Paper Digest: WWW 2025 Papers & Highlights
The Web Conference (WWW) is one of the top internet conferences in the world. To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights to quickly get the main idea of each paper.
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TABLE 1: Paper Digest: WWW 2025 Papers & Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Common Foundations for SHACL, ShEx, and PG-Schema Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Each language has its unique approach to defining constraints and validating graph data, leaving potential users in the dark about their commonalities and differences. In this paper, we provide concise formal definitions of the core components of these languages, employ a uniform framework to facilitate a comprehensive comparison between them, and identify a common set of functionalities, shedding light on both overlapping and distinctive features. |
Shqiponja Ahmetaj; Iovka Boneva; Jan Hidders; Katja Hose; Maxime Jakubowski; Jose Emilio Labra Gayo; Wim Martens; Fabio Mogavero; Filip Murlak; Cem Okulmus; Axel Polleres; Ognjen Savkovi\'{c}; Mantas \v{S}imkus; Dominik Tomaszuk; |
| 2 | TransBox: EL++-closed Ontology Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose EL++-closed ontology embeddings which are able to represent any logical expressions in DL EL++ via composition. |
Hui Yang; Jiaoyan Chen; Uli Sattler; |
| 3 | Spherical Embeddings for Atomic Relation Projection Reaching Complex Logical Query Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a symbolic learning-free approach using fuzzy logic to address the shape-closure problem that restricted geometric-based embedding models to only a few shapes (e.g. ConE) for answering complex logical queries. |
Chau D. M. Nguyen; Tim French; Michael Stewart; Melinda Hodkiewicz; Wei Liu; |
| 4 | Passage: Ensuring Completeness and Responsiveness of Public SPARQL Endpoints with SPARQL Continuation Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Ideally, we should not have to choose between completeness and responsiveness. To address this issue, we introduce and formalize the concept of SPARQL continuation queries. |
Thi Hoang Thi Pham; Gabriela Montoya; Brice N\'{e}delec; Hala Skaf-Molli; Pascal Molli; |
| 5 | Worst-Case-Optimal Joins on Graphs with Topological Relations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, despite all the recent advances in querying knowledge graphs, we still lack techniques specifically tailored for topological information. Applications looking to incorporate topological relations must either materialize the inferred relations, incurring high space and maintenance overheads, or query them with less efficient recursive algorithms, incurring high runtime overheads.In this paper we address the problem of leveraging topological information in knowledge graphs by designing efficient algorithms to process these queries. |
Jos\'{e} Fuentes-Sep\'{u}lveda; Adri\'{a}n G\'{o}mez-Brand\'{o}n; Aidan Hogan; Ayleen Irribarra-Cort\'{e}s; Gonzalo Navarro; Juan Reutter; |
| 6 | Subgraph-Aware Training of Language Models for Knowledge Graph Completion Using Structure-Aware Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a Subgraph-Aware Training framework for KGC (SATKGC) with two ideas: (i) subgraph-aware mini-batching to encourage hard negative sampling and to mitigate an imbalance in the frequency of entity occurrences during training, and (ii) new contrastive learning to focus more on harder in-batch negative triples and harder positive triples in terms of the structural properties of the knowledge graph. |
Youmin Ko; Hyemin Yang; Taeuk Kim; Hyunjoon Kim; |
| 7 | HySAE: An Efficient Semantic-Enhanced Representation Learning Model for Knowledge Hypergraph Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel knowledge hypergraph representation learning model, HySAE, which can achieve a satisfactory trade-off between effectiveness and efficiency. |
Zhao Li; Xin Wang; Jun Zhao; Feng Feng; Zirui Chen; Jianxin Li; |
| 8 | SymAgent: A Neural-Symbolic Self-Learning Agent Framework for Complex Reasoning Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce SymAgent, an innovative neural-symbolic agent framework that achieves collaborative augmentation between KGs and LLMs. |
Ben Liu; Jihai Zhang; Fangquan Lin; Cheng Yang; Min Peng; Wotao Yin; |
| 9 | Towards Multimodal Inductive Learning: Adaptively Embedding MMKG Via Prototypes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Intuitively, a MMKG model trained on DBpedia cannot infer on Freebase. To address above limitations, we make the first attempt towards inductive learning for MMKG and propose a multimodal Inductive MMKG model (IndMKG) that is universal and transferable to any MMKG. |
Shundong Yang; Jing Yang; Xiaowen Jiang; Yuan Gao; Laurence T. Yang; Ruikun Luo; Jieming Yang; |
| 10 | OntoTune: Ontology-Driven Self-training for Aligning Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: From this perspective, we propose an ontology-driven self-training framework called OntoTune, which aims to align LLMs with ontology through in-context learning, enabling the generation of responses guided by the ontology. |
Zhiqiang Liu; Chengtao Gan; Junjie Wang; Yichi Zhang; Zhongpu Bo; Mengshu Sun; Huajun Chen; Wen Zhang; |
| 11 | PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Nevertheless, locally converged parameters more accurately capture domain-specific knowledge, and current methods overlook the potential benefits of these parameters. To address these limitations, we propose PM-MoE architecture. |
Yu Feng; Yangli-ao Geng; Yifan Zhu; Zongfu Han; Xie Yu; Kaiwen Xue; Haoran Luo; Mengyang Sun; Guangwei Zhang; Meina Song; |
| 12 | Beyond Dataset Watermarking: Model-Level Copyright Protection for Code Summarization Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional watermarking for CSM copyright protection faces two main challenges: 1) dataset watermarking methods require separate design of triggers and watermark features based on the characteristics of different programming languages, which not only increases the computation complexity but also leads to a lack of generalization, 2) existing watermarks based on code style transformation are easily identifiable by automated detection, demonstrating poor concealment. To tackle these issues, we propose ModMark, a novel model-level digital watermark embedding method. |
Jiale Zhang; Haoxuan Li; Di Wu; Xiaobing Sun; Qinghua Lu; Guodong Long; |
| 13 | SheetAgent: Towards A Generalist Agent for Spreadsheet Reasoning and Manipulation Via Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To bridge the gap with the real-world requirements, we introduce SheetRM, a benchmark featuring long-horizon and multi-category tasks with reasoning-dependent manipulation caused by real-life challenges. |
Yibin Chen; Yifu Yuan; Zeyu Zhang; Yan Zheng; Jinyi Liu; Fei Ni; Jianye Hao; Hangyu Mao; Fuzheng Zhang; |
| 14 | MixedSAND: Semantic Annotation of Mixed-unit Numeric Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For instance, weight measurements may vary between entities, expressed in kilograms for some and pounds for others, with no accompanying unit information. We investigate the conditions for effectively annotating mixed-unit numeric data, introduce a benchmark for such an annotation task, and propose an algorithm that reliably detects semantic types (e.g., height) and links them to the corresponding types present in a knowledge graph. |
Amir Behrad Khorram Nazari; Davood Rafiei; Mario A. Nascimento; |
| 15 | Omni-SILA: Towards Omni-scene Driven Visual Sentiment Identifying, Locating and Attributing in Videos Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Prior studies on Visual Sentiment Understanding (VSU) primarily rely on the explicit scene information (e.g., facial expression) to judge visual sentiments, which largely ignore implicit scene information (e.g., human action, objection relation and visual background), while such information is critical for precisely discovering visual sentiments. Motivated by this, this paper proposes a new Omni-scene driven visual Sentiment Identifying, Locating and Attributing in videos (Omni-SILA) task, aiming to interactively and precisely identify, locate and attribute visual sentiments through both explicit and implicit scene information. |
Jiamin Luo; Jingjing Wang; Junxiao Ma; Yujie Jin; Shoushan Li; Guodong Zhou; |
| 16 | Large Language Models Empowered Personalized Web Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the absence of a comprehensive evaluation benchmark, we construct a Personalized Web Agent Benchmark (PersonalWAB), featuring user instructions, personalized user data, Web functions, and two evaluation paradigms across three personalized Web tasks. |
Hongru Cai; Yongqi Li; Wenjie Wang; Fengbin Zhu; Xiaoyu Shen; Wenjie Li; Tat-Seng Chua; |
| 17 | Unleash LLMs Potential for Sequential Recommendation By Coordinating Dual Dynamic Index Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, the neglect of user-related information hinders LLM-based sequential RSs from exploiting high-order user-item interaction patterns. In this paper, we propose the End-to-End Dual Dynamic (ED2) recommender, the first LLM-based sequential RS which adopts dual dynamic index mechanism, targeting resolving the above limitations simultaneously. |
Jun Yin; Zhengxin Zeng; Mingzheng Li; Hao Yan; Chaozhuo Li; Weihao Han; Jianjin Zhang; Ruochen Liu; Hao Sun; Weiwei Deng; Feng Sun; Qi Zhang; Shirui Pan; Senzhang Wang; |
| 18 | LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, the scalability and personalization of these models are often limited by their complexity and a lack of attention to the varying importance of different aspects in diverse reranking scenarios. To address these issues, we propose LLM4Rerank, a comprehensive LLM-based reranking framework designed to bridge the gap between various reranking aspects while ensuring scalability and personalized performance. |
Jingtong Gao; Bo Chen; Xiangyu Zhao; Weiwen Liu; Xiangyang Li; Yichao Wang; Wanyu Wang; Huifeng Guo; Ruiming Tang; |
| 19 | G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, existing methods often struggle with the integration of extracted CF information with LLMs due to its implicit representation and the modality gap between graph structures and natural language explanations. To address these challenges, we propose G-Refer, a framework using Graph Retrieval-augmented large language models (LLMs) for explainable recommendation. |
Yuhan Li; Xinni Zhang; Linhao Luo; Heng Chang; Yuxiang Ren; Irwin King; Jia Li; |
| 20 | Unleashing The Power of Large Language Model for Denoising Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce LLaRD, a framework leveraging LLMs to improve denoising in recommender systems, thereby boosting overall recommendation performance. |
Shuyao Wang; Zhi Zheng; Yongduo Sui; Hui Xiong; |
| 21 | Personalized Image Generation with Large Multimodal Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Worse still, there is a lack of supervised data for training personalized image generation models.To overcome the challenges, we propose a Personalized Image Generation Framework named Pigeon, which adopts exceptional large multimodal models with three dedicated modules to capture users’ visual preferences and needs from noisy user history and multimodal instructions. |
Yiyan Xu; Wenjie Wang; Yang Zhang; Biao Tang; Peng Yan; Fuli Feng; Xiangnan He; |
| 22 | When Large Vision Language Models Meet Multimodal Sequential Recommendation: An Empirical Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, their application in multimodal sequential recommendation (MSR) has not been extensively studied. To bridge this gap, we introduce MSRBench, the first comprehensive benchmark designed to systematically evaluate different LVLM integration strategies in web-based recommendation scenarios. |
Peilin Zhou; Chao Liu; Jing Ren; Xinfeng Zhou; Yueqi Xie; Meng Cao; Zhongtao Rao; You-Liang Huang; Dading Chong; Junling Liu; Jae Boum Kim; Shoujin Wang; Raymond Chi-Wing Wong; Sunghun Kim; |
| 23 | PerSRV: Personalized Sticker Retrieval with Vision-Language Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address these, we propose the Personalized Sticker Retrieval with Vision-Language Model framework, namely PerSRV, structured into offline calculations and online processing modules. |
Heng Er Metilda Chee; Jiayin Wang; Zhiqiang Guo; Weizhi Ma; Min Zhang; |
| 24 | Unleashing The Potential of Two-Tower Models: Diffusion-Based Cross-Interaction for Large-Scale Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the aforementioned challenges, we propose a cross-interaction decoupling architecture within our matching paradigm. |
Yihan Wang; Fei Xiong; Zhexin Han; Qi Song; Kaiqiao Zhan; Ben Wang; |
| 25 | Interactive Visualization Recommendation with Hier-SUCB Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For more interactive and accurate recommendations, we propose Hier-SUCB, a contextual combinatorial semi-bandit in the PVisRec setting. |
Songwen Hu; Ryan A. Rossi; Tong Yu; Junda Wu; Handong Zhao; Sungchul Kim; Shuai Li; |
| 26 | A Plug-in Critiquing Approach for Knowledge Graph Recommendation Systems Via Representative Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, we present a general Representative Items Sampling Framework for Critiquing on Knowledge Graph Recommendation (RISC) implemented as a plug-in, which offers a new paradigm for critiquing in mainstream recommendation scenarios. |
Huanyu Zhang; Xiaoxuan Shen; Baolin Yi; Jianfang Liu; Yinao Xie; |
| 27 | Graph Representation Learning Via Causal Diffusion for Out-of-Distribution Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we construct a Structural Causal Model (SCM) to analyze interaction data, revealing that environmental confounders (e.g., the COVID-19 pandemic) lead to unstable correlations in GNN-based models, thus impairing their generalization to OOD data. |
Chu Zhao; Enneng Yang; Yuliang Liang; Pengxiang Lan; Yuting Liu; Jianzhe Zhao; Guibing Guo; Xingwei Wang; |
| 28 | Dual Graph Denoising Model for Social Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although several recommender studies on data denoising exist, most either rely on heuristic assumptions, which limit their adaptability, or use a single model that combines denoising and recommendation, potentially imposing substantial demands on the model capacity. To address these issues, we propose a dual Graph Denoising Social Recommender (GDSR), which consists of two steps: graph denoising and user preference prediction. |
Anchen Li; Bo Yang; |
| 29 | GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces GraphHash, the first graph-based approach that leverages modularity-based bipartite graph clustering on user-item interaction graphs to reduce embedding table sizes. |
Xinyi Wu; Donald Loveland; Runjin Chen; Yozen Liu; Xin Chen; Leonardo Neves; Ali Jadbabaie; Mingxuan Ju; Neil Shah; Tong Zhao; |
| 30 | Model-Agnostic Social Network Refinement with Diffusion Models for Robust Social Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the denoising capability of generative diffusion models, we propose a Model-Agnostic Social Network Refinement framework with Diffusion Models for Robust Social Recommendation (ARD-SR). |
Youchen Sun; Zhu Sun; Yingpeng Du; Jie Zhang; Yew Soon Ong; |
| 31 | Value Function Decomposition in Markov Recommendation Process Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we argue that this paradigm involves a challenge of Mixing Random Factors: there exist two random factors from the stochastic policy and the uncertain user environment, but they are not separately modeled in the standard temporal difference (TD) learning, which may result in a suboptimal estimation of the long-term rewards and less effective action exploration. As a solution, we show that these two factors can be separately approximated by decomposing the original temporal difference loss. |
Xiaobei Wang; Shuchang Liu; Qingpeng Cai; Xiang Li; Lantao Hu; Han Li; Guangming Xie; |
| 32 | AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These changes pose significant challenges to existing RL algorithms for recommendations, leading to issues with dynamics and reward distribution shifts. This paper introduces a novel approach called Adaptive User Retention Optimization (AURO) to address this challenge. |
Zhenghai Xue; Qingpeng Cai; Bin Yang; Lantao Hu; Peng Jiang; Kun Gai; Bo An; |
| 33 | Policy-Guided Causal State Representation for Offline Reinforcement Learning Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, missing transitions in offline data make it challenging to accurately identify features that are most relevant to user satisfaction. To address these challenges, we propose Policy-Guided Causal Representation (PGCR), a novel two-stage framework for causal feature selection and state representation learning in offline RLRS. |
Siyu Wang; Xiaocong Chen; Lina Yao; |
| 34 | Off-policy Evaluation for Multiple Actions in The Presence of Unobserved Confounders Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel auxiliary variable-aided method for OPE in multi-action settings with unobserved confounders. |
Haolin Wang; Lin Liu; Jiuyong Li; Ziqi Xu; Jixue Liu; Zehong Cao; Debo Cheng; |
| 35 | Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Second, DMs are prone to data bias and tend to generate only the popular items that dominate the training dataset, thus failing to meet the personalized needs of different users. To address these issues, we propose Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation (DiQDiff), which aims to extract robust guidance to understand user interests and generate distinguished items for personalized user interests within DMs. |
Wenyu Mao; Shuchang Liu; Haoyang Liu; Haozhe Liu; Xiang Li; Lantao Hu; |
| 36 | Understanding and Scaling Collaborative Filtering Optimization from The Perspective of Matrix Rank Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we move beyond model-level modifications and study the properties of the embedding tables under different learning strategies. |
Donald Loveland; Xinyi Wu; Tong Zhao; Danai Koutra; Neil Shah; Mingxuan Ju; |
| 37 | Privacy-Friendly Cross-Domain Recommendation Via Distilling User-irrelevant Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we propose a conditional model inversion mechanism to accurately distill prototypes for individual users. |
Cheng Wang; Wenchao Xu; Haozhao Wang; Wei Liu; Ruixuan Li; |
| 38 | ESANS: Effective and Semantic-Aware Negative Sampling for Large-Scale Retrieval Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing techniques often suffer from false negatives, high cost for ensuring sampling quality and semantic information deficiency. To address these limitations, we propose Effective and Semantic-Aware Negative Sampling (ESANS), which integrates two key components: Effective Dense Interpolation Strategy (EDIS) and Multimodal Semantic-Aware Clustering (MSAC). |
Haibo Xing; Kanefumi Matsuyama; Hao Deng; Jinxin Hu; Yu Zhang; Xiaoyi Zeng; |
| 39 | D2K: Turning Historical Data Into Retrievable Knowledge for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence, in this work, we propose a framework that turns massive user behavior data to retrievable knowledge (D2K). |
Jiarui Qin; Weiwen Liu; Weinan Zhang; Yong Yu; |
| 40 | Unleashing The Potential of Multi-Channel Fusion in Retrieval for Personalized Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore advanced channel fusion strategies by assigning systematically optimized weights to each channel. |
Junjie Huang; Jiarui Qin; Jianghao Lin; Ziming Feng; Weinan Zhang; Yong Yu; |
| 41 | Node2binary: Compact Graph Node Embeddings Using Binary Vectors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Along this effort, we propose Node2Binary, which embeds graph nodes in as few as 128 binary bits, thereby reducing the memory footprint of vertex embedding vectors by several orders of magnitude. |
Niloy Talukder; Croix Gyurek; Mohammad Al Hasan; |
| 42 | Fair Network Communities Through Group Modularity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce two versions of group modularity, each grounded on a different null model, and propose fairness-aware community detection algorithms. |
Christos Gkartzios; Evaggelia Pitoura; Panayiotis Tsaparas; |
| 43 | Domain-Informed Negative Sampling Strategies for Dynamic Graph Embedding in Meme Stock-Related Social Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This limits the training and evaluation of DGE models in such social networks. To overcome this drawback, we propose novel negative sampling strategies based on the analysis of real meme stock-related social networks and financial knowledge. |
Yunming Hui; Inez Maria Zwetsloot; Simon Trimborn; Stevan Rudinac; |
| 44 | UniGO: A Unified Graph Neural Network for Modeling Opinion Dynamics on Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To fully leverage the advantages of unified opinion dynamics, we introduces UniGO, a framework for modeling opinion evolution on graphs. |
Hao Li; Hao Jiang; Yuke Zheng; Hao Sun; Wenying Gong; |
| 45 | Exposing Cross-Platform Coordinated Inauthentic Activity in The Run-Up to The 2024 U.S. Election Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Proposing an advanced coordination detection model, we reveal evidence of potential foreign interference, with Russian-affiliated media being systematically promoted across Telegram and 𝕏. |
Federico Cinus; Marco Minici; Luca Luceri; Emilio Ferrara; |
| 46 | In-Group Love, Out-Group Hate: A Framework to Measure Affective Polarization Via Contentious Online Discussions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a discrete choice model that captures decision-making within affectively polarized social networks and propose a statistical inference method estimate key parameters—in-group love and out-group hate—from social media data. |
Buddhika Nettasinghe; Ashwin Rao; Bohan Jiang; Allon G. Percus; Kristina Lerman; |
| 47 | Behavioral Homophily in Social Media Via Inverse Reinforcement Learning: A Reddit Case Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work introduces a novel approach for quantifying user homophily. |
Lanqin Yuan; Philipp J. Schneider; Marian-Andrei Rizoiu; |
| 48 | Causal Modeling of Climate Activism on Reddit Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion). |
Jacopo Lenti; Luca Maria Aiello; Corrado Monti; Gianmarco De Francisci Morales; |
| 49 | The Agenda-Setting Function of Social Media Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we investigate the issues and frames invoked in news article shares across Reddit over 16 years and measure their traditional media and social media partisanship. |
Rachel M. Kim; Ashton Anderson; |
| 50 | MSTI-Plus: Introducing Non-Sarcasm Reference Materials to Enhance Multimodal Sarcasm Target Identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by these limitations, this work reconstructs a more comprehensive MSTI benchmark by introducing both fine-grained non-sarcasm aspect annotations for existing sarcasm samples and non-sarcastic samples as non-sarcasm references to enable deep models to clearly perceive the mentioned information during training. |
Fengmao Lv; Mengting Xiong; Junlin Fang; Lingli Zhang; Tianze Luo; Weichao Liang; Tianrui Li; |
| 51 | Damage Analysis Via Bidirectional Multi-Task Cascaded Multimodal Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose the Bidirectional Multi-task Cascaded multimodal Fusion (BiMCF) approach towards joint multimodal damage analysis. |
Tao Liang; Siying Wu; Junfeng Fang; Guowu Yang; Wenya Wang; Fengmao Lv; |
| 52 | Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While humans rely on commonsense knowledge to interpret these complex expressions, current Multimodal Language Models (MLMs) struggle to capture these figurative aspects inherent in memes. To address this gap, we introduce a novel dataset, AxiOM, derived from the GAD anxiety questionnaire, which categorizes memes into six fine-grained anxiety symptoms. |
Abdullah Mazhar; Zuhair Hasan Shaik; Aseem Srivastava; Polly Ruhnke; Lavanya Vaddavalli; Sri Keshav Katragadda; Shweta Yadav; Md Shad Akhtar; |
| 53 | Thematic-LM: A LLM-based Multi-agent System for Large-scale Thematic Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Third, existing methods follow a sequential process, where codes are generated for individual samples without recalling previous codes and associated data, reducing the ability to analyse data holistically. To address these limitations, we propose Thematic-LM, an LLM-based multi-agent system for large-scale computational thematic analysis. |
Tingrui Qiao; Caroline Walker; Chris Cunningham; Yun Sing Koh; |
| 54 | Spatial-Temporal Analysis of Collective Emotional Resonance in China During Global Health Crisis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a percolation-based index to measure the nationwide emotional resonance level with which we illustrate the significant economic impact of the global health issue. |
Limiao Zhang; Xinyang Qi; Haiping Ma; Jie Gao; Xingyi Zhang; Yanqing Hu; Yaochu Jin; |
| 55 | ABO: Abandon Bayer Filter for Adaptive Edge Offloading in Responsive Augmented Reality Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The potential optimization in frame preprocessing of DNN offloading is yet to be investigated.To that end, we propose ABO, an adaptive RAW frame offloading framework that parallelizes demosaicing with DNN computation. |
Yongxuan Han; Shengzhong Liu; Fan Wu; Guihai Chen; |
| 56 | Boosting Asynchronous Decentralized Learning with Model Fragmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present DivShare, a novel asynchronous DL algorithm that achieves fast model convergence in the presence of communication stragglers. |
Sayan Biswas; Anne-Marie Kermarrec; Alexis Marouani; Rafael Pires; Rishi Sharma; Martijn de Vos; |
| 57 | Multivariate Time Series Anomaly Detection By Capturing Coarse-Grained Intra- and Inter-Variate Dependencies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce MtsCID, a novel semi-supervised multivariate time series anomaly detection method. |
Yongzheng Xie; Hongyu Zhang; Muhammad Ali Babar; |
| 58 | AERO: Enhancing Sharding Blockchain Via Deep Reinforcement Learning for Account Migration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose AERO, a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains. |
Mingxuan Song; Pengze Li; Bohan Zhou; Shenglin Yin; Zhen Xiao; Jieyi Long; |
| 59 | MAP The Blockchain World: A Trustless and Scalable Blockchain Interoperability Protocol for Cross-chain Applications Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose MAP, a trustless blockchain interoperability protocol that relays cross-chain transactions across heterogeneous chains with high scalability. |
Yinfeng Cao; Jiannong Cao; Dongbin Bai; Long Wen; Yang Liu; Ruidong Li; |
| 60 | MAML: Towards A Faster Web in Developing Regions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Users in these regions typically rely on low-end devices and limited bandwidth, which results in a poor user experience as they download and parse webpages bloated with excessive third-party CSS and JavaScript (JS). To address these challenges, we introduce the Mobile Application Markup Language (MAML), a flat layout-based web specification language that reduces computational and data transmission demands, while replacing the excessive bloat from JS with a new scripting language centered on essential (and popular) web functionalities. |
Ayush Pandey; Matteo Varvello; Syed Ishtiaque Ahmed; Shurui Zhou; Lakshmi Subramanian; Yasir Zaki; |
| 61 | Spache: Accelerating Ubiquitous Web Browsing Via Schedule-Driven Space Caching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we perform a systematic study to explore a pivotal problem facing the web community: is current distributed web cache ready for future satellite Internet? |
Qi Zhang; Qian Wu; Zeqi Lai; Jihao Li; Hewu Li; Yuyu Liu; Yuanjie Li; Jun Liu; |
| 62 | GL2GPU: Accelerating WebGL Applications Via Dynamic API Translation to WebGPU Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, considering the complex logic of WebGL applications and the still-evolving WebGPU specification, statically migrating existing WebGL applications to WebGPU from source code is labor-intensive. To address this issue, we propose GL2GPU, an intermediate layer that dynamically translates WebGL to WebGPU at JavaScript runtime to improve rendering performance. |
Yudong Han; Weichen Bi; Ruibo An; Deyu Tian; Qi Yang; Yun Ma; |
| 63 | GraphCSR: A Space and Time-Efficient Sparse Matrix Representation for Web-scale Graph Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional sparse matrix storage formats like compressed sparse row (CSR) face significant memory and performance bottlenecks in distributed, federated, and edge-based computing environments, which are increasingly central to the web. To address this challenge, we propose GraphCSR, a novel storage format that clusters vertices with identical edge degrees and stores only the starting index of each group. |
Xinbiao Gan; Tiejun Li; Qiang Zhang; Guang Wu; Bo Yang; Chunye Gong; Jie Liu; Kai Lu; |
| 64 | On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Shapley Value-guided Embedding Reduction (Shaver) as our response. |
Hung Vinh Tran; Tong Chen; Guanhua Ye; Quoc Viet Hung Nguyen; Kai Zheng; Hongzhi Yin; |
| 65 | GraphCom: Communication Hierarchy-aware Graph Engine for Distributed Model Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents GraphCom, a communication-efficient message graph engine for graph processing on supercomputers. |
Xinbiao Gan; Tiejun Li; Liang Wu; Qiang Zhang; Lingyun Song; Bo Yang; Jie Liu; Kai Lu; |
| 66 | PSSD: Making Large Language Models Self-denial Via Human Psyche Structure Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As the whole procedure conducts within LLMs, supporting and persuasive references are hard to acquire, while the absence of specific steps towards refining hidden mistakes persists even when errors are acknowledged. In response to the challenges, we present PSSD, which refers to and implements the human psyche structure such that three distinct and interconnected roles contribute to human reasoning. |
Jinzhi Liao; Zenghua Liao; Xiang Zhao; |
| 67 | FedRIR: Rethinking Information Representation in Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing FL methods typically prioritize either global generalization or local personalization, resulting in a trade-off between these objectives and limiting the full potential of diverse client data. To address this challenge, we propose a novel framework that enhances both global generalization and local personalization by Rethinking Information Representation in the Federated learning process (FedRIR). |
Yongqiang Huang; Zerui Shao; Ziyuan Yang; Zexin Lu; Yi Zhang; |
| 68 | Maverick: Personalized Edge-Assisted Federated Learning with Contrastive Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, our investigation reveals that it drives the global model towards clients with excessive local training, causing model drifts that undermine model performance for other clients. To tackle this problem, this paper presents Maverick, a new edge-assisted FL system that mitigates model drifts by training personalized local models for clients through contrastive local training. |
Kaibin Wang; Qiang He; Zeqian Dong; Rui Chen; Chuan He; Caslon Chua; Feifei Chen; Yun Yang; |
| 69 | SCOOT: SLO-Oriented Performance Tuning for LLM Inference Engines Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we observe that adjusting the parameters of LLM inference engines can improve service performance, and the optimal parameter configurations of different services are different. |
Ke Cheng; Zhi Wang; Wen Hu; Tiannuo Yang; Jianguo Li; Sheng Zhang; |
| 70 | Model Supply Chain Poisoning: Backdooring Pre-trained Models Via Embedding Indistinguishability Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel and severer backdoor attack, TransTroj, which enables the backdoors embedded in PTMs to efficiently transfer in the model supply chain. |
Hao Wang; Shangwei Guo; Jialing He; Hangcheng Liu; Tianwei Zhang; Tao Xiang; |
| 71 | NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on Adaptive Perturbation on Federated Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, all existing methods posting distributed backdoor attack on FedGL only focus on injecting distributed backdoor triggers into the training data of each malicious client, which will cause model performance degradation on original task and is not always effective when confronted with robust federated learning defense algorithms, leading to low success rate of attack. What’s more, the backdoor signals introduced by the malicious clients may be smoothed out by other clean signals from the honest clients, which potentially undermining the performance of the attack.To address the above significant shortcomings, we propose a non-intrusive graph distributed backdoor attack(NI-GDBA) that does not require backdoor triggers to be injected in the training data. |
Ken Li; Bin Shi; Jiazhe Wei; Bo Dong; |
| 72 | Dual Intention Escape: Penetrating and Toxic Jailbreak Attack Against Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by the mechanism in the psychology of human misjudgment, we propose a dual intention escape (DIE) jailbreak attack framework to generate more stealthy and toxic prompts to deceive LLMs to output harmful content. |
Yanni Xue; Jiakai Wang; Zixin Yin; Yuqing Ma; Haotong Qin; Renshuai Tao; Xianglong Liu; |
| 73 | You Can’t Eat Your Cake and Have It Too: The Performance Degradation of LLMs with Jailbreak Defense Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing research predominantly focuses on the effectiveness of defense strategies without thoroughly examining their impact on performance, leaving a gap in understanding the trade-offs between LLM safety and performance.Our research addresses this gap by conducting a comprehensive study on the utility degradation, safety elevation, and exaggerated-safety escalation of LLMs with jailbreak defense strategies. We propose USEBench, a novel benchmark designed to evaluate these aspects, along with USEIndex, a comprehensive metric for assessing overall model performance. |
Wuyuao Mai; Geng Hong; Pei Chen; Xudong Pan; Baojun Liu; Yuan Zhang; Haixin Duan; Min Yang; |
| 74 | FLock: Robust and Privacy-Preserving Federated Learning Based on Practical Blockchain State Channels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose FLock, a robust and privacy-preserving FL scheme based on practical blockchain state channels. |
Ruonan Chen; Ye Dong; Yizhong Liu; Tingyu Fan; Dawei Li; Zhenyu Guan; Jianwei Liu; Jianying Zhou; |
| 75 | Provably Robust Federated Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While FRL has advantages, its decentralized design makes it prone to poisoning attacks. To mitigate this, Byzantine-robust aggregation techniques tailored for FRL have been introduced. |
Minghong Fang; Xilong Wang; Neil Zhenqiang Gong; |
| 76 | Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel dataset-level membership inference method based on Self-Comparison. |
Jie Ren; Kangrui Chen; Chen Chen; Vikash Sehwag; Yue Xing; Jiliang Tang; Lingjuan Lyu; |
| 77 | Local Differentially Private Release of Infinite Streams With Temporal Relevance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, for the first time we present an online LDP publishing mechanism while preserving the inherent temporal relevance for the infinite stream, called the Sampling Period Perturbation Algorithm (SPPA). |
Runze Wang; Jiahao Liu; Miao Hu; Yipeng Zhou; Di Wu; |
| 78 | Dynamic Graph Unlearning: A General and Efficient Post-Processing Method Via Gradient Transformation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we study the dynamic graph unlearning for the first time and propose an effective, efficient, general, and post-processing method to implement DGNN unlearning. |
He Zhang; Bang Wu; Xiangwen Yang; Xingliang Yuan; Xiaoning Liu; Xun Yi; |
| 79 | 7 Days Later: Analyzing Phishing-Site Lifespan After Detected Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study provides an in-depth analysis of the lifespan and evolution of phishing websites, focusing on their survival strategies and evasion techniques. |
Kiho Lee; Kyungchan Lim; Hyoungshick Kim; Yonghwi Kwon; Doowon Kim; |
| 80 | What’s in Phishers: A Longitudinal Study of Security Configurations in Phishing Websites and Kits Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our research proposes a new approach to leverage weaknesses in phishing infrastructure, allowing defenders to take proactive actions to disable phishing sites earlier and reduce their effectiveness. |
Kyungchan Lim; Kiho Lee; Fujiao Ji; Yonghwi Kwon; Hyoungshick Kim; Doowon Kim; |
| 81 | CATALOG: Exploiting Joint Temporal Dependencies for Enhanced Phishing Detection on Ethereum Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current methods often fail to tackle these concerns and overlook the temporal sequence of transactions, resulting in suboptimal performance. In this paper, we aim to address these gaps by focusing on the alignment of two aspects: (1) User-specific local temporal behavior, and (2) Divergences from global activity patterns of the network. |
Medhasree Ghosh; Swapnil Srivastava; Apoorva Upadhyaya; Raju Halder; Joydeep Chandra; |
| 82 | 50 Shades of Deceptive Patterns: A Unified Taxonomy, Multimodal Detection, and Security Implications Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Through 4 unexplored case studies that inform security implications, we highlight the critical importance of the unified taxonomy in addressing the growing challenges of Internet deception. |
Zewei Shi; Ruoxi Sun; Jieshan Chen; Jiamou Sun; Minhui Xue; Yansong Gao; Feng Liu; Xingliang Yuan; |
| 83 | Harmful Terms and Where to Find Them: Measuring and Modeling Unfavorable Financial Terms and Conditions in Shopping Websites at Scale Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, there are no publicly available detection systems or datasets to systematically identify or mitigate these terms. In this paper, we take the first steps toward solving this problem with three key contributions.First, we introduce TermMiner, an automated data collection and topic modeling pipeline to understand the landscape of unfavorable financial terms. |
Elisa Tsai; Neal Mangaokar; Boyuan Zheng; Haizhong Zheng; Atul Prakash; |
| 84 | Gamblers or Delegatees: Identifying Hidden Participant Roles in Crypto Casinos Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes an unsupervised dual-stage role identification method to adaptively identify key roles and hidden delegatees in label-sparse crypto casinos. |
Jiaxin Wang; Qian’ang Mao; Hongliang Sun; Jiaqi Yan; |
| 85 | Serial Scammers and Attack of The Clones: How Scammers Coordinate Multiple Rug Pulls on Decentralized Exchanges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We explored the ubiquitous phenomenon of serial scammers, each of whom deployed dozens to thousands of addresses to conduct a series of similar Rug Pulls on popular decentralized exchanges. |
Phuong Duy Huynh; Son Hoang Dau; Nicholas Huppert; Joshua Cervenjak; Hoonie Sun; Hong Yen Tran; Xiaodong Li; Emanuele Viterbo; |
| 86 | The Poorest Man in Babylon: A Longitudinal Study of Cryptocurrency Investment Scams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These scams typically use professional-looking websites to lure unsuspecting victims with promises of unrealistically high returns. In this paper, we introduce Crimson, a distributed system designed to continuously detect cryptocurrency investment scam websites as they are created in the wild. |
Muhammad Muzammil; Abisheka Pitumpe; Xigao Li; Amir Rahmati; Nick Nikiforakis; |
| 87 | STGAN: Detecting Host Threats Via Fusion of Spatial-Temporal Features in Host Provenance Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing provenance-based detection methods primarily rely on single-dimensional feature analysis, which fails to capture the dynamic and multi-dimensional patterns of modern APT attacks, resulting in insufficient detection performance. To overcome this limitation, we introduce STGAN, a model that integrates spatial-temporal graphs into host provenance graph modeling. |
Anyuan Sang; Xuezheng Fan; Li Yang; Yuchen Wang; Lu Zhou; Junbo Jia; Huipeng Yang; |
| 88 | ACME++: A Secure Authorization Mechanism for ACME Clients in The Web PKI Ecosystem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the ACME Authz Cache Attack, whereby an attacker can obtain fraudulent certificates without domain control. |
Tianyu Zhang; Han Zhang; Yunze Wei; Yahui Li; Xingang Shi; Jilong Wang; Xia Yin; |
| 89 | Beyond Single Tabs: A Transformative Few-Shot Approach to Multi-Tab Website Fingerprinting Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce Few-shot Multi-tab Website Fingerprinting (FMWF), a novel approach designed to address the limitations of existing multi-tab WF attacks. |
Wenwen Meng; Chuan Ma; Ming Ding; Chunpeng Ge; Yuwen Qian; Tao Xiang; |
| 90 | HOLMES \& WATSON: A Robust and Lightweight HTTPS Website Fingerprinting Through HTTP Version Parallelism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose HOLMES, a novel approach that exploits HTTP version parallelism to extract enhanced application-layer features. |
Yifei Cheng; Yujia Zhu; Baiyang Li; Peishuai Sun; Yong Ding; Xinhao Deng; Qingyun Liu; |
| 91 | Peripheral Instinct: How External Devices Breach Browser Sandboxes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While the API specifications list initial security considerations, they shift the responsibility to (unprepared) device vendors. We systematically analyze the security implications of external devices exposed by such new APIs. |
Leon Trampert; Lorenz Hetterich; Lukas Gerlach; Mona Schappert; Christian Rossow; Michael Schwarz; |
| 92 | Dynamic Security Analysis of JavaScript: Are We There Yet? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we systematically evaluate the effectiveness of existing tools for the dynamic security analysis of client-side JavaScript, focusing in particular on information flow control. |
Stefano Calzavara; Samuele Casarin; Riccardo Focardi; |
| 93 | Broken Access: On The Challenges of Screen Reader Assisted Two-Factor and Passwordless Authentication Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Securing interactions with these services is crucial; however, currently deployed methods of web authentication mainly concentrate on sighted users, overlooking the specific needs of the blind and visually impaired community. In this paper, we address this critical gap by investigating the security and accessibility aspects of these web authentication methods when adopted by blind and visually impaired users. |
Md Mojibur Rahman Redoy Akanda; Ahmed Tanvir Mahdad; Nitesh Saxena; |
| 94 | Str-GCL: Structural Commonsense Driven Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Due to the lack of explicit information and clear guidance in general graph, identifying and integrating such structural commonsense in GCL poses a significant challenge. To address this gap, we propose a novel framework called Structural Commonsense Unveiling in Graph Contrastive Learning (Str-GCL). |
Dongxiao He; Yongqi Huang; Jitao Zhao; Xiaobao Wang; Zhen Wang; |
| 95 | SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel Structure Alignment framework for text-free Multi-domain Graph Pre-Training and cross-domain adaptation (SAMGPT). |
Xingtong Yu; Zechuan Gong; Chang Zhou; Yuan Fang; Hui Zhang; |
| 96 | RiemannGFM: Learning A Graph Foundation Model from Riemannian Geometry Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Herein, we present a universal pretraining model, RiemannGFM. |
Li Sun; Zhenhao Huang; Suyang Zhou; Qiqi Wan; Hao Peng; Philip Yu; |
| 97 | FG-CIBGC: A Unified Framework for Fine-Grained and Class-Incremental Behavior Graph Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The challenges are: (i) fine-grained emerging behavior graphs, and (ii) incremental model adaptations. To tackle these issues, we propose to (i) mine semantics in multi-source logs using Large Language Models (LLMs) under In-Context Learning (ICL), and (ii) bridge the gap between Out-Of-Distribution (OOD) detection and class-incremental graph learning. |
Zhibin Ni; Pan Fan; Shengzhuo Dai; Bo Zhang; Hai Wan; Xibin Zhao; |
| 98 | Unified and Generalizable Reinforcement Learning for Facility Location Problems on Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a unified and generalizable approach to tackle facility location problems on weighted graphs with deep reinforcement learning, demonstrating a keen awareness of complex graph structures. |
Wenxuan Guo; Runzhong Wang; Yanyan Xu; Yaohui Jin; |
| 99 | Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We provide detailed theoretical guarantees to clarify the reasonability of HEI. |
Jinluan Yang; Zhengyu Chen; Teng Xiao; Yong Lin; Wenqiao Zhang; Kun Kuang; |
| 100 | Subgraph Federated Unlearning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose ReGEnUnlearn, a subgraph federated unlearning framework for efficient and comprehensive unlearning of multiple target clients. |
Fan Liu; Hao Liu; |
| 101 | Federated Graph Anomaly Detection Via Disentangled Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This partitioning results in incomplete feature aggregation, as the connections between subgraphs are missing, ultimately reducing the performance of anomaly detection models. To overcome these challenges, a federated graph anomaly detection approach based on disentangled representation learning is proposed. |
Zhengyang Liu; Hang Yu; Xiangfeng Luo; |
| 102 | SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, we explore the theoretical implications of these patterns, demonstrating the potential benefits of ISP and NSP for NAD tasks. Motivated by these findings, we propose SmoothGNN, a novel unsupervised NAD framework. |
Xiangyu Dong; Xingyi Zhang; Yanni Sun; Lei Chen; Mingxuan Yuan; Sibo Wang; |
| 103 | SPEAR: A Structure-Preserving Manipulation Method for Graph Backdoor Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To enhance the stealthiness of graph backdoors, we propose SPEAR, a novel structure-preserving graph backdoor attack that avoids modifying the graph’s topology. |
Yuanhao Ding; Yang Liu; Yugang Ji; Weigao Wen; Qing He; Xiang Ao; |
| 104 | Exploring Hypergraph Condensation Via Variational Hyperedge Generation and Multi-Aspectual Amelioration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing size reduction methods primarily capture pairwise association pattern within conventional graphs, making them challenging to adapt to hypergraphs with high-order correlations. To fill this gap, we introduce a novel hypergraph condensation framework, HG-Cond, designed to distill large-scale hypergraphs into compact, synthetic versions while maintaining comparable HyperGNN performance. |
Zheng Gong; Shuheng Shen; Changhua Meng; Ying Sun; |
| 105 | Kronecker Generative Models for Power-Law Patterns in Real-World Hypergraphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In a wide range of real-world hypergraphs, we discover power-law or log-logistic distributions in eight structural properties. To simulate these observed patterns, we introduce HyRec, a tractable and realistic generative model leveraging the Kronecker product. |
Minyoung Choe; Jihoon Ko; Taehyung Kwon; Kijung Shin; Christos Faloutsos; |
| 106 | Generalization Performance of Hypergraph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we seek to develop margin-based generalization bounds for four representative classes of hypergraph neural networks, including convolutional-based methods (UniGCN), set-based aggregation (AllDeepSets), invariant and equivariant transformations (M-IGN), and tensor-based approaches (T-MPHN). |
Yifan Wang; Gonzalo R. Arce; Guangmo Tong; |
| 107 | SEHG: Bridging Interpretability and Prediction in Self-Explainable Heterogeneous Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing post-hoc HGNN explanation methods cannot impact the HGNN’s predictions. Self-explainable homogeneous models also perform poorly on heterogeneous graphs. To address these challenges, we present a Self-Explainable Heterogeneous Graph Neural Network (SEHG), a novel architecture that integrates explanation generation into the learning process of HGNN through two alternative stages. |
Zhenhua Huang; Wenhao Zhou; Yufeng Li; Xiuyang Wu; Chengpei Xu; Junfeng Fang; Zhaohong Jia; Linyuan L\{u}; Feng Xia; |
| 108 | TESA: A Trajectory and Semantic-aware Dynamic Heterogeneous Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose the TrajEctory and Semantic-Aware dynamic heterogeneous graph neural network (TeSa), which integrates trajectory-based evolution and semantic-aware aggregation to capture both the evolving dynamics and heterogeneous semantics entailed in continuous-time dynamic heterogeneous graphs. |
Xin Wang; Jiawei Jiang; Xiao Yan; Qiang Huang; |
| 109 | Coreness Maximization Through Budget-Limited Edge Insertion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes the first effective and polynomial-time approach to BLCM. |
Xiaowei Lv; Xiaojia Xu; Yongcai Wang; Haoyu Liu; Deying Li; |
| 110 | Scalable Algorithms for Forest-Based Centrality on Large Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on two forest-based centrality measures on undirected graphs: forest node centrality (FNC) and forest edge centrality (FEC), which capture the influence of nodes and edges through their participation in spanning forests. |
Yubo Sun; Haoxin Sun; Zhongzhi Zhang; |
| 111 | Revisiting Dynamic Graph Clustering Via Matrix Factorization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Dynamic graph clustering aims to detect and track time-varying clusters in dynamic graphs, revealing the evolutionary mechanisms of complex real-world dynamic systems. Matrix factorization-based methods are promising approaches for this task; however, these methods often struggle with scalability and can be time-consuming when applied to large-scale dynamic graphs. |
Dongyuan Li; Satoshi Kosugi; Ying Zhang; Manabu Okumura; Feng Xia; Renhe Jiang; |
| 112 | Diffusion-based Graph-agnostic Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing GNN-based approaches strongly rely on the homophilic assumption of the input graph, and thus, largely fail on heterophilic graphs and others embodying numerous missing or noisy links, which are widely present in real life.To bridge this gap, this paper presents DGAC, an effective graph-agnostic solution for graph clustering. |
Kun Xie; Renchi Yang; Sibo Wang; |
| 113 | Graph Wave Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we would like to depict more wave details in MP, since graph signals are essentially wave signals that can be seen as a superposition of a series of waves in the form of eigenvector. |
Juwei Yue; Haikuo Li; Jiawei Sheng; Yihan Guo; Xinghua Zhang; Chuan Zhou; Tingwen Liu; Li Guo; |
| 114 | Optimizing Revenue Through User Coupon Recommendations in Truthful Online Ad Auctions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We model the interaction between the platform and the advertisers as an extensive-form game, where advertisers first report coupon bids to the platform to receive coupon recommendations, and then participate in auctions by reporting their auction bids. Our research identifies a sufficient condition under which the advertisers’ optimal strategy is to report their valuations truthfully in both the recommendation and auction stages.We construct two mechanisms based on these findings. |
Xiaodong Liu; Xiao Lin; Yiming Ding; Changcheng Li; Peng Jiang; Weiran Shen; |
| 115 | Autobidding With Interdependent Values Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we initiate the study of autobidding where the signals for each bidder can be noisy and correlated. |
Martino Banchio; Kshipra Bhawalkar; Christopher Liaw; Aranyak Mehta; Andres Perlroth; |
| 116 | No-Regret Algorithms in Non-Truthful Auctions with Budget and ROI Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we design online autobidding algorithms to optimize value subject to ROI and budget constraints.We consider an item is being auctioned in each of T rounds. |
Gagan Aggarwal; Giannis Fikioris; Mingfei Zhao; |
| 117 | Networked Digital Public Goods Games with Heterogeneous Players and Convex Costs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We establish the presence of NE in this model and provide an in-depth analysis of the conditions under which NE is unique. |
Yukun Cheng; Xiaotie Deng; Yunxuan Ma; |
| 118 | Differentially Private Bayesian Persuasion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Similarly, hospitals may share patient data to attract research investments with the obligation to preserve patients’ privacy. To address these issues, we study Bayesian persuasion under differential privacy constraints, where the sender must design an optimal signaling scheme for persuasion while guaranteeing the privacy of each agent’s private information in the database. |
Yuqi Pan; Zhiwei Steven Wu; Haifeng Xu; Shuran Zheng; |
| 119 | Mitigating The Participation Bias By Balancing Extreme Ratings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider a robust rating aggregation task under the participation bias. |
Yongkang Guo; Yuqing Kong; Jialiang Liu; |
| 120 | Unlearning Incentivizes Learning Under Privacy Risk Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formulate a set of contract design problems for both unlearning-disabled and unlearning-enabled scenarios. |
Qiyuan Wang; Ruiling Xu; Shibo He; Randall Berry; Meng Zhang; |
| 121 | Relying on The Metrics of Evaluated Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To guide the choice of metrics in the face of this informational problem, we turn to the evaluated agents themselves, who may have more information about how to measure their own outcomes. We model this interaction as an agency game, where we ask: When does an agent have an incentive to reveal the observability of a metric to their evaluator? |
Serena Wang; Michael Jordan; Katrina Ligett; R. Preston McAfee; |
| 122 | Navigating The Deployment Dilemma and Innovation Paradox: Open-Source Versus Closed-source Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a game-theoretical model to examine the economic incentives behind the deployment choice and the impact of open-source engagement strategies on technological innovation. |
Yanxuan Wu; Haihan Duan; Xitong Li; Xiping Hu; |
| 123 | Query Design for Crowdsourced Clustering: Effect of Cognitive Overload and Contextual Bias Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work investigates the trade-off between query size and answer accuracy, revealing diminishing returns beyond 4-5 items per query. |
Yi Chen; Ramya Korlakai Vinayak; |
| 124 | Welcome to The Dark Side: Analyzing The Revenue Flows of Fraud in The Online Ad Ecosystem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We collect and study a large corpus of thousands of websites and show how ad transparency standards can be abused by bad actors to obscure ad revenue flows. |
Emmanouil Papadogiannakis; Nicolas Kourtellis; Panagiotis Papadopoulos; Evangelos Markatos; |
| 125 | Assessing Compliance in Digital Advertising: A Deep Dive Into Acceptable Ads Standards Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose enhancements to the exception list to better uphold user experience and ad integrity. |
Ahsan Zafar; Anupam Das; |
| 126 | Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Approach (ii) is also problematic because these measures tend to be ad-hoc and do not relate well to traditional relevance measures, like NDCG. Motivated by this, we present a new approach for jointly evaluating fairness and relevance in RSs: Distance to Pareto Frontier (DPFR). |
Theresia Veronika Rampisela; Tuukka Ruotsalo; Maria Maistro; Christina Lioma; |
| 127 | SuiGPT MAD: Move AI Decompiler to Improve Transparency and Auditability on Non-Open-Source Blockchain Smart Contract Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While code audits are one solution to this problem, the lack of smart contracts source code on many blockchain platforms, such as Sui, hinders the ease of auditing. A promising approach to this issue is the use of a decompiler to reverse-engineer smart contract bytecode. |
Eason Chen; Xinyi Tang; Zimo Xiao; Chuangji Li; Shizhuo Li; Tingguan Wu; Siyun Wang; Kostas Kryptos Chalkias; |
| 128 | Before \& After: The Effect of EU’s 2022 Code of Practice on Disinformation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we set out to explore what was the impact of the Code of Practice and especially to explore to what extent ad networks continue to advertise on dis-/mis-information sites. |
Emmanouil Papadogiannakis; Panagiotis Papadopoulos; Nicolas Kourtellis; Evangelos Markatos; |
| 129 | Responsible Diffusion Models Via Constraining Text Embeddings Within Safe Regions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel self-discovery approach to identifying a semantic direction vector in the embedding space to restrict text embedding within a safe region. |
Zhiwen Li; Die Chen; Mingyuan Fan; Cen Chen; Yaliang Li; Yanhao Wang; Wenmeng Zhou; |
| 130 | Semantics-Aware Cookie Purpose Compliance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce COOVER (cookie value examiner) to assess the non-compliance between the website-declared purpose and the semantic-intended purpose of cookies (denoted as potential cookie purpose violation ). |
Baiqi Chen; Jiawei Lyu; Tingmin Wu; Mohan Baruwal Chhetri; Guangdong Bai; |
| 131 | LoCal: Logical and Causal Fact-Checking with LLM-Based Multi-Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Logical and Causal fact-checking (LoCal), a novel fact-checking framework based on multiple LLM-based agents. |
Jiatong Ma; Linmei Hu; Rang Li; Wenbo Fu; |
| 132 | Retrieval with Learned Similarities Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We establish Mixture-of-Logits (MoL) as a universal approximator of similarity functions, demonstrate that MoL’s expressiveness can be realized empirically to achieve superior performance on diverse retrieval scenarios, and propose techniques to retrieve the approximate top-k results using MoL with tight error bounds. |
Bailu Ding; Jiaqi Zhai; |
| 133 | TourRank: Utilizing Large Language Models for Documents Ranking with A Tournament-Inspired Strategy Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, several significant challenges still persist in LLMs for ranking: (1) LLMs are constrained by limited input length, precluding them from processing a large number of documents simultaneously; (2) The output document sequence is influenced by the input order of documents, resulting in inconsistent ranking outcomes; (3) Achieving a balance between cost and ranking performance is challenging. To tackle these issues, we introduce a novel documents ranking method called TourRank1. |
Yiqun Chen; Qi Liu; Yi Zhang; Weiwei Sun; Xinyu Ma; Wei Yang; Daiting Shi; Jiaxin Mao; Dawei Yin; |
| 134 | SimEdge: A Scalable Transitivity-Aware Graph-Theoretic Similarity Model for Capturing Edge-to-Edge Relationships Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing node-to-node similarity measures from the SimRank family may violate the triangular inequality. When applied directly to assessing edge-to-edge similarity, such measures may fail to capture transitive relationships and misrepresent dissimilarity between nodes.In this paper, we propose a novel similarity measure, SimEdge, which can capture transitive relationships for assessing edge-to-edge similarity. |
Weiren Yu; |
| 135 | ImageScope: Unifying Language-Guided Image Retrieval Via Large Multimodal Model Collective Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose ImageScope, a training-free, three-stage framework that leverages collective reasoning to unify LGIR tasks. |
Pengfei Luo; Jingbo Zhou; Tong Xu; Yuan Xia; Linli Xu; Enhong Chen; |
| 136 | Behavior Modeling Space Reconstruction for E-Commerce Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper reexamines existing approaches through a unified lens using causal graphs and Venn diagrams, uncovering two prevalent yet significant issues: entangled preference and relevance effects, and a collapsed modeling space. To surmount these challenges, our research introduces a novel framework, DRP, which enhances search accuracy through two components to reconstruct the behavior modeling space. |
Yejing Wang; Chi Zhang; Xiangyu Zhao; Qidong Liu; Maolin Wang; Xuetao Wei; Zitao Liu; Xing Shi; Xudong Yang; Ling Zhong; Wei Lin; |
| 137 | PEAR: Position-Embedding-Agnostic Attention Re-weighting Enhances Retrieval-Augmented Generation with Zero Inference Overhead Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Position-Embedding-Agnostic attention Re-weighting (PEAR), which enhances the context awareness of LLMs with zero inference overhead. |
Tao Tan; Yining Qian; Ang Lv; Hongzhan Lin; Songhao Wu; Yongbo Wang; Feng Wang; Jingtong Wu; Xin Lu; Rui Yan; |
| 138 | MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods primarily utilize a paradigm of ”greedy selection”, i.e., selecting one document with the highest diversity score at a time or optimize an approximation of the objective function. These approaches tend to be inefficient and are easily trapped in a suboptimal state. To address these challenges, we introduce Multi-Agent reinforcement learning (MARL) for search result DIVersity, which called MA4DIV. |
Yiqun Chen; Jiaxin Mao; Yi Zhang; Dehong Ma; Long Xia; Jun Fan; Daiting Shi; Zhicong Cheng; Simiu Gu; Dawei Yin; |
| 139 | Assessing and Post-Processing Black Box Large Language Models for Knowledge Editing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address privacy leaks of editing data and style over-editing in existing approaches, we propose a new postEdit framework. |
Xiaoshuai Song; Zhengyang Wang; Keqing He; Guanting Dong; Yutao Mou; Jinxu Zhao; Weiran Xu; |
| 140 | HtmlRAG: HTML Is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, much of the structural and semantic information inherent in HTML, such as headings and table structures, is lost during this plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG. |
Jiejun Tan; Zhicheng Dou; Wen Wang; Mang Wang; Weipeng Chen; Ji-Rong Wen; |
| 141 | Unveiling Discrete Clues: Superior Healthcare Predictions for Rare Diseases Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, prediction for rare diseases remains challenging due to limited co-occurrence and inadequately tailored approaches. To address this issue, this paper proposes UDC, a novel method that unveils discrete clues to bridge consistent textual knowledge and CO signals within a unified semantic space, thereby enriching the representation semantics of rare diseases. |
Chuang Zhao; Hui Tang; Jiheng Zhang; Xiaomeng Li; |
| 142 | UniGraph2: Learning A Unified Embedding Space to Bind Multimodal Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Multimodal graphs (MMGs) represent such graphs where each node is associated with features from different modalities, while the edges capture the relationships between these entities.On the other hand, existing graph foundation models primarily focus on text-attributed graphs (TAGs) and are not designed to handle the complexities of MMGs. To address these limitations, we propose UniGraph2, a novel cross-domain graph foundation model that enables general representation learning on MMGs, providing a unified embedding space. |
Yufei He; Yuan Sui; Xiaoxin He; Yue Liu; Yifei Sun; Bryan Hooi; |
| 143 | Cluster Aware Graph Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing unsupervised graph anomaly detection methods often struggle with high-dimensionality issues, rely on strong assumptions about graph structures or fail to handle complex multi-view graphs. To address these challenges, we propose a cluster aware multi-view graph anomaly detection method, called CARE. |
Lecheng Zheng; John Birge; Haiyue Wu; Yifang Zhang; Jingrui He; |
| 144 | Pontus: A Memory-Efficient and High-Accuracy Approach for Persistence-Based Item Lookup in High-Velocity Data Streams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce Pontus, a novel approach that uses an approximate data structure (sketch) specifically designed for the efficient and accurate detection of persistent items. |
Weihe Li; Zukai Li; Beyza B\{u}t\{u}n; Alec F. Diallo; Marco Fiore; Paul Patras; |
| 145 | Revisiting Backdoor Attacks on Time Series Classification in The Frequency Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Drawing inspiration from the fact that DNN models inherently capture frequency domain features in time series data, we identify that improper perturbations in the frequency domain are the root cause of ineffective attacks. To address this, we propose to generate triggers both effectively and efficiently, guided by frequency analysis. |
Yuanmin Huang; Mi Zhang; Zhaoxiang Wang; Wenxuan Li; Min Yang; |
| 146 | Bridging The Gap: Aligning Language Model Generation with Structured Information Extraction Via Controllable State Transition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we define the generation process of the language model as the controllable state transition, aligning the generation and extraction processes to ensure the integrity of the output structure and adapt to the goals of the information extraction task. |
Hao Li; Yubing Ren; Yanan Cao; Yingjie Li; Fang Fang; Zheng Lin; Shi Wang; |
| 147 | Division-of-Thoughts: Harnessing Hybrid Language Model Synergy for Efficient On-Device Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Division-o f-Thoughts (DoT), a collaborative reasoning framework leveraging the synergy between locally deployed Smaller-scale Language Models (SLMs) and cloud-based LLMs. |
Chenyang Shao; Xinyuan Hu; Yutang Lin; Fengli Xu; |
| 148 | WebCode2M: A Real-World Dataset for Code Generation from Webpage Designs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To validate the effectiveness of WebCode2M, we introduce a baseline model based on the Vision Transformer (ViT), named WebCoder, and establish a benchmark for fair comparison. |
Yi Gui; Zhen Li; Yao Wan; Yemin Shi; Hongyu Zhang; Bohua Chen; Yi Su; Dongping Chen; Siyuan Wu; Xing Zhou; Wenbin Jiang; Hai Jin; Xiangliang Zhang; |
| 149 | UICopilot: Automating UI Synthesis Via Hierarchical Code Generation from Webpage Designs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The lengthy code poses challenges for the performance and efficiency of MLLMs, especially in capturing the structural information of UI designs. To address these challenges, this paper proposes UICopilot, a novel approach to automating UI synthesis via hierarchical code generation from webpage designs. |
Yi Gui; Yao Wan; Zhen Li; Zhongyi Zhang; Dongping Chen; Hongyu Zhang; Yi Su; Bohua Chen; Xing Zhou; Wenbin Jiang; Xiangliang Zhang; |
| 150 | ORFA: Exploring WebAssembly As A Turing Complete Query Language for Web APIs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While semantic enriched API query languages like GraphQL mitigates this problem, they still face expressiveness limitations for logical operations such as indirect queries and loop traversals. To address this, we propose ORFA (One Request For All), the first in literature that employs WebAssembly (Wasm) as a Web API query language to achieve complete expressiveness of client requests. |
Yuhao Gu; Chunyu Chen; Jiangsu Du; Xiaoxi Zhang; Xianwei Zhang; |
| 151 | MISE: Meta-knowledge Inheritance for Social Media-Based Stressor Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we cast the stressor estimation problem within a practical scenario few-shot learning setting, and propose a novel meta-learning based stressor estimation framework that is enhanced by a meta-knowledge inheritance mechanism. |
Xin Wang; Ling Feng; Huijun Zhang; Lei Cao; Kaisheng Zeng; Qi Li; Yang Ding; Yi Dai; David Clifton; |
| 152 | Mining User Preferences from Online Reviews with The Genre-aware Personalized Neural Topic Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, they often fail to mine user preferences and discover personalized topic profiles due to the absence of explicit user modeling. To address these limitations, we propose a novel Genre-aware Personalized neural Topic Model (GPTM), which incorporates product genre information into the topic modeling process to ensure the relevance between mined topics and product genres. |
Rui Wang; Jiahao Lu; Xincheng Lv; Shuyu Chang; Yansheng Wu; Yuanzhi Yao; Haiping Huang; Guozi Sun; |
| 153 | Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Anecdotal evidence suggests that webpages in developing and developed regions differ significantly. In this work, we test this hypothesis by measuring differences in web development practices across the two groups of countries, using multiple dimensions: webpages’ size, complexity, security, privacy, quality, technology adoption, and accessibility. |
Masudul Hasan Masud Bhuiyan; Matteo Varvello; Cristian-Alexandru Staicu; Yasir Zaki; |
| 154 | Chain-of-Factors Paper-Reviewer Matching Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, most of these studies focus on only one factor, resulting in an incomplete evaluation of the paper-reviewer relevance. To address this issue, we propose a unified model for paper-reviewer matching that jointly considers semantic, topic, and citation factors. |
Yu Zhang; Yanzhen Shen; SeongKu Kang; Xiusi Chen; Bowen Jin; Jiawei Han; |
| 155 | CROWN: A Novel Approach to Comprehending Users’ Preferences for Accurate Personalized News Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the breakthrough in personalized news recommendation, the following challenges have been rarely explored: (C1) Comprehending manifold intents coupled within a news article, (C2) Differentiating varying post-read preferences of news articles, and (C3) Addressing the cold-start user problem. To tackle these challenges together, we propose a novel personalized news recommendation framework (CROWN) that employs (1) category-guided intent disentanglement for (C1), (2) consistency-based news representation for (C2), and (3) GNN-enhanced hybrid user representation for (C3). |
Yunyong Ko; Seongeun Ryu; Sang-Wook Kim; |
| 156 | LLMCloudHunter: Harnessing LLMs for Automated Extraction of Detection Rules from Cloud-Based CTI Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous studies aimed at automating OSCTI analysis are limited since (1) they failed to provide actionable outputs, (2) they did not take advantage of images present in OSCTI sources, and (3) they focused on on-premises environments, overlooking the growing importance of cloud environments. To address these gaps, we propose LLMCloudHunter, a novel framework that leverages large language models (LLMs) to automatically generate generic-signature detection rule candidates from textual and visual OSCTI data. |
Yuval Schwartz; Lavi Ben-Shimol; Dudu Mimran; Yuval Elovici; Asaf Shabtai; |
| 157 | WasmGuard: Enhancing Web Security Through Robust Raw-Binary Detection of WebAssembly Malware Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our experiments, MINOS’s detection accuracy dropped to 49.90\% under adversarial attacks, revealing critical vulnerabilities. To address this, we introduce WasmGuard, a robust malware detection framework tailored for Wasm. |
Yuxia Sun; HuiHong Chen; Zhixiao Fu; Wenjian Lv; Zitao Liu; Haolin Liu; |
| 158 | Heterogeneous Graph Transfer Learning for Category-aware Cross-Domain Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing methods do not explore the item transition patterns across different domains and can also not be applied to a multi-domain scenario.Moreover, previous methods rely on overlapping users as bridges to transfer knowledge, which struggles to capture the complex associations across domains without sufficient overlapping users. In this paper, we introduce item attributes into CDSR, and propose a heterogeneous graph transfer learning method to address these issues.Specifically, we construct a cross-domain heterogeneous graph to allow the association of user, item, and category nodes from different domains,and enhance the flexibility of the model by enabling message propagation between more nodes through edge expansion based on the semantic similarity and co-occurrence probability.In addition, we devise meta-paths from different perspectives for nodes at item, user and category levels to guide information aggregation, which can transfer knowledge across domains and reduce the reliance on the number of overlapping users.We further design attention modules to capture users’ dynamic preferences from the item sequences they have interacted with in each domain, and explore the transition patterns within category sequences which reflect users’ coarse-grained preferences.Finally, we perform knowledge transfer across different domains, and predict the most likely items that users will interact with in each domain. |
Zitao Xu; Xiaoqing Chen; Weike Pan; Zhong Ming; |
| 159 | Explainable and Efficient Editing for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To solve these, we propose an Explainable and effiCient model Editing method, termed ECE. |
Tianyu Zhang; Junfeng Fang; Houcheng Jiang; Baolong Bi; Xiang Wang; Xiangnan He; |
| 160 | A Context-Aware Framework for Integrating Ad Auctions and Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, it cannot prevent strategic behavior by advertisers. Therefore, we propose a context-aware integrated framework to address these issues. |
Yuchao Ma; Weian Li; Yuejia Dou; Zhiyuan Su; Changyuan Yu; Qi Qi; |
| 161 | Hyperbolic Diffusion Recommender Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Subsequently, we propose a novel hyperbolic latent diffusion process specifically tailored for users and items. |
Meng Yuan; Yutian Xiao; Wei Chen; Chou Zhao; Deqing Wang; Fuzhen Zhuang; |
| 162 | Seed: Bridging Sequence and Diffusion Models for Road Trajectory Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, Seed adopts a conditional diffusion structure, where a Transformer models the movement of each trajectory along the road segments, and conditioned on the Transformer’s output, a diffusion model recovers the next road segment from random noise. |
Xuan Rao; Shuo Shang; Renhe Jiang; Peng Han; Lisi Chen; |
| 163 | Distributionally Robust Graph Out-of-Distribution Recommendation Via Diffusion Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: When the model overly focuses on fitting noise samples in the training data, it may learn irrelevant or meaningless features that cannot be generalized to OOD data. To address this challenge, we design a Distributionally Robust Graph model for OOD recommendation (DRGO). |
Chu Zhao; Enneng Yang; Yuliang Liang; Jianzhe Zhao; Guibing Guo; Xingwei Wang; |
| 164 | TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To alleviate human efforts, in this paper, we work on hierarchical text classification with a minimal amount of supervision: using the sole class name of each node as the only supervision. |
Yunyi Zhang; Ruozhen Yang; Xueqiang Xu; Rui Li; Jinfeng Xiao; Jiaming Shen; Jiawei Han; |
| 165 | Enabling Real-Time Inference in Online Continual Learning Via Device-Cloud Collaboration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose ELITE, an online CL framework with device-cloud collaboration, to realize on-device real-time inference on time-varying task streams with performance guarantee. |
Haibo Liu; Chen Gong; Zhenzhe Zheng; Shengzhong Liu; Fan Wu; |
| 166 | Not All Benignware Are Alike: Enhancing Clean-Label Attacks on Malware Classifiers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, clean-label attack methods are generally less effective compared to those that involve embedding triggers and altering sample labels to the target class (called corrupted-label attacks). To address this limitation, we propose a simple yet effective method that involves Poisoning Malware-Similar Benignware (PMSB) instead of random selection, thereby approximating the scenario of corrupted-label attacks and enhancing the effectiveness of clean-label attacks. |
Xutong Wang; Yun Feng; Bingsheng Bi; Yaqin Cao; Ze Jin; Xinyu Liu; Yuling Liu; Yunpeng Li; |
| 167 | Joint Optimal Transport and Embedding for Network Alignment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we aim to unify the embedding and OT-based methods in a mutually beneficial manner and propose a joint optimal transport and embedding framework for network alignment named JOENA. |
Qi Yu; Zhichen Zeng; Yuchen Yan; Lei Ying; R. Srikant; Hanghang Tong; |
| 168 | Explainable Multi-Modality Alignment for Transferable Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel Explainable multi-modality Alignment method for transferable Rec ommender systems, i.e., EARec. |
Shenghao Yang; Weizhi Ma; Zhiqiang Guo; Min Zhang; Haiyang Wu; Junjie Zhai; Chunhui Zhang; Yuekui Yang; |
| 169 | Traceback of Poisoning Attacks to Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce RAGForensics, the first traceback system for RAG, designed to identify poisoned texts within the knowledge database that are responsible for the attacks. |
Baolei Zhang; Haoran Xin; Minghong Fang; Zhuqing Liu; Biao Yi; Tong Li; Zheli Liu; |
| 170 | Robust Graph Learning Against Adversarial Evasion Attacks Via Prior-Free Diffusion-Based Structure Purification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, we propose a novel Diffusion-based Structure Purification framework named DiffSP, which creatively incorporates the graph diffusion model to learn intrinsic distributions of clean graphs and purify the perturbed structures by removing adversaries under the direction of the captured predictive patterns without relying on priors. |
Jiayi Luo; Qingyun Sun; Haonan Yuan; Xingcheng Fu; Jianxin Li; |
| 171 | Semi-Supervised Anomaly Detection Through Denoising-Aware Contrastive Distance Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Distance-based methods, in particular, typically rely on Euclidean distance metric, which lacks the flexibility to capture complex correlations across different data dimensions. To address the above challenges, we propose CAD, a denoising-aware Contrastive distance learning framework for semi-supervised AD. |
Jianling Gao; Chongyang Tao; Zhenchao Sun; Xiya Jiang; Shuai Ma; |
| 172 | Learning By Comparing: Boosting Multimodal Affective Computing Through Ordinal Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous studies on multimodal affective computing primarily focus on approximating predictions to annotated labels, often neglecting the ordinal nature of affective states. In this paper, we address this issue by exploring ordinal learning, and a Multimodal Ordinal Affective Computing (MOAC) framework is designed to enhance the understanding of the nature of affective concepts. |
Sijie Mai; Ying Zeng; Haifeng Hu; |
| 173 | Transfer Rule Learning Over Large Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a framework for KG rule learning based on transfer learning. |
Hong Liu; Zhe Wang; Kewen Wang; Xiaowang Zhang; Zhiyong Feng; |
| 174 | Private Order Flows and Builder Bidding Dynamics: The Road to Monopoly in Ethereum’s Block Building Market Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore the effect of PBS with private order flows. |
Shuzheng Wang; Yue Huang; Wenqin Zhang; Yuming Huang; Xuechao Wang; Jing Tang; |
| 175 | Enhancing Cross-domain Link Prediction Via Evolution Process Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes CrossLink, a novel framework for cross-domain link prediction. |
Xuanwen Huang; Wei Chow; Yize Zhu; Yang Wang; Ziwei Chai; Chunping Wang; Lei Chen; Yang Yang; |
| 176 | Brewing Vodka: Distilling Pure Knowledge for Lightweight Threat Detection in Audit Logs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Advanced Persistent Threats (APTs) are continuously evolving, leveraging their stealthiness and persistence to put increasing pressure on current provenance-based Intrusion Detection Systems (IDS). This evolution exposes several critical issues: (1) The dense interaction between malicious and benign nodes within provenance graphs introduces neighbor noise, hindering effective detection; (2) The complex prediction mechanisms of existing APTs detection models lead to the insufficient utilization of prior knowledge embedded in the data; (3) The high computational cost makes detection impractical.To address these challenges, we propose Vodka, a lightweight threat detection system built on a knowledge distillation framework, capable of node-level detection within audit log provenance graphs. |
Weiheng Wu; Wei Qiao; Wenhao Yan; Bo Jiang; Yuling Liu; Baoxu Liu; Zhigang Lu; Junrong Liu; |
| 177 | GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, we generate and curate large-scale graph-summary pair data with the assistance of LLMs, and introduce a novel graph-summary pretraining method, combined with invariant learning, to enhance graph foundation models with strong cross-domain zero-shot transferability. |
Yun Zhu; Haizhou Shi; Xiaotang Wang; Yongchao Liu; Yaoke Wang; Boci Peng; Chuntao Hong; Siliang Tang; |
| 178 | MixRec: Individual and Collective Mixing Empowers Data Augmentation for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, most data augmentation relies on elaborate manual design, which is not only not universal, but the bloated and redundant augmentation process may significantly slow down model training progress. To tackle these limitations, we propose a novel Dual Mixing-based Recommendation Framework (MixRec) to empower data augmentation as we wish. |
Yi Zhang; Yiwen Zhang; |
| 179 | Boosting Graph Convolution with Disparity-induced Structural Refinement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a Disparity-induced Structural Refinement (DSR) method that enables adaptive and selective message propagation in GNN, to enhance representation learning in heterophilous graphs. |
Sujia Huang; Yueyang Pi; Tong Zhang; Wenzhe Liu; Zhen Cui; |
| 180 | Tool Learning in The Wild: Empowering Language Models As Automatic Tool Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, we propose the AutoTools-Learning approach, training the LLMs with three learning tasks on 34k instances of high-quality synthetic data, including documentation understanding, relevance learning, and function programming. |
Zhengliang Shi; Shen Gao; Lingyong Yan; Yue Feng; Xiuyi Chen; Zhumin Chen; Dawei Yin; Suzan Verberne; Zhaochun Ren; |
| 181 | STKOpt: Automated Spatio-Temporal Knowledge Optimization for Traffic Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods address scenario-specific spatio-temporal heterogeneity by designing model architectures, often overlooking the importance of selecting optimal spatio-temporal knowledge (i.e., model inputs). In this paper, we propose an automated framework for spatio-temporal knowledge optimization to address this challenge. |
Yayao Hong; Liyue Chen; Leye Wang; Xiuhuai Xie; Guofeng Luo; Cheng Wang; Longbiao Chen; |
| 182 | ColaCare: Enhancing Electronic Health Record Modeling Through Large Language Model-Driven Multi-Agent Collaboration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce ColaCare, a framework that enhances Electronic Health Record (EHR) modeling through multi-agent collaboration driven by Large Language Models (LLMs). |
Zixiang Wang; Yinghao Zhu; Huiya Zhao; Xiaochen Zheng; Dehao Sui; Tianlong Wang; Wen Tang; Yasha Wang; Ewen Harrison; Chengwei Pan; Junyi Gao; Liantao Ma; |
| 183 | CTR-Driven Advertising Image Generation with Multimodal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To further improve the CTR of generated images, we propose a novel reward model to fine-tune pre-trained MLLMs through Reinforcement Learning (RL), which can jointly utilize multimodal features and accurately reflect user click preferences. |
Xingye Chen; Wei Feng; Zhenbang Du; Weizhen Wang; Yanyin Chen; Haohan Wang; Linkai Liu; Yaoyu Li; Jinyuan Zhao; Yu Li; Zheng Zhang; Jingjing Lv; Junjie Shen; Zhangang Lin; Jingping Shao; Yuanjie Shao; Xinge You; Changxin Gao; Nong Sang; |
| 184 | Fairness Evaluation with Item Response Theory Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Fair-IRT framework to evaluate a set of predictive models on a set of individuals, while simultaneously eliciting specific parameters, namely, the ability to make fair predictions (a feature of predictive models), as well as the discrimination and difficulty of individuals that affect the prediction results. |
Ziqi Xu; Sevvandi Kandanaarachchi; Cheng Soon Ong; Eirini Ntoutsi; |
| 185 | Path-LLM: A Multi-Modal Path Representation Learning By Aligning and Fusing with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To further optimize LLM training, we introduce a Two-stage Overlapping Curriculum Learning (TOCL) approach, which progressively increases the complexity of the training data. |
Yongfu Wei; Yan Lin; Hongfan Gao; Ronghui Xu; Sean Bin Yang; Jilin Hu; |
| 186 | Covering K-Cliques in Billion-Scale Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a hierarchical solution that computes a small cover without enumerating k-cliques. |
Kaiyu Chen; Dong Wen; Hanchen Wang; Zhengyi Yang; Wenjie Zhang; Xuemin Lin; |
| 187 | BATON: Enhancing Batch-wise Inference Efficiency for Large Language Models Via Dynamic Re-batching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this approach not only increases resource usage but also introduces idle computations to the batch due to the prefilling of newly added queries. Therefore, we propose BATON, an efficient batch-wise LLM inference scheme by dynamically adjusting processing batch, which can achieve near-zero idle computations without incurring additional resource consumption. |
Peizhuang Cong; Qizhi Chen; Haochen Zhao; Tong Yang; |
| 188 | Helios: Learning and Adaptation of Matching Rules for Continual In-Network Malicious Traffic Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Helios, an in-network malicious traffic detection system, for continual adaptation in attack-incremental scenarios. |
Zhenning Shi; Dan Zhao; Yijia Zhu; Guorui Xie; Qing Li; Yong Jiang; |
| 189 | Grasp The Key Takeaways from Source Domain for Few Shot Graph Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: (2) Apart from the biased nodes, the low-value nodes among the remaining nodes impede the GNN learning for the core nodes, like the limited target training nodes. To address the above issues, we propose a new method for FSGDA, named GraphInflu, whose core idea is to grasp the key takeaways from the source domain to facilitate the adaptation process. |
Xiangwei Lv; Jingyuan Chen; Mengze Li; Yongduo Sui; Zemin Liu; Beishui Liao; |
| 190 | From Data Deluge to Data Curation: A Filtering-WoRA Paradigm for Efficient Text-based Person Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We observe that only a subset of the data in these constructed datasets plays a decisive role. Therefore, we introduce a new Filtering-WoRA paradigm, which contains a filtering algorithm to identify this crucial data subset and WoRA (Weighted Low-Rank Adaptation) learning strategy for light fine-tuning. |
Jintao Sun; Hao Fei; Gangyi Ding; Zhedong Zheng; |
| 191 | Virtual Stars, Real Fans: Understanding The VTuber Ecosystem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Gaining a deeper insight into these viewers is critical for VTubers to enhance audience engagement, foster a more robust fan base, and attract a larger viewership. To address this gap, we conduct a comprehensive analysis of VTuber viewers on Bilibili, a leading livestreaming platform where nearly all VTubers in China stream. |
Yiluo Wei; Gareth Tyson; |
| 192 | MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, conventional RAG methods face inherent limitations because of two underlying requirements: 1) explicitly stated queries, and 2) well-structured knowledge. These conditions, however, do not hold in general long-context processing tasks.In this work, we propose MemoRAG, a novel RAG framework empowered by global memory-augmented retrieval. |
Hongjin Qian; Zheng Liu; Peitian Zhang; Kelong Mao; Defu Lian; Zhicheng Dou; Tiejun Huang; |
| 193 | Scenario-independent Uncertainty Estimation for LLM-based Question Answering Via Factor Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a plug-and-play scenario-independent framework to enhance unsupervised UE in LLMs by removing scenario-related noise and focusing on semantic information. |
Zhihua Wen; Zhizhao Liu; Zhiliang Tian; Shilong Pan; Zhen Huang; Dongsheng Li; Minlie Huang; |
| 194 | Fast Estimation and Optimization of Resistance Diameter on Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider the computation and optimization problems for resistance diameter of a graph. |
Zenan Lu; Xiaotian Zhou; Zhongzhi Zhang; |
| 195 | X-ClusterLink: An Efficient Cross-Cluster Communication Framework in Multi-Kubernetes Clusters Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods for cross-cluster communication either employ a centralized control plane, which becomes a communication bottleneck, or use numerous service-bound proxies, leading to increased management complexity and possibly compromised robustness in cross-cluster communication. To address the above challenges, we introduce X-ClusterLink, a framework designed for efficient cross-cluster communication in multi-Kubernetes clusters. |
Pengbo Wang; Gongming Zhao; Yuantao Wu; Hongli Xu; Haibo Wang; |
| 196 | DecETT: Accurate App Fingerprinting Under Encrypted Tunnels Via Dual Decouple-based Semantic Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose DecETT, a dual decouple-based semantic enhancement method for accurate AF under encrypted tunnels. |
Zheyuan Gu; Chang Liu; Xiyuan Zhang; Chen Yang; Gaopeng Gou; Gang Xiong; Zhen Li; Sijia Li; |
| 197 | Miresga: Accelerating Layer-7 Load Balancing with Programmable Switches Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nevertheless, the limited memory capacity and the relatively sluggish speed of table entry insertion and deletion of programmable switches have severely constrained their performance.To this end, we introduce Miresga, a hybrid and high-performance layer-7 load balancing system by co-designing hardware and software. |
Xiaoyi Shi; Lin He; Jiasheng Zhou; Yifan Yang; Ying Liu; |
| 198 | Reinforcement-Learning Based Covert Social Influence Operations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: And how successful can they be, given that both social platform bot detectors and humans might report them to the social platform? To answer these questions, we propose RL_CSIO, a methodology based on reinforcement learning (RL) for running CSIOs. |
Saurabh Kumar; Valerio La Gatta; Andrea Pugliese; Andrew Pulver; V.S. Subrahmanian; Jiazhi Zhang; Youzhi Zhang; |
| 199 | 2D-TPE: Two-Dimensional Positional Encoding Enhances Table Understanding for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we first empirically demonstrate the detrimental impact of such flattening operations on the performance of LLMs in capturing the spatial information of tables through two elaborate proxy tasks. Subsequently, we introduce a simple yet effective positional encoding method, termed 2D-TPE (Two-Dimensional Table Positional Encoding), to address this challenge. |
Jia-Nan Li; Jian Guan; Wei Wu; Zhengtao Yu; Rui Yan; |
| 200 | Bridging The Gap: Teacher-Assisted Wasserstein Knowledge Distillation for Efficient Multi-Modal Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite effectiveness, we argue that this approach overlooks the significant gap between the complex teacher MMRec and the lightweight, ID-based student MLPRec, which differ significantly in size, architecture, and input modalities, leading to ineffective knowledge transfer and suboptimal student performance. To bridge this gap, we propose TARec, a novel teacher-assisted Wasserstein Knowledge Distillation framework for compressing MMRecs into an efficient MLPRec. |
Ziyi Zhuang; Hanwen Du; Hui Han; Youhua Li; Junchen Fu; Joemon M. Jose; Yongxin Ni; |
| 201 | LUSTER: Link Prediction Utilizing Shared-Latent Space Representation in Multi-Layer Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel end-to-end framework namely: Link prediction Utilizing Shared-laTent spacE Representation (LUSTER) in multi-layer networks. |
Ruohan Yang; Muhammad Asif Ali; Huan Wang; Junyang Chen; Di Wang; |
| 202 | REACT: Residual-Adaptive Contextual Tuning for Fast Model Adaptation in Threat Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose REACT, a novel framework that adapts the model using a few unlabeled data and contextual insights. |
Jiayun Zhang; Junshen Xu; Bugra Can; Yi Fan; |
| 203 | ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a novel method for data-use auditing in the text-to-image generation model. |
Linkang Du; Zheng Zhu; Min Chen; Zhou Su; Shouling Ji; Peng Cheng; Jiming Chen; Zhikun Zhang; |
| 204 | DAGE: DAG Query Answering Via Relational Combinator with Logical Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This requirement limits the answerable queries to queries having a single free variable and being decomposable, which are called tree-form queries and correspond to the SROI- description logic. In this paper, we define a more general set of queries, called DAG queries and formulated in the ALCOIR description logic, propose a query embedding method for them, called DAGE, and a new benchmark to evaluate query embeddings on them. |
Yunjie He; Bo Xiong; Daniel Hern\'{a}ndez; Yuqicheng Zhu; Evgeny Kharlamov; Steffen Staab; |
| 205 | Highly-efficient Minimization of Network Connectivity in Large-scale Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we present a fast algorithm that is independent of the number of edges. |
Mingyang Zhou; Gang Liu; Kezhong Lu; Hao Liao; Rui Mao; |
| 206 | Multimodal Taylor Series Network for Misinformation Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel Multimodal Taylor Series (MTS) network for detecting multimodal misinformation. |
Jiahao Sun; Chen Chen; Chunyan Hou; Yike Wu; Xiaojie Yuan; |
| 207 | DVIB: Towards Robust Multimodal Recommender Systems Via Variational Information Bottleneck Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In contrast, we propose the DVIB framework to simultaneously address both issues in a simple manner. |
Wenkuan Zhao; Shanshan Zhong; Yifan Liu; Wushao Wen; Jinghui Qin; Mingfu Liang; Zhongzhan Huang; |
| 208 | Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by recent work on the practice of encoding human-interpretable concepts linearly within large language models, we treat truthfulness as a specially linearly encoded concept within LLMs, and introduce Adaptive Activation Steering (ACT), a tuning-free method that adaptively shifts LLM’s activations in the ”truthful” direction during inference. |
Tianlong Wang; Xianfeng Jiao; Yinghao Zhu; Zhongzhi Chen; Yifan He; Xu Chu; Junyi Gao; Yasha Wang; Liantao Ma; |
| 209 | Inferentially-Private Private Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our goal is to devise an inferentially-private private information structure that maximizes the informativeness of the released signal, following the Blackwell ordering principle, while adhering to inferential privacy constraints. |
Shuaiqi Wang; Shuran Zheng; Zinan Lin; Giulia Fanti; Zhiwei Steven Wu; |
| 210 | Frequency-Augmented Mixture-of-Heterogeneous-Experts Framework for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This evidently limits their ability to capture diverse user patterns, leading to suboptimal recommendations. To tackle this problem, we present FamouSRec, a Frequency-Augmented Mixture-of-Heterogeneous -Experts Framework for personalized Rec ommendations. |
Junjie Zhang; Ruobing Xie; Hongyu Lu; Wenqi Sun; Wayne Xin Zhao; Yu Chen; Zhanhui Kang; |
| 211 | Synergizing Large Language Models and Knowledge-Based Reasoning for Interpretable Feature Engineering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose ReaGen, an automated feature engineering (AutoFE) approach that combines knowledge graphs (KGs) with large language models (LLMs) to generate interpretable features. |
Mohamed Bouadi; Arta Alavi; Salima Benbernou; Mourad Ouziri; |
| 212 | Balancing Graph Embedding Smoothness in Self-supervised Learning Via Information-Theoretic Decomposition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a rigorous theoretical analysis of the effects of these loss functions, highlighting their significance from both the SSL and graph smoothness perspectives. |
Heesoo Jung; Hogun Park; |
| 213 | Disentangled Knowledge Tracing for Alleviating Cognitive Bias Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: After delving into the causal relations in the KT models, we identify the main cause as the confounder effect of students’ historical correct rate distribution over question groups on the student representation and prediction score. Towards this end, we propose a Disentangled Knowledge Tracing (DisKT) model, which separately models students’ familiar and unfamiliar abilities based on causal effects and eliminates the impact of the confounder in student representation within the model. |
Yiyun Zhou; Zheqi Lv; Shengyu Zhang; Jingyuan Chen; |
| 214 | Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Such assumption may not be feasible in the real world due to the difficulty of constructing tasks and the substantial costs involved. Therefore, we propose a SiMple yet effectIve approach for graph few-shot Learning with fEwer tasks, named SMILE. |
Yonghao Liu; Mengyu Li; Fausto Giunchiglia; Lan Huang; Ximing Li; Xiaoyue Feng; Renchu Guan; |
| 215 | BAT: Benchmark for Auto-bidding Task Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A significant obstacle to the development, evaluation, and refinement of real-time autobidding algorithms is the scarcity of comprehensive datasets and standardized benchmarks. To address this deficiency, we present an auction benchmark encompassing the two most prevalent auction formats. |
Alexandra Khirianova; Ekaterina Solodneva; Andrey Pudovikov; Sergey Osokin; Egor Samosvat; Yuriy Dorn; Alexander Ledovsky; Yana Zenkova; |
| 216 | Linking Souls to Humans: Blockchain Accounts with Credible Anonymity for Web 3.0 Decentralized Identity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work proposes zkBID, a zero-knowledge blockchain-account-based Web 3.0 decentralized identity scheme, to overcome endemic mistrust in blockchain account systems. |
Taotao Wang; Zibin Lin; Shengli Zhang; Long Shi; Qing Yang; Boris D\{u}dder; |
| 217 | Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection Via Role Recognition and Involvement Measurement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While detecting LLM-generated content is crucial for mitigating these risks, current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-LLM collaboration. To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content. |
Zihao Cheng; Li Zhou; Feng Jiang; Benyou Wang; Haizhou Li; |
| 218 | Plug and Play: Enabling Pluggable Attribute Unlearning in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first identify three main challenges of current methods in dynamic environments, i.e., irreversible operation, low efficiency, and unsatisfied recommendation preservation. To overcome these challenges, we propose a Pluggable Attribute Unlearning framework, PAU. |
Xiaohua Feng; Yuyuan Li; Fengyuan Yu; Li Zhang; Chaochao Chen; Xiaolin Zheng; |
| 219 | Personalized Federated Recommendation for Cold-Start Users Via Adaptive Knowledge Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, cold-start users need to match similar user information from warm clients for a collaborative recommendation, but directly sharing user information is a violation of privacy and unacceptable. To tackle these challenges, we propose an efficient and privacy-enhanced federated recommendation for cold-start users (FR-CSU) that each client can adaptively transfer both user and item knowledge separately from warm clients and implement recommendations with local and transferred knowledge fusion. |
Yichen Li; Yijing Shan; Yi Liu; Haozhao Wang; Wei Wang; Yi Wang; Ruixuan Li; |
| 220 | Posted Price Mechanisms for Online Allocation with Diseconomies of Scale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, significant challenges remain, particularly in achieving optimality with small or finite inventories and developing effective randomized posted price mechanisms. To bridge this gap, we propose a novel randomized dynamic pricing mechanism for \O{}SDoS, providing a tighter lower bound on the competitive ratio compared to prior work. |
Hossein Nekouyan Jazi; Bo Sun; Raouf Boutaba; Xiaoqi Tan; |
| 221 | LIRA: A Learning-based Query-aware Partition Framework for Large-scale ANN Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In the partition construction phase, all partition-based methods face the boundary problem that separates a query’s nearest neighbors to multiple partitions, resulting in a long-tailed kNN distribution and degrading the optimal nprobe (i.e., the number of probing partitions). To address this gap, we propose LIRA, a LearnIng-based queRy-aware pArtition framework. |
Ximu Zeng; Liwei Deng; Penghao Chen; Xu Chen; Han Su; Kai Zheng; |
| 222 | Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce the Key Propagation Graph Generator (KPG), a novel reinforcement learning-based framework, that generates contextually coherent and informative propagation patterns for events with insufficient topology information and identifies significant substructures in events with redundant and noisy propagation structures. |
Yusong Zhang; Kun Xie; Xingyi Zhang; Xiangyu Dong; Sibo Wang; |
| 223 | EAGER-LLM: Enhancing Large Language Models As Recommenders Through Exogenous Behavior-Semantic Integration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, LLMs are not designed for recommendations, leading to inefficient collaborative learning, weak result correlations, and poor integration of traditional RS features. To address these challenges, we propose EAGER-LLM, a decoder-only llm-based generative recommendation framework that integrates endogenous and exogenous behavioral and semantic information in a non-intrusive manner. |
Minjie Hong; Yan Xia; Zehan Wang; Jieming Zhu; Ye Wang; Sihang Cai; Xiaoda Yang; Quanyu Dai; Zhenhua Dong; Zhimeng Zhang; Zhou Zhao; |
| 224 | Biting Off More Than You Can Detect: Retrieval-Augmented Multimodal Experts for Short Video Hate Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches face significant challenges: hate expressions continuously evolve, hateful signals are dispersed across multiple modalities (audio, text, and vision), and the contribution of each modality varies across different hate content. To address these issues, we introduce MoRE(Mixture of Retrieval-augmented multimodal Experts), a novel framework designed to enhance SVHD. |
Jian Lang; Rongpei Hong; Jin Xu; Yili Li; Xovee Xu; Fan Zhou; |
| 225 | FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these approaches overlook the shared latent feature space among incomplete modalities across different nodes and fail to discriminate against low-quality nodes. To address this gap, we present FedMobile, a new knowledge contribution-aware multimodal FL framework designed for robust learning despite missing modalities. |
Yi Liu; Cong Wang; Xingliang Yuan; |
| 226 | Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, two significant gaps persist: 1) the difficulty in accurately generating missing data due to the limited ability to capture modality distributions; and 2) the critical but overlooked visibility bias, where items with missing modalities are more likely to be disregarded due to the prioritization of items’ multimodal data over user preference alignment. This bias raises serious concerns about the fair treatment of items. To bridge these two gaps, we propose a novel Modality-Diffused Counterfactual (MoDiCF) framework for incomplete multimodal recommendations. |
Jin Li; Shoujin Wang; Qi Zhang; Shui Yu; Fang Chen; |
| 227 | Nature Makes No Leaps: Building Continuous Location Embeddings with Satellite Imagery from The Web Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose SatCLE, a novel framework for Continuous Location Embeddings leveraging Satellite imagery. |
Xixuan Hao; Wei Chen; Xingchen Zou; Yuxuan Liang; |
| 228 | Dr. Docker: A Large-Scale Security Measurement of Docker Image Ecosystem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, it is unclear to what extent the threats within Docker images are distributed and propagated. In this paper, we investigate five potential security risks in Docker images and propose a security analysis framework DITector based on these security issues. |
Hequan Shi; Lingyun Ying; Libo Chen; Haixin Duan; Ming Liu; Zhi Xue; |
| 229 | TriG-NER: Triplet-Grid Framework for Discontinuous Named Entity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing methods predominantly rely on custom tagging schemes to handle these discontinuous entities, resulting in models tightly coupled to specific tagging strategies and lacking generalisability across diverse datasets. To address these challenges, we propose TriG-NER, a novel Triplet-Grid Framework that introduces a generalisable approach to learning robust token-level representations for discontinuous entity extraction. |
Rina Carines Cabral; Soyeon Caren Han; Areej Alhassan; Riza Batista-Navarro; Goran Nenadic; Josiah Poon; |
| 230 | Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this limitation, capturing broader-range semantics is essential yet challenging, as it introduces two primary types of noise: fully connecting sentences in news graphs often adds unnecessary structural noise, while highly similar but authenticity-irrelevant sentences introduce feature noise, complicating the detection process. To tackle these issues, we propose BREAK, a broad-range semantics model for fake news detection that leverages a fully connected graph to capture comprehensive semantics while employing dual denoising modules to minimize both structural and feature noise. |
Junwei Yin; Min Gao; Kai Shu; Wentao Li; Yinqiu Huang; Zongwei Wang; |
| 231 | Multi-Platform Autobidding with and Without Predictions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the problem of finding the optimal bidding strategy for an advertiser in a multi-platform auction setting. |
Gagan Aggarwal; Anupam Gupta; Xizhi Tan; Mingfei Zhao; |
| 232 | LLGformer: Learnable Long-range Graph Transformer for Traffic Flow Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, most of the existing advanced methods rely on manually constructed spatio-temporal graphs for joint modeling, or use pure spatial and pure temporal modules to separately model spatial and temporal features, which limits the learning of complex spatio-temporal patterns in traffic data due to structural inadequacies in the model. To address these issues, we propose a novel approach by constructing a learnable long-range spatio-temporal graph, which can better capture complex patterns in traffic data. |
Di Jin; Cuiying Huo; Jiayi Shi; Dongxiao He; Jianguo Wei; Philip S. Yu; |
| 233 | Towards Multimodal Empathetic Response Generation: A Rich Text-Speech-Vision Avatar-based Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing ERG research is predominantly confined to the singleton text modality, limiting its effectiveness since human emotions are inherently conveyed through multiple modalities. To combat this, we introduce an avatar-based Multimodal ERG (MERG) task, entailing rich text, speech, and facial vision information. |
Han Zhang; Zixiang Meng; Meng Luo; Hong Han; Lizi Liao; Erik Cambria; Hao Fei; |
| 234 | Joint Similarity Item Exploration and Overlapped User Guidance for Multi-Modal Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the Multi- Modal Cross-Domain Recommendation (MMCDR) problem where different items have multi-modal information while few users are overlapped across domains. |
Weiming Liu; Chaochao Chen; Jiahe Xu; Xinting Liao; Fan Wang; Xiaolin Zheng; Zhihui Fu; Ruiguang Pei; Jun Wang; |
| 235 | Mask-based Membership Inference Attacks for Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous work either relies solely on the RAG system’s judgment or is easily influenced by other documents or the LLM’s internal knowledge, which is unreliable and lacks explainability. To address these limitations, we propose a Mask-Based Membership Inference Attacks (MBA) framework. |
Mingrui Liu; Sixiao Zhang; Cheng Long; |
| 236 | Toward Effective Digraph Representation Learning: A Magnetic Adaptive Propagation Based Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This oversight results in sub-optimal performance. To address the above issues, we propose two key techniques: (1) MAP is crafted to be a plug-and-play complex-domain propagation optimization strategy, enabling seamless integration into any MagDG to improve predictions while enjoying high running efficiency. |
Xunkai Li; Daohan Su; Zhengyu Wu; Guang Zeng; Hongchao Qin; Rong-Hua Li; Guoren Wang; |
| 237 | P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While many existing methods have tackled matrix factorization-based social recommendations without direct social data access, developing GNN-based federated social recommendation models under similar conditions remains largely unexplored. To address this issue, we propose a novel vertical federated social recommendation method leveraging privacy-preserving two-party graph convolution networks (P4GCN) to enhance recommendation accuracy without requiring direct access to sensitive social information. |
Zheng Wang; Wanwan Wang; Yimin Huang; Zhaopeng Peng; Ziqi Yang; Ming Yao; Cheng Wang; Xiaoliang Fan; |
| 238 | NoTeNet: Normalized Mutual Information-Driven Tuning-free Dynamic Dependence Network Inference Method for Multimodal Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous methods, generally lacking utilization of multiple modalities, either struggle with computational efficiency due to the time-intensive manual hyperparameter tuning, or compromise prediction stability and robustness by neglecting temporal coherence. To address these challenges, we propose a Normalized mutual information-driven Tuning-free Dynamic Dependence Network inference method for multimodal data, namely NoTeNet. |
Xiao Tan; Yangyang Shen; Yan Zhang; Jingwen Shao; Dian Shen; Meng Wang; Beilun Wang; |
| 239 | Graph Meets LLM for Review Personalization Based on User Votes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we suggest using review votes rather than authorship for personalization. |
Sharon Hirsch; Lilach Zitnitski; Slava Novgorodov; Ido Guy; Bracha Shapira; |
| 240 | Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor Via Anomalous Circuit Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identification of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration. |
Yixuan Yao; Ming Yang; Zixia Liu; Kai Dong; Xiaodan Gu; Chunmian Wang; |
| 241 | ExpressPQDelivery: Toward Efficient and Immediately Deployable Post-Quantum Key Delivery for Web-of-Things Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the problem, we propose ExpressPQDelivery, which is, to the best of our knowledge, the first immediately deployable protocol to efficiently transport large keys. |
Jane Kim; Jung-Hun Kang; Hyunwoo Lee; Seung-Hyun Seo; |
| 242 | MDEval: Evaluating and Enhancing Markdown Awareness in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce MDEval, a comprehensive benchmark to assess Markdown Awareness for LLMs, by constructing a dataset with 20K instances covering 10 subjects in English and Chinese. |
Zhongpu Chen; Yinfeng Liu; Long Shi; Zhi-Jie Wang; Xingyan Chen; Yu Zhao; Fuji Ren; |
| 243 | EVA-MVC: Equitable View-weight Allocation for Generic Multi-View Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work introduces EVA-MVC, a simple yet effective algorithm designed for Equitable View-weight Allocation (EVA) seamlessly integrated with arbitrary Multi-view Clustering (MVC) methods. |
Yuan Fang; Xiaofeng Feng; Geping Yang; Ruichu Cai; Yiyang Yang; Zhiguo Gong; Zhifeng Hao; |
| 244 | Beyond Visual Confusion: Understanding How Inconsistencies in ENS Normalization Facilitate Homoglyph Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We also discovered the new attack scenario in ENS which may cause legitimate domains resolved into malicious addresses even when they are verified by authorities. To systematically evaluate this inconsistency, we designed a tool for detecting application-level discrepancies in domain normalization process without requiring access to the application’s source code. |
Jianwei Huang; Sridatta Raghavendra Chintapalli; Mengxiao Wang; Guofei Gu; |
| 245 | SAHSD: Enhancing Hate Speech Detection in LLM-Powered Web Applications Via Sentiment Analysis and Few-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces Sentiment-Aided Hate Speech Detection (SAHSD), a novel approach designed to enhance hate speech detection specifically in LLM-powered web applications. |
Yulong Wang; Hong Li; Ni Wei; |
| 246 | Surprisingly Popular Voting with Concentric Rank-Order Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we are yet to fully understand when SP-voting can recover the ground truth ranking, and if so, how many samples (votes and predictions) it needs. We answer this question by proposing two rank-order models and analyzing the sample complexity of SP-voting under these models. |
Hadi Hosseini; Debmalya Mandal; Amrit Puhan; |
| 247 | Rankformer: A Graph Transformer for Recommendation Based on Ranking Objective Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although personalized ranking is a fundamental aspect of RS, this critical property is often overlooked in the design of model architectures. To address this issue, we propose Rankformer, a ranking-inspired recommendation model. |
Sirui Chen; Shen Han; Jiawei Chen; Binbin Hu; Sheng Zhou; Gang Wang; Yan Feng; Chun Chen; Can Wang; |
| 248 | Leveraging Heterogeneous Spillover in Maximizing Contextual Bandit Rewards Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, influence susceptibility can vary for different people based on their preferences and the closeness of ties to other users which leads to heterogeneity in the spillover effects. Here, we present a framework that allows contextual multi-armed bandits to account for such heterogeneous spillovers when choosing the best arm for each user. |
Ahmed Sayeed Faruk; Elena Zheleva; |
| 249 | TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a TAilored Posterior diffErence (TAPE) method to provide unlearning auditing independently of original model training. |
Weiqi Wang; Zhiyi Tian; An Liu; Shui Yu; |
| 250 | Hyperbolic-Euclidean Deep Mutual Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Hyperbolic-Euclidean Deep Mutual Learning (H-EDML) framework, which leverages the unique properties of hyperbolic space to effectively capture the hierarchical relationships present in graph data, while also utilizes the familiar Euclidean space to handle local interactions. |
Haifang Cao; Yu Wang; Jialu Li; Pengfei Zhu; Qinghua Hu; |
| 251 | InfoMAE: Pair-Efficient Cross-Modal Alignment for Multimodal Time-Series Sensing Signals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes InfoMAE, a cross-modal alignment framework that tackles the challenge of multimodal pair efficiency under the SSL setting by facilitating efficient cross-modal alignment of pretrained unimodal representations. |
Tomoyoshi Kimura; Xinlin Li; Osama Hanna; Yatong Chen; Yizhuo Chen; Denizhan Kara; Tianshi Wang; Jinyang Li; Xiaomin Ouyang; Shengzhong Liu; Mani Srivastava; Suhas Diggavi; Tarek Abdelzaher; |
| 252 | Online Bidding Under RoS Constraints Without Knowing The Value Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This introduces a challenging exploration-exploitation dilemma: the advertiser must balance exploring different bids to estimate impression values with exploiting current knowledge to bid effectively. To address this, we propose a novel Upper Confidence Bound (UCB)-style algorithm that carefully manages this trade-off. |
Sushant Vijayan; Zhe Feng; Swati Padmanabhan; Karthikeyan Shanmugam; Arun Suggala; Di Wang; |
| 253 | Semi-supervised Node Importance Estimation with Informative Distribution Modeling for Uncertainty Regularization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose the first semi-supervised node importance estimation framework, i.e., EASING, to improve learning quality for unlabeled data in heterogeneous graphs. |
Yankai Chen; Taotao Wang; Yixiang Fang; Yunyu Xiao; |
| 254 | Beyond Neighbors: Distance-Generalized Graphlets for Enhanced Graph Characterization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce (d,s)-graphlets, a generalization of size-s graphlets that incorporates indirect connections between nodes up to distance d. |
Yeongho Kim; Yuyeong Kim; Geon Lee; Kijung Shin; |
| 255 | EdgeThemis: Ensuring Model Integrity for Edge Intelligence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes EdgeThemis, a novel mechanism for verifying the integrity of edge models through sentinel verification. |
Jiyu Yang; Qiang He; Zheyu Zhou; Xiaohai Dai; Feifei Chen; Cong Tian; Yun Yang; |
| 256 | AdvTG: An Adversarial Traffic Generation Framework to Deceive DL-Based Malicious Traffic Detection Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose AdvTG, an adversarial traffic generation framework to deceive DL-based malicious traffic based on the large language model (LLM) and reinforcement learning (RL). |
Peishuai Sun; Xiaochun Yun; Shuhao Li; Tao Yin; Chengxiang Si; Jiang Xie; |
| 257 | IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose IceBerg, a debiased self-training framework to address the class-imbalanced and few-shot challenges for GNNs at the same time. |
Zhixun Li; Dingshuo Chen; Tong Zhao; Daixin Wang; Hongrui Liu; Zhiqiang Zhang; Jun Zhou; Jeffrey Xu Yu; |
| 258 | Beast in The Cage: A Fine-grained and Object-oriented Permission System to Confine JavaScript Operations on The Web Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To restrict JavaScript operations on web content and data, we introduce a fine-grained, mandatory access control-based, and object-oriented permission system to browsers. |
Rui Zhao; |
| 259 | ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In such cases, the domain-specific knowledge carried by the current tokens may degrade performance. To address these challenges, we propose the A-B-Cross-to-Invariant Learning Recommender (ABXI). |
Qingtian Bian; Marcus de Carvalho; Tieying Li; Jiaxing Xu; Hui Fang; Yiping Ke; |
| 260 | Efficient and Practical Approximation Algorithms for Advertising in Content Feeds Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we revisit the native advertising problem in content feeds, initiated by Ieong et al. |
Guangyi Zhang; Ilie Sarpe; Aristides Gionis; |
| 261 | C3AI: Crafting and Evaluating Constitutions for Constitutional AI Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Constitutional AI (CAI) guides LLM behavior using constitutions, but identifying which principles are most effective for model alignment remains an open challenge. We introduce the C3AI framework (Crafting Constitutions for CAI models), which serves two key functions: (1) selecting and structuring principles to form effective constitutions before fine-tuning; and (2) evaluating whether fine-tuned CAI models follow these principles in practice. |
Yara Kyrychenko; Ke Zhou; Edyta Bogucka; Daniele Quercia; |
| 262 | Distinctiveness Maximization in Datasets Assemblage Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, given a user’s query set and budget, we aim to use the limited budget to help users assemble a set of datasets that can enrich a base dataset by introducing the maximum number of distinct tuples (i.e., maximizing distinctiveness). |
Tingting Wang; Shixun Huang; Zhifeng Bao; J. Shane Culpepper; Volkan Dedeoglu; Reza Arablouei; |
| 263 | Roles of Network and Identity in Hashtag Diffusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work offers a new framework for teasing apart the mechanisms underlying hashtag cascades. |
Aparna Ananthasubramaniam; Yufei ‘Louise’ Zhu; David Jurgens; Daniel M. Romero; |
| 264 | Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, our MATE model is built on a temporally variational inference architecture with the modality-shared and modality-specific prior networks for the disentanglement of latent variables. |
Ruichu Cai; Zhifan Jiang; Kaitao Zheng; Zijian Li; Weilin Chen; Xuexin Chen; Yifan Shen; Guangyi Chen; Zhifeng Hao; Kun Zhang; |
| 265 | Hyperbolic Variational Graph Auto-Encoder for Next POI Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these models face significant challenges, including the difficulty of capturing the hierarchical and tree-like structure of POIs in Euclidean space and the sparsity problem inherent in POI recommendations. To address these challenges, we propose a Hyperbolic Variational Graph Auto-Encoder (HVGAE) for next POI recommendation. |
Yuwen Liu; Lianyong Qi; Xingyuan Mao; Weiming Liu; Fan Wang; Xiaolong Xu; Xuyun Zhang; Wanchun Dou; Xiaokang Zhou; Amin Beheshti; |
| 266 | Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The proposed filter remains provable parameter efficiency, with a novel Taylor-based parameter decomposition that achieves streamlined, effective implementation. With this foundation, we propose TFGNN, a scalable spectral GNN operating in a decoupled paradigm. |
Guoming Li; Jian Yang; Shangsong Liang; Dongsheng Luo; |
| 267 | Hyper-Relational Knowledge Representation Learning with Multi-Hypergraph Disentanglement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, few works extract common and private information across multiple views to minimize noise and interference. This paper proposes a multi-hypergraph disentanglement method for HKRL to address the above issues. |
Jiecheng Li; Xudong Luo; Guangquan Lu; Shichao Zhang; |
| 268 | Preserving Label Correlation for Multi-label Text Classification By Prototypical Regularizations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we distinguish two types of label correlations: explicit co-occurring correlations and implicit semantic correlations, and propose regularizations on prototypical label embeddings for correlation preservation. |
Fanshuang Kong; Richong Zhang; Xiaohui Guo; Junfan Chen; Ziqiao Wang; |
| 269 | WBSan: WebAssembly Bug Detection for Sanitization and Binary-Only Fuzzing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose WBSan, the first Wasm binary sanitizer employing static analysis and Wasm binary instrumentation to detect memory bugs and undefined behaviors. |
Xiao Wu; Junzhou He; Liyan Huang; Cai Fu; Weihang Wang; |
| 270 | Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While large language models (LLMs) enhance CRS with their superior understanding of context-aware user preferences, they typically struggle to leverage behavioral data, which have proven to be important for classical collaborative filtering (CF)-based approaches. For this reason, we propose CRAG-Collaborative Retrieval Augmented Generation for LLM-based CRS. |
Yaochen Zhu; Chao Wan; Harald Steck; Dawen Liang; Yesu Feng; Nathan Kallus; Jundong Li; |
| 271 | Procurement Auctions with Best and Final Offers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This is in contrast to prior work, e.g., on descending auctions, where the options provided to each seller are to either accept a price reduction or reject it and drop out. As a result, the auctions that we consider induce different extensive form games and our goal is to study the subgame perfect equilibria of these games. |
Vasilis Gkatzelis; Randolph Preston McAfee; Renato Paes Leme; |
| 272 | Graph Embeddings Meet Link Keys Discovery for Entity Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel hybrid EM approach to guarantee the scalability link key extraction approach and improve the explainability of embedding-based EM methods. |
Chlo\'{e} Khadija Jradeh; Ensiyeh Raoufi; J\'{e}r\^{o}me David; Pierre Larmande; Fran\c{c}ois Scharffe; Konstantin Todorov; Cassia Trojahn; |
| 273 | Fact-based Counter Narrative Generation to Combat Hate Speech Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most of the generative models produce generic responses to hate speech and can hallucinate, reducing their effectiveness. To address the above limitations, we propose a counter narrative generation method that enhances CNs by providing non-aggressive, fact-based narratives with relevant background knowledge from two distinct sources, including a web search module. |
Brian Wilk; Homaira Huda Shomee; Suman Kalyan Maity; Sourav Medya; |
| 274 | FUNU: Boosting Machine Unlearning Efficiency By Filtering Unnecessary Unlearning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unlike traditional machine learning, where models are typically static once trained, machine unlearning facilitates dynamic updates that enable the model to ”forget” information without requiring complete retraining from scratch. There are various machine unlearning methods, some of which are more time-efficient when data removal requests are fewer.To decrease the execution time of such machine unlearning methods, we aim to reduce the size of data removal requests based on the fundamental assumption that the removal of certain data would not result in a distinguishable retrained model. |
Zitong Li; Qingqing Ye; Haibo Hu; |
| 275 | Fine-Grained Data Inference Via Incomplete Multi-Granularity Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To that end, this paper proposes a novel framework, a multi-granularity super-resolution data map inference framework (MGSR), designed to harness spatio-temporal information to transform incomplete coarse-grained multi-granularity data maps into fine-grained multi-granularity data maps. |
Hepeng Gao; Yijun Su; Funing Yang; Yongjian Yang; |
| 276 | Detecting and Understanding The Promotion of Illicit Goods and Services on Twitter Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we reveal, for the first time, popular online social networks (especially Twitter) are being extensively abused by miscreants to promote illicit goods and services of diverse categories. |
Hongyu Wang; Ying Li; Ronghong Huang; Xianghang Mi; |
| 277 | TensorJSFuzz: Effective Testing of Web-Based Deep Learning Frameworks Via Input-Constraint Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A key challenge is generating syntactically and semantically valid inputs while designing effective test oracles for web environments. To address this, we introduce TensorJSFuzz, a novel method for testing web-based DL frameworks. |
Lili Quan; Xiaofei Xie; Qianyu Guo; Lingxiao Jiang; Sen Chen; Junjie Wang; Xiaohong Li; |
| 278 | Motivation-Aware Session Planning Over Heterogeneous Social Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Motivation-Aware Session Planning (MASP) framework for session planning over heterogeneous social platforms. |
Chengkun He; Xiangmin Zhou; Yurong Cheng; Jie Shao; Guoren Wang; Iqbal Gondal; Zahir Tari; |
| 279 | Parallel Online Similarity Join Over Trajectory Streams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We further enhance join efficiency through pruning strategies and tailored approximation techniques. The POTSJ framework we propose, which incorporates these elements, is capable of processing online TS-Join while simultaneously satisfying three key objectives: real-time result updates, comprehensive trajectory evaluation, and scalability. |
Zhongjun Ding; Ke Li; Lisi Chen; Shuo Shang; |
| 280 | M2-VLP: Enhancing Multilingual Vision-Language Pre-Training Via Multi-Grained Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing methods learn semantic alignment at a coarse-grained level and fail to capture fine-grained correlations between different languages and modalities. To address this, we propose a Multi-grained Multilingual Vision-Language Pre-training (M2-VLP) model, which aims to learn cross-lingual cross-modal alignment at different semantic granular levels. |
Ahtamjan Ahmat; Lei Wang; Yating Yang; Bo Ma; Rui Dong; Kaiwen Lu; Rong Ma; Xinyue Wang; |
| 281 | NFTs As A Data-Rich Test Bed: Conspicuous Consumption and Its Determinants Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The NFT market merits deeper study for two key reasons: first, it is poorly understood relative to its economic scale; and second, it is unusually amenable to analysis because NFT transactions are publicly available on the blockchain, making them useful as a test bed for conspicuous consumption dynamics. This paper introduces a model that incorporates two previously identified elements of conspicuous consumption: the bandwagon effect (goods increase in value as they become more popular) and the snob effect (goods increase in value as they become rarer). |
Taylor Lundy; Narun Raman; Scott Duke Kominers; Kevin Leyton-Brown; |
| 282 | Learning Against Non-credible Second-Price Auctions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our goal is to characterize the landscape of online bidding in non-credible second-price auctions and understand the impact of the seller’s credibility on online bidding algorithm design under different information structures. |
Qian Wang; Xuanzhi Xia; Zongjun Yang; Xiaotie Deng; Yuqing Kong; Zhilin Zhang; Liang Wang; Chuan Yu; Jian Xu; Bo Zheng; |
| 283 | Multimodal Knowledge Graph Error Detection with Disentanglement VAE and Multi-Grained Triplet Confidence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we introduce a novel task of multimodal knowledge graph error detection (MKGED) in this paper, aiming at simultaneously identifying both modality errors and triplet errors. |
Xuhui Sui; Ying Zhang; Yu Zhao; Baohang Zhou; Xiaojie Yuan; |
| 284 | Mitigating Forgetting in Adapting Pre-trained Language Models to Text Processing Tasks Via Consistency Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, due to the differences in data, model, and tasks between the pre-training and fine-tuning processes, the fine-tuning process may suffer from catastrophic forgetting of pre-training knowledge, which may implicitly limit the model’s performance and generalization ability. To address these challenges, we propose a novel dual-model framework, termed as consistency alignment (CoAi). |
Jianqi Gao; Hao Wu; Yiu-ming Cheung; Jian Cao; Hang Yu; Yonggang Zhang; |
| 285 | Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing KG-based LLM reasoning methods face challenges like handling multi-hop reasoning, multi-entity questions, and effectively utilizing graph structures. To address these issues, we propose Paths-over-Graph (PoG), a novel method that enhances LLM reasoning by integrating knowledge reasoning paths from KGs, improving the interpretability and faithfulness of LLM outputs. |
Xingyu Tan; Xiaoyang Wang; Qing Liu; Xiwei Xu; Xin Yuan; Wenjie Zhang; |
| 286 | The Cost of Balanced Training-Data Production in An Online Data Market Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This trend raises a fascinating question: Can online data markets sustainably and efficiently address ethical issues in the broader machine-learning economy? In this work, we study this question in a stylized model of an online data market. |
Augustin Chaintreau; Roland Maio; Juba Ziani; |
| 287 | MSDZip: Universal Lossless Compression for Multi-source Data Via Stepwise-parallel and Learning-based Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To solve those problems, we propose a novel universal MSD lossless compressor called MSDZip via Stepwise-parallel and learning-based prediction technologies, it introduces two major designs: 1) We propose a Local-Global-Deep Mixing block in the learning-based prediction module to establish dependencies for MSD symbols, where designed Deep Mixing block solves the problem of unstable weights in the perceptual layers caused by cold-start problem to enhance the compression ratio significantly. |
Huidong Ma; Hui Sun; Liping Yi; Yanfeng Ding; Xiaoguang Liu; Gang Wang; |
| 288 | Achieving Personalized Privacy-Preserving Graph Neural Network Via Topology Awareness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nonetheless, existing DP-based privacy-preserving GNN learning frameworks generally overlook the local topological heterogeneity of graph nodes and tailor the same privacy budget for all nodes, which may lead to either overprotection or underprotection of some nodes, potentially diminishing model utility or posing privacy leakage risks. To address this issue, we propose a Topology-aware Differential Privacy Graph Neural Network learning framework, termed TDP-GNN, which can achieve personalized privacy protection for each node with improved privacy-utility guarantees. |
Dian Lei; Zijun Song; Yanli Yuan; Chunhai Li; Liehuang Zhu; |
| 289 | Tackling Sparse Facts for Temporal Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nonetheless, real-world knowledge-such as the progression of social network interactions and the unfolding of news events-is inherently dynamic, resulting in substantial sparsity issues in TKGs that profoundly impair the performance of TKGC models. To overcome this challenge, we introduce the Adaptive Neighborhood Enhancement Layer (ANEL), a novel module that can be effortlessly integrated into existing TKGC models to substantially elevate the representation quality of sparse entities. |
Yuchao Zhang; Xiangjie Kong; Kailun Ye; Guojiang Shen; Shangfei Zheng; |
| 290 | Two-stage Auction Design in Online Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose ad-wise selection metrics, named Max-Wel and Max-Rev, which optimize the platform’s welfare and revenue, respectively. |
Zhikang Fan; Lan Hu; Ruirui Wang; Zhongrui Ma; Yue Wang; Qi Ye; Weiran Shen; |
| 291 | Fairness-aware Prompt Tuning for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing graph prompt tuning overlooks such unfairness, leading to biased outputs towards certain demographic groups determined by sensitive attributes such as gender, age, and political ideology. To overcome this limitation, we propose a fairness-aware graph prompt tuning method to promote fairness while enhancing the generality of any pre-trained GNNs (named FPrompt). |
Zhengpin Li; Minhua Lin; Jian Wang; Suhang Wang; |
| 292 | HeatSnap: A Hot Page-Aware Continuous Snapshots System for Virtual Machines in Web Infrastructure Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These challenges are closely tied to the way memory pages are accessed during VM execution, where memory access patterns show significant disparities between frequently accessed hot pages and less-used cold pages. In this paper, we introduce HeatSnap, a continuous snapshot system designed to address these issues by leveraging the uneven access frequencies of memory pages. |
Kangyue Gao; Chuangyu Ouyang; Xinkui Zhao; Miao Ye; Chen Zhi; Guanjie Cheng; Yueshen Xu; Shuiguang Deng; Jianwei Yin; |
| 293 | Triangle Matters! TopDyG: Topology-aware Transformer for Link Prediction on Dynamic Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent Transformer-based link prediction methods on dynamic graphs not only fail to model the fine-grained structures such as triangles with the vanilla Transformers in the graph serialization process, but also amplify the imbalanced distribution of graphs because of their over-estimation of high-degree nodes. To tackle these issues, we propose a Topology-aware Transformer on Dynamic Graph (TopDyG) for link prediction, consisting of a topology injected Transformer (Ti-Transformer) and a mutual information learning (Mi-Learning). |
Xin Zhang; Fei Cai; Jianming Zheng; Zhiqiang Pan; Wanyu Chen; Honghui Chen; Chonghao Chen; |
| 294 | Epidemiology-informed Network for Robust Rumor Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Epidemiology-informed Network (EIN) that integrates epidemiological knowledge to enhance performance by overcoming data-driven methods’ sensitivity to data quality. |
Wei Jiang; Tong Chen; Xinyi Gao; Wentao Zhang; Lizhen Cui; Hongzhi Yin; |
| 295 | Fully Anonymous Decentralized Identity Supporting Threshold Traceability with Practical Blockchain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, existing anonymous credential schemes lack effective mechanisms for threshold traceability, not meeting Web3’s distributed governance requirements. In this paper, we propose FADID-TT, a Fully Anonymous DID system supporting Threshold Tracing with practical blockchain, to tackle the above challenges. |
Yizhong Liu; Zedan Zhao; Boyu Zhao; Feiang Ran; Xun Lin; Dawei Li; Zhenyu Guan; |
| 296 | Filtering Discomforting Recommendations with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The subjectivity of discomfort and the black-box nature of these algorithms make it challenging to effectively identify and filter such content. To address this, we first conducted a formative study to understand users’ practices and expectations regarding discomforting recommendation filtering. Then, we designed a Large Language Model (LLM)-based tool named DiscomfortFilter, which constructs an editable preference profile for a user and helps the user express filtering needs through conversation to mask discomforting preferences within the profile. |
Jiahao Liu; Yiyang Shao; Peng Zhang; Dongsheng Li; Hansu Gu; Chao Chen; Longzhi Du; Tun Lu; Ning Gu; |
| 297 | TimeChain: A Secure and Decentralized Off-chain Storage System for IoT Time Series Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, with the trend towards the Web of Things (WoT), lower transaction speeds and higher computational requirements limit their access to high-density data such as IoT. To address this, we propose TimeChain, an efficient off-chain blockchain storage system for IoT time series data. |
Yixiao Teng; Jiamei Lv; Ziping Wang; Yi Gao; Wei Dong; |
| 298 | BoxCD: Leveraging Contrastive Probabilistic Box Embedding for Effective and Efficient Learner Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, response prediction can be time-consuming. To address these issues, we propose BoxCD, a contrastive probabilistic box embedding model for cognitive diagnosis. |
Weibo Gao; Qi Liu; Linan Yue; Fangzhou Yao; Zhenya Huang; Zheng Zhang; Rui Lv; |
| 299 | Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Additionally, they primarily rely on implicit temporal information based solely on chronological ordering, neglecting explicit temporal signals that could more effectively represent dynamic user interest over time. To address these limitations, we propose a Hierarchical time-aware Mixture of experts for multi-modal Sequential Recommendation (HM4SR) with a two-level Mixture of Experts (MoE) and a multi-task learning strategy. |
Shengzhe Zhang; Liyi Chen; Dazhong Shen; Chao Wang; Hui Xiong; |
| 300 | IllusionCAPTCHA: A CAPTCHA Based on Visual Illusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our findings reveal that while LLMs can solve most CAPTCHAs, they struggle with those requiring complex reasoning-a type of CAPTCHA that also presents significant challenges for human users. Interestingly, our user study shows that the majority of human participants require a second attempt to pass these reasoning CAPTCHAs, a finding not reported in previous research.Based on empirical findings, we present IllusionCAPTCHA, a novel security mechanism employing the Human-Easy but AI-Hard paradigm. |
Ziqi Ding; Gelei Deng; Yi Liu; Junchen Ding; Jieshan Chen; Yulei Sui; Yuekang Li; |
| 301 | Self-Calibrated Listwise Reranking with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This not only increases computational costs but also restricts the LLM from fully capturing all the comparison information for all candidates. To address these challenges, we propose a novel self-calibrated listwise reranking method, which aims to leverage LLMs to produce global relevance scores for ranking. |
Ruiyang Ren; Yuhao Wang; Kun Zhou; Wayne Xin Zhao; Wenjie Wang; Jing Liu; Ji-Rong Wen; Tat-Seng Chua; |
| 302 | Reducing Symbiosis Bias Through Better A/B Tests of Recommendation Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a theoretical model of symbiosis bias and simulate the impact of each design in dynamic recommendation environments. |
Jennifer Brennan; Yahu Cong; Yiwei Yu; Lina Lin; Yajun Peng; Changping Meng; Ningren Han; Jean Pouget-Abadie; David M. Holtz; |
| 303 | What’s in A Query: Polarity-Aware Distribution-Based Fair Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we examine amortized fair ranking — where relevance and attention are cumulated over a sequence of user queries to make fair ranking more feasible in practice. |
Aparna Balagopalan; Kai Wang; Olawale Salaudeen; Asia Biega; Marzyeh Ghassemi; |
| 304 | WavePulse: Real-time Content Analytics of Radio Livestreams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. |
Govind Mittal; Sarthak Gupta; Shruti Wagle; Chirag Chopra; Anthony J. DeMattee; Nasir Memon; Mustaque Ahamad; Chinmay Hegde; |
| 305 | Supernotes: Driving Consensus in Crowd-Sourced Fact-Checking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While demonstrably effective at reducing misinformation’s impact when notes are displayed, there is an opportunity for notes to appear on many more posts: for 91\% of posts where at least one note is proposed, no notes ultimately achieve sufficient support from diverse users to be shown on the platform. This motivates the development of Supernotes: AI-generated notes that synthesize information from several existing community notes and are written to foster consensus among a diverse set of users. |
Soham De; Michiel A. Bakker; Jay Baxter; Martin Saveski; |
| 306 | Causal Insights Into Parler’s Content Moderation Shift: Effects on Toxicity and Factuality Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: After a month-long suspension, Parler returned with stricter guidelines, offering a unique opportunity to study the impact of platform-wide policy changes on user behavior and content outcomes. In this paper, we analyzed Parler data to assess the causal associations of these moderation changes on content toxicity and factuality. |
Nihal Kumarswamy; Mohit Singhal; Shirin Nilizadeh; |
| 307 | Hierarchical Vector Quantized Graph Autoencoder with Annealing-Based Code Selection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The Vector Quantized Variational Autoencoder (VQ-VAE) is a powerful autoencoder extensively used in fields such as computer vision; however, its application to graph data remains underexplored. In this paper, we provide an empirical analysis of vector quantization in the context of graph autoencoders, demonstrating its significant enhancement of the model’s capacity to capture graph topology. |
Long Zeng; Jianxiang Yu; Jiapeng Zhu; Qingsong Zhong; Xiang Li; |
| 308 | Aegis: Post-Training Attribute Unlearning in Federated Recommender Systems Against Attribute Inference Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, models trained using standard FedRec methods remain vulnerable to personal information leakage, particularly through attribute inference attacks, which can expose sensitive user attributes such as gender and race. In this paper, we address these user-sensitive attributes as targets for federated unlearning. |
Wenhan Wu; Jiawei Jiang; Chuang Hu; |
| 309 | Human-Centric Community Detection in Hybrid Metaverse Networks with Integrated AI Entities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Effective processing of MetaCD poses challenges due to the delicate trade-off between excluding certain AI nodes and maintaining community structure. To address this, we propose CUSA, an innovative framework incorporating AI-aware clustering techniques that navigate this trade-off by selectively retaining AI nodes that contribute to community integrity. |
Shih-Hsuan Chiu; Ya-Wen Teng; De-Nian Yang; Ming-Syan Chen; |
| 310 | Hypergraph-based Temporal Modelling of Repeated Intent for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, intents can be of repeated nature (e.g. yearly shopping for christmas gifts or buying a new phone), which has not been exploited by previous approaches. To navigate these impediments we propose the HyperHawkes model which models user sessions via hypergraphs and extracts user intents via contrastive clustering. |
Andreas Peintner; Amir Reza Mohammadi; Michael M\{u}ller; Eva Zangerle; |
| 311 | Robust Deep Signed Graph Clustering Via Weak Balance Theory Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We have identified two significant challenges in this domain: i) existing signed spectral methods are highly vulnerable to noise, which is prevalent in real-world scenarios; ii) the guiding principle an enemy of my enemy is my friend, rooted in Social Balance Theory, often narrows or disrupts cluster boundaries in mainstream signed graph neural networks. Addressing these challenges, we propose the Deep Signed Graph Clustering framework (DSGC), which leverages Weak Balance Theory to enhance preprocessing and encoding for robust representation learning. |
Peiyao Zhao; Xin Li; Zeyu Zhang; Mingzhong Wang; Xueying Zhu; Lejian Liao; |
| 312 | Understanding and Detecting File Knowledge Leakage in GPT App Ecosystem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop GPTs-Filtor, leveraging the unique characteristics of GPTs deployment, to perform an in-depth analysis and detection of file knowledge leakage at both user interaction (i.e., prompt) and network transmission levels. |
Chuan Yan; Bowei Guan; Yazhi Li; Mark Huasong Meng; Liuhuo Wan; Guangdong Bai; |
| 313 | XMTF: A Formula-Free Model for Reinforcement-Learning-Based Multi-Task Fusion in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The pre-defined formulas restrict the RL search space and become a bottleneck for MTF. To overcome this, we propose a formula-free MTF framework. |
Yang Cao; Changhao Zhang; Xiaoshuang Chen; Kaiqiao Zhan; Ben Wang; |
| 314 | Beyond Utility: Evaluating LLM As Recommender Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, there are unique evaluation aspects of LLM-based recommendation models, which have been largely ignored. To bridge this gap, we explore four new evaluation dimensions and propose a multidimensional evaluation framework. |
Chumeng Jiang; Jiayin Wang; Weizhi Ma; Charles L. A. Clarke; Shuai Wang; Chuhan Wu; Min Zhang; |
| 315 | Empowering Federated Graph Rationale Learning with Latent Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Data heterogeneity, characterized by non-IID data across clients, exacerbates this problem, leading to poor prediction performance. To address these challenges, we propose the Environment-aware Data Augmentation (EaDA) method for Federated Graph Rationalization. |
Linan Yue; Qi Liu; Yawen Li; Fangzhou Yao; Weibo Gao; Junping Du; |
| 316 | Counting Cohesive Subgraphs with Hereditary Properties Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To count HCS, we propose a general framework called HCSPivot, which can be applied to count all kinds of HCS. |
Rong-Hua Li; Xiaowei Ye; Fusheng Jin; Yu-Ping Wang; Ye Yuan; Guoren Wang; |
| 317 | Does Weighting Improve Matrix Factorization for Recommender Systems? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we conduct a systematic study of various weighting schemes and matrix factorization algorithms. |
Alex Ayoub; Samuel Robertson; Dawen Liang; Harald Steck; Nathan Kallus; |
| 318 | Beyond The Crawl: Unmasking Browser Fingerprinting in Real User Interactions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As a result, this paper presents a user study involving 30 participants over 10 weeks, capturing telemetry data from real browsing sessions across 3,000 top-ranked websites. |
Meenatchi Sundaram Muthu Selva Annamalai; Emiliano De Cristofaro; Igor Bilogrevic; |
| 319 | Facing Anomalies Head-On: Network Traffic Anomaly Detection Via Uncertainty-Inspired Inter-Sample Differences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we propose a novel approach, the Uncertainty-Inspired Inter-Sample Differences (UnDiff) method, which leverages model uncertainty to enhance anomaly detection capabilities, particularly in scenarios involving anomaly drift. |
Xinglin Lian; Chengtai Cao; Yan Liu; Xovee Xu; Yu Zheng; Fan Zhou; |
| 320 | Ranking on Dynamic Graphs: An Effective and Robust Band-Pass Disentangled Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While existing solutions achieve promising ranking performance, they leverage a single listwise loss to jointly optimize candidate sets, which leads to the gradient vanishing issue; and they employ neural networks to model complex temporal structures within a shared latent space, which fails to accurately capture multi-scale temporal patterns due to the frequency aliasing issue. To address these issues, we propose BandRank, a novel and robust band-pass disentangled ranking approach for dynamic graphs in the frequency domain. |
Yingxuan Li; Yuanyuan Xu; Xuemin Lin; Wenjie Zhang; Ying Zhang; |
| 321 | Community Detection in Large-Scale Complex Networks Via Structural Entropy Game Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, many current approaches are limited to specific graph types, such as unweighted or undirected graphs, reducing their broader applicability. To address these issues, we propose a novel heuristic community detection algorithm, termed CoDeSEG, which identifies communities by minimizing the network’s two-dimensional (2D) structural entropy within a potential game framework. |
Yantuan Xian; Pu Li; Hao Peng; Zhengtao Yu; Yan Xiang; Philip S. Yu; |
| 322 | Fitting Into Any Shape: A Flexible LLM-Based Re-Ranker With Configurable Depth and Width Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a flexible architecture called Matroyshka Re-Ranker, which is designed to facilitate runtime customization of model layers and sequence lengths at each layer based on users’ configurations. |
Zheng Liu; Chaofan Li; Shitao Xiao; Chaozhuo Li; Chen Jason Zhang; Hao Liao; Defu Lian; Yingxia Shao; |
| 323 | Robust Aggregation with Adversarial Experts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We aim to find the optimal aggregator that outputs a forecast minimizing regret under the worst information structure and adversarial strategies. |
Yongkang Guo; Yuqing Kong; |
| 324 | Pirates of Charity: Exploring Donation-based Abuses in Social Media Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we conduct a large-scale analysis of donation-based scams on social media platforms. |
Bhupendra Acharya; Dario Lazzaro; Antonio Emanuele Cin\`{a}; Thorsten Holz; |
| 325 | Dynamic Gradient Influencing for Viral Marketing Using Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a novel data-driven formulation of the problem. |
Saurabh Sharma; Ambuj Singh; |
| 326 | TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces TD3, a novel Tucker Decomposition based Dataset Distillation method within a meta-learning framework, designed for sequential recommendation. |
Jiaqing Zhang; Mingjia Yin; Hao Wang; Yawen Li; Yuyang Ye; Xingyu Lou; Junping Du; Enhong Chen; |
| 327 | Sherlock: Towards Multi-scene Video Abnormal Event Extraction and Localization Via A Global-local Spatial-sensitive LLM Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper proposes a Global-local Spatial-sensitive Large Language Model (LLM) named Sherlock, i.e., acting like Sherlock Holmes to track down the criminal events, for this M-VAE task. |
Junxiao Ma; Jingjing Wang; Jiamin Luo; Peiying Yu; Guodong Zhou; |
| 328 | Angular Distance-Guided Neighbor Selection for Graph-Based Approximate Nearest Neighbor Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper provides an extensive experimental analysis on the popular greedy search and other search optimization strategies. |
Sungjun Jung; Yongsang Park; Haeun Lee; Young H. Oh; Jae W. Lee; |
| 329 | Adversarial Style Augmentation Via Large Language Model for Robust Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study proposes adversarial style augmentation, AdStyle, designed to train a fake news detector that remains robust against various style-conversion attacks. |
Sungwon Park; Sungwon Han; Xing Xie; Jae-Gil Lee; Meeyoung Cha; |
| 330 | LP-DIXIT: Evaluating Explanations for Link Predictions on Knowledge Graphs Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose LP-DIXIT, the first protocol to evaluate the utility of explanations of link predictions. |
Roberto Barile; Claudia d’Amato; Nicola Fanizzi; |
| 331 | Exploiting Language Power for Time Series Forecasting with Exogenous Variables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we devise ExoLLM, an LLM-driven method to sufficiently utilize Exogenous variables for time series forecasting. |
Qihe Huang; Zhengyang Zhou; Kuo Yang; Yang Wang; |
| 332 | Graph Self-Supervised Learning with Learnable Structural and Positional Encodings Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This limitation stems from two main factors: (1) the inadequacy of conventional Graph Neural Networks (GNNs) in representing sophisticated topological features, and (2) the focus of self-supervised learning solely on final graph representations. To address these issues, we introduce GenHopNet, a GNN framework that integrates a k-hop message-passing scheme, enhancing its ability to capture local structural information without explicit substructure extraction. |
Asiri Wijesinghe; Hao Zhu; Piotr Koniusz; |
| 333 | Centralization in The Decentralized Web: Challenges and Opportunities in IPFS Data Management Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While this change contradicts the core decentralized ethos of IPFS and introduces risks of hurting the data replication level and thus availability, it also opens some opportunities for better data management and cost savings through deduplication.To explore these challenges and opportunities, we start by collecting an extensive dataset of IPFS internal traffic spanning the last three years with 20+ billion messages. By analyzing this long-term trace, we obtain a more complete and accurate view of how the status of centralization evolves over an extended period. |
Ruizhe Shi; Ruizhi Cheng; Yuqi Fu; Bo Han; Yue Cheng; Songqing Chen; |
| 334 | A Theory-Driven Approach to Inner Product Matrix Estimation for Incomplete Data: An Eigenvalue Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Addressing the critical challenge of data incompleteness in inner product matrix estimation, we introduce a novel eigenvalue correction method designed to precisely reconstruct true inner product matrices from incomplete data. |
Fangchen Yu; Yicheng Zeng; Jianfeng Mao; Wenye Li; |
| 335 | Dual Operation Aggregation Graph Neural Networks for Solving Flexible Job-Shop Scheduling Problem with Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, traditional FJSP-solving methods struggle to meet the efficiency and adaptability demands of cloud manufacturing due to generalization issues and excessive computational time, while reinforcement learning-based methods fail to learn relationships between FJSP nodes, such as interactions between operations of different jobs, leading to limited interpretability and performance. To address these issues, we propose a dual operation aggregation graph neural network (GNN) for solving FJSP. |
Peng Zhao; You Zhou; Di Wang; Zhiguang Cao; Yubin Xiao; Xuan Wu; Yuanshu Li; Hongjia Liu; Wei Du; Yuan Jiang; Liupu Wang; |
| 336 | On The Cross-Graph Transferability of Dynamic Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a cross-graph dynamic link predictor named CrossDyG, which achieves the cross-graph transferability in a one-many mechanism which trains on one single source graph and test on different target graphs. |
Zhiqiang Pan; Chen Gao; Fei Cai; Wanyu Chen; Xin Zhang; Honghui Chen; Yong Li; |
| 337 | ShapeShifter: Workload-Aware Adaptive Evolving Index Structures Based on Learned Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, the full reliance on learned models can increase vulnerability to attacks, compromising system stability. To address these challenges, we propose ShapeShifter, an adaptive evolutionary structure based on traditional indexes, capable of dynamically adjusting node structures according to the workload. |
Hui Wang; Xin Wang; Jiake Ge; Lei Liang; Peng Yi; |
| 338 | UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we develop UniDEC, a loss-independent, end-to-end trainable framework which trains the DE and classifier together in a unified manner with a multi-class loss, while reducing the computational cost by 4–16x. |
Siddhant Kharbanda; Devaansh Gupta; Gururaj K; Pankaj Malhotra; Amit Singh; Cho-Jui Hsieh; Rohit Babbar; |
| 339 | Instruction Vulnerability Prediction for WebAssembly with Semantic Enhanced Code Property Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel paradigm, namely IVPSEG, to understand the error propagation of bit flips within Wasm programs. |
Bao Wen; Jingjing Gu; Hao Han; Pengfei Yu; Yang Liu; |
| 340 | Quantitative Runtime Monitoring of Ethereum Transaction Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches to detecting attacks often rely on predefined rules or simplistic and overly-specialized models, which lack the flexibility to handle the wide spectrum of diverse and dynamically changing attack types. To address this challenge, we present a general and extensible framework, MoE (Monitoring Ethereum), that leverages runtime verification to detect a wide range of attacks on Ethereum. |
Xinyao Xu; Ziyu Mao; Jianzhong Su; Xingwei Lin; David Basin; Jun Sun; Jingyi Wang; |
| 341 | ETS-MM: A Multi-Modal Social Bot Detection Model Based on Enhanced Textual Semantic Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods may not always extract additional dimensions of semantic feature information that could offer a deeper understanding of users’ social patterns. To address this issue, we propose ETS-MM, a multimodal detection framework designed to augment multidimensional information from text and extract the semantic feature representation of user text information. |
Wei Li; Jiawen Deng; Jiali You; Yuanyuan He; Yan Zhuang; Fuji Ren; |
| 342 | ITMPRec: Intention-based Targeted Multi-round Proactive Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel intention-based targeted multi-round proactive recommendation method, dubbed ITMPRec. |
Yahong Lian; Chunyao Song; Tingjian Ge; |
| 343 | A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: (ii) The indiscriminate use of task demonstrations, retrieved through shallow similarity-based strategies, severely misleads LLMs during inference.In this paper, we introduce the cooperative multi-agent system (CMAS), a novel framework for zero-shot NER that uses the collective intelligence of multiple agents to address the challenges outlined above. |
Zihan Wang; Ziqi Zhao; Yougang Lyu; Zhumin Chen; Maarten de Rijke; Zhaochun Ren; |
| 344 | Training-free Graph Anomaly Detection: A Simple Approach Via Singular Value Decomposition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, prior deep learning-based GAD methods suffer from various limitations such as low accuracy, long training time, and limited scalability. To tackle these limitations, we propose TFGAD, a training-free graph anomaly detection approach. |
Cheng Zhou; Guangxia Li; Hao Weng; Yiyu Xiang; |
| 345 | Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the difficulty of aligning the retriever with the LLMs’ diverse knowledge preferences inevitably poses a challenge in developing a reliable RAG system. To address this issue, we propose DPA-RAG, a universal framework designed to align diverse knowledge preferences within RAG systems. |
Guanting Dong; Yutao Zhu; Chenghao Zhang; Zechen Wang; Ji-Rong Wen; Zhicheng Dou; |
| 346 | Towards Efficient Conversational Recommendations: Expected Value of Information Meets Bandit Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our approach applies to both Bayesian (Thompson Sampling) and frequentist (UCB) variants of conversational bandits. We introduce two new algorithms, ConTS-EVOI and ConUCB-EVOI, and rigorously prove that they achieve substantially tighter regret bounds, with both algorithms offering a √d improvement in their dependence on the time horizon T, where d is the dimension of the feature space. |
Zhuohua Li; Maoli Liu; Xiangxiang Dai; John C.S. Lui; |
| 347 | Compress and Mix: Advancing Efficient Taxonomy Completion with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose COMI, an efficient taxonomy completion framework that leverages large language models (LLMs) to capture both semantic and structural information in a unified manner. |
Hongyuan Xu; Yuhang Niu; Yanlong Wen; Xiaojie Yuan; |
| 348 | SANS: Efficient Densest Subgraph Discovery Over Relational Graphs Without Materialization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing dense subgraph discovery (DSD) approaches assume that a relational graph H is already derived from a heterogeneous data source and they focus on efficient discovery of the densest subgraph on the materialized H. Unfortunately, materializing relational graphs can be resource-intensive, which thus limits the practical usefulness of existing algorithms over large datasets. To mitigate this, we propose a novel Summary-bAsed deNsest Subgraph discovery (SANS) system. |
Yudong Niu; Yuchen Li; Jiaxin Jiang; Laks V.S. Lakshmanan; |
| 349 | WeInfer: Unleashing The Power of WebGPU on LLM Inference in Web Browsers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These inefficiencies primarily arise from underutilizing the full capabilities of WebGPU, particularly in resource management and execution synchronization. To address these limitations, we present WeInfer, an efficient Web-based LLM inference framework specifically designed to unleash the power of WebGPU. |
Zhiyang Chen; Yun Ma; Haiyang Shen; Mugeng Liu; |
| 350 | Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the efficiency of RankGPT models is limited by the maximum context length and relatively high latency of LLM inference. To address these issues, in this paper, we propose PE-Rank, leveraging the single passage embedding as a good context compression for efficient listwise passage reranking. |
Qi Liu; Bo Wang; Nan Wang; Jiaxin Mao; |
| 351 | SigScope: Detecting and Understanding Off-Chain Message Signing-related Vulnerabilities in Decentralized Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a holistic static-analysis framework, SigScope, that uniquely combines the insights extracted from DApp front-end code (HTML and Javascript) off-chain and back-end smart contracts on-chain. |
Sajad Meisami; Hugo Dabadie; Song Li; Yuzhe Tang; Yue Duan; |
| 352 | MER-Inspector: Assessing Model Extraction Risks from An Attack-Agnostic Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, we find that victim model accuracy, which shows a strong positive correlation with model extraction risk, can serve as an empirical metric. By integrating these two metrics, we propose a framework, namely Model Extraction Risk Inspector (MER-Inspector), to compare the extraction risks of models under different model architectures by utilizing relative metric values. |
Xinwei Zhang; Haibo Hu; Qingqing Ye; Li Bai; Huadi Zheng; |
| 353 | MGF-ESE: An Enhanced Semantic Extractor with Multi-Granularity Feature Fusion for Code Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we present a novel AST generation method that, based on controlling the scale of nodes, introduces syntactic description nodes to raise the semantic density of AST feature. |
Xiaolong Xu; Yuxin Cao; Hongsheng Hu; Haolong Xiang; Lianyong Qi; Junqun Xiong; Wanchun Dou; |
| 354 | FP-Rainbow: Fingerprint-Based Browser Configuration Identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unlike cookies, browser fingerprints are difficult to evade or delete, raising significant privacy concerns for users as they can be used to re-identify individuals over browsing sessions without their consent. Yet, there has been limited research on the impact of browser configuration settings on these fingerprints.This paper introduces FP-Rainbow, a novel approach to systematically explore and map the configuration space of Chromium-based web browsers aiming to identify the impact of configuration parameters on browser fingerprints and their changes over time. |
Maxime Huyghe; Walter Rudametkin; Cl\'{e}ment Quinton; |
| 355 | Breaking The Shield: Analyzing and Attacking Canvas Fingerprinting Defenses in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate four primary defense techniques designed to counter canvas fingerprinting, systematically analyzing their adoption across 18 browser extensions in Chrome and Firefox, as well as built-in protections from five major browsers: Chrome, Firefox, Brave, Tor, and Safari. |
Hoang Dai Nguyen; Phani Vadrevu; |
| 356 | DAGPrompT: Pushing The Limits of Graph Prompting with A Distribution-aware Graph Prompt Tuning Approach Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper identifies two key challenges in adapting graph prompting methods for complex graphs: (i) adapting the model to new distributions in downstream tasks to mitigate pre-training and fine-tuning discrepancies from heterophily and (ii) customizing prompts for hop-specific node requirements. To overcome these challenges, we propose Distribution-aware Graph Prompt Tuning (DAGPrompT), which integrates a GLoRA module for optimizing the GNN encoder’s projection matrix and message-passing schema through low-rank adaptation. |
Qin Chen; Liang Wang; Bo Zheng; Guojie Song; |
| 357 | Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we revisit existing GC optimization strategies and identify two pervasive issues therein: (1) various GC optimization strategies converge to coarse-grained class-level node feature matching between the original and condensed graphs; (2) existing GC methods rely on a Siamese graph network architecture that requires time-consuming bi-level optimization with iterative gradient computations. To overcome these issues, we propose a training-free GC framework termed Class-partitioned Graph Condensation (CGC), which refines the node distribution matching from the class-to-class paradigm into a novel class-to-node paradigm, transforming the GC optimization into a class partition problem which can be efficiently solved by any clustering methods. |
Xinyi Gao; Guanhua Ye; Tong Chen; Wentao Zhang; Junliang Yu; Hongzhi Yin; |
| 358 | IPdb: A High-Precision IP Level Industry Categorization of Web Services Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we present IPdb, an IP-level industry categorization dataset. |
Hongxu Chen; Guanglei Song; Zhiliang Wang; Jiahai Yang; Songyun Wu; Jinlei Lin; Lin He; Chenglong Li; |
| 359 | Decoupling Knowledge and Context: An Efficient and Effective Retrieval Augmented Generation Framework Via Cross Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Third, the effectiveness of knowledge injection is perturbed by the permutation of knowledge within the extended context, reducing the robustness of existing RAG methods. To tackle the above problems, we propose DecoupledRAG, a method that decouples external knowledge from the context within the RAG framework. |
Qian Dong; Qingyao Ai; Hongning Wang; Yiding Liu; Haitao Li; Weihang Su; Yiqun Liu; Tat-Seng Chua; Shaoping Ma; |
| 360 | Hidden Impact of Hardware Technologies on Throughput: A Case Study on A Brazilian Mobile Web Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present one of the first studies to understand the interplay between hardware characteristics (e.g., cellular and mobile) on expected network and application level performance in Brazil (the largest developing region in S. America). |
Eduardo C. Paim; Roberto I. T. Costa Filho; Valter Roesler; Theophilus A. Benson; Alberto Schaeffer-Filho; |
| 361 | MCNet: Monotonic Calibration Networks for Expressive Uncertainty Calibration in Online Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing calibration approaches may lack the ability to effectively model complex nonlinear relations, consider context features, and achieve balanced performance across different data subsets. To tackle these challenges, we introduce a novel model called Monotonic Calibration Networks, featuring three key designs: a monotonic calibration function (MCF), an order-preserving regularizer, and a field-balance regularizer. |
Quanyu Dai; Jiaren Xiao; Zhaocheng Du; Jieming Zhu; Chengxiao Luo; Xiao-Ming Wu; Zhenhua Dong; |
| 362 | Aggregate to Adapt: Node-Centric Aggregation for Multi-Source-Free Graph Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate multi-source-free unsupervised graph domain adaptation, i.e., adapting knowledge from multiple source domains to an unlabeled target domain without utilizing labeled source graphs but relying solely on source pre-trained models. |
Zhen Zhang; Bingsheng He; |
| 363 | Dealing with Noisy Data in Federated Learning: An Incentive Mechanism with Flexible Pricing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, for any given training task, only a portion of each client’s data is relevant and beneficial, while the rest may be redundant or noisy. Training with excessive noisy data can degrade performance. Motivated by this, we investigate the limitations of existing studies and develop an incentive mechanism with flexible pricing tailored for noisy data settings. |
Hengzhi Wang; Haoran Chen; Minghe Ma; Laizhong Cui; |
| 364 | MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes MedRAG, a RAG model enhanced by knowledge graph (KG)-elicited reasoning for the medical domain that retrieves diagnosis and treatment recommendations based on manifestations. |
Xuejiao Zhao; Siyan Liu; Su-Yin Yang; Chunyan Miao; |
| 365 | Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insights Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we focus on fair data summarization modeled as the fair k-supplier problem, where data consists of several groups, and a minimum number of centers must be selected from each group while minimizing the k-supplier objective. |
Ameet Gadekar; Aristides Gionis; Suhas Thejaswi; |
| 366 | Personalized Denoising Implicit Feedback for Robust Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To tackle these challenges, we further investigate the loss overlap and find that for a given user, there is a clear distinction between normal and noisy interactions in the user’s personal loss distribution. Based on this insight, we propose a resampling strategy to Denoise using the user’s Personal Loss distribution, named PLD, which reduces the probability of noisy interactions being optimized. |
Kaike Zhang; Qi Cao; Yunfan Wu; Fei Sun; Huawei Shen; Xueqi Cheng; |
| 367 | Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the MC recommendation using training-based collaborative filtering, requiring consideration of multiple ratings compared to single-criterion counterparts, often poses practical challenges in achieving state-of-the-art performance along with scalable model training. To solve this problem, we propose CA-GF, a training-free MC recommendation method, which is built upon criteria-aware graph filtering for efficient yet accurate MC recommendations. |
Jin-Duk Park; Jaemin Yoo; Won-Yong Shin; |
| 368 | Disentangled Condensation for Large-scale Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paradigm has considerably impeded the scalability of graph condensation, making it challenging to condense extremely large-scale graphs and generate high-fidelity condensed graphs. Therefore, we propose to disentangle the condensation process into a two-stage GNN-free paradigm, independently condensing nodes and generating edges while eliminating the need to optimize GNNs at the same time. |
Zhenbang Xiao; Yu Wang; Shunyu Liu; Bingde Hu; Huiqiong Wang; Mingli Song; Tongya Zheng; |
| 369 | Learning Feasible Causal Algorithmic Recourse: A Prior Structural Knowledge Free Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Since identifying counterfactuals without causal graph is impossible, we instead propose to approximate and constrain the variation of the perturbed components, i.e., the exogenous noise variables, by formulating the generation of AR as the structure-preserving intervention. |
Haotian Wang; Hao Zou; Xueguang Zhou; Shangwen Wang; Wenjing Yang; Peng Cui; |
| 370 | Hunting in The Dark Forest: A Pre-trained Model for On-chain Attack Transaction Detection in Web3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a universal approach for detecting on-chain attacks even when there are few or even no new on-chain attack samples. |
Zhiying Wu; Jiajing Wu; Hui Zhang; Zibin Zheng; Weiqiang Wang; |
| 371 | Logic-Aware Knowledge Graph Reasoning for Structural Sparsity Under Large Language Model Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: LoLLM secondly constructs reasoning paths instantiated from the first-order logic rules extracted from sparse KGs, and injects the logical semantics by a designed LLM-enhanced tuning strategy. We propose a textual loss (TL) and a logical loss (LL) in the optimization and obtain logical tuning embeddings of KG in this process. |
Yudai Pan; Jiajie Hong; Tianzhe Zhao; Lingyun Song; Jun Liu; Xuequn Shang; |
| 372 | Large Language Models As Narrative-Driven Recommenders Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although large language models (LLMs) have been shown to excel in processing general natural language queries, their effectiveness for handling such recommendation requests remains relatively unexplored. To close this gap, we compare the performance of 38 open- and closed-source LLMs of various sizes, such as LLama 3.2 and GPT-4o, in a movie recommendation setting. |
Lukas Eberhard; Thorsten Ruprechter; Denis Helic; |
| 373 | WaSCR: A WebAssembly Instruction-Timing Side Channel Repairer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A key security concern with WebAssembly is the threat of instruction-timing side-channel attacks, which exploit timing variations in branch instructions dependent on sensitive data, allowing attackers to infer sensitive information through timing measurement.In this paper, we introduce WaSCR, an automated WebAssembly instruction-timing Side-Channel Repairer. |
Liyan Huang; Junzhou He; Chao Wang; Weihang Wang; |
| 374 | Strong Equilibria in Bayesian Games with Bounded Group Size Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the ex-ante Bayesian k-strong equilibrium and the Bayesian k-strong equilibrium, where no group of at most k agents can benefit from deviation. |
Qishen Han; Grant Schoenebeck; Biaoshuai Tao; Lirong Xia; |
| 375 | Horizontal Federated Heterogeneous Graph Learning: A Multi-Scale Adaptive Solution to Data Distribution Challenges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These differences hinder models from learning shared representations and cause instability during training. To address these challenges, this paper proposes a novel multi-scale adaptive horizontal federated heterogeneous graph learning method MAFedHGL. |
Jia Wang; Yawen Li; Zhe Xue; Yingxia Shao; Zeli Guan; Wenling Li; |
| 376 | Price Stability and Improved Buyer Utility with Presentation Design: A Theoretical Study of The Amazon Buy Box Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We take a model of monopolistic competition and show that, on one hand, when all sellers have the same inspection costs, the market sees no stable price since the sellers always have incentives to undercut each other, and, on the other hand, the platform may stabilize the price by giving prominence to one seller chosen by a carefully designed mechanism. |
Ophir Friedler; Hu Fu; Anna Karlin; Ariana Tang; |
| 377 | Fair Personalized Learner Modeling Without Sensitive Attributes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore how to achieve fair personalized learner modeling without relying on any sensitive attribute input. |
Hefei Xu; Min Hou; Le Wu; Fei Liu; Yonghui Yang; Haoyue Bai; Richang Hong; Meng Wang; |
| 378 | Bridging Fairness and Uncertainty: Theoretical Insights and Practical Strategies for Equalized Coverage in GNNs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To bridge the gap between conformal prediction and fair coverage across different groups, we pose the fundamental question: Can fair GNNs enable the uncertainty estimates to be fairly applied across demographic groups? |
Longfeng Wu; Yao Zhou; Jian Kang; Dawei Zhou; |
| 379 | Towards Safe Machine Unlearning: A Paradigm That Mitigates Performance Degradation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study how to unlearn specific training data from a pre-trained model without accessing the remaining training data and protect model performance without dramatically changing the model’s parameters. |
Shanshan Ye; Jie Lu; Guangquan Zhang; |
| 380 | A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a Controllable, Scalable, and Human-Involved (CSHI) simulator framework that manages the behavior of user simulators across various stages via a plugin manager. |
Lixi Zhu; Xiaowen Huang; Jitao Sang; |
| 381 | MatriXSSed: A New Taxonomy for XSS in The Modern Web Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Cross-site scripting (XSS) has constantly remained one of the most prevalent attacks on the Web. In this work, we question its current taxonomy, i.e., the client- or server-side reflected (non-persistent) or stored (persistent) matrix. |
Doli\`{e}re Francis Som\'{e}; |
| 382 | Effective Influence Maximization with Priority Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We design an effective method AAS that achieves expected approximation guarantees. |
Jinghao Wang; Yanping Wu; Xiaoyang Wang; Chen Chen; Ying Zhang; Lu Qin; |
| 383 | Following Clues, Approaching The Truth: Explainable Micro-Video Rumor Detection Via Chain-of-Thought Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce ExMRD, a novel Explainable Micro-video Rumor Detection framework designed to generate detailed and coherent explanations for enhancing MVRD. |
Rongpei Hong; Jian Lang; Jin Xu; Zhangtao Cheng; Ting Zhong; Fan Zhou; |
| 384 | AI Model Modulation with Logits Redistribution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we propose AIM, a novel model modulation paradigm that enables a single model to exhibit diverse behaviors to meet the specific end requirements. |
Zihan Wang; Zhongkui Ma; Xinguo Feng; Zhiyang Mei; Ethan Ma; Derui Wang; Minhui Xue; Guangdong Bai; |
| 385 | Linear-Time Algorithms for Representative Subset Selection From Data Streams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result, these algorithms are only effective in limited scenarios and have super-linear time complexity, making them unsuitable for large-scale data streams. In this paper, we introduce the first linear-time streaming algorithms for this problem, without any assumptions on the data stream, while also minimizing memory usage. |
Shuang Cui; Kai Han; Jing Tang; |
| 386 | Towards Collaborative Anti-Money Laundering Among Financial Institutions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the first algorithm that supports performing AML over multiple institutions while protecting the security and privacy of local data.To evaluate, we construct Alipay-ECB, a real-world dataset comprising digital transactions from Alipay, the world’s largest mobile payment platform, alongside transactions from E-Commerce Bank (ECB). |
Zhihua Tian; Yuan Ding; Xiang Yu; Enchao Gong; Jian Liu; Kui Ren; |
| 387 | Ask, Acquire, Understand: A Multimodal Agent-based Framework for Social Abuse Detection in Memes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, previous methods have only been tested on limited datasets, providing insufficient evidence of their robustness. To address these limitations, we present a multimodal, agent-based framework designed to generate informative visual descriptions of memes by asking insightful questions to improve visual descriptions in zero-shot visual question-answering settings. |
Xuanrui Lin; Chao Jia; Junhui Ji; Hui Han; Usman Naseem; |
| 388 | ODNS Clustering: Unveiling Client-Side Dependency in Open DNS Infrastructure Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we measure the inter-dependence of open DNS resolvers and find that 1.9 million open DNS servers form only 81,636 ODNS clusters. |
Wenhao Wu; Zhaohua Wang; Qinxin Li; Zihan Li; Yi Li; Jin Yan; Zhenyu Li; |
| 389 | Conformal Graph-level Out-of-distribution Detection with Adaptive Data Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current approaches concentrate on how to learn better graph representations, but fail to provide any statistically guarantee on detection results, therefore impeding their deployments in the scenario where detection errors would result in serious consequences. To overcome this critical issue, we propose the Conformal Graph-level Out-of-distribution Detection (CGOD), extending the theory of conformal prediction to graph-level OOD detection with a rigorous control over the false positive rate. |
Xixun Lin; Yanan Cao; Nan Sun; Lixin Zou; Chuan Zhou; Peng Zhang; Shuai Zhang; Ge Zhang; Jia Wu; |
| 390 | LargePiG for Hallucination-Free Query Generation: Your Large Language Model Is Secretly A Pointer Generator Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent research on query generation has focused on using Large Language Models (LLMs), which, despite achieving state-of-the-art performance, also introduce hallucination issues in generated queries. In this work, we categorize these issues into relevance hallucination and factuality hallucination, proposing a new typology for hallucinations arising from LLM-based query generation. |
Zhongxiang Sun; Zihua Si; Xiaoxue Zang; Kai Zheng; Yang Song; Xiao Zhang; Jun Xu; |
| 391 | Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current QPL methods typically suffer from the pattern-entity alignment bias problem, leading to the learned defective query patterns limiting KGQE models’ performance. To address this problem, we propose an effective Query Instruction Parsing Plugin (QIPP) that leverages the context awareness of Pre-trained Language Models (PLMs) to capture latent query patterns from code-like query instructions. |
Xingrui Zhuo; Jiapu Wang; Gongqing Wu; Shirui Pan; Xindong Wu; |
| 392 | Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this approach fails to consider one crucial aspect of the systems: whether their outputs accurately reflect the users’ (post-purchase) sentiments, i.e., whether and why they would like and/or dislike the recommended items. To shed light on this issue, we introduce new datasets and evaluation methods that focus on the users’ sentiments. |
Ryotaro Shimizu; Takashi Wada; Yu Wang; Johannes Kruse; Sean O’Brien; Sai HtaungKham; Linxin Song; Yuya Yoshikawa; Yuki Saito; Fugee Tsung; Masayuki Goto; Julian McAuley; |
| 393 | On The Abuse and Detection of Polyglot Files Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we found that existing file-format and embedded-file detection tools, even those developed specifically for polyglot files, fail to reliably detect polyglot files used in the wild. |
Luke Koch; Sean Oesch; Amir Sadovnik; Brian Weber; Amul Chaulagain; Matthew Dixson; Jared Dixon; Mike Huettel; Cory Watson; Jacob Hartman; Richard Patulski; |
| 394 | Multimodal Graph-Based Variational Mixture of Experts Network for Zero-Shot Multimodal Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: But the existing methods ignore the fine-grained semantic correlation of text-image pairs and samples. Therefore, we propose the multimodal graph-based variational mixture of experts network (MG-VMoE) which takes the MoE network as the backbone and exploits it for aligning multimodal representations in a fine-grained way. |
Baohang Zhou; Ying Zhang; Yu Zhao; Xuhui Sui; Xiaojie Yuan; |
| 395 | Least Privilege Access for Persistent Storage Mechanisms in Web Browsers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The goal of our work is to design a mechanism to enforce fine-grained control of persistent storage objects. |
Gayatri Priyadarsini Kancherla; Dishank Goel; Abhishek Bichhawat; |
| 396 | Unveiling Network Performance in The Wild: An Ad-Driven Analysis of Mobile Download Speeds Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce adNPM, a novel technique for measuring download speed by embedded measurement code in ads displayed across web browsers and mobile apps, without requiring user participation. |
Miguel A. Bermejo-Agueda; Patricia Callejo; Rub\'{e}n Cuevas; \'{a}ngel Cuevas; Ramakrishnan Durairajan; Reza Rejaie; \'{a}lvaro Mayol; |
| 397 | Hypergraph-based Zero-shot Multi-modal Product Attribute Value Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, how to jointly learn the product representation given various product information in multiple modalities, such as textual modality (e.g., product titles and descriptions) and visual modality (e.g., product images), is also a challenging task. To address these limitations, we propose a novel method for extracting multi-label product attribute-value pairs from multiple modalities in the zero-shot scenario, where labeled data is absent during training. |
Jiazhen Hu; Jiaying Gong; Hongda Shen; Hoda Eldardiry; |
| 398 | Uncertainty-Aware Graph Structure Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Blindly connecting low-quality nodes and aggregating their ambiguous information can degrade the performance of other nodes. 2) The constructed graph structures are often constrained to be symmetric, which may limit the model’s flexibility and effectiveness.To overcome these limitations, we propose an Uncertainty-aware Graph Structure Learning (UnGSL) strategy. |
Shen Han; Zhiyao Zhou; Jiawei Chen; Zhezheng Hao; Sheng Zhou; Gang Wang; Yan Feng; Chun Chen; Can Wang; |
| 399 | A Scalable Crawling Algorithm Utilizing Noisy Change-Indicating Signals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Under the assumption that the change and request events, resp., to each web page follow independent Poisson processes, the optimal scheduling policy was derived by Azar et al. 2018. In this paper, we study an extension of this problem where side information indicating content changes, such as various types of web pings, for example, signals from sitemaps, content delivery networks, etc., is available. |
Julian Zimmert; R\'{o}bert Busa-Fekete; Andr\'{a}s Gy\{o}rgy; Linhai Qiu; Hyomin Choi; Tzu-Wei Sung; Hao Shen; Sharmila Subramaniam; Li Xiao; |
| 400 | Uncertainty Quantification and Decomposition for LLM-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We start by introducing a novel framework for estimating the predictive uncertainty to quantitatively measure the reliability of LLM-based recommendations. |
Wonbin Kweon; Sanghwan Jang; SeongKu Kang; Hwanjo Yu; |
| 401 | Safeguarding Blockchain Ecosystem: Understanding and Detecting Attack Transactions on Cross-chain Bridges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we collect the largest number of cross-chain bridge attack incidents to date, including 49 attacks that occurred between June 2021 and September 2024, of which 22 were attacks on cross-chain bridge business logic. |
Jiajing Wu; Kaixin Lin; Dan Lin; Bozhao Zhang; Zhiying Wu; Jianzhong Su; |
| 402 | Catalysts of Conversation: Examining Interaction Dynamics Between Topic Initiators and Commentors in Alzheimer’s Disease Online Communities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study investigated the user interaction dynamics within two large, popular ADRD communities, TalkingPoint and ALZConnected, focusing on topic initiator engagement, initial post content, and the linguistic patterns of comments at the thread level. |
Congning Ni; Qingxia Chen; Lijun Song; Patricia Commiskey; Qingyuan Song; Bradley Malin; Zhijun Yin; |
| 403 | Reembedding and Reweighting Are Needed for Tail Item Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite their advantages over traditional approaches, these models suffer more significant performance degradation on tail items against conventional ID-based solutions, which are largely overlooked by recent research. In this paper, we substantiate the above challenges as (1) all-in ground-truth, i.e., the standard cross-entropy (CE) loss focuses solely on the target items while treating all non-ground-truth equally, causing insufficient optimization for tail items, and (2) knowledge transfer tax, i.e., the knowledge encapsulated in LLMs and LVMs dominates the optimization process due to insufficient training for tail items. |
Zihao Li; Yakun Chen; Tong Zhang; Xianzhi Wang; |
| 404 | MerKury: Adaptive Resource Allocation to Enhance The Kubernetes Performance for Large-Scale Clusters Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present MerKury, a general and lightweight framework designed to enhance the Kubernetes performance for large-scale clusters. |
Jiayin Luo; Xinkui Zhao; Yuxin Ma; Shengye Pang; Jianwei Yin; |
| 405 | TEARS: Text Representations for Scrutable Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, these systems offer limited control to users over their recommendations. Inspired by recent work, we introduce TExtuAl Representations for Scrutable recommendations (TEARS) to address these challenges. |
Emiliano Penaloza; Olivier Gouvert; Haolun Wu; Laurent Charlin; |
| 406 | Automatic Instruction Data Selection for Large Language Models Via Uncertainty-Aware Influence Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing works tend to overlook external correlations between instruction examples during data selection process, which can introduce potential bias and lead to sub-optimal performance. To bridge this gap, we formalize this problem from graph influence maximization perspective and propose Uncertainty-aware influence Maximization (UniMax), a data selection framework that explicitly incorporates the complex inter-dependencies within instruction data. |
Jindong Han; Hao Liu; Jun Fang; Naiqiang Tan; Hui Xiong; |
| 407 | The First Early Evidence of The Use of Browser Fingerprinting for Online Tracking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our large-scale study reveals strong evidence of browser fingerprinting for ad tracking and targeting, shown by bid value disparities and reduced HTTP records after fingerprinting changes. |
Zengrui Liu; Jimmy Dani; Yinzhi Cao; Shujiang Wu; Nitesh Saxena; |
| 408 | BETag: Behavior-enhanced Item Tagging with Finetuned Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we introduce a novel method for automatic product tagging using LLMs to create behavior-enhanced tags (BETags). |
Shao-En Lin; Brian Liu; Miao-Chen Chiang; Ming-Yi Hong; Yu-Shiang Huang; Chuan-Ju Wang; Che Lin; |
| 409 | Ranking Items By The Current-Preferences and Profits: A List-wise Learning-to-Rank Approach to Profit Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Second, through the point-wise learning-to-rank (LTR), the model is optimized solely for the preference score of each item independently rather than being directly optimized for the overall ranking of items. To tackle these issues, we propose a novel MBA that involves three key steps: (S1) defining the Current Preference incorporated with Profit (i.e., CPP) for items; (S2) classifying items through CPP; and (S3) training the model by list-wise LTR based on CPP. |
Hong-Kyun Bae; Hae-Ri Jang; Won-Yong Shin; Sang-Wook Kim; |
| 410 | Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose and evaluate multiple strategies for generating tailored counterspeech that is adapted to the moderation context and personalized for the moderated user. |
Lorenzo Cima; Alessio Miaschi; Amaury Trujillo; Marco Avvenuti; Felice Dell’Orletta; Stefano Cresci; |
| 411 | Detecting Linguistic Bias in Government Documents Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methodologies often overlook the unique context and far-reaching impacts of governmental documents, potentially obscuring embedded biases that shape public policy and citizen-government interactions. To bridge this gap, we introduce the Dutch Government Data for Bias Detection (DGDB), a dataset sourced from the Dutch House of Representatives and annotated for bias by experts. |
Milena de Swart; Floris Den Hengst; Jieying Chen; |
| 412 | EBaaS: AIoT-Enabled EBike Battery-Swap As A Service for Last-Mile Delivery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these riders frequently encounter depleted batteries due to limited capacity and prolonged charging times, necessitating inconvenient swaps or recharges during deliveries. To address this issue, we propose the e-bike Battery Swap-as-a-Service (eBaaS), an innovative battery-swapping system that leverages an intelligent AIoT network for seamless battery swapping at distributed locations across urban areas. |
Donghui Ding; Zhao Li; Jiarun Zhang; Xuanwu Liu; Ji Zhang; Yuchen Li; Peng Cai; JianXun Liu; Guodong Long; |
| 413 | Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, they are less likely to consider the special baseline wander problem as well as the multi-view characteristics of medical time series, which largely hinders their prediction performance. To address these limitations, we propose a Multi-resolution Spatiotemporal Graph Learning framework, MedGNN, for medical time series classification. |
Wei Fan; Jingru Fei; Dingyu Guo; Kun Yi; Xiaozhuang Song; Haolong Xiang; Hangting Ye; Min Li; |
| 414 | MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Frequent fluctuations of client nodes in highly dynamic mobile clusters can lead to significant changes in feature space distribution and data drift, posing substantial challenges to the robustness of existing federated learning (FL) strategies. To address these issues, we proposed a mobile cluster federated learning framework (MoCFL). |
Kai Fang; Jiangtao Deng; Chengzu Dong; Usman Naseem; Tongcun Liu; Hailin Feng; Wei Wang; |
| 415 | SPRec: Self-Play to Debias LLM-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose SPRec, a novel self-play framework designed to mitigate over-recommendation and improve fairness without requiring additional data or manual intervention. |
Chongming Gao; Ruijun Chen; Shuai Yuan; Kexin Huang; Yuanqing Yu; Xiangnan He; |
| 416 | Analyzing User Characteristics of Hate Speech Spreaders on Social Media Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we analyze the role of user characteristics in hate speech resharing across different types of hate speech (e.g., political hate). |
Dominique Geissler; Abdurahman Maarouf; Stefan Feuerriegel; |
| 417 | Towards An Inclusive Mobile Web: A Dataset and Framework for Focusability in UI Accessibility Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our findings also reveal that the severity of issues varies across interaction stages, with earlier stages posing a more significant impact. Building on these insights, we propose a comprehensive framework of three accessibility stages: focusability, information, and functionality (FIF), encompassing 12 sub-tasks under 3 overarching tasks. |
Ming Gu; Lei Pei; Sheng Zhou; Ming Shen; Yuxuan Wu; Zirui Gao; Ziwei Wang; Shuo Shan; Wei Jiang; Yong Li; Jiajun Bu; |
| 418 | Enhancing Knowledge Tracing Through Decoupling Cognitive Pattern from Error-Prone Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Extracting cognitive patterns that accurately reflect students’ knowledge mastery from such error-prone data remains a significant challenge. Against this background, this paper proposes a novel KT method named RoubstKT, inspired by educational measurement theory and frequency-based decomposition. |
Teng Guo; Yu Qin; Yubin Xia; Mingliang Hou; Zitao Liu; Feng Xia; Weiqi Luo; |
| 419 | Evaluating Robustness of LLMs on Crisis-Related Microblogs Across Events, Information Types, and Linguistic Features Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper provides a detailed analysis of the performance of six well-known LLMs in processing disaster-related social media data from a large-set of real-world events. |
Muhammad Imran; Abdul Wahab Ziaullah; Kai Chen; Ferda Ofli; |
| 420 | Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a set of comprehensive metrics for quantifying gender bias in recommendations. |
Tahsin Alamgir Kheya; Mohamed Reda Bouadjenek; Sunil Aryal; |
| 421 | Modality Interactive Mixture-of-Experts for Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing multimodal fake news detection methods often emphasize cross-modal consistency but ignore the complex interactions between text and visual elements, which may complement, contradict, or independently influence the predicted veracity of a post. To address these challenges, we present Modality Interactive Mixture-of-Experts for Fake News Detection (MIMoE-FND), a novel hierarchical Mixture-of-Expert framework designed to enhance multimodal fake news detection by explicitly modeling modality interactions through an interaction gating mechanism. |
Yifan Liu; Yaokun Liu; Zelin Li; Ruichen Yao; Yang Zhang; Dong Wang; |
| 422 | Multi-Granularity Augmented Graph Learning for Spoofing Transaction Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Relying solely on local context makes it challenging to capture multi-granularity risk signals, especially for organized and covert spoofing.Additionally, existing methods fail to consider the differences and relative importance between features of varying granularity, leading to feature distortion and noise. Therefore, we propose a multi-granularity augmented graph learning method that differentially captures fraud signals at local, group, and global levels. |
Xin Liu; Haojun Rui; Dawei Cheng; Li Han; Zhongyun Zhou; Guoping Zhao; |
| 423 | Time-aware Medication Recommendation Via Intervention of Dynamic Treatment Regimes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, existing methods often overlook the time interval information over patients’ successive visits, which is critical to indicate patients’ treatment evolution. To address these significant gaps, we propose a Time-aware Medication Recommendation Framework via Intervention of Dynamic Treatment Regimes, called MR-DTR. |
Yishuo Li; Qi Zhang; Wenpeng Lu; Xueping Peng; Weiyu Zhang; Jiasheng Si; Yongshun Gong; Liang Hu; |
| 424 | Simulating Question-answering Correctness with A Conditional Diffusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a method called Diffusion-based Simulator (DSim), which takes advantage of diffusion to alleviate the bias accumulation. |
Ting Long; Li’ang Yin; Yi Chang; Wei Xia; Yong Yu; |
| 425 | InCo: Exploring Inter-Trip Cooperation for Efficient Last-mile Delivery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we design an inter-trip cooperation-based last-mile delivery system, InCo, aiming to minimize the average order delivery time. |
Wenjun Lyu; Shuxin Zhong; Guang Yang; Haotian Wang; Yi Ding; Shuai Wang; Yunhuai Liu; Tian He; Desheng Zhang; |
| 426 | DiGrI: Distorted Greedy Approach for Human-Assisted Online Suicide Ideation Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although current fully automated methods show promise, they may produce uncertain predictions, leading to flawed conclusions. To address this, we propose a novel model called DiGrI, or Distorted Greedy Approach for Human-Assisted Online Suicide Ideation Detection, which reformulates suicide ideation assessment as a selective, prioritized prediction problem. |
Usman Naseem; Liang Hu; Qi Zhang; Shoujin Wang; Shoaib Jameel; |
| 427 | Social Bots Meet Large Language Model: Political Bias and Social Learning Inspired Mitigation Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Through designing and implementing social experiments, we discover that this bias consistently manifests in the social behaviors of agents driven by diverse LLMs, across nine key political topics. Inspired by the social learning theory, we propose to mitigate political bias by guiding these agents to emulate how humans learn to behave. |
Jinghua Piao; Zhihong Lu; Chen Gao; Yong Li; |
| 428 | AuslanWeb: A Scalable Web-Based Australian Sign Language Communication System for Deaf and Hearing Individuals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Auslan, as the sign language specific to Australia, still lacks a reliable bidirectional translation tool for effective communication. To address these challenges, we propose AuslanWeb, a web-based system for bidirectional translation of both isolated and successive sign language. |
Xin Shen; Heming Du; Hongwei Sheng; Lincheng Li; Kaihao Zhang; |
| 429 | Effectiveness of Privacy-preserving Algorithms in LLMs: A Benchmark and Empirical Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, with plenty of algorithms emerging, it brings challenges for organizations or researchers to compare and evaluate these different algorithms to select the most suitable one for their certain requirements. To address these challenges, we introduce ”Privacy-preserving4LLM Benchmarking”, a systematic evaluation framework that systematically assesses different privacy-preserving algorithms’ utility-privacy trade-offs across different LLM architectures. |
Jinglin Sun; Basem Suleiman; Imdad Ullah; Imran Razzak; |
| 430 | Dual Pairwise Pre-training and Prompt-tuning with Aligned Prototypes for Interbank Credit Rating Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Limited excavation of the credit rating records and the temporal distribution shifts existed in different financial periods still pose challenges to improving the accuracy of the credit rating tasks. To address these challenges, in this work we propose a Dual Pairwise pre-training and prompt Tuning framework with Aligned Prototypes (DPTAP) for interbank credit rating, which enables dynamic credit updates. |
Jiehao Tang; Wenjun Wang; Dawei Cheng; Hui Zhao; Changjun Jiang; |
| 431 | Before It’s Too Late: A State Space Model for The Early Prediction of Misinformation and Disinformation Engagement Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present IC-Mamba, a novel state space model that forecasts social media engagement by modeling interval-censored data with integrated temporal embeddings. |
Lin Tian; Emily Booth; Francesco Bailo; Julian Droogan; Marian-Andrei Rizoiu; |
| 432 | Cross-Modal Transfer from Memes to Videos: Addressing Data Scarcity in Hateful Video Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our approach introduces a human-assisted reannotation pipeline to align meme dataset labels with video datasets, ensuring consistency with minimal labeling effort. |
Han Wang; Rui Yang Tan; Roy Ka-Wei Lee; |
| 433 | Sketching Very Large-scale Dynamic Attributed Networks More Practically Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a practical and sustainable framework of sketching very large-scale dynamic attributed networks called VLS2ketch, which incorporates incremental embedding updates alongside storage-efficient, binarized representation of both node attributes and topological variations. |
Wei Wu; Shiqi Li; Ling Chen; Fangfang Li; Chuan Luo; |
| 434 | Generative Dynamic Graph Representation Learning for Conspiracy Spoofing Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing spoofing detection methods, though effective in some scenarios, struggle to capture the complexity of dynamic and diverse, evolving inter-node relationships. To address these challenges, we propose a novel framework called the Generative Dynamic Graph Model (GDGM), which models dynamic trading behaviors and the relationships among nodes to learn representations for conspiracy spoofing detection. |
Sheng Xiang; Yidong Jiang; Yunting Chen; Dawei Cheng; Guoping Zhao; Changjun Jiang; |
| 435 | MDAM3: A Misinformation Detection and Analysis Framework for Multitype Multimodal Media Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These detectors are also usually designed to make judgments without providing explanations, reducing transparency and limiting their broader applicability. To address these issues, we propose MDAM3, a Misinformation Detection and Analysis Framework for Multitype Multimodal Media. |
Qingzheng Xu; Heming Du; Szymon \L{}ukasik; Tianqing Zhu; Sen Wang; Xin Yu; |
| 436 | A Macro- and Micro-Hierarchical Transfer Learning Framework for Cross-Domain Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Secondly, from a macro perspective, existing approaches ignore the relationship between user engagement and news content, which reveals shared behaviors of common users across domains and can facilitate more effective knowledge transfer. To address these limitations, we propose a novel macro- and micro- hierarchical transfer learning framework (MMHT) for cross-domain fake news detection. |
Xuankai Yang; Yan Wang; Xiuzhen Zhang; Shoujin Wang; Huaxiong Wang; Kwok Yan Lam; |
| 437 | Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, this narrows the differences in behavioral traits between them and benign users within the platform’s database, thereby making current GFD models lose efficiency. To address this problem, we propose a relation diffusion-based graph augmentation model Grad. |
Jie Yang; Rui Zhang; Ziyang Cheng; Dawei Cheng; Guang Yang; Bo Wang; |
| 438 | CAP: Causal Air Quality Index Prediction Under Interference with Unmeasured Confounding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existed methods often suffer from spurious correlations caused by unmeasured confounders and are lack of interpretability of the model, leading to sub-optimal prediction performance. This motivates us to propose a causal AQI prediction framework (CAP) that employs a structural causal model (SCM) to characterize the causal structural variability of various AQI factors for robust AQI prediction. |
Huayi Yang; Chunyuan Zheng; Guorui Liao; Shanshan Huang; Jun Liao; Zhili Gong; Haoxuan Li; Li Liu; |
| 439 | How Much Medical Knowledge Do LLMs Have? An Evaluation of Medical Knowledge Coverage for LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a systematic evaluation framework, MedKGEval, to assess the coverage of medical knowledge in LLMs through the lens of medical knowledge graphs (KGs). |
Ziheng Zhang; Zhenxi Lin; Yefeng Zheng; Xian Wu; |
| 440 | Perceiving Urban Inequality from Imagery Using Visual Language Models with Chain-of-Thought Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Two key challenges must be addressed: 1) accurately perceiving micro-level inequalities within neighborhoods, and 2) ensuring that this perception is interpretable for policy guidance. To address these gaps, we propose UI-CoT, a framework that leverages the power of urban imagery-based visual language models in urban inequality perceiving, enhanced by Chain-of-Thought prompting to improve reasoning capabilities. |
Yunke Zhang; Ruolong Ma; Xin Zhang; Yong Li; |
| 441 | Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose CoDiffMob, a diffusion model for urban mobility generation with collaborative noise priors, we emphasize the critical role of noise in diffusion models for generating mobility data. |
Yuheng Zhang; Yuan Yuan; Jingtao Ding; Jian Yuan; Yong Li; |
| 442 | From Predictions to Analyses: Rationale-Augmented Fake News Detection with Large Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these models lack the breadth of knowledge and the depth of language understanding, which results in unsatisfactory adaptability, generalization, and explainability performance. To address these issues, we attempt to introduce Large Vision-Language Models (LVLMs), aiming to leverage the common sense understanding and logical reasoning abilities of LVLMs for the MFND task. |
Xiaofan Zheng; Zinan Zeng; Heng Wang; Yuyang Bai; Yuhan Liu; Minnan Luo; |