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Companion Proceedings of the ACM on Web Conference 2025

作者： Lulu Yu Keping Bi Jiafeng Guo Shihao Liu Dawei Yin Xueqi Cheng CAS Key Lab of Network Data Science and Technology ICT Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China

ISBN: (纸本)9798400713316

Most existing unbiased learning-to-rank (ULTR) approaches are based on the user examination hypothesis, which assumes that users will click a result only if it is both relevant and observed (typically modeled by position). However, in real-world scenarios, users often click only one or two results after examining multiple relevant options, due to limited patience or because their information needs have already been satisfied. Motivated by this, we propose a query-level click propensity model to capture the probability that users will click on different result lists, allowing for non-zero probabilities that users may not click on an observed relevant result. We hypothesize that this propensity increases when more potentially relevant results are present, and refer to this user behavior as relevance saturation bias. Our method introduces a Dual Inverse Propensity Weighting (DualIPW) mechanism-combining query-level and position-level IPW-to address both relevance saturation and position bias. Through theoretical derivation, we prove that DualIPW can learn an unbiased ranking model. Experiments on the real-world Baidu-ULTR dataset demonstrate that our approach significantly outperforms state-of-the-art ULTR baselines. The code can be found at https://***/Trustworthy-Information-Access/DualIPW.

关键词： position bias

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Poisoning Attacks and Defenses to Learned Bloom Filters for Malicious URL Detection

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Dong, Fangming Wang, Pinghui Li, Rundong Cui, Xueyao Zhao, Junzhou Tao, Jing Zhang, Chen Guan, Xiaohong Zhejiang CreateLink Technology Co. Ltd Guangdong Guangzhou China Xi'an Jiaotong University MOE Key Laboratory for Intelligent Networks and Network Security Shaanxi Xi'an710049 China Tsinghua University Center for Intelligent and Networked Systems Tsinghua National Lab for Information Science and Technology Beijing China

Approximate membership query (AMQ) structures represented by the Bloom Filter and its variants have been popularly researched in recent years. Researchers have recently combined machine learning with this type of structure to reduce space consumption and computation overhead further and make remarkable progress. However, with the booming performance in space or other metrics, researchers tend to ignore the security of the trained model. The machine learning model is vulnerable to poisoning attacks, and naturally, we infer that the learning-based filters also have the same deficiencies. Hence, in this paper, to confirm the inference mentioned above, experiments on the real-world datasets of URLs are conducted and prove that it is necessary to consider the security issue when using learning-based filters. We show that by data poisoning, the attacker can deflect learned Bloom Filters to make a false identification, which can lead to a significant loss in some cases. Aiming to solve this issue, we put forward a method named Defensive Learned Bloom Filter (DLBF) to diminish the influence of data poisoning and achieve a better performance compared to types of learned Bloom Filters. © 2004-2012 IEEE.

关键词： Wiener filtering

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HiCoCS: High Concurrency Cross-Sharding on Permissioned Blockchains

arXiv

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arXiv 2025年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong School of Computer Science and Technology Xidian University Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China School of Computer Science and Technology Xidian University Shaanxi Key Laboratory of Network and System Security Xi’an710071 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210016 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Concurrency control

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Task Offloading and Resource Allocation Based on Reinforcement Learning and Load Balancing in Vehicular networking

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Shujuan Xiang, Shuhuan Zhou, Ziqi Dai, Haipeng Yu, Enze Deng, Qingyong Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science and School of Cyberspace Security Xiangtan411105 China Nanjing University School of Computer Science and Technology Nanjing211189 China Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guilin541004 China

Due to limited on-board resources and the mobility characteristics of vehicles in a multi-access edge computing (MEC)-based vehicular network, efficient task offloading and resource allocation schemes are essential for achieving low-latency and low-energy consumption applications in the Internet of Vehicles (IoV). The spatial distribution of vehicles, influenced by various factors, leads to significant workload variations across MEC servers. In this paper, we address task offloading and resource allocation as a joint optimization problem and propose a Load-Balancing Deep Deterministic Policy Gradient (LBDDPG) algorithm to achieve optimal results. The joint optimization problem is modeled as a Markov Decision Process (MDP), enabling the LBDDPG algorithm to systematically address the challenges of workload imbalance and resource inefficiency. The algorithm incorporates a load optimization strategy to balance workload distribution across MEC servers, mitigating disparities caused by uneven vehicle distributions. The reward function is designed to account for both energy consumption and delay, ensuring an optimal trade-off between these critical factors. To enhance training efficiency, a noise-based exploration strategy is employed, preventing ineffective exploration during the early stages. Additionally, constraints such as computational capacity and latency thresholds are embedded to ensure the algorithm's practical applicability. Experimental results demonstrate that the proposed LBDDPG algorithm achieves faster convergence and superior performance in terms of energy consumption and latency compared to other reinforcement learning algorithms. © 1975-2011 IEEE.

关键词： Markov processes

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ScanDTM: A Novel Dual-Temporal Modulation Scanpath Prediction Model for Omnidirectional Images

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zhu, Dandan Zhang, Kaiwei Min, Xiongkuo Zhai, Guangtao Yang, Xiaokang East China Normal University School of Computer Science and Technology Shanghai200333 China Shanghai Jiao Tong University Institute of Image Communication and Network Engineering Shanghai200240 China Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai200240 China

Scanpath prediction for omnidirectional images aims to effectively simulate the human visual perception mechanism to generate dynamic realistic fixation trajectories. However, the majority of scanpath prediction methods for omnidirectional images are still in their infancy as they fail to accurately capture the time-dependency of viewing behavior and suffer from sub-optimal performance along with limited generalization capability. A desirable solution should achieve a better trade-off between prediction performance and generalization ability. To this end, we propose a novel dual-temporal modulation scanpath prediction (ScanDTM) model for omnidirectional images. Such a model is designed to effectively capture long-range time-dependencies between various fixation regions across both internal and external time dimensions, thereby generating more realistic scanpaths. In particular, we design a Dual Graph Convolutional network (Dual-GCN) module comprising a semantic-level GCN and an image-level GCN. This module servers as a robust visual encoder that captures spatial relationships among various object regions within an image and fully utilizes similar images as complementary information to capture similarity relations across relevant images. Notably, the proposed Dual-GCN focuses on modeling temporal correlations from both local and global perspectives within the internal time dimension. Furthermore, drawing inspiration from the promising generalization capabilities of diffusion models across various generative tasks, we introduce a novel diffusion-guided saliency module. This module formulates the prediction issue as a conditional generative process for the saliency map, utilizing extracted semantic-level and image-level visual features as conditions. With the well-designed diffusion-guided saliency module, our proposed ScanDTM model acting as an external temporal modulator, we can progressively refine the generated scanpath from the noisy map. We conduct extensive expe

关键词： Prediction models

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An RTDNN-Based Hybrid Precoding and Combining Joint Optimization Algorithm for MUPC-MIMO Systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and networking 2025年

作者： Liu, Fulai Liu, Yubiao Yang, Xianghuan Duan, Zhuoyao Shi, Baozhu Ji, Li Du, Ruiyan Northeastern University at Qinhuangdao Lab of Key Technology of Millimeter-wave Large-scale MIMO System Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China Hebei University College of Mathematics and Information Science Baoding071002 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Qinhuangdao Vocational and Technical College Qinhuangdao066100 China School of Computer Science and Engineering Northeastern University Shenyang110004 China

To achieve a satisfactory balance between energy efficiency (EE) and spectral efficiency (SE) for hybrid precoding and combining (HPC), this paper presents a two-stage joint optimization HPC algorithm based on residual deep neural network (DNN) for multi-user partially connected (MUPC) structure, namely RTDNN algorithm. In the proposed algorithm, a two-stage framework is designed to optimize analog and digital coders at the transmitter and receiver simultaneously, which can effectively preserve coupling relationship of coders between the transmitter and receiver. Specially, the RTDNN architecture is mainly divided into two stage networks, which includes analog stage and digital stage networks. The analog stage network is developed to obtain the optimal analog precoder and combiner simultaneously. On this basis, the digital stage network is constructed to solve the digital precoder and combiner concurrently. To further improve the prediction accuracy of RTDNN-based HPC, residual modules are adopted into the developed two stage networks. Moreover, the proposed RTDNN is trained with unsupervised learning. Simulation results show that the EE of the proposed algorithm is at least enhanced by 0.6 bits/Hz/J with satisfactory SE than other methods. © 2017 IEEE.

关键词： Deep neural networks

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High-order diversity feature learning for pedestrian attribute recognition

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Neural networks 2025年 188卷 107463页

作者： Wu, Junyi Huang, Yan Gao, Min Niu, Yuzhen Chen, Yuzhong Wu, Qiang Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fujian Fuzhou China Australian Artificial Intelligence Institute University of Technology Sydney NSW Australia Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fujian Fuzhou China School of Electrical and Data Engineering University of Technology Sydney NSW Australia

Pedestrian attribute recognition (PAR) involves accurately identifying multiple attributes present in pedestrian images. There are two main approaches for PAR: part-based method and attention-based method. The former relies on existing segmentation or region detection methods to localize body parts and learn corresponding attribute-specific feature from the corresponding regions, where the performance heavily depends on the accuracy of body region localization. The latter adopts the embedded attention modules or transformer attention to exploit detailed feature. However, it can focus on certain body regions but often provide coarse attention, failing to capture fine-grained details, the learned feature may also be interfered with by irrelevant information. Meanwhile, these methods overlook the global contextual information. This work argues for replacing coarse attention with detailed attention and integrating it with global contextual feature from ViT to jointly represent attribute-specific regions. To tackle this issue, we propose a High-order Diversity Feature Learning (HDFL) method for PAR based on ViT. We utilize a polynomial predictor to design an Attribute-specific Detailed Feature Exploration (ADFE) module, which can construct the high-order statistics and gain more fine-grained feature. Our ADFE module is a parameter-friendly method that provides flexibility in deciding its utilization during the inference phase. A Soft-redundancy Perception Loss (SPLoss) is proposed to adaptively measure the redundancy between feature of different orders, which can promote diverse characterization of features. Experiments on several PAR datasets show that our method achieves a new state-of-the-art (SOTA) performance. On the most challenging PA100K dataset, our method outperforms previous SOTA by 1.69% and achieves the highest mA of 84.92%. © 2025 The Authors

关键词： Contrastive Learning

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RDG-TE: Link reliability-aware DRL-GNN-based traffic engineering in SDN

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Expert Systems with Applications 2025年 265卷

作者： Farhan, Muhammad Shah, Nadir Wang, Lei Muntean, Gabriel-Miro Song, Houbing Herbert School of Software Dalian University of Technology Dalian China Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Key Lab of Ubiquitous Network and Service Software of Liaoning Province China School of Electronic Engineering Dublin City University Dublin Ireland Department of Information Systems University of Maryland Baltimore United States

The existing advanced machine learning approaches based on Graph Neural networks (GNN) for efficient traffic engineering (TE) in Software Defined networking (SDN) overlook consideration of link reliability values. Link reliability is a very important parameter, directly linked to end-to-end delay for data packet transmission, and can be used to improve the delivery performance. This research article proposes two versions of RDG-TE, a novel model that integrates Deep Reinforcement Learning (DRL) and GNN in order to solve efficiently network TE problems by considering link reliability in both model training and reward computation. The proposed model enables improved performance by more accurately predicting the SDN network behavior in case of link failure. Testing results show that, in comparison to the closest state-of-art approach, our proposed approach reduces the number of disturbed flows by 24.19% and the hop count by 35.38% while also increases the reliable route prediction accuracy by 40.17%. © 2024 Elsevier Ltd

关键词： Deep reinforcement learning

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A Chinese multimodal depression dataset with personality labeling for older adults with underlying medical conditions

引用

IEEE Transactions on Affective Computing 2025年

作者： Zhao, Yuliang Zhang, Huawei Li, Jian Song, Siyang Lian, Chao Liu, Yinghao Wang, Yulin Fu, Changzeng School of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China Northeastern University SSTC NEU Hebei Key Laboratory of Marine Perception Network and Data Processing Hebei Qinhuangdao066004 China Osaka University Japan University of Cambridge AFAR Lab Department of Computer Science and Technology CambridgeCB3 0FT United Kingdom First Hospital of Qinhuangdao Qinhuangdao066004 China

Elderly individuals often suffer from underlying medical conditions, resulting in a significant decline in quality of life and a heightened susceptibility to depression. Presently, AI screening tools based on behavioral indicators offer an objective and effective approach to diagnosing depression. However, current AI depression screening tools are primarily tailored to adolescents and adults, exhibiting shortcomings in their applicability and accuracy for elderly individuals with underlying medical conditions. To address the above issues, firstly, this paper constructs a depression dataset for elderly people with underlying diseases by using semi-structured interviews. Secondly, based on cognitive science insights, it is recognized that personality factors significantly influence behavioral expressions and also determine the attitudes of elderly individuals toward current life circumstances/health issues. Therefore, besides annotating depression severity, the Big Five-10 personality scale was utilized to annotate participant personalities. Finally, a late fusion-based multi-task learning framework was proposed, and the effects of introducing gait information and personality annotation on the performance of depression assessment were investigated. The experimental findings affirm the importance of integrating gait information and personality assessment in improving depression detection effectiveness. This study provides valuable foundational resources, as well as beneficial references and insights, for the research on depression in the elderly. © 2010-2012 IEEE.

关键词： Depression Multimodal dataset Older adults Personality Underlying diseases

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HiCoCS: High Concurrency Cross-Sharding on Permissioned Blockchains

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IEEE Transactions on Computers 2025年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong Xidian University School of Computer Science and Technology China Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China Shaanxi Key Laboratory of Network and System Security Xi’an710071 China Jiangxi University of Finance and Economics School of Computing Nanchang330013 China Jiangxi University of Finance and Economics Nanchang330013 China

关键词： Scalability

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