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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Hongjia Wu Shanchen Zou Jiazhi Xia Hongxin Zhang Wei Chen State Key Lab of CAD&CG Zhejiang University Hangzhou China School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

We propose a visual query system for risky enterprises. Based on a small number of input samples of risky enterprises, users can interactively analyze and obtain SQL statements that express the query results, facilitating subsequent task operations and understanding risk criteria. Due to the diversity of enterprises and the complexity of economic indicators, understanding, evaluating, and querying risky enterprises is a challenging task for ordinary users. To solve this problem, our visual query system integrates multicriteria decision-making techniques and data mining through a visual analysis interface. The system facilitates efficient identification of risky enterprises, leveraging knowledge gained from a small number of known risk company samples. Our system includes an analytic hierarchy model to express domain knowledge, a query tree construction algorithm, and a well-designed visual interface. Two real cases demonstrate the effectiveness of our system.

关键词： Visualization Analytical models Law Economic indicators Time series analysis Decision making Data visualization

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Compromising Rechargeable Sensor Networks in Marine Environment

引用

IEEE Transactions on Mobile Computing 2025年

作者： Wang, Qiwei Lin, Chi Dai, Haipeng Obaidat, Mohammad S. Hsiao, Kuei-Fang Fan, Xin Dalian University of Technology School of Software Technology & DUT-RU International School of Information Science and Engineering China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116620 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China SRM University Department of Computational Intelligence School of Computing SRM Nagar TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan

Marine Wireless Rechargeable Sensor Networks (MWRSNs), enhanced by recent Wireless Power Transfer (WPT) technology, present a significant advancement in extending network life. Traditional methods improve network performance through algorithm optimization, but neglect charging security, exposing networks to potential attacks. This paper addresses this problem from an adversarial view and develops a novel attack for MWRSN through Denying of Charge (DoC) to maximize network destructiveness. We start by establishing a generalized on-demand charging model, essential for developing DoC tactics. Subsequently, we unveil the Collaborative DoC (CoDoC) algorithm, capable of manipulating and falsifying charging requests. Central to CoDoC is the Request Prediction Method (RPM), which forecasts the initiation of charging requests and facilitates rapid request surges to enhance the attack's efficacy. CoDoC is able to disguise the presence of the attack, which is able to escape from being detected by the base station. Theoretical analyses are provided to explore the features of the proposed scheme. To demonstrate the outperformed features of the proposed schemes, extensive simulations and test-bed experiments are conducted. Our analysis and extensive simulations demonstrate that CoDoC increases sensor node failures by 20% to 142% compared to traditional methods, highlighting its effectiveness in marine environments. © 2002-2012 IEEE.

关键词： Charging stations

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DivGCL: A Graph Contrastive Learning Model for Diverse Recommendation 39

DivGCL: A Graph Contrastive Learning Model for Diverse Recom...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Gong, Wenwen Geng, Yangliao Zhang, Dan Zhu, Yifan Xu, Xiaolong Xiang, Haolong Beheshti, Amin Zhang, Xuyun Qi, Lianyong School of Information and Electrical Engineering China Agricultural University China School of Computer Science & Technology Beijing Jiaotong University China Department of Computer Science and Technology Tsinghua University China School of Computer Science Beijing University of Posts and Telecommunications China School of Software Nanjing University of Information Science and Technology China School of Computing Macquarie University Australia College of Computer Science and Technology China University of Petroleum (East China China State Key Lab. for Novel Software Technology Nanjing University China

ISBN: (纸本)157735897X

Graph Contrastive Learning (GCL), as a primary paradigm of graph self-supervised learning, spurs a fruitful line of research in tackling the data sparsity issue by maximizing the consistency of user/item embeddings between different augmented views with random perturbations. However, diversity, as a crucial metric for recommendation performance and user satisfaction, has received rather little attention. In fact, there exists a challenging dilemma in balancing accuracy and diversity. To address these issues, we propose a new Graph Contrastive Learning model for Diversifying recommendations (DivGCL). Inspired by the excellence of the determinant point process (DPP), DivGCL adopts a DPP likelihood-based loss function to achieve an ideal trade-off between diversity and accuracy, optimizing it jointly with the advanced Gaussian noise-augmented GCL objective. Extensive experiments on four popular datasets demonstrate that DivGCL surpasses existing approaches in balancing accuracy and diversity, with an improvement of 23.47% at T@20 (abbreviation for trade-off metric) on ML-1M. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Self-supervised learning

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Cooperative Task Offloading for Internet of Vehicles in Cloud-edge Computing 23

Cooperative Task Offloading for Internet of Vehicles in Clou...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gu, Linjie Xu, Xiaolong Qi, Lianyong Zhang, Yiwen Zhang, Xuyun Dou, Wanchun Changwang School of Honors Nanjing University of Information Science and Technology Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China School of Information Science and Engineering Qufu Normal University China School of Computer Science and Technology Anhui University Anhui China Macquarie University The Department of Computing Sydney Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9781665494571

In order to support computing-intensive and service-sensitive Internet of Vehicles (IoV) applications, Digital Twin and edge computing are attractive solutions. Most of the existing IoV service offloading solutions only consider edge-cloud collaboration, but the cooperation between small cell eNodeB(SCeNB) nodes should not be ignored. Through reasonable cooperative computing between nodes, service delays far lower than offloading tasks to the cloud can be obtained. We generate and maintain the simulation of cooperation between SCeNB nodes by constructing a Digital twin to simulate SCeNB nodes in the small cell manager (SCM), thereby realizing user task offloading positions, sub-channel allocation and computing resource allocation. After that, we proposed an algorithm AUC-AC based on the dominant actor critic network and the auction-mechanism. In the DT of each SCeNB node, the global strategy is learned via deep reinforcement learning model. Numerical results show that our proposed experimental scheme is better than several baseline algorithms in terms of service delay. © 2021 IEEE.

关键词： Cloud computing Smart cities Computational modeling Reinforcement learning Digital twins Delays Numerical models

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Is It Enough to Recommend Tasks to Newcomers? Understanding Mentoring on Good First Issues

Is It Enough to Recommend Tasks to Newcomers? Understanding ...

引用

International Conference on software engineering (ICSE)

作者： Xin Tan Yiran Chen Haohua Wu Minghui Zhou Li Zhang State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China ShenYuan Honors College Beihang University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education School of Computer Science Peking University Beijing China

Newcomers are critical for the success and continuity of open source software (OSS) projects. To attract newcomers and facilitate their onboarding, many OSS projects recommend tasks for newcomers, such as good first issues (GFIs). Previous studies have preliminarily investigated the effects of GFIs and techniques to identify suitable GFIs. However, it is still unclear whether just recommending tasks is enough and how significant mentoring is for newcomers. To better understand mentoring in OSS communities, we analyze the resolution process of 48,402 GFIs from 964 repositories through a mix-method approach. We investigate the extent, the mentorship structures, the discussed topics, and the relevance of expert involvement. We find that ~70% of GFIs have expert participation, with each GFI usually having one expert who makes two comments. Half of GFIs will receive their first expert comment within 8.5 hours after a newcomer comment. Through analysis of the collaboration networks of newcomers and experts, we observe that community mentorship presents four types of structure: centralized mentoring, decentralized mentoring, collaborative mentoring, and distributed mentoring. As for discussed topics, we identify 14 newcomer challenges and 18 expert mentoring content. By fitting the generalized linear models, we find that expert involvement positively correlates with newcomers' successful contributions but negatively correlates with newcomers' retention. Our study manifests the status and significance of mentoring in the OSS projects, which provides rich practical implications for optimizing the mentoring process and helping newcomers contribute smoothly and suecessfully,

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Toward Covert and Reliable Communication for Anti-eavesdropping Transmission in V2X Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Xu, Rui Li, Gaolei Wu, Jun Li, Jianhua Zhao, Yue Liu, Yuchen Chen, Mingzhe Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200030 China Xidian University State Key Lab. of ISN School of Telecommunication Engineering Shaanxi 710071 China North Carolina State University Department of Computer Science Raleigh27607 United States University of Miami Department of Electrical and Computer Engineering Coral Gables33156 United States

The integration of covert communication in vehicle-to-everything (V2X) network has recently shown great potential to improve efficiency and reliability of data transmission under adversarial eavesdropping scenarios. In this paper, we propose a covert and reliable communication (CRC) framework for V2X networks, where the legitimate transmitter (Alice) attempts to communicate with a mobile receiver (Bob) in the presence of the location uncertainties of the eavesdropper (Willie). Specifically, the Bob adjusts the artificial noise power and position dynamically to communicate with Alice aided by full duplex antenna. In this context, we derive two key performance indicators of covert communication, namely the detection error probability and the effect covert throughput (ECT). Subsequently, we consider the worst case of CRC in the presence of single uncertain Willie, and derive the approximate maximum ECT expression by two-stage robust optimization. Building on this foundation, for more complex CRC scenario with multi uncertain Willies exist, we propose a deep reinforcement learning-empowered adaptation (DRLA) algorithm to maximize accumulated ECT. Extensive experiments compared to benchmarks (including stochastic selection, TD3 and DDPG) demonstrate the superiority of CRC. Specially, the designated DRLA algorithm not only can achieve a higher accumulated ECT but also can converge quickly compared with the benchmark schemes. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Reasoning about Strategic Abilities in Stochastic Multi-agent Systems

arXiv

引用

arXiv 2024年

作者： Zhang, Yedi Song, Fu Chen, Taolue Wu, Xuzhi Department of Computing Science National University of Singapore Singapore State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom College of Computer Science and Software Engineering Shenzhen University China

Reasoning about strategic abilities is key to AI systems comprising multiple agents, which provide a unified framework for formalizing various problems in game theory, social choice theory, etc. In this work, we propose a probabilistic extension of the alternating-time μ-calculus (AMC), named PAMC, for reasoning about the strategic abilities of agents in stochastic multi-agent systems. We show that PAMC subsumes two existing logics AMC and PμTL (a probabilistic extension of the modal μ-calculus), but is incomparable with the probabilistic alternating-time temporal logic (PATL). We study the problems of model checking and satisfiability checking for PAMC. We first give a model checking algorithm by leveraging algorithms for solving normal-form games and AMC model checking. We establish that the model checking problem of PAMC remains in UP∩co-UP, the same complexity class as the model checking problem for AMC and PμTL. We also provide a new reduction from the satisfiability problem of PAMC to solving parity games, by which we obtain an EXPTIME decision procedure, as well as the small model property which allows to construct a model for each satisfiable PAMC formula. Satisfiability in PAMC has the same complexity as in the modal μ-calculus, unlike PCTL and PATL whose satisfiability checking problems remain open. We have implemented both the model checking and satisfiability checking algorithms as open-source tools. Experimental results are reported, showcasing the practical applications and effectiveness of our approaches. © 2024, CC BY.

关键词： Stochastic systems

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Neuro-symbolic Learning Yielding Logical Constraints

arXiv

引用

arXiv 2024年

作者： Li, Zenan Huang, Yunpeng Li, Zhaoyu Yao, Yuan Xu, Jingwei Chen, Taolue Ma, Xiaoxing Lü, Jian State Key Lab of Novel Software Technology Nanjing University China Department of Computer Science University of Toronto Canada School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom

Neuro-symbolic systems combine neural perception and logical reasoning, representing one of the priorities of AI research. End-to-end learning of neuro-symbolic systems is highly desirable, but remains to be challenging. Resembling the distinction and cooperation between System 1 and System 2 of human thought (à la Kahneman), this paper proposes a framework that fuses neural network training, symbol grounding, and logical constraint synthesis to support learning in a weakly supervised setting. Technically, it is cast as a game with two optimization problems which correspond to neural network learning and symbolic constraint learning respectively. Such a formulation naturally embeds symbol grounding and enables the interaction between the neural and the symbolic part in both training and inference. The logical constraints are represented as cardinality constraints, and we use the trust region method to avoid degeneracy in learning. A distinguished feature of the optimization lies in the Boolean constraints for which we introduce a difference-of-convex programming approach. Both theoretical analysis and empirical evaluations substantiate the effectiveness of the proposed framework. © 2024, CC0.

关键词： Self-supervised learning

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Research on Microelectronic System of Antagonism Inhibiting Spasticity Based on Electro-Myo-Graphic Bridge 7

Research on Microelectronic System of Antagonism Inhibiting ...

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7th International Conference on Integrated Circuits and Microsystems, ICICM 2022

作者： Lou, Peng-Yu Feng, Yun-Hua Wang, Yun-Long Lu, Xiao-Ying Wang, Zhi-Gong Hohai University College of Computer and Information Nanjing China Pingdingshan University School of Software Pingdingshan China Southeast University State Key Lab of Bioelectronics Nanjing China Institute of RF-OE-ICs Southeast University Nanjing China

ISBN: (纸本)9781665460439

In this paper, based on the principle of stimulating antagonistic muscles, functional electrical stimulation (FES) is used to achieve the purpose of spasticity suppression. The microelectronic system for spasticity suppression was designed and studied, and then used to simulated spasticity of healthy people. First, based on the characteristics of spastic EMG signals, obtained by the EMG signal detection system of the research group, a detection module was designed to detect EMG signals of voluntary movement, simulated EMG signals of spastic patients. Furthermore, an adaptive pattern recognition algorithm for spasticity was designed to identify spasticity signals from the detected EMG signals. Then, on basis of the functional electrical stimulation core circuit of the research group, a microelectronic FES system based on the myoelectric bridge was designed. Finally, the functional verification experiment of the FES system to stimulate antagonist muscle to inhibit spasm was carried out, The results show that the system is effective for stimulating the wrist extensor muscle as an antagonist muscle to inhibit spasm. © 2022 IEEE.

关键词： Muscle

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A Swin transformer encoder-based StyleGAN for unbalanced endoscopic image enhancement

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computers in Biology and Medicine 2024年 175卷 108472-108472页

作者： Deng, Bo Zheng, Xiangwei Chen, Xuanchi Zhang, Mingzhe School of Information Science and Engineering Shandong Normal University Jinan250352 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250352 China State Key Laboratory of High–end Server & Storage Technology Jinan250101 China

With the rapid development of artificial intelligence, automated endoscopy-assisted diagnostic systems have become an effective tool for reducing the diagnostic costs and shortening the treatment cycle of patients. Typically, the performance of these systems depends on deep learning models which are pre-trained with large-scale labeled data, for example, early gastric cancer based on endoscopic images. However, the expensive annotation and the subjectivity of the annotators lead to an insufficient and class-imbalanced endoscopic image dataset, and these datasets are detrimental to the training of deep learning models. Therefore, we proposed a Swin Transformer encoder-based StyleGAN (STE-StyleGAN) for unbalanced endoscopic image enhancement, which is composed of an adversarial learning encoder and generator. Firstly, a pre-trained Swin Transformer is introduced into the encoder to extract multi-scale features layer by layer from endoscopic images. The features are subsequently fed into a mapping block for aggregation and recombination. Secondly, a self-attention mechanism is applied to the generator, which adds detailed information of the image layer by layer through recoded features, enabling the generator to autonomously learn the coupling between different image regions. Finally, we conducted extensive experiments on a private intestinal metaplasia grading dataset from a Grade-A tertiary hospital. The experimental results show that the images generated by STE-StyleGAN are closer to the initial image distribution, achieving a Fréchet Inception Distance (FID) value of 100.4. Then, these generated images are used to enhance the initial dataset to improve the robustness of the classification model, and achieved a top accuracy of 86 %. © 2024 Elsevier Ltd

关键词： Image enhancement

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