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18th International Conference on Wireless Artificial intelligent computing Systems and Applications, WASA 2024

作者： Ou, Pingjie Chen, Ningjiang Huang, Zizhan School of Computer and Electronic Information Guangxi University Guangxi Nanning530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Guangxi University Guangxi Nanning530004 China

ISBN: (纸本)9783031714696

Due to the limited resources of edge networks, the heterogeneity of user content requests, high-cost caching from direct resource hits, and redundancy in resource retention time hinder system performance. Traditional caching methods based on learning and popularity tend to encounter data skew when handling vehicle requests, leading to untimely cache data updates. To address these issues, this paper proposes an efficient edge cooperative caching strategy with recommendation awareness. Firstly, a time series prediction model based on a multi-dense multilayer perceptron is designed. This model segments historical request information into sub-sequence level slivers to enhance positional correlations between information, predicting user requests through historical time series and covariates. Secondly, to optimize cache utilization, comprehensively considers user mobility and contribution to enable recommendation awareness. Additionally, an optimized lifecycle is introduced to design a cache replacement strategy. Simulation experiments demonstrate that, compared to caching schemes based on content popularity and deep reinforcement learning, the proposed method achieves lower caching costs and higher cache hit rates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep reinforcement learning

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Deep Time Series Anomaly Detection with Local Temporal Pattern Learning

Deep Time Series Anomaly Detection with Local Temporal Patte...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yizhou Li Yijie Wang Hongzuo Xu Xiaohui Zhou National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China Intelligent Game and Decision Lab (IGDL) Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Self-supervised time series anomaly detection (TSAD) demonstrates remarkable performance improvement by extracting high-level data semantics through proxy tasks. Nonetheless, most existing self-supervised TSAD techniques rely on manual- or neural-based transformations when designing proxy tasks, overlooking the intrinsic temporal patterns of time series. This paper proposes a local temporal pattern learning-based time series anomaly detection (LTPAD). LTPAD first generates sub-sequences. Pairwise sub-sequences naturally manifest proximity relationships along the time axis, and such correlations can be used to construct supervision and train neural networks to facilitate the learning of temporal patterns. Time intervals between two sub-sequences serve as labels for sub-sequence pairs. By classifying these labeled data pairs, our model captures the local temporal patterns of time series, thereby modeling the temporal pattern-aware "normality". Abnormal scores of testing data are acquired by evaluating their conformity to these learned patterns shared in training data. Extensive experiments show that LTPAD significantly outperforms state-of-the-art competitors.

关键词： Time series analysis Semantics Neural networks Training data Manuals Signal processing Data models Speech processing Anomaly detection Testing

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An End-to-End Classification Network Model Based on Hybrid Supervision for Industrial Surface Defect Detection

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International Journal of Pattern Recognition and Artificial Intelligence 2025年第5期39卷

作者： Qin, Runbing Chen, Ningjiang Gan, Shukun Guangxi University School of Computer Electronics and Information Nanning530004 China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University Nanning530004 China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning530004 China

Industrial part surface defect detection aims to precisely locate defects in images, which is crucial for quality control in manufacturing. The traditional method needs to be designed in advance, but it has shortcomings in terms of generalization ability. Deep neural network-based defect detection faces issues such as low resolution, data scarcity, labeling costs, and high computation. Therefore, an improved solution is necessary to enhance industrial product quality control efficiency and accuracy. A new method is proposed that utilizes end-to-end training of a two-stage neural network based on segmentation with an extended training process. The gradient flow from the classification to the segmentation network is adjusted to prevent unstable features from interfering with learning. To address the problem of image oversampling and undersampling during training, a frequency sampling scheme for negative samples is introduced. Additionally, positive pixels in the region-based segmentation mask are weighted using the distance transform algorithm so that regions with a high probability of defects can be detected without detailed annotation. Experiments are conducted across three distinct defect datasets by applying hybrid supervision encompassing diverse conditions. In the optimal case, the AP rate of the proposed model on the DAGM and KolektorSDD datasets reaches 100%, and the AP rate on the Severstal Steel dataset reaches 98.99%. The experiments’ results show that the detection accuracy has improved greatly, showing how well the proposed method works in the industry. © 2025 World Scientific Publishing Company.

关键词： Deep neural networks

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A Multi-holder Role and Strange Attractor-Based Data Possession Proof in Medical Clouds 24th

A Multi-holder Role and Strange Attractor-Based Data Possess...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Guo, Jinyuan Sun, Lijuan Wu, Jingchen Chu, Chiawei Gao, Yutong Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Cybersecurity Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University Beijing China Intellectual Property Information Service Center BHU Beihang University Beijing China City University of Macau Taipa China Li’an International Education Innovation Pilot Zone Hainan International College of Minzu University of China Hainan Beijing China

ISBN: (纸本)9789819615278

Proof of Data Possession is a technique for ensuring the integrity of data stored in cloud storage. However, most audit schemes assume only one role for data owners, which is not suitable for complex Smart Healthcare Network with multiple roles. In such schemes addressing the risk of third-party auditors (TPA) colluding with other entities, blockchain is typically employed. However, this necessitates extensive computation, resulting in unacceptably high energy consumption and prolonged verification times. In this paper, we propose a Proof of Data Possession scheme tailored for medical network environments, which incorporates protocols designed for both healthcare professionals and patients, considering the diverse roles that generate data within medical network applications and the varying performance of the devices they use. During the generation of metadata for verification, the protocol incorporates the varying security risk levels associated with medical data. For subsequent metadata selection, an audit block extraction strategy based on strange attractors is employed, effectively mitigating the risk of collusion by third-party TPA, thereby enhancing the security and efficiency of the scheme. Additionally, we conducted experimental analysis and comparisons, demonstrating higher security and practical applicability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data integrity

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Towards intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework

arXiv

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arXiv 2025年

作者： Li, Xiaolong Wei, Jianhao Wang, Haidong Dong, Li Chen, Ruoyang Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin The Xiangjiang Laboratory Hunan Provincial General University Key Laboratory of IoT Intelligent Sensing and Distributed Collaborative Optimization Hunan University of Technology and Business Changsha410205 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China The Department of Electrical and Computer Engineering University of Concordia Montreal Canada The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada

In intelligent transportation systems (ITSs), incorporating pedestrians and vehicles in-the-loop is crucial for developing realistic and safe traffic management solutions. However, there is falls short of simulating complex real-world ITS scenarios, primarily due to the lack of a digital twin implementation framework for characterizing interactions between pedestrians and vehicles at different locations in different traffic environments. In this article, we propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop. Specifically, SVFDT builds comprehensive pedestrian-vehicle interaction models by leveraging multi-source traffic surveillance videos. Its architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources;(ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions;and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime. We analyze key design requirements and challenges and present core guidelines for SVFDT’s system implementation. A testbed evaluation demonstrates its effectiveness in optimizing traffic management. Comparisons with traditional terminal-server frameworks highlight SV-FDT’s advantages in mirroring delays, recognition accuracy, and subjective evaluation. Finally, we identify some open challenges and discuss future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Type-2 fuzzy description logic

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Frontiers of Computer Science 2011年第2期5卷 205-215页

作者： Ruixuan LI Kunmei WEN Xiwu GU Yuhua LI Xiaolin SUN Bing LI Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China

Description logics （DLs） are widely employed in recent semantic web application systems. However, classical description logics are limited when dealing with imprecise concepts and roles, thus providing the motivation for this work. In this paper, we present a type-2 fuzzy attributive concept language with complements （ALC） and provide its knowledge representation and reasoning algorithms. We also propose type-2 fuzzy web ontology language （OWL） to build a fuzzy ontology based on type- 2 fuzzy ALC and analyze the soundness, completeness, and complexity of the reasoning algorithms. Compared to type-1 fuzzy ALC, type-2 fuzzy ALC can describe imprecise knowledge more meticulously by using the membership degree interval. We implement a semantic search engine based on type-2 fuzzy ALC and carry out experiments on real data to test its performance. The results show that the type-2 fuzzy ALC can improve the precision and increase the number of relevant hits for imprecise information searches.

关键词： description logic (DL) type-2 fuzzy attributive concept language with complements (ALC) fuzzy ontology reasoning semantic search engine

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Optimizing ranking method using social annotations based on language model

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Artificial Intelligence Review 2014年第1期41卷 81-96页

作者： Wen, Kunmei Li, Ruixuan Xia, Jing Gu, Xiwu Intelligent and Distributed Computing laboratory of theHuaZhong University of Science and Technology WuHan China 709th Research Institute China Shipbuilding Industry Corporation WuHan China

Recent research has shown that more and more web users utilize social annotations to manage and organize their interested resources. Therefore, with the growing popularity of social annotations, it is becoming more and more important to utilize such social annotations to achieve effective web search. However, using a statistical model, there are no previous studies that examine the relationships between queries and social annotations. Motivated by this observation, we use social annotations to re-rank search *** intend to optimize retrieval ranking method by using the ranking strategy of integrating the queryannotation similarity into query-document similarity. Specifically, we calculate the queryannotation similarity by using a statistical language model, which in a shorter form we call simply a language model. Then the initial search results are re-ranked according to the computational weighted score of the query-document similarity score and the query-annotation similarity score. Experimental results showthat the proposed method can improve theNDCG score by 8.13%. We further conduct an empirical evaluation of the method by using a query set including about 300 popular social annotations and constructed phrases. More generally, the optimized results with social annotations based on a language model can be of significant benefit to web search.©Springer Science+Business Media B.V. 2011.

关键词： Query processing

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PDRL-CM: An efficient cooperative caching management method for vehicular networks based on deep reinforcement learning

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Ad Hoc Networks 2025年 176卷

作者： Ou, Pingjie Chen, Ningjiang Yang, Long School of Computer and Electronic Information Guangxi University Nanning Guangxi530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Nanning Guangxi530004 China The Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning Guangxi530004 China

In vehicular networks, onboard devices face the challenge of limited storage, and computational resources constrain their processing and storage capabilities. This limitation is particularly significant for applications that require complex computations and real-time responses. Additionally, limited storage capacity reduces the range of cacheable data, which can impact the immediate availability of data and the continuity of services. Therefore, improving cache utilization and meeting vehicles’ real-time data demands pose significant challenges. Deep reinforcement learning can optimize the issues arising from agents’ continuously changing state and action spaces due to increasing request demands. However, training the network may encounter instability and convergence difficulties in dynamic and complex environments or situations with sparse rewards. In response to these issues, this paper proposes a Priority-based Deep Reinforcement Learning Collaborative Cache Management method (PDRL-CM). PDRL-CM first designs a lightweight cache admission strategy that leverages data's inherent and combined attributes. It then makes cache admission decisions Using Monte Carlo sampling and a max-value search strategy combined with a feedforward neural network. Secondly, the method considers minimizing system latency and reducing vehicle energy consumption as joint optimization problems. An improved deep reinforcement learning algorithm solves this problem and makes cache-sharding decisions. A prioritized experience replay mechanism is incorporated to adjust the network prediction model quickly and accelerate the convergence process. Experimental results indicate that, compared to existing DRL-based caching methods, PDRL-CM offers faster data processing efficiency and higher cache hit rates under varying vehicle density, storage capacity, and content volume conditions. © 2025

关键词： Cache memory

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Towards automatic update of access control policy 24

Towards automatic update of access control policy

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24th Large Installation System Administration Conference, LISA 2010

作者： Hu, Jinwei Zhang, Yan Li, Ruixuan Intelligent Systems Laboratory School of Computing and Mathematics University of Western Sydney Sydney1797 Australia Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Role-based access control (RBAC) has significantly simplified the management of users and permissions in computing systems. In dynamic environments, systems are subject to changes, so that the associated configurations need to be updated accordingly in order to reflect the systems' evolution. Access control update is complex, especially for large-scale systems;because the updated system is expected to meet necessary constraints. This paper presents a tool, RoleUpdater, which answers administrators' high-level update request for role-based access control systems. RoleUpdater is able to automatically check whether a required update is achievable and, if so, to construct a reference model. In light of this model, administrators could fulfill the changes to RBAC systems. RoleUpdater is able to cope with practical update requests, e.g., that include role hierarchies and administrative rules in effect. Moreover, RoleUpdater can also provide minimal update in the sense that no redundant changes are implemented. © LISA 2010.

关键词： Access control

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AttentionFunc: Balancing FaaS Compute across Edge-Cloud Continuum with Reinforcement Learning 13

AttentionFunc: Balancing FaaS Compute across Edge-Cloud Cont...

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13th International Conference on the Internet of Things, IoT 2023

作者： Li, Kexin Nastic, Stefan Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China China Distributed System Group TU Wien Austria

ISBN: (纸本)9798400708541

Serverless computing is emerging as a promising paradigm to manage compute in Edge-Cloud continuum. However, distributing and balancing the computational load (serverless functions) across the continuum remains a significant challenge. In this paper, we introduce AttentionFunc-a novel framework for decentralized and efficient function offloading and computation balancing in the Edge-Cloud continuum. The AttentionFunc framework strives to introduce a fully decentralized decision-making model that accounts for the multi-objective nature of serverless workflows, the limitations of shared resources in the Edge-Cloud environment, and the dynamic behaviors such as resource contentions or cooperations among serverless functions. In addition, AttentionFunc incorporates an innovative multi-Agent offloading model based on the Markov Decision Process (MDP), designed to minimize functions' execution time and costs. The application of MDP allows the framework to efficiently address these issues using deep reinforcement learning approaches, with an aim to significantly improve function completion latency. Furthermore, AttentionFunc pioneers an attention-based optimization mechanism for multi-Agent deep reinforcement learning. This mechanism permits DRL agents to reach a consensus with minimal coordination information, leading to substantial reductions in communication and computation overhead. We evaluate AttentionFunc and compare it against select relevant state-of-The-Art approaches. Our experiments and simulations show that AttentionFunc outperforms state-of-The-Art approaches in terms of 1) the completion latency (up to 44.2% reduction), 2) the function success rate (up to 43.3% increase). Additionally, we provide the results of many experiments with different MEC scenarios to highlight the components of our approach that influence the results. We conclude that our approach reduces the low-latency challenge faced by most offloading models and improves the successful completio

关键词： Reinforcement learning

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