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Cost-Effective Edge Data Caching With Failure Tolerance and Popularity Awareness

IEEE Transactions on Mobile computing 2025年第6期24卷 5357-5369页

作者： Luo, Ruikun Zhang, Zujia He, Qiang Xu, Mengxi Chen, Feifei Dai, Xiaohai Wu, Song Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Deakin University School of Information Technology GeelongVIC3125 Australia

In the mobile edge computing environment, caching data in edge storage systems can significantly reduce data retrieval latency for users while saving the costs incurred by cloud-edge data transmissions for app vendors. Existing edge data caching (EDC) methods prioritize popular data and aim to minimize users’ data retrieval latency and system storage costs jointly. However, these EDC methods often rely on the assumption that data popularity always follows certain distributions. As a result, they cannot properly adapt to the fluctuations in data popularity due to user mobility or unexpected increases in user demands. Meanwhile, unlike cloud data centers, complex and fragile edge servers are more likely to experience physical failures or network outages, presenting new challenges for EDC strategies. Specifically, when an edge server fails or experiences an outage, cached data may become temporarily unavailable, leading to increased latency as requests are redirected to alternative servers or the cloud. In this paper, to enable uncertainty-aware edge data caching (uEDC), we first model the problem as a robust optimization problem and propose an optimal algorithm named uEDC-B to find the optimal uEDC solution. To address the high computational complexity of uEDC-B, we introduce an approximate algorithm named uEDC-L based on linear decision rules. Theoretical analysis and extensive experiments on a real-world dataset demonstrate that the proposed methods outperform two state-of-the-art approaches in handling the uncertainties in data popularity and edge server failure with a significant performance improvement of 59.27% in data retrieval latency and 55.07% in data caching cost. © 2002-2012 IEEE.

关键词： Mobile edge computing

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An iterated greedy algorithm based on NSGA-II for distributed hybrid flow shop scheduling problem 3

An iterated greedy algorithm based on NSGA-II for distribute...

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3rd International Conference on Communications, information System, and Data Science, CISDS 2024

作者： Liu, Dandan Liang, Xu Zou, Zhiyuan School of Computing Beijing Information Science and Technology University Beijing China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

The distributed hybrid flow shop scheduling problems (DHFSP) widely exist in various industrial production processes, and thus have received widespread attention. However, studies on HFSP considering green objective in distributed production environment are quite limited. Therefore, this paper investigated a distributed hybrid flow shop scheduling problem with objectives of minimization the makespan and total energy consumption (TEC). To solve it, an iterated greedy algorithm based on NSGA-II (NSGAIG) is developed. In the proposed algorithm, a random initialization strategy is used to generate the initial solution. Then, starting from the population initialization, a multi-objective local search is carried out for the current optimal solution in the population to obtain the global optimal solution. Next, random variation method is used to increase the exploration space of the algorithm. Finally, the proposed NSGAIG algorithm is compared with other multi-objective optimization algorithms. Experimental results indicate that the proposed NSGAIG outperforms its compared algorithms in solving this problem. © 2025 SPIE.

关键词： Optimization algorithms

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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RF-Sauron: Enabling Contact-Free Interaction on Eyeglass Using Conformal RFID Tag

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IEEE Internet of Things Journal 2025年第10期12卷 14320-14332页

作者： Yang, Baizhou Chen, Ling Peng, Xiaopeng Chen, Jiashen Tang, Yani Wang, Wei Fang, Dingyi Feng, Chao Northwest University Shaanxi Key Laboratory of Passive Internet of Things and Neural Computing School of Information Science and Technology Xi’an710127 China Northwest University Xi’an Key Laboratory of Advanced Computing and Software Security School of Information Science and Technology Xi’an710127 China College of Science Rochester Institute of Technology RochesterNY14623 United States Northwest University Xi’an Key Laboratory of Advanced Computing and System Security School of Information Science and Technology Xi’an710127 China Northwest University Shaanxi International Joint Research Centre for the Battery-Free Internet of Things School of Information Science and Technology Xi’an710127 China

Smart eyeglasses are emerging as a new medium for human-computer interaction. Existing solutions typically rely on cameras or touchpads, raising privacy invasion concerns or requiring users to physically interact with the glass frames. Here, we present RF-Sauron, a novel mid-air gesture interaction system for eyeglasses, based on radio frequency identification (RFID). The design of our RF-Sauron system involves embedding a conformal RFID tag into the eyeglass frame, where the received signals change with user gestures. We optimize the radiation gain of the tag to avoid view blockage while preserving a long working range. To discriminate different gestures, we propose an adaptively weighted multichannel fusion network to extract their respective distinctive features. To allow efficient adaptation of the pretrained network to new users, we also introduce a novel diagonal dot-product attention in our contrastive learning framework to uncover the feature similarities of different users. The proposed RF-Sauron system was evaluated through extensive experiments, demonstrating an average recognition accuracy of 98.86% across 20 users and a cross-user accuracy of 98.75%. © 2014 IEEE.

关键词： Contrastive Learning

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X-ECON: Explainable Event-Consumption Commonsense Reasoning

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IEEE Transactions on Big Data 2025年第3期11卷 1066-1077页

作者： Ding, Xiao Cai, Bibo Li, Xue Liu, Ting Qin, Bing Harbin Institute of Technology Social Computing and Information Retrieval Research Center Harbin150001 China Harbin Institute of Technology School of International Studies Harbin150001 China

This article investigates a novel commonsense inference task that comprises reasoning about the subsequent events chains and probable consumption intents, given an event described in brief free-form text. For instance, given the event "fall in love", the system anticipates ensuing events such as "get married"→"be pregnant"→"have a baby". Concurrently, the system predicts the likely consumption intents of the event participants, such as purchasing "jewelry", "maternity clothing", and "baby food". The event chains generated in this process provide explicit, meaningful explanations, aiding the understanding of inferred consumption intents. To facilitate this study, we construct a new crowdsourced corpus: x-ECON. This corpus comprises 10,144 event chains and 150 event-related product categories, offering a wide range of everyday consumer events and *** introduce a baseline reasoning framework that not only infers consumption intent but also generates an event chain to explain its inference. Our experimental results suggest that involving the event chain reasoning in the event-consumption reasoning system can help improve the neural networks' reason about the probable consumption intents of the event participants. Additionally, our method demonstrates the applicability of our approach in improving the performance of recommendation systems, by highlighting the role of explainable commonsense inference on the consumption *** also evaluated the performance ofChatGPTon our x-ECONdataset, showing that explainable event-consumption commonsense reasoning is a challenging task for large language models. © 2024 IEEE.

关键词： Crowdsourcing

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Identifying important nodes of hypergraph:An improved PageRank algorithm

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Chinese Physics B 2025年第4期34卷 162-171页

作者： Yu-Hao Piao Jun-Yi Wang Ke-Zan Li School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin 541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin 541004China Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory Guilin University of Electronic TechnologyGuilin 541004China

Hypergraphs can accurately capture complex higher-order relationships,but it is challenging to identify their important *** this paper,an improved PageRank(ImPageRank)algorithm is designed to identify important nodes in a directed *** algorithm introduces the Jaccard similarity of directed *** comparing the numbers of common neighbors between nodes with the total number of their neighbors,the Jaccard similarity measure takes into account the similarity between nodes that are not directly connected,and can reflect the potential correlation between *** improved susceptible–infected(SI)model in directed hypergraph is proposed,which considers nonlinear propagation mode and more realistic propagation *** addition,some important node evaluation methods are transferred from undirected hypergraphs and applied to directed ***,the ImPageRank algorithm is used to evaluate the performance of the SI model,network robustness and *** of real networks demonstrate the excellent performance of the proposed algorithm and provide a powerful framework for identifying important nodes in directed hypergraphs.

关键词： hypergraph important node PageRank susceptible-infected(SI)model centrality index

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Road Rage Detection System Using Deep Learning and Computer Vision 3

Road Rage Detection System Using Deep Learning and Computer ...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Lohare, Shaik Tanveer Maaz, Mohammed Razi, Mahammad Nehal, Mohammed Ahmed, Syed Thouheed Rashmi, C. School of Computing and Information Technology Reva University Bengaluru India

ISBN: (纸本)9798331508456

Road rage detection in India's chaotic traffic conditions demands robust, real-time solutions. This paper proposes a 3D CNN-based system that leverages transfer learning from a violence detection model (98 % accuracy on Kaggle datasets) to a road rage-specific model (94 % accuracy on a custom Indian dataset). By incorporating temporal smoothing with a 5-frame sliding window, the system reduces false positives by 15 % compared to existing methods. Experiments demonstrate its efficacy under varying illumination and traffic densities, validated through precision (0.99 for non-violence) and recall (0.99 for violence). The system's deployment in Bangalore highlights its potential for traffic monitoring, law enforcement, and insurance claim verification. Future work includes edge-device optimization and multimodal data integration. © 2025 IEEE.

关键词： Traffic control

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SDD-SLAM: Semantic-Driven Dynamic SLAM with Gaussian Splatting

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IEEE Robotics and Automation Letters 2025年第6期10卷 5721-5728页

作者： Liu, Haosong Wang, Long Luo, Haiyong Zhao, Fang Chen, Runze Chen, Yushi Xiao, Mingyu Yan, Jiaquan Luo, Dan Beijing100876 China Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China Beijing Forestry University School of Information Science and Technology School of Artificial Intelligence Beijing100083 China

Recently, significant advancements have been made in 3D Gaussian Splatting SLAM for dynamic environments. However, most existing methods primarily address active dynamic objects, such as people and vehicles, and fail to account for the impact of passive dynamic objects on localization and mapping. This results in the presence of numerous artifacts left by dynamic objects in the scene, which diminishes the accuracy of pose estimation. To address these challenges, we propose SDD-SLAM, a semantic-driven SLAM system based on 3D Gaussian Splatting. Extensive experiments conducted on the TUM and BONN datasets demonstrate that the proposed methods, including refined mask expansion, edge noise filtering, object-level dynamic object removal based on semantic Gaussians, and object-level density control strategy for Gaussian ellipsoids, significantly enhance the accuracy of camera pose estimation and the quality of map reconstruction in dynamic environments, achieving state-of-the-art performance. © 2016 IEEE.

关键词： Gaussian distribution

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PerFedKG: two-stage information-loop federated knowledge graph for personalized privacy-preserving recommendation systems

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Science China(information Sciences) 2025年第4期68卷 98-99页

作者： Fan WANG Xuyun ZHANG Weiming LIU Li LI Yuwen LIU Zhongyuan ZHANG Guanfeng LIU Shengye PANG Xiaolong XU Lianyong QI College of Computer Science and Technology Zhejiang University Department of Computing Macquarie University College of Computer Science and Technology China University of Petroleum (East China) School of Software Nanjing University of Information Science and Technology

Knowledge graphs(KGs) effectively mitigate data sparsity in recommendation systems(RSs) by providing valuable auxiliary information [1]. However, traditional centralized KG-based RSs increase the risk of user privacy *** learning(FL) enhances RS's privacy by enabling model training on decentralized data [2]. Although integrating KG and FL can address both data sparsity and privacy issues in RSs [3], several challenges persist. CH1,Each client's local model relies on a consistent global model from the server, limiting personalized deployment to endusers.

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Public Auditing with Aggregated Tags for Multiple Users 2nd

Public Auditing with Aggregated Tags for Multiple Users

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2nd International Conference on Data Security and Privacy Protection, DSPP 2024

作者： Li, Yumei Susilo, Willy Guo, Fuchun Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong Wollongong Australia

ISBN: (纸本)9789819785452

The proliferation of cloud services has significantly alleviated the data management, maintenance, and archival burdens for data owners by providing scalable storage solutions. However, data owners cannot guarantee the integrity and immutability of their data after uploading their data to the cloud server and deleting local copies. Public cloud data auditing protocols address this issue effectively by allowing data owners to verify that their data remains unchanged over time. While most protocols prioritize audit efficiency and reducing communication overhead, they often overlook the problem of redundant data storage. Specifically, when multiple users upload the same data repeatedly, the cloud server must store multiple tags for the same data, leading to unnecessary consumption of storage resources. To bridge this gap, this paper introduces an innovative public auditing framework that comes up with the concept of aggregated tags, which allows the cloud server to maintain a set of aggregated tags for each file. Notably, the size of the aggregated tag remains the same as that of a single tag, thereby conserving cloud-side storage space. Moreover, only an aggregated public key, rather than all users’ public keys, is needed for the file in the verification processes. Our framework also ensures that there is only one round of interaction between cloud servers and auditors without the involvement of data owners, which maintains the same efficiency level as previous public auditing protocols. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data integrity

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