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TRE-DSP: A traceable and revocable CP-ABE based data sharing scheme for IoV with partially hidden policy

Digital Communications and Networks 2025年第2期11卷 455-464页

作者： Yousheng Zhou Rundong Peng Yuanni Liu Pandi Vijayakumar Brij Gupta College of Computer Science and Technology Chongqing University of Posts and Telecommunications College of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Department of Computer Science and Engineering University College of Engineering Department of Computer Engineering National Institute of Technology Kurukshetra

With the popularity of the Internet of Vehicles(IoV), a large amount of data is being generated every day. How to securely share data between the IoV operator and various value-added service providers becomes one of the critical issues. Due to its flexible and efficient fine-grained access control feature, Ciphertext-Policy Attribute-Based Encryption(CP-ABE) is suitable for data sharing in IoV. However, there are many flaws in most existing CP-ABE schemes, such as attribute privacy leakage and key misuse. This paper proposes a Traceable and Revocable CP-ABE-based Data Sharing with Partially hidden policy for IoV(TRE-DSP). A partially hidden access structure is adopted to hide sensitive user attribute values, and attribute categories are sent along with the ciphertext to effectively avoid privacy exposure. In addition, key tracking and malicious user revocation are introduced with broadcast encryption to prevent key misuse. Since the main computation task is outsourced to the cloud, the burden of the user side is relatively low. Analysis of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV.

关键词： Privacy-preserving Attribute-based encryption Partially hidden policy Traceability User revocation Internet of vehicles

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A Novel Certificateless Signature-based Access Control Scheme for Named In-network Computing Service

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 38-45页

作者： Li, Wanji Liu, Qiangbin Zhu, Yi School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China School of Computer Science and Communication Engineering the Dean of Department of Communication Engineering Jiangsu University Zhenjiang China

Named in-network computing service (NICS) is a potential computing paradigm emerged recently. Benefitted from the characteristics of named addressing and routing, NICS can be flexibly deployed on NDN router side and provide nearby computing service to Internet users. But the NICS feature of dynamic deployment also causes serious security risk of access control. How to independently check out the subscription relationship between requester and requested computing service on NICS side become a challenge. To solve this problem, we propose a novel certificateless signature-based access control scheme (CS-ACS) in this paper. In CS-ACS, the entire user public-private key pairs consist of two parts, user side and source server side. Where, the user public-private key pairs (server side) are generated according to the user ID, service subscription relationship and subscription expiration time. Based on this special design, when authorized user signs the interest packet of invoking specific service using its private key, the NICS can verify the signature then check out whether the requester is a valid subscriber and the subscription is expired or not. Simulation results show that, comparing with fundamental solutions, CS-ACS can avoid extra secret key storage cost on NICS side and markedly shorten authentication delay. © (2025), (International Association of Engineers). All rights reserved.

关键词： Authentication

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Research on resource allocation methods for traditional Chinese medicine services based on deep reinforcement learning

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Neural Computing and Applications 2025年第3期37卷 1601-1616页

作者： Ma, Yuntao Fang, Xiaolin Qi, Jin Sun, Yanfei School of Computer Science and Engineering Southeast University Jiangsu Nanjing China School of Internet of Things Nanjing University of Posts and Telecommunications Jiangsu Nanjing China

Chinese medicine resources are the crystallization of traditional Chinese culture, and more and more people are choosing Chinese medicine services for their health. To address the problems of pluralistic heterogeneity, waste of service resources, and lagging demand response in the resource allocation model for traditional Chinese medicine (TCM) services, a deep reinforcement learning-based resource allocation method for TCM services is proposed. To address the fragmentation of TCM service resources, this paper presents a TCM service resource association method based on improved spectral clustering and establishes a good resource-demand matching model. For the problem of TCM service resource allocation after resource association, we establish a TCM service resource allocation model and collaboratively solve the TCM service resource allocation problem via the deep reinforcement learning method. The results show that the proposed solution can accelerate the demand response of TCM service resources, effectively reduce the cost of TCM services for patients, improve the quality of TCM services, and satisfy the demand for TCM services for patients. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep reinforcement learning

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PS-CoT-Adapter: adapting plan-and-solve chain-of-thought for scienceQA

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science China(Information sciences) 2025年第1期68卷 392-393页

作者： Qun LI Haixin SUN Fu XIAO Yiming WANG Xinping GAO Bir BHANU School of Computer Science Nanjing University of Posts and Telecommunications Purple Mountain Laboratories Department of Electrical and Computer Engineering University of California at Riverside

Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1-3]has investigated chain-of-thought (CoT) reasoning in complex multimodal scenarios,such as science question answering (scienceQA) tasks [4],by fine-tuning multimodal models through human-annotated CoT ***,collected CoT rationales often miss the necessary rea-soning steps and specific expertise.

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Resolution-aware beam scanning for joint detection and communication in ISAC systems

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science China(Information sciences) 2025年第5期68卷 250-266页

作者： Yiming XU Dongfang XU Zhanyuan XIE Zheng JIANG Shenghui SONG Derrick Wing Kwan NG Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China Telecom Research Institute School of Electrical Engineering and Telecommunications University of New South Wales

Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the design of sequential scanning beams for target detection with the required sensing resolution has not been tackled in the *** bridge this gap, this paper introduces a resolution-aware beam scanning design. In particular, the transmit information beamformer, the covariance matrix of the dedicated radar signal, and the receive beamformer are jointly optimized to maximize the average sum rate of the system while satisfying the sensing resolution and detection probability requirements.A block coordinate descent(BCD)-based optimization framework is developed to address the non-convex design problem. By exploiting successive convex approximation(SCA), S-procedure, and semidefinite relaxation(SDR), the proposed algorithm is guaranteed to converge to a stationary solution with polynomial time complexity. Simulation results show that the proposed design can efficiently handle the stringent detection requirement and outperform existing antenna-activation-based methods in the literature by exploiting the full degrees of freedom(DoFs) brought by all antennas.

关键词： integrated sensing and communication (ISAC) detection beam scanning resolution beamwidth

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Two-Timescale Hierarchical Contract for Joint Computation Offloading and Energy Management in Edge Computing System

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IEEE Transactions on Network science and engineering 2025年第3期12卷 1745-1760页

作者： Yan, Min Wang, Li Xu, Lianming Hou, Luyang Han, Zhu Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing100876 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

To mitigate the rising energy costs in edge computing, edge servers (ESs) can receive revenues from reducing their energy usage by contracting with virtual power plant (VPP). ESs also respond to user equipment (UE) by providing computation offloading services. However, such two-layer coordinated trading of computation offloading and energy management in a VPP-ES-UE architecture should address the information asymmetry issue and uncertain task arrivals, posing challenges to maximizing the stakeholders' utility. In this paper, we formulate the two-layer coordinated trading as a hierarchical contracting problem, which addresses the information asymmetry using two contract models. We design a VPP-ES energy contract on a large timescale and an ES-UE computation contract on a small timescale, where ESs need to coordinate the future task arrivals under the energy reduction target assigned by VPP. We construct a two-timescale virtual queue to achieve the energy reduction goal by the long-term queue stability constraint and employ Lyapunov optimization to transform the original problem into an online optimization problem without requiring future information. An online two-timescale hierarchical contract optimization algorithm is proposed to solve the transformed problem. The simulation results demonstrate that our method achieves higher social welfare compared to other benchmarks. © 2013 IEEE.

关键词： Computation offloading

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Efficient charging station deployment in unmanned aerial vehicle systems for enhanced mission efficiency

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Cluster Computing 2025年第5期28卷 1-12页

作者： Bany Salameh, Haythem Jararweh, Yaser College of Engineering Al Ain University Al Ain United Arab Emirates Telecommunications Engineering Department Yarmouk University Irbid21163 Jordan Computer Science Department Jordan University of Science and Technology Irbid Jordan

Unmanned Aerial Vehicles (UAVs) are flexible autonomous systems that enable efficient data collection and task execution across diverse applications. However, their limited battery life poses a significant challenge for long-duration missions, as frequent recharging interrupts operations and reduces efficiency. This paper addresses the problem of optimizing the number and placement of charging stations in a UAV operating environment, considering the limited battery capacity of UAVs, quantified by the maximum allowable travel distance to the nearest charging station. We first present an exhaustive search method that evaluates all possible charging station configurations to ensure optimal placement while keeping the UAV’s travel distance within the predefined maximum distance. To address the exponential growth in complexity, we propose an efficient algorithm that groups areas within the operational region of the UAV system into virtual sub-areas, each containing a single charging station. It reduces computational complexity while ensuring the maximum distance constraint. The proposed algorithm aims to minimize UAV downtime and extend mission durations. To validate the efficacy of the proposed charging station algorithm, we integrate it into a reinforcement learning-based UAV target detection system designed to operate in uncertain environments. The simulation results demonstrate that the proposed algorithm significantly improves the efficiency of UAV missions, particularly in terms of detection performance, compared to a benchmark charging station algorithm. This improvement is primarily due to the strategic placement of charging stations, which optimizes the UAV’s operational capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Drones

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Multi-domain feature-enhanced attribute updater for generalized zero-shot learning

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Neural Computing and Applications 2025年第14期37卷 8397-8414页

作者： Shi, Yuyan Jiang, Chenyi Song, Feifan Ye, Qiaolin Long, Yang Zhang, Haofeng School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China School of Information Science and Technology Nanjing Forestry University Jiangsu Nanjing210037 China Department of Computer Science Durham University DurhamDH1 3LE United Kingdom

The goal of generalized zero-shot learning (GZSL) is to transfer knowledge from seen classes to unseen classes. However, a significant challenge is the single-category attributes are often inadequate to capture the intra-class variability of diverse image samples. During training, the model tends to cluster samples of seen categories based solely on semantic descriptions, overlooking subtle yet important visual differences. To address these challenges, this paper introduces an instance-level attribute updating method, termed as multi-domain feature-enhanced attribute updater (MDAU), which establishes a one-to-one alignment between visual and semantic by obtaining multiple instance-level attributes that closely correspond to the natural intra-class distribution of the image, in contrast to the many-to-one alignment typically used in traditional GZSL approaches. Specifically, we initially compute feature weights for each individual image, and then, the visual information is incorporated as conditions to facilitate the personalized updating of class attributes. We evaluate our model on three benchmark datasets, the results show that our model can achieve the state-of-the-art performance and detailed analysis also illustrates the effectiveness of each proposed module. Code is available at https://***/S-Silvia/MDAU. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Zero-shot learning

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Mining Topics towards ChatGPT Using a Disentangled Contextualized-neural Topic Model 25

Mining Topics towards ChatGPT Using a Disentangled Contextua...

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18th ACM International Conference on Web Search and Data Mining, WSDM 2025

作者： Wang, Rui Liu, Xing Wang, Yanan Chang, Shuyu Yao, Yuanzhi Huang, Haiping School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China

ISBN: (纸本)9798400713293

Mining topics relevant to the advanced AI dialogue system, such as ChatGPT, from short-length posts on social media poses several challenges for existing topic-mining approaches. Firstly, Bag-Of-Words approaches, including probabilistic topic models and their embedding-based variants, may struggle to extract interpretable topics due to insufficient word co-occurrence. Secondly, contextualized based approaches, built on the autoencoding framework, often yield entangled topic spaces, resulting in the mixing of irrelevant words into topics. To address these limitations, we propose a novel Disentangled Contextualized-neural Topic Model (DisCTM) based on textual representation learning. DisCTM leverages a pre-trained transformer language model to incorporate word sequence information and deal with the sparsity in short text. Additionally, it employs a topic disentangling mechanism to decorrelate dimensions of the latent topic space, effectively separating semantically irrelevant words into different topics. Extensive experiments have been conducted on three publicly available text corpora, and the results demonstrate the effectiveness of DisCTM in extracting high-quality topics, as measured by topic coherence and diversity metrics. © 2025 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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Network Traffic Matrix Estimation Based on Link Loads Sampling

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IEEE Transactions on Network science and engineering 2025年第3期12卷 1524-1539页

作者： Sun, Wenyue Chen, Qian Song, Xuehua Bertino, Elisa Wang, Changda Jiangsu University School of Computer Science and Communication Engineering Zhenjiang212013 China Yangzhou Polytechnic College School of Information Engineering Yangzhou225009 China Purdue University Department of Computer Science West LafayetteIN47907 United States

Traffic Matrix (TM) represents traffic between all Origin-Destination (OD) node pairs in a network, playing a crucial role in network management. While the methods for TM acquisition typically require scaling each link load in a network, measurement costs arise drastically as the number of links grows exponentially with the number of nodes. To address this issue while achieving TM with acceptable accuracy aligned with various network management requirements, the paper proposes two novel methods. The first is the ME-TMEIM (TM Estimation with Incomplete Measurement Based on Maximum Entropy) method, an efficient approach with yet acceptable accuracy. The second is the D-TMEIM (Dynamic TMEIM) method. Compared to the ME-TMEIM method, the D-TMEIM method trades the TM's acquisition accuracy for efficiency. It adds temporal constraints on network traffic to improve the precision of the obtained missing link loads, based on which the TM is generated using the CS-OMP (Compressed Sensing-Orthogonal Matching Pursuit) algorithm. Experimental results using publicly available Abilene and GÉANT networks demonstrate that the proposed methods not only enhance TM acquisition efficiency but also maintain nearly the same accuracy as the known methods that acquire TM through exhaustive links measurement. © 2013 IEEE.

关键词： Efficiency

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