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作者： Li, Xinyang Zhao, Meng Ding, Yong Yang, Changsong Wang, Huiyong Liang, Hai Wang, Yujue Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin China Hangzhou Innovation Institute of Beihang University Hangzhou China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

In smart grid systems, the control center formulates strategies and provides services by analyzing electricity consumption data. However, ensuring the privacy and security of user data is a critical concern. While traditional data aggregation schemes can provide a certain level of privacy protection for users, they also impose limitations on the control center's access to fine-grained data. To address these challenges, we propose a privacy-preserving data aggregation scheme supporting data query (PAQ). We designed a multi-level data aggregation mechanism based on Paillier semi-homomorphic encryption to achieve efficient aggregation of user data in the control center. Additionally, a data query mechanism based on electricity consumption intervals is introduced, allowing the control center to query aggregated ciphertexts for different user categories from outsourced data on the cloud server. Security analysis demonstrates that PAQ design effectively solves security issues in data aggregation and query processes. Performance analysis indicates that the proposed scheme outperforms existing solutions in terms of efficiency. © 2024 John Wiley & Sons, Ltd.

关键词： Smart power grids

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Boosting cationic and anionic redox activity of Li-rich layered oxide cathodes via Li/Ni disordered regulation

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Journal of Energy Chemistry 2025年第1期100卷 533-543页

作者： Zewen Liu Zhen Wu Hao Wang Xudong Zhang Yuanzhen Chen Yongning Liu Shengwu Guo Shenghua Chen Yanli Nan Yan Liu State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina School of Materials Science and Engineering Xi’an University of Architecture and TechnologyXi’an 710055ShaanxiChina Hefei Advanced Computing Center Operation Management Corp Ltd Hefei 230088AnhuiChina Network Information Center Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina

Lithium-rich layered oxides (LLOs) are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries (LIBs).However,they suffer from voltage decay and low initial Coulombic efficiency (ICE) due to severe structural degradation caused by irreversible O ***,we introduce a three-in-one strategy of increasing Ni and Mn content,along with Li/Ni disordering and TM–O covalency regulation to boost cationic and anionic redox activity simultaneously and thus enhance the electrochemical activity of *** target material,Li_(1.2)Ni_(0.168)Mn_(0.558)Co_(0.074)O_(2)(L1),exhibits an improved ICE of 87.2%and specific capacity of 293.2 mA h g^(-1)and minimal voltage decay of less than 0.53 m V cycle-1over 300 cycles at 1C,compared to Li_(1.2)Ni_(0.13)Mn_(0.54)Co_(0.13)O_(2)(Ls)(274.4 mA h g^(-1)for initial capacity,73.8%for ICE and voltage decay of 0.84 mV/cycle over 300 cycles at 1C).Theoretical calculations reveal that the density of states (DOS) area near the Fermi energy level for L1 is larger than that of Ls,indicating higher anionic and cationic redox reactivity than ***,L1 exhibits increased O-vacancy formation energy due to higher Li/Ni disordering of 4.76%(quantified by X-ray diffraction Rietveld refinement) and enhanced TM–O covalency,making lattice O release more difficult and thus improving electrochemical *** increased Li/Ni disordering also leads to more Ni^(2+)presence in the Li layer,which acts as a pillar during Li+de-embedding,improving structural *** research not only presents a viable approach to designing low-Co LLOs with enhanced capacity and ICE but also contributes significantly to the fundamental understanding of structural regulation in high-performance LIB cathodes.

关键词： Low-Co Li-rich layered oxides Li/Ni disordering TM-O covalency Cationic and anionic redox activity

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Importance-Awareness Masking Network for Robust Document Retrieval

Importance-Awareness Masking Network for Robust Document Ret...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Junping He, Jiaqi Hu, Xinrong Yang, Wangli Yang, Jie Guo, Yi School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Computing and Information Technology University of Wollongong Wollongong Australia School of Computer Data and Mathematical Sciences Western Sydney University Australia

ISBN: (纸本)9798350368741

In this paper, we introduce the IMPortance-awaReness maskIng NeTwork (IMPRINT), a novel approach to enhance the robustness of document retrieval systems against query variations, particularly those containing misspellings. Unlike previous models that treat all query components (words/features) equally, IMPRINT prioritizes the most important components while masking out less relevant ones. Specifically, we propose a Mutual information-based measure to quantify component importance and integrate it into a dynamic masking mechanism that adjusts the retention probability of each component. Our method is evaluated on a combination of three benchmark datasets and three types of query variations. The experimental results show substantial performance gains compared to state-of-the-art models, achieving an average improvement of 1.2 absolute MRR@10 points in retrieval accuracy. © 2025 IEEE.

关键词： Document Retrieval Model Robustness Query Variations Word/Feature Importance

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Optimal Logistics Activities Based Deep Learning Enabled Traffic Flow Prediction Model

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Computers, Materials & Continua 2022年第12期73卷 5269-5282页

作者： Basim Aljabhan Mahmoud Ragab Sultanah M.Alshammari Abdullah S.Al-Malaise Al-Ghamdi Ports and Maritime Transportation Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia S 7Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia

Traffic flow prediction becomes an essential process for intelligent transportation systems(ITS).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missing data finds challenging for effective *** traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical *** recent developments of statistic and deep learning(DL)models pave a way for the effectual design of traffic flow prediction(TFP)*** this view,this study designs optimal attentionbased deep learning with statistical analysis for TFP(OADLSA-TFP)*** presentedOADLSA-TFP model intends to effectually forecast the level of traffic in the *** attain this,the OADLSA-TFP model employs attention-based bidirectional long short-term memory(ABLSTM)model for predicting traffic *** order to enhance the performance of the ABLSTM model,the hyperparameter optimization process is performed using artificial fish swarm algorithm(AFSA).A wide-ranging experimental analysis is carried out on benchmark dataset and the obtained values reported the enhancements of the OADLSA-TFP model over the recent approaches mean square error(MSE),root mean square error(RMSE),and mean absolute percentage error(MAPE)of 120.342%,10.970%,and 8.146%respectively.

关键词： Traffic flow prediction deep learning artificial fish swarm algorithm mass gatherings statistical analysis logistics

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SNCA:Semi-Supervised Node Classification for Evolving Large Attributed Graphs

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Big Data Mining and Analytics 2024年第3期7卷 794-808页

作者： Faima Abbasi Muhammad Muzammal Qiang Qu Farhan Riaz Jawad Ashraf Luxembourg Institute of Science and Technology and University of Luxembourg Esch-Sur-Alzette L-4362Luxembourg Department of Computer and Information Sciences Northumbria UniversityNewcastle NE18STUK Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Computer Science University of LincolnLincoln LN67TSUK Faculty of Computing Engineering and MediaDe Montfort UniversityLeicesterLE19BHUK

Attributed graphs have an additional sign vector for each ***,edge signs represent like or dislike relationship between the node *** has applications in domains,such as recommender systems,personalised search,***,limited availability of edge sign information in attributed networks requires inferring the underlying graph embeddings to fill-in the knowledge *** inference is performed by way of node classification which aims to deduce the node characteristics based on the topological structure of the graph and signed interactions between the *** study of attributed networks is challenging due to noise,sparsity,and class imbalance *** this work,we consider node centrality in conjunction with edge signs to contemplate the node classification problem in attributed *** propose Semi-supervised Node Classification in Attributed graphs(SNCA).SNCA is robust to underlying network noise,and has in-built class imbalance handling *** perform an extensive experimental study on real-world datasets to showcase the efficiency,scalability,robustness,and pertinence of the *** performance results demonstrate the suitability of the solution for large attributed graphs in real-world settings.

关键词： attributed networks node classification recommender systems

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Enabling Fuzzy Matching in Privacy-preserving Bilateral Task Recommendation 9

Enabling Fuzzy Matching in Privacy-preserving Bilateral Task...

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9th International Conference on Big Data computing and Communications, BigCom 2023

作者： Zhang, Chuan Luo, Xingqi Zhang, Weiting Zhao, Mingyang Liang, Jinwen Wu, Tong Zhu, Liehuang Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (纸本)9798350331240

Privacy-preserving bilateral task recommendation is an emerging functionality in mobile crowdsensing, enabling two-sided matching for both task publishers and workers while ensuring the confidentiality of both participants' attributes and task-matching policies. However, existing approaches rarely integrate fuzzy matching into privacy-preserving bilateral task recommendations, limiting the efficiency of two-sided matching because they can hardly find intersections between matched task publishers and matched workers. Intuitively leveraging fuzzy matching leaks whether the attributes of a participant satisfy a task recommendation policy. Motivated by this, this paper proposes PBTR, the first privacy-preserving bilateral task recommendation scheme that supports fuzzy matching while preserving matching privacy. Specifically, we leverage the Lagrange interpolation theorem-based secret sharing to enrich the matchmaking encryption technique to achieve fuzzy matching in bilateral task recommendations. To preserve matching privacy, we design a privacy-preserving matching proxy mechanism, where policy matches successfully if and only if the policies of both parties are satisfied. Formal security analysis proves the security of PBTR under the chosen-plaintext attack in the random oracle model. Experimental results demonstrate the feasibility of PBTR, which only requires several milliseconds to achieve privacy-preserving bilateral task recommendations. © 2023 IEEE.

关键词： Privacy-preserving techniques

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Fed-SC: One-Shot Federated Subspace Clustering over High-Dimensional Data 39

Fed-SC: One-Shot Federated Subspace Clustering over High-Dim...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Xie, Songjie Wu, Youlong Liao, Kewen Chen, Lu Liu, Chengfei Shen, Haifeng Tang, MingJian Sun, Lu ShanghaiTech University School of Information Science and Technology Shanghai China Australian Catholic University HilstLab Peter Faber Business School Sydney Australia Swinburne University of Technology Department of Computing Technologies Melbourne Australia Atlassian Australia

ISBN: (纸本)9798350322279

Recent work has explored federated clustering and developed an efficient k-means based method. However, it is well known that k-means clustering underperforms in high-dimensional space due to the so-called "curse of dimensionality". In addition, high-dimensional data (e.g., generated from healthcare, medical, and biological sectors) are pervasive in the big data era, which poses critical challenges to federated clustering in terms of, but not limited to, clustering effectiveness and communication efficiency. To fill this significant gap in federated clustering, we propose a one-shot federated subspace clustering scheme Fed-SC that can achieve remarkable clustering effectiveness on high-dimensional data while keeping communication cost low using only one round of communication for each local device. We further establish theoretical guarantees on the clustering effectiveness of one-shot Fed-SC and exploit the benefits of statistical heterogeneity across distributed data. Extensive experiments on synthetic and real-world datasets demonstrate significant effectiveness gains of Fed-SC compared with both subspace clustering and one-shot federated clustering methods. © 2023 IEEE.

关键词： K-means clustering

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Streamlining Task Planning Systems for Improved Enactment in Contemporary computing Surroundings

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SN Computer science 2024年第8期5卷 1-9页

作者： Menon, Sindhu Addula, Santosh Reddy Parkavi, A. Subbalakshmi, Ch. Dhandayuthapani, V. Bala Pokkuluri, Kiran Sree Soni, Anita School of Computing and Information Technology REVA University Karnataka Bangalore India Department of Information Technology University of the Cumberlands Williamsburg KY United States Dept of CSE M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Guru Nanak Institution Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telagana State Hyderabad 501506 India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Campus Oman Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India CSE Department IES University Bhopal Madhya Pradesh Bhopal India

Task planning algorithms are essential for maximizing efficiency and enhancing performance in modern computing systems, given the escalating demand for computational resources. This paper delves into the effectiveness of various scheduling algorithms across different computing environments, characterized by their unique workload dynamics and resource limitations. This method integrates adaptive algorithms and machine learning to dynamically optimize task planning, enhancing performance and efficiency in cloud, IoT, and distributed computing environments. These algorithms are crucial for optimizing key factors such as task completion times, job prioritization, and resource allocation while also addressing quality of service (QoS), cost, reliability, and specific resource needs. One significant challenge is that many task scheduling approaches do not account for the potential failure of tasks or resources. While some strategies effectively reduce overall completion time (makespan), they can result in severe workload imbalances. To address these issues, our study proposes a new approach that harnesses the processing capabilities of grid systems, thus boosting application performance and throughput. Our algorithm particularly focuses on balancing workload and accelerating scheduling operations, even in the face of grid node failures, by incorporating QoS metrics. This allows for a more robust and adaptable scheduling solution. Comparative analysis with existing methods demonstrates that our algorithm not only improves resource utilization but also significantly diminishes flow time and makespan, confirming its efficacy. Through this research, we contribute to the evolving field of autonomous task scheduling, presenting a solution that responds dynamically to changing environmental conditions and workload demands. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CPU Flow time Makespan MSHA QoS

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Health Care DNS Tunnelling Detection Method via Spiking Neural Network 3rd

Health Care DNS Tunnelling Detection Method via Spiking Neur...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Kumar, Narendra Surendiran, R. Samuel, G. K. Jabash Bhavana, N. Shirgire, Anil Malar, A. Jasmine Gnana Qalid, Aznul Faculty of Science and Technology NIET NIMS University Rajasthan Jaipur303121 India School of Information Science Annai College of Arts and Science Tamilnadu Kumbakonam612503 India Department of Electrical and Electronics Engineering Rohini College of Engineering and Technology Kanyakumari India School of Computing and Information Technology REVA University Karnataka Bengaluru560064 India Department of Civil Engineering Dr D Y Patil Institute of Technology Maharashtra Pimpri Pune411018 India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Artificial Intelligence Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9789819986453

A common DNS exploit that has been used for several decades to steal data is DNS tunneling. To avoid being detected as an intrusion from healthcare domain, the stolen health information is encoded and contained within DNS requests. Machine learning techniques for detection frequently make use of characteristics like DNS activity and healthcare network activity. The majority of features, such as time–frequency-related information, can only be obtained when data exfiltration occurs. Identifying a malicious query from a single DNS suggestion is key to preventing data exfiltration based on DNS tunneling. We can perceive DNS tunnelling before healthcare data exfiltration by not using internet traffic properties or DNS behavior information. In this paper, we describe a detection method based on deep learning that uses DNS query payloads as predictive variables in the models. Using the Spiking Neural network (SNN) in healthcare DNS Tunnelling Detection Method a DNS Tunnelling attack or not? Specificity, sensitivity, f1 score, accuracy, and Matthew's correlation coefficient (MCC) are the factors taken into account when evaluating the efficiency of the suggested model. In comparison to the Proposed Spiking neural network, the Existing Deep Neural Network (DNN), Mobile Net, and Recurrent Neural Network (RNN) technique improves the performance of a DNS tunneling detection method in a real-world network system. Results indicate that the suggested strategy is far more dependable than existing methods and achieves a 99.84% accuracy rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Recurrent neural networks

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