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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Miaoling Dai Chenhong Cao Tong Liu Meijia Su Yufeng Li Jiangtao Li School of Computer Engineering and Science Shanghai University China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal Unviersity Wuhu Anhui China

WiFi-based human gesture recognition has recently enjoyed increasing popularity in the Internet of Things (IoT) scenarios. Simultaneously recognizing user identities and user gestures is of great importance for enhancing the system security and user quality of experience (QoE). State-of-the-art approaches that perform dual tasks suffer from increased latency or degraded accuracy in cross-domain scenarios. In this paper, we present WiDual, a dual-task system that achieves accurate cross-domain gesture recognition and user identification based on WiFi in a real-time manner. The basic idea of WiDual is to use the attention mechanism to adaptively explore cross-domain features worthy of attention for dual tasks. WiDual employs a CSI (Channel Statement information) visualization method that transfers WiFi signals to images for further feature extraction and model training. In this way, WiDual mitigates the possible loss of useful information and excessive delays caused by extracting handcrafted features directly from the WiFi signal. Furthermore, WiDual utilizes a collaboration module to combine gesture features and user identity features to enhance the performance of dual-task recognition. We implement WiDual and evaluate its performance extensively on a public dataset including 6 gestures and 6 users performed across domains. Results show that WiDual outperforms state-of-the-art approaches, with 26% and 8% improvements on the accuracy of cross-domain user identification and gesture recognition respectively.

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Joint Optimization of Offloading and Caching in Full-Duplex-Enabled Edge Computing networks

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IEEE Transactions on Mobile Computing 2025年

作者： Dai, Xingxia Tian, Shujuan Liu, Haolin Li, Zhetao Jiang, Hongbo Deng, Qingyong Xiangtan University School of Computer Science School of Cyberspace Security Xiangtan411105 China Jinan University College of Information Science and Technology Guangzhou510632 China Hunan University College of Computer Science and Electronic Engineering Changsha Hunan410082 China Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China

Edge computing (EC) reduces task processing and content download delay by providing computation and caching resources directly to task offloading (TO) users and content request (CR) users. However, existing studies often focus exclusively on either TO users or CR users within EC networks, neglecting the interaction between these two groups. To address this gap, we investigate the offloading and caching decision-making in scenarios where TO and CR users coexist. Furthermore, we employ full-duplex (FD) technology to enhance spectral utilization for edge-end transmissions. Specifically, we jointly optimize offloading and caching in FD-enabled EC networks. To accomplish this, we decompose the formulated optimization problem into three sub-problems using the alternating optimization (AO) method. We then propose a three-subproblem alternating iterative delay minimization algorithm to effectively tackle the challenges of offloading and caching. Additionally, we analyze the convergence and complexity of our proposed algorithm. Finally, we conduct extensive simulations to evaluate the effectiveness of our approach. The simulation results demonstrate that the delay reduction achieved by our algorithm is between 24.78% and 89.23% greater than that of comparative algorithms. © 2002-2012 IEEE.

关键词： Computation offloading

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Intelligent Model to Predict Early Liver Disease using Machine Learning Technique

Intelligent Model to Predict Early Liver Disease using Machi...

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2022 International Conference on Business Analytics for Technology and security, ICBATS 2022

作者： Ghazal, Taher M. Rehman, Aziz Ur Saleem, Muhammad Ahmad, Munir Ahmad, Shabir Mehmood, Faisal Faculty of Information Science and Technology Ukm Network and Communication Technology Lab Center for Cyber Security Malaysia School of Computer Science NCBAandE Lahore Pakistan Gachon University Department of Computer Engineering Korea Republic of Gachon University Department of It Convergence Engineering Korea Republic of

ISBN: (纸本)9781665409209

Liver Disease (LD) is the main cause of death worldwide, affecting a large number of people. A variety of factors affect the liver, resulting in this disease. The diagnosis of this condition is both expensive and time-consuming. Machine Learning offers a lot of potential in terms of automated disease diagnosis. As a result, the purpose of this research is to assess the efficacy of various Machine Learning (ML) algorithms to lower the high cost of liver disease diagnosis through prediction. With the current rise in numerous liver disorders, it's more important than ever to detect liver disease early on. This research proposed intelligent model to predict liver disease using machine learning technique. This proposed model is more effective and comprehensive in terms of performance of 0.884 accuracy, and 0.116 miss-rate. © 2022 IEEE.

关键词： Forecasting

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A Quantum Temporal Difference Learning Method Based on Quantum World Model

A Quantum Temporal Difference Learning Method Based on Quant...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Peigen Zeng Ying He F. Richard Yu Jianbo Du College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xi’an China School of Information Technology Carleton University Ottawa Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Based on quantum parallelism theory and quantum phenomena such as superposition and entanglement, quantum reinforcement learning (QRL) has the potential to surpass classical reinforcement learning (RL). Although some excellent works have been done on QRL, existing RL methods encounter chanllenges when performing in environments with sparse rewards. In this paper, we provide a new perspective on conducting temporal difference (TD) learning in quantum computing, which can eliminate redundant exploration steps compared to classical methods. Specifically, we first use environment information to construct a world model with quantum circuits, enabling it to interact in a quantum way. Then, we perform the learning process by using the quantum world model and Grover’s algorithm to query backwards how to reach the recorded states with high TD-errors. Simulation results show that our proposed method has superior performance compared to classical RL algorithms.

关键词： Quantum entanglement Computational modeling Simulation Temporal difference learning Parallel processing Global communication Integrated circuit modeling Quantum circuit

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OFLGI: An Optimization-based Feature-Level Gradient Inversion Attack 23

OFLGI: An Optimization-based Feature-Level Gradient Inversio...

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23rd IEEE International Conference on Trust, security and Privacy in Computing and Communications, TrustCom 2024

作者： Lu, Yongwei Liang, Xiaoyan Du, Ruizhong Tian, Junfeng Hebei University School of Cyber Security and Computer Baoding070012 China Key Lab on High Trusted Information System of Hebei Province Baoding070012 China

Federated learning has become the leading paradigm for privacy-preserving distributed learning, as it only requires the upload of model gradients, not private data. However, recent studies have shown that these exchanged gradients can still lead to privacy leakage;for instance, attackers can recover private images through optimization-based gradient inversion attacks. In these attacks, pixel values in a dummy image are iteratively updated to approximate those of the private image. Yet, such optimization-based attacks often face challenges due to the large search space of the optimization task and struggle to accurately recover all labels in a batch. To tackle these, we propose the Optimization-based Feature-Level Gradient Inversion (OFLGI) attack, which focuses on optimizing image features rather than pixel values. This approach reduces the search space of the optimization task and enhances the quality of the reconstructed images. Our method formulates an optimization task to convert random noise features into natural image features, matching gradients while regularizing image fidelity. Notably, we are the first to utilize image features for gradient inversion attacks. Additionally, we introduce a novel batch label reconstruction algorithm that leverages the gradients of the biases in the final fully connected layer, surpassing existing methods even in the presence of duplicate labels. Furthermore, OFLGI demonstrates superior resistance to multiple gradient defense strategies, being able to recover high-quality private images even from degraded gradients. Extensive experiments have demonstrated the superiority of OFLGI over state-of-the-art gradient inversion techniques. © 2024 IEEE.

关键词： Federated learning

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An Efficient Energy Consumption Prediction Framework for High Performance Computing Cluster Jobs

An Efficient Energy Consumption Prediction Framework for Hig...

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International Conference on computer Engineering and Applications (ICCEA)

作者： Yantao Lou Jibin Wang Shoupeng Feng Xian Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Public Security of Shandong Province Shandong Intelligent Public Security Technology Innovation Center Jinan China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Energy consumption prediction is a crucial approach to enhance the operational efficiency of HPC (High Performance Computing) clusters. Existing predicting methods for the energy consumption mainly rely on time series mode, focusing on the instantaneous power during job execution. However, in practical applications, due to the large scale of HPC clusters, the prediction models need to process a vast amount of data, often resulting in poor performance. Moreover, it's challenging to provide effective predictions for jobs with significant power fluctuations. Thus we propose a more efficient energy consumption prediction framework for HPC cluster jobs, achieving two main objectives: On one hand, it uses a data encoding module to pre-encode the sampled data of energy consumption related indicators, simplifying the data that the time series prediction model needs to process and improving prediction efficiency. On the other hand, the prediction target is set to the power distribution range in a future period of time, rather than the instantaneous power at some specific time points, which improves the usability of the prediction results. Test results show that the proposed prediction framework can achieve more markable performance improvement while ensuring the effectiveness of the predictions.

关键词： Energy consumption Computational modeling High performance computing Time series analysis Power distribution Predictive models Feature extraction

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Identifying driver genes in cancer based on Pareto optimality consensus

Identifying driver genes in cancer based on Pareto optimalit...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Deng, Zheng Wu, Jingli Chen, Xiaorong Li, Gaoshi Guangxi Normal University College of Computer Science Information Technology Guilin China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Wuzhou University Guangxi Key Laboratory of Machine Vision Intelligent Control Wuzhou China

ISBN: (纸本)9781665468190

An important issue in cancer genomics is the identification of driver genes. It is significant for the discovery of key biomarkers and the development of effective personalized therapies. In this paper, a computated method PGScore is proposed. It scores genes at multilayer and integrates the scores to identify cancer driver genes based on Pareto Optimality Consensus(POC) strategy. PGScore uses random walks to reevaluate gene mutations, and integrates differential expression of mRNA and miRNA in normal and cancer samples. It measures the centrality of the gene in the network according to the weight of its direct and indirect neighbors, and finally integrates the above layers to get the final priority of the genes. We compare PGScore with state-of-the-art cancer driver genes prioritization methods on two real cancer datasets. The results show that PGScore can obtain better performance in identification accuracy and the partial area under the ROC(pAUC) curve on multiple reference databases. © 2022 IEEE.

关键词： Gene expression

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MEFold: Memory-Efficient Optimization for Protein Language Models via Chunk and Quantization

MEFold: Memory-Efficient Optimization for Protein Language M...

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International Joint Conference on Neural networks (IJCNN)

作者： Yanfeng Jiang Ning Sun Zhengxian Lu Shuang Peng Yi Zhang Fei Yang Tao Li College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Zhejiang Lab Hangzhou China Haihe Lab of ITAI Tianjin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Protein language models are currently experiencing a surge in demand owing to their remarkable accuracy in protein structure prediction. Nevertheless, their applications are hindered by the significant computation and memory requirements. The existing optimization strategies primarily focus on computational efficiency while often neglecting memory optimization, thereby restricting their suitability for devices with limited resources. In this paper, we propose MEFold, a novel memory-efficient optimization framework for protein language models that enables efficient inference on resource-constrained devices. MEFold consists of Look-up Table Chunk and Fine-grained Quantization. Look-up Table Chunk reduces the memory of intermediate activations by chunk and avoids the overhead of obtaining the optimal chunk size configuration through pre-computing. For the memory of model parameters, Fine-grained Quantization, delicately controls the scope of quantization to ensure that memory reduction is achieved while preventing declines in accuracy and computational speed. Experimental results show that, compared to the original model, for protein sequences ranging from 74 to 1024 in length, our method significantly reduces the peak memory during inference from 14.7-54.2GB to 6.0-14.4GB, while minimizing the impact on inference latency. On CASP14 and CAMEO datasets, the accuracy loss compared to the original model is below 1%. Moreover, our optimization provides various memory-saving alternatives. Our code is available at https://***/llwx593/MEFold.

关键词： Proteins Quantization (signal) Accuracy Computational modeling Memory management Neural networks Predictive models

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Robust Video Watermarking network Based on Channel Spatial Attention

Robust Video Watermarking Network Based on Channel Spatial A...

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International Joint Conference on Neural networks (IJCNN)

作者： Jian Li Tao Zuo Bin Ma Chunpeng Wang Peng Zhang Huanhuan Zhao Zhengzhong Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Robust video watermarking refers to the ability to extract the originally embedded watermark information from a video even after malicious modifications and attacks. Currently, traditional watermarking methods have the drawback of lacking robustness against multiple watermark attacks simultaneously. Neural network-based approaches have not fully considered the multi-scale features of videos and tend to lose information during the fusion of scale features. Therefore, we propose a video watermarking scheme based on Channel Spatial Attention. Our model can extract feature information at different scales, allowing the watermark to adapt to features of different scales in the video. Through a series of comparative experiments, our method has shown significant improvements over traditional video watermarking methods and deep learning-based video watermarking models.

关键词： Adaptation models Convolution Video sequences Neural networks Watermarking Interference Feature extraction

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A Novel Lightweight Dynamic Trust Evaluation Model for Edge Computing

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IEEE Transactions on network and Service Management 2025年

作者： Yang, Hao Guo, Liangmin Wang, Taochun Lv, Chengmei Anhui Technical College of Mechanical and Electrical Engineering School of Internet and Communication Wuhu241003 China Anhui Normal University School of Computer and Information Wuhu241003 China Anhui Provincial Key Laboratory of Network and Information Security Wuhu241003 China

The temporal decay of trust data in dynamic edge computing environments leads to inaccurate evaluation, and the recommended trust values from heterogeneous nodes are affected by subjective bias and are vulnerable to malicious attacks. To address these issues, this paper proposes a novel lightweight dynamic trust evaluation model. First, a time decay function is derived based on Newton’s law of cooling to effectively reflect the impact of trust timeliness on trust evaluation. On this basis, the real value is iteratively calculated and used as the recommended trust value through the truth discovery algorithm, enhancing the accuracy of trust evaluation. Then, the weight of the recommended trust value in the comprehensive trust value is determined based on the standard deviation of the weight of perceived data from each recommending node, balancing the influence of subjective and objective factors on trust evaluation results. Lastly, the comprehensive trust value is dynamically weighted, and an incentive mechanism is employed to update trust data based on feedback, reflecting the dynamic nature of trust. Theoretical analysis demonstrates that the model presented in this paper exhibits low time and space complexities, meeting the lightweight requirements of the edge computing environment. Experimental results indicate high recommendation trust accuracy and interaction success rates, as well as effective resistance against malicious attacks. © 2004-2012 IEEE.

关键词： Trusted computing

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