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High Performance Big Data and Intelligent Systems (HPBD&IS)

作者： Lin Yao Ziyuan Tian Jing Deng Guowei Wu Li Li DUT-RU International School of Information Science & Engineering Dalian University of Technology Dalian China School of Software Dalian University of Technology Dalian China The Department of Computer Science UNC Greensboro Greensboro UNC NC USA Department of Computer and Information Science University of Macau Macau China

Satellite-Terrestrial Integrated Networks (STINs) integrate satellite networks, the Internet, and mobile wireless networks and are able to provide powerful services to users. In STIN, satellite gateways play an important role of connecting space-based networks to terrestrial-based networks, but can be under frequent attacks such as Distributed Denial of Services (DDoS) attack. Therefore, intrusion detections become essential in STINs. In this work, we propose a feature reduction-based intrusion detection (FRID) technique with the help from deep reinforcement learning. In FRID, only features that are highly distinguishable, robust, and independent are selected for the later stages. Experimental results show the accuracy, recall, FAR, and F1 scores comparing FRID with several state-of-the-art schemes and demonstrate its superiority.

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Data-Driven DLT3 Federated Deep Reinforcement Learning for Secure and Efficient Autonomous Driving

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Rong Li, Qianxi Wang, Kan Khan, Fazlullah Alturki, Ryan Lan, Dapeng Xi’an University of Technology School of Computer and Engineering Xi’an710000 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Ningbo315104 China Umm Al-Qura University College of Computing Department of Software Engineering Makkah21955 Saudi Arabia Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110003 China

As autonomous vehicles (AVs) become increasingly widespread, the intelligent driving control and safety concerns have emerged. Recent advents in Internet of Things (IoTs) and 6G technologies have vastly boosted AVs’ sensing and communication capabilities, thereby enabling the high-speed and real-time data exchange. However, efficiently utilizing data while safeguarding the privacy remains challenges. Thus, we propose a federated learning (FL) framework with the privacy protection to facilitate the deep reinforcement learning (DRL) for longitudinal vehicle safety tracking control. First, the proposed framework enables the efficient information sharing and policy updates via encrypted policy gradient transmission. Then, the twin delayed deep deterministic policy gradient (TD3) method makes the velocity decision in the local model, while the dual long short-term memory (LSTM) network captures the temporal trajectory information. Finally, stacked denoising autoencoders (SDAE) is used to extract high-level environmental features, and the dual-LSTM-TD3 (DLT3)-SDAE model is introduced to enhance the policy decision efficiency and accuracy. Experimental results demonstrate that within the FL framework supported by homomorphic encryption, the DLT3-SDAE-based AV agent surpasses other DRL methods in terms of collision avoidance and average rewards, in the LAF environment. The safety, efficiency and comfort of the DLT3-SDAE AV agent are also evaluated and demonstrated. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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MoSA: Mixture of Sparse Adapters for Visual Efficient Tuning

arXiv

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arXiv 2023年

作者： Zhang, Qizhe Zou, Bocheng An, Ruichuan Liu, Jiaming Zhang, Shanghang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China University of Wisconsin-Madison United States School of Software Engineering Xi’an Jiaotong University China

With the rapid growth in the scale of pre-trained foundation models, parameter-efficient fine-tuning techniques have gained significant attention, among which Adapter Tuning is the most widely used. Despite achieving efficiency, it still underperforms full fine-tuning, and the performance improves at the cost of an increase in parameters. Recent efforts have either focused on training multiple adapter experts to increase model capacity or on pruning adapters to achieve parameter efficiency. However, both approaches introduce more parameters compared to the original adapter, hence are not computationally efficient. Motivated by this, we propose Mixture of Sparse Adapters, or MoSA, as a novel Adapter Tuning method to fully unleash the potential of each parameter in the adapter. We first split the standard adapter into multiple non-overlapping modules, then stochastically activate them for sparse training, and finally merge them to form a complete adapter after tuning. In this way, MoSA can achieve significantly better performance than standard adapters without any additional computational or storage overhead. Furthermore, we propose a hierarchical sparse strategy to better leverage limited training data. Extensive experiments on a series of 27 visual tasks demonstrate that MoSA consistently outperforms other Adapter Tuning methods as well as other baselines by a large margin. Furthermore, MoSA brings consistent improvements across various model scales, architectures, and different PEFT methods. Code will be released. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Designing Quantum-Safe Lattice-Based Multi-Authority CP-ABE Scheme for Blockchain-Enabled IoT-Based Consumer Healthcare Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Bagchi, Prithwi Bisht, Abhishek Das, Ashok Kumar Saxena, Neetesh Hossain, M. Shamim International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Cardiff University School of Computer Science and Informatics CardiffCF24 4AG United Kingdom King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh13273 Saudi Arabia

Despite edge computing reducing communication delays associated with cloud computing, privacy concerns remain a significant challenge when sharing data from edge-based consumer electronics (CE) or Internet-of-Things (IoT) devices. Ciphertext policy attribute-based encryption (CP-ABE) is a cryptographic tool that facilitates intricate and refined access control. It can deliver more flexible, secure, compact, and effective access control policies for the cloud data. In the case of a conventional CP-ABE scheme, the keys can only be issued and disseminated by a trusted central authority (CA). However, if the CA is compromised, the entire system becomes more susceptible to assaults or failures, which leads to a single-point failure. In this article, we propose a Ring-Learning with Errors (Ring-LWE)-based MA-CP-ABE scheme that is effectively resolved by the multi-authority CP-ABE (MA-CP-ABE), and it ensures the security against quantum attacks. The threshold secret sharing by Shamir and the Lagrange interpolation formula are applied in the key-generation and decryption procedures of the proposed scheme, which make it easier to segment and restore the private keys. The proposed scheme is implemented in the CE-enabled IoT-based smart healthcare applications using the blockchain technology as a secure storage. A detailed comparative study, security analysis and experimental results with the existing relevant schemes shows that the proposed scheme exhibits superior security and better efficiency as compared to other schemes, demonstrating its feasibility in practical IoT-based healthcare applications. © 2025 IEEE.

关键词： Quantum cryptography

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Analysis of android applications permissions 3

Analysis of android applications permissions

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3rd International Conference on Data science, E-Learning and Information Systems, DATA 2021

作者： Saif, Amal Al-Kilani, Hamzeh Qasaimeh, Malik Al-Refai, Abdullah Computer Science Department King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan Software Engineering Department Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9781450388382

As Android is one of the most popular mobile open-source platforms, it is very important to ensure the security and privacy of Android apps. Android has an authorization system that allows developers to announce their applications requiring essential services, and when running such applications, users need to comply with these requirements. Users frequently download applications, giving them infinite permissions easily without thinking about the impact on their privacy. In this paper, we analyze 222 applications manually to grant these permissions to see how they are compatible with user privacy depending on many criteria. © 2021 Association for Computing Machinery.

关键词： Android (operating system)

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A blockchain-based computation offloading method for edge computing in 5G networks

A blockchain-based computation offloading method for edge co...

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作者： Xu, Xiaolong Chen, Yi Zhang, Xuyun Liu, Qingxiang Liu, Xihua Qi, Lianyong School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Electrical and Computer Engineering University of Auckland Auckland New Zealand School of Information Science and Engineering Qufu Normal University Qufu China

Edge computing (EC) emerges as a novel computing paradigm to offload computing tasks from user equipments (UEs) to edge notes (ENs) in fifth-generation networks, which definitely breaks the resource limitation of UEs to a certain degree. However, it is troublesome to guarantee the overall operating performance of ENs due to the uneven distributed resource demands of UEs, the resulting transmission delay and the data loss for computation offloading between the covered EN and the deployed destination EN. In view of this challenge, a blockchain-based computation offloading method, named BCO, is proposed in this paper. Technically, since blockchain is a promising technique for the decentralized system, a blockchain-based EC framework is designed to degrade the data loss possibility by integrating blockchain and EC. Then, the nondominated sorting genetic algorithm, the third version (NSGA-III), is leveraged to acquire the balanced offloading strategies. Furthermore, by taking advantage of Simple Additive Weighting and Multiple Criteria Decision Making, the optimal offloading strategy is identified. Finally, systematic experiments and analyses on the comparative experiment are conducted to verify the efficiency of our proposed method BCO. © 2019 John Wiley & Sons, Ltd.

关键词： Blockchain

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A Self-Evolving Framework for Multi-Agent Medical Consultation Based on Large Language Models

A Self-Evolving Framework for Multi-Agent Medical Consultati...

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

作者： Kai Chen Ji Qi Jing Huo Pinzhuo Tian Fanyu Meng Xi Yang Yang Gao State Key Laboratory for Novel Software Technology Nanjing University Nanjing China China Mobile (Suzhou) Software Technology Co. Ltd. Suzhou China School of Computer Engineering and Science Shanghai University Shanghai China China Mobile Research Institute Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We propose a multi-agent approach (SeM-Agents) based on large language models for medical consultations. This framework incorporates various doctor roles and auxiliary roles, with agents communicating through natural language. Using a residual structure, the system conducts multi-round medical consultations based on the patient’s treatment background and symptoms. In the final summary and output stage of the consultation, it utilizes two experience databases—the Correct Consultation Experience Database and the Chain of Thought (CoT) Experience Database—which evolve with accumulated experience during consultations. This evolution drives the framework’s self-improvement, significantly enhancing the rationality and accuracy of the consultations. To ensure that the conclusions are safe, reliable, and aligned with human values, the final decisions undergo a safety review before being provided to the patient. This framework achieved accuracy rates of 89.2% and 83.1% on the MedQA and PubMedQA datasets, respectively.

关键词： Accuracy Pollution Databases Reviews Large language models Natural languages Signal processing Safety Reliability Speech processing

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LWS: A Framework for Log-based Workload Simulation in Session-based SUT

arXiv

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arXiv 2023年

作者： Han, Yongqi Du, Qingfeng Xu, Jincheng Zhao, Shengjie Chen, Zhekang Cao, Li Yin, Kanglin Pei, Dan School of Software Engineering Tongji University Shanghai201804 China Bizseer Beijing100083 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Artificial intelligence for IT Operations (AIOps) plays a critical role in operating and managing cloud-native systems and microservice-based applications but is limited by the lack of high-quality datasets with diverse scenarios. Realistic workloads are the premise and basis of generating such AIOps datasets, with the session-based workload being one of the most typical examples. Due to privacy concerns, complexity, variety, and requirements for reasonable intervention, it is difficult to copy or generate such workloads directly, showing the importance of effective and intervenable workload simulation. In this paper, we formulate the task of workload simulation and propose a framework for Log-based Workload Simulation (LWS) in session-based systems. LWS extracts the workload specification including the user behavior abstraction based on agglomerative clustering as well as relational models and the intervenable workload intensity from session logs. Then LWS combines the user behavior abstraction with the workload intensity to generate simulated workloads. The experimental evaluation is performed on an open-source cloud-native application with both well-designed and public real-world workloads, showing that the simulated workload generated by LWS is effective and intervenable, which provides the foundation of generating high-quality AIOps datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Abstracting

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Diagnosing Spinal Abnormalities Using Machine Learning: A Data-Driven Approach

Diagnosing Spinal Abnormalities Using Machine Learning: A Da...

引用

Artificial Intelligence (ICAI), International Conference on

作者： Zia Ul Islam Nasir Khalil Khan Momna Asghar School of Interdeciplanary Engineering & Sciences(SINES) National University of sciences & Technology (NUST) Isamabad Pakistan Department of Computer Software Engineering & Computer Science Pak-Austria Institute of Applied Science & Technology Haripur Pakistan Department of Electrical Engineering University of Azad Jammu & Kashmir Muzaffarabad A.K

Low back pain is a predominant condition which can affects people from different diaspora. The goal of this work is to use machine learning approach to forecast spinal abnormalities. Extratreesclassifier is utilized as a data preprocessing stage to choose the dataset's most prominent features. On a dataset of 310 samples, spinal anomalies are diagnosed using machine learning algorithms like the Support Vector Machine (SVM) and the multilayer perceptron (MLP). The purpose of this study is to determine the most crucial factors that produce backbone abnormalities and to predict them using supervised machine learning techniques. The classification of normal and abnormal spinal patients is investigated in terms of various aspects, including testing and training accuracy, precision, and recall. The observed accuracies for SVM and MLP with 80% training data are 92% and 90%, respectively. The result shows that these models can achieve high accuracy in predicting spinal abnormalities, with the SVM model performing the better. The result suggest that this approach has the potential to significantly improve the efficiency and accuracy of spinal abnormality diagnosis, leading to better patient outcomes.

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Temporal Feature Matters: A Framework for Diffusion Model Quantization

arXiv

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arXiv 2024年

作者： Huang, Yushi Gong, Ruihao Liu, Xianglong Liu, Jing Li, Yuhang Lu, Jiwen Tao, Dacheng Electrical and Computer Engineering Department Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Software Development Environment Beihang University China Department of Data Science and AI Faculty of IT Monash University Australia Electrical Engineering Department Yale University United States Department of Automation Tsinghua University China School of Computer Science and Engineering Nanyang Technological University Singapore

The Diffusion models, widely used for image generation, face significant challenges related to their broad applicability due to prolonged inference times and high memory demands. Efficient Post-Training Quantization (PTQ) is crucial to address these issues. However, unlike traditional models, diffusion models critically rely on the time-step for the multi-round denoising. Typically, each time-step is encoded into a hypersensitive temporal feature by several modules. Despite this, existing PTQ methods do not optimize these modules individually. Instead, they employ unsuitable reconstruction objectives and complex calibration methods, leading to significant disturbances in the temporal feature and denoising trajectory, as well as reduced compression efficiency. To address these challenges, we introduce a novel quantization framework that includes three strategies: 1) TIB-based Maintenance: Based on our innovative Temporal Information Block (TIB) definition, Temporal Information-aware Reconstruction (TIAR) and Finite Set Calibration (FSC) are developed to efficiently align original temporal features. 2) Cache-based Maintenance: Instead of indirect and complex optimization for the related modules, pre-computing and caching quantized counterparts of temporal features are developed to minimize errors. 3) Disturbance-aware Selection: Employ temporal feature errors to guide a fine-grained selection between the two maintenance strategies for further disturbance reduction. This framework preserves most of the temporal information and ensures high-quality end-to-end generation. Extensive testing on various datasets, diffusion models and hardware confirms our superior performance and acceleration. © 2024, CC BY.

关键词： Spatio-temporal data

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