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arXiv 2024年

作者： Zhang, Wenqin Chee, Yeow Meng Dau, Son Hoang Etzion, Tuvi Kiah, Han Mao Luo, Yuan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Dept. of Industrial Systems Engineering and Management National University of Singapore Singapore School of Computing Technologies RMIT University MelbourneVIC Australia Computer Science Faculty Technion Israel Institute of Technology School of Physical and Mathematical Sciences Nanyang Technological University Singapore

To measure repair latency at helper nodes, we introduce a new metric called skip cost that quantifies the number of contiguous sections accessed on a disk. We provide explicit constructions of zigzag codes and fractio...

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OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Ni, Liwei Wang, Rui Liu, Miao Meng, Xingyu Lin, Xiaoze Liu, Junfeng Luo, Guojie Chu, Zhufei Qian, Weikang Yang, Xiaoyan Xie, Biwei Li, Xingquan Li, Huawei Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China Pengcheng Laboratory Shenzhen518055 China University of Chinese Academy of Sciences Beijing101408 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing100049 China Peking University School of Computer Science Center for Energy-Efficient Computing and Applications Beijing100871 China Ninbo University Faculty of Electrical Engineering and Computer Science Ninbo315211 China Shanghai Jiao Tong University University of Michigan-Shanghai Jiao Tong University Joint Institute MoE Key Laboratory of Artificial Intelligence Shanghai200240 China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou311121 China

This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning (ML) applications within the logic synthesis process. Previous dataset generation flows were tailored for specific tasks or lacked integrated ML capabilities. While OpenLS-DGF supports various machine learning tasks by encapsulating the three fundamental steps of logic synthesis: Boolean representation, logic optimization, and technology mapping. It preserves the original information in both Verilog and ML-friendly GraphML formats. The Verilog files offer semi-customizable capabilities, enabling researchers to insert additional steps and incrementally refine the generated dataset. Furthermore, OpenLS-DGF includes an adaptive circuit engine that facilitates the final dataset management and downstream tasks. The generated OpenLS-D-v1 dataset comprises 46 combinational designs from established benchmarks, totaling over 966,000 Boolean circuits. OpenLS-D-v1 supports integrating new data features, making it more versatile for new tasks. This paper demonstrates the versatility of OpenLS-D-v1 through four distinct downstream tasks: circuit classification, circuit ranking, quality of results (QoR) prediction, and probability prediction. Each task is chosen to represent essential steps of logic synthesis, and the experimental results show the generated dataset from OpenLSDGF achieves prominent diversity and applicability. The source code and datasets are available at https://***/Logic-Factory/ACE/blob/master/OpenLS-DGF. © 1982-2012 IEEE.

关键词： Information management

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Autonomous navigation of a tracked unmanned ground vehicle*

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IFAC-PapersOnLine 2022年第14期55卷 120-125页

作者： Marija Seder Anđela Jurić Ana Šelek Filip Marić Marija Lovrić Ivan Petrović University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics (LAMOR) Zagreb Croatia University of Zagreb Croatian Military Academy Dr. Franjo Tuđman Zagreb Croatia

This work proposes a complete autonomous navigation system for a tracked vehicle. The system enables a complete autonomous execution of waypoint and patrolling tasks selected by the user. It also enables user-vehicle shared autonomy, switching between the user teleoperation and the vehicle autonomous operation. Our navigation system uses the model predictive control scheme based on a navigation function. We propose the navigation function which takes into account changing environments, any-shape footprint, and non-holonomic motion of the tracked vehicle. Besides the waypoint and patrolling tasks, we implemented a fail-safe scenario in case of the user-vehicle communication loss, in which the vehicle returns autonomously to the previously visited goal where the communication was stable. The efficiency of the proposed system is validated by experimental results on the Komodo tracked vehicle.

关键词： Robot navigation path planning obstacle detection avoidance motion control map building

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PrivBox: Privacy-Preserving Deep Packet Inspection with Dual Double-masking Obfuscated Rule Generation

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Wu, Pengfei Ning, Jianting Huang, Xinyi Chen, Rongmao Zhang, Kai Liang, Kaitai Wuhan University School of Cyber Science and Engineering Wuhan China Singapore Management University School of Computing and Information Systems Singapore City University of Macau Faculty of Data Science Macau China Fujian Normal University College of Computer and Cyber Security Fuzhou China Jinan University College of Cyber Security Guangzhou China National University of Defense Technology College of Computer Changsha China Shanghai University of Electric Power College of Computer Science and Technology Shanghai China Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Netherlands

Many network middleboxes have been deployed to perform deep packet inspection (DPI) over packet payloads. However, such middleboxes cannot accomplish their tasks when the traffic is encrypted. BlindBox (SIGCOMM 2015) provided the first solution for performing DPI over encrypted traffic. To improve its efficiency, a later proposal PrivDPI (CCS 2019) introduced a practical technique to generate encrypted rules. However, a recent proposal P2DPI (ASIACCS 2021) showed that the rule generator in PrivDPI can comprise the user's privacy. In this paper, we present a new attack on P2DPI and show that the privacy of its endpoints can still be compromised by the rule generator. We comprehensively analyze the vulnerability of prior studies and present PrivBox, a new DPI system that achieves the same privacy guarantee as BlindBox while maintaining practical efficiency. This is based on a new technique called dual double-masking obfuscated rule generation. For a ruleset of 3,000, PrivBox achieves connection establishment time on the endpoint side comparable to PrivDPI and supports up to 4,672 token encryptions per second, which is sufficient for a number of real-world applications. Overall, our experiment demonstrates that PrivBox is practical and well-suited for short, frequently established sessions, especially when token repeating is common. ©2004-2012 IEEE.

关键词： Differential privacy

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Gemini+: Enhancing Real-Time Video Analytics with Dual-Image FPGAs

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Wu, Jingrou Hu, Chuang Zhang, Jin Wang, Dan Jiang, Jing University of Technology Sydney Australian Artificial Intelligence Institute SydneyNSW2007 Australia Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems The Department of Computer Science and Engineering Shenzhen518055 China Wuhan University School of Computer Science Wuhan430072 China Southern University of Science and Technology Department of Computer Science and Engineering The Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

Real-time video analytics demand intensive computing resources, often exceeding device capabilities. Heterogeneous computing resources like CPU and GPU, usually work collaboratively to ensure real-time performance. GPU manage data-intensive computing, while CPU handle instruction-intensive tasks. However, analytics accuracy can degrade because of the imbalance between CPU-GPU workloads and resources, and static resources in most systems limit adaptability to dynamic workloads. In addition to CPU-GPU resource control, accuracy is highly dependent on video analytics configuration including resolution, frame rate, and model selection. In this paper, we propose Gemini+, a hardware-accelerated video analytics pipeline empowered by dual-image FPGAs. It enables flexible CPU-GPU resource adaptation by providing near-instantaneous switching between two pre-configured images. We investigate CPU-GPU resource control and video analytics configuration adaptation in a dual-image FPGA-based video analytics pipeline. We study and formulate two problems of practical importance, a single-camera and multi-camera dual computing resource control problem, i.e., SC-DCRC and MC-DCRC. We analyze the problem complexity and develop optimal and sub-optimal algorithms. We evaluate our algorithms through simulation and build a prototype for verification. The results show that Gemini+ can improve analytic accuracy by 25% compared to fixed CPU-GPU resource systems and 88% compared to GPU-dominant systems. © 2025 IEEE.

关键词： Graphics processing unit

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Evaluation of the Montenegrin Academic Digital Innovation Hub 27

Evaluation of the Montenegrin Academic Digital Innovation Hu...

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27th International Conference on Information Technology, IT 2023

作者： Lakovic, Luka Kara, Peter A. Ognjanovic, Ivana Bokor, Laszlo Sendelj, Ramo Reich, Christoph Roganovic, Milovan Zoulias, Emmanouil Erakovic, Jevto Radusinovic, Tanja Rakocevic, Nada Simon, Aniko University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Faculty of Science Engineering and Computing Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science School of Computer Science and Mathematics LondonKT1 2EE United Kingdom University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinical Centre of Montenegro Clinic for Neurology Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi 11527 Greece Chamber of Economy of Montenegro Podgorica81000 Montenegro Sigma Technology Budapest1093 Hungary

ISBN: (纸本)9798350397512

In the era of global digitalization, digital innovation hubs have become essential, as they function as transforming forces. In this paper, we provide an analysis of the Montenegrin Academic Digital Innovation Hub by applying multi-criteria qualitative evaluation over several criteria: process flow of fully digitized operations of the Hub (with aspects of the innovation life-cycle, ethics, and security), resource management, strategic goals, and feasibility. The paper also proposes a model of comprehensive resource management via the perspective of smart specialization priorities of Montenegro. © 2023 IEEE.

关键词： Life cycle

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Provably Secure and Efficient One-to-Many Authentication and Key Agreement Protocol for Resource-Asymmetric Smart Environments

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IEEE Internet of Things Journal 2025年

作者： Li, Long Hsu, Chingfang Cui, Jianqun Au, Man Ho Harn, Lein Li, Quanrun Central China Normal University Computer School Wuhan430079 China Central China Normal University Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Wuhan430079 China Department of Computing Hong Kong Polytechnic University 25809 Hong Kong University of Missouri-Kansas City Department of Computer Science Electrical Engineering MO64110 United States Zhejiang Sci-Tech University School of Cyber Science and Engineering Hangzhou310020 China

The smart environment is a crucial application of the Internet of Things(IoT). Due to its growing security and efficiency needs, recent years have seen the proposal of numerous authentication and key agreement (AKA) protocols. Unfortunately, most of existing AKA protocols only support one-to-one AKA and rely on the elliptic curve cryptosystem, resulting in huge overhead. In addition, these protocols fail to consider the resource-asymmetric characteristics of this scenario. That is, the resources on the gateway side are abundant, while the resources on user sides and device sides are limited. In order to achieve efficient and secure one-to-many AKA establishment in this scenario, where one-to-many means that users can realize key agreements with multiple smart devices at the same time. For the first time, this paper uses the one-to-many computing structure of the Chinese Remainder Theorem (CRT) to design an efficient one-to-many AKA establishment, which is perfectly adapted to resource-asymmetric allocation in smart environments. Compared with existing solutions, this solution has the following advantages. Firstly, our protocol is suitable for resource-asymmetric environments, where the gateway acts as an intermediate node and uses rich resources to integrate multiple AKA requests. Secondly, the solution supports users to negotiate session keys with multiple smart devices at the same time. Thirdly, we prove the protocol’s security under the Real-or-Random (ROR) model. In addition, we perform formal security verification of the protocol using the Automated Validation of Internet Security Protocols and Applications(AVISPA) tool. Finally, the security and efficiency of this solution are superior to similar solutions. Specifically, our solution can meet 18 security and functionality requirements. Compared with the latest similar scheme, assuming that the number of smart devices is 10, our scheme reduces the computational cost by 75.75%. At the same time, in terms of comm

关键词： Authentication Protocol

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An Improved Machine Learning Technique with Effective Heart Disease Prediction System

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computers, Materials & Continua 2021年第12期69卷 4169-4181页

作者： Mohammad Tabrez Quasim Saad Alhuwaimel Asadullah Shaikh Yousef Asiri Khairan Rajab Rihem Farkh Khaled Al Jaloud College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology P.O.Box 6086Riyadh11442Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar University1002Tunisia

Heart disease is the leading cause of death *** heart disease is challenging because it requires substantial experience and *** research studies have found that the diagnostic accuracy of heart disease is *** coronary heart disorder determines the state that influences the heart valves,causing heart *** indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep *** work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial *** first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)*** this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was *** CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods *** proposed method offers an illustrative framework that helps predict heart attacks with high *** proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.

关键词： Machine learning deep recurrent neural network effective heart disease prediction framework

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HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Wang, Xianbin Cheng, Zhipeng Hosseinalipour, Seyyedali Dai, Huaiyu Jiao, Zhenzhen School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Japan Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Suzhou China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering NC State University RaleighNC United States iF-Labs Beijing Teleinfo Technology Company Ltd. CAICT Beijing China

The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient machine learning (ML) solutions that can handle high vehicular mobility and decentralized data. This has motivated the emergence of Hierarchical Federated Learning over vehicle-edge-cloud architectures (VEC-HFL). Nevertheless, one aspect which is underexplored in the literature on VEC-HFL is that vehicles often need to execute multiple ML tasks simultaneously, where this multi-model training environment introduces crucial challenges. First, improper aggregation rules can lead to model obsolescence and prolonged training times. Second, vehicular mobility may result in inefficient data utilization by preventing the vehicles from returning their models to the network edge. Third, achieving a balanced resource allocation across diverse tasks becomes of paramount importance as it majorly affects the effectiveness of collaborative training. We take one of the first steps towards addressing these challenges via proposing a framework for multi-model training in dynamic VEC-HFL with the goal of minimizing global training latency while ensuring balanced training across various tasks—a problem that turns out to be NP-hard. To facilitate timely model training, we introduce a hybrid synchronous-asynchronous aggregation rule. Building on this, we present a novel method called Hybrid Evolutionary And gReedy allocaTion (HEART). The framework operates in two stages: first, it achieves balanced task scheduling through a hybrid heuristic approach that combines improved Particle Swarm Optimization (PSO) and Genetic Algorithms (GA);second, it employs a low-complexity greedy algorithm to determine the training priority of assigned tasks on vehicles. Experiments on real-world datasets demonstrate the superiority of HEART over existing methods. © 2025, CC BY-NC-ND.

关键词： Federated learning

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Reinforcement Learning-Based Optimal Multiple Waypoint Navigation

Reinforcement Learning-Based Optimal Multiple Waypoint Navig...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Christos Vlachos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos Department of Electrical and Computer Engineering University of Patras Greece School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the parameters of the underlying potential field are adjusted through a policy gradient algorithm in order to minimize a cost function. The main novelty of the proposed scheme lies in the method that provides optimal policies for multiple final positions, in contrast to most existing methodologies that consider a single final configuration. An assessment of the optimality of our results is conducted by comparing our novel motion planning scheme against a RRT* method.

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