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Automated and controlled patch generation for enhanced fixing of communication software vulnerabilities

Intelligent and Converged Networks 2024年第3期5卷 222-236页

作者： Shuo Feng Shuai Yuan Zhitao Guan Xiaojiang Du School of Control and Computer Engineering North China Electric Power UniversityBeijing 100029China Department of Finance Operationsand Information SystemsBrock UniversitySt.Catharines L2S3A1Canada Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA

Software is a crucial component in the communication systems,and its security is of paramount ***,it is susceptible to different types of attacks due to potential ***,significant time and effort is required to fix such *** propose an automated program repair method based on controlled text generation ***,we utilize a fine-tuned language model for patch generation and introduce a discriminator to evaluate the generation process,selecting results that contribute most to vulnerability ***,we perform static syntax analysis to expedite the patch verification *** effectiveness of the proposed approach is validated using QuixBugs and Defects4J datasets,demonstrating significant improvements in generating correct patches compared to other existing methods.

关键词： automatic program repair controlled text generation communication software security program language model

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Modelling and simulation of energy management of power-split hybrid electric vehicles using the discrete EVent system specification

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International Journal of Vehicle Systems Modelling and Testing 2024年第1期18卷 33-61页

作者： Maatoug, Abdelfettah Kharoubi, Sahraoui Maaskri, Moustafa Belalem, Ghalem Mahmoudi, Said Faculty of Applied Sciences Science and Technology Department University of Tiaret Tiaret14000 Algeria Faculty of Mathematics and Computer Science Computer Science Department University of Tiaret Tiaret14000 Algeria Faculty of Applied Sciences Electrical Engineering Department University of Tiaret Tiaret14000 Algeria Faculty of Exact and Applied Sciences Computer Science Department University of Oran 1 Ahmed Ben Bella Oran31000 Algeria Faculty of Engineering Computer Science Department University of Mons Mons7000 Belgium

This work provides a basis for studying energy management optimisation in power-split hybrid electric vehicles (PSHEVs) to reduce fuel consumption and increase powertrain efficiency by enforcing a strategy related to battery level and vehicle speed. Three modes were used to operate the vehicle to meet different situations related to available energy and desired speed. These are particularly abundance mode, ascetic mode and optimal mode. For this, a system was designed based on several sub-models written in the discrete EVent system (DEVS) specification. The vehicle's power train and control strategy models were created in the DEVSimPy environment, and a joint simulation was carried out from the composite sub models until the overall model is reached. Simulation results under several driving cycles showed that the proposed system improves the energy consumption of the vehicle in different operating modes of the engines and gives more opportunities to adopt electric motors in vehicle transmission. © 2024 Inderscience Enterprises Ltd.

关键词： Energy management

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Edge-Enabled Metaverse:The Convergence of Metaverseand Mobile Edge Computing

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Tsinghua Science and technology 2024年第3期29卷 795-805页

作者： Nyothiri Aung Sahraoui Dhelim Liming Chen Huansheng Ning Luigi Atzori Tahar Kechadi Information Engineering College Xinjiang Institute of EngineeringUrumqi 830023Chinaand also with School of Computer ScienceUniversity College DublinDublinD04 V1W8Ireland School of Computing Ulster UniversityBelfastBT151EDUK School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Electrical and Electronic Engineering University of CagliariSardinia 09124Italy School of Computer Science University College DublinDublinD04 V1W8Ireland

Metaverse is a virtual environment where users are represented by their avatars to navigate a virtual world having strong links with its physical *** state-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering *** current centralized architecture of such systems is unfavorable as it suffers from several drawbacks caused by the long latency of cloud access,such as low-quality *** this end,we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing *** applications leverage edge devices’computing power to perform the required computations for heavy tasks,such as collision detection in the virtual universe and high-computational 3D physics in virtual *** computational costs of a Metaverse entity,such as collision detection or physics emulation,are performed at the device of the associated physical *** validate the effectiveness of the proposed architecture,we simulate a distributed social Metaverse *** simulation results show that the proposed architecture can reduce the latency by 50%when compared with cloud-based Metaverse applications.

关键词： industrial Metaverse edge computing Blockchain virtual environment task offloading fog computing

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Deep Learning for MANET Routing

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2023年 1卷 412-424页

作者： Danilchenko, Kiril Azoulay, Rina Reches, Shulamit Haddad, Yoram University of Waterloo Department of Electrical and Computer Engineering WaterlooN2L 3G1 Canada Jerusalem College of Technology Department of Computer Science Jerusalem91160 Israel

Power control and scheduling are among the most well-known resource allocation challenges in wireless networks, and are often solved as optimization problems with constraints. However, solving these optimization challenges by using optimal algorithms often incurs a significant time complexity, which creates considerable discrepancies between the theoretical results and real-time processing required. In this study, we propose a novel machine learning-based perspective to address this issue. We propose a scheduling and power control deep neural network SPCDNet method and its modification SPCDNetR. SPCDNet solves the scheduling problem for point-to-point transmission requests while SPCDNetR solves the more complex problem, where the input transmission list is composed of ordered routes which should be satisfied. Both SPCDNet and SPCDNetR are trained in a supervised manner and show near-optimal performance on the test set. Our results demonstrate that SPCDNet and SPCDNetR can serve as a computationally inexpensive solution (regarding time complexity), compared with state-of-the-art schemes, while showing to be near-optimal approximation solutions to the time scheduling and power control challenges. Moreover, we found that both SPCDNet and SPCDNetR reach efficient solutions for large problem instances, even though they were trained on small problems. © 2023 CCBY.

关键词： Time division multiple access

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Evolutionary Optimization with a Simplified Helper Task for High-Dimensional Expensive Multiobjective Problems

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-32页

作者： Wu, Xunfeng Lin, Qiuzhen Zhou, Junwei Liu, Songbai Coello Coello, Carlos A. Leung, Victor C. M. College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Mexico City07300 Mexico Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In recent years, surrogate-Assisted evolutionary algorithms (SAEAs) have been sufficiently studied for tackling computationally expensive multiobjective optimization problems (EMOPs), as they can quickly estimate the qualities of solutions by using surrogate models to substitute for expensive evaluations. However, most existing SAEAs only show promising performance for solving EMOPs with no more than 10 dimensions, and become less efficient for tackling EMOPs with higher dimensionality. Thus, this article proposes a new SAEA with a simplified helper task for tackling high-dimensional EMOPs. In each generation, one simplified task will be generated artificially by using random dimension reduction on the target task (i.e., the target EMOPs). Then, two surrogate models are trained for the helper task and the target task, respectively. Based on the trained surrogate models, evolutionary multitasking optimization is run to solve these two tasks so that the experiences of solving the helper task can be transferred to speed up the convergence of tackling the target task. Moreover, an effective model management strategy is designed to select new promising samples for training the surrogate models. When compared to five competitive SAEAs on four well-known benchmark suites, the experiments validate the advantages of the proposed algorithm on most test cases. © 2024 held by the owner/author(s).

关键词： Multitasking

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Spatio-Temporal Service Analysis in Multi-Layer Non-Terrestrial Networks

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Journal of Communications and Information Networks 2024年第1期9卷 43-55页

作者： Qian Chen Weixiao Meng Shuai Han Cheng Li School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China School of Engineering Science Simon Fraser UniversityBurnabyBC V5A 1S6Canada Department of Electrical and Computer Engineering Memorial UniversitySt.John’sNL A1B 3X5Canada

In the advent of the 6G era, non-terrestrial networks (NTN) with expansive coverage are being increasingly recognized as a vital supplement to cellular networks for facilitating seamless communication. The intricate interplay between network performance and service quality necessitates a thorough investigation into the modeling and analysis of services for efficient construction of *** studies on service analysis, predominantly focused on terrestrial networks,fall short in addressing the unique challenges posed by NTN,particularly those related to platform distribution and antenna gain modeling. This deficiency in research,coupled with the varying preferences of users for different network types, forms the basis of this study. This paper explores the spatio-temporal characteristics of services within a multi-layered NTN *** this context,the spatial distribution of the platforms is modeled using a binomial point process, and the antennas are characterized by a sectorized beam pattern. We derive the closed-form expressions for the association probability,the number of accessed users, and the arrival rate of services with certain delay requirements towards different types of NTN. Simulation results are provided to evaluate the influence of various parameters on the association probability, the number of accessed users, and the total arrival rate of services. The number of satellites can be determined to achieve the optimal system utility,balancing the accessed services, offloading effects, and launching costs. This initial investigation lays the groundwork for further theoretical progress in the service analysis and platform deployment of NTN.

关键词： association strategy non-terrestrial networks service analysis stochastic geometry system utility

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EVA2.0:Investigating Open-domain Chinese Dialogue Systems with Large-scale Pre-training

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Machine Intelligence Research 2023年第2期20卷 207-219页

作者： Yuxian Gu Jiaxin Wen Hao Sun Yi Song Pei Ke Chujie Zheng Zheng Zhang Jianzhu Yao Lei Liu Xiaoyan Zhu Minlie Huang The Conversational AI Group Tsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Department of Electrical Engineering and Computer Science York UniversityToronto M3J1P3Canada

Large-scale pre-training has shown remarkable performance in building open-domain dialogue ***,previous works mainly focus on showing and evaluating the conversational performance of the released dialogue model,ignoring the discussion of some key factors towards a powerful human-like chatbot,especially in Chinese *** this paper,we conduct extensive experiments to investigate these under-explored factors,including data quality control,model architecture designs,training approaches,and decoding *** propose EVA2.0,a large-scale pre-trained open-domain Chinese dialogue model with 2.8 billion parameters,and will make our models and codes publicly *** and human evaluations show that EVA2.0 significantly outperforms other open-source *** also discuss the limitations of this work by presenting some failure cases and pose some future research directions on large-scale Chinese open-domain dialogue systems.

关键词： Natural language processing deep learning(DL) large-scale pre-training dialogue systems Chinese open-domain conversational model

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Price-Discrimination Game for Distributed Resource Management in Federated Learning

IEEE Networking Letters

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IEEE Networking Letters 2024年第2期6卷 124-128页

作者： Zhang, Han Yang, Halvin Zhang, Guopeng China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China University College London Department of Electronic and Electrical Engineering LondonWC1E 7JE United Kingdom

In federated learning (FL) systems, the parameter server (PS) and clients form a monopolistic market, where the number of PS is far less than the number of clients. To improve the performance of FL and reduce the cost to incentive clients, this letter suggests distinguishing the pricing of FL services provided by different clients, rather than applying the same pricing to them. The price is differentiated based on the performance improvements brought to FL by clients and their heterogeneity in computing and communication capabilities. To this end, a price-discrimination game (PDG) is formulated to comprehensively address the distributed resource management problems in FL, including multi-objective trade-off, client selection, and incentive mechanism. As the PDG includes a mixed-integer nonlinear programming problem, a distributed semi-heuristic algorithm with low computational complexity and low communication overhead is designed to solve the Nash equilibrium (NE) of the PDG. The simulation result verifies that the NE achieves a good tradeoff between the training loss, training time, and the cost of motivating clients to participate in FL. © 2019 IEEE.

关键词： Energy utilization

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Recent Advances in In-Memory Computing:Exploring Memristor and Memtransistor Arrays with 2D Materials

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Nano-Micro Letters 2024年第7期16卷 1-30页

作者： Hangbo Zhou Sifan Li Kah-Wee Ang Yong-Wei Zhang Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis Way#16-16 ConnexisSingapore 138632Republic of Singapore Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Republic of Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research(A*STAR)2 Fusionopolis WaySingapore 138634Republic of Singapore

The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing *** response,in-memory computing has emerged as a promising alternative architecture,enabling computing operations within memory arrays to overcome these *** devices have gained significant attention as key components for in-memory computing due to their high-density arrays,rapid response times,and ability to emulate biological *** these devices,two-dimensional(2D)material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing,thanks to their exceptional performance driven by the unique properties of 2D materials,such as layered structures,mechanical flexibility,and the capability to form *** review delves into the state-of-the-art research on 2D material-based memristive arrays,encompassing critical aspects such as material selection,device perfor-mance metrics,array structures,and potential ***,it provides a comprehensive overview of the current challenges and limitations associated with these arrays,along with potential *** primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing,leveraging the potential of 2D material-based memristive devices.

关键词： 2D materials Memristors Memtransistors Crossbar array In-memory computing

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Blockchain and Federated Reinforcement Learning for Vehicle-to-Everything Energy Trading in Smart Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 839-853页

作者： Moniruzzaman, Md Yassine, Abdulsalam Benlamri, Rachid Department of Electrical and Computer Engineering Lakehead University Thunder BayONP7B 5E1 Canada Department of Software Engineering Lakehead University Thunder BayONP7B 5E1 Canada University of Doha for Science and Technology Doha24449 Qatar

The proliferation of electric vehicles (EVs) and the advancement of vehicle-to-everything energy trading systems are expected to play a crucial role in alleviating the stress on the electric grid during peak hours. However, the wide adoption of these paradigms requires intelligent mechanisms that protect the security and privacy of EV users. This article proposes a novel federated reinforcement learning system combined with blockchain technology to maximize EV users' utility while preserving the security and privacy of trading transactions. Furthermore, we develop the concept of proof of state of charge as a consensus mechanism to determine the winning EVs and reward them as block miners in the blockchain. The proposed system is validated through comprehensive simulation experiments utilizing a real-world dataset. The model is implemented on the Avalanche blockchain platform to demonstrate its real-world feasibility. The test results show that the proposed scheme improves EV users' utility significantly compared to the existing studies. The obtained simulation results indicate the effectiveness and robustness of the proposed system. © 2023 IEEE.

关键词： Reinforcement learning

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