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arXiv 2024年

作者： Ridoy, Shahriyar Zaman Shaon, Md. Shazzad Hossain Cuzzocrea, Alfredo Akter, Mst Shapna Department of Electrical and Computer Engineering North South University Dhaka Bangladesh Department of Computer Science and Engineering Oakland University RochesterMI48309 United States iDEA Lab University of Calabria Rende Italy The Department of Computer Science University of Paris City Paris France Department of Computer Science Oakland University MI United States

Automated detection of software vulnerabilities is critical for enhancing security, yet existing methods often struggle with the complexity and diversity of modern codebases. In this paper, we introduce EnStack, a novel ensemble stacking framework that enhances vulnerability detection using natural language processing (NLP) techniques. Our approach synergizes multiple pre-trained large language models (LLMs) specialized in code understanding—CodeBERT for semantic analysis, GraphCodeBERT for structural representation, and UniXcoder for cross-modal capabilities. By fine-tuning these models on the Draper VDISC dataset and integrating their outputs through meta-classifiers such as Logistic Regression, Support Vector Machines (SVM), Random Forest, and XGBoost, EnStack effectively captures intricate code patterns and vulnerabilities that individual models may overlook. The meta-classifiers consolidate the strengths of each LLM, resulting in a comprehensive model that excels in detecting subtle and complex vulnerabilities across diverse programming contexts. Experimental results demonstrate that EnStack significantly outperforms existing methods, achieving notable improvements in accuracy, precision, recall and F1-score. This work highlights the potential of ensemble LLM approaches in code analysis tasks and offers valuable insights into applying NLP techniques for advancing automated vulnerability detection. © 2024, CC BY.

关键词： Semantics

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Joint Channel Estimation and Data Detection for AFDM Receivers With Oversampling

Joint Channel Estimation and Data Detection for AFDM Receive...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Kuranage Roche Rayan Ranasinghe Yao Ge Giuseppe Thadeu Freitas de Abreu Yong Liang Guan School of Computer Science and Engineering Constructor University Bremen Germany Continental-NTU Corporate Lab Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

In this paper, we propose a novel low complexity time domain (TD) oversampling receiver framework under affine frequency division multiplexing (AFDM) waveforms for joint channel estimation and data detection (JCEDD). Leveraging a generalized doubly-dispersive channel model, we first derive the input-output (I/O) relationship for arbitrary waveforms when oversampled in the TD and present the I/O relationship for AFDM as an example. Subsequently, utilizing the multiple sample streams created via the oversampling procedure, we use the parametric bilinear Gaussian belief propagation (PBiGaBP) technique to conduct JCEDD for decoding the transmitted data and estimating the complex channel coefficients. Simulation results verify significant performance improvements both in terms of data decoding and complex channel coefficient estimation with improved robustness against a varying number of pilots over a conventional Nyquist sampling rate receiver.

关键词： Simulation Channel estimation Symbols Receivers Performance gain Robustness Decoding Numerical models Time-domain analysis Streams

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A data analysis of political polarization using random matrix theory

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science China(Information sciences) 2020年第2期63卷 236-238页

作者： Hui CHEN Xiaofeng TAO Na LI Zhu HAN National Engineering Lab for Mobile Network Technologies Beijing University of Posts and Telecommunications Electrical and Computer Engineering and Computer Science University of Houston

Dear editor,Data analysis science is currently a hot topic in industry and academia due to ubiquitous and rapidly growing data [1, 2]. Data analysis technology has been applied in a variety of fields and has provided efficient and profitable data-driven decisions. For instance, since the beginning of the20th century, large-scale opinion polls have been

关键词： A data analysis of political polarization using random matrix theory RMT

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Musculoskeletal Risk Modeling Using Sensors and Machine Learning

Musculoskeletal Risk Modeling Using Sensors and Machine Lear...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Vora, H. Davoudi Kakhki, F. Moghadam, A. Kyrarini, M. Machine Learning & Safety Analytics Lab Santa Clara University Santa ClaraCA95053 United States Department of Computer Science and Engineering Santa Clara University Santa ClaraCA95053 United States Department of General Engineering Santa Clara University Santa ClaraCA95053 United States Department of Technology College of Engineering San Jose State University San JoseCA95192 United States Department of Electrical & Computer Engineering Santa Clara University Santa ClaraCA95053 United States

ISBN: (纸本)9798350386097

This study develops a predictive modeling approach for occupational ergonomic risk in manual lifting tasks to enhance real-time biomechanical risk assessment, aimed at mitigating occupational musculoskeletal disorders (MSDs). Wearable Electromyography (EMG) sensors were used to collect data on muscle activity of eight upper extremity muscles across ten participants (n=10) engaged in both low-risk and high-risk tasks for MSDs. A machine learning model was developed and optimized for lifting task risk classification. The model shows high accuracy, precision, and recall values in classifying low and high-risk lifting tasks. This approach significantly surpasses traditional occupational risk assessment methods, which often rely on historical data and are reactive rather than proactive. The integration of wearable sensors with machine learning provides precise risk classification and facilitates the implementation of effective safety interventions across various occupational settings. This strategy has potential to improves safety planning and can contribute to substantial reduction in the occurrence and severity of MSDs, with ultimate goal of enhancing overall workplace safety and health. © 2024 IEEE.

关键词： Electromyography

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Toward Covert and Reliable Communication for Anti-eavesdropping Transmission in V2X Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Xu, Rui Li, Gaolei Wu, Jun Li, Jianhua Zhao, Yue Liu, Yuchen Chen, Mingzhe Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200030 China Xidian University State Key Lab. of ISN School of Telecommunication Engineering Shaanxi 710071 China North Carolina State University Department of Computer Science Raleigh27607 United States University of Miami Department of Electrical and Computer Engineering Coral Gables33156 United States

The integration of covert communication in vehicle-to-everything (V2X) network has recently shown great potential to improve efficiency and reliability of data transmission under adversarial eavesdropping scenarios. In this paper, we propose a covert and reliable communication (CRC) framework for V2X networks, where the legitimate transmitter (Alice) attempts to communicate with a mobile receiver (Bob) in the presence of the location uncertainties of the eavesdropper (Willie). Specifically, the Bob adjusts the artificial noise power and position dynamically to communicate with Alice aided by full duplex antenna. In this context, we derive two key performance indicators of covert communication, namely the detection error probability and the effect covert throughput (ECT). Subsequently, we consider the worst case of CRC in the presence of single uncertain Willie, and derive the approximate maximum ECT expression by two-stage robust optimization. Building on this foundation, for more complex CRC scenario with multi uncertain Willies exist, we propose a deep reinforcement learning-empowered adaptation (DRLA) algorithm to maximize accumulated ECT. Extensive experiments compared to benchmarks (including stochastic selection, TD3 and DDPG) demonstrate the superiority of CRC. Specially, the designated DRLA algorithm not only can achieve a higher accumulated ECT but also can converge quickly compared with the benchmark schemes. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Double-Blockchain Assisted Secure and Anonymous Data Aggregation for Fog-Enabled Smart Grid

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engineering 2022年第1期8卷 159-169页

作者： Siguang Chen Li Yang Chuanxin Zhao Vijayakumar Varadarajan Kun Wang Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal UniversityWuhu 241000China School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

As a future energy system,the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable ***,this efficient and reliable service relies on collecting and analyzing users’electricity consumption data frequently,which induces various security and privacy *** address these challenges,we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named ***,we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain,which provides strong support for achieving efficient and secure data collection in smart ***,we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption,batch aggregation signature and anonymous *** particular,the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data ***,the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.

关键词： Blockchain Fog computing Homomorphic encryption Smart grid Anonymity

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Learned Disentangled Latent Representations for Scalable Image Coding for Humans and Machines

Learned Disentangled Latent Representations for Scalable Ima...

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Data Compression Conference (DCC)

作者： Ezgi Özyılkan Mateen Ulhaq Hyomin Choi Fabien Racapé Dept. of Electrical and Computer Engineering New York University InterDigital - Emerging Technologies Lab School of Engineering Science Simon Fraser University

As an increasing amount of image and video content will be analyzed by machines, there is demand for a new codec paradigm that is capable of compressing visual input primarily for the purpose of computer vision inference, while secondarily supporting input reconstruction. In this work, we propose a learned compression architecture that can be used to build such a codec. We introduce a novel variational formulation that explicitly takes feature data relevant to the desired inference task as input at the encoder side. As such, our learned scalable image codec encodes and transmits two disentangled latent representations for object detection and input reconstruction. We note that compared to relevant benchmarks, our proposed scheme yields a more compact latent representation that is specialized for the inference task. Our experiments show that our proposed system achieves a bit rate savings of 40.6% on the primary object detection task compared to the current state-of-the-art, albeit with some degradation in performance for the secondary input reconstruction task.

关键词： Visualization Codecs Image coding Scalability Redundancy Object detection Transforms

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Optical spatiotemporal vortices

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eLight 2023年第1期3卷 156-168页

作者： Chenhao Wan Andy Chong Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Optical and Electronic Information and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074HubeiChina Department of Physics Pusan National UniversityGeumjeong-GuBusan 46241South Korea Zhangjiang Laboratory 100 Haike RoadShanghai 201204China Shanghai Key Lab of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China

Spatiotemporal vortices of light,featuring transverse orbital angular momentum(OAM)and energy circulation in the spatiotemporal domain,have received increasing attention *** experimental realization of the controllable generation of spatiotemporal vortices triggers a series of research in this *** review article covers the latest developments of spatiotemporal vortices of light ranging from theoretical physics,experimental generation schemes,and characterization methods,to applications and future *** new degree of freedom in photonic OAM endowed by spatiotemporal vortices paves the way to the discovery of novel physical mechanisms and photonic applications in light science.

关键词： Spatiotemporal optical vortex Orbital angular momentum Spin-orbit interaction Linear momentum density Toroidal vortex Topological charge

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Energy-Efficient and Latency-Aware Task Offloading for Industrial cloud-Edge Systems with Heterogeneous CPUs and GPUs

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IEEE Internet of Things Journal 2025年

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

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Compound-Action DRL for Timely Data Collection in UAV-Assisted IoT with Stochastic Arrival 23

Compound-Action DRL for Timely Data Collection in UAV-Assist...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Yi, Mengjie Wang, Xijun Liu, Juan Zhang, Yan Hou, Ronghui School of Cyber Engineering Xidian University Xi'an710071 China School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China School of Electrical Engineering and Computer Science Ningbo University Zhejiang 315211 China Information Science Institute Xidian University State Key Lab of Integrated Service Networks Shaanxi Xi'an710071 China

ISBN: (纸本)9798350325959

The use of unmanned aerial vehicles (UAVs) in IoT networks can provide an efficient means of collecting mission-critical data. However, ensuring the freshness of collected data is a significant challenge due to the stochastic status updates of sensor nodes (SN s), In this paper, we delve into the issue of timely data collection aided by a UAV in IoT networks, where the UAV collects status updates from SNs and offloads the data to the data center at an opportune location. SNs sample information from their surroundings at stochastic intervals to generate new status updates. To minimize the average total age of information (AoI), we describe the problem as a Markov decision process and propose a compound-action deep reinforcement learning (CADRL) approach to jointly optimize the trajectory, scheduling of SNs, offloading strategy, and offloading power of the UAV, which can handle both continuous and discrete actions of the UAV. Simulation results indicate that our algorithm effectively reduces the AoI when contrasted with baseline methods. © 2023 IEEE.

关键词： Age of information compound-action deep reinforcement learning Internet of things unmanned aerial vehicle

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