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International Conference on Theoretical and Applicative Aspects of computer Science (ICTAACS)

作者： Abdelhak Mesbah Ibtihel Baddari Mohamed Amine Riahla Department of Computer Science Faculty of Sciences LIMOSE Laboratory University M'Hamed Bougara of Boumerdes Boumerdes Algeria Department of Electrical Systems Engineering Faculty of Technology LIMOSE Laboratory University M'Hamed Bougara of Boumerdes Boumerdes Algeria

The growing popularity of the Android platform makes it a target of malware authors. The effective identification of such malware is an ongoing challenge. Several methods using machine learning have been proposed to prevent this threat. These methods are usually conventionally evaluated without considering the extent of performance over time. Given the evolving nature of both malware and benign apps, conventional evaluation may lack information. To imitate reality, this study compares the longitudinal performance of different machine learning models, using different strategies that combine permissions and API calls as features extracted through static analysis. Thus, to determine which strategy of features on which classifier are most effective to characterize malware for building a robust malware detector. To achieve this goal, on the one hand, we use a large real-world app set consisting of 100K (50k benign, 50k malware) apps date-labeled, collected across ten years, first seen between 2013 and 2022. On the other hand, each feature's strategy is fed into five classifiers (i.e., SVM, RF, LR, DT, and ANN), using old apps for the training and new apps for the evaluation. Among the assessed machine learning models, the SVM achieves the most promising results over time by employing the combination strategy of the high difference usage of API calls and permissions.

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Joint Trajectory and Resource Optimization of MEC-Assisted UAVs in Sub-THz Networks: A Resources-based Multi-Agent Proximal Policy Optimization DRL with Attention Mechanism

arXiv

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arXiv 2022年

作者： Park, Yu Min Hassan, Sheikh Salman Tun, Yan Kyaw Han, Zhu Hong, Choong Seon Department of Computer Science and Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of Teletraffic Systems Division of Network and Systems Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Brinellvägen 8 Stockholm114 28 Sweden Electrical and Computer Engineering Department University of Houston HoustonTX77004 United States

Terahertz (THz) band communication technology will be used in the sixth-generation (6G) networks to enable high-speed and high-capacity data service demands. However, THz-communication losses arise owing to limitations, i.e., molecular absorption, rain attenuation, and coverage range. Furthermore, to maintain steady THz-communications and overcome coverage distances in rural and suburban regions, the required number of base stations (BSs) is very high. Consequently, a new communication platform that enables aerial communication services is required. Furthermore, the airborne platform supports line-of-sight (LoS) communications rather than non-LoS (NLoS) communications, which helps overcome these losses. Therefore, in this work, we investigate the deployment and resource optimization for multi-access edge computing (MEC)-enabled unmanned aerial vehicles (UAVs), which can provide THz-based communications in remote regions. To this end, we formulate an optimization problem to minimize the sum of the energy consumption of both MEC-UAV and mobile users (MUs) and the delay incurred by MUs under the given task information. The formulated problem is a mixed-integer nonlinear programming (MINLP) problem, which is NP-hard. We decompose the main problem into two subproblems to address the formulated problem. We solve the first subproblem with a standard optimization solver, i.e., CVXPY, due to its convex nature. To solve the second subproblem, we design a resources-based multi-agent proximal policy optimization (RMAPPO) deep reinforcement learning (DRL) algorithm with an attention mechanism. The considered attention mechanism is utilized for encoding a diverse number of observations. This is designed by the network coordinator to provide a differentiated fit reward to each agent in the network. The simulation results show that the proposed algorithm outperforms the benchmark and yields a network utility which is 2.22%, 15.55%, and 17.77% more than the benchmarks. Copyright © 2

关键词： Resource allocation

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Exploring Fairness for FAS-assisted Communication systems: from NOMA to OMA

arXiv

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arXiv 2024年

作者： Yao, Junteng Zhou, Liaoshi Wu, Tuo Jin, Ming Pan, Cunhua Elkashlan, Maged Wong, Kai-Kit The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Yonsei Frontier Laboratory The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of

This paper addresses the fairness issue within fluid antenna system (FAS)-assisted non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) systems, where a single fixed-antenna base station (BS) transmits superposition-coded signals to two users, each with a single fluid antenna. We define fairness through the minimization of the maximum outage probability for the two users, under total resource constraints for both FAS-assisted NOMA and OMA systems. Specifically, in the FAS-assisted NOMA systems, we study both a special case and the general case, deriving a closed-form solution for the former and applying a bisection search method to find the optimal solution for the latter. Moreover, for the general case, we derive a locally optimal closed-form solution to achieve fairness. In the FAS-assisted OMA systems, to deal with the non-convex optimization problem with coupling of the variables in the objective function, we employ an approximation strategy to facilitate a successive convex approximation (SCA)-based algorithm, achieving locally optimal solutions for both cases. Empirical analysis validates that our proposed solutions outperform conventional NOMA and OMA benchmarks in terms of fairness. © 2024, CC BY.

关键词： Approximation algorithms

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Data security and privacy computing in artificial intelligence

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Journal of Information and Intelligence 2024年第2期2卷 99-101页

作者： Dengguo Feng Hui Li Rongxing Lu Zheli Liu Jianbing Ni Hui Zhu Institute of Software Academy of SciencesBeijing 100190China State Key Laboratory of Integrated Services Networks School of Cyber EngineeringXidian UniversityXi’an 710071China Faculty of Computer Science University of New BrunswickFredericton E3B 5A3Canada College of Computer Science Nankai UniversityTianjin 300071China Department of Electrical and Computer Engineering Queen’s UniversityKingston K7L 3N6Canada

Artificial intelligence(AI)is a field of computer science dedicated to creating systems and algorithms that can perform tasks typically requiring human intelligence,such as learning,problem-solving,language understanding,and decision-making,contributing to a wide array of applications across diverse *** development of AI,such as machine learning and deep learning,has revolutionized data processing and *** transformation is rapidly changing human life and has allowed for many practical AI based applications,including biometric recognition,text/sentimental analysis,and attack detection in the fields of health care,finance,autonomous vehicles,personalized ***,the potential benefits of AI are hindered by issues,such as insecurity and privacy violations in data processing and communication.

关键词： such has finance

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Transient Behavior of Gossip Opinion Dynamics with Community Structure

arXiv

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arXiv 2022年

作者： Xing, Yu Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden

We study transient behavior of gossip opinion dynamics, in which agents randomly interact pairwise over a weighted graph with two communities. Edges within a community have identical weights different from edge weights between communities. We first derive an upper bound for the second moment of agent opinions. Using this result, we obtain upper bounds for probability that a large proportion of agents have opinions close to average ones. The results imply a phase transition of transient behavior of the process: When edge weights within communities are larger than those between communities and those between regular and stubborn agents, most agents in the same community hold opinions close to the average opinion of that community with large probability, at an early stage of the process. However, if the difference between intra- and inter-community weights is small, most of the agents instead hold opinions close to everyone’s average opinion at the early stage. In contrast, when the influence of stubborn agents is large, agent opinions settle quickly to steady state. We then conduct numerical experiments to validate the theoretical results. Different from traditional asymptotic analysis in most opinion dynamics literature, the paper characterizes the influences of stubborn agents and community structure on the initial phase of the opinion evolution. © 2022, CC BY.

关键词： Dynamics

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Vision-Based Potential Field Path Planning for Robot Obstacle Avoidance Under Field-of-View Constraints

Vision-Based Potential Field Path Planning for Robot Obstacl...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Zesong Wu Weibing Li Ping Wang Kun Cao Yongping Pan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Advanced Manufacturing Sun Yat-sen University Shenzhen China

Path planning improves the performance and robustness of vision-based robot control in unstructured environments. Visual servo path planning usually focuses only on the path of the camera in the Cartesian space or the path of the feature points in the image space, which means that the collision between robots and obstacles in the workspace cannot be avoided. This paper proposes a vision-based potential field path planning method for robot manipulators with field-of-view constraints in the image space and obstacle avoidance in the Cartesian space. A hybrid potential field function is proposed to integrate Fo V limits in the image space and obstacle avoidance in the Cartesian space. A probability-based search strategy is presented for path planning to avoid oscillation caused by gradient descent. The proposed method is applied to a collaborative robot with 7 degrees of freedom equipped with an eye-in-hand camera. Experiments in two different environments have verified the superiority of the proposed method regarding spanned image areas, camera path lengths, iteration times, and obstacle avoidance compared to the classical gradient descent-based method.

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Next-Gen Decoding: Non-Binary LDPC Algorithms for Emerging Power Line and Visible Light Communications

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IEEE Access 2025年 13卷 101224-101245页

作者： Waheed Ullah Fengfan Yang Kwonhue Choi Dushantha Nalin K. Jayakody School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China School of Computer Science and Engineering Yeungnam University Gyeongsan South Korea COPELABS Lusófona University Lisbon Portugal LASI Center of Technology and Systems (UNINOVA-CTS) Costa de Caparica Portugal CIET/EEE Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

Non-Binary Low-Density Parity-Check (LDPC) codes have gained significant attention due to their remarkable error correction capabilities in various communication systems. Decoding algorithms play a pivotal role in realizing the potential of non-binary LDPC codes. This paper provides a comprehensive review and analysis of non-binary LDPC decoding algorithms, focusing on their efficiency, complexity, and performance. Furthermore, recent advancements and innovations in non-binary LDPC decoding algorithms are discussed, such as improved message passing strategies, layered decoding techniques, and adaptive algorithms. The review also highlights challenges and open research directions in non-binary LDPC decoding, such as mitigating error floors, reducing decoding complexity, and integrating with emerging communication technologies. Finally, the paper draws conclusions on the current state of non-binary LDPC decoding algorithms, underscoring their promising applications in wireless communication, visible light communication (VLC), and power line communication (PLC). Simulation results demonstrate a marked improvement in bit error rate performance for both VLC and PLC systems, highlighting the practical potential of these advanced decoding techniques.

关键词： Codes Decoding Reliability Iterative decoding Symbols Visible light communication Logic Computational complexity Reviews Message passing

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Plasma Jet Deposition and Self-Sintering of Gold Nanoparticle Ink for Flexible Electronics

Plasma Jet Deposition and Self-Sintering of Gold Nanoparticl...

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International Flexible Electronics Technology Conference (IFETC)

作者： Jacob Manzi Tony Varghese Anupama Dhamala Lakshmi Prakasan Josh Eixenberger Nirmala Kandadai David Estrada Harish Subbaraman School of Electrical Engineering and Computer Science Oregon State University Corvallis OR USA Micron School of Material Science and Engineering Boise State University Boise ID USA Department of Electrical and Computer Engineering Boise State University Boise ID USA Center for Advanced Energy Studies Boise State University Boise ID USA Idaho National Laboratory Idaho Falls ID USA

Additive manufacturing has become a promising method for the fabrication of inexpensive, green, flexible electronics. Printed electronics on low-temperature substrates like paper are very appealing for the flexible hybrid electronics market for their use in disposable and biocompatible electronic applications and in areas like packaging, wearables, and consumer electronics. Plasma-jet printing uses a dielectric barrier discharge plasma to focus aerosolized nanoparticles onto a target substrate. The same plasma can be used to change the properties of the printed material and even sinter in situ. The technology can also be utilized in space and microgravity environments since the plasma-assisted deposition is independent of gravity. In this work, we show plasma voltage effect on deposition of gold nanoparticles and direct printing of flexible, conductive gold structures onto low-temperature paper substrates without the need for thermal or photonic post-processing. The effects of plasma parameters on the conductivity and flexible reliability of the printed films are studied, and a paper-based LED electrode is demonstrated.

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Exploring Communication-efficient Federated Learning via Stateless In-network Aggregation

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IEEE Transactions on Mobile Computing 2025年

作者： Xia, Junxu Cheng, Geyao Wu, Wenfei Luo, Lailong Guo, Deke National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha Hunan410073 China National University of Defense Technology State Key Laboratory of Complex & Critical Software Environment College of Information and Communication Changsha Hunan410073 China Peking University School of Computer Science Beijing100091 China

As an ambitious training paradigm, federated learning has garnered increasing attention in recent years, which enables collaborative training of a global model without accessing users' private data. However, due to the simultaneous and constant model updates gathering from massive distributed clients, the central server generally becomes a performance bottleneck. Additionally, the stateful aggregation (retaining all the updates from each client) conducted by the central server further poses potential threats to privacy, since it may recover the raw data based on such model updates inversely. The state-of-the-art methodologies, however, fail to address these two problems concurrently and efficiently. To this end, we propose GAIN, a secure aggregation acceleration service for federated learning. At its core, GAIN leverages programmable switches deployed at the edge network to aggregate model updates in a stateless manner before transmitting them to the central server. Consequently, GAIN can accelerate the transmission and aggregation of model updates while eliminating the chance of recovering private data. We evaluate the performance of GAIN through FPGA-based experiments and large-scale simulations. The results show that GAIN can effectively reduce bandwidth overhead and achieve up to 4.11× training throughput acceleration while prioritizing privacy protection. © 2025 IEEE.

关键词： Differential privacy

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Energy Efficient Collaborative Federated Learning Design: A Graph Neural Network based Approach

Energy Efficient Collaborative Federated Learning Design: A ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In this paper, we consider the design of an energy efficient collaborative federated learning (CFL) methodology where devices exchange their local FL parameters with a subset of their neighbors without reliance on a parameter server. In the considered model, mobile devices implement the designed CFL to train their local FL models using their own datasets over a realistic wireless network. Due to the limited wireless resources and user movements, each device may not be able to transmit its FL parameters with all neighboring devices. Therefore, each device must select a subset of devices to share its FL parameters and optimize the transmit power. This problem is formulated as an optimization problem, whose goal is to minimize CFL training energy consumption while satisfying the delay and CFL training loss requirements. To solve this problem, a two-stage solution is proposed. At the first stage, a graph neural network (GNN) based algorithm is proposed, which enables each device to individually determine the subset of devices to transmit FL parameters using its neighboring devices' location and connection information. Compared to standard iterative algorithms that need to iteratively optimize device connections and transmit power, the proposed GNN based method can directly obtain the optimal device connections without iterative optimization. Given the optimal device connections, at the second stage, each device can directly obtain the optimal transmit power. Simulation results show that the proposed algorithm can decrease energy consumption by up to 46% compared to the algorithm where each device will directly connect to its first and second nearest neighbors.

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