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arXiv 2022年

作者： Krishna, Ghanta Sai Supriya, Kundrapu Vardhan, Jai Mallikharjuna Rao, K. Data Science and Artificial Intelligence Department IIIT Naya Raipur India Computer science and Engineering IIIT Naya Raipur India

Human drivers have distinct driving techniques, knowledge, and sentiments due to unique driving traits. Driver drowsiness has been a serious issue endangering road safety;therefore, it is essential to design an effective drowsiness detection algorithm to bypass road accidents. Miscellaneous research efforts have been approached the problem of detecting anomalous human driver behaviour to examine the frontal face of the driver and automobile dynamics via computer vision techniques. Still, the conventional methods cannot capture complicated driver behaviour features. However, with the origin of deep learning architectures, a substantial amount of research has also been executed to analyze and recognize driver's drowsiness using neural network algorithms. This paper introduces a novel framework based on vision transformers and YoloV5 architectures for driver drowsiness recognition. A custom YoloV5 pre-trained architecture is proposed for face extraction with the aim of extracting Region of Interest (ROI). Owing to the limitations of previous architectures, this paper introduces vision transformers for binary image classification which is trained and validated on a public dataset UTA-RLDD. The model had achieved 96.2% and 97.4% as it's training and validation accuracies respectively. For the further evaluation, proposed framework is tested on a custom dataset of 39 participants in various light circumstances and achieved 95.5% accuracy. The conducted experimentations revealed the significant potential of our framework for practical applications in smart transportation systems. © 2022, CC BY.

关键词： Face recognition

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Smart Farming: Intelligent Animal Detection Framework in Agriculture using IoT Sensors

Smart Farming: Intelligent Animal Detection Framework in Agr...

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International Conference on Edge Computing and Applications (ICECAA)

作者： S Priyanka S A Suje D Selvapandian V Narasimharaj Department of Information Technology SNS College of Engineering Coimbatore India Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Mechatronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Agriculture, the study and practice of plant cultivation, is crucial to the development of a subsistence farming economy. In India, farming supports more than half of the population. The agricultural sector's expansion has been severely hampered by obstacles. Ineffective crop management and crop raiding by external sources, notably human-wildlife conflict, provide the greatest challenge to farmers. Intricately highlighted by means of integrated systems is a cumulative strategy that employs Internet-of-Things (IoT) and conventional farming practices, as well as barriers to prevent crop destruction. A device that can detect the occurrence of a little alive object using a PIR Sensor or an Ultrasonic Sensor, particularly creatures, fenced terrestrial to be cultivated, this module aims to make agriculture smart. This module also proposes smart irrigation control and real-time data analysis. The GSM Module can send this information as text messages, or the Blynk software can be used to visualize it. The suggested system includes animal identification and a laser security system to minimize noise pollution from animals. Multiple issues have been addressed to aid the farmer in fixing them all at once.

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An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering

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Array 2025年 26卷

作者： Abualigah, Laith Alomari, Saleh Ali Almomani, Mohammad H. Zitar, Raed Abu Migdady, Hazem Saleem, Kashif Smerat, Aseel Snasel, Vaclav Ezugwu, Absalom E. Computer Science Department Al al-Bayt University Mafraq25113 Jordan Faculty of Science and Information Technology Jadara University Irbid21110 Jordan Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates CSMIS Department Oman College of Management and Technology Barka320 Oman Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh11362 Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman19328 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava 70800 Czech Republic Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom2520 South Africa

data clustering plays a crucial role in various domains, such as image processing, pattern recognition, and data mining. Traditional clustering techniques often suffer from limitations like sensitivity to initialization, poor convergence, and entrapment in local optima. To address these challenges, this paper proposes an Enhanced Walrus Optimizer (IWO) tailored for clustering tasks. The proposed IWO integrates two powerful strategies–Opposition-Based Learning (OBL) and Mutation Search Strategy (MSS)–to improve population diversity and prevent premature convergence, thereby enhancing both exploration and exploitation capabilities. These enhancements enable more accurate and stable identification of cluster centers. The effectiveness of IWO is validated through extensive experiments on multiple benchmark clustering datasets and compared against several state-of-the-art metaheuristic algorithms, including PSO, GWO, AOA, and others. The results demonstrate that IWO achieves better results, indicating improved compactness and separation of clusters. Statistical validation using p-values and ranking scores further confirms the superiority of the proposed method. These findings suggest that IWO offers a robust and flexible framework for solving complex clustering problems. Future work will explore hybrid deep learning-integrated models and parallel implementations to enhance scalability. © 2025

关键词： Swarm intelligence

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GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Liang, Ruihuai Yang, Bo Chen, Pengyu Cao, Xuelin Yu, Zhiwen Debbah, Merouane Niyato, Dusit Poor, H. Vincent Yuen, Chau Northwestern Polytechnical University School of Computer Science Shaanxi Xi'an710129 China Xidian University School of Cyber Engineering Shaanxi Xi'an710071 China Harbin Engineering University Heilongjiang Harbin150001 China Khalifa University of Science and Technology Center for 6G Technology Abu Dhabi United Arab Emirates Nanyang Technological University College of Computing and Data Science Singapore Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Nanyang Technological University School of Electrical and Electronics Engineering Singapore

Optimization is crucial for the efficiency and reliability of multi-access edge computing (MEC) networks. Many optimization problems in this field are NP-hard and do not have effective approximation algorithms. Consequently, there is often a lack of optimal (ground-truth) data, which limits the effectiveness of traditional deep learning approaches. Most existing learning-based methods require a large amount of optimal data and do not leverage the potential advantages of using suboptimal data, which can be obtained more efficiently. To illustrate this point, we focus on the multi-server multi-user computation offloading (MSCO) problem, a common issue in MEC networks that lacks efficient optimal solution methods. In this paper, we introduce the graph diffusion-based solution generator (GDSG), designed to work with suboptimal datasets while still achieving convergence to the optimal solution with high probability. We reformulate the network optimization challenge as a distribution-learning problem and provide a clear explanation of how to learn from suboptimal training datasets. We develop GDSG, a multi-task diffusion generative model that employs a graph neural network (GNN) to capture the distribution of high-quality solutions. Our approach includes a straightforward and efficient heuristic method to generate a sufficient amount of training data composed entirely of suboptimal solutions. In our implementation, we enhance the GNN architecture to achieve better generalization. Moreover, the proposed GDSG can achieve nearly 100% task orthogonality, which helps prevent negative interference between the discrete and continuous solution generation training objectives. We demonstrate that this orthogonality arises from the diffusion-related training loss in GDSG, rather than from the GNN architecture itself. Finally, our experiments show that the proposed GDSG outperforms other benchmark methods on both optimal and suboptimal training datasets. Regarding the minimization of

关键词： Graph neural networks

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Algorithmic Techniques for Necessary and Possible Winners

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ACM/IMS Transactions on data science 2021年第3期2卷 1–23页

作者： Chakraborty, Vishal Delemazure, Theo Kimelfeld, Benny Kolaitis, Phokion G. Relia, Kunal Stoyanovich, Julia Computer Science and Engineering Department Jack Baskin School of Engineering University of California Santa Cruz Santa CruzCA95064 United States Ecole Normale Superieure 45 Rue d'Ulm Paris75005 France Henry and Marilyn Taub Faculty of Computer Science Technion Haifa3200003 Israel Computer Science and Engineering Department Jack Baskin School of Engineering University of California Santa Cruz Santa CruzCA95064 United States Computer Science and Engineering Department Tandon School of Engineering New York University 370 Jay Street BrooklynNY11201 United States Center for Data Science New York University 60 5th Avenue New YorkNY10011 United States

We investigate the practical aspects of computing the necessary and possible winners in elections over incomplete voter preferences. In the case of the necessary winners, we show how to implement and accelerate the polynomial-time algorithm of Xia and Conitzer. In the case of the possible winners, where the problem is NP-hard, we give a natural reduction to Integer Linear Programming (ILP) for all positional scoring rules and implement it in a leading commercial optimization solver. Further, we devise optimization techniques to minimize the number of ILP executions and, oftentimes, avoid them altogether. We conduct a thorough experimental study that includes the construction of a rich benchmark of election data based on real and synthetic data. Our findings suggest that, the worst-case intractability of the possible winners notwithstanding, the algorithmic techniques presented here scale well and can be used to compute the possible winners in realistic scenarios. © 2021 Association for Computing Machinery.

关键词： Integer programming

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Future of Cybersecurity

Future of Cybersecurity

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Recent Trends in Information Technology (CSCTIT), College of science International Conference of

作者： Yasamin Alagrash Senior lecturer in the computer science department college of science Mustansiriyah University Baghdad Iraq Department of computer science Ph.D. in computer science and informatics college of engineering Oakland university MI USA Computer science department MSC in data security university of technology Baghdad Iraq

Cybersecurity is critical to the digital world's ongoing progress. Whether you are a company owner or a customer, you must be prepared to adapt to new technology, policies, and methods that will aid in the creation of a safer and more secure online environment. As a result, the frequency of exposed network surfaces compels us to invest in more advanced protection measures.

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VULSEYE: Detect Smart Contract Vulnerabilities via Stateful Directed Graybox Fuzzing

arXiv

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arXiv 2024年

作者： Liang, Ruichao Chen, Jing Wu, Cong He, Kun Wu, Yueming Cao, Ruochen Du, Ruiying Liu, Yang Zhao, Ziming Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China Cyber Security Lab College of Computing and Data Science Nanyang Technological University Singapore Department of Computer Science and Engineering University at Buffalo New York United States

Smart contracts, the cornerstone of decentralized applications, have become increasingly prominent in revolutionizing the digital landscape. However, vulnerabilities in smart contracts pose great risks to user assets and undermine overall trust in decentralized systems. Fuzzing, a prominent security testing technique, is extensively explored to detect vulnerabilities. But current smart contract fuzzers fall short of expectations in testing efficiency for two primary reasons. Firstly, smart contracts are stateful programs, and existing approaches, primarily coverage-guided, lack effective feedback from the contract state. Consequently, they struggle to effectively explore the contract state space. Secondly, coverage-guided fuzzers, aiming for comprehensive program coverage, may lead to a wastage of testing resources on benign code areas. This wastage worsens in smart contract testing, as the mix of code and state spaces further complicates comprehensive testing. To address these challenges, we propose VULSEYE, a stateful directed graybox fuzzer for smart contracts guided by vulnerabilities. Different from prior works, VULSEYE achieves stateful directed fuzzing by prioritizing testing resources to code areas and contract states that are more prone to vulnerabilities. We introduce Code Targets and State Targets into fuzzing loops as the testing targets of VULSEYE. We use static analysis and pattern matching to pinpoint Code Targets, and propose a scalable backward analysis algorithm to specify State Targets. We design a novel fitness metric that leverages feedback from both the contract code space and state space, directing fuzzing toward these targets. With the guidance of code and state targets, VULSEYE alleviates the wastage of testing resources on benign code areas and achieves effective stateful fuzzing. In comparison with state-of-the-art fuzzers, VULSEYE demonstrated superior effectiveness and efficiency. Notably, it uncovered 4,845 vulnerabilities in 42,738 rea

关键词： Software testing

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Secure Physical Layer Communications for Low-Altitude Economy Networking: A Survey

arXiv

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arXiv 2025年

作者： Cai, Lingyi Wang, Jiacheng Zhang, Ruichen Zhang, Yu Jiang, Tao Niyato, Dusit Wang, Xianbin Jamalipour, Abbas Shen, Xuemin Research Center of 6G Mobile Communications School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada School of Electrical and Computer Engineering University of Sydney Australia Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

The Low-Altitude Economy Networking (LAENet) is emerging as a transformative paradigm that enables an integrated and sophisticated communication infrastructure to support aerial vehicles in carrying out a wide range of economic activities within low-altitude airspace. However, the physical layer communications in the LAENet face growing security threats due to inherent characteristics of aerial communication environments, such as signal broadcast nature and channel openness. These challenges highlight the urgent need for safeguarding communication confidentiality, availability, and integrity. In view of the above, this survey comprehensively reviews existing secure countermeasures for physical layer communication in the LAENet. We explore core methods focusing on anti-eavesdropping and authentication for ensuring communication confidentiality. Subsequently, availability-enhancing techniques are thoroughly discussed for anti-jamming and spoofing defense. Then, we review approaches for safeguarding integrity through anomaly detection and injection protection. Furthermore, we discuss future research directions, emphasizing energy-efficient physical layer security, multi-drone collaboration for secure communication, AI-driven security defense strategy, space-air-ground integrated security architecture, and 6G-enabled secure UAV communication. This survey may provide valuable references and new insights for researchers in the field of secure physical layer communication for the LAENet. Copyright © 2025, The Authors. All rights reserved.

关键词： Aircraft communication

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Generating CGAN-based Synthetic Dermoscopy Images and Classifying with Deep Lightweight CNN

Generating CGAN-based Synthetic Dermoscopy Images and Classi...

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Computing, Networking, Telecommunications & engineering sciences Applications (CoNTESA), International Conference on

作者： Suriya Sultana Israt Yasmin Muhammed J. A. Patwary Mohammad Arif Sobhan Bhuiyan Iftekhar Salam Mahdi H. Miraz Department of Computer Science and Engineering International Islamic University Chittagong School of Electrical and Electronic Engineering Xiamen University Malaysia Sepang Malaysia School of Computing and Data Science (SCDS) Xiamen University Malaysia Sepang Malaysia

Effective dermatological diagnosis and treatments significantly depend on the accurate categorisation of the skin lesions. It is difficult to classify skin lesions using automated diagnostic techniques due to class-imbalance datasets and the absence of labelled data. The applications of various deep learning methods have demonstrated superior performance in medical diagnosis in the recent past. Unfortunately, training these models requires a substantial quantity of labelled instances. A novel approach, combining Deep Learning convolutional neural networks (CNNs) as well as conditional generative adversarial networks (CGANs), for classifying skin lesion images, has been presented in this study. CGAN is an enhanced version of the GAN, in which the generator takes input images with specific class labels. Applying conditions for minority classes can help to equalise the dataset by increasing the sample size for those minority classes. In this research, CGANs were used to produce synthetic images of minority classes for a skin lesion dataset named PAD UFES-20. The images generated from CGANS were added then to the original dataset to train a lightweight deep-learning CNN network named MobileNetv2. The analysis of the results shows an improvement of 6% average accuracy and 5% recall value, when the synthetic data augmentation was added to the original dataset. This synthetic data augmentation approach can also widely be used in various medical classification applications to reduce the imbalance problems, resulting in enhanced diagnosis.

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Regularized Newton Methods for Monotone Variational Inequalities with Hölder Continuous Jacobians

arXiv

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arXiv 2022年

作者： Liu, Chengchang Luo, Luo Department of Computer Science and Engineering The Chinese Unversity of Hong Kong Hong Kong School of Data Science Fudan University China

This paper considers the problems of solving monotone variational inequalities with Hölder continuous Jacobians. By employing the knowledge of Hölder parameter ν, we propose the ν-regularized extra-Newton method within at most O(ǫ−2/(2+ν)) iterations to obtain an ǫ-accurate solution. In the case of ν is unknown, we propose the universal regularized extra-Newton method within O(ǫ−4/(3(1+ν))) iteration complexity. © 2022, CC BY.

关键词： Newton-Raphson method

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