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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning 41

S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning

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41st International Conference on Machine Learning, ICML 2024

作者： Wan, Guancheng Tian, Yijun Huang, Wenke Chawla, Nitesh V. Ye, Mang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Department of Computer Science University of Notre Dame United States Taikang Center for Life and Medical Sciences Wuhan University Wuhan China

Graph Contrastive Learning (GCL) has emerged as a highly effective self-supervised approach in graph representation learning. However, prevailing GCL methods confront two primary challenges: 1) They predominantly operate under homophily assumptions, focusing on low-frequency signals in node features while neglecting heterophilic edges that connect nodes with dissimilar features. 2) Their reliance on neighborhood aggregation for inference leads to scalability challenges and hinders deployment in real-time applications. In this paper, we introduce S3GCL, an innovative framework designed to tackle these challenges. Inspired by spectral GNNs, we initially demonstrate the correlation between frequency and homophily levels. Then, we propose a novel cosine-parameterized Chebyshev polynomial as low/high-pass filters to generate biased graph views. To resolve the inference dilemma, we incorporate an MLP encoder and enhance its awareness of graph context by introducing structurally and semantically neighboring nodes as positive pairs in the spatial domain. Finally, we formulate a cross-pass GCL objective between full-pass MLP and biased-pass GNN filtered features, eliminating the need for augmentation. Extensive experiments on real-world tasks validate S3GCL proficiency in generalization to diverse homophily levels and its superior inference efficiency. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Remote Sensing Data Processing Process Scheduling Based on Reinforcement Learning in Cloud Environment

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computer Modeling in engineering & Sciences 2023年第6期135卷 1965-1979页

作者： Ying Du Shuo Zhang Pu Cheng Rita Yi Man Li Xiao-Guang Yue School of Computer and Information Engineering Henan UniversityKaifeng475004China Henan Province Engineering Research Center of Spatial Information Processing Henan UniversityKaifeng475004China School of Software Henan UniversityKaifeng475004China Department of Economics and Finance/Sustainable Real Estate Research Center Hong Kong Shue Yan UniversityHong Kong999077China Department of Computer Science and Engineering European University CyprusNicosia1516Cyprus CIICESI-ESTG Politécnico do PortoFelgueiras4610-156Portugal

Task scheduling plays a crucial role in cloud computing and is a key factor determining cloud computing *** solve the task scheduling problem for remote sensing data processing in cloud computing,this paper proposes a workflow task scheduling algorithm—Workflow Task Scheduling Algorithm based on Deep Reinforcement Learning(WDRL).The remote sensing data process modeling is transformed into a directed acyclic graph scheduling ***,the algorithm is designed by establishing a Markov decision model and adopting a fitness calculation ***,combine the advantages of reinforcement learning and deep neural networks to minimize make-time for remote sensing data processes from *** experiment is based on the development of CloudSim and Python and compares the change of completion time in the process of remote sensing *** results showthat compared with several traditionalmeta-heuristic scheduling algorithms,WDRL can effectively achieve the goal of optimizing task scheduling efficiency.

关键词： Cloud computing reinforcement learning remote sensing task scheduling

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Comparative Analysis of Random Forest and Multilayer Perceptron Algorithms for Predicting Gross Domestic Product 4

Comparative Analysis of Random Forest and Multilayer Percept...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Azeta, Ambrose A. Adewale, Muyideen D. Omote, Patrick O. Onaolapo, Funmi O. Bakare, Idowo A. Tjiraso, Steven Namibia University of Science and Technology Department of Software Engineering Namibia Africa Centre of Excellence on Technology Enhanced Learning National Open University of Nigeria Nigeria Crawford University Department of Computer and Mathematical Sciences Ogun StateNeria Nigeria

ISBN: (纸本)9798350353266

This study examines the efficacy of machine learning techniques, particularly Multilayer Perceptron (MLP), and Random Forest and in forecasting Nigeria's Gross Domestic Product (GDP). Traditional statistical methods frequently fail to capture intricate, nonlinear relationships, thus necessitating advanced techniques. The dataset employed in this research encompasses 22 instances from 2000 to 2021. It includes variables such as GDP, healthcare expenditure, population, and the index of economic freedom obtained from the World Bank as well as the Nigerian Perceptions Index for corruption. The data was prepared to undergo training and testing following the CRISP-DM methodology. The models were trained and evaluated using a five-member cross-validation method. Performance metrics indicated that MLP outperformed Random Forest, with MAE values of 16.8228 and 25.3074 and RMSE values of 23.2111 and 33.2027, respectively. These findings suggest that MLP offers more accurate GDP predictions for Nigeria, effectively managing complex data relationships. This study provides valuable insights for enhancing predictive accuracy and supporting more effective economic planning and decision-making. © 2024 IEEE.

关键词： Decision trees

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Exploring Local Governance and Sustainability Challenges in Community Reserves: A Data Science Perspective with Global Insights 3

Exploring Local Governance and Sustainability Challenges in ...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Sachin Pavithran, A.P. Banerjee, Anurima Pradeep Ghantasala, G.S. Rajesh Sharma, R. Ananthanagu, U. Natarajan, Suresh Kumar Vidyullatha, Pellakuri Department of Humanities and Social Sciences Ramanagara India Department of Humanities and Social Sciences India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bengaluru India Alliance University Center of Excellence in Immersive Technologies Bengaluru India Ramanagara India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur Vaddeswaram India

ISBN: (纸本)9798350354379

As one of the furthermost noticeable industries, tourism strives for to magnify the situation in numerous forms, effectually addressing the dissimilar matters and concerns into the world nowadays. Frequently, these issues disturb the ecologically momentous areas and the local communities on a higher note. It's the need of the hour to check the forecasts of sustainable and community-based coastal tourism, whichever targets to promote the area's tourism potential over vigorous involvement by the local community although preservative the aboriginal ecological balance. Kerala, a classic tourism destination in India, provides significant opportunities for livelihood options to the local community who depend on natural resources. The contemporaneous research paper was centered on a case study of Kadalundi- Vallikkunnu Community Reserve in Kerala, the local authority and several influences it could need onto the local community then the recommendations whichever could be measured to discourse a interrelated issues. A vast portion of the respondents, having tourism activities as their primary source of income, have identified the problem of environmental degradation as prevalent in the area. In contrast, the rest are unaware of the harmful effects happening to the natural ecosystem, primarily because of waste disposal and water pollution. The paper is an endeavor to shed graceful on the local governance system of community reserve and socio-economic aspects of the local communities on the development of tourism initiatives in Kerala. © 2024 IEEE.

关键词： Community Based Coastal Tourism Community Reserve Environmental Degradation Rural Tourism Sustainability

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Transferring fashion to surveillance with weak labels

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Neural Computing and Applications 2023年第18期35卷 13021-13035页

作者： Zheng, Qi He, Zheng Liang, Chao Chen, Jun Lin, Chia-Wen Tao, Dapeng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China Collaborative Innovation Center of Geospatial Technology Wuhan430079 China Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan School of Information Science and Engineering Yunnan University Yunnan Kunming650504 China

In this paper, we address the problem of automatic clothing parsing in surveillance images using the information from user-generated tags, such as "jeans" and "T-shirt." Although clothing parsing has achieved great success in the fashion domain, it is quite challenging to parse target under practical surveillance conditions due to the presence of complex environmental interference, such as that from low resolution, viewpoint variations and lighting changes. Our method is developed to capture target information from the fashion domain and apply this information to a surveillance domain by weakly supervised transfer learning. Most target tags convey strong location information (e.g., "T-shirt" is always shown in the upper region), which can be used as weak labels for our transfer method. Both quantitative and qualitative experiments conducted on practical surveillance datasets demonstrate the effectiveness of the proposed surveillance data enhancing method. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Monitoring

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Student Perceptions of Authentic Learning to Learn White-box Testing 2024

Student Perceptions of Authentic Learning to Learn White-box...

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55th ACM Technical Symposium on computer Science Education, SIGCSE 2024

作者： Rahman, Akond Zhang, Yue Wu, Fan Shahriar, Hossain Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Computer Science Tuskegee University TuskegeeAL United States Center for Cybersecurity University of West Florida PensacolaFL United States

ISBN: (纸本)9798400704246

The pivotal role of white-box testing with respect to software quality assurance, necessitates dissemination of education materials related to white-box testing in the course curriculum. In this poster, we describe our experiences in conducting an authentic learning-based exercise related to white-box testing. From a conducted survey with 124 students, we observe the authentic learning-based exercise to be helpful for students to learn about white-box testing. © 2024 Owner/Author.

关键词： Students

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Design and Implementation of a Following Vehicle Based on ROS and UWB 7

Design and Implementation of a Following Vehicle Based on RO...

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7th International Conference on Automation, Control and Robots, ICACR 2023

作者： Yu, Yun-Shuai Chen, Chin-Wei National Formosa University Department of Computer Science and Information Engineering Yunlin Taiwan Software Research & Development Center New Taipei City Taiwan

ISBN: (纸本)9798350302882

Autonomous vehicle technology has gained significant attention in various industries recently. This paper presents the design and implementation of a following vehicle that utilizes the Robot Operating System (ROS) and Ultra-Wideband (UWB) RF transceiver modules to achieve precise target localization, map the surrounding environment, and navigate accordingly. A mobile application has also been developed to track the following vehicle's position. © 2023 IEEE.

关键词： Radio transceivers

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Classification of Adversarial Attacks Using Ensemble Clustering Approach

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computers, Materials & Continua 2023年第2期74卷 2479-2498页

作者： Pongsakorn Tatongjai Tossapon Boongoen Natthakan Iam-On Nitin Naik Longzhi Yang Center of Excellence in AI&Emerging Technologies School of ITMae Fah Luang UniversityChiang RaiThailand Department of Computer Science Aberystwyth UniversityAberystwythUnited Kingdom School of Informatics and Digital Engineering Aston UniversityBirminghamUnited Kingdom Department of Computer and Information Sciences Northumbria UniversityNewcastleUnited Kingdom

As more business transactions and information services have been implemented via communication networks,both personal and organization assets encounter a higher risk of *** safeguard these,a perimeter defence likeNIDS(network-based intrusion detection system)can be effective for known *** has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks,where obfuscation techniques are applied to disguise patterns of intrusive *** current research focuses on non-payload connections at the TCP(transmission control protocol)stack level that is applicable to different network *** contrary to the wrapper method introduced with the benchmark dataset,three new filter models are proposed to transform the feature space without knowledge of class *** ECT(ensemble clustering based transformation)techniques,i.e.,ECT-Subspace,ECT-Noise and ECT-Combined,are developed using the concept of ensemble clustering and three different ensemble generation strategies,i.e.,random feature subspace,feature noise injection and their *** on the empirical study with published dataset and four classification algorithms,new models usually outperform that original wrapper and other filter alternatives found in the *** is similarly summarized from the first experiment with basic classification of legitimate and direct attacks,and the second that focuses on recognizing obfuscated *** addition,analysis of algorithmic parameters,i.e.,ensemble size and level of noise,is provided as a guideline for a practical use.

关键词： Intrusion detection adversarial attack machine learning feature transformation ensemble clustering

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Two Industrial Reference Demonstrators for High Throughput and Low Latency in 5G-Standalone (SA) Network Setups 27

Two Industrial Reference Demonstrators for High Throughput a...

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27. VDE ITG-Fachtagung Mobilkommunikation: Technologien und Anwendungen - 27th VDE ITG Symposium on Mobile Communication: Technologies and Applications

作者： John, Fabian Sievers, Björn Sellhorn, Ole Hendrik Hellbrück, Horst University of Applied Sciences Lübeck - Technische Hochschule Department of Electrical Engineering and Computer Science Center of Excellence CoSA Lübeck Germany

ISBN: (纸本)9783800760947

The fifth generation of mobile communications (5G) is known as a new key technology for industry. Industry and research institutes are enabled to operate their private 5G Campus networks with commercially available hardware and open-source implementations. Due to the higher costs for a 5G deployment compared to other wireless network systems, industrial network expansion is slow. With the demonstration of industrial use cases in the 5G system shows its advantages, and it will help the deployment of 5G campus networks. To demonstrate the advantages of high data throughput and low latency in 5G networks, we propose two wireless demonstrators as software applications that visualize and evaluate the key parameters in the industrial context. The first demonstrator implements the streaming of uncompressed high-resolution images and shows the importance of high data rates. The second demonstrator visualizes the importance of low latency for real-time applications with a control loop of an inverted pendulum. The advantage of the approach is that there is no need for physical hardware, and the solution is adaptable to all wireless technologies. For evaluation, we present measurement results with our demonstrators in a real 5G testbed deployment. © 2023 Mobilkommunikation: Technologien und Anwendungen - 27. VDE ITG-Fachtagung. All rights reserved.

关键词： 5G mobile communication systems

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