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IEEE Conference on Wireless Communications and Networking

作者： Yushun Yao Jiansong Miao Tao Zhang Xiangyun Tang Jiawen Kang Dusit Niyato School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Software Engineering Beijing Jiaotong University Beijing China School of Information Engineering Minzu University of China Beijing China School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Unmanned aerial vehicles (UAVs) are integrated into existing networks to enhance coverage, increase network capacity and provide ubiquitous access service. However, the channel in the UAV network is prone to noise and interference due to the complex environments. Reconfigurable intelligent surface (RIS), as an emerging technology in recent years, can be applied to the UAV network to establish the transmission environment by intelligibly adjusting signal characteristics, which can achieve significant gains in coverage and spectral efficiency. Thus, we consider RIS aided UAV networks for virtual reality (VR) content transmission under the presence of eavesdroppers, and maximize the time average sum secrecy energy efficiency (SEE) via adjusting UAV trajectory, beamforming matrix of UAV and RIS jointly by the deep reinforcement learning (DRL) approach. To eliminate the time correlation and the coupling of variables, we propose a Lyapunov guided decay twin-delayed deep deterministic policy gradient (TD3) scheme to tackle the decoupled problem. Simulations demonstrate the effectiveness of the proposed scheme and its outperformance in SEE compared with other benchmarks.

关键词： Wireless networks Surveillance Forensics Noise Oral communication Reconfigurable intelligent surfaces Benchmark testing

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Fine-Grained Alignment in Vision-and-Language Navigation through Bayesian Optimization

arXiv

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arXiv 2024年

作者： Song, Yuhang Gianni, Mario Yang, Chenguang Lin, Kunyang Chiu, Te-Chuan Nguyen, Anh Lee, Chun-Yi Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan Department of Computer Science National Tsing Hua University Hsinchu300 Taiwan Department of Computer Science National Tsing Hua University Taiwan School of Software Engineering South China University of Technology Guangzhou510006 China

This paper addresses the challenge of fine-grained alignment in Vision-and-Language Navigation (VLN) tasks, where robots navigate realistic 3D environments based on natural language instructions. Current approaches use contrastive learning to align language with visual trajectory sequences. Nevertheless, they encounter difficulties with fine-grained vision negatives. To enhance cross-modal embeddings, we introduce a novel Bayesian Optimization-based adversarial optimization framework for creating fine-grained contrastive vision samples. To validate the proposed methodology, we conduct a series of experiments to assess the effectiveness of the enriched embeddings on fine-grained vision negatives. We conduct experiments on two common VLN benchmarks R2R and REVERIE, experiments on the them demonstrate that these embeddings benefit navigation, and can lead to a promising performance enhancement. Our source code and trained models are available at: https://***/r/FGVLN. © 2024, CC BY.

关键词： Contrastive Learning

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Personalized Alarming System for Asthma Management Based on Lung Functionality: Observational Study

Personalized Alarming System for Asthma Management Based on ...

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Advances in Science and engineering technology International Conferences (ASET)

作者： Fatima Alblooshi Reem Albadi Khawla Alblooshi Emma Ishimirwe Yvettte Uwisoneye Fady Alnajjar Dua'a Mehiar College of Information Technology United Arab Emirates Universty Al Ain UAE Department of Computer Science and Software Engineering College of IT United Arab Emirates Universty Al Ain UAE

Asthma is a chronic respiratory disease that can be challenging to manage. To improve asthma management, we designed a personalized alarm system that utilizes lung function data to monitor and alert individuals to potentially harmful air quality. Our pilot observation included seven individuals who were exposed to two different air-quality environments, and we observed significant variations in their lung function responses. Based on these observations, we developed customized alarm systems for each participant that continuously monitored air quality and provided notifications when it reached levels that could potentially exacerbate their asthma symptoms. This personalized approach has the potential to enable individuals to proactively protect their respiratory health and avoid asthma attacks by taking timely and appropriate action based on their own specific needs.

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Network congestion control and reliability optimization with multiple time delays from the perspective of information security

Network congestion control and reliability optimization with...

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2020 International Conference on Advance in Ambient Computing and Intelligence, ICAACI 2020

作者： Xudong, Yang Wuhan Polytechnic College of Computer Technology and Software Engineering Wuhan430070 China

ISBN: (纸本)9781728197531

As a new type of complex system, multi delay network in the field of information security undertakes the important responsibility of solving information congestion, balancing network bandwidth and traffic. The problems of data loss, program failure and a large number of system downtime still exist in the conventional multi delay system when dealing with the problem of information jam, which makes the corresponding reliability of the whole system greatly reduced. Based on this, this paper mainly studies and analyzes the stability system and reliability of the corresponding multi delay system in the information security perspective. In this paper, the stability and reliability analysis of multi delay systems based on linear matrix and specific function environment is innovatively proposed. Finally, the sufficient conditions of robust asymptotic stability of multi delay systems are obtained. At the same time, the relevant stability conditions and robust stability conditions of multi delay feedback switched systems are given by simulation. In the experimental part, the corresponding data and conclusions are simulated. The simulation results show that the reliability and stability analysis data of multi delay system proposed in this paper have certain experimental value. © 2020 IEEE.

关键词： Time delay

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Security Analysis for a VANET Privacy Protection Scheme

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Journal of Cyber Security 2022年第1期4卷 57-64页

作者： Yuzhen Liu Xiaoliang Wang Zhoulei Cao Frank Jiang School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411201China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201China School of Engineering and IT University of New South WalesNSWAustralia

Vehicular ad hoc network(VANET)is a self-organizing wireless sensor network model,which is extensively used in the existing *** to the openness of wireless channel and the sensitivity of traffic information,data transmission process in VANET is vulnerable to leakage and *** of vehicle identitywhile protecting vehicle privacy information is an advantageous way to improve the security of *** propose a scheme based on fair blind signature and secret sharing *** this paper,we prove that the scheme is feasible through security analysis.

关键词： Vehicular ad hoc network anonymous authentication fair blind signature secret sharing algorithm security analysis BAN logic ideology

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Research on Recognition Algorithm of Abnormal Behavior of Workers in Two-Stream Convolutional Network

Research on Recognition Algorithm of Abnormal Behavior of Wo...

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2020 International Conference on computer Science and Communication technology, ICCSCT 2020

作者： Zhao, Yinong Li, Ruoxi Zhang, Xuedong Cao, Yuhan Chen, Xiaojing Computer Science and Software Engineering University of Science and Technology Anshan Liaoning China

In order to identify the abnormal behaviors of workers in the factory, this paper proposes an improved algorithm for identifying abnormal behaviors of workers in a two-stream convolutional network. The workers' body contour shape information extracted from the convolutional neural network is input into the LSTM network in order to extract timing information between frames. Secondary extraction of the dense optical flow image of the video image, sparse extraction of pixels with small changes in optical flow value in the dense optical flow image, and then put the new continuous optical flow image into the continuous optical flow image network. The two networks are fused after softmax classification to get the final recognition result. Experiments on the CAVIAR dataset, CASIA dataset, and self-built behavior dataset show that compared with other abnormal behavior detection methods and traditional two-stream convolution algorithms, the accuracy of the improved algorithm in this paper is improved by 1%-4%. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Extraction

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Study on collaborative filtering recommendation algorithm based on prediction for item rating

Study on collaborative filtering recommendation algorithm ba...

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2020 International Conference on engineering Management of Communication and technology, EMCTECH 2020

作者： Xiao, Shaozhang Ling, Haonan Lu, Yi Tang, Zhaoxia Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an Jiangsu China

ISBN: (数字)9781728196688

ISBN: (纸本)9781665404488

As the Internet is developing rapidly and people are becoming increasingly dependent on e-commerce, whether the users can be provided with accurate recommendation information has become the key to attracting users. Under this background, corresponding personalized recommendation systems have been proposed, and collaborative filtering algorithm is one of the algorithms commonly used in the recommendation systems. Collaborative filtering recommends the information in which the user is interested through analyzing the preferences of a group with similar interests and common experience. With the increasing number of users and items in the e-commerce system, the collaborative filtering algorithm is limited by data sparsity in operation, which will influence the quality of the recommendation results. This paper studied the collaborative filtering recommendation algorithm based on the prediction for item rating, and enhanced the accuracy of the recommendation algorithm by designing an improved similarity-based algorithm for predicting ratings based on the Pearson correlation coefficient. © 2020 IEEE.

关键词： Collaborative filtering

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Strategies for predictive power: Machine learning models in city-scale load forecasting

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2023年 6卷

作者： Nooruldeen, Orhan Baker, Mohammed Rashad Aleesa, A.M. Ghareeb, Ahmed Shaker, Ehab Hashim Software Department College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Department of Mechanical Engineering University of Kirkuk Kirkuk Iraq Department of Computer Techniques Engineering College of Information Technology Imam Ja'afar Al-Sadiq University Baghdad Iraq

This study focuses on enhancing machine learning (ML) algorithms' performance in predicting daily loads for Kirkuk, Iraq—an essential element in energy planning, resource allocation, and policymaking. We explore single and ensemble learning algorithms, including AdaBoost, Bagging, Support Vector Regression (SVR), and Decision Tree (DT). To assess accuracy, we employ critical metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). MSE and RMSE gauge precision through squared differences between predictions and actual energy consumption. MAPE reveals percentage deviations, while R² quantifies model fit, indicating its ability to capture energy consumption variance. Our results highlight Bagging's superiority, particularly in day-ahead forecasts, demonstrating superior accuracy over Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). These findings underscore the importance of feature reduction methods in enhancing performance. Bagging exhibits promise across various feature reduction scenarios, proficiently predicting energy consumption. Furthermore, the AdaBoost algorithm demonstrates commendable performance. The application of voting ensemble learning emerges as a particularly insightful approach, effectively reducing the squared differences and deviations in energy consumption forecasts. The significant implications of these findings suggest that the models, with their impressive performance, could serve as valuable tools for energy planners and policymakers in Kirkuk, playing a key role in optimizing resource allocation for efficient energy utilization. © 2023 The Author(s)

关键词： Electricity planning Ensemble learning Feature reduction Kirkuk city Load forecasting Machine learning

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Convergence of 5G with Internet of Things for Enhanced Privacy

Convergence of 5G with Internet of Things for Enhanced Priva...

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Future of Information and Communication Conference, FICC 2021

作者： Batool, Amreen Sun, Baoshan Saleem, Ali Ali, Jawad School of Computer Science and Software Engineering Tiangong University Tianjin China Department of Computer Science COMSATS University Islamabad Lahore Pakistan Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9783030730994

In this paper, we address the issue of privacy in 5th generation (5G) driven Internet of Things (IoT) and related technologies while presenting a comparison with previous technologies for communication and unaddressed issues in 5G. Initially, an overview of 5G driven IoT is presented with details about both technologies and eventually leading to problems that 5th generation will face. Details about 5G are also presented while comparing them with previous technologies. The architecture of 5G is presented hence explaining layers of 5G and technologies like SDN, NFV and cloud computing that compose these layers. The architecture for 5g based IoT is also presented for providing visual understanding as well as explained based on how this addresses the issues present in 4G. Privacy is highlighted in 5G driven IoT while providing details about how SDN, NFV and cloud computing helps in elimination of this issue. The issues presented will be compared with 4G based IoT and solutions are provided about mitigation of these issues particularly bandwidth and security. Moreover, techniques used by 4G and 5G technologies for handling the issues of privacy in IoT are presented in a nutshell as a table. Paper also presents a detailed overview of technologies making 5G possible meanwhile giving an explanation about how these technologies resolve privacy issues in 5G. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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