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IEEE Conference on Vehicular Technology (VTC)

作者： Shihao Chen Helin Yang Liang Xiao Changyuan Xu Xianzhong Xie Wanting Yang Zehui Xiong Department of Information & Communication Engineering Xiamen University Xiamen China Chongqing Key Laboratory of Computer Network and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China Pillar of Information System Technology & Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

The space-air-ground integrated network (SAGIN) is a crucial technology for sixth-generation (6G) wireless communication networks to achieve seamless coverage and high throughput. In this paper, we propose an unmanned aerial vehicle (UAV)-assisted SAGIN structure, where the UAV is responsible for collecting data from ground users (GUs) and transmitting it to low-earth orbit (LEO) satellites. This paper also formulates a joint energy-efficient and fair resource scheduling optimization problem under jamming attacks and limited energy constraints, where the line-of-sight (LoS) links between the UAV and GUs are susceptible to being jammed. Due to the non-convex problem and dynamic environments, a deep reinforcement learning (DRL)-based twin delayed deep deterministic policy gradient (TD3) is developed to search optimal UAV trajectory to maximize energy efficiency (EE) and fairness against jamming. Simulation results verify that the proposed intelligent resource scheduling algorithm outperforms the baseline algorithms in terms of EE and fairness index in different settings.

关键词： Wireless communication Vehicular and wireless technologies Simulation Autonomous aerial vehicles Space-air-ground integrated networks Energy efficiency Trajectory

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The Knowledge of Cyber-Security Vulnerabilities in an Institution of Higher and University Education. A Case of ISP-Bukavu (Institut Supérieur Pédagogique de Bukavu) (TTC = Teachers’ Training College)

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Journal of computer and Communications 2023年第4期11卷 12-32页

作者： Dominique Wasso Kiseki Vincent Havyarimana Therence Niyonsaba Désiré Lumonge Zabagunda Walumbuka Ilundu Wail Thabo Semong Computer Engineering University of Burundi (UB) Bujumbura Burundi Ecole Normale Supérieure (ENS) Bujumbura Burundi University Research Laboratory in Modeling and Applied Statistical Engineering (LURMISTA) Nyamugerera Bujumbura Department of Physics and Technology ISP Bukavu Democratic Republic of Congo Department of Management Computer Science ISP Bukavu Democratic Republic of Congo Departement of Computer science and Information System Bostwana International University of Science and Technology Palapye Bostwana

This study pursues the objective of analyzing and verifying the knowledge of the agents of the Institut Supérieur Pédagogique/ISP-Bukavu (TTC = Teachers’ training College) in relation to the practical flaws resulting from the lack of knowledge of the observable rules in information system security. In a clearer way, it aims to verify the level of knowledge of the vulnerabilities, to verify the level of use of the antivirus software, to analyze the frequency of use of Windows update, the use of an anti-spyware software as well as a firewall software on the computer. Through a survey conducted on a sample of 100 agents of the Institut Supérieur Pédagogique/ISP-Bukavu (TTC = Teachers’ training College), the results revealed that 48% of the sample has no knowledge on computer vulnerabilities;for the use of antivirus software: 47% do not use the antivirus;for Windows update: 29% never update the Windows operating system;for anti-spyware: 48% never use;for the firewall: 50% are not informed. In fine, our results proposed a protection model VMAUSP (Vulnerability Measurability Measures Antivirus, Update, Spyware and Firewall) to users based on the behavioral approach, learning how the model works.

关键词： Threats Vulnerabilities Cybersecurity Anti-Spyware Firewall Cyber-Attack Antivirus

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An Haptic Display for Visually Impaired Pedestrian Navigation 1st

An Haptic Display for Visually Impaired Pedestrian Navigatio...

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1st International Conference of Advanced Computing and Informatics, ICACIN 2020

作者： Alzighaibi, Sara Bouaziz, Rahma Kammoun, Slim Information System Department College of Computer Science and Engineering Taibah University Medina Saudi Arabia Computer Science Department College of Computer Science and Engineering Taibah University Medina Saudi Arabia ReDCAD University of Sfax Sfax Tunisia Research Laboratory of Technologies of Information and Communication & Electrical Engineering University of Tunis Tunis Tunisia

ISBN: (纸本)9789811560477

Sight is one of the major senses that allows people to travel safely, freely, and independently of others. In their daily life, visually impaired people are confronting challenging tasks such as reading or writing. Navigation and direction finding, particularly in the unfamiliar environment, are remaining as one of the main issues for visually impaired (VI) individuals. Several ways have been emerged to help VI orientation and mobility such as electronic orientation aids which can be further studied and developed. This work aims to improve VI navigation by proposing a new tactile interface that will be designed and implemented with the use of vibrators and Arduino board. The proposed interface targets to increase the quality of life and independence for VI. Also, evaluation performed with VI pedestrian will give us further information and knowledge regarding haptic perceptions and effectiveness of such interface and interaction technique. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Navigation

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Hybrid Variational Autoencoder for Collaborative Filtering 25

Hybrid Variational Autoencoder for Collaborative Filtering

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Liu, Jinxin Xiao, Yingyuan Zhu, Ke Zheng, Wenguang Hsu, Ching-Hsien Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9781665405270

In recent years, Variational AutoEncoder (VAE) based methods have made many important achievements in the field of collaborative filtering recommendation system. VAE is a kind of Bayesian model which combines latent variable model with variational inference, but its optimization is often troubled by posterior collapse. By comparing the optimization process of VAE and ordinary autoencoder, we observe that the mismatch between poorly optimized encoder and decoder with too strong characterization capabilities makes it difficult to learn the mapping from the data manifold to the parameterized graph. Since the learning of a posteriori network corresponds to the encoder, we think that the problem of a posteriori collapse can be alleviated by balancing the encoder and decoder better. Therefore, we proposed Hy-VAE, which combines conventional VAE with deterministic autoencoder, and has the advantages of both VAE and deterministic autoencoder. Experiments on three real-world recommendation data sets show that our method alleviates the posterior crash problem in VAE and improves the recommendation performance. © 2022 IEEE.

关键词： Collaborative filtering

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Robust Imagined Speech Production Using AI-Generated Content network for Patients with Language Impairments

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IEEE Transactions on Consumer Electronics 2024年

作者： Xu, Xu Fu, Chong Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110004 China

Imagined speech production is critical for brain-computer interface systems. It is able to provide the communication ability for patients with language impairments. Nowadays, many studies have developed algorithms for this purpose. However, they often struggle with an issue that is the noise in physiological signals from various devices. This makes current methods have low robustness when performing speech generation. To this end, we propose a framework to robustly producing imagined speech for patients with language impairments. It is based on artificial intelligent-generated content (AIGC). In specific, this work develops a diffusion model to remove noise from brain signals, which are then converted and normalized into log-mel spectrograms. Next, the product feeds into a transformer-based model to extract local and global features. Finally, these enhanced features are inputted into full connected layers to generate the imagined speech. The effectiveness of our model is validated using the high-quality, i.e., single-word-production-Dutch-iBIDS dataset. It achieves Pearson correlation scores above 0.8 and standard deviations below 0.2 across different volunteers. Experimental results well demonstrate that our model is robust and has advantages over peer methods. © 2024 IEEE.

关键词： Deep learning

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HIPAA Controlled Patient Information Exchange and Traceability in Clinical Processes 9

HIPAA Controlled Patient Information Exchange and Traceabili...

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9th IEEE International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, SETIT 2022

作者： Essefi, Intidhar Boussi Rahmouni, Hanen Solomonides, Tony Fethi Ladeb, Mohamed University of Tunis El Manar Higher Institute of Medical Technologies Research Laboratory of Biophysics and Medical Technologies Tunis Tunisia University of the West of England Computer Science Research Center Bristol United Kingdom NorthShore University Health System Outcomes Research Network Research Institute United States University of Tunis EL Manar Radiology Department Kassab Orthopedics Institute Manouba Tunisia

ISBN: (数字)9781728184425

ISBN: (纸本)9781728184425

The digital transformation of healthcare processes is deeply changing the quality of healthcare services offered to the patient. Although it is often seen as highly beneficial, the move to digital connected health systems exposes both health providers and individuals to many risks ranging from privacy violations to medical identity usurpation Throughout this process of digitisation, it is essential that privacy protection and systemic compliance to personal data regulations such as HIPAA and GDPR are ensured by all healthcare stakeholders. This is essential to setup the boundaries and limitation imposed by the legislative framework with relation to the legitimate processing of sensitive data. In this paper, we are aiming to represent privacy and security controls as tags/labels to data elements highlighted in clinical processes and their automation as privacy protection filters to the data at user interface layers of the healthcare information system. As a data model we rely on the HL7-CDA standard for medical documents architecture thanks to its seamless and extensible data integration capabilities while exchanging EHR data. © 2022 IEEE.

关键词： Sensitive data

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning

arXiv

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arXiv 2024年

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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Space-Air-Ground Integrated network (SAGIN) for 6G: Requirements, Architecture and Challenges

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China Communications 2022年第2期19卷 90-108页

作者： Huanxi Cui Jun Zhang Yuhui Geng Zhenyu Xiao Tao Sun Ning Zhang Jiajia Liu Qihui Wu Xianbin Cao School of Electronic and Information Engineering Beijing Key Laboratory for Network-Based Cooperative Air application ManagementBeihang UniversityBeijing 100191China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China China Mobile Research Institute Beijing 100053China Department of Electrical and Computer Engineering University of WindsorONCanadaN9B 3P4 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology the School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China College of Electronics and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210007China

As the fifth-generation(5G)mobile communication network may not meet the requirements of emerging technologies and applications,including ubiquitous coverage,industrial internet of things(IIoT),ubiquitous artificial intelligence(AI),digital twins(DT),etc.,this paper aims to explore a novel space-air-ground integrated network(SAGIN)architecture to support these new requirements for the sixth-generation(6G)mobile communication network in a flexible,low-latency and efficient ***,we first review the evolution of the mobile communication network,followed by the application and technology requirements of *** the current 5G non-terrestrial network(NTN)architecture in supporting the new requirements is deeply *** that,we proposes a new flexible,low-latency and flat SAGIN architecture,and presents corresponding use ***,the future research directions are discussed.

关键词： 6G AI DT SAGIN NTN network architecture

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Multi-modal Data based Semi-Supervised Learning for Vehicle Positioning

arXiv

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arXiv 2024年

作者： Huan, Ouwen Yang, Yang Luo, Tao Chen, Mingzhe Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In this paper, a multi-modal data based semisupervised learning (SSL) framework that jointly use channel state information (CSI) data and RGB images for vehicle positioning is designed. In particular, an outdoor positioning system where the vehicle locations are determined by a base station (BS) is considered. The BS equipped with several cameras can collect a large amount of unlabeled CSI data and a small number of labeled CSI data of vehicles, and the images taken by cameras. Although the collected images contain partial information of vehicles (i.e. azimuth angles of vehicles), the relationship between the unlabeled CSI data and its azimuth angle, and the distances between the BS and the vehicles captured by images are both unknown. Therefore, the images cannot be directly used as the labels of unlabeled CSI data to train a positioning model. To exploit unlabeled CSI data and images, a SSL framework that consists of a pretraining stage and a downstream training stage is proposed. In the pretraining stage, the azimuth angles obtained from the images are considered as the labels of unlabeled CSI data to pretrain the positioning model. In the downstream training stage, a small sized labeled dataset in which the accurate vehicle positions are considered as labels is used to retrain the model. Simulation results show that the proposed method can reduce the positioning error by up to 30% compared to a baseline where the model is not pretrained. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Minimizing Age of Collection for Multiple Access in Wireless Industrial Internet of Things

arXiv

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arXiv 2023年

作者： Liang, Jiaxin Chan, Tse-Tin Pan, Haoyuan Computer Network and Protocol Research Laboratory Huawei Technologies Shenzhen518060 China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

This paper investigates the information freshness of Industrial Internet of Things (IIoT) systems, where each IoT device makes a partial observation of a common target and transmits the information update to a central receiver to recover the complete observation. We consider the age of collection (AoC) performance as a measure of information freshness. Unlike the conventional age of information (AoI) metric, the instantaneous AoC decreases only when all cooperative packets for a common observation are successfully received. Hence, effectively allocating wireless time-frequency resources among IoT devices to achieve a low average AoC at the central receiver is paramount. Three multiple access schemes are considered in this paper: time-division multiple access (TDMA) without retransmission, TDMA with retransmission, and frequency-division multiple access (FDMA). First, our theoretical analysis indicates that TDMA with retransmission outperforms the other two schemes in terms of average AoC. Subsequently, we implement information update systems based on the three schemes on software-defined radios. Experimental results demonstrate that considering the medium access control (MAC) overhead in practice, FDMA achieves a lower average AoC than TDMA with or without retransmission in the high signal-to-noise ratio (SNR) regime. In contrast, TDMA with retransmission provides a stable and relatively low average AoC over a wide SNR range, which is favorable for IIoT applications. Overall, we present a theoretical-plus-experimental investigation of AoC in IIoT information update systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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