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Green Floating Blockchain-Empowered Co-Trust Security Mechanism with Energy Efficiency Against Attack Threat for 6G-IoV

Green Floating Blockchain-Empowered Co-Trust Security Mechan...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yibing Liu Lijun Huo Hansong Xu Qianqian Pan Jun Wu State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China School of Engineering The University of Tokyo Tokyo Japan Graduate School of Information Production and Systems Waseda University Fukuoka Japan

The Internet of Vehicles (IoV) based on the 6-th Generation (6G) communication brings convenience, but also raises anxiety about information security. Researchers have developed static security schemes based on the blockchain, but it results in excessive resource occupation. And it is difficult to resist some attacks such as desynchronization, Denial of Service (DoS), and covert intrusion. In response to the above problems, this paper proposes the Floating Blockchain Consensus Security (FBCS) scheme. It models the attack risk of Road Side Unit (RSU) through the traffic flow prediction to present the credibility evaluation and constructs the floating blockchain based on the results. And the security capability is adjusted through the dynamic joining and exit mode of trusted nodes and untrusted nodes. FBCS also establishes a relationship between blockchain size and energy consumption. Once the system resources are found to be insufficient, it applies the cloud center-based supplementary certification mechanism to provide authentication to untrusted nodes to enhance the stability of the *** analysis and simulation experiments prove that the FBCS can afford data privacy, maintain moderate security capabilities, and adapt defense capabilities according to the attack environment, reduce resource occupation to save cost.

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Bridging Gaps in HCI: Advancing Education, Research, and Careers in Asia

Bridging Gaps in HCI: Advancing Education, Research, and Car...

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2025 CHI Conference on Human Factors in computing systems, CHI EA 2025

作者： Gamage, Dilrukshi Cheng, Shiwei Mudliar, Preeti Lu, Zhicong Zhao, Shengdong Ahmed, Nova Ma, Xiaojuan Lee, Uichin Wang, Ding Department of Computation and Intelligent Systems University of Colombo School of Computing Colombo Sri Lanka Zhejiang University of Technology Hangzhou China International Institute of Information Technology Bangalore Bangalore India Department of Computer Science City University of Hong Kong Hong Kong School of Creative Media City University of Hong Kong Hong Kong Design Inclusion and Access Lab. Department of Electrical and Computer Engineering North South University Dhaka Dhaka Bangladesh Hong Kong University of Science and Technology Hong Kong Hong Kong School of Computing KAIST Daejeon South Korea Google Atlanta GA United States

ISBN: (纸本)9798400713958

Asia’s Human-computer Interaction (HCI) landscape is rapidly evolving, yet it faces distinct challenges in curriculum development, research establishment, and career navigation. This panel discussion, hosted by Asia SIGCHI Committee (ASC) at CHI2025, will bring together distinguished experts to address these challenges and explore strategies for fostering a robust and inclusive HCI community in Asia. Panelists will discuss the integration of culturally relevant content in HCI education, approaches to enhancing research visibility on international platforms, and initiatives to bridge gaps between academic training and industry expectations. Through an engaging discussion and audience interaction, the panel aims to identify actionable solutions, promote collaboration, and inspire future initiatives that strengthen HCI’s growth across the region. Participants will gain valuable insights into overcoming systemic barriers while building sustainable and impactful HCI programs tailored to Asia’s unique sociocultural dynamics. This panel seeks to advance the global relevance and contributions of HCI in Asia. © 2025 Copyright held by the owner/author(s).

关键词： Asia Curricula HCI HCI Education HCI Teaching

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Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Gaolei Li Changchuan Yin Tony Q.S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

关键词： Location awareness Simulation Collaboration Reinforcement learning Gaussian distribution Autonomous aerial vehicles Distance measurement Trajectory Jamming Optimization

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Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged Photonic-electrical Silicon Transceiver for High-speed Optical Interconnects

Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged P...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Qin, Jun Yang, Qipeng Han, Changhao Zhou, Yan Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Liu, Siming Li, Yueqin Sun, Jian Min, Miao Hu, Weiwei He, Zhixue Wang, Lei Yu, Shaohua Shu, Haowen Wang, Xingjun Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100192 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China Peng Cheng Laboratory Shenzhen518055 China State Key Laboratory of Information Photonics and Optical Communications BUPT Beijing100876 China

We explore implementing a multilevel deep neural network to enhance the performance of a 4-channel photonic-electrical hybrid-packaged silicon transceiver. Stable transmission and reception of 150 Gbps/λ PAM4 signals... 详细信息

ISBN: (纸本)9781957171395

关键词： Optical interconnects

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Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged Photonic-electrical Silicon Transceiver for High-speed Optical Interconnects

Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged P...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Optical interconnects

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Distributed Ledger Technology in Energy Services: The InEExS Project Objectives and Approach

Distributed Ledger Technology in Energy Services: The InEExS...

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International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Aikaterini Papapostolou Ioanna Andreoulaki Sokratis Divolis Filippos Anagnostopoulos Vangelis Marinakis Decision Support Systems Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Institute for European Energy and Climate Policy (IEECP) Amsterdam The Netherlands

The digitalization trend is prominent in a wide variety of sectors, and the energy sector is no exception. The incorporation of blockchain, a Distributed Ledger Technology (DLT), in energy services has been examined in literature and quite a few endeavors of DLT adoption in energy applications have been implemented by companies, universities and other organizations. The European LIFE project “InEExS” pursues to apply DLT through specific business cases, so as to offer improved digital energy services and encourage energy efficiency. In this paper, a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis has been conducted as a first stage of DLT potential usages evaluation in the contexts of the InEExS, with the purpose of demonstrating the reasons why blockchain could support the digitalization of energy applications and acceleration of energy transition, while pointing out the most important barriers that should be addressed to ensure that DLT integration would be truly beneficial. The decentralized nature of blockchain, combined with the transparency and safety it provides, make it a very promising technology for energy management and trading implementations, among others. However, technical constraints, such as the scalability problem, security threats, as well as sociopolitical and regulatory barriers should not be neglected. The findings of our SWOT analysis are combined with an assessment of prospective blockchain usages in the business cases deployed by the InEExS, so that the best practices to optimally exploit DLT in various energy applications, within and beyond the project, are identified.

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Design and Application of a One-Stop Mobile Application Sharing Platform for Steel Enterprises 42

Design and Application of a One-Stop Mobile Application Shar...

引用

42nd Chinese Control Conference, CCC 2023

作者： Chen, Xinzhe Chen, Luefeng Hu, Jie Yang, Qingfeng Wu, Min Pedrycz, Witold School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada

ISBN: (纸本)9789887581543

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Informatization

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Learning Frequency Nadir From Multi-Fidelity Data For Dynamic Secure Microgrid Islanding

Learning Frequency Nadir From Multi-Fidelity Data For Dynami...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Zhang, Yichen Li, Yan Qiu, Feng Hong, Tianqi Markel, Lawrence Argonne National Laboratory Energy Systems Division IL60439 United States School of Electrical Engineering and Computer Science The Pennsylvania State University University ParkPA16801 United States Oak Ridge National Laboratory Electrification and Energy Infrastructures Division KnoxvilleTN37932 United States

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

A frequency-constrained microgrid scheduling model has been used to obtain dispatch commands that ensure dynamic frequency security under event-triggered islanding. Among all frequency constraint encoding approaches, data-driven methods show superiority in terms of capturing more generic frequency behavior but are limited by the quality of data. In this paper, we introduce a deep learning method for frequency nadir prediction that can be trained using multi-fidelity data. Without loss of generality, we consider the training dataset consists of large-size low-fidelity data and small-size high-fidelity data. Instead of directly performing the training over either dataset, we first learn the correlation between the low- and high-fidelity dataset. Then, this correlation model can be used to generate a large size of synthetic high-fidelity data with negligible computation effort. The multi-fidelity training admits a 95% error reduction in out-of-sample testing. Once being encoded into the microgrid scheduling model, less conservative dispatch commands can be obtained. © 2022 IEEE.

关键词： Deep neural networks

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StressBal: Personalized Just-in-time Stress Intervention with Wearable and Phone Sensing

StressBal: Personalized Just-in-time Stress Intervention wit...

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Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous computing and the 2022 ACM International Symposium on Wearable computers

作者： Yunjo Han Hyemin Lee Kobiljon E. Toshnazarov Youngtae Noh Uichin Lee Korea Advanced Institute of Science & Technology (KAIST) Korea Republic of Hanyang University Korea Republic of Electrical and Computer Engineering / Engineering school / Networked Systems Lab Inha University Korea Republic of KENTECH Korea Republic of School of Computing KAIST Korea Republic of

ISBN: (纸本)9781450394239

This paper proposes StressBal, a mobile system that offers personalized just-in-time stress intervention with mobile and wearable sensing. The system runs entirely on-device, detecting stress in real-time and providing respiratory exercise-based interventions. It also updates the stress detection algorithm continuously, based on user feedback. We envision an open mobile platform that helps researchers to design and evaluate automatic, seamless, and unobtrusive stress interventions during everyday activities.

关键词： Mobile and Wearable computing Stress Intervention Just-in-time Intervention Stress Detection

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Semantic enrichment of pretrained embedding output for unsupervised IR

Semantic enrichment of pretrained embedding output for unsup...

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2021 AAAI Spring Symposium on Combining Machine Learning and Knowledge engineering, AAAI-MAKE 2021

作者： Dervakos, Edmund Filandrianos, Giorgos Thomas, Konstantinos Mandalios, Alexios Zerva, Chrysoula Stamou, Giorgos Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece National Centre for Text Mining School of Computer Science University of Manchester Manchester United Kingdom

The rapid growth of scientific literature in the biomedical and clinical domain has significantly complicated the identification of information of interest by researchers as well as other practitioners. More importantly, the rapid emergence of new topics and findings, often hinders the performance of supervised approaches, due to the lack of relevant annotated data. The global COVID-19 pandemic further highlighted the need to query and navigate uncharted ground in the scientific literature in a prompt and efficient way. In this paper we investigate the potential of semantically enhancing deep transformer architectures using SNOMED-CT in order to answer user queries in an unsupervised manner. Our proposed system attempts to filter and re-rank documents related to a query that were initially retrieved using BERT models. To achieve that, we enhance queries and documents with SNOMED-CT concepts and then impose filters on concept co-occurrence between them. We evaluate this approach on OHSUMED dataset and show competitive performance and we also present our approach for adapting such an approach to full papers, such as kaggle's CORD-19 full-text dataset challenge. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Query processing

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