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arXiv 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan The Beijing Key Laboratory of Network System Architecture and Convergence The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing China Singapore The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Computer Science North Carolina State University RaleighNC27695 United States The Beijing Key Laboratory of Network System Architecture and Convergence China The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025, CC BY-NC-SA.

关键词： Markov processes

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An avoiding overlap method between acoustic shadow and organ for automated ultrasound diagnosis and treatment 11

An avoiding overlap method between acoustic shadow and organ...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Matsuyama, Momoko Koizumi, Norihiro Nishiyama, Yu Tsumura, Ryosuke Tsukihara, Hiroyuki Numata, Kazushi The University of Electro-Communications Department of Mechanical and Intelligent Systems Engineering Chofu Japan The University of Electro-Communications Department of Computer and Network Engineering Chofu Japan Health and Medical Research Institute National Institute of Advanced Industrial Science and Technology Line 4 Tsukuba Japan The University of Tokyo Department of Cardiothoracic Surgery Bunnkyo-Ku Japan Yokohama City University Medical Center Gastroenterological Center Yokohama Japan

ISBN: (纸本)9781665492324

Ultrasound diagnosis and treatment have attracted attention because they are noninvasive and allow real-time observation of lesions. However, it is difficult to accurately estimate the location of the treatment area because of information loss due to acoustic shadows caused by the reflection of sound waves from hard tissues such as bone. In this study, we propose a probe manipulation method that uses deep learning to avoid overlap between organs and shadows for automated diagnosis. © 2022 IEEE.

关键词： Ultrasonics

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A Road Map of Prompt engineering for ChatGPT in Healthcare: A Perspective Study

A Road Map of Prompt Engineering for ChatGPT in Healthcare: ...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Abhari, Shahabeddin Fatahi, Somayeh Saragadam, Ashish Chumachenko, Dmytro Pelegrini Morita, Plinio School of Public Health Sciences University of Waterloo WaterlooON Canada Department of Computer Science University of Saskatchewan SaskatoonSK Canada Department of Mathematical Modelling and Artificial Intelligence National Aerospace University "Kharkiv Aviation Institute" Kharkiv Ukraine Department of Systems Design Engineering University of Waterloo WaterlooON Canada Research Institute for Aging University of Waterloo WaterlooON Canada Centre for Digital Therapeutics Techna Institute University Health Network TorontoON Canada Institute of Health Policy Management and Evaluation Dalla Lana School of Public Health University of Toronto TorontoON Canada

ISBN: (纸本)9781643685335

Generative AI models, such as ChatGPT, have significantly impacted healthcare through the strategic use of prompts to enhance precision, relevance, and ethical standards. This perspective explores the application of prompt engineering to tailor outputs specifically for healthcare stakeholders: patients, providers, policymakers, and researchers. A nine-stage process for prompt engineering in healthcare is proposed, encompassing identifying applications, understanding stakeholder needs, designing tailored prompts, iterative testing and refinement, ethical considerations, collaborative feedback, documentation, training, and continuous updates. A literature review focused on "Generative AI" or "ChatGPT," prompts, and healthcare informed this study, identifying key prompts through qualitative analysis and expert input. This systematic approach ensures that AI-generated prompts align with stakeholder requirements, offering valuable insights into symptoms, treatments, and prevention, thereby supporting informed decision-making among patients. © 2024 The Authors.

关键词： Decision making

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Risk-Aware Distributed Multi-Agent Reinforcement Learning

arXiv

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arXiv 2023年

作者： Al Maruf, Abdullah Niu, Luyao Ramasubramanian, Bhaskar Clark, Andrew Poovendran, Radha Network Security Lab Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Electrical and Computer Engineering Western Washington University BellinghamWA98225 United States Electrical and Systems Engineering Washington University in St. Louis St. LouisMO63130 United States

Autonomous cyber and cyber-physical systems need to perform decision-making, learning, and control in unknown environments. Such decision-making can be sensitive to multiple factors, including modeling errors, changes in costs, and impacts of events in the tails of probability distributions. Although multi-agent reinforcement learning (MARL) provides a framework for learning behaviors through repeated interactions with the environment by minimizing an average cost, it will not be adequate to overcome the above challenges. In this paper, we develop a distributed MARL approach to solve decision-making problems in unknown environments by learning risk-aware actions. We use the conditional value-at-risk (CVaR) to characterize the cost function that is being minimized, and define a Bellman operator to characterize the value function associated to a given state-action pair. We prove that this operator satisfies a contraction property, and that it converges to the optimal value function. We then propose a distributed MARL algorithm called the CVaR QD-Learning algorithm, and establish that value functions of individual agents reaches consensus. We identify several challenges that arise in the implementation of the CVaR QD-Learning algorithm, and present solutions to overcome these. We evaluate the CVaR QD-Learning algorithm through simulations, and demonstrate the effect of a risk parameter on value functions at consensus. © 2023, CC BY.

关键词： Decision making

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Exploring Y1 Communications and Services in O-RAN: Background, Privacy, and Security

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4638-4666页

作者： Sachinkumar B. Mallikarjun Mohammad Asif Habibi Mihirraj Dixit Xavier Costa-Pérez Mérouane Debbah Hans D. Schotten Department of Electrical and Computer Engineering Division of Wireless Communications and Radio Navigation University of Kaiserslautern (RPTU) Kaiserslautern Germany 6G Networks Research and Development Department NEC Laboratories Europe Heidelberg Germany Network Engineering Department Universitat Politècnica de Catalunya Barcelona Spain Department of AI Driven Systems i2CAT Research Center Barcelona Spain Department of AI Driven Systems Catalan Institution for Research and Advanced Studies (ICREA) Barcelona Spain Department of Computer and Information Engineering KU 6G Research Center Khalifa University of Science and Technology Abu Dhabi UAE CentraleSupelec University Paris-Saclay Gif-sur-Yvette France Intelligent Networking Research Group German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

In the latest open radio access network (O-RAN) architecture, the Y1 interface has been introduced to grant authorized parties, known as Y1 consumers, access to and utilization of Y1 data within the near-real-time RAN intelligent controller (Near-RT RIC). The primary objective of enabling communication between Y1 consumers and the Near-RT RIC is to facilitate the introduction of value-added services to O-RAN. This paper comprehensively explores Y1 communications, services, interface, data, and other essential concepts required to understand the subject matter thoroughly. We examine the security and privacy aspects of Y1 communications to ensure that all authorized parties involved in these interactions are equipped to withstand potential threats and vulnerabilities. More importantly, our research proposes a novel architectural framework for Y1 communications that integrates advanced functionalities and specifies secure communication protocols. This framework addresses the limitations of the state-of-the-art Y1 architecture by enhancing its robustness, adaptability, and efficiency. In addition, we outline the procedural flow for Y1 communications within the proposed framework, detailing the functionalities and processes it introduces. Beyond security and privacy procedures, this paper elaborates on the various phases of Y1 communications. Furthermore, a series of experiments are conducted to evaluate the effectiveness of the proposed Y1 framework. Our assessment focuses on the authentication and authorization schemes, revealing that the mutual transport layer security (mTLS) 1.3-based certificate-bound access token (CBAT) scheme demonstrated superior performance in ensuring secure and efficient communication. Additionally, the robustness of the proposed architecture is validated by evaluating its functionality in both subscription and query scenarios. Moreover, the paper discusses the proposed Y1 framework, primarily focusing on its role and functionality within the

关键词： Open RAN Security Privacy Protocols computer architecture Authentication Standards Robustness Guidelines Authorization

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Navigation Design to Improve Interaction Technique in Multi-Browser Virtual Environment

Navigation Design to Improve Interaction Technique in Multi-...

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Mochammad Hannats Hanafi Ichsan Cecilia Sik-Lanyi Tibor Guzsvinecz Department of Electrical Engineering and Information Systems Faculty of Information Technology University of Pannonia Veszprem Hungary Department of Informatics Faculty of Computer Science Brawijaya University Malang Indonesia Hungarian Research Network Budapest Hungary Department of Information Technology and its Applications Faculty of Information Technology University of Pannonia Zalaegerszeg Hungary

ISBN: (数字)9798350378245

ISBN: (纸本)9798350378252

Efficient, precise, and intuitive navigation in a Virtual Environment (VE) or in desktop Virtual Reality (VR) is critical to improving user experience. This research introduces three navigation designs for physical embodiment inside VE using a computer mouse for multi-browser VE in desktop VR based on navigation possibilities. Navigation is intended for the First-Person Shooter (FPS) perspective: Intuitive Navigation (IntuNav), Edge Shift Navigation (EdgeNav), and Twist Shift Navigation (TwistNav). IntuNav uses mouse x and y coordinate movements to move and rotate the camera. EdgeNav relies on the cursor positioning at the edge of the screen for camera rotation and movement. TwistNav uses a middle click and right mouse click for camera movement and rotation. The current navigation design has been implemented in Unity with C#. This research proposed three model designs based on feasible navigation possibilities, which shows that each navigation model has unique advantages that can be adapted to user needs and preferences. Based on the test results using the System Usability Scale, IntuNav and EdgeNav obtained good results and can be used for further development.

关键词： Solid modeling Adaptation models Navigation Virtual environments Cameras User experience C# languages Usability

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Proactive Robust Hardening of Resilient Power Distribution network: Decision-Dependent Uncertainty Modeling and Fast Solution Strategy

arXiv

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arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Jia, Qing-Shan Chen, Chen Zhai, Qiaozhu Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based on resilience importance indices are designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitudes. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic programming

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Robust Co-Optimization of Distribution network Hardening and Mobile Resource Scheduling with Decision-Dependent Uncertainty

arXiv

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arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Chen, Chen Zhai, Qiaozhu Jia, Qing-Shan Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

This paper studies the robust co-planning of proactive network hardening and mobile hydrogen energy resources (MHERs) scheduling, which is to enhance the resilience of power distribution network (PDN) against the disastrous events. A decision-dependent robust optimization model is formulated with min − max resilience constraint and discrete recourse structure, which helps achieve the load survivability target considering endogenous uncertainties. Different from the traditional model with a fixed uncertainty set, we adopt a dynamic representation that explicitly captures the endogenous uncertainties of network contingency as well as the available hydrogen storage levels of MHERs, which induces a decision-dependent uncertainty (DDU) set. Also, the multi-period adaptive routing and energy scheduling of MHERs are modeled as a mixed-integer recourse problem for further decreasing the resilience cost. Then, a nested parametric column-and-constraint generation (N-PC&CG) algorithm is customized and developed to solve this challenging formulation. By leveraging the structural property of the DDU set as well as the combination of discrete recourse decisions and the corresponding extreme points, we derive a strengthened solution scheme with nontrivial enhancement strategies to realize efficient and exact computation. Numerical results on 14-bus test system and 56-bus real-world distribution network demonstrate the resilience benefits and economical feasibility of the proposed method under different damage severity levels. Moreover, the enhanced NPC&CG shows a superior solution capability to support prompt decisions for resilient planning with DDU models. Copyright © 2025, The Authors. All rights reserved.

关键词： Hardening

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Defensive Auto-Updatable and Adaptable Bot Recommender System (DAABRS): A New Architecture Approach in Cloud Computing systems

Defensive Auto-Updatable and Adaptable Bot Recommender Syste...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Sufian Khwaldeh Mohit Y Khushwant S Department of Network and Information Security College of Information Technology University of Fujairah UAE Department of Information Technology Faculty of Information Technology and Systems The University of Jordan Aqaba Jordan Department of Mathematics University Institute of Sciences Chandigarh University Mohali Punjab India Department of Computer Science & Engineering University Institute of Engineering and Technology M. D. University Rohtak India

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

Cloud server data security makes extensive use of data centers and instantly accessible edge systems for possible transactions involving sizable and confidential data. These Edge systems are chosen for rental based on their usefulness in situations where new privacy and security difficulties must be overcome. The use of Edge systems is subject to dynamic mobility, which increases the danger of attacking threats. With this suggested approach, the Defensive Auto-updatable and Adaptable Bot Recommender approach (DAABRS) is used by the Cloud Center to identify the safeguarded Edge System. Cloud Data Centers with Edge systems contain a variety of users and environments, making it simple for attackers to enter anonymously and compromise Edge System privacy. The most effective security measure against increasing risks and assaults for Edge systems that rent more cloud storage is to use Stealth Mode Bots inside of data segments. In addition to anticipating and averting data breaches in server partitioning, tenant systems, edge systems, and containers by integrating stealth mode bots into each. Suggested use cases: Tenants, Edge systems, or Containers for effective security-enhanced Stealth Mode Bots that are activated when a particular data segment is unplugged. Updatable and adaptive bots are used in the suggested strategy to counter Edge strategy assaults. Additionally, it uses Stealth Mode Bots to stop heavily attacked Data Containers, and machine learning will be used to update specifications to identify and prevent further assaults.

关键词： Cloud computing Data centers Privacy Machine learning Containers Chatbots Servers

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Improving Quality of Experience in Cloud Gaming Using Speculative Execution

Improving Quality of Experience in Cloud Gaming Using Specul...

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International Conference on Mobile Computing and Ubiquitous networking (ICMU)

作者： Takumasa Ishioka Tatsuya Fukui Ryouhei Tsugami Toshihito Fujiwara Satoshi Narikawa Takuya Fujihashi Shunsuke Saruwatari Takashi Watanabe Graduate School of Information Science and Technology Osaka University Japan Department of Information and Computer Science Faculty of Engineering Kyoto Tachibana University Japan NTT Access Network Service Systems Laboratories NTT Corporation Japan

Cloud gaming has surged in popularity with the rise of mobile games. Cloud gaming systems allow players to enjoy the latest high-performance games from any location on their mobile devices. However, cloud gaming presents challenges, notably the increased latency due to network delays, creating an issue of end-to-end latency. Minimizing network delays like processing, transmission, and propagation is crucial to offer a user experience similar to playing a local game. Our research explores a speculative video transmission method on cloud gaming servers to overcome these limitations and reduce network latency. In our proposed method, the cloud game server analyzes input patterns from the user, then pre-encodes and sends the corresponding game frames. This process reduces the perceived network delay, making the game more responsive. This paper evaluates the estimated Quality of Experience (QoE) based on an existing QoE model.

关键词：

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