检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Yao, Jiawei Mao, Yijie Chen, Mingzhe Hu, Ye School of Information Science and Technology ShanghaiTech University Shanghai201210 China Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States

Reconfigurable Intelligent Surface (RIS) has been recognized as a promising solution for enhancing localization accuracy. Traditional RIS-based localization methods typically rely on prior channel knowledge, beam scanning, and pilot-based assistance. These approaches often result in substantial energy and computational overhead, and require real-time coordination between the base station (BS) and the RIS. To address these challenges, in this work, we move beyond conventional methods and introduce a novel data-driven, multiple RISs-assisted passive localization approach (RAPL). The proposed method includes two stages, the angle-of-directions (AoDs) between the RISs and the user is estimated by using the conditional sample mean in the first stage, and then the user’s position is determined based on the estimated multiple AoD pairs in the second stage. This approach only utilizes the existing communication signals between the user and the BS, relying solely on the measurement of received signal power at each BS antenna for a set of randomly generated phase shifts across all RISs. Moreover, by obviating the need for real-time RIS phase shift optimization or user-to-BS pilot transmissions, the method introduces no additional communication overhead, making it highly suitable for deployment in real-world networks. The proposed scheme is then extended to multi-RIS scenarios considering both parallel and cascaded RIS topologies. Numerical results show that the proposed RAPL improves localization accuracy while significantly reducing energy and signaling overhead compared to conventional methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

来源：评论

学校读者我要写书评

暂无评论

Optimizing AIGC Services Using Learning-based Stackelberg Game in Vehicular Metaverses

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Lai, Bingkun Luo, Xiaofeng Kang, Jiawen Gao, Xiaozheng Yang, Zuyuan Niyato, Dusit Mao, Shiwen Guangdong University of Technology School of Automation Guangzhou510006 China Beijing Institute of Technology School of Information and Electronics Beijing10081 China Nanyang Technological University College of Computing and Data Science Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL United States

The emerging vehicular metaverse embodies the next-generation vehicular networking paradigm. In the vehicular metaverses, Artificial Intelligence-Generated Content (AIGC) technology as a powerful content generation tool, is capable of providing an immersive experience for Vehicular Metaverse Users (VMUs). Due to limited computational resources within vehicles, VMUs rely on AIGC Service Providers (ASPs) to execute resource-intensive AIGC tasks within vehicular metaverses. However, large-scale AIGC service requests can lead to resource scarcity within the ASP, ultimately leading to declining service quality for VMUs. To tackle this challenge, we introduce a novel Stackelberg game framework utilizing the Generative Diffusion Model (GDM) for AIGC services, in which we experimentally reveal a relationship between image quality and diffusion steps. A Transformer-based Deep Reinforcement Learning (TDRL) algorithm is employed to find the optimal Stackelberg equilibrium under incomplete information. Numerical results indicate that our method converges to equilibrium efficiently, with superior utilities compared to baseline approaches. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Generative AI-enabled Wireless Communications for Robust Low-Altitude Economy Networking

arXiv

引用

arXiv 2025年

作者： Zhao, Changyuan Wang, Jiacheng Zhang, Ruichen Niyato, Dusit Sun, Geng Du, Hongyang Kim, Dong In Jamalipour, Abbas College of Computing and Data Science Nanyang Technological University Singapore CNRS@CREATE 1 Create Way 08-01 Create Tower 138602 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Jilin University 130012 China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of School of Electrical and Computer Engineering University of Sydney Australia

Low-Altitude Economy Networks (LAENets) have emerged as significant enablers of social activities, offering low-altitude services such as the transportation of packages, groceries, and medical supplies. Unlike traditional terrestrial networks, LAENets are characterized by control mechanisms and ever-changing operational factors, which make them more complex and susceptible to vulnerabilities. As applications of LAENet continue to expand, robustness of these systems becomes crucial. In this paper, we investigate a novel application of Generative Artificial Intelligence (GenAI) to improve the robustness of LAENets. We conduct a systematic analysis of robustness requirements for LAENets, complemented by a comprehensive review of robust Quality of Service (QoS) metrics from the wireless physical layer perspective. We then investigate existing GenAI-enabled approaches for robustness enhancement. This leads to our proposal of a novel diffusion-based optimization framework with a Mixture of Expert (MoE)-transformer actor network. In the robust beamforming case study, the proposed framework demonstrates its effectiveness by optimizing beamforming under uncertainties, achieving a more than 44% increase in the worst-case achievable secrecy rate. These findings highlight the significant potential of GenAI in strengthening LAENet robustness. © 2025, CC BY.

关键词： Beamforming

来源：评论

学校读者我要写书评

暂无评论

All-optical scalable and programmable VCSEL-based Ising annealer with parallel feedback

引用

Optics Express 2025年第11期33卷 22119-22131页

作者： Dewen Zhang Zifeng Yuan Thanh Xuan Hoang Wujie Fu Ching Eng Png Soon Thor Lim Aaron Danner National University of Singapore Department of Electrical and Computer Engineering 4 Engineering Drive 3 117583 Singapore Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A★STAR) 1 Fusionopolis Way #16-16 Connexis 138632 Singapore

An Ising machine stands as a compelling solution for large-scale combinatorial optimization problems, leveraging its unconventional computational architecture. We propose an optical parallel feedback system, employing mutual injection locking of vertical-cavity surface-emitting lasers (VCSELs) and programmable spatial light modulators (SLMs), specifically aimed at removing electrical bottlenecks to achieve all-optical computation. Through simulation results, the ability to solve a 16-bit square-lattice MAXCUT optimization problem is analyzed. As a proof-of-concept, we demonstrate an optical injection lock using single-mode master and slave VCSELs. Remarkably, we achieve polarization switching with less than 1% of the master VCSEL’s injection power. Building upon this modest achievement, we outline a roadmap envisioning the integration of advanced nanophotonic devices, particularly metasurface-enabled SLMs and VCSELs. This strategic evolution targets scalability and programmability while upholding an all-optical computing paradigm.

关键词： Diffractive optical elements Fiber lasers Light propagation Optical parametric oscillators Spatial light modulators Vertical cavity surface emitting lasers

来源：评论

学校读者我要写书评

暂无评论

Understanding Cybercrime: A Study of Malware Analysis, Phishing Attacks, and Sentiment Trends

Understanding Cybercrime: A Study of Malware Analysis, Phish...

引用

Intelligent and Innovative Technologies in computing, electrical and Electronics (IITCEE), International Conference

作者： U. Kumaran Sreeraag G Advik Narendran V. P. Meena Sushil Kumar Gupta Abhishek Appaji Department of Computer Science and Engineering Amrita School of Computing Bangalore Amrita Vishwa Vidyapeetham India Department of Electrical Engineering National Institute of Technology Jamshedpur Jharkhand India Department of Medical Electronics Engineering BMS College of Engineering Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

With the rapid evolution of computer systems and technologies, individuals face an increasing threat of cybercrime. This research explores the changing landscape of cybercrime by focusing on three key areas: malware analysis, phishing attack analysis, and sentiment analysis. The study aims to identify malware strategies for evading detection and analyze the prevalence of spyware by examining diverse malware samples. Additionally, trend analysis highlights the rising complexity and frequency of phishing attacks, with a notable shift toward targeting reputable platforms. Sentiment analysis of media headlines surrounding data breaches uncovers a paradox: while concerns about cybersecurity remain, there is a growing confidence in the ability to manage cybersecurity crises. These findings provide insights into emerging threats and evolving public perceptions, offering a comprehensive understanding of the current cyber- crime landscape.

关键词： Sentiment analysis Phishing Focusing data breach Media Market research Malware Complexity theory computer crime Spyware

来源：评论

学校读者我要写书评

暂无评论

Explainable AI in Blockchain System for Decentralized Governance

Explainable AI in Blockchain System for Decentralized Govern...

引用

Pervasive Computational Technologies (ICPCT), International Conference on

作者： Suman Chahar Kawaljit Kaur Kuldeep Singh Kaswan Jagjit Singh Dhatterwal University Institute of Computing Chandigarh University Mohali India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Decentralized governance is going through radical changes due to the innovative use of blockchain which allows decisions to be made in a transparent, secure and more im- portantly, immutable way, without the authority of a single individual or party. Thus, the introduction of artificial intelligence (AI) in blockchain governing systems complicates such thing as transparency and trust, especially in the case of deep AI systems. But more on this later – this is where Explainable AI or XAI comes into the picture. XAI methods enable understanding of the reasons behind the choices made by AI models and solves the "black box" problem that is predominant in machine learn- ing. When applied to decentralised governance, XAI provides clarifications of AI’s decision making in such areas as voting, resource distribution and the enactment of smart contracts. This way, the stakeholders are confident that the decision-making is fair and impartial and this is made possible by the use of XAI. This paper aims to find out more about the applicability of XAI concept in blockchain –based governance systems in terms of bias and fairness in automated decisions and explanation of the decisions made. The reasons why interpretability plays the crucial role in decentralized systems are explained with the help of the comparison between the classical artificial intelligence and the Xin Intelligent. The study also looks at various use cases such as, Decentralized Autonomous Organizations (DAOs), and voting systems implemented on blockchain platforms and how XAI can enhance the performance of the governance systems. As this study shows, XAI is vital in building trust, improving responsibility, and guaranteeing balanced majorities in decentralized blockchain governance structures.

关键词： Explainable AI Decentralized autonomous organization Computational modeling Smart contracts Decision making Blockchains Resource management Stakeholders Security Decision trees

来源：评论

学校读者我要写书评

暂无评论

Generative AI based Secure Wireless Sensing for ISAC Networks

引用

IEEE Transactions on Information Forensics and Security 2025年

作者： Wang, Jiacheng Du, Hongyang Liu, Yinqiu Sun, Geng Niyato, Dusit Mao, Shiwen Kim, Dong In Shen, Xuemin Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Jilin University College of Computer Science and Technology 130012 China Auburn University Department of Electrical and Computer Engineering Auburn United States Sungkyunkwan University Department of Electrical and Computer Engineering Suwon 16419 South Korea University of Waterloo Department of Electrical and Computer Engineering Canada

Integrated sensing and communications (ISAC) is one of the crucial technologies for 6G, and channel state information (CSI) based sensing serves as an essential part of ISAC. However, current research on ISAC focuses mainly on improving sensing performance, overlooking security issues, particularly the unauthorized sensing of users. Hence, this paper proposes a diffusion model based secure sensing system (DFSS). Specifically, we first propose a discrete conditional diffusion model to generate graphs with nodes and edges, which guides the ISAC system to appropriately activate wireless links and nodes, ensuring the sensing performance while minimizing the operation cost. Using the activated links and nodes, DFSS then employs the continuous conditional diffusion model to generate safeguarding signals, which are next modulated onto the pilot at the transmitter to mask fluctuations caused by user activities. As such, only authorized ISAC devices with the safeguarding signals can extract the true CSI for sensing, while unauthorized devices are unable to perform the effective sensing. Experiment results demonstrate that DFSS can reduce the activity recognition accuracy of the unauthorized devices by approximately 70%, effectively shield the user from the illegitimate surveillance. © 2005-2012 IEEE.

关键词： Generative AI integrated sensing and communication wireless sensing security

来源：评论

学校读者我要写书评

暂无评论

AI-enabled driver assistance: monitoring head and gaze movements for enhanced safety

引用

Complex and Intelligent Systems 2025年第7期11卷 1-28页

作者： Mudassar Shah, Sayyed Zengkang, Gan Sun, Zhaoyun Hussain, Tariq Zaman, Khalid Alwabli, Abdullah Jaffar, Amar Y. Ali, Farman School of Civil and Transportation Engineering Shenzhen University Guangdong 518060 China School of Computer Science and Technology Zhejiang Gongshang University Hangzhou 310018 China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic University Guangdong Shenzhen 518000 China Department of Electrical Engineering College of Engineering and Computing in Alqunfudah Umm Al-Qura University Mecca 21955 Saudi Arabia Computer and Network Engineering Department College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science and Engineering School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea

This paper introduces a real-time head-pose detection and eye-gaze estimation system for Automatic Driver Assistance Technology (ADAT) aimed at enhancing driver safety by accurately collecting and transmitting data on the driver’s head position and eye gaze to mitigate potential risks. Existing methods are constrained by significant limitations, including reduced accuracy under challenging conditions such as varying head orientations and lighting, higher latency in real-time applications (e.g., Faster-RCNN and TPH-YOLOv5), and computational inefficiency, which hinders their deployment in resource-constrained environments. To address these challenges, we propose a novel framework using the Transformer Detection of Gaze Head - YOLOv7 (TDGH-YOLOv7) object detector. The key contributions of this work include the development of a reference image dataset encompassing diverse vertical and horizontal gaze positions alongside the implementation of an optimized detection system that achieves state-of-the-art performance in terms of accuracy and latency. The proposed system achieves superior precision, with a weighted accuracy of 95.02% and Root Mean Square Errors of 2.23 and 1.68 for vertical and horizontal gaze estimation, respectively, validated on the MPII-Gaze and DG-Unicamp datasets. A comprehensive comparative analysis with existing models, such as CNN, SSD, Faster-RCNN, and YOLOv8, underscores the robustness and efficiency of the proposed approach. Finally, the implications of these findings are discussed, and potential avenues for future research are outlined. © The Author(s) 2025.

关键词： Eye gaze estimation Eye tracking Facial detection Gaze Head pose detection

来源：评论

学校读者我要写书评

暂无评论

A systematic literature review to implement large language model in higher education: issues and solutions

引用

Discover Education 2025年第1期4卷 1-25页

作者： Guizani, Sghaier Mazhar, Tehseen Shahzad, Tariq Ahmad, Wasim Bibi, Afsha Hamam, Habib College of Engineering Alfaisal University Riyadh 11533 Saudi Arabia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Computing School of Arts and Creative Technology University of Greater Manchester Manchester BL15AB United Kingdom Department of Computer and Software Technology University of Swat Swat 19200 Pakistan Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Artificial intelligence-driven Chatbots, especially large language models (LLMs) like GPT-4, represent significant progress in digital education. These models excel in mimicking human-like text and transforming learning and teaching methods. This study examines the development, application, and impact of LLMs in education. It highlights their role in automating instructional tasks and promoting personalized learning experiences. Despite integration concerns and ethical debates, LLMs showcase the potential of AI to improve educational practices. Our research concludes that LLMs offer transformative opportunities for education. However, their incorporation requires careful ethical considerations, data privacy measures, and a balance between human educators and AI technologies. The findings suggest strategies for integrating LLMs into educational frameworks to enhance learning outcomes while preserving educational integrity. © The Author(s) 2025.

关键词： Artificial intelligence Education Large language models Learning systems Natural language processing systems Neural networks

来源：评论

学校读者我要写书评

暂无评论

Secrecy Energy Efficiency Maximization in IRS-Assisted VLC MISO Networks with RSMA: A DS-PPO Approach

引用

IEEE Transactions on Wireless Communications 2025年

作者： Guo, Yangbo Fan, Jianhui Zhang, Ruichen Chang, Baofang Ng, Derrick Wing Kwan Niyato, Dusit Kim, Dong In Henan Normal University School of Computer and Information Engineering Xinxiang453007 China Engineering Lab of Intelligence Business and Internet of Things Henan453007 China Nanyang Technological University College of Computing and Data Science Singapore Singapore University of New South Wales School of Electrical Engineering and Telecommunications NSW2052 Australia Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of

This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting multiple access (RSMA) scheme. In these networks, an eavesdropper (Eve) attempts to eavesdrop on communications intended for legitimate users (LUs). To enhance information security and energy efficiency simultaneously, we formulate a secrecy energy efficiency (SEE) maximization problem by jointly optimizing the beamforming vectors, RSMA common rates, direct current (DC) bias, and IRS alignment matrices. The problem is constrained by total power budget, quality of service (QoS) requirements, linear operating region of light emitting diodes (LEDs), and common information rate allocation. Due to the non-convex and NP-hard nature of the formulated problem, we propose a deep reinforcement learning (DRL)-based dual-sampling proximal policy optimization (DS-PPO) approach. The approach leverages dual sample strategies and generalized advantage estimation (GAE). In addition, the maximum ratio transmission (MRT) and zero-forcing (ZF) are adopted to design the beamforming vectors. Simulation results show that the proposed DS-PPO approach outperforms traditional baseline approaches. Moreover, the implementation of the RSMA scheme and IRS contributes to overall system performance, achieving approximately 19.67% improvement over traditional multiple access schemes and 25.74% improvement over networks without IRS deployment. © 2002-2012 IEEE.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：