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arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Kim, Dong In Letaief, Khaled B. The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Hong Kong

AI agents based on multimodal large language models (LLMs) are expected to revolutionize human-computer interaction and offer more personalized assistant services across various domains like healthcare, education, manufacturing, and entertainment. Deploying LLM agents in 6G networks enables users to access previously expensive AI assistant services via mobile devices democratically, thereby reducing interaction latency and better preserving user privacy. Nevertheless, the limited capacity of mobile devices constrains the effectiveness of deploying and executing local LLMs, which necessitates offloading complex tasks to global LLMs running on edge servers during long-horizon interactions. In this article, we propose a split learning system for LLM agents in 6G networks leveraging the collaboration between mobile devices and edge servers, where multiple LLMs with different roles are distributed across mobile devices and edge servers to perform user-agent interactive tasks collaboratively. In the proposed system, LLM agents are split into perception, grounding, and alignment modules, facilitating inter-module communications to meet extended user requirements on 6G network functions, including integrated sensing and communication, digital twins, and task-oriented communications. Furthermore, we introduce a novel model caching algorithm for LLMs within the proposed system to improve model utilization in context, thus reducing network costs of the collaborative mobile and edge LLM agents. © 2024, CC BY.

关键词： Learning systems

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Myopically Verifiable Probabilistic Certificates for Safe Control and Learning

arXiv

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arXiv 2024年

作者： Wang, Zhuoyuan Jing, Haoming Kurniawan, Christian Chern, Albert Nakahira, Yorie The Department of Electrical and Computer Engineering Carnegie Mellon Universty PA15213 United States The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore The Department of Computer Science and Engineering University of California San DiegoCA92093 United States

This paper addresses the design of safety certificates for stochastic systems, with a focus on ensuring long-term safety through fast real-time control. In stochastic environments, set invariance-based methods that restrict the probability of risk events in infinitesimal time intervals may exhibit significant long-term risks due to cumulative uncertainties/risks. On the other hand, reachability-based approaches that account for the long-term future may require prohibitive computation in real-time decision making. To overcome this challenge involving stringent long-term safety vs. computation tradeoffs, we first introduce a novel technique termed 'probabilistic invariance'. This technique characterizes the invariance conditions of the probability of interest. When the target probability is defined using long-term trajectories, this technique can be used to design myopic conditions/controllers with assured long-term safe probability. Then, we integrate this technique into safe control and learning. The proposed control methods efficiently assure long-term safety using neural networks or model predictive controllers with short outlook horizons. The proposed learning methods can be used to guarantee long-term safety during and after training. Finally, we demonstrate the performance of the proposed techniques in numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesian Network Structure Learning

arXiv

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arXiv 2023年

作者： Ouyang, Hui Chen, Cheng Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China

Dynamic Bayesian Networks (DBNs), renowned for their interpretability, have become increasingly vital in representing complex stochastic processes in various domains such as gene expression analysis, healthcare, and traffic prediction. Structure learning of DBNs from data is challenging, particularly for datasets with thousands of variables. Most current algorithms for DBN structure learning are adaptations from those used in static Bayesian Networks (BNs), and are typically focused on small-scale problems. In order to solve large-scale problems while taking full advantage of existing algorithms, this paper introduces a novel divide-and-conquer strategy, originally developed for static BNs, and adapts it for large-scale DBN structure learning. In this work, we specifically concentrate on 2 Time-sliced Bayesian Networks (2-TBNs), a special class of DBNs. Furthermore, we leverage the prior knowledge of 2-TBNs to enhance the performance of the strategy we introduce. Our approach significantly improves the scalability and accuracy of 2-TBN structure learning. Experimental results demonstrate the effectiveness of our method, showing substantial improvements over existing algorithms in both computational efficiency and structure learning accuracy. On problem instances with more than 1,000 variables, our approach improves two accuracy metrics by 74.45% and 110.94% on average, respectively, while reducing runtime by 93.65% on average. Copyright © 2023, The Authors. All rights reserved.

关键词： Bayesian networks

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Modeling and Simulation of Total Attenuation of P2P NLOS 5G and Beyond Wireless Communication systems Operating in FR2 Bands in Nigeria 2

Modeling and Simulation of Total Attenuation of P2P NLOS 5G ...

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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Alozie, Emmanuel Faruk, Nasir Abdulkarim, Abubakar Usman, Aliyu D. Imam-Fulani, Yusuf Olayinka Garba, Salisu Adewole, Kayode S. Oloyede, Abdulkarim A. Sowande, Olugbenga A. Baba, Bashir Abdullahi Musa, Abdulwaheed Imoize, Agbotiname Lucky Adediran, Yinusa A. Olagunju, Hawau I. Taura, Lawan S. Sule Lamido University Department of Information Technology Kafin Hausa Nigeria Sule Lamido University Directorate of Information and Communication Technology Kafin Hausa Nigeria Ahmadu Bello University Department of Electrical Engineering Nigeria Kampala International University Department of Electrical and Telecommunications Engineering Kansanga Kampala Uganda Ahmadu Bello University Department of Electronics and Telecommunication Engineering Nigeria University of Ilorin Department of Telecommunication Science Ilorin Nigeria Sule Lamido University Department of Software Engineering Kafin Hausa Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria Sule Lamido University Department of Cyber Security Kafin Hausa Nigeria Kwara State University Department of Electrical and Computer Engineering Malete Nigeria University of Johannesburg Institute for Intelligent Systems Johannesburg South Africa University of Lagos Department of Electrical and Electronics Engineering Lagos Akoka Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum Germany University of Ilorin Department of Electrical and Electronics Engineering Ilorin Nigeria Sule Lamido University Department of Physics Kafin Hausa Nigeria

ISBN: (纸本)9798350358834

Signal propagation in a particular region differs from another due to differences in atmospheric, climatic and environmental properties, distinct terrain and clutter features. Adequate analysis is essential to understand the radio propagation behavior in a particular region. The ITU-R designated four rain regions, M, N, P, and Q, for Nigeria representing the rain rate distribution and also provided further classifications based on ground conductivity, among other salient parameters. Based on these classifications, this paper utilized the EDX Signal Pro software® to model and simulate a typical Point-to-Point (P2P), Non-Line of Sight (NLOS) link scenarios for 5G networks. The objective was to estimate and compare the total loss, excess loss, and flat fade margins for each rain region in Nigeria. Results obtained from the comparison showed that signals propagating in region N experience the highest level of losses and fading, while, region Q has the least losses and fading. © 2023 IEEE.

关键词： 5G mobile communication systems

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Quantum Computing's Double-Edged Sword Unravelling the Vulnerabilities in Quantum Key Distribution for Enhanced Network Security

Quantum Computing's Double-Edged Sword Unravelling the Vulne...

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Next Generation Electronics (NEleX), International Conference on

作者： I. Anantraj B. Umarani C. Karpagavalli C. Usharani S. Jothi Lakshmi Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Namakkal India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India

Quantum computing has emerged as both a boon and a bane in the realm of network security. This paper delves into the intriguing interplay between quantum computing and the vulnerabilities of Quantum Key Distribution (QKD) protocols, a cornerstone of secure quantum communication. While QKD offers unmatched security through the principles of quantum mechanics, it is not impervious to attacks in the age of rapidly advancing quantum computing. We examine these vulnerabilities, highlighting the challenges of ensuring the long-term security of quantum networks in a post-quantum world. Through a comprehensive exploration of threat models and the practicality of QKD, we uncover potential weaknesses in quantum network security. In particular, we investigate QKD's susceptibility to evolving quantum algorithms, hardware vulnerabilities, and post-processing security concerns. This work aims to provide a roadmap for understanding and addressing these vulnerabilities, ensuring the continued robustness of quantum communication networks, and fostering the development of post-quantum cryptographic solutions. As quantum computing advances, its implications for quantum network security are far-reaching. This study underlines the critical need for research and innovation in securing quantum communication. By unravelling the vulnerabilities within Quantum Key Distribution and exploring post-quantum cryptographic alternatives, this research contributes to the development of more resilient and secure quantum networks, safeguarding sensitive information and communications in an increasingly quantum-enabled world.

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Parasitic Parameters Effect on the Operation of High Frequency Electric Drive System

Parasitic Parameters Effect on the Operation of High Frequen...

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International Conference on Electrical Machines, ICEM

作者： Fawzy A. Abdo Kotb B. Tawfiq Peter Sergeant Department of Electromechanical Systems and Metal Engineering Ghent University Ghent Belgium Flanders Make@UGent Core Lab MIRO Leuven Belgium Department of Electrical Engineering Menoufia University Menoufia Egypt Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350370607

ISBN: (纸本)9798350370614

The rapid switching of semiconductor power modules in electric drive systems generates high frequency (HF) common mode voltage at the motor terminals which causes bearing failure and winding degradation. Therefore, accurate characterization of the inverter and motor parameters - including parasitic effects - is vitally important to understand the high frequency performance as well as to model and mitigate the switching oscillations of the electric drive system. In this paper, the high frequency operation of the inverter in the electric drive system, as well as the direct effect of the parasitic components on the complete system are thoroughly investigated. Moreover, a detailed extraction process of the high frequency parasitic parameters is introduced. An equivalent circuit model of the complete drive system based on the impedance characteristics is introduced. Additionally, a HF model of a 750W three-phase induction motor is developed with parameter values determined from measurements on this motor.

关键词： Semiconductor device measurement Voltage measurement Induction motors Switches Inverters Frequency measurement High frequency Integrated circuit modeling Oscillators Equivalent circuits

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Machine Learning Algorithms for Intrusion Detection Performance Evaluation and Comparative Analysis

Machine Learning Algorithms for Intrusion Detection Performa...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： B. Md. Irfan V. Poornima S Mohana Kumar Upendra Singh Aswal N. Krishnamoorthy Ramya Maranan Department of Information Technology Nalsar University of Law Hyderabad Telengana India Department of Computer Science VISTASPallavaram Chennai Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Software Systems and Engineering Vellore Institute of Technology Vellore Campus Vellore Tamilnadu Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

The security of computer networks is increasingly difficult to maintain due to the rising complexity and frequency of cyber-attacks. Important tools for finding and neutralizing these dangers are intrusion detection systems. This study sets out to do a thorough examination and comparison of the efficacy of several machine learning algorithms for use in intrusion detection. This research study evaluates the efficacy of several machine learning algorithms in correctly categorizing instances of network traffic as normal or invasive via extensive experiments performed on representative datasets. Algorithms like random forests, decision trees, SVMs, DL models and NNs are all being tested and rated. Effectiveness is measured and compared using a variety of performance indicators including accuracy, recall, precision, false positive rate, and F1-score. The results of this study emphasize the potential of deep learning models and Random Forests for use in intrusion detection and add to the body of knowledge around machine learning methods for this task. Professionals in the field of network security might use the results to their advantage when building intrusion detection systems. Future research areas are also mentioned, which will hopefully lead to even greater improvements in the field and safer, more reliable intrusion detection systems.

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BatteryCDE: A Transferable Future Capacity Estimation Method for Battery Degradation with Irregular Sampling and Missing Data

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IEEE Transactions on Transportation Electrification 2025年

作者： Wang, Tianjing Ren, Chao Xiong, Houbo Song, Qun Dong, ZhaoYang City University of Hong Kong Department of Electrical Engineering Hong Kong KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden Zhejiang University School of Electrical Engineering China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore

To overcome critical limitations in existing methods for battery capacity estimation, including long-term estimation, handling of irregular sampling and missing data, and transferability across different battery types, this study proposes a novel estimation method, termed batteryCDE, by integrating neural controlled differential equations (CDEs) with attention mechanisms. It first constructs a continuous data path using cubic spline interpolation, followed by neural CDEs that generate feature-wise and cycle-wise attention features to capture essential battery characteristics. These attention-weighted features are processed by another neural CDE to model differential relationships, leading to precise capacity estimations. This study also provides a theoretical analysis of the advantages of batteryCDE over conventional discrete models in terms of long-term estimation, handling of irregular sampling and missing data, and transferability. Extensive experiments evaluate batteryCDE’s performance, utilizing three scenarios: varying estimation horizons, missing data, and cross-battery transfer. Results show that batteryCDE outperforms traditional models like LSTM, Transformer and neural CDE networks. Even with 50% missing data, an estimation horizon of 100 cycles, and application to different batteries, batteryCDE maintains an estimation error below 0.05. Compared to other methods, batteryCDE reduces estimation errors by 6.59% for long-term predictions, 15.09% for handling missing data, and 17.01% for cross-battery transferability. © 2025 IEEE. All rights reserved.

关键词： Battery degradation capacity irregular and missing data neural controlled differential equations transferability

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Handoff and Deposit: Designing Temporal Coordination in Cross-Device Transfer Techniques for Mixed-Focus Collaboration

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Proceedings of the ACM on Human-computer Interaction 2022年第CSCW2期6卷 1-23页

作者： Homaeian, Leila Wallace, James R. Scott, Stacey D. School of Public Health and Health Systems University of Waterloo WaterlooONN2L 3G1 Canada School of Computer Science University of Guelph GuelphONN1G 2W1 Canada Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

When working together, people frequently share information with each other to enable division of labour, assistance, and delegation of responsibility. The literature has explored both synchronous and asynchronous transfer techniques, known as Handoff and Deposit, respectively. However, current cross-device environments tend to only provide a single mechanism. Moreover, we have little understanding of the impact of different techniques on collaborative process. To understand how Handoff and Deposit may be designed to support complex sensemaking tasks, we followed a Research through Design process to iteratively design Handoff and Deposit techniques using paper and digital sketches and high-fidelity prototypes. We consulted the HCI literature to corroborate our findings with studies and descriptions of existing cross-device transfer designs and to understand the potential impact of those designs on mixed-focus collaboration. We learned that as we move away from a restricted physical workspace and leverage the flexibility of digital personal devices, there is a large design space for realizing cross-device transfer. To inform these designs, we provide five design considerations for cross-device transfer techniques: Transfer Acceptance, Action Dependencies, Immediate Usability, Interruption Potential, and Connection Actions. © 2022 ACM.

关键词： Design

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PupilRec: Leveraging Pupil Morphology for Recommending on Smartphones

PupilRec: Leveraging Pupil Morphology for Recommending on Sm...

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作者： Shen, Xiangyu Jiang, Hongbo Liu, Daibo Yang, Kehua Deng, Feiyang Lui, John C. S. Liu, Jiangchuan Dustdar, Schahram Luo, Jun Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China The City University of Hong Kong School of Graduate Studies Hong Kong The Chinese University of Hong Kong Computer Science and Engineering Department Hong Kong Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada Vienna University of Technology Distributed Systems Group Vienna1040 Austria Nanyang Technological University School of Computer Engineering 639798 Singapore

As mobile shopping has gradually become the mainstream shopping mode, recommendation systems are gaining an increasingly wide adoption. Existing recommendation systems are mainly based on explicit and implicit user behaviors. However, these user behaviors may not directly indicate users' inner feelings, causing erroneous user preference estimation and thus leading to inaccurate recommendations. Inspired by our key observation on the correlation between pupil size and users' inner feelings, we consider using the change of pupil size when browsing to model users' preferences, so as to achieve targeted recommendations. To this end, we propose PupilRec as a computer-vision-based recommendation framework involving a mobile terminal and a server side. On the mobile terminal, PupilRec collects users' pupil size change information through the front camera of smartphones;it then preprocesses the raw pupil size data before transmitting them to the server. On the server side, PupilRec utilizes the Tsfresh package and Random Forest algorithm to figure out the key time-series features directly implying user preferences. PupilRec then trains a neural network to fit a user preference model. Using this model, PupilRec predicts user preference to obtain a user-product matrix and further simplifies it by singular value decomposition. Finally, the real-time recommendation is achieved by a collaborative filtering module that retrieves recommended contents to users smartphones. We prototype PupilRec and conduct both experiments and field studies to comprehensively evaluate the effectiveness of PupilRec by recruiting 67 volunteers. The overall results show that PupilRec can accurately estimate users' preference and can recommend products users interested in. © 2014 IEEE.

关键词： Smartphones

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