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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Laso, Sergio Martín, Luis Herrera, Juan Luis Galán-Jiménez, Jaime Berrocal, Javier Murillo, Juan M. Cáceres Spain University of Extremadura Dept. of Computer Systems and Telematics Engineering Spain University of Bologna Dipartamento di Informatica Scienza e Ingegneria Italy Cáceres Spain

ISBN: (纸本)9798350370218

Thanks to the Internet of Things (IoT) paradigm, it is now possible to develop computer applications that automate real, intensive processes, by processing sensor information using techniques such as artificial intelligence or computer vision. Nonetheless, due to the criticality of the processes they automate, intensive IoT applications have very strict Quality of Service (QoS) requirements to function properly, motivating their deployment throughout the Computing Continuum to optimize the obtained QoS. Evaluating and guaranteeing the QoS of this application can be a challenging endeavor, as current QoS evaluation tools are unable to predict future changes to the real world, evaluate multiple adaption alternatives, or act accordingly. To fulfill these needs, in this paper, we present the evolution of a Computing Continuum emulator towards Dantalion, a complete Digital Twin for the Computing Continuum. © 2023 IEEE.

关键词： Computing Continuum Digital Twin In-ternet of Things Quality of Service

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Deep Learning-Based Energy Efficiency Maximization in Massive MIMO-NOMA Networks With Multiple RISs

Deep Learning-Based Energy Efficiency Maximization in Massiv...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Ridho Hendra Yoga Perdana Toan-Van Nguyen Yushintia Pramitarini Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea Department of Electrical and Computer Engineering San Diego State University San Diego CA USA Dept. of Software and Communications Engineering Hongik University Republic of Korea

In this paper, we study a deep learning (DL)-based energy efficiency maximization (EEM) problem in massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) networks with multiple reconfigurable intelligent surfaces (RISs). These RISs are deployed randomly at the edge of the near radius to assist the base station (BS) in communicating with near and far users. We formulate the problem of jointly optimizing the precoding matrix and phase shift of the RISs to maximize the overall energy efficiency subject to the quality-of-service of each user, phase shift of RISs, and power budget of the BS constraints. To address this challenging non-convex problem with mixed-integer constraints, the original problem is decoupled into phase shift and beamforming sub-problems, then addressed them separately. We introduce the bisection search algorithm to address the challenge of the phase shifts optimization problem. For the beamforming optimization, it is transformed into an equivalent non-convex problem but with a more tractable form. Then, we propose an iterative algorithm based on the inner approximation method for its solution. To support real-time optimization, we design a deep learning framework to predict optimal solutions of phase shifts at RISs and precoding matrix under different parameter settings. Simulation results show that the proposed DL-based approach can predict the optimal solution with high accuracy in a short time compared to the conventional approach. Additionally, the effect of the maximum power budget at the base station, the number of RISs, and BS's antennas are evaluated thoroughly.

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Phoneme Classification in Imagined Speech Using Explainable Machine Learning

Phoneme Classification in Imagined Speech Using Explainable ...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Sejin Kim Hyunwook Kang Ji-Hoon Jeong Minji Lee Dept. Artificial Intelligence The Catholic University of Korea Bucheon South Korea Dept. Electrical and Computer Engineering Inha University Incheon South Korea Dept. Computer Science Chungbuk National University Cheongju South Korea Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon South Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Imagined speech is gaining attention as a next-generation paradigm for brain-computer interfaces in terms of its intuitiveness in communication. Many studies have focused on classifying imagined words as the basis of communication, but some studies on phoneme classification based on imagined speech have begun to be conducted in terms of the fact that combinations of phonemes can further expand sound images. In addition, explainability is a major issue in classification results in machine learning, but there have been few studies that analyzed the explainability of imagined speech classification. In this study, we examined explainability using SHAP for phoneme classification based on imagined speech. FEIS dataset was used, and 16 phonemes were classified using the XGBoost classifier. In 21 subjects, the average accuracy was 40.36 %, the average precision was 41.7%, the average recall was 40.34%, and the average F1-score was 40.7%. In addition, when looking at Sub 04, which had the highest performance, the SHAP values were high in AF3 and F7 channels, which correspond to Wernicke's area related to speech. In this regard, we could observe the feature importance of this area. These results prove that Wernicke's area actually plays an important role in classifying phoneme units in imagined speech, and can provide important insights to improve the performance of imagined speech for intuitive communication based on brain-computer interface in the future.

关键词： Accuracy Machine learning algorithms Closed box Machine learning Predictive models Brain modeling Brain-computer interfaces Classification algorithms Reliability Next generation networking

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Wide-Band Signal Generation Using Egyptian Fractions and Application to Instantaneous Impedance Measurements

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IEEE Sensors Journal 2025年第11期25卷 19555-19562页

作者： Majzoub, Sohaib Allagui, Anis Elwakil, Ahmed S. Zhang, Di University of Sharjah Depts. of Electrical Eng. and Computer Eng PO Box 27272 Sharjah United Arab Emirates University of Sharjah Dept. of Sustainable and Renewable Energy Eng PO Box Sharjah27272 United Arab Emirates Florida International University Dept. of Mechanical and Materials Eng MiamiFL33174 United States University of Sharjah Dept. of Electrical Eng P.O. Box 27272 Sharjah United Arab Emirates Nile University Nanoelectronics Integrated Systems Center Cairo Egypt University of Calgary Dept. of Electrical and Software Eng AB Canada

In this work, we investigate the use of Egyptian fractions to construct wide-band signals with a near-flat power spectrum. In particular, it is well-known that wide-band signals can be obtained by using composite multi-sine signals. However, the choice of the frequencies and/or phase shifts in these sinusoids is critical in order to approach a flat power spectrum with a low crest factor. A number of known methods, such as using Rudin-Shapiro polynomials or random phases, have been employed in this problem. However, the use of Egyptian fractions has not been explored yet;despite being easier to generate and implement. Here, we construct multi-sine signals (in the form of a voltage) based on different Egyptian fraction sequences and use them to measure the linear time-invariant impedance of an RC circuit and then the instantaneous impedance of nonlinear time-varying solar cell. Two methods are used to generate these signals: (i) the Egyptian fraction sequences are assigned to the phase angles or (ii) they are used to set the values of the angular frequencies. The accuracy of measurements is assessed via comparison with the standard sine-sweep method using a research-grade Biologic VSP-300 electrochemical station showing acceptable results. © 2001-2012 IEEE.

关键词： Sheet resistance

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A Study on a Personalized Robot Golf Trainer System with Multi-Persona and Ethical Guidelines for Robots

A Study on a Personalized Robot Golf Trainer System with Mul...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Younglim Choi BoJeong Park JongWoo Kim SeoYeong Mun Hyunseok Kim dept. of Software Innovation Center Dong-A University Busan South Korea dept. of Electronic Engineering Dong-A University Busan South Korea dept. of Mechanical Engineering Dong-A University Busan South Korea dept. of Computer Engineering Dong-A University Busan South Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This paper proposes a multi-persona and robot ethics system for a personalized robot golf trainer. The system combines a wheeled mobile robot without arms and GolfPoseNet for analyzing golf postures and integrates a MediaPipe Pose-based user command recognition system to enhance the golf learning experience. To improve the learning process for users of all ages, the system incorporates a multipersona approach. It features custom training designed according to the Robot Service Personality (RSP) model developed by the author, with interaction styles tailored to each service personality type. Additionally, the system optimizes feedback style and training speed based on each user's preferences and learning style, resulting in a highly personalized experience. In an outdoor golf setting, the system aims to support efficient learning by utilizing both an external camera and the robot's camera for accurate posture analysis, offering real-time feedback. Furthermore, by integrating robot ethics, the system ensures user privacy and safety while fostering trust in AI robots. This study provides foundational insights into the potential for personalized AI service robot development in golf posture analysis and human-robot interaction, serving as groundwork for future system advancements.

关键词： Training Ethics Service robots Robot vision systems Human-robot interaction Manipulators Cameras Mobile robots Artificial intelligence Robots

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A Study of Emotional Expression Interface for Wheeled Mobile Robot to Enhance Emotional Bonding

A Study of Emotional Expression Interface for Wheeled Mobile...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： SeoYeong Mun BoJeong Park JongWoo Kim Younglim Choi Hyunseok Kim dept. of Mechanical Engineering Dong-A University Busan South Korea dept. of Electronic Engineering Dong-A University Busan South Korea dept. of Software Innovation Center Dong-A University Busan South Korea dept. of Computer Engineering Dong-A University Busan South Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This study aims to investigate how the emotional expression of robots and their physical design significantly influence the establishment of emotional bonds with users in Human-Robot Interaction (HRI). Utilizing the commercially available Wheeled Mobile Robot(WMR) designed for service tasks as our experimental platform, we've developed an intuitive interface that enhances emotional and physical interactions. By enriching the robot's emotional expression capabilities and incorporating thoughtful design features-such as LED color variations and ear-like elements-we pave the way for more authentic user experiences. Our upcoming user studies will rigorously evaluate the emotional connections, intimacy, and satisfaction levels that users feel during their interactions with the WMR, employing quantitative and qualitative analyses to measure the effectiveness of each design element. Ultimately, this research underscores the potential for robots to form meaningful relationships with users, making technology more relatable and engaging.

关键词： Ethics Systematics Human-robot interaction Speech recognition Light emitting diodes User experience Mobile robots Bonding Physical design Guidelines

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Purple-teaming LLMs with Adversarial Defender Training

arXiv

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arXiv 2024年

作者： Zhou, Jingyan Li, Kun Li, Junan Kang, Jiawen Hu, Minda Wu, Xixin Meng, Helen Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong Dept. of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong

Existing efforts in safeguarding LLMs are limited in actively exposing the vulnerabilities of the target LLM and readily adapting to newly emerging safety risks. To address this, we present Purple-teaming LLMs with Adversarial Defender training (PAD), a pipeline designed to safeguard LLMs by novelly incorporating the red-teaming (attack) and blue-teaming (safety training) techniques. In PAD, we automatically collect conversational data that cover the vulnerabilities of an LLM around specific safety risks in a self-play manner, where the attacker aims to elicit unsafe responses and the defender generates safe responses to these attacks. We then update both modules in a generative adversarial network style by training the attacker to elicit more unsafe responses and updating the defender to identify them and explain the unsafe reason. Experimental results demonstrate that PAD significantly outperforms existing baselines in both finding effective attacks and establishing a robust safe guardrail. Furthermore, our findings indicate that PAD excels in striking a balance between safety and overall model quality. We also reveal key challenges in safeguarding LLMs, including defending multi-turn attacks and the need for more delicate strategies to identify specific risks. © 2024, CC BY-NC-ND.

关键词： Generative adversarial networks

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Course Recommendation System for Company Job Placement Using Collaborative Filtering and Hybrid Model 13

Course Recommendation System for Company Job Placement Using...

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13th International Conference on Data Science, Technology and Applications, DATA 2024

作者： Park, Jaeheon Lee, Suan Lee, Woncheol Kim, Jinho Kangwon Joint Program of Software Convergence Course Kangwon National University Chuncheon Korea Republic of School of Computer Science Semyung University Jecheon Korea Republic of SeedsSoft Co. Ltd. Chuncheon Korea Republic of Dept. of Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of

ISBN: (纸本)9789897587078

This study introduces a novel recommendation system aimed at enhancing university career counseling by adapting it to more accurately align with students' interests and career trajectories. Recognizing the challenges students face in selecting courses that complement their career goals, our research explores the efficacy of employing both collaborative filtering and a hybrid model approach in the development of this system. Uniquely, this system utilizes a company-course recommendation method, diverging from the traditional student-course paradigm, to generalize company-course relationships, thereby enhancing the system's recommendation precision. Through meticulous feature engineering, we improved the performance of the NeuMF model. Our experiments demonstrate that the proposed method outperforms other models by 10% to 79% based on the mAP metric, suggesting that the proposed model can effectively recommend courses for employment. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Collaborative learning

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A Social Referencing Disambiguation Framework for Domestic Service Robots

A Social Referencing Disambiguation Framework for Domestic S...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Kevin Fan Melanie Jouaiti Ali Noormohammadi-As Chrystopher L. Nehaniv Kerstin Dautenhahn Dept. of Electrical & Computer Engineering University of Waterloo ON Canada Dept. of Systems Design Engineering University of Waterloo ON Canada

The successful integration of domestic service robots into home environments can bring significant services and convenience to the general population and possibly mitigate important societal issues, such as care provision for older adults. However, home environments are complex, dynamic and object-rich. It is, thus, very probable that service robots will encounter ambiguity while interacting with household items. To enable service robots to be more adaptive, we proposed a learning so-cial referencing computational framework and experimentally evaluated the framework on a mobile manipulator robot, Fetch, in object selection scenarios. The framework allows the robot to (1) detect and analyze the ambiguity level based on the robot's view and user's command, (2) assess the human's attention level and attract their attention, (3) disambiguate references to objects using human feedback and (4) learn novel objects after clarification from the user. System evaluation results are presented. The framework is modular and can be applied to different robotic platforms.

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On Detecting Low-pass Graph Signals under Partial Observations

arXiv

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arXiv 2024年

作者： Nguyen, Hoang-Son Wai, Hoi-To Dept. of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong

The application of graph signal processing (GSP) on partially observed graph signals with missing nodes has gained attention recently. This is because processing data from large graphs are difficult, if not impossible due to the lack of availability of full observations. Many prior works have been developed using the assumption that the generated graph signals are smooth or low pass filtered. This paper treats a blind graph filter detection problem under this context. We propose a detector that certifies whether the partially observed graph signals are low pass filtered, without requiring the graph topology knowledge. As an example application, our detector leads to a pre-screening method to filter out non low pass signals and thus robustify the prior GSP algorithms. We also bound the sample complexity of our detector in terms of the class of filters, number of observed nodes, etc. Numerical experiments verify the efficacy of our method. © 2024, CC BY.

关键词： Data handling

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