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Joint Downlink-Uplink Beamforming for Wireless Multi-Antenna Federated Learning 21

Joint Downlink-Uplink Beamforming for Wireless Multi-Antenna...

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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Zhang, Chong Dong, Min Liang, Ben Afana, Ali Ahmed, Yahia Dept. of Electrical and Computer Engineering University of Toronto Canada Ericsson Canada Canada Dept. of Electrical Computer and Software Engineering Ontario Tech University Canada

ISBN: (纸本)9783903176553

We study joint downlink-uplink beamforming design for wireless federated learning (FL) with a multi-antenna base station. Considering analog transmission over noisy channels and uplink over-the-air aggregation, we derive the global model update expression over communication rounds. We then obtain an upper bound on the expected global loss function, capturing the downlink and uplink beamforming and receiver noise effect. We propose a low-complexity joint beamforming algorithm to minimize this upper bound, which employs alternating optimization to breakdown the problem into three subproblems, each solved via closed-form gradient updates. Simulation under practical wireless system setup shows that our proposed joint beamforming design solution substantially outperforms the conventional separate-link design approach and nearly attains the performance of ideal FL with error-free communication links. © 2023 IFIP.

关键词： Beamforming

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Unveiling the Arsenal of User Data Protection Tools and Practices 1

Unveiling the Arsenal of User Data Protection Tools and Prac...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Moiz, Syed Abro, Abdul Ahad Ebrahim, Mansoor Abro, Abdul Basit Iqra University Main Campus Dept. of Computer Science Karachi Pakistan Iqra University Main Campus Dept. of Software Engineering Karachi Pakistan Indus University Dept. of Computer Science Karachi Pakistan

ISBN: (纸本)9798350373042

In our rapidly evolving digital landscape, the imperative of safeguarding personal data has surged in significance. As lives increasingly intertwine with digital technologies, personal information has grown markedly, parallel to the escalating threats posed by cyberattacks and data breaches. This research paper provides a comprehensive exploration of data security and privacy, aiming to impart a profound understanding of the intrinsic value of data. It meticulously examines essential methodologies such as encryption, access control, multifactor authentication, and systems, all of which collectively form the bedrock of a resilient security framework. Additionally, this study delves into the intricate regulatory landscape that governs data protection, placing particular emphasis on well-established frameworks such as the General Data Protection Regulation and the Health Insurance Portability and Accountability Act. It highlights emergent trends in data security, including the integration of artificial intelligence and machine learning for threat detection, while also addressing the distinctive challenges posed by the proliferation of Internet of Things (IoT) devices. In conclusion, this paper underscores the paramount importance of personal data protection in our digital age, offering invaluable insights and guidance for organizations and individuals endeavoring to bolster their defenses and uphold data integrity within our interconnected world. © 2024 IEEE.

关键词： Data privacy

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Development of AI-Based Diagnostic Systems for Wound Images 15

Development of AI-Based Diagnostic Systems for Wound Images

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Jeong, Seoyeon Kim, Jaeho Choi, Kyusung Cho, Young-Rae Yonsei University Dept. of Computer and Telecommunications Engineering Wonju26493 Korea Republic of Yonsei University Dept. of Software Wonju26493 Korea Republic of

ISBN: (纸本)9798350364637

Recent advancements in AI technology are revolutionizing the medical field by enhancing diagnostic accuracy, optimizing treatment plans, and providing personalized medical services. AI-based algorithms are particularly effective in analyzing medical images to detect diseases early. Telemedicine, which allows remote medical consultations, benefits from these techniques by improving access to healthcare and enabling accurate diagnoses through efficient data exchange. This work developed an AI-based diagnostic system using a Convolutional Neural Network (CNN) model to analyze wound images. The wound dataset from Kaggle, consisting of 377 images across seven categories, was augmented 25 times to improve model performance. The app allows users to take or upload wound images, which are then classified by the model, providing treatment and aftercare information. This app achieved a high accuracy of 95.716% in classifying wounds and provides basic treatment suggestions. Despite challenges like limited dataset size, the project successfully demonstrated the potential for real-time wound analysis in telemedicine and healthcare. Future improvements should include a more diverse dataset and advanced treatment recommendations to enhance the app's utility. © 2024 IEEE.

关键词： Convolutional neural networks

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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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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

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

ISBN: (纸本)9798331510756

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. © 2025 IEEE.

关键词： Human robot interaction

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Phoneme Classification in Imagined Speech Using Explainable Machine Learning 13

Phoneme Classification in Imagined Speech Using Explainable ...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

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

ISBN: (纸本)9798331521929

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. © 2025 IEEE.

关键词： Adversarial machine learning

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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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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

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

ISBN: (纸本)9798331510756

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. © 2025 IEEE.

关键词： Human robot interaction

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Bluetooth Low Energy-based Adaptive Scheme for IoT Services 14

Bluetooth Low Energy-based Adaptive Scheme for IoT Services

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14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Shan, Gaoyang Choi, Geunkyung Roh, Byeong-Hee Ajou University Dept. of Software and Computer Engineering Suwon16499 Korea Republic of Ajou University Dept. of Ai Convergence Nework Dept. of Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798350335385

To adapt to various IoT services, Bluetooth Low Energy (BLE) specification has defined a variety of communication modes, such as advertising, extended advertising, and periodic advertising events. As services' environments, such as the number of users and QoS requirements, are changed, modes of protocols should be also adjusted to meet those services' needs. This paper proposes an adaptive BLE scheme to reflect dynamically changing services' environments. The effectiveness of the proposed scheme is verified through simulation experiments. © 2023 IEEE.

关键词： Internet of things

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Macula Swelling Detection using Retinal Fundus Images

Macula Swelling Detection using Retinal Fundus Images

引用

2023 IEEE International Conference on Imaging Systems and Techniques, IST 2023

作者： Khan, Rukham Mumtaz Ali Khan, Fazal Saadat Khan, Fauzia Hassan, Rafia National University of Sciences and Technology Dept. of Computer Engineering Islamabad Pakistan University of Tartu Dept. of Computer Science Tartu Estonia Bahria University Dept. of Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350330830

Diabetic Macular Edema (DME) is an advanced symptom of Diabetic Retinopathy (DR) and can lead to irreversible vision loss. Macular Edema (ME) is the swelling of the macula, the part of the eye accountable for thorough central vision. The utilization of image processing in the detection of diabetic macular edema (DME) plays a crucial role in the field of medical imaging, effectively reducing the time and manual effort required by medical specialists to identify this disease. In this paper, we proposed a method that uses digital fundus images to analyze the swelling of the macula. The proposed algorithm consists of four steps, i.e., (i) retinal image dataset collection, (ii) retinal image pre-processing, (iii) feature extraction, and (iv) classification to categorize the disease into three different stages, i.e., normal, stage 1 (mild) and stage 2 (severe). The proposed method is tested on three databases, i.e., MESSIDOR, DMED, and the local dataset AFIO obtained from the local hospital in Pakistan. We use three parameters to check the validity of the proposed algorithm, i.e., visual inspection, accuracy, and area under calculation time. Our method achieves an average accuracy of 96.07 % with a sensitivity of 93.74 % and specificity of 97.32%. We also compare our results with other proposed methods, which shows our method performs better. © 2023 IEEE.

关键词： Eye protection

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Multi-Label Classification of CS Papers Using Natural Language Processing Models 22

Multi-Label Classification of CS Papers Using Natural Langua...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Zhang, Zheng Das, Amit Rahgouy, Mostafa Bao, Yida Baskiyar, Sanjeev Auburn University Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics AuburnAL United States

ISBN: (纸本)9798350345346

computer science (CS) is a rapidly evolving field of knowledge with a significant volume and significance of scientific papers. Consequently, meticulous management and categorization of CS papers are crucial to ensure efficient access and retrieval of relevant information. This study aims to evaluate the effectiveness of eight traditional machine learning models, as well as Recurrent Neural Network (RNN) models, such as LSTM and BiLSTM, and state-of-the-art pre-trained self-attention-based natural language processing (NLP) models, including BERT, XLNet, RoBERTa, and DistilBERT, for the task of multi-label classification of arXiv CS paper classification. The results demonstrate that pre-trained self-attention-based models consistently outperform the other models regarding classification performance. Moreover, self-attention-based models exhibited superior performance and achieved a new state-of-the-art result. To the best of our knowledge, our paper represents the first endeavor to evaluate machine learning models, specifically on computer science-related documents, as part of a multi-label classification task. © 2023 IEEE.

关键词： BERT Deep Learning LSTM Machine learning multi-label classification

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CoML: Machine Learning based approach for COVID-19 related Suicidal Ideation detection 6

CoML: Machine Learning based approach for COVID-19 related S...

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6th International Conference on Intelligent Systems and computer Vision, ISCV 2024

作者： Bouktif, Salah Ud Din Khanday, Akib Mohi Ouni, Ali CIT United Arab Emirates University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates École de Technologie Supérieure University of Quebec Dept. of Software Engineering and IT MontrealQC Canada

ISBN: (纸本)9798350350180

The pervasive use of online social media platforms has changed the communication habits and subsequently the content and the frequency of the human exchanges. People share their thoughts on social media more frequently and freely. In particular and due to the societal stigma, people find less obstacle to discuss mental health problems on social media compared to face-o-face discussion. Mining social media for the sake of analysing suicidal intention is an attractive and at the same time challenging task especially during COVID-19 era. The main objective is to leverage machine learning to classify the social media user suicidal behavior while considering the impact of COVID-19 circumstances. We propose a rigorous feature engineering technique based on TF/IDF and Bag of Words. Machine learning classifiers and ensemble models are compared to find that the Neural Network(NN) classifier outperforms the benchmarks with a precision of 94%, a Recall of 94%, an F1 Score of 94%, and an overall accuracy of 94%. In the future, more feature selection techniques and deep learning can be used. © 2024 IEEE.

关键词： Adversarial machine learning

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