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11th International Conference on Internet of Things: Systems, management and Security, IOTSMS 2024

作者： Chang, Tien-Chiao Wu, Yu-Chi Han, Chin-Chuan Chang, Chao-Shu National United University Dept. of Electrical Engineering Miao-Li Taiwan National United University Dept. of Computer Science and Information Engineering Miao-Li Taiwan National United University Dept. of Information Management Miao-Li Taiwan

ISBN: (纸本)9798350366501

With the development of virtual reality (VR) and augmented reality (AR) devices, handheld controllers and camera-based hand tracking are the most common methods for interacting with the virtual world. This paper proposes arm motion tracking and hand gesture recognition methods based on a single IMU (Inertial Measurement Unit) sensor embedded in a wristband. This approach is more convenient than handheld controllers and more energy-efficient than camera-based tracking. We utilize two deep learning models: one for arm motion tracking and the other for hand gesture recognition. When training the arm motion tracking model, we use two IMU sensors-one placed on the wrist to provide forearm pose data and the other placed above the elbow to provide upper arm pose data. We use the elbow's IMU data as the reference for training the model and calculate the real-time upper arm pose vector based on the wrist's pose data. We train the hand gesture recognition model using only the wrist-mounted IMU sensor. This model captures different vibrations in the IMU during various gestures (such as grab, release, snap, and tap) for training. We achieve real-time arm motion tracking and hand gesture recognition using only a single IMU sensor. The angle error for arm motion tracking is only 15 degrees, and the hand gesture recognition accuracy reaches 95%. © 2024 IEEE.

关键词： Gesture recognition

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Neuro-Symbolic Empowered Denoising Diffusion Probabilistic Models for Real-Time Anomaly Detection in Industry 4.0: Wild-And-Crazy-Idea Paper

Neuro-Symbolic Empowered Denoising Diffusion Probabilistic M...

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2023 Forum on Specification and Design Languages, FDL 2023

作者： Capogrosso, Luigi Mascolini, Alessio Girella, Federico Skenderi, Geri Gaiardelli, Sebastiano Dall'Ora, Nicola Ponzio, Francesco Fraccaroli, Enrico Di Cataldo, Santa Vinco, Sara Macii, Enrico Fummi, Franco Cristani, Marco Dept. of Engineering for Innovation Medicine University of Verona Italy Dept. of Control and Computer Engineering Polytechnic of Turin Italy Dept. of Regional and Urban Studies and Planning Polytechnic of Turin Italy Dept. of Computer Science University of North Carolina at Chapel Hill United States

ISBN: (纸本)9798350307375

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-Time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before. copy;2023 IEEE. © 2023 IEEE computer Society. All rights reserved.

关键词： Industry 4.0

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Machine Learning-Based Wine Quality Prediction 2

Machine Learning-Based Wine Quality Prediction

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Dhondiyal, Shiv Ashish Kumar, Rohit Verma, Himanshu Dhyani, Ashish Singh, Sumeshwar Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India GNIOT Institute of Professional Studies Greater Noida India Dept. of Hospitality Management Graphic Era Deemed to be University India Dept. of Computer Science and Engineering Jaypee Institute of information technology sector 62 India

ISBN: (纸本)9798350383867

The main goal of this attempt is to assess the quality of wine and determine if it is excellent or terrible. Humans have been engaging in the act of tasting for generations and have consistently made predictions using their sensory organs. A number of industries have recently embraced new technology and used it to a variety of uses. However, human knowledge is required in a number of businesses, such as product quality assurance. With the increasing popularity of the product, modern innovations have increased the cost of the treatment. To ensure the quality of the output, this study looks at a number of machines learning algorithms, including Random Forest Classification, Decision Tree classifiers, and Support Vector Machines (SVM). These methods make use of the product's current attributes to simplify the quality control procedure. © 2024 IEEE.

关键词： Adversarial machine learning

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Blockchain-Based Music Streaming Services: A Novel Approach to Transparent Artist control 3

Blockchain-Based Music Streaming Services: A Novel Approach ...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Ashtagi, Rashmi Deshmukh, Tanmay Jadhav, Swati Bidwe, Ranjeet Akkalkot, Afsha Buchade, Amar Uke, Nilesh Banerjee, Sudipta Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India Symbiosis Institute of Technology Dept. of Cse Pune India Coep Technological University Dept. Name of Computer Engineering Pune India Data Science Vishwakarma Institute Technology Dept. of Artificial Intelligence Pune India Indira College of Engineering and Management Pune India Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798350354348

Artists have historically used music as a form of personal expression. However, due to the centralized nature of music streaming services, artists receive only a small percentage of the revenue generated and have no control over their content produced. To address these issues, a revolutionary technique is needed that provides artists with greater control and transparency over their output. This paper focuses on a decentralized strategy that utilizes blockchain technology to facilitate direct interaction between artists and users, enabling artists to upload their music, sell songs, and receive royalties. The implementation of blockchain technology could potentially revolutionize the music industry by ensuring that rightful owners receive payment for their music while reducing the waste of paying intermediaries. Without intermediaries involved in the distribution process, the music industry could pay out more royalties to rights holders automatically and instantaneously. The proposed solution also addresses piracy concerns through the use of encryption and watermarking techniques. Blockchains provide security, confidentiality, tamper-proofing, traceability, control, and oversight over information. By developing a secure, self-installing, cross-platform reader positioned on the header of the digital content file, The research empowers data owners to manage the flow of information even after it has been made available. The suggested approach enables content owners to control their digital content and monitor its most popular components after distribution. The blockchain-based system also benefits from token distribution among the content's owner, distributor, and consumer. The simulation results demonstrate that our approach is capable of overcoming the flaws of the existing system. © 2024 IEEE.

关键词： Blockchain

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Reinforcement Learning for Autonomous Vehicles 1

Reinforcement Learning for Autonomous Vehicles

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Vinod Kumawat, Parwati Kumar, Pankaj Yaminipriya, S. Chamarthi, Subbarao Kumar, Jitendra SLP College Dept of CSE Gwalior India Madhav University Dept. of Commerce & Management Pindwara India Dhanalakshmi Srinivasan College of Engineering Dept. of Biomedical Engineering Coimbatore India Lingayas Vidyapeeth Dept. of Mechanical Engineering Faridabad India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

Reinforcement Learning (RL) has emerged as a transformative technology for autonomous vehicles, enabling sophisticated decision-making systems that enhance driving safety, efficiency, & adaptability. This paper explores the application of RL algorithms in the context of autonomous vehicle control, focusing on the development of intelligent agents capable of learning optimal driving policies through interaction with complex environments. The study reviews various RL techniques, including Deep Q-Learning, Policy Gradient methods, & Actor-Critic frameworks, evaluating their effectiveness in addressing key challenges such as real-time decision making, dynamic environment adaptation, & multi-agent interactions. Emphasis is placed on the design of reward functions, exploration strategies, & simulation environments, which are crucial for training robust & reliable autonomous driving systems. Case studies demonstrate the application of RL in scenarios such as lane-keeping, adaptive cruise control, & collision avoidance. The paper also discusses advancements in simulation platforms & hardware acceleration that facilitate scalable RL experiments. By integrating theoretical insights with practical implementations, this work provides a comprehensive overview of current developments in RL for autonomous vehicles & identifies future research directions aimed at overcoming limitations & achieving safer, more efficient. © 2024 IEEE.

关键词： Reinforcement learning

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The Impact of Emojis on User Engagement with Trolling Content in an Online Platform

The Impact of Emojis on User Engagement with Trolling Conten...

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2024 IEEE Digital Platforms and Societal Harms, DPSH 2024

作者： Saraf, Diya Liu, Yuhong Jazaieri, Hooria Santa Clara University Dept. of Computer Science & Engineering Santa ClaraCA United States Santa Clara University Dept. of Management Santa ClaraCA United States

ISBN: (纸本)9798350351309

This paper investigates the impact of emojis on user engagement in trolling content on a social media platform. Utilizing a comprehensive dataset collected from Reddit, this study categorizes emojis by emotional content and popularity to analyze their effects on user comments and upvotes. Findings indicate that user engagement can be significantly enhanced by more popular emojis and emojis that convey emotionality while reduced by excessive or misplaced informational emojis. This research offers critical insights for strategic emoji use to foster positive online interactions and mitigate trolling, benefiting content moderators and digital communicators. © 2024 IEEE.

关键词： Emoji Emotion Social Media Platforms Trolling User Engagement

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Empowering citizens by a blockchain-Based Robinson list

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International Journal of computers and Applications 2022年第10期44卷 920-928页

作者： Cirillo, Albenzio Dalena, Vito Mauro, Antonio Mogavero, Francesco Pennino, Diego Pizzonia, Maurizio Vitaletti, Andrea Zecchini, Marco Fondazione Ugo Bordoni Roma Italy Roma Tre University–Eng. Dept. Roma Italy Sapienza University of Rome Department of Computer Control and Management Engineering Roma Italy

Robinson lists are adopted in many countries to protect phone subscribers against commercial spam calls. In its essence, they collect the denial of the subscribers to be contacted by marketing operators. Nowadays, all Robinson lists run as centralised services. Namely, citizens have to rely on trusted third parties for the management of their own choices. The Blockchain allows us to envision the development of a decentralised Robinson list in which citizens are finally empowered to autonomously manage their choices. In this paper, we present a proof-of-concept (PoC) of a decentralised Robinson list developed with the Algorand technology. To the best of our knowledge, this is the first attempt to implement a Robinson list on a blockchain technology. The PoC has been designed to support the requirements provided by Fondazione Ugo Bordoni, the manager of the Italian Robinson list. The evaluation of the PoC shows that our decentralised solution provides satisfactory performance, in terms of scalability, latency and costs, when compared to the current centralised service. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Blockchain

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EHRGPT: Leveraging language models for generating synthetic health records

EHRGPT: Leveraging language models for generating synthetic ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Pamisetty, Giridhar Batreddy, Subbareddy Verma, Priya Babu, Ch Sobhan Indian Institute of Technology Dept. of Electrical Engineering Hyderabad India Administrative Staff College of India Centre for Management Studies Hyderabad India Indian Institute of Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350362480

Electronic health records (EHRs) are the comprehensive digital records containing patient health information, which help in various domains like public health monitoring, predictive modeling, data-driven research, etc. However, there are strict regulations about data sharing due to the sensitivity of the EHR data. So, there is an increasing demand for generating high-quality synthetic EHR data that mitigates privacy concerns. Most of the existing approaches to EHR generation are limited as they predominantly generate binary decisions about affecting a disease or count-based summaries of disease occurrences for a patient, failing to capture the full patient trajectory contained in the real data and hence reducing the downstream applications. With the success of language models in generative approaches, we propose a language modeling approach using transformers to generate the complete health record of a patient for each admission. We propose to group the patient's EHRs as it enhances the model to capture temporal coherence and the medical history of the corresponding patient. The proposed model can generate high-fidelity privacy preserved EHR data with the statistical properties of the real data. © 2024 IEEE.

关键词： Electronic health record

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Adaptive Neighborhood Sampling and KAN-Based Edge Features for Enhanced Fraud Detection

Adaptive Neighborhood Sampling and KAN-Based Edge Features f...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Batreddy, Subbareddy Pamisetty, Giridhar Kothuri, Manish Verma, Priya Babu, Ch Sobhan Indian Institute of Technology Dept. of Electrical Engineering Hyderabad India Indian Institute of Technology Dept. of Computer Science and Engineering Hyderabad India Administrative Staff College of India Centre for Management Studies Hyderabad India

ISBN: (纸本)9798350362480

Fraud detection models have shown immense improvement in performance with the incorporation of graph neural networks (GNNs), as they are naturally capable of modeling relational data and capturing complex relations between users. However, real-world data has a significant class imbalance with very few fraudulent labels, resulting in a bias toward predicting the majority class as the decision boundaries are skewed towards it. To alleviate this class imbalance, we propose adaptive neighborhood sampling, an effective and optimal neighborhood sampling approach that uses sampling probability comprising of class frequency and global importance of the node through the page rank to reduce the dilution of the fraud nodes. We also propose a novel Kolmogorov-Arnold Network (KAN) based edge feature extraction approach, which treats incoming and outgoing edges separately to effectively capture the directional patterns that are crucial in distinguishing the patterns of both labels. In addition to this, we employed topology-aware margin loss to further improve the class imbalanced node classification. Incorporating these proposed approaches in the GNN-based fraud detection model helped outperform the state-of-the-art fraud models on open-source and complex real-world datasets. © 2024 IEEE.

关键词： Graph neural networks

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Secrecy Capacity Optimization in RF/FSO Systems: Impact of Mixed Rayleigh and Log-Normal Fading with Atmospheric Turbulence

Secrecy Capacity Optimization in RF/FSO Systems: Impact of M...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

This paper investigates the secrecy capacity optimization in RF/FSO systems with mixed Rayleigh and log-normal fading, while masking eavesdroppers channel state information () as well as suffering from atmospheric turbulence. We explore the impact of several parameters including beamforming angles, modulation schemes and hybrid RF/FSO link configurations through a combination of simulations as well as analytical models. Secrecy capacity is enhanced for an improved SNR and vice versa under higher turbulence conditions and cooler influents eavesdropped scenarios. Performance of the hybrid RF/FSO systems dominates considerably over single-link solutions, especially at longer distances. All these mechanisms appreciably increase the secrecy capacity, while beamforming and advanced modulation techniques offer most of the improvement. The study also demonstrates the interdependencies of system parameters, providing guidelines for designing secure communication systems under different environmental and operational conditions. © 2024 IEEE.

关键词： Beamforming

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