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IEEE International Conference on Robotics and Automation (ICRA)

作者： Kazuki Shibata Tomohiko Jimbo Takamitsu Matsubara Autonomous Distributed Cooperative Control Program Data Analytics Research-Domain Toyota Central R&D Labs. Inc. Japan Graduate School of Science and Technology Division of Information Science Nara Institute of Science and Technology Nara Japan

In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport. Typical end-to-end deep neural network policies may be insufficient for covering communication and control; these methods cannot decide the timing of communication and can only work with fixed-rate communications. Therefore, our framework exploits event-triggered architecture, namely, a feedback controller that computes the communication input and a triggering mechanism that determines when the input has to be updated again. Such event-triggered control policies are efficiently optimized using a multi-agent deep deterministic policy gradient. We confirmed that our approach could balance the transport performance and communication savings through numerical simulations.

关键词： Deep learning Automation Conferences Reinforcement learning Computer architecture Numerical simulation Timing

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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Rapid Electronic Transport Channel of Co-P with Mo in a Heterostructure Embedded with P, N Dual Doped Porous Carbon for Electrocatalytic Oxygen and Hydrogen Evolution

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ChemNanoMat 2024年第7期10卷

作者： Dey, Tapan Rajput, Anubha Jhaa, Gaurav Matsagar, Babasahab M. Chen, Norman C.-R Kumar, Nitish Salunkhe, Rahul Wu, Kevin C.-W. Chakraborty, Biswarup Dutta, Saikat Electrochemical Energy & Sensor Research Laboratory Amity Institute of Click Chemistry Research & Studies Amity University Noida India Department of Chemistry Indian Institute of Technology Delhi Hauz Khas New Delhi110016 India Chemical Information Sciences Laboratory Department of Chemistry Pondicherry University Kalapet Pondicherry605014 India Department of Chemical Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei10617 Taiwan Molecular Science and Technology Program Taiwan International Graduate Program Academia Sinica Taiwan Materials Research Laboratory Department of Physics Indian Institute of Technology Jammu Jammu India Department of Chemical Engineering and Materials Science Yuan Ze University Taoyuan Chung-Li Taiwan Department of Chemical Engineering Chung Yuan Christian University No. 200 Zhongbei Rd. Zhongli Dist. Taoyuan City320 Taiwan

We developed a molybdenum (Mo)-doped cobalt (Co)-heterostructure embedded on a phosphorous (P) and nitrogen (N) dual-doped porous carbon which exhibits an intrinsic electronic transport channel of Co to Mo and P. The P,Mo,O−Co/PNC/NF (NF=Nickel foam) electrode offers 335 mV overpotential at 10 mA cm−2 in OER as compared with PMA-ZIF67-NC/NF and ZIF67-NC/NF electrode with an overpotential of 357 and 373 mV respectively. Linear sweep voltammetry (LSV) of overall water splitting (OWS) supports that the current density gradually increased at a cell potential of 1.6 V with a maximum of 40 mA with a corresponding cell potential of 1.79 V at a current density of 10 mA cm−2. Density functional theory (DFT) calculations for water adsorption on optimized [111] surface of Co, CoMo, and CoMoP2 with adsorbed H2O and corresponding lattice determine the electron density difference of [111] surface with adsorbed H2O for Eads (eV) 4.23 corresponds to adsorption energy for CoMoP2. XANE-EXAFS spectroscopy of P,Mo,O−Co/PNC at Co K edge and Mo K edge suggests the presence of higher valence of both Cox+ and Mox+ without metallic Co and Mo and Co−P and Mo−P bonds as major structural units due to phosphidation as determined by R-space FT-EXAFS spectra. © 2024 Wiley-VCH GmbH.

关键词： Cobalt

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A Platform for Monitoring Student Commuting in the Use of School Transport in Smart Cities - A Facial Recognition Based Approach

A Platform for Monitoring Student Commuting in the Use of Sc...

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International Conference on Smart Communications and Networking (SmartNets)

作者： Jessé Da Costa Rocha Marcela Alves de Souza Evelin Helena Silva Cardoso Nandamudi Vijaykumar Jasmine Priscyla Leite de Araújo Renato Lisboa Francês Graduate Program in Electrical Engineering Institute of Technology Federal University of Pará (UFPA) Belém Pará Brazil Information and Communication Technology Center Federal University of the South and Southeast of Para (Unifesspa) Marabá Pará Brazil Department of Computer Science Federal Rural University of the Amazon (UFRA) Capitão Poço Pará Brazil Coordination of Applied Research and Technological Development National Institute for Space Research (INPE) São Paulo Brazil

This article proposes an intelligent platform for monitoring students' steps on their way to school until they leave the school to their homes. This platform can identify students and notify those responsible and competent authorities in various situations of school life, such as: entering and leaving the school bus, entering and leaving school, entering the school cafeteria, etc. The first application aims to control access to the school bus through facial recognition. In the tests carried out, the recognition system achieved excellent results in all metrics.

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Hybrid descriptor definition for content based image classification using fusion of handcrafted features to convolutional neural network features

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International Journal of information technology (Singapore) 2021年第4期13卷 1365-1374页

作者： Das, Rik Kumari, Khusbu De, Sourav Manjhi, P.K. Thepade, Sudeep Department of Post Graduate Program in Information Technology Xavier Institute of Social Service Ranchi Jharkhand India Department of Computer Applications Yogoda Satsanga Mahavidyalaya Ranchi Jharkhand India Department of Computer Science & Engineering Cooch Behar Government Engineering College Cooch Behar India Department of Mathematics Vinoba Bhave University Hazaribag Jharkhand India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India

The efficacy of content-based image classification is dependent on the richness of the feature vectors extracted from the image data. Traditional feature extraction techniques highlight single low level image characteristics like shape, size, texture, color etc. for feature generation. The process often fails to extract meaningful descriptors since considering a single image characteristic will ignore other rich properties of image contents. Mass adoption of Convolutional Neural Network (CNN) has significantly improved classification performances for content-based image data. Recent literature has documented high level of precision for image classification by carrying out transfer learning with open source pre trained Convolutional Neural Network (CNN). However, the concept of transfer learning experiences major setback in case of limited training data due to overfitting of training instances during finetuning. This work has identified this challenge and has attempted to capture probability distribution of input images to a pre trained Convolution Neural Network by utilizing it as a fixed weight feature extractor. The proposed approach has addressed the overfitting problem by removing the finetuning step involved in transfer learning. Further investigation on robust descriptor definition is carried out by concatenating the pre-trained CNN features to handcrafted features. The hybrid architecture has encouraging outcomes that have outclassed the classification accuracies of state-of-the-art handcrafted techniques. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Augmented feature learning Feature fusion Handcrafted features Image classification Pre trained Convolutional Neural Network (CNN) Representation learning

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Parameter Optimization of Support Vector Regression Using Harris Hawks Optimization

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Procedia Computer science 2021年 179卷 17-24页

作者： I Nyoman Setiawan Robert Kurniawan Budi Yuniarto Rezzy Eko Caraka Bens Pardamean Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Department of Information Management College of Informatics Chaoyang University of Technology Taiwan (ROC) Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia 11530 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia. 11530.

Support Vector Regression (SVR) is often used in forecasting. Adjustment of parameters in the SVR affects the results of forecasting. This study aims to analyze the SVR method that is optimized using Harris Hawks Optimization (HHO), hereinafter referred to as HHO-SVR. The HHO-SVR was evaluated using five benchmark datasets to determine the performance of this method. The HHO process is also compared based on the type of kernel and other metaheuristic algorithms. The results showed that the HHO-SVR has almost the same performance as other methods but is less efficient in terms of time. In addition, the type of kernel also affects the process and results.

关键词： SVR harris hawks optimization parameter optimization kernel forecasting

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Exploring Tourist Feedback on Riau Attractions Through Indonesian Language YouTube Opinion Using Naïve Bayes Algorithm

Exploring Tourist Feedback on Riau Attractions Through Indon...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology (IAICT)

作者： Rahmad Kurniawan Iwan Iskandar Fitra Lestari Habibi Al Rasyid Harpizon Ilyas Husti Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Riau Pekanbaru Indonesia Department of Informatics Engineering Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Department of Industrial Engineering Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

YouTube is a widely-used platform in Indonesia, with 93.8% of its users. As such, it presents a valuable opportunity for marketing tourist destinations, particularly in Riau province, which aims to become Indonesia’s top Halal travel destination. Tourism is a vital contributor to the economic growth of regions, and each province in Indonesia competes to promote its tourist attractions to attract more visitors every year. However, the large volume of data can challenge the manual analysis of feedback from YouTube’s features, such as likes, dislikes, and comments. A literature review suggests that the Naive Bayes algorithm, which uses machine learning, is helpful for sentiment analysis. Therefore, this study aims to analyze public sentiment toward tourist destinations in Riau province by analyzing YouTube comments using the Naïve Bayes algorithm. The study used 1680 opinions collected from 10 YouTube videos showcasing tourist destinations in Riau. The Naive Bayes algorithm classified 60% of the comments as positive, 32% as neutral, and 8% as negative. The experimental results indicated an accuracy and precision of 73%, a recall of 94%, and an F-1 Score of 82%. The study used the word frequency technique to reveal that Riau could become a popular halal tourist destination based on several frequently occurring words in the comments.

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Robust shape estimation with false-positive contact detection

arXiv

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arXiv 2021年

作者： Shibata, Kazuki Miyano, Tatsuya Jimbo, Tomohiko Matsubara, Takamitsu Autonomous Distributed Cooperative Control Program Data Analytics Research-Domain Toyota Central RandD Labs. Inc. 41-1 Yokomichi Nagakute Aichi Japan Graduate School of Science and Technology Division of Information Science Nara Institute of Science and Technology Nara Japan

We propose a means of omni-directional contact detection using accelerometers instead of tactile sensors for object shape estimation using touch. Unlike tactile sensors, our contact-based detection method tends to induce a degree of uncertainty with false-positive contact data because the sensors may react not only to actual contact but also to the unstable behavior of the robot. Therefore, it is crucial to consider a robust shape estimation method capable of handling such false-positive contact data. To realize this, we introduce the concept of heteroscedasticity into the contact data and propose a robust shape estimation algorithm based on Gaussian process implicit surfaces (GPIS). We confirmed that our algorithm not only reduces shape estimation errors caused by false-positive contact data but also distinguishes false-positive contact data more clearly than the GPIS through simulations and actual experiments using a quadcopter. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Polarization entanglement enabled by orthogonally stacked van der Waals NbOCl2 crystals

arXiv

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arXiv 2024年

作者： Guo, Qiangbing Wu, Yun-Kun Zhang, Di Zhang, Qiuhong Guo, Guang-Can Alù, Andrea Ren, Xi-Feng Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Centre for Advanced 2D Materials Graphene Research Centre National University of Singapore Singapore Singapore CAS Key Laboratory of Quantum Information CAS Synergetic Innovation Center of Quantum Information & Quantum Physics University of Science and Technology of China Hefei China Hefei National Laboratory University of Science and Technology of China Hefei China Photonics Initiative Advanced Science Research Center City University of New York New York United States Physics Program Graduate Center City University of New York New York United States The Key Laboratory of Weak-Light Nonlinear Photonics Ministry of Education School of Physics TEDA Applied Physics Institute Nankai University Tianjin China School of Chemistry and Materials Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China

Polarization entanglement holds significant importance for photonic quantum technologies. Recently emerging subwavelength nonlinear quantum light sources, e.g., GaP and LiNbO3 thin films, benefiting from the relaxed phase-matching constraints and volume confinement, has shown intriguing properties, such as high-dimensional hyperentanglement and robust entanglement anti-degradation. Van der Waals (vdW) NbOCl2 crystal, renowned for its superior optical nonlinearities, has emerged as one of ideal candidates for ultrathin quantum light sources [Nature 613, 53 (2023)]. However, polarization-entanglement is inaccessible in NbOCl2 crystal due to its unfavorable nonlinear susceptibility tensor. Here, by leveraging the twist-stacking degree of freedom inherently in vdW systems, we showcase the preparation of tunable polarization entanglement and quantum Bell states. Our work not only provides a new and tunable polarization-entangled vdW photon-pair source, but also introduces a new knob in engineering the entanglement state of quantum light at the nanoscale. © 2024, CC BY-NC-ND.

关键词： Van der Waals forces

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Developing the “Healthcare CEO App” for patients with type 1 diabetes transitioning from adolescence to young adulthood: A mixed-methods study

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Nursing Open 2023年第3期10卷 1755-1766页

作者： Chiang, Yueh-Tao Chang, Chi-Wen Yu, Hsing-Yi Tsay, Pei-Kwei Lo, Fu-Sung Chen, Chi-Wen Lin, Wen-Yen Hsu, Chien-Lung An, Chi Moons, Philip School of Nursing College of Medicine Chang-Gung University Taoyuan Taiwan Division of Pediatric Endocrinology & Genetics Department of Pediatrics Chang-Gung Memorial Hospital Taoyuan Taiwan Department of Nursing Chang-Gung Memorial Hospital Taoyuan Taiwan Department of Public Health and Center of Biostatistics College of Medicine Chang-Gung University Taoyuan Taiwan College of Medicine Chung-Gung University Taoyuan Taiwan College of Nursing National Yang Ming Chiao Tung University Taipei Taiwan Department of Information Management National Taichung University of Science and Technology Taichung Taiwan Department of Information Management Chang-Gung University Taoyuan Taiwan Graduate Institute of Management Chang Gung University Tao-Yuan Taiwan Master of Science Degree Program in Innovation for Smart Medicine Tao-Yuan Taiwan Department of Psychiatry Chang Gung Memorial Hospital at Linkou Tao-Yuan Taiwan Department of Public Health and Primary Care KU Leuven-University of Leuven Leuven Belgium Institute of Health and Care Sciences University of Gothenburg Gothenburg Sweden Department of Paediatrics and Child Health University of Cape Town Cape Town South Africa

Aim: To develop and test a mobile application that supports the disease self-management of adolescents with type 1 diabetes during their transition to early adulthood. Design: A sequential mixed-methods design was employed. Methods: The application content was designed according to previously identified care needs and expectations, followed by application development on the Android operating system. From the outpatient clinic of the Department of Paediatric Endocrinology and Metabolism at a medical centre in northern Taiwan, 35 individuals aged between 16–25 years participated in application testing. Results: The overall median score of the QUIS was 4–5, most of the 25% quartile was 4–5, and all of the 75% quartile was 5, indicating adequate user interaction satisfaction. © 2022 The Authors. Nursing Open published by John Wiley & Sons Ltd.

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