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arXiv 2023年

作者： Wang, Xinshun Zhang, Wanying Wang, Can Gao, Yuan Liu, Mengyuan the National Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School China the School of Intelligent Systems Engineering Sun Yat-sen University China the Multimedia Information Processing Laboratory the Department of Computer Science Kiel University Germany Peking University China the Unit of Computing Sciences CS Faculty of Information Technology and Communication Sciences ITC Tampere University Finland

Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task. Nevertheless, how to construct a graph from a skeleton sequence and how to perform message passing on the graph are still open problems, which severely affect the performance of GCN. To solve both problems, this paper presents a Dynamic Dense Graph Convolutional Network (DD-GCN), which constructs a dense graph and implements an integrated dynamic message passing. More specifically, we construct a dense graph with 4D adjacency modeling as a comprehensive representation of motion sequence at different levels of abstraction. Based on the dense graph, we propose a dynamic message passing framework that learns dynamically from data to generate distinctive messages reflecting sample-specific relevance among nodes in the graph. Extensive experiments on benchmark Human 3.6M and CMU Mocap datasets verify the effectiveness of our DD-GCN which obviously outperforms state-of-the-art GCN-based methods, especially when using long-term and our proposed extremely long-term protocol. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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Supporting collective physical activities by interactive floor projection in a special-needs school setting

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International Journal of Child-Computer Interaction 2022年 32卷

作者： Oki, Mika Akizuki, Shuichi Bourreau, Baptiste Takahashi, Issey Aoki, Yoshimitsu Yamamoto, Junichi Suzuki, Kenji Artificial Intelligence Laboratory University of Tsukuba Ibaraki 305-8573 Japan School of Engineering Chukyo University Aichi 466-8666 Japan Faculty of Science and Technology Keio University Kanagawa 223-8522 Japan Faculty of Letters Keio University Tokyo 108-8345 Japan

This paper presents an algorithm to provide floor projection feedback according to the local distance and density of individuals. It is realized by a large-space floor projection system with a feedback function based on human tracking with laser ranging image sensors. The purpose is to support the cognition of spatial–temporal structures of groups of adolescents with neurodevelopmental disorders (NDs) that are conducting organized physical activity (PA). Observation and evaluation of behavioral changes in adolescents with NDs, when they were active with or without the floor projection based on the proposed algorithm, were conducted to validate its effectiveness. We observed that the proposed algorithm can be implemented in different organized PAs. It had the effect to help individuals in a behavior to keep a close distance to each other as a group rather than to keep the same distance apart from each other while walking. © 2021 The Authors

关键词： Behavior change Collective physical activity Interactive floor projection Neurodevelopmental disorders Robust human tracking

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Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact

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engineering Analysis with Boundary Elements 2023年 146卷 252-262页

作者： Tao Hai Hayder A. Dhahad Masood Ashraf Ali Vishal Goyal Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Khaled Twfiq Almoalimi Farah Qasim Ahmed Alyousuf School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Mechanical Engineering Department University of Technology Baghdad Iraq Department of Industrial Engineering College of Engineering Prince Sattam bin Abdulaziz University Alkharj 16273 Saudi Arabia Department of Electronics and Communication Engineering GLA University Mathura India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq

This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts.

关键词： Machine learning Entropy generation Numerical calculations Convection Nanofluid

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data-Driven Design of High-Curie Temperature Full-Heusler Alloys for Spintronic Applications

SSRN

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SSRN 2024年

作者： Ho, Thi H. Nguyen, Quynh Anh T. Tien, Tran Bao Kawazoe, Yoshiyuki Bui, Viet Q. University of Ulsan Korea Republic of Laboratory for Computational Physics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam Advanced Institute of Science and Technology The University of Danang 41 Le Duan Danang Viet Nam Department of Academic Affairs The University of Danang 41 Le Duan Danang Viet Nam New Industry Creation Hatchery Center Tohoku University Sendai980-8579 Japan School of Physics Institute of Science Suranaree University of Technology 111 University Avenue Nakhon Ratchasima30000 Thailand SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

In this study, we employ density functional theory (DFT) and subgroup discovery (SGD) to explore the structural and magnetic properties of full cubic Heusler compounds, with a particular emphasis on their Curie temperatures (Tc) and magnetic stability. Our comprehensive examination of 2,904 structures across both L21 and Xα phases identifies configurations that exhibit both structural stability and superior magnetic properties. Notable among these, compounds such as Co2FeSi, Co2FeAl, and Cr2VGe exhibit remarkable magnetic stability, maintaining their ferromagnetic properties well above room temperature. Co2FeSi displays a substantial magnetic moment of 5.49 μB and maintains its ferromagnetic properties up to a Curie temperature of 992 K, underscoring its suitability for high-temperature applications. Similarly, Co2FeAl, with a magnetic moment of 4.72 μB, transitions to a paramagnetic state at a higher temperature of 1099 K, demonstrating enhanced thermal durability. Moreover, Cr2VGe, notable for its robust magnetic moment of 2.81 μB, retains its ferromagnetic characteristics until an exceptional 2412 K, making it extremely valuable for thermally intensive environments. These findings underscore the potential of these materials in developing durable and efficient spintronic devices that operate under extreme thermal conditions. By mapping the interplay between electronic structure and magnetic properties, our study provides a predictive framework for optimizing the performance of spintronic materials. © 2024, The Authors. All rights reserved.

关键词： Ternary alloys

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Wireless Sensor Networks and Green Networking for 6G communication- An Overview

Wireless Sensor Networks and Green Networking for 6G communi...

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Computational intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Dibyahash Bordoloi Satya Prakash Yadav Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Turkey Faculty of Engineering Research Center for AI and IoT University of Kyrenia Kyrenia Turkey Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun RS CSE Graphic Era University Dehradun Department of Computer Science and Engineering G.L. Bajaj Institute of technology and Management (GLBITM) Greater Noida India

Wireless sensor networks and green networking have been major research areas in the field of communication technology for the past few decades. In particular, the development of 6G communication systems has brought renewed focus on these areas, as the demands for higher data rates, more diverse applications, and lower energy consumption continue to increase. The use of wireless sensor networks in 6G communication systems has been widely explored in the literature, and has shown great potential for a wide range of applications, including environmental monitoring, industrial process control, and healthcare. However, a key challenge in the deployment of these networks is ensuring their energy efficiency, as the demands on the network will be much higher than in previous generations. To address this challenge, researchers have proposed various green networking techniques, such as energy harvesting and low-power communication protocols, which have been shown to significantly reduce the energy consumption of the sensors. In addition to improving the energy efficiency of wireless sensor networks, researchers have also studied their reliability, scalability, and adaptability. These properties are important in 6G communication systems, as the demands on the network are expected to be much higher than in previous generations. Researchers have proposed various methods to improve the reliability, scalability, and adaptability of wireless sensor networks, including the use of error-correction codes, redundant communication channels, and machine learning algorithms.

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A Bidirectional LSTM Model for Classifying Chatbot Messages

A Bidirectional LSTM Model for Classifying Chatbot Messages

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International Joint Symposium on artificial intelligence and Natural Language Processing (iSAI-NLP)

作者： Nunthawat Lhasiw Nuttapong Sanglerdsinlapachai Tanatorn Tanantong Faculty of Science and Technology Thammasat University Pathum Thani Thailand Strategic Analytics Networks With Machine Learning and AI Research Team National Electronics and Computer Technology Center Pathum Thani Thailand Thammasat Research Unit in Data Innovation and Artificial Intelligence Faculty of Science and Technology Thammasat University Pathum Thani Thailand

ISBN: (纸本)9781665409483

Online channels, e.g., Facebook Messenger and Line, are widely used especially in COVID-19 pandemic. To quickly respond to their customer, chatbot system are implemented in many companies or organizations, connected to those channels. The Office of Registrar, Thammasat University also implements a chatbot to answer questions from students. An important step in the chatbot system is to know an intention of a question message. A bidirectional LSTM model is employed to classify a question message from the chatbot system into five intention classes. The experimental results shows that the obtained model yields an accuracy of 0.80 on our validation dataset.

关键词： Training COVID-19 Social networking (online) Pandemics data integrity Memory management Companies

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Analysis of Potential Health and Environmental Risks Associated with 6G Wireless Communication Networks

Analysis of Potential Health and Environmental Risks Associa...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Ramakrishnan Raman R Ravi Kumar Neha Garg Kapil Joshi Biju G. Pillai Unnati Joshi Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Uttaranchal Institute of Technology Uttaranchal University Department of IT & Business Analytics Faculty of Management Sri Balaji University Pune (SBUP) Department of PIET Parul University

Over the last several decades, there has been a meteoric rise in the number of people wanting to connect wirelessly. Due to rapid growth of new applications (such as real-time and interactive services) and smart terminals, Wireless data traffic has dramatically expanded, yet existing cellular networks (including the impending 5G) cannot fully meet the constantly expanding technological requirements. The sixth-generation (6G) mobile network is poised to set a new technological benchmark with its innovative use of spectrum and efficient transmission methods in preparation for future difficulties. In this project, we provide a high-level review of the current state of research on the promising approaches that are transitioning to 6G, which have lately garnered a lot of interest, and outline the prospective needs for their implementation. In addition, we describe a variety of important technological issues and possible solutions related to 6G, such as physical-layer transmission methods, network designs, security approaches, and testbed advancements.

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MetaGCD: Learning to Continually Learn in Generalized Category Discovery

arXiv

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arXiv 2023年

作者： Wu, Yanan Chi, Zhixiang Wang, Yang Feng, Songhe Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Electrical and Computer Engineering University of Toronto TorontoM5G1V7 Canada Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel classes. The goal is to continually discover novel classes while maintaining the performance in known classes. We name the setting Continual Generalized Category Discovery (C-GCD). Existing methods for novel class discovery cannot directly handle the C-GCD setting due to some unrealistic assumptions, such as the unlabeled data only containing novel classes. Furthermore, they fail to discover novel classes in a continual fashion. In this work, we lift all these assumptions and propose an approach, called MetaGCD, to learn how to incrementally discover with less forgetting. Our proposed method uses a meta-learning framework and leverages the offline labeled data to simulate the testing incremental learning process. A meta-objective is defined to revolve around two conflicting learning objectives to achieve novel class discovery without forgetting. Furthermore, a soft neighborhood-based contrastive network is proposed to discriminate uncorrelated images while attracting correlated images. We build strong baselines and conduct extensive experiments on three widely used benchmarks to demonstrate the superiority of our method. Our code is available at https://***/ynanwu/MetaGCD. © 2023, CC0.

关键词： Learning systems

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Cardiovascular Diagnostics Detection Using Multilayer Perception Approach

SSRN

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SSRN 2025年

作者： Auvee, Rafath Bin Zafar Fuad, M.D. Fakhrul Islam Hossain, Yeasir Kafi, Hasan Muhammad Miah, Abu Saleh Musa Al Farid, Fahmid Ahmed, Saadaldeen Rashid Karim, Hezerul Abdul Department of Computer Science and Engineering Bangladesh Army University of Science and Technology Saidpur Saidpur5311 Bangladesh Faculty of Engineering Multimedia University Cyberjaya Cyberjaya 63100 Malaysia Artificial Intelligence Engineering Department College of Engineering Al-Ayen University Thi-Qar Iraq

Cardiovascular disease (CVD) affects the heart and blood vessels, causing symptoms like chest pain, shortness of breath, and dizziness. CVD is worsened by unhealthy habits and conditions such as high blood pressure, high cholesterol, and diabetes. Early identification and treatment are crucial. Recent advancements in machine learning (ML) and deep learning (DL) offer new methods for predicting CVD and improving patient outcomes. Current CVD prediction methods are limited by small sample sizes, outdated techniques, and lack of diverse data, hindering their reliability and effectiveness. This study aims to evaluate the performance of various ML and DL algorithms for predicting CVD. We conducted an exploratory data analysis during the feature engineering *** applied seven ML algorithms, including Random Forest, Logistic Regression, KNN, Gradient Boosting, Naive Bayes, SVM and SVM with SGD classifier, and two DL models, including MLP, and CNN, using tools like Matplotlib, Scikit-learn [19], and Tensorflow [22]. The MLP model achieved the highest accuracy at 94.5%, outperforming previous studies and demonstrating the potential of advanced algorithms in CVD prediction. This study provides a comprehensive comparison of ML, DL algorithms, highlighting their effectiveness in predicting CVD. Our findings indicate that ML and DL algorithms, particularly the MLP model, can significantly enhance CVD prediction, offering valuable insights for future research and clinical applications. © 2025, The Authors. All rights reserved.

关键词： Clinical research

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ConTIG: Continuous representation learning on temporal interaction graphs

arXiv

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arXiv 2021年

作者： Yan, Xu Fan, Xiaoliang Yang, Peizhen Wu, Zonghan Pan, Shirui Chen, Longbiao Zang, Yu Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Computer Science & Technology Department Xiamen University Xiamen China Centre for Artificial Intelligence FEIT University of Technology Sydney Australia Department of Data Science and AI Faculty of Information Technology Monash University Australia

—Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems. Existing dynamic embedding methods on TIG discretely update node embeddings merely when an interaction occurs. They fail to capture the continuous dynamic evolution of embedding trajectories of nodes. In this paper, we propose a two-module framework named ConTIG, a continuous representation method that captures the continuous dynamic evolution of node embedding trajectories. With two essential modules, our model exploit three-fold factors in dynamic networks which include latest interaction, neighbor features and inherent characteristics. In the first update module, we employ a continuous inference block to learn the nodes’ state trajectories by learning from time-adjacent interaction patterns between node pairs using ordinary differential equations. In the second transform module, we introduce a self-attention mechanism to predict future node embeddings by aggregating historical temporal interaction information. Experiments results demonstrate the superiority of ConTIG on temporal link prediction, temporal node recommendation and dynamic node classification tasks compared with a range of state-of-the-art baselines, especially for long-interval interactions prediction. © 2021, CC BY.

关键词： Ordinary differential equations

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