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ReMP: Reusable Motion Prior for Multi-domain 3D Human Pose Estimation and Motion Inbetweening

ReMP: Reusable Motion Prior for Multi-domain 3D Human Pose E...

引用

2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Jang, Hojun Kim, Young Min Seoul National University Dept. of Electrical and Computer Engineering Korea Republic of Interdisciplinary Program in Artificial Intelligence and Inmc Seoul National University Korea Republic of

ISBN: (纸本)9798331510831

We present Reusable Motion prior (ReMP), an effective motion prior that can accurately track the temporal evolution of motion in various downstream tasks. Inspired by the success of foundation models, we argue that a robust spatio-temporal motion prior can encapsulate underlying 3D dynamics applicable to various sensor modalities. We learn the rich motion prior from a sequence of complete parametric models of posed human body shape. Our prior can easily estimate poses in missing frames or noisy measurements despite significant occlusion by employing a temporal attention mechanism. More interestingly, our prior can guide the system with incomplete and challenging input measurements to quickly extract critical information to estimate the sequence of poses, significantly improving the training efficiency for mesh sequence recovery. ReMP consistently outperforms the baseline method on diverse and practical 3D motion data, including dept. point clouds, LiDAR scans, and IMU sensor data. Project page is available in https://***/ReMP. © 2025 IEEE.

关键词： Reusability

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

引用

2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Using textile capacitive sensors to train synchronized movement of hands

Using textile capacitive sensors to train synchronized movem...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Yang, Chang-Ming Wu, Shu-Cing Chen, Shih-Hung Wu, Ze-We Liao, Kuo-Cheng Chen, Chi-Chun Ming Young Biomedical Corp. Miaoli Taiwan National Chin-Yi University of Technology Program Prospective Technology of Electrical Engineering and Computer Science Taichung Taiwan National Chin-Yi University of Technology Dept. of Computer Science and Information Engineering Taichung Taiwan National Chin-Yi University of Technology Dept. of Electronic Engineering Taichung Taiwan

ISBN: (纸本)9798350324136

This study introduced a capacitive sensing interactive game platform aimed at promoting emotional stability, which we have named the 'Sunrise and Sunset' game. This game primarily consists of two pieces of regular textile fabric enveloping conductive silver fabric. A microcontroller was employed to extract the sensed capacitive values, and a game named 'Sunrise and Sunset' is designed to complement the slow raising and lowering of both hands. The development of this gaming platform has the potential to offer a novel method of emotional management, particularly in high-stress living environments. It can serve as an effective relaxation tool, aiding individuals in emotional balance, anxiety reduction, and stress alleviation. Simultaneously, this platform can contribute to the promotion of mental well-being, providing an engaging and beneficial means for people to manage their emotions and moods. © 2024 IEEE.

关键词： capacitive sensing emotional stability game relaxation tool silver fabric

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MSMA: Multi-Agent Trajectory Prediction in Connected and Autonomous Vehicle Environment with Multi-Source Data Integration 24

MSMA: Multi-Agent Trajectory Prediction in Connected and Aut...

引用

24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Chen, Xi Bhadani, Rahul Sun, Zhanbo Head, Larry Dept. of Systems and Industrial Engineering Univ. of Arizona TucsonAZ United States Dept. of Electrical and Computer Engineering Univ. of Alabama in Huntsville HuntsvilleAL United States School of Transportation and Logistics Southwest Jiaotong Univ. Chengdu China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

The prediction of surrounding vehicle trajectories is crucial for collision-free path planning. In this study, we focus on a scenario where a connected and autonomous vehicle (CAV) serves as the central agent, utilizing both sensors and communication technologies to perceive its surrounding traffics consisting of autonomous vehicles (AVs), connected vehicles (CVs), and human-driven vehicles (HDVs). Our trajectory prediction task is aimed at all the detected surrounding vehicles. To effectively integrate the multi-source data from both sensor and communication technologies, we propose a deep learning framework called MSMA utilizing a cross-attention module for multi-source data fusion. Vector map data is utilized to provide contextual information. The trajectory data set is collected in CARLA simulator with synthesized data errors introduced. Numerical experiments demonstrate that in a mixed traffic flow scenario, the integration of data from different sources enhances our understanding of the environment. This notably improves trajectory prediction accuracy, particularly in situations with a high CV market penetration rate. © ASCE.

关键词： Data fusion

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A song emotion identification system from lyrics using heterogeneous ensemble learning

A song emotion identification system from lyrics using heter...

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2023 IEEE Silchar Subsection Conference, SILCON 2023

作者： Mukherjee, Himadri Marciano, Matteo Dhar, Ankita Roy, Kaushik West Bengal State University Dept. of Computer Science India Music Program New York University Gazelien Records Lab Dept. of Arts and Humanities Abu Dhabi United Arab Emirates Sister Nivedita University Dept. of Computer Science & Engineering India

ISBN: (纸本)9798350314144

The number of songs is increasing at an explosive rate which has led to the development of automatic song categorization systems. Songs are categorized in different ways like genre, artist, beats per minute (BPM), etc. It is also very important to categorize songs based on their emotional content as the audience often prefers songs of a particular mood. The lyrics of a song become available online instantly after the release of the song itself. This sets the stage for an automated song classification system based on lyrics. In this paper, a system is presented to address this problem. The system was tested with 400 songs from 2 categories namely happy and sad. The dataset was composed of disparate artists, genres, and timelines wherein the highest accuracy of 83.25% was obtained using a heterogeneous ensemble learning-based approach. © 2023 IEEE.

关键词： Learning systems

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Online Complaint Management System using Image Recognition 8

Online Complaint Management System using Image Recognition

引用

8th International Conference on Communication and Electronics systems, ICCES 2023

作者： Balakrishnan, S. Janet, J. Rohith, T. Sakthivel, R. Sanjaiy Kumar, T.N. Sri Krishna College of Engg. and Technology Dept. of Computer Science and Business Systems Tamilnadu Coimbatore India Sri Krishna College of Engg. and Technology Dept. of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350396638

Clean and safe environments are the responsibility of the government. Currently, people have to lodge their complaints with the Municipal Corporation, and then workers take time to respond to them. In this paper we have made a platform to overcome the communication gap between the user and the municipal corporation. This research study proposes an online complaint management system in a web application, where the citizen can lodge a complaint by taking a picture. After the picture being uploaded, image recognition is used to automatically detect the problem raised by the user and Google map API to detect the location automatically. As soon as the problem has been resolved, the municipal corporation must upload a completed status picture to verify that the work has been completed. The aim of this application is to overcome the communication gap between user and Municipal Corporation, to simplify the process of raising complaints and to maintain a clean and safe environment. © 2023 IEEE.

关键词： Image recognition

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Study Case on Implementation Elevator 2 Floor System on PLC OMRON CP1E N30 9

Study Case on Implementation Elevator 2 Floor System on PLC ...

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9th International Conference on Education and Technology, ICET 2023

作者： Habibi, Muhammad Afnan Wibawa, Arif Fadlika, Irham Irfani, Mochamad Farhan Ali Firmansyah, Mohamad Farel Baskoro, Angger Study Program of Power Plant Engineering Technology Malang Indonesia Universitas Negeri Malang Dept. of Electrical Engineering Malang Indonesia National Yang Ming Chiao Tung University Dept. of Electrical Engineering and Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350358292

The swift progression of industrial technology underscores the need for adept.professionals capable of skillfully implementing and managing intricate systems. Vocational education assumes a pivotal role in cultivating competent intermediaries proficient in translating theoretical knowledge into practical applications. At the core of industrial automation lies the programmable Logic Controller (PLC), a specialized digital computer that interfaces with sensors, switches, and actuators to execute real-time control programs, thus facilitating meticulous industrial processes. This study concentrates on the practical deployment of a two-floor elevator system utilizing the OMRON CP1E N30 PLC. Elevators, integral to contemporary structures, mandate precision for secure vertical conveyance. Leveraging input signals from elevator buttons, sensors, and switches, the PLC orchestrates efficient motor control and dependable door operations. This hands-on application underscores the PLC's significance within real-time control contexts. Nonetheless, Vocational Higher Education graduates frequently grapple with the challenge of transitioning to industry settings due to limited practical exposure. This partnership harmonizes with the center's objective of enriching vocational education by integrating sophisticated technologies like PLCs, equipping the workforce with the acumen to bridge the gap between theoretical foundations and the pragmatic demands of industry. © 2023 IEEE.

关键词： Elevators

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19 53

Investigating Online Searching Behavior Based on Google Tren...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Belkacem, Abdelkader Nasreddine Jamil, Nuraini Alrabaee, Saed College of It Uae University Dept. of Computer and Network Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Information Systems and Security Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning. © 2023 IEEE.

关键词： COVID-19 Google Trends MENA Online learning Remote learning Web Behavior

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Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybrid Edge-Cloud Computing

Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybr...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Haitao Wang, Shen Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization. © 2024 IEEE.

关键词： Mobile edge computing

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids 21

Integrating Noise Classification and Speech Enhancement Mode...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Hung, Ying-Hsiu Chang, Yen-Ching Lai, Shin-Chi Juang, Wen-Ho Sheu, Ming-Hwa Lee, Jeng-Dao National Yunlin University of Science and Technology Dept. Electronics Engineering Douliu Taiwan National Formosa University Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Huwei Taiwan National Formosa University Dept. Computer Science and Information Engineering Huwei Taiwan National Formosa University Dept. Automation Engineering Huwei Taiwan

ISBN: (纸本)9798350377088

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm. © 2024 IEEE.

关键词： Generative adversarial networks

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