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Multi-View Dynamic Kernelized Evidential Clustering

IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinese 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Jingshen Chen, Xinglu Qiu, Xinying Wang, Zhimin Feng, Wenhe School of Information Science and Technology Guangzhou China Faculty of Chinese Language and Culture Guangzhou China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9780000000002

Chinese sentence simplification faces challenges due to the lack of large-scale labeled parallel corpora and the prevalence of idioms. To address these challenges, we propose Readability-guided Idiom-aware Sentence Simplification (RISS), a novel framework that combines data augmentation techniques. RISS introduces two key components: (1) Readability-guided Paraphrase Selection (RPS), a method for mining high-quality sentence pairs, and (2) Idiom-aware Simplification (IAS), a model that enhances the comprehension and simplification of idiomatic expressions. By integrating RPS and IAS using multi-stage and multi-task learning strategies, RISS outperforms previous state-of-the-art methods on two Chinese sentence simplification datasets. Furthermore, RISS achieves additional improvements when fine-tuned on a small labeled dataset. Our approach demonstrates the potential for more effective and accessible Chinese text simplification. © 2024 China National Conference on Computational Linguistics.

关键词： Computational linguistics

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Smart Environmental Monitoring System for Air and Water Quality Index

Smart Environmental Monitoring System for Air and Water Qual...

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IEEE Students' Conference on Electrical, Electronics and Computer science (SCEECS)

作者： Ansh Tripathi Saransh Dubry P. Rama V. Angayarkanni J. Kalaivani Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Department of Computing Technologies Faculty of Engineering and Technology SRMIST Kattankulathur

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The quality of air and water is essential for human health, environmental sustainability, and ecosystem balance. However, pollution from industrial activities, urbanization, and agricultural practices has made effective environmental monitoring a global priority. Traditional monitoring methods, which rely on manual sampling and laboratory analysis, are often time-consuming and lack real-time capabilities. This paper presents a Smart Environmental Monitoring (SEM) system leveraging IoT and sensor technology to monitor air and water quality in real time using two key sensors: the MQ135 for Air Quality Index (AQI) and the pH sensor for Water Quality Index (WQI). By focusing on these two critical parameters, the SEM system offers a streamlined yet robust solution for assessing environmental quality. The system collects data continuously, transmits it to a cloud platform for processing, and employs machine learning algorithms to forecast future pollution trends. A user-friendly dashboard displays real-time AQI and WQI levels, historical trends, and predictive insights, empowering users to respond proactively to environmental risks. Deployment results demonstrate the SEM system’s capability in detecting pollution patterns, identifying high-risk areas, and predicting pollution spikes during peak traffic and in industrial zones. Comparative analysis with traditional monitoring highlights the SEM system’s superior accuracy, scalability, and predictive abilities. Although the SEM system has higher initial setup costs, its operational efficiency and long-term benefits in mitigating environmental risks make it a sustainable choice for public health and environmental management.

关键词： Environmentally friendly manufacturing techniques Scalability Water quality Machine learning Market research Real-time systems Water pollution Indexes Environmental monitoring Public healthcare

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An Empirical Investigation on Employing Machine Learning for Balancing Home Agent Loads in Next Generation IP Mobility 2

An Empirical Investigation on Employing Machine Learning for...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Khatri, Anshu Mathi, Senthilkumar Ashiq Sheriff, A. Deepthika, R. Naveen Raaghavendran, G. Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Mobile IPv6 is a key technology to enable the mobility of devices on next-generation internet protocol networks. Home agents provide simple services to registered mobile nodes. In addition, the use of multiple domestic operators for load-balancing devices faces problems of synchronization and signalling overhead. Thus, most of the existing load balancing methods present a significant delay in the transmission of data packets. The machine learning approach provides an efficient hardware storage solution for balancing the load of the home agent by improving the state-work schedule. It provides a method for dynamic load balancing based on the research-consumption system. Thus, the present paper investigates the use of machine learning, specifically reinforcement learning, to enhance home agent load balancing in next-generation IP traffic. The reinforcement learning-based home agent decisions, guided by mobile node speed and registration queries, reduce latency, improve network performance, and provide dynamic solutions. The proposed method significantly reduces the delay for packet delivery by balancing the home agent load with the incorporation of reward parameters. The empirical investigation found a remarkable improvement in network reliability, as manifested by a 2.31% increase in packet delivery ratio compared to systems with existing methods. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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Real Time Motion Capture for Physical Rehabilitation Using Kinect

Real Time Motion Capture for Physical Rehabilitation Using K...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Singireddy Srijay Reddy Nagumalla Yasash P. Rama V. Angayarkanni J. Kalaivani Faculty of Engineering and Technology Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Department of Computing Technologies Faculty of Engineering and Technology SRMIST Kattankulathur

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

This Project is a Kinect-based rehabilitation system designed to assist patients with movement disorders by performing targeted physical exercises at home. This system enables the evaluation of whether patients are correctly executing the prescribed rehabilitation exercises. By utilizing the skeletal tracking capabilities of the Microsoft Kinect sensor, each movement of the patient during the exercises is recognized and *** research introduces a real-time motion capture system designed for physical rehabilitation using Microsoft's Kinect sensor. The system addresses the growing demand for remote, data-driven healthcare solutions that assist in rehabilitation exercises by tracking and analysing skeletal movements. The Kinect's ability to track 25 joints in real time provides a unique opportunity to offer non-intrusive monitoring, real-time feedback, and data collection for therapists and patients. While Kinect offers several advantages, including cost-effectiveness and ease of use, our study identifies several limitations in precision, especially for complex movements and occlusion scenarios. This paper details the system's design, testing, and evaluation, highlighting areas where future improvements are necessary.

关键词： Accuracy Target tracking Tracking Smart systems Motors Real-time systems Motion capture Monitoring System analysis and design Testing

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The Development of Assistive Robotics: A Comprehensive Analysis Integrating Machine Learning, Robotic Vision, and Collaborative Human Assistive Robots 2nd

The Development of Assistive Robotics: A Comprehensive Analy...

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2nd International Conference on Digital Transformation in Education and Artificial Intelligence Applications, MoStart 2024

作者： Crnokić, Boris Peko, Ivan Gotlih, Janez Faculty of Mechanical Engineering Computing and Electrical Engineering University of Mostar Matice Hrvatske b.b. Mostar8800 Bosnia and Herzegovina Faculty of Science University of Split Ruđera Boškovića 33 Split21000 Croatia Faculty of Mechanical Engineering University of Maribor Smetanova ul. 17 Maribor2000 Slovenia

ISBN: (纸本)9783031620577

With integration of collaborative robotics, robotic vision, and machine learning, the high-end frontier reached in assistive robotics is likely to help develop promising solutions to the challenges associated with the needs of aging populations and persons with disabilities. This paper examines recent developments, challenges, and opportunities that relate to the application of these technologies for human assistance. The paper will begin with an explanation of the basic concepts and the rationales driving assistive robotics, and then move on to the technological ground with basic collaborative robots, robotic vision, and machine learning, stressing how they can make human assistance better. Case studies and real-world applications will help to present the potential of integrated assistive robotics not only in healthcare and rehabilitation, but also in domains such as elder care and living on one’s own. This paper also highlights some of the critical challenges inclusive of robustness, reliability, human-robot interaction, safety, and ethical considerations along with some of the emerging trends and future research directions related to it. The paper sets up a visionary outlook, where integrated assistive robotics—realized fully through human-centered design, interdisciplinary collaboration, and innovation in technology development—could revolutionize care and support toward fostering the inclusivity, dignity, and independence of those with diverse needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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Diversity and Freshness-Aware Regret Minimizing Set Queries 29th

Diversity and Freshness-Aware Regret Minimizing Set Queries

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29th International computing and Combinatorics Conference, COCOON 2023

作者： Guo, Hongjie Li, Jianzhong Shen, Fangyao Gao, Hong Faculty of Computing Harbin Institute of Technology Heilongjiang Harbin China School of Computer Science and Technology Zhejiang Normal University Zhejiang Jinhua China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783031491924

Multi-criteria decision-making often involves selecting a small representative set from a database. A recently proposed method is the regret minimization set (RMS) queries. It aims to rectify the shortcomings of needing a utility function in top-k queries and the overly large result size of skyline queries. However, the existing definition of RMS only ensures one result under any utility function, and do not consider the diversity and freshness of the returned results. In this paper, we define a strong regret set, which guarantees the utility value error of k data points under any utility function. Given this new definition, we propose two problems, namely the Minimum Size problem and the Max-sum Diversity and Freshness problem. Both proposed problems have been proven to be NP-hard. Correspondingly, we devise approximation algorithms for them, and analyze algorithms’ time complexities and the approximation ratios of the solutions obtained. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Approximation algorithms

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Relationship between land use and user’s behavior along the street:A case study of Da Nang City,Viet Nam

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Frontiers of Architectural Research 2024年第1期13卷 144-163页

作者： Duy Thinh Do Duy Thao Do Faculty of Civil Engineering Ho Chi Minh City University of Technology and Education(HCMUTE)Ho Chi Minh City 71307Viet Nam School of Computing Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea

Human activities on street spaces are affected by both physical and non-physical settings on *** two aspects are primarily impacted by land use which results in the uneven distribution of different activities on *** research investigates land use patterns and their characteristics in association to user’s *** using mixed qualitative and quantitative research methods,e.g.,place-centered behavioral map,observation,visual encounter surveys,machine learning,the relationship between user’s behavior and land use characteristics along the street is *** user behaviors along both types of streets were classified into six main categories,with 26 *** observation results show that the residential use of land along the street was transformed into the commercial use for various types of shophouses based on the resident’s *** is a great correlation between land use and user’s *** findings give support to authorities to manage urban streets and develop a sustainable policy for improving street ***,this research contributes useful information to urban designers and planners in creating a successful street space that is appropriate for the Vietnam Community.

关键词： Land use pattern User’s behavior Da Nang Street space Data mining

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Insulator Defect Detection in Power Lines Based on Improved Convolution Neural Network 6

Insulator Defect Detection in Power Lines Based on Improved ...

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6th IEEE Conference on Energy Conversion, CENCON 2023

作者： Joseph, Annie Anak Rafiq, Mohd Rahul Bin Mohd Kipli, Kuryati Bt Chin, Kho Lee Zulcaffle, Tengku Mohd Afendi Voon, Charlie Sia Chin Universiti Malaysia Sarawak Department of Electrical and Electronic Engineering Kota Samarahan94300 Malaysia Swinburne University of Technology Faculty of Engineering Computing and Science Kuching93350 Malaysia

ISBN: (纸本)9798350325096

In a transmission line architecture, an insulator is essential for preventing the unintended dissipation of electrical current from the conductive elements into the surrounding environment. This purpose is accomplished by effectively isolating the conductors from the supporting framework. A defect in the insulator may cause several malfunctions in the transmission line. It can range from a minor failure to catastrophic damage. Previous studies have investigated some insulator defect detection technologies using image processing methods. In modern research, classifiers are frequently used for this function in widespread detection systems. However, there are still some issues with computational effectiveness and detecting accuracy. This paper introduces an innovative approach by proposing a hybrid system based on You Only Look Once (YOLOv5) and Residual Neural Network (Resnet50) architectures. The proposed methodology achieves an excellent accuracy of 99.0 ± 0.233%. It takes 25 minutes to complete the training process for a dataset containing 1,000 photos of insulators. The suggested method can transform the inspection procedure for high-altitude insulators by smoothly merging the advantages of YOLOv5 and Resnet50 through a carefully thought-out hybrid approach. © 2023 IEEE.

关键词： Hybrid systems

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PraK Tool V3: Enhancing Video Item Search Using Localized Text and Texture Queries 31st

PraK Tool V3: Enhancing Video Item Search Using Localized Te...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

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