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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Nisarg Doshi Sagar Bhavsar Rajeswari D Srinivasan R Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

In dusty and hazzy circumstances, pictures caputured tend to be unclear. To tackle this challenge researchers have founded different image-dehazing approaches. Lately, high quality pictures which could be used to extract maximal data from autonomous systems are in great demand. In order to minimize the haze from the picture, this research study uses various deep learning (also known as DL) architectures to identify the information recovered and extract key details from the image. The research paper focuses on utilizing deep learning techniques to remove haze from clear *** first phase of the suggested architecture involves applying multiple pre-processing methods, including estimating the airlight, implementing contextual regularization and boundary constraints, as well as contrast enhancement techniques (CS, CLAHE, HE) and Image Fusion. The subsequent goal of this study is to pinpoint the optimal deep learning model that can produce clear images based on the resulting dehazed images. The quality of the dehazed images is evaluated by comparing them with clear images using metrics such as PSNR and SSIM. Experimental findings shows that Alex Net upon the implementation of pre-processing beats other state of the art techniques with regard to PSNR value and holds its own w.r.t SSIM values.

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ANFIS Model for Cost Analysis in a Dual Source Multi-Destination System 4

ANFIS Model for Cost Analysis in a Dual Source Multi-Destina...

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4th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Okwu, M.O. Tartibu, L.K. Ojo, E.O. Adume, S. Gidiagba, J.O. Fadeyi, J. Department of Mechanical Industrial Engineering Tech. University of Johannesburg South Africa Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Mathematics and Data Science Dept. Computing Science and Mathematics Division Faculty of Natural Science University of Stirling United Kingdom Faculty of Engineering and Applied Sc. Memorial University Newfoundland Canada Department of Industrial and Systems Engineering University of Buffalo United States

Managers face uncertainties while making allocation decisions, especially in dual or multi-source multi-destination inventory systems. Regrettably, many studies focus on classical methods as a technique for product distribution, which has never guaranteed a satisfactory solution. Real-life problems are non-deterministic polynomial-time hard (NP-hard), and solving such problems is relatively challenging. Such complicated problems need an efficient and robust computational hybrid algorithm. This study emphasises the need for a hybrid intelligent technique for effective product distribution. Soft computing hybrid algorithm, ANFIS was applied to product distribution in a double source multi-destination system. Rules were developed from available input datasets. Distributing products from dual manufacturing plants to fifteen available depots using the creative algorithm resulted in an overall 13.5% decrease in cost compared to the existing method adopted by the company. The result showed that the proposed method is relatively satisfactory and adequate for cost modelling. In addition, it is easy to use and outperforms the classical approach. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Soft computing

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Swarm Intelligence Algorithm Inspired by Route Choice Behavior

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Journal of Bionic Engineering 2016年第4期13卷 669-678页

作者： Daxin Tian Junjie Hu Zhengguo Sheng Yunpeng Wang Jianming Ma Jian Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China School of Transportation Science and Engineering Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beihang University Beijing 1 O0191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Nanjing 210096 China Department of Engineering and Design University of Sussex Brighton BN1 9RH UK The Texas Department of Transportation Austin TX78750 USA College of Computer Science and Technology Jilin University Changchun 130012 China

Travelers＇ route choice behavior, a dynamical learning process based on their own experience, traffic information, and influence of others, is a type of cooperation optimization and a constant day-to-day evolutionary process. Travelers adjust their route choices to choose the best route, minimizing travel time and distance, or maximizing expressway use. Because route choice behavior is based on human beings, the most intelligent animals in the world, this swarm behavior is expected to in- corporate more intelligence. Unlike existing research in route choice behavior, the influence of other travelers is considered for updating route choices on account of the reality, which makes the route choice behavior from individual to swarm. Anew swarm intelligence algorithm inspired by travelers＇ route choice behavior for solving mathematical optimization problems is introduced in this paper. A comparison of the results of experiments with those of the classical global Particle Swarm Optimization （PSO） algorithm demonstrates the efficacy of the Route Choice Behavior Algorithm （RCBA）. The novel algorithm provides a new approach to solving complex problems and new avenues for the study of route choice behavior.

关键词： swarm intelligence route choice behavior particle swarm optimization mathematical optimization

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Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

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International Conference on Communication, computing and Internet of Things (IC3IoT)

作者： J. Uma Mageswaran G. Indumathi A.V. Kalpana G. Elangovan S.D. Lalitha S. Nalini Mayank Kumar Sohanda Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur Dist Tamil Nadu SRM Institute of Science and Technology Ramapuram Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Department of Data Science and Business Systems SRM Institute of Science and Technology Department of Computer Science and Engineering R.M.K Engineering College Department of Computing Technologies SRM Institute of Science and Technolog Kattankulathur

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus.

关键词： Sociology Telecommunication computing Internet of Things Statistics RFID tags Monitoring Accidents

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Nonblocking execution in GraphBLAS

Nonblocking execution in GraphBLAS

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Aristeidis Mastoras Sotiris Anagnostidis Albert– Jan N. Yzelman Computing Systems Laboratory Zurich Research Center Huawei Technologies Switzerland Data Analytics Laboratory Department of Computer Science ETH Zurich Switzerland

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

GraphBLAS is a recent standard that allows the expression of graph algorithms in the language of linear algebra and enables automatic code parallelization and optimization. GraphBLAS operations are executed either in blocking or in non-blocking mode. Although there exist multiple implementations of GraphBLAS for efficient blocking execution on both shared-and distributed-memory systems, none of these implementations supports full nonblocking execution to improve data locality. In this paper, we present a preliminary evaluation for two algorithms, Pagerank and Conjugate Gradient, that confirms the importance of nonblocking execution, by showing promising speedups over the corresponding blocking execution.

关键词： Analytical models Distributed processing Heuristic algorithms Instruction sets Distributed databases Manuals Linear algebra

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MVMoE: multi-task vehicle routing solver with mixture-of-experts 24

MVMoE: multi-task vehicle routing solver with mixture-of-exp...

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Proceedings of the 41st International Conference on Machine Learning

作者： Jianan Zhou Zhiguang Cao Yaoxin Wu Wen Song Yining Ma Jie Zhang Chi Xu College of Computing and Data Science Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Department of Information Systems Eindhoven University of Technology The Netherlands Institute of Marine Science and Technology Shandong University China College of Computing and Data Science Nanyang Technological University Singapore and Singapore Institute of Manufacturing Technology (SIMTech) Agency for Science Technology and Research (A*STAR) Singapore

Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants simultaneously. Specifically, we propose a multitask vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in computation. We further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational complexity. Experimentally, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark instances. We further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution data. The source code is available at: https://***/RoyalSkye/Routing-MVMoE.

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MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts

arXiv

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arXiv 2024年

作者： Zhou, Jianan Cao, Zhiguang Wu, Yaoxin Song, Wen Ma, Yining Zhang, Jie Xu, Chi College of Computing and Data Science Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Department of Information Systems Eindhoven University of Technology Netherlands Institute of Marine Science and Technology Shandong University China Singapore

Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants simultaneously. Specifically, we propose a multi-task vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in computation. We further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational complexity. Experimentally, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark instances. We further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution data. The source code is available at: https://***/RoyalSkye/Routing-MVMoE. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicle routing

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Skeleton-OOD: An End-to-End Skeleton-Based Model for Robust Out-of-Distribution Human Action Detection

arXiv

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arXiv 2024年

作者： Xu, Jing Zhu, Anqi Lin, Jingyu Ke, Qiuhong Chen, Cunjian Department of Data Science and AI Monash University MelbourneVIC3800 Australia Department of Computing and Information Systems University of Melbourne MelbourneVIC3052 Australia

Human action recognition is crucial in computer vision systems. However, in real-world scenarios, human actions often fall outside the distribution of training data, requiring a model to both recognize in-distribution (ID) actions and reject out-of-distribution (OOD) ones. Despite its importance, there has been limited research on OOD detection in human actions. Existing works on OOD detection mainly focus on image data with RGB structure, and many methods are post-hoc in nature. While these methods are convenient and computationally efficient, they often lack sufficient accuracy, fail to consider the exposure of OOD samples, and ignore the application in skeleton structure data. To address these challenges, we propose a novel end-to-end skeleton-based model called Skeleton-OOD, which is committed to improving the effectiveness of OOD tasks while ensuring the accuracy of ID recognition. Through extensive experiments conducted on NTU-RGB+D 60, NTU-RGB+D 120, and Kinetics-400 datasets, Skeleton-OOD demonstrates the superior performance of our proposed approach compared to state-of-the-art methods. Our findings underscore the effectiveness of classic OOD detection techniques in the context of skeleton-based action recognition tasks, offering promising avenues for future research in this field. Code is available at https://***/YilliaJing/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Musculoskeletal system

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis 4

Personalized Federated Learning with Collaborative Aggregati...

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4th International Conference on Industrial Automation, Robotics and Control Engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract) 39

Copula Guided Parallel Gibbs Sampling for Nonparametric and ...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Lin, Lihui Rao, Yanghui Xie, Haoran Lau, Raymond Y. K. Yin, Jian Wang, Fu Lee Li, Qing Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Lingnan University Department of Computing and Decision Sciences New Territories Hong Kong City University of Hong Kong Department of Information Systems Kowloon Hong Kong Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Hong Kong Metropolitan University School of Science and Technology Kowloon Hong Kong The Hong Kong Polytechnic University Department of Computing Kowloon Hong Kong

ISBN: (纸本)9798350322279

In terms of the generative process, the Gamma-Gamma-Poisson Process (G2PP) is equivalent to the nonparametric topic model of Hierarchical Dirichlet Process (HDP). Considering the high computational cost of estimating parameters in HDP, a parallel G2PP was developed to generate topics efficiently via multi-threading. Unfortunately, the above model needs to predefine the number of topics. To address this issue, we first propose a Topic Self-Adaptive Model (TSAM) for nonparametric and parallel topic discovery. In TSAM, a monitor-executor mechanism is developed to manage the global topic information using a hierarchical structure of threads. Based on the apparatus of copulas, we further extend our TSAM to TSAMcop for coherent topic modeling by exploiting a copula guided parallel Gibbs sampling algorithm. Extensive experiments validate the effectiveness of both TSAM and TSAMcop. © 2023 IEEE.

关键词： Information management

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