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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Ya'nan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China Eth Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech Advanced Research Center KKCompany Taiwan ByteDance China Mgtv China Fuzhou University China Shopee Information Technology Co. Ltd. S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China Insa Rennes Cnrs Ietr - Umr 6164 Rennes France National Higher School of Telecommunications and Ict Oran Algeria Buaa Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798350302493

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. © 2023 IEEE.

关键词： Image enhancement

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HKTGNN: Hierarchical Knowledge Transferable Graph Neural Network-based Supply Chain Risk Assessment

HKTGNN: Hierarchical Knowledge Transferable Graph Neural Net...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zhanting Zhou Kejun Bi Yuyanzhen Zhong Chao Tang Dongfen Li Shi Ying Ruijin Wang School of Information Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Sichuan University Chengdu China Chongqing CEPREI Industrial Technology Research Institute Co. Ltd Chongqing China Sichuan Changhong Electronic Holding Group Co. Ltd Chengdu China College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China School of Computer Science Wuhan University Wuhan China

The strength of a supply chain is an important measure of a country’s or region’s technical advancement and overall competitiveness. Establishing supply chain risk assessment models for effective management and mitigation of potential risks has become increasingly crucial. As the number of businesses grows, the important relationships become more complicated and difficult to measure. This emphasizes the need of extracting relevant information from graph data. Previously, academics mostly employed knowledge inference to increase the visibility of links between nodes in the supply chain. However, they have not solved the data hunger problem of single node feature characteristics. We propose a hierarchical knowledge transferable graph neural network-based (HKTGNN 1 ) supply chain risk assessment model to address these issues. Our approach is based on current graph embedding methods for assessing corporate investment risk assessment. We embed the supply chain network corresponding to individual goods in the supply chain using the graph embedding module, resulting in a directed homogeneous graph with just product nodes. This reduces the complicated supply chain network into a basic product network. It addresses difficulties using the domain difference knowledge transferable module based on centrality, which is presented by the premise that supply chain feature characteristics may be biased in the actual world. Meanwhile, the feature complement and message passing will alleviate the data hunger problem, which is driven by domain differences. Our model outperforms in experiments on a real-world supply chain dataset. We will give an equation to prove that our comparative experiment is both effective and fair. 1 Accepted by The 21st IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA 2023).

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HKTGNN: Hierarchical Knowledge Transferable Graph Neural Network-based Supply Chain Risk Assessment

arXiv

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arXiv 2023年

作者： Zhou, Zhanting Bi, Kejun Zhong, Yuyanzhen Tang, Chao Li, Dongfen Ying, Shi Wang, Ruijin School of Information Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Sichuan University Chengdu China Chongqing CEPREI Industrial Technology Research Institute Co. Ltd Chongqing China Sichuan Changhong Electronic Holding Group Co. Ltd Chengdu China College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China School of Computer Science Wuhan University Wuhan China

The strength of a supply chain is an important measure of a country's or region's technical advancement and overall competitiveness. Establishing supply chain risk assessment models for effective management and mitigation of potential risks has become increasingly crucial. As the number of businesses grows, the important relationships become more complicated and difficult to measure. This emphasizes the need of extracting relevant information from graph data. Previously, academics mostly employed knowledge inference to increase the visibility of links between nodes in the supply chain. However, they have not solved the data hunger problem of single node feature characteristics. We propose a hierarchical knowledge transferable graph neural network-based (HKTGNN) supply chain risk assessment model to address these issues. Our approach is based on current graph embedding methods for assessing corporate investment risk assessment. We embed the supply chain network corresponding to individual goods in the supply chain using the graph embedding module, resulting in a directed homogeneous graph with just product nodes. This reduces the complicated supply chain network into a basic product network. It addresses difficulties using the domain difference knowledge transferable module based on centrality, which is presented by the premise that supply chain feature characteristics may be biased in the actual world. Meanwhile, the feature complement and message passing will alleviate the data hunger problem, which is driven by domain differences. Our model outperforms in experiments on a real-world supply chain dataset. We will give an equation to prove that our comparative experiment is both effective and fair. Copyright © 2023, The Authors. All rights reserved.

关键词： Supply chains

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Negative Binomial Regression Model for Predicting the Number of Tourists in National Parks of Thailand

Negative Binomial Regression Model for Predicting the Number...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Sthaporn Thepsumritporn Anuchit Jitpattanakul Sakorn Mekruksavanich Wikanda Phaphan Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand Research Group in Statistical Learning and Inference

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This article examines the use of count data models to predict the number of tourists visiting Thailand’s national parks. The dataset, encompassing observations from 2016 to 2022 across 146 national parks, exhibits characteristics such as discreteness, over dispersion, and non-negative integer values. Two models— Poisson (POI), and Negative Binomial (NB)—were compared to identify the most effective approach for modeling and prediction. Model evaluation metrics, including the Akaike information Criterion (AIC) and log-likelihood, were employed. Results demonstrate that the Negative Binomial regression model best captures the data’s overdispersion compared to the POI model. The NB model achieved the lowest AIC and highest log-likelihood, affirming its superior performance for this application. Our findings underscore the NB model’s utility in predicting tourism-related count data and suggest its broader applicability to other domains characterized by overdispersed count data.

关键词： Natural resources Measurement Analytical models Accuracy Predictive models Data models Planning Resource management Dispersion

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CSI-Based Human Activity Recognition using Convolutional Neural Networks 11

CSI-Based Human Activity Recognition using Convolutional Neu...

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11th International Conference on computer engineering and Knowledge, ICCKE 2021

作者： Moshiri, Parisa Fard Nabati, Mohammad Shahbazian, Reza Ghorashi, Seyed Ali Shahid Beheshti University G. C. Cognitive Telecommunication Research Group Department of Electrical Engineering Tehran Iran Standard Research Institute Faculty of Technology and Engineering Department of Electrical Engineering Alborz31745-139 Iran University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom

ISBN: (纸本)9781665402088

Human activity recognition (HAR) as an emerging technology can have undeniable impacts on several applications such as health monitoring, context-aware systems, transportation, robotics, and smart cities. Among the main research methods in HAR (sensor, image, and WiFi-based), the WiFi-based method has attracted considerable attention due to the ubiquity of WiFi devices. WiFi devices can be utilized to distinguish daily activities such as "walk", "run", and "sleep". These activities affect WiFi signal propagation and can be further used to recognize activities. This paper proposes a Deep Learning method for HAR tasks using channel state information (CSI). A new model is developed in which CSI data are converted to grayscale images. These images are then fed into a 2D-Convolutional Neural Network (CNN) for activity classification. We take advantage of CNN's high accuracy on image classification along with WiFi-based ubiquity. The experimental results demonstrate that our proposed approach achieves acceptable performance in HAR tasks. © 2021 IEEE.

关键词： Channel state information

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Dealing with Imbalanced Dataset Leveraging Boundary Samples Discovered by Support Vector Data Description

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computers, Materials & Continua 2021年第3期66卷 2691-2708页

作者： Zhengbo Luo Hamïd Parvïn Harish Garg Sultan Noman Qasem Kim-Hung Pho Zulkefli Mansor Graduate School of Information Production and SystemsWaseda UniversityTokyoJapan Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science Nourabad Mamasani BranchIslamic Azad UniversityMamasaniIran School of Mathematics Thapar Institute of Engineering and TechnologyDeemed UniversityPatialaPunjab147004India Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic UniversityRiyadhSaudi Arabia Computer Science Department Faculty of Applied ScienceTaiz UniversityTaizYemen Fractional Calculus Optimization and Algebra Research GroupFaculty of Mathematics and StatisticsTon Duc Thang UniversityHo Chi Minh CityVietnam Fakulti Teknologi dan Sains Maklumat Universiti Kebangsaan MalaysiaSelangorMalaysia

These days,imbalanced datasets,denoted throughout the paper by ID,(a dataset that contains some(usually two)classes where one contains considerably smaller number of samples than the other(s))emerge in many real world problems(like health care systems or disease diagnosis systems,anomaly detection,fraud detection,stream based malware detection systems,and so on)and these datasets cause some problems(like under-training of minority class(es)and over-training of majority class(es),bias towards majority class(es),and so on)in classification process and ***,these datasets take the focus of many researchers in any science and there are several solutions for dealing with this *** main aim of this study for dealing with IDs is to resample the borderline samples discovered by Support Vector Data Description(SVDD).There are naturally two kinds of resampling:Under-sampling(U-S)and oversampling(O-S).The O-S may cause the occurrence of over-fitting(the occurrence of over-fitting is its main drawback).The U-S can cause the occurrence of significant information loss(the occurrence of significant information loss is its main drawback).In this study,to avoid the drawbacks of the sampling techniques,we focus on the samples that may be *** data points that can be misclassified are considered to be the borderline data points which are on border(s)between the majority class(es)and minority class(es).First by SVDD,we find the borderline examples;then,the data resampling is applied over *** the next step,the base classifier is trained on the newly created ***,we compare the result of our method in terms of Area Under Curve(AUC)and F-measure and G-mean with the other state-of-the-art *** show that our method has betterresults than the other state-of-the-art methods on our experimental study.

关键词： Imbalanced learning classification borderline examples

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Investigating the interrelationships between the barriers of green computing adoption: TISM-MICMAC-DEMATEL method

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Quality and Quantity 2024年第1期59卷 507-570页

作者： Dalvi-Esfahani, Mohammad Nilashi, Mehrbakhsh Abumalloh, Rabab Ali Thurasamy, Ramayah Ahmad, Muhammad Shakil Blockchain Fintech and Cryptocurrency Research Group University of Southampton Malaysia Johor Iskandar Puteri 79100 Malaysia UCSI Graduate Business School UCSI University No. 1 Jalan Menara Gading UCSI Heights Cheras Kuala Lumpur 56000 Malaysia Department of Computer Science and Engineering Qatar University Doha 2713 Qatar School of Management Universiti Sains Malaysia Minden Penang 11800 Malaysia Department of Information Technology and Management Daffodil International University Birulia Bangladesh Department of Management Sunway Business School (SBS) Selangor Petaling Jaya 47500 Malaysia University Center for Research & amp Development (UCRD) Chandigarh University Punjab Ludhiana 140413 India Faculty of Economics and Business Universitas Indonesia (UI) West Java Depok City 16424 Indonesia Management Information Systems Department School of Business The University of Jordan (UJ) Amman 11942 Jordan TUIBS Leadership Management & amp Human Resources Teesside University Middlesbrough United Kingdom Faculty of Business Sohar University Sohar P.C 311 Oman

While a considerable body of research addresses firms’ endeavors toward environmental sustainability, the prominence of green computing has been somewhat eclipsed by impediments hindering its swift integration and development. To address this gap, this study conducted a comprehensive analysis, identifying twelve distinct barriers categorized into adoption costs, technological challenges, and internal obstacles. Through consultation with fifteen field experts, the study employed Interpretive Structural Modeling (ISM) and Matrice d’Impacts Croises Multipication Applique´ an Classment (MICMAC) methodologies to scrutinize the influences among these barriers. The findings illuminated that firm size and legal constraints emerge as pivotal and autonomous factors exerting substantial influence on the adoption. Additionally, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was deployed to ascertain the relative importance of these barriers and delineate their causal relationships. Notably, the DEMATEL results underscored that, according to experts, adoption cost barriers hold greater significance compared to other dimensions. Transactional risk, technological complexity, and switching cost were identified as pivotal barriers within their respective dimensions. The decision-making framework formulated in this study offers valuable insights for managers and practitioners, providing a nuanced understanding of the interplay between identified barriers and their implications for implementing green computing initiatives. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Barriers DEMATEL Green computing MICMAC Total interpretive structural modeling

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Metaverse for Industry 5.0 in NextG Communications: Potential Applications and Future Challenges

arXiv

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arXiv 2023年

作者： Prabadevi, B. Deepa, N. Victor, Nancy Gadekallu, Thippa Reddy Maddikunta, Praveen Kumar Reddy Yenduri, Gokul Wang, Wei Pham, Quoc Viet Huynh-The, Thien Liyanage, Madhusanka The School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India The Department of Computer Science School of Engineering and Technology Pondicherry University Karaikal Campus Puducherry Union Territory Karaikal609605 India The Zhongda Group Haiyan County Zhejiang Province Jiaxing City314312 China The Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon The College of Information Science and Engineering Jiaxing University Jiaxing314001 China The Division of Research and development Lovely Professional University Phagwara India The School of Medical Technology Beijing Institute of Technology Beijing China Department of Engineering Shenzhen MSU-BIT University Shenzhen China The School of Computer Science and Statistics Trinity College Dublin The University of Dublin DublinD02PN40 Ireland Department of Computer and Communication Engineering Ho Chi Minh City University of Technology and Education Viet Nam The School of Computer Science UCD Ireland

With the advent of new technologies and endeavors for automation in almost all day-to-day activities, the recent discussions on the metaverse life have a greater expectation. Furthermore, we are in the era of the fifth industrial revolution, where machines and humans collaborate to maximize productivity with the effective utilization of human intelligence and other resources. Hence, Industry 5.0 in the metaverse may have tremendous technological integration for a more immersive experience and enhanced *** technological amalgamations are suitable for the present environment and entirely different from the previous perception of virtual technologies. This work presents a comprehensive review of the applications of the metaverse in Industry 5.0 (so-called industrial metaverse). In particular, we first provide a preliminary to the metaverse and industry 5.0 and discuss key enabling technologies of the industrial metaverse, including virtual and augmented reality, 3D modeling, artificial intelligence, edge computing, digital twin, blockchain, and 6G communication networks. This work then explores diverse metaverse applications in Industry 5.0 vertical domains like Society 5.0, agriculture, supply chain management, healthcare, education, and transportation. A number of research projects are presented to showcase the conceptualization and implementation of the industrial metaverse. Furthermore, various challenges in realizing the industrial metaverse, feasible solutions, and future directions for further research have been presented. © 2023, CC BY.

关键词： Virtual reality

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Optimal Reordering Trace Files for Improving Software Testing Suitcase

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computers, Materials & Continua 2021年第4期67卷 1225-1239页

作者： Yingfu Cai Sultan Noman Qasem Harish Garg Hamïd Parvïn Kim-Hung Pho Zulkefli Mansor School of Measurement and Communication Harbin University of Science&TechnologyHarbinChina Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia Department of Computer Science Faculty of Applied ScienceTaiz UniversityTaizYemen School of Mathematics Thapar Institute of Engineering and TechnologyDeemed UniversityPatiala147004India Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science Nourabad Mamasani BranchIslamic Azad UniversityMamasaniIran Fractional Calculus Optimization and Algebra Research GroupFaculty of Mathematics and StatisticsTon Duc Thang UniversityHo Chi Minh CityVietnam Fakulti Teknologi dan Sains Maklumat Universiti Kebangsan MalaysiaUKM BangiSelangor43600Malaysia

An invariant can be described as an essential relationship between program *** invariants are very useful in software checking and *** tools that are used to detect invariants are invariant *** are two types of invariant detectors:dynamic invariant detectors and static invariant *** software is an available computer program that implements a special case of a dynamic invariant detection *** proposes a dynamic invariant detection algorithm based on several runs of the tested program;then,it gathers the values of its variables,and finally,it detects relationships between the variables based on a simple statistical *** method has some *** of its biggest drawbacks is its overwhelming time *** is observed that the runtime for the Daikon invariant detection tool is dependent on the ordering of traces in the trace file.A mechanism is proposed in order to reduce differences in adjacent trace *** is done by applying some special techniques of mutation/crossover in genetic algorithm(GA).An experiment is run to assess the benefits of this *** findings reveal that the runtime of the proposed dynamic invariant detection algorithm is superior to the main approach with respect to these improvements.

关键词： Dynamic invariant detection software testing genetic algorithm

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Global Challenge for Safe and Secure LLMs Track 1

arXiv

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arXiv 2024年

作者： Jia, Xiaojun Huang, Yihao Liu, Yang Tan, Peng Yan Yau, Weng Kuan Mak, Mun-Thye Sim, Xin Ming Ng, Wee Siong Ng, See Kiong Liu, Hanqing Zhou, Lifeng Yan, Huanqian Sun, Xiaobing Liu, Wei Wang, Long Qian, Yiming Liu, Yong Yang, Junxiao Zhang, Zhexin Lei, Leqi Chen, Renmiao Lu, Yida Cui, Shiyao Wang, Zizhou Li, Shaohua Wang, Yan Goh, Rick Siow Mong Zhen, Liangli Zhang, Yingjie Zhao, Zhe Nanyang Technological University Singapore AI Singapore Singapore Hangzhou Innovation Institute Beihang University China School of Computer Science and Technology Anhui University China Qiyuan laboratory China IHPC Agency for Science Technology and Research Singapore CoAI Group Tsinghua University China Singapore Institute of Information Engineering Chinese Academy of Sciences China RealAI China

This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks. With the increasing integration of LLMs in critical sectors such as healthcare, finance, and public administration, ensuring these models are resilient to adversarial attacks is vital for preventing misuse and upholding ethical standards. This competition focused on two distinct tracks designed to evaluate and enhance the robustness of LLM security frameworks. Track 1 tasked participants with developing automated methods to probe LLM vulnerabilities by eliciting undesirable responses, effectively testing the limits of existing safety protocols within LLMs. Participants were challenged to devise techniques that could bypass content safeguards across a diverse array of scenarios, from offensive language to misinformation and illegal activities. Through this process, Track 1 aimed to deepen the understanding of LLM vulnerabilities and provide insights for creating more resilient models. Copyright © 2024, The Authors. All rights reserved.

关键词： Public administration

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