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6th International Conference on information Systems and Management science, ISMS 2023

作者： Saxena, Virat Raj Yadav, Rajeev Parveen, Azra Sadiq, Mohd. Department of Computer Science and Engineering Mangalayatan University Uttar Pradesh Aligarh202001 India Indraprastha Research Laboratory Indraprastha Institute of Information Sciences Private Limited New Delhi110025 India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi110025 India

ISBN: (纸本)9783031664090

Various techniques have been developed for the identification of different types of requirements like interview, questionnaire, group elicitation techniques, attributed goal-oriented requirements analysis, fuzzy based goal-oriented requirements analysis method, non-functional requirements framework, etc. Based on the critical analysis of requirements elicitation methods, we found that single elicitation technique is not suitable to understand the need of the stakeholders. In real life applications, multiple elicitation techniques are required to elicit and select the requirements of an information system because each technique has its strength and limitations. Therefore, to address this issue, this paper presents a mathematical model for the selection of requirements elicitation techniques so that complete set of requirements along with its priority can be identified before the development of an information system. The institute examination system is considered to show the applicability of the proposed mathematical model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： software testing

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SCAF-DG: A Multi-site Medical Image Denoising with A Domain-Generalized Spatial-Channel Attention Fusion 6

SCAF-DG: A Multi-site Medical Image Denoising with A Domain-...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Fiasam, Linda Delali Rao, Yunbo Sey, Collins Klugah-Brown, Benjamin Tettey, Obed Nartey Aggrey, Esther Stacy E.B. Agyemang, Isaac Osei Adjei-Mensah, Isaac Yang, Yang University of Electronic Science Technology of China School of Information and Software Engineering Chengdu China University of Electronic Science Technology of China School of Life Science and Technology Chengdu China University of Electronic Science Technology of China School of Information and Communication Engineering Chengdu China Sichuan Institute of Computer Sciences Chengdu China

ISBN: (纸本)9798350325485

Images from multiple medical sites usually contain varying noise levels that can affect the generalization performance of the denoising models. Domain generalization (DG), which seeks to learn a model that can generalize to an unseen test domain, has not been extensively explored in medical image denoising for multi-site images. We present an approach for multi-site medical image denoising, which addresses the issue of varying noise levels across different imaging sites. Our proposed method, called SCAF-DG (Spatial-Channel Attention Fusion for Domain Generalization), employs two attention units to extract both domain-specific channel features and mutually-invariant spatial features from the noisy images. These features are then combined to form a rich feature space. Additionally, we utilized skip connections to preserve structural information during the denoising process. We evaluate SCAF-DG on publicly available brain magnetic resonance image (MRI) datasets from three different imaging sites, and demonstrate an average increase of 1.01 in peak signal-To-noise ratio (PSNR) and 0.016 in structural similarity index measure (SSIM) for denoising images. Our results demonstrate that SCAF-DG has the potential to generalize well to new, unseen test domains, and outperforms existing state-of-The-Art denoising models on multi-site medical images. © 2023 IEEE.

关键词： Image denoising

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Secure Blockchain Integration Approach for Knowledge Discovery in Industrial Internet of Things

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4772-4787页

作者： Rohan Tripathi Ritam Rao Vivekananda Bhat K. Abhishek Kumar Pandey Ashok Kumar Das Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computer Science and Engineering College of Informatics Korea University Seoul South Korea

The field of Industrial Internet of Things (IIoT) is now being duly recognized as a revolutionary area in industrial automation. With the budding interest of knowledge discovery that helps the businesses in applying technology on a large scale, researchers are now faced with several major security concerns. Blockchain integration is one of the suggested applications of technology that could help secure the data during transmission, storage, and knowledge discovery. Moreover, by integrating smart contracts, a secure architecture could also assimilate accountability during data exchange. Thus, we propose a four-layer security architecture that isolates IIoT devices from user-oriented layers and maintains a record of all IIoT devices registered in the organization to prevent malicious devices from corrupting the database and discovery process. Further, by choosing proof of authority (PoA), we ensure the fair functioning of the Blockchain nodes. Through proxy re-encryption for clients, PoA consensus for Blockchain nodes, and Whitelist-based access control for IIoT Devices, we ensure the legitimacy of all participating nodes. We also implement a prototype using a private Ethereum network with proof of authority consensus and present the time taken for the entire exchange and the gas (ethereum currency) consumed per exchange by the contracts. Also we implemented the secure knowledge discovery to understand the significance of the developed scheme. The results show that the exchange can be implemented in an IIoT environment and work with a reasonable amount of resource consumption.

关键词： Blockchains Industrial Internet of Things computer architecture Security Knowledge discovery computer science Cloud computing Prototypes Authentication Analytical models

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A Mathematical Model for the Selection of software Requirements Elicitation Techniques 14

A Mathematical Model for the Selection of Software Requireme...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Saxena, Virat Raj Yadav, Rajeev Parveen, Azra Sadiq, Mohd. Mangalayatan University Department of Computer Science and Engineering Uttar Pradesh Aligarh202001 India Indraprastha Institute of Information Sciences Private Limited Indraprastha Research Laboratory New Delhi110025 India Upfet Jamia Millia Islamia A Central University Software Engineering Laboratory Computer Engineering Section New Delhi110025 India

ISBN: (纸本)9798350344837

关键词： information systems

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Exploring Natural Language Processing Model Acceleration in Molecular Dynamics Simulation Using High-Performance Computing and Machine Learning

Exploring Natural Language Processing Model Acceleration in ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Du, Qi Wang, Feng Huang, Hui Wan, Heng Wu, Xiaoyu Wu, Chengkun Hunan University College of Computer Science and Electronic Engineering Hunan Changsha China Hunan Institute of Scientific and Technical Information Hunan Changsha China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment Hunan Changsha China

ISBN: (纸本)9798350386226

In molecular dynamics simulations, the integral methods used to calculate molecular trajectories, such as the Verlet integral method, involve significant computational costs. On the premise of ensuring the accuracy of simulation, how to effectively reduce the amount of computation is always a challenging problem. This paper applies Natural Language Processing models to simulate molecular motion trajectories in molecular dynamics, integrating the MPI programming model and machine learning on a high-performance computing platform to achieve MPI parallelization during model training and inference. The results indicate that different types of neural network architectures have a significant impact on inference performance and accuracy. When using the deep spatio-temporal networks model, the mean absolute error is 0.0032. At an atomic scale of 3M, the parallel efficiency with 1024 computational nodes exceeds 90%, demonstrating excellent parallel performance. © 2024 IEEE.

关键词： Natural language processing systems

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Multi-class multi-object detection and tracking system in 3 dimensions using YOLOv5 3

Multi-class multi-object detection and tracking system in 3 ...

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3rd International Conference on Range Technology, ICORT 2023

作者： Bhateja, Aditi Sharma, Sanyam Krishna, Abhishek Chaudhary, Himanshu Lall, Brejesh Kalra, Prem K Computer Science Indian Institute of Technology Delhi India Delhi Technological University Software Engineering Delhi India Delhi Technological University Information Technology Delhi India Securities and Exchange Board of India India Electrical Engineering Indian Institute of Technology Delhi India

ISBN: (纸本)9781665493314

It is of great significance to detect and track multiple objects belonging to multiple classes in 3 dimensions and the same can be applied to various research domains. One such application pertains to multi-fish detection and tracking The problem is of significance to marine researchers who want to study the behaviour of fish in a constrained environment i.e. in an aquarium, using different sensors. For this study, a data set consisting of different fishes in an aquarium was constructed and Microsoft Kinect was used to capture the movements in 3 dimensions. The data set was cleaned, aligned, and calibration was done to make it suitable for object detection and tracking In this paper, YOLOv5s model was improvised, hereafter referred as YOLOv5-With Attention Mechanism (YOLOv5-WAM), and an object detection and tracking pipeline was designed to detect and track underwater objects (fishes in our work) belonging to multiple classes in 3D. YOLOv5-WAM can be adapted for use on devices having low compute availability, making it suitable for real time object detection and tracking using low cost experimental setup. YOLOv5-WAM showed about 1% improvement in mAP for our custom dataset while for Fish4Knowkdge and COCO dataset, the improvements were to the tune of 7% when compared to YOLOv5s. The results and analysis of the approach are presented in this paper. © 2023 IEEE.

关键词： Fish

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Knowledge Distillation With Fast CNN for License Plate Detection

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-7页

作者： Tian, Chunwei Zhang, Xuanyu Liang, Xu Li, Bo Sun, Yougang Zhang, Shichao School of Software Northwestern Polytechnical University Xi'an Shaanxi China School of Software Northwestern Polytechnical University Xi'an China School of Electronics and Information Northwestern Polytechnical University Xi'an Shaanxi China Institute of Rail Transit National Maglev Transportation Engineering R&D Center Tongji university Shanghai China School of Computer Science and Engineering Central South University Changsha Hunan China

Deep convolutional neural networks (CNNs) can improve recognition rate in license plate to improve traffic. However, these methods may refer to big computational costs and a lot of parameters. In this paper, we propose a knowledge distillation with a fast CNN for license plate detection (KDNet). KDNet uses knowledge distillation to guide a CNN to optimize parameters and quickly obtain a detector for license plate. To overcome naive effect of local information, a non-local similarity mechanism is used into a CNN to enhance effect of global information for extracting salient information in license plate detection. Experimental results that this proposed KDNet is superior to detection speed for license plate. The code of KDNet can be obtained at https://***/hellloxiaotian/KDNet. IEEE

关键词： Data mining

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Introduction to the Special Issue on Advanced Security for FutureMobile Internet:A Key Challenge for the Digital Transformation

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computer Modeling in engineering & sciences 2024年第12期141卷 1907-1909页

作者： Ilsun You Xiaofeng Chen Vishal Sharma Gaurav Choudhary Department of Financial Information Security Kookmin UniversitySeoul-si02707Republic of Korea School of Network and Information Security Xidian UniversityXi’an710071China School of Electronics Electrical Engineering and Computer ScienceQueen’s University Belfast(QUB)BelfastBT71NNUK Centre for Industrial Software(CIS) The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSønderborg6400Denmark

Mobile internet technologies have transformed our daily lives,allowing us to connect,communi-cate,and access various services and applications anytime and *** technologies are set to play a significant role in the next generation of digital transformation,further increasing their impact by integrating with emerging technologies like 6G,quantum computing,and generative AI.

关键词： services Internet internet

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Primary User-Awareness-Based Energy-Efficient Duty-Cycle Scheme in Cognitive Radio Networks

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computers, Materials & Continua 2022年第3期70卷 5991-6005页

作者： Zilong Jin Chengbo Zhang Kan Yao Dun Cao Seokhoon Kim Yuanfeng Jin School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science and Technology Nanjing210044China Jiangsu Institute of Economic and Information Technology Nanjing210003China School of Computer and Communications Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Rep.of Korea Department of Mathematics Yanbian UniversityYanji133002China

Cognitive radio devices can utilize the licensed channels in an opportunistic manner to solve the spectrum scarcity issue occurring in the unlicensed ***,these cognitive radio devices(secondary users)are greatly affected by the original users(primary users)of licensed *** users have to adjust operation parameters frequently to adapt to the dynamic network environment,which causes extra energy *** consumption can be reduced by predicting the future activity of primary ***,the traditional prediction-based algorithms require large historical data to achieve a satisfying precision accuracy which will consume a lot of time and memory ***,many of these schemes lack methods to deal with the very busy network *** this paper,one semi-supervised learning algorithm,i.e.,tri-training,has been employed to investigate the prediction of primary *** on the prediction results of tri-training,a duty-cycle mechanism and an intermediate node selection approach are proposed to improve the energy *** results show the effectiveness of the proposed algorithm.

关键词： Cognitive radio tri-training duty-cycle intermediate node energy efficiency

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Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

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computer Systems science & engineering 2023年第5期45卷 1695-1709页

作者： Xiaorui Zhang Feng Xu Wei Sun Yan Jiang Yi Cao Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorONN9B 3P4Canada

3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one *** existing methods reconstruct the mesh surface of complex objects,the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework;the 3D topology is easily limited by predefined templates and inflexible,and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology,thus destroying the surface details;the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices,and the training time of the reconstructed network is too *** this paper,we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network(GCN)and topology *** use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the ***,a feature fusion method is proposed to make full use of the features of each stage of the image *** use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training *** experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces,but also has better qualitative and quantitative results.

关键词： 3D surface reconstruction deep learning GCN topology modification end-to-end framework

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