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Intelligent Communication technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Mahesh Kumar Mishra Krishnamoorthy Selvaraj Kathari Santosh Mohd Aarif S.Suma Christal Mary B Kiran Bala Delta System and Software Inc McKinney Texas USA Dept of ERP Encloud service LLC Keller Texas US Department of MBA CMR Institute of Technology Bengaluru Bengaluru India IOSRP Noida UP India Department of Information Technology Panimalar Engineering College Poonamalle Chennai Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy

Project management and organisation are completely transformed by the introduction of 5G technology, which has brought in a new era of connection. This research investigates how 5G networks will affect Enterprise Resource Planning (ERP), Agile Project Management (APM) and the larger context of the Technology-Organization-Environment (TOE) paradigm through a cross-industry examination. Agile approaches may benefit from improved adaptive design, cooperation and connectivity as a result of 5G technology being included into project management procedures. A team of developers can work together more easily and make decisions more quickly with the real-time data transfer made possible by 5G networks' higher capacity and speed. This study looks at how Agile methods for managing projects are more effective and flexible when using 5G's low latency and high data throughput. This research explores how Enterprise Resource Planning (ERP) systems may be affected by 5G. With 5G networks' enhanced capabilities, current information synchronisation is made possible in new ways, enabling ERP systems to function with increased precision and speed. This study investigates how supply chain administration, resource allocation and general organisational agility are improved by 5G-enabled ERP. This study evaluates how various sectors' organisational structures, procedures, and objectives are affected by the deployment of 5G infrastructure. This research outlines critical elements that influence the successful implementation of 5G into project management and ERP systems, including technology preparedness, organisational culture, and external factors. This research provides important insights into the potential for transformation of 5G technology through a thorough cross-industry assessment. It also gives organisations an established basis on which to adapt their Agile Project Management and Enterprise Resource Planning practises in the dynamic context of today's digital world.

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CM-UNet++: A Multi-Level information Optimized Network for Urban Water Body Extraction from High-Resolution Remote Sensing Imagery

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Remote Sensing 2025年第6期17卷

作者： Cai, Jiangchen Tao, Liufeng Li, Yang National Engineering Research Center of Geographic Information System China University of Geosciences Wuhan 430074 China School of Computer Science China University of Geosciences Wuhan 430074 China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resource Shenzhen518000 China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan 430074 China School of Geophysics and Geomatics China University of Geosciences Wuhan 430074 China Anhui Urban & Rural Planning and Design Institute Co. Ltd. Hefei230022 China

Urban water bodies are crucial in urban planning and flood detection, and they are susceptible to changes due to climate change and rapid urbanization. With the development of high-resolution remote sensing technology and the success of semantic segmentation using deep learning in computer vision, it is possible to extract urban water bodies from high-resolution remote sensing images. However, many urban water bodies are small, oddly shaped, silted, or spectrally similar to other objects, making their extraction extremely challenging. In this paper, we propose a neural network named CM-UNet++, a combination of the dense-skip module based on UNet++ and the CSMamba module to encode different levels’ information with interactions and then extract global and local information at each level. We use a size-weighted auxiliary loss function to balance feature maps of different levels. Additionally, features beyond RGB are incorporated into the input of the neural network to enhance the distinction between water bodies and other objects. We produced a labeled urban water extraction dataset, and experiments on this dataset show that CM-UNet++ attains 0.8781 on the IOU (intersection over union) metric, which indicates that this method outperforms other recent semantic segmentation methods and achieves better completeness, connectivity, and boundary accuracy. The proposed dense-skip module and CSMamba module significantly improve the extraction of small and spectrally indistinct water bodies. Furthermore, experiments on a public dataset confirm the method’s robustness. © 2025 by the authors.

关键词： Semantic Segmentation

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Experimental Proposal for Validation of W-state Protocol for Qubits and Qudits

Experimental Proposal for Validation of W-state Protocol for...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Rohit K Ramakrishnan Aravinth Balaji Ravichandran Srinivas Talabattula Anindya Banerji Nijil Lal Sarika Mishra R P Singh Peter P Rohde Electrical Communication Engineering Department Indian Institute of Science Bangalore India Faculty of Mathematics Physics and Informatics University of Gdansk Poland Physical Research Laboratory Quantum Technologies (QuTe) Laboratory Ahemdabad India Centre for Quantum Technologies National University of Singapore Singapore cqtCentre for Quantum Software & Information University of Technology Sydney NSW Australia

This paper presents an experimental scheme to validate W-state encoding, a passive linear optics error-correction scheme for qubits and qudits. We use the orbital angular momentum of photons to realize qubits and qudits.

ISBN: (纸本)9781957171258

关键词： Fourier transforms Photons Quantum communications Quantum memories Spatial light modulators Vortex beams

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Solving the Two-Criteria Maximum Coverage Location Problem of Continuous Area Monitored by Sensor Network

Solving the Two-Criteria Maximum Coverage Location Problem o...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications

作者： Sergiy Yakovlev Adam Wojciechowski Dmytro Podzeha Oleg Illiashenko Iryna Yakovleva National Aerospace University “KhAI” Kharkiv Ukraine Lodz University of Technology Lodz Poland Software Engineering & Dependable Computing Laboratory Institute of Information Science and Technologies “Alessandro Faedo”—ISTI CNR Pisa Italy National University of Urban Economy in Kharkiv Kharkiv Ukraine

The paper investigates the problem of optimizing a sensor network for monitoring a continuous area, considering the bounded coverage areas of sensors. This task is formulated in terms of the maximum coverage location problem. A mathematical model is proposed as a two-criteria optimization problem. The objective functions are the maximum area of the covered part of the region and the minimum total overlapping of sensor coverage areas. This model is transformed into an elastic (quasi-physical quasi-human) model, which differs from the known one in forming the extrusion potential energy function. To solve the problem, an original approach was implemented, combining local and global optimization stages. At the stage of local optimization, the Broyden-Fletcher-Goldfarb-Shanno method was used, in which the gradients were calculated analytically or from first-order differences depending on the shape of sensor coverage areas. At the stage of local optimization, the multistart method was used. The implementation of the approach has been tested for the polygonal shape of the region and elliptical shapes of the sensor coverage areas.

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Towards Evidence-Based Cybersecurity Assessment of Programmable systems to Ensure the Protection of Critical IT Infrastructure

Towards Evidence-Based Cybersecurity Assessment of Programma...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications

作者： Oleg Illiashenko Vyacheslav Kharchenko Oleg Odarushchenko Department of computer systems and networks National aerospace university “KhAI” Kharkiv Ukraine Software Engineering & Dependable Computing Laboratory Institute of Information Science and Technologies “Alessandro Faedo”—ISTI CNR Pisa Italy Research and Production Enterprise RPC Radics LLC Kropyvnytskyi Ukraine

The paper is devoted to developing scientific principles, methods, means, and information technology of model-oriented verification and evidence-based assessment using functional safety and cybersecurity cases for programmable systems of critical applications (PSCA). In particular, information and control systems important for the safety of nuclear power plants (NPPs), aerospace systems, railway domains, etc., developed using Field Programmable Gate Arrays (FPGAs) and hardware platforms are reviewed. The goal is to ensure the guaranteed completeness and reliability of its functional safety and cybersecurity assessment by developing and implementing a set of formal and semi-formal methods and tools that consider defects of different nature – physical, design, trojans, and vulnerabilities that can be attacked and lead to a fatal system failure, which results in damaging the critical IT infrastructure. The methods shown are based on integrating algebraic, tabular, graph models, and case assessment methodology. The methods are implemented as appropriate technologies for evidence-based verification and evaluation of PSCA. For formal methods, a prototype of a translator of Very High-Speed Integrated Circuits Hardware Description Language (VHDL) code is developed into an algebra of behaviors. It provides evidence-based verification and a framework and tools to design reports on assessing the cybersecurity and functional safety of programmable systems.

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Application of Neural Networks for Network Traffic Monitoring and Analysis

Application of Neural Networks for Network Traffic Monitorin...

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International Symposium on Multidisciplinary Studies and Innovative technologies (ISMSIT)

作者： Oleksandr Kushnerov Pierre Murr Serhii Herasymov Stanislav Milevskyi Marharyta Melnyk Sergii Golovashych Department of Economic Cybernetics Sumy State University Sumy Ukraine Computer Engineering Department International University of Science and Technology in Kuwait Kuwait Kuwait Cybersecurity department National Technical University “Kharkiv polytechnic institute” Kharkiv Ukraine Department of Cyber Security and information protection SET University Kyiv Ukraine Department of Software Engineering and Management Intelligent Technologies National Technical University “Kharkiv polytechnic institute” Kharkiv Ukraine

ISBN: (数字)9798350354423

ISBN: (纸本)9798350354430

Monitoring and analyzing network traffic are critically important tasks in the context of increasing cyber threats. Traditional data processing methods often struggle to handle the large volumes and complexity of modern network traffic, necessitating the use of advanced technologies such as neural networks and machine learning. This article demonstrates the application of neural networks for monitoring and analyzing network traffic, highlighting their ability to learn from large datasets to detect complex patterns and anomalies. Using TensorFlow, we developed a neural network model based on an autoencoder that effectively identifies and classifies anomalies in network traffic. The model's effectiveness was validated using a synthetic dataset from Kaggle, showing high accuracy on both training and testing data. Our approach significantly improves the efficiency and accuracy of threat detection compared to traditional methods, making it a valuable tool for enhancing cybersecurity.

关键词： Training Accuracy Neural networks Telecommunication traffic Machine learning Data models Threat assessment Computer security Monitoring Testing

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BugListener: Identifying and Synthesizing Bug Reports from Collaborative Live Chats

arXiv

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arXiv 2022年

作者： Shi, Lin Mu, Fangwen Zhang, Yumin Yang, Ye Chen, Junjie Chen, Xiao Jiang, Hanzhi Jiang, Ziyou Wang, Qing Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China School of Systems and Enterprises Stevens Institute of Technology HobokenNJ United States Tianjin University College of Intelligence and Computing Tianjin China

In community-based software development, developers frequently rely on live-chatting to discuss emergent bugs/errors they encounter in daily development tasks. However, it remains a challenging task to accurately record such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the task of identifying and synthesizing bug reports from community live chats, and propose a novel approach, named BugListener, to address the challenges. Specifically, BugListener automates three sub-tasks: 1) Disentangle the dialogs from massive chat logs by using a Feed-Forward neural network;2) Identify the bug-report dialogs from separated dialogs by leveraging the Graph neural network to learn the contextual information;3) Synthesize the bug reports by utilizing Transfer Learning techniques to classify the sentences into: observed behaviors (OB), expected behaviors (EB), and steps to reproduce the bug (SR). BugListener is evaluated on six open source projects. The results show that: for bug report identification, BugListener achieves the average F1 of 77.74%, improving the best baseline by 12.96%;and for bug report synthesis task, BugListener could classify the OB, EB, and SR sentences with the F1 of 84.62%, 71.46%, and 73.13%, improving the best baselines by 9.32%, 12.21%, 10.91%, respectively. A human evaluation study also confirms the effectiveness of BugListener in generating relevant and accurate bug reports. These demonstrate the significant potential of applying BugListener in community-based software development, for promoting bug discovery and quality improvement. Copyright © 2022, The Authors. All rights reserved.

关键词： software design

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Highly sensitive and miniature microfiber-based ultrasound sensor for photoacoustic tomography

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Opto-Electronic Advances 2022年第6期5卷 55-62页

作者： Liuyang Yang Yanpeng Li Fang Fang Liangye Li Zhijun Yan Lin Zhang Qizhen Sun School of Optical and Electronic Information&National Engineering Laboratory for Next Generation Internet Access System(NGIA)&Wuhan National Laboratory for Optoelectronics(WNLO) Huazhong University of Science and TechnologyWuhan 430074China Aston Institute of Photonic Technologies Aston UniversityBirmingham B47ETUK.

A microfiber with large evanescent field encapsulated in PDMS is proposed and demonstrated for ultrasound *** compact size and large evanescent field of microfiber provide an excellent platform for the interaction between optical signal and ultrasound wave,exhibiting a high sensitivity of 3.5 mV/kPa,which is approximately 10 times higher than the single-mode fiber ***,a phase feedback stabilization module is introduced into the coherent demodulation system for long-term stable *** addition,a photoacoustic tomography experiment with the microfiber ultrasound sensor is implemented to verify the excellent performance on imaging,with the depth of 12 mm,the highest lateral resolution of 65μm and axial resolution of 250μm,*** highly sensitive microfiber ultrasound sensor provides a competitive alternative for various applications,such as industrial non-destructive testing,biomedical ultrasound and photoacoustic imaging.

关键词： ultrasound sensor microfiber photoacoustic tomography

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BugListener: Identifying and Synthesizing Bug Reports from Collaborative Live Chats

BugListener: Identifying and Synthesizing Bug Reports from C...

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International Conference on software engineering (ICSE)

作者： Lin Shi Fangwen Mu Yumin Zhang Ye Yang Junjie Chen Xiao Chen Hanzhi Jiang Ziyou Jiang Qing Wang Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Systems and Enterprises Stevens Institute of Technology Hoboken NJ USA College of Intelligence and Computing Tianjin University Tianjin China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Science &#x0026 Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665495899

In community-based software development, developers frequently rely on live-chatting to discuss emergent bugs/errors they encounter in daily development tasks. However, it remains a challenging task to accurately record such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the task of identifying and synthesizing bug reports from commu-nity live chats, and propose a novel approach, named BugListener, to address the challenges. Specifically, BugListener automates three sub-tasks: 1) Disentangle the dialogs from massive chat logs by using a Feed-Forward neural network; 2) Identify the bug-report dialogs from separated dialogs by leveraging the Graph neural net-work to learn the contextual information; 3) Synthesize the bug reports by utilizing Transfer Learning techniques to classify the sentences into: observed behaviors (OB), expected behaviors (EB), and steps to reproduce the bug (SR). BugListener is evaluated on six open source projects. The results show that: for bug report identification, BugListener achieves the average Fl of 77.74%, im-proving the best baseline by 12.96%; and for bug report synthesis task, BugListener could classify the OB, EB, and SR sentences with the F1 of 84.62%, 71.46%, and 73.13%, improving the best baselines by 9.32%,12.21%,10.91%, respectively. A human evaluation study also confirms the effectiveness of Bug Listener in generating relevant and accurate bug reports. These demonstrate the significant potential of applying BugListener in community-based software development, for promoting bug discovery and quality improvement.

关键词： Computer bugs Transfer learning Neural networks Collaboration Predictive models software Behavioral sciences

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The Parables of the Mustard Seed and the Yeast: Extremely Low-Budget, High-Performance Nighttime Semantic Segmentation 39

The Parables of the Mustard Seed and the Yeast: Extremely Lo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Shiqin Xu, Xin Chen, Haoyang Jiang, Kui Wang, Zheng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)157735897X

Nighttime Semantic Segmentation (NSS) is essential to many cutting-edge vision applications. However, existing technologies overly rely on massive labeled data, whose annotation is time-consuming and laborious. In this paper, we pioneer a new task focusing on exploring the potential of training strategy and framework design with limited annotation to achieve high-performance NSS. Insufficient information at very low labeling budgets can easily lead to under-optimization or overfitting of the model. Our solution comprises two main components: i) a novel region-based active sampling strategy called Contextual-Aware Region Query (CARQ), which identifies highly informative target nighttime regions for labeling;and ii) an innovative Fragmentation Synergy Active Domain Adaptation framework (FS-ADA), which progressively broadcasts the limited annotation to the unlabeled regions, achieving high performance with a minimal annotation budget. Extensive experiments demonstrate that our method outperforms state-of-the-art UDA-NSS & ADA-SS methods across four day-to-nighttime benchmarks, and generalizes well to foggy, rainy, & snowy scenes. In particular only with 1% target nighttime data annotation, our method is on par with the mainstream fully-supervised methods on the BDD100K-Night val dataset. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Budget control

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