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作者： Kang, Jonggu Ryu, Duksan Baik, Jongmoon School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Software Engineering Jeonbuk National University Jeonju-si Korea Republic of

Background: The importance of software in maritime transportation is rapidly increasing as the industry seeks to develop and utilize innovative future ships, which can be realized using software technology. Due to the safety-critical nature of ships, software quality assurance (SQA) has become an essential prerequisite for such development. Objective: Based on the unique characteristics of the maritime domain, the purpose of this study was to achieve effective SQA resource allocation to reduce post-release quality costs. Method: software defect prediction (SDP) is employed to predict defects in newly developed software based on models trained with past software defects and to update information using machine learning. This study demonstrated that just-in-time SDP is applicable to maritime domain practice and can reduce post-release quality costs via combination with an estimation model, qCOPLIMO. Results: Using real-world datasets collected from the maritime industry, performance and cost-benefit analyses of SDP were performed. A successful model was obtained that meets the performance criterion of 0.75 in within-project defect prediction (WPDP) but not cross-project defect prediction (CPDP). In addition, the cost-benefit analysis results showed that 20% effort enables the detection of 56% of defects on average and that the post-release quality cost can be reduced by 37.3% in the maritime domain. Conclusion: SDP can be successfully applied to the maritime domain. Further, it is desirable to utilize WPDP instead of CPDP once minimum high-quality commits are available that can be identified as defective or not. Finally, SDP can help reduce review effort and post-release quality costs. © 2020 John Wiley & Sons Ltd

关键词： Defects

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Intelligent Operational Decision Support System for Sugar Factory Resource Efficiency 5

Intelligent Operational Decision Support System for Sugar Fa...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Junge, Stefan National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine Dept. of Automation and Robotic Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine Berlin University of Applied Sciences Dept. of Packaging Technology Berlin Germany

ISBN: (纸本)9798350359787

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%. © 2023 IEEE.

关键词： Decision support systems

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WR-Former: Vision Transformer with Weight Reallocation Module for Robust Facial Expression Recognition

WR-Former: Vision Transformer with Weight Reallocation Modul...

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International Conference on Computer Supported Cooperative Work in Design

作者： Rui Wang Haitao Wang Peng Zou Xiao Sun Institute of Advanced Technology University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China College of Software Engineering Southeast University Nanjing China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Facial expression recognition(FER) is an exceedingly challenging task in the field of computer vision, primarily due to variant head poses, occlusions and illumination conditions. Previous methods focus on extracting more facial features to improve the performance on the FER benchmarks. However, most of them ignore the weights of the key regions. To address the above problem, this paper proposes a vision Transformer with weight reallocation module termed as WR-Former. The weight reallocation module utilizes a hybrid multi-head self-attention and can dynamically iterate during the training process. It guides the model to reallocate tokens based on the importance of different facial regions. Besides, a simple yet effective data augmentation method is proposed to expand training samples and improve the robustness. Experimental results and visualizations demonstrate that our WR-Former outperforms the previous state-of-the-art methods and has the ability to better extract the features conveyed by the key facial regions.

关键词： Training Federated learning Face recognition Lighting Data visualization Transformers Feature extraction

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BMRMIA:A Platform for Radiologists to Systematically Learn Automated Medical Image Analysis by Three Dimensional Medical Decision Support System

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Computer Modeling in engineering & Sciences 2022年第5期131卷 851-863页

作者： Yankun Cao Lina Xu Zhi Liu Xiaoyan Xiao Mingyu Wang Qin Li Hongji Xu Geng Yang School of Software Shandong UniversityJinan250101China School of Information Science and Engineering Shandong UniversityQingdao266237China School of Computer Science and Technology Shandong Jianzhu UniversityJinan250012China Department of Nephrology Qilu Hospital of Shandong UniversityJinan250012China State Key Laboratory of ASIC&Systems The School of MicroelectronicsFudan UniversityShanghai200433China Shenzhen Institute of Information Technology Shenzhen518172China

Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis *** platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary ***:In recent years,deep learning has been widely used in academia,industry,*** increasing number of companies are starting to recruit a large number of professionals in the field of deep *** numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis *** now,however,there is no practical training platform that can help radiologists learn automated medical image analysis ***:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this *** platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.

关键词： BMR deep learning three dimensional medical decision support system deep learning engineer standard

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Multi-Unit Floor Plan Recognition and Reconstruction Using Improved Semantic Segmentation of Raster-Wise Floor Plans

arXiv

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arXiv 2024年

作者： Kratochvila, Lukas de Jong, Gijs Arkesteijn, Monique Bilík, Šimon Zemčík, Tomáš Horak, Karel Rellermeyer, Jan S. Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands Department of Management in the Built Environment Faculty of Architecture and the Built Environment TU Delft Delft Netherlands Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Dependable and Scalable Software Systems Institute of Systems Engineering Faculty of Electrical Engineering and Computer Science Leibniz University Hannover Hannover Germany

Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and faster rescue intervention. Nevertheless, creating the twins still remains a largely manual effort, due to a lack of 3D-representations, which are available only in limited amounts for some new buildings. Thus, in this paper we aim to synthesize 3D information from commonly available 2D architectural floor plans. We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures with improved skip connections, an attention mechanism, and a training objective together with a reconstruction part of the pipeline, which vectorizes the segmented plans to create a 3D model. The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets. On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes, outperforming the other pixel-wise approaches tested. We have also made our code publicly available to support research in the field. © 2024, CC BY.

关键词： Semantic Segmentation

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Forecasting Potential Sales of Bread Products at Stores by Network Embedding 5

Forecasting Potential Sales of Bread Products at Stores by N...

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5th IEEE International Conference on Cybernetics, CYBCONF 2021

作者： Takahashi, Kohei Goto, Yusuke Iwate Prefectural University Graduate School of Software and Information Science Iwate Japan Shibaura Institute of Technology College of Systems Engineering and Science Saitama Japan

ISBN: (纸本)9781665403207

In this work, we study the forecast of the potential sales of out-of-stock products in retail stores using factory shipment data. A precise prediction of the potential sales of out-of-stock products in retail stores is beneficial for both baking factories and retail stores because it optimizes the supply chain by introducing a new product in proper quantity at retail stores, and it also creates new opportunities for baking factories to sell their products to retail stores. This study uses high-dimensional and sparse baking factory shipment data, which are unsuitable for prediction using conventional methods because the data have a high computation time and missing values. We employ a network embedding method, LINE, to derive similar stores based on their sales and predict their potential sales. We confirmed that our proposed method outperforms a simple prediction method (Baseline) and t-SNE for accurate product sales prediction via simulation experiments. We also verified our proposed method's applicability when the forecasting target is expanded to products sold in fewer stores and with lower volume. © 2021 IEEE.

关键词： Forecasting

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PowerGen: Resources Utilization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud Computing

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Procedia Computer Science 2024年 238卷 385-395页

作者： Leila Ismail Huned Materwala Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios.

关键词： cloud computing edge computing dataset generation energy-efficiency green computing machine learning internet of things power consumption modeling resource management scheduling

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Beyond Entanglement Routing: Source Assignment and All-Optical Switching-Based Distribution

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IEEE/ACM Transactions on Networking 2024年

作者： Zhu, Qiucheng Zhao, Yangming Xu, Hongli Huang, Liusheng Qiao, Chunming State Key Laboratory for Novel Software Technology Nanjing University Nanjing210008 China School of Intelligent Software and Engineering Nanjing University Suzhou Campus Suzhou215163 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China Department of Computer Science and Engineering University at Buffalo State University of New York BuffaloNY14260 United States

Entanglement routing plays a vital role in distributed quantum computing and quantum networks. Previous works on entanglement routing have addressed several design challenges due to limited quantum resources, failures to establish entanglement, and decoherence of established entanglement, but considered neither the limitations imposed by having a limited number of entangled photon sources (EPSes), nor the benefits of using all-optical (or quantum) switching to distribute entangled photons. In this paper, we first explore the problem of jointly optimizing entanglement routing and EPS assignment, assuming no all-optical switching capability. In other words, a pair of entangled photons generated by one EPS can be distributed to two neighboring quantum nodes. We then relax the above assumption so as to allow a pair of entangled photons generated by one EPS to be distributed to non-adjacent nodes using all-optical switching. We propose two corresponding solutions, namely, entanglement routing and EPS assignment (or ERSA), and ERSAwith all-optical switching-based distribution (or ERSA+D) that aim to maximize the number of entanglement connections between the given set of source-destination (SD) pairs while avoiding starvation and achieving fairness among the SD pairs. In order to obtain efficient solutions in a large discrete solution space in a timely manner, we first formulate each optimization problem as an Integer Linear Programming (ILP), and then propose efficient algorithms to derive near-optimal solutions based on relaxation, Lagrangian decomposition, duality iteration, and rounding techniques. Extensive simulations show that ERSA+Dcan increase network throughput by up to 1652% and 113%, respectively, thanks to EPS assignment optimization, and all-optical switching based entanglement distribution. © 1993-2012 IEEE.

关键词： Quantum computers

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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IEEE International Conference on Data Mining (ICDM)

作者： Yifan Zhang Haolong Xiang Xuyun Zhang Xiaolong Xu Wei Fan Qin Zhang Lianyong Qi School of software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia Uffield Department of Women's & Reproductive Health University of Oxford England College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering University of Petroleum (East China) China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods.

关键词： Learning systems Training Accuracy Clustering algorithms Forestry Network security Fraud Time complexity Parallel algorithms Anomaly detection

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A KDC-enhanced closest point method for thermal analysis on curved geometries using LS-GFDM

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Applied Mathematics Letters 2025年 171卷

作者： Tang, Zhuochao Gu, Jiming Chen, Bin Fu, Zhuojia School of Management Science and Engineering Anhui University of Technology Maanshan243002 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Maanshan243002 China Center for Numerical Simulation Software in Engineering and Sciences College of Mechanics and Engineering Science Hohai University Nanjing211100 China

This paper presents a Krylov-deferred correction (KDC)-enhanced closest point method (CPM) integrated with the least-squares generalized finite difference method (LS-GFDM) for thermal analysis on curved geometries. To overcome the inherent limitations of conventional single-step/multi-step temporal schemes in long-time simulations, we systematically unify three advanced components: (i) CPM's embedding strategy that transforms surface PDEs into extended problems over Cartesian grids, (ii) LS-GFDM's spatial discretization employing dual point sets to enhance applicability beyond conventional GFDM, and (iii) KDC's predictor–corrector framework that iteratively refines temporal solutions to achieve high-order accuracy. Building upon our previous work (Tang et al. 2025) where LS-GFDM was integrated with CPM using explicit Euler temporal schemes, this study constitutes a critical advancement by replacing low-order temporal discretization with the KDC methodology. Numerical experiments demonstrate that our KDC-CPM-LSGFDM framework achieves superior numerical performance. © 2025 Elsevier Ltd

关键词： Least squares approximations

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