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检索条件"任意字段=18th International Conference on Software Engineering and Knowledge Engineering"

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ShellFusion: An Answer Generator for Shell Programming Tasks via knowledge Fusion

ShellFusion: An Answer Generator for Shell Programming Tasks...

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international conference on software engineering - Companion, ICSE, Companion

作者： Zhongqi Chen Neng Zhang Pengyue Si Qinde Chen Chao Liu Zibin Zheng School of Software Engineering Sun Yat-sen University China School of Big Data and Software Engineering Chongqing University China

Shell programming is widely used to accomplish various tasks in Unix and Linux platforms. However, the large number of shell commands available, e.g., 50,000+ commands are documented in the Ubuntu Manual Pages (MPs), makes it a big challenge for programmers to find appropriate commands for a task. Although there are some tutorials (e.g., TLDR) with examples manually created to address the challenge, the tutorials only cover a limited number of frequently used commands for shell beginners and provide limited support for users to search commands by a task. In this paper, we introduce a novel web-based tool, ShellFusion, which can automatically generate comprehensive answers (including relevant commands, scripts, and explanations) for shell programming tasks by fusing multisource knowledge mined from Q&A posts, Ubuntu MPs, and TLDR tutorials. Our evaluation on 434 shell programming tasks shows that ShellFusion significantly outperforms the state-of-the-art approaches by at least 179.6% in terms of MRR@K and MAP@K. A user study conducted with 20 shell programmers further shows that ShellFusion can help users address programming tasks more efficiently and accurately. ShellFusion Tool: http://***/ Demo Video: https://***/P0YJzpKBmnA

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Preventing Diabetes: Substituting Processed Foods and Nutritional Chatbot Assistance 1

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5th international conference on Applied Technologies, ICAT 2023

作者： Solano, Pablo Herrera, Víctor Abril-Ulloa, Victoria Espinoza-Mejía, Mauricio Faculty of Engineering University of Cuenca Cuenca Ecuador Technologies Applied to Health Research Group Faculty of Medical Sciences University of Cuenca Cuenca Ecuador Computer Science Department University of Cuenca Cuenca Ecuador

ISBN: (数字)9783031589539

ISBN: (纸本)9783031589522

Type 2 Diabetes Mellitus (T2DM) is one of the biggest threats to Ecuador’s health. the intake of processed foods has been linked to a higher risk of T2DM. this paper proposes FoodSub, a mobile application to recommend substitutes for processed foods using the NOVA Classification. Nutrient-based food clustering is used to identify substitute pairs between processed and unprocessed foods. the recommendations are supported and personalized using a knowledge graph that contains foods, dietary guidelines, and user information. In addition, a chatbot is implemented to answer simple questions about foods. this chatbot is developed using a Large Language Model (LLM) to query the knowledge graph. the mobile application and the chatbot are evaluated in terms of usability;both perform well, but there is room for improvement. Additionally, the recommendations’ performance is evaluated through expert verification. the recommendations perform well when issues like food transformation processes, flavor, context, or meal time are not relevant. Future work will consider the enhancement of the chatbot and the improvement of substitute recommendations for the relevant cases. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Nutrition

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ANALYSIS, DESIGN AND EXPERIMENT OF MMC BASED ON 6500VIGCT-PLUS DEVICE 18

ANALYSIS, DESIGN AND EXPERIMENT OF MMC BASED ON 6500VIGCT-PL...

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18th international conference on AC and DC Power Transmission, ACDC 2022

作者： Bai, Ruihang Jia, Na Zhao, Biao Tang, Bojin Qu, Lu Guo, Mingzhu Yu, Zhanqing Wang, Yifan Tang, Xueteng Zeng, Rang Department of Electrical Engineering Tsinghua University Haidian Beijing China China Three Gorges Corporation China

ISBN: (数字)9781839537615

ISBN: (纸本)9781839537615

Integrated-Gate-Commutated thyristor (IGCT) devices with the block voltage up to 6500V have been manufactured. Its application in modular multilevel converter (MMC) can significantly reduce the number of modules to optimize the converter. this paper firstly discusses the influence of increasing the rated voltage of modules on MMC characteristics such as the number of modules, dc support capacitance, weight, volume, cost, loss, harmonic. the rated parameters and component of module are designed based on IGCT-plus device of 6500V and 3800A. On this basis, the loss of the converter is analyzed by means of average equivalent method, and compared with that of MMC based on 4500V-IGCT and IEGT. Finally, both high-current switch-off and power-cycling running tests are carried out to verify the performance of designed module. the result shows that MMC module based on 6500V-IGCT has good stability and reliability. It has reference value for the application of IGCT with higher block voltage in MMC, thus improving power density and technical economy of MMC. © ACDC 2022. All rights reserved.

关键词： Capacitance

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LOCOMOTIVE 5G Core for 6G ready Resilient and Highly Available Network Slices and SFCs 18

LOCOMOTIVE 5G Core for 6G ready Resilient and Highly Availab...

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18th international conference of Network and Service Management, CNSM 2022

作者： Sarkar, Sourav Vittal, Shwetha Antony Franklin, A.A. Indian Institute of Technology Hyderabad Department of Computer Science and Engineering India

ISBN: (纸本)9783903176515

the presence of a Load Balancer (LB)s is much significant to keep up the High Availability (HA) and resilience of the scalable 5G Core (5GC). the whole system may collapse just because of inefficient LB at any NF, resulting in total disruption to the High Availability (HA) service. In this paper, we present the LOCOMOTIVE 5GC which outperforms the traditional hot standby in both HA and resilience during various dynamic conditions. LOCOMOTIVE serves 16% (at least) more user requests compared to hot standby in the control plane while handling unexpected overloaded conditions (without the failure of LB). During the failures of LB, it drops 22% lesser user requests than hot standby. With this outstanding resilience, LOCOMOTIVE even achieves 4% better availability than the hot standby in an active-Active cluster configuration. To prove the feasibility of LOCOMOTIVE and to encourage further research works in the world of LBs, we developed its entire framework in a 3GPP compliant 5G test-bed system along with eXpress Data Path (XDP) and extended Berkeley Packet Filter (eBPF) framework. © 2022 IFIP.

关键词： 5G mobile communication systems

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Multitask Graph Neural Network for knowledge Graph Link Prediction 9th

Multitask Graph Neural Network for Knowledge Graph Link Pred...

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9th international conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Wang, Ye Yang, Jianhua Li, Lijie Yao, Jian College of Computer Science and Technology Harbin Engineering University Harbin China TaoBao Software Co. Ltd Hangzhou China

ISBN: (纸本)9789819959709

Predicting entities in knowledge graphs is a crucial research area, and convolutional neural networks (CNNs) have exhibited significant performance due to their ability to generate expressive feature embeddings. However, several existing methods in this field tend to disrupt entities and relational embeddings, disregarding the original translation characteristics in triples, leading to incomplete feature *** address this issue and preserve the translation characteristics of triples, the present study introduces a novel representation technique, termed MultiGNN. the suggested approach uses a graph convolutional neural network for encoding and implements a parameter sharing technique. It employs a convolutional neural network and a translation model as decoders. the model’s parameter space is expanded to effectively integrate translation characteristics into the convolutional neural network, which allows it to capture these characteristics and enhance the model’s performance. the proposed method in this paper has demonstrated significant enhancements in several metrics on the public benchmark dataset when compared to the baseline method. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Exploring Innovative Approaches for software Development Risk Assessment and Management

Exploring Innovative Approaches for Software Development Ris...

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international conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Setya Uswatun Hasanah Paulus Insap Santosa Ridi Ferdiana Department of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350368970

ISBN: (纸本)9798350368987

the success of software development projects is significantly impacted by various risk factors, both predictable and unpredictable. this literature review aims to explore and evaluate innovative risk assessment and management techniques in software development. Using the Systematic Literature Review (SLR) method, this study reviews existing research on new software development risk assessment and management methods. this extensive review highlights various approaches, tools, and strategies for identifying, evaluating, and mitigating risks in software projects. the results underscore the importance of creative solutions in tackling risk management challenges. Practitioners are encouraged to adopt innovative methods for risk assessment and management as the field of software development evolves. Furthermore, this study recommends that researchers continue to investigate emerging trends and challenges in risk management. Emphasis is placed on developing hybrid models that combine traditional and innovative approaches, examining risk management practices in new technologies such as AI and machine learning, and addressing cultural and organisational barriers to adopting new techniques. Collaboration and knowledge sharing between practitioners and researchers are expected to improve risk management practices in software development significantly.

关键词： Machine learning algorithms Prevention and mitigation Machine learning Predictive models Market research software Risk management Information technology software development management Resilience

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10th EAI international conference on Industrial Networks and Intelligent Systems, INISCOM 2024

10th EAI International Conference on Industrial Networks and...

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10th EAI international conference on Industrial Networks and Intelligent Systems, INISCOM 2024

ISBN: (纸本)9783031673566

the proceedings contain 18 papers. the special focus in this conference is on Industrial Networks and Intelligent Systems. the topics include: Joint Online Adaptive Optimal Tracking Control and Frequency-Response Method for Speed of PMSM and DC-Link Voltage Peak Controller Design in Bi-directional Quasi Z-Source Inverter;Variational Quantum Eigensolver for Optimizing Network Scheduling Using QUBO Formulation;optimal Task Scheduling in 6G Networks: A Variational Quantum Computing Approach;multi-agent Quantum Reinforcement Learning for Digital Twin Placement in 6G Multi-tier Systems;Multi-user Ambient Backscatter Communication-Based and STAR-RIS-Aided Mobile Edge Computing Network with Uplink NOMA Scheme: A Joint Design;Hybrid Federated and Multi-agent DRL-Based Resource Allocation in Digital Twin-IoV Networks;performance Comparison in Traffic Sign Recognition Using Deep Learning;energy and Distance Aware Clustering-Based Routing for Low-Power IoT-Enabled Wireless Sensor Networks;Enhancing Energy Harvesting Efficiency for IRS-Aided TS-SWIPT Network with Practical Phase Shifts;contactless Palmprint Recognition Using Discriminant Multi-directional Feature Codes;a Survey on Wireless Data Aggregation through Over-the-Air Computation;CLIP-Prefix for Image Captioning and an Experiment on Blind Image Guessing;Features Inspired PM2.5 Prediction: A Belfast City Case Study;Understanding the Security Implications in O-RAN with Abusive Adversaries;Joint Secrecy and Latency Performance Analysis for UAV-Assisted Uplink NOMA-Based IoT Network with Mobile Edge Computing;crossCert: A Privacy-Preserving Cross-Chain System for Educational Credential Verification Using Zero-knowledge Proof.

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Vehicle type recognition: a case study of MobileNetV2 for an image classification task 28th

Vehicle type recognition: a case study of MobileNetV2 for an...

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28th international conference on knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Kobiela, Dariusz Groth, Jan Hajdasz, Michal Erezman, Mateusz Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Software Engineering Gabriela Narutowicza 11/12 Gdansk80-233 Poland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Algorithms and Systems Modelling Gabriela Narutowicza 11/12 Gdansk80-233 Poland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gabriela Narutowicza 11/12 Gdansk80-233 Poland

the goal of the research was to demonstrate the full data science lifecycle through a use case of the MobileNetv2 model for vehicle image classification task using various validation and test sets, each with different difficulty level. Diverse model variations were employed, each designed to recognize images of ground vehicles and classify them into one of five possible classes: car, truck, motorcycle, bicycle, or bus. In terms of validation accuracy, the highest results were obtained by the model trained with uniformly designed train and val sets (with data normalization and augmentation), where train set also contained validation set. this model also obtained the highest accuracy results on both test sets. the superiority of MODEL 3 BASELINE is confirmed by other metrics as well: test loss, f1-score, AUC and confusion matrices (for both test sets). Results between MODEL 1 BASELINE and MODEL 2 BASELINE differed according to the test set 1 and 2 and other metrics and it was not possible to declare the superiority of one method of datasets preparation over another (original class distribution [no data normalization and no data augmentation] versus uniformly designed [with data normalization and augmentation]). the article also presents challenges and findings - the problems, key issues, and their solutions that arose during the process of data collection and tagging, as well as the preparation and evaluation of the model. © 2024 the Authors.

关键词： Classification (of information)

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Automatic Classification of software Requirements using Artificial Neural Networks: A Systematic Literature Review 9

Automatic Classification of Software Requirements using Arti...

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9th international conference in software engineering Research and Innovation (CONISOFT)

作者： Alejandro Lopez-Hernandez, Delmer Octavio Ocharan-Hernandez, Jorge Mezura-Montes, Efren Sanchez-Garcia, Angel J. Univ Veracruzana Sch Stat & Informat Xalapa Veracruz Mexico Univ Veracruzana Artificial Intelligence Res Inst Xalapa Veracruz Mexico

ISBN: (纸本)9781665443616

software requirements classification is a human-intensive task performed during the requirements analysis phase in software development. this literature review analyzes the state-of-the-art of the classification of software requirements using Artificial Neural Networks. Fourteen articles were selected to conduct the review. Sixteen different techniques to classify requirements were identified where, besides artificial neural networks, the most popular are Naive Bayes and the Support Vector Machine. Among the reported Artificial Neural Networks, we identify Convolutional Neural Networks and a Shallow Neural Network. We also found seven approaches that classify functional and non-functional requirements, six that classify only non-functional requirements, and one of them that classifies only functional requirements. the most used metrics to express classification results were accuracy, recall, and F-score. Finally, the results of the classifiers are gathered and reported.

关键词： software Requirements Classification Artificial Neural Networks Machine Learning Systematic Literature Review

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A Novel RVFL-Based Algorithm Selection Approach for software Model Checking 15th

A Novel RVFL-Based Algorithm Selection Approach for Software...

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15th international conference on knowledge Science, engineering, and Management (KSEM)

作者： Cao, Weipeng Wu, Yuhao Wang, Qiang Zhang, Jiyong Zhang, Xingjian Qiu, Meikang Civil Aviat Univ China CAAC Key Lab Civil Aviat Wide Surveillance Safe operat Management & Control Technol Tianjin Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Chinese Acad Mil Sci Inst Syst Engn Beijing Peoples R China Hangzhou Dianzi Univ Sch Automat Hangzhou Peoples R China Texas A&M Univ Commerce Dept Comp Sci Commerce TX USA

ISBN: (纸本)9783031109898;9783031109881

software model checking is the technique that automatically verifies whether software meets the given correctness properties. In the past decades, a large number of model checking techniques and tools have been developed, reaching a point where modern model checkers are sophisticated enough to handle large-scale software systems. However, due to the fact that the software model checkering techniques are diverse and each of them is designed and optimized for a specific type of software system, it remains a hard problem for engineers to efficiently combine them to verify the complex software systems in practice. To alleviate this problem, we propose a novel algorithm selection approach based on Random Vector Functional Link net (RVFL) for software model checking, namely Kaleidoscopic RVFL (K-RVFL). the novel design of feature hybridization and fusion enables K-RVFL to extract more diverse and multi-level features. We have also carried out a thorough experimental evaluation on a publicly available data set and compared K-RVFL with a number of neural networks, including RVFL, Extreme Learning Machine (ELM), Stochastic Configuration Network (SCN), Back Propagation algorithm (BP), and Supporting Vector Machine (SVM). the experimental results demonstrate the usefulness and effectiveness of K-RVFL.

关键词： software verification software model checking Algorithm selection Random vector functional link Neural networks

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