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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3227 条记录，以下是71-80 订阅

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Arabic Cyberbullying Detection Using machine learning: State of the Art Survey 23

Arabic Cyberbullying Detection Using Machine Learning: State...

引用

27th international conference on Evaluation and Assessment in software engineering (EASE)

作者： Alsunaidi, Norah Aljbali, Sara Yasin, Yasmin Aljamaan, Hamoud King Fahd Univ Petr & Minerals Informat & Comp Sci Dept Dhahran Saudi Arabia Saudi Aramco Dhahran Saudi Arabia

ISBN: (纸本)9798400700446

Cyberbullying (CB) is a global dilemma that is growing rapidly to affect more individuals including minors. The devastating consequences of CB indicate a pressing necessity to regulate unethical or illegal users' online behaviors. A remarkable number of researchers attempted to harness the potential of machine learning to detect and prevent such harmful behaviors, however, the existing studies targeting Arabic-based content are still emerging. Therefore, this paper provides a comprehensive review of the published empirical studies in CB detection in Arabic-based content with an emphasis on the adapted methodologies, gaps, and challenges. We hope this work would support researchers in the area of CB-detection to foster a safe online environment and protect against any harmful consequences of CB among users.

关键词： Cyberbullying Detection Arabic Cyberbullying Classification machine learning

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Extensible Modeling Framework for Reliable machine learning System Analysis 2

Extensible Modeling Framework for Reliable Machine Learning ...

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IEEE/ACM 2nd international conference on AI engineering - software engineering for AI (CAIN)

作者： Husen, Jati H. Washizaki, Hironori Tun, Hnin Thandar Yoshioka, Nobukazu Fukazawa, Yoshiaki Takeuchi, Hironori Tanaka, Hiroshi Munakata, Kazuki Waseda Univ Tokyo Japan Telkom Univ Bandung Indonesia Musashi Univ Tokyo Japan Fujitsu Ltd Kanagawa Japan

ISBN: (纸本)9798350301137

machine learning system analysis requires different approaches for each different task and domain. Selecting a proper set of analytic models can be challenging for a specific problem. This paper discusses the extensibility of the Multi-View Modeling Framework for ML Systems approach using process mapping and extensible metamodel. We conducted a case study to evaluate the feasibility of such extensibility by extending the approach to facilitate an activity-driven analysis for an optical character recognition system. Based on the result of the case study, we found that Multi-View Modeling Framework for ML Systems is likely to be extensible.

关键词： machine learning system analysis extensibility

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COMET: A ML-Based Tool for Evaluating the Effectiveness of software Design Communication

COMET: A ML-Based Tool for Evaluating the Effectiveness of S...

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ACM/IEEE international conference on Model Driven engineering Languages and Systems (MODELS)

作者： Lindgren, Marcus Haapasaari Persson, Jon Jolak, Rodi Dobslaw, Felix Mid Sweden Univ Dept Commun Qual Management & Informat Syst Ostersund Sweden

ISBN: (纸本)9798350324983

Communication is one of the most demanding activities in software development. The effectiveness of communication can be measured by analyzing three interpersonal communication dimensions: Active Discussion, Creative conflict, and Conversation Management. Previous work relied on manually labeling the communication dimensions to analyze the effectiveness of software design discussions, a process that is time-consuming and not applicable to real-time use. In this study, natural language processing and supervised machine learning are used to create COMET, a tool for automatic classification and assessment of the effectiveness of interpersonal communication during software design meetings. To determine the optimal classification approach, nine different classifiers are examined. The classifier model that performed the best is Random Forest which managed to achieve 93.66% accuracy, 93.76% precision, and 93.63% recall when trained and tested with a stratified 10-fold cross-validation technique.

关键词： software engineering software Design machine learning Natural Language Processing Communication

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Analysis Of software Defect Prediction Using machine-learning Techniques 3

Analysis Of Software Defect Prediction Using Machine-Learnin...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Chimankar, Abhijeet Gajanan Saranya, S. Krishnamoorthy, P. Mohanraj, A. ul Islam, Atowar Mallireddy, Naresh Maharashtra Pandharpur431136 India Department of Computer Science and Engineering Hindustan Institute of Technology Coimbatore641032 India Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Andhra Pradesh Puttur India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore India Department of Computer Science and Electronics University of Science and Technology Meghalaya Ri-Bhoi Techno city Killing Road Meghalaya Baridua793101 India Department of Computer Science and Engineering Aditya college of Engineering Andhra Pradesh Surampalem533437 India

ISBN: (纸本)9798350399264

- The development of the group based on the machine learning process is being generated with the support of software analysis. Development of the ideas is being generated and proper predictions about the research topic have been determined. Moreover, improving the problem-solving capacity is being generated and it helps to analyze the hybridized approaches. Analytical data is defined in this portion. Moreover, the prediction of the machine learning capacity is highlighted and it tries to focus on the software-based system. On the other hand, the input of the attribution is highlighted and it helps to improve the significance value of the research topic. Assisting activities have to be generated and the growth of the organization has to be highlighted. © 2023 IEEE.

关键词： machine learning

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Design and implementation of intelligent analysis software for teaching behavior based on near-field speech

Design and implementation of intelligent analysis software f...

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international conference on Internet of Things and machine learning, IoTML 2023

作者： Sun, Zhenyue Cheng, Yun Yu, Ming Dai, Yaoyao School of Education Huanggang Normal University Hubei Huanggang China

ISBN: (纸本)9781510671805

The use of a finer-grained classroom language analysis coding form facilitates a more in-depth analysis of teachers' classroom language behavior. This study uses a finer-grained T-coding system to encode texts converted from teachers' near-field recordings, makes a preliminary exploration of multiple methods of visual teacher teaching strategies for coding sequences, and writes Python scripting software based on machine learning algorithms. This software can effectively assist teachers in self-observation and improve teaching quality. © 2023 SPIE.

关键词： learning algorithms

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Determining the Most Significant Metadata Features to Indicate Defective software Commits 21

Determining the Most Significant Metadata Features to Indica...

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21st IEEE/ACIS international conference on software engineering Research, Management and Applications, SERA 2023

作者： Dey, Rupam Kumar Khojandi, Anahita Perumalla, Kalyan The University of Tennessee Industrial and Systems Engineering Knoxville United States

ISBN: (纸本)9798350345889

Defects are largely inevitable in the software development life cycle. Since we cannot avoid them during the development process, we can only desire to fight back with our limited resources in terms of time and monetary investment. Like in many other fields, machine learning models can be of help to mitigate the problem of defects by predicting both bug frequency and defective modules at different granularity levels. However, machine learning models are as good as the quality of the pre-selected set of features under consideration. Therefore, importance must be given while selecting only the necessary features from the original set of features. In this study, we compared various machine learning models with varying feature selection techniques and found the superiority of random forest-based machine learning techniques with wrapper methods. Random forest-based models with the wrapper method were able to detect all the buggy classes successfully on the validation data set. © 2023 IEEE.

关键词： Defects

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Detection of a Novel Object-Detection-Based Cheat Tool for First-Person Shooter Games Using machine learning 21

Detection of a Novel Object-Detection-Based Cheat Tool for F...

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21st IEEE/ACIS international conference on software engineering Research, Management and Applications, SERA 2023

作者： Xiao, Zhang Goto, Takaaki Ghosh, Partha Kirishima, Tadaaki Tsuchida, Kensei Toyo University Graduate School of Information Sciences and Arts Saitama Japan Toyo University Faculty of Information Sciences and Arts Saitama Japan Academy of Technology Department of Computer Science and Engineering West Bengal India

ISBN: (纸本)9798350345889

Detection of novel game cheating tools is critical for ensuring fair online play. Such cheating tools are visual-based and effectively avoid detection because they do not change the data of game software. With the development and popularity of artificial intelligence technology, it has become easier for individuals to develop cheating tools, such as a new cheating tool for first-person shooter games that searches for characters on the game screen and automatically targets them. Therefore, in this study, a new cheat detection method is proposed using machine learning. The proposed method can be used to detect new cheating tools based on object detection. © 2023 IEEE.

关键词： machine learning

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software Maintenance Prediction Using Stack Ensemble Deep learning Algorithms 3rd

Software Maintenance Prediction Using Stack Ensemble Deep Le...

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3rd international conference on machine Vision and Augmented Intelligence, MAI 2023

作者： Chirania, Shristi Tahreem, Hera Ayushi Agrawalla, Bikash Reddy, B. Ramachandra Department of Computer Science and Engineering NIT Jamshedpur Jamshedpur India

ISBN: (纸本)9789819743582

software Maintenance is of utmost importance for any industry. So, to predict the value of software maintenance beforehand also becomes very important, hence many software maintenance prediction algorithms have been devised previously. Here we are also trying to calculate the software maintenance prediction values by taking current and previous datasets of the same software and comparing the lines of code in both of them. In this paper, performance of various machine learning algorithms and ensemble learning using deep neural networks has been compared. The machine learning algorithms used here are Decision Trees, Linear regression, Support Vector machines, XGBoost, Catboost, and Random forest. The ensemble learning technique used is Stacking and each model of the ensemble model is of deep neural network. The models are evaluated with RMSE and MSE. By observing the error values of all the algorithms, the ensemble deep neural network gave the minimum error and outperformed all the other ML algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Reproducibility Requires Consolidated Artifacts 2

Reproducibility Requires Consolidated Artifacts

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IEEE/ACM 2nd international conference on AI engineering - software engineering for AI (CAIN)

作者： Fostiropoulos, Iordanis Brown, Bowman Itti, Laurent Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9798350301137

machine learning is facing a 'reproducibility crisis' where a significant number of works report failures when attempting to reproduce previously published results. We evaluate the sources of reproducibility failures using a meta-analysis of 142 replication studies from ReScience C and 204 code repositories. We find that missing experiment details such as hyperparameters are potential causes of unreproducibility. We experimentally show the bias of different hyperparameter selection strategies and conclude that consolidated artifacts with a unified framework can help support reproducibility.

关键词： machine learning Systems Meta Science Reproducibility

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Causality-Aided Trade-off Analysis for machine learning Fairness 38

Causality-Aided Trade-off Analysis for Machine Learning Fair...

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38th IEEE/ACM international conference on Automated software engineering (ASE)

作者： Ji, Zhenlan Ma, Pingchuan Wang, Shuai Li, Yanhui Hong Kong Univ Sci & Technol Hong Kong Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9798350329964

There has been an increasing interest in enhancing the fairness of machine learning (ML). Despite the growing number of fairness-improving methods, we lack a systematic understanding of the trade-offs among factors considered in the ML pipeline when fairness-improving methods are applied. This understanding is essential for developers to make informed decisions regarding the provision of fair ML services. Nonetheless, it is extremely difficult to analyze the trade-offs when there are multiple fairness parameters and other crucial metrics involved, coupled, and even in conflict with one another. This paper uses causality analysis as a principled method for analyzing trade-offs between fairness parameters and other crucial metrics in ML pipelines. To practically and effectively conduct causality analysis, we propose a set of domain-specific optimizations to facilitate accurate causal discovery and a unified, novel interface for trade-off analysis based on well-established causal inference methods. We conduct a comprehensive empirical study using three real-world datasets on a collection of widely-used fairness-improving techniques. Our study obtains actionable suggestions for users and developers of fair ML. We further demonstrate the versatile usage of our approach in selecting the optimal fairness-improving method, paving the way for more ethical and socially responsible AI technologies.

关键词： Causality Analysis Fairness machine learning

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