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检索条件"任意字段=4th International Conference on Software Engineering and Development, ICSED 2022"

共 6379 条记录，以下是41-50 订阅

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Empowerment of Large Language Models in Psychological Counseling through Prompt engineering 4

Empowerment of Large Language Models in Psychological Counse...

引用

4th IEEE international conference on software engineering and Artificial Intelligence (SEAI)

作者： Huang, Shanshan Fu, Fuxiang Yang, Ke Zhang, Ke Yang, Fan Commun Univ Zhejiang Sch Media Engn Hangzhou Peoples R China

ISBN: (纸本)9798350374353;9798350374346

the rapid advancement of artificial intelligence (AI) has led to significant strides in the development of large language models, which are increasingly adept at comprehending and processing natural language. these models possess substantial memory capacity and can exhibit logical reasoning. However, their efficacy in addressing the emotional and empathetic aspects of human interaction remains a challenge. Current models may provide superficial advice that fails to penetrate the depth of an individual's emotional *** introduce a novel approach to enhance the emotional intelligence of large language models, aiming to fostering a more empathetic and emotionally attuned interaction with users. We conducted experiments with several prominent models, including ChatGLM and ERNIE Bot, employing a variety of promptings. these ranged from presenting examples without explicit directives to providing a limited set of examples and extending narratives from a given context. We simulated therapeutic conversations to evaluate the models' performance in emotionally charged scenarios. Our findings indicate that by refining the guidance mechanisms for these models, it is possible substantially to improve their capacity for emotional engagement and understanding.

关键词： Large language model Prompt engineering artificial intelligence psychological counseling

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Research on Object Detection and Tracking based on Deep Learning 4

Research on Object Detection and Tracking based on Deep Lear...

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4th international conference on Machine Learning and Intelligent Systems engineering (MLISE)

作者： Pan, Jing Jiangxi Agr Univ Software Coll Nanchang Jiangxi Peoples R China

ISBN: (纸本)9798350375084;9798350375077

With the advancement of artificial intelligence technology, object detection technology in the field of computer vision has played a key role. this article aims to address the accuracy and speed of existing methods in processing live video streams. With the development of deep learning technology, we propose a novel framework of convolutional neural network (CNN) architecture, which optimizes the process of feature extraction and object classification, and significantly improves the detection accuracy. In addition, we have integrated recurrent neural networks (RNNs) to improve the tracking continuity of targets in video sequences. through the fusion of these technologies, our model not only performs well in multi-target detection, but also reliably tracks targets in the case of occlusion and fast movement. Experimental results show that compared with the existing deep learning methods, the performance of our model on the standard dataset is significantly improved, with a 20% increase in detection speed and a 15% increase in accuracy.

关键词： Objecti detection Convolutional neural network Recurrent neural network Deep learning

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Evaluating the Extent of Application of software engineering Concepts in Graduation Projects: A Case Study 4

Evaluating the Extent of Application of Software Engineering...

引用

4th IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2024

作者： Ali, Taghrid Z. Hagal, Mohamed A. Elmarzaki, Howayda A. Maatuk, Abdelsalam M. Higher Institute of Engineering Technologies Software Development Department Benghazi Libya University of Benghazi Faculty of Information Technology Benghazi Libya

ISBN: (纸本)9798350372632

Despite the efforts to narrow the gap between software engineering learning and the need for practice in the software industry, beginners often find it difficult to match their academic skills with professional life practices. therefore, there is an urgent need to know the best ways to provide students with the practical experiences they need to build professional competencies in the field of software engineering. this research aims to evaluate the extent to which students can employ software engineering concepts and apply them to their graduation projects. Besides, this study seeks to shed light on the methods used in teaching software engineering courses at the Faculty of Information Technology at the University of Benghazi, to improve them. To achieve this goal, we conducted a study spanning two semesters. the study includes a questionnaire to collect the answers of 74 university students studying in their final semester in the software engineering Department and working on their graduation project. We analyzed the results with responses from 30 students. the results show that students have some necessary skills that prepare them for future work in software engineering. the results also showed that there is a gap among students between the concepts they received and their practical application in their graduation projects. Some guidelines are presented that may contribute to improving the educational environment for teaching software engineering. © 2024 IEEE.

关键词： Students

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Choosing the Right Automated software Testing Tools 4

Choosing the Right Automated Software Testing Tools

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4th IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2024

作者： Aburas, Ali University of Tripoli Computer Science Department Tripoli Libya

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372632

the primary objective of a software project is to get a high-quality software product while reducing the cost and the time required to complete the project. To do that, the software needs to be tested before being released. software testing is a set of activities conducted to assure the correctness of software processes and has always been essential during the software development life cycle. software testers evaluate the quality of the software with the help of various automated testing tools. Automation testing tools help developers find defects that are hard to find during manual testing. Choosing the right automated software testing tools is not a trivial task and is considered one of the most critical steps before testing any software. To simplify the task, we studied 16 automated testing tools that are widely used and considered important by software testers and developers. We organized and compared them based on chosen criteria, such as the platform where they can be employed and what type of testing the functionality they involve. the main goal of this paper is to provide a comparative view of popular test automation tools based on specific criteria. this comparison can benefit software developers, testers, and researchers. It also serves as a reference for university courses in software testing. © 2024 IEEE.

关键词： software testing

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Mapping software-engineering Industry AI Use to software-engineering Curriculum: Developing the AI-USE Framework 4

Mapping Software-Engineering Industry AI Use to Software-Eng...

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4th international conference on AI Research, ICAIR 2024

作者： Lilholt, Addison Heverin, thomas The Baldwin School Bryn Mawr United States

ISBN: (纸本)9798331309466

Estimates predict a global deficit of 4 million software engineers by 2025, further complicated by the software engineering (SE) industry's escalating use of artificial intelligence (AI). To tackle this issue, our research suggests that computer science (CS) curricula in middle and high schools need to be updated to incorporate SE industry segments that significantly employ AI. this strategic curriculum alignment is significant for preparing a workforce equipped to meet future industry demands. Our initial analysis involved reviewing nine international AI education guidelines to evaluate current methods for integrating AI into SE education. the findings indicated a pronounced lack of specific guidance connecting AI applications in the SE industry with educational content. To address this, we performed a systematic literature review of 12 research papers focusing on AI's role across the SE industry, followed by multiple rounds of inductive content analysis. An industry segment was deemed "essential" if it was referenced in 75% or more of the papers' findings. through this method, we identified 10 essential SE industry segments for inclusion in CS education: software development, software maintenance, process improvement, software economics, knowledge management, project management, software testing, software security, quality assurance, and deployment and operations (DevOps). these findings led to the creation of the AI-USE (Artificial Intelligence Usage in software engineering) framework, which maps these 10 key segments to the predominant uses of SE in the industry as identified in the literature. Further inductive content analysis helped us develop subsegments for these essential areas. Ongoing framework development involves refining these subsegments and gathering feedback from industry and academic professionals. We anticipate that the fully developed AI-USE framework will significantly enhance SE education, equipping the next generation of software engineers wi

关键词： Project management

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Discussion on Internet of things and cyber security and its Effects on ecosystem 4

Discussion on Internet of things and cyber security and its ...

引用

4th international conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Singla, Neeraj ChitkaraUniversity Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350364729

the Internet of things (IoT) is changing our world drastically but its development presents a complex challenge: cyber security. Number of interconnected devices highlights the security risks related with IoT. this paper discusses the vulnerabilities that hackers can misuse while suggesting the security measures on the other hand. Further, it focuses on the importance of user education for reducing IoT-related risks. By providing users with the knowledge to execute strong security practices such as updating passwords and keeping the software updated, the overall security status of IoT ecosystem. Moreover, the alliance between the producers, security researchers and policy makers is important to ensure that the technology is developed in a responsible way. By recognizing these cybersecurity challenges of IoT, we can guarantee the safe and sustainable development of this evolutionary technology. © 2024 IEEE.

关键词： Artificial Intelligence (AI) cybersecurity data privacy Internet of things IoT machine learning risk analysis threats

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Research on bank credit risk control based on AE-CatBoost 4

Research on bank credit risk control based on AE-CatBoost

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4th international conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Yu, Tingting East China Normal University School of Software Engineering Putuo Shanghai China

ISBN: (纸本)9798350394375

With the continuous progress of society, the scale of credit business is also expanding. this thesis conducts research on machine learning to accurately assess borrowers' credit risks based on a public data set of loan records from a certain credit platform. By combining the deep learning model AutoEncoder and the integrated learning model CatBoost, the AE-CatBoost model was proposed. Various evaluation indicators were compared through experiments and it was found that the AUC value of AE-CatBoost model is about 0.034 higher than that of CatBoost model. the proposal of AE-CatBoost has important practical significance for the development of bank credit risk control. © 2024 IEEE.

关键词： Risk assessment

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Daiquiri: A Framework for GO 4

Daiquiri: A Framework for GO

引用

4th international conference on Multimedia Processing, Communication and Information Technology, MPCIT 2024

作者： Srivastava, Harshit Srivastava, Harshal Annapurna, V.K. Bisht, Harshita Bhosale, Arya Arun The National Institute of Engineering Department of Computer Science and Engineering Mysuru India

ISBN: (纸本)9798350375466

the Go programming language, introduced by Google in 2009, is valued for its simplicity, efficiency, and strong concurrency support. Its widespread adoption across various domains necessitates a purpose-built framework tailored to Go's principles. Such a framework encapsulates best practices, performance optimizations, and standardized patterns, streamlining development and enabling scalability. It offers modularity, productivity tools, and abstractions that simplify common tasks, allowing developers to focus on domain-specific challenges. By leveraging a Go framework, developers can efficiently build scalable, concurrent applications, ensuring agility, scalability, and reliability. this alignment with Go's principles equips developers with the tools needed to navigate the complexities of contemporary software development. © 2024 IEEE.

关键词： Scalability

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Propensity-Dependent Model for Unbiased Learning-to-Rank 4

Propensity-Dependent Model for Unbiased Learning-to-Rank

引用

4th international conference on software engineering and development, icsed 2022

作者： Zhang, Haochen Wu, Ziqing Peng, Zhuoran Luo, Tiancheng Weng, Xianna School of Mathematical Sciences Peking University Beijing China College of Professional Studies Northeastern University Boston United States College of Arts & Science New York University New YorkNY United States School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Agriculture China Agricultural University Beijing China

ISBN: (纸本)9781450397940

Most present unbiased learning-to-rank models are based on the trust bias assumption to learn a ranking policy by Inverse Propensity Scoring (IPS). the trust bias assumption improves the unrealistic noise-free assumption in the Position-Based model, but it assumes that the propensities are independent. In this paper, we improve this assumption and consider that the propensities of different positions are relevant. In particular, we model the relationship between different propensities as a Propensity-Dependent model and use both IPS estimator and the Doubly Robust estimator to learn the optimal ranking policy. Finally, we generate the dataset in a simulated study and then evaluate the model's performance. © 2022 ACM.

关键词： Learning systems

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Blood Cell Image Classification Based on Improved EfficientNetB3 Model 4

Blood Cell Image Classification Based on Improved EfficientN...

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4th international conference on Electronic Information engineering and Computer Science, EIECS 2024

作者： Feng, Haiwen Wu, Yuhan Software College of Shenyang University of Technology Shenyang Intelligent Key Laboratory of High-end Industrial Equipment Manufacturing Shenyang China Software College of Shenyang University of Technology Shenyang China

ISBN: (纸本)9798331531409

Accurate classification of blood cells is very important for clinical diagnosis, but traditional classification methods are not only time-consuming, inefficient, but also easy to make subjective judgments. With the development of technology, neural network has made great progress in the field of blood cell analysis, especially deep learning technology has shown broad application prospects in the field of medical images. By using the lightweight EfficientNetB3 model to classify four kinds of cells, CBAM attention mechanism is added to the network to improve efficiency. the experimental results show that this method has excellent recognition ability and is helpful for the diagnosis and treatment of blood diseases in the future. © 2024 IEEE.

关键词： Deep learning

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