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检索条件"任意字段=Knowledge-Based Software Engineering Conference"

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An intelligent question answering system based on RoBERTa-WWM under home appliance knowledge graph 35

An intelligent question answering system based on RoBERTa-WW...

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35th International conference on software engineering and knowledge engineering, SEKE 2023

作者： Gao, Xiaoyan Chen, Ji Ding, Xiangqian Shi, Shuo Jia, Junhua Xin, Xiaowei Faculty of Information Science and Engineering Ocean University of China Qingdao China

Recently, the intelligent question answering (QA) system for home appliances has attracted wide attention in China because it can provide users with reasonable suggestions in time. However, the fuzzy boundaries of the Chinese "word groups" and the absence of a unified standard for Chinese question classification result in low efficiency of QA, which has hindered the development of the system for a long time. In this work, we developed a QA model based on RoBERTa-WWM to answer questions about home appliances in specific fields. More importantly, the RoBERTa-WWM-BiLSTM-CRF named entity recognition model and RoBERTa-WWM-TextCNN question semantic classification model were constructed to parse the correct semantics of questions from users, which provided algorithm support for the home appliance QA system. The results showed that the method outperformed baselines, and the QA model showed high interpretability and good performance. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Domestic appliances

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Non-Functional Requirement Extracting Methods for AI-based Systems: A Survey 13

Non-Functional Requirement Extracting Methods for AI-based S...

引用

13th International conference on Computer and knowledge engineering, ICCKE 2023

作者： Damirchi, Reza Amini, Amineh Karaj Branch Islamic Azad University Department of Computer Engineering Karaj Iran

ISBN: (纸本)9798350330151

Artificial intelligence (AI) systems are growing rapidly in a variety of fields and have become a part of everyday human life. Traditional software development methods, especially in the requirements engineering domain, are not very efficient in the development of AI-based Systems. Given that the elicitation and analysis of software project requirements is the key to producing a high-quality and efficient product, there must be new approaches to identifying and extracting requirements in the field of AI-based systems. This is done in the research area of Requirements engineering for Artificial Intelligence (RE4AI). Non-functional requirements are defined in accordance with the system's objectives and operational requirements. They specify the behaviors, limitations, and overall quality of the system;for this reason, they are very important for the success of the system, and lack of attention or neglect to accurately identify it will increase the probability of project failure. In this Paper, we aim to provide a comprehensive overview of the approaches and techniques for extracting non-functional requirements for AI-based systems by reviewing recent papers in this field. The review of these studies shows that there are currently no accurate methods for identifying and extracting non-functional requirements of AI-based Systems, but by relying on traditional methods and redefining their concepts, most of these methods can also be used in this domain. Additionally, new requirements for the field of AI have been introduced that are of high importance. © 2023 IEEE.

关键词： Requirements engineering

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Formalization and Verification of Data Auction Mechanism based on Smart Contract Using CSP 35

Formalization and Verification of Data Auction Mechanism Bas...

引用

35th International conference on software engineering and knowledge engineering, SEKE 2023

作者： Du, Yingjia Fei, Yuan Chen, Sini Zhu, Huibiao Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai China

Nowadays, the utilization of online auction platforms is becoming increasingly prevalent. Online auction provides a common and practical way for global buyers to compete fairly. Nevertheless, the anonymous environment may bring collusion among entities with effects on results. Compared with traditional mechanisms which rely on third-party platforms, the data auction based on smart contract can create a decentralized environment to avoid the occurrence of collusion. Meanwhile, there exists few research on the verification of its reliability and safety which is worth investigating from the perspective of formal methods. In this paper, we apply Process Algebra CSP in modeling the data auction communicating system among five key entities. In addition, we use Process Analysis Toolkit (PAT) to realize the mechanism and verify five crucial properties, including deadlock freedom, data reachability, data correctness, anti-collusion capability and data security. The verification results indicate that the architecture of data auction based on smart contract can satisfy all the above requirements. Especially, the design of asymmetric encryption for the fundamental information ensures the non-occurrence of collusion in the auction. Additionally, the digital signature generated by private key attached to the message guarantees the safety of the interaction. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Smart contract

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An Empirical Study on the Characteristics of Connected knowledge Subgraphs on Stack Overflow 24

An Empirical Study on the Characteristics of Connected Knowl...

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24th IEEE International conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Sun, Tianyue Gong, Lina Zhang, Jingxuan Wei, Mingqiang Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China

ISBN: (纸本)9798350365658

In the Stack Overflow (SO) community, users typically employ links (including internal and external links) in their posts or comments to better present their cited knowledge. Prior studies have already demonstrated that external links bring valuable information, thus enhancing the software engineering knowledge quality within the community. Nowadays, the act of users referencing internal links has formed connected knowledge subgraphs with question threads as nodes and internal links as edges. However, the characteristics of the subgraphs have not been investigated. It is still unclear how and why users reference internal links within the subgraphs. In addition, the impact of internal links within the subgraphs on the community is yet to be explored. Therefore, we constructed subgraphs through internal links and analyzed their structural stability based on the official SO data dump. Subsequently, we employed qualitative analysis methods to explore how and why developers reference internal links within the subgraphs. Finally, we conducted a quantitative analysis to assess the impact of the subgraphs on the community. We observed the subgraphs are structurally stable, with a slowing expansion rate. Meanwhile, We found that 82.4% of links were cited without summarizing the content. Notably, 64.3% of links without summaries are likely to hinder knowledge comprehension once they become obsolete. This is especially pronounced for links cited for 'providing solutions to subproblem', reaching as high as 96.9%. Our quantitative analysis has uncovered the last updated time of question threads within the subgraphs is closer to the present. Simultaneously, they also exhibit a lower deletion rate. based on our findings, we provide actionable suggestions for developers, SO community, and researchers. For example, we encourage researchers to develop a visualization tool that can visualize question threads and their internal links from the 'linked' list as graphs. This facilitates i

关键词： Connected knowledge Subgraphs Empirical software engineering Internal Link Stack Overflow

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Model-Driven Optimization for Quantum Program Synthesis with MOMoT

Model-Driven Optimization for Quantum Program Synthesis with...

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

作者： Gemeinhardt, Felix Eisenberg, Martin Klikovits, Stefan Wimmer, Manuel Johannes Kepler Univ Linz Inst Business Informat Software Engn Linz Austria

ISBN: (纸本)9798350324983

In the realm of classical software engineering, model-driven optimization has been widely used for different problems such as (re)modularization of software systems. In this paper, we investigate how techniques from model-driven optimization can be applied in the context of quantum software engineering. In quantum computing, creating executable quantum programs is a highly non-trivial task which requires significant expert knowledge in quantum information theory and linear algebra. Although different approaches for automated quantum program synthesis exist-e.g., based on reinforcement learning and genetic programming-these approaches represent tailor-made solutions requiring dedicated encodings for quantum programs. This paper applies the existing model-driven optimization approach MOMoT to the problem of quantum program synthesis. We present the resulting platform for experimenting with quantum program synthesis and present a concrete demonstration for a well-known quantum algorithm.

关键词： Quantum Circuit Synthesis Model-Driven Optimization Quantum software engineering

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Application of Data Mining in the Development and Management of software engineering in Cloud Computing Platform 6th

Application of Data Mining in the Development and Management...

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6th International conference on Cyber Security Intelligence and Analytics, CSIA 2024

作者： Tan, Qing School of Information Technology Luoyang Normal University Luoyang China

ISBN: (纸本)9783031882869

Data Mining is a process of extracting valuable information and knowledge that people have not observed from a large number of complicated data. Massive data mining based on cloud computing can optimize the dynamic resource scheduling and allocation of information. The application of data mining technology in computer software engineering can quickly find out the data which is beneficial to software engineering management, thus providing useful information for software development and management. Data mining technology has significant application advantages in software vulnerability detection, fault repair, source code development and other fields. This paper proposes the application of data mining technology to software engineering on the basis of cloud computer. The experimental results show that the data mining technology proposed in this paper can effectively improve the quality of software engineering development and management under the cloud computing platform. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Application programs

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KGLM-QA: A Novel Approach for knowledge Graph-Enhanced Large Language Models for Question Answering 15

KGLM-QA: A Novel Approach for Knowledge Graph-Enhanced Large...

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15th International conference on Information and knowledge Technology, IKT 2024

作者： Safaei, Alireza Akhavan Saboori, Pegah Ramezani, Reza Nematbakhsh, Mohammadali Department of Software Engineering University of Isfahan Isfahan Iran

ISBN: (纸本)9798331522254

Large language models excel in various natural language processing tasks but often struggle with knowledge-intensive queries, particularly those involve rare entities or require precise factual information. This paper presents a novel framework that enhances capabilities of an LLM-based question answering system by incorporating structured knowledge from knowledge graphs. Our approach employs entity extraction, semantic similarity scoring, and adaptive graph exploration to efficiently navigate and extract relevant information from knowledge graphs. The core of the presented solution is a knowledge graph-enhanced language model process that iteratively refines subgraph exploration and answer generation, complemented by a fallback mechanism for robustness across diverse question types. Experiments on location-based questions from the Entity Questions dataset demonstrate significant improvements in the quality of responses. Using the Gemini 1.5 Flash model, our system achieved an accuracy increase from 36% to 71% for partially correct answers and from 22% to 69% for exactly correct answers, as evaluated by human assessors. This approach offers a promising direction for developing more reliable and accurate question answering systems, particularly for queries involving long-tail entities or specific factual knowledge. © 2024 IEEE.

关键词： knowledge graph

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An Exploratory Study on Upper-Level Computing Students' Use of Large Language Models as Tools in a Semester-Long Project

An Exploratory Study on Upper-Level Computing Students' Use ...

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2024 ASEE Annual conference and Exposition

作者： Tanay, Ben Arie Arinze, Lexy Chiwete Joshi, Siddhant Sanjay Davis, Kirsten A. Davis, James C. Purdue Engineering Education United States Purdue University West Lafayette United States

Background: Large Language Models (LLMs) have begun to influence software engineering practice since the public release of GitHub's Copilot and OpenAI's ChatGPT in 2022. Tools built on LLM technology could revolutionize the way software engineering is practiced, offering interactive "assistants" that can answer questions and prototype software. It falls to software engineering educators to teach future software engineers how to use such tools well, by incorporating them into their pedagogy. While some institutions have banned ChatGPT, other institutions have opted to issue guidelines for its use. Additionally, researchers have proposed strategies to address potential issues in the educational and professional use of LLMs. As of yet, there have been few studies that report on the use of LLMs in the classroom. It is, therefore, important to evaluate students' perception of LLMs and possible ways of adapting the computing curriculum to these shifting paradigms. Purpose: The purpose of this study is to explore computing students' experiences and approaches to using LLMs during a semester-long software engineering project. We investigated the impacts of a low-cost intervention. While there have been studies on the use of LLMs in the classroom, there have been limited works on the use within a project-based course in the computing classroom. Our study helps fill this knowledge gap. Design/Method: We collected data from a senior-level software engineering course at Purdue University, a large public R1 university in the Midwest. This course uses a project-based learning (PBL) design with a semester-long team project. In Fall 2023, the students were required to use LLMs such as ChatGPT and Copilot as they completed their projects. A sample of these student teams were interviewed in the middle and at the end of the semester to understand: (1) how they used LLMs in their projects;and (2) whether and how their perspectives on LLMs changed over the course of the semester. We ana

关键词： Students

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Dispatching and Scheduling Dependent Tasks based on Multi-agent Deep Reinforcement Learning 35

Dispatching and Scheduling Dependent Tasks Based on Multi-ag...

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35th International conference on software engineering and knowledge engineering, SEKE 2023

作者： Feng, Shuaishuai Wu, Xi Zhao, Yongxin Li, Yongjian Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China The University of Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

With the development of edge computing, a large number of tasks can be offloaded to the edge server for computing, among which the dispatching and scheduling of dependent tasks has attracted extensive attention. The offloading of dependent tasks mainly has the following problems: how to select an appropriate edge server for dispatching, how to arrange the scheduling order of edge servers to better schedule tasks, and how to solve the task dependency problem. In this paper, we proposal a dispatching and scheduling method DAMD, based on reinforcement learning and multi-agent reinforcement learning, to solve the above three problems. Specifically, as the first step of DAMD, a reinforcement learning approach is designed to estimate the network load and dynamically dispatch tasks to the appropriate edge servers. Each edge server is regarded as an agent by a multi-agent reinforcement learning method, the second step of DAMD, which comprehensively considers the dependency relationship between tasks and the scheduling relationship between servers to achieve the efficiency and fairness of task scheduling. Finally, the results show that our method can better complete the task within the deadline and greatly reduce the average response time according to the time sensitivity requirement. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Reinforcement learning

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Development of a Gamification Application 'OSINT Maniac' to Enhance Open-Source Intelligence (OSINT) Skills 10

Development of a Gamification Application 'OSINT Maniac' to ...

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10th International conference on Education and Technology, ICET 2024

作者： Siddiq, Syubbanul Qomariasih, Nurul Wijayanti, Rheva Anindya Cryptography Engineering Cyber and Crypto Institute Bogor Indonesia

ISBN: (纸本)9798331522483

Open-Source Intelligence (OSINT) plays a crucial role in cybersecurity and intelligence by providing valuable insights and information from publicly available sources. In today's digital age, where vast amounts of data are generated and shared online, OSINT serves as a powerful tool for gathering intelligence, monitoring threats, and identifying potential risks. By leveraging OSINT, organizations can enhance their situational awareness, detect emerging threats, and proactively mitigate security vulnerabilities. However, mastering OSINT skills requires not only theoretical knowledge but also practical experience and proficiency in effectively utilizing various online resources. Utilizing the Personal Xtreme Programming (PXP) software Development Lifecycle (SDLC), the Osint Maniac gamification application is crafted as a practical OSINT training platform. based on questionnaire results distributed to respondents, the Osint Maniac gamification application has been shown to enhance users' knowledge and practical OSINT skills. © 2024 IEEE.

关键词： software design

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