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

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Intelligent Tutoring for Large-Scale Personalized Programming Learning based on knowledge Graph 53

Intelligent Tutoring for Large-Scale Personalized Programmin...

引用

53rd IEEE ASEE Frontiers in Education International conference, FIE 2023

作者： Li, Ying Qiu, Jincheng Yang, Runze Zhu, Tongyu Sheng, Hao Gui, ShiJie Liang, Yu School of Computer Science and Engineering Beihang University Beijing China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

ISBN: (纸本)9798350336429

The development of learning analytics technology has created the necessary conditions for the implementation of large-scale personalized education. By taking advantage of the current advancements available, we can adopt a deep integration of 'artificial intelligence + education', take educational big data as the research object, aim at achieving precise teaching, and use cognitive diagnosis as a means to construct an intelligent guidance model. The model will conform to a learner's cognitive patterns and curriculum characteristics to meet his personalized learning needs under different knowledge states. Firstly, use knowledge map and abstract syntax trees to extract the characteristics of teaching objectives from questions and standard programing codes. Secondly, Use 'target characteristics - learning characteristics - learning sequence - learning effect' cognitive diagnosis model to assess student achievement in learning. Finally, according to the correlation between knowledge points, the initial learning path consisting of all relevant knowledge points is identified from a knowledge map. And the most optimal learning path is selected based on the importance of knowledge points and student's learning situation. There are three innovation points in this paper. First, the traditional method which only focuses on discovering the one-dimensional knowledge features implicated in the questions, is extended to construct a knowledge-ability multidimensional feature matrix. Secondly, it optimized static cognitive diagnosis into dynamic cognitive arbitrariness, which has a modeling capability of dynamic temporal sequence. Thirdly, it regarded the correlation between knowledge points, and the importance of knowledge points as the heuristic conditions for constructing a learning path. © 2023 IEEE.

关键词： Cognitive Diagnosis Intelligent Guidance Model knowledge Graph Personalized Learning Path

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Bug Localization in Game software engineering: Evolving Simulations to Locate Bugs in software Models of Video Games 22

Bug Localization in Game Software Engineering: Evolving Simu...

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ACM/IEEE 25th International conference on Model Driven engineering Languages and Systems (MODELS)

作者： Casamayor, Rodrigo Arcega, Lorena Perez, Francisca Cetina, Carlos Univ San Jorge Escuela Arquitectura & Tecnol Zaragoza Spain

ISBN: (纸本)9781450394666

Video games have characteristics that differentiate their development and maintenance from classic software development and maintenance. These differences have led to the coining of the term Game software engineering to name the emerging subfield that intersects software engineering and video games. One of these differences is that video game developers perceive more difficulties than other non-game developers when it comes to locating bugs. Our work proposes a novel way to locate bugs in video games by means of evolving simulations. As the baseline, we have chosen BLiMEA, which targets classic software engineering and uses bug reports and the defect localization principle to locate bugs. We also include Random Search as a sanity check in the evaluation. We evaluate the approaches in a commercial video game (Kromaia). The results for F-measure range from 46.80%. to 70.28% for five types of bugs. Our approach improved the results of the baseline by 20.29% in F-measure. To the best of our knowledge, this is the first approach that is designed specifically for bug localization in video games. A focus group with professional video game developers has confirmed the acceptance of our approach. Our approach opens a new research direction for bug localization for both game software engineering and possibly classic software engineering.

关键词： Bug Localization Video Games Search-based software engineering Model-Driven engineering

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Minimizing Inconsistency in Pairwise Comparison Matrices Using Genetic Algorithm 59

Minimizing Inconsistency in Pairwise Comparison Matrices Usi...

引用

59th Annual conference on Information Sciences and Systems, CISS 2025

作者： Sayadi, Atiyeh Shajari, Baran Janicki, Ryszard McMaster University Department of Computing and Software Hamilton Canada

ISBN: (纸本)9798331513269

This paper introduces a novel approach to reduce inconsistencies in pairwise comparison matrices using a genetic algorithm inspired by the process of natural selection. The method applies a distance-based inconsistency index as the fitness function within an evolutionary process. Through experiments, we show that our genetic algorithm greatly reduces inconsistencies over generations. Smaller matrices quickly become consistent in just a few generations, showing the method's effectiveness and efficiency for different scenarios and use cases. Larger matrices, however, require more generations to reach the acceptable consistency level. The algorithm works well for different matrix sizes, adapting effectively to various challenging situations. © 2025 IEEE.

关键词： Genetic algorithms

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Impact of Deep Feature Synthesis on Deep Learning in Electronic Transaction Fraud Detection 3

Impact of Deep Feature Synthesis on Deep Learning in Electro...

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3rd IEEE International conference on software engineering and Artificial Intelligence, SEAI 2023

作者： Uddin, Mohammed Najim Azad, Salahuddin Hossain, Md Rahat Chugh, Ritesh CQUniversity School of Engineering and Technology Rockhampton Australia CQUniversity School of Engineering and Technology Melbourne Australia

ISBN: (纸本)9798350337952

Electronic transaction fraud has been a severe threat in recent years, causing substantial financial losses and devaluing the reputation of financial institutions. Various machine learning and deep learning models have been cited in the literature to detect electronic transaction fraud effectively. Feature selection is crucial in enhancing the performance of a machine learning model. Automatic feature engineering techniques outweigh the manual feature selection process regarding the time spent and adapting to the changing nature of the dataset. However, to the best of our knowledge, the impact of state-of-the-art feature selection techniques, such as Deep Feature Synthesis (DFS), on the performance of deep learning-based electronic transaction fraud detection has not been thoroughly studied. Our study uses the DFS algorithm to generate features automatically from the labelled credit card transaction dataset collected from open-source sites. The data is fed into a baseline deep learning model, i.e., Convolutional Neural Networks (CNN), in two separate cases- with DFS and without DFS. A comparison of results from both cases shows that CNN with DFS outperforms the standalone CNN significantly in terms of accuracy, precision, recall, and F1 scores. © 2023 IEEE.

关键词： Convolutional neural networks

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Feature Selection in High-Dimensional Set-Valued Decision System 18

Feature Selection in High-Dimensional Set-Valued Decision Sy...

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18th International conference on Intelligent Systems and knowledge engineering, ISKE 2023

作者： Wu, Zhengjiang Li, Ruimin Lian, Tao Wu, Xuyang School of Software Henan Polytechnic University Jiaozuo China School of Computer Science and Technology Henan Polytechnic University Jiaozuo China

ISBN: (纸本)9798350318401

In the decision system, the lower approximation set keeps expanding with adding features dynamically. But when enough features are added, the lower approximation set stabilizes. This provides a criterion for feature selection in high-dimensional decision set. Semi-monolayer covering rough set is an approximate representation of uncertain knowledge in set-valued decision system. This paper proposes a feature selection algorithm for high-dimensional set-valued decision system based on incremental update theory (abbr. SMCDA). Specifically, the trend of lower DA0 approximation set under the variation of features in the set-value information system is described, and the incremental limit of the lower DA0 approximation set is illustrated. The incremental algorithm of the lower DA0 approximation under the variation of features is designed. Next, the lower DA0 approximation set is continuously updated using the incremental algorithm until stable. At this point, the already added features are the result of feature selection for SMCDA. Experimental results show that the SMCDA algorithm has a good feature reduction effect. © 2023 IEEE.

关键词： Monolayers

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Ridesharing in Rail-Freight Transport and Use of Digital Aggregator: Prospects and Difficulties - A Developer's Perspective 15

Ridesharing in Rail-Freight Transport and Use of Digital Agg...

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15th International conference on software, knowledge, Information Management and Applications, SKIMA 2023

作者： Ahmed, Md Shakil Koirala, Ravi Chandra Dahal, Keshav Prokhorchenko, Andrii School of Computing Engineering and Physical Sciences University of the West of Scotland Glasgow United Kingdom Ukrainian State University of Railway Transport Operational Work Management Kharkiv Ukraine

ISBN: (纸本)9798350316551

The adoption of ridesharing principles, inspired by the Uber model, in road transport has presented a promising perspective for cost and time reduction. Particularly within the expanding urban transport networks, this concept has significantly enhanced passenger comfort. The implementation of an aggregator, a software program that gathers data from various sources and consolidates it based on user requirements or history, facilitates swift decision-making across different interfaces. This service model is employed in the context of rail-freight transport with ridesharing. The investigation aims to comprehend how an aggregator in rail-freight ridesharing can modernize freight operations, leading to cost and time savings and making the overall business model appealing to stakeholders. © 2023 IEEE.

关键词： Decision making

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Leveraging knowledge Graphs for Personalized Internship Recommendations: A Case Study on software engineering Courses

Leveraging Knowledge Graphs for Personalized Internship Reco...

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Cybernetics and Innovations (ICCI), International conference on

作者： Supitsara Seetaworn Phuree Phenhiran Perapard Ngokpol Montira Innoy Sorayuth Ingboon Tharathon Utasri Akkharawoot Takhom Faculty of Engineering Thammasat School of Engineering Pathum Thani Thailand

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

Internships play a vital role in bridging academic learning with industry requirements. This paper presents a knowledge graph-based framework for generating personalized internship recommendations by integrating course syllabi, student competencies, and job descriptions. The approach leverages structured knowledge representation and graph traversal to dynamically match students' academic progress with relevant opportunities. Additionally, Large Language Models (LLMs) are employed for entity extraction to enhance recommendation accuracy. A case study involving software engineering subjects within a Programme of Innovative engineering demonstrates the framework's ability to provide tailored internship suggestions and identify skill gaps. The results indicate that knowledge graphs offer a scalable and adaptable solution for aligning academic curricula with evolving industry needs.

关键词： Industries Technological innovation Accuracy Large language models knowledge graphs Cybernetics software engineering

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A software Failure Mode Analysis Method based on Test knowledge Graph 9

A Software Failure Mode Analysis Method Based on Test Knowle...

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9th International conference on Dependable Systems and Their Applications, DSA 2022

作者： Yang, Wansheng Hu, Chi Ma, Siyou Institute of Computer Application China Academy of Engineering Physics Sichuan Mianyang China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

software failure mode analysis (SFMA) is an important process for analyzing failure propagation by identifying possible software failure points, and then summarizing software failure modes so as to improve software quality. However, the traditional SFMA is carried out mainly by software safety analysis method, which possesses heavy workload, low efficiency, and over-dependence on the experience of safety analysts. In this paper, a SFMA is proposed method based on knowledge graph. It collects the failure factors, failure mechanism and failure performance of the software under test into a fixed Resource Description Framework (RDF) triple store by establishing a knowledge graph of data assets in the software testing process. Then, the method of text clustering is applied to realize the automatic analysis of software failure mode. This method mainly covers three steps: 1) Extract failure factors - the RDF data of the descriptive text of various entity nodes related to software defect reports from the software test knowledge graph;2) Combine the RDF triples into a normalizing text in a unified format, an easy way of further natural language processing;3) Generate software defects with similar characteristics and similar failure behaviors into 'defect modes' or 'failure modes' by using clustering algorithms. The empirical study has proven that the SFMA proposed in this paper presents a novel, feasible solution for processing extremely large-scale data. © 2022 IEEE.

关键词： Failure modes

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Residential Electricity Power Forecasting using GRU-based CNN and SAE Ensembler

Residential Electricity Power Forecasting using GRU-based CN...

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2023 IEEE International conference on Emerging Trends in engineering, Sciences and Technology, ICES and T 2023

作者： Raza, Ali Ali, Usman Ali, Zain Saeed, Muhammad Hamza Khan, Raja Jalees Ul Hussen Ayub, Nasir COMSATS University Islamabad Department of Computer Science WahCantt Campus Pakistan Riphah International University Computing Department Faisalabad Pakistan HITEC University Department of Electrical Engineering Taxila Pakistan National Textile University Department of Computer Science Faisalabad Pakistan Superior University Gold Campus Department of Software Engineering Lahore Pakistan Capital University of Science and Technology Department of Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665455602

Smart grids play an important role to resolves issues related to electricity. Electrical load forecasting can be performed through smart grids to acquire knowledge about the electrical load that will be needed in the future. A combination of Stacked Autoencoding (SAE) and Gated Recurrent Unit (GRU) and a combination of Convolutional Neural Network (CNN), Grey Wolf Optimization (GWO), and GRU are proposed in this paper. Firstly, the historical load data is preprocessed and then preprocessed data is given to the feature selection and extraction methods to measure the importance of each feature. Features that have highest values are selected and input to the proposed classifiers. According to the results the proposed CNN-GRU-GWO gave the highest accuracy rate of 96.31%. This application of this article will help the utility purchase the exact amount of electricity from the grid, which will lead to grid stability. © 2023 IEEE.

关键词： Smart power grids

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Chinese Relation Extraction with Bi-directional Context-based Lattice LSTM 16th

Chinese Relation Extraction with Bi-directional Context-Base...

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16th International conference on knowledge Science, engineering and Management (KSEM)

作者： Ding, Chengyi Wu, Lianwei Liu, Pusheng Wang, Linyong Northwestern Polytech Univ Sch Software Xian Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China

ISBN: (纸本)9783031402883;9783031402890

Chinese entity relation extraction (Chinese RE) is a crucial task for various NLP applications. It aims to automatically extract relationships between entities in Chinese texts, thereby enhancing the accuracy of natural language understanding. Although existing hybrid methods can overcome some of the shortcomings of character-based and word-based methods, they still suffer from polysemy ambiguity, which results in inaccuracy when representing the relationships between entities in text. To address the issue, we propose a Bi-directional Contextbased Lattice (BC-Lattice) model for Chinese RE task. In detail, our BC-Lattice consists of: (1) A context-based polysemy weighting (CPW) module allocates weights to multiple senses of polysemous words from external knowledge base by modeling context-level information, thus obtaining more accurate representations of polysemous words;(2) A cross-attention semantic interaction-enhanced (CSI) classifier promotes exchange of semantic information between hidden states from forward and backward perspectives for more comprehensive representations of relation types. In experiments conducted on two public datasets from distinct domains, our method yields improved F1 score by up to 3.17%.

关键词： Information extraction Relation extraction NLP Polysemy disambiguation Lattice architecture External knowledge

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