2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)最新文献

A Node Importance Assessment Method for Complex Networks Based on the Neighborhood Influence 基于邻域影响的复杂网络节点重要性评估方法

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217612

Yuhao Liu, Shaofei Chen, Longfei Wang

引用次数: 0

The Design and Practice of the OpenKylin Build and Management System OpenKylin构建与管理系统的设计与实践

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217673

Long Peng, Xiaodong Liu, Jie Yu, Xia Liao, N. Tashi, Xinyan Xu, Wenzhu Wang, Duojie Rengzeng, Mao Zhou

引用次数: 0

A Software Credibility Framework with a Decision Making Component for Patient Portals 一个带有决策组件的病人门户软件可信度框架

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217572

M. A. Rawlins, Xuejun Yu

引用次数: 0

A Chinese Knowledge Graph Q&A System Based on Dense Relationship Retrieval 基于密集关系检索的中文知识图谱问答系统

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217649

Kaifeng Guo, Ri Huang

{"title":"A Chinese Knowledge Graph Q&A System Based on Dense Relationship Retrieval","authors":"Kaifeng Guo, Ri Huang","doi":"10.1109/SEAI59139.2023.10217649","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217649","url":null,"abstract":"Currently, most knowledge graph question answering (KGQA) systems need to retrieve all entities when dealing with complex problems involving multiple entities and relationships, which results in high time complexity and resource consumption. The response time of KGQA systems is an important indicator for evaluating their performance. To address this issue, this paper proposes a Chinese KGQA system based on dense relationship retrieval. The system uses the Faiss index mechanism for vector similarity retrieval, quickly extracts the top K relationships from the pre-built vector relationship library, and constructs paths to reduce a large number of irrelevant semantic paths in most KGQA tasks, thereby improving the time cost and computational resource overhead caused by the exponential growth of path numbers in second-order and higher-order questions. By using this method, the response time of the QA system can be shortened to within 1 second with minimal loss of accuracy. This paper combines pre-training models to complete tasks such as text semantic similarity and entity mention recognition, and achieves an average F1 value of 71.3% on the CCKS2019-CKBQA test set. Comparing with other systems demonstrates the superiority of our method in terms of accuracy and efficiency.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127229544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Meta-Heuristics for Solving Curve Fitting Problems in Optical-Diffraction Based Image Depth Reconstruction 基于光学衍射的图像深度重建中曲线拟合问题的元启发式求解

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217365

Qianyao Zhu, Kaizhou Gao, Yongjun Liu

引用次数: 0

Attention Based Graph Neural Networks 基于注意力的图神经网络

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217656

Md Ibrahim Khalil, Muhammad Affan Abbas

{"title":"Attention Based Graph Neural Networks","authors":"Md Ibrahim Khalil, Muhammad Affan Abbas","doi":"10.1109/SEAI59139.2023.10217656","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217656","url":null,"abstract":"The performance of graph neural networks (GNNs) in a variety of graph-related tasks, such as node categorization, has been remarkably good. Existing GNN models, especially when working with big and sparse graphs, are constrained in how well they can capture complicated graph topologies. In order to overcome this issue, we incorporate an attention mechanism into the GNN design in this study. During the message-passing phase, our proposed model, Attention-Based Graph Neural Networks (AB-GNN), uses a learned attention mechanism to differentially weight the significance of surrounding nodes. Using numerous benchmark datasets for node classification, we test the performance of the AB-GNN and demonstrate that it outperforms current state-of-the-art GNN models. Our tests specifically show that AB-GNN improves accuracy by up to 1% in comparison to the top baseline model. According to our findings, the attention mechanism enhances the model's capacity to detect critical aspects in the graph, resulting in more precise node classification on Cora and CiteSeer datasets in our case. Comprehensively, our work demonstrates the potential of attention mechanisms to enhance the functionality of GNN models and offers directions for further study in this field.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129203090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Highlight Generation Method for Game Commentary Using Acoustic Features 使用声音特征的游戏解说高光生成方法

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217476

Taiki Yoshida, Tomomi Ogawa

引用次数: 0

Wordle Results Distribution Prediction Model and Difficulty Classification Model Using Word Linguistic Properties 基于词语言属性的世界结果分布预测模型和难度分类模型

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217669

Zhuo Yang, Ruijie Yang, Yuyan Wang

{"title":"Wordle Results Distribution Prediction Model and Difficulty Classification Model Using Word Linguistic Properties","authors":"Zhuo Yang, Ruijie Yang, Yuyan Wang","doi":"10.1109/SEAI59139.2023.10217669","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217669","url":null,"abstract":"Wordle is a word guessing game offered daily by the New York Times. The aim of our study is to use selected word linguistic properties to predict the percentages of successfully guessing a word in one to six tries or failing and classify words by difficulty. A prediction model was established based on stepwise linear regression and normal distribution probability density function (PDF). The percentages of a word were predicted using stepwise linear regression. The Jarque Bera test using MATLAB function indicated that 97.5% of word attempt distributions followed a corresponding normal distribution. Thus, these seven percentages were used as independent variables to predict the mean and variance of the corresponding normal distribution. The PDF was then obtained to correct percentages. Additionally, a word classification model was also established based on K-Means and support vector machines (SVM) to classify words by difficulty. The elbow method was used to divide words into two categories. The classification model was trained based on SVM. The prediction results showed that the sum of predicted percentages for each word fell between 99 and 101, which was superior to performing a single stepwise linear regression. The average root mean square error per word was 3.22. The classification model results showed that, based on 5-fold cross validation, area under curve, accuracy, recall and precision were 0.83, 77.1%, 75.16% and 73.72%, respectively, indicating the reliability of our method.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121607843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Promoting Pre-service Teachers’ AI-Supported Application of Self-Efficacy 促进职前教师ai支持下的自我效能感应用

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217712

C. Chou, Tsu-Chuan Shen, Tsu-Chi Shen, C. Shen, Tzu-Ling Liu

引用次数: 0

WeatherPon: A Weather and Machine Learning-Based Coupon Recommendation Mechanism in Digital Marketing WeatherPon:数字营销中基于天气和机器学习的优惠券推荐机制

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217467

Liyuan Liu, Meng Han

引用次数: 0