Proceedings of the 2023 6th International Conference on Software Engineering and Information Management最新文献_第4页

How to Find Social Robots exactly? 如何找到社交机器人?

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584873

Jianwei Ding, Zhouguo Chen

{"title":"How to Find Social Robots exactly?","authors":"Jianwei Ding, Zhouguo Chen","doi":"10.1145/3584871.3584873","DOIUrl":"https://doi.org/10.1145/3584871.3584873","url":null,"abstract":"With the rapid development of artificial intelligence and natural language processing, there are more and more social robots applied in the social networks such as Twitter, intended to lead public opinion or crawling private information illegally. The problem of detection social robots, which is automated social accounts governed by artificial intelligence software, pretend to be a human user. There are some technologies proposed to detect the social robots automatically applied to the real social network for verification. Hence, conventional social robot detecting technologies proposed before are applied to detect by the account's metadata or account posted tweet content respectively. With the help of pre-trained language model such as BERT, this paper propose a deep neural network model based on contextual long short-term memory (LSTM) architecture named DeepBot, which exploits tweet content and account's metadata features. The architecture of DeepBot contains three phases: (1) it uses the pretrained model such as BERT to extract the embedding vector from the tweet content of the specific account, and (2) it choose more discriminative account metadata to extract a metadata vector, and then (3) it combines the auxiliary embedding vector and metadata vector into decoder layer to train a detecting model. What's more, in this paper, we review the labelling social robots datasets proposed in public, and get a mixture datasets of labelling social datasets to verify and compare the experimental results of our proposed DeepBot and other conventional methods. We also present empirical results of DeepBot and our ongoing experimentation with it, as we have gained experience applying it to the mixture labeling social robot dataset, including over 10000 accounts. The experimental results show that DeepBot outperforms previous state-of-the-art methods, with leveraging a small and interpretable set of features.","PeriodicalId":173315,"journal":{"name":"Proceedings of the 2023 6th International Conference on Software Engineering and Information Management","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128137433","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: 1

Study on the influence of social network for college students' mental health through big data technology 利用大数据技术研究社交网络对大学生心理健康的影响

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584892

Lei Hong

{"title":"Study on the influence of social network for college students' mental health through big data technology","authors":"Lei Hong","doi":"10.1145/3584871.3584892","DOIUrl":"https://doi.org/10.1145/3584871.3584892","url":null,"abstract":"At present, mental issues have become the main factor leading to college students' suicide, crime and other malignant events. As the key process in mental health education, it is vital to utilize technology to predict psychological problems in advance for the healthy growth of college students. In the past, the commonly used psychological crisis screening methods only consider individual psychological scale, a great quantity of data from social network has not been analyzed in depth. Thanks to data mining technologies, it is possible to build psychological portrait for each student to deeply excavate some hidden information and knowledge. Based on the survey and behavior research of college students' Internet social networking in the era of Internet plus, this paper analyses the influence of social network on college students' mental health according to the law of college students' psychological development. Moreover, this paper also explores the mental health education strategy in the behavior guidance of social network, providing theoretical support for the research and work of ideological and political education in colleges and universities.","PeriodicalId":173315,"journal":{"name":"Proceedings of the 2023 6th International Conference on Software Engineering and Information Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130407247","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

An Artificial Neural Network Model based on Binary Particle Swarm Optimization for enhancing the efficiency of Software Defect Prediction 基于二元粒子群优化的人工神经网络模型提高了软件缺陷预测的效率

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584885

R. Malhotra, Sonali Chawla, Anjali Sharma

{"title":"An Artificial Neural Network Model based on Binary Particle Swarm Optimization for enhancing the efficiency of Software Defect Prediction","authors":"R. Malhotra, Sonali Chawla, Anjali Sharma","doi":"10.1145/3584871.3584885","DOIUrl":"https://doi.org/10.1145/3584871.3584885","url":null,"abstract":"With the rise in the growth of the software industry, it is essential to identify software defects in earlier stages to save costs and improve the efficiency of the software development lifecycle process. We have devised a hybrid software defect prediction (SDP) model that integrates Binary Particle Swarm Optimization (Binary PSO), Synthetic Minority Oversampling Technique (SMOTE), and Artificial Neural Network (ANN). BPSO is applied as a wrapper feature selection process utilizing AUC as a fitness function, SMOTE handles the dataset imbalance, and ANN is used as a classification algorithm for predicting software defects. We analyze the proposed BPSO-SMOTE-ANN model's predictive capability using the AUC and G-mean performance metrics. The proposed hybrid model is found helpful in predicting software defects. The statistical results suggest the enhanced performance of the proposed hybrid model concerning AUC and G-mean values. Also, the hybrid model was found to be competitive with other machine learning(ML) algorithms in determining software defects.","PeriodicalId":173315,"journal":{"name":"Proceedings of the 2023 6th International Conference on Software Engineering and Information Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123746746","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

Effectiveness of Hybrid Learning as Technology Based Learning Model in the New Normal Era 新常态下基于技术的混合学习模式的有效性

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584899

Dian Anggraini Kusumajati, R. Chairiyani, F. A. Kusumastuti, N. Setianingsih

{"title":"Effectiveness of Hybrid Learning as Technology Based Learning Model in the New Normal Era","authors":"Dian Anggraini Kusumajati, R. Chairiyani, F. A. Kusumastuti, N. Setianingsih","doi":"10.1145/3584871.3584899","DOIUrl":"https://doi.org/10.1145/3584871.3584899","url":null,"abstract":"Hybrid learning (HL) has become one of the learning models used when entering the new normal. Although various studies have measured HL, the effectiveness of HL model usage remains a concern for universities, especially Bina Nusantara University (BINUS) Jakarta. The main problem is that the use of hybrid learning conducted by BINUS is new. The purpose of this study was to determine the effectiveness of hybrid learning as technology-based learning by students in BINUS Jakarta. The population in this study was active students of BINUS Jakarta, totaling 127 students. The technique of collecting the sample was using random sampling. The statistical method uses Pearson correlation analysis and description. Based on the descriptive analysis the results found that hybrid learning was reflected in the role of technology (helps students in finding the information needed for learning by using gadgets and computers), campus commitment (providing internet facilities, Wi-Fi, computers, audio connectors, television, cameras, internet, and InFocus in each class), learning activities (presentations and discussions, independent assignments, group assignments in the form of projects (problem based), and final tests), and student readiness (previously using a blended learning process). There are 81.1% of students in BINUS Jakarta who feel that the hybrid learning system is effective in the learning process carried out during the Covid-19 condition, at the same time preparing for the new normal era. Hybrid learning, which is most effective for students in BINUS Jakarta, can be felt when there is the role of technology, campus commitment, learning activities, and students' readiness to participate in learning.","PeriodicalId":173315,"journal":{"name":"Proceedings of the 2023 6th International Conference on Software Engineering and Information Management","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121117048","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

Interpretable Fake News Detection on Social Media 社交媒体上可解读的假新闻检测

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584913

Xiwei Xu, Ke Qin

{"title":"Interpretable Fake News Detection on Social Media","authors":"Xiwei Xu, Ke Qin","doi":"10.1145/3584871.3584913","DOIUrl":"https://doi.org/10.1145/3584871.3584913","url":null,"abstract":"With the development of information technology, public opinion can quickly spread to all over the world, permeate every corner of social life, and have a great impact on human's lives. Extracted from large-scale and multi-mode social media, user-generated information is anonymous and noisy. It is found that users' social interaction helps to detect fake news. At present, most methods focus on effectively detecting fake news with potential characteristics, but most models are lack of interpretability. Therefore, this paper studies the interpretable detection of fake information in public social media platform, and proposes a model based on in-depth sentence-comment interactive reasoning network. The model uses fake information content and user comments to capture the most worth checking information sentences from user comments, in order to detect fake information and provide some explanation. This paper solves the following challenges: (1) how to detect fake news while improving the detection performance and interpretability; (2) how to extract the correlation between false content and user comments in the social media platform.","PeriodicalId":173315,"journal":{"name":"Proceedings of the 2023 6th International Conference on Software Engineering and Information Management","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133215951","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

Deepening Research and Upgrading of Configuration Software ViGET 组态软件ViGET的深化研究与升级

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584881

Yawei Chang, Weipeng Liu, Huan Hu, Haibin Liu

引用次数: 0

Evaluation of Price Prediction Models for Cryptocurrencies based on convolutional neural networks trained on Candlestick Charts 基于烛台图训练的卷积神经网络的加密货币价格预测模型评估

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584875

Tomohiko Hagio, Mutsuo Sano

引用次数: 0

Application of Customer Clustering and Analytical Hierarchy Process in Optimizing Delivery of Government Science and Technology Interventions 客户聚类与层次分析法在政府科技干预优化交付中的应用

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584898

Giselle Eve O. Siladan, M. Samonte

引用次数: 0

Methodology for the execution of programs based on different programming languages 基于不同编程语言的程序执行方法

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584882

Yoni Nicolas-Rojas, W. Auccahuasi, Sandra Meza, Tamara Pando-Eszurra, Oscar Linares, K. Urbano

引用次数: 0

An Explainable Machine Learning-based Prediction Model for In-hospital Mortality in Acute Myocardial Infarction Patients with Typical Chest Pain 典型胸痛急性心肌梗死患者住院死亡率的可解释机器学习预测模型

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584877

Huilin Zheng, Malik Muhammad Waqar, Saba Arif, Syed Waseem Abbas Sherazi, Sang Hyeok Son, Jong Yun Lee

{"title":"An Explainable Machine Learning-based Prediction Model for In-hospital Mortality in Acute Myocardial Infarction Patients with Typical Chest Pain","authors":"Huilin Zheng, Malik Muhammad Waqar, Saba Arif, Syed Waseem Abbas Sherazi, Sang Hyeok Son, Jong Yun Lee","doi":"10.1145/3584871.3584877","DOIUrl":"https://doi.org/10.1145/3584871.3584877","url":null,"abstract":"Acute myocardial infarction (AMI) is the leading cause of hospital admissions and death all over the world and chest pain is the most common presenting complaint of AMI. Therefore, this paper proposes a machine learning (ML)-based prediction model for the in-hospital mortality in AMI patients with typical chest pain. To understand the principle of the black-box prediction model, a Shapley additive explanations (SHAP) method is applied to the ML-based prediction model. The experimental framework mainly includes three steps. First, we extract the experimental data from the Korea Acute Myocardial Infarction Registry National Institutes of Health (KAMIR-NIH), and then preprocess the selected data with missing value imputation, data normalization, and splitting. Thereafter, two kinds of data sampling methods such as synthetic minority oversampling techniques (SMOTE) and Adaptive Synthetic (ADASYN), are applied to handle the class imbalance problem on the experimental data. Second, different ML models such as decision tree, random forest, extreme gradient boosting (XGBoost), support vector machine, and logistic regression, are trained and evaluated on the preprocessed AMI patient data. Finally, the SHAP method is used to explain the best ML-based prediction model. The experimental results showed that the logistic regression with the ADASYN approach achieved the highest performance. Moreover, the SHAP technique enhanced the transparency of the ML model and can be a good reference for doctors to support their decisions in real life.","PeriodicalId":173315,"journal":{"name":"Proceedings of the 2023 6th International Conference on Software Engineering and Information Management","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126900949","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