{"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}
{"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}
{"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}
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}
{"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}
{"title":"Deepening Research and Upgrading of Configuration Software ViGET","authors":"Yawei Chang, Weipeng Liu, Huan Hu, Haibin Liu","doi":"10.1145/3584871.3584881","DOIUrl":"https://doi.org/10.1145/3584871.3584881","url":null,"abstract":"ViGET is a Programmable Logic Controller (PLC) programming software for HVDC engineering. It is used to complete the editing, compiling, on-line and monitoring of control and protection programs in HVDC engineering. In view of the shortcomings of ViGET software and the user needs, this paper completes the reconstruction of the software framework and the embedding of the editor through Visual Studio Shell and Visual Studio Package technology, and completes the optimization and upgrading of the CFC graphical editor, version management, On-line and other functions. Improve the usability of ViGET software and the overall operation efficiency of the program, ViGET V2.0 has been released to provide better services for secondary engineering development.","PeriodicalId":173315,"journal":{"name":"Proceedings of the 2023 6th International Conference on Software Engineering and Information Management","volume":"25 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":"124891056","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}
{"title":"Evaluation of Price Prediction Models for Cryptocurrencies based on convolutional neural networks trained on Candlestick Charts","authors":"Tomohiko Hagio, Mutsuo Sano","doi":"10.1145/3584871.3584875","DOIUrl":"https://doi.org/10.1145/3584871.3584875","url":null,"abstract":"In the past few years, there has been a growing interest in cryptocurrencies. However, the risk of incurring losses is high due to their large price fluctuations. Therefore, we want to reduce this risk by predicting the rise and fall of their prices. In this study, we use a convolutional neural network model trained on candlestick charts to make price predictions. In this experiment, the system was trained on the image pattern data of a set of five candlesticks, and predictions were made on whether the price would go up or down. The novelty of this research is that we apply the stock price prediction method using visual candlestick patterns, which has been empirically judged, to virtual currency prediction based on their visual pattern transition model with deep learning. The model trained on the data from 1-minute intervals gave the best results, with a predictive accuracy of 58.69% and a bankruptcy probability of only 1.678%.","PeriodicalId":173315,"journal":{"name":"Proceedings of the 2023 6th International Conference on Software Engineering and Information Management","volume":"63 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":"121802381","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}
{"title":"Application of Customer Clustering and Analytical Hierarchy Process in Optimizing Delivery of Government Science and Technology Interventions","authors":"Giselle Eve O. Siladan, M. Samonte","doi":"10.1145/3584871.3584898","DOIUrl":"https://doi.org/10.1145/3584871.3584898","url":null,"abstract":"Prioritization helps entities provide services to the socially disadvantaged while efficiently using their resources, especially when working within a limited budget. It is, therefore, crucial for an organization to prioritize initiatives to become effective in its dedication to service. This article focuses on using the Analytical Hierarchy Process (AHP) in evaluating five alternatives to provide a picture of the clients served by the Department of Science and Technology Regional Office XII (DOST XII). This study involved eight decision-makers in evaluating four criteria after considering the results of the customer clustering. Results showed that location, necessity, appropriateness, and cost are the key factors considered when prioritizing.","PeriodicalId":173315,"journal":{"name":"Proceedings of the 2023 6th International Conference on Software Engineering and Information Management","volume":"8 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":"127940081","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}
Yoni Nicolas-Rojas, W. Auccahuasi, Sandra Meza, Tamara Pando-Eszurra, Oscar Linares, K. Urbano
{"title":"Methodology for the execution of programs based on different programming languages","authors":"Yoni Nicolas-Rojas, W. Auccahuasi, Sandra Meza, Tamara Pando-Eszurra, Oscar Linares, K. Urbano","doi":"10.1145/3584871.3584882","DOIUrl":"https://doi.org/10.1145/3584871.3584882","url":null,"abstract":"Abstract: Currently, in the programming ecosystems, there are different programming languages, each of the languages work with their libraries dedicated to special tasks, among the most used languages are Python, R, Matlab, C, C++ among others, in this work, we demonstrate a method to perform work with the use of different programming languages, in order to exploit the benefits of each one, all of them in a single development environment, which takes advantage of the available hardware that we can have in the workstations, as is the case of CPUs and GPUs that may have. As a result we present the architecture and programming modes that can be developed with each language, the programming mode considered is to perform partial jobs, defined in taking the file to work, perform the necessary processes, then store them in new files so that it can be worked by another language, the method can be applied in multiple tasks mainly in those that can work with matrices and vectors.","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":"129309565","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}
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}