Proceedings of the 3rd International Conference on Software Engineering and Information Management最新文献

{"title":"Anti-disturbance Control Based On Uncertain Data","authors":"P. Cheng, F. Pan, Yanyan Yin, Lingshuang Kong, Song Wang","doi":"10.1145/3378936.3378975","DOIUrl":"https://doi.org/10.1145/3378936.3378975","url":null,"abstract":"In this paper, we describe a novel method for constructing probabilistic robust disturbance rejection control for systems contain uncertain data in which a scenario optimization method is used to deal with the nonlinear and unbounded uncertainties. For anti-disturbance, a reduced order disturbance observer is considered and a state-feedback controller is designed. Sufficient conditions are presented to ensure that the resulting closed-loop system is stable and a prescribed H∞ performance index is satisfied. A numerical example is presented to illustrate the effectiveness of the techniques proposed and analyzed.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133879890","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":"A Predictive Analytics Approach in Determining the Predictors of Student Attrition in the Higher Education Institutions in the Philippines","authors":"Markdy Y. Orong, Roseclaremath A. Caroro, Geraldine d. Durias, J. A. Cabrera, Herwina Lonzon, Gretel T. Ricalde","doi":"10.1145/3378936.3378956","DOIUrl":"https://doi.org/10.1145/3378936.3378956","url":null,"abstract":"The paper identified the predictors of student attrition in the Higher Education Institution (HEI) through predictive analytics approach. The prediction model used in the study includes variable optimization through Genetic Algorithm (GA) and decision tree generation phase through C4.5 algorithm. The college student leavers' data from one of the Higher Education in the Philippines from the school year 2008-2009 until the school year 2018-2019 was used as datasets of the study. Out of forty identified reasons for leaving as variables, there were nine (9) identified predictors of student attrition. Through the identified predictors, administrators of educational institutions may design intervention plans related to the student attrition.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134013985","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":"Internet Addiction and Mental Health Prediction Using Ensemble Learning Based on Web Browsing History","authors":"B. Purwandari, Wayan Surya Wibawa, Nilam Fitriah, Mellia Christia, D. R. Bintari","doi":"10.1145/3378936.3378947","DOIUrl":"https://doi.org/10.1145/3378936.3378947","url":null,"abstract":"The widespread prevalence of Web browsing may lead to Internet Addiction Disorder (IAD), which impacts negatively on Web users' general health. Young people who are very active online are prone to suffer from IAD. It negatively affects their academic performance and social lives. The earlier the detection, the better the treatment. Therefore, this pilot study aimed to predict IAD among the youth to encourage early treatment. The sample included 30 undergraduate students at Universitas Indonesia (UI). Their Web browsing histories for five weeks were recorded from their laptops and analyzed using the support vector machine (SVM) with radial basis function (RBF) kernel as a machine learning method for prediction. The results were subsequently compared using ensemble learning, such as random forest (RF) and gradient boosting (GB). It was then matched with respondents' responses to an Internet Addiction Test (IAT) questionnaire, which measures IAD levels. Respondents' general health data were collected with the 12-item General Health Questionnaire (GHQ-12). Features from Web browsing histories were extracted to classify activities in five types. These are information retrieval (IR), instant messaging (IM), social networking services (SNS), leisure, and online shopping (OS). The extracted features became input to classify participants' IAD. The results were compared with their IAD results from the IAT questionnaire. Machine learning was also employed to classify the input into respondents' general health (GH) status, which was matched with their responses to the GHQ-12 questionnaire. The findings revealed that the prediction accuracies were 66.67% for the IAD status and 65.17% for the GH status employing SVM. The precisions for predicting IAD and GH were 63.33% and 44.33%, according to RF. Moreover, the accuracies were 63.33% and 67.17%, according to GB. Results indicated that RF decreased prediction accuracies, but GB was slightly different from SVM. For each classifier, IAD status was predicted more accurately than GH status. An alternative to improve the outcomes is gaining data from the Internet firewall instead of the Web browsing history from users' laptops. It can provide richer and more realistic records of Web access, which are collected from any devices connected to the university's computer networks. However, it requires consent from the participants and authority managing the infrastructure. If each class has a balanced example, we plan to add more features and employ other types of ensemble learning for higher accuracy. Furthermore, performing a multiclass prediction can demonstrate specific IAD severity levels and the class of mental health status, i.e., anxiety and depression.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130275558","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":"Research on Security Location of Malicious Node Filtering for Environmental Monitoring Sensor Networks","authors":"Chaoyang Wang, Mingliang Li, Xuewen Liu, Xuejiao Wu","doi":"10.1145/3378936.3378963","DOIUrl":"https://doi.org/10.1145/3378936.3378963","url":null,"abstract":"In order to solve the problem that the positioning accuracy is reduced due to the difficult filtering of malicious nodes in wireless sensor networks. Aiming at the reason that the traditional DV-HOP positioning algorithm is vulnerable, an improved DV-HOP algorithm is proposed. The algorithm can combine multiple communication radii to reduce the hop error of the anchor node in communication with other nodes, and make the positioning more accurate by the hop-weighted hyperbolic algorithm. Further, consistency checking mechanism between packet information aggregator implemented filtering malicious nodes. The simulation results show that the improved DV-HOP algorithm has significantly higher positioning accuracy than the traditional DV-HOP algorithm under the condition that the detection mechanism filters malicious nodes.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129885538","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":"Weak Sparsity Adaptive Matching Pursuit Algorithm based on Environmental Monitoring Sensor Network Data","authors":"Peipei Zhao, Xuewen Liu, Mingliang Li, J. Ding","doi":"10.1145/3378936.3378961","DOIUrl":"https://doi.org/10.1145/3378936.3378961","url":null,"abstract":"Due to the undetermined signal sparsity in environmental monitoring applications, the compressed sensing reconstruction algorithm with sparsity adaptive characteristics has better application value. In order to improve the reconstruction accuracy of the reconstruction algorithm, this paper proposes a weak sparsity adaptive matching pursuit algorithm. Firstly, the algorithm constructs the candidate set by weak selection, and then introduces the backtracking idea to filter the candidate set atoms and form a support set. In addition, the algorithm applies the idea of variable step size, and selects different step sizes for different iterations to achieve more accurate and complete reconstruction. Simulation experiments show that the improved algorithm proposed in this paper has higher reconstruction accuracy than similar algorithms.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116021861","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":"Software Engineering Wastes - A Perspective of Modern Code Review","authors":"N. Fatima, Sumaira Nazir, S. Chuprat","doi":"10.1145/3378936.3378953","DOIUrl":"https://doi.org/10.1145/3378936.3378953","url":null,"abstract":"Identification and eradication of waste are the principal emphases of lean thinking. Waste is defined as any activity that consumes resources but does not deliver any value to the stakeholder and it can also be demarcated as an impediment to process flow. Lean thinking has been applied in the software engineering domain concerning overall software development, however, still, there is a need to take action regarding waste identification and elimination concerning specific software engineering activities. This paper describes the wastes generated during Modern Code Review (MCR). MCR is a socio-technical software engineering activity and acknowledged as a lightweight process for defect identification, code improvement and software quality enhancement. It involves coordination and communication among multiple software engineers having different personalities, preferences, and technical skills, thus it can generate multiple types of wastes. Therefore, the study has two objectives that are to recognize and report various wastes generated during MCR and to map the identified MCR wastes on the existing software engineering wastes. Systematic Literature Review and grounded theory has been utilized to recognize and produce a unique list of the waste generated during MCR. The identified unique list of MCR wastes and their mapping on existing software engineering wastes are validated through software engineering experts. The study findings report 28 unique wastes out of which 25 wastes map to the existing software engineering wastes. However, 3 wastes such as negative emotions, inequality/biasness and insignificant feedback are not reported in the existing software engineering literature. The study will be useful for researchers to identify the wastes in same context or for other software engineering activities and to provide the strategies to minimize the generation of identified wastes.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131537248","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":"A Transfer Learning Approach for Handwritten Numeral Digit Recognition","authors":"Le Zhang","doi":"10.1145/3378936.3378970","DOIUrl":"https://doi.org/10.1145/3378936.3378970","url":null,"abstract":"Handwritten numeral digit recognition is a classical problem in the field of computer vision, which has a wide range of applications in various fields including financial and post services. The accuracy of handwritten numeral digit recognition has been greatly improved by using deep learning in the past few years. However, deep learning relies on a large amount of training data and time-consuming calculation. In this paper, we adopt a transfer learning approach for handwritten numeral digit recognition and use both the multi-layer perceptron and convolutional neural network models to share the feature extraction process among five handwritten numerical datasets, namely, Tibetan, Arabic, Bangla, Devanagari, and Telugu. We compare the transfer learning scheme with the model based on a single dataset. We find that using the transfer learning method can significantly reduce the training time of the deep learning models, and slightly reduces the recognition accuracy.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130963536","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":"Improving Core Topics Discovery in Semantic Markup Literature: A Combined Approach","authors":"Carlos Montenegro, Rosa Navarrete","doi":"10.1145/3378936.3378939","DOIUrl":"https://doi.org/10.1145/3378936.3378939","url":null,"abstract":"This research configures a corpus of articles related to the aspects being investigated in Semantic Markup, knowledge domain that has evolved and expanded over the last decade and conduct a manual process to identify the Topics being addressed. Then, it is used LDA, an unsupervised probabilistic topic model, and other tools, for automatically recognize the topics of interest within this corpus; this aims to interpret, validate and complement the results manually obtained. The results let us argue that using combined techniques contribute to improving the human expert analysis, and it is helpfully for the discovery of core topics in Semantic Markup Literature.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127027494","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":"A Cooperative Trust Evaluation Scheme for Tactical Wireless Sensor Networks","authors":"Jihun Lim, Dooho Keum, Young-Bae Ko","doi":"10.1145/3378936.3378943","DOIUrl":"https://doi.org/10.1145/3378936.3378943","url":null,"abstract":"In this paper, we address an important issue of trust evaluation for tactical wireless sensor networks, where energy-hungry sensor nodes are deployed in harsh and hostile areas for the purpose of surveillance and reconnaissance. In particular, due to the limited resources of the node, it is of great importance to detect and isolate malicious nodes in an energy-efficient manner. Most of previous work adopt some proactive scanning method, requiring periodical exchange of control messages even in ordinary times and thus significant computational overhead for updating trust values per all nodes over the network. In order to provide more energy efficient evaluation method (while yet preventing any intelligent attacks such as selective forwarding and false positive problem attacks), we propose a novel cooperative trust evaluation scheme in which the initial trust level of nodes measured by on- demand trust rating approach is re-evaluated later on by a root or gateway node based on a pattern analysis. Through a preliminary simulation study using OPNET simulator, we compare our scheme to the existing work and prove that it can achieve the higher detection rate with much less energy consumption.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124637610","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":"Big Data Analyses of ZeroNet Sites for Exploring the New Generation DarkWeb","authors":"Jianwei Ding, Xiaoyu Guo, Zhouguo Chen","doi":"10.1145/3378936.3378981","DOIUrl":"https://doi.org/10.1145/3378936.3378981","url":null,"abstract":"ZeroNet is a new generation typical dark web, which uses the Bitcoin encryption algorithm and BitTorrent technology to build a distributed and censored-resistant communication network. Based on our cumulative studies on the onion router, we present a big data analyses framework for automated multi-categorization of ZeroNet websites to facilitate analyst situational awareness of new content that emerges from this dynamic landscape. Over the last two years, our team has developed a distributed crawling infrastructure called ZeroCrawler that automatically crawls and updates ZeroNet websites in realtime. It stores data into a research repository designed to help better understand ZeroNet's hidden service ecosystem. The analysis component of our framework is called Automated Multi-Categorization Labeling (AMCL), which introduces a three-stage thematic labeling strategy: (1) it learns descriptive and discriminative keywords for different categories, and (2) get a probability distribution of the keywords for different categories, and then (3) uses these terms to map ZeroNet website content to several labels. We also present empirical results of AMCL and our ongoing experimentation with it, as we have gained experience applying it to the entirety of our ZeroNet repository, now over 3000 indexed websites. The experimental results show that AMCL can discover categories on previously unlabeled websites, and we discuss applications of AMCL in supporting various analyses and investigations of the ZeroNet websites.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121784716","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}