Pub Date : 2023-10-13DOI: 10.1142/s219688882340002x
Saori Iwanaga
This study proposes a discrete mathematical Susceptible–Exposed–Preinfectious–Infectious–Recovered (SEPIR) states model for seasonal influenza. In a previous study, focusing on infections by preinfectious people using preexisting data, the author showed that the super-spreading of seasonal influenza occurred before the day that the first patients were discovered (D-day). In addition, when people do not take precautionary measures, the infectivity rate (from preinfected people) was determined as 0.041. After D-day in the community, the implementation of countermeasures was observed to reduce the infectivity rate to 0.002 and 0.013 in working and living spaces, respectively. The number of infectious people can be estimated by summing up each group in the community. This study performed a multiagent simulation (MAS) of seasonal influenza from preinfectious people in closed spaces based on decomposability. Then, the basic simulation is validated and the appropriateness of infective rates, changed infectivity rates, and near decomposability is confirmed.
{"title":"Multiagent Simulation of Seasonal Influenza from Preinfectious People in Closed Spaces","authors":"Saori Iwanaga","doi":"10.1142/s219688882340002x","DOIUrl":"https://doi.org/10.1142/s219688882340002x","url":null,"abstract":"This study proposes a discrete mathematical Susceptible–Exposed–Preinfectious–Infectious–Recovered (SEPIR) states model for seasonal influenza. In a previous study, focusing on infections by preinfectious people using preexisting data, the author showed that the super-spreading of seasonal influenza occurred before the day that the first patients were discovered (D-day). In addition, when people do not take precautionary measures, the infectivity rate (from preinfected people) was determined as 0.041. After D-day in the community, the implementation of countermeasures was observed to reduce the infectivity rate to 0.002 and 0.013 in working and living spaces, respectively. The number of infectious people can be estimated by summing up each group in the community. This study performed a multiagent simulation (MAS) of seasonal influenza from preinfectious people in closed spaces based on decomposability. Then, the basic simulation is validated and the appropriateness of infective rates, changed infectivity rates, and near decomposability is confirmed.","PeriodicalId":30898,"journal":{"name":"Vietnam Journal of Computer Science","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858777","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}
Pub Date : 2023-08-25DOI: 10.1142/s2196888823500136
J. Bernacki, Rafal Scherer
{"title":"Remarks on Speeding up the Digital Camera Identification using Convolutional Neural Networks","authors":"J. Bernacki, Rafal Scherer","doi":"10.1142/s2196888823500136","DOIUrl":"https://doi.org/10.1142/s2196888823500136","url":null,"abstract":"","PeriodicalId":30898,"journal":{"name":"Vietnam Journal of Computer Science","volume":"3 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79927614","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}
Pub Date : 2023-08-25DOI: 10.1142/s2196888823500148
Nhan Vo Minh, Khue Nguyen Tran Minh, Long H. B. Nguyen, D. Dinh
{"title":"Exploring Composite Indexes for Domain Adaptation in Neural Machine Translation","authors":"Nhan Vo Minh, Khue Nguyen Tran Minh, Long H. B. Nguyen, D. Dinh","doi":"10.1142/s2196888823500148","DOIUrl":"https://doi.org/10.1142/s2196888823500148","url":null,"abstract":"","PeriodicalId":30898,"journal":{"name":"Vietnam Journal of Computer Science","volume":"37 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89961390","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}
Pub Date : 2023-08-18DOI: 10.1142/s2196888823500124
Falguni Roy, M. Hasan
{"title":"An Item-Item Collaborative Filtering Recommender System Based on Item Reviews: An Approach with Deep Learning","authors":"Falguni Roy, M. Hasan","doi":"10.1142/s2196888823500124","DOIUrl":"https://doi.org/10.1142/s2196888823500124","url":null,"abstract":"","PeriodicalId":30898,"journal":{"name":"Vietnam Journal of Computer Science","volume":"7 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87194703","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}
Pub Date : 2023-07-28DOI: 10.1142/s2196888823500070
K. Skracic, J. Petrović, P. Pale
This paper presents an approach to improve the file fragment classification by proposing new features for classification and evaluating them on a dataset that includes both low- and high-entropy file fragments. High-entropy fragments, belonging to compressed and encrypted files, are particularly challenging to classify because they lack exploitable patterns. To address this challenge, the proposed feature vectors are constructed based on the byte frequency distribution (BFD) of file fragments, along with discrete Fourier transform coefficients and several randomness measures. These feature vectors are tested using three machine learning models: Support vector machines (SVMs), artificial neural networks (ANNs), and random forests (RFs). The proposed approach is evaluated on the govdocs1 dataset, which is freely available and widely used in this field, to enable reproducibility and fair comparison with other published research. The results show that the proposed approach outperforms existing methods and achieves better classification accuracy for both low- and high-entropy file fragments.
{"title":"Classification of Low- and High-Entropy File Fragments Using Randomness Measures and Discrete Fourier Transform Coefficients","authors":"K. Skracic, J. Petrović, P. Pale","doi":"10.1142/s2196888823500070","DOIUrl":"https://doi.org/10.1142/s2196888823500070","url":null,"abstract":"This paper presents an approach to improve the file fragment classification by proposing new features for classification and evaluating them on a dataset that includes both low- and high-entropy file fragments. High-entropy fragments, belonging to compressed and encrypted files, are particularly challenging to classify because they lack exploitable patterns. To address this challenge, the proposed feature vectors are constructed based on the byte frequency distribution (BFD) of file fragments, along with discrete Fourier transform coefficients and several randomness measures. These feature vectors are tested using three machine learning models: Support vector machines (SVMs), artificial neural networks (ANNs), and random forests (RFs). The proposed approach is evaluated on the govdocs1 dataset, which is freely available and widely used in this field, to enable reproducibility and fair comparison with other published research. The results show that the proposed approach outperforms existing methods and achieves better classification accuracy for both low- and high-entropy file fragments.","PeriodicalId":30898,"journal":{"name":"Vietnam Journal of Computer Science","volume":"2016 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86357727","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}
Pub Date : 2023-07-21DOI: 10.1142/s2196888823500100
Christine Dewi, Rung-Ching Chen, Henoch Juli Christanto, Francesco Cauteruccio
{"title":"Multinomial Naive Bayes Classifier for Sentiment Analysis of Internet Movie Database","authors":"Christine Dewi, Rung-Ching Chen, Henoch Juli Christanto, Francesco Cauteruccio","doi":"10.1142/s2196888823500100","DOIUrl":"https://doi.org/10.1142/s2196888823500100","url":null,"abstract":"","PeriodicalId":30898,"journal":{"name":"Vietnam Journal of Computer Science","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79949781","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}
Pub Date : 2023-06-28DOI: 10.1142/s2196888823300016
Van Hieu Bui, H. Phan
{"title":"The Computational Complexity of Hierarchical Clustering Algorithms for Community Detection: A Review","authors":"Van Hieu Bui, H. Phan","doi":"10.1142/s2196888823300016","DOIUrl":"https://doi.org/10.1142/s2196888823300016","url":null,"abstract":"","PeriodicalId":30898,"journal":{"name":"Vietnam Journal of Computer Science","volume":"72 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90316421","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}
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