Pub Date : 2023-10-20DOI: 10.3390/informatics10040080
Patrick Cheong-Iao Pang, Megan Munsie, Shanton Chang
People are increasingly seeking complex health information online. However, how they access this information and how influential it is on their health choices remains poorly understood. Google Analytics (GA) is a widely used web analytics tool and it has been used in academic research to study health information-seeking behaviors. Nevertheless, it is rarely used to study the navigation flows of health websites. To demonstrate the usefulness of GA data, we adopted both top-down and bottom-up approaches to study how web visitors navigate within a website delivering complex health information about stem cell research using GA’s device, traffic and path data. Custom Treemap and Sankey visualizations were used to illustrate the navigation flows extracted from these data in a more understandable manner. Our methodology reveals that different device and traffic types expose dissimilar search approaches. Through the visualizations, popular web pages and content categories frequently browsed together can be identified. Information on a website that is often overlooked but needed by many users can also be discovered. Our proposed method can identify content requiring improvements, enhance usability and guide a design for better addressing the needs of different audiences. This paper has implications for how web designers can use GA to help them determine users’ priorities and behaviors when navigating complex information. It highlights that even where there is complex health information, users may still want more direct and easy-to-understand navigations to retrieve such information.
{"title":"A Method for Analyzing Navigation Flows of Health Website Users Seeking Complex Health Information with Google Analytics","authors":"Patrick Cheong-Iao Pang, Megan Munsie, Shanton Chang","doi":"10.3390/informatics10040080","DOIUrl":"https://doi.org/10.3390/informatics10040080","url":null,"abstract":"People are increasingly seeking complex health information online. However, how they access this information and how influential it is on their health choices remains poorly understood. Google Analytics (GA) is a widely used web analytics tool and it has been used in academic research to study health information-seeking behaviors. Nevertheless, it is rarely used to study the navigation flows of health websites. To demonstrate the usefulness of GA data, we adopted both top-down and bottom-up approaches to study how web visitors navigate within a website delivering complex health information about stem cell research using GA’s device, traffic and path data. Custom Treemap and Sankey visualizations were used to illustrate the navigation flows extracted from these data in a more understandable manner. Our methodology reveals that different device and traffic types expose dissimilar search approaches. Through the visualizations, popular web pages and content categories frequently browsed together can be identified. Information on a website that is often overlooked but needed by many users can also be discovered. Our proposed method can identify content requiring improvements, enhance usability and guide a design for better addressing the needs of different audiences. This paper has implications for how web designers can use GA to help them determine users’ priorities and behaviors when navigating complex information. It highlights that even where there is complex health information, users may still want more direct and easy-to-understand navigations to retrieve such information.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135569799","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 : 2023-10-17DOI: 10.3390/informatics10040079
Anam Shahil-Feroz, Haleema Yasmin, Sarah Saleem, Zulfiqar Bhutta, Emily Seto
This study assessed the usability of the smartphone app, named “Raabta” from the perspective of pregnant women at high risk of preeclampsia to improve the Raabta app for future implementation. Think-aloud and task-completion techniques were used with a purposive sample of 14 pregnant women at high risk of preeclampsia. The sessions were audio-recorded and later professionally transcribed for thematic analysis. The study generated learnings associated with four themes: improving the clarity of instructions, messaging, and terminology; accessibility for non-tech savvy and illiterate Urdu users; enhancing visuals and icons for user engagement; and simplifying navigation and functionality. Overall, user feedback emphasized the importance of enhancing the clarity of instructions, messaging, and terminology within the Raabta app. Voice messages and visuals were valued by users, particularly among the non-tech savvy and illiterate Urdu users, as they enhance accessibility and enable independent monitoring. Suggestions were made to enhance user engagement through visual improvements such as enhanced graphics and culturally aligned color schemes. Lastly, users highlighted the need for improved navigation both between screens and within screens to enhance the overall user experience. The Raabta app prototype will be modified based on the feedback of the users to address the unique needs of diverse groups.
{"title":"Remote Moderated Usability Testing of a Mobile Phone App for Remote Monitoring of Pregnant Women at High Risk of Preeclampsia in Karachi, Pakistan","authors":"Anam Shahil-Feroz, Haleema Yasmin, Sarah Saleem, Zulfiqar Bhutta, Emily Seto","doi":"10.3390/informatics10040079","DOIUrl":"https://doi.org/10.3390/informatics10040079","url":null,"abstract":"This study assessed the usability of the smartphone app, named “Raabta” from the perspective of pregnant women at high risk of preeclampsia to improve the Raabta app for future implementation. Think-aloud and task-completion techniques were used with a purposive sample of 14 pregnant women at high risk of preeclampsia. The sessions were audio-recorded and later professionally transcribed for thematic analysis. The study generated learnings associated with four themes: improving the clarity of instructions, messaging, and terminology; accessibility for non-tech savvy and illiterate Urdu users; enhancing visuals and icons for user engagement; and simplifying navigation and functionality. Overall, user feedback emphasized the importance of enhancing the clarity of instructions, messaging, and terminology within the Raabta app. Voice messages and visuals were valued by users, particularly among the non-tech savvy and illiterate Urdu users, as they enhance accessibility and enable independent monitoring. Suggestions were made to enhance user engagement through visual improvements such as enhanced graphics and culturally aligned color schemes. Lastly, users highlighted the need for improved navigation both between screens and within screens to enhance the overall user experience. The Raabta app prototype will be modified based on the feedback of the users to address the unique needs of diverse groups.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136032832","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 : 2023-10-12DOI: 10.3390/informatics10040078
Andreas Dengel, Rupert Gehrlein, David Fernes, Sebastian Görlich, Jonas Maurer, Hai Hoang Pham, Gabriel Großmann, Niklas Dietrich genannt Eisermann
In the current era of artificial intelligence, large language models such as ChatGPT and BARD are being increasingly used for various applications, such as language translation, text generation, and human-like conversation. The fact that these models consist of large amounts of data, including many different opinions and perspectives, could introduce the possibility of a new qualitative research approach: Due to the probabilistic character of their answers, “interviewing” these large language models could give insights into public opinions in a way that otherwise only interviews with large groups of subjects could deliver. However, it is not yet clear if qualitative content analysis research methods can be applied to interviews with these models. Evaluating the applicability of qualitative research methods to interviews with large language models could foster our understanding of their abilities and limitations. In this paper, we examine the applicability of qualitative content analysis research methods to interviews with ChatGPT in English, ChatGPT in German, and BARD in English on the relevance of computer science in K-12 education, which was used as an exemplary topic. We found that the answers produced by these models strongly depended on the provided context, and the same model could produce heavily differing results for the same questions. From these results and the insights throughout the process, we formulated guidelines for conducting and analyzing interviews with large language models. Our findings suggest that qualitative content analysis research methods can indeed be applied to interviews with large language models, but with careful consideration of contextual factors that may affect the responses produced by these models. The guidelines we provide can aid researchers and practitioners in conducting more nuanced and insightful interviews with large language models. From an overall view of our results, we generally do not recommend using interviews with large language models for research purposes, due to their highly unpredictable results. However, we suggest using these models as exploration tools for gaining different perspectives on research topics and for testing interview guidelines before conducting real-world interviews.
{"title":"Qualitative Research Methods for Large Language Models: Conducting Semi-Structured Interviews with ChatGPT and BARD on Computer Science Education","authors":"Andreas Dengel, Rupert Gehrlein, David Fernes, Sebastian Görlich, Jonas Maurer, Hai Hoang Pham, Gabriel Großmann, Niklas Dietrich genannt Eisermann","doi":"10.3390/informatics10040078","DOIUrl":"https://doi.org/10.3390/informatics10040078","url":null,"abstract":"In the current era of artificial intelligence, large language models such as ChatGPT and BARD are being increasingly used for various applications, such as language translation, text generation, and human-like conversation. The fact that these models consist of large amounts of data, including many different opinions and perspectives, could introduce the possibility of a new qualitative research approach: Due to the probabilistic character of their answers, “interviewing” these large language models could give insights into public opinions in a way that otherwise only interviews with large groups of subjects could deliver. However, it is not yet clear if qualitative content analysis research methods can be applied to interviews with these models. Evaluating the applicability of qualitative research methods to interviews with large language models could foster our understanding of their abilities and limitations. In this paper, we examine the applicability of qualitative content analysis research methods to interviews with ChatGPT in English, ChatGPT in German, and BARD in English on the relevance of computer science in K-12 education, which was used as an exemplary topic. We found that the answers produced by these models strongly depended on the provided context, and the same model could produce heavily differing results for the same questions. From these results and the insights throughout the process, we formulated guidelines for conducting and analyzing interviews with large language models. Our findings suggest that qualitative content analysis research methods can indeed be applied to interviews with large language models, but with careful consideration of contextual factors that may affect the responses produced by these models. The guidelines we provide can aid researchers and practitioners in conducting more nuanced and insightful interviews with large language models. From an overall view of our results, we generally do not recommend using interviews with large language models for research purposes, due to their highly unpredictable results. However, we suggest using these models as exploration tools for gaining different perspectives on research topics and for testing interview guidelines before conducting real-world interviews.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135968937","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 : 2023-10-11DOI: 10.3390/informatics10040077
Fan Zhang, Melissa Petersen, Leigh Johnson, James Hall, Raymond F. Palmer, Sid E. O’Bryant
The Health and Aging Brain Study–Health Disparities (HABS–HD) project seeks to understand the biological, social, and environmental factors that impact brain aging among diverse communities. A common issue for HABS–HD is missing data. It is impossible to achieve accurate machine learning (ML) if data contain missing values. Therefore, developing a new imputation methodology has become an urgent task for HABS–HD. The three missing data assumptions, (1) missing completely at random (MCAR), (2) missing at random (MAR), and (3) missing not at random (MNAR), necessitate distinct imputation approaches for each mechanism of missingness. Several popular imputation methods, including listwise deletion, min, mean, predictive mean matching (PMM), classification and regression trees (CART), and missForest, may result in biased outcomes and reduced statistical power when applied to downstream analyses such as testing hypotheses related to clinical variables or utilizing machine learning to predict AD or MCI. Moreover, these commonly used imputation techniques can produce unreliable estimates of missing values if they do not account for the missingness mechanisms or if there is an inconsistency between the imputation method and the missing data mechanism in HABS–HD. Therefore, we proposed a three-step workflow to handle missing data in HABS–HD: (1) missing data evaluation, (2) imputation, and (3) imputation evaluation. First, we explored the missingness in HABS–HD. Then, we developed a machine learning-based multiple imputation method (MLMI) for imputing missing values. We built four ML-based imputation models (support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and lasso and elastic-net regularized generalized linear model (GLMNET)) and adapted the four ML-based models to multiple imputations using the simple averaging method. Lastly, we evaluated and compared MLMI with other common methods. Our results showed that the three-step workflow worked well for handling missing values in HABS–HD and the ML-based multiple imputation method outperformed other common methods in terms of prediction performance and change in distribution and correlation. The choice of missing handling methodology has a significant impact on the accompanying statistical analyses of HABS–HD. The conceptual three-step workflow and the ML-based multiple imputation method perform well for our Alzheimer’s disease models. They can also be applied to other disease data analyses.
{"title":"A Machine Learning-Based Multiple Imputation Method for the Health and Aging Brain Study–Health Disparities","authors":"Fan Zhang, Melissa Petersen, Leigh Johnson, James Hall, Raymond F. Palmer, Sid E. O’Bryant","doi":"10.3390/informatics10040077","DOIUrl":"https://doi.org/10.3390/informatics10040077","url":null,"abstract":"The Health and Aging Brain Study–Health Disparities (HABS–HD) project seeks to understand the biological, social, and environmental factors that impact brain aging among diverse communities. A common issue for HABS–HD is missing data. It is impossible to achieve accurate machine learning (ML) if data contain missing values. Therefore, developing a new imputation methodology has become an urgent task for HABS–HD. The three missing data assumptions, (1) missing completely at random (MCAR), (2) missing at random (MAR), and (3) missing not at random (MNAR), necessitate distinct imputation approaches for each mechanism of missingness. Several popular imputation methods, including listwise deletion, min, mean, predictive mean matching (PMM), classification and regression trees (CART), and missForest, may result in biased outcomes and reduced statistical power when applied to downstream analyses such as testing hypotheses related to clinical variables or utilizing machine learning to predict AD or MCI. Moreover, these commonly used imputation techniques can produce unreliable estimates of missing values if they do not account for the missingness mechanisms or if there is an inconsistency between the imputation method and the missing data mechanism in HABS–HD. Therefore, we proposed a three-step workflow to handle missing data in HABS–HD: (1) missing data evaluation, (2) imputation, and (3) imputation evaluation. First, we explored the missingness in HABS–HD. Then, we developed a machine learning-based multiple imputation method (MLMI) for imputing missing values. We built four ML-based imputation models (support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and lasso and elastic-net regularized generalized linear model (GLMNET)) and adapted the four ML-based models to multiple imputations using the simple averaging method. Lastly, we evaluated and compared MLMI with other common methods. Our results showed that the three-step workflow worked well for handling missing values in HABS–HD and the ML-based multiple imputation method outperformed other common methods in terms of prediction performance and change in distribution and correlation. The choice of missing handling methodology has a significant impact on the accompanying statistical analyses of HABS–HD. The conceptual three-step workflow and the ML-based multiple imputation method perform well for our Alzheimer’s disease models. They can also be applied to other disease data analyses.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136098365","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 : 2023-09-26DOI: 10.3390/informatics10040076
Samuel R. Schrader, Eren Gultepe
The evaluation of similarities between natural languages often relies on prior knowledge of the languages being studied. We describe three methods for building phylogenetic trees and clustering languages without the use of language-specific information. The input to our methods is a set of document vectors trained on a corpus of parallel translations of the Bible into 22 Indo-European languages, representing 4 language families: Indo-Iranian, Slavic, Germanic, and Romance. This text corpus consists of a set of 532,092 Bible verses, with 24,186 identical verses translated into each language. The methods are (A) hierarchical clustering using distance between language vector centroids, (B) hierarchical clustering using a network-derived distance measure, and (C) Deep Embedded Clustering (DEC) of language vectors. We evaluate our methods using a ground-truth tree and language families derived from said tree. All three achieve clustering F-scores above 0.9 on the Indo-Iranian and Slavic families; most confusion is between the Germanic and Romance families. The mean F-scores across all families are 0.864 (centroid clustering), 0.953 (network partitioning), and 0.763 (DEC). This shows that document vectors can be used to capture and compare linguistic features of multilingual texts, and thus could help extend language similarity and other translation studies research.
{"title":"Analyzing Indo-European Language Similarities Using Document Vectors","authors":"Samuel R. Schrader, Eren Gultepe","doi":"10.3390/informatics10040076","DOIUrl":"https://doi.org/10.3390/informatics10040076","url":null,"abstract":"The evaluation of similarities between natural languages often relies on prior knowledge of the languages being studied. We describe three methods for building phylogenetic trees and clustering languages without the use of language-specific information. The input to our methods is a set of document vectors trained on a corpus of parallel translations of the Bible into 22 Indo-European languages, representing 4 language families: Indo-Iranian, Slavic, Germanic, and Romance. This text corpus consists of a set of 532,092 Bible verses, with 24,186 identical verses translated into each language. The methods are (A) hierarchical clustering using distance between language vector centroids, (B) hierarchical clustering using a network-derived distance measure, and (C) Deep Embedded Clustering (DEC) of language vectors. We evaluate our methods using a ground-truth tree and language families derived from said tree. All three achieve clustering F-scores above 0.9 on the Indo-Iranian and Slavic families; most confusion is between the Germanic and Romance families. The mean F-scores across all families are 0.864 (centroid clustering), 0.953 (network partitioning), and 0.763 (DEC). This shows that document vectors can be used to capture and compare linguistic features of multilingual texts, and thus could help extend language similarity and other translation studies research.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134961006","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}
The aim of this study is to conceptualize website usability and develop a survey instrument to measure related concepts from the perspective of end users. We designed a three-stage methodology. First, concepts related to website usability were derived using content analysis technique. A total of 16 constructs measuring website usability were defined with their explanations and corresponding open codes. Second, a survey instrument was developed according to the defined open codes and the literature. The instrument was first validated using face validity, pilot testing (n = 30), and content validity (n = 40). Third, the survey instrument was validated using explanatory and confirmatory analyses. In the explanatory analysis, 785 questionnaires were collected from e-commerce website users to validate the factor structure of website usability. For confirmatory factor analysis, a new sample collected from 1086 users of e-commerce websites was used to confirm the measurement model. In addition, nomological validation was conducted by analyzing the effect of website usability concepts on three key factors: “continued intention to use”, “satisfaction”, and “brand loyalty”.
{"title":"Conceptualization and Survey Instrument Development for Website Usability","authors":"Nevcihan Toraman, Aycan Pekpazar, Cigdem Altin Gumussoy","doi":"10.3390/informatics10030075","DOIUrl":"https://doi.org/10.3390/informatics10030075","url":null,"abstract":"The aim of this study is to conceptualize website usability and develop a survey instrument to measure related concepts from the perspective of end users. We designed a three-stage methodology. First, concepts related to website usability were derived using content analysis technique. A total of 16 constructs measuring website usability were defined with their explanations and corresponding open codes. Second, a survey instrument was developed according to the defined open codes and the literature. The instrument was first validated using face validity, pilot testing (n = 30), and content validity (n = 40). Third, the survey instrument was validated using explanatory and confirmatory analyses. In the explanatory analysis, 785 questionnaires were collected from e-commerce website users to validate the factor structure of website usability. For confirmatory factor analysis, a new sample collected from 1086 users of e-commerce websites was used to confirm the measurement model. In addition, nomological validation was conducted by analyzing the effect of website usability concepts on three key factors: “continued intention to use”, “satisfaction”, and “brand loyalty”.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136313236","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 : 2023-09-18DOI: 10.3390/informatics10030074
Bisni Fahad Mon, Asma Wasfi, Mohammad Hayajneh, Ahmad Slim, Najah Abu Ali
The utilization of reinforcement learning (RL) within the field of education holds the potential to bring about a significant shift in the way students approach and engage with learning and how teachers evaluate student progress. The use of RL in education allows for personalized and adaptive learning, where the difficulty level can be adjusted based on a student’s performance. As a result, this could result in heightened levels of motivation and engagement among students. The aim of this article is to investigate the applications and techniques of RL in education and determine its potential impact on enhancing educational outcomes. It compares the various policies induced by RL with baselines and identifies four distinct RL techniques: the Markov decision process, partially observable Markov decision process, deep RL network, and Markov chain, as well as their application in education. The main focus of the article is to identify best practices for incorporating RL into educational settings to achieve effective and rewarding outcomes. To accomplish this, the article thoroughly examines the existing literature on using RL in education and its potential to advance educational technology. This work provides a thorough analysis of the various techniques and applications of RL in education to answer questions related to the effectiveness of RL in education and its future prospects. The findings of this study will provide researchers with a benchmark to compare the usefulness and effectiveness of commonly employed RL algorithms and provide direction for future research in education.
{"title":"Reinforcement Learning in Education: A Literature Review","authors":"Bisni Fahad Mon, Asma Wasfi, Mohammad Hayajneh, Ahmad Slim, Najah Abu Ali","doi":"10.3390/informatics10030074","DOIUrl":"https://doi.org/10.3390/informatics10030074","url":null,"abstract":"The utilization of reinforcement learning (RL) within the field of education holds the potential to bring about a significant shift in the way students approach and engage with learning and how teachers evaluate student progress. The use of RL in education allows for personalized and adaptive learning, where the difficulty level can be adjusted based on a student’s performance. As a result, this could result in heightened levels of motivation and engagement among students. The aim of this article is to investigate the applications and techniques of RL in education and determine its potential impact on enhancing educational outcomes. It compares the various policies induced by RL with baselines and identifies four distinct RL techniques: the Markov decision process, partially observable Markov decision process, deep RL network, and Markov chain, as well as their application in education. The main focus of the article is to identify best practices for incorporating RL into educational settings to achieve effective and rewarding outcomes. To accomplish this, the article thoroughly examines the existing literature on using RL in education and its potential to advance educational technology. This work provides a thorough analysis of the various techniques and applications of RL in education to answer questions related to the effectiveness of RL in education and its future prospects. The findings of this study will provide researchers with a benchmark to compare the usefulness and effectiveness of commonly employed RL algorithms and provide direction for future research in education.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135202815","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 : 2023-09-09DOI: 10.3390/informatics10030073
Vanessa García-Pineda, Alejandro Valencia-Arias, Juan Camilo Patiño-Vanegas, Juan José Flores Cueto, Diana Arango-Botero, Angel Marcelo Rojas Coronel, Paula Andrea Rodríguez-Correa
This article aims to examine the research trends in the development of mobile networks from machine learning. The methodological approach starts from an analysis of 260 academic documents selected from the Scopus and Web of Science databases and is based on the parameters of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Quantity, quality and structure indicators are calculated in order to contextualize the documents’ thematic evolution. The results reveal that, in relation to the publications by country, the United States and China, who are competing for fifth generation (5G) network coverage and are responsible for manufacturing devices for mobile networks, stand out. Most of the research on the subject focuses on the optimization of resources and traffic to guarantee the best management and availability of a network due to the high demand for resources and greater amount of traffic generated by the many Internet of Things (IoT) devices that are being developed for the market. It is concluded that thematic trends focus on generating algorithms for recognizing and learning the data in the network and on trained models that draw from the available data to improve the experience of connecting to mobile networks.
本文旨在探讨机器学习在移动网络发展中的研究趋势。方法方法从对Scopus和Web of Science数据库中选择的260篇学术文献的分析开始，并基于系统评价和元分析首选报告项目(PRISMA)声明的参数。计算了数量、质量和结构指标，以便将文件的主题演变置于背景中。结果显示，就各国的出版物而言，正在争夺第五代(5G)网络覆盖并负责制造移动网络设备的美国和中国脱颖而出。关于该主题的大多数研究都集中在资源和流量的优化上，以保证网络的最佳管理和可用性，因为正在为市场开发的许多物联网(IoT)设备对资源的高需求和更大的流量。结论是，主题趋势侧重于生成识别和学习网络数据的算法，以及从可用数据中提取的训练模型，以改善连接到移动网络的体验。
{"title":"Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research Agenda","authors":"Vanessa García-Pineda, Alejandro Valencia-Arias, Juan Camilo Patiño-Vanegas, Juan José Flores Cueto, Diana Arango-Botero, Angel Marcelo Rojas Coronel, Paula Andrea Rodríguez-Correa","doi":"10.3390/informatics10030073","DOIUrl":"https://doi.org/10.3390/informatics10030073","url":null,"abstract":"This article aims to examine the research trends in the development of mobile networks from machine learning. The methodological approach starts from an analysis of 260 academic documents selected from the Scopus and Web of Science databases and is based on the parameters of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Quantity, quality and structure indicators are calculated in order to contextualize the documents’ thematic evolution. The results reveal that, in relation to the publications by country, the United States and China, who are competing for fifth generation (5G) network coverage and are responsible for manufacturing devices for mobile networks, stand out. Most of the research on the subject focuses on the optimization of resources and traffic to guarantee the best management and availability of a network due to the high demand for resources and greater amount of traffic generated by the many Internet of Things (IoT) devices that are being developed for the market. It is concluded that thematic trends focus on generating algorithms for recognizing and learning the data in the network and on trained models that draw from the available data to improve the experience of connecting to mobile networks.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":"368 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136192677","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 : 2023-09-04DOI: 10.3390/informatics10030072
D. Renza, D. Ballesteros
CNN models can have millions of parameters, which makes them unattractive for some applications that require fast inference times or small memory footprints. To overcome this problem, one alternative is to identify and remove weights that have a small impact on the loss function of the algorithm, which is known as pruning. Typically, pruning methods are compared in terms of performance (e.g., accuracy), model size and inference speed. However, it is unusual to evaluate whether a pruned model preserves regions of importance in an image when performing inference. Consequently, we propose a metric to assess the impact of a pruning method based on images obtained by model interpretation (specifically, class activation maps). These images are spatially and spectrally compared and integrated by the harmonic mean for all samples in the test dataset. The results show that although the accuracy in a pruned model may remain relatively constant, the areas of attention for decision making are not necessarily preserved. Furthermore, the performance of pruning methods can be easily compared as a function of the proposed metric.
{"title":"Sp2PS: Pruning Score by Spectral and Spatial Evaluation of CAM Images","authors":"D. Renza, D. Ballesteros","doi":"10.3390/informatics10030072","DOIUrl":"https://doi.org/10.3390/informatics10030072","url":null,"abstract":"CNN models can have millions of parameters, which makes them unattractive for some applications that require fast inference times or small memory footprints. To overcome this problem, one alternative is to identify and remove weights that have a small impact on the loss function of the algorithm, which is known as pruning. Typically, pruning methods are compared in terms of performance (e.g., accuracy), model size and inference speed. However, it is unusual to evaluate whether a pruned model preserves regions of importance in an image when performing inference. Consequently, we propose a metric to assess the impact of a pruning method based on images obtained by model interpretation (specifically, class activation maps). These images are spatially and spectrally compared and integrated by the harmonic mean for all samples in the test dataset. The results show that although the accuracy in a pruned model may remain relatively constant, the areas of attention for decision making are not necessarily preserved. Furthermore, the performance of pruning methods can be easily compared as a function of the proposed metric.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45946946","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}
Vulnerabilities in cyber defense in the countries of the Latin American region have favored the activities of cybercriminals from different parts of the world who have carried out a growing number of cyberattacks that affect public and private services and compromise the integrity of users and organizations. This article describes the most representative vulnerabilities related to cyberattacks that have affected different sectors of countries in the Latin American region. A systematic review of repositories and the scientific literature was conducted, considering journal articles, conference proceedings, and reports from official bodies and leading brands of cybersecurity systems. The cybersecurity vulnerabilities identified in the countries of the Latin American region are low cybersecurity awareness, lack of standards and regulations, use of outdated software, security gaps in critical infrastructure, and lack of training and professional specialization.
{"title":"A Comprehensive Analysis of the Worst Cybersecurity Vulnerabilities in Latin America","authors":"Omar Flor-Unda, Freddy Simbaña, Xavier Larriva-Novo, Ángel Acuña, Rolando Tipán, Patricia Acosta-Vargas","doi":"10.3390/informatics10030071","DOIUrl":"https://doi.org/10.3390/informatics10030071","url":null,"abstract":"Vulnerabilities in cyber defense in the countries of the Latin American region have favored the activities of cybercriminals from different parts of the world who have carried out a growing number of cyberattacks that affect public and private services and compromise the integrity of users and organizations. This article describes the most representative vulnerabilities related to cyberattacks that have affected different sectors of countries in the Latin American region. A systematic review of repositories and the scientific literature was conducted, considering journal articles, conference proceedings, and reports from official bodies and leading brands of cybersecurity systems. The cybersecurity vulnerabilities identified in the countries of the Latin American region are low cybersecurity awareness, lack of standards and regulations, use of outdated software, security gaps in critical infrastructure, and lack of training and professional specialization.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42639268","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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