Pub Date : 2022-01-01DOI: 10.5220/0011300700003277
A. Machado, Heitor Cardoso, Plinio Moreno, Alexandre Bernardino
Abstract
摘要
{"title":"Active Data Collection of Health Data in Mobile Devices","authors":"A. Machado, Heitor Cardoso, Plinio Moreno, Alexandre Bernardino","doi":"10.5220/0011300700003277","DOIUrl":"https://doi.org/10.5220/0011300700003277","url":null,"abstract":"Abstract","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"65 1","pages":"160-167"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81092476","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 : 2022-01-01DOI: 10.5220/0011301100003277
D. Silva, António Fernandes
{"title":"Bridging the Gap between Real and Synthetic Traffic Sign Repositories","authors":"D. Silva, António Fernandes","doi":"10.5220/0011301100003277","DOIUrl":"https://doi.org/10.5220/0011301100003277","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"67 1","pages":"44-54"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81132026","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 : 2022-01-01DOI: 10.1007/978-3-031-37317-6_4
D. Silva, A. Fernandes
{"title":"Traffic Sign Repositories: Bridging the Gap Between Real and Synthetic Data","authors":"D. Silva, A. Fernandes","doi":"10.1007/978-3-031-37317-6_4","DOIUrl":"https://doi.org/10.1007/978-3-031-37317-6_4","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"33 1","pages":"56-77"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79341350","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 : 2022-01-01DOI: 10.5220/0011315200003277
Kamil Choromański, J. Kozakiewicz, M. Sobucki, M. Pilarska-Mazurek, R. Olszewski
: Deep learning analysis of multisource Martian data (both from orbiter and rover) allows for the separation and classification of different geomorphological settings. However, it is difficult to determine the optimal neural network model for unambiguous semantic segmentation due to the specificity of Martian data and blurring of the boundary of individual settings (which is its immanent property). In this paper, the authors describe several variants of multisource deep learning processing system for Martian data and develop a methodology for semantic segmentation of geomorphological settings for this planet based on the combination of selected solutions output. Network ensemble with use of the weighted averaging method improved results comparing to single network. The paper also discusses the decision rule extraction method of individual Martian geomorphological landforms using fuzzy inference systems. The results obtained using FIS tools allow for the extraction of single geomorphological forms, such as ripples.
{"title":"Analysis of Ensemble of Neural Networks and Fuzzy Logic Classification in Process of Semantic Segmentation of Martian Geomorphological Settings","authors":"Kamil Choromański, J. Kozakiewicz, M. Sobucki, M. Pilarska-Mazurek, R. Olszewski","doi":"10.5220/0011315200003277","DOIUrl":"https://doi.org/10.5220/0011315200003277","url":null,"abstract":": Deep learning analysis of multisource Martian data (both from orbiter and rover) allows for the separation and classification of different geomorphological settings. However, it is difficult to determine the optimal neural network model for unambiguous semantic segmentation due to the specificity of Martian data and blurring of the boundary of individual settings (which is its immanent property). In this paper, the authors describe several variants of multisource deep learning processing system for Martian data and develop a methodology for semantic segmentation of geomorphological settings for this planet based on the combination of selected solutions output. Network ensemble with use of the weighted averaging method improved results comparing to single network. The paper also discusses the decision rule extraction method of individual Martian geomorphological landforms using fuzzy inference systems. The results obtained using FIS tools allow for the extraction of single geomorphological forms, such as ripples.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"40 1","pages":"184-192"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80834348","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 : 2022-01-01DOI: 10.1007/978-3-031-37317-6_5
Marco Parola, Federico A. Galatolo, Matteo Torzoni, M. G. Cimino
{"title":"Convolutional Neural Networks for Structural Damage Localization on Digital Twins","authors":"Marco Parola, Federico A. Galatolo, Matteo Torzoni, M. G. Cimino","doi":"10.1007/978-3-031-37317-6_5","DOIUrl":"https://doi.org/10.1007/978-3-031-37317-6_5","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"25 1","pages":"78-97"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74891908","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 : 2022-01-01DOI: 10.5220/0011289300003277
K. Ramakrishnan, Vimala Balakrishnan, Kumanan Govaichelvan
Growing number of online discourses enables the development of emotion mining models using natural language processing techniques. However, language diversity and cultural disparity alters the sentiment orientation of words depending on the community and context. Therefore, this study investigates the impacts of linguistic features, namely lexical and syntactic, in predicting the presence two emotions among Malaysian YouTube users, anger and anticipation. Term Frequency-Inverse Document Frequency (TF-IDF), Unigrams, Bigrams and Parts-of-Speech Tags were used as features to observe the classification performance. The dataset used in this study contains 2500 YouTube comments by Malaysian users on 46 Covid-19 related videos. Comments were extracted from three prominent Malaysian-centric English news channels: Channel News Asia (CNA), The Star News, and New Strait Times, ranging from 16 March 2020 - 30 April 2020 (i.e., first lockdown phase). Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbour and Multinomial Naive Bayes were the six classification algorithms tested, with results indicating Support Vector Machine with TF-IDF provided the best performance, achieving accuracy of 76% and 73% for anger and anticipation, respectively.
{"title":"Linguistic Feature-based Classification for Anger and Anticipation using Machine Learning","authors":"K. Ramakrishnan, Vimala Balakrishnan, Kumanan Govaichelvan","doi":"10.5220/0011289300003277","DOIUrl":"https://doi.org/10.5220/0011289300003277","url":null,"abstract":"Growing number of online discourses enables the development of emotion mining models using natural language processing techniques. However, language diversity and cultural disparity alters the sentiment orientation of words depending on the community and context. Therefore, this study investigates the impacts of linguistic features, namely lexical and syntactic, in predicting the presence two emotions among Malaysian YouTube users, anger and anticipation. Term Frequency-Inverse Document Frequency (TF-IDF), Unigrams, Bigrams and Parts-of-Speech Tags were used as features to observe the classification performance. The dataset used in this study contains 2500 YouTube comments by Malaysian users on 46 Covid-19 related videos. Comments were extracted from three prominent Malaysian-centric English news channels: Channel News Asia (CNA), The Star News, and New Strait Times, ranging from 16 March 2020 - 30 April 2020 (i.e., first lockdown phase). Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbour and Multinomial Naive Bayes were the six classification algorithms tested, with results indicating Support Vector Machine with TF-IDF provided the best performance, achieving accuracy of 76% and 73% for anger and anticipation, respectively.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"46 1","pages":"140-147"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90790926","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 : 2022-01-01DOI: 10.5220/0011270400003277
Tadele Mengiste, B. Belay, Bezawork Tilahun, Tsiyon Worku, Tesfa Tegegne
: Variations in fonts, styles, and ways to write a character have been the major bottlenecks in OCR research. Such problems are swiftly tackled through advancements in deep neural networks (DNNs). However, the number of network parameters and feature reusability are still the issues when applying Deep Convolutional Neural networks(DCNNs) for character image recognition. To address these challenges, in this paper, we propose an extensible and recursive-residual ConvNet architecture (RRConvNet) for real-life character image recognition. Unlike the standard DCCNs, RRConvNet incorporates two extensions: recursive-supervision and skip-connection. To enhance the recognition performance and reduce the number of parameters for extra convolutions, layers of up to three recursions are proposed. Feature maps are used after each recursion for reconstructing the target character. For all recursions of the reconstruction method, the reconstruction layers are the same. The second enhancement is to use a short skip-connection from the input to the reconstruction output layer to reuse the character features maps that are already learned from the prior layer. This skip-connection could be also used as an alternative path for gradients where the gradient is too small. With an overall character recognition accuracy of 98.2 percent, the proposed method achieves a state-of-the-art result on both publicly available and private test datasets.
{"title":"RRConvNet: Recursive-residual Network for Real-life Character Image Recognition","authors":"Tadele Mengiste, B. Belay, Bezawork Tilahun, Tsiyon Worku, Tesfa Tegegne","doi":"10.5220/0011270400003277","DOIUrl":"https://doi.org/10.5220/0011270400003277","url":null,"abstract":": Variations in fonts, styles, and ways to write a character have been the major bottlenecks in OCR research. Such problems are swiftly tackled through advancements in deep neural networks (DNNs). However, the number of network parameters and feature reusability are still the issues when applying Deep Convolutional Neural networks(DCNNs) for character image recognition. To address these challenges, in this paper, we propose an extensible and recursive-residual ConvNet architecture (RRConvNet) for real-life character image recognition. Unlike the standard DCCNs, RRConvNet incorporates two extensions: recursive-supervision and skip-connection. To enhance the recognition performance and reduce the number of parameters for extra convolutions, layers of up to three recursions are proposed. Feature maps are used after each recursion for reconstructing the target character. For all recursions of the reconstruction method, the reconstruction layers are the same. The second enhancement is to use a short skip-connection from the input to the reconstruction output layer to reuse the character features maps that are already learned from the prior layer. This skip-connection could be also used as an alternative path for gradients where the gradient is too small. With an overall character recognition accuracy of 98.2 percent, the proposed method achieves a state-of-the-art result on both publicly available and private test datasets.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"57 1","pages":"110-116"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89143033","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 : 2022-01-01DOI: 10.5220/0011367300003277
S. Marrone, Carlo Sansone
: Recognising users’ emotional states is among the most pursued tasks in the field of affective computing. Despite several works show promising results, they usually require expensive or intrusive hardware. Keystroke Dynamics (KD) is a behavioural biometric, whose typical aim is to identify or confirm the identity of an individual by analysing habitual rhythm patterns as they type on a keyboard. This work focuses on the use of KD as a way to continuously predict users’ emotional states during message writing sessions. In particular, we introduce a time-windowing approach that allows analysing users’ writing sessions in different batches, even when the considered writing window is relatively small. This is very relevant in the field of social media, where the exchanged messages are usually very small and the typing rhythm is very fast. The obtained results suggest that even very short writing windows (in the order of 30”) are sufficient to recognise the subject’s emotional state with the same level of accuracy of systems based on the analysis of larger writing sessions (i.e., up to a few minutes).
{"title":"Identifying Users' Emotional States through Keystroke Dynamics","authors":"S. Marrone, Carlo Sansone","doi":"10.5220/0011367300003277","DOIUrl":"https://doi.org/10.5220/0011367300003277","url":null,"abstract":": Recognising users’ emotional states is among the most pursued tasks in the field of affective computing. Despite several works show promising results, they usually require expensive or intrusive hardware. Keystroke Dynamics (KD) is a behavioural biometric, whose typical aim is to identify or confirm the identity of an individual by analysing habitual rhythm patterns as they type on a keyboard. This work focuses on the use of KD as a way to continuously predict users’ emotional states during message writing sessions. In particular, we introduce a time-windowing approach that allows analysing users’ writing sessions in different batches, even when the considered writing window is relatively small. This is very relevant in the field of social media, where the exchanged messages are usually very small and the typing rhythm is very fast. The obtained results suggest that even very short writing windows (in the order of 30”) are sufficient to recognise the subject’s emotional state with the same level of accuracy of systems based on the analysis of larger writing sessions (i.e., up to a few minutes).","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"14 1","pages":"207-214"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83987961","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 : 2022-01-01DOI: 10.5220/0011274000003277
Daniel Lehmann, M. Ebner
{"title":"Calculating the Credibility of Test Samples at Inference by a Layer-wise Activation Cluster Analysis of Convolutional Neural Networks","authors":"Daniel Lehmann, M. Ebner","doi":"10.5220/0011274000003277","DOIUrl":"https://doi.org/10.5220/0011274000003277","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"140 1","pages":"34-43"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83282574","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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