Pub Date : 1900-01-01DOI: 10.22364/bjmc.2019.7.3.07
Š. Karolčík, Inga Zilinskiene, Asta Slotkienė, E. Čipková
The rapid changes in technologies for representing learning content impact the modern concept of e-learning. They enable the implementation of personalized learning, development of a friendly, flexible and simulation-based learning environment. In this paper, the e-learning environment for Geography was analysed. In order to implement personalized active learning in Geography teaching and learning, the requirements of an adaptive tool for Geography teaching and learning are discussed and the theoretical framework for personalized e-learning environment is proposed. Based on previous experimental pedagogical research (carried out in Slovakia between 2008–2013 within the national project “Modernisation of the Educational Process in Elementary and Secondary Schools”), a new geospatial technological approach and theoretical framework for active learning process is discussed, and the prototype of a new Mapker for Geography teaching and learning is presented.
{"title":"Analysis of e-Learning Environment for Geography: Opportunities for Personalized Active Learning","authors":"Š. Karolčík, Inga Zilinskiene, Asta Slotkienė, E. Čipková","doi":"10.22364/bjmc.2019.7.3.07","DOIUrl":"https://doi.org/10.22364/bjmc.2019.7.3.07","url":null,"abstract":"The rapid changes in technologies for representing learning content impact the modern concept of e-learning. They enable the implementation of personalized learning, development of a friendly, flexible and simulation-based learning environment. In this paper, the e-learning environment for Geography was analysed. In order to implement personalized active learning in Geography teaching and learning, the requirements of an adaptive tool for Geography teaching and learning are discussed and the theoretical framework for personalized e-learning environment is proposed. Based on previous experimental pedagogical research (carried out in Slovakia between 2008–2013 within the national project “Modernisation of the Educational Process in Elementary and Secondary Schools”), a new geospatial technological approach and theoretical framework for active learning process is discussed, and the prototype of a new Mapker for Geography teaching and learning is presented.","PeriodicalId":431209,"journal":{"name":"Balt. J. Mod. Comput.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126557435","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 : 1900-01-01DOI: 10.22364/bjmc.2019.7.4.07
D. Haritonova
The objective of this study is to discover the geodynamic processes of the Earth’s crust in the territory of Latvia occurred due to the effect of the Baltic Sea non-tidal loading, by way of using GNSS permanent station daily coordinate time series and tide gauge data to find correlations between two data sets for the period from 2012 up to 2018. For this study observations of 31 Latvian and 2 Estonian GNSS stations were used. Stations belong to the LatPos, EUPOS-Riga, EPN and EstPos networks. Station daily coordinate time series were computed using Bernese GNSS software v5.2 in a double-difference mode with 9 fiducial stations from International GNSS Service and EUREF Permanent GNSS Network. The analysis of obtained data significantly increases understanding of the Earth’s surface displacements occurring due to the loading effect in
{"title":"The Impact of the Baltic Sea Non-tidal Loading on GNSS Station Coordinate Time Series: the Case of Latvia","authors":"D. Haritonova","doi":"10.22364/bjmc.2019.7.4.07","DOIUrl":"https://doi.org/10.22364/bjmc.2019.7.4.07","url":null,"abstract":"The objective of this study is to discover the geodynamic processes of the Earth’s crust in the territory of Latvia occurred due to the effect of the Baltic Sea non-tidal loading, by way of using GNSS permanent station daily coordinate time series and tide gauge data to find correlations between two data sets for the period from 2012 up to 2018. For this study observations of 31 Latvian and 2 Estonian GNSS stations were used. Stations belong to the LatPos, EUPOS-Riga, EPN and EstPos networks. Station daily coordinate time series were computed using Bernese GNSS software v5.2 in a double-difference mode with 9 fiducial stations from International GNSS Service and EUREF Permanent GNSS Network. The analysis of obtained data significantly increases understanding of the Earth’s surface displacements occurring due to the loading effect in","PeriodicalId":431209,"journal":{"name":"Balt. J. Mod. Comput.","volume":"253 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115856889","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 : 1900-01-01DOI: 10.22364/BJMC.2018.6.4.01
Jelena Liutvinavičiene, O. Kurasova
This research focuses on massive data visualization that is based on dimensionality reduction methods. We propose a new methodology, which divides the whole data visualization process into separate interactive steps. In each step, some part of data can be selected for further analysis and visualization. The different dimensionality method can be chosen/changed in each step. The decision which methods to be chosen depends on desirable accuracy measures and visualization samples. In addition, there are provided statistical measures of the identified clusters. We have developed a special tool, which implements the proposed methodology. R language and Shiny package were used for developing the tool. In the paper, the principles of the methodology and features of the tool are presented by describing the specific use case.
{"title":"Multi-level Massive Data Visualization: Methodology and Use Cases","authors":"Jelena Liutvinavičiene, O. Kurasova","doi":"10.22364/BJMC.2018.6.4.01","DOIUrl":"https://doi.org/10.22364/BJMC.2018.6.4.01","url":null,"abstract":"This research focuses on massive data visualization that is based on dimensionality reduction methods. We propose a new methodology, which divides the whole data visualization process into separate interactive steps. In each step, some part of data can be selected for further analysis and visualization. The different dimensionality method can be chosen/changed in each step. The decision which methods to be chosen depends on desirable accuracy measures and visualization samples. In addition, there are provided statistical measures of the identified clusters. We have developed a special tool, which implements the proposed methodology. R language and Shiny package were used for developing the tool. In the paper, the principles of the methodology and features of the tool are presented by describing the specific use case.","PeriodicalId":431209,"journal":{"name":"Balt. J. Mod. Comput.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124390191","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 : 1900-01-01DOI: 10.22364/bjmc.2022.10.4.05
T. Robal, Kevin Basov, U. Reinsalu, Mairo Leier
{"title":"A Study into Elevator Passenger In-Cabin Behaviour on a Smart-Elevator Platform","authors":"T. Robal, Kevin Basov, U. Reinsalu, Mairo Leier","doi":"10.22364/bjmc.2022.10.4.05","DOIUrl":"https://doi.org/10.22364/bjmc.2022.10.4.05","url":null,"abstract":"","PeriodicalId":431209,"journal":{"name":"Balt. J. Mod. Comput.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124559742","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 : 1900-01-01DOI: 10.22364/bjmc.2023.11.1.12
I. Laktionov, O. Vovna, M. Kabanets
{"title":"Computer-oriented Method of Adaptive Monitoring and Control of Temperature and Humidity Mode of Greenhouse Production","authors":"I. Laktionov, O. Vovna, M. Kabanets","doi":"10.22364/bjmc.2023.11.1.12","DOIUrl":"https://doi.org/10.22364/bjmc.2023.11.1.12","url":null,"abstract":"","PeriodicalId":431209,"journal":{"name":"Balt. J. Mod. Comput.","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116462172","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 : 1900-01-01DOI: 10.22364/bjmc.2018.6.4.02
Arjun Neervannan
Deep Reinforcement Learning algorithms have shown to perform well on complex tasks, such as video games and chess. However, when it comes to locomotive tasks, picking the right algorithm and hyperparameters continues to be a challenge for many researchers. This project addressed that issue by determining which one of three reinforcement learning algorithms worked most effectively to help a computer learn to walk, without any external supervision or guidance, in a simulated environment. In addition, the project also determined the best learning rate for the algorithms by testing out 6 learning rates. A walking environment was used as it is considered to be a good representative for a large class of reinforcement learning problems. Proximal policy optimization was found to be the most effective, followed by the trust-region policy optimization and the vanilla policy gradient. The algorithms worked best with learning rate 1e-3.
{"title":"Evaluating the Effectiveness of Deep Reinforcement Learning Algorithms in a Walking Environment","authors":"Arjun Neervannan","doi":"10.22364/bjmc.2018.6.4.02","DOIUrl":"https://doi.org/10.22364/bjmc.2018.6.4.02","url":null,"abstract":"Deep Reinforcement Learning algorithms have shown to perform well on complex tasks, such as video games and chess. However, when it comes to locomotive tasks, picking the right algorithm and hyperparameters continues to be a challenge for many researchers. This project addressed that issue by determining which one of three reinforcement learning algorithms worked most effectively to help a computer learn to walk, without any external supervision or guidance, in a simulated environment. In addition, the project also determined the best learning rate for the algorithms by testing out 6 learning rates. A walking environment was used as it is considered to be a good representative for a large class of reinforcement learning problems. Proximal policy optimization was found to be the most effective, followed by the trust-region policy optimization and the vanilla policy gradient. The algorithms worked best with learning rate 1e-3.","PeriodicalId":431209,"journal":{"name":"Balt. J. Mod. Comput.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121933068","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 : 1900-01-01DOI: 10.22364/bjmc.2020.8.4.08
Jolanta Miliauskaitė, D. Kalibatienė
Nowadays, data-driven fuzzy inference systems (FIS) have become popular to solve different vague, imprecise, and uncertain problems in various application domains. However, plenty of authors have identified different challenges and issues of FIS development because of its complexity that also influences FIS quality attributes. Still, there is no common agreement on a systematic view of these complexity issues and their relationship to quality attributes. In this paper, we present a systematic literature review of 1340 scientific papers published between 1991 and 2019 on the topic of FIS complexity issues. The obtained results were systematized and classified according to the complexity issues as computational complexity, complexity of fuzzy rules, complexity of membership functions, data complexity, and knowledge representation complexity. Further, the current research was extended by extracting FIS quality attributes related to the found complexity issues. The key, but not all, FIS quality attributes found are performance, accuracy, efficiency, and interpretability.
{"title":"Complexity in Data-Driven Fuzzy Inference Systems: Survey, Classification and Perspective","authors":"Jolanta Miliauskaitė, D. Kalibatienė","doi":"10.22364/bjmc.2020.8.4.08","DOIUrl":"https://doi.org/10.22364/bjmc.2020.8.4.08","url":null,"abstract":"Nowadays, data-driven fuzzy inference systems (FIS) have become popular to solve different vague, imprecise, and uncertain problems in various application domains. However, plenty of authors have identified different challenges and issues of FIS development because of its complexity that also influences FIS quality attributes. Still, there is no common agreement on a systematic view of these complexity issues and their relationship to quality attributes. In this paper, we present a systematic literature review of 1340 scientific papers published between 1991 and 2019 on the topic of FIS complexity issues. The obtained results were systematized and classified according to the complexity issues as computational complexity, complexity of fuzzy rules, complexity of membership functions, data complexity, and knowledge representation complexity. Further, the current research was extended by extracting FIS quality attributes related to the found complexity issues. The key, but not all, FIS quality attributes found are performance, accuracy, efficiency, and interpretability.","PeriodicalId":431209,"journal":{"name":"Balt. J. Mod. Comput.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117085603","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 : 1900-01-01DOI: 10.22364/bjmc.2022.10.1.02
Paulis F. Barzdins, A. Kalnins, E. Celms, J. Barzdins, A. Sprogis, Mikus Grasmanis, Sergejs Rikacovs, Guntis Barzdins
. This paper outlines our Deep Learning Lifecycle Data Management system. It consists of two major parts: the LDM Core Tool – a simple data logging tool; and an Extension Mechanism – this mechanism allows the user to extend the simple LDM Core Tool to match their specific requirements. Current extensions support adding new visualisations for data stored on the server. Our approach allows the Core Tool to be a complete black box; we need only a metamodel denoting the logical structure of the stored data. By then specialising this metamodel we can define an Extension Metamodel which, when communicated to the tool through configuration, allows us to define and thus add the extensions.
{"title":"Metamodel Specialisation based Tool Extension","authors":"Paulis F. Barzdins, A. Kalnins, E. Celms, J. Barzdins, A. Sprogis, Mikus Grasmanis, Sergejs Rikacovs, Guntis Barzdins","doi":"10.22364/bjmc.2022.10.1.02","DOIUrl":"https://doi.org/10.22364/bjmc.2022.10.1.02","url":null,"abstract":". This paper outlines our Deep Learning Lifecycle Data Management system. It consists of two major parts: the LDM Core Tool – a simple data logging tool; and an Extension Mechanism – this mechanism allows the user to extend the simple LDM Core Tool to match their specific requirements. Current extensions support adding new visualisations for data stored on the server. Our approach allows the Core Tool to be a complete black box; we need only a metamodel denoting the logical structure of the stored data. By then specialising this metamodel we can define an Extension Metamodel which, when communicated to the tool through configuration, allows us to define and thus add the extensions.","PeriodicalId":431209,"journal":{"name":"Balt. J. Mod. Comput.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117219971","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 : 1900-01-01DOI: 10.22364/bjmc.2019.7.4.03
S. Listopad
The problems of dynamic systems’ management, in particular the regional power distribution grid, are characterized by heterogeneity, lack of time for decision-making, distribution and partial observability of the control object, as well as the interdependence of actions performed and decisions made. Traditional abstract mathematical methods used in electric power industry are not relevant to such problems due to their inherent non-factors, and therefore solve only well formalized parts of these problems. To provide information support for solving problems in dynamic environments, a new class of intelligent systems is proposed, which simulate collective decision-making under the guidance of a facilitator, namely hybrid intelligent multi-agent systems of heterogeneous thinking. The presence of a hybrid component in these systems provides the opportunity to work with the heterogeneity of problems, and the presence of intelligent selforganizing agents make it possible to relevantly model effective problem-solving practices of expert teams in order to provide operational dispatching personnel with relevant solutions under time pressure. The paper considers the architecture of such a system for solving the problem of restoring the regional power distribution grid after large-scale accidents.
{"title":"Architecture of the Hybrid Intelligent Multi-agent System of Heterogeneous Thinking for Planning of Distribution Grid Restoration","authors":"S. Listopad","doi":"10.22364/bjmc.2019.7.4.03","DOIUrl":"https://doi.org/10.22364/bjmc.2019.7.4.03","url":null,"abstract":"The problems of dynamic systems’ management, in particular the regional power distribution grid, are characterized by heterogeneity, lack of time for decision-making, distribution and partial observability of the control object, as well as the interdependence of actions performed and decisions made. Traditional abstract mathematical methods used in electric power industry are not relevant to such problems due to their inherent non-factors, and therefore solve only well formalized parts of these problems. To provide information support for solving problems in dynamic environments, a new class of intelligent systems is proposed, which simulate collective decision-making under the guidance of a facilitator, namely hybrid intelligent multi-agent systems of heterogeneous thinking. The presence of a hybrid component in these systems provides the opportunity to work with the heterogeneity of problems, and the presence of intelligent selforganizing agents make it possible to relevantly model effective problem-solving practices of expert teams in order to provide operational dispatching personnel with relevant solutions under time pressure. The paper considers the architecture of such a system for solving the problem of restoring the regional power distribution grid after large-scale accidents.","PeriodicalId":431209,"journal":{"name":"Balt. J. Mod. Comput.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123941683","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 : 1900-01-01DOI: 10.22364/bjmc.2022.10.3.23
Henry Härm, Tanel Alumäe
. We present an approach for generating abstractive summaries for Estonian spoken news stories in a low-resource setting. Given a recording of a radio news story, the goal is to create a summary that captures the essential information in a short format. The approach consists of two steps: automatically generating the transcript and applying a state-of-the-art text summarization system to generate the result. We evaluated a number of models, with the best-performing model leveraging the large English BART model pre-trained on CNN/DailyMail dataset and fine-tuned on machine-translated in-domain data, and with the test data translated to English and back. The method achieved a ROUGE-1 score of 17.22, improving on the alternatives and achieving the best result in human evaluation. The applicability of the proposed solution might be limited in languages where machine translation systems are not mature. In such cases multilingual BART should be considered, which achieved a ROUGE-1 score of 17.00 overall and a score of 16.22 without machine translation based data augmentation.
{"title":"Abstractive Summarization of Broadcast News Stories for Estonian","authors":"Henry Härm, Tanel Alumäe","doi":"10.22364/bjmc.2022.10.3.23","DOIUrl":"https://doi.org/10.22364/bjmc.2022.10.3.23","url":null,"abstract":". We present an approach for generating abstractive summaries for Estonian spoken news stories in a low-resource setting. Given a recording of a radio news story, the goal is to create a summary that captures the essential information in a short format. The approach consists of two steps: automatically generating the transcript and applying a state-of-the-art text summarization system to generate the result. We evaluated a number of models, with the best-performing model leveraging the large English BART model pre-trained on CNN/DailyMail dataset and fine-tuned on machine-translated in-domain data, and with the test data translated to English and back. The method achieved a ROUGE-1 score of 17.22, improving on the alternatives and achieving the best result in human evaluation. The applicability of the proposed solution might be limited in languages where machine translation systems are not mature. In such cases multilingual BART should be considered, which achieved a ROUGE-1 score of 17.00 overall and a score of 16.22 without machine translation based data augmentation.","PeriodicalId":431209,"journal":{"name":"Balt. J. Mod. Comput.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124056018","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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