The paper presents an overview of possible application fields of big data to the Technology-Enhanced Learning (TEL), with the many different facets this could imply. Many are the benefits for e-learning when approaching the collection of data, especially when elearning is delivered in a life-long learning perspective. All these benefits could impact the future of eLearning, by revolutionizing the way we analyze and assess the eLearning experience. On one side, we present our experience in enriching the persistence layer of an LMS with a deeper log system on users’ actions, in the perspective of collecting volumes of data compatible with big data tools and technologies, while highlighting some related issues. On the other hand we will deal with the first applications of cognitive systems that are responsible for catalysing the big data in analytics aimed at e-learning
{"title":"Is e-learning ready for big data? And how big data would be useful to e-learning?","authors":"P. Maresca, A. Molinari","doi":"10.18293/DMS2016-042","DOIUrl":"https://doi.org/10.18293/DMS2016-042","url":null,"abstract":"The paper presents an overview of possible application fields of big data to the Technology-Enhanced Learning (TEL), with the many different facets this could imply. Many are the benefits for e-learning when approaching the collection of data, especially when elearning is delivered in a life-long learning perspective. All these benefits could impact the future of eLearning, by revolutionizing the way we analyze and assess the eLearning experience. On one side, we present our experience in enriching the persistence layer of an LMS with a deeper log system on users’ actions, in the perspective of collecting volumes of data compatible with big data tools and technologies, while highlighting some related issues. On the other hand we will deal with the first applications of cognitive systems that are responsible for catalysing the big data in analytics aimed at e-learning","PeriodicalId":297195,"journal":{"name":"J. Vis. Lang. Sentient Syst.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115796169","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}
Social robotics is related to the robotic systems and human interaction. Social robots have applications in elderly care, health care, home care, customer service and reception in industrial settings. Human-Robot Interaction (HRI) requires better understanding of human emotion. There are few multimodal fusion systems that integrate limited amount of facial expression, speech and gesture analysis. In this paper, we describe the implementation of a semantic algebra based formal model that integrates six basic facial expressions, speech phrases and gesture trajectories. The system is capable of real-time interaction. We used the decision level fusion approach for integration and the prototype system has been implemented using Matlab. KeywordsAffective computing, Emotion recognition, Humanmachine interaction, Multimedia, Multimodal, Decision level fusion, Social robotics.
{"title":"Towards Formal Multimodal Analysis of Emotions for Affective Computing","authors":"M. Ghayoumi, Maha Thafar, A. Bansal","doi":"10.18293/DMS2016-030","DOIUrl":"https://doi.org/10.18293/DMS2016-030","url":null,"abstract":"Social robotics is related to the robotic systems and human interaction. Social robots have applications in elderly care, health care, home care, customer service and reception in industrial settings. Human-Robot Interaction (HRI) requires better understanding of human emotion. There are few multimodal fusion systems that integrate limited amount of facial expression, speech and gesture analysis. In this paper, we describe the implementation of a semantic algebra based formal model that integrates six basic facial expressions, speech phrases and gesture trajectories. The system is capable of real-time interaction. We used the decision level fusion approach for integration and the prototype system has been implemented using Matlab. KeywordsAffective computing, Emotion recognition, Humanmachine interaction, Multimedia, Multimodal, Decision level fusion, Social robotics.","PeriodicalId":297195,"journal":{"name":"J. Vis. Lang. Sentient Syst.","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124937889","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}
Traffic microscopic traffic simulation models have become extensively used in both transportation operations and management analyses, which are very useful in reflecting the dynamic nature of transportation system in a stochastic manner. As far as the microscopic traffic flow simulation users are concerned, the one of the major concerns would be the appropriate calibration of the simulation models. In this paper a parameter calibration method of microscopic traffic flow simulation models based on orthogonal genetic algorithm is presented. In order to improve the capacity of locating a possible solution in solution space, the proposed method incorporates the orthogonal experimental design method into the genetic algorithm. The proposed method is applied to an arterial section of Ronghua Road in Beijing. Through comparing with the parameter calibration method based on genetic algorithm, the advantage of the proposed method is shown. Keywords-Microscopic traffic flow simulation model; Parameter calibration; Orthogonal genetic algorithm; VISSIM
{"title":"Parameter Calibration Method of Microscopic Traffic Flow Simulation Models based on Orthogonal Genetic Algorithm","authors":"Yong Qin, Honghui Dong, Qing Zhang, Yanfang Yang","doi":"10.18293/DMS2016-047","DOIUrl":"https://doi.org/10.18293/DMS2016-047","url":null,"abstract":"Traffic microscopic traffic simulation models have become extensively used in both transportation operations and management analyses, which are very useful in reflecting the dynamic nature of transportation system in a stochastic manner. As far as the microscopic traffic flow simulation users are concerned, the one of the major concerns would be the appropriate calibration of the simulation models. In this paper a parameter calibration method of microscopic traffic flow simulation models based on orthogonal genetic algorithm is presented. In order to improve the capacity of locating a possible solution in solution space, the proposed method incorporates the orthogonal experimental design method into the genetic algorithm. The proposed method is applied to an arterial section of Ronghua Road in Beijing. Through comparing with the parameter calibration method based on genetic algorithm, the advantage of the proposed method is shown. Keywords-Microscopic traffic flow simulation model; Parameter calibration; Orthogonal genetic algorithm; VISSIM","PeriodicalId":297195,"journal":{"name":"J. Vis. Lang. Sentient Syst.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126307028","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}
Graphs long have been valued as a pictorial way of representing relationships between entities. Contemporary applications use graphs to model social networks, protein interactions, chemical structures, and a variety of other systems. In many cases, it is useful to detect patterns within graphs. For example, one could be interested in identifying frequently occurring subgraphs, which is known as the frequent subgraph mining problem. A complete solution to this problem can result in numerous subgraphs and can be time-consuming to compute. An approximate solution is faster, but is subject to static heuristics that are beyond the control of the user. Herein we present VisCFSM, a visual, constraint-based, frequent subgraph mining system which allows the user to dynamically specify a variety of constraints on the subgraphs to be found while the mining algorithm is running. The constraint specification interactions are performed through a visual user interface, thereby facilitating a form of visual algorithm steering. This approach can be integrated with any frequent subgraph mining algorithm. Most importantly, this approach has the potential for the user to better, and more quickly, find the information that is of most interest to him/her in a graph. Keywords-graph; data mining; visual algorithm steering
{"title":"VisCFSM: Visual, Constraint-Based, Frequent Subgraph Mining","authors":"Nathan Eloe, C. Sabharwal, J. Leopold","doi":"10.18293/DMS2016-026","DOIUrl":"https://doi.org/10.18293/DMS2016-026","url":null,"abstract":"Graphs long have been valued as a pictorial way of representing relationships between entities. Contemporary applications use graphs to model social networks, protein interactions, chemical structures, and a variety of other systems. In many cases, it is useful to detect patterns within graphs. For example, one could be interested in identifying frequently occurring subgraphs, which is known as the frequent subgraph mining problem. A complete solution to this problem can result in numerous subgraphs and can be time-consuming to compute. An approximate solution is faster, but is subject to static heuristics that are beyond the control of the user. Herein we present VisCFSM, a visual, constraint-based, frequent subgraph mining system which allows the user to dynamically specify a variety of constraints on the subgraphs to be found while the mining algorithm is running. The constraint specification interactions are performed through a visual user interface, thereby facilitating a form of visual algorithm steering. This approach can be integrated with any frequent subgraph mining algorithm. Most importantly, this approach has the potential for the user to better, and more quickly, find the information that is of most interest to him/her in a graph. Keywords-graph; data mining; visual algorithm steering","PeriodicalId":297195,"journal":{"name":"J. Vis. Lang. Sentient Syst.","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126118389","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}
C. Gravino, M. Risi, G. Scanniello, G. Tortora, R. Francese
Project-based learning is a kind of learning activity which has great educative effect, but which presents also several issues. In particular, if we consider an university course that requires the design and the implementation of a software project, may be difficult to estimate the number of hours that a team of students has to take to accomplish that project. There is the risk to underestimate the project (too difficult) or to overestimate it (too easy) with respect to the other projects of the same course and the amount of foreseen work hours. In this paper, we present the experience we gained in the adoption of Software Project and Product Measures for addressing the project size of projects performed during a Mobile Application Development course for Computer Science students at the University of Salerno. The course foresaw a project work conducted by students organized in teams. The goal of the project work was to design and develop an Android-based application with back-end for smart devices. Software estimation project measures are applied to some metrics extracted in the requirement analysis phase to get an estimation of the effort in terms of man/hours and consequently to adjust the project size by adding/reducing requirements. The metrics extracted from the projects of academic year 2013/14 have been used in the successive year for estimating the project effort and intervene on the project size variables.
{"title":"Supporting Mobile Development Project-Based learning by Software Project and Product Measures","authors":"C. Gravino, M. Risi, G. Scanniello, G. Tortora, R. Francese","doi":"10.18293/DMS2016-045","DOIUrl":"https://doi.org/10.18293/DMS2016-045","url":null,"abstract":"Project-based learning is a kind of learning activity which has great educative effect, but which presents also several issues. In particular, if we consider an university course that requires the design and the implementation of a software project, may be difficult to estimate the number of hours that a team of students has to take to accomplish that project. There is the risk to underestimate the project (too difficult) or to overestimate it (too easy) with respect to the other projects of the same course and the amount of foreseen work hours. In this paper, we present the experience we gained in the adoption of Software Project and Product Measures for addressing the project size of projects performed during a Mobile Application Development course for Computer Science students at the University of Salerno. The course foresaw a project work conducted by students organized in teams. The goal of the project work was to design and develop an Android-based application with back-end for smart devices. Software estimation project measures are applied to some metrics extracted in the requirement analysis phase to get an estimation of the effort in terms of man/hours and consequently to adjust the project size by adding/reducing requirements. The metrics extracted from the projects of academic year 2013/14 have been used in the successive year for estimating the project effort and intervene on the project size variables.","PeriodicalId":297195,"journal":{"name":"J. Vis. Lang. Sentient Syst.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122785061","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 learning assessment in e-learning contexts is one of the latest challenges for educational technology researchers. One of the main issues to be addressed is the definition of dimensions that should be used to measure the learning effectiveness. In this perspective, the research work aims at defining the engagement indicators useful to assess the active participation of students in social learning environments. Moreover, the paper presents the design and implementation of Learning Dashboards aimed at visualizing the student engagement in online communities where the engagement and involvement of students are the key factors for successful learning. KeywordsAssessment; Learning Analytics; Learning dashboard; Social learning environments; Engagement.
{"title":"Visualizing student engagement in e-learning environment","authors":"T. Roselli, Veronica Rossano, Enrica Pesare","doi":"10.18293/DMS2016-028","DOIUrl":"https://doi.org/10.18293/DMS2016-028","url":null,"abstract":"The learning assessment in e-learning contexts is one of the latest challenges for educational technology researchers. One of the main issues to be addressed is the definition of dimensions that should be used to measure the learning effectiveness. In this perspective, the research work aims at defining the engagement indicators useful to assess the active participation of students in social learning environments. Moreover, the paper presents the design and implementation of Learning Dashboards aimed at visualizing the student engagement in online communities where the engagement and involvement of students are the key factors for successful learning. KeywordsAssessment; Learning Analytics; Learning dashboard; Social learning environments; Engagement.","PeriodicalId":297195,"journal":{"name":"J. Vis. Lang. Sentient Syst.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130912271","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}
Safety analysis ensuring the normal operation of an engineering system is important. The existing safety analysis methods are limited to relatively simple fact description and statistical induction level. Besides, many of them enjoy poor generality, and fail to achieve comprehensive safety evaluation given a system structure and collected information. This work describes a new safety analysis method, called a GO-Bayes algorithm. It combines structural modeling of the GO method and probabilistic reasoning of the Bayes method. It can be widely used in system analysis. The work takes a metro vehicle braking system as an example to verify its usefulness and accuracy. Visual implementation by Extendsim software shows its feasibility and advantages in comparison with the Fault Tree Analysis (FTA) method.
{"title":"GO-Bayes Method for System Modeling and Safety Analysis","authors":"Guo-qiang Cai","doi":"10.18293/VLSS2015-052","DOIUrl":"https://doi.org/10.18293/VLSS2015-052","url":null,"abstract":"Safety analysis ensuring the normal operation of an engineering system is important. The existing safety analysis methods are limited to relatively simple fact description and statistical induction level. Besides, many of them enjoy poor generality, and fail to achieve comprehensive safety evaluation given a system structure and collected information. This work describes a new safety analysis method, called a GO-Bayes algorithm. It combines structural modeling of the GO method and probabilistic reasoning of the Bayes method. It can be widely used in system analysis. The work takes a metro vehicle braking system as an example to verify its usefulness and accuracy. Visual implementation by Extendsim software shows its feasibility and advantages in comparison with the Fault Tree Analysis (FTA) method.","PeriodicalId":297195,"journal":{"name":"J. Vis. Lang. Sentient Syst.","volume":"270 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116493741","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}
We describe RankFrag: a technique which uses machine learning to detect corner points in hand-drawn digital curves. RankFrag classifies the stroke points by iteratively extracting them from a list of corner candidates. The points extracted in the last iterations are said to have a higher rank and are more likely to be corners. The technique has been tested on three different datasets described in the literature. We observed that, considering both accuracy and efficiency, RankFrag performs better than other state-of-art techniques.
{"title":"RankFrag: A Machine Learning-Based Technique for Finding Corners in Hand-Drawn Digital Curves","authors":"G. Costagliola, Mattia De Rosa, V. Fuccella","doi":"10.18293/VLSS2015-043","DOIUrl":"https://doi.org/10.18293/VLSS2015-043","url":null,"abstract":"We describe RankFrag: a technique which uses machine learning to detect corner points in hand-drawn digital curves. RankFrag classifies the stroke points by iteratively extracting them from a list of corner candidates. The points extracted in the last iterations are said to have a higher rank and are more likely to be corners. The technique has been tested on three different datasets described in the literature. We observed that, considering both accuracy and efficiency, RankFrag performs better than other state-of-art techniques.","PeriodicalId":297195,"journal":{"name":"J. Vis. Lang. Sentient Syst.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128159860","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}
L. Greco, F. Colace, V. Moscato, F. Amato, A. Picariello
{"title":"A Quick Survey on Sentiment Analysis Techniques: a lexical based perspective","authors":"L. Greco, F. Colace, V. Moscato, F. Amato, A. Picariello","doi":"10.18293/VLSS2015-020","DOIUrl":"https://doi.org/10.18293/VLSS2015-020","url":null,"abstract":"","PeriodicalId":297195,"journal":{"name":"J. Vis. Lang. Sentient Syst.","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127368412","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}
Object indexing is a challenging task that enables the retrieval of relevant images in pictorial databases. In this paper, we present an incremental indexing approach of picture objects based on clustering of object shapes. A semisupervised fuzzy clustering algorithm is used to group similar objects into a number of clusters by exploiting a-priori knowledge expressed as a set of pre-labeled objects. Each cluster is represented by a prototype that is manually labeled and used to annotate objects. To capture eventual updates that may occur in the pictorial database, the previously discovered prototypes are added as pre-labeled objects to the current shape set before clustering. The proposed incremental approach is evaluated on a benchmark image dataset, which is divided into chunks to simulate the progressive availability of picture objects during time.
{"title":"Incremental indexing of objects in pictorial databases","authors":"G. Castellano, A. Fanelli, M. Torsello","doi":"10.18293/VLSS2015-010","DOIUrl":"https://doi.org/10.18293/VLSS2015-010","url":null,"abstract":"Object indexing is a challenging task that enables the retrieval of relevant images in pictorial databases. In this paper, we present an incremental indexing approach of picture objects based on clustering of object shapes. A semisupervised fuzzy clustering algorithm is used to group similar objects into a number of clusters by exploiting a-priori knowledge expressed as a set of pre-labeled objects. Each cluster is represented by a prototype that is manually labeled and used to annotate objects. To capture eventual updates that may occur in the pictorial database, the previously discovered prototypes are added as pre-labeled objects to the current shape set before clustering. The proposed incremental approach is evaluated on a benchmark image dataset, which is divided into chunks to simulate the progressive availability of picture objects during time.","PeriodicalId":297195,"journal":{"name":"J. Vis. Lang. Sentient Syst.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114287556","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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