COMPUTATIONAL ANALYSIS OF KNOWLEDGE SHARING IN COLLABORATIVE DISTANCE LEARNING

The rapid advance of distance learning and networking technology has enabled universities and corporations to reach out and educate students across time and space barriers. This technology supports structured, on-line learning activities, and provides facilities for assessment and collaboration. Structured collaboration, in the classroom, has proven itself a successful and uniquely powerful learning method. Online collaborative learners, however, do not enjoy the same benefits as face-to-face learners because the technology provides no guidance or direction during online discussion sessions. Integrating intelligent facilitation agents into collaborative distance learning environments may help bring the benefits of the supportive classroom closer to distance learners.In this dissertation, I describe a new approach to analyzing and supporting online peer interaction. The approach applies Hidden Markov Models, and Multidimensional Scaling with a threshold-based clustering method, to analyze and assess sequences of coded on-line student interaction. These analysis techniques were used to train a system to dynamically recognize when and why students may be experiencing breakdowns while sharing knowledge and learning from each other. I focus on knowledge sharing interaction because students bring a great deal of specialized knowledge and experiences to the group, and how they share and assimilate this knowledge shapes the collaboration and learning processes. The results of this research could be used to dynamically inform and assist an intelligent instructional agent in facilitating knowledge sharing interaction, and helping to improve the quality of online learning interaction

Risk-based decision support system for life cycle management of industrials plants

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de ComputadoresThe objective of this thesis is to contribute for a better understanding of the decision making process in industrial plants specifically in situations with impact in the long term performance of the plant. The way decisions are made, and especially the motivations that lead to the selection of a specific course of action, are sometimes unclear and lack on justification. This is particularly critical in cases where inappropriate decisions drive to an increase on the production costs. Industrial plants are part of these cases, specifically the ones that are still lacking enhanced monitoring technologies and associated decision support systems. Maintenance has been identified as one of the critical areas regarding impact on performance. This is due to the fact that maintenance costs still represent a considerable slice of the production costs. Thus, understanding the way maintenance procedures are executed, and more important, the methods used to decide when maintenance should be developed and how, have been a concern of decision makers in industrial plants. This thesis proposes a methodology to efficiently transform the existing information on the plant behaviour into knowledge that may be used to support the decision process in maintenance activities. The development of an appropriate knowledge model relating the core aspects of the process enables the extraction of new knowledge based on the past experience. This thesis proposes also a methodology to calculate the risk associated to each maintenance situation and, based on the possible actions and on the impacts they may have in the plant costs performance, suggests the most appropriate course. The suggestion is made aiming the minimization of the life cycle costs. Results have been validated in test cases performed both at simulation and real industrial environments. The results obtained at the tests cases demonstrated the feasibility of the developed methodology as well as its adequateness and applicability in the domain of interest

An Overview and Tutorial of the Repertory Grid Technique in Information Systems Research

Interest in the repertory grid technique has been growing in the IS field. This article seeks to inform the reader on the proper use and application of the technique in IS research. The methodology has unique advantages that make it suitable for many research settings. In this tutorial, we describe the technique, its theoretical underpinnings, and how it may be used by IS researchers. We conclude by detailing many IS research opportunities that exist in respect to the repertory grid technique

Using artificial intelligence for pattern recognition in a sports context

Optimizing athlete’s performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired using wearable devices, have been useful to identify patterns, thus leading to a better understanding of the game and, consequently, providing the opportunity to improve the athletic performance. Even though there is a panoply of research in pattern recognition, there is a gap when it comes to non-controlled environments, as during sports training and competition. This research paper combines the use of physiological and positional data as sequential features of different artificial intelligence approaches for action recognition in a real match context, adopting futsal as its case study. The traditional artificial neural networks (ANN) is compared with a deep learning method, Long Short-Term Memory Network, and also with the Dynamic Bayesian Mixture Model, which is an ensemble classification method. The methods were used to process all data sequences, which allowed to determine, based on the balance between precision and recall, that Dynamic Bayesian Mixture Model presents a superior performance, with an F1 score of 80.54% against the 33.31% achieved by the Long Short-Term Memory Network and 14.74% achieved by ANN.info:eu-repo/semantics/publishedVersio

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)

1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020 Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option

Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"