Goran Trajkovski

Application of wavelet neural-networks in wireless sensor networks

Some of the algorithms developed within the artificial neural-networks tradition can be easily adopted to wireless sensor network platforms and in the same time they can meet the requirements for sensor networks like: simple parallel distributed computation, distributed storage, data robustness and auto-classification of sensor readings. Dimensionality reduction, obtained simply from the outputs of the neural-networks clustering algorithms, leads to lower communication costs and energy savings. Two different data aggregation architectures are presented, with algorithms which use wavelets for initial data-processing of the sensory inputs and artificial neural-networks which use unsupervised learning methods for categorization of the sensory inputs. They are analyzed on a data obtained from a set of several motes, equipped with several sensors each. Results from deliberately simulated malfunctioning sensors show the data robustness of these architectures.

Transitioning from e-Learning to m-Learning: Present Issues and Future Challenges

This paper places mobile learning in the space of the existing learning methods. The three main groups of challenges - technological, development and pedagogical - in the transition from e-learning to m-learning are defined. The influence they make over the main participants in the m-learning process - developers, educators and students is also examined

Handbook of Research on Computational Arts and Creative Informatics

As technology becomes further meshed into our culture and everyday lives, new mediums and outlets for creative expression and innovation are necessary. The Handbook of Research on Computational Arts and Creative Informatics covers a comprehensive range of topics regarding the interaction of the sciences and the arts. Exploring new uses of technology and investigating creative insights into concepts of art and expression, this cutting-edge Handbook of Research offers a valuable resource to academicians, researchers, and field practitioners.

Handbook of Research on Agent-Based Societies: Social and Cultural Interactions

In a given environment, agents interact with each other, imitating, communicating, exchanging, and competing. Based on these heterogeneous modalities of interaction, a variety of socialities may emerge including language and communication, identities, economies, and cultures. The Handbook of Research on Agent-Based Societies: Social and Cultural Interactions addresses the emergence of societal phenomena in the interactions of systems of agents. Comprising authoritative chapters by numerous international authors, this reference book goes well beyond describing the next generation of multi-agent systems in simulations and system engineering and analyzes existing systems to stimulate the development of new ones. It features: 23 authoritative contributions by over 40 of the worlds leading experts on agent-based societies from 7 countriesComprehensive coverage of each specific topic, highlighting recent trends and describing the latest advances in the fieldMore than 830 references to existing literature and research on agent-based societiesAnd a compendium of over 260 key terms with detailed definitions. It is organized by topic and indexed, making it a convenient method of reference for all IT/IS scholars and professionals. It includes cross-referencing of key terms, figures, and information pertinent to agent-based societies.

MASIVE: A Case Study in Multiagent Systems

The project MASIVE (Multi-Agent Systems Interactive Virtual Environments) is the multi-agent extension of our Interactivist-Expectative Theory on Agency and Learning (IETAL). The agents in the environment learn expectations from their interactions with the environment. In addition to that, they are equipped with special sensors for sensing akin agents, and interchange their knowledge of the environment (their intrinsic representations) during their imitation conventions. In this paper we discuss the basics of the theory, and the social consequences of such an environment from the perspective of learning, knowledge dissemination, and emergence of language.

Scouting for imprecise temporal associations to support effectiveness of drugs during clinical trials

The field of data mining is dedicated to the analysis of data to find underlying connections and the discovery of new patterns. This research targets the analysis of imprecise temporal associations through the modification of a standard market basket analysis approach by means of fuzzy set relations to classify the associations among different sources of data. The domain that is taken into consideration in this work is the one of medicine. We used data recorded within an Intensive Care Unit from a 8 month old infant that suffers from Acute Respiratory Distress Syndrome. In particular, we analyzed the response of the partial pressure of oxygen within the bloodstream to the application of a respirator. The results of this research show that it is possible to investigate such relations with the help of fuzzy set classification for temporal associations, and the result of such exploration is as easily understandable as the standard Market Basket algorithm. The findings support the physiological response, suggesting that this approach is worthy of notice. We are confident that such an algorithm will show its capabilities when applied to the clinical trials part of drug testing, given the results outlined in this article.

Effects of Computer Competency on Usability and Learning Experience in Online Learning Environments

Many institutions in higher education are offering at least some of their curriculum online and use online course management systems to support these learning environments. Successful participation in online learning depends on many factors, and may especially be influenced by the degree of computer competency users bring with them to the learning environment. The purpose of this study is to evaluate the effects of several areas of computer competency on student learning experiences and learning environment usability in online learning environments. Subject of evaluation was a multi-section course consisting of eight sections taught in hybrid format; approximately 50% of course work was conducted using the World Wide Web. Results of the study have direct implications in online course design and development

Data mining for imprecise temporal associations

The field of data mining is dedicated to the analysis of data in order to find underlying connections and the discovery of new patterns. Since the volume of data to be analyzed is sometimes quite significant, there is the need for efficient data mining algorithms to be implemented. The market-basket algorithm can represent a breakthrough in data mining techniques. As the associations that are to be analyzed grow more and more abstract, the market-basket approach is unable to deal with imprecise temporal associations, leaving a big area uncharted. This research is dedicated to the analysis of temporal imprecise associations through the modification of a standard a-priori approach by means of fuzzy set relations to classify the associations relating different sources of data. The results of this research show that it is possible to investigate such relations with the help of fuzzy set classification for temporal associations, and the result of such exploration is as easily understandable as the standard a-priori algorithm.

Building infrastructure for an honors research robotics lab

This paper overviews the undergraduate research and teaching activities at the Cognitive Agency and Robotics Laboratory (CARoL) at Towson University. The lab emerged out of an experiment conducted in a small classroom setting. Today, it can be used as a recipe for building a setting where quality undergraduate (and graduate) research projects in robotics can be conducted on a shoestring.

Extending MASIVE: the impact of stress on imitation-based learning

In this paper we model the conjecture that stress has a negative impact on imitation-based learning, in the context of the IETAL and MASIVE theories. We pose a central question: Which is worse - a loss of available information due to stress, or the acquisition of misinformation due to stress? This question is addressed by modeling a modified IETAL agent in the MASIVE environment for which imitation-based learning is affected by stress. We present an algebraic representation of the modified agent, thus giving an extension of earlier work designed to explore learning phenomena in human subjects. Our goal is to break ground in a new direction in order to develop a richer theory based on our previous work in the domain of cognitive robotics and multiagent systems.