Professor Vincenzo Loia University of Salerno
Bridging Granular Computing and Situational Awareness : Perspective, Opportunities, Problems
Situational Awareness is usually defined in terms of what information is important for a particular job or goal. Most of the problems with Situational Awareness occurs at the level “Perception” and “Comprehension”
because of missing information, information perceived in a wrong way or also information not pertinent with respect to the specific goal. Situational Awareness requirements are different for different domains and human roles. This demands taking into account different views and levels of “granularity” of information. Situational Awareness oriented systems have to organize information around goals and provide a proper level of abstraction of meaningful information. To answer these issues, the talk presents general human-oriented perception model which consist of three processes (not strictly separated):
- Sensing which deals with the basic experience generated as stimuli fall on our sensory systems
- Perception which deals with the interpretation of the sensations, giving them meaning and organization
- Cognition: acquisition which deals with the retrieval and exploitation of the information
In the talk we will discuss how some Granular Computing approaches can be useful if employed in the three processes and the pros/cons of the derived experiences.
Professor Vincenzo Loia received B.S. degree in computer science from University of Salerno , Italy in 1985 and the M.S. and Ph.D. degrees in computer science from University of Paris VI, France, in 1987 and 1989, respectively. From 1989 he is Faculty member at the University of Salerno. He is currently a Full Professor of Computer Science at Department of Management and Innovation Systems. He was principal investigator in a number of industrial R&D projects and in academic research projects. He is author of over 390 original research papers in international journals, book chapters, and in international conference proceedings. He edited four research books around agent technology, Internet, and soft computing methodologies. His research interests include Computational Intelligence, Soft Computing, Brain computer interfaces, Knowledge Discovery and Situational Awareness. He is Senior Member IEEE. He is Editor-in-Chief (EIC) and Founder of Journal of Ambient Intelligence and Humanized Computing, Springer-Verlag and he is also Co-Editor-in-Chief (EIC) of Softcomputing Journal, Springer-Verlag. He is Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics (Systems) and IEEE Transactions on Industrial Informatics.
Professor Minjuan Wang Shanghai International Studies University (Oriental Scholar), China & San Diego State University, USA
Augmented Reality: the Emerging Trend in Education
Augmented Reality (AR) is the layering of virtual information over the real, 3-D world to produce a blended reality. AR has been considered a significant tool in education for many years. It adds new layers of interactivity, context, and information for learners which can deepen and enrich the learning experience. The combination of real and virtual allows the student to engage in learning about a topic from multiple perspectives and data sources at levels that are not always available in traditional classroom settings and interactions.
As the usage of mobile devices in formal settings continues to rise, so does the opportunity to harness the power of augmented reality (AR) to enhance teaching and learning. Many educators have experimented with AR, but has it proven to improve what students grasp and retain? Is AR just another fun way to engage students, with little transformation of learning? This keynote speaking will introduce augmented reality as an emerging trend in education, provide an overview of its current development, explore examples of curriculum integration, and also suggest approaches for success.
Professor Minjuan Wang is adjunct faculty at Shanghai International Studies University in China; Professor of Learning Design and Technology at San Diego State University (SDSU), and Program Manager in the Chancellor’s Office at California State University, both in the USA. Additionally, she is a research associate for British Telecom. Her current research specialties focus on the sociocultural aspects of online learning, mobile learning, intelligent systems, and cloud learning. Currently, she conducts research on teaching and learning in international multicultural settings and the use of mobile learning in formal and informal learning. Minjuan serves on the editorial boards for three outstanding journals: Open Education Research, International Journal on E-Learning (IJEL), and the Open Education Journal. She has also been a long-time reviewer for the premier journal—Educational Technology Research and Development, and a reviewer for more than 10 other international journals. In addition, she edited the Handbook of Research on Hybrid Learning Models. A winner of several research awards, Minjuan has more than 100 peer-reviewed articles published in premier journals, such as Educational Technology Research and Development, IEEE Transactions on Education, Computers and Education, British Journal of Educational Technology, and Educational Technology and Society. She has also published several book chapters on Best practices in teaching online or hybrid courses, Cross-cultural issues in online learning, Cybergogy for interactive learning online, and assessment of mobile learning in large classrooms. The Cybergogy for Engaged Learning Model she created has been recognized as an instructional design model (http://edutechwiki.unige.ch/en/Cybergogy).
Professor Jonathan Garibaldi University of Nottingham
Modelling Variation in Reasoning to Improve Decision Making
Medical decision making is often difficult, requiring complex decisions in the precence of much uncertainty (both in data and in domain knowledge). In this talk, I shall present some of the recent work we have carried out in modelling uncertainty whilst performing various clustering and classification tasks to support medical decision making. In particular, I will focus on the variability exhibited by human decision makers, and how modelling this may lead to improved decision making.
Professor Jon Garibaldi is a professor of computer science at the University of Nottingham and the head of the IMA research group. His main research interest is in the modelling of human decision making, primarily in the context of medical applications. His work to date has concentrated on utilising fuzzy logic to model the imprecision and uncertainty inherent in medical knowledge representation and decision making. This has been applied in areas such as classification of breast cancer, identification of Alzheimer’s disease, and the assessment of immediate neonatal outcome. A particular interest is in the transfer of medical intelligent systems into clinical use and this has led to the study of methods of evaluating intelligent systems and mechanisms for their implementation. Prof Garibaldi also has an interest in generic machine learning, such as clustering, classification and optimisation, particularly when applied to the optimisation of decision making models, and in the study of adaptive and time-varying behaviour.
Dr Annie Louis University of Essex
Worth my reading time? Computational methods for analyzing the writing style and quality of documents
When we read articles, we spontaneously make judgements about whether it is well-written or not, boring or interesting, too dense or not contentful enough. In this talk, I will describe how Computational Linguistics techniques are starting to get computers to make similar judgements about writing style and quality. Being able to do this task well has great potential for recommending articles, improving web search, assessing student writing, and improving the quality of texts generated by automatic systems. But until recent years, research on this problem was limited to spelling, and grammar issues where models analyze words or individual sentences.
In this talk, I will present my work aiming to predict a richer set of quality aspects such as coherence, interest value, and verbosity. I will show how this impact can be created by language processing models that
1) identify deep linguistic structure, and
2) are able to find intentions and perceptions expressed through language.
Annie Louis is a Lecturer in the School of Computer Science and Electronic Engineering at the University of Essex. Her research interests are in natural language processing, machine learning, and the application of language processing technology to solve problems in information retrieval and social media analysis. Previously, Annie was
a Research Associate (2015) and a Newton International Fellow (2013 to 2015) at the University of Edinburgh. She obtained her PhD from the University of Pennsylvania in 2013, and is the recipient of a Best Student Paper Award in 2010 (SIGDIAL) and a Best Paper Award in 2012 (EMNLP-CoNLL).