In the Developmental Intelligence Laboratory, we are interested in understanding fundamental cognitive mechanisms of human intelligence, human learning, and human interaction and communication in everyday activities. To do so, we collect and analyze micro-level multimodal behavioral data using state-of-the-art sensing and computational techniques. One of our primary research aims is to understand human learning and early development. How do young children acquire fundamental knowledge of the world? How do they select and process the information around them and learn from scratch? How do they learn to move their bodies and to communicate and interact with others? Learning this kind of knowledge and skills is the core of human intelligence. To understand how human learners achieve the learning goal, the primary approach in our research is to attach GoPro-like cameras on the head of young children to record egocentric video from their point of view. Using this innovative approach, we've been collecting video data of children’s everyday activities, such as playing with their parents and their peers, reading books with parents and caregivers, and playing outside. We've been using state-of-the-art machine learning and data mining approaches to analyze high-density behavioral data. This research line will ultimately solve the mystery on why human children are such efficient learners. Moreover, the findings from our research will be used to help improve learning of children with developmental deficits. A complimentary research line is to explore how human learning can teach us about how machines can learn. Can we model and simulate how a human child learns and develops? To this end, our research aims at bridging and connecting developmental science in psychology and machine learning and computer vision in computer science.
This paper provides a summary account of Activity-Centred Analysis and Design (ACAD). ACAD offers a practical approach to analysing complex learning situations, in a way that can generate knowledge that is reusable in subsequent (re)design work. ACAD has been developed over the last two decades. It has been tested and refined through collaborative analyses of a large number of complex learning situations and through research studies involving experienced and inexperienced design teams. The paper offers a definition and high level description of ACAD and goes on to explain the underlying motivation. The paper also provides an overview of two current areas of development in ACAD: the creation of explicit design rationales and the ACAD toolkit for collaborative design meetings. As well as providing some ideas that can help teachers, design teams and others discuss and agree on their working methods, ACAD has implications for some broader issues in educational technology research and development. It questions some deep assumptions about the framing of research and design thinking, in the hope that fresh ideas may be useful to people involved in leadership and advocacy roles in the field.
G. Singh. (2013)By comparing historical data of trading like daily Open, High, Low, Close, Volume, Number of Trades, Turnover, Delivery percentage etc. of a particular stock with its Peer Group companies and Non Peer Group companies stocks for a particular period, we can find some unusual observations which are also known as outliers. In this paper we have tried to detect the observations, which are very different from the other observations using a Data Mining Technique for Outlier Detection-“Multiple Linear Regression Analysis”..
G. Singh. (2012)Fraud Detection is of great importance to financial institutions. In this paper we have tried to study the Outlier Analysis in Stock Market Fraud Detection. Outlier Analysis is a fundamental issue in Data Mining, specifically in Fraud Detection. While observing the Indian Stock Market, we could detect that some of the Trading Entities have suspicious trading patterns that give rise to a doubt of having some malpractices in stock transactions within Indian Stock Market. All the facts are presented on the basis of data obtained from the official sites of BSE (Bombay Stock Exchange), NSE (National Stock Exchange) and SEBI (Securities and Exchange Board of India)..
A. Grigoryan, and S. Agaian. Applied Mathematics and Sciences: An International Journal (MathSJ), volume 1 of IFIP Advances in Information and Communication Technology, page 23-39. Springer, (December 2014)
P. D, C. Veeramani, B. Shalini, and R. Karthika. International Journal of Innovative Science and Modern Engineering (IJISME), 2 (10):
41-45(September 2014)
A. Park, B. Beck, D. Fletche, P. Lam, and H. Tsang. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), page 880-883. (August 2016)
F. Haak. Information between Data and Knowledge, volume 74 of Schriften zur Informationswissenschaft, Werner Hülsbusch, Glückstadt, Gerhard Lustig Award Papers.(2021)
R. O'Donnell. (2021)cite arxiv:2105.10386Comment: First edition originally published April 2014, in hardcover book format by Cambridge University Press, and electronically on the author's website. This arXiv version corrects 100+ typos and errors, but is otherwise essentially the same.
J. Kim, P. Guo, D. Seaton, P. Mitros, K. Gajos, and R. Miller. Proceedings of the First ACM Conference on Learning @ Scale Conference, page 31–40. New York, NY, USA, Association for Computing Machinery, (2014)