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.
The main objective of FLOSSMETRICS is to construct, publish and analyse a large scale database with information and metrics about libre software development coming from several thousands of software projects, using existing methodologies, and tools already developed.
This journal is a multi-disciplinary focus for activities relating to the development, assessment and management of energy-related programs. It is hoped that this publication will prove to be an important factor in raising the standards of discussions, analyses, and evaluations relating to energy programs. The following are among the topical areas on which important contributions are solicited: input-output analyses relating to energy-consuming systems, careful resource or reserve assessments of all types, energy-conservation measures and their implementations, incisive evaluation of energy-systems managements, environmental-impact assessments, and policy alternatives stressing economic implications.
Glassbox is a troubleshooting agent for Java applications that automatically
diagnoses common problems.
Because Glassbox’s troubleshooting knowledge is built in, anyone can isolate a failing connection or a slow-running query instantly. You don’t need to analyze log files or graphs. Just one click, and Glassbox tells you in plain English what broke.
H. Zhang. misc, (September 2017)1. WU Qing—gang. The Current Situation and the Countermeasures of China Cold Chain Logistics Development. China Business and Market, 2011. 2. Zhang Zixiang, Cui Yan. Study on Fresh Farm Product E-commerce Mode Based on SWOT Analysis. Logistics technology, 2016..
R. Klopper, S. Gruner, and D. Kourie. Proceedings of the 2007 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries, page 56--65. Port Elizabeth, South Africa, ACM, (2007)