University of Massachusetts Boston

About: University of Massachusetts Boston is a education organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Population & Health care. The organization has 6541 authors who have published 12918 publications receiving 411731 citations. The organization is also known as: UMass Boston.

Unleashing the Power of Planning to Create Disaster-Resistant Communities

Abstract: Artist Vita Marie Lovett's art quilt Toto, I've a Feeling We're Not in Miami Anymore includes debris found in her south Florida yard after Hurricane Andrew struck in 1994. She describes it as a “photo documentary of Hurricane Andrew's destruction whirling against a background of broken fabric roof trusses and window frames.” It is dedicated to her friend Jackie Parker Koger who lost her life as a result of the storm, which was the costliest natural disaster in U.S. history to that time. After the hurricane, Lovett relocated to Marietta, Georgia, where she creates art quilts with architectural themes from her home studio. Human suffering and losses of lives and property in natural disasters can be reduced with appropriate planning for hazardous areas. Federal policies addressing these problems, however, have yet to recognize the importance of planning as the cornerstone of effective local hazard mitigation. In fact, federal programs make planning more difficult because they encourage the intensive...

Disgust: characteristic features, social manifestations, and clinical implications

Abstract: Emotions have been a long–standing cornerstone of research in social and clinical psychology. Although the systematic examination of emotional processes has yielded a rather comprehensive theoretical and scientific literature, dramatically less empirical attention has been devoted to disgust. In the present article, the nature, experience, and other associated features of disgust are outlined. We also review the domains of disgust and highlight how these domains have expanded over time. The function of disgust in various social constructions, such as cigarette smoking, vegetarianism, and homophobia, is highlighted. Disgust is also becoming increasingly recognized as an influential emotion in the onset, maintenance, and treatment of various phobic states, Obsessive–Compulsive Disorder, and eating disorders. In comparison to the other emotions, disgust offers great promise for future social and clinical research efforts, and prospective studies designed to improve our understanding of disgust are o...

Using mLearning and MOOCs to understand chaos, emergence, and complexity in education

Abstract: In this paper, we look at how the massive open online course (MOOC) format developed by connectivist researchers and enthusiasts can help analyze the complexity, emergence, and chaos at work in the field of education today. We do this through the prism of a MobiMOOC, a six-week course focusing on mLearning that ran from April to May 2011. MobiMOOC embraced the core MOOC components of self-organization, connectedness, openness, complexity, and the resulting chaos, and, as such, serves as an interesting paradigm for new educational orders that are currently emerging in the field. We discuss the nature of participation in MobiMOOC, the use of mobile technology and social media, and how these factors contributed to a chaotic learning environment with emerging phenomena. These emerging phenomena resulted in a transformative educational paradigm.

Online Feature Selection with Streaming Features

Abstract: We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.