University of North Carolina at Charlotte

About: University of North Carolina at Charlotte is a education organization based out in Charlotte, North Carolina, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 8772 authors who have published 22239 publications receiving 562529 citations. The organization is also known as: UNC Charlotte & UNCC.

Incubators of Terror: Do Failed and Failing States Promote Transnational Terrorism?

Abstract: A growing body of scholars and policymakers have raised concerns that failed and failing states pose a danger to international security because they produce conditions under which transnational terrorist groups can thrive. This study devises an empirical test of this proposition, along with counter-theories, using simple descriptive statistics and a timeseries, cross-national negative binomial analysis of 197 countries from 1973 to 2003. It finds that states plagued by chronic state failures are statistically more likely to host terrorist groups that commit transnational attacks, have their nationals commit transnational attacks, and are more likely to be targeted by transnational terrorists themselves. Addressing the problem of failed and failing states will undoubtedly yield significant security and humanitarian dividends for the international system. Is a reduction in transnational terrorism one of them? United States policymakers regard failed and failing states such as Afghanistan, Somalia, and Sudan to be festering incubators of terrorism, and lament that for too long United States foreign policy has ignored the threat that these types of states pose to the international order and to national security. Post September 11th national security documents explicitly describe failed states as, ‘‘…safe havens for terrorists’’ (National Security Council 2006, 15), while Secretary of State Condoleezza Rice proclaims, ‘‘Today…the greatest threats to our security are defined more by the dynamics within weak and failing states than by the borders between strong and aggressive

The impacts of information quality and system quality on users' continuance intention in information-exchange virtual communities

Abstract: An information-exchange virtual community (VC) is an IT-supported virtual space that is composed of a group of people for accessing, sharing and disseminating topic-related experiences and knowledge through communication and social interaction 36,43]. With the increasing number of VCs and low switching cost, it is challenging to retain existing users and encourage their continued participation. By integrating the IS post-adoption research and IS Success model, we propose a research framework to investigate VC users' continuance intention from a quality perspective. Based on a field survey, we find that information and system quality directly affect perceived individual benefits and user satisfaction, which ultimately determine user continuance intention to consume and to provide information. Furthermore, by modeling information quality and system quality as multifaceted constructs, our results reveal key quality concerns in information-exchange VCs. Implications for VC design and management are also discussed.

Leadership, affect and emotions: A state of the science review

Abstract: This paper presents a selective, qualitative review of affect, emotions, and emotional competencies in leadership theory and research published in ten management and organizational psychology journals, book chapters and special issues of journals from 1990 to 2010. Three distinct themes emerged from this review: (1) leader affect, follower affect and outcomes, (2) discrete emotions and leadership, and (3) emotional competencies and leadership. Within each of these themes, we examine theory (construct definition and theoretical foundation) and methods (design, measurement and context) and summarize key findings. Our findings indicate that the study of affect and emotions in leadership fares well with regard to construct definitions across the first two themes, but not in the last theme above. Design and measurement issues across all three themes are a little less advanced. One serious gap is in a lack of focus on levels-of-analysis theoretically and methodologically. Our review concludes with recommendations for future theoretical and empirical work in this area.

What to Do Next: Modeling User Behaviors by Time-LSTM.

Abstract: Recently, Recurrent Neural Network (RNN) solutions for recommender systems (RS) are becoming increasingly popular. The insight is that, there exist some intrinsic patterns in the sequence of users’ actions, and RNN has been proved to perform excellently when modeling sequential data. In traditional tasks such as language modeling, RNN solutions usually only consider the sequential order of objects without the notion of interval. However, in RS, time intervals between users’ actions are of significant importance in capturing the relations of users’ actions and the traditional RNN architectures are not good at modeling them. In this paper, we propose a new LSTM variant, i.e. Time-LSTM, to model users’ sequential actions. Time-LSTM equips LSTM with time gates to model time intervals. These time gates are specifically designed, so that compared to the traditional RNN solutions, Time-LSTM better captures both of users’ shortterm and long-term interests, so as to improve the recommendation performance. Experimental results on two real-world datasets show the superiority of the recommendation method using TimeLSTM over the traditional methods.

Multi-Scale Progressive Fusion Network for Single Image Deraining

Abstract: Rain streaks in the air appear in various blurring degrees and resolutions due to different distances from their positions to the camera. Similar rain patterns are visible in a rain image as well as its multi-scale (or multi-resolution) versions, which makes it possible to exploit such complementary information for rain streak representation. In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi-scale progressive fusion network (MSPFN) for single image rain streak removal. For the similar rain streaks at different positions, we employ recurrent calculation to capture the global texture, thus allowing to explore the complementary and redundant information at the spatial dimension to characterize target rain streaks. Besides, we construct multi-scale pyramid structure, and further introduce the attention mechanism to guide the fine fusion of these correlated information from different scales. This multi-scale progressive fusion strategy not only promotes the cooperative representation, but also boosts the end-to-end training. Our proposed method is extensively evaluated on several benchmark datasets and achieves the state-of-the-art results. Moreover, we conduct experiments on joint deraining, detection, and segmentation tasks, and inspire a new research direction of vision task driven image deraining. The source code is available at https://github.com/kuihua/MSPFN.