Building an IT Taxonomy with Co-occurrence Analysis, Hierarchical Clustering, and Multidimensional Scaling

Different information technologies (ITs) are related in complex ways. How can the relationships among a large number of ITs be described and analyzed in a representative, dynamic, and scalable way? In this study, we employed co-occurrence analysis to explore the relationships among 50 information technologies discussed in six magazines over ten years (1998-2007). Using hierarchical clustering and multidimensional scaling, we have found that the similarities of the technologies can be depicted in hierarchies and two-dimensional plots, and that similar technologies can be classified into meaningful categories. The results imply reasonable validity of our approach for understanding technology relationships and building an IT taxonomy. The methodology that we offer not only helps IT practitioners and researchers make sense of numerous technologies in the iField but also bridges two related but thus far largely separate research streams in iSchools - information management and IT management

Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks

Recently, skeleton based action recognition gains more popularity due to cost-effective depth sensors coupled with real-time skeleton estimation algorithms. Traditional approaches based on handcrafted features are limited to represent the complexity of motion patterns. Recent methods that use Recurrent Neural Networks (RNN) to handle raw skeletons only focus on the contextual dependency in the temporal domain and neglect the spatial configurations of articulated skeletons. In this paper, we propose a novel two-stream RNN architecture to model both temporal dynamics and spatial configurations for skeleton based action recognition. We explore two different structures for the temporal stream: stacked RNN and hierarchical RNN. Hierarchical RNN is designed according to human body kinematics. We also propose two effective methods to model the spatial structure by converting the spatial graph into a sequence of joints. To improve generalization of our model, we further exploit 3D transformation based data augmentation techniques including rotation and scaling transformation to transform the 3D coordinates of skeletons during training. Experiments on 3D action recognition benchmark datasets show that our method brings a considerable improvement for a variety of actions, i.e., generic actions, interaction activities and gestures.Comment: Accepted to IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 201

The effects of intense fire on headwater streams of the Colville National Forest, WA

Thesis (M.S.) University of Alaska Fairbanks, 2006Forest fires play an important role in shaping ecosystems, and there has been growing concern on the effects of high intensity fires on forest and aquatic ecosystems. Headwater streams are highly connected to riparian and surrounding terrestrial systems, and to downstream aquatic systems, partly through prey and organic matter transfers via aquatic invertebrate drift and emergence. Because of their small size, headwater streams may experience the greatest initial impact from forest fire, but may also return to pre-fire conditions quicker than larger streams. In this study, headwater streams from replicated burned and control watersheds were sampled in the two years following an intense forest fire in northeastern Washington. Benthic, drift and emergence samples of aquatic invertebrates were taken and analyzed for differences in density, biomass and community composition between watershed types. There was significantly higher density of invertebrates in burned sites, but no difference in biomass except in invertebrate emergence which was greater at burned sites. There was lower diversity in the burned watersheds, and the invertebrate community was dominated by chironomids. These changes in invertebrate density and community composition could influence the food resources available to aquatic and riparian consumers

Time series classification based on fractal properties

The article considers classification task of fractal time series by the meta algorithms based on decision trees. Binomial multiplicative stochastic cascades are used as input time series. Comparative analysis of the classification approaches based on different features is carried out. The results indicate the advantage of the machine learning methods over the traditional estimating the degree of self-similarity.Comment: 4 pages, 2 figures, 3 equations, 1 tabl