Evaluation of trend localization with multi-variate visualizations

Multi-valued data sets are increasingly common, with the number of dimensions growing. A number of multi-variate visualization techniques have been presented to display such data. However, evaluating the utility of such techniques for general data sets remains difficult. Thus most techniques are studied on only one data set. Another criticism that could be levied against previous evaluations of multi-variate visualizations is that the task doesn't require the presence of multiple variables. At the same time, the taxonomy of tasks that users may perform visually is extensive. We designed a task, trend localization, that required comparison of multiple data values in a multi-variate visualization. We then conducted a user study with this task, evaluating five multivariate visualization techniques from the literature (Brush Strokes, Data-Driven Spots, Oriented Slivers, Color Blending, Dimensional Stacking) and juxtaposed grayscale maps. We report the results and discuss the implications for both the techniques and the task.     

Annotation-based access control for collaborative information spaces

Web 2.0 social platforms (e.g., Flickr, YouTube) and Collaborative Working Environments (e.g., Microsoft SharePoint, BSCW) provide Web-based collaborative information spaces which enable common users and/or professionals to work together and share their online resources. Most of these collaborative information spaces provide role-based or group-based, coarse-grained access control policies which cannot successfully cope with the requirements posed by massive and open collaboration. In this paper, we present an annotation-based access control (AnBAC) model supported by a Collaboration Vocabulary (CoVoc) as a more flexible and user-centric access control approach. Based on this, we developed two tools: Uncle-Share is a gadget that provides annotation-based access control and can be equipped with CoVoc for annotating collaborative relationships. Who-With-Whom uses CoVoc to visualize extended social networks in order to help users to select appropriate contacts to grant access to resources.     

Studies and advances on joint source-channel encoding/decoding techniques in flow media communications

Joint source-channel coding/decoding (JSCC/JSCD) techniques in flow media communications have become a state-of-the-art and one of the challenging research subjects in the spatial communication area. They have great application prospective and deep impact in various manned space flights, satellite missions, mobile radio communications and deep-space explorations. In the last few years, there have been influential achievements in JSCC/JSCD studies. This paper aims at an introduction to the basic principles o...     

Support vector classification of proteomic profile spectra based on feature extraction with the bi-orthogonal discrete wavelet transform

Automatic classification of high-resolution mass spectrometry data has increasing potential to support physicians in diagnosis of diseases like cancer. The proteomic data exhibit variations among different disease states. A precise and reliable classification of mass spectra is essential for a successful diagnosis and treatment. The underlying process to obtain such reliable classification results is a crucial point. In this paper such a method is explained and a corresponding semi automatic parameterization procedure is derived. Thereby a simple straightforward classification procedure to assign mass spectra to a particular disease state is derived. The method is based on an initial preprocessing stage of the whole set of spectra followed by the bi-orthogonal discrete wavelet transform (DWT) for feature extraction. The approximation coefficients calculated from the scaling function exhibit a high peak pattern matching property and feature a denoising of the spectrum. The discriminating coefficients, selected by the Kolmogorov–Smirnov test are finally used as features for training and testing a support vector machine with both a linear and a radial basis kernel. For comparison the peak areas obtained with the it ClinProt-System ¹ [33] were analyzed using the same support vector machines. The introduced approach was evaluated on clinical MALDI-MS data sets with two classes each originating from cancer studies. The cross validated error rates using the wavelet coefficients where better than those obtained from the peak areas².     

Cognitive-bias toward gaming-related words and disinhibition in World of Warcraft gamers

This study investigated cognitive biases toward gaming-related words and differences in cognitive performance among twelve World of Warcraft players (WWP) and thirty non-players (NP). We measured response to valenced common English and WoW jargon words using a computer-based go/no-go task. Sometimes positive valence words were the targets for the ‘go’ response, with negative-valence words as the distracters, sometimes the reverse. Target discrimination (d′) and response disinhibition (C) were calculated using a signal detection analysis. Based on questionnaire responses, there were no differences between groups in depression, anxiety, smoking or drinking, but WWP reported significantly more screen and gaming time (17.31 h/week versus 4.12 among NP). WWP had faster reaction time (RT) and better discrimination of targets from distracters (high d′) but also showed higher disinhibition (low C). WWP also showed cognitive-bias toward game-related words in the form of higher d′ for WoW jargon than common English and more disinhibition to positive-valence WoW jargon. Similar to past studies which have found alcoholics to have cognitive biases toward alcohol-related words, WWP who play frequently showed cognitive biases toward words related to the World of Warcraft game.