Problematic Internet use and psychosocial well-being among MMO players

The current study examined problematic Internet use (PIU) among people who play MMO games and sought to determine whether aspects of the MMO experience are useful predictors of PIU. The study sought to determine whether game-related variables could predict PIU scores after accounting for their relationships with psychosocial well-being. Novel methods allowed us, for the first time, to connect in-game behaviors with survey results of over 4000 MMO players. The results revealed that MMO gaming variables contributed a substantively small, but statistically significant amount of explained variance to PIU scores.     

Benchmarking stereo-derived DSMs to lidar by land cover and slope for resource development in northern Ontario,Canada

Topographic and elevation data are essential in the development of supporting infrastructure around mining sites. The de facto standard for acquiring elevation data is through light detection and ranging (lidar). The high labour and monetary cost of acquiring lidar has fostered more cost-effective approaches for creating elevation models that use stereo photogrammetry. To assess the accuracy of stereo-photogrammetry-derived elevation models and their potential application, we benchmark satellite (Worldview-2) and aircraft (South Central Ontario Orthoimagery Project; SCOOP) stereo-derived digital surface models (DSMs) against a lidar-derived DSM. Our results show that both stereo-derived DSMs have strong monotonic correlations with lidar across a range of land-cover types and slopes. The overall vertical accuracy of Worldview-2 and SCOOP DSMs are similar and do not meet the United States National Digital Elevation Program (NDEP) standards. However, accuracy assessment across land-cover types and slope categories show that specific land cover types (i.e. grass, row crops/pasture, sparse vegetation and marsh) on gently sloping terrain compare well to lidar data and meet NDEP accuracy standards. We situate the presented research in the context of northern resource development and discuss opportunities to improve the vertical accuracy of stereo-derived DSMs, for example, through unmanned aerial systems.     

Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning

Physical activity monitoring for youth is an area of increasing scientific and public health interest due to the high prevalence of obesity and downward trend in physical activity. However, accurate assessment of such activity remains a challenging problem because of the complex nature in which certain activities are performed. In this study, we formulated the issue as a machine learning problem—using a diverse set of 19 physical activities commonly performed by youth—via two approaches: activity recognition and intensity estimation. With the aid of training data, we implemented a distance metric learning method called DML-KNN that utilizes time-frequency features and is capable of effectively classifying both continuous and intermittent movement in youth subjects. Four different time-frequency feature extraction methods were then systematically evaluated. Our results show that the DML-KNN method performed competitively, especially when using features extracted by the Tamura method for intensity estimation, and by the Square Coefficient method for activity recognition.     

Big data in the public sector: Uncertainties and readiness

Big data is being implemented with success in the private sector and science. Yet the public sector seems to be falling behind, despite the potential value of big data for government. Government organizations do recognize the opportunities of big data but seem uncertain about whether they are ready for the introduction of big data, and if they are adequately equipped to use big data. This paper addresses those uncertainties. It presents an assessment framework for evaluating public organizations’ big data readiness. Doing so demystifies the concept of big data, as it is expressed in terms of specific and measureable organizational characteristics. The framework was tested by applying it to organizations in the Dutch public sector. The results suggest that organizations may be technically capable of using big data, but they will not significantly gain from these activities if the applications do not fit their organizations and main statutory tasks. The framework proved helpful in pointing out areas where public sector organizations could improve, providing guidance on how government can become more big data ready in the future.     

Removal of partial occlusion from single images

This paper examines large partial occlusions in an image which occur near depth discontinuities when the foreground object is severely out of focus. We model these partial occlusions using matting, with the alpha value determined by the convolution of the blur kernel with a pinhole projection of the occluder. The main contribution is a method for removing the image contribution of the foreground occluder in regions of partial occlusion, which improves the visibility of the background scene. The method consists of three steps. First, the region of complete occlusion is estimated using a curve evolution method. Second, the alpha value at each pixel in the partly occluded region is estimated. Third, the intensity contribution of the foreground occluder is removed in regions of partial occlusion. Experiments demonstrate the method's ability to remove the effects of partial occlusion in single images with minimal user input.     

Detecting global predicates in distributed systems with clocks

Summary. This paper proposes a framework for detecting global state predicates in systems of processes with approximately-synchronized real-time clocks. Timestamps from these clocks are used to define two orderings on events: “definitely occurred before” and “possibly occurred before”. These orderings lead naturally to definitions of 3 distinct detection modalities, i.e., 3 meanings of “predicate held during a computation”, namely: (“ possibly held”), (“ definitely held”), and (“ definitely held in a specific global state”). This paper defines these modalities and gives efficient algorithms for detecting them. The algorithms are based on algorithms of Garg and Waldecker, Alagar and Venkatesan, Cooper and Marzullo, and Fromentin and Raynal. Complexity analysis shows that under reasonable assumptions, these real-time-clock-based detection algorithms are less expensive than detection algorithms based on Lamport's happened-before ordering. Sample applications are given to illustrate the benefits of this approach. Received: January 1999 / Accepted: November 1999     

Design and implementation of a vehicle interface protocol using an IEEE 1394 network

A wide variety of in-vehicle devices such as camera sensors, navigation systems, telematics and communication equipments have been incorporated into a vehicle to realize Intelligent Transport Systems (ITS) applications. Because an efficient standardized network is required, ITS Data Bus (IDB) has been discussed to carry high-speed multimedia data for audio, video and other real-time ITS applications. For connecting devices in a standardized manner, the IDB network has architecture with a gateway called vehicle interface which is located between automaker’s proprietary network and the standardized IDB network. IEEE 1394 (also known as iLink or FireWire), which can transport multimedia data for consumer electronics, is a good candidate for IDB network. In this paper, we analyze the issues for existing AV/C protocol (application layer protocol over IEEE 1394) to comprise the IDB network. In addition, we designed and implemented the vehicle interface protocol as a higher layer of IEEE 1394 to address the AV/C protocol issues for realizing the whole IDB network architecture.     

Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG

We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model.     

Topology-controlled volume rendering

Topology provides a foundation for the development of mathematically sound tools for processing and exploration of scalar fields. Existing topology-based methods can be used to identify interesting features in volumetric data sets, to find seed sets for accelerated isosurface extraction, or to treat individual connected components as distinct entities for isosurfacing or interval volume rendering. We describe a framework for direct volume rendering based on segmenting a volume into regions of equivalent contour topology and applying separate transfer functions to each region. Each region corresponds to a branch of a hierarchical contour tree decomposition, and a separate transfer function can be defined for it. The novel contributions of our work are: 1) a volume rendering framework and interface where a unique transfer function can be assigned to each subvolume corresponding to a branch of the contour tree, 2) a runtime method for adjusting data values to reflect contour tree simplifications, 3) an efficient way of mapping a spatial location into the contour tree to determine the applicable transfer function, and 4) an algorithm for hardware-accelerated direct volume rendering that visualizes the contour tree-based segmentation at interactive frame rates using graphics processing units (GPUs) that support loops and conditional branches in fragment programs     

Intermittent microclimate cooling during exercise-heat stress in US army chemical protective clothing

The effectiveness of intermittent, microclimate cooling for men who worked in US Army chemical protective clothing (modified mission-oriented protective posture level 3; MOPP 3) was examined. The hypothesis was that intermittent cooling on a 2 min on-off schedule using a liquid cooling garment (LCG) covering 72% of the body surface area would reduce heat strain comparably to constant cooling. Four male subjects completed three experiments at 30 degrees C, 30% relative humidity wearing the LCG under the MOPP 3 during 80 min of treadmill walking at 224 +/- 5 W . m(-2). Water temperature to the LCG was held constant at 21 degrees C. The experiments were; 1) constant cooling (CC); 2) intermittent cooling at 2-min intervals (IC); 3) no cooling (NC). Core temperature increased (1.6 +/- 0.2 degrees C) in NC, which was greater than IC (0.5 +/- 0.2 degrees C) and CC (0.5 +/- 0.3 degrees C) ( p < 0.05). Mean skin temperature was higher during NC (36.1 +/- 0.4 degrees C) than IC (33.7 +/- 0.6 degrees C) and CC (32.6 +/- 0.6 degrees C) and mean skin temperature was higher during IC than CC ( p < 0.05). Mean heart rate during NC (139 +/- 9 b . min(-1)) was greater than IC (110 +/- 10 b . min(-1)) and CC (107 +/- 9 b . min(-1)) ( p < 0.05). Cooling by conduction (K) during NC (94 +/- 4 W . m(-2)) was lower than IC (142 +/- 7 W . m(-2)) and CC (146 +/- 4 W . m(-2)) ( p < 0.05). These findings suggest that IC provided a favourable skin to LCG gradient for heat dissipation by conduction and reduced heat strain comparable to CC during exercise-heat stress in chemical protective clothing.