How Does Mobile Computing Develop Transactive Memory in Virtual Team? A Social Identification View

The advancement in mobile computing technologies has shown great potential to drive efficiency and effectiveness of knowledge work in virtual teams. Despite their ubiquity, theoretical and empirical research investigating the impact of mobile computing artifacts on development of transactive memory in virtual teams is in its infancy. Drawing on the social psychology literature, we propose a social identity based view to understand how the use of mobile computing artifacts is associated with the development of transactive memory system (TMS) in virtual teams. Specially, the use of four categories of mobile computing artifacts (i.e., ubiquitous co-presence, status disclosure, context search, and customized notification) is proposed to enhance social identification, which thereafter promotes TMS development in terms of specialization, credibility, and coordination. This study offers a new perspective on the mechanisms through which mobile computing artifacts facilitate TMS development, and it yields important implications for the design of mobile strategy in organizations

Efficient Regularization of Squared Curvature

Curvature has received increased attention as an important alternative to length based regularization in computer vision. In contrast to length, it preserves elongated structures and fine details. Existing approaches are either inefficient, or have low angular resolution and yield results with strong block artifacts. We derive a new model for computing squared curvature based on integral geometry. The model counts responses of straight line triple cliques. The corresponding energy decomposes into submodular and supermodular pairwise potentials. We show that this energy can be efficiently minimized even for high angular resolutions using the trust region framework. Our results confirm that we obtain accurate and visually pleasing solutions without strong artifacts at reasonable run times.Comment: 8 pages, 12 figures, to appear at IEEE conference on Computer Vision and Pattern Recognition (CVPR), June 201

Towards a Tool-based Development Methodology for Pervasive Computing Applications

Despite much progress, developing a pervasive computing application remains a challenge because of a lack of conceptual frameworks and supporting tools. This challenge involves coping with heterogeneous devices, overcoming the intricacies of distributed systems technologies, working out an architecture for the application, encoding it in a program, writing specific code to test the application, and finally deploying it. This paper presents a design language and a tool suite covering the development life-cycle of a pervasive computing application. The design language allows to define a taxonomy of area-specific building-blocks, abstracting over their heterogeneity. This language also includes a layer to define the architecture of an application, following an architectural pattern commonly used in the pervasive computing domain. Our underlying methodology assigns roles to the stakeholders, providing separation of concerns. Our tool suite includes a compiler that takes design artifacts written in our language as input and generates a programming framework that supports the subsequent development stages, namely implementation, testing, and deployment. Our methodology has been applied on a wide spectrum of areas. Based on these experiments, we assess our approach through three criteria: expressiveness, usability, and productivity

trackr: A Framework for Enhancing Discoverability and Reproducibility of Data Visualizations and Other Artifacts in R

Research is an incremental, iterative process, with new results relying and building upon previous ones. Scientists need to find, retrieve, understand, and verify results in order to confidently extend them, even when the results are their own. We present the trackr framework for organizing, automatically annotating, discovering, and retrieving results. We identify sources of automatically extractable metadata for computational results, and we define an extensible system for organizing, annotating, and searching for results based on these and other metadata. We present an open-source implementation of these concepts for plots, computational artifacts, and woven dynamic reports generated in the R statistical computing language

Fuzzy Fibers: Uncertainty in dMRI Tractography

Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research

A Pluggable Framework for Lightweight Task Offloading in Parallel and Distributed Computing

Multicore processors have quickly become ubiquitous in supercomputing, cluster computing, datacenter computing, and even personal computing. Software advances, however, continue to lag behind. In the past, software designers could simply rely on clock-speed increases to improve the performance of their software. With clock speeds now stagnant, software designers need to tap into the increased horsepower of multiple cores in a processor by creating software artifacts that support parallelism. Rather than forcing designers to write such software artifacts from scratch, we propose a pluggable framework that designers can reuse for lightweight task offloading in a parallel computing environment of multiple cores, whether those cores be colocated on a processor within a compute node, between compute nodes in a tightly-coupled system like a supercomputer, or between compute nodes in a loosely-coupled one like a cloud computer. To demonstrate the efficacy of our framework, we use the framework to implement lightweight task offloading (or software acceleration) for a popular parallel sequence-search application called mpiBLAST. Our experimental results on a 9-node, 36-core AMD Opteron cluster show that using mpiBLAST with our pluggable framework results in a 205% speed-up

Multi-Sensor Context-Awareness in Mobile Devices and Smart Artefacts

The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this article we consider how to augment mobile devices with awareness of their environment and situation as context. Most work to date has been based on integration of generic context sensors, in particular for location and visual context. We propose a different approach based on integration of multiple diverse sensors for awareness of situational context that can not be inferred from location, and targeted at mobile device platforms that typically do not permit processing of visual context. We have investigated multi-sensor context-awareness in a series of projects, and report experience from development of a number of device prototypes. These include development of an awareness module for augmentation of a mobile phone, of the Mediacup exemplifying context-enabled everyday artifacts, and of the Smart-Its platform for aware mobile devices. The prototypes have been explored in various applications to validate the multi-sensor approach to awareness, and to develop new perspectives of how embedded context-awareness can be applied in mobile and ubiquitous computing

An accurate method to correct atmospheric phase delay for InSAR with the ERA5 global atmospheric model

Differential SAR Interferometry (DInSAR) has proven its unprecedented ability and merits of monitoring ground deformation on a large scale with centimeter to millimeter accuracy. However, atmospheric artifacts due to spatial and temporal variations of the atmospheric state often affect the reliability and accuracy of its results. The commonly-known Atmospheric Phase Screen (APS) appears in the interferograms as ghost fringes not related to either topography or deformation. Atmospheric artifact mitigation remains one of the biggest challenges to be addressed within the DInSAR community. State-of-the-art research works have revealed that atmospheric artifacts can be partially compensated with empirical models, point-wise GPS zenith path delay, and numerical weather prediction models. In this study, we implement an accurate and realistic computing strategy using atmospheric reanalysis ERA5 data to estimate atmospheric artifacts. With this approach, the Line-of-Sight (LOS) path along the satellite trajectory and the monitored points is considered, rather than estimating it from the zenith path delay. Compared with the zenith delay-based method, the key advantage is that it can avoid errors caused by any anisotropic atmospheric phenomena. The accurate method is validated with Sentinel-1 data in three different test sites: Tenerife island (Spain), Almería (Spain), and Crete island (Greece). The effectiveness and performance of the method to remove APS from interferograms is evaluated in the three test sites showing a great improvement with respect to the zenith-based approach.Peer ReviewedPostprint (published version