Context-Based Workplace Awareness

Maintaining an awareness of the working context of fellow co-workers is crucial to successful cooperation in a workplace. For mobile, non co-located workers, however, such workplace awareness is hard to maintain. This paper investigates how context-aware computing can be used to facilitate workplace awareness. In particular, we present the concept of Context-Based Workplace Awareness, which is derived from years of in-depth studies of hospital work and the design of computer supported cooperative work technologies to support the distributed collaboration and coordination of clinical work within large hospitals. This empirical background has revealed that an awareness especially of the social, spatial, temporal, and activity context plays a crucial role in the coordination of work in hospitals. The paper then presents and discusses technologies designed to support context-based workplace awareness, namely the AWARE architecture, and the AwarePhone and AwareMedia applications. Based on almost 2 year’ deployment of the technologies in a large hospital, the paper discuss how the four dimension of context-based workplace awareness play out in the coordination of clinical work.     

Improving convergence of Iterative Feedback Tuning

Iterative Feedback Tuning constitutes an attractive control loop tuning method for processes in the absence of an accurate process model. It is a purely data driven approach aiming at optimizing the closed loop performance. The standard formulation ensures an unbiased estimate of the loop performance cost function gradient with respect to the control parameters. This gradient is important in a search algorithm. The extension presented in this paper further ensures informative data to improve the convergence properties of the method and hence reduce the total number of required plant experiments especially when tuning for disturbance rejection. Informative data is achieved through application of an external probing signal in the tuning algorithm. The probing signal is designed based on a constrained optimization which utilizes an approximate black box model of the process. This model estimate is further used to guarantee nominal stability and to improve the parameter update using a line search algorithm for determining the iteration step size. The proposed algorithm is compared to the classical formulation in a simulation study of a disturbance rejection problem. This type of problem is notoriously difficult for Iterative Feedback Tuning due to the lack of excitation from the reference.     

Learning nonlinear image manifolds by global alignment of local linear models

Appearance-based methods, based on statistical models of the pixel values in an image (region) rather than geometrical object models, are increasingly popular in computer vision. In many applications, the number of degrees of freedom (DOF) in the image generating process is much lower than the number of pixels in the image. If there is a smooth function that maps the DOF to the pixel values, then the images are confined to a low-dimensional manifold embedded in the image space. We propose a method based on probabilistic mixtures of factor analyzers to 1) model the density of images sampled from such manifolds and 2) recover global parameterizations of the manifold. A globally nonlinear probabilistic two-way mapping between coordinates on the manifold and images is obtained by combining several, locally valid, linear mappings. We propose a parameter estimation scheme that improves upon an existing scheme and experimentally compare the presented approach to self-organizing maps, generative topographic mapping, and mixtures of factor analyzers. In addition, we show that the approach also applies to finding mappings between different embeddings of the same manifold.     

Cultivation and engineering of a software metrics program

Abstract. This paper reports from a case study of an organization that implements a software metrics program to measure the effects of its improvement efforts. The program measures key indicators of all completed projects and summarizes progress information in a quarterly management report. The implementation turns out to be long and complex, as the organization is confronted with dilemmas based on contradictory demands and value conflicts. The process is interpreted as a combination of a rational engineering process in which a metrics program is constructed and put into use, and an evolutionary cultivation process in which basic values of the software organization are confronted and transformed. The analysis exemplifies the difficulties and challenges that software organizations face when bringing known principles for software metrics programs into practical use. The article discusses the insights gained from the case in six lessons that may be used by Software Process Improvement managers in implementing a successful metrics program.     

Open-Source Change Logs

A recent editorial in Empirical Software Engineering suggested that open-source software projects offer a great deal of data that can be used for experimentation. These data not only include source code, but also artifacts such as defect reports and update logs. A common type of update log that experimenters may wish to investigate is the ChangeLog, which lists changes and the reasons for which they were made. ChangeLog files are created to support the development of software rather than for the needs of researchers, so questions need to be asked about the limitations of using them to support research. This paper presents evidence that the ChangeLog files provided at three open-source web sites were incomplete. We examined at least three ChangeLog files for each of three different open-source software products, namely, GNUJSP, GCC-g++, and Jikes. We developed a method for counting changes that ensures that, as far as possible, each individual ChangeLog entry is treated as a single change. For each ChangeLog file, we compared the actual changes in the source code to the entries in the ChangeLog> file and discovered significant omissions. For example, using our change-counting method, only 35 of the 93 changes in version 1.11 of Jikes appear in the ChangeLog file—that is, over 62% of the changes were not recorded there. The percentage of omissions we found ranged from 3.7 to 78.6%. These are significant omissions that should be taken into account when using ChangeLog files for research. Before using ChangeLog files as a basis for research into the development and maintenance of open-source software, experimenters should carefully check for omissions and inaccuracies.     

Parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds

A mandatory component for many point set algorithms is the availability of consistently oriented vertex‐normals (e.g. for surface reconstruction, feature detection, visualization). Previous orientation methods on meshes or raw point clouds do not consider a global context, are often based on unrealistic assumptions, or have extremely long computation times, making them unusable on real‐world data. We present a novel massively parallelized method to compute globally consistent oriented point normals for raw and unsorted point clouds. Built on the idea of graph‐based energy optimization, we create a complete kNN‐graph over the entire point cloud. A new weighted similarity criterion encodes the graph‐energy. To orient normals in a globally consistent way we perform a highly parallel greedy edge collapse, which merges similar parts of the graph and orients them consistently. We compare our method to current state‐of‐the‐art approaches and achieve speedups of up to two orders of magnitude. The achieved quality of normal orientation is on par or better than existing solutions, especially for real‐world noisy 3D scanned data.