An Interoperable Grid Information System for Integrated Resource Monitoring Based on Virtual Organizations

In many Grid infrastructures different kinds of information services are in use, which utilize different incompatible data structures and interfaces to encode and provide their data. Homogeneous monitoring of these infrastructures with the monitoring data being accessible everywhere independently of the middleware which provided it, is the basis for a consistent status reporting on the Grids’ resources and services. Thus, interoperability or interoperation between the different information services in a heterogeneous Grid infrastructure is required. Monitoring data must contain the identity of the affected Virtual Organization (VO) so that it can be related to the resources and services the VO has allocated to enable VO-specific information provision. This paper describes a distributed architecture for an interoperable information service, which combines data unification and categorization with policies for VO membership, VO resource management and data transformations. This service builds the basis for an integrated and interoperating monitoring of Grids, which provide their data to more than one VO and utilize heterogeneous information services.     

3D Semantic Scene Completion: A Survey

International Journal of Computer Vision - Semantic scene completion (SSC) aims to jointly estimate the complete geometry and semantics of a scene, assuming partial sparse input. In the last years...     

St. Francis Hospice: Medicare and health care reform

The St. Francis Hospice Program is symbolic of more than 100 years of Franciscan dedication to the people of Hawaii. Since Mother Marianne's arrival in November of 1883, the Sisters of the Third Franciscan Order Syracuse, New York have responded to the calling; "the charity of Christ impels us." It is through this calling that care and comfort for the terminally ill is a part of the mission of St. Francis Healthcare System. The magnificent spirit through which Hospice services have been made possible, is a reflection of God's great generosity to us throughout the years.     

Computer-assisted techniques to enhance transformative learning in first-year literature courses

When students use computers as learning tools, the whole process of learning, and, indeed, the learners themselves, are transformed. This article illustrates some techniques that foster transformative learning in computer-assisted first-year literature classes: first, a lesson plan on A Valediction: Forbidding Mourning that uses Microsoft Word functions, including format painter, tables, and annotation to explore meaning in context; second, a plan for learners to use subconference options in the Daedalus Interactive Writing Environment to analyze Oedipus Rex; finally, a demonstration of how students engage in a meta-reflection process as they explore Barn Burning with Freelance Graphics.Marguerite Jamieson is an English instructor at Anne Arundel Community College in Arnold, Maryland, and a doctoral student at George Mason University. Her research interests include forming bridges between adult learning theory and contemporary literary theory — especially drawing on transformational learning theory and the work of Mikhail Bakhtin and Lev Vygotsky.Rebecca Kajs holds a doctorate in English from Texas Woman's University with a concentration in rhetoric. For ten years, she taught the use of heuristic tools for reading analysis at the University of Texas at Arlington. She is currently an associate professor of English and Philosophy at Anne Arundel Community College.Anne Agee holds a doctorate in rhetoric from The Catholic University of America. A professor of English and formerly director of the Humanities Computer Center at Anne Arundel Community College, she is currently the college's Coordinator of Instructional Technology. Dr. Agee and Professor Jamieson have collaborated in a study of the learning environment in a computer classroom, the results of which were published in the Fall 1995 issue of Teaching/Learning Conversations. Dr. Agee has also published Using [Daedalus] InterChange as a Teachers' Journal in the Fall 1995 issue of Wings.     

A review of stochastic algorithms with continuous value function approximation and some new approximate policy iteration algorithms for multidimensional continuous applications

We review the literature on approximate dynamic programming, with the goal of better understanding the theory behind practical algorithms for solving dynamic programs with continuous and vector-valued states and actions and complex information processes. We build on the literature that has addressed the well-known problem of multidimensional (and possibly continuous) states, and the extensive literature on model-free dynamic programming, which also assumes that the expectation in Bellman’s equation cannot be computed. However, we point out complications that arise when the actions/controls are vector-valued and possibly continuous. We then describe some recent research by the authors on approximate policy iteration algorithms that offer convergence guarantees (with technical assumptions) for both parametric and nonparametric architectures for the value function.     

Two nonlinear optimization methods for black box identification compared

In this paper, two nonlinear optimization methods for the identification of nonlinear systems are compared. Both methods estimate the parameters of e.g. a polynomial nonlinear state-space model by means of a nonlinear least-squares optimization of the same cost function. While the first method does not estimate the states explicitly, the second method estimates both states and parameters adding an extra constraint equation. Both methods are introduced and their similarities and differences are discussed utilizing simulation data. The unconstrained method appears to be faster and more memory efficient, but the constrained method has a significant advantage as well: it is robust for unstable systems of which bounded input-output data can be measured (e.g. a system captured in a stabilizing feedback loop). Both methods have successfully been applied on real-life measurement data.     

C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization

In recent years, evolutionary algorithms (EAs) have been extensively developed and utilized to solve multi-objective optimization problems. However, some previous studies have shown that for certain problems, an approach which allows for non-greedy or uphill moves (unlike EAs), can be more beneficial. One such approach is simulated annealing (SA). SA is a proven heuristic for solving numerical optimization problems. But owing to its point-to-point nature of search, limited efforts has been made to explore its potential for solving multi-objective problems. The focus of the presented work is to develop a simulated annealing algorithm for constrained multi-objective problems. The performance of the proposed algorithm is reported on a number of difficult constrained benchmark problems. A comparison with other established multi-objective optimization algorithms, such as infeasibility driven evolutionary algorithm (IDEA), Non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective Scatter search II (MOSS-II) has been included to highlight the benefits of the proposed approach.     

Locating computer clubs in multicultural neighborhoods: How collaborative project work fosters integration processes

Located in socially and culturally diverse neighborhoods, we have built a network of intercultural computer clubs, called come_IN. These clubs offer a place to share practices among children and adults of diverse ethnical backgrounds. We show how this initiative ties into the striving for the integration of migrant communities and host society in Germany. In this paper, we analyze how collaborative project work and the use of mobile media and technologies contribute to integration processes in multicultural neighborhoods. Qualitative data gathered from interviews with club participants, participative observation in the computer clubs, as well as the analysis of artifacts created during project work provides the background needed to match local needs and peculiarities with (mobile) technologies. Based on these findings we present two approaches to add to the technological infrastructure: (1) a mesh-network extending the clubs into the neighborhood and (2) a project management tool, which supports projects and stimulates the sharing of ideas among projects.     

From monolithic to component-based performance evaluation of software architectures

Model-based performance evaluation methods for software architectures can help architects to assess design alternatives and save costs for late life-cycle performance fixes. A recent trend is component-based performance modelling, which aims at creating reusable performance models; a number of such methods have been proposed during the last decade. Their accuracy and the needed effort for modelling are heavily influenced by human factors, which are so far hardly understood empirically. Do component-based methods allow to make performance predictions with a comparable accuracy while saving effort in a reuse scenario? We examined three monolithic methods (SPE, umlPSI, Capacity Planning (CP)) and one component-based performance evaluation method (PCM) with regard to their accuracy and effort from the viewpoint of method users. We conducted a series of three experiments (with different levels of control) involving 47 computer science students. In the first experiment, we compared the applicability of the monolithic methods in order to choose one of them for comparison. In the second experiment, we compared the accuracy and effort of this monolithic and the component-based method for the model creation case. In the third, we studied the effort reduction from reusing component-based models. Data were collected based on the resulting artefacts, questionnaires and screen recording. They were analysed using hypothesis testing, linear models, and analysis of variance. For the monolithic methods, we found that using SPE and CP resulted in accurate predictions, while umlPSI produced over-estimates. Comparing the component-based method PCM with SPE, we found that creating reusable models using PCM takes more (but not drastically more) time than using SPE and that participants can create accurate models with both techniques. Finally, we found that reusing PCM models can save time, because effort to reuse can be explained by a model that is independent of the inner complexity of a component. The tasks performed in our experiments reflect only a subset of the actual activities when applying model-based performance evaluation methods in a software development process. Our results indicate that sufficient prediction accuracy can be achieved with both monolithic and component-based methods, and that the higher effort for component-based performance modelling will indeed pay off when the component models incorporate and hide a sufficient amount of complexity.