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.     

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.     

Improving literacy and metacognition with electronic portfolios: Teaching and learning with ePEARL

Can an electronic portfolio that is both a multimedia container for student work and a tool to support key learning processes have a positive impact on the literacy practices and self-regulated learning skills of students? This article presents the findings of a yearlong study conducted in three Canadian provinces during the 2007–2008 school year initially involving 32 teachers and 388 students. Due to varying levels of implementation our final data set included 14 teachers and 296 students. Using a non-equivalent pre-test/post-test design, we found that grade 4–6 students who were in classrooms where the teacher provided regular and appropriate use of the electronic portfolio tool ePEARL (i.e., medium–high implementation condition, n = 7 classrooms and 121 students), compared to control students (n = 7 classrooms and 175 students) who did not use ePEARL, showed significant improvements (p < .05) in their writing skills on a standardized literacy measure (i.e., the constructed response subtest of the Canadian Achievement Test-4th ed.) and certain metacognitive skills measured via student self-report. The results of this study indicate that teaching with ePEARL has positive impacts on students’ literacy and self-regulated learning skills when the tool is used regularly and integrated into classroom instruction.     

Enacting language games: the development of a sense of ‘we‐ness’ in online forums

A sense of ‘we‐ness’– enacted through collective identity and culture – is both crucial in online, remote contexts, and particularly difficult to develop in such settings. Using Wittgenstein's concept of language games, we examine how participants of two online forums construct collective identity and culture through their discursive practices. We suggest a strong performative interpretation of the notion of language games, i.e. members of a community produce a sense of we‐ness through their participation in the language game while also defining their expected behaviours and actions. We illustrate how the notion of language games offers an approach for researching and analysing the emergence of collective identity and culture in online forums.     

IT professional identity: needs,perceptions, and belonging

A theoretical model of professional identification is developed and empirically examined as a means to understanding information technology (IT) workers’ attachment to the IT profession. Professional identification represents oneness with or belonging to a profession and provides a unique means of investigating and evaluating the IT profession. Results from a survey of 305 IT workers indicate that professional identification is directly impacted by three factors: (1) the individual's need for professional identification; (2) the individual's perceived similarity to others in the IT profession; and (3) the individual's perceptions of the IT profession, signifying the importance of internalization to identification. Professional identification is also indirectly impacted by the public's perception of the IT profession.     

Are digital natives a myth or reality? University students’ use of digital technologies

This study investigated the extent and nature of university students’ use of digital technologies for learning and socialising. The findings show that students use a limited range of mainly established technologies. Use of collaborative knowledge creation tools, virtual worlds, and social networking sites was low. ‘Digital natives’ and students of a technical discipline (Engineering) used more technology tools when compared to ‘digital immigrants’ and students of a non-technical discipline (Social Work). This relationship may be mediated by the finding that Engineering courses required more intensive and extensive access to technology than Social Work courses. However, the use of technology between these groups is only quantitatively rather than qualitatively different. The study did not find evidence to support popular claims that young people adopt radically different learning styles. Their attitudes to learning appear to be influenced by lecturers’ teaching approaches. Students appear to conform to traditional pedagogies, albeit with minor uses of tools delivering content. The outcomes suggest that although the calls for transformations in education may be legitimate it would be misleading to ground the arguments for such change in students’ shifting patterns of learning and technology use.     

Subspace sums for extracting non-random data from massive noise

An algorithm is introduced that distinguishes relevant data points from randomly distributed noise. The algorithm is related to subspace clustering based on axis-parallel projections, but considers membership in any projected cluster of a given side length, as opposed to a particular cluster. An aggregate measure is introduced that is based on the total number of points that are close to the given point in all possible 2 ^d projections of a d-dimensional hypercube. No explicit summation over subspaces is required for evaluating this measure. Attribute values are normalized based on rank order to avoid making assumptions on the distribution of random data. Effectiveness of the algorithm is demonstrated through comparison with conventional outlier detection on a real microarray data set as well as on time series subsequence data.

A Stochastic Filtering Technique for Fluid Flow Velocity Fields Tracking

In this paper, we present a method for the temporal tracking of fluid flow velocity fields. The technique we propose is formalized within a sequential Bayesian filtering framework. The filtering model combines an Itô diffusion process coming from a stochastic formulation of the vorticity-velocity form of the Navier-Stokes equation and discrete measurements extracted from the image sequence. In order to handle a state space of reasonable dimension, the motion field is represented as a combination of adapted basis functions, derived from a discretization of the vorticity map of the fluid flow velocity field. The resulting nonlinear filtering problem is solved with the particle filter algorithm in continuous time. An adaptive dimensional reduction method is applied to the filtering technique, relying on dynamical systems theory. The efficiency of the tracking method is demonstrated on synthetic and real-world sequences.     

Effects of auditory,haptic and visual feedback on performing gestures by gaze or by hand

Modern interaction techniques like non-intrusive gestures provide means for interacting with distant displays and smart objects without touching them. We were interested in the effects of feedback modality (auditory, haptic or visual) and its combined effect with input modality on user performance and experience in such interactions. Therefore, we conducted two exploratory experiments where numbers were entered, either by gaze or hand, using gestures composed of four stroke elements (up, down, left and right). In Experiment 1, a simple feedback was given on each stroke during the motor action of gesturing: an audible click, a haptic tap or a visual flash. In Experiment 2, a semantic feedback was given on the final gesture: the executed number was spoken, coded by haptic taps or shown as text. With simultaneous simple feedback in Experiment 1, performance with hand input was slower but more accurate than with gaze input. With semantic feedback in Experiment 2, however, hand input was only slower. Effects of feedback modality were of minor importance; nevertheless, semantic haptic feedback in Experiment 2 showed to be useless at least without extensive training. Error patterns differed between both input modes, but again not dependent on feedback modality. Taken together, the results show that in designing gestural systems, choosing a feedback modality can be given a low priority; it can be chosen according to the task, context and user preferences.