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.     

Dust off Those Encyclopedias: Using Reference Sources to Teach the ACRL Framework Concepts

What if the ideal tools for teaching undergraduate students the most critical information literacy concepts have been sitting in the stacks all along collecting dust? Reference sources are an optimal medium to introduce all six of the Association of College &; Research Libraries' (ACRL) Framework's central concepts for information literacy. Additionally, by understanding a reference source's place in the information search process, students learn to consciously avoid the common pitfall of neglecting exploratory research before specifying research inquiries. Thus, incorporating reference sources thoughtfully into instructional design contributes to the development of both information literacy and metacognition.     

Delivering public value through open government data initiatives in a Smart City context

By using ICT in an innovative way, governments can improve the delivery of services and interaction with stakeholders. Open data is a way to help public organizations became more open and improve interaction with stakeholders. This paper aims to identify what are the public values enhancements acquired on smart city environment that discloses open data. We propose a conceptual model to analyze the smart city initiative. We contextualized the model taking a smart city domain by analyzing three related-initiatives that comprises open data in a smart city case carried at Rio de Janeiro Operations Center (COR) in Brazil by seven deep-interviewees directly involved - from inside and outside – in this case. The findings reveal evidences that open data initiatives contribute to enhance the delivery of public value in smart city contexts.     

Towards the creation of a CNL adapted to requirements writing by combining writing recommendations and spontaneous regularities: example in a space project

The Quality Department of the French National Space Agency (CNES, Centre National d’Études Spatiales) wishes to design a writing guide based on the real and regular writing of requirements. As a first step in this project, the present article proposes a linguistic analysis of requirements written in French by CNES engineers. One of our goals is to determine to what extent they conform to several rules laid down in two existing Controlled Natural Languages (CNLs), namely the Simplified Technical English developed by the AeroSpace and Defense Industries Association of Europe and the Guide for Writing Requirements proposed by the International Council on Systems Engineering. Indeed, although CNES engineers are not obliged to follow any controlled language in their writing of requirements, we believe that language regularities are likely to emerge from this task, mainly due to the writers’ experience. We are seeking to identify these regularities in order to use them as a basis for a new CNL for the writing of requirements. The issue is approached using natural language processing tools to identify sentences that do not comply with the rules or contain specific linguistic phenomena. We further review these sentences to understand why the recommendations cannot (or should not) always be applied when specifying large-scale projects.     

Static worksharing strategies for heterogeneous computers with unrecoverable interruptions

One has a large computational workload that is “divisible” (its constituent tasks’ granularity can be adjusted arbitrarily) and one has access to p remote computers that can assist in computing the workload. How can one best utilize the computers? Two features complicate this question. First, the remote computers may differ from one another in speed. Second, each remote computer is subject to interruptions of known likelihood that kill all work in progress on it. One wishes to orchestrate sharing the workload with the remote computers in a way that maximizes the expected amount of work completed. We deal with three versions of this problem. The simplest version ignores communication costs but allows computers to differ in speed (a heterogeneous set of computers). The other two versions account for communication costs, first with identical remote computers (a homogeneous set of computers), and then with computers that may differ in speed. We provide exact expressions for the optimal work expectation for all three versions of the problem - via explicit closed-form expressions for the first two versions, and via a recurrence that computes this optimal value for the last, most general version.     

Modeling and Retrieval of Moving Objects

This paper presents a symbolic formalism for modeling and retrieving video data via the moving objects contained in the video images. The model integrates the representations of individual moving objects in a scene with the time-varying relationships between them by incorporating both the notions of object tracks and temporal sequences of PIRs (projection interval relationships). The model is supported by a set of operations which form the basis of a moving object algebra. This algebra allows one to retrieve scenes and information from scenes by specifying both spatial and temporal properties of the objects involved. It also provides operations to create new scenes from existing ones. A prototype implementation is described which allows queries to be specified either via an animation sketch or using the moving object algebra.     

Enlarging the Margins in Perceptron Decision Trees

Capacity control in perceptron decision trees is typically performed by controlling their size. We prove that other quantities can be as relevant to reduce their flexibility and combat overfitting. In particular, we provide an upper bound on the generalization error which depends both on the size of the tree and on the margin of the decision nodes. So enlarging the margin in perceptron decision trees will reduce the upper bound on generalization error. Based on this analysis, we introduce three new algorithms, which can induce large margin perceptron decision trees. To assess the effect of the large margin bias, OC1 (Journal of Artificial Intelligence Research, 1994, 2, 1–32.) of Murthy, Kasif and Salzberg, a well-known system for inducing perceptron decision trees, is used as the baseline algorithm. An extensive experimental study on real world data showed that all three new algorithms perform better or at least not significantly worse than OC1 on almost every dataset with only one exception. OC1 performed worse than the best margin-based method on every dataset.     

Classification of dissimilarity data with a new flexible Mahalanobis-like metric

Statistical pattern recognition traditionally relies on feature-based representation. For many applications, such vector representation is not available and we only possess proximity data (distance, dissimilarity, similarity, ranks, etc.). In this paper, we consider a particular point of view on discriminant analysis from dissimilarity data. Our approach is inspired by the Gaussian classifier and we defined decision rules to mimic the behavior of a linear or a quadratic classifier. The number of parameters is limited (two per class). Numerical experiments on artificial and real data show interesting behavior compared to Support Vector Machines and to kNN classifier: (a) lower or equivalent error rate, (b) equivalent CPU time, (c) more robustness with sparse dissimilarity data.     

A local approach to concept generation

Generating concepts defined by a binary relation between a set of properties and a set of objects is one of the important current problems encountered in Data Mining and Knowledge Discovery in Databases. We present a new algorithmic process which computes all the concepts, without requiring an exponential-size data structure, and with a good worst-time complexity analysis, which makes it competitive with the best existing algorithms for this problem. Our algorithm can be used to compute the edges of the lattice as well at no extra cost.