Rudolf Fleischer

Exploring the role of visualization and engagement in computer science education

Visualization technology can be used to graphically illustrate various concepts in computer science. We argue that such technology, no matter how well it is designed, is of little educational value unless it engages learners in an active learning activity. Drawing on a review of experimental studies of visualization effectiveness, we motivate this position against the backdrop of current attitudes and best practices with respect to visualization use. We suggest a new taxonomy of learner engagement with visualization technology. Grounded in Blooms well-recognized taxonomy of understanding, we suggest metrics for assessing the learning outcomes to which such engagement may lead. Based on these taxonomies of engagement and effectiveness metrics, we present a framework for experimental studies of visualization effectiveness. Interested computer science educators are invited to collaborate with us by carrying out studies within this framework.

Evaluating the educational impact of visualization

The educational impact of visualization depends not only on how well students learn when they use it, but also on how widely it is used by instructors. Instructors believe that visualization helps students learn. The integration of visualization techniques in classroom instruction, however, has fallen far short of its potential. This paper considers this disconnect, identifying its cause in a failure to understand the needs of a key member in the hierarchy of stakeholders, namely the instructor. We describe these needs and offer guidelines for both the effective deployment of visualizations and the evaluation of instructor satisfaction. We then consider different forms of evaluation and the impact of student learning styles on learner outcomes.

On‐line scheduling revisited

We present a new on-line algorithm, MR, for non-preemptive scheduling of jobs with known processing times on m identical machines which beats the best previous algorithm for m⩾64. For m∞ its competitive ratio approaches 1+\sqrt{(1+1{\rm n} 2)/2}<1.9201. Copyright 2000

Approximate motion planning and the complexity of the boundary of the union of simple geometric figures

We study rigid motions of a rectangle amidst polygonal obstacles. The best known algorithms for this problem have a running time of Ω(n2), wheren is the number of obstacle corners. We introduce thetightness of a motion-planning problem as a measure of the difficulty of a planning problem in an intuitive sense and describe an algorithm with a running time ofO((a/b · 1/ɛcrit + 1)n(logn)2), wherea ≥b are the lengths of the sides of a rectangle and ɛcrit is the tightness of the problem. We show further that the complexity (= number of vertices) of the boundary ofn bow ties (see Figure 1) isO(n). Similar results for the union of other simple geometric figures such as triangles and wedges are also presented.

Online Routing in Convex Subdivisions

We consider online routing algorithms for finding paths between the vertices of plane graphs. We show (1) there exists a routing algorithm for arbitrary triangulations that has no memory and uses no randomization, (2) no equivalent result is possible for convex subdivisions, (3) there is no competitive online routing algorithm under the Euclidean distance metric in arbitrary triangulations, and (4) there is no competitive online routing algorithm under the link distance metric even when the input graph is restricted to be a Delaunay, greedy, or minimum-weight triangulation.

Low-Resolution Gait Recognition

Unlike other biometric authentication methods, gait recognition is noninvasive and effective from a distance. However, the performance of gait recognition will suffer in the low-resolution (LR) case. Furthermore, when gait sequences are projected onto a nonoptimal low-dimensional subspace to reduce the data complexity, the performance of gait recognition will also decline. To deal with these two issues, we propose a new algorithm called superresolution with manifold sampling and backprojection (SRMS), which learns the high-resolution (HR) counterparts of LR test images from a collection of HR/LR training gait image patch pairs. Then, we incorporate SRMS into a new algorithm called multilinear tensor-based learning without tuning parameters (MTP) for LR gait recognition. Our contributions include the following: 1) With manifold sampling, the redundancy of gait image patches is remarkably decreased; thus, the superresolution procedure is more efficient and reasonable. 2) Backprojection guarantees that the learned HR gait images and the corresponding LR gait images can be more consistent. 3) The optimal subspace dimension for dimension reduction is automatically determined without introducing extra parameters. 4) Theoretical analysis of the algorithm shows that MTP converges. Experiments on the USF human gait database and the CASIA gait database show the increased efficiency of the proposed algorithm, compared with previous algorithms.

Exploring an unknown graph efficiently

We study the problem of exploring an unknown, strongly connected directed graph. Starting at some node of the graph, we must visit every edge and every node at least once. The goal is to minimize the number of edge traversals. It is known that the competitive ratio of online algorithms for this problem depends on the deficiency d of the graph, which is the minimum number of edges that must be added to make the graph Eulerian. We present the first deterministic online exploration algorithm whose competitive ratio is polynomial in d (it is O(d8)).

Online Scheduling Revisited

We present a newon line algorithm, MR, for nonpreemptive scheduling of jobs with known processing times on m identical machines which beats the best previous algorithm for m ≥ 64. For m → ∞ its competitive ratio approaches 1 + √1+ln2/2 < 1.9201.

Efficient exact geometric computation made easy

We show that the combination of the CGAL framework for geometric computation and the number type ledareal yields easy-to-write, correct and efficient geometric programs.

Tight bounds on maximal and maximum matchings

In this paper, we study lower bounds on the size of maximal and maximum matchings in 3-connected planar graphs and graphs with bounded maximum degree. For each class, we give a lower bound on the size of matchings, and show that the bound is tight for some graph within the class.