This research investigated how the similarity of the rendering parameters of background and foreground objects affected egocentric depth perception in indoor virtual and augmented environments. We refer to the similarity of the rendering parameters as visual 'congruence'. Study participants manipulated the depth of a sphere to match the depth of a designated target peg. In the first experiment, the sphere and peg were both virtual, while in the second experiment, the sphere is virtual and the peg is real. In both experiments, depth perception accuracy was found to depend on the levels of realism and congruence between the sphere, pegs, and background. In Experiment 1, realistic backgrounds lead to overestimation of depth, but resulted in underestimation when the background was virtual, and when depth cues were applied to the sphere and target peg. In Experiment 2, background and target pegs were real but matched with the virtual sphere; in comparison to Experiment 1, realistically rendered targets prompted an underestimation and more accuracy with the manipulated object. These findings suggest that congruence can affect distance estimation and the underestimation effect in the AR environment resulted from increased graphical fidelity of the foreground target and background.

Engaging student in Computer Science courses has been linked to higher performance and retention in the major. Yet many of the assignments that we deploy in our courses tend to be fairly academic exercises that do not engage students particularly well.

HS BRIDGES (https://bridgesuncc.github.io/bridges-hs/) is a collection of programming projects, including "student scaffolds" and "teacher walkthroughs", that use UNC Charlotte's BRIDGES Java Libraries (https://bridgesuncc.github.io/) in order to enable students' creations of data structure- and real world data visualizations.

This workshop will provide instructors in early CS courses with tools and strategies for designing and building high quality courses by structure and content, that are student centered, aligned with stated learning outcomes, and with access to engaging learning materials. Workshop participants will be introduced to two software toolkits, CS Materials and BRIDGES, towards achieving these goals. These tools permit searches for learning materials that meet specific learning outcomes, while at the same time provide access to engaging materials that demonstrate core CS relevance to real world problems and applications. Workshop participants will be exposed to strategies for designing courses, materials and tools that can engage today's students and meet their expectations. Although this workshop will be based on CS Materials and BRIDGES, the lessons learnt are independently beneficial to participants.

Computing proficiency is an increasingly vital component of the modern workforce, and computer science programs are faced with the challenges of engaging and retaining students to meet the growing need in that sector. However, administrators and instructors often find themselves either reinventing the wheel or relying too heavily on intuition, despite the availability of national curriculum standards. To address these issues, we present CS Materials, an open-source resource targeted at computing educators for designing and analyzing courses for coverage of recommended guidelines, and alignment between the various components within a course, between sections of the same course, or course sequences within a program. The system works by facilitating mapping educational materials to national curriculum standards. A side effect of the system is that it centralizes the design of the courses and the materials used therein. The curriculum guidelines act as a lingua franca that allows examination of and comparison between materials and courses. More relevant to instructors, the system enables a more precise search for materials that match particular topics and learning outcomes, and dissemination of high quality materials and course designs. This paper discusses the system, and analyzes the costs and benefits of its features and usage. While adding courses and materials requires some overhead, having a centralized repository of courses and materials with a shared structure and vocabulary serves students, instructors, and administrators, by promoting a data-driven approach to rigor and alignment with national standards.

Many high school mathematics teachers have stepped up to the charge of learning computer science and offering CS courses to their students. As CS grows in popularity, more students are completing AP CS A as sophomores or juniors, and looking for advanced opportunities while still in high school.

Grounding computer science concepts in real-world and socially relevant problems can be a key to increasing students' motivation and engagement in computing. BRIDGES provides an infrastructure for use in early CS courses that allows students to easily integrate real-world data into their routine course assignments and visualize the data and data structures of their implementations. The BRIDGES API provides several key advantages, First, minimal effort is needed to accessing and using interesting real-world datasets in CS homework assignments. Available data sets in BRIDGES span multiple domains, including entertainment, science, geographic data, and literature. Second, BRIDGES can be used by students to create and explore a visualization of the data used and data structures implemented in their assignments. Such visualizations can be used to illustrate concepts of underlying algorithm or data structure, or important data features. Third, the BRIDGES Game API allows students to implement 2D games, to emphasize basic concepts, such as control structures, looping and logic, while providing a fun experience. Finally, students can explore the benchmarking of algorithms in BRIDGES by using real and large data sets, emphasizing algorithm performance and computational complexity. Workshop attendees will engage in hands-on experience with BRIDGES with multiple datasets and will have opportunities to discuss how BRIDGES can be used in their own courses.

Early programming courses such as CS1 are an important time to capture the interest of students while imparting critical technical knowledge. Yet many CS1 courses are being taught using toy assignments and activities that tend to make students uninterested or doubt the usefulness of the content. In this poster, we demonstrate an enriching experience for students by coupling interesting datasets with visual representations and interactive applications, without having to change the content of that course. Our approach utilizes extensions to BRIDGES, an API in use for sophomore level CS courses for the past 5 years. BRIDGES provides easy access to external datasets and helps build interactive applications. The assignments we present are all scaffolded in a way that can be directly integrated into most early programming courses to make routine topics compelling and exciting.

We present a course design model for applying project-based learning to an online undergraduate object oriented systems course. In our model, projects and reflection are central to the curriculum. Our model challenges students through modularized, repetitive project cycles beginning with analysis and design (i.e. using pseudo- code, flowcharts, diagrams) then coding, debugging, testing, and finally, reflection. We analyzed student reflection responses from two semesters to extract major themes and sub-themes, then mapped these to the MUSIC model (eMpowerment, Usefulness, Success, Interest, Caring) to understand our model's influence on student engagement and motivation. We found that a rhythmic project cycle encourages self-regulation in online students to formulate project plans, track their progress, and evaluate their solutions. Online students feel empowered when course projects promote choice, flexibility, creativity, experimentation, and extensions to other applications. Online student success is dependent on the clarity of instructions, course scaffolding, level of challenge, instructor feedback, and opportunities to reflect on personal failure, success, and challenge. Online students are interested in projects that are familiar, real-world, and fun, but expect to be situated in team-based environments. Students appreciate instructors who are caring and accommodating to personal needs. We recommend six salient strategies for improving online course and project design: design a visible, rhythmic structure; set transparent expectations and instructions; encourage design before implementation; connect to real-world applications and tools; experience happy challenges; infuse sustained reflection.

This workshop provides instructors with a hands-on introduction to BRIDGES, a software infrastructure for programming assignments in early computer science courses, including introductory programming (CS1, CS2), data structures, and algorithm analysis. BRIDGES provides capabilities for creating more engaging programming assignments, including: (1) a simplified API for accessing real-world data sets, including from social networks; scientific, government, and civic organizations; and movie, music, and literature collections; (2) interesting visualizations of the data, (3) an easy to use API that supports creation of games that leverage real-world data, and, (4) algorithm benchmarking. Workshop attendees will engage in hands-on experience with BRIDGES with multiple datasets and will have the opportunity to discuss how BRIDGES can be used in their own courses.

Many newcomers to programming and computational thinking have been brought up on interactive, gamified learning environments. Introductory computer science courses at the university level need to dig deeper into these topics, but must do so with similarly engaging technologies and projects. To address this need, we have built a framework for a grid-based game API with event-based blocking and continuous non-blocking interfaces. The framework abstracts away much of the complexity of inputs and rendering and exposes a simple game grid similar to a 2D array indexed by rows and columns. As such, our project helps reinforce basic computing concepts (arrays, loops, OOP, recursion) with a customizable and engaging game interface. We have discussed the valuable influence of visual representations of student's data structures using BRIDGES in previous publications, and believe our game API can provide significance and intrigue for students in introductory courses and beyond. Our Bridges Games App website (http://bridges-games.herokuapp.com/) presents descriptions and instructions.

Traditionally, students are introduced to programming oriented courses by covering a series of topics and having a programming project due at the end of each topic. Assigned projects are often not connected to each other, but are rather used to expose students to a variety programming constructs and concepts. Students take the most direct approach such as trial and error to complete these projects. While this scenario utilizes projects, it approaches the use of projects as a means to assess learning rather than a means for learning. In this poster, we present results from a project-based online course in object-oriented analysis and design. We present a model for structuring project modules that provide opportunities for students to prepare, design, implement and reflect on each project module. By providing multiple points for feedback and reflection, as well as assigning projects with visual components, the course aims to promote engagement of undergraduate students, while at the same time maintaining rigor. Preliminary results from this course are encouraging, and could potentially become a model for project based learning.

This workshop introduces participants to the concepts and use of BRIDGES, a software infrastructure for programming assignments in data structures and algorithms courses. BRIDGES provides two key capabilities, (1) easy to use interface to real world datasets spanning social networks, entertainment (movies on IMDB, song lyrics), scientific data (real-time USGIS Earthquake Data), civic issues (crime data), and literature (books); and (2) a visualization of the acquired data can be used in assignments by students to populate their implemented data structures, including the capability to bring out attributes of the dataset. The visualizations are displayed on the BRIDGES website and are easily shared (with family, friends, peers, etc) via a weblink. Workshop attendees will engage in hands-on experience with BRIDGES and multiple datasets and will have the opportunity to discuss how BRIDGES can be used in their own courses, as well as partner with the BRIDGES team.

Working with real-world data has increasingly become a popular context for introductory computing courses. As a valuable 21st century skill, preparing students to be able to divine meaning from data can be useful to their long-term careers. Because Data Science aligns so closely with computing, many of the topics and problems it affords as a context can support the core learning objectives in introductory computing classes. In many instances, incorporating a real-world dataset to provide concrete context for an activity or assignment can improve student engagement and understanding of the abstract educational content being presented. However, there are many problems inherent to bringing real-world data into introductory courses. How do instructors, with finite amounts of time and energy, find and prepare suitable datasets for their pedagogical needs? Once the datasets are ready, how can students conveniently interact with and draw meaning from the datasets, especially when they are used in complex projects that are typical of later introductory courses? On the other hand, how does an instructor balance the complexities of using real-world datasets in the classroom, making sure that students appreciate the meaningfulness of course activities and their connection to learning objectives? This panel brings together experts with experience in using real-world data in introductory computing courses. Each panelist provides unique perspectives and skills to the problem of preparing, interacting, visualizing, and using pedagogical datasets. This panel should be of particular interest to instructors who are considering integrating current and real-world data into their assignments and projects, and to educational developers who want to create and manage datasets for pedagogical purposes. The panel will follow a conventional format: 5 minutes of introduction, 10 minutes for each panelist to present, and then 30 minutes for audience Q&A.

In recent years, interactive textbooks have gained prominence in an effort to overcome student reluctance to routinely read textbooks, complete assigned homeworks, and to better engage students to keep up with lecture content. Interactive textbooks are more structured, contain smaller amounts of textual material, and integrate media and assessment content. While these are an arguable improvement over traditional methods of teaching, issues of academic integrity and engagement remain. In this work we demonstrate preliminary work on building interactive teaching modules for data structures and algorithms courses with the following characteristics, (1) the modules are highly visual and interactive, (2) training and assessment are tightly integrated within the same module, with sufficient variability in the exercises to make it next to impossible to violate academic integrity, (3) a data logging and analytic system that provides instantaneous student feedback and assessment, and (4) an interactive visual analytic system for the instructor to see students/ performance at the individual, sub-group or class level, allowing timely intervention and support for selected students. Our modules are designed to work within the infrastructure of the OpenDSA system, which will promote rapid dissemination to an existing user base of CS educators. We demonstrate a prototype system using an example dataset.

Effective communication using visualization relies in part on the use of viable encoding strategies. For example, a viewer's ability to rapidly and accurately discern between two or more categorical variables in a chart or figure is contingent upon the distinctiveness of the encodings applied to each variable. Research in perception suggests that color is a more salient visual feature when compared to shape and although that finding is supported by visualization studies, characteristics of shape also yield meaningful differences in distinctiveness. We propose that open or closed shapes (that is, whether shapes are composed of line segments that are bounded across a region of space or not) represent a salient characteristic that influences perceptual processing. Three experiments were performed to test the reliability of the open/closed category; the first two from the perspective of attentional allocation, and the third experiment in the context of multi-class scatterplot displays. In the first, a flanker paradigm was used to test whether perceptual load and open/closed feature category would modulate the effect of the flanker on target processing. Results showed an influence of both variables. The second experiment used a Same/Different reaction time task to replicate and extend those findings. Results from both show that responses are faster and more accurate when closed rather than open shapes are processed as targets, and there is more processing interference when two competing shapes come from the same rather than different open or closed feature categories. The third experiment employed three commonly used visual analytic tasks - perception of average value, numerosity, and linear relationships with both single and dual displays of open and closed symbols. Our findings show that for numerosity and trend judgments, in particular, that different symbols from the same open or closed feature category cause more perceptual interference when they are presented together in a plot than symbols from different categories. Moreover, the extent of the interference appears to depend upon whether the participant is focused on processing open or closed symbols.

This workshop introduces participants to the concepts and use of BRIDGES, a software infrastructure designed to facilitate hands-on experience for solving traditional problems in sophomore level computer science courses (data structures, algorithms) using data from real-world systems that are of interest to students, such as social networks (Twitter, Facebook), scientific or engineering datasets (USGIS Earthquake data), Google Maps, etc. BRIDGES provides easy access (typically function calls) to real-world data sets for use in routine data structures programming assignments, without requiring students to work with complex and varied APIs to acquire such data. BRIDGES also provides visualization capabilities, allowing the students to visualize the data structure they have created as part of their assignment. BRIDGES visualizations can be easily shared, via a web link, with peers, friends, and family. Workshop attendees will engage in hands-on experience with BRIDGES and multiple data sets, and will have the opportunity to discuss how BRIDGES can be used to support various introductory computer science courses. A laptop with internet connection is required to participate in hands-on activities.

Although undergraduate enrollment in Computer Science has remained strong and seen substantial increases in the past decade, retention of majors remains a significant concern, particularly for students at the freshman and sophomore level that are tackling foundational courses on algorithms and data structures. In this work, we present BRIDGES, a software infrastructure designed to enable the creation of more engaging assignments in introductory data structures courses by providing students with a simplified API that allows them to populate their own data structure implementations with live, real-world, and interesting data sets, such as those from popular social networks (e.g., Twitter, Facebook). BRIDGES also provides the ability for students to create and explore {\em visualizations} of the execution of the data structures that they construct in their course assignments, which can promote better understanding of the data structure and its underlying algorithms; these visualizations can be easily shared via a weblink with peers, family, and instructional staff. In this paper, we present the BRIDGES system, its design, architecture and its use in our data structures course over two semesters.

This demo introduces participants to the concepts and application of BRIDGES, a software infrastructure designed to facilitate hands-on experience for solving traditional problems in introductory computer science courses using data from real-world systems that are of interest to students, such as Facebook, Twitter, and Google Maps. BRIDGES provides access to real-world data sets for use in traditional data structures programming assignments, without requiring students to work with complex and varied APIs to acquire such data. BRIDGES also helps the students to explore and understand the use of data structures by providing each student with a visualization of operations performed on the student's own implementation of a data structure. BRIDGES visualizations can be easily shared (via a weblink) with peers, friends, and family. Demo attendees will see (and possibly engage in) hands-on experience with BRIDGES and will have the opportunity to discuss how BRIDGES can be used to support various introductory computer science courses. Additionally, the demo will complement our oral presentation of our work at SIGCSE, by providing hands-on demonstrations of BRIDGES.

No abstract available.

We present a new constraint-based implicit active contour, which shares desirable properties of both parametric and implicit active contours. Like parametric approaches, their representation is compact and can be manipulated interactively. Like other implicit approaches, they can naturally adapt to nonsimple topologies. Unlike implicit approaches using level-set methods, representation of the contour does not require a dense mesh. Instead, it is based on specified on-curve and off-curve constraints, which are interpolated using radial basis functions. These constraints are evolved according to specified forces drawn from the relevant literature of both parametric and implicit approaches. This new type of active contour is demonstrated through synthetic images, photographs, and medical images with both simple and nonsimple topologies. For complex input, this approach produces results comparable to those of level set or parameterized finite-element active models, but with a compact analytic representation. As with other active contours they can also be used for tracking, especially for multiple objects that split or merge.

DNA microarray provides a powerful basis for analysis of gene expression. Data mining methods such as clustering have been widely applied to microarray data to link genes that show similar expression patterns. However, this approach usually fails to unveil gene-gene interactions in the same cluster. We propose to combine graphical model based interaction analysis with other data mining techniques (e.g., association rule, hierarchical clustering) for this purpose. For interaction analysis, we propose the use of graphical Gaussian model to discover pairwise gene interactions and loglinear model to discover multigene interactions. We have constructed a prototype system that permits rapid interactive exploration of gene relationships.

This paper investigates the problem of augmenting labeled data with unlabeled data to improve classification accuracy. This is significant for many applications such as image classification where obtaining classification labels is expensive, while large unlabeled examples are easily available. We investigate an Expectation Maximization (EM) algorithm for learning from labeled and unlabeled data. The reason why unlabeled data boosts learning accuracy is because it provides the information about the joint probability distribution. A theoretical argument shows that the more unlabeled examples are combined in learning, the more accurate the result. We then introduce B-EM algorithm, based on the combination of EM with bootstrap method, to exploit the large unlabeled data while avoiding prohibitive I/O cost. Experimental results over both synthetic and real data sets that the proposed approach has a satisfactory performance.

Describes algebraic methods for creating implicit surfaces using linear combinations of radial basis interpolants to form complex models from scattered surface points. Shapes with arbitrary topology are easily represented without the usual interpolation or aliasing errors arising from discrete sampling. These methods were first applied to implicit surfaces by V.V. Savchenko, et al. (1995) and later developed independently by G. Turk and J.F. O'Brien (1998) as a means of performing shape interpolation. Earlier approaches were limited as a modeling mechanism because of the order of the computational complexity involved. We explore and extend these implicit interpolating methods to make them suitable for systems of large numbers of scattered surface points by using compactly supported radial basis interpolants. The use of compactly supported elements generates a sparse solution space, reducing the computational complexity and making the technique practical for large models. The local nature of compactly supported radial basis functions permits the use of computational techniques and data structures such as k-d trees for spatial subdivision, promoting fast solvers and methods to divide and conquer many of the subproblems associated with these methods. Moreover, the representation of complex models permits the exploration of diverse surface geometry. This reduction in computational complexity enables the application of these methods to the study of the shape properties of large, complex shapes.

The discrete space representation of most scientific datasets, generated through instruments or by sampling continuously defined fields, while being simple, is also verbose and structureless. We propose the use of a particular spatial structure, the binary space partitioning tree as a new representation to perform efficient geometric computation in discretely defined domains. The ease of performing affine transformations, set operations between objects, and correct implementation of transparency makes the partitioning tree a good candidate for probing and analyzing medical reconstructions, in such applications as surgery planning and prostheses design. The multiresolution characteristics of the representation can be exploited to perform such operations at interactive rates by smooth variation of the amount of geometry. Application to ultrasound data segmentation and visualization is proposed. The paper describes methods for constructing partitioning trees from a discrete image/volume data set. Discrete space operators developed for edge detection are used to locate discontinuities in the image from which lines/planes containing the discontinuities are fitted by using either the Hough transform or a hyperplane sort. A multiresolution representation can be generated by ordering the choice of hyperplanes by the magnitude of the discontinuities. Various approximations can be obtained by pruning the tree according to an error metric. The segmentation of the image into edgeless regions can yield significant data compression. A hierarchical encoding schema for both lossless and lossy encodings is described.

The binary space partitioning tree is a method of converting a discrete space representation to a particular continuous space representation. The conversion is accomplished using standard discrete space operators developed for edge detection, followed by a Hough transform to generate candidate hyperplanes that are used to construct the partitioning tree. The result is a segmented and compressed image represented in continuous space suitable for elementary computer vision operations and improved image transmission/storage. Examples of 256*256 medical images for which the compression is estimated to range between 1 and 0.5 b/pixel are given.>

The authors present a ray-tracing algorithm for volume rendering designed to work efficiently when the data of interest is distributed sparsely through the volume. A simple preprocessing step identifies the voxels representing features of interest. Frequently this set of voxels, arbitrarily distributed in three-dimensional space, is a small fraction of the original voxel grid. A median-cut space partitioning scheme, combined with bounding volumes to prune void spaces in the resulting search structure, is used to store the voxels of interest in a k-d tree. The k-d tree is used as a data structure. The tree is then efficiently ray-traced to render the voxel data. The k-d tree is view independent, and can be used for animation sequences involving changes in positions of the viewer or positions of lights. This search structure has been applied to render voxel data from MRI, CAT scan, and electron density distributions.>