Dynamic programming is one of the most challenging algorithm design techniques for computer programmers. Students frequently struggle with dynamic programming algorithms in Data Structures and Algorithms courses, and software developers often find it difficult to solve dynamic programming problems during job interviews. To address this issue, we have developed a new visualization technique to help students better understand, analyze, and design dynamic programming algorithms. Previous dynamic programming visualizations have primarily focused on illustrating the execution sequence of programs. Our visualizations are designed to help students recognize the common logical patterns shared by a diverse set of problems. Additionally, students can use these visualizations as visual aids to assist them in designing dynamic programming algorithms.

The definition and measurement of complexity have been studied extensively in many scientific fields. But a comprehensive discussion of data visualization complexity is still missing. In this paper, we attempt to address this issue by reviewing the existing complexity metrics for data visualization. The complexity metrics from related fields and their applicability to data visualization are also discussed. The goal is to provide a preliminary theoretical foundation and a set of practical methods for evaluating the complexity of data visualization.

This work is about data-driven storytelling in tennis. Tennis has a peculiar scoring system that makes it challenging for the general audience to understand tennis matches. But from a storytelling perspective, the tennis scoring system has unique benefits because it creates a nested story structure. Dividing a tennis match into sets and games helps manage the tempo and rhythm of a story while creating suspense on multiple levels. The ad-scoring rule helps prolong the struggle, delay the outcome, and create suspense. However, the traditional sports news media has failed to present tennis matches as nested stories, and the suspense is often lost in the news report and match highlight videos. In this paper, we describe a novel visualization method to present tennis matches as nested stories. This visualization can help readers better understand the narratives of tennis matches without the barriers of the scoring system and the written language. In essence, this is a universal visual language that presents the stories of tennis matches in an intuitive way. Such visualizations can supplement the traditional textual and video coverage of tennis matches. It can also be applied to other sports with similar nested story structures. Our preliminary user studies showed highly positive user first impressions.

The quality evaluation is essential to creating effective data visualization designs. The data visualization research community has produced many quality metrics for evaluating data visualization. However, these quality metrics are rarely integrated into popular data visualization tools. As a result, most data visualization creators are either not aware of these quality metrics or do not know how to apply these metrics to the visualization creation process. In this paper, we propose a novel quality evaluation method that integrates quality metrics into popular data visualization programming tools. Our main contribution is a code-space quality evaluation method, different from the traditional image-space or data-space quality evaluation method. Using our method, a visualization programmer passes a coded data visualization design to a quality evaluation function that generates warnings, comments, and design recommendations. This allows users to integrate quality checks into the design process.

Kinematic motion analysis is widely used in health-care, sports medicine, robotics, biomechanics, sports science, etc. Motion capture systems are essential for motion analysis. There are three types of motion capture systems: marker-based capture, vision-based capture, and volumetric capture. Marker-based motion capture systems can achieve fairly accurate results but attaching markers to a body is inconvenient and time-consuming. Vision-based, marker-less motion capture systems are more desirable because of their non-intrusiveness and flexibility. Volumetric capture is a newer and more advanced marker-less motion capture system that can reconstruct realistic, full-body, animated 3D character models. But volumetric capture has rarely been used for motion analysis because volumetric motion data presents new challenges. We propose a new method for conducting kinematic motion analysis using volumetric capture data. This method consists of a three-stage pipeline. First, the motion is captured by a volumetric capture system. Then the volumetric capture data is processed using the Iterative Closest Points (ICP) algorithm to generate virtual markers that track the motion. Third, the motion tracking data is imported into the biomechanical analysis tool OpenSim for kinematic motion analysis. Our motion analysis method enables users to apply numerical motion analysis to the skeleton model in OpenSim while also studying the full-body, animated 3D model from different angles. It has the potential to provide more detailed and in-depth motion analysis for areas such as healthcare, sports science, and biomechanics.

Suspense is one of the most important emotions for the enjoyment of games. Many techniques have been developed to generate and manage suspense in games, but a theoretical framework for this body of work is missing. In this paper, we present a framework that identifies the main components for suspense management in games. This framework includes both the cognitive model and computational model of suspense. For the computational model of suspense, we identify the affective loop between Player Suspense Model, Suspense Manager, and game content. This framework can help game designers better understand the various options and tools for creating and managing suspense and provides a road map for developing new techniques.

Spatial-temporal data visualization is an important sub-field of data visualization. Many spatial-temporal data visualization techniques have been developed and published. Although there are several surveys on this subject, some are based on a task-centric or data-centric framework. Some surveys focus on the temporal data visualization for a particular type of spatial data visualization, such as a map or space-time cube. In this paper, we present a new taxonomy and interactive survey of spatial-temporal data visualization. Based on our taxonomy, a wide variety of spatial-temporal data visualization techniques can be described by a small set of visualization elements. This is the first step towards creating a grammar for spatial-temporal visualization.

Baseball is one of the many sports where visual acuity is felt to play a critical role in the ability to successfully make contact with a ball traveling upwards of 95 miles per hour from a distance of 60 feet, six inches. The correlation between elite baseball players’ visual acuity and their on-field performance has been the subject of many previous research studies. But realistic computer simulation is rarely used in those studies. We have developed a computer graphics program to simulate a pitched baseball at different speeds and rotations, and used it to evaluate the visual abilities of US college Division I level athletes in baseball and softball to correctly identify the spin, seam count, and a projected geometric shape on the simulated baseball. Each athlete who completed the test was then given a set of Visual Acuity Test scores. These results were then correlated with their individual batting statistics. The results showed a statistically significant, positive correlation between the seam recognition ability and many offensive performance indicators such as batting averages, slugging percentages, home runs, RBIs, etc. Spin recognition was trended towards the same conclusion. This study shows that visual simulation can be a viable tool for sports performance analysis and prediction.

In recent years, Various mechanisms have been proposed to optimize for players' emotional experience. In this paper, we focus on suspense, one of the key emotions in gameplay. Most previous research on suspense management in games focused on narratives. Instead, we propose a new computational model of Suspense for Non-Narrative Gameplay (SNNG). SNNG is built around a Player Suspense Model (PSM) with three key factors: hope, fear, and uncertainty. These three factors are modeled as three sensors that can be triggered by particular game objects (e.g., NPCs) and game mechanics (e.g., health). A player's feeling of suspense can be adjusted by altering the level of hope, fear, and uncertainty. Therefore, an SNNG-enhanced game engine could manage a player's level of suspense by adding or removing game objects, diverting NPCs, adjusting game mechanics, and giving or withholding information. We tested our model by integrating SNNG into a Pacman game. Our preliminary experiment with nine subjects was encouraging.

A story is a chain of events. Each event has multiple dimensions, including time, location, characters, actions, and context. Storyline visualizations attempt to visually present the many dimensions of a story's events and their relationships. Integrating the temporal and spatial dimension in a single visualization view is often desirable but highly challenging. One of the main reasons is that spatial data is inherently 2D while temporal data is inherently 1D. We propose a storyline visualization technique that integrate both time and location information in a single view. We have developed a timeline-based visualization method that maintains regular ordering of temporal and spatial dimensions. We also developed a heuristic method to reduce excessive line crossovers for large data sets. Through case studies, we show that this visualization method provide a good balance in integrating temporal and spatial dimensions.

Level-based tree drawing is a common algorithm that produces intuitive and clear presentations of hierarchically structured information. However, new applications often introduces new aesthetic requirements that call for new tree drawing methods. In this paper, we propose an indented level-based tree drawing algorithm for visualizing parse trees of English language. This algorithm displays a tree with an aspect ratio that fits the aspect ratio of the newer computer displays, while presenting the words in a way that is easy to read. We discuss the design of the algorithm and its application in text visualization for linguistic analysis and language learning. An efficient and practical implementation of the algorithm is also presented.

Flocking behavior in animals has long been an area of interest in computer animation. Various tools have been developed to help animators simulate flocking behavior without coding. Most of these tools are based on the classic flocking behavior model, which has simplistic escape behavior rules. Certain species, such as fish, has much more sophisticated escape behaviors. In this paper, present a new plug-in for Blender (FishEscapeSim) that can simulate more sophisticated fish escape behavior and predator-prey interactions. Our fish escape behavior model is based on the observations from biological research. We discuss the design and implementation of this tool and demonstrate its usage. This tool can help animators quickly create more realistic fish escape behavior without having to write code.

Sports data analysis and visualization can be a useful tool for gaining insights into the games. In this paper, we present a new technique to analyze and visualize shot patterns in tennis matches. Tennis is a complicated game that involves a rich set of tactics and strategies. The current tennis data analyses are usually conducted at a high level that often fail to show useful patterns and nuances embedded in low level data. Based on a very detailed database of professional tennis matches, we have developed a system to analyze the serve and shot patterns so that a user can explore questions such as "What are the favorite patterns of this player? What are the most effective patterns for this player?" This can help tennis experts, players, and fans gain deeper insight into the sport.

Abstract Fish exhibit a wide variety of behavior, including swimming and schooling. Animating a school of fish is difficult because of the large number of fish and because a school of fish often swims in distinctive and coordinated patterns, particularly when they are attacked by a predator. Previous work in computer graphics has not provided satisfactory models to simulate the many distinctive interactions between a school of prey fish and their predator. In this paper, we present a fish school escape behavior model that can simulate 12 fish escape maneuvers identified in biological studies: compact, avoid, fast avoid, skitter, fountain, flash, ball, split, join, herd, vacuole, and hourglass. This behavior animation model can free an animator from dealing with the low‐level animations but instead controls the fish behavior on a higher level by modifying a state machine and a small set of system parameters.

A school of fish exhibit a variety of distinctive maneuvers to escape from predators. Although these escape maneuvers have long been observed and studied in the fields of biology and ecology, they have not been sufficiently modeled in computer graphics. For example, a school of fish often use fountain and flash maneuvers at the time of final attacks by their predators. Previous works on fish animation provide relatively simple escape behavior model and lack variety. We propose a behavioral model to simulate two fish escape maneuvers at the time of a single predator attack: fountain and flash maneuvers. We demonstrate our results with simulations of predator attacks.

Real-time 3D graphics programs are based on a 3D pipeline structure. In a 3D pipeline, data is loaded, prepared, transferred from CPU to GPU, and then processed on GPU to create images. Regular debuggers and special graphics debuggers primarily focus on displaying data but do not show how data is transferred from CPU to GPU. If there is an error in data transfer, programmers usually need to locate the error by reading the source code, which many of our students find difficult. We have developed a tool to visualize data flow between CPU and GPU in a 3D pipeline. The visualization helps students understand 3D graphics programs, especially some of the implicit data connections in 3D graphics APIs. It also helps students quickly locate bugs related to data transfer in a 3D pipeline. We demonstrate our tool with an example that shows how a subtle error can be quickly detected in our visualization.

A biomarker is a measurable substance that can be used as an indicator of a particular disease. In the research field of cancer biomarker analysis, data visualization has generally been utilized as a technique for presenting results, with very little application of integrating data visualization as a tool for biological data exploration or pattern discovery. To address this issue, we have developed a visualization platform, CancerVis, for visual and interactive analysis of cancer data and pattern discovery. CancerVis provides multiple interactive views from different prospective of a dataset. The views are synchronized so that users can easily link the same data entry in multiple views. CancerVis's visualization technologies also supports common data mining techniques for pattern discovery in cancer biomarker research, such as visualization of optimal cutoff point and its variability over time. The usability of CancerVis is verified by a clinical dataset.

We present a set of online materials that teach debugging skills in college level computer graphics courses. Almost all the computer graphics textbooks use OpenGL and OpenGL Shading Language (GLSL). Most of the college level computer graphics courses are also taught using OpenGL/GLSL. Most students find OpenGL/GLSL programming very challenging. One of the reasons is that debugging is particularly difficult for computer graphics programming because many traditional debugging techniques can no longer be used. Debugging graphics programs requires a good understanding of OpenGL's error reporting mechanisms and different debugging techniques. Students also need to learn special GPU debugging tools. However, debugging is rarely covered in computer graphics textbooks and courses. Online resources on debugging OpenGL/GLSL programs are also scarce. To address this issue, we have developed online materials that teach students how to systematically debug shader-based OpenGL programs. This web site [1] provides error classification, debugging techniques, a list of common bugs, description of the OpenGL error reporting mechanisms, description of GPU debugging tools, symptom checker, code samples, and tutorials. This online resource fills an important gap in the current computer graphics curriculum. Students can use it to learn OpenGL/GLSL debugging skills. Instructors can use it as a supplement to their existing teaching materials.

Free/Libre and Open source software are generally maintained by a group of developers contributing to the software voluntarily without the presence of any governing institution. Online collaboration platforms and sub-version systems allow developers from all over the world with the necessary skills to contribute to the software. Recently, there has been much interest in analyzing the developers' geographic location since it also serves as a socio-economic marker. In this paper we present our spatio-temporal visualization technique called Storygraph that shows the developers, developer locations and the frequency of commits based on the commit log in one integrated view. We also apply our techniques to the VCS of Rails, Homebrew and D3.js obtained from GitHub.

In this paper, we report our attempt to evaluate the degree of online collaboration with quantitative analysis. Since most online collaborations are carried out through natural language, such quantitative analysis must rely on natural language processing. Here we discuss our work in analyzing an online social visualization web site using the semantic similarity measures of collaborative textual snippets. We use word-based textual similarity measures to study the relationships among user comments. We also detect and visualize the patterns of user collaborations.

In this paper, we present a new, shader based polygon stitching algorithm that takes full advantage of the geometry shader. Based on this algorithm, we propose a novel method to simulate deformable terrain, particularly the simulation of tire tracks left on the soft terrain such as snow, mud, or sand. Existing methods for simulating deformable terrain include texture decals, bump mapping, displacement mapping, and voxel based methods, but they all have drawbacks. In our algorithm, a pre-modeled tire track is stitched with the terrain mesh on GPU, behind a running vehicle. Compared with previous methods, our method generates more realistic terrain deformation while using fewer polygons. The proposed shader based polygon stitching algorithm can be used to simulate other destructible environments.

Learning how write and communicate effectively is a common challenge that spans most academic disciplines. "Can a model be developed that visually captures the structural and linguistic complexity of text?" This paper proposes a model for visualizing sentence complexity.

Composition style is often an important factor in readers' selection of reading materials. For example, a reader may seek out articles written in similar style as his or her favorite writer. We present a new method for providing recommendations based on the composition style. Our algorithm analyzes and encodes the readability index and syntactical structure of a model document, and then searches for articles with similar readability index and structure. The text readability and syntactical structures are visualized to help readers compare the documents and make the selection. Our method adds a "search by style" component to the traditional keyword based search, and provides recommendation that fits the user's personal preferences better. We demonstrate our method by applying it to product review recommendation based on user preferred composition style.

In the past few years, online social data visualization has emerged as a new platform for users to construct, share, and comment on data visualizations online. The most well known online data visualization tools include Many Eyes, Swivel, and Tableau Public. In this paper, we report our analysis of Many Eyes - an IBM research project. By analyzing all the data visualizations constructed by users from 2007 to 2010, we provide insight into online user behavior as well as patterns and trends in social data visualization.

Computational modeling and simulation of knee joint can help expand our understanding of the knee biomechanics, and thus improve orthopedic practice. Although many computational knee models have been developed, very few of them include a deformable menisci model. In addition, patient specific modeling and simulation are still not widely adopted, although they are shown to be more effective than generic modeling. In this paper, we present two new methods for simulating and visualizing menisci-femur contact area during gait cycles, using patient-specific knee models and motion data. Specifically we propose a new template based 3D model reconstruction method that generates patient-specific knee models from medical images. We also propose a new deformable menisci model that combines physics-based deformation and spatial deformation, making it easier for users to balance the physical realism and performance needs. These new methods will benefit knee biomechanical analysis as well as interactive orthopedic surgery planning and training.

For collaborative environments to be successful, it is critical that participants have the ability to generate effective references. Given the heterogeneity of the objects and the myriad of possible scenarios for collaborative augmented reality environments, generating meaningful references within them can be difficult. Participants in co-located physical spaces benefit from non-verbal communication, such as eye gaze, pointing and body movement; however, when geographically separated, this form of communication must be synthesized using computer-mediated techniques. We have conducted an exploratory study using a collaborative building task of constructing both physical and virtual models to better understand inter-referential awareness -- or the ability for one participant to refer to a set of objects, and for that reference to be understood. Our contributions are not necessarily in presenting novel techniques, but in narrowing the design space for referencing in collaborative augmented reality. This study suggests collaborative reference preferences are heavily dependent on the context of the workspace.

We present a novel design for managing competing user interests and privacy protection cooperation in a collaborative virtual environment for molecular modeling. Based on the user feedback, this design includes four levels of access control for collaborative sessions and provides dynamic action priority specification for manipulations on shared molecular models. Furthermore, we implement a messaging system that includes a text chatting tool and system broadcasting functions. Our design supports flexible user group formation and interaction. We have implemented our design on a set of Microsoft Windows PCs connected to a Linux molecule simulation server. A GUI interface developed in Python is also illustrated

For collaborative environments to be successful, a fundamental requirement is that they provide support for inter-referential awareness - or the ability for one participant to refer to a set of objects, and for that reference to be understood by others. Participants in co-located collaboration benefit from the availability of non-verbal communication, including gestures, eye gaze and body movements. However, when geographically separated, they can experience difficulty in communicating - as computer-mediated cues are often compromised. Given the heterogeneity of media and myriad of interaction techniques that exist in groupware, supporting inter-referential awareness can be difficult. We present a unified and systematic way of encapsulating the numerous factors related to this form of awareness through the creation of process-driven ontology. Our framework provides a formal method for describing inter-referential awareness, and serves as approach that interface designers can use to better comprehend the relevant factors involved. This research stems from our previous investigations in inter-referential awareness in collaborative augmented reality environments

The most common computer authentication method is to use alphanumerical usernames and passwords. This method has been shown to have significant drawbacks. For example, users tend to pick passwords that can be easily guessed. On the other hand, if a password is hard to guess, then it is often hard to remember. To address this problem, some researchers have developed authentication methods that use pictures as passwords. In this paper, we conduct a comprehensive survey of the existing graphical password techniques. We classify these techniques into two categories: recognition-based and recall-based approaches. We discuss the strengths and limitations of each method and point out the future research directions in this area. We also try to answer two important questions: "Are graphical passwords as secure as text-based passwords?"; "What are the major design and implementation issues for graphical passwords?" This survey will be useful for information security researchers and practitioners who are interested in finding an alternative to text-based authentication methods

Molecular modeling is an important research area, helping scientists develop new drugs against diseases such as AIDS and cancer. Prior studies have demonstrated that immersive virtual environments have unique advantages over desktop systems in visualizing molecular models. However, exploration and interaction in existing molecular modeling virtual environments is often limited to a single user, lacking strong support for collaboration. In addition, scientists are often reluctant to adopt these systems because of their lack of availability and high cost. We propose an affordable immersive system that allows biologists and chemists to manipulate molecular models via natural gestures, receive and visualize real-time feedback from a molecular dynamics simulator, allow the sharing of customized views, and provide support for both local and remote collaborative research.

This paper describes the design and development of a VR system that can assist orthopedic diagnosis and surgery. A realistic 3D knee surface model, integrated with motion analysis, is used to visualize the geometrical and biomechanical characteristics of human knee joint. The system can be used to perform pre-operative surgery simulation and evaluation in a VR environment.