Abstract-Existing research efforts into tennis visualization have primarily focused on using ball and player tracking data to enhance professional tennis broadcasts and to aid coaches in helping their students. Gathering and analyzing this data typically requires the use of an array of synchronized cameras, which are expensive for non-professional tennis matches. In this paper, we propose TenniVis, a novel tennis match visualization system that relies entirely on data that can be easily collected, such as score, point outcomes, point lengths, service information, and match videos that can be captured by one consumer-level camera. It provides two new visualizations to allow tennis coaches and players to quickly gain insights into match performance. It also provides rich interactions to support ad hoc hypothesis development and testing. We first demonstrate the usefulness of the system by analyzing the 2007 Australian Open men's singles final. We then validate its usability by two pilot user studies where two college tennis coaches analyzed the matches of their own players. The results indicate that useful insights can quickly be discovered and ad hoc hypotheses based on these insights can conveniently be tested through linked match videos.

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Figure 1: FDIVE learns to distinguish relevant from irrelevant data through an iteratively improving classification model by learning the best-fitting feature descriptor and distance function. (1) Users express their notion of relevance by labeling a set of query items, in this case, images. (2) These labels are used to rank all similarity measures by their ability to distinguish relevant from irrelevant data. (3) The system applies the selected similarity measure to learn a Self-Organizing Map (SOM)-based relevance model. Users explore and refine the model by supplying relevance labels in uncertain data regions, especially near the decision boundaries. ABSTRACTThe detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity. Therefore, analysts require automated support for the extraction of relevant patterns. In this paper, we present FDIVE, a visual active learning system that helps to create visually explorable relevance models, assisted by learning a pattern-based similarity. We use a small set of user-provided labels to rank similarity measures, consisting of feature descriptor and distance function combinations, by their ability to distinguish relevant from irrelevant data. Based on the best-ranked similarity measure, the system calculates an interactive Self-Organizing Map-based relevance model, which classifies data according to the cluster affiliation. It also automatically prompts further relevance feedback to improve its accuracy. Uncertain areas, especially near the decision boundaries, are highlighted and can be refined by the user. We evaluate our approach by comparison to state-of-the-art feature selection techniques and demonstrate the usefulness of our approach by a case study classifying electron microscopy images of brain cells. The results show that FDIVE enhances both the quality and understanding of relevance models and can thus lead to new insights for brain research.

Urban heat islands are local areas where the temperature is much higher than in the vicinity and are a modern phenomenon that occurs mainly in highly developed areas, such as large cities. This effect has a negative impact on energy management in buildings and also has a direct impact on human health, especially for elderly people. With the advent of volunteered geographic information from private weather station networks, more high resolution data is now available within cities to better analyze this effect. However, such data sets are large and have heterogeneous characteristics requiring visualinteractive applications to support further analysis. We use machine learning methods to predict urban heat islands occurrences and utilize temporal and spatio-temporal visualizations to contextualize the emergence of urban heat islands to comprehend the influencing causes and their effects. Subsequently, we demonstrate the analysis capabilities of our application by presenting two use cases.

Match analysis has become an important task in everyday work at professional soccer clubs in order to improve team performance. Video analysts regularly spend up to several days analyzing and summarizing matches based on tracked and annotated match data. Although there already exists extensive capabilities to track the movement of players and the ball from multimedia data sources such as video recordings, there is no capability to sufficiently detect dynamic and complex events within these data. As a consequence, analysts have to rely on manually created annotations, which are very time-consuming and expensive to create. We propose a novel method for the semi-automatic definition and detection of events based entirely on movement data of players and the ball. Incorporating Allen's interval algebra into a visual analytics system, we enable analysts to visually define as well as search for complex, hierarchical events. We demonstrate the usefulness of our approach by quantitatively comparing our automatically detected events with manually annotated events from a professional data provider as well as several expert interviews. The results of our evaluation show that the required annotation time for complete matches by using our system can be reduced to a few seconds while achieving a similar level of performance.

In order to review the syllabi, the program developed a rubric to focus on 12 criteria, which were comprised of WI requirements and best practices for teaching writing. The rubric was tested and redrafted several times in order to ensure a more consistent data collection. The following numbers represent the summary of data collected on the 12 criteria. BACKGROUNDEstablished in 1993, the Writing Across the Curriculum Program at Mason was designed to develop both our students' understanding of their disciplines and their ability to communicate as professionals in those disciplines. The curricular approach is based on the following beliefs: § writing is an important tool for learning and discovery, § students gain proficiency as writers when they are given frequent opportunities to write for various audiences and purposes, § faculty across share responsibility for helping students learn the conventions and writing practices of their disciplines, § students benefit from revising their writing based on meaningful feedback from their instructors, § and writing instruction must be continuous throughout a student's education.The frequent opportunities for writing, receiving feedback, and revising help students to think more creatively and critically, engage more deeply in their learning, and transfer their learning from context to context.

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