The book reports on the development and validation of a computerized adaptive EFL test, which is used as an exit requirement for non-English-major undergraduates at a major Chinese university. Unlike the tests reported in previous research on computerized adaptive language test (CALT), which were mostly designed to assess vocabulary, grammar and reading, this test consists of listening and reading components using both dichotomously-scored stand-alone items and polytomously-scored testlet-based items. The book is an essential and stimulating read for researchers and graduate students in the testing domain, particularly for those who are interested in CALT, item response theory (IRT), and quantitative research methods in general. It also constitutes a solid reference resource for L2 teachers and test developers. One of the distinctive features of the book is its operationalization of assessment use argument (AUA) in the investigation of the construct validity of the CALT. Another strength of this book is that it provides insight into the specific procedures that are followed in the development of CALTs and addresses a few key issues, such as local item dependence and DIF detection that were ignored in previous CALT development research.

Direct volume rendering (DVR) is an effective way to visualize 3D vascular images for diagnosis of different vascular pathologies and planning of surgical treatments. Angiograms are typically noisy, fuzzy, and contain thin vessel structures. Therefore, some kinds of enhancements are usually needed before direct volume rendering can start. However, without visualizing the 3D structures in angiograms, users may find it difficult to select appropriate parameters and assess the effectiveness of the enhancement results. In addition, traditional enhancement techniques cannot easily separate the vessel voxels from other contextual structures with the same or very similar intensity. In this paper, we propose a framework to integrate enhancement and direct volume rendering into one visualization pipeline using multi-dimensional transfer function tailored for visualizing the curvilinear and line structures in angiograms. Furthermore, we present a feature preserving interpolation method to render very thin vessels which are usually missed using traditional approaches. To ease the difficulty in vessel selection, a MIP-guided method is suggested to assist the process.

In this paper, we present some novel animation techniques to help users understand complex structures contained in volumetric data from the medical imaging and scientific simulation fields. Because of the occlusion of 3D objects, these complex structures and their 3D relationships usually cannot be revealed using one image. By our animation techniques, the focus regions, the context regions, and their relationships can be better visualized at the different stages of an animation. We propose an image-centric method which employs layered-depth images (LDIs) to get correct depth cues, and a data-centric method which exploits a novel transfer function fusing technique to guarantee the smooth transition between frames. The experimental results on real volume data demonstrate the advantages of our techniques over traditional image blending and transfer function interpolation methods.

In this paper, we present a novel viewpoint selection framework for angiographic volume data. We propose several view descriptors based on typical concerns of clinicians for the view evaluation. Compared with conventional approaches, our method can deliver a more representative global optimal view by sampling at a much higher rate in the view space. Instead of performing analysis on sample views individually, we construct a solution space to estimate the quality of the views. Descriptor values are propagated to the solution space where an efficient searching process can be performed. The best viewpoint can be found by analyzing the accumulated descriptor values in the solution space based on different visualization goals.

Node-link diagrams are widely used in information visualization to show relationships among data. However, when the size of data becomes very large, node-link diagrams will become cluttered and visually confusing for users. In this paper, we propose a novel controllable edge clustering method based on Delaunay triangulation to reduce visual clutter for node-link diagrams. Our method uses curves instead of straight lines to represent links and these curves can be grouped together according to their relative positions and directions. We further introduce progressive edge clustering to achieve continuous level-of-details for large networks.

Hyperlinks among webpages are very important information and are widely used for webpage clustering and webpage ranking. With the explosive growth in the number of webpages available online, the exploration of hyperlinks among webpages becomes a very challenging problem. Information visualization provides an effective way of visualizing hyperlinks and can help users gain insights into the relationships of webpages.

In this paper, we present some novel computer graphics techniques to visualize the hyperlinks among webpages. We propose a visual encoding scheme for five dimensional hyperlinks data and two constrained 3D layout techniques for incoming and outgoing links of a single webpage. To reveal the hierarchical structure of webpages as well as the hyperlinks information, we extend the treemap representation. Our representations are visually appealing and can effectively reveal linkage patterns among webpages. Experimental results and a user study demonstrate the effectiveness of our system. Our system can facilitate E-learning and help students understand the complex structures and hidden patterns in network datasets.

Multimodal volume data commonly found in medical imaging applications present both opportunities and challenges to segmentation and visualization tasks. This paper presents a user directed volume segmentation system. Through a spreadsheets interface, the user can interactively examine and refine segmentation results obtained from automatic clustering. In addition, the user can isolate or highlight a feature of interest in a volume based on different modalities, and see the corresponding segmented results. Our system is easy to use since the preliminary segmentation results are organized and presented to the user in a relation-aware fashion based on the spatial relations between the segmented regions. We demonstrate this system using two multimodal datasets.

In this paper, we present VoxelBars as an informative interface for volume visualization. VoxelBars arrange voxels into a 2D space and visually encode multiple attributes of voxels into one display. VoxelBars allow users to easily find out clusters of interesting voxels, set opacities and colors of a specific group of voxels, and achieve various sophisticated visualization tasks at voxel level. We provide results on real volume data to demonstrate the advantages of the VoxelBars over scatterplots and traditional transfer function specification methods. Some novel visualization techniques including visibility-aware transfer function design and selective clipping based on VoxelBars are also introduced.

Interpersonal ties, such as strong ties and weak ties, describe the information carried by an edge in social network. Tracking the dynamic changes of interpersonal ties can thus enhance our understanding of the evolution of a complex network. Nevertheless, existing studies in dynamic network visualization mostly focus on the temporal changes of nodes or structures of the network without an adequate support of analysis and exploration of the temporal changes of interpersonal ties. In this paper, we introduce a new visual analytics method that enables interactive analysis and exploration of the dynamic changes of interpersonal ties. The method integrates four well-linked visualizations, including a scatterplot, a pixelbar chart, a layered graph, and a node-link diagram, to allow for multi-perspective analysis of the evolution of interpersonal ties. The scatterplot created by multi-dimensional scaling can help reveal the clusters of ties and detect abnormal ties, while other visualizations allow users to explore the clusters of ties interactively from different perspectives. A case study has been conducted to demonstrate the effectiveness of our approach.

This book is a compilation of selected papers from the 14th International Symposium on Computer Science in Sport (IACSS 2023), held on September 27-30, 2023 in Hangzhou, China. The work focuses on the application of computer science and technology in the field of sports (such as intelligent data collection, data mining, visual analysis of game data, virtual reality, machine learning, computer vision, match prediction models and performance analysis). The contents make valuable contributions to academic researchers, college students, coaches and athletes, and sports management personnel (such as managers of sports associations, training bases, and professional clubs). Additionally, readers will encounter new ideas for realizing a more efficient and convenient training and exercise system.