In sports analytics, tactical visualization is widely used to convey valuable insights. However, due to the complex domain knowledge and contextual information involved in tactical visualizations, it is challenging for users to connect high-level tactical insights to corresponding visual patterns. This requires users to engage in a reasoning process to interpret insights within game contexts, which remains insufficiently supported in existing visual-text linking studies. In this work, we propose InsightChaser, a novel approach to bridge tactical insights and soccer visualizations through visual-text linking and visual reasoning enhancement. InsightChaser constructs knowledge graphs to represent both visual elements and contextual game information. Integrating large language models (LLMs), our approach retrieves relevant visual elements and establishes explicit links with insights. Moreover, InsightChaser utilizes LLMs to enhance these visual-text links by providing reasoning explanations and visual effects. We further develop an interactive visualization system that supports navigation and explanation of enhanced visual-text links. Users can explore linked tactical insights interactively and reason through enhanced visual explanations. We conduct two case studies using real-world soccer data and a user study to demonstrate the effectiveness of our approach.

People-to-people recommendation involves suggesting connections or relationships between individuals based on shared interests, skills, or other relevant factors, such as preferred field of study or geographical location. It is a common matching task across various domains, notably in education, where it assists in forming study groups, mentorship programs, and collaborative projects. However, generating people-to-people recommendations that satisfy users’ preferences is laborious, requiring extensive profile analysis and thus prompting the need for interactive visualization systems. In this work, we collaborated with experts from various education domains and developed CustMatcher which coordinates automatic matching algorithms and visualizations to enable efficient people-to-people recommendations. We first propose a steerable matching framework considering both the flexibility and the efficiency. A constraint space is defined to allow users to express their explicit preferences and implicit preferences about the matching. Visualizations and interactions are designed based on the framework and the constraint space to help users generate the initial matching result, handle the conflicts between preferences, and improve the matching result progressively. We evaluate the effectiveness and usability of the system with a user study and a case study.

Software-Defined Wide Area Networking (SD-WAN) has emerged as a popular solution for today’s enterprise networks, where Traffic Engineering (TE) plays a crucial role in optimizing traffic distribution across different overlay tunnels. During network congestion, traditional SD-WAN approaches often switch traffic to backup Multiprotocol Label Switching (MPLS) tunnels with high economic costs. To address this issue, we introduce Dynamic Path Switching (DPS), a novel SD-WAN TE solution that leverages underlay path diversity within an Internet overlay tunnel to maintain high Quality of Service (QoS) while substantially reducing economic costs. DPS operates on a Virtual extensible Local Area Network (VXLAN) overlay and controls the 5-tuple flow ID in the outer encapsulation header of traffic flows at SD-WAN gateways, which enables Internet Service Providers (ISPs) to dynamically switch traffic flows across multiple underlay paths based on the hashing results of their flow IDs. Moreover, we leverage extended Berkeley Packet Filter (eBPF) to implement DPS with high efficiency. Our prototype implementation, evaluated on a real Internet testbed, demonstrates that DPS can provide 99.974% service availability for enterprise traffic while reducing economic costs by 64.26% compared to traditional MPLS-based SD-WAN TE solutions.

This research examines the resource scheduling problem in wireless networks utilizing deep reinforcement learning, with the objective of optimizing the Age of Information (AoI) in multi-class heterogeneous sensing tasks. This research presents a scheduling approach that integrates Distributional Deep Deterministic Policy Gradient (DDPG) with a reward scaling mechanism, and develops a task-aware system model for multi-user wireless networks, enhancing the model’s capacity to represent system timeliness and task characteristics. The Distributional DDPG design effectively captures randomness and uncertainty in the scheduling process, yielding a stable scheduling policy. The implementation of a reward scaling mechanism optimizes the stability and convergence rate of the training process. The experimental findings indicate that the proposed method outperforms the conventional strategy for task completion rate and average AoI, hence validating its applicability in delay-sensitive sensing systems.

In the context of the Industrial Internet of Things (IIoT), Industrial Control Systems (ICS) necessitate optimized coordination among communication, computation, and control. Traditional designs, often overlooking the inherent coupling between communication and control, struggle to meet stringent real-time performance and stability requirements. This paper investigates an Integrated Communication, Computation, and Control (IC3) loop and proposes a resource allocation scheme aimed at minimizing the Linear Quadratic Regulator (LQR) cost function, used here to quantify closed-loop control performance. The modeling approach considers the sensor and controller as an integrated entity, incorporates energy consumption and resource constraints, and derives the relationship between bandwidth/time allocation and transmit power. To address the resulting non-linear coupling, an Alternating Optimization (AO) algorithm is employed to iteratively allocate resources. Simulation results demonstrate that the proposed scheme effectively reduces the LQR cost under energy constraints and outperforms traditional approaches by achieving a balance between control performance and resource efficiency.

Coaching is critical for learning table tennis skills.However, amateur table tennis players often lack access to professional coaches due to high costs and a limited number of coaches.While recent multimodal large language models show promise as virtual coaches, most of the existing approaches merely rely on video analysis, which is not comprehensive enough.In table tennis, many important kinematic details (e.g., strength, acceleration) cannot be captured by videos.They can only be tracked using sensors.To address this gap, we present T3Set (Table Tennis Training Set), a multimodal dataset that synchronizes inertial measurement unit (IMU) data from sensors mounted on 32 players' rackets with video recordings.The sensor data has 16 dimensions and a sample rate of 100Hz.This dataset covers 7 fundamental techniques across 380 training rounds, totaling 8655 annotated strokes, with 8395 targeted suggestions from coaches.The key features of T3Set include (1) temporal alignment between sensor data, video data, and text data.(2) high-quality targeted suggestions which are consistent with predefined suggestion taxonomy.Based on T3Set, we propose a novel two-stage framework that effectively integrates motion perception with generative reasoning as a virtual coach.Our method quantitatively outperforms baseline methods.The dataset, code, and documentation are available at

In basketball, decision-making is one of the core skills for players. For example, when a player is holding the ball, the success of the team’s offense is primarily determined by her/his decisions (i.e., pass, shoot, or dribble) in response to the dynamics of the game. Understanding players’ decision-making processes in changing game situations can help coaches develop effective strategies, which is critical for the success of a team. However, the decision-making process is influenced by various factors (e.g., player’s playing style, opponents’ defense, and time remaining), making understanding a challenging problem. In this study, we propose HoopScouter, a visual analytics system to help understand ball handlers’ decisions in basketball games. Based on a careful investigation of the analysis requirements, we first introduce a representation learning method that characterizes ball handlers’ decision-making styles. We then design a sketch panel with integrated time information to support exploration of player decisions under similar game scenarios. Facet views and coordinated interactions are also provided to identify the strengths and weaknesses of the ball handler’s decision-making, and to understand when and why ball handlers would make certain decisions. To validate the effectiveness of HoopScouter, we conduct two case studies on real-world basketball games and receive positive feedback from domain experts.

In soccer, player scouting aims to find players suitable for a team to increase the winning chance in future matches. To scout suitable players, coaches and analysts need to consider whether the players will perform well in a new team, which is hard to learn directly from their historical performances. Match simulation methods have been introduced to scout players by estimating their expected contributions to a new team. However, they usually focus on the simulation of match results and hardly support interactive analysis to navigate potential target players and compare them in fine-grained simulated behaviors. In this work, we propose a visual analytics method to assist soccer player scouting based on match simulation. We construct a two-level match simulation framework for estimating both match results and player behaviors when a player comes to a new team. Based on the framework, we develop a visual analytics system, Team-Scouter, to facilitate the simulative-based soccer player scouting process through player navigation, comparison, and investigation. With our system, coaches and analysts can find potential players suitable for the team and compare them on historical and expected performances. For an in-depth investigation of the players' expected performances, the system provides a visual comparison between the simulated behaviors of the player and the actual ones. The usefulness and effectiveness of the system are demonstrated by two case studies on a real-world dataset and an expert interview.

To enhance the appeal and informativeness of data news, there is an increasing reliance on data analysis techniques and visualizations, which poses a high demand for journalists' abilities. While numerous visual analytics systems have been developed for deriving insights, few tools specifically support and disseminate viewpoints for journalism. Thus, this work aims to facilitate the automatic creation of sports news from natural language insights. To achieve this, we conducted an extensive preliminary study on the published sports articles. Based on our findings, we propose a workflow - 1) exploring the data space behind insights, 2) generating narrative structures, 3) progressively generating each episode, and 4) mapping data spaces into communicative visualizations. We have implemented a human-AI interaction system called SNIL, which incorporates user input in conjunction with large language models (LLMs). It supports the modification of textual and graphical content within the episode-based structure by adjusting the description. We conduct user studies to demonstrate the usability of SNIL and the benefit of bridging the gap between analysis tasks and communicative tasks through expert and fan feedback.

Tactics play an important role in team sports by guiding how players interact on the field. Both sports fans and experts have a demand for analyzing sports tactics. Existing approaches allow users to visually perceive the multivariate tactical effects. However, these approaches require users to experience a complex reasoning process to connect the multiple interactions within each tactic to the final tactical effect. In this work, we collaborate with basketball experts and propose a progressive approach to help users gain a deeper understanding of how each tactic works and customize tactics on demand. Users can progressively sketch on a tactic board, and a coach agent will simulate the possible actions in each step and present the simulation to users with facet visualizations. We develop an extensible framework that integrates large language models (LLMs) and visualizations to help users communicate with the coach agent with multimodal inputs. Based on the framework, we design and develop Smartboard, an agent-based interactive visualization system for fine-grained tactical analysis, especially for play design. Smartboard provides users with a structured process of setup, simulation, and evolution, allowing for iterative exploration of tactics based on specific personalized scenarios. We conduct case studies based on real-world basketball datasets to demonstrate the effectiveness and usefulness of our system.

In team sports like basketball, understanding and executing tactics—coordinated plans of movements among players—are crucial yet complex, requiring extensive practice. These tactics require players to develop a keen sense of spatial and situational awareness. Traditional coaching methods, which mainly rely on basketball tactic boards and video instruction, often fail to bridge the gap between theoretical learning and the real-world application of tactics, due to shifts in view perspectives and a lack of direct experience with tactical scenarios. To address this challenge, we introduce VisCourt, a Mixed Reality (MR) tactic training system, in collaboration with a professional basketball team. To set up the MR training environment, we employed semi-automatic methods to simulate realistic 3D tactical scenarios and iteratively designed visual in-situ guidance. This approach enables full-body engagement in interactive training sessions on an actual basketball court and provides immediate feedback, significantly enhancing the learning experience. A user study with athletes and enthusiasts shows the effectiveness and satisfaction with VisCourt in basketball training and offers insights for the design of future SportsXR training systems.

Participation in sports provides individuals with disabilities opportunities for social inclusion, improved physical and mental health, skill development, and increased self-confidence, ultimately empowering them. Sitting volleyball, a popular para-sport adapted from traditional volleyball, has been played in more than 75 countries since its development in 1956. However, the limited availability of dedicated sitting volleyball courts creates a significant gap for individuals with disabilities interested in playing the sport. To address the challenges encountered by amateur sitting volleyball players due to the lack of specialized facilities, we encompass a pioneering design study on VR para-sports training and introduce VolleyNaut - an innovative virtual reality (VR) training system. Developed in close collaboration with professional coaches, this immersive system faithfully replicates the daily drills and realistic ball pitches experienced by players. It offers four specialized basic defensive drill scenarios, contributing to skill adjustment and enhancement. In our user study, we recruited volleyball players from college teams and clubs to assess the engagement factor of VolleyNaut, and we also included national sitting volleyball players and coaches to evaluate the system’s effectiveness as a training tool. Our comprehensive analysis, combining quantitative and qualitative data, revealed consistently positive results across all user groups.

The location context can benefit a broad range of context-aware applications, where recognizing and locating contextual objects, such as hair dryers, coffee machines, or water faucets, which are not equipped with any smart modules and thus unable to emit modulated signals, provide fine-grained contextual information. While there have been extensive researches on localizing smart mobile devices, little has been done for locatingcontextual objects, let alone for recognizing and locating them together. In this article, we aim to study the problem of simultaneously recognizing and locating such contextual objects and present CORAL, a contextual object recognition and locating scheme by the usage of unmodulated acoustic signals from the working contextual objects recorded by the commercial off-the-shelf smartphones of users. Specifically, CORAL exploits the frequency and power features of these signals to build a mel-frequency cepstral coefficients (MFCCs) data set for contextual objects, and constructs a classifier for contextual object recognition by using bidirectional LSTM (BiLSTM) and a regression model for object-to-device distance computation by using LightGBM, which is then used for object locating with the help of the user’s trace. We implement a prototype of CORAL and extensive experiments show that the CORAL achieves high recognition accuracy and locating accuracy, even when there are concurrent working contextual objects or ambient noises.

High-resolution remote sensing data enables the extraction of fine-detailed boundaries of open-pit minefields, which is crucial for various applications such as ecological restoration, environment impact assessment, mining field disaster minoring, etc. In the last decade, a variety of convolutional neural networks (CNNs) and vision transformers (ViT) approaches have been developed for extracting the boundary and coverage of open-pit minefields. However, these deep learning approaches are always computationally expensive in pretraining and fine-tuning the network parameters. In addition, the diverse land cover/land use of different open-pit minefields poses a big challenge in building a large-scale benchmark dataset. To conduct efficient open-pit minefield extraction with limited labelled data, this paper employs a large-scale foundation model called Segment Anything (SAM) to develop the few-shot learning strategy for extracting open-pit minefield with slightly fine-tuning SAM and without fine-tuning SAM, respectively. The experiment demonstrates that the proposed SAM-enhanced few-shot learning outperforms pretraining the-state-of-the-art semantic segmentation approaches in terms of extraction precision and time cost. We hope our work can provide a solution for complex open-pit minefield extraction with a small number of labelled datasets.

In soccer, player action evaluation provides a fine-grained method to analyze player performance and plays an important role in improving winning chances in future matches. However, previous studies on action evaluation only provide a score for each action, and hardly support inspecting and comparing player actions integrated with complex match context information such as team tactics and player locations. In this work, we collaborate with soccer analysts and coaches to characterize the domain problems of evaluating player performance based on action scores. We design a tailored visualization of soccer player actions that places the action choice together with the tactic it belongs to as well as the player locations in the same view. Based on the design, we introduce a visual analytics system, Action-Evaluator, to facilitate a comprehensive player action evaluation through player navigation, action investigation, and action explanation. With the system, analysts can find players to be analyzed efficiently, learn how they performed under various match situations, and obtain valuable insights to improve their action choices. The usefulness and effectiveness of this work are demonstrated by two case studies on a real-world dataset and an expert interview.

Numerous patterns found in urban phenomena, such as air pollution and human mobility, can be characterized as many directed geospatial networks (geo-networks) that represent spreading processes in urban space. These geo-networks can be analyzed from multiple levels, ranging from the macro-level of summarizing all geo-networks, meso-level of comparing or summarizing parts of geo-networks, and micro-level of inspecting individual geo-networks. Most of the existing visualizations cannot support multilevel analysis well. These techniques work by: 1) showing geo-networks separately with multiple maps leads to heavy context switching costs between different maps; 2) summarizing all geo-networks into a single network can lead to the loss of individual information; 3) drawing all geo-networks onto one map might suffer from the visual scalability issue in distinguishing individual geo-networks. In this study, we propose GeoNetverse, a novel visualization technique for analyzing aggregate geo-networks from multiple levels. Inspired by metro maps, GeoNetverse balances the overview and details of the geo-networks by placing the edges shared between geo-networks in a stacked manner. To enhance the visual scalability, GeoNetverse incorporates a level-of-detail rendering, a progressive crossing minimization, and a coloring technique. A set of evaluations was conducted to evaluate GeoNetverse from multiple perspectives.

Mediation analysis is crucial for diagnosing indirect causal relations in many scientific fields. However, mediation analysis of nominal variables requires examining and comparing multiple total effects and their corresponding direct/indirect causal effects derived from mediation models. This process is tedious and challenging to achieve with classical analysis tools such as Excel tables. In this study, we worked closely with experts from two scientific domains to design MediVizor, a visualization system that enables experts to conduct visual mediation analysis of nominal variables. The visualization design allows users to browse and compare multiple total effects together with the direct/indirect effects that compose them. The design also allows users to examine to what extent the positive and negative direct/indirect effects contribute to and reduce the total effects, respectively. We conducted two case studies separately with the experts from the two domains, sports and communication science, and a user study with common users to evaluate the system and design. The positive feedback from experts and common users demonstrates the effectiveness and generalizability of the system.

As the final stage of questionnaire analysis, causal reasoning is the key to turning responses into valuable insights and actionable items for decision-makers. During the questionnaire analysis, classical statistical methods (e.g., Differences-in-Differences) have been widely exploited to evaluate causality between questions. However, due to the huge search space and complex causal structure in data, causal reasoning is still extremely challenging and time-consuming, and often conducted in a trial-and-error manner. On the other hand, existing visual methods of causal reasoning face the challenge of bringing scalability and expert knowledge together and can hardly be used in the questionnaire scenario. In this work, we present a systematic solution to help analysts effectively and efficiently explore questionnaire data and derive causality. Based on the association mining algorithm, we dig question combinations with potential inner causality and help analysts interactively explore the causal sub-graph of each question combination. Furthermore, leveraging the requirements collected from the experts, we built a visualization tool and conducted a comparative study with the state-of-the-art system to show the usability and efficiency of our system.

Table tennis is a sport that demands high levels of technical proficiency and body coordination from players. Biomechanical fingerprints can provide valuable insights into players' habitual movement patterns and characteristics, allowing them to identify and improve technical weaknesses. Despite the potential, few studies have developed effective methods for generating such fingerprints. To address this gap, we propose TacPrint, a framework for generating a biomechanical fingerprint for each player. TacPrint leverages machine learning techniques to extract comprehensive features from biomechanics data collected by inertial measurement units (IMU) and employs the attention mechanism to enhance model interpretability. After generating fingerprints, TacPrint provides a visualization system to facilitate the exploration and investigation of these fingerprints. In order to validate the effectiveness of the framework, we designed an experiment to evaluate the model's performance and conducted a case study with the system. The results of our experiment demonstrated the high accuracy and effectiveness of the model. Additionally, we discussed the potential of TacPrint to be extended to other sports.

In recent years, with growing health consciousness, at-home workout has become increasingly popular for its convenience and safety. Most people choose to follow video guidance during exercising. However, our preliminary study revealed that fitness-minded people face challenges when watching exercise videos on handheld devices or fixed monitors, such as limited movement comprehension due to static camera angles and insufficient feedback. To address these issues, we reviewed popular workout videos, identified user requirements, and came up with an augmented reality (AR) solution. Following a user-centered iterative design process, we proposed a design space of AR visual cues for workouts and implemented an AR-based application. Specifically, we captured users’ exercise performance with pose-tracking technology and provided feedback via AR visual cues. Two user experiments showed that incorporating AR visual cues could improve movement comprehension and enable users to adjust their movements based on real-time feedback. Finally, we presented several suggestions to inspire future design and apply AR visual cues to sports training.

Abstract Anticipation skill is important for elite racquet sports players. Successful anticipation allows them to predict the actions of the opponent better and take early actions in matches. Existing studies of anticipation behaviors, largely based on the analysis of in‐lab behaviors, failed to capture the characteristics of in‐situ anticipation behaviors in real matches. This research proposes a data‐driven approach for research on anticipation behaviors to gain more accurate and reliable insight into anticipation skills. Collaborating with domain experts in table tennis, we develop a complete solution that includes data collection, the development of a model to evaluate anticipation behaviors, and the design of a visual analytics system called Tac‐Anticipator. Our case study reveals the strengths and weaknesses of top table tennis players' anticipation behaviors. In a word, our work enriches the research methods and guidelines for visual analytics of anticipation behaviors.

Lineup selection is an essential and important task in soccer matches. To win a match, coaches must consider various factors and select appropriate players for a planned formation. Computation-based tools have been proposed to help coaches on this complex task, but they are usually based on over-simplified models on player performances, do not support interactive analysis, and overlook the inputs by coaches. In this article, we propose a method for visual analytics of soccer lineup selection by tackling two challenges: characterizing essential factors involved in generating optimal lineup, and supporting coach-driven visual analytics of lineup selection. We develop a lineup selection model that integrates such important factors, such as spatial regions of player actions and defensive interactions with opponent players. A visualization system, Team-Builder, is developed to help coaches control the process of lineup generation, explanation, and comparison through multiple coordinated views. The usefulness and effectiveness of our system are demonstrated by two case studies on a real-world soccer event dataset.

Augmented sports videos, which combine visualizations and video effects to present data in actual scenes, can communicate insights engagingly and thus have been increasingly popular for sports enthusiasts around the world. Yet, creating augmented sports videos remains a challenging task, requiring considerable time and video editing skills. On the other hand, sports insights are often communicated using natural language, such as in commentaries, oral presentations, and articles, but usually lack visual cues. Thus, this work aims to facilitate the creation of augmented sports videos by enabling analysts to directly create visualizations embedded in videos using insights expressed in natural language. To achieve this goal, we propose a three-step approach - 1) detecting visualizable entities in the text, 2) mapping these entities into visualizations, and 3) scheduling these visualizations to play with the video - and analyzed 155 sports video clips and the accompanying commentaries for accomplishing these steps. Informed by our analysis, we have designed and implemented Sporthesia, a proof-of-concept system that takes racket-based sports videos and textual commentaries as the input and outputs augmented videos. We demonstrate Sporthesia's applicability in two exemplar scenarios, i.e., authoring augmented sports videos using text and augmenting historical sports videos based on auditory comments. A technical evaluation shows that Sporthesia achieves high accuracy (F1-score of 0.9) in detecting visualizable entities in the text. An expert evaluation with eight sports analysts suggests high utility, effectiveness, and satisfaction with our language-driven authoring method and provides insights for future improvement and opportunities.

We propose SimuExplorer, a visualization system to help analysts explore how player behaviors impact scoring rates in table tennis. Such analysis is indispensable for analysts and coaches, who aim to formulate training plans that can help players improve. However, it is challenging to identify the impacts of individual behaviors, as well as to understand how these impacts are generated and accumulated gradually over the course of a game. To address these challenges, we worked closely with experts who work for a top national table tennis team to design SimuExplorer. The SimuExplorer system integrates a Markov chain model to simulate individual and cumulative impacts of particular behaviors. It then provides flow and matrix views to help users visualize and interpret these impacts. We demonstrate the usefulness of the system with case studies and expert interviews. The experts think highly of the system and have obtained insights into players' behaviors using it.

Conventional racket sports training highly relies on coaches' knowledge and experience, leading to biases in the guidance. To solve this problem, smart wearable devices based on Internet of Things technology (IoT) have been extensively investigated to support data-driven training. Considerable studies introduced methods to extract valuable information from the sensor data collected by IoT devices. However, the information cannot provide actionable insights for coaches due to the large data volume and high data dimensions. We proposed an IoT + VA framework, Tac-Trainer, to integrate the sensor data, the information, and coaches' knowledge to facilitate racket sports training. Tac-Trainer consists of four components: device configuration, data interpretation, training optimization, and result visualization. These components collect trainees' kinematic data through IoT devices, transform the data into attributes and indicators, generate training suggestions, and provide an interactive visualization interface for exploration, respectively. We further discuss new research opportunities and challenges inspired by our work from two perspectives, VA for IoT and IoT for VA.

In a basketball play, players who are not in possession of the ball (i.e., off-ball players) can still effectively contribute to the team's offense, such as making a sudden move to create scoring opportunities. Analyzing the movements of off-ball players can thus facilitate the development of effective strategies for coaches. However, common basketball statistics (e.g., points and assists) primarily focus on what happens around the ball and are mostly result-oriented, making it challenging to objectively assess and fully understand the contributions of off-ball movements. To address these challenges, we collaborate closely with domain experts and summarize the multi-level requirements for off-ball movement analysis in basketball. We first establish an assessment model to quantitatively evaluate the offensive contribution of an off-ball movement considering both the position of players and the team cooperation. Based on the model, we design and develop a visual analytics system called OBTracker to support the multifaceted analysis of off-ball movements. OBTracker enables users to identify the frequency and effectiveness of off-ball movement patterns and learn the performance of different off-ball players. A tailored visualization based on the Voronoi diagram is proposed to help users interpret the contribution of off-ball movements from a temporal perspective. We conduct two case studies based on the tracking data from NBA games and demonstrate the effectiveness and usability of OBTracker through expert feedback.

In complex and heterogeneous geoenvironments, landslides exhibit varying features in different environments, and data in landslide inventories are imbalanced. Existing data-driven landslide susceptibility evaluation (LSE) methods overlook environmental heterogeneity and cannot reliably predict regions with few samples. Alternatively, global random negative sampling strategies may produce imbalanced positive and negative samples in some environments, contributing to inaccurate predictions. This article proposes a graph neural network (GNN) constrained by environmental consistency (GNN-EC) to overcome these problems. The GNN-EC consists of graphs with nodes, and edges. A graph represents the environmental relationships in the study area. Nodes are geographic units delineated from terrain polygon approximation. Edges capture the relationships between node-pairs. Additionally, the weights of edges reflect the similarity between two node environments. A GNN aggregates node information in the graph for LSE. Our experiment showed that the proposed method outperformed the common machine learning methods: increasing prediction accuracy by approximately 7, 5–6 and 3–4% compared to the artificial neural network (ANN), the support vector machine (SVM) and the random forest (RF), respectively. Moreover, our method can maintain high prediction accuracy, even with a small training set.

The representation of landform element varies over multiple scales, or multiresolution digital elevation model (DEM) and its derivatives. When more details of land surfaces are available to be characterized based on the existing high spatial resolution elevation products, the influence of scale variation might become more significant. This poses a demand for determining a scale-independent approach being competent to support multiscale landform element detection on high-resolution DEMs. Although the practicability of the state-of-the-art scale-independent approaches have been reported on moderate-resolution DEMs, how these approaches perform based on the multiscale data including high and moderate spatial-resolution DEMs is still unexplored. This letter evaluates the performance of four scale-independent techniques including filtering, spatial pyramid, multiscale segmentation, and spatial-contextual approach in landform element detection on different spatial resolution DEMs. The experimental results show that spatial–contextual approach is more effective to support multiscale landform element detection than others.

Tactic analysis is a major issue in badminton as the effective usage of tactics is the key to win. The tactic in badminton is defined as a sequence of consecutive strokes. Most existing methods use statistical models to find sequential patterns of strokes and apply 2D visualizations such as glyphs and statistical charts to explore and analyze the discovered patterns. However, in badminton, spatial information like the shuttle trajectory, which is inherently 3D, is the core of a tactic. The lack of sufficient spatial awareness in 2D visualizations largely limited the tactic analysis of badminton. In this work, we collaborate with domain experts to study the tactic analysis of badminton in a 3D environment and propose an immersive visual analytics system, TIVEE, to assist users in exploring and explaining badminton tactics from multi-levels. Users can first explore various tactics from the third-person perspective using an unfolded visual presentation of stroke sequences. By selecting a tactic of interest, users can turn to the first-person perspective to perceive the detailed kinematic characteristics and explain its effects on the game result. The effectiveness and usefulness of TIVEE are demonstrated by case studies and an expert interview.

The spatial time series generated by city sensors allow us to observe urban phenomena like environmental pollution and traffic congestion at an unprecedented scale. However, recovering causal relations from these observations to explain the sources of urban phenomena remains a challenging task because these causal relations tend to be time-varying and demand proper time series partitioning for effective analyses. The prior approaches extract one causal graph given long-time observations, which cannot be directly applied to capturing, interpreting, and validating dynamic urban causality. This paper presents Compass, a novel visual analytics approach for in-depth analyses of the dynamic causality in urban time series. To develop Compass, we identify and address three challenges: detecting urban causality, interpreting dynamic causal relations, and unveiling suspicious causal relations. First, multiple causal graphs over time among urban time series are obtained with a causal detection framework extended from the Granger causality test. Then, a dynamic causal graph visualization is designed to reveal the time-varying causal relations across these causal graphs and facilitate the exploration of the graphs along the time. Finally, a tailored multi-dimensional visualization is developed to support the identification of spurious causal relations, thereby improving the reliability of causal analyses. The effectiveness of Compass is evaluated with two case studies conducted on the real-world urban datasets, including the air pollution and traffic speed datasets, and positive feedback was received from domain experts.

How to achieve academic career success has been a long-standing research question in social science research. With the growing availability of large-scale well-documented academic profiles and career trajectories, scholarly interest in career success has been reinvigorated, which has emerged to be an active research domain called the Science of Science (i.e., SciSci). In this study, we adopt an innovative dynamic perspective to examine how individual and social factors will influence career success over time. We propose ACSeeker, an interactive visual analytics approach to explore the potential factors of success and how the influence of multiple factors changes at different stages of academic careers. We first applied a Multi-factor Impact Analysis framework to estimate the effect of different factors on academic career success over time. We then developed a visual analytics system to understand the dynamic effects interactively. A novel timeline is designed to reveal and compare the factor impacts based on the whole population. A customized career line showing the individual career development is provided to allow a detailed inspection. To validate the effectiveness and usability of ACSeeker, we report two case studies and interviews with a social scientist and general researchers.

Circular glyphs are used across disparate fields to represent multidimensional data. However, although these glyphs are extremely effective, creating them is often laborious, even for those with professional design skills. This paper presents GlyphCreator, an interactive tool for the example-based generation of circular glyphs. Given an example circular glyph and multidimensional input data, GlyphCreator promptly generates a list of design candidates, any of which can be edited to satisfy the requirements of a particular representation. To develop GlyphCreator, we first derive a design space of circular glyphs by summarizing relationships between different visual elements. With this design space, we build a circular glyph dataset and develop a deep learning model for glyph parsing. The model can deconstruct a circular glyph bitmap into a series of visual elements. Next, we introduce an interface that helps users bind the input data attributes to visual elements and customize visual styles. We evaluate the parsing model through a quantitative experiment, demonstrate the use of GlyphCreator through two use scenarios, and validate its effectiveness through user interviews.

The popularity of racket sports (e.g., tennis and table tennis) leads to high demands for data analysis, such as notational analysis, on player performance. While sports videos offer many benefits for such analysis, retrieving accurate information from sports videos could be challenging. In this paper, we propose EventAnchor, a data analysis framework to facilitate interactive annotation of racket sports video with the support of computer vision algorithms. Our approach uses machine learning models in computer vision to help users acquire essential events from videos (e.g., serve, the ball bouncing on the court) and offers users a set of interactive tools for data annotation. An evaluation study on a table tennis annotation system built on this framework shows significant improvement of user performances in simple annotation tasks on objects of interest and complex annotation tasks requiring domain knowledge.

This paper reports methods to compute the equilibrium stances of a new snake-like robot designed to stabilize its head on a free water surface. To adjust rapidly the stability of the robot, this bio-inspired robot can rotate independently each body-shell, and modify the level of immersion of each module. To predict the stable stance accessible by this additional degree of freedom, a model is developed to compute the equilibrium configurations of the robot from a given parametrization of the body shape. Then, an algorithm is introduced to compute a sequence of controlled body deformations, such that the head configuration relatively to the water surface remains unchanged. Finally, we explore in simulation stances and quasi-static gaits, and investigate to what extent the buoyancy and the body deformations can be used to stabilize the head of the snake-like robot.

Stroke evaluation is critical for coaches to evaluate players' performance in table tennis matches. However, current methods highly demand proficient knowledge in table tennis and are time-consuming. We collaborate with the Chinese national table tennis team and propose Tac-Valuer, an automatic stroke evaluation framework for analysts in table tennis teams. In particular, to integrate analysts' knowledge into the machine learning model, we employ the latest effective framework named abductive learning, showing promising performance. Based on abductive learning, Tac-Valuer combines the state-of-the-art computer vision algorithms to extract and embed stroke features for evaluation. We evaluate the design choices of the approach and present Tac-Valuer's usability through use cases that analyze the performance of the top table tennis players in world-class events.

This article is focused on a challenging topic emerging from the registration of point clouds, specifically the registration of dynamic objects with low overlapping ratio. This problem is especially difficult when the static scanner is installed on a floating platform, and the objects it scans are also floating. These issues make most of the automatic registration methods and software solutions invalid. To solve this problem, explicit exploration of the static region is necessary for both the coarse and fine registration steps. Fortunately, determining the corresponding regions can be eased by the intuitive realization that in urban environments, natural objects neither present straight boundaries nor stack vertically. This intuition has guided the authors to develop a robust approach for the detection of static regions using planar structures. Then, silhouettes of the objects are extracted from the planar structures, which assist in the determination of an SE(2) transformation in the horizontal direction by a novel line matching method. The silhouettes also enable identification of the correspondences of planes in the step of fine registration using a variant of the iterative closest point method. Experimental evaluations using point clouds of cargo ships with different sizes and shapes reveal the robustness and efficiency of the proposed method, which gives 100% success and reasonable accuracy in rapid time, suitable for an online system. In addition, the proposed method is evaluated systematically with regard to several practical situations caused by the floating platform, and it demonstrates good robustness to limited scanning time and noise.

Using causal relations to guide decision making has become an essential analytical task across various domains, from marketing and medicine to education and social science. While powerful statistical models have been developed for inferring causal relations from data, domain practitioners still lack effective visual interface for interpreting the causal relations and applying them in their decision-making process. Through interview studies with domain experts, we characterize their current decision-making workflows, challenges, and needs. Through an iterative design process, we developed a visualization tool that allows analysts to explore, validate, and apply causal relations in real-world decision-making scenarios. The tool provides an uncertainty-aware causal graph visualization for presenting a large set of causal relations inferred from high-dimensional data. On top of the causal graph, it supports a set of intuitive user controls for performing what-if analyses and making action plans. We report on two case studies in marketing and student advising to demonstrate that users can effectively explore causal relations and design action plans for reaching their goals.

In soccer, passing is the most frequent interaction between players and plays a significant role in creating scoring chances. Experts are interested in analyzing players' passing behavior to learn passing tactics, i.e., how players build up an attack with passing. Various approaches have been proposed to facilitate the analysis of passing tactics. However, the dynamic changes of a team's employed tactics over a match have not been comprehensively investigated. To address the problem, we closely collaborate with domain experts and characterize requirements to analyze the dynamic changes of a team's passing tactics. To characterize the passing tactic employed for each attack, we propose a topic-based approach that provides a high-level abstraction of complex passing behaviors. Based on the model, we propose a glyph-based design to reveal the multi-variate information of passing tactics within different phases of attacks, including player identity, spatial context, and formation. We further design and develop PassVizor, a visual analytics system, to support the comprehensive analysis of passing dynamics. With the system, users can detect the changing patterns of passing tactics and examine the detailed passing process for evaluating passing tactics. We invite experts to conduct analysis with PassVizor and demonstrate the usability of the system through an expert interview.

How could soccer player migration impact national team performance, or vice versa? The answer to this question could play an essential role in making appropriate decisions and policies regarding the international mobility of soccer players. However, answering such a question faces two main challenges, including the complex relationship between variables in multi-attribute temporal data describing migrated players and national team performance, and the interpretation of analysis results in policymaking scenarios. In this work, we have closely collaborated with domain experts and characterized the problems of soccer player migration analysis. To address the first challenge, we adapt a cross-lagged panel analysis model into the player migration analysis problem. This cross-lagged panel analysis model is effective to evaluate the impact strength between player migration and national team performance, and straightforward to reveal the causal relationship. To address the second challenge, we design and develop a visual analytics system, MIG-Viewer, to help the experts to interpret the results of the proposed model efficiently. With MIG-Viewer, the experts can navigate the countries of interest in accordance with migration strategy, conduct comprehensive analysis with the comparison of impact strength, and adjust player migration and inspect further details of a specific country. We present two case studies using global player migration data since 1992 with three soccer analysis experts to demonstrate the effectiveness and usefulness of the system.

Simulative analysis in competitive sports can provide prospective insights, which can help improve the performance of players in future matches. However, adequately simulating the complex competition process and effectively explaining the simulation result to domain experts are typically challenging. This work presents a design study to address these challenges in table tennis. We propose a well-established hybrid second-order Markov chain model to characterize and simulate the competition process in table tennis. Compared with existing methods, our approach is the first to support the effective simulation of tactics, which represent high-level competition strategies in table tennis. Furthermore, we introduce a visual analytics system called Tac-Simur based on the proposed model for simulative visual analytics. Tac-Simur enables users to easily navigate different players and their tactics based on their respective performance in matches to identify the player and the tactics of interest for further analysis. Then, users can utilize the system to interactively explore diverse simulation tasks and visually explain the simulation results. The effectiveness and usefulness of this work are demonstrated by two case studies, in which domain experts utilize Tac-Simur to find interesting and valuable insights. The domain experts also provide positive feedback on the usability of Tac-Simur. Our work can be extended to other similar sports such as tennis and badminton.

The rapid development of massively multiplayer online role-playing games (MMORPGs) has led operators to record huge amounts of fine-grained data from the in-game activities of players. These data provide considerable opportunities with which to study the dynamic interplay among player behaviors and investigate the roles of various social structures that underlie such interplay. However, modeling and visualizing these behavioral data remain a challenge. In this study, we propose a novel influence-susceptible model to measure the dynamic interplay among multiple behaviors. Based on this model, we introduce a new visual analytics system called BeXplorer. BeXplorer enables analysts to interactively explore the dynamic interplay between player purchase and communication behaviors and to examine the manner in which this interplay is bound by social structures where players are embedded.

The wireless video transmission environment of unmanned aerial vehicles (UAVs) is complex and unstable given the high mobility and changeable working conditions of UAVs, which lead to burst and consecutive errors and high error rates. A compressed video stream is extremely sensitive to transmission errors, such that even a single bit error sharply degrades the video quality. Hence, we propose an intraframe pixel-row-interleaved error concealment algorithm that interleaves pixel rows to generate high similarity in different parts of a frame, thereby achieving intraframe error resilience. Subsequently, we suggest an interframe time-field-interleaved alternative motion-compensated prediction that allows for automatic error elimination and recovers at least four consecutive frames in wireless video communications. The experiments demonstrate that the proposed algorithms recover frames with excellent subjective and objective effects. Moreover, these algorithms can provide reliable and robust video transmission for UAVs.

Regarded as a high-level tactic in soccer, a team formation assigns players different tasks and indicates their active regions on the pitch, thereby influencing the team performance significantly. Analysis of formations in soccer has become particularly indispensable for soccer analysts. However, formations of a team are intrinsically time-varying and contain inherent spatial information. The spatio-temporal nature of formations and other characteristics of soccer data, such as multivariate features, make analysis of formations in soccer a challenging problem. In this study, we closely worked with domain experts to characterize domain problems of formation analysis in soccer and formulated several design goals. We design a novel spatio-temporal visual representation of changes in team formation, allowing analysts to visually analyze the evolution of formations and track the spatial flow of players within formations over time. Based on the new design, we further design and develop ForVizor, a visual analytics system, which empowers users to track the spatio-temporal changes in formation and understand how and why such changes occur. With ForVizor, domain experts conduct formation analysis of two games. Analysis results with insights and useful feedback are summarized in two case studies.

Interactive visualization of large image collections is important and useful in many applications, such as personal album management and user profiling on images. However, most prior studies focus on using low-level visual features of images, such as texture and color histogram, to create visualizations without considering the more important semantic information embedded in images. This paper proposes a novel visual analytic system to analyze images in a semantic-aware manner. The system mainly comprises two components: a semantic information extractor and a visual layout generator. The semantic information extractor employs an image captioning technique based on convolutional neural network (CNN) to produce descriptive captions for images, which can be transformed into semantic keywords. The layout generator employs a novel co-embedding model to project images and the associated semantic keywords to the same 2D space. Inspired by the galaxy metaphor, we further turn the projected 2D space to a galaxy visualization of images, in which semantic keywords and images are visually encoded as stars and planets. Our system naturally supports multi-scale visualization and navigation, in which users can immediately see a semantic overview of an image collection and drill down for detailed inspection of a certain group of images. Users can iteratively refine the visual layout by integrating their domain knowledge into the co-embedding process. Two task-based evaluations are conducted to demonstrate the effectiveness of our system.

With the rapid development of massively multiplayer online role-playing games (MMORPGs), a huge amount of fine-grained data on the in-game activities of players have been recorded by MMORPGs operators. These data provide considerable opportunities with which to study the dynamic interplay between player behaviors and investigate the roles of various social structures that underlie such interplay. However, it is challenging to model and visualize these behavioral data. This study proposes a novel influence-susceptible model to measure the dynamic interplay between behaviors. Based on this model, we introduce a new visual analytics system called BeXplorer. This system enables analysts to interactively explore the dynamic interplay between player purchase and communication behaviors and to examine the manner in which this interplay is bound by social structures where players are embedded. Three case studies and a task-based evaluation are conducted to demonstrate the effectiveness and applicability of our method.

Aiming at satisfying the users' navigation requirements in complex indoor environments, an indoor route planning method with environment awareness is proposed in this paper. Considering the accessibility, simplicity and comfortable experience of user's navigation, the paper first describes the modeling method of indoor navigation network and defines the expressions of indoor environment semantics. And then a navigation cost function is presented, which includes environment semantics such as path complexity, crowded degree and blocking events into indoor navigation network model. Finally, this paper proposes an optimal indoor route planning method by introducing the navigation cost function and environment semantics into traditional Dijkstra algorithm. The experimental results show that the proposed method can effectively enhance the user's comfort experience in indoor navigation.

Abstract Pie and doughnut charts nicely convey the part–whole relationship and they have become the most recognizable chart types for representing proportions in business and data statistics. Many experiments have been carried out to study human perception of the pie chart, while the corresponding aspects of the doughnut chart have seldom been tested, even though the doughnut chart and the pie chart share several similarities. In this paper, we report on a series of experiments in which we explored the effect of a few fundamental design parameters of doughnut charts, and additional visual cues, on the accuracy of such charts for proportion estimates. Since mobile devices are becoming the primary devices for casual reading, we performed all our experiments on such device. Moreover, the screen size of mobile devices is limited and it is therefore important to know how such size constraint affects the proportion accuracy. For this reason, in our first experiment we tested the chart size and we found that it has no significant effect on proportion accuracy. In our second experiment, we focused on the effect of the doughnut chart inner radius and we found that the proportion accuracy is insensitive to the inner radius, except the case of the thinnest doughnut chart. In the third experiment, we studied the effect of visual cues and found that marking the centre of the doughnut chart or adding tick marks at 25% intervals improves the proportion accuracy. Based on the results of the three experiments, we discuss the design of doughnut charts and offer suggestions for improving the accuracy of proportion estimates.

Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.

Task scheduling can improve the performance of parallel execution through optimizing the utilization of on-chip computing resources, and thus it has been widely studied. Most of the previous work uses data access locality to predict cache behaviors for task scheduling, but usually suffering accuracy and computational time complexity issues. This paper proposes an efficient task assignment algorithm to minimize the contention for shared caches on multi-core processors among parallel independent process level tasks. The proposed algorithm leverages the property of footprint to approximately estimate the locality parameter of parallel tasks, choosing the best grouping of tasks with minimum locality value in a quick way for task assignment. The calculation time is therefore significantly reduced and the algorithm complexity is O(nlog2n). Meanwhile, the algorithm accuracy is very high. On an Intel 8 cores dual-processor system, the experimental results show that the task assignment algorithm achieves over 99% of the actual optimal performance on average and outperforms the default Linux task scheduling method by an average of over 5% for two sets of different parallel tasks.

Precipitation is important in many fields. It is meaningful and valuable to estimate the spatial distribution of precipitation. However, existing methods introduced for precipitation interpolation are not satisfactory. In this paper, geographic and topographic factors are taken into consideration and put into Cokriging method to interpolate the precipitation maps of annual precipitation in Poyang Lake Watershed of China. At the same time, IDW (Inverse distance weight) method, Ordinary Kriging method and Cokriging method considering elevation only has been used to interpolate the precipitation. Evaluating by MAE (mean absolute error), MRE (mean relative error), as well as RMSIE(Root mean squared interpolation error). The results indicate that Cokriging method considering geographic and topographic facotors is suoprior than IDW method and Cokriging method considering elevation only, and it has no obvious advantage compared with ordinary Kriging method.