Generative AI is reshaping education, but it also raises concerns about instability and overreliance. In programming classrooms, we aim to leverage its feedback capabilities while reinforcing the educator’s role in guiding student–AI interactions. We developed ClassAid, a real-time orchestration system that integrates TA Agents to provide personalized support and an AI-driven dashboard that visualizes student–AI interactions, enabling instructors to dynamically adjust TA Agent modes. Instructors can configure the Agent to provide technical feedback (direct coding solutions), heuristic feedback (hint-based guidance), automatic feedback (autonomously selecting technical or heuristic support), or silent operation (no AI support). We evaluated ClassAid through three aspects: (1) the TA Agents’ performance, (2) feedback from 54 students and one instructor during a classroom deployment, and (3) interviews with eight educators. Results demonstrate that dynamic instructor control over AI supports effective real-time personalized feedback and provides design implications for integrating AI into authentic educational settings.

With the rapid proliferation of IoT devices, the volume of generated data is growing at an unprecedented pace. Due to the limited resources of edge devices, a significant portion of this data must be transmitted to the cloud for in-depth processing, large-scale analysis, long-term storage, and archival purposes. Consequently, the performance has become a critical concern. While identifying prevailing challenges and research gaps in this domain requires a systematic review, such efforts remain largely absent from existing survey literature. This article addresses this gap by offering a structured review of recent optimization approaches. It begins by categorizing the literature into three main strategies: lossless transmission, lossy transmission, and hybrid approaches. In the context of lossless transmission, we analyze techniques such as data compression algorithms and incremental versus full synchronization mechanisms. For lossy strategies, we analyze approaches including lossy compression and predictive methods. In addition, we investigate hybrid strategies that integrate both lossless and lossy techniques to leverage their complementary advantages. Finally, we discuss the limitations of existing studies and highlight promising directions for future research in optimizing edge-to-cloud data transmission.

Vision-language pre-trained models (VLMs) have shown impressive cross-modal understanding, yet their “compositional understanding” ability remains under investigation. We introduce CompoVis, a framework for visually probing cross-modal gaps in VLMs. CompoVis optimizes the grid layout to highlight alignment clusters and boundaries, visually interprets multi-head attention and semantic drift, and enables interactive fine-tuning unconstrained by closed datasets or offline models. Quantitative experiments and case studies explore key insights: VLMs rely on entity shortcuts rather than comprehension-driven; stubborn global modality isolation and suboptimal fine-grained alignment remain; fine-tuning with negative samples does not fundamentally alleviate the gaps. Approximately 89% of participants ($n=27$) found that, compared to methods relying solely on data metrics, CompoVis offers a more innovative and effective approach for investigating modality gaps in VLMs.

Porcelain, as a significant component of traditional Chinese culture, carries a profound historical legacy and rich cultural connotations. Its study involves a complex knowledge system spanning multiple dynasties and regions. Traditional research methods often rely on documentary analysis and artifact examination, which may not fully reveal the artistic and cultural characteristics of porcelain. In recent years, the rapid development of digital technologies has presented new opportunities for the research, preservation, and dissemination of cultural heritage. Therefore, this paper leverages image processing techniques and large language models to conduct a multidimensional quantitative analysis of the artistic features of porcelain, employing scientific methods to investigate its artistic value. Additionally, we developed an interactive visualization system that enables users to comprehend the development of porcelain from a spatiotemporal perspective and engage in interactive exploration of its artistic features at both macro and micro levels. Case studies and user evaluations demonstrate the system’s high usability and efficiency, providing a novel academic perspective and tools for the in-depth research and digital dissemination of Chinese cultural heritage.

Small-scale object detection remains a major challenge in semi-supervised object detection (SSOD), particularly in medical image analysis. Conventional teacher models often struggle to accurately capture the features of low-contrast small lesions, leading to noisy pseudo-labels in both localization and classification, which introduces severe uncertainty and degrades detection performance. To address this issue, we propose Dual Teacher, a novel multimodal semi-supervised detection framework designed to enhance pseudo-label reliability and improve small-scale lesion detection. Specifically, we introduce two complementary teacher models: Hybrid-Scale Teacher, which exploits downsampled views to strengthen multi-scale feature learning, and Entropy-Based Multi-Modal Teacher, which leverages entropy maps to refine the quality of small-scale pseudo-labels. To effectively fuse predictions from both teachers and resolve conflicts, we propose a Dempster-Shafer-based Dual-Teacher pseudo-label fusion strategy that explicitly models uncertainty and optimizes classification confidence. Additionally, we introduce a class-adaptive threshold mechanism that dynamically adjusts pseudo-label selection based on dual-teacher predictions, further boosting the recall of small-scale lesions. Extensive experiments on the Dental Disease Dataset, ChestX-Det and M3FD demonstrate that our method consistently surpasses state-of-the-art SSOD approaches. Code is available at: https://github.com/z316910/Dual-Teacher.git.

Time series data is ubiquitous in people’s daily production and life, and visualizations augmented with annotations can significantly facilitate the understanding of such data and promote downstream tasks. Consequently, numerous annotation tools have been developed to detect and narrate useful patterns within time series visualizations. However, most existing tools can only identify basic factual insights (e.g., increasing or decreasing trends) that are already present in the charts. When users need deeper insights (e.g., predicting future trends) and richer contextual information (e.g., associative patterns between dimensions within and beyond the chart), these tools often fall short. To address this challenge, we present AutoMA, a system that automatically generates multi-level annotations for time series visualizations. We introduce an LLM-based pattern extraction method that supports the identification of seven distinct temporal patterns. Furthermore, we present a multi-level annotation design space that encompasses six specific annotation tasks, aimed at delivering richer contextual information. The generated annotations span a spectrum of information, ranging from directly observable temporal patterns to deeper insights obtained through further computation, and ultimately to advanced inter-dimensional association patterns. Finally, we demonstrate the effectiveness of our approach through experiments and user evaluations. The results indicate that AutoMA significantly enhances users’ ability to comprehend and explore time series data.

Image-based person re-identification (Re-ID) aims to identify and track individuals across multiple camera views using query images. While machine learning methods have made progress, their real-world performance remains limited. A key challenge lies in the nature of person retrieval, which requires users to perform fine-grained matching and filtering. This process often involves manually comparing and evaluating a large number of candidate images, resulting in low retrieval efficiency and being time-consuming. To address these issues, we introduce textual information to assist users in performing fine-grained retrieval tasks. Specifically, we utilize vision-language models fine-tuned with domain knowledge to generate hierarchical textual descriptions as retrieval cues. We also provide a visual analysis tool, which adopts multi-view and adjustable visual encodings to aggregate and present image data, supporting interactive browsing and retrieval. Finally, we conduct a case study and visual analysis experiments to evaluate the effectiveness of the textual retrieval cues. The evaluation results reveal the potential of textual information in optimizing person retrieval and offers insights for future work.

Despite exploratory data analysis (EDA) is a powerful approach for uncovering insights from unfamiliar datasets, existing EDA tools face challenges in assisting users to assess the progress of exploration and synthesize coherent insights from isolated findings. To address these challenges, we present FactExplorer, a novel fact-based EDA system that shifts the analysis focus from raw data to data facts. FactExplorer employs a hybrid logical-visual representation, providing users with a comprehensive overview of all potential facts at the outset of their exploration. Moreover, FactExplorer introduces fact-mining techniques, including topic-based drill-down and transition path search capabilities. These features facilitate in-depth analysis of facts and enhance the understanding of interconnections between specific facts. Finally, we present a usage scenario and conduct a user study to assess the effectiveness of FactExplorer. The results indicate that FactExplorer facilitates the understanding of isolated findings and enables users to steer a thorough and effective EDA.

Humans encounter a vast array of sensory stimuli in their everyday lives. However, many visualization techniques primarily utilize visual feedback, which may disregard certain intricate details. Relying on a single visual channel may overlook complex layouts. However, how haptic force feedback can be used to assist visualization remained under-explored. In this work, we initially conducted a literature review to identify potential problems in the visualization of large datasets and engaged in discussions with domain experts to explore the potential of haptic force feedback and visual collision representation. Subsequently, we designed an innovative haptic force feedback knob, which included 3 primary modules and 29 elements. To evaluate the clarity and usefulness of this design space, we conducted a workshop and devised “recommended solutions” for the identified visualization problems. Finally, we implemented a prototype of the haptic force feedback knob and assessed its performance on scatterplot and parallel coordinate plot tasks using large datasets. The results indicated that the knob prototype could reduce visual strain and enhance the efficiency of visualization tasks.

Visual analysis has been increasingly integrated into the exploration of temporal networks, as visualization methods have the capability to present time-varying attributes and relationships of entities in an easy-to-read manner. Visualization techniques have been employed in a variety of dynamic network datasets, including social media networks, academic citation networks, and financial transaction networks. However, effectively visualizing dynamic basketball player network data, which consists of numerical networks, intensive timestamps, and subtle changes, remains a challenge for analysts. To address this issue, we propose a snapshot extraction algorithm that involves human-in-the-loop methodology to help users divide a series of networks into hierarchical snapshots for subsequent network analysis tasks, such as node exploration and network pattern analysis. Furthermore, we design and implement a prototype system, called DBNetVizor, for dynamic basketball player network data visualization. DBNetVizor integrates a graphical user interface to help users extract snapshots visually and interactively, as well as multiple linked visualization charts to display macro- and micro-level information of dynamic basketball player network data. To demonstrate the usability and efficiency of our proposed methods, we present two case studies based on dynamic basketball player network data in a competition. Additionally, we conduct an evaluation and receive positive feedback.

In the complex landscape of the Internet, inter-domain routing systems are essential for ensuring seamless connectivity and reachability across autonomous systems. However, the lack of dependable security validation mechanisms in these systems poses persistent challenges. Vulnerabilities such as prefix hijacking, path forgery, and route leakage not only compromise network operators and users, but also threaten the stability and accessibility of the Internet’s core infrastructure. To address this, visualization and visual analytics techniques are adept at identifying and detecting security threats, offering network administrators effective methods to monitor and maintain network operations. This paper presents a comprehensive survey of the state-of-the-art research in visualization and visual analytics for inter-domain routing security. We delineate four scenarios for tasks analysis in network visualization: monitoring, detection, verification, and discovery. Each category is explored in detail, focusing on the employed data sources and visualization techniques. Several key findings are presented at the end of each category, aimed at providing researchers and practitioners with research inspiration. Furthermore, we examine the trends of academic interest observed in recent decades and propose potential directions for future research in visual analytics pertaining to Internet infrastructure security.

Identifying causality behind complex systems plays a significant role in different domains, such as decision-making, policy implementations, and management recommendations. However, existing causality studies on temporal event sequence data mainly focus on individual causal discovery, which is incapable of capturing combined causality. To address the gap in combined causality discovery on temporal event sequence data, eliminating and recruiting principles are defined to balance the effectiveness and controllability of cause combinations. We also leverage the Granger causality algorithm based on the Reactive point processes to describe impelling or inhibiting behavior patterns among entities. In addition, we design an informative and aesthetic visual metaphor of "electrocircuit" to encode aggregated causality for ensuring that our causality visualization exhibits no node-overlap, no edge-intersection, and no link-ambiguity. Aggregation layout, diverse sorting strategies, and smooth interactions are also integrated into our directed, weighted, and parallel-based hypergraph for illustrating combined causality. Our developed combined causality visual analysis system, namely VAC$^{2}$2, can help users effectively explore combined causes as well as individual causes. This interactive system supports multi-level causality exploration with diverse ordering strategies and a focus+context technique to help users obtain different levels of information abstraction. The usefulness and effectiveness of our work are further evaluated by conducting two case studies and a controlled user study on event sequence data.

pixel-based visual encoding attribute-based visual encoding image-based visual encoding Figure 1: The interface of the system.(A) the probe panel which allows users to select the person-of-interest as a probe and set up the visual parameter of the search space.(B) the ranking list composed of the pixel-based visual encoding which allows users to quickly retrieve strong negative samples.(C) the search space view which supports visual exploration and enables users to provide feedback on the samples.(D) the spatiotemporal view which summarizes the spatiotemporal information of the retrieval results.(E) a cluster sample at three different visualization scales.

Video data refers to digital information in the form of a series of frames or images representing continuous motion captured by a video recording device. In various domains such as security, sports, education, and entertainment, a significant amount of video data is generated and stored daily. However, analyzing these videos manually is challenging due to their intrinsic characteristics, including large-scale, redundancy, contextual dependencies, and multimodality. Consequently, researchers have extensively explored visualization techniques to address these complexities. In this investigation, we review the state-of-the-art techniques in video visualization and visual analysis. Initially, we provide an overview of the design space for video visualization and visual analysis techniques. Subsequently, we organize and classify these techniques based on visual analysis tasks and application scenarios, providing detailed descriptions within each category. Drawing upon a comprehensive review of existing research, we provide a critical evaluation and propose potential opportunities for future research. Additionally, we have developed a web-based survey browser for convenient exploration of our created classification framework and the associated scholarly articles (https://zjutvis.github.io/VOVideo/).

Vision algorithms face challenges of limited visual presentation and unreliability in pedestrian activity assessment. In this article, we introduce LANDER, an interactive analysis system for visual exploration of pedestrian activity and uncertainty in surveillance videos. This visual analytics system focuses on three common categories of uncertainties in object tracking and action recognition. LANDER offers an overview visualization of activity and uncertainty, along with spatio-temporal exploration views closely associated with the scene. Expert evaluation and user study indicate that LANDER outperforms traditional video exploration in data presentation and analysis workflow. Specifically, compared to the baseline method, it excels in reducing retrieval time ($p< $0.01), enhancing uncertainty identification ($p< $0.05), and improving the user experience ($p< $0.05).

Previous methods based on 3DCNN, convLSTM, or optical flow have achieved great success in video salient object detection (VSOD). However, these methods still suffer from high computational costs or poor quality of the generated saliency maps. To address this, we design a space-time memory (STM)-based network that employs a standard encoder-decoder architecture. During the encoding stage, we extract high-level temporal features from the current frame and its adjacent frames, which is more efficient and practical than methods reliant on optical flow. During the decoding stage, we introduce an effective fusion strategy for both spatial and temporal branches. The semantic information of the high-level features is used to improve the object details in the low-level features. Subsequently, spatiotemporal features are methodically derived step by step to reconstruct the saliency maps. Moreover, inspired by the boundary supervision prevalent in image salient object detection (ISOD), we design a motion-aware loss that predicts object boundary motion, and simultaneously perform multitask learning for VSOD and object motion prediction. This can further enhance the model's capability to accurately extract spatiotemporal features while maintaining object integrity. Extensive experiments on several datasets demonstrate the effectiveness of our method and can achieve state-of-the-art metrics on some datasets. Our proposed model does not require optical flow or additional preprocessing, and can reach an impressive inference speed of nearly 100 FPS.

The narrative progression of events, evolving into a cohesive story, relies on the entity-entity relationships. Among the plethora of visualization techniques, storyline visualization has gained significant recognition for its effectiveness in offering an overview of story trends, revealing entity relationships, and facilitating visual communication. However, existing methods for storyline visualization often fall short in accurately depicting the specific relationships between entities. In this study, we present E 2 Storyline, a novel approach that emphasizes simplicity and aesthetics of layout while effectively conveying entity-entity relationships to users. To achieve this, we begin by extracting entity-entity relationships from textual data and representing them as subject-predicate-object (SPO) triplets, thereby obtaining structured data. By considering three types of design requirements, we establish new optimization objectives and model the layout problem using multi-objective optimization (MOO) techniques. The aforementioned SPO triplets, together with time and event information, are incorporated into the optimization model to ensure a straightforward and easily comprehensible storyline layout. Through a qualitative user study, we determine that a pixel-based view is the most suitable method for displaying the relationships between entities. Finally, we apply E 2 Storyline to real-world data, including movie synopses and live text commentaries. Through comprehensive case studies, we demonstrate that E 2 Storyline enables users to better extract information from stories and comprehend the relationships between entities.

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.

Machine vision measurement (MVM) is an essential approach that measures the area or length of a target efficiently and non-destructively for product quality control. The result of MVM is determined by its configuration, especially the lighting scheme design in image acquisition and the algorithmic parameter optimization in image processing. In a traditional workflow, engineers constantly adjust and verify the configuration for an acceptable result, which is time-consuming and significantly depends on expertise. To address these challenges, we propose a target-independent approach, visual interactive image clustering, which facilitates configuration optimization by grouping images into different clusters to suggest lighting schemes with common parameters. Our approach has four steps: data preparation, data sampling, data processing, and visual analysis with our visualization system. During preparation, engineers design several candidate lighting schemes to acquire images and develop an algorithm to process images. Our approach samples engineer-defined parameters for each image and obtains results by executing the algorithm. The core of data processing is the explainable measurement of the relationships among images using the algorithmic parameters. Based on the image relationships, we develop VMExplorer, a visual analytics system that assists engineers in grouping images into clusters and exploring parameters. Finally, engineers can determine an appropriate lighting scheme with robust parameter combinations. To demonstrate the effectiveness and usability of our approach, we conduct a case study with engineers and obtain feedback from expert interviews.

Mathematical optimization is the process of determining the set of globally or locally optimal parameters in a finite or infinite search space. It has been extensively employed in the research areas of computer science, engineering, operations research, and economics. The application of mathematical optimization has also been extended to data visualization, where it can enhance data processing, structure visualization, and facilitate exploration. However, the current state of summarization in the application of mathematical optimization in data visualization remains inadequate. In this article, we review and classify the existing techniques for advanced mathematical optimization in the fields of data visualization and visual analytics. The classification is conducted based on a classical visualization pipeline, including data enhancement and transformation, representation and rendering, as well as interactive exploration and analysis. We also discuss various mathematical optimization models and their solution methods to help readers gain a better understanding of the relationship among models, visualization, and application scenarios. We additionally provide an online exploration demo, which could enable users to interactively find relevant articles. Based on the limitations and potential trends revealed in the existing literature, we define future challenges in the cross-disciplinary of mathematical optimization and data visualization.

Visualization has the capacity of converting auditory perceptions of music into visual perceptions, which consequently opens the door to music visualization (e.g., exploring group style transitions and analyzing performance details). Current research either focuses on low-level analysis without constructing and comparing music group characteristics, or concentrates on high-level group analysis without analyzing and exploring detailed information. To fill this gap, integrating the high-level group analysis and low-level details exploration of music, we design a musical semantic sequence visualization analytics prototype system (MUSE) that mainly combines a distribution view and a semantic detail view, assisting analysts in obtaining the group characteristics and detailed interpretation. In the MUSE, we decompose the music into note sequences for modeling and abstracting music into three progressively fine-grained pieces of information (i.e., genres, instruments and notes). The distribution view integrates a new density contour, which considers sequence distance and semantic similarity, and helps analysts quickly identify the distribution features of the music group. The semantic detail view displays the music note sequences and combines the window moving to avoid visual clutter while ensuring the presentation of complete semantic details. To prove the usefulness and effectiveness of MUSE, we perform two case studies based on real-world music MIDI data. In addition, we conduct a quantitative user study and an expert evaluation.

We present Rigel, an interactive system for rapid transformation of tabular data. Rigel implements a new declarative mapping approach that formulates the data transformation procedure as direct mappings from data to the row, column, and cell channels of the target table. To construct such mappings, Rigel allows users to directly drag data attributes from input data to these three channels and indirectly drag or type data values in a spreadsheet, and possible mappings that do not contradict these interactions are recommended to achieve efficient and straightforward data transformation. The recommended mappings are generated by enumerating and composing data variables based on the row, column, and cell channels, thereby revealing the possibility of alternative tabular forms and facilitating open-ended exploration in many data transformation scenarios, such as designing tables for presentation. In contrast to existing systems that transform data by composing operations (like transposing and pivoting), Rigel requires less prior knowledge on these operations, and constructing tables from the channels is more efficient and results in less ambiguity than generating operation sequences as done by the traditional by-example approaches. User study results demonstrated that Rigel is significantly less demanding in terms of time and interactions and suits more scenarios compared to the state-of-the-art by-example approach. A gallery of diverse transformation cases is also presented to show the potential of Rigel's expressiveness.

Dimensionality reduction is commonly used for identifying and analyzing patterns in the visual analysis of multi-dimensional datasets. The selection of subspaces is a core building block in projecting high-dimensional data to low-dimensional space, which is usually illustrated as a scatterplot for analysts to easily understand and explore. This process involves human prior knowledge and domain-specific requirements. Thus, quantifying and tracking the changes of dimensionality reduction results across subspaces remain challenging. Existing methods can neither quantify the subsets-based changes of dimensionality reduction results when switching subspaces, nor automatically and comprehensively display the overall and subtle differences among dimensionality reduction results. To address this, we developedEvoSets, a novel visual analytics system designed to help users understand how subspaces affect dimensionality reduction results. The effects are quantified based on the distribution of subsets within projections to tracking the sensitivity of dimensionality reduction results across subspaces. In addition, the system supports the exploration of the overall evolution of the dimensionality reduction results for helping users track the convergence and divergence behavior changes of subsets based on an extendedBubble Setsvisualization. Similarities are intuitively illustrated, and dissimilarities are highlighted among the generated dimensionality reduction results across subspaces based on different layout constraints. The usefulness and effectiveness of the system are further evaluated with a user study and two case studies on multi-dimensional datasets.

Acoustic quality detection is vital in the manufactured products quality control field since it represents the conditions of machines or products. Recent work employed machine learning models in manufactured audio data to detect anomalous patterns. A major challenge is how to select applicable audio features to meliorate model’s accuracy and precision. To relax this challenge, we extract and analyze three audio feature types including Time Domain Feature, Frequency Domain Feature, and Cepstrum Feature to help identify the potential linear and non-linear relationships. In addition, we design a visual analysis system, namely AFExplorer, to assist data scientists in extracting audio features and selecting potential feature combinations. AFExplorer integrates four main views to present detailed distribution and relevance of the audio features, which helps users observe the impact of features visually in the feature selection. We perform the case study with AFExplore according to the ToyADMOS and MIMII Dataset to demonstrate the usability and effectiveness of the proposed system.

With the development of production technology and social needs, sectors of manufacturing are constantly improving. The use of sensors and computers has made it increasingly convenient to collect multimedia data in manufacturing. Targeted, rapid, and detailed analysis based on the type of multimedia data can make timely decisions at different stages of the entire manufacturing process. Visualization and visual analytics are frequently adopted in multimedia data analysis of manufacturing because of their powerful ability to understand, present, and analyze data intuitively and interactively. In this paper, we present a literature review of visualization and visual analytics specifically for manufacturing multimedia data. We classify existing research according to visualization techniques, interaction analysis methods, and application areas. We discuss the differences when visualization and visual analytics are applied to different types of multimedia data in the context of particular examples of manufacturing research projects. Finally, we summarize the existing challenges and prospective research directions.

With the popularity of online learning in recent decades, MOOCs (Massive Open Online Courses) are increasingly pervasive and widely used in many areas. Visualizing online learning is particularly important because it helps to analyze learner performance, evaluate the effectiveness of online learning platforms, and predict dropout risks. Due to the large-scale, high-dimensional, and heterogeneous characteristics of the data obtained from online learning, it is difficult to find hidden information. In this paper, we review and classify the existing literature for online learning to better understand the role of visualization in online learning. Our taxonomy is based on four categorizations of online learning tasks: behavior analysis, behavior prediction, learning pattern exploration and assisted learning. Based on our review of relevant literature over the past decade, we also identify several remaining research challenges and future research work.

With the rapid development of mobile Internet, the popularity of video capture devices has brought a surge in multimedia video resources. Utilizing machine learning methods combined with well-designed features, we could automatically obtain video summarization to relax video resource consumption and retrieval issues. However, there always exists a gap between the summarization obtained by the model and the ones annotated by users. How to help users understand the difference, provide insights in improving the model, and enhance the trust in the model remains challenging in the current study. To address these challenges, we propose VSumVis under a user-centered design methodology, a visual analysis system with multi-feature examination and multi-level exploration, which could help users explore and analyze video content, as well as the intrinsic relationship that existed in our video summarization model. The system contains multiple coordinated views, i.e., video view, projection view, detail view, and sequential frames view. A multi-level analysis process to integrate video events and frames are presented with clusters and nodes visualization in our system. Temporal patterns concerning the difference between the manual annotation score and the saliency score produced by our model are further investigated and distinguished with sequential frames view. Moreover, we propose a set of rich user interactions that enable an in-depth, multi-faceted analysis of the features in our video summarization model. We conduct case studies and interviews with domain experts to provide anecdotal evidence about the effectiveness of our approach. Quantitative feedback from a user study confirms the usefulness of our visual system for exploring the video summarization model.

Volume rendering techniques have been developed for decades and have been widely applied in many research fields, such as medical image visualization, geological exploration, scientific computing. etc. With the maturity of volume visualization techniques, one may have many choices to analyze volume data. However, facing different application requirements in specific cases, one may need pertinent methods to visualize volume data and highlight specific volume features. In this paper, we review and classify the existing literature on feature enhancement volume rendering. The classification is conducted based on the enhancement of four types of features (the external feature, the internal feature, the structure feature, and the ideographic feature) in volume data. Finally, we conclude this survey with future challenges in feature enhancement volume visualization.

Automatic infographics generators employ machine learning algorithms/user-defined rules and visual embellishments into the creation of infographics. It is an emerging topic in the field of information visualization that has requirements in many sectors, such as dashboard design, data analysis, and visualization recommendation. The growing popularity of visual analytics in recent years brings increased attention to automatic infographics. This creates the need for a broad survey that reviews and assesses the significant advances in this field. Automatic tools aim to lower the barrier for visually analyzing data by automatically generating visualizations for analysts to search and make a choice, instead of manually specifying. This survey reviews and classifies automatic tools and papers of visualization recommendations into a set of application categories including network-graph visualizations, annotation visualizations, and storytelling visualization. More importantly, this report presents several challenges and promising directions for future work in the field of automatic infographics and visualization recommendations.

Rapid advancement of social media tremendously facilitates and accelerates the information diffusion among users around the world. How and to what extent will the information on social media achieve widespread diffusion across the world? How can we quantify the interaction between users from different geolocations in the diffusion process? How will the spatial patterns of information diffusion change over time? To address these questions, a dynamic social gravity model (SGM) is proposed to quantify the dynamic spatial interaction behavior among social media users in information diffusion. The dynamic SGM includes three factors that are theoretically significant to the spatial diffusion of information: geographic distance, cultural proximity, and linguistic similarity. Temporal dimension is also taken into account to help detect recency effect, and ground-truth data is integrated into the model to help measure the diffusion power. Furthermore, SocialWave, a visual analytic system, is developed to support both spatial and temporal investigative tasks. SocialWave provides a temporal visualization that allows users to quickly identify the overall temporal diffusion patterns, which reflect the spatial characteristics of the diffusion network. When a meaningful temporal pattern is identified, SocialWave utilizes a new occlusion-free spatial visualization, which integrates a node-link diagram into a circular cartogram for further analysis. Moreover, we propose a set of rich user interactions that enable in-depth, multi-faceted analysis of the diffusion on social media. The effectiveness and efficiency of the mathematical model and visualization system are evaluated with two datasets on social media, namely, Ebola Epidemics and Ferguson Unrest.

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.

Analysis and exploration of spatio-temporal data such as traffic flow and vehicle trajectories have become important in urban planning and management. In this paper, we present a novel visualization technique called route-zooming that can embed spatio-temporal information into a map seamlessly for occlusion-free visualization of both spatial and temporal data. The proposed technique can broaden a selected route in a map by deforming the overall road network. We formulate the problem of route-zooming as a nonlinear least squares optimization problem by defining an energy function that ensures the route is broadened successfully on demand while the distortion caused to the road network is minimized. The spatio-temporal information can then be embedded into the route to reveal both spatial and temporal patterns without occluding the spatial context information. The route-zooming technique is applied in two instantiations including an interactive metro map for city tourism and illustrative maps to highlight information on the broadened roads to prove its applicability. We demonstrate the usability of our spatio-temporal visualization approach with case studies on real traffic flow data. We also study various design choices in our method, including the encoding of the time direction and choices of temporal display, and conduct a comprehensive user study to validate our embedded visualization design.

Geo-tagged microblog data covers billions of movement patterns on a global and local scale. Understanding these patterns could guide urban and traffic planning or help coping with disaster situations. We present a visual analytics system to investigate travel trajectories of people reconstructed from microblog messages. To analyze seasonal changes and events and to validate movement patterns against other data sources, we contribute highly interactive visual comparison methods that normalize and contrast trajectories as well as density maps within a single view. We also compute an adaptive hierarchical graph from the trajectories to abstract individual movements into higher-level structures. Specific challenges that we tackle are, among others, the spatio-temporal sparsity of the data, the volume of data varying by region, and a diverse mix of means of transportation. The applicability of our approach is presented in three case studies.

We introduce a visual analytics method to analyze eye-tracking data and saliency models for dynamic stimuli, such as video or animated graphics. The focus lies on the analysis of the different performance of saliency models in contrast to human observers to identify trends in the general viewing behavior, including time sequences of attentional synchrony and objects with a strong attentional focus. By using a space-time cube visualization in combination with clustering, the dynamic stimuli and associated eye gazes as well as the attention maps from saliency models can be analyzed in a static three-dimensional representation. We propose algorithms to keep the appearance of the computer's attention data in line with the human's eye-tracking data. The analytical process is supported by multiple coordinated views that allow the user to focus on different aspects of spatial and temporal information in eye gaze data and saliency map. By comparing attention data from both human and computer incorporated with the spatiotemporal characteristics, we are able to find the different patterns within human and computer algorithms. We list our key findings to help developing better saliency detection algorithms.

It is often necessary to analyze spatio-temporal data such as traffic flow, air pollution, and vehicle trajectories in a city. A map is often used to show the spatial context while various temporal displays like time series plots can be used to present the changes in the data over time. In this paper, we present a novel visualization that can seamlessly embed temporal displays into a map for occlusion-free visualization of both the spatial and temporal attributes of the data. We first extend the seam carving algorithm to broaden the roads of interest in a map with the least distortion to other areas, and then embed temporal displays into the roads to reveal temporal patterns without the occlusion of map information. We study various design choices in our method, including the encoding of the time direction and temporal display, and conduct two comprehensive user studies to validate our design decisions. We also demonstrate the usability of our approach with case studies on real traffic flow data in a major city.

Cooperation and competition (jointly called "coopetition") are two modes of interactions among a set of concurrent topics on social media. How do topics cooperate or compete with each other to gain public attention? Which topics tend to cooperate or compete with one another? Who plays the key role in coopetition-related interactions? We answer these intricate questions by proposing a visual analytics system that facilitates the in-depth analysis of topic coopetition on social media. We model the complex interactions among topics as a combination of carry-over, coopetition recruitment, and coopetition distraction effects. This model provides a close functional approximation of the coopetition process by depicting how different groups of influential users (i.e., "topic leaders") affect coopetition. We also design EvoRiver, a time-based visualization, that allows users to explore coopetition-related interactions and to detect dynamically evolving patterns, as well as their major causes. We test our model and demonstrate the usefulness of our system based on two Twitter data sets (social topics data and business topics data).