Rui Wang, Ce Zhang, Jun-Yu Ma, Jianshu Zhang, Hongru Wang, Yi Chen, Boyang Xue, Tianqing Fang, Zhisong Zhang, Hongming Zhang, Haitao Mi, Dong Yu, Kam-Fai Wong. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Visualizing and analyzing food cold chain data is essential to ensure food safety and quality in modern supply chains. However, the visualization and effective representation of such data is a huge challenge for food domain analysts due to their multidimensional, correlated, and time-varying characteristics. To address these issues, we propose VIS-Llama, an automated visualization generation model based on large language models (LLMs). VIS-Llama leverages the Llama3.1 model, which is fine-tuned with Low-Rank Adaptation (LoRA) technology and trained on a specialized corpus (FDvis) containing food cold chain data along with corresponding Vega-Lite grammar, to generate high-quality visualizations tailored to the needs of food cold chain data analysis task. Based on this model, we develop AIG-FDvis, a system that provides convenient visualization recommendations for cold chain analysts. We demonstrate the effectiveness and usefulness of VIS-Llama through quantitative and qualitative evaluations, including performance metric comparisons and case studies. This work highlights the potential of LLMs for domain-specific data visualization generation and learning complex strategies. The source code is available at https://github.com/lili0223/VIS-Llama.git.

Microbial time-series data usually need to be obtained by professionals through biological experiments, and the amount of data obtained from such manual experiments is very limited. Deep learning generative models leverage the superior learning capabilities of neural networks to generate high-quality synthetic data, which provides a promising approach to address the above shortcomings. However, the black-box nature of deep learning models makes it difficult for domain experts to trust the generated data, limiting the application of these techniques. Thus, we propose MTvis, an interactive visualization system designed to help experts understand and optimize data generated by MT-GAN, which is a microbial time-series data augmentation model proposed by us. MT-GAN not only introduces the temperature lag effect but also captures the dynamics of microbial time-series data via adversarial and joint learning. MTvis provides two exploration modes which enable users to gain insights into the structure of the model and adjust hyperparameters dynamically. The system also provides real-time observation of distribution differences between generated and real data. Experimental results show that MT-GAN effectively improves the fidelity of the generated data, while MTvis enhances the credibility and practicality of synthetic data for domain experts.

Data videos are widely used in media and education, but can overwhelm viewers if poorly organized. We assess whether a hierarchical drill-down structure improves comprehension and reduces extraneous cognitive load in linear, non-interactive data videos. Building on cognitive load theory and narrative visualization research, we propose a conceptual model that divides a narrative into successive layers of detail. We conducted an online between-subjects experiment (N = 100) comparing a drill-down video with an equivalent flat baseline. To isolate visual-structuring effects and reflect common sound-off contexts (e.g., autoplay feeds, public displays), we used short, caption-free videos without audio. Independent-samples t-tests showed slightly better recall with drill-down but no statistically significant differences in recall, cognitive load, or self-reported comprehension. Qualitative feedback highlighted that fast pacing and high visual density in both videos imposed substantial cognitive demands, likely overshadowing any structural benefits. Our findings encourage designs that combine drill-down structuring with adaptive pacing, persistent visual anchors, and multimedia cues.

The extensive range of food safety standards poses a significant challenge to efficiently accessing specific information within this domain, necessitating innovative solutions to streamline the process. In response, researchers are focusing on constructing a knowledge graph based on food safety standards to facilitate efficient associative querying. Named entity recognition is a pivotal element in this endeavor due to its critical impact on the accuracy and quality of the knowledge graph. To address the nuanced challenges of accurately identifying nested entity boundaries and rectifying entity class imbalances in food safety standards, we present PGD-GP, a novel Chinese named entity recognition model. This model is based on Projected Gradient Descent for adversarial training and Global Pointer. The model innovatively refines the Chinese Bert model at the encoding layer, employing the adversarial training method PGD to iteratively introduce perturbations to character vectors, thereby significantly enhancing the model's robustness and adaptability to texts. The decoding layer leverages Global Pointer to accurately determine dependencies and relative positional relationships between characters, thus facilitating more precise recognition of entity boundaries. To combat the issue of class imbalance, Circle Loss is utilized as the loss function. We developed and annotated the Food Safety Standard Dataset using a specifically tailored ontology rule for food safety standards. Comparative experiments conducted on the Food Safety Standard Dataset and the public Resume dataset demonstrate that PGD-GP surpasses six mainstream baseline models in performance, thereby validating the effectiveness and robustness of PGD-GP. Building upon the foundation of PGD-GP and the Food Safety Standard Dataset, we implemented a prototype system that integrates a food safety standard-based knowledge graph with associated queries. This system serves as an efficient, accurate, and comprehensive intelligent assistant, enabling researchers to effectively acquire food safety standard information.

Rui Wang, Hongru Wang, Fei Mi, Boyang Xue, Yi Chen, Kam-Fai Wong, Ruifeng Xu. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Blockchain-based cryptocurrencies, such as Bitcoin (BTC) and Ethereum (ETH), are newly emerging financial assets. Cryptocurrency exchanges are marketplaces for cryptocurrency circulation while becoming a new venue for money laundering. In this work, we cooperate with a cryptocurrency exchange to investigate new solutions for anti-money laundering in cryptocurrency exchanges. First, we learn the domain knowledge of cryptocurrency transactions and summarize data analytical requirements of transaction supervisors in their daily work of anti-money laundering. Then, we propose a visual analysis approach to support their daily work. The approach consists of a new algorithm that automatically detects suspicious money laundering accounts and a multiviewed user interface that visualizes the algorithm results and relevant transaction data. An abacus-inspired visualization is designed in the interface to depict transaction patterns contained in numerous cryptocurrency transactions, which can help supervisors find money laundering clues and deduce the trading tactic adopted by launderers. Finally, an algorithm performance experiment, a case study, and a field study are conducted with real-world data to demonstrate the effectiveness of our solution.

Rui Wang, Jianzhu Bao, Fei Mi, Yi Chen, Hongru Wang, Yasheng Wang, Yitong Li, Lifeng Shang, Kam-Fai Wong, Ruifeng Xu. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, combining COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from local government area-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

Immersive techniques, such as virtual reality, augmented reality, and mixed reality, take immersive displays as carriers to provide immersive experience. A large number of approaches focus on the visualization of scientific data in immersive environments while just a few methods concentrate on interactive information visualization (InfoVis) in an immersive environment, although InfoVis has been extended to the 3-D space for a long time. In the era of data explosion, the traditional 2-D space is unable to convey large amounts of abstract information in an intuitive way. Meanwhile, desktop-based 3-D InfoVis generally leads to visual conflict and confusion owing to limited display size and field of vision. In this survey, we search for the interactive techniques in immersive InfoVis and summarize their commonalities and discuss their differences and potential trends. The data types of abstract information in InfoVis can be categorized into graph/network data, high-dimensional and multivariate data, time-varying data, and text and document data. Besides, the visual presentation of information in immersive environments is also summarized, especially for charts, plots, and diagrams, which are some basic components of InfoVis techniques. We also described the immersive applications of InfoVis techniques, including the tools or frameworks on immersive analytics and infographics. The discussion about the traditional nonimmersive and the immersive methods in data visualizations show that the latter one has the potential to become an alternative to explore massive information in the future.

The metaverse is a visual world that blends the physical world and digital world. At present, the development of the metaverse is still in the early stage, and there lacks a framework for the visual construction and exploration of the metaverse. In this paper, we propose a framework that summarizes how graphics, interaction, and visualization techniques support the visual construction of the metaverse and user-centric exploration. We introduce three kinds of visual elements that compose the metaverse and the two graphical construction methods in a pipeline. We propose a taxonomy of interaction technologies based on interaction tasks, user actions, feedback and various sensory channels, and a taxonomy of visualization techniques that assist user awareness. Current potential applications and future opportunities are discussed in the context of visual construction and exploration of the metaverse. We hope this paper can provide a stepping stone for further research in the area of graphics, interaction and visualization in the metaverse.

Large-scale data clustering is an essential key for big data problem. However, no current existing approach is “optimal” for big data due to high complexity, which remains it a great challenge. In this article, a simple but fast approximate DBSCAN, namely, KNN-BLOCK DBSCAN, is proposed based on two findings: 1) the problem of identifying whether a point is a core point or not is, in fact, a kNN problem and 2) a point has a similar density distribution to its neighbors, and neighbor points are highly possible to be the same type (core point, border point, or noise). KNN-BLOCK DBSCAN uses a fast approximate kNN algorithm, namely, FLANN, to detect core-blocks (CBs), noncore-blocks, and noise-blocks within which all points have the same type, then a fast algorithm for merging CBs and assigning noncore points to proper clusters is also invented to speedup the clustering process. The experimental results show that KNN-BLOCK DBSCAN is an effective approximate DBSCAN algorithm with high accuracy, and outperforms other current variants of DBSCAN, including ρ-approximate DBSCAN and AnyDBC.

Hierarchical structures are very common in the real world for recording all kinds of relational data generated in our daily life and business procedures. A very popular visualization method for displaying such data structures is called "Tree". So far, there are a variety of Tree visualization methods that have been proposed and most of them can only visualize one hierarchical dataset at a time. Hence, it raises the difficulty of comparison between two or more hierarchical datasets.In this paper, we proposed Pansy Tree which used a tree metaphor to visualize merged hierarchies. We design a unique icon named pansy to represent each merged node in the structure. Each Pansy is encoded by three colors mapping data items from three different datasets in the same hierarchical position (or tree node). The petals and sepal on Pansy are designed for showing each attribute’s values and hierarchical information. We also redefine the links in force layout encoded by width and animation to better convey hierarchical information. We further apply Pansy Tree into CNCEE datasets and demonstrate two use cases to verify its effectiveness.The main contribution of this work is to merge three datasets into one tree that makes it much easier to explore and compare the structures, data items and data attributes with visual tools.

Traditional radio monitoring and management largely depend on radio spectrum data analysis, which requires considerable domain experience and heavy cognition effort and frequently results in incorrect signal judgment and incomprehensive situation awareness. Faced with increasingly complicated electromagnetic environments, radio supervisors urgently need additional data sources and advanced analytical technologies to enhance their situation awareness ability. This paper introduces a visual analytics approach for electromagnetic situation awareness. Guided by a detailed scenario and requirement analysis, we first propose a signal clustering method to process radio signal data and a situation assessment model to obtain qualitative and quantitative descriptions of the electromagnetic situations. We then design a two-module interface with a set of visualization views and interactions to help radio supervisors perceive and understand the electromagnetic situations by a joint analysis of radio signal data and radio spectrum data. Evaluations on real-world data sets and an interview with actual users demonstrate the effectiveness of our prototype system. Finally, we discuss the limitations of the proposed approach and provide future work directions.

Word cloud is a text visualization technique which is widely applied in helping improve semantic understanding about target materials. One of the most important features is the font size, which represents words frequencies of a document. As the result, in this paper, we explore how to set font sizes of words, and its influence on semantic understanding through people's performance with qualitative and controlled experiments. Adopting an machine learning algorithm LDA (Latent Dirichlet Allocation) topic model, we quantify semantics of the document and judge participants' accuracy performance. The experimental results show the influence of different font size on semantic understanding performance and provide insights for ways in promoting semantic understanding of word cloud.

Fuzzy clustering assigns a probability of membership for a datum to a cluster, which veritably reflects real-world clustering scenarios but significantly increases the complexity of understanding fuzzy clusters. Many studies have demonstrated that visualization techniques for multi-dimensional data are beneficial to understand fuzzy clusters. However, no empirical evidence exists on the effectiveness and efficiency of these visualization techniques in solving analytical tasks featured by fuzzy clusters. In this paper, we conduct a controlled experiment to evaluate the ability of fuzzy clusters analysis to use four multi-dimensional visualization techniques, namely, parallel coordinate plot, scatterplot matrix, principal component analysis, and Radviz. First, we define the analytical tasks and their representative questions specific to fuzzy clusters analysis. Then, we design objective questionnaires to compare the accuracy, time, and satisfaction in using the four techniques to solve the questions. We also design subjective questionnaires to collect the experience of the volunteers with the four techniques in terms of ease of use, informativeness, and helpfulness. With a complete experiment process and a detailed result analysis, we test against four hypotheses that are formulated on the basis of our experience, and provide instructive guidance for analysts in selecting appropriate and efficient visualization techniques to analyze fuzzy clusters.

Abstract The symmetrizable and converged Laplace–Beltrami operator ( ) is an indispensable tool for spectral geometrical analysis of point clouds. The , introduced by Liu et al. [LPG12] is guaranteed to be symmetrizable, but its convergence degrades when it is applied to models with sharp features. In this paper, we propose a novel , which is not only symmetrizable but also can handle the point‐sampled surface containing significant sharp features. By constructing the anisotropic Voronoi diagram in the local tangential space, the can be well constructed for any given point. To compute the area of anisotropic Voronoi cell, we introduce an efficient approximation by projecting the cell to the local tangent plane and have proved its convergence. We present numerical experiments that clearly demonstrate the robustness and efficiency of the proposed for point clouds that may contain noise, outliers, and non‐uniformities in thickness and spacing. Moreover, we can show that its spectrum is more accurate than the ones from existing for scan points or surfaces with sharp features.

How to reduce energy consumption, improve resource utilization and put forward efficient resource management model so as to improve the media cloud performance and mobile users’ quality of service (QoS) is the problem needed to be addressed. Our proposed media cloud distributed scheduling model aims to maximize the utility of media cloud. The media cloud distributed scheduling policy for resource-intensive mobile application includes media service provisioning and cloud resource scheduling among media cloud datacenter. The media cloud service scheduling optimization algorithms include two sub-algorithms. The practical example of video streaming service for mobile users is also given. The experiments study the performance of media cloud distributed scheduling algorithm and related algorithms. The experiment results show that proposed algorithm has better performance than related algorithms.

Fuzzy clustering analysis is an effective method to describe the uncertainty relationship between data objects and clusters. However, fuzzy clustering results will become complex and high-dimensional membership degree matrixes when they contain a large number of data points and multiple clusters. In this paper, we propose a Radviz-based interactive visualization to help users understand fuzzy clustering results. Firstly, we utilize the projection mechanism of Radviz to map the membership degree matrixes onto planar and radial pictures, in which data points with low membership uncertainty are located near Radviz circumference, while the others are scattered in the center of Radviz circle. To provide an informative interactive visualization, we then improve traditional Radviz visualization in many aspects, including implementing an optimal and uneven placement of dimension anchors by using the Prim algorithm, designing visual codings of data points and dimension arcs to express statistical information, combining chord diagram to depict the sharing relationship between clusters, and offering a set of interactions to support deeper exploration. Finally, we use a case study to illustrate the effectiveness and usefulness of our visualization.

Automatic control on business process with workflow is the core part of a project management system. A process-oriented workflow system model which can support workflow configuration, process control, task assignment, task submitting and approval, and cooperative work is presented. The model is a hierachical architecture that consists of three layers, i.e. database layer, workflow engine layer, and user interface layer. It is based on Browser/Server architecture which can provide multi-user, distributed and access control properties. The workflow engine is based on XPDL and relational database technologies and can support cooperative work, branch selection and workflow configuration. The model is implemented on Windows .NET platform, and is applied in an international enterprise project management system. The application results demonstrate that this workflow system model can satisfy the requirement of cooperative project management.