Visual Informatics最新文献

With the incredible growth of the scale and complexity of datasets, creating proper visualizations for users becomes more and more challenging in large datasets. Though several visualization recommendation systems have been proposed, so far, the lack of practical engineering inputs is still a major concern regarding the usage of visualization recommendations in the industry. In this paper, we proposed AVA, an open-sourced web-based framework for Automated Visual Analytics. AVA contains both empiric-driven and insight-driven visualization recommendation methods to meet the demands of creating aesthetic visualizations and understanding expressible insights respectively. The code is available at https://github.com/antvis/AVA.

Influence maximization (IM) algorithms play a significant role in hypergraph analysis tasks, such as epidemic control analysis, viral marketing, and social influence analysis, and various IM algorithms have been proposed. The main challenge lies in IM algorithm evaluation, due to the complexity and diversity of the spreading processes of different IM algorithms in different hypergraphs. Existing evaluation methods mainly leverage statistical metrics, such as influence spread, to quantify overall performance, but do not fully unravel spreading characteristics and patterns. In this paper, we propose an exploratory visual analytics system, IMVis, to assist users in exploring and evaluating IM algorithms at the overview, pattern, and node levels. A spreading pattern mining method is first proposed to characterize spreading processes and extract important spreading patterns to facilitate efficient analysis and comparison of IM algorithms. Novel visualization glyphs are designed to comprehensively reveal both temporal and structural features of IM algorithms’ spreading processes in hypergraphs at multiple levels. The effectiveness and usefulness of IMVis are demonstrated through two case studies and expert interviews.

Augmented Reality (AR), as a novel data visualization tool, is advantageous in revealing spatial data patterns and data-context associations. Accordingly, recent research has identified AR data visualization as a promising approach to increasing decision-making efficiency and effectiveness. As a result, AR has been applied in various decision support systems to enhance knowledge conveying and comprehension, in which the different data-reality associations have been constructed to aid decision-making.

However, how these AR visualization strategies can enhance different decision support datasets has not been reviewed thoroughly. Especially given the rise of big data in the modern world, this support is critical to decision-making in the coming years. Using AR to embed the decision support data and explanation data into the end user’s physical surroundings and focal contexts avoids isolating the human decision-maker from the relevant data. Integrating the decision-maker’s contexts and the DSS support in AR is a difficult challenge. This paper outlines the current state of the art through a literature review in allowing AR data visualization to support decision-making.

To facilitate the publication classification and analysis, the paper proposes one taxonomy to classify different AR data visualization based on the semantic associations between the AR data and physical context. Based on this taxonomy and a decision support system taxonomy, 37 publications have been classified and analyzed from multiple aspects. One of the contributions of this literature review is a resulting AR visualization taxonomy that can be applied to decision support systems. Along with this novel tool, the paper discusses the current state of the art in this field and indicates possible future challenges and directions that AR data visualization will bring to support decision-making.

Composite index is always derived with the weighted aggregation of hierarchical components, which is widely utilized to distill intricate and multidimensional matters in economic and business statistics. However, the composite indices always present inevitable anomalies at different levels oriented from the calculation and expression processes of hierarchical components, thereby impairing the precise depiction of specific economic issues. In this paper, we propose VisCI, a visualization framework for anomaly detection and interactive optimization of composite index. First, LSTM-AE model is performed to detect anomalies from the lower level to the higher level of the composite index. Then, a comprehensive array of visual cues is designed to visualize anomalies, such as hierarchy and anomaly visualization. In addition, an interactive operation is provided to ensure accurate and efficient index optimization, mitigating the adverse impact of anomalies on index calculation and representation. Finally, we implement a visualization framework with interactive interfaces, facilitating both anomaly detection and intuitive composite index optimization. Case studies based on real-world datasets and expert interviews are conducted to demonstrate the effectiveness of our VisCI in commodity index anomaly exploration and anomaly optimization.

Creating realistic materials is essential in the construction of immersive virtual environments. While existing techniques for material capture and conditional generation rely on flash-lit photos, they often produce artifacts when the illumination mismatches the training data. In this study, we introduce DiffMat, a novel diffusion model that integrates the CLIP image encoder and a multi-layer, cross-attention denoising backbone to generate latent materials from images under various illuminations. Using a pre-trained StyleGAN-based material generator, our method converts these latent materials into high-resolution SVBRDF textures, a process that enables a seamless fit into the standard physically based rendering pipeline, reducing the requirements for vast computational resources and expansive datasets. DiffMat surpasses existing generative methods in terms of material quality and variety, and shows adaptability to a broader spectrum of lighting conditions in reference images.

To help determine in what ways virtual reality (VR) technologies may benefit reservoir engineering workflows, we conducted a usability study on a prototype VR tool for performing reservoir model analysis tasks. By leveraging the strengths of VR technologies, this tool’s aim is to help advance reservoir analysis workflows beyond conventional methods by improving how one understands, analyzes, and interacts with reservoir model visualizations. To evaluate our tool’s VR approach to this, the study presented herein was conducted with reservoir engineering experts who used the VR tool to perform three common reservoir model analysis tasks: the spatial filtering of model cells using movable planes, the cross-comparison of multiple models, and well path planning. Our study found that accomplishing these tasks with the VR tool was generally regarded as easier, quicker, more effective, and more intuitive than traditional model analysis software while maintaining a feeling of low task workload on average. Overall, participants provided positive feedback regarding their experience with using VR to perform reservoir engineering work tasks, and in general, it was found to improve multi-model cross-analysis and rough object manipulation in 3D. This indicates the potential for VR to be better than conventional means for some work tasks and participants also expressed they could see it best utilized as an addition to current software in their reservoir model analysis workflows. There were, however, some concerns voiced when considering the full adoption of VR into their work that would be best first addressed before this took place.

Malicious webshells currently present tremendous threats to cloud security. Most relevant studies and open webshell datasets consider malicious webshell defense as a binary classification problem, that is, identifying whether a webshell is malicious or benign. However, a fine-grained multi-classification is urgently needed to enable precise responses and active defenses on malicious webshell threats. This paper introduces a malicious webshell family dataset named MWF to facilitate webshell multi-classification researches. This dataset contains 1359 malicious webshell samples originally obtained from the cloud servers of Alibaba Cloud. Each of them is provided with a family label. The samples of the same family generally present similar characteristics or behaviors. The dataset has a total of 78 families and 22 outliers. Moreover, this paper introduces the human–machine collaboration process that is adopted to remove benign or duplicate samples, address privacy issues, and determine the family of each sample. This paper also compares the distinguished features of the MWF dataset with previous datasets and summarizes the potential applied areas in cloud security and generalized sequence, graph, and tree data analytics and visualization.

Medical image generation has recently garnered significant interest among researchers. However, the primary generative models, such as Generative Adversarial Networks (GANs), often encounter challenges during training, including mode collapse. To address these issues, we proposed the AE-COT-GAN model (Autoencoder-based Conditional Optimal Transport Generative Adversarial Network) for the generation of medical images belonging to specific categories. The training process of our model comprises three fundamental components. The training process of our model encompasses three fundamental components. First, we employ an autoencoder model to obtain a low-dimensional manifold representation of real images. Second, we apply extended semi-discrete optimal transport to map Gaussian noise distribution to the latent space distribution and obtain corresponding labels effectively. This procedure leads to the generation of new latent codes with known labels. Finally, we integrate a GAN to train the decoder further to generate medical images. To evaluate the performance of the AE-COT-GAN model, we conducted experiments on two medical image datasets, namely DermaMNIST and BloodMNIST. The model’s performance was compared with state-of-the-art generative models. Results show that the AE-COT-GAN model had excellent performance in generating medical images. Moreover, it effectively addressed the common issues associated with traditional GANs.

Traffic congestion is becoming increasingly severe as a result of urbanization, which not only impedes people’s ability to travel but also hinders the economic development of cities. Modeling the correlation between congestion and its influencing factors using machine learning methods makes it possible to quickly identify congested road segments. Due to the intrinsic black-box character of machine learning models, it is difficult for experts to trust the decision results of road congestion prediction models and understand the significance of congestion-causing factors. In this paper, we present a model interpretability method to investigate the potential causes of traffic congestion and quantify the importance of various influencing factors using the SHAP method. Due to the multidimensionality of these factors, it can be challenging to visually represent the impact of all factors. In response, we propose TCEVis, an interactive visual analytics system that enables multi-level exploration of road conditions. Through three case studies utilizing actual data, we demonstrate that the TCEVis system offers advantages for assisting traffic managers in analyzing the causes of traffic congestion and elucidating the significance of various influencing factors.