Visual Informatics最新文献

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.

This paper presents a survey on automatic or semi-automatic recommendation systems that help users create dashboards. It starts by showing the important role that dashboards play in data science, and give an informal definition of dashboards, i.e., a set of visualizations possibly with linkage, a screen layout and user feedback. We are mainly interested in systems that use a fully or partially automatic mechanism to recommend dashboards to users. This automation includes the suggestion of data and visualizations, the optimization of the layout and the use of user feedback. We position our work with respect to existing surveys. Starting from a set of over 1000 papers, we have selected and analyzed 19 papers/systems along several dimensions. The main dimensions were the set of considered visualizations, the suggestion method, the utility/objective functions, the layout, and the user interface. We conclude by highlighting the main achievements in this domain and by proposing perspectives.

Data have become valuable assets for enterprises. Data governance aims to manage and reuse data assets, facilitating enterprise management and enabling product innovations. A data lineage graph (DLG) is an abstracted collection of data assets and their data lineages in data governance. Analyzing DLGs can provide rich data insights for data governance. However, the progress of data governance technologies is hindered by the shortage of available open datasets for DLGs. This paper introduces an open dataset of DLGs, including the DLG model, the dataset construction process, and applied areas. This real-world dataset is sourced from Huawei Cloud Computing Technology Company Limited, which contains 18 DLGs with three types of data assets and two types of relations. To the best of our knowledge, this dataset is the first open dataset of DLGs for data governance. This dataset can also support the development of other application areas, such as graph analytics and visualization.

As computer graphics technology supports pursuing a photorealistic style, replicated artworks with a photorealistic style overwhelmingly predominate in the computer-generated art circle. Along with the progression of generative technology, this trend may make generative art a virtual world of photorealistic fake, in which the single criterion of expressive style imperils art into the context of a single boring stereotype. This article focuses on the issue of style diversity and its technical feasibility by artistic experiments of generating flower images in StyleGAN. The author insisted that photo both technology and artistic style should not be confined merely for realistic purposes. This proposition was validated in the GAN generation experiment by changing the training materials.

Mixed reality offers a larger visualization space and more intuitive means of interaction for data exploration, and many works have been dedicated to combining 2D visualizations on screen with mixe reality. However, for each combination, we need to customize the implementation of the corresponding mixed reality 3D visualization. It is a challenge to simplify this development process and enable agile building of mixed reality 3D visualizations for 2D visualizations. In addition, many existing 2D visualizations do not provide interfaces oriented to immersive analytics, so how to extend the mixed reality 3D space from existing 2D visualizations is another challenge. This work presents an agile and flexible approach to interactively transfer visualizations from 2D screens to mixed reality 3D spaces. We designed an interactive process for spatial generation of mixed-reality 3D visualizations, defined a unified data transfer framework, integrated data deconstruction techniques for 2D visualizations, implemented interfaces to immersive visualization building tool-kits, and encapsulated these techniques into a tool named X-Space. We validated that the approach is feasible and effective through 2D visualization cases including scatter plots, stacked bar charts, and adjacency matrix. Finally, we conducted expert interviews to discuss the usability and value of the method.

One main challenge for simplifying node-link diagrams of large-scale social networks lies in that simplified graphs generally contain dense subgroups or cohesive subgraphs. Graph triangles quantify the solid and stable relationships that maintain cohesive subgraphs. Understanding the mechanism of triangles within cohesive subgraphs contributes to illuminating patterns of connections within social networks. However, prior works can hardly handle and visualize triangles in cohesive subgraphs. In this paper, we propose a triangle-based graph simplification approach that can filter and visualize cohesive subgraphs by leveraging a triangle-connectivity called $k$-truss and a force-directed algorithm. We design and implement TriGraph, a web-based visual interface that provides detailed information for exploring and analyzing social networks. Quantitative comparisons with existing methods, two case studies on real-world datasets, and feedback from domain experts demonstrate the effectiveness of TriGraph.

Art therapy as an intervention has been shown to alleviate social impairment in people with AD. Meanwhile, digital technology (DTS) has been shown to perform well in different degenerative dementias through mobile devices and apps. However, it is unclear whether digital art creation therapy has an impact on the speech function of people with early AD. Therefore, the aim of this study was to confirm whether digital art creation therapy has an ameliorating effect on language decline in AD patients through the KnowU social teleprompter. This study was a controlled trial in which 16 patients with early AD worked with us and were divided into a paper-based art creation therapy group (control group) and a KnowU social teleprompter therapy group for a 6-week intervention. In the digital art creation intervention group we introduced the KnowU digital kit, consisting of a creation plug-in for the Procreate app on a tablet and a wearable device and its app. The entire treatment process is recorded and combined with a quantitative analysis of the McNemar $\chi$2 test to analyze the differences in outcomes of verbal communication function in early AD patients after different therapies. Ultimately, it is shown that early AD patients utilizing​ the KnowU social teleprompter are more effective in the intervention treatment of language decline in the real social domain compared to the paper-based art creation therapy group. The discussion further demonstrates that DTs and art therapy can provide a better social experience, creative approach and emotional recall of language loss in early AD patients, as well as increase the collaborative relationship between early AD patients and their caregivers.