Malicious webshell family dataset for webshell multi-classification research

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 multiclassification 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.

Empirically evaluating virtual reality’s effect on reservoir engineering tasks

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.

Exploring visual quality of multidimensional time series projections

Dimensionality reduction is often used to project time series data from multidimensional to two-dimensional space to generate visual representations of the temporal evolution.In this context,we address the problem of multidimensional time series visualization by presenting a new method to show and handle projection errors introduced by dimensionality reduction techniques on multidimensional temporal data.For visualization,subsequent time instances are rendered as dots that are connected by lines or curves to indicate the temporal dependencies.However,inevitable projection artifacts may lead to poor visualization quality and misinterpretation of the temporal information.Wrongly projected data points,inaccurate variations in the distances between projected time instances,and intersections of connecting lines could lead to wrong assumptions about the original data.We adapt local and global quality metrics to measure the visual quality along the projected time series,and we introduce a model to assess the projection error at intersecting lines.These serve as a basis for our new uncertainty visualization techniques that use different visual encodings and interactions to indicate,communicate,and work with the visualization uncertainty from projection errors and artifacts along the timeline of data points,their connections,and intersections.Our approach is agnostic to the projection method and works for linear and non-linear dimensionality reduction methods alike.

AvatarWild:Fully controllable head avatars in the wild

Recent advancements in the field have resulted in significant progress in achieving realistic head reconstruction and manipulation using neural radiance fields(NeRF).Despite these advances,capturing intricate facial details remains a persistent challenge.Moreover,casually captured input,involving both head poses and camera movements,introduces additional difficulties to existing methods of head avatar reconstruction.To address the challenge posed by video data captured with camera motion,we propose a novel method,AvatarWild,for reconstructing head avatars from monocular videos taken by consumer devices.Notably,our approach decouples the camera pose and head pose,allowing reconstructed avatars to be visualized with different poses and expressions from novel viewpoints.To enhance the visual quality of the reconstructed facial avatar,we introduce a view-dependent detail enhancement module designed to augment local facial details without compromising viewpoint consistency.Our method demonstrates superior performance compared to existing approaches,as evidenced by reconstruction and animation results on both multi-view and single-view datasets.Remarkably,our approach stands out by exclusively relying on video data captured by portable devices,such as smartphones.This not only underscores the practicality of our method but also extends its applicability to real-world scenarios where accessibility and ease of data capture are crucial.

Visual exploration of multi-dimensional data via rule-based sample embedding

We propose an approach to learning sample embedding for analyzing multi-dimensional datasets.The basic idea is to extract rules from the given dataset and learn the embedding for each sample based on the rules it satisfies.The approach can filter out pattern-irrelevant attributes,leading to significant visual structures of samples satisfying the same rules in the projection.In addition,analysts can understand a visual structure based on the rules that the involved samples satisfy,which improves the projection’s pattern interpretability.Our research involves two methods for achieving and applying the approach.First,we give a method to learn rule-based embedding for each sample.Second,we integrate the method into a system to achieve an analytical workflow.Cases on real-world dataset and quantitative experiment results show the usability and effectiveness of our approach.

Autoencoder-based conditional optimal transport generative adversarial network for medical image generation

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 AECOT-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.

RelicCARD:Enhancing cultural relics exploration through semantics-based augmented reality tangible interaction design

Cultural relics visualization brings digital archives of relics to broader audiences in many applications,such as education,historical research,and virtual museums.However,previous research mainly focused on modeling and rendering the relics.While enhancing accessibility,these techniques still provide limited ability to improve user engagement.In this paper,we introduce RelicCARD,a semantics-based augmented reality(AR)tangible interaction design for exploring cultural relics.Our design uses an easily available tangible interface to encourage the users to interact with a large collection of relics.The tangible interface allows users to explore,select,and arrange relics to form customized scenes.To guide the design of the interface,we formalize a design space by connecting the semantics in relics,the tangible interaction patterns,and the exploration tasks.We realize the design space as a tangible interactive prototype and examine its feasibility and effectiveness using multiple case studies and an expert evaluation.Finally,we discuss the findings in the evaluation and future directions to improve the design and implementation of the interactive design space.

An open dataset of data lineage graphs for data governance research

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.

VisCI:A visualization framework for anomaly detection and interactive optimization of composite index

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.

VisAhoi:Towards a library to generate and integrate visualization onboarding using high-level visualization grammars

Visualization onboarding supports users in reading,interpreting,and extracting information from visual data representations.General-purpose onboarding tools and libraries are applicable for explaining a wide range of graphical user interfaces but cannot handle specific visualization requirements.This paper describes a first step towards developing an onboarding library called VisAhoi,which is easy to integrate,extend,semi-automate,reuse,and customize.VisAhoi supports the creation of onboarding elements for different visualization types and datasets.We demonstrate how to extract and describe onboarding instructions using three well-known high-level descriptive visualization grammars—Vega-Lite,Plotly.js,and ECharts.We show the applicability of our library by performing two usage scenarios that describe the integration of VisAhoi into a VA tool for the analysis of high-throughput screening(HTS)data and,second,into a Flourish template to provide an authoring tool for data journalists for a treemap visualization.We provide a supplementary website(https://datavisyn.github.io/visAhoi/)that demonstrates the applicability of VisAhoi to various visualizations,including a bar chart,a horizon graph,a change matrix/heatmap,a scatterplot,and a treemap visualization.

DiffMat:Latent diffusion models for image-guided material generation

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,crossattention 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.

Current state of the art and future directions: Augmented reality data visualization to support decision-making

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.

TCEVis:Visual analytics of traffic congestion influencing factors based on explainable machine learning

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.

RenderKernel:High-level programming for real-time rendering systems

Real-time rendering applications leverage heterogeneous computing to optimize performance.However,software development across multiple devices presents challenges,including data layout inconsistencies,synchronization issues,resource management complexities,and architectural disparities.Additionally,the creation of such systems requires verbose and unsafe programming models.Recent developments in domain-specific and unified shading languages aim to mitigate these issues.Yet,current programming models primarily address data layout consistency,neglecting other persistent challenges.In this paper,we introduce RenderKernel,a programming model designed to simplify the development of real-time rendering systems.Recognizing the need for a high-level approach,RenderKernel addresses the specific challenges of real-time rendering,enabling development on heterogeneous systems as if they were homogeneous.This model allows for early detection and prevention of errors due to system heterogeneity at compile-time.Furthermore,RenderKernel enables the use of common programming patterns from homogeneous environments,freeing developers from the complexities of underlying heterogeneous systems.Developers can focus on coding unique application features,thereby enhancing productivity and reducing the cognitive load associated with real-time rendering system development.

MILG:Realistic lip-sync video generation with audio-modulated image inpainting

Existing lip synchronization(lip-sync)methods generate accurately synchronized mouths and faces in a generated video.However,they still confront the problem of artifacts in regions of non-interest(RONI),e.g.,background and other parts of a face,which decreases the overall visual quality.To solve these problems,we innovatively introduce diverse image inpainting to lip-sync generation.We propose Modulated Inpainting Lip-sync GAN(MILG),an audio-constraint inpainting network to predict synchronous mouths.MILG utilizes prior knowledge of RONI and audio sequences to predict lip shape instead of image generation,which can keep the RONI consistent.Specifically,we integrate modulated spatially probabilistic diversity normalization(MSPD Norm)in our inpainting network,which helps the network generate fine-grained diverse mouth movements guided by the continuous audio features.Furthermore,to lower the training overhead,we modify the contrastive loss in lipsync to support small-batch-size and few-sample training.Extensive experiments demonstrate that our approach outperforms the existing state-of-the-art of image quality and authenticity while keeping lip-sync.

Demers cartogram with rivers

Cartograms serve as representations of geographical and abstract data,employing a value-by-area mapping technique.As a variant of the Dorling cartogram,the Demers cartogram utilizes squares instead of circles to represent regions.This alternative approach allows for a more intuitive comparison of regions,utilizing screen space more efficiently.However,a drawback of the Dorling cartogram and its variants lies in the potential displacement of regions from their original positions,ultimately compromising legibility,readability,and accuracy.To tackle this limitation,we propose a novel hybrid cartogram layout algorithm that incorporates topological elements,such as rivers,into Demers cartograms.The presence of rivers significantly impacts both the layout and visual appearance of the cartograms.Through a user study conducted on an Electronic Health Records(EHR)dataset,we evaluate the efficacy of the proposed hybrid layout algorithm.The obtained results illustrate that this approach successfully retains key aspects of the original cartogram while enhancing legibility,readability,and overall accuracy.

JobViz:Skill-driven visual exploration of job advertisements

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities nowadays.However,the majority of these job sites are limited to offering fundamental filters such as job titles,keywords,and compensation ranges.This often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of listings.Thus,we propose well-coordinated visualizations to provide job seekers with three levels of details of job information:a skill-job overview visualizes skill sets,employment posts as well as relationships between them with a hierarchical visualization design;a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users’swift comprehension of the pertinent skills necessitated by respective positions;a post detail view lists the specifics of selected job posts for profound analysis and comparison.By using a real-world recruitment advertisement dataset collected from 51Job,one of the largest job websites in China,we conducted two case studies and user interviews to evaluate JobViz.The results demonstrated the usefulness and effectiveness of our approach.

Corrigendum to“An open dataset of data lineage graphs for data governance research”[Vis.Inform.8(1)(2024)1-5]

The authors regret that they did not include the link to download the dataset.Below please find the link:https://github.com/csuvis/DataAssetGraphData The authors would like to apologise for any inconvenience caused.

AVA:An automated and AI-driven intelligent visual analytics framework

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.

IMVis: Visual analytics for influence maximization algorithm evaluation in hypergraphs

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.