Existing Visualization Techniques Research Articles

Revealing hidden patterns in deep neural network feature space continuum via manifold learning

Deep neural networks (DNNs) extract thousands to millions of task-specific features during model training for inference and decision-making. While visualizing these features is critical for comprehending the learning process and improving the performance of the DNNs, existing visualization techniques work only for classification tasks. For regressions, the feature points lie on a high dimensional continuum having an inherently complex shape, making a meaningful visualization of the features intractable. Given that the majority of deep learning applications are regression-oriented, developing a conceptual framework and computational method to reliably visualize the regression features is of great significance. Here, we introduce a manifold discovery and analysis (MDA) method for DNN feature visualization, which involves learning the manifold topology associated with the output and target labels of a DNN. MDA leverages the acquired topological information to preserve the local geometry of the feature space manifold and provides insightful visualizations of the DNN features, highlighting the appropriateness, generalizability, and adversarial robustness of a DNN. The performance and advantages of the MDA approach compared to the existing methods are demonstrated in different deep learning applications.

A novel interpolation-MLP coupled reconstruction method for turbulent natural convection thermal environment reconstruction of MW-class offshore wind turbine nacelles

Localized overheating in the nacelle due to heat loss in high-power offshore wind turbines of 10 MW or more, where localized overheating affects the operation of other components, it is particularly important to visualize the thermal environment in the nacelle accurately and in real time in order to control the start-up and shutdown of cooling methods. However, the complexity of the high turbulence intensity (Rayleigh number (Ra) > 109) thermal environment in the nacelle of high-power units leads to significant errors in existing visualization techniques. Therefore, this paper presents a novel coupled interpolation-MLP reconstruction technique based on a data-driven deep neural network. Based on the interpolation method as primary reconstruction, a secondary reconstruction neural network model is obtained by training the thermal environment parameters (static pressure P, tangential velocity U, normal velocity V and temperature T) of specific computing nodes in the nacelle. The model is used to predict the values of thermal environment parameters at each time point for computing nodes with large gradients. An improved PSO algorithm is used to arrange the positions of a defined number of sensors to save sensor resources during the measurement process and to and ensure computing stability. The above improvements to the traditional interpolation reconstruction algorithms have led to an increase in the accuracy of the thermal environment reconstruction.

Preparation of future science teachers for cognitive visualization of the educational process

The issue of designing a qualitative educational environment for the formation of a flexible personality in the light of global world changes has been actualized in the article. Its main essential characteristic is the ability to adapt to the changing conditions of the modern natural and social environment. We consider the visualization of the educational process as a means of improving the quality of educational services, forming a holistic perception of the environment that combines logical and visual thinking. According to the purpose of the article, based on the analysis of existing visualization techniques and their expert evaluation, the most effective ones for mastering the content of natural sciences disciplines as well as professional and methodological training of future natural sciences teachers have been identified and substantiated. The significance of visualization techniques for the educational environment has been determined by the method of expert evaluation according to the criterion of «didactic quality». Its indicators are: the possibility to use information educational environment of the higher education institution on the base of existing material and technical support; importance for creating a positive emotional background for teaching; importance for organizing interactive pedagogical interaction of participants in the educational process; accessibility for perception of educational information; expediency of use in the future natural sciences teachers professional training; easy to use both for teachers and students. The five most effective visualization techniques for ensuring a holistic perception of knowledge by combining logical and imaginative thinking (scribing, mind map, infographics, timeline, tag cloud) have been identified by ranking. The effectiveness of the scribing technique and its influence on the formation of indicators of the fully assimilated knowledge quality such as depth and awareness has been experimentally proved.

Distinguishable Colorimetric Biosensor for Diagnosis of Prostate Cancer Bone Metastases.

Castration-resistant prostate cancer (PCa) causes severe bone metastasis (BM), which significantly increases mortality in men with PCa. Imaging tests and radiometric scanning require long analysis times, expensive equipment, specialized personnel, and a slow turnaround. New visualization technologies are expected to solve the above problems. Nonetheless, existing visualization techniques barely meet the urgency for precise diagnosis because the human eyes cannot recognize and capture even slight variations in visual information. By using dye differentiated superposition enhancement colorimetric biosensors, an effective method to diagnose prostate cancer bone metastases (PCa-BM) with excellent accuracy for naked-eye quantitative detection of alkaline phosphatase (ALP) is developed. The biomarker ALP specific hydrolytic product ascorbic acid can be detected by rhodamine derivatives (Rd) as gold nanobipyramids (Au NBPs) are deposited and grown. Color-recombining enhancement effects between Rd and Au NBPs significantly improved abundance. The 150UL-1 threshold between normal and abnormal can be identified by color. And with color enhancement effect and double signal response, the ALP index is visually measured to diagnose PCa-BM and provide handy treatment recommendations. Additionally, the proposed colorimetric sensing strategy can be used to diagnose other diseases.

The State of the Art in Visualizing Dynamic Multivariate Networks

AbstractMost real‐world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes and their evolution concurrently. Many real‐world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research.

FlexEvent: going beyond Case‐Centric Exploration and Analysis of Multivariate Event Sequences

AbstractIn many domains, multivariate event sequence data is collected focused around an entity (the case). Typically, each event has multiple attributes, for example, in healthcare a patient has events such as hospitalization, medication, and surgery. In addition to the multivariate events, also the case (a specific attribute, e.g., patient) has associated multivariate data (e.g., age, gender, weight). Current work typically only visualizes one attribute per event (label) in the event sequences. As a consequence, events can only be explored from a predefined case‐centric perspective. However, to find complex relations from multiple perspectives (e.g., from different case definitions, such as doctor), users also need an event‐ and attribute‐centric perspective. In addition, support is needed to effortlessly switch between and within perspectives. To support such a rich exploration, we present FlexEvent: an exploration and analysis method that enables investigation beyond a fixed case‐centric perspective. Based on an adaptation of existing visualization techniques, such as scatterplots and juxtaposed small multiples, we enable flexible switching between different perspectives to explore the multivariate event sequence data needed to answer multi‐perspective hypotheses. We evaluated FlexEvent with three domain experts in two use cases with sleep disorder and neonatal ICU data that show our method facilitates experts in exploring and analyzing real‐world multivariate sequence data from different perspectives.

Transportation data visualization with a focus on freight: a literature review

ABSTRACT Road-based freight movements are a critical component of the supply chain and transportation networks. With the drive to invest in big data collection capabilities, most of the collected freight movement data remain underutilized. To improve the efficiency and resiliency of the supply chain, it is essential to enhance the visibility of goods movements on road networks. To this end, we provide in this paper a comprehensive literature review on this topic and analyze the previous research from different perspectives, such as data levels of abstraction and existing visualization techniques. In addition, we provide a taxonomy of freight transportation visualization according to the underlying analytic objective. Furthermore, we propose a decision support tool to aid freight data analysts in selecting the right visualization tools. Finally, we identify research gaps in the field of freight transportation visualization.

Interactive Visual Exploration of Human Mobility Correlation Based on Smart Card Data

Public transportation agencies call for an intuitive, interactive, and reusable visualization tool to detect patterns of crime (i.e. pickpockets and gangs) or missing commuters on public transportation systems. Few existing visualization techniques have visually explored mobility correlations of targets and their companions, who are characterized in diverse mobility types, by using discrete travel hints extracted from a massive amount of data. To fill this gap, a visual analytical system is provided to conduct a group-based and individual-based exploration of mobility correlations of passengers of interest, based on an auto integration of multiple queries. How passengers differ from or correlate with each other are further examined based on their spatiotemporal distributions in trajectories and ODs. Real-world case studies, as well as user feedback made by 30 participants, demonstrate the effectiveness of the system in detecting specific targets and their companions featured in diverse mobility types, or in characterizing their spatiotemporal aggregation patterns for a further tracking on public transportation systems.

Image-based benchmarking and visualization for large-scale global optimization

In the context of optimization, visualization techniques can be useful for understanding the behaviour of optimization algorithms and can even provide a means to facilitate human interaction with an optimizer. Towards this goal, an image-based visualization framework, without dimension reduction, that visualizes the solutions to large-scale global optimization problems as images is proposed. In the proposed framework, the pixels visualize decision variables while the entire image represents the overall solution quality. This framework affords a number of benefits over existing visualization techniques including enhanced scalability (in terms of the number of decision variables), facilitation of standard image processing techniques, providing nearly infinite benchmark cases, and explicit alignment with human perception. To the best of the authors’ knowledge, this is the first realization of a dimension-preserving, scalable visualization framework that embeds the inherent relationship between decision space and objective space. The proposed framework is utilized with different mapping schemes on an image-reconstruction problem that encompass continuous, discrete, constrained, dynamic, and multi-objective optimization. The proposed framework is then demonstrated on arbitrary benchmark problems with known optima. Experimental results elucidate the flexibility and demonstrate how valuable information about the search process can be gathered via the proposed visualization framework. Results of a user survey strongly support that users perceive a correlation between objective fitness values and the quality of the corresponding images generated by the proposed framework.

Skyscraper visualization of multiple time-dependent scalar fields on surfaces

We present a system to visualize multiple time-dependent scalar fields defined on 3D surfaces as they arise from numerical simulations. Domain experts want to understand correlations between multiple scalar fields to verify underlying processes. However, existing visualization techniques require high exploration effort to perceive temporal changes and relations of multiple fields on 3D surfaces. This makes analysis time-consuming and carries a high risk of overlooking regions with specific correlations.Therefore, we introduce a novel concept to facilitate exploration of multiple time-dependent scalar fields simultaneously. The basic idea is to use a novel glyph-based depiction, called skyline visualization, which is inspired by the skyscraper metaphor from information visualization. The skyscrapers are represented by 2D and 3D bar charts, where their floors show the temporal behavior of scalar fields of the corresponding surface region. The user can interact with the skyline visualization to see at which surface areas the scalar fields take on striking values or whether correlations exist between them. Furthermore, the user can immediately see in which regions scalar fields change strongly over time or show strongly inverted behavior. We evaluated our system with several CFD engineers, radiologists and visualization experts. Compared to existing systems, they were able to find regions of interest quickly and with high accuracy.

Visualizing Semantic Structure of a Clinical Text Document

The increasing use of Electronic Health Records (EHRs) in healthcare delivery settings has led to increase availability of electronic clinical data. They generate a lot of patients’ clinical data each day, requiring physicians to review them to find clinically relevant information of different patients during care episodes. The availability of electronically collected healthcare data has created the need of computational tools to analyze them. One of the types of data which doctors have access to is clinical notes that resides in electronic health records. These notes are useful as they provide comprehensive information about patients’ health histories with many practical uses. For example, doctors always review these notes during care episodes to appraise themselves about the health history of a patient. These reviews are currently manual where a doctor reads a patient’s chart while looking for specific clinical information. Without the proper support, this manual process leads to information overload and increases physician cognitive workload. Current electronic health records (EHRs) do not provide support to help physicians reduce cognitive workload when completing clinical tasks. This is especially true for long clinical documents which require quick review at the point of care. The growing amount of clinical documentation available in EHRs has arose the need of tools that support synthesize of information in EHRs. The use of visual analytics to explore healthcare data is one such research direction to address this problem. However, existing visualization techniques are mainly based on structured electronic health record and rarely support therapeutic activities. Therefore, visualization of unstructured clinical records to support clinical practice is required. In this paper we propose a unique approach for graphically representing and visualizing the semantic structure of a clinical text document to aid doctors in reviewing electronic clinical notes. A user evaluation demonstrates that the proposed method for visualizing and navigating a document’s semantic structure facilitates a user’s document information exploration.

Direct Visualization of Microscale Dynamics of Water Droplets on under-Oil-Hydrophilic Membranes by Using Synchrotron White-Beam X-ray Microimaging Techniques.

Despite considerable academical and practical interests on separation of water-in-oil emulsion via special wettable membranes, fundamental understanding on microscale dynamics of water droplets on under-oil-hydrophilic membranes (UOHMs) at early stages during separation is still very preliminary due to temporal and spatial resolution of existing visualization techniques. To this end, we here succeed in a direct microscopic visualization of separation processes of water droplets on the UOHMs by employing a high-speed, two-dimensional synchrotron white-beam X-ray microimaging technique. During the separation of water-in-oil emulsion, microscale dynamic behaviors of water droplets on hydrophilic membrane surfaces immersed in the different oil media (i.e., hexane, kerosene, and light and heavy mineral oils) and oil films between water droplets and membrane surfaces are visualized and analyzed.

COVID-19 Data Visualization through Automatic Phase Detection

This article presents a new interactive visualization system to represent and compare rate of spread of COVID-19 pandemic across different countries over time. We surveyed existing visualization techniques used in various websites and media outlets and introduced use of a knee detection algorithm that divides the exponential spread in multiple linear components. A set of use cases demonstrates the utility of the system in comparing the spread across different countries for both analysis and prediction.

A critical review of graphics for subgroup analyses in clinical trials.

SUMMARYSubgroup analyses are a routine part of clinical trials to investigate whether treatment effects are homogeneous across the study population. Graphical approaches play a key role in subgroup analyses to visualise effect sizes of subgroups, to aid the identification of groups that respond differentially, and to communicate the results to a wider audience. Many existing approaches do not capture the core information and are prone to lead to a misinterpretation of the subgroup effects. In this work, we critically appraise existing visualisation techniques, propose useful extensions to increase their utility and attempt to develop an effective visualisation approach. We focus on forest plots, UpSet plots, Galbraith plots, subpopulation treatment effect pattern plot, and contour plots, and comment on other approaches whose utility is more limited. We illustrate the methods using data from a prostate cancer study.

Combining the richness of GIS techniques with visualisation tools to better understand the spatial distribution of data - a case study of Chicago City crime analysis

This study aims to: 1) to explore the benefits of adding a spatial GIS layer of analysis to other existing visualisation techniques; 2) to identify and evaluate the patterns in selected crime data by analysing Chicago's open dataset; 3) provide a better understanding of patterns and prediction of crime trends within the selected geographical location. We conclude that Chicago seems to be on course to have both the lowest violent crime rate since 1972, and the lowest murder frequency since 1967. Chicago has witnessed a vigorous drop in most crimes types over the last few years in compares to the previous crime index data. Also, Chicago crime naturally upsurges during summer months and declines during winter months. Our study results align with previous several decades of studies and analysis of Chicago crimes, in which the same communities of highest crime rates still experience the mainstream of crime.

Using the Chou’s 5-steps rule to predict splice junctions with interpretable bidirectional long short-term memory networks

Neural models have been able to obtain state-of-the-art performances on several genome sequence-based prediction tasks. Such models take only nucleotide sequences as input and learn relevant features on their own. However, extracting the interpretable motifs from the model remains a challenge. This work explores various existing visualization techniques in their ability to infer relevant sequence information learnt by a recurrent neural network (RNN) on the task of splice junction identification. The visualization techniques have been modulated to suit the genome sequences as input. The visualizations inspect genomic regions at the level of a single nucleotide as well as a span of consecutive nucleotides. This inspection is performed based on the modification of input sequences (perturbation based) or the embedding space (back-propagation based). We infer features pertaining to both canonical and non-canonical splicing from a single neural model. Results indicate that the visualization techniques produce comparable performances for branchpoint detection. However, in the case of canonical donor and acceptor junction motifs, perturbation based visualizations perform better than back-propagation based visualizations, and vice-versa for non-canonical motifs. The source code of our stand-alone SpliceVisuL tool is available at https://github.com/aaiitggrp/SpliceVisuL.

Comparative Analysis of Flight Search Representations

Visualization has been extensively used in almost all the fields, but unfortunately, there is very little work on the visualization of flight search. The literature discusses only two representations: textual representation and linear representation. In this study, the authors introduce a new form of visualization called circular representation and compare it with the existing visualization techniques. The authors found that each representation has its own merits and demerits. More specifically, they found that users were able to find required flight circular efficiently when using circular representation, while most errors were committed by the user when using textual representation, and users ranked circular representation low in terms of qualitative satisfaction.

Experimental Investigation of Volume Fraction in an Annulus Using Electrical Resistance Tomography

Summary Horizontal drilling technology has shown to improve the production and cost–effectiveness of the well by generating multiple extraction points from a single vertical well. The efficiency of hole cleaning is reduced because of the solid–cuttings accumulation in the annulus in cases of extended–reach drilling. It is difficult to study the complex flow behavior in a drilling annulus using the existing visualization techniques. In this study, experiments were carried out in the multiphase flow–loop system consisting of a simulated drilling annulus using electrical resistance tomography (ERT) and a high–speed camera. Real–time tomographic images (quantitative visualization) of multiphase flow from ERT were compared to the actual photographs of the flow conditions in a drilling annulus. The quantitative analysis demonstrates that ERT has a wide potential application in studying the hole–cleaning issues in the drilling industry.

Use of Data Visualisation for Zero-Day Malware Detection

With the explosion of Internet of Things (IoT) worldwide, there is an increasing threat from malicious software (malware) attackers that calls for efficient monitoring of vulnerable systems. Large amounts of data collected from computer networks, servers, and mobile devices need to be analysed for malware proliferation. Effective analysis methods are needed to match with the scale and complexity of such a data-intensive environment. In today’s Big Data contexts, visualisation techniques can support malware analysts going through the time-consuming process of analysing suspicious activities thoroughly. This paper takes a step further in contributing to the evolving realm of visualisation techniques used in the information security field. The aim of the paper is twofold: (1) to provide a comprehensive overview of the existing visualisation techniques for detecting suspicious behaviour of systems and (2) to design a novel visualisation using similarity matrix method for establishing malware classification accurately. The prime motivation of our proposal is to identify obfuscated malware using visualisation of the extended x86 IA-32 (opcode) similarity patterns, which are hard to detect with the existing approaches. Our approach uses hybrid models wherein static and dynamic malware analysis techniques are combined effectively along with visualisation of similarity matrices in order to detect and classify zero-day malware efficiently. Overall, the high accuracy of classification achieved with our proposed method can be visually observed since different malware families exhibit significantly dissimilar behaviour patterns.

Design of visualization plots of industrial alarm and event data for enhanced alarm management

The availability of large volumes of alarm & event data in complex industrial facilities has prompted the development of alarm management techniques and also resulted in a great demand to transform such data and derived results into effective visual forms. Even though good visualization applications can be found in many existing studies, systematic studies to the design of visualization plots are still rare in the area of industrial alarm monitoring. More efforts need to be devoted to enriching the family of visualization techniques, so as to help industrial practitioners in better understanding the behavior of alarm systems and to facilitate decision making for the enhancement of alarm management. This paper presents timely work in the design of visualization plots of alarm & event data. First, a comprehensive literature survey is carried out to investigate existing visualization techniques, which are categorized into three classes based on the input information. Problems in the existing studies are summarized and design requirements for visual analytics are presented. Then, design studies on the development of visualization plots are presented in three categories, including visualization towards overall performance, visualization towards pattern insights, and visualization towards realtime applications. Examples are provided to demonstrate the effectiveness and utility of these visualization techniques.