A secure visual framework for multi-index protection evaluation in networks

Mining the core value of Industrial Internet of Things(IIoT)data safely and reducing the risk of malicious attacks are the inherent requirements of industrial data visualization.Visualization technology has become the main tool for data aggregation,mining and analysis of IIoT data through graphical representation.However,visualization technology still has two shortcomings in big data calculation and analysis scenarios.On the one hand,visual results will lead to the disclosure of sensitive privacy.On the other hand,most visualization tools can't provide an interactive framework for users to select the suitable solutions.To address these problems,we present an open accessible Visual framework based on Differential Privacy theory(VisDP),which provides Multi-index Quantitative comprehensive Evaluation technology(MQE)for data mining results.Considering the advantages of interactive mechanism,VisDP provides rich optional schemes,including the operating web,calling API and the downloading SDK.Finally,we verify the availability and privacy of MQE through mathematical proofs,analyze the hospital medical waste detection system that actually applies the framework,and the experimental results have showed the effectiveness and practicality of the proposed platform.

A visual analysis approach for data imputation via multi-party tabular data correlation strategies

Data imputation is an essential pre-processing task for data governance,aimed at filling in incomplete data.However,conventional data imputation methods can only partly alleviate data incompleteness using isolated tabular data,and they fail to achieve the best balance between accuracy and eficiency.In this paper,we present a novel visual analysis approach for data imputation.We develop a multi-party tabular data association strategy that uses intelligent algorithms to identify similar columns and establish column correlations across multiple tables.Then,we perform the initial imputation of incomplete data using correlated data entries from other tables.Additionally,we develop a visual analysis system to refine data imputation candidates.Our interactive system combines the multi-party data imputation approach with expert knowledge,allowing for a better understanding of the relational structure of the data.This significantly enhances the accuracy and eficiency of data imputation,thereby enhancing the quality of data governance and the intrinsic value of data assets.Experimental validation and user surveys demonstrate that this method supports users in verifying and judging the associated columns and similar rows using theirdomain knowledge.

Visual interactive image clustering:a target-independent approach for configuration optimization in machine vision measurement

Machine vision measurement(MVM)is an essential approach that measures the area or length of a target efficiently and non-destructively for product quality control.The result of MVM is determined by its configuration,especially the lighting scheme design in image acquisition and the algorithmic parameter optimization in image processing.In a traditional workflow,engineers constantly adjust and verify the configuration for an acceptable result,which is time-consuming and significantly depends on expertise.To address these challenges,we propose a target-independent approach,visual interactive image clustering,which facilitates configuration optimization by grouping images into different clusters to suggest lighting schemes with common parameters.Our approach has four steps:data preparation,data sampling,data processing,and visual analysis with our visualization system.During preparation,engineers design several candidate lighting schemes to acquire images and develop an algorithm to process images.Our approach samples engineer-defined parameters for each image and obtains results by executing the algorithm.The core of data processing is the explainable measurement of the relationships among images using the algorithmic parameters.Based on the image relationships,we develop VMExplorer,a visual analytics system that assists engineers in grouping images into clusters and exploring parameters.Finally,engineers can determine an appropriate lighting scheme with robust parameter combinations.To demonstrate the effiectiveness and usability of our approach,we conduct a case study with engineers and obtain feedback from expert interviews.

INPHOVIS:Interactive visual analytics for smartphone-based digital phenotyping

Digital phenotyping is the characterization of human behavior patterns based on data from digital devices such as smartphones in order to gain insights into the users’state and especially to identify ailments.To support supervised machine learning,digital phenotyping requires gathering data from study participants’smartphones as they live their lives.Periodically,participants are then asked to provide ground truth labels about their health status.Analyzing such complex data is challenging due to limited contextual information and imperfect health/wellness labels.We propose INteractive PHOne-o-typing VISualization(INPHOVIS),an interactive visual framework for exploratory analysis of smartphone health data to study phone-o-types.Prior visualization work has focused on mobile health data with clear semantics such as steps or heart rate data collected using dedicated health devices and wearables such as smartwatches.However,unlike smartphones which are owned by over 85 percent of the US population,wearable devices are less prevalent thus reducing the number of people from whom such data can be collected.In contrast,the‘‘low-level"sensor data(e.g.,accelerometer or GPS data)supported by INPHOVIS can be easily collected using smartphones.Data visualizations are designed to provide the essential contextualization of such data and thus help analysts discover complex relationships between observed sensor values and health-predictive phone-o-types.To guide the design of INPHOVIS,we performed a hierarchical task analysis of phone-o-typing requirements with health domain experts.We then designed and implemented multiple innovative visualizations integral to INPHOVIS including stacked bar charts to show diurnal behavioral patterns,calendar views to visualize day-level data along with bar charts,and correlation views to visualize important wellness predictive data.We demonstrate the usefulness of INPHOVIS with walk-throughs of use cases.We also evaluated INPHOVIS with expert feedback and received encouraging responses.

Towards better analysis of machine learning models:A visual analytics perspective

Interactive model analysis,the process of understanding,diagnosing,and refining a machine learning model with the help of interactive visualization,is very important for users to efficiently solve real-world artificial intelligence and data mining problems.Dramatic advances in big data analytics have led to a wide variety of interactive model analysis tasks.In this paper,we present a comprehensive analysis and interpretation of this rapidly developing area.Specifically,we classify the relevant work into three categories:understanding,diagnosis,and refinement.Each category is exemplified by recent influential work.Possible future research opportunities are also explored and discussed.

Interactive Visual Analysis on the Attack and Defense Drill of Grid Cyber-physical Systems

The open and distributed connection of the powersystem makes it vulnerable to various potential cyber-attacks,which may lead to power outages and even casualties. Therefore,the construction of attack and defense drill (ADD) platforms forattack mechanism investigation and protection strategy evaluationhas become a research hotspot. However, for the massiveand heterogeneous security analysis data generated during thedrill, it is rare to have a comprehensive and intuitive methodto visually and efficiently display the perspective of the attackerand defender. In order to solve this problem, this paper proposesa visual analysis scheme of an ADD framework for a grid cyberphysicalsystem (GCPS) based on the interactive visual analysismethod. Specifically, it realizes system weakness discovery basedon knowledge visualization, optimization of the detection modeland visualization interaction. Finally, the case study on thesimulation platform of ADD proves the effectiveness of theproposed method.

Development of a component-based interactive visualization system for the analysis of ocean data

With the continuous development of various types of fixed marine observation equipment,satellite remote sensing technology and computer simulation technology,modern marine scientific research has entered the era of big data.Interactive ocean visuali-zation has become ubiquitous owing to the use of ocean data in studies of marine disasters,global climate change and fisheries.However,the primary challenge in analyzing large amounts of ocean data originates from the complexity of the data themselves.Therefore,an interactive multi-scale,multivariate visualization sys-tem with dynamic expansion potential is needed for analyzing larger volumes of ocean data.In this study,a unified visual data service was constructed,and a component-based interactive visua-lization structure for multi-dimensional,spatiotemporal ocean data is presented in this paper.Based on this structure,users can easily customize the system to visualize other types of scientific data.

USEVis:Visual analytics of attention-based neural embedding in information retrieval

Neural attention-based encoders,which effectively attend sentence tokens to their associated context without being restricted by long-term distance or dependency,have demonstrated outstanding performance in embedding sentences into meaningful representations(embeddings).The Universal Sentence Encoder(USE)is one of the most well-recognized deep neural network(DNN)based solutions,which is facilitated with an attention-driven transformer architecture and has been pre-trained on a large number of sentences from the Internet.Besides the fact that USE has been widely used in many downstream applications,including information retrieval(IR),interpreting its complicated internal working mechanism remains challenging.In this work,we present a visual analytics solution towards addressing this challenge.Specifically,focused on semantics and syntactics(concepts and relations)that are critical to domain clinical IR,we designed and developed a visual analytics system,i.e.,USEVis.The system investigates the power of USE in effectively extracting sentences’semantics and syntactics through exploring and interpreting how linguistic properties are captured by attentions.Furthermore,by thoroughly examining and comparing the inherent patterns of these attentions,we are able to exploit attentions to retrieve sentences/documents that have similar semantics or are closely related to a given clinical problem in IR.By collaborating with domain experts,we demonstrate use cases with inspiring findings to validate the contribution of our work and the effectiveness of our system.

Visual exploration of movement and event data with interactive time masks

We introduce the concept of time mask,which is a type of temporal filter suitable for selection of multiple disjoint time intervals in which some query conditions fulfil.Such a filter can be applied to time-referenced objects,such as events and trajectories,for selecting those objects or segments of trajectories that fit in one of the selected time intervals.The selected subsets of objects or segments are dynamically summarized in various ways,and the summaries are represented visually on maps and/or other displays to enable exploration.The time mask filtering can be especially helpful in analysis of disparate data(e.g.,event records,positions of moving objects,and time series of measurements),which may come from different sources.To detect relationships between such data,the analyst may set query conditions on the basis of one dataset and investigate the subsets of objects and values in the other datasets that co-occurred in time with these conditions.We describe the desired features of an interactive tool for time mask filtering and present a possible implementation of such a tool.By example of analysing two real world data collections related to aviation and maritime traffic,we show the way of using time masks in combination with other types of filters and demonstrate the utility of the time mask filtering.

ARGUS: Interactive visual analysis of disruptions in smartphone-detected Bio-Behavioral Rhythms

Human Bio-Behavioral Rhythms(HBRs)such as sleep-wake cycles(Circadian Rhythms),and the degree of regularity of sleep and physical activity have important health ramifications.Ubiquitous devices such as smartphones can sense HBRs by continuously analyzing data gathered passively by built-in sensors to discover important clues about the degree of regularity and disruptions in behavioral patterns.As human behavior is complex and smartphone data is voluminous with many channels(sensor types),it can be challenging to make meaningful observations,detect unhealthy HBR deviations and most importantly pin-point the causes of disruptions.Prior work has largely utilized computational methods such as machine and deep learning approaches,which while accurate,are often not explainable and present few actionable insights on HBR patterns or causes.To assist analysts in the discovery and understanding of HBR patterns,disruptions and causes,we propose ARGUS,an interactive visual analytics framework.As a foundation of ARGUS,we design an intuitive Rhythm Deviation Score(RDS)that analyzes users’smartphone sensor data,extracts underlying twenty-four-hour rhythms and quantifies their degree of irregularity.This score is then visualized using a glyph that makes it easy to recognize disruptions in the regularity of HBRs.ARGUS also facilitates deeper HBR insights and understanding of causes by linking multiple visualization panes that are overlaid with objective sensor information such as geo-locations and phone state(screen locked,charging),and user-provided or smartphone-inferred ground truth information.This array of visualization overlays in ARGUS enables analysts to gain a more comprehensive picture of HBRs,behavioral patterns and deviations from regularity.The design of ARGUS was guided by a goal and task analysis study involving an expert versed in HBR and smartphone sensing.To demonstrate its utility and generalizability,two different datasets were explored using ARGUS and our use cases and designs were strongly validated in evaluation sessions with expert and non-expert users.

lncRInter:A database of experimentally validated long non-coding RNA interaction

Non-coding regions are the major component of human genomes and the long non-coding RNA（IncRNA）is a class of pervasive genes located in noncoding regions（Morris and Mattick,2014）.IncRNAs play a wide range of regulatory roles in gene transcription,translation,epigenetic modification and protein function by interacting with different types of molecules including DNA,

Concise provenance of interactive network analysis

Large,complex networks are commonly found in many application domains,such as sociology,biology,and software engineering.Analyzing such networks can be a non-trivial task,as it often takes many interactions to derive a finding.It is thus beneficial to capture and summarize the important steps in an analysis.This provenance would then effectively support recalling,reusing,reproducing,and sharing the analysis process and results.However,the provenance of analyzing a large,complex network would often be a long interaction record.To automatically compose a concise visual summarization of network analysis provenance,we introduce a ranking model together with a reduction algorithm.The model identifies and orders important interactions used in the network analysis.Based on this model,our algorithm is able to minimize the provenance,while still preserving all the essential steps for recalling and sharing the analysis process and results.We create a prototype system demonstrating the effectiveness of our model and algorithm with two usage scenarios.

ARSlice:Head-Mounted Display Augmented with Dynamic Tracking and Projection

Computed tomography(CT)generates cross-sectional images of the body.Visualizing CT images has been a challenging problem.The emergence of the augmented and virtual reality technology has provided promising solutions.However,existing solutions suffer from tethered display or wireless transmission latency.In this paper,we present ARSlice,a proof-of-concept prototype that can visualize CT images in an untethered manner without wireless transmission latency.Our ARSlice prototype consists of two parts,the user end and the projector end.By employing dynamic tracking and projection,the projector end can track the user-end equipment and project CT images onto it in real time.The user-end equipment is responsible for displaying these CT images into the 3D space.Its main feature is that the user-end equipment is a pure optical device with light weight,low cost,and no energy consumption.Our experiments demonstrate that our ARSlice prototype provides part of six degrees of freedom for the user,and a high frame rate.By interactively visualizing CT images into the 3D space,our ARSlice prototype can help untrained users better understand that CT images are slices of a body.

A comprehensive review of tools for exploratory analysis of tabular industrial datasets

Exploratory data analysis plays a major role in obtaining insights from data.Over the last two decades,researchers have proposed several visual data exploration tools that can assist with each step of the analysis process.Nevertheless,in recent years,data analysis requirements have changed significantly.With constantly increasing size and types of data to be analyzed,scalability and analysis duration are now among the primary concerns of researchers.Moreover,in order to minimize the analysis cost,businesses are in need of data analysis tools that can be used with limited analytical knowledge.To address these challenges,traditional data exploration tools have evolved within the last few years.In this paper,with an in-depth analysis of an industrial tabular dataset,we identify a set of additional exploratory requirements for large datasets.Later,we present a comprehensive survey of the recent advancements in the emerging field of exploratory data analysis.We investigate 50 academic and non-academic visual data exploration tools with respect to their utility in the six fundamental steps of the exploratory data analysis process.We also examine the extent to which these modern data exploration tools fulfill the additional requirements for analyzing large datasets.Finally,we identify and present a set of research opportunities in the field of visual exploratory data analysis.