DevLan/vispubs huggingface dataset:
GraphProtector: A Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms
Authors: Xumeng Wang, Wei Chen 0001, Jia-Kai Chou, Chris Bryan, Huihua Guan, Wenlong Chen, Rusheng Pan, Kwan-Liu Ma
Published at VAST, 2018

Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysis can also lead to privacy exposure (whether intentionally or inadvertently): leaking the real-world identity of ostensibly anonymous individuals. Most sanitization strategies modify the graph's structure based on hypothesized tactics that an adversary would employ. While combining multiple anonymization schemes provides a more comprehensive privacy protection, deciding the appropriate set of techniques-along with evaluating how applying the strategies will affect the utility of the anonymized results-remains a significant challenge. To address this problem, we introduce GraphProtector, a visual interface that guides a user through a privacy preservation pipeline. GraphProtector enables multiple privacy protection schemes which can be simultaneously combined together as a hybrid approach. To demonstrate the effectiveness of GraphPro tector, we report several case studies and feedback collected from interviews with expert users in various scenarios.
Identification of Temporally Varying Areas of Interest in Long-Duration Eye-Tracking Data Sets
Authors: Prithiviraj K. Muthumanickam, Katerina Vrotsou, Aida Nordman, Jimmy Johansson 0001, Matthew Cooper 0001
Published at VAST, 2018

Eye-tracking has become an invaluable tool for the analysis of working practices in many technological fields of activity. Typically studies focus on short tasks and use static expected areas of interest (AoI) in the display to explore subjects' behaviour, making the analyst's task quite straightforward. In long-duration studies, where the observations may last several hours over a complete work session, the AoIs may change over time in response to altering workload, emergencies or other variables making the analysis more difficult. This work puts forward a novel method to automatically identify spatial AoIs changing over time through a combination of clustering and cluster merging in the temporal domain. A visual analysis system based on the proposed methods is also presented. Finally, we illustrate our approach within the domain of air traffic control, a complex task sensitive to prevailing conditions over long durations, though it is applicable to other domains such as monitoring of complex systems.
iForest: Interpreting Random Forests via Visual Analytics
Authors: Xun Zhao, Yanhong Wu, Dik Lun Lee, Weiwei Cui
Published at VAST, 2018

As an ensemble model that consists of many independent decision trees, random forests generate predictions by feeding the input to internal trees and summarizing their outputs. The ensemble nature of the model helps random forests outperform any individual decision tree. However, it also leads to a poor model interpretability, which significantly hinders the model from being used in fields that require transparent and explainable predictions, such as medical diagnosis and financial fraud detection. The interpretation challenges stem from the variety and complexity of the contained decision trees. Each decision tree has its unique structure and properties, such as the features used in the tree and the feature threshold in each tree node. Thus, a data input may lead to a variety of decision paths. To understand how a final prediction is achieved, it is desired to understand and compare all decision paths in the context of all tree structures, which is a huge challenge for any users. In this paper, we propose a visual analytic system aiming at interpreting random forest models and predictions. In addition to providing users with all the tree information, we summarize the decision paths in random forests, which eventually reflects the working mechanism of the model and reduces users' mental burden of interpretation. To demonstrate the effectiveness of our system, two usage scenarios and a qualitative user study are conducted.
InkPlanner: Supporting Prewriting via Intelligent Visual Diagramming
Authors: Zhicong Lu, Mingming Fan 0001, Yun Wang 0012, Jian Zhao 0010, Michelle Annett, Daniel J. Wigdor
Published at VAST, 2018

Prewriting is the process of generating and organizing ideas before drafting a document. Although often overlooked by novice writers and writing tool developers, prewriting is a critical process that improves the quality of a final document. To better understand current prewriting practices, we first conducted interviews with writing learners and experts. Based on the learners' needs and experts' recommendations, we then designed and developed InkPlanner, a novel pen and touch visualization tool that allows writers to utilize visual diagramming for ideation during prewriting. InkPlanner further allows writers to sort their ideas into a logical and sequential narrative by using a novel widget- NarrativeLine. Using a NarrativeLine, InkPlanner can automatically generate a document outline to guide later drafting exercises. Inkplanner is powered by machine-generated semantic and structural suggestions that are curated from various texts. To qualitatively review the tool and understand how writers use InkPlanner for prewriting, two writing experts were interviewed and a user study was conducted with university students. The results demonstrated that InkPlanner encouraged writers to generate more diverse ideas and also enabled them to think more strategically about how to organize their ideas for later drafting.
KnowledgePearls: Provenance-Based Visualization Retrieval
Authors: Holger Stitz, Samuel Gratzl, Harald Piringer, Thomas Zichner, Marc Streit
Published at VAST, 2018

Storing analytical provenance generates a knowledge base with a large potential for recalling previous results and guiding users in future analyses. However, without extensive manual creation of meta information and annotations by the users, search and retrieval of analysis states can become tedious. We present KnowledgePearls, a solution for efficient retrieval of analysis states that are structured as provenance graphs containing automatically recorded user interactions and visualizations. As a core component, we describe a visual interface for querying and exploring analysis states based on their similarity to a partial definition of a requested analysis state. Depending on the use case, this definition may be provided explicitly by the user by formulating a search query or inferred from given reference states. We explain our approach using the example of efficient retrieval of demographic analyses by Hans Rosling and discuss our implementation for a fast look-up of previous states. Our approach is independent of the underlying visualization framework. We discuss the applicability for visualizations which are based on the declarative grammar Vega and we use a Vega-based implementation of Gapminder as guiding example. We additionally present a biomedical case study to illustrate how KnowledgePearls facilitates the exploration process by recalling states from earlier analyses.
Lessons Learned Developing a Visual Analytics Solution for Investigative Analysis of Scamming Activities
Authors: Jay Koven, Cristian Felix, Hossein Siadati, Markus Jakobsson, Enrico Bertini
Published at VAST, 2018

The forensic investigation of communication datasets which contain unstructured text, social network information, and metadata is a complex task that is becoming more important due to the immense amount of data being collected. Currently there are limited approaches that allow an investigator to explore the network, text and metadata in a unified manner. We developed Beagle as a forensic tool for email datasets that allows investigators to flexibly form complex queries in order to discover important information in email data. Beagle was successfully deployed at a security firm which had a large email dataset that was difficult to properly investigate. We discuss our experience developing Beagle as well as the lessons we learned applying visual analytic techniques to a difficult real-world problem.
Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models
Authors: Jiawei Zhang 0003, Yang Wang, Piero Molino, Lezhi Li, David S. Ebert
Published at VAST, 2018

Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. Conventional techniques usually focus on visualizing the internal logic of a specific model type (i.e., deep neural networks), lacking the ability to extend to a more complex scenario where different model types are integrated. To this end, Manifold is designed as a generic framework that does not rely on or access the internal logic of the model and solely observes the input (i.e., instances or features) and the output (i.e., the predicted result and probability distribution). We describe the workflow of Manifold as an iterative process consisting of three major phases that are commonly involved in the model development and diagnosis process: inspection (hypothesis), explanation (reasoning), and refinement (verification). The visual components supporting these tasks include a scatterplot-based visual summary that overviews the models' outcome and a customizable tabular view that reveals feature discrimination. We demonstrate current applications of the framework on the classification and regression tasks and discuss other potential machine learning use scenarios where Manifold can be applied.
MAQUI: Interweaving Queries and Pattern Mining for Recursive Event Sequence Exploration
Authors: Po-Ming Law, Zhicheng Liu, Sana Malik, Rahul C. Basole
Published at VAST, 2018

Exploring event sequences by defining queries alone or by using mining algorithms alone is often not sufficient to support analysis. Analysts often interweave querying and mining in a recursive manner during event sequence analysis: sequences extracted as query results are used for mining patterns, patterns generated are incorporated into a new query for segmenting the sequences, and the resulting segments are mined or queried again. To support flexible analysis, we propose a framework that describes the process of interwoven querying and mining. Based on this framework, we developed MAQUI, a Mining And Querying User Interface that enables recursive event sequence exploration. To understand the efficacy of MAQUI, we conducted two case studies with domain experts. The findings suggest that the capability of interweaving querying and mining helps the participants articulate their questions and gain novel insights from their data.
MotionRugs: Visualizing Collective Trends in Space and Time
Authors: Juri Buchmüller, Dominik Jäckle, Eren Cakmak, Ulrik Brandes, Daniel A. Keim
Published at VAST, 2018

Understanding the movement patterns of collectives, such as flocks of birds or fish swarms, is an interesting open research question. The collectives are driven by mutual objectives or react to individual direction changes and external influence factors and stimuli. The challenge in visualizing collective movement data is to show space and time of hundreds of movements at the same time to enable the detection of spatiotemporal patterns. In this paper, we propose MotionRugs, a novel space efficient technique for visualizing moving groups of entities. Building upon established space-partitioning strategies, our approach reduces the spatial dimensions in each time step to a one-dimensional ordered representation of the individual entities. By design, MotionRugs provides an overlap-free, compact overview of the development of group movements over time and thus, enables analysts to visually identify and explore group-specific temporal patterns. We demonstrate the usefulness of our approach in the field of fish swarm analysis and report on initial feedback of domain experts from the field of collective behavior.
RegressionExplorer: Interactive Exploration of Logistic Regression Models with Subgroup Analysis
Authors: Dennis Dingen, Marcel van 't Veer, Patrick Houthuizen, Eveline H. J. Mestrom, Hendrikus H. M. Korsten, R. Arthur Bouwman, Jarke J. van Wijk
Published at VAST, 2018

We present RegressionExplorer, a Visual Analytics tool for the interactive exploration of logistic regression models. Our application domain is Clinical Biostatistics, where models are derived from patient data with the aim to obtain clinically meaningful insights and consequences. Development and interpretation of a proper model requires domain expertise and insight into model characteristics. Because of time constraints, often a limited number of candidate models is evaluated. RegressionExplorer enables experts to quickly generate, evaluate, and compare many different models, taking the workflow for model development as starting point. Global patterns in parameter values of candidate models can be explored effectively. In addition, experts are enabled to compare candidate models across multiple subpopulations. The insights obtained can be used to formulate new hypotheses or to steer model development. The effectiveness of the tool is demonstrated for two uses cases: prediction of a cardiac conduction disorder in patients after receiving a heart valve implant and prediction of hypernatremia in critically ill patients.
RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
Authors: Bum Chul Kwon, Min-Je Choi, Joanne Taery Kim, Edward Choi, Young-bin Kim, Soonwook Kwon, Jimeng Sun 0001, Jaegul Choo
Published at VAST, 2018

We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients' diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is often challenging for users to understand why the model makes a particular prediction. Such black-box nature of RNNs can impede its wide adoption in clinical practice. Furthermore, we have no established methods to interactively leverage users' domain expertise and prior knowledge as inputs for steering the model. Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers. Following the iterative design process between the experts, we design, implement, and evaluate a visual analytics tool called RetainVis, which couples a newly improved, interpretable, and interactive RNN-based model called RetainEX and visualizations for users' exploration of EMR data in the context of prediction tasks. Our study shows the effective use of RetainVis for gaining insights into how individual medical codes contribute to making risk predictions, using EMRs of patients with heart failure and cataract symptoms. Our study also demonstrates how we made substantial changes to the state-of-the-art RNN model called RETAIN in order to make use of temporal information and increase interactivity. This study will provide a useful guideline for researchers that aim to design an interpretable and interactive visual analytics tool for RNNs.
RuleMatrix: Visualizing and Understanding Classifiers with Rules
Authors: Yao Ming, Huamin Qu, Enrico Bertini
Published at VAST, 2018

With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large number of potential but neglected users are the domain experts with little knowledge of machine learning but are expected to work with machine learning systems. In this paper, we present an interactive visualization technique to help users with little expertise in machine learning to understand, explore and validate predictive models. By viewing the model as a black box, we extract a standardized rule-based knowledge representation from its input-output behavior. Then, we design RuleMatrix, a matrix-based visualization of rules to help users navigate and verify the rules and the black-box model. We evaluate the effectiveness of RuleMatrix via two use cases and a usability study.
Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts
Authors: Po-Ming Law, Yanhong Wu, Rahul C. Basole
Published at VAST, 2018

Getting the overall picture of how a large number of ego-networks evolve is a common yet challenging task. Existing techniques often require analysts to inspect the evolution patterns of ego-networks one after another. In this study, we explore an approach that allows analysts to interactively create spatial layouts in which each dot is a dynamic ego-network. These spatial layouts provide overviews of the evolution patterns of ego-networks, thereby revealing different global patterns such as trends, clusters and outliers in evolution patterns. To let analysts interactively construct interpretable spatial layouts, we propose a data transformation pipeline, with which analysts can adjust the spatial layouts and convert dynamic ego-networks into event sequences to aid interpretations of the spatial positions. Based on this transformation pipeline, we develop Segue, a visual analysis system that supports thorough exploration of the evolution patterns of ego-networks. Through two usage scenarios, we demonstrate how analysts can gain insights into the overall evolution patterns of a large collection of ego-networks by interactively creating different spatial layouts.
Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models
Authors: Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch 0001, Adam Perer, Hanspeter Pfister, Alexander M. Rush
Published at VAST, 2018

Neural sequence-to-sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work with a five-stage blackbox pipeline that begins with encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction and “what if”-style exploration of trained sequence-to-sequence models through each stage of the translation process. The aim is to identify which patterns have been learned, to detect model errors, and to probe the model with counterfactual scenario. We demonstrate the utility of our tool through several real-world sequence-to-sequence use cases on large-scale models.
SIRIUS: Dual, Symmetric, Interactive Dimension Reductions
Authors: Michelle Dowling, John E. Wenskovitch, J. T. Fry, Scotland Leman, Leanna House, Chris North 0001
Published at VAST, 2018

Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights.
Situ: Identifying and Explaining Suspicious Behavior in Networks
Authors: John R. Goodall, Eric D. Ragan, Chad A. Steed, Joel W. Reed, G. David Richardson, Kelly M. T. Huffer, Robert A. Bridges, Jason A. Laska
Published at VAST, 2018

Despite the best efforts of cyber security analysts, networked computing assets are routinely compromised, resulting in the loss of intellectual property, the disclosure of state secrets, and major financial damages. Anomaly detection methods are beneficial for detecting new types of attacks and abnormal network activity, but such algorithms can be difficult to understand and trust. Network operators and cyber analysts need fast and scalable tools to help identify suspicious behavior that bypasses automated security systems, but operators do not want another automated tool with algorithms they do not trust. Experts need tools to augment their own domain expertise and to provide a contextual understanding of suspicious behavior to help them make decisions. In this paper we present Situ, a visual analytics system for discovering suspicious behavior in streaming network data. Situ provides a scalable solution that combines anomaly detection with information visualization. The system's visualizations enable operators to identify and investigate the most anomalous events and IP addresses, and the tool provides context to help operators understand why they are anomalous. Finally, operators need tools that can be integrated into their workflow and with their existing tools. This paper describes the Situ platform and its deployment in an operational network setting. We discuss how operators are currently using the tool in a large organization's security operations center and present the results of expert reviews with professionals.
SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach
Authors: Michael Blumenschein, Michael Behrisch 0001, Stefanie Schmid, Simon Butscher, Deborah Wahl, Karoline Villinger, Britta Renner, Harald Reiterer, Daniel A. Keim
Published at VAST, 2018

We present SMARTEXPLORE, a novel visual analytics technique that simplifies the identification and understanding of clusters, correlations, and complex patterns in high-dimensional data. The analysis is integrated into an interactive table-based visualization that maintains a consistent and familiar representation throughout the analysis. The visualization is tightly coupled with pattern matching, subspace analysis, reordering, and layout algorithms. To increase the analyst's trust in the revealed patterns, SMARTEXPLORE automatically selects and computes statistical measures based on dimension and data properties. While existing approaches to analyzing high-dimensional data (e.g., planar projections and Parallel coordinates) have proven effective, they typically have steep learning curves for non-visualization experts. Our evaluation, based on three expert case studies, confirms that non-visualization experts successfully reveal patterns in high-dimensional data when using SMARTEXPLORE.