Understanding embedding relationships is crucial for neural network interpretability, structural analysis, and data exploration. However, visually comparing embeddings is challenging due to the difficulty of mentally aligning structures across views. In this paper, we address this challenge by constructing multiple hierarchies of data points from different perspectives to facilitate meaningful comparisons. We introduce Parallel Clusters (ParaClus), a visual analytics system that enables multi-scale exploration of embedding structures. This is achieved through cluster formations across embeddings and attributes, along with an adaptive thresholding mechanism that defines neighborhoods. By dynamically adjusting this threshold, users can explore structures at varying scales. Our interface employs a parallel axes design, where clusters derived from the same embedding or attribute are aligned along individual axes. This layout allows users to easily compare neighboring clusters and observe relationships across embeddings. Furthermore, interactive subdivision mechanisms enable users to refine clusters based on their connections to other clusters, providing deeper insights into structural dependencies. Additionally, our system seamlessly integrates labels and scalar attributes into the clustering process, offering a unified approach to analyzing multi-attribute, time-varying, and network-derived embeddings. We evaluate the effectiveness of ParaClus through expert assessments and case studies.

Explorative flow visualization allows domain experts to analyze complex flow structures by interactively investigating flow patterns. However, traditional visual interfaces often rely on specialized graphical representations and interactions, which require additional effort to learn and use. Natural language interaction offers a more intuitive alternative, but teaching machines to recognize diverse scientific concepts and extract corresponding structures from flow data poses a significant challenge. In this paper, we introduce an automated framework that aligns flow pattern representations with the semantic space of large language models (LLMs), eliminating the need for manual labeling. Our approach encodes streamline segments using a denoising autoencoder and maps the generated flow pattern representations to LLM embeddings via a projector layer. This alignment empowers semantic matching between textual embeddings and flow representations through an attention mechanism, enabling the extraction of corresponding flow patterns based on textual descriptions. To enhance accessibility, we develop an interactive interface that allows users to query and visualize flow structures using natural language. Through case studies, we demonstrate the effectiveness of our framework in enabling intuitive and intelligent flow exploration.

Identifying biomarkers from human brain networks is critical in early detection of neurological disorders and understanding disease mechanisms. Existing visual analytics approaches show a remarkable ability to assist experts in discovering and validating biomarkers through exploration. However, these approaches often focus only on the diffusion features of fiber bundles and evaluate individual bundles and regions separately. This may hinder their ability to accurately represent the complex brain network for investigation from various perspectives. In this paper, we present NeuroLens, a visual analytics system that integrates comprehensive information across multiple levels, including fiber bundles, local regions and entire brain networks. Specifically, to model the bundles more precisely, we enhance the features by incorporating the joint distribution of geometric features. The bundle information is further aggregated to form representations at the region and the brain level using an attention-based graph neural network. The region-level representation describes complex structures involving multiple bundles, and the brain-level representation enables comparisons between subjects and groups. The NeuroLens interface enables comparative exploration of this multi-level and multi-faceted information. To verify the findings during exploration, NeuroLens leverages the large language model to query related information from existing literature. We collaborate with domain experts to examine the effectiveness of NeuroLens. Their exploration, findings, and feedback are discussed.

Exploring volumetric data is crucial for interpreting scientific datasets. However, selecting optimal viewpoints for effective navigation can be challenging, particularly for users without extensive domain expertise or familiarity with 3D navigation. In this paper, we propose a novel framework that leverages natural language interaction to enhance volumetric data exploration. Our approach encodes volumetric blocks to capture and differentiate underlying structures. It further incorporates a CLIP Score mechanism, which provides semantic information to the blocks to guide navigation. The navigation is empowered by a reinforcement learning framework that leverage these semantic cues to efficiently search for and identify desired viewpoints that align with the user's intent. The selected viewpoints are evaluated using CLIP Score to ensure that they best reflect the user queries. By automating viewpoint selection, our method improves the efficiency of volumetric data navigation and enhances the interpretability of complex scientific phenomena.

Flow fields are often partitioned into data blocks for massively parallel computation and analysis based on blockwise relationships. However, most of the previous techniques only consider the first-order dependencies among blocks, which is insufficient in describing complex flow patterns. In this work, we present FlowHON, an approach to construct higher-order networks (HONs) from flow fields. FlowHON captures the inherent higher-order dependencies in flow fields as nodes and estimates the transitions among them as edges. We formulate the HON construction as an optimization problem with three linear transformations. The first two layers correspond to the node generation and the third one corresponds to edge estimation. Our formulation allows the node generation and edge estimation to be solved in a unified framework. With FlowHON, the rich set of traditional graph algorithms can be applied without any modification to analyze flow fields, while leveraging the higher-order information to understand the inherent structure and manage flow data for efficiency. We demonstrate the effectiveness of FlowHON using a series of downstream tasks, including estimating the density of particles during tracing, partitioning flow fields for data management, and understanding flow fields using the node-link diagram representation of networks.

The success of deep learning heavily relies on the large amount of training samples. However, in scientific visualization, due to the high computational cost, only few data are available during training, which limits the performance of deep learning. A common technique to address the data sparsity issue is data augmentation. In this paper, we present a comprehensive study on nine data augmentation techniques (i.e., noise injection, interpolation, scale, flip, rotation, variational auto-encoder, generative adversarial network, diffusion model, and implicit neural representation) for understanding their effectiveness on two scientific visualization tasks, i.e., spatial super-resolution and ambient occlusion prediction. We compare the data quality, rendering fidelity, optimization time, and memory consumption of these data augmentation techniques using several scientific datasets with various characteristics. We investigate the effects of data augmentation on the method, quantity, and diversity for these tasks with various deep learning models. Our study shows that increasing the quantity and single-domain diversity of augmented data can boost model performance, while the method and cross-domain diversity of the augmented data do not have the same impact. Based on our findings, we discuss the opportunities and future directions for scientific data augmentation.

Ordering has been extensively studied in many visualization applications, such as axis and matrix reordering, for the simple reason that the order will greatly impact the perceived pattern of data. Many quality metrics concerning data pattern, perception, and aesthetics are proposed, and respective optimization algorithms are developed. However, the optimization problems related to ordering are often difficult to solve (e.g., TSP is NP-complete), and developing specialized optimization algorithms is costly. In this paper, we propose Versatile Ordering Network (VON), which automatically learns the strategy to order given a quality metric. VON uses the quality metric to evaluate its solutions, and leverages reinforcement learning with a greedy rollout baseline to improve itself. This keeps the metric transparent and allows VON to optimize over different metrics. Additionally, VON uses the attention mechanism to collect information across scales and reposition the data points with respect to the current context. This allows VONs to deal with data points following different distributions. We examine the effectiveness of VON under different usage scenarios and metrics. The results demonstrate that VON can produce comparable results to specialized solvers.

Flow visualization is an essential tool for domain experts to understand and analyze flow fields intuitively. In the past decades, various interactive techniques were developed to customize flow visualization for exploration. However, these techniques usually use specifically designed graphical interfaces, requiring considerable learning and usage effort. Recently, FlowNL Huang et al., (2023) introduces a natural language interface to reduce the effort, but it still struggles with natural language ambiguities due to the lack of domain knowledge and provides limited ability to understand the context in dialogues. To address these issues, we propose an explorative flow visualization powered by a large language model that interacts with users. Our approach leverages an extensive dataset of flow-related queries to train the model, enhancing its ability to interpret a wide range of natural language expressions and maintain context over multi-turn interactions. Additionally, we introduce an advanced dialogue management system that supports interactive continuous communication between users and the system. Our empirical evaluations demonstrate significant improvements in user engagement and accuracy of flow structure extraction. These enhancements are crucial for expanding the applicability of flow visualization systems in real-world scenarios, where effective and intuitive user interfaces are paramount.

Neural networks attract significant attention in almost every field due to their widespread applications in various tasks. However, developers often struggle with debugging due to the black-box nature of neural networks. Visual analytics provides an intuitive way for developers to understand the hidden states and underlying complex transformations in neural networks. Existing visual analytics tools for neural networks have been demonstrated to be effective in providing useful hints for debugging certain network architectures. However, these approaches are often architecture-specific with strong assumptions of how the network should be understood. This limits their use when the network architecture or the exploration goal changes. In this paper, we present a general model and a programming toolkit, Neural Network Visualization Builder (NNVisBuilder), for prototyping visual analytics systems to understand neural networks. NNVisBuilder covers the common data transformation and interaction model involved in existing tools for exploring neural networks. It enables developers to customize a visual analytics interface for answering their specific questions about networks. NNVisBuilder is compatible with PyTorch so that developers can integrate the visualization code into their learning code seamlessly. We demonstrate the applicability by reproducing several existing visual analytics systems for networks with NNVisBuilder. The source code and some example cases can be found at https://github.com/sysuvis/NVB.

Ranking data items is a core step of decision making in many scenarios, such as investigating companies in finance and evaluating players in sports. While sorting by a single attribute is often trivial, ranking based on multi-attribute is often fuzzy, meaning that the goals, constraints, and weights of factors are not well-defined. Existing techniques use aggregation or dimension reduction to map the data along a single axis, which destroys the high-dimensional structure of data. In this paper, we aim to reduce the scope of data instead of the dimensionality in ranking tasks. In this way, users make decisions among a few data items based on complete information, instead of ranking many items based on inaccurate information. We introduce the concept of Pareto frontier for partitioning the data into multiple groups. A visual analytic system with two coordinated views is designed for users to rank data in an individual group or compare items in multiple groups. We evaluate the effectiveness of the proposed system through case studies and usability through a user study.

Comprehensively evaluating and comparing researchers’ academic performance is complicated due to the intrinsic complexity of scholarly data. Different scholarly evaluation tasks often require the publication and citation data to be investigated in various manners. In this article, we present an interactive visualization framework, SD$^{2}$, to enable flexible data partition and composition to support various analysis requirements within a single system. SD$^{2}$features the hierarchical histogram, a novel visual representation for flexibly slicing and dicing the data, allowing different aspects of scholarly performance to be studied and compared. We also leverage the state-of-the-art set visualization technique to select individual researchers or combine multiple scholars for comprehensive visual comparison. We conduct multiple rounds of expert evaluation to study the effectiveness and usability of SD$^{2}$and revise the design and system implementation accordingly. The effectiveness of SD$^{2}$is demonstrated via multiple usage scenarios with each aiming to answer a specific, commonly raised question.

Flow visualization is essentially a tool to answer domain experts' questions about flow fields using rendered images. Static flow visualization approaches require domain experts to raise their questions to visualization experts, who develop specific techniques to extract and visualize the flow structures of interest. Interactive visualization approaches allow domain experts to ask the system directly through the visual analytic interface, which provides flexibility to support various tasks. However, in practice, the visual analytic interface may require extra learning effort, which often discourages domain experts and limits its usage in real-world scenarios. In this paper, we propose FlowNL, a novel interactive system with a natural language interface. FlowNL allows users to manipulate the flow visualization system using plain English, which greatly reduces the learning effort. We develop a natural language parser to interpret user intention and translate textual input into a declarative language. We design the declarative language as an intermediate layer between the natural language and the programming language specifically for flow visualization. The declarative language provides selection and composition rules to derive relatively complicated flow structures from primitive objects that encode various kinds of information about scalar fields, flow patterns, regions of interest, connectivities, etc. We demonstrate the effectiveness of FlowNL using multiple usage scenarios and an empirical evaluation.

Exploring flow features and patterns hidden behind the data has received extensive academic attention in flow visualization. In this paper, we introduce an importance-guided surface generation and exploration scheme to explore the features and their connections. The features are expressed as an importance field, which can either be derived from a scalar field or be specified as a flow pattern. Guided by the importance field, we sample a pool of seeding curves along the binormal direction and construct stream surfaces to fit the regions of high- importance values. Our scheme evaluates candidate seeding curves by collecting importance scores from the curve and corresponding streamlines. The candidate seeding curves are refined using the high-score segments to identify the optimal surfaces. Comparative visualization among different kinds of flow features across time steps can be easily derived for flow structure analysis. In order to reduce the visual complexity, we leverage SurfRiver to achieve clearer observation by flattening and aligning the surface. Finally, we apply our surface generation scheme guided by flow patterns and scalar fields to evaluate the effectiveness of the proposed tool.

Visually identifying effective bio-markers from human brain networks poses non-trivial challenges to the field of data visualization and analysis. Existing methods in the literature and neuroscience practice are generally limited to the study of individual connectivity features in the brain (e.g., the strength of neural connection among brain regions). Pairwise comparisons between contrasting subject groups (e.g., the diseased and the healthy controls) are normally performed. The underlying neuroimaging and brain network construction process is assumed to have 100% fidelity. Yet, real-world user requirements on brain network visual comparison lean against these assumptions. In this work, we present MV^2Net, a visual analytics system that tightly integrates multi-variate multi-view visualization for brain network comparison with an interactive wrangling mechanism to deal with data uncertainty. On the analysis side, the system integrates multiple extraction methods on diffusion and geometric connectivity features of brain networks, an anomaly detection algorithm for data quality assessment, single- and multi-connection feature selection methods for bio-marker detection. On the visualization side, novel designs are introduced which optimize network comparisons among contrasting subject groups and related connectivity features. Our design provides level-of-detail comparisons, from juxtaposed and explicit-coding views for subject group comparisons, to high-order composite view for correlation of network comparisons, and to fiber tract detail view for voxel-level comparisons. The proposed techniques are inspired and evaluated in expert studies, as well as through case analyses on diffusion and geometric bio-markers of certain neurology diseases. Results in these experiments demonstrate the effectiveness and superiority of MV^2Net over state-of-the-art approaches.

We present SurfRiver, a new visual transformation approach that flattens stream surfaces in 3D to rivers in 2D for comparative visualization. Leveraging the TextFlow-like visual metaphor, SurfRiver untangles the convoluted individual stream surfaces along the flow direction and maps them along the horizontal direction of the abstract river view. It stacks multiple surfaces along the vertical direction of the river view. This visual mapping makes it easy for users to track along the flow direction and align stream surfaces for comparative study. Through brushing and linking, the river view is connected to the spatial surface view for collective reasoning. SurfRiver can be used to examine a single stream surface, investigate seeding sensitivity or variability of a family of surfaces from a group of related seeding curves, or explore a collection of representative surfaces. We describe our optimization solution to achieve the desirable mapping, present SurfRiver interface and interactions, and report results from different flow fields to demonstrate its efficacy. Feedback from a domain expert also indicates the promise of SurfRiver.

A user's localness (i.e., whether a user is a local resident in a city or not) and a venue's local attractiveness (i.e., the likelihood of a venue to attract local people) are important information for many location-based applications related with Cyber-Physical Systems (CPS), such as participatory sensing, urban planning, traffic control and localized travel recommendations. Previous effort has been devoted to geo-locating users in a city using supervised learning approaches, which depend on the availability of high quality training datasets. However, it is difficult to obtain such training datasets in the real-world CPS applications due to the issue of privacy. In this work, we develop an unsupervised approach, called a Physical-Social-Aware Inference (PSAI) scheme, to jointly infer a user's localness and a venue's local attractiveness by exploring both the physical and social information embedded in the location-based social networks (LBSN). We further implement a parallel PSAI framework on the platform of a Graphic Processing Unit (GPU) to enhance its ability to process large-scale data. Our extensive experiments on the real-world LBSN datasets demonstrate the effectiveness and efficiency of the PSAI scheme compared to the state-of-the-art baselines.

For effective flow visualization, identifying representative flow lines or surfaces is an important problem which has been studied. However, no work can solve the problem for both lines and surfaces. In this paper, we present FlowNet, a single deep learning framework for clustering and selection of streamlines and stream surfaces. Given a collection of streamlines or stream surfaces generated from a flow field data set, our approach converts them into binary volumes and then employs an autoencoder to learn their respective latent feature descriptors. These descriptors are used to reconstruct binary volumes for error estimation and network training. Once converged, the feature descriptors can well represent flow lines or surfaces in the latent space. We perform dimensionality reduction of these feature descriptors and cluster the projection results accordingly. This leads to a visual interface for exploring the collection of flow lines or surfaces via clustering, filtering, and selection of representatives. Intuitive user interactions are provided for visual reasoning of the collection with ease. We validate and explain our deep learning framework from multiple perspectives, demonstrate the effectiveness of FlowNet using several flow field data sets of different characteristics, and compare our approach against state-of-the-art streamline and stream surface selection algorithms.

Successfully detecting, analyzing, and reasoning about collective anomalies is important for many real-life application domains (e.g., intrusion detection, fraud analysis, software security). The primary challenges to achieving this goal include the overwhelming number of low-risk events and their multimodal relationships, the diversity of collective anomalies by various data and anomaly types, and the difficulty in incorporating the domain knowledge of experts. In this paper, we propose the novel concept of the faceted High-Order Correlation Graph (HOCG). Compared with previous, low-order correlation graphs, HOCG achieves better user interactivity, computational scalability, and domain generality through synthesizing heterogeneous types of objects, their anomalies, and the multimodal relationships, all in a single graph. We design elaborate visual metaphors, interaction models, and the coordinated multiple view based interface to allow users to fully unleash the visual analytics power of the HOCG. We conduct case studies for three application domains and collect feedback from domain experts who apply our method to these scenarios. The results demonstrate the effectiveness of the HOCG in the overview of point anomalies, the detection of collective anomalies, and the reasoning process of root cause analyses.

We present a novel visual representation and interface named the matrix of isosurface similarity maps (MISM) for effective exploration of large time-varying multivariate volumetric data sets. MISM synthesizes three types of similarity maps (i.e., self, temporal, and variable similarity maps) to capture the essential relationships among isosurfaces of different variables and time steps. Additionally, it serves as the main visual mapping and navigation tool for examining the vast number of isosurfaces and exploring the underlying time-varying multivariate data set. We present temporal clustering, variable grouping, and interactive filtering to reduce the huge exploration space of MISM. In conjunction with the isovalue and isosurface views, MISM allows users to identify important isosurfaces or isosurface pairs and compare them over space, time, and value range. More importantly, we introduce path recommendation that suggests, animates, and compares traversal paths for effectively exploring MISM under varied criteria and at different levels-of-detail. A silhouette-based method is applied to render multiple surfaces of interest in a visually succinct manner. We demonstrate the effectiveness of our approach with case studies of several time-varying multivariate data sets and an ensemble data set, and evaluate our work with two domain experts.

Detecting, analyzing and reasoning collective anomalies is important for many real-life application domains such as facility monitoring, software analysis and security. The main challenges include the overwhelming number of low-risk events and their multifaceted relationships which form the collective anomaly, the diversity in various data and anomaly types, and the difficulty to incorporate domain knowledge in the anomaly analysis process. In this paper, we propose a novel concept of high-order correlation graph (HOCG). Compared with the previous correlation graph definition, HOCG achieves better user interactivity, computational scalability, and domain generality through synthesizing heterogeneous types of nodes, attributes, and multifaceted relationships in a single graph. We design elaborate visual metaphors, interaction models, and the coordinated multiple view based interface to allow users to fully unleash the visual analytics power over HOCG. We conduct case studies in two real-life application domains, i.e., facility monitoring and software analysis. The results demonstrate the effectiveness of HOCG in the overview of point anomalies, detection of collective anomalies, and reasoning process of root cause analysis.

We present a semi-automatic approach for stream surface generation. Our approach is based on the conjecture that good seeding curves can be inferred from a set of streamlines. Given a set of densely traced streamlines over the flow field, we design a sketch-based interface that allows users to describe their perceived flow patterns through drawing simple strokes directly on top of the streamline visualization results. Based on the 2D stroke, we identify a 3D seeding curve and generate a stream surface that captures the flow pattern of streamlines at the outermost layer. Then, we remove the streamlines whose patterns are covered by the stream surface. Repeating this process, users can peel the flow by replacing the streamlines with customized surfaces layer by layer. Furthermore, we propose an optimization scheme to identify the optimal seeding curve in the neighborhood of an original seeding curve based on surface quality measures. To support interactive optimization, we design a parallel surface quality estimation strategy that estimates the quality of a seeding curve without generating the surface. Our sketch-based interface leverages an intuitive painting metaphor which most users are familiar with. We present results using multiple data sets to show the effectiveness of our approach.

Visual exploration of flow fields is important for studying dynamic systems. We introduce semantic flow graph (SFG), a novel graph representation and interaction framework that enables users to explore the relationships among key objects (i.e., field lines, features, and spatiotemporal regions) of both steady and unsteady flow fields. The objects and their relationships are organized as a heterogeneous graph. We assign each object a set of attributes, based on which a semantic abstraction of the heterogeneous graph is generated. This semantic abstraction is SFG. We design a suite of operations to explore the underlying flow fields based on this graph representation and abstraction mechanism. Users can flexibly reconfigure SFG to examine the relationships among groups of objects at different abstraction levels. Three linked views are developed to display SFG, its node split criteria and history, and the objects in the spatial volume. For simplicity, we introduce SFG construction and exploration for steady flow fields with critical points being the only features. Then we demonstrate that SFG can be naturally extended to deal with unsteady flow fields and multiple types of features. We experiment with multiple data sets and conduct an expert evaluation to demonstrate the effectiveness of our approach.

We present an efficient GPU-based solution to compute isosurface similarity maps for scientific volume data sets. Our approach first replaces exact isosurface extraction with a binary volume indicating whether each voxel intersects the surface or not. We then employ bounding volume hierarchy (BVH)-trees to speed up the distance field computation. Finally, a self-similarity map is generated from which we identify representative isosurfaces. We apply our approach to compute isosurface similarity maps from different volume data sets of varying sizes and characteristics. The results demonstrate significant speed gain with acceptable loss of accuracy, showing the potential of our solution for handling large-scale time-varying multivariate data sets.

Unlike the conventional first-order network (FoN), the higher-order network (HoN) provides a more accurate description of transitions by creating additional nodes to encode higher-order dependencies. However, there exists no visualization and exploration tool for the HoN. For applications such as the development of strategies to control species invasion through global shipping which is known to exhibit higher-order dependencies, the existing FoN visualization tools are limited. In this paper, we present HoNVis, a novel visual analytics framework for exploring higher-order dependencies of the global ocean shipping network. Our framework leverages coordinated multiple views to reveal the network structure at three levels of detail (i.e., the global, local, and individual port levels). Users can quickly identify ports of interest at the global level and specify a port to investigate its higher-order nodes at the individual port level. Investigating a larger-scale impact is enabled through the exploration of HoN at the local level. Using the global ocean shipping network data, we demonstrate the effectiveness of our approach with a real-world use case conducted by domain experts specializing in species invasion. Finally, we discuss the generalizability of this framework to other real-world applications such as information diffusion in social networks and epidemic spreading through air transportation.

Fake reviews have become a pervasive problem in online review systems, wherein fraudulent users manipulate the perception of an object (e.g., a restaurant) by fabricating fake reviews. Extensive work has been devoted to identifying fake reviews via modeling different factors separately, such as user features, object characteristics, and user-object bipartite relations. However, this problem remains challenging due to the fact that more advanced camouflage strategies are utilized by malicious users. In real-world scenarios, spammers may pretend to be normal users by giving fake reviews with the similar score distribution as normal users. To address these issues, we propose to explore the temporal patterns of users' review behavior, because spammers prefer to promote or demote the target businesses in a short period of time. In this work, we present a unified framework Reliable Fake Review Detection (RFRD) that explicitly models temporal patterns of users' review behavior into a probabilistic generative model. Moreover, the RFRD framework models users' underlying review credibility and objects' highly-skewed review distributions. We conduct experiments on two Yelp datasets, demonstrating the effectiveness of the proposed RFRD framework.

Abstract Graphs represent general node‐link diagrams and have long been utilized in scientific visualization for data organization and management. However, using graphs as a visual representation and interface for navigating and exploring scientific data sets has a much shorter history, yet the amount of work along this direction is clearly on the rise in recent years. In this paper, we take a holistic perspective and survey graph‐based representations and techniques for scientific visualization. Specifically, we classify these representations and techniques into four categories, namely partition‐wise, relationship‐wise, structure‐wise and provenance‐wise. We survey related publications in each category, explaining the roles of graphs in related work and highlighting their similarities and differences. At the end, we reexamine these related publications following the graph‐based visualization pipeline. We also point out research trends and remaining challenges in graph‐based representations and techniques for scientific visualization.

Inferring the localness of people is to classify people who are local residents in a city from people who visit the city by analyzing online check-in points that are contributed by online users. This information is critical for the urban planning, user profiling, and localized recommendation systems. Supervised learning approaches have been developed to infer the location of people in a city by assuming the availability of high-quality training datasets with complete geotemporal information. In this article, we develop an unsupervised model to accurately identify local people in a city by using the incomplete online check-in data that are publicly available. In particular, we develop an incomplete geotemporal expectation maximization (IGT-EM) scheme, which incorporates a set of hidden variables to represent the localness of people and a set of estimation parameters to represent the likelihood of venues to attract local and nonlocal people, respectively. Our solution can accurately classify local people from nonlocal nones without requiring any training data. We also implement a parallel IGT-EM algorithm by leveraging the computing power of a graphic processing unit (GPU) that consists of 2,496 cores. In the evaluation, we compare our new approach with the existing solutions through four real-world case studies using data from the New York City, Chicago, Boston, and Washington, DC. The results show that our approach can identify the local people and significantly outperform the compared baselines in estimation accuracy and execution time.

This paper describes a visualization tool AESvisual that helps students learn and instructors teach the AES cipher. The software allows the user to visualize all the major steps of AES encryption and decryption. The demo mode is useful and efficient for classroom presentation and the practice mode provides the user with an environment to practice AES encryption with error checking. AESvisual is quite versatile, providing support for both beginners learning how to encrypt and decrypt, and also for the more advanced users wishing to see all the details, including the GF(28) addition and multiplication operations. Classroom evaluation of the tool was positive.

Measuring the similarity of integral curves is fundamental to many important flow data analysis and visualization tasks such as feature detection, pattern querying, streamline clustering, and hierarchical exploration. In this paper, we introduce FlowString, a novel vocabulary approach that extracts shape invariant features from streamlines and utilizes a string-based method for exploratory streamline analysis and visualization. Our solution first resamples streamlines by considering their local feature scales. We then classify resampled points along streamlines based on the shape similarity around their local neighborhoods. We encode each streamline into a string of well-selected shape characters, from which we construct meaningful words for querying and retrieval. A unique feature of our approach is that it captures intrinsic streamline similarity that is invariant under translation, rotation and scaling. We design an intuitive interface and user interactions to support flexible querying, allowing exact and approximate searches for partial streamline matching. Users can perform queries at either the character level or the word level, and define their own characters or words conveniently for customized search. We demonstrate the effectiveness of FlowString with several flow field data sets of different sizes and characteristics. We also extend FlowString to handle multiple data sets and perform an empirical expert evaluation to confirm the usefulness of this approach.

This paper describes a visualization tool VIGvisual that helps students learn and instructors teach the Vigenère cipher. The software allows the user to visualize both encryption and decryption through a variety of cipher tools. The demo mode is useful and efficient for classroom presentation. The practice mode allows the user to practice encryption and decryption. VIGvisual is quite versatile, providing support for both beginners learning how to encrypt and decrypt, and also for the more advanced users wishing to practice cryptanalysis in the attack mode. Classroom evaluation of the tool was positive.

Measuring the similarity of integral curves is fundamental to many important flow data analysis and visualization tasks such as feature detection, pattern querying, streamline clustering and hierarchical exploration. In this paper, we introduce FlowString, a novel approach that extracts shape invariant features from streamlines and utilizes a string-based method for exploratory streamline analysis and visualization. Our solution first resamples streamlines by considering their local feature scales. We then classify resampled points along streamlines based on the shape similarity around their local neighborhoods. We encode each streamline into a string of well-selected shape characters, from which we construct meaningful words for querying and retrieval. A unique feature of our approach is that it captures intrinsic streamline similarity that is invariant under translation, rotation and scaling. Leveraging the suffix tree, we enable efficient search of streamline patterns with arbitrary lengths with the complexity linear to the size of the respective pattern. We design an intuitive interface and user interactions to support flexible querying, allowing exact and approximate searches for robust partial streamline similarity matching. Users can perform queries at either the character level or the word level, and define their own characters or words conveniently for customized search. We demonstrate the effectiveness of FlowString with several flow field data sets of different sizes and characteristics.

This poster presents a visualization tool SHAvisual for instructors to teach and students to learn the SHA-512 algorithm visually with demo and practice modes. This poster will also discuss some findings of classroom use and student reactions, which are very positive and encouraging.

This paper describes a visualization tool RSAvisual that helps students learn and instructors teach the RSA cipher. This tool permits the user to visualize the steps of the RSA cipher, do encryption and decryption, learn simple factorization algorithms, and perform some elementary attacks. The demo mode of RSAvisual can be used for classroom presentation and self-study. With the practice mode, the user may go through steps in encryption, decryption, the Extended Euclidean algorithm, two simple factorization algorithms and three elementary attacks. The user may compute the output of each operation and check for correctness. This helps students learn the primitive operations and how they are used in the RSA cipher. The opportunity for self-study provides an instructor with greater flexibility in selecting a lecture pace for the detailed materials. Classroom evaluation was positive and very encouraging.

Striking a careful balance among coverage, occlusion, and complexity is a resounding theme in the visual understanding of large and complex three-dimensional flow fields. In this paper, we present a novel deformation framework for focus+context streamline visualization that reduces occlusion and clutter around the focal regions while compacting the context region in a full view. Unlike existing techniques that vary streamline densities, we advocate a different approach that manipulates streamline positions. This is achieved by partitioning the flow field's volume space into blocks and deforming the blocks to guide streamline repositioning. We formulate block expansion and block smoothing into energy terms and solve for a deformed grid that minimizes the objective function under the volume boundary and edge flipping constraints. Leveraging a GPU linear system solver, we demonstrate interactive focus+context visualization with 3D flow field data of various characteristics. Compared to the fisheye focus+context technique, our method can magnify multiple streamlines of focus in different regions simultaneously while minimizing the distortion through optimized deformation. Both automatic and manual feature specifications are provided for flexible focus selection and effective visualization.

We treat streamline selection and viewpoint selection as symmetric problems which are formulated into a unified information-theoretic framework. This is achieved by building two interrelated information channels between a pool of candidate streamlines and a set of sample viewpoints. We define the streamline information to select best streamlines and in a similar manner, define the viewpoint information to select best viewpoints. Furthermore, we propose solutions to streamline clustering and viewpoint partitioning based on the representativeness of streamlines and viewpoints, respectively. Finally, we define a camera path that passes through all selected viewpoints for automatic flow field exploration. We demonstrate the robustness of our approach by showing experimental results with different flow data sets, and conducting rigorous comparisons between our algorithm and other seed placement or streamline selection algorithms based on information theory.

This paper describes a visualization tool ECvisual that helps students understand and instructors teach elliptic curve based ciphers. This tool permits the user to visualize elliptic curves over the real field and over a finite field of prime order, perform arithmetic operations, do encryption and decryption, and convert plaintext to a point on an elliptic curve. The demo mode of ECvisual can be used for classroom presentation and self-study. With the practice mode, the user may go through steps in finite field computations, encryption, decryption and plaintext conversion. The user may compute the output for each operation check each answer for correctness. This helps students understand the primitive operations and how they are used in an elliptic curve cipher. The opportunity for self-study provides an instructor greater flexibility in selecting a lecture pace for this detail-filled material. Classroom evaluation was positive and very encouraging.