This paper presents GraphFederator, a novel approach to construct federated representations of multi-party graphs and supports privacy-preserving visual analysis of graphs. Inspired by the concept of federated learning, we reformulate the analysis of multi-party graphs into a decentralization process. The new federation framework consists of a shared module that is responsible for federated modeling and analysis, and a set of local modules that run on respective graph data. Specifically, we propose a Federated Graph Representation Model (FGRM) that is learned from encrypted characteristics of multi-party graphs in local modules. We also design multiple visualization tools for federated visualization, exploration, and analysis of multi-party graphs. Experimental results on two datasets demonstrate the effectiveness of our approach.

In the design of visual analytics systems, good understanding of user intents can make systems adapt to user needs and help users better complete analytical tasks. However, user intent is difficult to observe directly. Current work tends to focus more on analyzing user behaviors and overlook the potential connections between data. In this paper, we propose an approach to understanding user intents by automatically extracting data features and combining them with user interaction history. We develop a framework for understanding user intents based on graph neural networks to support two high-level tasks: 1) real-time recommendation for the next interaction based on interaction history, and 2) real-time storytelling to characterize user intents. In our framework, we apply an SR-GATNE model based on graph neural networks to real-time recommendations and story generation. We incorporate the framework in a visual analytics system for industry analysis and evaluating the system. Results of evaluation show that our approach can help users complete the tasks better and improve their experience in analytical tasks.

Target selection is one of essential operation made available by interaction techniques in virtual reality (VR) environments. However, effectively positioning or selecting occluded objects is under-investigated in VR, especially in the context of high-density or a high-dimensional data visualization with VR. In this paper, we propose ClockRay, an occluded-object selection technique that can maximize the intrinsic human wrist rotation skills through the integration of emerging ray selection techniques in VR environments. We describe the design space of the ClockRay technique and then evaluate its performance in a series of user studies. Drawing on the experimental results, we discuss the benefits of ClockRay compared to two popular ray selection techniques - RayCursor and RayCasting. Our findings can inform the design of VR-based interactive visualization systems for high-density data.

The visualization of streaming high-dimensional data often needs to consider the speed in dimensionality reduction algorithms, the quality of visualized data patterns, and the stability of view graphs that usually change over time with new data. Existing methods of streaming high-dimensional data visualization primarily line up essential modules in a serial manner and often face challenges in satisfying all these design considerations. In this research, we propose a novel parallel framework for streaming high-dimensional data visualization to achieve high data processing speed, high quality in data patterns, and good stability in visual presentations. This framework arranges all essential modules in parallel to mitigate the delays caused by module waiting in serial setups. In addition, to facilitate the parallel pipeline, we redesign these modules with a parametric non-linear embedding method for new data embedding, an incremental learning method for online embedding function updating, and a hybrid strategy for optimized embedding updating. We also improve the coordination mechanism among these modules. Our experiments show that our method has advantages in embedding speed, quality, and stability over other existing methods to visualize streaming high-dimensional data.

Visual analytic systems usually provide multiple coordinated views (MCVs) to support data analysis and exploration. Coordination in visual graphics plays an important role in facilitating comprehensive analytical tasks, such as data comparison and cognitive inference. However, individual views in MCVs are probably designed for a specific purpose based on a particular type of data, and insufficient consideration of the intricate relationships among views may lead to inconsistency in visual representation and user interaction across different views. To better understand the inconsistency issues in MCVs and their impacts on user behaviors, this paper reports a study on the analysis and classification of visualization inconsistency based on the reviews of interactive visualization designs and visual analytic systems, and the interviews with stakeholders. We find that inconsistencies are prevalent in MCVs and frequently lead to misleading or even incorrect results. We classify the discovered inconsistencies based on a coordination model of MCVs, and develop an empirical guide for systematic and efficient visualization consistency checking in the design, implementation, and evaluation stage.

Abstract Anticipation skill is important for elite racquet sports players. Successful anticipation allows them to predict the actions of the opponent better and take early actions in matches. Existing studies of anticipation behaviors, largely based on the analysis of in‐lab behaviors, failed to capture the characteristics of in‐situ anticipation behaviors in real matches. This research proposes a data‐driven approach for research on anticipation behaviors to gain more accurate and reliable insight into anticipation skills. Collaborating with domain experts in table tennis, we develop a complete solution that includes data collection, the development of a model to evaluate anticipation behaviors, and the design of a visual analytics system called Tac‐Anticipator. Our case study reveals the strengths and weaknesses of top table tennis players' anticipation behaviors. In a word, our work enriches the research methods and guidelines for visual analytics of anticipation behaviors.

Lineup selection is an essential and important task in soccer matches. To win a match, coaches must consider various factors and select appropriate players for a planned formation. Computation-based tools have been proposed to help coaches on this complex task, but they are usually based on over-simplified models on player performances, do not support interactive analysis, and overlook the inputs by coaches. In this article, we propose a method for visual analytics of soccer lineup selection by tackling two challenges: characterizing essential factors involved in generating optimal lineup, and supporting coach-driven visual analytics of lineup selection. We develop a lineup selection model that integrates such important factors, such as spatial regions of player actions and defensive interactions with opponent players. A visualization system, Team-Builder, is developed to help coaches control the process of lineup generation, explanation, and comparison through multiple coordinated views. The usefulness and effectiveness of our system are demonstrated by two case studies on a real-world soccer event dataset.

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization.

Arguably the most representative application of artificial intelligence, autonomous driving systems usually rely on computer vision techniques to detect the situations of the external environment. Object detection underpins the ability of scene understanding in such systems. However, existing object detection algorithms often behave as a black box, so when a model fails, no information is available on When, Where and How the failure happened. In this paper, we propose a visual analytics approach to help model developers interpret the model failures. The system includes the micro- and macro-interpreting modules to address the interpretability problem of object detection in autonomous driving. The micro-interpreting module extracts and visualizes the features of a convolutional neural network (CNN) algorithm with density maps, while the macro-interpreting module provides spatial-temporal information of an autonomous driving vehicle and its environment. With the situation awareness of the spatial, temporal and neural network information, our system facilitates the understanding of the results of object detection algorithms, and helps the model developers better understand, tune and develop the models. We use real-world autonomous driving data to perform case studies by involving domain experts in computer vision and autonomous driving to evaluate our system. The results from our interviews with them show the effectiveness of our approach.

In the design of gesture-based user interfaces, traditional gesture elicitation studies suffer from the legacy bias problem. In this paper, we conducted an exploratory study about the practical effects of frame-based design for gestural interaction with immersive VR shopping applications. In this study, we derived gestures via the traditional guessability and the framed guessability approaches. Experimental results indicated that priming participants with a frame, or a scenario, could significantly reduce the impact of legacy bias, and result in superior gesture vocabulary. However, no evidence was found that the priming technique would generate more gesture types, which may lead to lower agreement scores due to the reduction of legacy bias. Based on our findings, we propose some concrete design guidelines for gesture-based interaction. We highlight the implications of this work for the design of all gesture-based applications.

Autonomous driving technologies often use state-of-the-art artificial intelligence algorithms to understand the relationship between the vehicle and the external environment, to predict the changes of the environment, and then to plan and control the behaviors of the vehicle accordingly. The complexity of such technologies makes it challenging to evaluate the performance of autonomous driving systems and to find ways to improve them. The current approaches to evaluating such autonomous driving systems largely use a single score to indicate the overall performance of a system, but domain experts have difficulties in understanding how individual components or algorithms in an autonomous driving system may contribute to the score. To address this problem, we collaborate with domain experts on autonomous driving algorithms, and propose a visual evaluation method for autonomous driving. Our method considers the data generated in all components during the whole process of autonomous driving, including perception results, planning routes, prediction of obstacles, various controlling parameters, and evaluation of comfort. We develop a visual analytics workflow to integrate an evaluation mathematical model with adjustable parameters, support the evaluation of the system from the level of the overall performance to the level of detailed measures of individual components, and to show both evaluation scores and their contributing factors. Our implemented visual analytics system provides an overview evaluation score at the beginning and shows the animation of the dynamic change of the scores at each period. Experts can interactively explore the specific component at different time periods and identify related factors. With our method, domain experts not only learn about the performance of an autonomous driving system, but also identify and access the problematic parts of each component. Our visual evaluation system can be applied to the autonomous driving simulation system and used for various evaluation cases. The results of using our system in some simulation cases and the feedback from involved domain experts confirm the usefulness and efficiency of our method in helping people gain in-depth insight into autonomous driving systems.

Data-driven scrollytelling has become a prevalent way of visual communication because of its comprehensive delivery of perspectives derived from the data. However, creating an expressive scrollytelling story requires both data and design literacy and is time-consuming. As a result, scrollytelling has been mainly used only by professional journalists to disseminate opinions. In this paper, we present an automatic method to generate expressive scrollytelling visualization, which can present easy-to-understand data facts through a carefully arranged sequence of views. The method first enumerates data facts of a given dataset, and scores and organizes them. The facts are further assembled, sequenced into a story, with reader input taken into consideration. Finally, visual graphs, transitions, and text descriptions are generated to synthesize the scrollytelling visualization. In this way, non-professionals can easily explore and share interesting perspectives from selected data attributes and fact types. We demonstrate the effectiveness and usability of our method through both use cases and an in-lab user study.

The popularity of racket sports (e.g., tennis and table tennis) leads to high demands for data analysis, such as notational analysis, on player performance. While sports videos offer many benefits for such analysis, retrieving accurate information from sports videos could be challenging. In this paper, we propose EventAnchor, a data analysis framework to facilitate interactive annotation of racket sports video with the support of computer vision algorithms. Our approach uses machine learning models in computer vision to help users acquire essential events from videos (e.g., serve, the ball bouncing on the court) and offers users a set of interactive tools for data annotation. An evaluation study on a table tennis annotation system built on this framework shows significant improvement of user performances in simple annotation tasks on objects of interest and complex annotation tasks requiring domain knowledge.

Creating thematic sessions based on accepted papers is important to the success of a conference. Facing a large number of papers from multiple topics, conference organizers need to identify the topics of papers and group them into sessions by considering the constraints on session numbers and paper numbers in individual sessions. In this paper, we present a system using visualization and topic modeling to help the construction of conference sessions. The system provides multiple automatically generated session schemes and allows users to create, evaluate, and manipulate paper sessions with given constraints. A case study based on our system on the VAST papers shows that our method can help users successfully construct coherent conference sessions. In addition to conference session management, our method can be extended to other tasks, such as event and class schedule.

In the cut-in scenario of the ACC system, there is often a lack of harmony between people and cars due to the limitations of sensors and control strategies. Finding and solving the conflict between the driver and the machine is essential to achieve harmonious Human-Machine Cooperation. This research is to understand the conflict between the driver and ACC system in the cut-in scenario based on the previous work of driver trust experiment. The research selected eight drivers for in-depth interview, and the results showed that the biggest conflict between the driver and ACC was that the driver's cognitive and behavioural patterns were significantly different from the ACC system. It is mainly reflected on three aspects: the different definition of the cut-in scenario, the risk perception and the stress of the impending danger, and the perceptual process of cut-in scenario. In order to reduce human-machine conflict, the research proposed three design strategies: (1) Redefine the cut-in scenario based on the driver's cognition. (2) Keep the ACC human-machine interface consistent with the driver's psychological perception. (3) Help drivers cope with dangerous scenario with three levels of warning signals: guidance information, warning information and takeover information.

Gesture elicitation studies, which are a popular technology for collecting requirements and expectations by involving real users in gesture design processes, often suffer from gesture disagreement and legacy bias and may not generate optimal gestures for a target system in practice. This paper reports a research project on user-defined gestures for interacting with immersive VR shopping applications. The main contribution of this work is the proposal of a more practical method for deriving more reliable gestures than traditional gesture elicitation studies. We applied this method to a VR shopping application and obtained empirical evidence for the benefits of deriving two gestures in the a priori stage and selecting the top-two gestures in the a posteriori stage of traditional elicitation studies for each referent. We hope that this research can help lay a theoretical foundation for freehand-gesture-based user interface design and be generalised to all freehand-gesture-based applications.

Accurately understanding a user’s intention is often essential to the success of any interactive system. An information retrieval system, for example, should address the vocabulary problem (Furnas et al., 1987) to accommodate different query terms users may choose. A system that supports natural user interaction (e.g., full-body game and immersive virtual reality) must recognize gestures that are chosen by users for an action. This article reports an experimental study on the gesture choice for tasks in three application domains. We found that the chance for users to produce the same gesture for a given task is below 0.355 on average, and offering a set of gesture candidates can improve the agreement score. We discuss the characteristics of those tasks that exhibit the gesture disagreement problem and those tasks that do not. Based on our findings, we propose some design guidelines for free-hand gesture-based interfaces.

When analyzing a visualized network, users need to explore different sections of the network to gain insight. However, effective exploration of large networks is often a challenge. While various tools are available for users to explore the global and local features of a network, these tools usually require significant interaction activities, such as repetitive navigation actions to follow network nodes and edges. In this paper, we propose a structure-based suggestive exploration approach to support effective exploration of large networks by suggesting appropriate structures upon user request. Encoding nodes with vectorized representations by transforming information of surrounding structures of nodes into a high dimensional space, our approach can identify similar structures within a large network, enable user interaction with multiple similar structures simultaneously, and guide the exploration of unexplored structures. We develop a web-based visual exploration system to incorporate this suggestive exploration approach and compare performances of our approach under different vectorizing methods and networks. We also present the usability and effectiveness of our approach through a controlled user study with two datasets.

In contrast to the extensive studies on static target pointing, much less formal understanding of moving target acquisition can be found in the HCI literature. We designed a set of experiments to identify regularities in 1D unidirectional moving target selection, and found a Ternary-Gaussian model to be descriptive of the endpoint distribution in such tasks. The shape of the distribution as characterized by μ and σ in the Gaussian model were primarily determined by the speed and size of the moving target. The model fits the empirical data well with 0.95 and 0.94 R2 values for μ and σ , respectively. We also demonstrated two extensions of the model, including 1) predicting error rates in moving target selection; and 2) a novel interaction technique to implicitly aid moving target selection. By applying them in a game interface design, we observed good performances in both predicting error rates (e.g., 2.7% mean absolute error) and assisting moving target selection (e.g., 33% or a greater increase in pointing accuracy).

Massive Open Online Courses (MOOCs) often provide online discussion forum tools to facilitate learner interaction and communication. Having massive forum messages posted by learners everyday, MOOC forums are regarded as an important source for understanding learners activities and opinions. However, the high volume and heterogeneity of MOOC forum contents make it challenging to analyze forum data effectively from different perspectives of discussions and to integrate diverse information into a coherent understanding of issues of concern. In this paper, we report a study on the design of a visual analytics tool to facilitate the multifaceted analysis of online discussion forums. This tool, called MessageLens, aims at helping MOOC instructors to gain a better understanding of forum discussions from three facets: discussion topic, learner attitude, and communication among learners. With various visualization tools, instructors can investigate learner activities from different perspectives. We report a case study with real-world MOOC forum data to present the features of MessageLens and a preliminary evaluation study on the benefits and areas of improvement of the system. Our research suggests an approach to analyzing rich communication contents as well as dynamic social interactions among people.

While instrumental ensemble playing can benefit children's music education and collaboration skill development, it requires extensive training on music and instruments, which many school children lack. To help children with limited music training experience instrumental ensemble playing, we created EnseWing, an interactive system that offers such an experience. In this paper, we report the design of the EnseWing experience and a two-month field study. Our results show that EnseWing preserves the music and ensemble skills from traditional instrumental ensemble and provides more collaboration opportunities for children.

Abstract In recent years, deep learning techniques have been applied to the diagnosis of pulmonary nodules. In order to improve the pulmonary nodule diagnostic performance effectively, we propose a novel pulmonary nodule diagnosis method using dual‐modal deep supervised autoencoder based on extreme learning machine for which discriminative features are automatically learnt from the input data. The network is fed with nodule images in pairs obtained from computed tomography and positron emission tomography respectively. For each pair image, the high‐level discriminative features of nodules in computed tomography and positron emission tomography are extracted from stacked supervised autoencoder layers. The outputs of the proposed architecture are combined using an ideal fusion method to get the final classification. In the experiments, 5‐fold cross‐validation method is used to validate the proposed method on 1,600 pulmonary nodule images and our method reaches high‐classification sensitivities of 91.75% at 1.58 false positives per scan. Meanwhile, compared with other deep learning diagnosis methods, our method achieves better discriminative results and is highly suited to be used for pulmonary nodule diagnosis.

Body-based motion gestures have been gaining popularity in designing interactive systems. However, theories and design guidelines on the use of body movement in design have not been fully evaluated. This article investigates human ability to perform discrete target selection tasks using stretching action through two controlled experiments. The experimental results indicate that: (1) the range of the discrete levels of depth that users can easily discriminate with arm stretching is up to 16 with full visual feedback, but is down to 4 without the feedback; (2) dwelling, a gesture with keeping a hand motionless and the cursor within a target area for a certain amount of time, may be the best gesture for confirmation command; (3) full visual feedback can improve the user performance; and (4) arm stretching action can be modeled using Fitts’ law. We also discuss the design potentials for Stretch Widgets based on these results.

Online dating arises as a popular venue for finding romantic partners in recent years. Many online dating sites adopt recommender systems to help their users. However, few of current research provides solutions to cold start problem, i.e., providing recommendations to new users. In this research, we propose a new approach of providing reciprocal online dating recommendations to new users. Specifically, we detect communities from existing users, match new users to these communities, and take advantage of reciprocal activities of those community members to provide recommendations to new users. Using data from a popular U.S. online dating site, experiments show that our approach greatly outperforms existing methods.

Social media data with geotags can be used to track people's movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of people's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns.

To solve the energy limitation problems of wireless sensor networks used in forest fire detection applications, a new algorithm based on a fire probability threshold switch is proposed. The method uses the weighted average method to obtain the weights of each sensor node, and then calculates the fire probability for each node based on a logistic regression to obtain the fire probability threshold, based on this, the numbers of sensor nodes sending data to the sink node are obtained. The proposed algorithm is applied to detection of a wood fire in a simulated situation. The results show that the proposed method reduced the transmission energy used by 34%. These results indicate that the method can reduce the unnecessary energy usage effectively while guaranteeing the reliability of the transmission data.

Wildfires are highly destructive disasters that spread quickly. The use of advanced technology to achieve early warnings of wildfires is essential for the protection of wilderness resources. Nowadays, the method of using wireless sensor networks for wildfire warning has been extensively studied by many researchers. In this paper, we propose and have implemented a multi-sensor data fusion algorithm for wildfire monitoring and warning based on adaptive weighted fusion algorithm (AWFA) and Dempster -- Shafer theory (DST) of evidence. At the same time, we also have put forward some auxiliary algorithms for fire warning, including heterogeneous sensor data homogenization methods, a judgment algorithm for sensor numerical errors, and an evidence conflict solution of Dempster -- Shafer theory of evidence. Experimental results show that this algorithm can ensure the timeliness and accuracy of the wildfire warning, effectively reduce the amount of data transmission of sensor nodes and the whole network, and reduce the energy consumption, thus prolonging the network lifetime.

Global Positioning System (GPS) is currently the most frequently used wayfinding aid for driving. Yet, GPS is designed to act as a driving guide rather than to help users gain spatial knowledge. Accordingly, GPS might be less usable in situations where such knowledge is required or highly desirable. In this study, we experimentally study the influence of GPS display scales (single-scale vs. dual-scale) using simulated driving tasks in a virtual environment. The single-scale GPS is similar to the regular GPS view. The dual-scale GPS aid is a dual-scale navigation tool that provides two levels of detail, including both detailed and contextual information. The results demonstrate that the dual-scale GPS was more efficient in leading the participants to the destination during the simulated driving and was more useful for the participants to establish spatial awareness and a cognitive map; the dual-scale GPS participants also reported higher subjective evaluations. The proposed dual-scale GPS design and experimental results show some indications for designing new wayfinding aids aimed at increasing wayfinding performance while simultaneously helping users construct a cognitive map.

In China, hundreds of millions of migrant workers have moved to cities or coastal regions for more or better-paid jobs and have left their children behind at their rural homes. Separated by thousands of kilometers, these "left-behind" children and their migrant parents use mobile phones as their primary - and often only - method of maintaining family connections. To better understand the use of technology in this long-distance communication, we conducted a multi-phased study using interviews and surveys in three different Chinese rural areas. In this paper, we report our findings on how these children communicate with their migrant parents and what information they exchange. We also discuss design implications derived from these findings that may improve communication between left-behind children and their parents.

Global Positioning System (GPS) is currently the most often used wayfinding aid for driving. Yet GPS is originally designed to provide a driving guide rather than to help users gain spatial knowledge. Accordingly, GPS might be less usable in situations where spatial knowledge is required. This study experimentally compared two wayfinding aids using simulated driving tasks in a virtual environment: a simulated GPS and a dual-scale exploration aid (DSEA). The DSEA, which provides two levels of details—both detailed and contextual information—was proposed to support participants in finding and selecting routes by themselves. The results show that although DSEA was less helpful in leading participants to their destination and corresponded to more turning errors in simulated driving, it was more useful for the corresponding participants to establish spatial awareness and a cognitive map. The influence of participants' spatial ability test score on wayfinding performance was measured and discussed. The proposed DSEA design and experimental results show some indications for designing new wayfinding aids aimed at reducing wayfinding errors and constructing cognitive maps while still providing easy navigation.

Gesture inputs on multi-touch tabletops usually involve multiple fingers (more than two) and casual touchdowns or liftoffs of fingers. This flexibility of touch gestures allows more natural user interaction, but also poses new challenges for accurate recognition of multi-touch gestures. To address these challenges, we propose a new approach to recognize flexible multi-touch stroke gestures on tabletops. Based on a user study on multi-touch unistroke gestures, we develop a gesture recognition method by extracting key strokes embedded in flexible multi-touch input. Our evaluation study result shows that this method can greatly improve the recognition accuracy of flexible multi-touch unistroke gestures on tabletops.

With the fast economic growth and urbanization of many developing countries come concerns that their children now have fewer opportunities to express creativity and develop collaboration skills, or to experience their local cultural heritage. We propose to address these concerns by creating technologies inspired by traditional arts, and allowing children to create and collaborate through playing with them. ShadowStory is our first attempt in this direction, a digital storytelling system inspired by traditional Chinese shadow puppetry. We present the design and implementation of ShadowStory and a 7-day field trial in a primary school. Findings illustrated that ShadowStory promoted creativity, collaboration, and intimacy with traditional culture among children, as well as interleaved children's digital and physical playing experience.

Comprehensive yet personalized information for a location is usually desired by mobile users in situ. Traditional navigation systems provide complete static information, such as address, contact, even photos and reviews for a certain place. However, such information does not reflect the real time situation (e.g. popularity/crowdness). Location-based social networks provide opportunity to build social dynamics between the place and potential visitors. In this work, we propose a design by leveraging public online information with users' social network resources to provide real time exploration in novel environments. A mobile application is implemented using Wikipedia, Panoramio, and Foursquare data to provide complete, updated, and trustworthy information. Design highlights and implementation are reported.

We introduce the graph vertex similarity measure, Relation Strength Similarity (RSS), that utilizes a network's topology to discover and capture similar vertices. The RSS has the advantage that it is asymmetric; can be used in a weighted network; and has an adjustable "discovery range" parameter that enables exploration of friend of friend connections in a social network. To evaluate RSS we perform experiments on a coauthorship network from the CiteSeerX database. Our method significantly outperforms other vertex similarity measures in terms of the ability to predict future coauthoring behavior among authors in the CiteSeerX database for the near future 0 to 4 years out and reasonably so for 4 to 6 years out.

Friend recommendation is popular in social network services to help people make new friends and expand their networks. Friend recommendation is either based on topological structures of a social network, or derived from profile information of users. However, dynamically recommending friends by considering both social connections and a context of social connections (e.g., similar interest) in a way of visual exploration is not well supported by existing tools. In this paper, we propose a novel visual system, SFViz (Social Friends Visualization), to support users to explore and find friends interactively under a context of interest. Our approach leverages both semantic structure of activity data and topological structures in social networks. In SFViz, a hierarchical structure of social tags is generated to help users navigate through a network of interest. Multiscale and cross-scale aggregations of similarity among people are presented in the hierarchy to support users to seek potential friends. We report a case study using SFViz to explore the recommended friends based on people's tagging behaviors in a music community, Last.fm. The results indicate that our system can enhance users' awareness of their social networks under different interest contexts, and help users seek potential friends sharing similar interests in an interactive way.

Recently, pen tilting has been explored in pen-based user interfaces and has shown potential to improve user interaction in various tasks (e.g., menu selection, modeless object manipulation). However, some basic questions concerning pen tilting behaviors, such as the ideal range, azimuth size, and direction of pen tilting, have not been thoroughly investigated. In this paper, we report our empirical studies on user performances in basic pen tilting tasks. First, we conducted a baseline study, which helps us to determine tilting directions, tilting ranges, and the thresholds that separate incidental pen tilting actions from intentional actions used for interaction. Based on the results from the baseline study, we designed an experiment to investigate user performances in goal tilting in different tilting ranges, azimuth sizes, and directions. Drawing on the results of our data analyses on task completion time, error rate, and pen tip movements, we discussed values of tilting parameters like titling range, minimal azimuth size, and tilting direction.

Establishing and maintaining communication between decision makers, professional responders and general public is vital in many emergency planning and rescue situations. In this paper we present our research on using first-hand information collected by mobile users to increase the flexibility and richness in collaborative emergency management. We propose our design to support direct positioning, quick assessment, and rich communication by leveraging mobile uploaded geo-tagged photos as shared media.

Abstract Online Social Networks (OSNs) facilitate the creation and maintenance of interpersonal online relationships. Unfortunately, the availability of personal data on social networks may unwittingly expose users to numerous privacy risks. As a result, establishing effective methods to control personal data and maintain privacy within these OSNs have become increasingly important. This research extends the current access control mechanisms employed by OSNs to protect private information shared among users of OSNs. The proposed approach presents a system of collaborative content management that relies on an extended notion of a “content stakeholder.” A tool, Collaborative Privacy Management (CoPE), is implemented as an application within a popular social‐networking site, facebook.com , to ensure the protection of shared images generated by users. We present a user study of our CoPE tool through a survey‐based study (n=80). The results demonstrate that regardless of whether Facebook users are worried about their privacy, they like the idea of collaborative privacy management and believe that a tool such as CoPE would be useful to manage their personal information shared within a social network.

Concept maps are an important tool to organize, represent, and share knowledge. Building a concept map involves creating text-based concepts and specifying their relationships with line-based links. Current concept map tools usually impose specific task structures for text and link construction, and may increase cognitive burden to generate and interact with concept maps. While pen-based devices (e.g., tablet PCs) offer users more freedom in drawing concept maps with a pen or stylus more naturally, the support for hand-drawn concept map creation and manipulation is still limited, largely due to the lack of methods to recognize the components and structures of hand-drawn concept maps. This article proposes a method to understand hand-drawn concept maps. Our algorithm can extract node blocks, or concept blocks, and link blocks of a hand-drawn concept map by combining dynamic programming and graph partitioning, recognize the text content of each concept node, and build a concept-map structure by relating concepts and links. We also design an algorithm for concept map retrieval based on hand-drawn queries. With our algorithms, we introduce structure-based intelligent manipulation techniques and ink-based retrieval techniques to support the management and modification of hand-drawn concept maps. Results from our evaluation study show high structure recognition accuracy in real time of our method, and good usability of intelligent manipulation and retrieval techniques.

Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns.

Computer-based geometry systems have been widely used for teaching and learning, but largely based on mouse-and-keyboard interaction, these systems usually require users to draw figures by following strict task structures defined by menus, buttons, and mouse and keyboard actions. Pen-based designs offer a more natural way to develop geometry theorem proofs with hand-drawn figures and scripts. This paper describes a pen-based geometry theorem proving system that can effectively recognize hand-drawn figures and hand-written proof scripts, and accurately establish the correspondence between geometric components and proof steps. Our system provides dynamic and intelligent visual assistance to help users understand the process of proving and allows users to manipulate geometric components and proof scripts based on structures rather than strokes. The results from evaluation study show that our system is well perceived and users have high satisfaction with the accuracy of sketch recognition, the effectiveness of visual hints, and the efficiency of structure-based manipulation.

Social tags reflect personal and shared vocabulary, and provide opportunities for people to organize and search information. However, tags are usually not structured. To find relevant tags and associated documents, people often need to invest significant amount of cognitive resources to make sense of the relationships among tags. To help the sensemaking of social tags and exploration of knowledge structure of them, we propose an approach of tag networks, TagNet, in which tags are linked by their corresponding documents and a multiscale tag hierarchy are derived with network clustering and aggregation techniques. We also present TagNetLens, an interactive tool that allows users to explore a tag network and its tag hierarchy. We report a case study of TagNet and TagNetLens based on social tags and documents from CiteULike. The results indicate that our TagNet approach can provide users with knowledge structures that are similar to cognitive structures of concepts in people's minds, and TagNetLens can help people to better explore the space of social tags and may have potentials to facilitate the understanding of the knowledge structure in social tags.

Increasing mobility for modern life requires people to access information and make decisions "on the go". Mobil applications place special constraints that challenge the existing ways of visual analytic design where relatively stable and interaction-rich contexts are assumed. In tasks like learning unfamiliar places, current visualizations on mobile devices are not capable of helping users to form a good cognitive map of the environment. How can we take advantage of rich physical environmental cues and supplement with computational power to help people make sense of the environment? In this paper, we present a framework of sensemaking to model how people get insight into the environment as they navigate around. Based on this framework, we present design guidelines for mobile visual analytics that could enhance the spatial awareness of the navigator and analyze several existing designs through the discussion.

To improve the search results for socially-connect users, we propose a ranking framework, Social Network Document Rank (SNDocRank). This framework considers both document contents and the similarity between a searcher and document owners in a social network and uses a Multi-level Actor Similarity (MAS) algorithm to efficiently calculate user similarity in a social network. Our experiment results based on YouTube data show that compared with the tf-idf algorithm, the SNDocRank method returns more relevant documents of interest. Our findings suggest that in this framework, a searcher can improve search by joining larger social networks, having more friends, and connecting larger local communities in a social network.

Emergency management and planning often involves multiple domain experts with diverse knowledge backgrounds and responsibilities. Current practices in emergency management and planning have not leveraged the state-of-art technologies in information sharing, synthesis, and analysis. The proposed research will investigate the process of collaborative sensemaking in emergency planning and implement a map-based online system to support this process.

Teams of specialized experts, such as emergency management planning teams, while making decisions need to efficiently pool domain-specific knowledge, synthesize relevant information, and keep track of collaborators activities at a low interaction cost. This requires tools that allow monitoring both low-level information (e.g., individual actions and external events) and higher-order activities (e.g., how members contribute to groupwork). This paper presents design of CIVIL, a system prototype developed to support map-based decision-making. We report our empirical evaluation of the effects of visualizations on the decision process and the final product.

Concept maps are an important tool to knowledge organization, representation, and sharing. Most current concept map tools do not provide full support for hand-drawn concept map creation and manipulation, largely due to the lack of methods to recognize hand-drawn concept maps. This paper proposes a structure recognition method. Our algorithm can extract node blocks and link blocks of a hand-drawn concept map by combining dynamic programming and graph partitioning and then build a concept-map structure by relating extracted nodes and links. We also introduce structure-based intelligent manipulation technique of hand-drawn concept maps. Evaluation shows that our method has high structure recognition accuracy in real time, and the intelligent manipulation technique is efficient and effective.

It is difficult for a navigator to find directions to a given target in an unfamiliar environment, especially a virtual environment. The commonly used overview maps can show survey knowledge only on one particular scale but cannot provide spatial knowledge at other scales. In this study, three wayfinding aids (a view-in-view map, animation guide, and human system collaboration) were compared experimentally in terms of effectiveness, efficiency, and users' satisfaction. Results show that although these three aids all can effectively help participants find targets quicker and easier, their usefulness is different, with the view-in-view map being the best and human system collaboration the worst. Their usefulness also appears to be different for people with different spatial abilities. The results indicate that the design of wayfinding tools in virtual environments should consider the type and the presentation style of spatial information based on wayfinding tasks and users' spatial ability.

We present a new technique called 'Tilt Menu' for better extending selection capabilities of pen-based interfaces. The Tilt Menu is implemented by using 3D orientation information of pen devices while performing selection tasks. The Tilt Menu has the potential to aid traditional one-handed techniques as it simultaneously generates the secondary input (e.g., a command or parameter selection) while drawing/interacting with a pen tip without having to use the second hand or another device. We conduct two experiments to explore the performance of the Tilt Menu. In the first experiment, we analyze the effect of parameters of the Tilt Menu, such as the menu size and orientation of the item, on its usability. Results of the first experiment suggest some design guidelines for the Tilt Menu. In the second experiment, the Tilt Menu is compared to two types of techniques while performing connect-the-dot tasks using freeform drawing mechanism. Results of the second experiment show that the Tilt Menu perform better in comparison to the Tool Palette, and is as good as the Toolglass.

Making sense of research literature is a complicated process that involves various information seeking and compre-hension tasks. The lack of support for sensemaking in existing systems presents important design challenges and opportunities. This research proposes the design of an integral environment to support literature search, selection, organization and comprehension. Our system prototype, CiteSense, offers lightweight interaction tools and a smooth transition among various information activities. This research deepens our understanding of the design of systems that support the sensemaking of research literature.

The proliferation of GPS units in our life has greatly changed how people find their ways in the real world. However, overreliance on step-by-step route directions from these automated navigation systems may result in less consciously building spatial knowledge of the environment, which is critical to wayfinding. Also, GPS devices may divert people's attention from objects in the physical world to virtual representations on screen, and make people less engaged with the real environment. Consequently, when GPS device are out of access, malfunction, or simply give wrong directions, people may not be well prepared to react to unexpected environmental conditions and find alternative action plans. Such mental unreadiness may cause safety problems in emergency situations. In this paper, we discuss the importance of building spatial awareness in wayfinding activities, analyze GPS features and constraints, and propose the context-awareness design principles that may supplement the efficiency of GPS as well as keep people actively engaged in exploration.