Robots interacting with humans pose data privacy risks, potentially leading to uncontrollable leaks of personal information. Robots interact with older adults in public places, homes, care facilities, and hospitals. Older adults may have concerns about privacy issues related to robots and would benefit from developing their robot literacy skills regarding data privacy. Research on older adults’ robot literacy in relation to data privacy is scarce. We conducted a qualitative and explorative human-centered design study with care home residents (N=9) to explore their perceptions of and interest in robot data privacy literacy. In the study, they interacted with an early prototype of a robot-assisted learning application implemented on the social robot QTrobot. Participants were concerned about the "superpowers" and data storage of robots. Based on our findings and existing literature, we redesigned the prototype into "Myth Buster," a robot-assisted learning application aimed at enhancing older adults’ data privacy literacy regarding robots. Our work contributes to the understanding of older adults’ data privacy literacy, which is currently under-researched in Human-Robot Interaction. We also present design-relevant insights for developing robot-assisted data privacy learning applications to enhance robot literacy of older adults.

There are situations where we need to understand a person's health register data, get an overview of it and inspect the details of interest, preferably in a short period of time. Typical situations are a doctor seeing a patient for the very first time, or a researcher trying to understand what makes a person an outlier in a patient cohort. We have developed a visualization tool, called LifeTrack, that will show a person's whole health register history in a single view. The structure of the LifeTrack data view is based on concepts familiar to the target audience, and allows one to see both the overview and details of the data.

Whenever a diagnosis is given, a procedure is performed, or a drug is prescribed, it leads to an entry into an electronic health record (EHR) system. Previously, this data was difficult to utilize because of rules regarding confidentiality, but new security approaches and pseudonymization have enabled us to work with this data. Health-related data is voluminous and complex, and it can be difficult to abstract a meaningful overview. One of the complexities is its longitudinality. Often medical research is cross-sectional - we often take a point in time for analysis, when instead, it might be more beneficial to see the trajectory that led to the point in time. We are currently developing a trajectory visualization tool for longitudinal electronic health data. It is a web-based tool that interfaces with the OHDSI data infrastructure and visualizes the cohorts and concept sets (groups of medical codes) defined via the OHDSI Atlas GUI. Currently, our tool is in user testing and it will be deployed to a wider user group during the spring. The user feedback has been positive. Users find the tool especially useful in understanding and debugging their OHDSI Atlas cohort definitions.

We present an investigation of sharing the focus of visual attention between two players in a collaborative game, so that where one player was looking was visible to the other. The difference between using head-gaze and eye-gaze to estimate the point of regard was studied, the motive being that recording head-gaze is easier and cheaper than eye-gaze. Two experiments are reported, the first investigates the effect of a high immersion presentation of the game in VR Head Mounted Display compared with a lower immersion desktop presentation. The second examines the high immersion condition in more detail. The studies show that in spite of there being many factors that could affect the outcome of a relatively short period of game play, sharing eye-gaze in the high immersion condition produces shorter overall durations and better subjective ratings of team work than does sharing head-gaze. This difference is not apparent in the low immersion condition. The findings are a good argument for exploiting the opportunities for including and using eye tracking within head mounted displays in the context of collaborative games.

Visualization of proportions is a very common need and there are many techniques for it. The pie chart is popular among practitioners and general audience, but many prominent experts advise against using it. This paper reports an experiment where the pie chart is compared to stacked bar charts with a baseline condition of table. The aim is to study if the performance differences justify the bad reputation of pie charts. In the experiment the participants were requested to list out the elements of a visualization in descending order of size. The task time and the answer were recorded. The results show that the pie chart is slower and less accurate than the stacked bar chart, especially when the difference between the elements is small, but the participants find it slightly more pleasant to use. The participants also perceive the stacked bar chart as the most effective visualization.

Collaboration improves efficiency, avoids duplication of efforts, improves goal-awareness, and makes working generally more pleasurable. While collaboration is desirable, it introduces additional costs because of the required coordination. In this article, we study how visual search is affected by gaze-sharing collaboration. There is evidence that pairs of visual searchers using gaze-only sharing are more efficient than single searchers. We extend this result by investigating if groups of three searchers are more efficient, and if and how the groups of searchers develop their search strategy. We conducted an experiment to understand how the collaboration develops when groups of one to three participants perform a visual search task by collaborating with shared gaze. The task was to state if the target was present among distractors. Our results show that users are able to develop an efficient search and division-of-labor strategy when the only collaboration method is gaze-sharing.

To use eye trackers in a school classroom, children need to be able to calibrate their own tracker unsupervised and on repeated occasions. A game designed specifically around the need to maintain their gaze in fixed locations was used to collect calibration and verification data. The data quality obtained was compared with a standard calibration procedure and another game, in two studies carried out in three elementary schools. One studied the effect on data quality over repeated occasions and the other studied the effect of age on data quality. The first showed that accuracy obtained from unsupervised calibration by children was twice as good after six occasions with the game requiring the fixed gaze location compared with the standard calibration, and as good as standard calibration by group of supervised adults. In the second study, age was found to have no effect on performance in the groups of children studied.

Principal Component Analysis (PCA) is an established and efficient method for finding structure in a multidimensional data set. PCA is based on orthogonal transformations that convert a set of multidimensional values into linearly uncorrelated variables called principal components.The main disadvantage to the PCA approach is that the procedure and outcome are often difficult to understand. The connection between input and output can be puzzling, a small change in input can yield a completely different output, and the user may often wonder if the PCA is doing the right thing.We introduce a user interface that makes the procedure and result easier to understand. We have implemented an interactive PCA view in our text visualization tool called Text Variation Explorer. It allows the user to interactively study the result of PCA, and provides a better understanding of the process.We believe that although we are addressing the problem of interactive principal component analysis in the context of text visualization, these ideas should be useful in other contexts as well.

Digitalization is changing how research is carried out in all areas of science. Humanities is no exception - materials that used to be hand-written or printed on paper are increasingly available in digital form. This development is changing how scholars are interacting with their material. We are addressing the problem of interactive text visualization in the context of sociolinguistic language study. When a scholar is reading and analyzing text from a computer screen instead of a paper, we can support this by providing a dashboard for reading, and by creating visualizations of the text structure, variation, and change. We have designed and developed a software tool called Text Variation Explorer (TVE) for sociolinguistic language study. It is based on interactive visualization with a direct manipulation user interface, and aimed for exploratory corpus linguistics. The TVE software tool has proven to be useful in supporting the study of language variation and change in its social contexts, or sociolinguistics. It is, to a certain degree, language-independent, and generic enough to be useful in other linguistic contexts as well. We are now in the process of designing and implementing the next iteration of TVE. We present the lessons learned from the first version, discuss the old and the new design, and welcome feedback from the communities involved.

In this preliminary research we examine the suitability of hierarchical strategies of multi-class support vector machines for classification of induced pluripotent stem cell (iPSC) colony images. The iPSC technology gives incredible possibilities for safe and patient specific drug therapy without any ethical problems. However, growing of iPSCs is a sensitive process and abnormalities may occur during the growing process. These abnormalities need to be recognized and the problem returns to image classification. We have a collection of 80 iPSC colony images where each one of the images is prelabeled by an expert to class bad, good or semigood. We use intensity histograms as features for classification and we evaluate histograms from the whole image and the colony area only having two datasets. We perform two feature reduction procedures for both datasets. In classification we examine how different hierarchical constructions effect the classification. We perform thorough evaluation and the best accuracy was around 54% obtained with the linear kernel function. Between different hierarchical structures, in many cases there are no significant changes in results. As a result, intensity histograms are a good baseline for the classification of iPSC colony images but more sophisticated feature extraction and reduction methods together with other classification methods need to be researched in future.

We often encounter experimental results that are difficult to quantify although the visual recognition of a phenomenon is apparent. We are able to detect patterns and features with our vision that are complex to specify algorithmically. This is because our brain has evolved into a powerful pattern matching machine. One important class of measurement results in medical research are signals that describe a cycling pattern of some phenomenon. In this work, we developed a software tool, Anomaly Explorer, to assist in classification of Ca2+signals emitted by cardiomyocytes. Anomaly Explorer was designed and implemented to analyze and classify Ca2+signals with criteria similar to the criteria used by laboratory personnel. The software tool is based on interactive visualization with a direct manipulation user interface. Our Anomaly Explorer has proven to be a valuable tool as it makes the analysis faster, more accurate, and person-independent. The definition of signal anomalies in terms of visual properties is more flexible than numerical or analytical specification. Our approach is not limited to the Ca2+signal analysis. The anomaly types are domain-specific, but the interaction and visualization ideas can be applied in any signal analysis problem.

In curriculum visualization, information visualization techniques are used to communicate the structure and content of a curriculum to the stakeholders, such as students, lecturers, and administrators. Curriculum visualization has recently drawn a lot of interest as there are growing demands to form larger educational units and to find savings by eliminating overlap. We have developed a novel approach to analyse and visualize the contents of a curriculum. Our software tool for curriculum visualization uses a fast heuristic to automatically lay out a curriculum diagram, provides tools to process the content information, and offers several coordinated views to visualize the curriculum contents and overlap. The approach and software tool have already been used successfully in one survey and two others are underway.

A well-designed visualization can make the exploration of music collections an intuitive and entertaining experience. In this paper, four different methods (colors, icons, fonts and avatars) for visualizing musical genres are presented and compared against each other. The performance of the different visualizations was studied in a series of online surveys with mostly Finnish participants, and the findings were then utilized in designing novel GUIs for an existing music recommendation system. Based on the results, the best performance can be achieved by combining different visualization methods together; while colors alone were not a good method for visualizing musical genres, the best performing method (avatars) was partially based on the findings of color and icon questionnaires. The easiest genre to visualize was metal; it performed best in both online questionnaires and practical prototype design.

Accessing personal and online music libraries with thousands of songs has become an everyday activity. Instead of textual lists, the libraries can also be accessed using graphical visualizations such as adaptive avatars. To develop the idea further, we designed 17 stereotypical avatars representing different musical genres. To study how well the avatars were recognized, an online questionnaire with 71 participants was arranged. This paper discusses the design of the avatars in more detail, explains which musical genres were selected for the study and why, and presents the results of the questionnaire.

Abstract Emotion‐ and attention‐related subjective and physiological responses to virtual proximity and facial expressions of embodied computer agents (ECA) were studied. Thirty participants viewed female and male characters with a neutral, unpleasant, or pleasant facial expression. Agents' size was used to simulate three levels of proximity. Participants' electrical facial muscle and heart activity were registered, and subjective ratings of emotional and attentional experiences collected. Unpleasant and large (i.e., closer) agents were more alerting (i.e., unpleasant, arousing, and dominating) and attracted more stimulus‐driven attention than neutral, pleasant, and smaller (i.e., further away) agents. Pleasant agents attracted more voluntary attention than neutral and unpleasant agents. Heart rate (HR) responded to agent proximity, while the valence of the agent affected electrical facial muscle activity. Thus, the imitation of human social emotional cues in embodied computer agents (ECAs) could be used to regulate human–computer interaction. Copyright © 2010 John Wiley & Sons, Ltd.

Mobile Web search engines represent retrieved results using primarily text based result surrogates. Users could benefit from information visualization techniques that provide more information about the results as basis of relevance judgments. We present a query-biased visualization approach that incorporates query occurrence visualization into the conventional mobile Web search interface. Textual and graphical versions of the design were evaluated in a study with 18 users. Eleven of the participants preferred either one of the visualizations to the traditional search interface. The results suggest that the key benefit of the proposed visualization is its unobtrusiveness. It does not distract from normal use yet when necessary it can be used to help with relevance assessment, for example when the search engine fails to provide good result descriptors.However, the utility of the proposed query occurrence visualization is dependent on how well it matches the user's mental model and existing search strategies.

Parallel coordinates is a visualization technique that provides an unbiased representation of high-dimensional data. The parallel configuration of axes treats data dimensions uniformly and is well suited for exploratory visualization. However, first-time users of parallel coordinate visualizations can find the representation confusing and difficult to understand.We used eye tracking to study how parallel coordinate visualizations are perceived, and compared the results to the optimal visual scan path required to complete the tasks. The results indicate that even first-time users quickly learn how to use parallel coordinate visualizations, pay attention to the correct task-specific areas in the visualization, and become rapidly proficient with it.

The aim in this work was to develop a method to visualize log files produced by a pilot study of an experimental e-learning platform. The e-learning system running on a web server generated detailed descriptions of its usage that were analyzed. The log files were produced to allow the project's researchers to study offline how the system was utilized. We used open-source software tools to create a flexible system that processed a large amount of log data into concise visual representations. The visualizations revealed a set of different interaction patterns during the pilot study and showed that each teacher had a large effect on students' interaction with the system. Thus, the results showed that the suggested visualization method can be used to support the evaluation and training of students' proficiency in a learning environment.

Parallel coordinate visualizations have a reputation of being difficult to understand, expert-only representations. We argue that this reputation may be partially unfounded, because many of the parallel coordinate browser implementations lack essential features. This paper presents a survey of current interaction techniques for parallel coordinate browsers and compares them to the visualization design guidelines in the literature. In addition, we report our experiences with parallel coordinate browser prototypes, and describe an experiment where we studied the immediate usability of parallel coordinate visualizations. In the experiment, 16 database professionals performed a set of tasks both with the SQL query language and a parallel coordinate browser. The results show that although the subjects had doubts about the general usefulness of the parallel coordinate technique, they could perform the tasks more efficiently with a parallel coordinate browser than with their familiar query language interface.

Interactive visualization of multidimensional data is an important activity in many information acquisition tasks. We introduce a new interaction method for glyphs, an adoption of the 'small multiples' presentation method. It has been used extensively in printed graphics and also in scientific visualization, but its applications in information visualization have been few. We propose a method for interacting with small multiple displays that allows rapid and easy experimentation with glyph property assignments. In addition, we present findings on the relation between the glyphs and the data. The results of our experiment with 18 subjects support the idea that a data-related glyph is more favored by the users and about 13% more accurate than an unrelated glyph in information acquisition tasks. Additionally, the proposed visualization technique may prove to be efficient enough for non-experts to explore multi-dimensional data sets.

One of the first questions we pose when we have a new data set to explore is: are there any obvious groupings in the data? These clusters reveal the structure of the data set and make the data easier to understand. Cluster analysis is a classic problem and there are many tools for it. The problem is that these methods often give conflicting and too fine-grained results for practical use. As well, they are accompanied with the danger of over-interpreting the data. Another drawback in these techniques is that they do not usually provide an opportunity for interactive exploration. We propose a simple user interface and method to interactively explore clusters in data sets. The method is based on a rapid dynamic query and it can detect both hierarchical and partitional clusters.

This paper introduces two novel techniques to manipulate parallel coordinates. Both techniques are dynamic in nature as they encourage one to experiment and discover new information through interacting with a data set. The first technique, polyline averaging, makes it possible to dynamically summarise a set of polylines and can hence replace computationally much more demanding methods, such as hierarchical clustering. The other new technique interactively visualises correlation coefficients between polyline subsets, helping the user to discover new information in the data set. Both techniques are implemented in a Java-based parallel coordinate browser. In conclusion, examples of the use of these techniques in visual data mining are also explored.

The Reorderable Matrix is a simple visualization method for quantitative tabular data. The paper examines how first-time users interact with the Reorderable Matrix and how well they perform a simple task of finding correlating attributes. Visualizing a set of data is a common task in various activities such as decision making or opinion forming. Typical situations are a person making business related decisions, a doctor examining test results of a patient or an engineer making choices between different constructs. All these situations involve examining complex data interactions in a limited time. In this experiment the participants were interacting with the Reorderable Matrix for the first time and tried to find correlating attributes from an unfamiliar set of data.

We consider the problem of discovering the functional and inclusion dependencies that a given database instance satisfies. This technique is used in a database design tool that uses example databases to give feedback to the designer. If the examples show deficiencies in the design, the designer can directly modify the examples. the tool then infers new dependencies and the database schema can be modified, if necessary. the discovery of the functional and inclusion dependencies can also be used in analyzing an existing database. the problem of inferring functional dependencies has several connections to other topics in knowledge discovery and machine learning. In this article we discuss the use of examples in the design of databases, and give an overview of the complexity results and algorithms that have been developed for this problem. © 1992 John Wiley & Sons, Inc.