Background

Our aim is to highlight the benefits and limitations of open and non-anonymized peer review. Our argument is based on the literature and on responses to a survey on the reviewing process of alt.chi, a more or less open review track within the so-called Computer Human Interaction (CHI) conference, the predominant conference in the field of human-computer interaction. This track currently is the only implementation of an open peer review process in the field of human-computer interaction while, with the recent increase in interest in open scientific practices, open review is now being considered and used in other fields.

Methods

We ran an online survey with 30 responses from alt.chi authors and reviewers, collecting quantitative data using multiple-choice questions and Likert scales. Qualitative data were collected using open questions.

Results

Our main quantitative result is that respondents are more positive to open and non-anonymous reviewing for alt.chi than for other parts of the CHI conference. The qualitative data specifically highlight the benefits of open and transparent academic discussions. The data and scripts are available on https://osf.io/vuw7h/, and the figures and follow-up work on http://tiny.cc/OpenReviews.

Conclusion

While the benefits are quite clear and the system is generally well-liked by alt.chi participants, they remain reluctant to see it used in other venues. This concurs with a number of recent studies that suggest a divergence between support for a more open review process and its practical implementation.

Global resource flows and indicators for emerging challenges relevant to the realization of sustainable development goals are of high interest for research on sustainable development and geopolitics. Such complex sets of information require advanced methodological approaches that enable effective communication of data and participatory data exploration. To address this need, we present ResFlow, an interactive web-based application for the visualization and exploration of flow data, in particular for analysing resource flows between countries. ResFlow visualizes resource flows to and from countries using 3D arcs and provides tailored interaction and filtering techniques to facilitate flexible exploration of the data at hand.

Parallel coordinates are commonly used for non-temporal multivariate data, but there is little support for their usability for displaying temporal multivariate data. In this paper, we introduce a study evaluating the usability of 2D and 3D parallel coordinates for pattern identification in temporal multivariate data. The results indicate that 3D parallel coordinates have higher usability, as measured with higher accuracy and faster response time as well as subjective ratings, compared to 2D.

Eye-Tracking experiments have proven to be of great assistance in understanding human computer interaction across many fields. Most eye-tracking experiments are non-intrusive and so do not affect the behaviour of the subject. Such experiments usually last for just a few minutes and so the spatio- temporal data generated by the eye-tracker is quite easy to analyze using simple visualization techniques such as heat maps and animation. Eye tracking experiments in air traffic control, or maritime or driving simulators can, however, last for several hours and the analysis of such long duration data becomes much more complex. We have developed an analysis pipeline, where we identify visual spatial areas of attention over a user interface using clustering and hierarchical cluster merging techniques. We have tested this technique on eye tracking datasets generated by air traffic controllers working with Swedish air navigation services, where each eye tracking experiment lasted for ∼90 minutes. We found that our method is interactive and effective in identification of interesting patterns of visual attention that would have been very difficult to locate using manual analysis.

Eye tracking is used to analyze and compare user behaviour within numerous domains, but long duration eye tracking experiments across multiple users generate millions of eye gaze samples, making the data analysis process complex. Usually the samples are labelled into Areas of Interest (AoI) or Objects of Interest (OoI), where the AoI approach aims to understand how a user monitors different regions of a scene while OoI identification uncovers distinct objects in the scene that attract user attention. Using scalable clustering and cluster merging techniques that require minimal user input, we label AoIs across multiple users in long duration eye tracking experiments. Using the common AoI labels then allows direct comparison of the users as well as the use of such methods as Hidden Markov Models and Sequence mining to uncover common and distinct behaviour between the users which, until now, has been prohibitively difficult to achieve.

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

The study of human activity in space and time is an inherent part of human geography. In order to perform such studies, data on the time use of individuals, in terms of sequence and timing of performed activities, are collected and analysed. A common assumption when analysing individuals’ time use is that groups that exhibit similar background and demographic characteristics also display similarities in how they use their time to structure their daily lives. In this article, we set out to investigate the correctness of such assumptions. We propose a visual analytics process based on sequence similarity measures tailored to event-based data such as performed activity sequences. The process allows an analyst to retrieve similarly behaving records according to user-selected similarity preferences and interactively explore aspects of this similarity in a multiple linked-view environment.

In recent years, several different attempts have been made to extend the well-known technique of parallel coordinates using a three-dimensional display. This article presents an evaluation that investigates the performance of the three-dimensional parallel coordinates technique and compares it with standard, two-dimensional parallel coordinates for analysis of two-dimensional relationships. Three-dimensional parallel coordinates, based on parallel planes instead of parallel axes, have been used for many years within the information visualization community for a variety of applications. Despite its quite common use, no formal evaluation detailing its usefulness for different tasks has been conducted. The evaluation presented in this article is the first step towards determining the usefulness of this type of three-dimensional parallel coordinates. The study compared three-dimensional parallel coordinates, using two different axis configurations commonly seen in the literature, with standard two-dimensional parallel coordinates for identification of two-dimensional relationships between variables in multivariate data. This type of task and the relationships to be judged are known to be well supported by two-dimensional parallel coordinates and multi-relational three-dimensional parallel coordinates. The results show that for identification of two-dimensional relationships, two-dimensional parallel coordinates are superior to the three-dimensional extensions, in terms of both response time and accuracy. Subjective opinions were also in favour of two-dimensional parallel coordinates. This study adds to the much-needed body of work examining the usability of three-dimensional representations in information visualization and for what tasks and data a proposed method is or is not appropriate.

Activity diaries are a powerful data source for studying the time use of individuals and for creating awareness of individuals' daily activity patterns. The presented project is concerned with the development of an easily accessible method for collecting and analyzing diary data which will be applicable across a wide range of industrial, governmental, social science and medical domains. The PODD (POrtable Diary Data collection) is composed of a smartphone application for data registration, a web interface for user registration and an administration system for configuring the application according to the focus of the data collection.