POLAR is a prototype data visualisation system developed for use by Intelligence Analysts conducting Patterns-of-Life (PoL) analysis. PoL analysis involves exploring data to identify behaviour patterns of individuals or groups. POLAR began with a series of requirements interviews with an ex-military analyst. The design process revealed a series of design alternatives that were explored. One of these related to whether, contrary to suggestions in the literature, animation might provide an advantage to the analyst. We report the design of the system and an evaluation study to assess the effect of animation on users. Whilst animation didn't result in significant improvements in performance, the use of animation with trails did result in significantly better usability assessments and higher user-engagement ratings. Post task interviews with analysts suggested where animation might offer a performance advantage.

This paper presents the preliminary results of our initial, descriptive, practical, hybrid argumentation model, designed for the use by criminal intelligence analysts (from now on referred to as analysts) working with sophisticated visual analytical software in uncertain sense-making environments. Analysts are required to create exhibits (as evidence) for a court of law or as input for decision-making in intelligence-led policing. These exhibits are required to be accurate, relevant and unbiased. Eight experienced criminal intelligence analysts from West Midlands police and the Belgium police evaluated a low-fidelity prototype resembling the first-order argumentation concepts of our initial argumentation model. The evaluation was to assess the applicability and practicality of the first-order argumentation concepts within our model. The preliminary results presented in this paper indicate that most of the first-order argumentation concepts are both applicable and practical and that the participants would use such concepts to construct their rationale from the onset of an analytical activity, if it were included as part of a software application.

The Criminal Intelligence Analyst's role is to create exhibits which are relevant, accurate and unbiased. Exhibits can be used as input to assist decision-making in intelligence-led policing. It may also be used as evidence in a court of law. The aim of this study was to determine how Criminal Intelligence Analysts recognise and manage significant information as a method to determine what is relevant for their attention and for the creation of exhibits. This in turn may provide guidance on how to design and incorporate loose and flexible argumentation schemas into sense-making software. The objective is to be informed on how to design software, which affords Criminal Intelligence Analysts with the ability to effortlessly determine the relevance of information, which subsequently could assist with the process of assessing and defending the quality of exhibits.

In this paper, we describe an experimental study, which evaluates how 3D stereoscopy affects player immersion in a possibly very distracting local multiplayer game. The game "Nicely Dicely" was specifically developed for this purpose, with 3D stereoscopy in mind, right from the beginning. Groups of participants were competitively playing the game in non-3D monoscopic and 3D stereoscopic presentations via a 3D compatible projector and corresponding active shutter glasses. In the following, we elaborate on the game and our quantitative and qualitative hybrid experiment design and methodology. An analysis of the resulting data will show that, indeed 3D stereoscopy significantly increases spatial presence, involvement and player immersion, even in a local multiplayer situation. Furthermore, some guiding insights relating the game's design will be illustrated.

Technology for older people is typically concerned either with health care or accessibility of existing systems. In this paper we take a more 'entertainment-oriented' approach to developing experiences aimed at older users. We describe here the design, development and a user study of the V-Armchair, a virtual reality and motion platform based roller coaster experience. The V-Armchair constitutes a blueprint for the digital archiving of physical ride experiences through the simultaneous capture of 360 video, sound and motion. It gives access to thrill experiences to those who may not be able to go on real thrill rides, such as older riders, and it can be considered as a class of technology that could help to support 'active aging' as defined by the World Health Organisation. We discuss strategies for capturing and then 'toning down' motion experiences to make them accessible for older users. We present a study which explores the user experience of the V-Armchair with an older group (median age 63) using a DK2 headset, and a younger group (median age 25) using a CV1 headset, via thematic analysis of semi-structured interviews and a modified version of the Game Experience Questionnaire, and discuss emergent themes such as the role of the presenter, reminiscence, presence and immersion.

Panoramic 360 video is a rapidly growing part of interactive TV viewing experience due to the increase of both production by consumers and professionals and the availability of consumer headsets used to view it. Recent years have also seen proposals for the development of home systems that could ultimately approximate CAVE-like experiences. The question arises as to the nature of the user experience of viewing panoramic video in head mounted displays compared to CAVE-like systems. User preference seems hard to predict. Accordingly, this study took a qualitative approach to describing user experience of viewing a panoramic video on both platforms, using a thematic analysis. Sixteen users tried both viewing conditions and equal numbers expressed preferences for each display system. The differences in user experience by viewing condition are discussed in detail via themes emerging from the analysis.

In the following, we discuss the process of applying virtual reality to digital games. We named this process “VRification” and will elaborate on some of its opportunities and issues. Based on a literature survey and professional practice, this work covers several examples of VR games, which were intended as such from the beginning (Job Simulator and Lucky’s Tale) and others, which were ported to VR after their initial release (DOOM VR and LizzE). We conclude that, for VR games, it is essential to be optimized for the full potential of targeted interface technologies. Furthermore, porting former-non-VR games to VR can create successful user experiences, when aiming for the same high standard of optimization, especially regarding simulator sickness.

In this paper, we describe an experiment outline on investigating design and user experience related aspects of several virtual reality locomotion and virtual object interaction mechanics. These mechanics will be based on consumer hardware like a common game controllers, an infrared hand and finger tracking device, VR hand controllers and an omni-directional treadmill. Corresponding related work will contextualize and motivate this research. The projected experimental study will be based on user test sessions with a specifically developed 1st person VR puzzle horror game, called Gooze. A hybrid approach of self-assessment, in-game parameter tracking and session observations will be proposed for the investigation. Statistical analysis methods will be suggested to evaluate results. Furthermore, this paper will give an overview of the game and elaborate on design, gameplay and user experience related insights of already conducted informal pre-studies with it.

Sensemaking has been described as a process involving information structuring. However, there are few detailed accounts of how this manifests in practice, particularly in relation to the creation and use of external representations such as data visualisations, and how such structuring aids sensemaking. To explore these questions in depth, we present an interview study of police crime analysts from which a model of their analysis process is developed. We describe the model focusing on the notion of 'think-steps', which for the analysts acted as a primary structuring concept. We describe how 'think-steps' propagate throughout the analysis process captured in the model. For the analysts, 'think-steps' are extensible templates that decompose a case into elements, provide a way of storing and visually structuring data, support generation of requests for information, focus research, simulate a case, and shape reporting. We reflect on the implications that our findings might have for design, including the possibility of a repertoire of evolving, sharable and reusable templates for sensemaking within a community of practice.

3D gesture based systems are becoming ubiquitous and there are many mid-air hand gestures that exist for interacting with digital surfaces and displays. There is no well defined gesture set for 3D mid-air hand gestures which makes it difficult to develop applications that have consistent gestures. To understand what gestures exist we conducted the first comprehensive systematic literature review on mid-air hand gestures following existing researchmethods. The results of the review identified 65 paperswhere themid-air hand gestures supported tasks for selection, navigation, and manipulation. We also classified the gestures according to a gesture classification scheme and identified how these gestures have been empirically evaluated. The results of the review provide a richer understanding of what mid-air hand gestures have been designed, implemented, and evaluated in the literature which can help developers design better user experiences for digital interactive surfaces and displays.

We describe and evaluate five different level design independent modes of handling camera behavior in the 3rd person game LizzE – And the Light of Dreams in Virtual Reality. The behavior of the different modes will each be illustrated in detail. To evaluate the modes A: Fast circling, B: Lazy Circling, C: No Circling, D: Blink circling and E: Buffered pulling, an experimental study with 33 subjects was conducted. An analysis of the resulting data will show why Buffered pulling seems to be the most promising of the examined modes. We elaborate on the quantitative and qualitative hybrid experiment design and methodology. Eventually the advantages and disadvantages of the five tested modes are discussed in terms of supporting the gameplay, player enjoyment, in game performance and the tendency to induce nausea.

Panoramic video arises at the convergence of TV and virtual reality, and it is necessary to understand how these technologies interact to affect user experience in order to produce useful content. TV and film makers have developed a sophisticated language and set of techniques to achieve directed linear story telling on fixed screens, whereas virtual worlds more often emphasise user led exploration of possibly non-linear narrative and aspects such as presence and immersion in navigable 3D environments. This study focused on the user experience of panoramic video as viewed over two conditions, on a VR headset and using a handheld phone, and compared this to watching on a static screen thus emphasising the differences between traditional and panoramic TV. A qualitative approach to analysis was taken where users participated in semi-structured interviews. A thematic analysis was performed which produced thematic maps describing user experience for each condition. A detailed and nuanced account of emerging themes is given. Subsequently, key themes were identified and graphed to produce user response profiles to the three viewing conditions that highlight differences in user experience in terms of presence, attention, engagement, concentration on story, certainty, comfort and social ease.

Intelligence analysis activities are increasingly seen as a sense-making activity. Information systems supporting these activities have, therefore, to be designed in a way to enable analysts to engage in sensemaking in an efficient manner. In the context of the VALCRI project, we developed guidelines for an appropriate design of intelligence analysis systems. Such guidelines can be very valuable in the design process, but their application is sometimes not straightforward. We describe such problems and also suggest possible solutions for these application problems within the context of intelligence analysis.

At the end of the criminal intelligence analysis process there are relatively well established and understood approaches to explicit externalisation and representation of thought that include theories of argumentation, narrative and hybrid approaches that include both of these. However the focus of this paper is on the little understood area of how to support users in the process of arriving at such representations from an initial starting point where little is given. The work is based on theoretical considerations and some initial studies with end users. In focusing on process we discuss the requirements of fluidity and rigor and how to gain traction in investigations, the processes of thinking involved including abductive, deductive and inductive reasoning, how users may use thematic sorting in early stages of investigation and how tactile reasoning may be used to externalize and facilitate reasoning in a productive way. In the conclusion section we discuss the issues raised in this work and directions for future work.

We developed the Patterns of Life (PoL) suite for the IEEE VAST 2014 Mini-Challenge 2. The suite comprises PoL Atlas for visualising movement and transactions spatiotemporally, PoL Classifier for classifying locations and identifying trips, and PoL Location Timeline, for visualising where time is spent.

Since the cell assembly (CA) was hypothesised, it has gained substantial support and is believed to be the neural basis of psychological concepts. A CA is a relatively small set of connected neurons, that through neural firing can sustain activation without stimulus from outside the CA, and is formed by learning. Extensive evidence from multiple single unit recording and other techniques provides support for the existence of CAs that have these properties, and that their neurons also spike with some degree of synchrony. Since the evidence is so broad and deep, the review concludes that CAs are all but certain. A model of CAs is introduced that is informal, but is broad enough to include, e.g. synfire chains, without including, e.g. holographic reduced representation. CAs are found in most cortical areas and in some sub-cortical areas, they are involved in psychological tasks including categorisation, short-term memory and long-term memory, and are central to other tasks including working memory. There is currently insufficient evidence to conclude that CAs are the neural basis of all concepts. A range of models have been used to simulate CA behaviour including associative memory and more process- oriented tasks such as natural language parsing. Questions involving CAs, e.g. memory persistence, CAs' complex interactions with brain waves and learning, remain unanswered. CA research involves a wide range of disciplines including biology and psychology, and this paper reviews literature directly related to the CA, providing a basis of discussion for this interdisciplinary community on this important topic. Hopefully, this discussion will lead to more formal and accurate models of CAs that are better linked to neuropsychological data.

Recently there has been increasing research interest in displaying graphs with curved edges to produce more readable visualizations. While there are several automatic techniques, little has been done to evaluate their effectiveness empirically. In this paper we present two experiments studying the impact of edge curvature on graph readability. The goal is to understand the advantages and disadvantages of using curved edges for common graph tasks compared to straight line segments, which are the conventional choice for showing edges in node-link diagrams. We included several edge variations: straight edges, edges with different curvature levels, and mixed straight and curved edges. During the experiments, participants were asked to complete network tasks including determination of connectivity, shortest path, node degree, and common neighbors. We also asked the participants to provide subjective ratings of the aesthetics of different edge types. The results show significant performance differences between the straight and curved edges and clear distinctions between variations of curved edges.

CABot3, the third Cell Assembly roBot, is an agent implemented entirely in simulated neurons. It is situated in a virtual 3D environment and responds to commands from a user in that environment. It parses the user’s natural language commands to set goals, uses those goals to drive its planning system, views the environment, moves through it, and learns a spatial cognitive map of it. Some systems (e.g. parsing) perform perfectly, but others (e.g. planning) are not always successful. So, CABot3 acts as a proof of concept, showing a simulated neural agent can function in a 3D environment.

The paper presents results of using advanced techniques such as Self-Organizing feature Map (SOM) to incorporate a GIS data layer to compensate for the limited amount of real-time ground-truth data available for land-use and land-cover mapping in wet-season conditions in Bangladesh based on multi-temporal RADARSAT-1 SAR images. The experimental results were compared with those of traditional statistical classifiers such as Maximum Likelihood, Mahalanobis Distance, and Minimum Distance, which are not suitable for incorporating low-level GIS data in the image classification process. The performances of the classifiers were evaluated in terms of the classification accuracy with respect to the collected real-time ground truth data. The SOM neural network provided the highest overall accuracy when a GIS layer of land type classification with respect to the depth and duration of regular flooding was used in the network. Using this method, the overall accuracy was around 15% higher than the previously mentioned traditional classifiers at 79.6% where the training data covered only 0.53% of the total image. It also achieved higher accuracies for more classes in comparison to the other classifiers.