Information layering to de-clutter displays in emergency ambulance dispatch.

In this paper we report on a study to examine the usefulness of the MLD (Multi-Layered Display) as a device for creating physically distinct but visually overlapping information, what we refer to as 'information layering'. The technique was applied to emergency ambulance control, as a method for reducing visual clutter and information complexity in displays used by controllers. The results of the study show that participants completing simulated dispatch tasks in the MLD condition performed better on all categories of task difficulty compared to participants using a standard single layer display. However the improvements in performance were not significantly different

Identifying information seeking behaviours of low and high literacy users: combined cognitive task analysis.

Motivation – According to the UK’s National Skills for Life survey carried out in 2003, 16% or equivalent to 5.2 million of the UK population presented low levels of literacy (Williams, et al. 2003). In this study we investigate the differences in information seeking behaviours between low and high literacy users of an on-line social service system. Research approach – Ten volunteers participated in the study. Using the National Skills for Life Survey, five were classified as high literacy; five as low literacy. All participants were asked to think-aloud whilst carrying out the information search using the “Adviceguide” website. The four tasks were of varying difficulty; easy, medium and difficult. Observations, video recording, and a semi structured interview technique that uses cognitive probes were used. The qualitative data were transcribed and analysed using Strauss and Corbin’s (1998) Grounded Theory and Wong and Blandford (2002) Emergent Themes Analysis approach. Findings/Design – We identified eight themes or characteristics from this study; Verification, Reading, Recovery, Trajectories, Abandon, Focus, Satisfied, and Perception. Results showed that low and high literacy users demonstrated critically different characteristics. Take away message – To better support the low and high literacy users with information seeking, we plan to look at information seeking behaviour models as theoretical lenses to analyse their behaviour from the identified characteristics (Makri, Blandford & Cox, 2008). The behaviour models will better inform the development of interface design for low and high literacy users

Provenance for intelligence analysis using visual analytics.

In this paper we discuss various aspects in intelligence analysis relating to provenance and the new requirements resulting from the changed nature of terrorist activities. We propose a three-layer provenance model which relates the scope of provenance to the intelligence workflow and the idea of a ’provenance reasoning workspace’ for integrating provenance information into visual analytic tools

Algorithmic opacity: making algorithmic processes transparent through abstraction hierarchy

In this paper we introduce the problem of algorithmic opacity and the challenges it presents to ethical decision-making in criminal intelligence analysis. Machine learning algorithms have played important roles in the decision-making process over the past decades. Intelligence analysts are increasingly being presented with smart black box automation that use machine learning algorithms to find patterns or interesting and unusual occurrences in big data sets. Algorithmic opacity is the lack visibility of computational processes such that humans are not able to inspect its inner workings to ascertain for themselves how the results and conclusions were computed. This is a problem that leads to several ethical issues. In the VALCRI project, we developed an abstraction hierarchy and abstraction decomposition space to identify important functional relationships and system invariants in relation to ethical goals. Such explanatory relationships can be valuable for making algorithmic process transparent during the criminal intelligence analysis process

Behavioural markers: bridging the gap between art of analysis and science of analytics in criminal intelligence

Studying how intelligence analysts use interaction in visualization systems is an important part of evaluating how well these interactions support analysis needs, like generating insights or performing tasks. Intelligence analysis is inherently a fluid activity involving transitions between mental and interaction states through analytic processes. A gap exists to complement these transitions at micro-analytic level during data exploration or task performance. We propose Behavioural markers (BMs) which are representatives of the action choices that analysts make during their analytical processes as the bridge between human cognition and computation through semantic interaction. A low level semantic action sequence computation technique has been proposed to extract these BMs from captured process log. Our proposed computational technique can supplement the problems of existing qualitative approaches to extract such BMs

Semantic based image retrieval through combined classifiers of deep neural network and wavelet decomposition of image signal

Semantic Gap, High retrieval efficiency, and speed are important factors for content-based image retrieval system (CBIR). Recent research towards semantic gap reduction to improve the retrieval accuracy of CBIR is shifting towards machine learning methods, relevance feedback, object ontology etc. In this research study, we have put forward the idea that semantic gap can be reduced to improve the performance accuracy of image retrieval through a two-step process. It should be initiated with the identification of the semantic category of the query image in the first step, followed by retrieving of similar images from the identified semantic category in the second step. We have later demonstrated this idea through constructing a global feature vector using wavelet decomposition of color and texture information of the query image and then used feature vector to identify its semantic category. We have trained a stacked classifier consisting of deep neural network and logistic regression as base classifiers for identifying the semantic category of input image. The image retrieval process in the identified semantic category was achieved through Gabor Filter of the texture information of query image. This proposed algorithm has shown better precision rate of image retrieval than that of other researchers work

INVISQUE: Intuitive information exploration through interactive visualization

In this paper we present INVISQUE, a novel system designed for interactive information exploration. Instead of a conventional list-style arrangement, in INVISQUE information is represented by a two-dimensional spatial canvas, with each dimension representing user-defined semantics. Search results are presented as index cards, ordered in both dimensions. Intuitive interactions are used to perform tasks such as keyword searching, results browsing, categorizing, and linking to online resources such as Google and Twitter. The interaction-based query style also naturally lends the system to different types of user input such as multi-touch gestures. As a result, INVISQUE gives users a much more intuitive and smooth experience of exploring large information spaces

Interaction log and provenance for sensemaking

This paper describes two visual analytic tools designed to support sensemaking through the visualisation of interaction log and analytic provenance. The first tool, SensePath, aims to reduce the time required for the transcription and coding during qualitative analysis such as thematic analysis (making sense of the experiment data). The second tool, SenseMap, is designed to help online sensemaking with everyday tasks such as buying a digital camera. User evaluation leads to early insight of how the visualisation of interaction log and analytic provenance can help these sensemaking tasks

Editorial: In use, in situ: extending field research methods

A case for evaluating in use and in-situ Many authors have argued the need for a broader understanding of context and the situatedness of activity when approaching the evaluation of systems. However, prevailing practice often still tends towards attempting to understand the use of designed artefacts by focusing on a core set of tasks that are thought to define the system. A consequence of such focus is that other tasks are considered peripheral and outside the scope of design and evaluation activities. To illustrate the point, consider the experience, familiar to many of us, of being involved in an evaluation activity where participants provide unstructured qualitative feedback. Irrespective of whether the activity is carried out in a laboratory, in a high fidelity simulation or in a naturalistic setting, participants will frequently volunteer unsolicited feedback about tasks and goals that were not originally within the ambit of the design activity. This unprompted feedback, we suggest, is a cue for the evaluators to pay attention to the relationship between the tool and the practice in which it will be used. In other words a cue to consider the situations in which artefact will be used, the tasks and activities that may be affected by the new system, and so on. These are empirical questions that cannot be answered a priori by the development team, whether the evaluation is taking place in “artificial” or “natural” setting

Associative search through formal concept analysis in criminal intelligence analysis

Criminal Intelligence Analysis often requires a search different from the semantic and keyword based searching to reveal the associations among semantically and operationally connected objects within a crime knowledge base. In this paper we introduce associative search as a search along the networks of association between objects like people, places, other organizations, products, events, services, and so on. We also propose an associative search model based on the 5WH associated concepts of a crime, i.e. WHAT (what has happened), WHO (who was involved in the crime), WHEN (the temporal information of the crime), WHERE (the geo-spatial information of the crime) HOW (the modus-operandi used in committing a crime). We have employed Formal Concept Analysis theory to reveal the associations, highlighting Hot Spots, offender‘s profile and its associated offenders in a criminal activit