Interactive visualisation for low literacy users

Sixteen percent (5.2 million) of the UK population possess low levels of literacy. The Government and other non-profit organisations, due to funding reforms, are forced to reduce the provision of face-to-face advice, and therefore, are pushing advice services via telephone or internet. As a consequence, low literacy users are experiencing difficulties finding the information they need to solve their day to day problems online. This thesis evaluates how walk in clients of a local Citizens Advice Bureau (CAB) who come to get social service information, obtain information online using the Adviceguide website. The thesis presents two challenges: (i) knowing the users in a way that can help consider design solutions that are probably not in a typical designer’s standard repertoire of design patterns, and (ii) knowing what is the problem that needs to be addressed. It is not simply an issue of usability or the need for simpler language, but understanding that these low literacy users are very different from the high literacy users. These low literacy users need this information to solve their day-to-day problems and are likely to be less successful in doing so. By providing an information architecture that permits them of a reasoning space and context, while supporting less abstract skills by visualized information in an unconventional way. The above challenges leave us with these research questions to address: what is the basis of such a design, how can these designs be incorporated into existing non-traditional interface proof of concept and finally how can these designs be evaluated

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

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

Providing a foundation for interpretable autonomous agents through elicitation and modeling of criminal investigation pathways

Criminal investigations are guided by repetitive and time-consuming information retrieval tasks, often with high risk and high consequence. If Artificial intelligence (AI) systems can automate lines of inquiry, it could reduce the burden on analysts and allow them to focus their efforts on analysis. However, there is a critical need for algorithmic transparency to address ethical concerns. In this paper, we use data gathered from Cognitive Task Analysis (CTA) interviews of criminal intelligence analysts and perform a novel analysis method to elicit question networks. We show how these networks form an event tree, where events are consolidated by capturing analyst intentions. The event tree is simplified with a Dynamic Chain Event Graph (DCEG) that provides a foundation for transparent autonomous investigations

Pan: conversational agent for criminal investigations

We present an early prototype conversational agent (CA), called Pan, for retrieving information to support criminal investigations. Our approach tackles the issue of algorithmic transparency, which is critical in unpredictable, high risk, and high consequence domains. We present a novel method to flexibly model CA intentions and provide transparency of attributes that is underpinned with human recognition. We propose that Pan can be used for experimentation to probe analyst requirements and to evaluate the effectiveness of our explanation structure

How analysts think: a preliminary study of human needs and demands for AI-based conversational agents

For conversational agents to provide benefit to intelligence analysis they need to be able to recognise and respond to the analysts intentions. Furthermore, they must provide transparency to their algorithms and be able to adapt to new situations and lines of inquiry. We present a preliminary analysis as a first step towards developing conversational agents for intelligence analysis: that of understanding and modeling analyst intentions so they can be recognised by conversational agents. We describe in-depth interviews conducted with experienced intelligence analysts and implications for designing conversational agent intentions using Formal Concept Analysis

Developing conversational agents for use in criminal investigations

The adoption of artificial intelligence (AI) systems in environments that involve high risk and high consequence decision making is severely hampered by critical design issues. These issues include system transparency and brittleness, where transparency relates to (i) the explainability of results and (ii) the ability of a user to inspect and verify system goals and constraints, and brittleness (iii) the ability of a system to adapt to new user demands. Transparency is a particular concern for criminal intelligence analysis, where there are significant ethical and trust issues that arise when algorithmic and system processes are not adequately understood by a user. This prevents adoption of potentially useful technologies in policing environments. In this paper, we present a novel approach to designing a conversational agent (CA) AI system for intelligence analysis that tackles these issues.We discuss the results and implications of three different studies; a Cognitive Task Analysis to understand analyst thinking when retrieving information in an investigation, Emergent Themes Analysis to understand the explanation needs of different system components, and an interactive experiment with a prototype conversational agent. Our prototype conversational agent, named Pan, demonstrates transparency provision and mitigates brittleness by evolving new CA intentions. We encode interactions with the CA with human factors principles for situation recognition and use interactive visual analytics to support analyst reasoning. Our approach enables complex AI systems, such as Pan, to be used in sensitive environments and our research has broader application than the use case discussed

Pulsed Eddy Current Sensing for Condition Assessment of Reinforced Concrete

© 2019 IEEE. Reinforced concrete (i.e., concrete wall-like structures having steel reinforcement rods embedded within) are commonly available as civil infrastructures. Such concrete structures, especially the walls of sewers, are vulnerable to bacteria and gas induced acid attacks which contribute to deterioration of the concrete and subsequent concrete wall loss. Therefore, quantification of concrete wall loss becomes important in determining the health and structural integrity of concrete walls. An effective strategy that can be formulated to quantify concrete wall loss is, locating a reinforcement rod and determining the thickness of concrete overlaying the rod via Non-destructive Testing and Evaluation (NDT E). Pulsed Eddy Current (PEC) sensing is commonly used for NDT E of metallic structures, including ferromagnetic materials. Since steel reinforcement rods that are commonly embedded in concrete also are ferromagnetic, this paper contributes by presenting experimental results, which suggest the usability of PEC sensing for reinforced concrete assessment, via executing the aforementioned strategy

Some convolution and scale transformation techniques to enhance GPR images

© 2019 IEEE. Locating reinforcement rods embedded inside concrete wall-like structures, as well as locating subsurface features such as voids, cracks, and interfaces is an essential part of structural health monitoring of concrete infrastructure. The Ground Penetrating Radar (GPR) technique has been commonly used as a means of Non-destructive Testing and Evaluation (NDT E) which suits the purpose. In the recent past, the interest of using GPR to assess the crowns (i.e., top) of concrete sewers has been rising. Moisture is well known to be a challenge for GPR imaging as moisture tends to influence GPR waves. This challenge becomes more common and persistent inside sewers since sewer walls contain considerable surface and subsurface moisture as a result of the humid environment created by the waste water flowing through sewers as well as the bacteria and gas induced acid attacks. Forming a part of sewer condition assessment-related research with the objective of assessing moist concrete, this paper presents some preliminary results which demonstrate how some simple scale transformations and convolution can help in enhancing GPR images in grey-scale. A set of raw GPR signals captured on a moist concrete block inside a laboratory environment is considered. The effect of enhancement is demonstrated against a benchmark image constructed by mapping the raw signals directly onto grey-scale

Developing conversational agents for use in criminal investigations

The adoption of artificial intelligence (AI) systems in environments that involve high risk and high consequence decision-making is severely hampered by critical design issues. These issues include system transparency and brittleness, where transparency relates to (i) the explainability of results and (ii) the ability of a user to inspect and verify system goals and constraints; and brittleness, (iii) the ability of a system to adapt to new user demands. Transparency is a particular concern for criminal intelligence analysis, where there are significant ethical and trust issues that arise when algorithmic and system processes are not adequately understood by a user. This prevents adoption of potentially useful technologies in policing environments. In this article, we present a novel approach to designing a conversational agent (CA) AI system for intelligence analysis that tackles these issues. We discuss the results and implications of three different studies; a Cognitive Task Analysis to understand analyst thinking when retrieving information in an investigation, Emergent Themes Analysis to understand the explanation needs of different system components, and an interactive experiment with a prototype conversational agent. Our prototype conversational agent, named Pan, demonstrates transparency provision and mitigates brittleness by evolving new CA intentions. We encode interactions with the CA with human factors principles for situation recognition and use interactive visual analytics to support analyst reasoning. Our approach enables complex AI systems, such as Pan, to be used in sensitive environments, and our research has broader application than the use case discussed