Situation awareness approach to context-aware case-based decision support.

Context-aware case-based decision support systems (CACBDSS) use the context of users as one of the features for similarity assessment to provide solutions to problems. The combination of a context-aware case-based reasoning (CBR) with general domain knowledge has been shown to improve similarity assessment, solving domain specific problems and problems of uncertain knowledge. Whilst these CBR approaches in context awareness address problems of incomplete data and domain specific problems, future problems that are situation-dependent cannot be anticipated due to lack of data by the CACBDSS to make predictions. Future problems can be predicted through situation awareness (SA), a psychological concept of knowing what is happening around you in order to know the future. The work conducted in this thesis explores the incorporation of SA to CACBDSS. It develops a framework to decouple the interface and underlying data model using an iterative research and design methodology. Two new approaches of using situation awareness to enhance CACBDSS are presented: (1) situation awareness as a problem identification component of CACBDSS (2) situation awareness for both problem identification and solving in CACBDSS. The first approach comprises of two distinct parts; SA, and CBR parts. The SA part understands the problem by using rules to interpret cues from the environment and users. The CBR part uses the knowledge from the SA part to provide solutions. The second approach is a fusion of the two technologies into a single case-based situation awareness (CBSA) model for situation awareness based on experience rather than rule, and problem solving predictions. The CBSA system perceives the users context and the environment and uses them to understand the current situation by retrieving similar past situations. The futures of new situations are predicted through knowledge of the history of similar past situations. Implementation of the two approaches in flow assurance control domain to predict the formation of hydrate shows improvements in both similarity assessment and problem solving predictions compared to CACBDSS without SA. Specifically, the second approach provides an improved decision support in scenarios where there are experienced situations. In the absence of experienced situations, the second approach offers more reliable solutions because of its rule-based capability. The adaptation of the user interface of the approaches to the current situation and the presentation of a reusable sequence of tasks in the situation reduces memory loads on operators. The integrated research-design methodology used in realising these approaches links theory and practice, thinking and doing, achieving practical as well as research objectives. The action research with practitioners provided the understanding of the domain activities, the social settings, resources, and goals of users. The user-centered design process ensures an understanding of the users. The agile development model ensures an iterative work, enables faster development of a functional prototype, which are more easily communicated and tested, thus giving better input for the next iteration

Integrated children’s services: conformity, diversity and managing the market: sharing our experience, practitioner-led research 2008-2009

This report is part of CWDC’s Practitioner-Led Research (PLR) programme. Now in its third year, the programme gives practitioners the opportunity to explore, describe and evaluate ways in which services are currently being delivered within the children’s workforce. Working alongside mentors from Making Research Count (MRC), practitioners design and conduct their own small-scale research and then produce a report which is centred around the delivery of Integrated Working

Academic Integrity Resources - links and guides

an online tutorial, a pdf version, a powerpoint presentation, links to regulations

Child–parent interaction in relation to road safety education : Part 2 – main report

• Children and young people are particularly vulnerable road users. • Child pedestrian injury rates are poor compared with the rest of Europe. • The factors that impact on children’s road safety and their capability in traffic are numerous, multi-faceted and complex. • • The systematic review conducted by Cattan et al. (2008) as the initial phase of this study shows that: • parents see themselves as being responsible for developing their children’s road safety awareness and skills; • holding hands is the most common road-crossing interaction between parents and children; • adults rarely make use of road-crossing events to give oral instructions; • few parents and children are consistent in their road-crossing behaviour; • roadside training by volunteer parents for groups of children can lead to significant improvements in children’s road safety behaviour; • belief in fate seems to influence the likelihood of parents using restraints, such as seat belts or car seats, with their children; and • parents’ understanding of the child’s perspective in carrying out road safety tasks and their motivation to actively involve their child in making decisions at the roadside can be improved through training. • Social Cognitive Theory (Bandura, 1986) suggests that the modelling role of parents can make a significant contribution to children’s learning about road use and their development of traffic competence whether or not parents are aware of this. • The main aim of this study was to explore the way parents influence children and young people aged 0–16 years to be safer road users. • This study included children and young people aged 5–16 and parents of children aged 0–16 years old

Employing community data to investigate social and structural dimensions of urban neighborhoods: An early childhood education example

The present study sought to define neighborhood context by examining relationships among data from city-level administrative databases at the level of the census block group. The present neighborhood investigation included 1,801 block groups comprising a large, northeastern metropolitan area. Common factor analyses and multistage, hierarchical cluster analyses yielded two dimensions (i.e., Social Stress, Structural Danger) and two typologies (i.e., Racial Composition, Property Structure Composition) of neighborhood context. Simultaneous multiple regression analyses revealed small but statistically significant associations between neighborhood variables and academic outcomes for public school kindergarten children

Knowledge Base Population using Semantic Label Propagation

A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we present a method that maximizes the effectiveness of newly trained relation extractors at a minimal annotation cost. Manual labeling can be significantly reduced by Distant Supervision, which is a method to construct training data automatically by aligning a large text corpus with an existing knowledge base of known facts. For example, all sentences mentioning both 'Barack Obama' and 'US' may serve as positive training instances for the relation born_in(subject,object). However, distant supervision typically results in a highly noisy training set: many training sentences do not really express the intended relation. We propose to combine distant supervision with minimal manual supervision in a technique called feature labeling, to eliminate noise from the large and noisy initial training set, resulting in a significant increase of precision. We further improve on this approach by introducing the Semantic Label Propagation method, which uses the similarity between low-dimensional representations of candidate training instances, to extend the training set in order to increase recall while maintaining high precision. Our proposed strategy for generating training data is studied and evaluated on an established test collection designed for knowledge base population tasks. The experimental results show that the Semantic Label Propagation strategy leads to substantial performance gains when compared to existing approaches, while requiring an almost negligible manual annotation effort.Comment: Submitted to Knowledge Based Systems, special issue on Knowledge Bases for Natural Language Processin

Semantic-Based Policy Composition for Privacy-Demanding Data Linkage

Record linkage can be used to support current and future health research across populations however such approaches give rise to many challenges related to patient privacy and confidentiality including inference attacks. To address this, we present a semantic-based policy framework where linkage privacy detects attribute associations that can lead to inference disclosure issues. To illustrate the effectiveness of the approach, we present a case study exploring health data combining spatial, ethnicity and language information from several major on-going projects occurring across Australia. Compared with classic access control models, the results show that our proposal outperforms other approaches with regards to effectiveness, reliability and subsequent data utility