Indexing, learning and content-based retrieval for special purpose image databases

This chapter deals with content-based image retrieval in special purpose image databases. As image data is amassed ever more effortlessly, building efficient systems for searching and browsing of image databases becomes increasingly urgent. We provide an overview of the current state-of-the art by taking a tour along the entir

An approach to human-machine teaming in legal investigations using anchored narrative visualisation and machine learning

During legal investigations, analysts typically create external representations of an investigated domain as resource for cognitive offloading, reflection and collaboration. For investigations involving very large numbers of documents as evidence, creating such representations can be slow and costly, but essential. We believe that software tools, including interactive visualisation and machine learning, can be transformative in this arena, but that design must be predicated on an understanding of how such tools might support and enhance investigator cognition and team-based collaboration. In this paper, we propose an approach to this problem by: (a) allowing users to visually externalise their evolving mental models of an investigation domain in the form of thematically organized Anchored Narratives; and (b) using such narratives as a (more or less) tacit interface to cooperative, mixed initiative machine learning. We elaborate our approach through a discussion of representational forms significant to legal investigations and discuss the idea of linking such representations to machine learning

Development, implementation and evaluation of a programme to facilitate critical thinking in nursing education

D.Cur. (Nursing Education)Abstract: The purpose of the study is to describe the development, implementation and evaluation of a programme to facilitate critical thinking in nursing education. The researcher departed deductively from the recommendation of a Delphi study by exponents of critical thinking that researchers are to develop programmes and assessment tools of critical thinking. They came up with a consensus definition resulting from a concept analysis and defined critical thinking as a purposeful, self-regulatory judgement which results into interpretation, analysis, evaluation and inference including explanation of the critical thinking process of contextual, conceptual, methodological, evidential and criteriological considerations on which the judgment is based. The researcher made use of the critical thinking framework that included contextual, conceptual, methodological, evidential and criteriological dimensions of critical thinking to develop a conceptual framework to facilitate critical thinking. The study is a qualitative, explorative and descriptive design for programme development that is contextual in nature

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

The Development and Validation of the Growing Disciples Inventory (GDI) as a Curriculum-aligned Self-assessment for Christian Education

Although numerous norm-referenced measures of religiosity and spirituality exist for adults, no assessment of the holistic goals for Christian spiritual development in the context of evangelical Protestant schools, geared to adolescents, and using emerging technologies, was found. Addressing this lacuna, the purpose of this curriculum study was to develop and validate the Growing Disciples Inventory (GDI) as a curriculum-aligned self-assessment for Christian education. Using a mixed methods approach, the GDI was constructed in the first phase of this educational design research. Experts in the fields of curriculum, assessment, Christian education and/or discipleship evaluated the extent to which proposed items were aligned to the Growing Disciples (GD) curriculum framework, and were appropriate to adolescent learners participating in Christian education. At least four items were included for each of 21 constructs within the four GD curriculum processes. The 100-item GDI was further refined through two development cycles of usability testing with adolescents. Using a think-aloud protocol, a proportional quota convenience sample of 16 learners completed the GDI online, reviewed their online reports, and took the exit survey. Minor refinements were made with the data from these individual interviews. During the second phase, evidence for the validity of the GDI was evaluated with data from a purposive sample of nine educators and 595 Grade 7 through 12 students in 8 American, South African, and Australian Seventh-day Adventist schools. High reliability was found in terms of internal consistency (Cronbach’s alphas of .855 to .943) and structural equation modelling (standardized correlation coefficients of .59 to .95) for the four cyclical and lifelong Christian spiritual development processes of Connecting,Understanding, Ministering, and Equipping. Confirmatory factor analysis through structural equation modelling provided evidence of construct validity with an adequate model fit. Moderate inter-factor correlations compared to higher correlations within factors indicated discriminant validity. Learner responses to 7 GDI exit survey items further supported the GDI’s design and ease-of-use online. Answers to 3 open-ended GDI exit survey questions supplied rich qualitative data that corroborated quantitative responses, and added perceptions of the utility and relevance of the GDI as a formative self-assessment tool to facilitate exploration of strengths and growth points through reflection and metacognition. The majority of educator interviews indicated favourable perceptions of the GDI’s utility and relevance within their sphere of the global Seventh-day Adventist education system. Structural equation model fit evaluation and correlations demonstrated that the GDI is a consistent self-assessment across gender and grade level. Although a weak correlation between country and learner scores was found, qualitative data supports the relevance of the GDI in each country. Further validation studies are recommended with larger samples international samples to adequately demonstrate generalizability within the context of evangelical Protestant education. Analysis of emerging themes in learner responses corroborated quantitative findings, triangulating evidence for learner engagement and the positive potential for the GDI’s use to facilitate Christian spiritual development. Each study of reliability and validity undertaken in this mixed methods curriculum research added moderate to strong evidence in support of the GDI as a curriculum-aligned self-assessment for adolescents participating in Christian education