A model based framework for air quality indices and population risk evaluation, with an application to the analysis of Scottish air quality data

The paper is devoted to the development of a statistical framework for air quality assessment at the country level and for the evaluation of the ambient population exposure and risk with respect to airborne pollutants. The framework is based on a multivariate space–time model and on aggregated indices defined at different levels of aggregation in space and time. The indices are evaluated, uncertainty included, by considering both the model outputs and the information on the population spatial distribution. The framework is applied to the analysis of air quality data for Scotland for 2009 referring to European and Scottish air quality legislation

An AIHW framework for assessing data sources for population health monitoring: working paper

This paper outlines the Australian Institute of Health and Welfare\u27s (AIHW) assessment framework for determining the suitability of specific data sources for population health monitoring. AIHW\u27s Assessment Framework When identifying potential data sources for population health monitoring, it is important to ensure they are \u27fit-for-purpose\u27. The AIHW has developed a 3-step process to assess potential data sources for population health monitoring: Step 1 collects information about the data source Step 2 identifies the potential to inform key monitoring areas Step 3 assesses the quality of the data, using a modified version of the Australian Bureau of Statistics (ABS) Data Quality Framework (ABS 2009), to determine its \u27fitness-for-purpose\u27 by establishing its utility, strengths and limitations. The assessment framework has been designed for use by the AIHW and others with an interest in assessing new data sources for use in population health monitoring. With adaptation, it may also have wider applications in other sectors or subject areas. For an example of the application of the assessment framework, see the AIHW working paper Assessment of the Australian Rheumatology Association Database for national population health monitoring (AIHW 2014a)

Information quality assessment and effects on inventory decision-making

Information quality has become a critical concern to the success of organisations. Numerous business initiatives have been delayed or even cancelled, citing poor-quality information as the main reason. Previous research indicated that under-standing the effects of information quality is critical to the success of organisations. However, little research has been done to analyse the effects of information quality in an organisational context. In order to address this drawback, the objective of this thesis is to systematically analyse the effects of information quality on decision-making. In order to achieve this objective, we propose a practical assessment framework that allows us to measure information quality dimensions and categories. This framework was validated using a real-world database. With the help of this assessment framework we gained in-depth insights into the effects of information quality on decision quality. Our results showed that the categories of intrinsic and contextual information quality are positively related to decision quality. However decision quality is not significantly affected by representational information quality. It is also found that in contrast to consistency, increasing information accuracy and completeness can significantly improve decision quality. From our results we concluded that not all the aspects of information quality are equally effective for the improvement of decision quality. Decision-makers could decide to pay little or no attention to the improvement of representational information quality and information consistency. This finding will directly reduce the cost of information quality improvement. For practical implementations, our results concluded a validated framework that allows software engineers to implement assessment. Comparing our framework with other common frameworks that require soft-ware engineers to understand information quality theory, our framework helps soft-ware engineers to follow a step-by-step procedure to build an application of information quality assessment. It will directly increase software engineers' work efficiency

FaceQnet: Quality Assessment for Face Recognition based on Deep Learning

In this paper we develop a Quality Assessment approach for face recognition based on deep learning. The method consists of a Convolutional Neural Network, FaceQnet, that is used to predict the suitability of a specific input image for face recognition purposes. The training of FaceQnet is done using the VGGFace2 database. We employ the BioLab-ICAO framework for labeling the VGGFace2 images with quality information related to their ICAO compliance level. The groundtruth quality labels are obtained using FaceNet to generate comparison scores. We employ the groundtruth data to fine-tune a ResNet-based CNN, making it capable of returning a numerical quality measure for each input image. Finally, we verify if the FaceQnet scores are suitable to predict the expected performance when employing a specific image for face recognition with a COTS face recognition system. Several conclusions can be drawn from this work, most notably: 1) we managed to employ an existing ICAO compliance framework and a pretrained CNN to automatically label data with quality information, 2) we trained FaceQnet for quality estimation by fine-tuning a pre-trained face recognition network (ResNet-50), and 3) we have shown that the predictions from FaceQnet are highly correlated with the face recognition accuracy of a state-of-the-art commercial system not used during development. FaceQnet is publicly available in GitHub.Comment: Preprint version of a paper accepted at ICB 201

Quality Assurance of Learning Assessments in Large Information Systems and Decision Analysis Courses

As Information Systems courses have become both more data-focused and student numbers have increased, there has emerged a greater need to assess technical and analytical skills more efficiently and effectively. Multiple-choice examinations provide a means for accomplishing this, though creating effective multiple-choice assessment items within a technical course context can be challenging. This study presents an iterative quality improvement framework based on Plan-Do-Study-Act (PDSA) quality assurance cycle for developing and improving such multiple-choice assessments. Integral to this framework, we also present a rigorous, reliable, and valid measure of assessment and item quality using discrimination efficiency and the KR-20 assessment reliability measure. We demonstrate the effectiveness of our approach across exams developed and administered for two courses — one, a highly technical Information Systems introductory course and the other, an introductory data analytics course. Using this approach, we show that assessment quality iteratively improves when instructors measure items and exams rigorously and apply this PDSA framework

Peer assessment of research: how many publications per staff?

The UK's higher education funding councils have proposed reducing the number of submitted outputs from four to three in the forthcoming Research Excellence Framework to reduce the burden on panel members. This reduction is considered to be sufficient for panels to form a robust view of the achievements of individuals and their departments. The key issue is whether the subject panels would have sufficient information to judge the quality of research at departmental level with details of only three outputs per staff. Two journal quality indicators are used in this note to test the assumption that three publications is likely to be as useful to the panels as four to measure research quality in three cognate units of assessment (business & management, economics & econometrics and accounting & finance). In fact, the results indicate that two publications would be sufficient, thereby providing more time for a careful assessment of submitted outputs

Sustaining Quality Assessment Processes in User-Centred Health Information Portals

Information portals are quality-controlled intermediaries, through which consumers can access online information of high relevance and quality. Developing and maintaining a portal’s content repository involves resource identification, selection and description processes undertaken by domain experts. Among these processes, the less standardised, manual quality assessment procedures are highlighted, where new solutions are imperative to solve its scalability and sustainability issues. Results of a qualitative analysis implicate that quality assessment is fundamentally a subjective issue that needs human intervention. For this reason, this research proposes a semi-automated quality assessment approach, in which a user-centred quality framework, an indicator-based quality model and a decision support tool are devised to address the identified domain expert needs for intelligent support. The system development methodology within design science framework is adopted by this research and the tool prototyping within the context of health information portals is underway to evaluate the feasibility and usefulness of the proposed approach

A framework of quality assessment methods for crowdsourced geographic information : a systematic literature review

Collaboration is the foundation to strengthen disaster preparedness and for effective emergency response actions at all levels. Some studies have highlighted that remote volunteers, i.e., volunteers supported by Web 2.0 technologies, possess the potential to strengthen humanitarian relief organizations by offering information regarding disaster-affected people and infrastructure. Although studies have explored various aspects of this topic, none of those provided an overview of the state-of-the-art of researches on the collaboration among humanitarian organizations and communities of remote volunteers. With the aim of overcoming this gap, a systematic literature review was conducted on the existing research works. Therefore, the main contribution of this work lies in examining the state of research in this field and in identifying potential research gaps. The results show that most of the research works addresses the general domain of disaster management, whereas only few of them address the domain of humanitarian logistics. Collaboration among Humanitarian Relief Organizations and Volunteer Technical Communities: Identifying Research Opportunities and Challenges through a Systematic Literature Review (PDF Download Available). Available from: https://www.researchgate.net/publication/315790817_Collaboration_among_Humanitarian_Relief_Organizations_and_Volunteer_Technical_Communities_Identifying_Research_Opportunities_and_Challenges_through_a_Systematic_Literature_Review [accessed May 26, 2017]