Conducting field research in a primary school setting : methodological considerations for maximizing response rates, data quality and quantity

Background: Schools are an ideal setting in which to involve children in research. Yet for investigators wishing to work in these settings, there are few method papers providing insights into working efficiently in this setting.

Objective: The aim of this paper is to describe the five strategies used to increase response rates, data quality and quantity in the TRansport Environment and Kids (TREK) project.

Setting: The TREK project examined the association between neighbourhood urban design and active transport in Grade 5–7 school children (n = 1480) attending 25 primary schools in metropolitan Perth, Western Australia during 2007.

Method: Children completed several survey components during school time (i.e. questionnaire, mapping activity, travel diary and anthropometric measurements) and at home (i.e. pedometer study, parent questionnaire).

Results: Overall, 69.4% of schools and 56.6% of children agreed to participate in the study and, of these, 89.9% returned a completed travel diary, 97.8% returned their pedometer and 88.8% of parents returned their questionnaire. These return rates are superior to similar studies. Five strategies appeared important: (1) building positive relationships with key school personnel; (2) child-centred approaches to survey development; (3) comprehensive classroom management techniques to standardize and optimize group sessions; (4) extensive follow-up procedures for collecting survey items; and (5) a specially designed data management/monitoring system.

Conclusion: Sharing methodological approaches for obtaining high-quality data will ensure research opportunities within schools are maximized. These methodological issues have implications for planning, budgeting and implementing future research.

StenoCipher to provide data confidentiality and tampered data recovery for RFID tag

Radio Frequency Identification (RFID) is an emerging wireless object identification technology with many potential applications such as supply chain management, personnel tracking and healthcare. However, security vulnerabilities of the RFID system have been a serious concern for its wide adoption in many applications. Although much work has been done to provide privacy and anonymity, little focus has been given to ensure RFID data confidentiality, integrity and to address the tampered data recovery problem. To this end, we propose a lightweight stenographic-based approach to ensure RFID data confidentiality and integrity as well as the recovery of tampered RFID data.

Embedded restricted Boltzmann machines for fusion of mixed data types and applications in social measurements analysis

Analysis and fusion of social measurements is important to understand what shapes the public’s opinion and the sustainability of the global development. However, modeling data collected from social responses is challenging as the data is typically complex and heterogeneous, which might take the form of stated facts, subjective assessment, choices, preferences or any combination thereof. Model-wise, these responses are a mixture of data types including binary, categorical, multicategorical, continuous, ordinal, count and rank data. The challenge is therefore to effectively handle mixed data in the a unified fusion framework in order to perform inference and analysis. To that end, this paper introduces eRBM (Embedded Restricted Boltzmann Machine) – a probabilistic latent variable model that can represent mixed data using a layer of hidden variables transparent across different types of data. The proposed model can comfortably support largescale data analysis tasks, including distribution modelling, data completion, prediction and visualisation. We demonstrate these versatile features on several moderate and large-scale publicly available social survey datasets.

Enhancing RFID data quality and reliability

This thesis addressed the problem of data quality, reliability and energy consumption of networked Radio Frequency Identification systems for business intelligence applications decision making processes. The outcome of the research substantially improved the accuracy and reliability of RFID generated data as well as energy depletion thus prolonging RFID system lifetime.

Malware detection and prevention in RFID systems

The threat that malware poses to RFID systems was identified only recently. Fortunately, all currently known RFID malware is based on SQLIA. Therefore, in this chapter we propose a dual pronged, tag based SQLIA detection and prevention method optimized for RFID systems. The first technique is a SQL query matching approach that uses simple string comparisons and provides strong security against a majority of the SQLIA types possible on RFID systems. To provide security against second order SQLIA, which is a major gap in the current literature, we also propose a tag data validation and sanitization technique. The preliminary evaluation of our query matching technique is very promising, showing 100% detection rates and 0% false positives for all attacks other than second order injection.

Hybrid approach to ensure data confidentiality and tampered data recovery for RFID tag

Radio Frequency Identification (RFID) is an emerging wireless object identification technology with many potential applications such as supply chain management, personnel tracking and healthcare. However, security vulnerabilities of the RFID system have been a serious concern for its wide adoption in many applications. Although there are lots of work to provide privacy and anonymity, little focus has been given to ensure confidentiality and integrity of RFID tag data. To this end, we propose a lightweight hybrid approach based on stenographic and watermarking to ensure data confidentiality, linkability resistance and integrity on the RFID tags data. The proposed technique is capable of tampered data recovering and restoring for RFID tag. It has been validated and tested on EPC class 1 gen2 tags.

A modified fuzzy min-max neural network for data clustering and its application to power quality monitoring

When no prior knowledge is available, clustering is a useful technique for categorizing data into meaningful groups or clusters. In this paper, a modified fuzzy min-max (MFMM) clustering neural network is proposed. Its efficacy for tackling power quality monitoring tasks is demonstrated. A literature review on various clustering techniques is first presented. To evaluate the proposed MFMM model, a performance comparison study using benchmark data sets pertaining to clustering problems is conducted. The results obtained are comparable with those reported in the literature. Then, a real-world case study on power quality monitoring tasks is performed. The results are compared with those from the fuzzy c-means and k-means clustering methods. The experimental outcome positively indicates the potential of MFMM in undertaking data clustering tasks and its applicability to the power systems domain.

Selected approaches and frameworks to carry out genomic data provision and analysis on the cloud

While High Performance Computing clouds allow researchers to process large amounts of genomic data, complex resource and software configuration tasks must be carried out beforehand. The current trend exposes applications and data as services, simplifying access to clouds. This paper examines commonly used cloud-based genomic analysis services, introduces the approach of exposing data as services and proposes two new solutions (HPCaaS and Uncinus) which aim to automate service development, deployment process and data provision. By comparing and contrasting these solutions, we identify key mechanisms of service creation, execution and data access required to support non-computing specialists employing clouds.