A data quality control program for computer-assisted personal interviews

Researchers strive to optimize data quality in order to ensure that study findings are valid and reliable. In this paper, we describe a data quality control program designed to maximize quality of survey data collected using computer-assisted personal interviews. The quality control program comprised three phases: (1) software development, (2) an interviewer quality control protocol, and (3) a data cleaning and processing protocol. To illustrate the value of the program, we assess its use in the Translating Research in Elder Care Study. We utilize data collected annually for two years from computer-assisted personal interviews with 3004 healthcare aides. Data quality was assessed using both survey and process data. Missing data and data errors were minimal. Mean and median values and standard deviations were within acceptable limits. Process data indicated that in only 3.4% and 4.0% of cases was the interviewer unable to conduct interviews in accordance with the details of the program. Interviewers’ perceptions of interview quality also significantly improved between Years 1 and 2. While this data quality control program was demanding in terms of time and resources, we found that the benefits clearly outweighed the effort required to achieve high-quality data.

Image retrieval using noisy query

In conventional content based image retrieval (CBIR) employing relevance feedback, one implicit assumption is that both pure positive and negative examples are available. However it is not always true in the practical applications of CBIR. In this paper, we address a new problem of image retrieval using several unclean positive examples, named noisy query, in which some mislabeled images or weak relevant images present. The proposed image retrieval scheme measures the image similarity by combining multiple feature distances. Incorporating data cleaning and noise tolerant classifier, a twostep strategy is proposed to handle noisy positive examples. Experiments carried out on a subset of Corel image collection show that the proposed scheme outperforms the competing image retrieval schemes.

Content based image retrieval using unclean positive examples

Conventional content-based image retrieval (CBIR) schemes employing relevance feedback may suffer from some problems in the practical applications. First, most ordinary users would like to complete their search in a single interaction especially on the web. Second, it is time consuming and difficult to label a lot of negative examples with sufficient variety. Third, ordinary users may introduce some noisy examples into the query. This correspondence explores solutions to a new issue that image retrieval using unclean positive examples. In the proposed scheme, multiple feature distances are combined to obtain image similarity using classification technology. To handle the noisy positive examples, a new two-step strategy is proposed by incorporating the methods of data cleaning and noise tolerant classifier. The extensive experiments carried out on two different real image collections validate the effectiveness of the proposed scheme.

Securing RFID systems from SQLIA

While SQL injection attacks have been plaguing web applications for years the threat they pose to RFID systems have only identified recently. Because the architecture of web systems and RFID systems differ considerably the prevention and detection techniques proposed for web applications are not suitable for RFID systems. In this paper we propose a system to secure RFID systems against tag based SQLIA. Our system is optimized for the architecture of RFID systems and consists of a query structure matching technique and tag data cleaning technique. The novelty of the proposed system is that it's specifically aimed at RFID systems and has the ability to detect and prevent second order injections which is a problem most current solutions haven't addressed. The preliminary evaluation of our query matching technique is very promising showing very high detection rate with minimal false positives.

When is a team a team? 'Teamworking' and the reorganisation of work in commercial cleaning

Purpose – The article seeks to analyse and explore the contradictions and variations in the concepts “team” and “teamwork” and their use in the NSW, Australia, commercial cleaning industry.

Design/methodology/approach – The article utilises an ethnographic study of a large Australian cleaning firm. Data were collected using participant observation, field notes, and interviews with managers.

Findings – The study provides evidence for the limited uptake of the idealised form of teamwork in commercial cleaning and suggests that teamworking is another means of coordinating groups of workers. Furthermore, the findings support previous research into the paradox of teams without teamwork.

Originality/value – The research provides an insight into the largely neglected area of the reorganisation of work in commercial cleaning. It also provides a critique of the concept of teams and teamworking.