Experts vs. Discounters: Consumer Free Riding and Experts Withholding Advice in Markets for Credence Goods

This paper studies the incentives for credence goods experts to invest effort in diagnosis if effort is both costly and unobservable, and if they face competition by discounters who are not able to perform a diagnosis. The unobservability of diagnosis effort and the credence characteristic of the good induce experts to choose incentive compatible tariff structures. This makes them vulnerable to competition by discounters. We explore the conditions under which honestly diagnosing experts survive competition by discounters; we identify situations in which experts misdiagnose consumers in order to prevent them from free-riding on experts' advice; and we discuss policy options to solve the free-riding consumers–cheating experts problem.

Automatic solder joint defect classification using the Log-Gabor filter

This paper proposes the validity of a Gabor filter bank for feature extraction of solder joint images on Printed Circuit Boards (PCBs). A distance measure based on the Mahalanobis Cosine metric is also presented for classification of five different types of solder joints. From the experimental results, this methodology achieved high accuracy and a well generalised performance. This can be an effective method to reduce cost and improve quality in the production of PCBs in the manufacturing industry.

The properties of the international classification of the external cause of injury when used as an instrument for injury prevention research

Objective: To demonstrate properties of the International Classification of the External Cause of Injury (ICECI) as a tool for use in injury prevention research. Methods: The Childhood Injury Prevention Study (CHIPS) is a prospective longitudinal follow up study of a cohort of 871 children 5–12 years of age, with a nested case crossover component. The ICECI is the latest tool in the International Classification of Diseases (ICD) family and has been designed to improve the precision of coding injury events. The details of all injury events recorded in the study, as well as all measured injury related exposures, were coded using the ICECI. This paper reports a substudy on the utility and practicability of using the ICECI in the CHIPS to record exposures. Interrater reliability was quantified for a sample of injured participants using the Kappa statistic to measure concordance between codes independently coded by two research staff. Results: There were 767 diaries collected at baseline and event details from 563 injuries and exposure details from injury crossover periods. There were no event, location, or activity details which could not be coded using the ICECI. Kappa statistics for concordance between raters within each of the dimensions ranged from 0.31 to 0.93 for the injury events and 0.94 and 0.97 for activity and location in the control periods. Discussion: This study represents the first detailed account of the properties of the ICECI revealed by its use in a primary analytic epidemiological study of injury prevention. The results of this study provide considerable support for the ICECI and its further use.

Overview of the INEX 2009 XML mining track : clustering and classification of XML documents

This report explains the objectives, datasets and evaluation criteria of both the clustering and classification tasks set in the INEX 2009 XML Mining track. The report also describes the approaches and results obtained by the different participants.

Use of face masks by non-scrubbed operating room staff : a randomized controlled trial.

Background: Ambiguity remains about the effectiveness of wearing surgical face masks. The purpose of this study was to assess the impact on surgical site infections when non-scrubbed operating room staff did not wear surgical face masks. Design: Randomised controlled trial. Participants: Patients undergoing elective or emergency obstetric, gynecological, general, orthopaedic, breast or urological surgery in an Australian tertiary hospital. Intervention: 827 participants were enrolled and complete follow-up data was available for 811 (98.1%) patients. Operating room lists were randomly allocated to a ‘Mask roup’ (all non-scrubbed staff wore a mask) or ‘No Mask group’ (none of the non-scrubbed staff wore masks). Primary end point: Surgical site infection (identified using in-patient surveillance; post discharge follow-up and chart reviews). The patient was followed for up to six weeks. Results: Overall, 83 (10.2%) surgical site infections were recorded; 46/401 (11.5%) in the Masked group and 37/410 (9.0%) in the No Mask group; odds ratio (OR) 0.77 (95% confidence interval (CI) 0.49 to 1.21), p = 0.151. Independent risk factors for surgical site infection included: any pre-operative stay (adjusted odds ratio [aOR], 0.43 (95% CI, 0.20; 0.95), high BMI aOR, 0.38 (95% CI, 0.17; 0.87), and any previous surgical site infection aOR, 0.40 (95% CI, 0.17; 0.89). Conclusion: Surgical site infection rates did not increase when non-scrubbed operating room personnel did not wear a face mask.

On the Classification of Recursive Languages

A one-sided classifier for a given class of languages converges to 1 on every language from the class and outputs 0 infinitely often on languages outside the class. A two-sided classifier, on the other hand, converges to 1 on languages from the class and converges to 0 on languages outside the class. The present paper investigates one-sided and two-sided classification for classes of recursive languages. Theorems are presented that help assess the classifiability of natural classes. The relationships of classification to inductive learning theory and to structural complexity theory in terms of Turing degrees are studied. Furthermore, the special case of classification from only positive data is also investigated.

Innovative and entrepreneurial activity in the public sector : the changing face of public sector institutions

This paper investigates the elements which support innovative and entrepreneurial activity in New Zealand’s state owned enterprises (SOEs). An inductive case study design, involving interview data, textual analysis, and observation, was applied to three SOEs. Findings reveal that those aspects typically associated with entrepreneurship, such as innovation, risk acceptance, pro-activeness and growth, are often supported by a number of unexpected elements within the public sector. These elements include culture, branding, operational excellence, cost efficiency, and knowledge transfer. The implications are twofold. First, that innovative and entrepreneurial activity in the public sector can go beyond policy-making, with SOEs representing an important policy decision and sector of the New Zealand Government. And second, that the impact of several SOEs on international markets suggests competition on the global stage will increasingly come from both public and private sector organizations.

Vision-based classification of solder joint defects

Inspection of solder joints has been a critical process in the electronic manufacturing industry to reduce manufacturing cost, improve yield, and ensure product quality and reliability. The solder joint inspection problem is more challenging than many other visual inspections because of the variability in the appearance of solder joints. Although many research works and various techniques have been developed to classify defect in solder joints, these methods have complex systems of illumination for image acquisition and complicated classification algorithms. An important stage of the analysis is to select the right method for the classification. Better inspection technologies are needed to fill the gap between available inspection capabilities and industry systems. This dissertation aims to provide a solution that can overcome some of the limitations of current inspection techniques. This research proposes two inspection steps for automatic solder joint classification system. The “front-end” inspection system includes illumination normalisation, localization and segmentation. The illumination normalisation approach can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image. The “back-end” inspection involves the classification of solder joints by using Log Gabor filter and classifier fusion. Five different levels of solder quality with respect to the amount of solder paste have been defined. Log Gabor filter has been demonstrated to achieve high recognition rates and is resistant to misalignment. Further testing demonstrates the advantage of Log Gabor filter over both Discrete Wavelet Transform and Discrete Cosine Transform. Classifier score fusion is analysed for improving recognition rate. Experimental results demonstrate that the proposed system improves performance and robustness in terms of classification rates. This proposed system does not need any special illumination system, and the images are acquired by an ordinary digital camera. In fact, the choice of suitable features allows one to overcome the problem given by the use of non complex illumination systems. The new system proposed in this research can be incorporated in the development of an automated non-contact, non-destructive and low cost solder joint quality inspection system.

A new approach for finding an appropriate combination of texture parameters for classification

This paper suggests an approach for finding an appropriate combination of various parameters for extracting texture features (e.g. choice of spectral band for extracting texture feature, size of the moving window, quantization level of the image, and choice of texture feature etc.) to be used in the classification process. Gray level co-occurrence matrix (GLCM) method has been used for extracting texture from remotely sensed satellite image. Results of the classification of an Indian urban environment using spatial property (texture), derived from spectral and multi-resolution wavelet decomposed images have also been reported. A multivariate data analysis technique called ‘conjoint analysis’ has been used in the study to analyze the relative importance of these parameters. Results indicate that the choice of texture feature and window size have higher relative importance in the classification process than quantization level or the choice of image band for extracting texture feature. In case of texture features derived using wavelet decomposed image, the parameter ‘decomposition level’ has almost equal relative importance as the size of moving window and the decomposition of images up to level one is sufficient and there is no need to go for further decomposition. It was also observed that the classification incorporating texture features improves the overall classification accuracy in a statistically significant manner in comparison to pure spectral classification.

The old face of 'new' social networks : the republic of letters as a virtual community

This paper explores the idea of virtual participation through the historical example of the republic of letters in early modern Europe (circa 1500-1800). By reflecting on the construction of virtuality in a historical context, and more specifically in a pre-digital environment, this paper calls attention to accusations of technological determinism in ongoing research concerning the affordances of the Internet and related media of communication. It argues that ‘the virtual’ is not synonymous with ‘the digital’ and suggests that, in order to articulate what is novel about modern technologies, we must first understand the social interactions underpinning the relationships which are facilitated through those technologies. By analysing the construction of virtuality in a pre-digital environment, this paper thus offers a baseline from which scholars might consider what is different about the modes of interaction and communication being engaged in via modern media.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.