Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline.

Court-provided trial technology : efficiency and fairness for criminal trials

In Australia, trials conducted as 'electronic trials' have ordinarily run with the assistance of commercial service providers, with the associated costs being borne by the parties. However, an innovative approach has been taken by the courts in Queensland. In October 2007 Queensland became the first Australian jurisdiction to develop its own court-provided technology, to facilitate the conduct of an electronic trial. This technology was first used in the conduct of civil trials. The use of the technology in the civil sphere highlighted its benefits and, more significantly, demonstrated the potential to achieve much greater efficiencies. The Queensland courts have now gone further, using the court-provided technology in the high proffle criminal trial of R v Hargraves, Hargraves and Stoten, in which the three accused were tried for conspiracy to defraud the Commonwealth of Australia of about $3.7 million in tax. This paper explains the technology employed in this case and reports on the perspectives of all of the participants in the process. The representatives for all parties involved in this trial acknowledged, without reservation, that the use of the technology at trial produced considerable overall efficiencies and costs savings. The experience in this trial also demonstrates that the benefits of trial technology for the criminal justice process are greater than those for civil litigation. It shows that, when skilfully employed, trial technology presents opportunities to enhance the fairness of trials for accused persons. The paper urges governments, courts and the judiciary in all jurisdictions to continue their efforts to promote change, and to introduce mechanisms to facilitate more broadly a shift from the entrenched paper-based approach to both criminal and civil procedure to one which embraces more broadly the enormous benefits trial technology has to offer.

Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

Use of virtual index for measuring efficiency of innovation systems : a cross-country study

In this study we propose a virtual index for measuring the relative innovativeness of countries. Using a multistage virtual benchmarking process, the best and rational benchmark is extracted for inefficient ISs. Furthermore, Tobit and Ordinary Least Squares (OLS) regression models are used to investigate the likelihood of changes in inefficiencies by investigating country-specific factors. The empirical results relating to the virtual benchmarking process suggest that the OLS regression model would better explain changes in the performance of innovation- inefficient countries.

Wide baseline correspondence extraction beyond local features

Robust, affine covariant, feature extractors provide a means to extract correspondences between images captured by widely separated cameras. Advances in wide baseline correspondence extraction require looking beyond the robust feature extraction and matching approach. This study examines new techniques of extracting correspondences that take advantage of information contained in affine feature matches. Methods of improving the accuracy of a set of putative matches, eliminating incorrect matches and extracting large numbers of additional correspondences are explored. It is assumed that knowledge of the camera geometry is not available and not immediately recoverable. The new techniques are evaluated by means of an epipolar geometry estimation task. It is shown that these methods enable the computation of camera geometry in many cases where existing feature extractors cannot produce sufficient numbers of accurate correspondences.

Information extraction from web services : a comparison of Tokenisation algorithms

Most web service discovery systems use keyword-based search algorithms and, although partially successful, sometimes fail to satisfy some users information needs. This has given rise to several semantics-based approaches that look to go beyond simple attribute matching and try to capture the semantics of services. However, the results reported in the literature vary and in many cases are worse than the results obtained by keyword-based systems. We believe the accuracy of the mechanisms used to extract tokens from the non-natural language sections of WSDL files directly affects the performance of these techniques, because some of them can be more sensitive to noise. In this paper three existing tokenization algorithms are evaluated and a new algorithm that outperforms all the algorithms found in the literature is introduced.

The Relative Efficiency of International, Domestic, and Budget Airlines : Nonparametric Evidence

This study determines whether the inclusion of low-cost airlines in a dataset of international and domestic airlines has an impact on the efficiency scores of so-called ‘prestigious’ purportedly ‘efficient’ airlines. This is because while many airline studies concern efficiency, none has truly included a combination of international, domestic and budget airlines. The present study employs the nonparametric technique of data envelopment analysis (DEA) to investigate the technical efficiency of 53 airlines in 2006. The findings reveal that the majority of budget airlines are efficient relative to their more prestigious counterparts. Moreover, most airlines identified as inefficient are so largely because of the overutilization of non-flight assets.

Productivity, Technical and Efficiency Change in Singapore’s Services Sector, 2005 to 2008

The current study was motivated by statements made by the Economic Strategies Committee that Singapore’s recent productivity levels in services were well below countries such as the US, Japan and Hong Kong. Massive employment of foreign workers was cited as the reason for poor productivity levels. To shed more light on Singapore’s falling productivity, a nonparametric Malmquist productivity index was employed which provides measures of productivity change, technical change and efficiency change. The findings reveal that growth in total factor productivity was attributed to technical change with no improvement in efficiency change. Such results suggest that gains from TFP were input-driven rather than from a ‘best-practice’ approach such as improvements in operations or better resource allocation.

Surveying the state of higher education in energy efficiency, in Australian engineering curriculum

This paper presents the method and results of a survey of 27 of the 33 Australian universities teaching engineering education in late 2007, undertaken by The Natural Edge Project (hosted by Griffith University and the Australian National University) and supported by the National Framework for Energy Efficiency. This survey aimed to ascertain the extent of energy efficiency (EE) education, and to identify preferred methods to assist in increasing the extent to which EE education is embedded in engineering curriculum. In this paper the context for the survey is supported by a summary of the key results from a variety of surveys undertaken over the last decade internationally. The paper concludes that EE education across universities and engineering disciplines in Australia is currently highly variable and ad hoc. Based on the results of the survey, this paper highlights a number of preferred options to support educators to embed sustainability within engineering programs, and future opportunities for monitoring EE, within the context of engineering education for sustainable development (EESD).