A soft-computing-based method for the automatic discovery of fuzzy rules in databases:uses for academic research and management support in marketing

The study here highlights the potential that analytical methods based on Knowledge Discovery in Databases (KDD) methodologies have to aid both the resolution of unstructured marketing/business problems and the process of scholarly knowledge discovery. The authors present and discuss the application of KDD in these situations prior to the presentation of an analytical method based on fuzzy logic and evolutionary algorithms, developed to analyze marketing databases and uncover relationships among variables. A detailed implementation on a pre-existing data set illustrates the method. © 2012 Published by Elsevier Inc.

Automatic summary assessment for intelligent tutoring systems

Summary writing is an important part of many English Language Examinations. As grading students' summary writings is a very time-consuming task, computer-assisted assessment will help teachers carry out the grading more effectively. Several techniques such as latent semantic analysis (LSA), n-gram co-occurrence and BLEU have been proposed to support automatic evaluation of summaries. However, their performance is not satisfactory for assessing summary writings. To improve the performance, this paper proposes an ensemble approach that integrates LSA and n-gram co-occurrence. As a result, the proposed ensemble approach is able to achieve high accuracy and improve the performance quite substantially compared with current techniques. A summary assessment system based on the proposed approach has also been developed.

Automatic identification of best answers in online enquiry communities

Online communities are prime sources of information. The Web is rich with forums and Question Answering (Q&A) communities where people go to seek answers to all kinds of questions. Most systems employ manual answer-rating procedures to encourage people to provide quality answers and to help users locate the best answers in a given thread. However, in the datasets we collected from three online communities, we found that half their threads lacked best answer markings. This stresses the need for methods to assess the quality of available answers to: 1) provide automated ratings to fill in for, or support, manually assigned ones, and; 2) to assist users when browsing such answers by filtering in potential best answers. In this paper, we collected data from three online communities and converted it to RDF based on the SIOC ontology. We then explored an approach for predicting best answers using a combination of content, user, and thread features. We show how the influence of such features on predicting best answers differs across communities. Further we demonstrate how certain features unique to some of our community systems can boost predictability of best answers.

Feature LDA:a supervised topic model for automatic detection of web API documentations from the web

Web APIs have gained increasing popularity in recent Web service technology development owing to its simplicity of technology stack and the proliferation of mashups. However, efficiently discovering Web APIs and the relevant documentations on the Web is still a challenging task even with the best resources available on the Web. In this paper we cast the problem of detecting the Web API documentations as a text classification problem of classifying a given Web page as Web API associated or not. We propose a supervised generative topic model called feature latent Dirichlet allocation (feaLDA) which offers a generic probabilistic framework for automatic detection of Web APIs. feaLDA not only captures the correspondence between data and the associated class labels, but also provides a mechanism for incorporating side information such as labelled features automatically learned from data that can effectively help improving classification performance. Extensive experiments on our Web APIs documentation dataset shows that the feaLDA model outperforms three strong supervised baselines including naive Bayes, support vector machines, and the maximum entropy model, by over 3% in classification accuracy. In addition, feaLDA also gives superior performance when compared against other existing supervised topic models.

Automatic object segmentation from calibrated images

This paper addresses the problem of automatically obtaining the object/background segmentation of a rigid 3D object observed in a set of images that have been calibrated for camera pose and intrinsics. Such segmentations can be used to obtain a shape representation of a potentially texture-less object by computing a visual hull. We propose an automatic approach where the object to be segmented is identified by the pose of the cameras instead of user input such as 2D bounding rectangles or brush-strokes. The key behind our method is a pairwise MRF framework that combines (a) foreground/background appearance models, (b) epipolar constraints and (c) weak stereo correspondence into a single segmentation cost function that can be efficiently solved by Graph-cuts. The segmentation thus obtained is further improved using silhouette coherency and then used to update the foreground/background appearance models which are fed into the next Graph-cut computation. These two steps are iterated until segmentation convergences. Our method can automatically provide a 3D surface representation even in texture-less scenes where MVS methods might fail. Furthermore, it confers improved performance in images where the object is not readily separable from the background in colour space, an area that previous segmentation approaches have found challenging. © 2011 IEEE.