An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

G-Asks: An Intelligent Automatic Question Generation System for Academic Writing Support

Many electronic feedback systems have been proposed for writing support. However, most of these systems only aim at supporting writing to communicate instead of writing to learn, as in the case of literature review writing. Trigger questions are potentially forms of support for writing to learn, but current automatic question generation approaches focus on factual question generation for reading comprehension or vocabulary assessment. This article presents a novel Automatic Question Generation (AQG) system, called G-Asks, which generates specific trigger questions as a form of support for students' learning through writing. We conducted a large-scale case study, including 24 human supervisors and 33 research students, in an Engineering Research Method course at The University of Sydney and compared questions generated by G-Asks with human generated question. The results indicate that G-Asks can generate questions as useful as human supervisors (`useful' is one of five question quality measures) while significantly outperforming Human Peer and Generic Questions in most quality measures after filtering out questions with grammatical and semantic errors. Furthermore, we identified the most frequent question types, derived from the human supervisors' questions and discussed how the human supervisors generate such questions from the source text

Generating indicative-informative summaries with SumUM

We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies

MI as a predictor of students’ performance in reading competency

The purpose of this study was to examine whether performance in MI could predict the performance in reading competency. The other objectives were to identify the components of MI which are correlated with the reading test scores, and to determine the relationship between the multiple intelligences and reading proficiency. A descriptive and ex post facto design was employed to ascertain relationships among the variables. The participants were 128 randomly chosen pre-university students (grade12, 18-19 years old) of both genders studying in Tehran in the academic year 2008-2009. Three instruments were utilized in this study: 1) a demographic questionnaire; 2) the Persian version of Mckenzie’s MI Inventory; and 3) a standardized reading proficiency test which was selected from retrieved paper-based TOEFL® tests. Results of the correlation analysis revealed no significant relationship between the two variables of MI and reading scores of the students. Furthermore, the results of the correlation analysis revealed that there was a low significant, negative relationship between musical-rhythmic intelligence and reading which suggests that when the reading score of a student increases, musical-rhythmic intelligence of the same student decreases and vice versa. Overall, three categories of MI (musical-rhythmic, verbal-linguistic, bodily-kinesthetic) were found to be predictive of reading proficiency

Third Conference on Artificial Intelligence for Space Applications, part 2

Topics relative to the application of artificial intelligence to space operations are discussed. New technologies for space station automation, design data capture, computer vision, neural nets, automatic programming, and real time applications are discussed

Comparison of Machine Learning Classifiers for Recognition of Online and Offline Handwritten Digits*

Automated recognition of handwritten digits has applications in several industries such as Postal and Banking for reading of addressed packages and cheques respectively. This paper compares four machine learning classifiers namely Naive Bayes, Instance Based Learner, Decision Tree and Neural Network for single digit recognition. Our experiments were conducted using the WEKA machine learning tool on two datasets; the MNIST offline handwritten digits and a collection of online ISGL handwritten digits acquired with a pen digitiser. Experiments were designed to allow for comparison within the datasets in a cross validation and across them where the online dataset is used for training and the offline dataset for testing and vice versa. We also compared classification accuracy at different levels of down sampling. Results indicate that the lazy learning instance based classifier performed slightly better than the neural network with a maximal accuracy of 97.86% and they both outperformed the other two classifiers: Naive Bayes and Decision Tree. The decision tree gave the worst performance of the four classifiers. We also discovered that better results were obtained with using the online digits when tested in a cross validation experiment. However, the pre-processed MNIST offline digits gave higher accuracies when used for training and tested with the online ISGL digits not vice versa. Also, we discovered down sampled size of 14x14 gave the best results for most of the four classifiers although these were not significantly different from the other down sampled sizes of 7x7, 21x21 and 28x28. We intend to investigate the performance of these classifiers in recognition of other characters (alphabets, punctuation and other symbols) as well as extend the recognition task to other levels of text granularity such as words, sentences and paragraphs. Keywords: Digits recognition, machine learning, classifiers, handwritten character recognition, Wek