Abstract This study investigates the effectiveness of integrating short stories with task-based learning activities in English as a foreign language (EFL) class to promote vocabulary development and motivation among L2 learners. Six were selected by participants based on their interests, pre- post-tests conducted evaluate acquisition questionnaire was used gather information about perception af...

BACKGROUND Automated laboratory analyzers that mass produce data have been linked to information systems for more than two decades, but little progress has been made in developing more comprehensible report forms. Results are still reported in computer-generated printouts containing hundreds of numbers crowded into columns on each printed page. METHODS We developed three software applications...

Ensemble methods have become very well known for being powerful pattern recognition algorithms capable of achieving high accuracy. However, Ensemble methods produces learners that are not comprehensible or transferable thus making them unsuitable for tasks that require a rational justification for making a decision. Rule Extraction methods can resolve this limitation by extracting comprehensibl...

Symbolic machine learning techniques can extract exible and comprehensible knowledge from empirical data of material behavior. The diversity of symbolic machine learning techniques ooers potential to match the requirements of many tasks when models of material behavior need to be created from data. We develop a series of steps for generating material behavior from empirical data and exemplify s...

Bateson's difference-based epistemology can be simulated by a Boolean network model. Bateson proposed that taking differences in differences would produce emergent hierarchies of knowledge. This study simulated Bateson's proposal by taking differences in differences in a Boolean model. The crucial result is that constancies in the dynamics of the flow of differences in the model (a) define perc...

The ultimate goal of data mining is to extract knowledge from massive data. Knowledge is ideally represented as human-comprehensible patterns from which end-users can gain intuitions and insights. Axis-parallel hyper-rectangles provide interpretable generalizations for multi-dimensional data points with numerical attributes. In this dissertation, we study the fundamental problem of rectangle-ba...