Topic models have been widely used to identify topics in text corpora. It is also known that purely unsupervised models often result in topics that are not comprehensible in applications. In recent years, a number of knowledge-based models have been proposed, which allow the user to input prior knowledge of the domain to produce more coherent and meaningful topics. In this paper, we go one step...

This paper presents a novel framework for combining Microsoft Kinect device (Kinect) and Leap Motion sensor with Java Monkey Engine and utilizing it for natural human computer interaction. The framework supports the standard input devices (keyboard, mouse) as well as selected motion tracking sensors Microsoft Kinect, Leap Motion). To demonstrate its aplicability, the framework was subsequently ...

Everyday methods providing managers with elaborated information making more comprehensible the results obtained of queries over OLAP systems are required. This problem is relatively recent due to the huge amount of information they store, but so far there are few proposals facing this issue, and they are mainly focused on presenting the information to the user in a comprehensible language (natu...

This paper presents a complete inductive learning system that aims to produce comprehensible theories for XML document classifications. The knowledge representation method is based on a higherorder logic formalism which is particularly suitable for structured-data learning systems. A systematic way of generating predicates is also given. The learning algorithm of the system is a modified standa...

In machine learning, symbolic approaches usually yield comprehensible results without free parameters for further (incremental) retraining. On the other hand, non-symbolic (connectionist or neural network based) approaches usually yield black-boxes which are diicult to understand and reuse. The goal of this study is to propose a machine learner that is both incrementally retrainable and compreh...

The paper addresses comprehensible proof presentation for teaching and learning that can be provided by an automated proof planner that is a component of the proof development environment mega. Starting from empirically discovered requirements for the comprehen-sibility of proofs we show that, given a proof plan representation of a proof, the problem of automatically presenting a mathematical p...

A preference for a consistent point of view pervades narrative comprehension, memory, and production. Subjects read statements exhibiting a consistent point of view faster than statements exhibiting a change in point of view, and they rated the consistent statements as more comprehensible than change statements. Futhermore, subjects tended to misrecall change statements as consistent statements...

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...