Revista Romana de Interactiune Om-Calculator

81-96

Authors: Claudiu Musat, Marian-Andrei Rizoiu, Stefan Trausan-Matu1

Topic modeling is a growing research field and novel ways of interpreting and evaluating results are necessary. We propose a method for evaluating and improving the performance of topic models generating algorithms relying on WordNet data. We first propose a measure for determining a topic model’s fitness factoring in its broadness and redundancy. Then, for each individual topic, the amount of relevant information it provides, along with its most important words and related concepts are determined by defining a cohesion function based on the topic’s projection on WordNet concepts. The model as a whole is improved by eliminating each topic’s outliers with respect to the ontology projection. We define a inter topic ontology based distance and we further use it to investigate the impact of removing redundant topics from a model with regard to the overlap between topics’ ontological projections. Clustering similar topics into conceptually cohesive groups is tried as an alternative to pruning less relevant topics. Results show that evaluating and improving statistical models with WordNet is a promising research track that leads to more coherent topic models.

125-138

Authors: Dragos Daniel Iordache

Nowadays, the e-learning applications are integrated in most of curricular areas and scholar cycles. In this context, the perceived utility of these applications at end-user level is an important issue. A series of theories and models (Technology Acceptance Model - TAM, Innovation Diffusion Theory – IDT, Student Integration Model - SIM) have been developed regarding the acceptance of informatics technologies in general and the e-learning technologies in particular. This paper is focussing on the main aspects of the acceptance theories and models in the context of e-learning applications.

157-170

Authors: Mihai Anitei, Mihaela Chraif, Claudiu Papasteri, Andrei Neacsşu

The facial microexpresions had been situated in the main stream of research for over 5 decades. Therefore, Eakman highlighted two main directions: the possibility to learn the facial microepressions and to learn how to code and analyze the facial microexpresion in different situations. This study highlights the importance of facial microexpressions simulation in human memory retrieval. Learning to code and analyze the facial microexpressions od relevant for selection interview, education, selling and marketing. This way people can express conscious and unconscious the facial microexpressions. The role of the psychologist is focused on the facial microexpressions interpretation making the differences between the true or simulated emotions.