Automatic Generation of Cognitive Theories using Genetic Programming

Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming. Our approach evolves from experimental data cognitive theories that explain “the mental program” that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories

Survey of data mining approaches to user modeling for adaptive hypermedia

The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio

The role of human factors in stereotyping behavior and perception of digital library users: A robust clustering approach

To deliver effective personalization for digital library users, it is necessary to identify which human factors are most relevant in determining the behavior and perception of these users. This paper examines three key human factors: cognitive styles, levels of expertise and gender differences, and utilizes three individual clustering techniques: k-means, hierarchical clustering and fuzzy clustering to understand user behavior and perception. Moreover, robust clustering, capable of correcting the bias of individual clustering techniques, is used to obtain a deeper understanding. The robust clustering approach produced results that highlighted the relevance of cognitive style for user behavior, i.e., cognitive style dominates and justifies each of the robust clusters created. We also found that perception was mainly determined by the level of expertise of a user. We conclude that robust clustering is an effective technique to analyze user behavior and perception

Evaluation of a personalized digital library based on cognitive styles: Adaptivity vs. adaptability

Personalization can be addressed by adaptability and adaptivity, which have different advantages and disadvantages. This study investigates how digital library users react to these two techniques. More specifically, we develop a personalized digital library to suit the needs of different cognitive styles based on the findings of our previous work (Frias-Martinez, et al., in press). The personalized digital library includes two versions: adaptive version and adaptable version. The results showed that users not only performed better in the adaptive version, but also they perceived more positively to the adaptive version. In addition, cognitive styles have great effects on users’ responses to adaptability and adaptivity. These results provide guidance for designers to select suitable techniques to develop personalized digital libraries

Modeling human behavior in user-adaptive systems: recent advances using soft computing techniques

Adaptive Hypermedia systems are becoming more important in our everyday activities and users are expecting more intelligent services from them. The key element of a generic adaptive hypermedia system is the user model. Traditional machine learning techniques used to create user models are usually too rigid to capture the inherent uncertainty of human behavior. In this context, soft computing techniques can be used to handle and process human uncertainty and to simulate human decision-making. This paper examines how soft computing techniques, including fuzzy logic, neural networks, genetic algorithms, fuzzy clustering and neuro-fuzzy systems, have been used, alone or in combination with other machine learning techniques, for user modeling from 1999 to 2004. For each technique, its main applications, limitations and future directions for user modeling are presented. The paper also presents guidelines that show which soft computing techniques should be used according to the task implemented by the application

Sobre la poesía de Camilo Pessanha

Camilo Pessanha nació en 1867, en Coimbra (Portugal), y murió en Macao (1926). Licenciado en derecho (1891), fue profesor de filosofía del liceo de Macao, a donde llegó en 1894, y también registrador de propiedad y magistrado. Se interesó por la estética y la literatura oriental, y reunió una significativa colección de arte chino. Desde 1885 publicó en periódicos y revistas su poesía, que en parte sería recogida en el volumen Clepsydra (Lisboa, 1920)

Bone mechanical stimulation with piezoelectric materials

This chapter summarized explores in vivo use of a piezoelectric polymer for bone mechanical stimulatio

Experiência no curso de estudantes de 1º ano – um estudo no âmbito das tutorias de acompanhamento na Universidade de Évora

O presente estudo pretende conhecer a experiência no curso de estudantes de 1º ano que ingressaram na Universidade de Évora no final do 1º semestre. Estudos realizados sobre a percepção dos estudantes relativa ao contexto de aprendizagem no ensino superior indicam forte relação com as abordagens à aprendizagem e apresentam forte relevância para a compreensão da forma como os estudantes acedem ao conhecimento e para a definição de processos de aprendizagem de elevada qualidade (Entwistle, 2009; Chaleta & Entwistle, 2011). Os dados foram obtidos através da aplicação do CEQP (Ramsden, 2005; 2006; Chaleta et al, 2012) com 565 estudantes de diferentes cursos e áreas científicas. Os resultados indicaram que a experiência no curso é positiva para o conjunto dos estudantes havendo necessidade de observar com mais atenção as questões relacionadas com a avaliação. A grande maioria dos estudantes revela também satisfação com o curso que frequenta. Palavras-Chave: Experiência no Curso; Tutorias de Acompanhamento; CEQP; Ensino Superior. Abstract This study examine the experience in the course of the 1st year students who entered at the University of Évora. Studies on the perception of students on the learning environment in higher education indicate a strong relationship with the approaches to learning and have strong relevance to the understanding how students access the knowledge and the definition of high quality learning processes (Entwistle, 2009; Chaleta & Entwistle, 2011). The data were obtained by applying the CEQP (Ramsden, 2005, 2006; Chaleta et al, 2012) with 565 students from different courses and scientific areas. The results indicated that the course experience is positive for all the students but we need to look more closely at the issues related to assessment. The vast majority of students also reveals satisfaction with the course who attends Keywords: Course Experience; Mentor Monitoring; CEQP; Higher Education

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information