[show abstract][hide abstract] ABSTRACT: Multi-agents are increasingly gaining the attention of researchers and developers of virtual games. The agents can control the user performance, adapting the interface and automatically changing the difficulty level of tasks. The objective of this paper is to describe the development of a game that combines the technologies of Virtual Reality and Multi-Agent. This system aims at improving the cognitive functions in patients with neuropsychiatric disorders. The integration of different technologies, the development methodology and the test procedures are described throughout the paper.
Virtual Reality (SVR), 2011 XIII Symposium on; 06/2011
[show abstract][hide abstract] ABSTRACT: Therapies for cognitive stimulation must be developed when some of the cognitive functions are not working properly. In many
applications there is a strong dependence on therapist’s intervention to control the patient’s navigation in the environment
and to change the difficulty level of a task. In general, these interventions, cause distractions, reducing the level of user
immersion in the activities. As an alternative, the inclusion of intelligent agents can help to alleviate this problem by
reducing the need of therapist involvement. This paper presents a serious game that combines the technologies of Virtual Reality
and Multi-Agent Systems designed to improve the cognitive functions in patients with neuropsychiatric disorders. The integration
of different technologies and the modelling methodology are described and open new software development perspectives for 3D
Virtual and Mixed Reality - Systems and Applications - International Conference, Virtual and Mixed Reality 2011, Held as Part of HCI International 2011, Orlando, FL, USA, July 9-14, 2011, Proceedings, Part II; 01/2011
[show abstract][hide abstract] ABSTRACT: Grid computing represents the main solution to integrate distributed and heterogeneous resources in global scale. However, the infrastructure necessary for maintaining a global grid in production is huge. Such fact has led to excessive power consumption. On the other hand, most green strategies for data centers are DVS (Dynamic Voltage Scaling)-based and become difficult to implement them in global grids. This paper proposes the HGreen heuristic (Heavier Tasks on Maximum Green Resource) and defines a workflow scheduling algorithm in order to implement it on global grids. HGreen algorithm aims to prioritize energy-efficient resources and explores workflow application profiles. Simulation results have shown that the proposed algorithm can significantly reduce the power consumption in global grids.
IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing, DASC 2011, 12-14 December 2011, Sydney, Australia; 01/2011
[show abstract][hide abstract] ABSTRACT: This paper proposes to model the extraction of acronyms and their meaning from unstructured text as a stochastic process using hidden Markov models (HMMs). The underlying, or hidden, chain is derived from the acronym, where the states in the chain are made by the acronyms characters. The transition between two states happens when the origin state emits a signal. Signals recognizable by the HMM are the tokens extracted from text. Observations are the sequence of tokens also extracted from text. Given a set of observations, the acronym definition will be the observation with the highest probability to emerge from the HMM. Modeling this extraction probabilistically allows us to deal with two difficult aspects of this process: ambiguity and noise. We characterize ambiguity when there is no unique alignment between the characters in the acronym with a token in the expansion, while the feature-characterizing noise is the absence of such alignment. Our experiments have proven that this approach has high precision (93.50%) and recall (85.50%) rates in an environment, where acronym coinage is ambiguous and noisy, such as the biomedical domain. Processing and comparing the approached described in this paper with different others showed that the former reaches the highest F1 score (89.40%) on the same corpus.
IEEE Transactions on Biomedical Engineering 12/2010; · 2.35 Impact Factor
[show abstract][hide abstract] ABSTRACT: In this work, we propose a mathematical model that describes how the mesothalamic dopamine pathway modulates the attentional
focus via the thalamocortical loop, and how mesothalamic dopamine alterations can promote inattention symptoms in patients
with Parkinson’s disease (PD) and attention deficit hyperactivity disorder (ADHD). We model the thalamocortical loop with
a neuronal network where each thalamic neuron is described by a system of coupled differential equations reflecting neurophysiological
properties. The computational simulations reflect neurochemical features of PD and ADHD. Our results suggest that the mesothalamic
dopamine hypoactivity causes difficulties in attentional shifting. Conversely, the mesothalamic dopamine hyperactivity hinders
the attentional focus consolidation. Furthermore, regardless of the amount of mesothalamic dopamine activity, the mesocortical
dopamine hypoactivity leads to loss of attentional focus. Finally, we identify a unique neuronal mechanism underlying attention
deficits in PD and ADHD and relate different inattention symptoms in ADHD to different dopaminergic levels in the brain circuit
[show abstract][hide abstract] ABSTRACT: In earlier work, we have proposed a neural network model that describes some mental processes involved in neuroses, by an
associative memory mechanism, where modules corresponding to sensorial and symbolic memories interact, representing unconscious
and conscious mental activity. Here, we relate our neuroses model with two models which have been proposed for modelling cognitive
functions underlying consciousness: the CODAM model and the Dehaene-Kerszberg-Changeux Global Workspace model, to support
that memory and attentional mechanisms are essential for consciousness.
Artificial Neural Networks - ICANN 2010 - 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part III; 01/2010
[show abstract][hide abstract] ABSTRACT: We review concepts introduced in earlier work, where a neural network mechanism describes some mental processes in neurotic pathology and psychoanalytic working-through, as associative memory functioning, according to the findings of Freud. We developed a complex network model, where modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's idea that consciousness is related to symbolic and linguistic memory activity in the brain. We have introduced a generalization of the Boltzmann machine to model memory associativity. Model behavior is illustrated with simulations and some of its properties are analyzed with methods from statistical mechanics.
[show abstract][hide abstract] ABSTRACT: We compare the use of Generalized Simulated Annealing (GSA) to the traditional Boltzmann Machine (BM), to model memory functioning,
in a neural network model that describes conscious and unconscious processes involved in neurosis, which we proposed in earlier
work. Modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental activity.
We previously developed an algorithm, based on known microscopic mechanisms that control synaptic properties, and showed that
the network self-organizes to a hierarchical, clustered structure. Some properties of the complex networks which result from
this self-organization indicate that the use of GSA may be more appropriate than the BM, to model memory access mechanisms.
We illustrate the model with simulations.
[show abstract][hide abstract] ABSTRACT: We have previously described the mental pathology known as neurosis, in terms of its relation to memory function. We proposed neural network mechanisms, whereby neurotic behavior is described as a brain associative memory process, and the symbolic associativity involved in psychoanalytic working-through can be mapped onto a corresponding network reconfiguration process. Microscopic mechanisms that control synaptic properties self-organize the memory networks to a hierarchical, clustered structure. Modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. Here, we review these concepts and illustrate, with simulations, some of these complex networks’ behaviors and properties.
[show abstract][hide abstract] ABSTRACT: Four binary-encoded models describing some aspects of the phylogenetics evolution in an artificial immune system have been
proposed and analyzed. The first model has focused on the evolution of a paratope’s population, considering a fixed group
of epitopes, to simulate a hypermutation mechanism and observe how the system would self-adjust to cover the epitopes. In
the second model, the evolution involves a group of antibodies adapting to a given antigenic molecules’ population. The third
model simulated the coevolution between antibodies’ generating gene libraries and antigens. The objective was to simulate
somatic recombination mechanisms to obtain final libraries apt to produce antibodies to cover any possible antigen that would
appear in the pathogens’ population. In the fourth model, the coevolution involves a new population of self-molecules whose
function was to establish restrictions in the evolution of libraries’ population. For all the models implemented, evolutionary
algorithms (EA) were used to form adaptive niching inspired in the coevolutionary shared niching strategy ideas taken from
a monopolistic competition economic model where “businessmen” locate themselves among geographically distributed “clients”
so as to maximize their profit. Numerical experiments and conclusions are shown. These considerations present many similarities
to biological immune systems and also some inspirations to solve real-world problems, such as pattern recognition and knowledge
discovery in databases.
[show abstract][hide abstract] ABSTRACT: Abstract� Knowledge,discovery in databases usually face the problem,of miss- ing values. Thus there are several preprocessing,mechanisms,that aim to make data imputation. However, these mechanisms normally deal with univariate cases, i.e. cases that present missing values in only one column. Iterative im- putation mechanisms,are capable,of dealing with cases that present missing values in several columns, imputing values for one column at a time, but offer several implementation possibilities, from which the data analists find it diffi- cult to choose. This paper presents a workflow-based platform to allow the easy setup, experimentation, and analisys of several iterative imputation techniques. It shows the usage of the platform and a sample experiment. Resumo� Uma,dificuldade comum,aos processos,de descoberta de conheci-
XXIII Simpósio Brasileiro de Banco de Dados, 13-15 de Outubro, Campinas, São Paulo, Brasil, Anais; 01/2008
[show abstract][hide abstract] ABSTRACT: Albeit the Seminar on “The Purloined Letter” is one of the clearest of the writings of Lacan, in a special part of the text a dense logical-mathematical formalism makes the rationale difficult to understand for the majority of the readers. In addition, some not explicitly assumed hypothesis lead the reader to consider as general some conclusions that are not absolute at all. This paper proposes a detailed analysis of these specific parts of the text, bringing light, not only over the subsumed hypothesis, but also over Lacan’s theory properly. Some results from the Theory of Complexity, specially the self-organization phenomena, are used to update Lacan ideas about the genesis of the symbolic structure of the unconscious.
[show abstract][hide abstract] ABSTRACT: Inattention symptoms observed in patients with attention deficit hyperactivity disorder (ADHD) are mostly related to a hypoactivity in the mesocortical dopaminergic pathway. However, mesothalamic dopaminergic variations also affect the attentional control, and possibly lead to attention alterations in ADHD.
Elaborating a neurocomputational model from biochemical knowledge of mesocortical and mesothalamic dopamine systems, to investigate how different levels of mesothalamic dopamine influence the thalamocortical loop, leading to some attention deficits observed in ADHD.
First, we model physiological properties of thalamic neurons with a set of mathematical equations. Next, we simulate computationally the modeled thalamocortical loop under different levels of mesothalamic dopamine, and also the mesocortical dopaminergic decrease.
Low levels of mesothalamic dopamine hinders the attentional shift and, high levels of such neuromodulator lead to distraction. When such alterations occur together with a decrease in the mesocortical dopamine level, the attention deficit turns into incapacity of perceiving environmental stimuli, due to a no winner competition between low activated thalamic areas. Inattention in ADHD also has its origins in dopaminergic disturbs throughout the mesothalamic pathway, which enhance a high focusing or do not allow the attention focus consolidation.
In ADHD, the inattention is related to dopaminergic alterations that are not restricted to the mesocortical system.
Arquivos de Neuro-Psiquiatria 01/2008; 65(4A):1043-9. · 0.83 Impact Factor
[show abstract][hide abstract] ABSTRACT: We have previously described neurotic psychopathology and psychoanalytic
working-through by an associative memory mechanism, based on a neural
network model, where memory was modelled by a Boltzmann machine (BM).
Since brain neural topology is selectively structured, we simulated
known microscopic mechanisms that control synaptic properties, showing
that the network self-organizes to a hierarchical, clustered structure.
Here, we show some statistical mechanical properties of the complex
networks which result from this self-organization. They indicate that a
generalization of the BM may be necessary to model memory.
[show abstract][hide abstract] ABSTRACT: This work presents a mathematical-computational model of the development process of Alzheimer’s disease, based on the assumption that cholesterol plays a key role in the formation of hallmark neuropathological lesions that characterize the disease: the senile amyloid plaques and neurofibrillary tangles. The final model, conceived as a system of equations, was implemented as a computer program and, thereafter, two sets of tests were carried out. In the first set of tests, aimed at validating the model, the results obtained from the simulations carried out were qualitatively coherent with in vivo or in vitro experiments found in the consulted literature. In the second set, we performed simulations in order to test a number of hypotheses about the development process of the disease, collected from the literature but yet without experimental confirmation. From the results of these simulations, it was possible to validate those hypotheses and to draw some conclusions about the development process of the disease.
Proceedings of the 2007 Summer Computer Simulation Conference, SCSC 2007, San Diego, California, USA, July 16-19, 2007; 01/2007
[show abstract][hide abstract] ABSTRACT: We proposed a mechanism in  whereby neurotic behavior may be understood as an associative memory process in the brain, and the symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neuronal network. Memory was modeled by a Boltzmann machine represented by a complete graph, as a first approximation. However, it is known that brain neuronal topology is selectively structured and here, we further develop the memory model by including known microscopic mechanisms that control synaptic properties, showing that the network self organizes to a hierarchical, clustered structure. We propose a memory organization where two modules corresponding to sensorial and declarative memory interact, producing sensorial and symbolic activity, representing unconscious and conscious mental processes. The model is illustrated through a computer simulation, where we show some mathematical properties of the resulting complex network.
[show abstract][hide abstract] ABSTRACT: This paper presents part of a mathematical model constructed to study oxidative stress actions in Alzheimer's disease. There will be shown the production, oxidation, reduction and loss of proteins caused by oxidants and antioxidants substances. These processes, among others, were formalized into mathematical equations that were converted into a computer program, which, from some known initial condition, simulates the system evolution along the time. Finally, the results of some simulations are qualitatively analyzed and discussed.