MMORPGS and cognitive performance: A study with 1280 Brazilian high school students

The present paper attempts to empirically study the cognitive impacts of Massive Multiplayer Online Role-Playing Games (MMORPGs) in uncontrolled contexts in light of the Cognitive Mediation Networks Theory, a new model of human intelligence that aims to explain cognition as the result of brain activity combined with the information-processing done by external structures such as tools, social groups and culture. A sample of 1280 students Brazilian high school students answered a form inquiring about socio-demographic information plus the use of computer games, and also was submitted to a short knowledge exam and a mini psychometric test. The findings indicated that, due to their underlying structure and sociocultural nature, MMORPGs are associated to a greater level of insertion into the Digital Age, higher levels of logical-numerical performance, and better scholastic ability. Finally, suggestions are made for future studies on the subject.     

Introduction to the special issue of the 19th International Conference on Runtime Verification

International Journal on Software Tools for Technology Transfer -     

Nonlinear Versus Linear Learning Devices: A Procedural Perspective

We provide a discussion of bounded rationality learning behind traditional learning mechanisms, i.e., Recursive Ordinary Least Squares and Bayesian Learning . These mechanisms lack for many reasons a behavioral interpretation and, following the Simon criticism, they appear to be substantively rational. In this paper, analyzing the Cagan model, we explore two learning mechanisms which appear to be more plausible from a behavioral point of view and somehow procedurally rational: Least Mean Squares learning for linear models and Back Propagation for Artificial Neural Networks . The two algorithms look for a minimum of the variance of the error forecasting by means of a steepest descent gradient procedure. The analysis of the Cagan model shows an interesting result: non-convergence of learning to the Rational Expectations Equilibrium is not due to the restriction to linear learning devices; also Back Propagation learning for Artificial Neural Networks may fail to converge to the Rational Expectations Equilibrium of the model.     

Betweenness estimation in OLSR-based multi-hop networks for distributed filtering

In traditional networks special efforts are put to secure the perimeter with firewalls: particular routers that analyze and filter the traffic to separate zones with different levels of trust. In wireless multi-hop networks the perimeter is a concept extremely hard to identify, thus, it is much more effective to enforce control on the nodes that will route more traffic. But traffic filtering and traffic analysis are costly activities for the limited resources of mesh nodes, so a trade-off must be reached limiting the number of nodes that enforce them. This work shows how, using the OLSR protocol, the centrality of groups of nodes with reference to traffic can be estimated with high accuracy independently of the network topology or size. We also show how this approach greatly limits the impact of an attack to the network using a number of firewalls that is only a fraction of the available nodes.     

Effective and Efficient Knowledge Base Refinement

This paper presents the STALKER knowledge base refinement system. Like its predecessor KRUST, STALKER proposes many alternative refinements to correct the classification of each wrongly classified example in the training set. However, there are two principal differences between KRUST and STALKER. Firstly, the range of misclassified examples handled by KRUST has been augmented by the introduction of inductive refinement operators. Secondly, STALKER's testing phase has been greatly speeded up by using a Truth Maintenance System (TMS). The resulting system is more effective than other refinement systems because it generates many alternative refinements. At the same time, STALKER is very efficient since KRUST's computationally expensive implementation and testing of refined knowledge bases has been replaced by a TMS-based simulator.     

Identification of channelling and recirculation parameters of agitated pulp stock chests

This paper describes a mechanism for identifying the dynamics of non-ideal mixing processes. The object is to study two of the non-ideal behaviours of agitated pulp stock chests: recirculation and channelling. An initial continuous-time model, which contains physically relevant parameters, is transformed into its discrete-time counterpart. This transformation introduces some challenging identification problems, as the discrete-time parameters become a non-linear combination of the original continuous-time parameters. A system identification methodology that addresses these challenges is developed and demonstrated by means of computer simulation. The analysis of data collected from experiments on a laboratory scale model of an industrial chest shows the potential of the techniques developed in this paper.     

Compressed Multiresolution Hierarchies for High‐Quality Precomputed Shadows

The quality of shadow mapping is traditionally limited by texture resolution. We present a novel lossless compression scheme for high‐resolution shadow maps based on precomputed multiresolution hierarchies. Traditional multiresolution trees can compactly represent homogeneous regions of shadow maps at coarser levels, but require many nodes for fine details. By conservatively adapting the depth map, we can significantly reduce the tree complexity. Our proposed method offers high compression rates, avoids quantization errors, exploits coherency along all data dimensions, and is well‐suited for GPU architectures. Our approach can be applied for coherent shadow maps as well, enabling several applications, including high‐quality soft shadows and dynamic lights moving on fixed‐trajectories.     

Output feedback of Markov jump linear systems with no mode observation: An automotive throttle application

The note presents an output feedback control strategy for Markov jump linear systems with no mode observation. Based on minimizing a finite‐time quadratic cost, we derive an algorithm that generates output feedback gains that satisfy a necessary optimality condition. These gains can be computed off‐line relying only on the initial condition of the system. This result expands a previous one from the literature that considered state‐feedback only. To illustrate the usefulness of the approach, real‐time laboratory experiments were performed to control an automotive electronic throttle valve subject to Markov‐driven voltage fluctuations. Copyright © 2015 John Wiley & Sons, Ltd.     

Learning Temperature Dynamics on Agar-Based Phantom Tissue Surface During Single Point CO $$_2$$ 2 Laser Exposure

Neural Processing Letters - This paper presents an approach to determine a model of superficial tissue temperature dynamics during continuous wave CO $$_2$$ laser irradiation. The main contribution...     

Spectral nephelometry for making extravirgin olive oil fingerprints

In spectral nephelometry, absorption spectroscopy and nephelometry are innovatively combined to provide simultaneous online monitoring of color and turbidity of edible oils. Spectral nephelometry instrumentation consists of an optoelectronic device that measures the absorption spectrum of the oil sample at different angles. Data processing, carried out by principal component analysis (PCA), allows identification of the oil sample and creates a two-dimensional map as a fingerprint of oil types.