A aprendizagem de inglês mediada por jogos eletrônicos do tipo MMORPG

Massively Multiplayer Online Role-Playing Games (MMORPGs) are role-playing games that, through the Internet, can integrate thousands of players interacting at the same time in at least one virtual world. This way, these games can provide, further than fun, a greater familiarity with the additional language and opportunity to improve the linguistic proficiency in a real context. Hence, what is proposed in this study is extended knowledge about the learning of an additional language mediated by MMORPGs for teachers to know how, if relevant, to present, use or encourage this practice to their students. Based on this major purpose, we seek to answer the following research questions: (a) what distinguishes the learning profile of the gamers and non-gamers; (b) if MMORPGs can, through a hybrid and systematic approach, assist the development of proficiency of the additional language and (c) what the think-aloud protocols show about the learning mediated by the MMORPG Allods Online. Following an experimental method (NUNAN, 1997), 16 students of the curricular component Reading and Writing Practices in English Language have comprised the control group and 17 students of the same class formed the experimental group and were submitted to a pre and post-test adapted from the Key English Test (KET) by the Cambridge University (2008). The tests were conducted before and after a period of 5 weeks of 3 hours of practice with Allods Online a week (experimental group), and classes of the curricular component (both groups). A quantitative analysis of the questionnaires about the exposure to English profiles of the participants, a quantitative analysis of the tests scores and a qualitative analysis of the thinkaloud protocols collected during the experiment were conducted based on the theories of (a) motivation (GARDNER, 1985, WILLIAMS & BURDEN, 1997, BROWN, 2007, HERCULANO-HOUZEL, 2005); (b) active learning (GASS, 1997, GEE, 2008, MATTAR, 2010); (c) interaction and collaborative learning (KRASHEN, 1991, GASS, 1997, VYGOTSKY, 1978); (d) situated learning (DAMASIO, 1994; 1999; 2003, BROWN, 2007, GEE, 2003) and (e), tangential learning (PORTNOW, 2008; MATTAR, 2010). The results indicate that the participants of the experimental group (gamers) seem to be more engaged in tangential English learning activities, such as playing games, listening to music in English, communicating with foreigners and reading in English. We also deduced that the period of experiment possibly generated positive results on the gamers proficiency scores, mainly in the parts related to orthographic development, reading and comprehension, writing with focus on content and orthographic accuracy. Lastly, the think-aloud protocols presented evidences that the gamers have engaged in active English language learning, they have interacted in English with other players, and learned linguistic aspects through the experience with the MMORPG Allods Online

Mapping the BDA20 Gene to BTAX

The lipocalin family is a large group of proteins that exhibits great structural and functional variation both within and among species, including a significant number of animal-derived aeroallergens, such as the bovine BDA20 (major cow dander allergen). This protein is classified as an occupational allergen causing asthma and other work-related allergic disorders among dairy farmers. Using a somatic cell panel the BDA20 gene was assigned to the bovine X chromosome (BTAX) with a significant concordant value of 97% to the previously mapped reference marker MAF45. A radiation hybrid (RH) mapping approach confirmed the assignment of BDA20 to BTAX. Two-point LOD scores showed that BDA20 is linked to XBM451 with a LOD score of 22.1 for a 0 value of 0.03.

Análisis del uso de las redes neuronales artificiales en el diseño de filtros antispam : una propuesta basada en arquitecturas neuronales no supervisadas

[ES]El spam, o correo no deseado enviado masivamente, es una amenaza que afecta al correo electrónico y otros medios de comunicación telemática. Su alto volumen de circulación genera pérdidas temporales y económicas considerables. Se presenta una solución a este problema: un sistema inteligente híbrido de filtrado antispam, basado en redes neuronales artificiales (RNA) no supervisadas. Consta de una etapa de preprocesado y de otra de procesado, basadas en distintos modelos de computación: programada (con 2 fases: manual y computacional) y neuronal (mediante mapas autoorganizados de Kohonen, SOM), respectivamente. Este sistema ha sido optimizado usando, como cuerpo de datos, ham de “Enron Email” y spam de dos fuentes diferentes. Se analiza la calidad y el rendimiento del mismo mediante diferentes métricas.

Scheduler-dependent inter-cell interference and its impact on LTE uplink performance at flow level

The Long Term Evolution (LTE) cellular technology is expected to extend the capacity and improve the performance of current 3G cellular networks. Among the key mechanisms in LTE responsible for traffic management is the packet scheduler, which handles the allocation of resources to active flows in both the frequency and time dimension. This paper investigates for various scheduling scheme how they affect the inter-cell interference characteristics and how the interference in turn affects the user’s performance. A special focus in the analysis is on the impact of flow-level dynamics resulting from the random user behaviour. For this we use a hybrid analytical/simulation approach which enables fast evaluation of flow-level performance measures. Most interestingly, our findings show that the scheduling policy significantly affects the inter-cell interference pattern but that the scheduler specific pattern has little impact on the flow-level performance.

Comparison of particle dispersion obtained with simple stochastic models and les in pipe flow

A hybrid Eulerian-Lagrangian approach is employed to simulate heavy particle dispersion in turbulent pipe flow. The mean flow is provided by the Eulerian simulations developed by mean of JetCode, whereas the fluid fluctuations seen by particles are prescribed by a stochastic differential equation based on normalized Langevin. The statistics of particle velocity are compared to LES data which contain detailed statistics of velocity for particles with diameter equal to 20.4 µm. The model is in good agreement with the LES data for axial mean velocity whereas rms of axial and radial velocities should be adjusted.

Vibration reduction for vision systems on board unmanned aerial vehicles using a neuro-fuzzy controller

In this paper, an intelligent control approach based on neuro-fuzzy systems performance is presented, with the objective of counteracting the vibrations that affect the low-cost vision platform onboard an unmanned aerial system of rotating nature. A scaled dynamical model of a helicopter is used to simulate vibrations on its fuselage. The impact of these vibrations on the low-cost vision system will be assessed and an intelligent control approach will be derived in order to reduce its detrimental influence. Different trials that consider a neuro-fuzzy approach as a fundamental part of an intelligent semi-active control strategy have been carried out. Satisfactory results have been achieved compared to those obtained by means of vibration reduction passive techniques.

Integration of different models in the design of chemical processes: Application to the design of a power plant

With advances in the synthesis and design of chemical processes there is an increasing need for more complex mathematical models with which to screen the alternatives that constitute accurate and reliable process models. Despite the wide availability of sophisticated tools for simulation, optimization and synthesis of chemical processes, the user is frequently interested in using the ‘best available model’. However, in practice, these models are usually little more than a black box with a rigid input–output structure. In this paper we propose to tackle all these models using generalized disjunctive programming to capture the numerical characteristics of each model (in equation form, modular, noisy, etc.) and to deal with each of them according to their individual characteristics. The result is a hybrid modular–equation based approach that allows synthesizing complex processes using different models in a robust and reliable way. The capabilities of the proposed approach are discussed with a case study: the design of a utility system power plant that has been decomposed into its constitutive elements, each treated differently numerically. And finally, numerical results and conclusions are presented.

Parallel relaxed and extrapolated algorithms for computing PageRank

In this paper, parallel Relaxed and Extrapolated algorithms based on the Power method for accelerating the PageRank computation are presented. Different parallel implementations of the Power method and the proposed variants are analyzed using different data distribution strategies. The reported experiments show the behavior and effectiveness of the designed algorithms for realistic test data using either OpenMP, MPI or an hybrid OpenMP/MPI approach to exploit the benefits of shared memory inside the nodes of current SMP supercomputers.

A knowledge-based system for liquid retaining structure design with blackboard architecture

Owing to the high degree of vulnerability of liquid retaining structures to corrosion problems, there are stringent requirements in its design against cracking. In this paper, a prototype knowledge-based system is developed and implemented for the design of liquid retaining structures based on the blackboard architecture. A commercially available expert system shell VISUAL RULE STUDIO working as an ActiveX Designer under the VISUAL BASIC programming environment is employed. Hybrid knowledge representation approach with production rules and procedural methods under object-oriented programming are used to represent the engineering heuristics and design knowledge of this domain. It is demonstrated that the blackboard architecture is capable of integrating different knowledge together in an effective manner. The system is tailored to give advice to users regarding preliminary design, loading specification and optimized configuration selection of this type of structure. An example of application is given to illustrate the capabilities of the prototype system in transferring knowledge on liquid retaining structure to novice engineers. (C) 2004 Elsevier Ltd. All rights reserved.

An Autonomic Framework for Mobile Cloud Computing

This thesis presents the formal definition of a novel Mobile Cloud Computing (MCC) extension of the Networked Autonomic Machine (NAM) framework, a general-purpose conceptual tool which describes large-scale distributed autonomic systems. The introduction of autonomic policies in the MCC paradigm has proved to be an effective technique to increase the robustness and flexibility of MCC systems. In particular, autonomic policies based on continuous resource and connectivity monitoring help automate context-aware decisions for computation offloading. We have also provided NAM with a formalization in terms of a transformational operational semantics in order to fill the gap between its existing Java implementation NAM4J and its conceptual definition. Moreover, we have extended NAM4J by adding several components with the purpose of managing large scale autonomic distributed environments. In particular, the middleware allows for the implementation of peer-to-peer (P2P) networks of NAM nodes. Moreover, NAM mobility actions have been implemented to enable the migration of code, execution state and data. Within NAM4J, we have designed and developed a component, denoted as context bus, which is particularly useful in collaborative applications in that, if replicated on each peer, it instantiates a virtual shared channel allowing nodes to notify and get notified about context events. Regarding the autonomic policies management, we have provided NAM4J with a rule engine, whose purpose is to allow a system to autonomously determine when offloading is convenient. We have also provided NAM4J with trust and reputation management mechanisms to make the middleware suitable for applications in which such aspects are of great interest. To this purpose, we have designed and implemented a distributed framework, denoted as DARTSense, where no central server is required, as reputation values are stored and updated by participants in a subjective fashion. We have also investigated the literature regarding MCC systems. The analysis pointed out that all MCC models focus on mobile devices, and consider the Cloud as a system with unlimited resources. To contribute in filling this gap, we defined a modeling and simulation framework for the design and analysis of MCC systems, encompassing both their sides. We have also implemented a modular and reusable simulator of the model. We have applied the NAM principles to two different application scenarios. First, we have defined a hybrid P2P/cloud approach where components and protocols are autonomically configured according to specific target goals, such as cost-effectiveness, reliability and availability. Merging P2P and cloud paradigms brings together the advantages of both: high availability, provided by the Cloud presence, and low cost, by exploiting inexpensive peers resources. As an example, we have shown how the proposed approach can be used to design NAM-based collaborative storage systems based on an autonomic policy to decide how to distribute data chunks among peers and Cloud, according to cost minimization and data availability goals. As a second application, we have defined an autonomic architecture for decentralized urban participatory sensing (UPS) which bridges sensor networks and mobile systems to improve effectiveness and efficiency. The developed application allows users to retrieve and publish different types of sensed information by using the features provided by NAM4J's context bus. Trust and reputation is managed through the application of DARTSense mechanisms. Also, the application includes an autonomic policy that detects areas characterized by few contributors, and tries to recruit new providers by migrating code necessary to sensing, through NAM mobility actions.

A comparison of variational and Markov chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a Hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while conditional variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother. © 2008 Springer Science + Business Media LLC.

Emotions and a Prior Knowledge Representation in Artificial General Intelligence

In this paper a prior knowledge representation for Artificial General Intelligence is proposed based on fuzzy rules using linguistic variables. These linguistic variables may be produced by neural network. Rules may be used for generation of basic emotions – positive and negative, which influence on planning and execution of behavior. The representation of Three Laws of Robotics as such prior knowledge is suggested as highest level of motivation in AGI.

Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units

Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit. Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and possibilistic clustering approaches can be implemented on the base of the presented spiking neural network.

Distribuição de derivados de petróleo por redes de polidutos: uma abordagem através de algoritmos evolucionários híbridos para um problema triobjetivo

An important problem faced by the oil industry is to distribute multiple oil products through pipelines. Distribution is done in a network composed of refineries (source nodes), storage parks (intermediate nodes), and terminals (demand nodes) interconnected by a set of pipelines transporting oil and derivatives between adjacent areas. Constraints related to storage limits, delivery time, sources availability, sending and receiving limits, among others, must be satisfied. Some researchers deal with this problem under a discrete viewpoint in which the flow in the network is seen as batches sending. Usually, there is no separation device between batches of different products and the losses due to interfaces may be significant. Minimizing delivery time is a typical objective adopted by engineers when scheduling products sending in pipeline networks. However, costs incurred due to losses in interfaces cannot be disregarded. The cost also depends on pumping expenses, which are mostly due to the electricity cost. Since industrial electricity tariff varies over the day, pumping at different time periods have different cost. This work presents an experimental investigation of computational methods designed to deal with the problem of distributing oil derivatives in networks considering three minimization objectives simultaneously: delivery time, losses due to interfaces and electricity cost. The problem is NP-hard and is addressed with hybrid evolutionary algorithms. Hybridizations are mainly focused on Transgenetic Algorithms and classical multi-objective evolutionary algorithm architectures such as MOEA/D, NSGA2 and SPEA2. Three architectures named MOTA/D, NSTA and SPETA are applied to the problem. An experimental study compares the algorithms on thirty test cases. To analyse the results obtained with the algorithms Pareto-compliant quality indicators are used and the significance of the results evaluated with non-parametric statistical tests.

Comparison of DES predictions of turbulent flow and heat transfer in a 45° ribbed duct

Numerical predictions of the turbulent flow and heat transfer of a stationary duct with square ribs 45° angled to the main flow direction are presented. The rib height to channel hydraulic diameter is 0.1, the rib pitch to rib height is 10. The calculations have been carried out for a bulk Reynolds number of 50,000. The flows generated by ribs are dominated by separating and reattaching shear layers with vortex shedding and secondary flows in the cross-section. The hybrid RANS-LES approach is adopted to simulate such flows at a reasonable computation cost. The capability of the various versions of DES method, depending the RANS model, such as DES-SA, DES-RKE, DES-SST, have been compared and validated against the experiment. The significant effect of RANS model on the accuracy of the DES prediction has been shown. The DES-SST method, which was able to reproduce the correct physics of flow and heat transfer in a ribbed duct showed better performance than others.