Educational robotics as a learning aid for disabled children

Severe disabled children have little chance of environmental and social exploration and discovery, and due this lack of interaction and independency, it may lead to an idea that they are unable to do anything by themselves. This idea is called learned helplessness and is very negative for the child cognitive development and social development as well. With this entire situation it is very likely that the self-steam and mood of this child. Trying to help these children on this situation, educational robotics can offer and aid, once it can give them a certain degree of independency in exploration of environment. The system developed in this work allows the child to transmit the commands to a robot. Sensors placed on the child's body can obtain information from head movement or muscle pulses to command the robot to carry the tasks. Also, this system can be used with a variety of robots, being necessary just a previous configuration. It is expected that, with the usage of this system, the disabled children have a better cognitive development and social interaction, balancing in a certain way, the negative effects of their disabilities. © 2011 IEEE.

Precipitates segmentation from scanning electron microscope images through machine learning techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.

Mensuração de estratégias de estudo e aprendizagem de alunos universitários: learning and study strategies inventory (lassi) adaptação e validação para o Brasil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Unsupervised manifold learning using Reciprocal kNN Graphs in image re-ranking and rank aggregation tasks

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

A transmission power self-optimization technique for wireless sensor networks

Wireless sensor networks (WSNs) are generally used to monitor hazardous events in inaccessible areas. Thus, on one hand, it is preferable to assure the adoption of the minimum transmission power in order to extend as much as possible the WSNs lifetime. On the other hand, it is crucial to guarantee that the transmitted data is correctly received by the other nodes. Thus, trading off power optimization and reliability insurance has become one of the most important concerns when dealing with modern systems based on WSN. In this context, we present a transmission power self-optimization (TPSO) technique for WSNs. The TPSO technique consists of an algorithm able to guarantee the connectivity as well as an equally high quality of service (QoS), concentrating on the WSNs efficiency (Ef), while optimizing the transmission power necessary for data communication. Thus, the main idea behind the proposed approach is to trade off WSNs Ef against energy consumption in an environment with inherent noise. Experimental results with different types of noise and electromagnetic interference (EMI) have been explored in order to demonstrate the effectiveness of the TPSO technique.

Particle competition and cooperation for semi-supervised learning with label noise

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Preliminary diagnosis of ophtalmological diseases through machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Effects of Self-Assessment on Math Homework

In this action research study of my eighth grade differentiated Algebra students, I investigated the effects of students using self-assessment on their homework. Students in my class were unmotivated and failed test objectives consistently. I wanted students to see that they controlled their learning and could be motivated to succeed. Formative assessment tells students how they need to improve. Learning needs to happen before they can be assessed. Self-assessment is one tool that helps students know if they are learning. A rubric scoring guide, daily documentation sheet and feedback on homework and test correlations were used to help students monitor their learning. Students needed time to develop the skill to self-assess. Students began to understand the relationship between homework and performing well on tests by the end of the action research period. Early in the period, most students encountered difficulty understanding that they controlled their learning and did not think homework was important. By the end of the year, all students said homework was important and that it helped them on quizzes and tests. Motivating students to complete homework is difficult. Teaching them to self-assess and to keep track of their learning helps them stay motivated.

Do Students Progress if They Self-Assess? A Study in Small-Group Work

In this action research study of my classroom of 8th grade mathematics, I investigated the effects of self-assessment on student group work. Data was collected to see how self-assessment affected small-group work, usage of precise mathematical vocabulary, and student attitudes toward mathematics. Self-assessment allowed the students to periodically evaluate their own learning and their involvement in math class. I discovered that the vast majority of students enjoy working in small-groups, and they feel they are good group members. Evidence in regard to use of precise mathematical vocabulary showed an increased awareness in the importance of its usage. Student attitudes toward mathematics remained positive and unchanged throughout the research. As a result of this research, I plan to continue use of small-group work and selfassessment. I will continue emphasis on the inclusion of precise mathematical vocabulary as well as on training on cooperative learning strategies.

A novel self consistent calculation approach for the capacitance-voltage characteristics of semiconductor quantum wire transistors based on a split-gate configuration

Purpose - The purpose of this paper is to develop an efficient numerical algorithm for the self-consistent solution of Schrodinger and Poisson equations in one-dimensional systems. The goal is to compute the charge-control and capacitance-voltage characteristics of quantum wire transistors. Design/methodology/approach - The paper presents a numerical formulation employing a non-uniform finite difference discretization scheme, in which the wavefunctions and electronic energy levels are obtained by solving the Schrodinger equation through the split-operator method while a relaxation method in the FTCS scheme ("Forward Time Centered Space") is used to solve the two-dimensional Poisson equation. Findings - The numerical model is validated by taking previously published results as a benchmark and then applying them to yield the charge-control characteristics and the capacitance-voltage relationship for a split-gate quantum wire device. Originality/value - The paper helps to fulfill the need for C-V models of quantum wire device. To do so, the authors implemented a straightforward calculation method for the two-dimensional electronic carrier density n(x,y). The formulation reduces the computational procedure to a much simpler problem, similar to the one-dimensional quantization case, significantly diminishing running time.

Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

Localising and quantifying damage by means of a multi-chromosome genetic algorithm

This paper presents a structural damage detection methodology based on genetic algorithms and dynamic parameters. Three chromosomes are used to codify an individual in the population. The first and second chromosomes locate and quantify damage, respectively. The third permits the self-adaptation of the genetic parameters. The natural frequencies and mode shapes are used to formulate the objective function. A numerical analysis was performed for several truss structures under different damage scenarios. The results have shown that the methodology can reliably identify damage scenarios using noisy measurements and that it results in only a few misidentified elements. (C) 2012 Civil-Comp Ltd and Elsevier Ltd. All rights reserved.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.