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Working memory (WM) is particularly important for higher cognitive tasks. Previous studies have shown that there are several brain networks under WM task or training, however, it is still unknown how many networks are involved in WM. In this paper, we utilize the method of modularity in the graph theory to explore the module distribution and the degree of coupling of the brain network under the real-time...
Different dynamic classifier selection techniques have been proposed in the literature to determine among diverse classifiers available in a pool which should be used to classify a test instance. The individual competence of each classifier in the pool is usually evaluated taking into account its accuracy on the neighborhood of the test instance in a validation dataset. In this work we investigate...
Tool use constitutes a range of complex behaviors that generally require a sophisticated level of cognition, and is only found in higher mammals and a number of avian species. In this paper, we will examine how different strategies for using a tool emerge during the simulated evolution of a two degree-of-freedom articulated limb in a reaching task environment. The limb is controlled by recurrent neural...
Recurrent neural networks (RNNs) often show very complicated temporal behavior. In this paper, we investigate the dynamics of a simple recurrent neural network used in a nontrivial articulated limb control task in a tool use domain. The RNN for the task is evolved by the NeuroEvolution of Augmenting Topologies (NEAT) algorithm. As a non-linear dynamical system, RNNs exhibit strong correlation between...
A multi-robot system conceived for accomplishing surveillance tasks is designed. The surveillance task addressed differs from that usually considered in the sense that additional requirements must be satisfied, namely: the environment must be virtually partitioned into so many disjoint areas of equal task cost as the number of robots; and each robot must execute the surveillance task restricted to...
Random forests are a class of ensemble methods for classification and regression with randomizing mechanism in bagging instances and selecting feature subspace. For high dimensional data, the performance of random forests degenerates because of the random sampling feature subspace for each node in the construction of decision trees. To address the issue, in this paper, we propose a new Principal Component...
Many real-world problems are usually unbalanced, where datasets present skewed class distributions, such as failure diagnosis, spam detection, anomaly detection, fraud detection, oil spillage detection and medical diagnosis, etc. Deep Belief Network (DBN) is a competitive machine learning technique with good performance in many applications. However, some machine learning methods are likely to give...
Creating a neural network based classification model is commonly accomplished using the trial and error technique. However, this technique has several difficulties in terms of time wasted and the availability of experts. In this article, an algorithm that simplifies structuring neural network classification models is proposed. The algorithm aims at creating a large enough structure to learn models...
Feature selection is an effective technique for dimensionality reduction to get the most useful information from huge raw data. Many spectral feature selection algorithms have been proposed to address the unsupervised feature selection problem, but most of them fail to pay attention to the noises induced during the feature selection process. In this paper, we not only consider the feature structural...
Recently, the number of features in different problem domains has grown enormously. In order to select the best representation (attributes) for these problems, a deep knowledge of the problem domain is required. As this type of knowledge is not always possible, feature selection needs to be applied as an automatic selection process of the most relevant attributes in a dataset. In this paper, we propose...
We demonstrate how to map Local Binary Patterns (LBP), a class of leading feature extractors, onto a neuromorphic processor such as TrueNorth, a silicon expression of a non-von Neumann, low-power, spiking-based, brain-inspired processor. The application is presented in the form of a texture feature extractor that can process 8-bit grayscale video at 30fps. While consuming less than 140mW of power,...
Utilizing object proposals as a preprocessing procedure has been shown its significance in many multimedia computing tasks. Most state-of-the-art methods devoted to finding a generic objectness measure for rating the possibilities of the initial sliding windows with or without objects. In fact, the object criteria vary from one objectness measure to another, which leads to the definite bottleneck...
Kernel independent component analysis (KICA) has an important application in blind source separation, in which how to select the optimal kernel, including the kernel functional form and its parameters, is the key issue for obtaining the optimal performance. In practices, a single kernel is usually chosen as the kernel model of KICA in light of experience. However, selecting a suitable kernel model...
Auto-encoder is a popular representation learning technique which can capture the generative model of data via a encoding and decoding procedure typically driven by reconstruction errors in an unsupervised way. In this paper, we propose a semi-supervised manifold learning based auto-encoder (named semAE). semAE is based on a regularized auto-encoder framework which leverages semi-supervised manifold...
Research on pedestrian detection still presents a lot of space for improvements, both on speed and detection accuracy. State-of-the-art object proposals approach has shown the very effective computational efficiency in object detection. In this paper, we present a framework for pedestrian detection based on the object proposals. Instead of scaling the test image to different sizes, we generate a pyramid...
Previous experimental studies on rabbits using electrocorticograms (ECoGs) over the cortical surface indicate spatio-temporal dynamics in the form of amplitude modulation (AM) patterns, which intermittently collapse at theta rates and give rise to rapidly propagating phase modulated (PM) patterns. The observed dynamics have been shown to be of cognitive relevance carrying useful information on the...
In community-based question answering (CQA) domain, there are two main tasks, i.e., question retrieval and answer ranking. Previous studies addressed these two tasks in an independent manner or in a sequential fashion without information communication. In this work we propose a novel method to improve the performance of CQA by mutually promoting the two tasks with the help of each other. Specifically,...
Network is a powerful paradigm for representing complex relationships and finding the community structure of networks can help people better understand the real world. Infomap, which employs the minimum description length as the optimization objective, is a competent algorithm for community structure analysis. In this paper, we propose a novel algorithm combining flow-based ensemble learning and Label...
We present an extension to a previously proposed Deep ELM architecture, and make the network end-to-end trainable using backpropagation. This significantly increases the network's performance for lower numbers of hidden units, and hence is well suited for resource constrained applications. The new architecture offers classification results of over 98% on the MNIST handwritten digits dataset for hidden...
A time series is a sequence of observations collected over fixed sampling intervals. Several real-world dynamic processes can be modeled as a time series, such as stock price movements, exchange rates, temperatures, among others. As a special kind of data stream, a time series may present concept drift, which affects negatively time series analysis and forecasting. Explicit drift detection methods...
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