Computational Intelligence and Neuroscience The latest articles from Hindawi Publishing Corporation © 2015 , Hindawi Publishing Corporation . All rights reserved. Inclusion of Neuropsychological Scores in Atrophy Models Improves Diagnostic Classification of Alzheimer’s Disease and Mild Cognitive Impairment Mon, 25 May 2015 11:35:26 +0000 Brain atrophy in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) are difficult to demarcate to assess the progression of AD. This study presents a statistical framework on the basis of MRI volumes and neuropsychological scores. A feature selection technique using backward stepwise linear regression together with linear discriminant analysis is designed to classify cognitive normal (CN) subjects, early MCI (EMCI), late MCI (LMCI), and AD subjects in an exhaustive two-group classification process. Results show a dominance of the neuropsychological parameters like MMSE and RAVLT. Cortical volumetric measures of the temporal, parietal, and cingulate regions are found to be significant classification factors. Moreover, an asymmetrical distribution of the volumetric measures across hemispheres is seen for CN versus EMCI and EMCI versus AD, showing dominance of the right hemisphere; whereas CN versus LMCI and EMCI versus LMCI show dominance of the left hemisphere. A 2-fold cross-validation showed an average accuracy of 93.9%, 90.8%, and 94.5%, for the CN versus AD, CN versus LMCI, and EMCI versus AD, respectively. The accuracy for groups that are difficult to differentiate like EMCI versus LMCI was 73.6%. With the inclusion of the neuropsychological scores, a significant improvement (24.59%) was obtained over using MRI measures alone. Mohammed Goryawala, Qi Zhou, Warren Barker, David A. Loewenstein, Ranjan Duara, and Malek Adjouadi Copyright © 2015 Mohammed Goryawala et al. All rights reserved. Intelligent Surveillance Robot with Obstacle Avoidance Capabilities Using Neural Network Sun, 24 May 2015 11:36:08 +0000 For specific purpose, vision-based surveillance robot that can be run autonomously and able to acquire images from its dynamic environment is very important, for example, in rescuing disaster victims in Indonesia. In this paper, we propose architecture for intelligent surveillance robot that is able to avoid obstacles using 3 ultrasonic distance sensors based on backpropagation neural network and a camera for face recognition. 2.4 GHz transmitter for transmitting video is used by the operator/user to direct the robot to the desired area. Results show the effectiveness of our method and we evaluate the performance of the system. Widodo Budiharto Copyright © 2015 Widodo Budiharto. All rights reserved. Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations Thu, 21 May 2015 07:07:30 +0000 Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions. Yi Zhang, Jinchang Ren, and Jianmin Jiang Copyright © 2015 Yi Zhang et al. All rights reserved. A Hybrid alldifferent-Tabu Search Algorithm for Solving Sudoku Puzzles Wed, 20 May 2015 13:19:02 +0000 The Sudoku problem is a well-known logic-based puzzle of combinatorial number-placement. It consists in filling a grid, composed of columns, rows, and subgrids, each one containing distinct integers from 1 to . Such a puzzle belongs to the NP-complete collection of problems, to which there exist diverse exact and approximate methods able to solve it. In this paper, we propose a new hybrid algorithm that smartly combines a classic tabu search procedure with the alldifferent global constraint from the constraint programming world. The alldifferent constraint is known to be efficient for domain filtering in the presence of constraints that must be pairwise different, which are exactly the kind of constraints that Sudokus own. This ability clearly alleviates the work of the tabu search, resulting in a faster and more robust approach for solving Sudokus. We illustrate interesting experimental results where our proposed algorithm outperforms the best results previously reported by hybrids and approximate methods. Ricardo Soto, Broderick Crawford, Cristian Galleguillos, Fernando Paredes, and Enrique Norero Copyright © 2015 Ricardo Soto et al. All rights reserved. Theory and Simulation for Traffic Characteristics on the Highway with a Slowdown Section Mon, 18 May 2015 13:08:01 +0000 We study the traffic characteristics on a single-lane highway with a slowdown section using the deterministic cellular automaton (CA) model. Based on the theoretical analysis, the relationships among local mean densities, velocities, traffic fluxes, and global densities are derived. The results show that two critical densities exist in the evolutionary process of traffic state, and they are significant demarcation points for traffic phase transition. Furthermore, the changing laws of the two critical densities with different length of limit section are also investigated. It is shown that only one critical density appears if a highway is not slowdown section; nevertheless, with the growing length of slowdown section, one critical density separates into two critical densities; if the entire highway is slowdown section, they finally merge into one. The contrastive analysis proves that the analytical results are consistent with the numerical ones. Dejie Xu, Baohua Mao, Yaping Rong, and Wei Wei Copyright © 2015 Dejie Xu et al. All rights reserved. Fast Distributed Dynamics of Semantic Networks via Social Media Sun, 17 May 2015 07:01:32 +0000 We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS), based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network. Facundo Carrillo, Guillermo A. Cecchi, Mariano Sigman, and Diego Fernández Slezak Copyright © 2015 Facundo Carrillo et al. All rights reserved. Stabilization Methods for a Multiagent System with Complex Behaviours Wed, 13 May 2015 09:00:54 +0000 The main focus of the paper is the stability analysis of a class of multiagent systems based on an interaction protocol which can generate different types of overall behaviours, from asymptotically stable to chaotic. We present several interpretations of stability and suggest two methods to assess the stability of the system, based on the internal models of the agents and on the external, observed behaviour. Since it is very difficult to predict a priori whether a system will be stable or unstable, we propose three heuristic methods that can be used to stabilize such a system during its execution, with minimal changes to its state. Florin Leon Copyright © 2015 Florin Leon. All rights reserved. An Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization Sun, 10 May 2015 07:57:43 +0000 An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate. Zhen-Lun Yang, Angus Wu, and Hua-Qing Min Copyright © 2015 Zhen-Lun Yang et al. All rights reserved. Log-Spiral Keypoint: A Robust Approach toward Image Patch Matching Tue, 05 May 2015 12:28:37 +0000 Matching of keypoints across image patches forms the basis of computer vision applications, such as object detection, recognition, and tracking in real-world images. Most of keypoint methods are mainly used to match the high-resolution images, which always utilize an image pyramid for multiscale keypoint detection. In this paper, we propose a novel keypoint method to improve the matching performance of image patches with the low-resolution and small size. The location, scale, and orientation of keypoints are directly estimated from an original image patch using a Log-Spiral sampling pattern for keypoint detection without consideration of image pyramid. A Log-Spiral sampling pattern for keypoint description and two bit-generated functions are designed for generating a binary descriptor. Extensive experiments show that the proposed method is more effective and robust than existing binary-based methods for image patch matching. Kangho Paek, Min Yao, Zhongwei Liu, and Hun Kim Copyright © 2015 Kangho Paek et al. All rights reserved. MapReduce Based Personalized Locality Sensitive Hashing for Similarity Joins on Large Scale Data Thu, 30 Apr 2015 12:55:22 +0000 Locality Sensitive Hashing (LSH) has been proposed as an efficient technique for similarity joins for high dimensional data. The efficiency and approximation rate of LSH depend on the number of generated false positive instances and false negative instances. In many domains, reducing the number of false positives is crucial. Furthermore, in some application scenarios, balancing false positives and false negatives is favored. To address these problems, in this paper we propose Personalized Locality Sensitive Hashing (PLSH), where a new banding scheme is embedded to tailor the number of false positives, false negatives, and the sum of both. PLSH is implemented in parallel using MapReduce framework to deal with similarity joins on large scale data. Experimental studies on real and simulated data verify the efficiency and effectiveness of our proposed PLSH technique, compared with state-of-the-art methods. Jingjing Wang and Chen Lin Copyright © 2015 Jingjing Wang and Chen Lin. All rights reserved. Forecasting Nonlinear Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm Mon, 27 Apr 2015 13:16:30 +0000 The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior. Jun Wang, Bi-hua Zhou, Shu-dao Zhou, and Zheng Sheng Copyright © 2015 Jun Wang et al. All rights reserved. Evaluating a Pivot-Based Approach for Bilingual Lexicon Extraction Thu, 23 Apr 2015 16:51:55 +0000 A pivot-based approach for bilingual lexicon extraction is based on the similarity of context vectors represented by words in a pivot language like English. In this paper, in order to show validity and usability of the pivot-based approach, we evaluate the approach in company with two different methods for estimating context vectors: one estimates them from two parallel corpora based on word association between source words (resp., target words) and pivot words and the other estimates them from two parallel corpora based on word alignment tools for statistical machine translation. Empirical results on two language pairs (e.g., Korean-Spanish and Korean-French) have shown that the pivot-based approach is very promising for resource-poor languages and this approach observes its validity and usability. Furthermore, for words with low frequency, our method is also well performed. Jae-Hoon Kim, Hong-Seok Kwon, and Hyeong-Won Seo Copyright © 2015 Jae-Hoon Kim et al. All rights reserved. A Fuzzy Computing Model for Identifying Polarity of Chinese Sentiment Words Thu, 23 Apr 2015 16:51:42 +0000 With the spurt of online user-generated contents on web, sentiment analysis has become a very active research issue in data mining and natural language processing. As the most important indicator of sentiment, sentiment words which convey positive and negative polarity are quite instrumental for sentiment analysis. However, most of the existing methods for identifying polarity of sentiment words only consider the positive and negative polarity by the Cantor set, and no attention is paid to the fuzziness of the polarity intensity of sentiment words. In order to improve the performance, we propose a fuzzy computing model to identify the polarity of Chinese sentiment words in this paper. There are three major contributions in this paper. Firstly, we propose a method to compute polarity intensity of sentiment morphemes and sentiment words. Secondly, we construct a fuzzy sentiment classifier and propose two different methods to compute the parameter of the fuzzy classifier. Thirdly, we conduct extensive experiments on four sentiment words datasets and three review datasets, and the experimental results indicate that our model performs better than the state-of-the-art methods. Bingkun Wang, Yongfeng Huang, Xian Wu, and Xing Li Copyright © 2015 Bingkun Wang et al. All rights reserved. Initialization by a Novel Clustering for Wavelet Neural Network as Time Series Predictor Wed, 22 Apr 2015 06:59:41 +0000 The architecture and parameter initialization of wavelet neural network are discussed and a novel initialization method is proposed. The new approach can be regarded as a dynamic clustering procedure which will derive the neuron number as well as the initial value of translation and dilation parameters according to the input patterns and the activating wavelets functions. Three simulation examples are given to examine the performance of our method as well as Zhang's heuristic initialization approach. The results show that the new approach not only can decide the WNN structure automatically, but also provides superior initial parameter values that make the optimization process more stable and quickly. Rong Cheng, Hongping Hu, Xiuhui Tan, and Yanping Bai Copyright © 2015 Rong Cheng et al. All rights reserved. A Low-Noise, Modular, and Versatile Analog Front-End Intended for Processing In Vitro Neuronal Signals Detected by Microelectrode Arrays Tue, 21 Apr 2015 13:28:55 +0000 The collection of good quality extracellular neuronal spikes from neuronal cultures coupled to Microelectrode Arrays (MEAs) is a binding requirement to gather reliable data. Due to physical constraints, low power requirement, or the need of customizability, commercial recording platforms are not fully adequate for the development of experimental setups integrating MEA technology with other equipment needed to perform experiments under climate controlled conditions, like environmental chambers or cell culture incubators. To address this issue, we developed a custom MEA interfacing system featuring low noise, low power, and the capability to be readily integrated inside an incubator-like environment. Two stages, a preamplifier and a filter amplifier, were designed, implemented on printed circuit boards, and tested. The system is characterized by a low input-referred noise (<1 μV RMS), a high channel separation (>70 dB), and signal-to-noise ratio values of neuronal recordings comparable to those obtained with the benchmark commercial MEA system. In addition, the system was successfully integrated with an environmental MEA chamber, without harming cell cultures during experiments and without being damaged by the high humidity level. The devised system is of practical value in the development of in vitro platforms to study temporally extended neuronal network dynamics by means of MEAs. Giulia Regalia, Emilia Biffi, Giancarlo Ferrigno, and Alessandra Pedrocchi Copyright © 2015 Giulia Regalia et al. All rights reserved. Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI Tue, 21 Apr 2015 12:33:16 +0000 This paper presents an investigation aimed at drastically reducing the processing burden required by motor imagery brain-computer interface (BCI) systems based on electroencephalography (EEG). In this research, the focus has moved from the channel to the feature paradigm, and a 96% reduction of the number of features required in the process has been achieved maintaining and even improving the classification success rate. This way, it is possible to build cheaper, quicker, and more portable BCI systems. The data set used was provided within the framework of BCI Competition III, which allows it to compare the presented results with the classification accuracy achieved in the contest. Furthermore, a new three-step methodology has been developed which includes a feature discriminant character calculation stage; a score, order, and selection phase; and a final feature selection step. For the first stage, both statistics method and fuzzy criteria are used. The fuzzy criteria are based on the S-dFasArt classification algorithm which has shown excellent performance in previous papers undertaking the BCI multiclass motor imagery problem. The score, order, and selection stage is used to sort the features according to their discriminant nature. Finally, both order selection and Group Method Data Handling (GMDH) approaches are used to choose the most discriminant ones. Juan-Antonio Martinez-Leon, Jose-Manuel Cano-Izquierdo, and Julio Ibarrola Copyright © 2015 Juan-Antonio Martinez-Leon et al. All rights reserved. Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based -Means Clustering Mon, 20 Apr 2015 06:35:32 +0000 Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based -means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based -means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based -means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed. Suraj, Purnendu Tiwari, Subhojit Ghosh, and Rakesh Kumar Sinha Copyright © 2015 Suraj et al. All rights reserved. On the Relationship between Variational Level Set-Based and SOM-Based Active Contours Sun, 19 Apr 2015 11:02:24 +0000 Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses. Mohammed M. Abdelsamea, Giorgio Gnecco, Mohamed Medhat Gaber, and Eyad Elyan Copyright © 2015 Mohammed M. Abdelsamea et al. All rights reserved. Memory Dynamics in Attractor Networks Sun, 19 Apr 2015 10:25:20 +0000 As can be represented by neurons and their synaptic connections, attractor networks are widely believed to underlie biological memory systems and have been used extensively in recent years to model the storage and retrieval process of memory. In this paper, we propose a new energy function, which is nonnegative and attains zero values only at the desired memory patterns. An attractor network is designed based on the proposed energy function. It is shown that the desired memory patterns are stored as the stable equilibrium points of the attractor network. To retrieve a memory pattern, an initial stimulus input is presented to the network, and its states converge to one of stable equilibrium points. Consequently, the existence of the spurious points, that is, local maxima, saddle points, or other local minima which are undesired memory patterns, can be avoided. The simulation results show the effectiveness of the proposed method. Guoqi Li, Kiruthika Ramanathan, Ning Ning, Luping Shi, and Changyun Wen Copyright © 2015 Guoqi Li et al. All rights reserved. Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study Sun, 19 Apr 2015 07:54:55 +0000 This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed. H. A. Hashim and M. A. Abido Copyright © 2015 H. A. Hashim and M. A. Abido. All rights reserved. Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration Sun, 19 Apr 2015 07:04:31 +0000 This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance. The contributions can be divided into three parts. Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects. Secondly, the robust similarity measure is proposed based on adaptive principal component. Finally, the robust registration algorithm is derived based on the RAPCA. The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images. Chengcai Leng, Jinjun Xiao, Min Li, and Haipeng Zhang Copyright © 2015 Chengcai Leng et al. All rights reserved. Encoding Sequential Information in Semantic Space Models: Comparing Holographic Reduced Representation and Random Permutation Tue, 07 Apr 2015 13:18:14 +0000 Circular convolution and random permutation have each been proposed as neurally plausible binding operators capable of encoding sequential information in semantic memory. We perform several controlled comparisons of circular convolution and random permutation as means of encoding paired associates as well as encoding sequential information. Random permutations outperformed convolution with respect to the number of paired associates that can be reliably stored in a single memory trace. Performance was equal on semantic tasks when using a small corpus, but random permutations were ultimately capable of achieving superior performance due to their higher scalability to large corpora. Finally, “noisy” permutations in which units are mapped to other units arbitrarily (no one-to-one mapping) perform nearly as well as true permutations. These findings increase the neurological plausibility of random permutations and highlight their utility in vector space models of semantics. Gabriel Recchia, Magnus Sahlgren, Pentti Kanerva, and Michael N. Jones Copyright © 2015 Gabriel Recchia et al. All rights reserved. Harmony Search Method: Theory and Applications Tue, 07 Apr 2015 10:43:26 +0000 The Harmony Search (HS) method is an emerging metaheuristic optimization algorithm, which has been employed to cope with numerous challenging tasks during the past decade. In this paper, the essential theory and applications of the HS algorithm are first described and reviewed. Several typical variants of the original HS are next briefly explained. As an example of case study, a modified HS method inspired by the idea of Pareto-dominance-based ranking is also presented. It is further applied to handle a practical wind generator optimal design problem. X. Z. Gao, V. Govindasamy, H. Xu, X. Wang, and K. Zenger Copyright © 2015 X. Z. Gao et al. All rights reserved. High-Frequency Electroencephalographic Activity in Left Temporal Area Is Associated with Pleasant Emotion Induced by Video Clips Thu, 26 Mar 2015 14:09:31 +0000 Recent findings suggest that specific neural correlates for the key elements of basic emotions do exist and can be identified by neuroimaging techniques. In this paper, electroencephalogram (EEG) is used to explore the markers for video-induced emotions. The problem is approached from a classifier perspective: the features that perform best in classifying person’s valence and arousal while watching video clips with audiovisual emotional content are searched from a large feature set constructed from the EEG spectral powers of single channels as well as power differences between specific channel pairs. The feature selection is carried out using a sequential forward floating search method and is done separately for the classification of valence and arousal, both derived from the emotional keyword that the subject had chosen after seeing the clips. The proposed classifier-based approach reveals a clear association between the increased high-frequency (15–32 Hz) activity in the left temporal area and the clips described as “pleasant” in the valence and “medium arousal” in the arousal scale. These clips represent the emotional keywords amusement and joy/happiness. The finding suggests the occurrence of a specific neural activation during video-induced pleasant emotion and the possibility to detect this from the left temporal area using EEG. Jukka Kortelainen, Eero Väyrynen, and Tapio Seppänen Copyright © 2015 Jukka Kortelainen et al. All rights reserved. Estimating Latent Attentional States Based on Simultaneous Binary and Continuous Behavioral Measures Thu, 26 Mar 2015 12:55:01 +0000 Cognition is a complex and dynamic process. It is an essential goal to estimate latent attentional states based on behavioral measures in many sequences of behavioral tasks. Here, we propose a probabilistic modeling and inference framework for estimating the attentional state using simultaneous binary and continuous behavioral measures. The proposed model extends the standard hidden Markov model (HMM) by explicitly modeling the state duration distribution, which yields a special example of the hidden semi-Markov model (HSMM). We validate our methods using computer simulations and experimental data. In computer simulations, we systematically investigate the impacts of model mismatch and the latency distribution. For the experimental data collected from a rodent visual detection task, we validate the results with predictive log-likelihood. Our work is useful for many behavioral neuroscience experiments, where the common goal is to infer the discrete (binary or multinomial) state sequences from multiple behavioral measures. Zhe Chen Copyright © 2015 Zhe Chen. All rights reserved. Learning Document Semantic Representation with Hybrid Deep Belief Network Mon, 23 Mar 2015 08:26:27 +0000 High-level abstraction, for example, semantic representation, is vital for document classification and retrieval. However, how to learn document semantic representation is still a topic open for discussion in information retrieval and natural language processing. In this paper, we propose a new Hybrid Deep Belief Network (HDBN) which uses Deep Boltzmann Machine (DBM) on the lower layers together with Deep Belief Network (DBN) on the upper layers. The advantage of DBM is that it employs undirected connection when training weight parameters which can be used to sample the states of nodes on each layer more successfully and it is also an effective way to remove noise from the different document representation type; the DBN can enhance extract abstract of the document in depth, making the model learn sufficient semantic representation. At the same time, we explore different input strategies for semantic distributed representation. Experimental results show that our model using the word embedding instead of single word has better performance. Yan Yan, Xu-Cheng Yin, Sujian Li, Mingyuan Yang, and Hong-Wei Hao Copyright © 2015 Yan Yan et al. All rights reserved. Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments Sun, 22 Mar 2015 11:34:07 +0000 Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand. Renata De Paris, Christian V. Quevedo, Duncan D. Ruiz, Osmar Norberto de Souza, and Rodrigo C. Barros Copyright © 2015 Renata De Paris et al. All rights reserved. Analysis of Human Standing Balance by Largest Lyapunov Exponent Wed, 18 Mar 2015 14:07:36 +0000 The purpose of this research is to analyse the relationship between nonlinear dynamic character and individuals’ standing balance by the largest Lyapunov exponent, which is regarded as a metric for assessing standing balance. According to previous study, the largest Lyapunov exponent from centre of pressure time series could not well quantify the human balance ability. In this research, two improvements were made. Firstly, an external stimulus was applied to feet in the form of continuous horizontal sinusoidal motion by a moving platform. Secondly, a multiaccelerometer subsystem was adopted. Twenty healthy volunteers participated in this experiment. A new metric, coordinated largest Lyapunov exponent was proposed, which reflected the relationship of body segments by integrating multidimensional largest Lyapunov exponent values. By using this metric in actual standing performance under sinusoidal stimulus, an obvious relationship between the new metric and the actual balance ability was found in the majority of the subjects. These results show that the sinusoidal stimulus can make human balance characteristics more obvious, which is beneficial to assess balance, and balance is determined by the ability of coordinating all body segments. Kun Liu, Hongrui Wang, Jinzhuang Xiao, and Zahari Taha Copyright © 2015 Kun Liu et al. All rights reserved. Modelling Coupled Oscillations in the Notch, Wnt, and FGF Signaling Pathways during Somitogenesis: A Comprehensive Mathematical Model Tue, 17 Mar 2015 12:47:10 +0000 Somite formation in the early stage of vertebrate embryonic development is controlled by a complicated gene network named segmentation clock, which is defined by the periodic expression of genes related to the Notch, Wnt, and the fibroblast growth factor (FGF) pathways. Although in recent years some findings about crosstalk among the Notch, Wnt, and FGF pathways in somitogenesis have been reported, the investigation of their crosstalk mechanisms from a systematic point of view is still lacking. In this study, a more comprehensive mathematical model was proposed to simulate the dynamics of the Notch, Wnt, and FGF pathways in the segmentation clock. Simulations and bifurcation analyses of this model suggested that the concentration gradients of both Wnt, and FGF signals along the presomitic mesoderm (PSM) are corresponding to the whole process from start to stop of the segmentation clock. A number of highly sensitive parameters to the segmentation clock’s oscillatory pattern were identified. By further bifurcation analyses for these sensitive parameters, and several complementary mechanisms in respect of the maintenance of the stable oscillation of the segmentation clock were revealed. Hong-yan Wang, Yan-xin Huang, Li-hua Zheng, Yong-li Bao, Lu-guo Sun, Yin Wu, Chun-lei Yu, Zhen-bo Song, Ying Sun, Guan-nan Wang, Zhi-qiang Ma, and Yu-xin Li Copyright © 2015 Hong-yan Wang et al. All rights reserved. Sentiment Analysis Using Common-Sense and Context Information Tue, 17 Mar 2015 11:17:54 +0000 Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this paper, we propose a novel sentiment analysis model based on common-sense knowledge extracted from ConceptNet based ontology and context information. ConceptNet based ontology is used to determine the domain specific concepts which in turn produced the domain specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods. Basant Agarwal, Namita Mittal, Pooja Bansal, and Sonal Garg Copyright © 2015 Basant Agarwal et al. All rights reserved.