Computational Intelligence and Neuroscience The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. An Improved Genetic Fuzzy Logic Control Method to Reduce the Enlargement of Coal Floor Deformation in Shearer Memory Cutting Process Wed, 27 Apr 2016 11:19:30 +0000 In order to reduce the enlargement of coal floor deformation and the manual adjustment frequency of rocker arms, an improved approach through integration of improved genetic algorithm and fuzzy logic control (GFLC) method is proposed. The enlargement of coal floor deformation is analyzed and a model is built. Then, the framework of proposed approach is built. Moreover, the constituents of GA such as tangent function roulette wheel selection (Tan-RWS) selection, uniform crossover, and nonuniform mutation are employed to enhance the performance of GFLC. Finally, two simulation examples and an industrial application example are carried out and the results indicate that the proposed method is feasible and efficient. Chao Tan, Rongxin Xu, Zhongbin Wang, Lei Si, and Xinhua Liu Copyright © 2016 Chao Tan et al. All rights reserved. Spike Code Flow in Cultured Neuronal Networks Wed, 27 Apr 2016 11:11:23 +0000 We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of “1101” and “1011,” which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the “maximum cross-correlations” among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network. Shinichi Tamura, Yoshi Nishitani, Chie Hosokawa, Tomomitsu Miyoshi, Hajime Sawai, Takuya Kamimura, Yasushi Yagi, Yuko Mizuno-Matsumoto, and Yen-Wei Chen Copyright © 2016 Shinichi Tamura et al. All rights reserved. Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks Wed, 27 Apr 2016 11:01:59 +0000 It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a “signature” of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence. Shinichi Tamura, Yoshi Nishitani, Chie Hosokawa, Tomomitsu Miyoshi, and Hajime Sawai Copyright © 2016 Shinichi Tamura et al. All rights reserved. Parallelizing Backpropagation Neural Network Using MapReduce and Cascading Model Wed, 27 Apr 2016 09:12:36 +0000 Artificial Neural Network (ANN) is a widely used algorithm in pattern recognition, classification, and prediction fields. Among a number of neural networks, backpropagation neural network (BPNN) has become the most famous one due to its remarkable function approximation ability. However, a standard BPNN frequently employs a large number of sum and sigmoid calculations, which may result in low efficiency in dealing with large volume of data. Therefore to parallelize BPNN using distributed computing technologies is an effective way to improve the algorithm performance in terms of efficiency. However, traditional parallelization may lead to accuracy loss. Although several complements have been done, it is still difficult to find out a compromise between efficiency and precision. This paper presents a parallelized BPNN based on MapReduce computing model which supplies advanced features including fault tolerance, data replication, and load balancing. And also to improve the algorithm performance in terms of precision, this paper creates a cascading model based classification approach, which helps to refine the classification results. The experimental results indicate that the presented parallelized BPNN is able to offer high efficiency whilst maintaining excellent precision in enabling large-scale machine learning. Yang Liu, Weizhe Jing, and Lixiong Xu Copyright © 2016 Yang Liu et al. All rights reserved. A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis Wed, 27 Apr 2016 07:40:22 +0000 Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms’ performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting “building blocks” into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis. Giulia Regalia, Stefania Coelli, Emilia Biffi, Giancarlo Ferrigno, and Alessandra Pedrocchi Copyright © 2016 Giulia Regalia et al. All rights reserved. Inversion for Refractivity Parameters Using a Dynamic Adaptive Cuckoo Search with Crossover Operator Algorithm Tue, 26 Apr 2016 13:25:28 +0000 Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg’s 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter. Zhihua Zhang, Zheng Sheng, Hanqing Shi, and Zhiqiang Fan Copyright © 2016 Zhihua Zhang et al. All rights reserved. Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications Tue, 26 Apr 2016 12:30:15 +0000 Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method. Guoqi Qian, Yuehua Wu, Davide Ferrari, Puxue Qiao, and Frédéric Hollande Copyright © 2016 Guoqi Qian et al. All rights reserved. Evaluation of a Home Biomonitoring Autonomous Mobile Robot Sun, 24 Apr 2016 13:22:46 +0000 Increasing population age demands more services in healthcare domain. It has been shown that mobile robots could be a potential solution to home biomonitoring for the elderly. Through our previous studies, a mobile robot system that is able to track a subject and identify his daily living activities has been developed. However, the system has not been tested in any home living scenarios. In this study we did a series of experiments to investigate the accuracy of activity recognition of the mobile robot in a home living scenario. The daily activities tested in the evaluation experiment include watching TV and sleeping. A dataset recorded by a distributed distance-measuring sensor network was used as a reference to the activity recognition results. It was shown that the accuracy is not consistent for all the activities; that is, mobile robot could achieve a high success rate in some activities but a poor success rate in others. It was found that the observation position of the mobile robot and subject surroundings have high impact on the accuracy of the activity recognition, due to the variability of the home living daily activities and their transitional process. The possibility of improvement of recognition accuracy has been shown too. Enrique Dorronzoro Zubiete, Keigo Nakahata, Nevrez Imamoglu, Masashi Sekine, Guanghao Sun, Isabel Gomez, and Wenwei Yu Copyright © 2016 Enrique Dorronzoro Zubiete et al. All rights reserved. Planning the City Logistics Terminal Location by Applying the Green -Median Model and Type-2 Neurofuzzy Network Tue, 19 Apr 2016 13:13:57 +0000 The paper herein presents green -median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parameters on planning the location for the city logistics terminal (CLT) within the discrete network. CLT shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections. By applying the green -median model (GMM), negative effects on environment and health in urban areas caused by delivery vehicles may be reduced to minimum. This model creates real possibilities for making the proper investment decisions so as profitable investments may be realized in the field of transport infrastructure. The paper herein also includes testing of GMM in real conditions on four CLT locations in Belgrade City zone. Dragan Pamučar, Ljubislav Vasin, Predrag Atanasković, and Milica Miličić Copyright © 2016 Dragan Pamučar et al. All rights reserved. Online Knowledge-Based Model for Big Data Topic Extraction Tue, 19 Apr 2016 11:16:02 +0000 Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half. Muhammad Taimoor Khan, Mehr Durrani, Shehzad Khalid, and Furqan Aziz Copyright © 2016 Muhammad Taimoor Khan et al. All rights reserved. A Driving Behaviour Model of Electrical Wheelchair Users Mon, 11 Apr 2016 12:49:43 +0000 In spite of the presence of powered wheelchairs, some of the users still experience steering challenges and manoeuvring difficulties that limit their capacity of navigating effectively. For such users, steering support and assistive systems may be very necessary. To appreciate the assistance, there is need that the assistive control is adaptable to the user’s steering behaviour. This paper contributes to wheelchair steering improvement by modelling the steering behaviour of powered wheelchair users, for integration into the control system. More precisely, the modelling is based on the improved Directed Potential Field (DPF) method for trajectory planning. The method has facilitated the formulation of a simple behaviour model that is also linear in parameters. To obtain the steering data for parameter identification, seven individuals participated in driving the wheelchair in different virtual worlds on the augmented platform. The obtained data facilitated the estimation of user parameters, using the ordinary least square method, with satisfactory regression analysis results. S. O. Onyango, Y. Hamam, K. Djouani, B. Daachi, and N. Steyn Copyright © 2016 S. O. Onyango et al. All rights reserved. An Interactive Astronaut-Robot System with Gesture Control Mon, 11 Apr 2016 08:46:33 +0000 Human-robot interaction (HRI) plays an important role in future planetary exploration mission, where astronauts with extravehicular activities (EVA) have to communicate with robot assistants by speech-type or gesture-type user interfaces embedded in their space suits. This paper presents an interactive astronaut-robot system integrating a data-glove with a space suit for the astronaut to use hand gestures to control a snake-like robot. Support vector machine (SVM) is employed to recognize hand gestures and particle swarm optimization (PSO) algorithm is used to optimize the parameters of SVM to further improve its recognition accuracy. Various hand gestures from American Sign Language (ASL) have been selected and used to test and validate the performance of the proposed system. Jinguo Liu, Yifan Luo, and Zhaojie Ju Copyright © 2016 Jinguo Liu et al. All rights reserved. Gait Planning and Stability Control of a Quadruped Robot Sun, 10 Apr 2016 12:31:24 +0000 In order to realize smooth gait planning and stability control of a quadruped robot, a new controller algorithm based on CPG-ZMP (central pattern generator-zero moment point) is put forward in this paper. To generate smooth gait and shorten the adjusting time of the model oscillation system, a new CPG model controller and its gait switching strategy based on Wilson-Cowan model are presented in the paper. The control signals of knee-hip joints are obtained by the improved multi-DOF reduced order control theory. To realize stability control, the adaptive speed adjustment and gait switch are completed by the real-time computing of ZMP. Experiment results show that the quadruped robot’s gaits are efficiently generated and the gait switch is smooth in the CPG control algorithm. Meanwhile, the stability of robot’s movement is improved greatly with the CPG-ZMP algorithm. The algorithm in this paper has good practicability, which lays a foundation for the production of the robot prototype. Junmin Li, Jinge Wang, Simon X. Yang, Kedong Zhou, and Huijuan Tang Copyright © 2016 Junmin Li et al. All rights reserved. Characterization of Visual Scanning Patterns in Air Traffic Control Thu, 07 Apr 2016 12:06:36 +0000 Characterization of air traffic controllers’ (ATCs’) visual scanning strategies is a challenging issue due to the dynamic movement of multiple aircraft and increasing complexity of scanpaths (order of eye fixations and saccades) over time. Additionally, terminologies and methods are lacking to accurately characterize the eye tracking data into simplified visual scanning strategies linguistically expressed by ATCs. As an intermediate step to automate the characterization classification process, we (1) defined and developed new concepts to systematically filter complex visual scanpaths into simpler and more manageable forms and (2) developed procedures to map visual scanpaths with linguistic inputs to reduce the human judgement bias during interrater agreement. The developed concepts and procedures were applied to investigating the visual scanpaths of expert ATCs using scenarios with different aircraft congestion levels. Furthermore, oculomotor trends were analyzed to identify the influence of aircraft congestion on scan time and number of comparisons among aircraft. The findings show that (1) the scanpaths filtered at the highest intensity led to more consistent mapping with the ATCs’ linguistic inputs, (2) the pattern classification occurrences differed between scenarios, and (3) increasing aircraft congestion caused increased scan times and aircraft pairwise comparisons. The results provide a foundation for better characterizing complex scanpaths in a dynamic task and automating the analysis process. Sarah N. McClung and Ziho Kang Copyright © 2016 Sarah N. McClung and Ziho Kang. All rights reserved. Online Sequential Projection Vector Machine with Adaptive Data Mean Update Thu, 07 Apr 2016 10:11:51 +0000 We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including the projection vectors for dimension reduction, the input weights, biases, and output weights, and the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM. Lin Chen, Ji-Ting Jia, Qiong Zhang, Wan-Yu Deng, and Wei Wei Copyright © 2016 Lin Chen et al. All rights reserved. Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization Mon, 04 Apr 2016 09:36:28 +0000 Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method. Zhongqi Wang, Bo Yang, Yonggang Kang, and Yuan Yang Copyright © 2016 Zhongqi Wang et al. All rights reserved. Intelligent Flow Friction Estimation Sun, 03 Apr 2016 14:47:48 +0000 Nowadays, the Colebrook equation is used as a mostly accepted relation for the calculation of fluid flow friction factor. However, the Colebrook equation is implicit with respect to the friction factor (). In the present study, a noniterative approach using Artificial Neural Network (ANN) was developed to calculate the friction factor. To configure the ANN model, the input parameters of the Reynolds Number (Re) and the relative roughness of pipe () were transformed to logarithmic scales. The 90,000 sets of data were fed to the ANN model involving three layers: input, hidden, and output layers with, 2, 50, and 1 neurons, respectively. This configuration was capable of predicting the values of friction factor in the Colebrook equation for any given values of the Reynolds number (Re) and the relative roughness () ranging between 5000 and 108 and between 10−7 and 0.1, respectively. The proposed ANN demonstrates the relative error up to 0.07% which had the high accuracy compared with the vast majority of the precise explicit approximations of the Colebrook equation. Dejan Brkić and Žarko Ćojbašić Copyright © 2016 Dejan Brkić and Žarko Ćojbašić. All rights reserved. A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm Thu, 31 Mar 2016 18:10:33 +0000 In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN) and support vector machine (SVM) methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others. Zhongbin Wang, Xihua Xu, Lei Si, Rui Ji, Xinhua Liu, and Chao Tan Copyright © 2016 Zhongbin Wang et al. All rights reserved. Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance Wed, 30 Mar 2016 13:21:34 +0000 An advanced fuzzy potential field method for mobile robot obstacle avoidance is proposed. The potential field method primarily deals with the repulsive forces surrounding obstacles, while fuzzy control logic focuses on fuzzy rules that handle linguistic variables and describe the knowledge of experts. The design of a fuzzy controller—advanced fuzzy potential field method (AFPFM)—that models and enhances the conventional potential field method is proposed and discussed. This study also examines the rule-explosion problem of conventional fuzzy logic and assesses the performance of our proposed AFPFM through simulations carried out using a mobile robot. Jong-Wook Park, Hwan-Joo Kwak, Young-Chang Kang, and Dong W. Kim Copyright © 2016 Jong-Wook Park et al. All rights reserved. Global Exponential Stability of Almost Periodic Solution for Neutral-Type Cohen-Grossberg Shunting Inhibitory Cellular Neural Networks with Distributed Delays and Impulses Wed, 30 Mar 2016 11:35:40 +0000 A kind of neutral-type Cohen-Grossberg shunting inhibitory cellular neural networks with distributed delays and impulses is considered. Firstly, by using the theory of impulsive differential equations and the contracting mapping principle, the existence and uniqueness of the almost periodic solution for the above system are obtained. Secondly, by constructing a suitable Lyapunov functional, the global exponential stability of the unique almost periodic solution is also investigated. The work in this paper improves and extends some results in recent years. As an application, an example and numerical simulations are presented to demonstrate the feasibility and effectiveness of the main results. Lijun Xu, Qi Jiang, and Guodong Gu Copyright © 2016 Lijun Xu et al. All rights reserved. MEG Analysis of Neural Interactions in Attention-Deficit/Hyperactivity Disorder Tue, 29 Mar 2016 12:03:20 +0000 The aim of the present study was to explore the interchannel relationships of resting-state brain activity in patients with attention-deficit/hyperactivity disorder (ADHD), one of the most common mental disorders that develop in children. Magnetoencephalographic (MEG) signals were recorded using a 148-channel whole-head magnetometer in 13 patients with ADHD (range: 8–12 years) and 14 control subjects (range: 8–13 years). Three complementary measures (coherence, phase-locking value, and Euclidean distance) were calculated in the conventional MEG frequency bands: delta, theta, alpha, beta, and gamma. Our results showed that the interactions among MEG channels are higher for ADHD patients than for control subjects in all frequency bands. Statistically significant differences were observed for short-distance values within right-anterior and central regions, especially at delta, beta, and gamma-frequency bands (; Mann-Whitney test with false discovery rate correction). These frequency bands also showed statistically significant differences in long-distance interactions, mainly among anterior and central regions, as well as among anterior, central, and other areas. These differences might reflect alterations during brain development in children with ADHD. Our results support the role of frontal abnormalities in ADHD pathophysiology, which may reflect a delay in cortical maturation in the frontal cortex. Amine Khadmaoui, Carlos Gómez, Jesús Poza, Alejandro Bachiller, Alberto Fernández, Javier Quintero, and Roberto Hornero Copyright © 2016 Amine Khadmaoui et al. All rights reserved. A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection Sun, 27 Mar 2016 07:50:59 +0000 This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of parameter. Dalton Meitei Thounaojam, Thongam Khelchandra, Kh. Manglem Singh, and Sudipta Roy Copyright © 2016 Dalton Meitei Thounaojam et al. All rights reserved. MEG Connectivity and Power Detections with Minimum Norm Estimates Require Different Regularization Parameters Tue, 22 Mar 2016 14:12:54 +0000 Minimum Norm Estimation (MNE) is an inverse solution method widely used to reconstruct the source time series that underlie magnetoencephalography (MEG) data. MNE addresses the ill-posed nature of MEG source estimation through regularization (e.g., Tikhonov regularization). Selecting the best regularization parameter is a critical step. Generally, once set, it is common practice to keep the same coefficient throughout a study. However, it is yet to be known whether the optimal lambda for spectral power analysis of MEG source data coincides with the optimal regularization for source-level oscillatory coupling analysis. We addressed this question via extensive Monte-Carlo simulations of MEG data, where we generated 21,600 configurations of pairs of coupled sources with varying sizes, signal-to-noise ratio (SNR), and coupling strengths. Then, we searched for the Tikhonov regularization coefficients (lambda) that maximize detection performance for (a) power and (b) coherence. For coherence, the optimal lambda was two orders of magnitude smaller than the best lambda for power. Moreover, we found that the spatial extent of the interacting sources and SNR, but not the extent of coupling, were the main parameters affecting the best choice for lambda. Our findings suggest using less regularization when measuring oscillatory coupling compared to power estimation. Ana-Sofía Hincapié, Jan Kujala, Jérémie Mattout, Sebastien Daligault, Claude Delpuech, Domingo Mery, Diego Cosmelli, and Karim Jerbi Copyright © 2016 Ana-Sofía Hincapié et al. All rights reserved. Support Vector Machine with Ensemble Tree Kernel for Relation Extraction Tue, 22 Mar 2016 13:52:59 +0000 Relation extraction is one of the important research topics in the field of information extraction research. To solve the problem of semantic variation in traditional semisupervised relation extraction algorithm, this paper proposes a novel semisupervised relation extraction algorithm based on ensemble learning (LXRE). The new algorithm mainly uses two kinds of support vector machine classifiers based on tree kernel for integration and integrates the strategy of constrained extension seed set. The new algorithm can weaken the inaccuracy of relation extraction, which is caused by the phenomenon of semantic variation. The numerical experimental research based on two benchmark data sets (PropBank and AIMed) shows that the LXRE algorithm proposed in the paper is superior to other two common relation extraction methods in four evaluation indexes (Precision, Recall, -measure, and Accuracy). It indicates that the new algorithm has good relation extraction ability compared with others. Xiaoyong Liu, Hui Fu, and Zhiguo Du Copyright © 2016 Xiaoyong Liu et al. All rights reserved. EyeTribe Tracker Data Accuracy Evaluation and Its Interconnection with Hypothesis Software for Cartographic Purposes Mon, 21 Mar 2016 09:16:13 +0000 The mixed research design is a progressive methodological discourse that combines the advantages of quantitative and qualitative methods. Its possibilities of application are, however, dependent on the efficiency with which the particular research techniques are used and combined. The aim of the paper is to introduce the possible combination of Hypothesis with EyeTribe tracker. The Hypothesis is intended for quantitative data acquisition and the EyeTribe is intended for qualitative (eye-tracking) data recording. In the first part of the paper, Hypothesis software is described. The Hypothesis platform provides an environment for web-based computerized experiment design and mass data collection. Then, evaluation of the accuracy of data recorded by EyeTribe tracker was performed with the use of concurrent recording together with the SMI RED 250 eye-tracker. Both qualitative and quantitative results showed that data accuracy is sufficient for cartographic research. In the third part of the paper, a system for connecting EyeTribe tracker and Hypothesis software is presented. The interconnection was performed with the help of developed web application HypOgama. The created system uses open-source software OGAMA for recording the eye-movements of participants together with quantitative data from Hypothesis. The final part of the paper describes the integrated research system combining Hypothesis and EyeTribe. Stanislav Popelka, Zdeněk Stachoň, Čeněk Šašinka, and Jitka Doležalová Copyright © 2016 Stanislav Popelka et al. All rights reserved. Rhythmic Oscillations of Excitatory Bursting Hodkin-Huxley Neuronal Network with Synaptic Learning Thu, 17 Mar 2016 06:32:33 +0000 Rhythmic oscillations of neuronal network are actually kind of synchronous behaviors, which play an important role in neural systems. In this paper, the properties of excitement degree and oscillation frequency of excitatory bursting Hodkin-Huxley neuronal network which incorporates a synaptic learning rule are studied. The effects of coupling strength, synaptic learning rate, and other parameters of chemical synapses, such as synaptic delay and decay time constant, are explored, respectively. It is found that the increase of the coupling strength can weaken the extent of excitement, whereas increasing the synaptic learning rate makes the network more excited in a certain range; along with the increasing of the delay time and the decay time constant, the excitement degree increases at the beginning, then decreases, and keeps stable. It is also found that, along with the increase of the synaptic learning rate, the coupling strength, the delay time, and the decay time constant, the oscillation frequency of the network decreases monotonically. Qi Shi, Fang Han, Zhijie Wang, and Caiyun Li Copyright © 2016 Qi Shi et al. All rights reserved. A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users Wed, 16 Mar 2016 12:40:06 +0000 Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented. Logesh Ravi and Subramaniyaswamy Vairavasundaram Copyright © 2016 Logesh Ravi and Subramaniyaswamy Vairavasundaram. All rights reserved. A Human Activity Recognition System Using Skeleton Data from RGBD Sensors Wed, 16 Mar 2016 12:37:00 +0000 The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed. Enea Cippitelli, Samuele Gasparrini, Ennio Gambi, and Susanna Spinsante Copyright © 2016 Enea Cippitelli et al. All rights reserved. Developmental and Evolutionary Lexicon Acquisition in Cognitive Agents/Robots with Grounding Principle: A Short Review Wed, 16 Mar 2016 11:15:03 +0000 Grounded language acquisition is an important issue, particularly to facilitate human-robot interactions in an intelligent and effective way. The evolutionary and developmental language acquisition are two innovative and important methodologies for the grounding of language in cognitive agents or robots, the aim of which is to address current limitations in robot design. This paper concentrates on these two main modelling methods with the grounding principle for the acquisition of linguistic ability in cognitive agents or robots. This review not only presents a survey of the methodologies and relevant computational cognitive agents or robotic models, but also highlights the advantages and progress of these approaches for the language grounding issue. Nadia Rasheed and Shamsudin H. M. Amin Copyright © 2016 Nadia Rasheed and Shamsudin H. M. Amin. All rights reserved. List-Based Simulated Annealing Algorithm for Traveling Salesman Problem Sun, 13 Mar 2016 13:41:19 +0000 Simulated annealing (SA) algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. Parameters’ setting is a key factor for its performance, but it is also a tedious work. To simplify parameters setting, we present a list-based simulated annealing (LBSA) algorithm to solve traveling salesman problem (TSP). LBSA algorithm uses a novel list-based cooling schedule to control the decrease of temperature. Specifically, a list of temperatures is created first, and then the maximum temperature in list is used by Metropolis acceptance criterion to decide whether to accept a candidate solution. The temperature list is adapted iteratively according to the topology of the solution space of the problem. The effectiveness and the parameter sensitivity of the list-based cooling schedule are illustrated through benchmark TSP problems. The LBSA algorithm, whose performance is robust on a wide range of parameter values, shows competitive performance compared with some other state-of-the-art algorithms. Shi-hua Zhan, Juan Lin, Ze-jun Zhang, and Yi-wen Zhong Copyright © 2016 Shi-hua Zhan et al. All rights reserved.