Computational Intelligence and Neuroscience The latest articles from Hindawi © 2017 , Hindawi Limited . All rights reserved. Deep Learning for Plant Identification in Natural Environment Mon, 22 May 2017 00:00:00 +0000 Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large-scale plant classification in natural environment. The proposed model achieves a recognition rate of 91.78% on the BJFU100 dataset, demonstrating that deep learning is a promising technology for smart forestry. Yu Sun, Yuan Liu, Guan Wang, and Haiyan Zhang Copyright © 2017 Yu Sun et al. All rights reserved. Applications of Computational Intelligence in Time Series Mon, 22 May 2017 00:00:00 +0000 Francisco Martínez-Álvarez, Alicia Troncoso, Jorge Reyes, María Martínez-Ballesteros, and José C. Riquelme Copyright © 2017 Francisco Martínez-Álvarez et al. All rights reserved. Robust Grape Detector Based on SVMs and HOG Features Thu, 18 May 2017 00:00:00 +0000 Detection of grapes in real-life images is a serious task solved by researchers dealing with precision viticulture. In the case of white wine varieties, grape detectors based on SVMs classifiers, in combination with a HOG descriptor, have proven to be very efficient. Simplified versions of the detectors seem to be the best solution for practical applications. They offer the best known performance versus time-complexity ratio. As our research showed, a conversion of RGB images to grayscale format, which is implemented at an image preprocessing level, is ideal means for further improvement of performance of the detectors. In order to enhance the ratio, we explored relevance of the conversion in a context of a detector potential sensitivity to a rotation of berries. For this purpose, we proposed a modification of the conversion, and we designed an appropriate method for a tuning of such modified detectors. To evaluate the effect of the new parameter space on their performance, we developed a specialized visualization method. In order to provide accurate results, we formed new datasets for both tuning and evaluation of the detectors. Our effort resulted in a robust grape detector which is less sensitive to image distortion. Pavel Škrabánek and Petr Doležel Copyright © 2017 Pavel Škrabánek and Petr Doležel. All rights reserved. Refining Automatically Extracted Knowledge Bases Using Crowdsourcing Sun, 14 May 2017 07:07:37 +0000 Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost. Chunhua Li, Pengpeng Zhao, Victor S. Sheng, Xuefeng Xian, Jian Wu, and Zhiming Cui Copyright © 2017 Chunhua Li et al. All rights reserved. Development of a Novel Motor Imagery Control Technique and Application in a Gaming Environment Tue, 09 May 2017 09:41:44 +0000 We present a methodology for a hybrid brain-computer interface (BCI) system, with the recognition of motor imagery (MI) based on EEG and blink EOG signals. We tested the BCI system in a 3D Tetris and an analogous 2D game playing environment. To enhance player’s BCI control ability, the study focused on feature extraction from EEG and control strategy supporting Game-BCI system operation. We compared the numerical differences between spatial features extracted with common spatial pattern (CSP) and the proposed multifeature extraction. To demonstrate the effectiveness of 3D game environment at enhancing player’s event-related desynchronization (ERD) and event-related synchronization (ERS) production ability, we set the 2D Screen Game as the comparison experiment. According to a series of statistical results, the group performing MI in the 3D Tetris environment showed more significant improvements in generating MI-associated ERD/ERS. Analysis results of game-score indicated that the players’ scores presented an obvious uptrend in 3D Tetris environment but did not show an obvious downward trend in 2D Screen Game. It suggested that the immersive and rich-control environment for MI would improve the associated mental imagery and enhance MI-based BCI skills. Ting Li, Jinhua Zhang, Tao Xue, and Baozeng Wang Copyright © 2017 Ting Li et al. All rights reserved. Comparison of Brain Activation during Motor Imagery and Motor Movement Using fNIRS Thu, 04 May 2017 00:00:00 +0000 Motor-activity-related mental tasks are widely adopted for brain-computer interfaces (BCIs) as they are a natural extension of movement intention, requiring no training to evoke brain activity. The ideal BCI aims to eliminate neuromuscular movement, making motor imagery tasks, or imagined actions with no muscle movement, good candidates. This study explores cortical activation differences between motor imagery and motor execution for both upper and lower limbs using functional near-infrared spectroscopy (fNIRS). Four simple finger- or toe-tapping tasks (left hand, right hand, left foot, and right foot) were performed with both motor imagery and motor execution and compared to resting state. Significant activation was found during all four motor imagery tasks, indicating that they can be detected via fNIRS. Motor execution produced higher activation levels, a faster response, and a different spatial distribution compared to motor imagery, which should be taken into account when designing an imagery-based BCI. When comparing left versus right, upper limb tasks are the most clearly distinguishable, particularly during motor execution. Left and right lower limb activation patterns were found to be highly similar during both imagery and execution, indicating that higher resolution imaging, advanced signal processing, or improved subject training may be required to reliably distinguish them. Alyssa M. Batula, Jesse A. Mark, Youngmoo E. Kim, and Hasan Ayaz Copyright © 2017 Alyssa M. Batula et al. All rights reserved. Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines Thu, 04 May 2017 00:00:00 +0000 Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, -norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble). The validity of AELME is confirmed through classification on several real-world benchmark datasets. Adnan O. M. Abuassba, Dezheng Zhang, Xiong Luo, Ahmad Shaheryar, and Hazrat Ali Copyright © 2017 Adnan O. M. Abuassba et al. All rights reserved. Fast Recall for Complex-Valued Hopfield Neural Networks with Projection Rules Wed, 03 May 2017 00:00:00 +0000 Many models of neural networks have been extended to complex-valued neural networks. A complex-valued Hopfield neural network (CHNN) is a complex-valued version of a Hopfield neural network. Complex-valued neurons can represent multistates, and CHNNs are available for the storage of multilevel data, such as gray-scale images. The CHNNs are often trapped into the local minima, and their noise tolerance is low. Lee improved the noise tolerance of the CHNNs by detecting and exiting the local minima. In the present work, we propose a new recall algorithm that eliminates the local minima. We show that our proposed recall algorithm not only accelerated the recall but also improved the noise tolerance through computer simulations. Masaki Kobayashi Copyright © 2017 Masaki Kobayashi. All rights reserved. A Decision-Based Modified Total Variation Diffusion Method for Impulse Noise Removal Thu, 27 Apr 2017 00:00:00 +0000 Impulsive noise removal usually employs median filtering, switching median filtering, the total variation method, and variants. These approaches however often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A new method to remove noise is proposed in this paper to overcome this limitation, which divides pixels into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the pixels are divided into corrupted and noise-free; if the image is corrupted by random valued impulses, the pixels are divided into corrupted, noise-free, and possibly corrupted. Pixels falling into different categories are processed differently. If a pixel is corrupted, modified total variation diffusion is applied; if the pixel is possibly corrupted, weighted total variation diffusion is applied; otherwise, the pixel is left unchanged. Experimental results show that the proposed method is robust to different noise strengths and suitable for different images, with strong noise removal capability as shown by PSNR/SSIM results as well as the visual quality of restored images. Hongyao Deng, Qingxin Zhu, Xiuli Song, and Jinsong Tao Copyright © 2017 Hongyao Deng et al. All rights reserved. Extra Facial Landmark Localization via Global Shape Reconstruction Sun, 23 Apr 2017 00:00:00 +0000 Localizing facial landmarks is a popular topic in the field of face analysis. However, problems arose in practical applications such as handling pose variations and partial occlusions while maintaining moderate training model size and computational efficiency still challenges current solutions. In this paper, we present a global shape reconstruction method for locating extra facial landmarks comparing to facial landmarks used in the training phase. In the proposed method, the reduced configuration of facial landmarks is first decomposed into corresponding sparse coefficients. Then explicit face shape correlations are exploited to regress between sparse coefficients of different facial landmark configurations. Finally extra facial landmarks are reconstructed by combining the pretrained shape dictionary and the approximation of sparse coefficients. By applying the proposed method, both the training time and the model size of a class of methods which stack local evidences as an appearance descriptor can be scaled down with only a minor compromise in detection accuracy. Extensive experiments prove that the proposed method is feasible and is able to reconstruct extra facial landmarks even under very asymmetrical face poses. Shuqiu Tan, Dongyi Chen, Chenggang Guo, and Zhiqi Huang Copyright © 2017 Shuqiu Tan et al. All rights reserved. Identification of Functionally Interconnected Neurons Using Factor Analysis Sun, 16 Apr 2017 06:37:33 +0000 The advances in electrophysiological methods have allowed registering the joint activity of single neurons. Thus, studies on functional dynamics of complex-valued neural networks and its information processing mechanism have been conducted. Particularly, the methods for identifying neuronal interconnections are in increasing demand in the area of neurosciences. Here, we proposed a factor analysis to identify functional interconnections among neurons via spike trains. This method was evaluated using simulations of neural discharges from different interconnections schemes. The results have revealed that the proposed method not only allows detecting neural interconnections but will also allow detecting the presence of presynaptic neurons without the need of the recording of them. Jorge H. Soletta, Fernando D. Farfán, Ana L. Albarracín, Alvaro G. Pizá, Facundo A. Lucianna, and Carmelo J. Felice Copyright © 2017 Jorge H. Soletta et al. All rights reserved. Comparison of the BCI Performance between the Semitransparent Face Pattern and the Traditional Face Pattern Sun, 09 Apr 2017 08:07:06 +0000 Brain-computer interface (BCI) systems allow users to communicate with the external world by recognizing the brain activity without the assistance of the peripheral motor nervous system. P300-based BCI is one of the most common used BCI systems that can obtain high classification accuracy and information transfer rate (ITR). Face stimuli can result in large event-related potentials and improve the performance of P300-based BCI. However, previous studies on face stimuli focused mainly on the effect of various face types (i.e., face expression, face familiarity, and multifaces) on the BCI performance. Studies on the influence of face transparency differences are scarce. Therefore, we investigated the effect of semitransparent face pattern (STF-P) (the subject could see the target character when the stimuli were flashed) and traditional face pattern (F-P) (the subject could not see the target character when the stimuli were flashed) on the BCI performance from the transparency perspective. Results showed that STF-P obtained significantly higher classification accuracy and ITR than those of F-P ( < 0.05). Jiao Cheng, Jing Jin, and Xingyu Wang Copyright © 2017 Jiao Cheng et al. All rights reserved. Corrigendum to “Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation” Wed, 05 Apr 2017 09:07:30 +0000 Ju-Chi Liu, Hung-Chyun Chou, Chien-Hsiu Chen, Yi-Tseng Lin, and Chung-Hsien Kuo Copyright © 2017 Ju-Chi Liu et al. All rights reserved. Progress in EEG-Based Brain Robot Interaction Systems Wed, 05 Apr 2017 00:00:00 +0000 The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques. Xiaoqian Mao, Mengfan Li, Wei Li, Linwei Niu, Bin Xian, Ming Zeng, and Genshe Chen Copyright © 2017 Xiaoqian Mao et al. All rights reserved. Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem Tue, 04 Apr 2017 09:50:34 +0000 The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria. M. Rajeswari, J. Amudhavel, Sujatha Pothula, and P. Dhavachelvan Copyright © 2017 M. Rajeswari et al. All rights reserved. Memetic Differential Evolution with an Improved Contraction Criterion Tue, 04 Apr 2017 07:08:53 +0000 Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimization. In this paper, we present an improved memetic differential evolution algorithm for solving global optimization problems. The proposed approach, called memetic DE (MDE), hybridizes differential evolution (DE) with a local search (LS) operator and periodic reinitialization to balance the exploration and exploitation. A new contraction criterion, which is based on the improved maximum distance in objective space, is proposed to decide when the local search starts. The proposed algorithm is compared with six well-known evolutionary algorithms on twenty-one benchmark functions, and the experimental results are analyzed with two kinds of nonparametric statistical tests. Moreover, sensitivity analyses for parameters in MDE are also made. Experimental results have demonstrated the competitive performance of the proposed method with respect to the six compared algorithms. Lei Peng, Yanyun Zhang, Guangming Dai, and Maocai Wang Copyright © 2017 Lei Peng et al. All rights reserved. PAIRS: Prediction of Activation/Inhibition Regulation Signaling Pathway Sun, 02 Apr 2017 00:00:00 +0000 Uncovering the signaling architecture in protein-protein interaction (PPI) can certainly benefit the understanding of disease mechanisms and promise to facilitate the therapeutic interventions. Therefore, it is important to reveal the signaling relationship from one protein to another in terms of activation and inhibition. In this study, we propose a new measurement to characterize the regulation relationship of a PPI pair. By utilizing both Gene Ontology (GO) functional annotation and protein domain information, we developed a tool called Prediction of Activation/Inhibition Regulation Signaling Pathway (PAIRS) that takes protein interaction pairs as input and gives both known and predicted result of the human protein regulation relationship in terms of activation and inhibition. It helps to give prognostic regulation information for further signaling pathway reconstruction. Tengjiao Wang, Yanghe Feng, and Qi Wang Copyright © 2017 Tengjiao Wang et al. All rights reserved. A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling Tue, 28 Mar 2017 09:40:59 +0000 Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with iteration times is first used to obtain the initial population , in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed. Qianwang Deng, Guiliang Gong, Xuran Gong, Like Zhang, Wei Liu, and Qinghua Ren Copyright © 2017 Qianwang Deng et al. All rights reserved. A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm Tue, 28 Mar 2017 00:00:00 +0000 The k-modes clustering algorithm has been widely used to cluster categorical data. In this paper, we firstly analyzed the k-modes algorithm and its dissimilarity measure. Based on this, we then proposed a novel dissimilarity measure, which is named as GRD. GRD considers not only the relationships between the object and all cluster modes but also the differences of different attributes. Finally the experiments were made on four real data sets from UCI. And the corresponding results show that GRD achieves better performance than two existing dissimilarity measures used in k-modes and Cao’s algorithms. Hongfang Zhou, Yihui Zhang, and Yibin Liu Copyright © 2017 Hongfang Zhou et al. All rights reserved. Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs Thu, 16 Mar 2017 00:00:00 +0000 In this paper, we propose novel methods for measuring depth of anesthesia (DOA) by quantifying dominant information flow in multichannel EEGs. Conventional methods mainly use few EEG channels independently and most of multichannel EEG based studies are limited to specific regions of the brain. Therefore the function of the cerebral cortex over wide brain regions is hardly reflected in DOA measurement. Here, DOA is measured by the quantification of dominant information flow obtained from principle bipartition. Three bipartitioning methods are used to detect the dominant information flow in entire EEG channels and the dominant information flow is quantified by calculating information entropy. High correlation between the proposed measures and the plasma concentration of propofol is confirmed from the experimental results of clinical data in 39 subjects. To illustrate the performance of the proposed methods more easily we present the results for multichannel EEG on a two-dimensional (2D) brain map. Kab-Mun Cha, Byung-Moon Choi, Gyu-Jeong Noh, and Hyun-Chool Shin Copyright © 2017 Kab-Mun Cha et al. All rights reserved. Image Encryption Algorithm Based on Hyperchaotic Maps and Nucleotide Sequences Database Tue, 14 Mar 2017 07:01:22 +0000 Image encryption technology is one of the main means to ensure the safety of image information. Using the characteristics of chaos, such as randomness, regularity, ergodicity, and initial value sensitiveness, combined with the unique space conformation of DNA molecules and their unique information storage and processing ability, an efficient method for image encryption based on the chaos theory and a DNA sequence database is proposed. In this paper, digital image encryption employs a process of transforming the image pixel gray value by using chaotic sequence scrambling image pixel location and establishing superchaotic mapping, which maps quaternary sequences and DNA sequences, and by combining with the logic of the transformation between DNA sequences. The bases are replaced under the displaced rules by using DNA coding in a certain number of iterations that are based on the enhanced quaternary hyperchaotic sequence; the sequence is generated by Chen chaos. The cipher feedback mode and chaos iteration are employed in the encryption process to enhance the confusion and diffusion properties of the algorithm. Theoretical analysis and experimental results show that the proposed scheme not only demonstrates excellent encryption but also effectively resists chosen-plaintext attack, statistical attack, and differential attack. Ying Niu, Xuncai Zhang, and Feng Han Copyright © 2017 Ying Niu et al. All rights reserved. Random Deep Belief Networks for Recognizing Emotions from Speech Signals Sun, 05 Mar 2017 00:00:00 +0000 Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition. Guihua Wen, Huihui Li, Jubing Huang, Danyang Li, and Eryang Xun Copyright © 2017 Guihua Wen et al. All rights reserved. Smart Data: Where the Big Data Meets the Semantics Sun, 26 Feb 2017 07:53:02 +0000 Trong H. Duong, Hong Q. Nguyen, and Geun S. Jo Copyright © 2017 Trong H. Duong et al. All rights reserved. Ranking of Sites for Installation of Hydropower Plant Using MLP Neural Network Trained with GA: A MADM Approach Sun, 26 Feb 2017 07:38:45 +0000 Every energy system which we consider is an entity by itself, defined by parameters which are interrelated according to some physical laws. In recent year tremendous importance is given in research on site selection in an imprecise environment. In this context, decision making for the suitable location of power plant installation site is an issue of relevance. Environmental impact assessment is often used as a legislative requirement in site selection for decades. The purpose of this current work is to develop a model for decision makers to rank or classify various power plant projects according to multiple criteria attributes such as air quality, water quality, cost of energy delivery, ecological impact, natural hazard, and project duration. The case study in the paper relates to the application of multilayer perceptron trained by genetic algorithm for ranking various power plant locations in India. Benjamin A. Shimray, Kh. Manglem Singh, Thongam Khelchandra, and R. K. Mehta Copyright © 2017 Benjamin A. Shimray et al. All rights reserved. Patch Based Multiple Instance Learning Algorithm for Object Tracking Wed, 22 Feb 2017 07:49:35 +0000 To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL) algorithm is proposed. The algorithm divides an object into many blocks. Then, the online MIL algorithm is applied on each block for obtaining strong classifier. The algorithm takes account of both the average classification score and classification scores of all the blocks for detecting the object. In particular, compared with the whole object based MIL algorithm, the P-MIL algorithm detects the object according to the unoccluded patches when partial occlusion occurs. After detecting the object, the learning rates for updating weak classifiers’ parameters are adaptively tuned. The classifier updating strategy avoids overupdating and underupdating the parameters. Finally, the proposed method is compared with other state-of-the-art algorithms on several classical videos. The experiment results illustrate that the proposed method performs well especially in case of illumination changes or pose variations and partial occlusion. Moreover, the algorithm realizes real-time object tracking. Zhenjie Wang, Lijia Wang, and Hua Zhang Copyright © 2017 Zhenjie Wang et al. All rights reserved. Mexican Hat Wavelet Kernel ELM for Multiclass Classification Tue, 21 Feb 2017 00:00:00 +0000 Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems. To some extent, it solves the existing problems of the invalid nodes and the large computational complexity in ELM. However, the traditional KELM classifier usually has a low test accuracy when it faces multiclass classification problems. In order to solve the above problem, a new classifier, Mexican Hat wavelet KELM classifier, is proposed in this paper. The proposed classifier successfully improves the training accuracy and reduces the training time in the multiclass classification problems. Moreover, the validity of the Mexican Hat wavelet as a kernel function of ELM is rigorously proved. Experimental results on different data sets show that the performance of the proposed classifier is significantly superior to the compared classifiers. Jie Wang, Yi-Fan Song, and Tian-Lei Ma Copyright © 2017 Jie Wang et al. All rights reserved. An Evolutionary Method for Financial Forecasting in Microscopic High-Speed Trading Environment Mon, 20 Feb 2017 13:54:28 +0000 The advancement of information technology in financial applications nowadays have led to fast market-driven events that prompt flash decision-making and actions issued by computer algorithms. As a result, today’s markets experience intense activity in the highly dynamic environment where trading systems respond to others at a much faster pace than before. This new breed of technology involves the implementation of high-speed trading strategies which generate significant portion of activity in the financial markets and present researchers with a wealth of information not available in traditional low-speed trading environments. In this study, we aim at developing feasible computational intelligence methodologies, particularly genetic algorithms (GA), to shed light on high-speed trading research using price data of stocks on the microscopic level. Our empirical results show that the proposed GA-based system is able to improve the accuracy of the prediction significantly for price movement, and we expect this GA-based methodology to advance the current state of research for high-speed trading and other relevant financial applications. Chien-Feng Huang and Hsu-Chih Li Copyright © 2017 Chien-Feng Huang and Hsu-Chih Li. All rights reserved. A Novel Graph Constructor for Semisupervised Discriminant Analysis: Combined Low-Rank and -Nearest Neighbor Graph Mon, 20 Feb 2017 00:00:00 +0000 Semisupervised Discriminant Analysis (SDA) is a semisupervised dimensionality reduction algorithm, which can easily resolve the out-of-sample problem. Relative works usually focus on the geometric relationships of data points, which are not obvious, to enhance the performance of SDA. Different from these relative works, the regularized graph construction is researched here, which is important in the graph-based semisupervised learning methods. In this paper, we propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and -nearest neighbor (LRKNN) graph. In our LRKNN graph, we map the data to the LR feature space and then the is adopted to satisfy the algorithmic requirements of SDA. Since the low-rank representation can capture the global structure and the -nearest neighbor algorithm can maximally preserve the local geometrical structure of the data, the LRKNN graph can significantly improve the performance of SDA. Extensive experiments on several real-world databases show that the proposed LRKNN graph is an efficient graph constructor, which can largely outperform other commonly used baselines. Baokai Zu, Kewen Xia, Yongke Pan, and Wenjia Niu Copyright © 2017 Baokai Zu et al. All rights reserved. An Efficient Framework for EEG Analysis with Application to Hybrid Brain Computer Interfaces Based on Motor Imagery and P300 Sun, 19 Feb 2017 00:00:00 +0000 The hybrid brain computer interface (BCI) based on motor imagery (MI) and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI. Jinyi Long, Jue Wang, and Tianyou Yu Copyright © 2017 Jinyi Long et al. All rights reserved. A Robust Shape Reconstruction Method for Facial Feature Point Detection Sun, 19 Feb 2017 00:00:00 +0000 Facial feature point detection has been receiving great research advances in recent years. Numerous methods have been developed and applied in practical face analysis systems. However, it is still a quite challenging task because of the large variability in expression and gestures and the existence of occlusions in real-world photo shoot. In this paper, we present a robust sparse reconstruction method for the face alignment problems. Instead of a direct regression between the feature space and the shape space, the concept of shape increment reconstruction is introduced. Moreover, a set of coupled overcomplete dictionaries termed the shape increment dictionary and the local appearance dictionary are learned in a regressive manner to select robust features and fit shape increments. Additionally, to make the learned model more generalized, we select the best matched parameter set through extensive validation tests. Experimental results on three public datasets demonstrate that the proposed method achieves a better robustness over the state-of-the-art methods. Shuqiu Tan, Dongyi Chen, Chenggang Guo, and Zhiqi Huang Copyright © 2017 Shuqiu Tan et al. All rights reserved.