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Computational Intelligence and Neuroscience

A-Yeong Kim, Hyun-Je Song, Seong-Bae Park
Dialog state tracking in a spoken dialog system is the task that tracks the flow of a dialog and identifies accurately what a user wants from the utterance. Since the success of a dialog is influenced by the ability of the system to catch the requirements of the user, accurate state tracking is important for spoken dialog systems. This paper proposes a two-step neural dialog state tracker which is composed of an informativeness classifier and a neural tracker. The informativeness classifier which is implemented by a CNN first filters out noninformative utterances in a dialog...
2018: Computational Intelligence and Neuroscience
Duk Shin, Hiroyuki Kambara, Natsue Yoshimura, Yasuharu Koike
Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studies, there still remain solving works for the purpose of realizing an ECoG-based prosthesis. We suggest a neuromuscular interface to control robot using decoded muscle activities and joint angles. We used sparse linear regression to find the best fit between band-passed ECoGs and electromyograms (EMG) or joint angles...
2018: Computational Intelligence and Neuroscience
Hedian Jin, Chunguang Li, Jiacheng Xu
Rehabilitation training is essential for motor dysfunction patients, and the training through their subjective motion intention, comparing to passive training, is more conducive to rehabilitation. This study proposes a method to identify motion intention of different walking states under the normal environment, by using the functional near-infrared spectroscopy (fNIRS) technology. Twenty-two healthy subjects were recruited to walk with three different gaits (including small-step with low-speed, small-step with midspeed, midstep with low-speed)...
2018: Computational Intelligence and Neuroscience
Junghoon Hah, Woojin Lee, Jaewook Lee, Saerom Park
This paper describes a new image generation algorithm based on generative adversarial network. With an information-theoretic extension to the autoencoder-based discriminator, this new algorithm is able to learn interpretable representations from the input images. Our model not only adversarially minimizes the Wasserstein distance-based losses of the discriminator and generator but also maximizes the mutual information between small subset of the latent variables and the observation. We also train our model with proportional control theory to keep the equilibrium between the discriminator and the generator balanced, and as a result, our generative adversarial network can mitigate the convergence problem...
2018: Computational Intelligence and Neuroscience
Haijin Ji, Song Huang
Kernel entropy component analysis (KECA) is a newly proposed dimensionality reduction (DR) method, which has showed superiority in many pattern analysis issues previously solved by principal component analysis (PCA). The optimized KECA (OKECA) is a state-of-the-art variant of KECA and can return projections retaining more expressive power than KECA. However, OKECA is sensitive to outliers and accused of its high computational complexities due to its inherent properties of L2-norm. To handle these two problems, we develop a new extension to KECA, namely, KECA-L1, for DR or feature extraction...
2018: Computational Intelligence and Neuroscience
Guohui Li, Xiao Ma, Hong Yang
The change of the number of sunspots has a great impact on the Earth's climate, agriculture, communications, natural disasters, and other aspects, so it is very important to predict the number of sunspots. Aiming at the chaotic characteristics of monthly mean of sunspots, a novel hybrid model for forecasting sunspots time-series based on variational mode decomposition (VMD) and backpropagation (BP) neural network improved by firefly algorithm (FA) is proposed. Firstly, a set of intrinsic mode functions (IMFs) are obtained by VMD decomposition of the monthly mean time series of the sunspots...
2018: Computational Intelligence and Neuroscience
Xingsi Xue, Jie Chen, Junfeng Chen, Dongxu Chen
Over the recent years, ontologies are widely used in various domains such as medical records annotation, medical knowledge representation and sharing, clinical guideline management, and medical decision-making. To implement the cooperation between intelligent applications based on biomedical ontologies, it is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called biomedical ontology matching. Although Evolutionary algorithms (EAs) are one of the state-of-the-art methodologies to match the heterogeneous ontologies, huge memory consumption, long runtime, and the bias improvement of the solutions hamper them from efficiently matching biomedical ontologies...
2018: Computational Intelligence and Neuroscience
Saad Albawi, Oguz Bayat, Saad Al-Azawi, Osman N Ucan
Recently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implement a social touch recognition system for raw input samples (sensor data) only. The touch gesture recognition is performed using a dataset previously measured with numerous subjects that perform varying social gestures. This dataset is dubbed as the corpus of social touch, where touch was performed on a mannequin arm...
2018: Computational Intelligence and Neuroscience
Jeng-Fung Chen, Shih-Kuei Lo, Quang Hung Do
Forecasting short-term traffic flow is a key task of intelligent transportation systems, which can influence the traveler behaviors and reduce traffic congestion, fuel consumption, and accident risks. This paper proposes a fuzzy wavelet neural network (FWNN) trained by improved biogeography-based optimization (BBO) algorithm for forecasting short-term traffic flow using past traffic data. The original BBO is enhanced by the ring topology and Powell's method to advance the exploration capability and increase the convergence speed...
2018: Computational Intelligence and Neuroscience
Shaohua Cui, Hui Zhao, Hui Chen, Cuiping Zhang
For the environmental friendliness of the technology on battery electric vehicles, there is growing attention on it. However, the market share of battery electric vehicles remains low due to the range anxiety. As a remedy, the mobile charging services could offer charging service at any time or locations requested. For profitability of the services, the operator should route the charging vehicles in a more efficient manner. For this consideration, we formulate the mobile charging vehicle routing problem as a mixed integer linear program based on the classical vehicle routing problem with time windows...
2018: Computational Intelligence and Neuroscience
Mushtaq Hussain, Wenhao Zhu, Wu Zhang, Syed Muhammad Raza Abidi
Several challenges are associated with e-learning systems, the most significant of which is the lack of student motivation in various course activities and for various course materials. In this study, we used machine learning (ML) algorithms to identify low-engagement students in a social science course at the Open University (OU) to assess the effect of engagement on student performance. The input variables of the study included highest education level , final results , score on the assessment , and the number of clicks on virtual learning environment (VLE) activities, which included dataplus , forumng , glossary , oucollaborate , oucontent , resources , subpages , homepage , and URL during the first course assessment...
2018: Computational Intelligence and Neuroscience
Yongkai Ye, Xinwang Liu, Qiang Liu, Xifeng Guo, Jianping Yin
In real-world applications of multiview clustering, some views may be incomplete due to noise, sensor failure, etc. Most existing studies in the field of incomplete multiview clustering have focused on early fusion strategies, for example, learning subspace from multiple views. However, these studies overlook the fact that clustering results with the visible instances in each view could be reliable under the random missing assumption; accordingly, it seems that learning a final clustering decision via late fusion of the clustering results from incomplete views would be more natural...
2018: Computational Intelligence and Neuroscience
Quan Zhang, HongWei Xiu
The interval multiple attribute decision-making problems are studied in this paper, where the preference information on attributes is expressed with preference orderings, linguistic terms, interval numbers, and inequality constraints among partial attribute weights. An approach is proposed to determine the attribute weights based on the preference information on attributes and the interval decision matrix. Firstly, preference orderings, linguistic terms, and interval numbers are normalized and aggregated into the group opinions, based on which an optimization model is set up to calculate the subjective attribute weights by including inequality constraints among partial attribute weights in the model...
2018: Computational Intelligence and Neuroscience
Nhat-Duc Hoang
To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. The new approach relies on image processing techniques and computational intelligence algorithms. The image processing techniques of Laplacian pyramid and projection integral are employed to extract numerical features from digital images. Least squares support vector machine (LSSVM) and Differential Flower Pollination (DFP) are the two computational intelligence algorithms that are employed to construct the crack classification model based on the extracted features...
2018: Computational Intelligence and Neuroscience
Zhenrui Peng, Kangli Dong, Hong Yin, Yu Bai
Artificial fish swarm algorithm easily converges to local optimum, especially in solving the global optimization problem of multidimensional and multiextreme value functions. To overcome this drawback, a novel fish swarm algorithm (LFFSA) based on Lévy flight and firefly behavior is proposed. LFFSA incorporates the moving strategy of firefly algorithm into two behavior patterns of fish swarm, i.e., chasing behavior and preying behavior. Furthermore, Lévy flight is introduced into the searching strategy. To limit the search band, nonlinear view and step size based on dynamic parameter are considered...
2018: Computational Intelligence and Neuroscience
Enrica Modica, Giulia Cartocci, Dario Rossi, Ana C Martinez Levy, Patrizia Cherubino, Anton Giulio Maglione, Gianluca Di Flumeri, Marco Mancini, Marco Montanari, Davide Perrotta, Paolo Di Feo, Alessia Vozzi, Vincenzo Ronca, Pietro Aricò, Fabio Babiloni
It is well known that the evaluation of a product from the shelf considers the simultaneous cerebral and emotional evaluation of the different qualities of the product such as its colour, the eventual images shown, and the envelope's texture (hereafter all included in the term "product experience"). However, the measurement of cerebral and emotional reactions during the interaction with food products has not been investigated in depth in specialized literature. The aim of this paper was to investigate such reactions by the EEG and the autonomic activities, as elicited by the cross-sensory interaction (sight and touch) across several different products...
2018: Computational Intelligence and Neuroscience
Enrica Modica, Dario Rossi, Giulia Cartocci, Davide Perrotta, Paolo Di Feo, Marco Mancini, Pietro Aricò, Bianca M S Inguscio, Fabio Babiloni
Over the past few decades, antismoking public service announcements (PSAs) have been used by governments to promote healthy behaviours in citizens, for instance, against drinking before the drive and against smoke. Effectiveness of such PSAs has been suggested especially for young persons. By now, PSAs efficacy is still mainly assessed through traditional methods (questionnaires and metrics) and could be performed only after the PSAs broadcasting, leading to waste of economic resources and time in the case of Ineffective PSAs...
2018: Computational Intelligence and Neuroscience
Hsiou-Hsiang Liu, Lung-Cheng Chang, Chien-Wei Li, Cheng-Hong Yang
The tourism industry has become one of the most important economic sectors for governments worldwide. Accurately forecasting tourism demand is crucial because it provides useful information to related industries and governments, enabling stakeholders to adjust plans and policies. To develop a forecasting tool for the tourism industry, this study proposes a method that combines feature selection (FS) and support vector regression (SVR) with particle swarm optimization (PSO), named FS-PSOSVR. To ensure high forecast accuracy, FS and a PSO algorithm are employed to, respectively, select reliable input variables and to identify the optimal initial parameters of SVR...
2018: Computational Intelligence and Neuroscience
ZhiFei Lai, HuiFang Deng
Medical image classification is a key technique of Computer-Aided Diagnosis (CAD) systems. Traditional methods rely mainly on the shape, color, and/or texture features as well as their combinations, most of which are problem-specific and have shown to be complementary in medical images, which leads to a system that lacks the ability to make representations of high-level problem domain concepts and that has poor model generalization ability. Recent deep learning methods provide an effective way to construct an end-to-end model that can compute final classification labels with the raw pixels of medical images...
2018: Computational Intelligence and Neuroscience
Fangzhao Li, Changjian Wang, Xiaohui Liu, Yuxing Peng, Shiyao Jin
[This corrects the article DOI: 10.1155/2018/4149103.].
2018: Computational Intelligence and Neuroscience
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