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Computational dentistry uses computerized methods and mathematical models for dental image analysis. One of the fundamental problems in computational dentistry is accurate tooth instance segmentation in high-resolution mesh data of intra-oral scans (IOS). This paper presents a new computational model based on deep neural networks, called Mask-MCNet , for end-to-end learning of tooth instance segmentation in 3D point cloud data of IOS. The proposed Mask-MCNet localizes each tooth instance by predicting its 3D bounding box and simultaneously segments the points that belong to each individual tooth instance. The proposed model processes the input raw 3D point cloud in its original spatial resolution without employing a voxelization or down-sampling technique. Such a characteristic preserves the finely detailed context in data like fine curvatures in the border between adjacent teeth and leads to a highly accurate segmentation as required for clinical practice (e.g. orthodontic planning). The experiments show that the Mask-MCNet outperforms state-of-the-art models by achieving 98% Intersection over Union (IoU) score on tooth instance segmentation which is very close to human expert performance. [ABSTRACT FROM AUTHOR]

Abstract Neural network models for dynamic systems can be trained either in parallel or in series-parallel configurations. Influenced by early arguments, several papers justify the choice of series-parallel rather than parallel configuration claiming it has a lower computational cost, better stability properties during training and provides more accurate results. Other published results, on the other hand, defend parallel training as being more robust and capable of yielding more accurate long-term predictions. The main contribution of this paper is to present a study comparing both methods under the same unified framework with special attention to three aspects: (i) robustness of the estimation in the presence of noise; (ii) computational cost; and, (iii) convergence. A unifying mathematical framework and simulation studies show situations where each training method provides superior validation results and suggest that parallel training is generally better in more realistic scenarios. An example using measured data seems to reinforce such a claim. Complexity analysis and numerical examples show that both methods have similar computational cost although series-parallel training is more amenable to parallelization. Some informal discussion about stability and convergence properties is presented and explored in the examples. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The approaches used in these methods are discussed with their respective states of art and applicability. The measures of similarity as well as the evaluation criteria, which are the central components of clustering, are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted. [ABSTRACT FROM AUTHOR]

In this paper, the global dissipativity and exponential state estimation for quaternion-valued memristive neural networks are investigated. By starting from basic quaternion properties, several algebraic conditions ensuring the global dissipativity and state estimation problems are derived. It is noteworthy that, based on the vector ordering approach, one can compare the "magnitude" of two different quaternion-valued, and thus the closed convex hull derived by two different quaternion-valued connections can be derived. Simulation results are given to show the validate of the mathematical model and the derived conclusions of the memristive system. [ABSTRACT FROM AUTHOR]

This paper is concerned with the non-fragile H ∞ state estimation problem for a class of discrete-time networked system with probabilistic diverging disturbance and multiple missing measurements. The measurement missing phenomenon is assumed to occur randomly and the missing probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution over the interval [ 0 , 1 ] . The aim of this paper is to estimate the networked system by designing a non-fragile H ∞ estimator such that the augmented estimation error system is asymptotically mean square stable with a prescribed H ∞ disturbance attention level γ . By using the Lyapunov method and stochastic analysis, we derive a sufficient condition for the existence of the desired estimator. By solving the linear matrix inequalities (LMIs), the estimator gain matrix is given. Two numerical examples are employed to demonstrate the effectiveness and applicability of the proposed design technique. [ABSTRACT FROM AUTHOR]

Image set representation and classification is an important problem in computer vision and pattern recognition area. It has been widely used in many computer vision applications. In this paper, a new image set representation and classification method has been proposed. The main contributions of this paper are twofold: (1) a new image set representation model, called attributed covariate-relation graph (ACRG), has been proposed for image set representation and modeling. ACRG aims to represent image set with an attributed graph model which involves both image features and their spatial structure simultaneously. (2) A new graph data based sparse representation and classification method, called Graph Sparse Representation Classification (GSRC) has been proposed to achieve ACRG classification. Experimental results on several datasets demonstrate the benefits of the proposed ACRG representation and GSRC classification. [ABSTRACT FROM AUTHOR]

Rolling bearings are necessary parts in rotary machines. However, the problem of early fault diagnosis for rolling bearings is difficult to solve due to its low signal-to-noise ratio and non-linear and non-stationary signal. Based on a detailed investigation of rolling bearing vibration signals, this paper proposes a method for determining whether a fault occurs by comparing the high-frequency band power. If a fault occurs, we first de-noise the vibration signals using wavelet de-noising and then extract the fault characteristics in both the time domain and the time-frequency domain to avoid the limitations of using only one domain. Finally, the fault location is identified using the grey correlation method. According to the method application results, the recognition accuracy using the method proposed in this paper is satisfactory, proving that the method has superior performance. [ABSTRACT FROM AUTHOR]

In this paper, the problem of adaptive synchronization for time-delay neutral-type multi-slave neural networks with Markovian jumping parameters and stochastic noise is researched. The adaptive synchronization model which contains a drive system and multiple response systems is presented. By using of the generalized It o ^ ’ s formula and the M-matrix method, the sufficient condition is obtained to guarantee that the error control system is stable, and the update law of the feedback controller is designed to ensure that every slave system synchronizes with master system. Finally, a numerical example is given to illustrate the effectiveness of the method and result obtained in this paper. [ABSTRACT FROM AUTHOR]

Thanks to social media platforms, topics are diffused online. The ones which diffuse rapidly and widely are known as viral topics. Predicting the popularity of viral topics is important to online recommendation systems and marketing services. However, the task is still challenging due to the fact that the popularity of viral topics is highly dynamic and little research has been focused on how to predict the popularity accurately for viral topics. This paper first uses data collected from the largest BBS in China to find high correlations for the short-term popularity of viral topics. Based on this finding, this paper then utilizes a time series feature space to capture the behaviors of popularity of viral topics and presents a method for predicting the short-term popularity of a given viral topic by using only data of historical popularity of the topic. This paper demonstrates that our method outperforms a baseline model and one of the most sophisticated methods in the terms of MAPE (mean absolute percentage error) and RAE (relative absolute error). Furthermore, this paper gives out how long the minimum observation period is in order to predict promptly. Finally, this paper uses two cases to show the effectiveness and simplicity of our method. [ABSTRACT FROM AUTHOR]

This paper is concerned with an improved event-triggered control for a class of networked control systems (NCSs) under stochastic cyber-attacks with limited communication bandwidths. In order to reduce the unnecessary data transmissions, an adaptive event-triggered approach is employed to locally determine the sensor measurements whether to be transmitted or not, where the triggering thresholds can take much real-time information on the NCSs into consideration, such as the triggering error, last transmitted signal, and current sampled one. It is assumed that the network transmissions may be attacked by two types of randomly occurring cyber-attacks, then a novel mathematical model is achieved and three sufficient conditions are derived to guarantee the asymptotical stability of the closed-loop system. Especially, two augmented Lyapunov-Krasovskii functionals (LKFs) are constructed and an improved reciprocal convex technique is proposed to reduce the conservatism. Finally, two numerical examples are presented to show the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

Abstract Layout optimization of large-scale oil–gas gathering system is a kind of NP-hard problem in the field of system optimization. It involves a large number of network nodes, coupled optimization variables, complex network structures and hydraulic constraints, which cause the great difficulty in constructing optimization models and solution methods. In this paper, a high-dimensional mixed integer nonlinear layout optimization mathematical model involving the pipeline network structure parameters and pipeline design parameters is established, which can be applied to large-scale oil gathering system and gas gathering system universally. A modified particle swarm optimization (MPSO) algorithm with global search ability is proposed. The convergence theorem of the stochastic optimization algorithm is established based on the Poincare cycle theory. Global convergence of MPSO algorithm is proved, and the performance of MPSO algorithm is analyzed by numerical experiments. Based on dimension reduction planning and modularization thought, the grid dissection set partition method is proposed, and the theoretical foundation and complexity of the grid dissection method are discussed. In order to reduce the dimension of the layout optimization problem, the concept of the fuzzy set of adjacent position, and a novel approach for the well-station connection mode optimization are put forward. Based on the MPSO algorithm, grid dissection set partition method and solution method by fuzzy set of adjacent position, a combined optimization strategy for layout optimization model is proposed. The reliability and practicality of the proposed layout optimization model and combined optimization strategy are verified by the successful application of a real-world large-scale oil field with 661 wells. [ABSTRACT FROM AUTHOR]

Abstract Recent studies propose that different dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TPs). Climate TPs can severely affect millions of lives on Earth so that an active scientific community is working on finding early warning signals. This paper deals with the development of a time series segmentation algorithm for paleoclimate data in order to find segments sharing common statistical patterns. The proposed algorithm uses a clustering-based approach for evaluating the solutions and six statistical features, most of which have been previously considered in the detection of early warning signals in paleoclimate TPs. Due to the limitations of classical statistical methods, we propose the use of a genetic algorithm to automatically segment the series, together with a method to compare the segmentations. The final segments provided by the algorithm are used to construct a prediction model, whose promising results show the importance of segmentation for improving the understanding of a time series. [ABSTRACT FROM AUTHOR]

Highlights • A novel neural network model CID-STNN is firstly proposed to forecasting crude oil time series. • Complexity invariant distance is firstly used on learning rate to improve forecast accuracy. • Intrinsic mode functions by ensemble empirical mode decomposition are used to train the model. • The comparison results show that CID-STNN model has a better fitting ability than other models. Abstract In view of the applications of artificial neural networks in economic and financial forecasting, it is vital to improve the prediction methods and the forecasting accuracy for the neural networks. In this paper, a neural network architecture with novel learning rate which is controlled by the complexity invariant distance (CID) is developed for energy market forecasting, where the CID is generally utilized to measure the complexity differences between two time series by employing the Euclidean distance. Moreover, stochastic time strength neural network (STNN) is a kind of supervised neural network which is introduced to forecast the time series. Based on the above theories, a new neural network model called CID-STNN is proposed in this work, in an attempt to improve the forecasting accuracy. For comparing the forecasting performance of CID-STNN and STNN deeply, the ensemble empirical mode decomposition (EEMD) is applied to decompose time series into several intrinsic mode functions (IMFs), and these IMFs are utilized to train the models. Further, the empirical research is performed in testing the prediction effect of WTI and Brent by evaluating predicting ability of the proposed model, and the corresponding superiority is also demonstrated. [ABSTRACT FROM AUTHOR]

Abstract The recognition of license plates is very important for intelligent transportation systems. Generally, the performance of an intelligent recognition algorithm is greatly affected by different shooting angles, illumination conditions and backgrounds of the license plate images. This paper presents a sequence recognition approach for intelligent recognition of Chinese license plates. Firstly, a spatial transformer network (STN) is employed to adjust the inclined and deformed license plates such that all the plates have a uniform orientation and thus are easier to be recognized. Then, an improved convolutional neural network (CNN) is designed to extract sequence features of the rectified license plates. The features of different convolutional layers are integrated as input to a bi-directional recurrent neural network (BRNN), where the character segmentation is not needed. Finally, the recognition is accomplished by the BRNN and connectionist temporal classification (CTC). Due to the lack of adequate Chinese license plates, an effective training method is presented in which the network is pre-trained by sufficiently enough synthetic license plates and is fine-tuned by our collected real Chinese license plates. The experimental results show that our model achieves better recognition accuracy and lower average edit distance than some existing methods. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose a novel end-to-end architecture termed Spatio-Temporal Convolutional features with Nested LSTM (STC-NLSTM), which learns the muti-level appearance features and temporal dynamics of facial expressions in a joint fashion. More precisely, 3DCNN is used to extract spatio-temporal convolutional features from the image sequences that represent facial expressions, and the dynamics of expressions are modeled by Nested LSTM, which is actually coupled by two sub-LSTMs, saying T-LSTM and C-LSTM. Namely, T-LSTM is used to model the temporal dynamics of the spatio-temporal features in each convolutional layer, and C-LSTM is adopted to integrate the outputs of all T-LSTMs together so as to encode the multi-level features encoded in the intermediate layers of the network. We conduct experiments on four benchmark databases, CK+, Oulu-CASIA, MMI and BP4D, and the results show that the proposed method achieves a performance superior to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Abstract Feature reduction is nowadays an important topic in machine learning as it reduces the complexity of the final model and makes it easier to interpret. In some applications, the features arise from multiple sources and it is not so important to select the individual features as to select the important sources. This leads to a group feature selection problem. In this paper, we consider the group feature selection in the multiclass classification setting based on the framework of support vector machines. We reformulate it as a sparse problem by prescribing the maximum number of active groups and propose a novel method based on the ADMM algorithm. We proposed the method in such a way that the main computational load is performed in the first iteration and the remaining iterations can be computed fast. This allows us to handle large problems. We demonstrate the good performance of our method on several real-world datasets. [ABSTRACT FROM AUTHOR]

Abstract The problem of disturbance rejection/attenuation for constant-input delayed linear multi-agent systems (MASs) with the directed communication topology is tackled in this paper, where a classic model reduction technique is introduced to transform the delayed MAS into the delay-free one. First, when the leader has no control input, a novel adaptive predictive extended state observer (ESO) using only relative state information of neighboring agents is designed to achieve disturbance-rejected consensus tracking. The stabilization analysis is presented via the Lyapunov function and sufficient conditions are derived in terms of linear matrix inequalities. Then the result is extended to disturbance-attenuated case where the leader has bounded control input which is only known by a portion of followers. Finally, two numerical examples are presented to illustrate the effectiveness of proposed strategies. The main contribution focuses on the design of adaptive predictive ESO protocols with the fully distributed property. [ABSTRACT FROM AUTHOR]

Abstract This paper suggests a novel way of dramatically improving the Naïve Bayes text classifier with our semantic tensor space model for document representation. In our work, we intend to achieve a perfect text classification with the semantic Naïve Bayes learning that incorporates the semantic concept features into term feature statistics; for this, the Naïve Bayes learning is semantically augmented under the tensor space model where the ‘concept’ space is regarded as an independent space equated with the ‘term’ and ‘document’ spaces, and it is produced with concept-level informative Wikipedia pages associated with a given document corpus. Through extensive experiments using three popular document corpora including Reuters-21578, 20Newsgroups , and OHSUMED corpora, we prove that the proposed method not only has superiority over the recent deep learning-based classification methods but also shows nearly perfect classification performance. [ABSTRACT FROM AUTHOR]

Abstract Data-driven graphs constitute the cornerstone of many machine learning approaches. Recently, it was shown that sparse graphs (sparse representation based graphs) provide a powerful approach to graph-based semi-supervised classification. In this paper, we introduce a new structured sparse graph that is derived by integrating manifold-type constraints on the sparse coefficients without any a priori graph or similarity matrix. Furthermore, we introduce a direct and efficient solution to the proposed optimization problem. Unlike recent sparse graph construction methods that are based on the use of hand-crafted constraints or a predefined reference similarity matrix, our constraints are directly defined on the graph weights themselves, and can provide additional information to both local and global structures of the sparse graph. Experiments conducted on several image databases show that the proposed graph can give better results than many state-of-the-art sparse graphs when applied to the problem of graph-based label propagation. [ABSTRACT FROM AUTHOR]

Inspired by the fact that the matrix formed by nonlocal similar patches in a natural image is of low rank, the nuclear norm minimization (NNM) has been widely used in various image processing studies. Nonetheless, nuclear norm based convex surrogate of the rank function usually over-shrinks the rank components since it treats different components equally, and thus may produce a result far from the optimum. To alleviate the aforementioned limitations of the nuclear norm, in this paper we propose a new method for image restoration via the non-convex weighted ℓ p nuclear norm minimization (NCW-NNM), which is able to accurately impose the image structural sparsity and self-similarity simultaneously. To make the proposed model tractable and robust, the alternating direction method of multiplier (ADMM) framework is adopted to solve the associated non-convex minimization problem. Experimental results on various image restoration problems, including image deblurring, image inpainting and image compressive sensing (CS) recovery, demonstrate that the proposed method outperforms many current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, the H ∞ filtering problem is investigated for a class of discrete-time arbitrary switched neural networks with missing measurements, stochastic perturbations, and communication delays. Based on the average dwell time approach and a set of Kronecker delta functions, a unified measurement model is established to represent the phenomena of missing measurements, time delays and nonlinearities. The aim of this paper is to design an H ∞ filter such that the filter error dynamics is exponentially mean-square stable and the H ∞ performance requirement is satisfied simultaneously. By using the Lyapunov stability theory and the matrix technology, the design method of the desired filter is given in terms of a matrix inequality which can be solved by using the available software. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the wide use of consumer electronics and the rapid development of online shopping, more and more ad videos are developed for IDTV and mobile users. However, a huge amount of time spending on the Internet advertising somehow brings users uncomfortable viewing experience rather than effectively generates high consumption of advertised products. Therefore, it is urgent to find a viewer-friendly and advertiser-beneficial solution to launch ads. This paper is the first attempt to improve the effectiveness of advertising through combining online shopping information with an ad video and directing viewers to proper online shopping places. The proposed ActiveAd framework includes four main components. Firstly, an ad video analysis component detects both syntactic and semantic elements from ad videos, e.g. FMPIs (Frame Marked with Production Information), visual concepts, and textual keywords within the ad videos. Our ad video analysis provides a comprehensive solution to extract meaningful elements from ad videos. Secondly, a visual linking by search component is proposed to collect websites which contain similar images as FMPIs. Features used for the visual search are weighted and fused in order to ensure the uniformity of search results. Thirdly, different kinds of tags and product categories extracted from collected websites are aggregated in order to identify the representative text of the product. Finally, query keywords are selected through calculating cosine similarity from two kinds of keywords, i.e. keywords identified from tag aggregation and keywords obtained through ad video analysis (visual concept detection and textual keyword detection). A query vector is generated by selected keywords and used to retrieve product online. In this paper, a powerful cross-media contextual search including visual search, tag aggregation and textual search is achieved with the help of ad video analysis. Experiments demonstrate that our proposed ActiveAd achieves product recommendation effectively and efficiently. [ABSTRACT FROM AUTHOR]

The problem of H ∞ control for a fish population logistic model with the invasion of alien species is studied via a T–S fuzzy control approach in this paper. Firstly, the harvested capability induced by economic factors and purification capability induced by invasion of alien species are analyzed. Secondly, the corresponding bio-economic model is established by taking the above two factors into account. Thirdly, the singularity-induced bifurcation (SIB) and impulsive behavior of the resultant bio-economic model are investigated. After that, a T–S fuzzy system is used to describe the nonlinear system for the bio-economic model with added input disturbance. A sufficient condition is proposed to satisfy H ∞ norm of the system by using the Lyapunov theory and a linear matrix inequality approach. Finally, a modified differential transform method is exploited to present the invasion system to analyze the characters. The application of H ∞ controller according to actual events demonstrates the effectiveness of the method used in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes an online tracking algorithm that employs a confidence combinatorial map model. Drifting is a problem that easily occurs in object tracking and most of the recent tracking algorithms have attempted to solve this problem. In this paper, we propose a confidence combinatorial map that describes the structure of the object, based on which the confidence combinatorial map model is developed. The model associates the relationship between the object in the current frame and that in the previous frame. On the strength of this relationship, more precisely classified samples can be selected and are employed in the model update stage, which directly influences the occurrence of the tracking drift. The proposed algorithm was estimated on several public video sequences and the performance was compared with several state-of-the-art algorithms. The experiments demonstrate that the proposed algorithm outperforms other comparative algorithms and gives a very good performance. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of H ∞ fuzzy filtering design with immeasurable premise variables for nonlinear systems presented as T–S model. First, the time derivatives of the membership functions are discussed and some linear matrices inequalities (LMIs) are obtained to bound it. Then, a new non-quadratic Lyapunov function which has the diagonal form is constructed to get some LMIs to design the filtering. In the end, two numerical examples are given to show that better H ∞ performance can be obtained by the method in this paper than others in the literature. [ABSTRACT FROM AUTHOR]

The novel method of deception detecting based on speech signal is proposed in this study. Extracting prosodic and non-linear dynamics (NLD) feature sets from speech signal and applying relevance vector machine (RVM) classification method is the primary target of this paper. Here, the sustained speaker-depended phonation samples of deception and non-deception were applied. In this paper, 30 prosodic features and 18 NLD features were selected which show significant correlations to deception state. Moreover, the RVM classification model based on sparse bayesian learning (SBL) was introduced which is a bayesian extension of the support vector machine (SVM) and not be restricted by the Mercer׳s condition. In the experiments, the deception corpus of Soochow University is exploited to test our approach. Firstly, the experiment of feature performance test was carried out. It is demonstrated that the combination of prosodic and NLD features is the most effectively method for detecting deceptive speech. Secondly, the property of RVM classification model is measured. The experiment results show that RVM technology requires much fewer basic functions and demands much less decision time than the SVM algorithm. Furthermore, during the test of correct classification accuracy of RVM, it shows that RVM is much higher than the classical model of SVM and radial basis function neural network (RBFNN). Finally, the robustness test of RVM is processed and received better performance than SVM and RBFNN methods when the signal to noise ratio (SNR) falls. In general, the proposed deception detecting method based on the combined features and RVM classifier is novel, convenient and practical, moreover, it achieves higher classification accuracy, less detection time, property of generalization, and the strong robustness. [ABSTRACT FROM AUTHOR]

Support vector regression with chaotic sequence and simulated annealing algorithm in previous forecasting research paper has shown its superiority to effectively avoid trapping into a local optimum. However, the proposed chaotic simulated annealing (CSA) algorithm in previous published literature as well as the original SA algorithm could not realize the mechanism of temperature decreasing continuously. In addition, lots of chaotic sequences adopt Logistic mapping function which is distributed at both ends in the interval [0,1], thus, it could not excellently strengthen the chaotic distribution characteristics. To continue exploring any possible improvements of the proposed CSA and chaotic sequence, this paper employs the innovative cloud theory to be hybridized with CSA to overcome the discrete temperature annealing process, and applies the Cat mapping function to ensure the chaotic distribution characteristics. Furthermore, seasonal mechanism is also proposed to well arrange with the cyclic tendency of electric load, caused by economic activities or climate cyclic nature. This investigation eventually presents a load forecasting model which hybridizes the seasonal support vector regression model and chaotic cloud simulated annealing algorithm (namely SSVRCCSA) to receive more accurate forecasting performance. Experimental results indicate that the proposed SSVRCCSA model yields more accurate forecasting results than other alternatives. [ABSTRACT FROM AUTHOR]

As a support vector machine (SVM) has good generalization ability, it has been implemented in various applications. Yet in the process of resolving the mathematical model, it needs to compute the kernel matrix, the dimension of which is equal to the number of data points in the dataset, thereby costing a very high amount of memory. Some improved algorithms are proposed to extract the boundary of the dataset so that the number of data points participating in the training process decreases and the training process can be accelerated. But the prediction accuracy of most of these algorithms is so low that many support vectors are discarded. Moreover, those methods all need to perform the main computation by the kernel function in linear feature space, which increases the computational cost. In this paper, the concept “relative density” is proposed to extract the subset containing support vectors. The measure “relative density” is designed to be more meticulous so that the new method performs more precisely than existing methods. The proposed method makes use of the fact that it has good local characteristics to perform the computations in original space without having to use any kernel function. Therefore, efficiency is also improved. Furthermore, the proposed method can be used to detect noise data, by which an inseparable problem can be transformed into a separable problem so that cross validation can be avoided in various SVM algorithms. This is an advantage that none of the existing SVM methods has. Yet another advantage of this method is that it can be considered as a framework to be used in various SVM methods. This paper presents the details of the proposed accelerated algorithm, having a time complexity of O( n log n ), that decreases training time significantly without decreasing prediction accuracy. The effectiveness and efficiency of the method is demonstrated through experiments on artificial and public datasets. [ABSTRACT FROM AUTHOR]

This paper provides a simple forecasting framework for nonlinear and non-stationary time series using Wavelet based nonlinear models. The proposed method exploits the ability of wavelets to detect non-stationarities that may be present in a given time series in combination with higher order nonlinear Volterra Models. The utility of the proposed model is verified using two examples: the first based on a synthetically generated times series with nonlinear and non stationary features; the second case study examined in the paper pertains to forecasting of number of pilgrims visiting the well known religious shrine at Katra in the state of Jammu and Kashmir in India. Further, the proposed model was applied to 3 time series from M3 competition. The results show that the proposed models perform better when compared with the performance of some well known benchmark models. The long term predictive capability of the wavelet based nonlinear models has also been studied separately. [ABSTRACT FROM AUTHOR]

This paper focuses on a new kind of artificial neural networks – the Laplace transform artificial neural networks (LTANN). It is proposed to use the Laplace transform instead of ordinary weights and a linear activation function of an artificial neuron. This extension allows to use artificial neural networks in new areas. The ordinary description of artificial neural networks is a special case of the description proposed in this paper. Three models of different geological structures based on the LTANN are discussed in this paper. Using these models, it is possible to classify an unknown geological structure as the structure without anomaly, the structure with a sinkhole or the structure with a loose zone. [ABSTRACT FROM AUTHOR]

Abstract: An artificial neural network (ANN) is a powerful mathematical framework used to either model complex relationships between inputs and outputs or find patterns in data. It is based on an interconnected group of artificial neurons, and it employs a connectionist approach to computation when processing information. ANNs have been successfully used for a great variety of applications, such as decision making, quantum chemistry, radar systems, face identification, gesture recognition, handwritten text recognition, medical diagnosis, financial applications, robotics, data mining, and e-spam filtering. In the speech community, neural architectures have been used since the beginning of the 1980s, and ANNs have been proven useful to accomplish several speech processing tasks, e.g., to extract linguistically motivated features, to perform speech detection, and to generate local scores to be used for different goals. In recent years, there has been a renewed interest in the use of ANNs for speech applications due to a major advance made in pre-training the weights in deep neural networks (DNNs). It seems that a new trend to move the speech technology forward through the use of NNs has begun, and it can therefore be instructive to review key ANN applications to automatic speech processing. In this paper, several ANN-based applications for speech processing will be presented, ranging from speech attribute extraction to phoneme estimation and/or classification. Furthermore, it will be shown that ANNs play a key role in several important speech applications, such as large vocabulary continuous speech recognition (LVCSR) and automatic language recognition. The goal of the paper is to summarize chief ANN approaches to speech processing using the experience gathered in the last seven years in our laboratories. [Copyright &y& Elsevier]

Abstract: Similarity measure plays an important role in many image processing fields. This paper introduces the medium mathematic systems, and establishes a novel image medium similarity measure between two pixels and that of between two image sets based on the measure of medium truth degree. Moreover, an image edge detection algorithm, an image matching algorithm and an image medium fidelity measure method governed by the medium similarity measure are discussed in this paper. Experimental results show that the proposed similarity measure is effective. [Copyright &y& Elsevier]

Bladder wall segmentation from Magnetic Resonance (MR) images plays an important role in diagnosis. Since the thickness of the bladder wall is a key indication of bladder cancer. There are several methods that have been used for bladder wall segmentation, such as level sets and Active Shape Model (ASM). However, the weak boundaries, the artifacts inside bladder lumen and the complex background outside the bladder wall make the bladder wall segmentation very challenging. To overcome these difficulties and obtain accurate bladder walls, in this paper, a shape prior constrained particle swarm optimization (SPC-PSO) model is proposed to segment the inner and outer boundaries of the bladder wall. The bladder walls are divided into two categories: strong boundaries and weak boundaries by the proposed model. For the strong boundaries, the proposed model can reserve it. For the weak boundaries, the model applies the shape prior to guide the process of segmentation. Compared with some state-of-the-art methods, better results were obtained on bladder MR images from 11 patients by our proposed method. [ABSTRACT FROM AUTHOR]

Entity Linking (EL) is the task of resolving mentions to referential entities in a knowledge base, which facilitates applications such as information retrieval, question answering, and knowledge base population. In this paper, we propose a novel embedding method specifically designed for EL. The proposed model jointly learns word and entity embeddings which are located in different distributed spaces, and a bilinear model is introduced to simulate the interaction between words and entities. We treat EL as a ranking problem, and utilize a pairwise learning-to-rank framework with features constructed with learned embeddings as well as conventional EL features. Experimental results show the proposed model produces effective embeddings which improve the performance of our EL algorithm. Our method yields the state-of-the-art performances on two benchmark datasets CoNLL and TAC-KBP 2010. [ABSTRACT FROM AUTHOR]

A type of machine vision based data glove with certain encoded features on its surface, strip glove is proposed in this paper. The machine vision device replaces the sensors attached on the glove and captures the codes on the glove to obtain the hand motion data. To handle the complex geometrical transformations in the strip glove motion, conformal geometric algebra is introduced as the mathematical tool. In the framework of conformal geometric algebra, the degrees of freedom in hand motion are assign to different planes geometrically to form palm and finger planes. The mathematical model of strip glove is established for the pose solution of the strip glove. The motion tracking of the strip glove is implemented in a binocular system. The particle filter algorithm is applied for tracking the colored blocks on a plane. A continuous motion model of finger segments is constructed in the binocular machine vision system by conformal geometric algebra according to hand motion characteristics, and the hand motion data is obtained during the tracking. Based on particle filter algorithm, the conformal geometric algebra method is applied to set particle parameters for the solution of the local occlusion problems caused by hand pose variation, and the tracking stability of the strip glove is improved. [ABSTRACT FROM AUTHOR]

Stochastic Gradient Descent (SGD) with variance reduction techniques has been proved powerful to train the parameters of various machine learning models. However, it cannot support the distributed systems trivially due to the intrinsic design. Although conventional studies such as PetuumSGD perform well for distributed machine learning tasks, they mainly focus on the optimization of the communication protocol, which does not exploit the potential benefits of a specific machine learning algorithm. In this paper, we analyze the asynchronous communication protocol in PetuumSGD, and propose a distributed version of variance reduced SGD named DisSVRG. DisSVRG adopts the variance reduction technique to update the parameters in a model. After that, those newly learned parameters across nodes are shared by using the asynchronous communication protocol. Besides, we accelerate DisSVRG by using the adaptive learning rate with an acceleration factor. Additionally, an adaptive sampling strategy is proposed in DisSVRG. The proposed methods greatly reduce the wait time during the iterations, and accelerate the convergence of DisSVRG significantly. Extensive empirical studies verify that DisSVRG converges faster than the state-of-the-art variants of SGD, and gains almost linear speedup in a cluster. [ABSTRACT FROM AUTHOR]

Recently, single image super-resolution (SR) models based on deep convolutional neural network (CNN) have achieved significant advances in accuracy and speed. However, these models are not efficient enough for the image SR task. Firstly, we find that generic deep CNNs learn the low frequency features in all layers, which is redundant and leads to a slow training speed. Secondly, rectified linear unit (ReLU) only activate the positive response of the neuron, while the negative response is also worth being activated. In this paper, we propose a novel joint residual network (JRN) with three subnetworks, in which two shallow subnetworks aim to learn the low frequency information and one deep subnetwork aims to learn the high frequency information. In order to activate the negative part of the neurons and to preserve the sparsity of activation function, we propose a paired ReLUs activation scheme: one of the ReLUs is for positive activation and the other is for negative activation. The above two innovations lead to a much faster training, as well as a more efficient local structure. The proposed JRN achieves the same accuracy of a generic CNN with only 10.5% training iterations. The experiments on a wide range of images show that JRN is superior to the state-of-the-art methods both in accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

A model-free based neural network control with time-delay estimation (TDE-MFNNC) for lower extremity exoskeleton is presented in this paper. The lower limb exoskeleton which has 5 DOFs for each leg is established in Solidworks as a virtual prototype which is used as a platform to build the control system in SimMechanics. In an attempt to get the effective tracking trajectory, a neural network and time-delay estimation technique is added to model-free based iPD controller. The proposed controller is simulated and tested on virtual prototype to comparing with PD controller, neural network and model-free based iPD controller. This comparative study validates TDE-MFNNC as more stable and effective than the traditional controllers. Further, the kinematic model of lower limb exoskeleton added to exoskeleton also proves the stability and effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

We consider a large-scale minimization problem (not necessarily convex) with non-smooth separable convex penalty. Problems in this form widely arise in many modern large-scale machine learning and signal processing applications. In this paper, we present a new perspective towards the parallel Block Coordinate Descent (BCD) methods. Specifically we explicitly give a concept of so-called two-layered block variable updating loop for parallel BCD methods in modern computing environment comprised of multiple distributed computing nodes. The outer loop refers to the block variable updating assigned to distributed nodes, and the inner loop involves the updating step inside each node. Each loop allows to adopt either Jacobi or Gauss–Seidel update rule. In particular, we give detailed theoretical convergence analysis to two practical schemes: Jacobi/Gauss–Seidel and Gauss–Seidel/Jacobi that embodies two algorithms respectively. Our new perspective and behind theoretical results help devise parallel BCD algorithms in a principled fashion, which in turn lend them a flexible implementation for BCD methods suited to the parallel computing environment. The effectiveness of the algorithm framework is verified on the benchmark tasks of large-scale ℓ 1 regularized sparse logistic regression and non-negative matrix factorization. [ABSTRACT FROM AUTHOR]

This paper introduces novel cooperative control scheme based on adaptive critics for islanded Microgrids in the presence of disturbances. The interactions between the distributed generation sources are governed by a communication graph. Synchronization ideas are used to couple the dynamics of the distributed generation sources. A utility function is selected to reflect the objectives of the optimization problem. An online adaptive learning technique based on particle filtering is developed to estimate the dynamics of the distributed generation sources. Once the separation principle is held, the control input of every generation source and the estimator can be designed independently. The online adaptive learning technique uses incomplete knowledge about the dynamics of the distributed generation sources. Means of adaptive critics are used to implement the solution in real-time. [ABSTRACT FROM AUTHOR]

With rapid development of financial services and products, credit risk assessment has recently gained considerable attention in the field of financial risk management. In this paper, an improved credit risk assessment approach is presented. Based on the credit data from China Banking Regulatory Commission (CBRC), a multi-dimensional and multi-level credit risk indicator system is constructed. In particular, we present an improved sequential minimal optimization (SMO) learning algorithm, named four-variable SMO (FV-SMO), for credit risk classification model. At each iteration, it jointly selects four variables into the working set and an theorem is proposed to guarantee the analytical solution of sub-problem. The assessment is made on China credit dataset and two benchmark credit datasets from UCI database and CD-ROM database. Experimental results demonstrate FV-SMO is competitive in saving the computational cost and outperforms other five state-of-the-art classification methods in credit risk assessment accuracy. [ABSTRACT FROM AUTHOR]

Minimum Spanning Tree (MST) based clustering algorithms have been employed successfully to detect clusters of heterogeneous nature. Given a dataset of n random points, most of the MST-based clustering algorithms first generate a complete graph G of the dataset and then construct MST from G . The first step of the algorithm is the major bottleneck which takes O ( n 2 ) time. This paper proposes an algorithm namely MST-based clustering on partition-based nearest neighbor graph for reducing the computational overhead. By using a centroid based nearest neighbor rule, the proposed algorithm first generates a sparse Local Neighborhood Graph ( LNG ) and then the approximate MST is constructed from LNG . We prove that both size and computational time to construct the graph ( LNG ) is O ( n 3/2 ), which is a O ( n ) factor improvement over the traditional algorithms. The approximate MST is constructed from LNG in O ( n 3 / 2 lg n ) time, which is asymptotically faster than O ( n 2 ). Experimental analysis on both synthetic and real datasets demonstrates that the computational time has been reduced significantly by maintaining the quality of clusters obtained from the approximate MST. [ABSTRACT FROM AUTHOR]

Although deep learning has achieved excellent performance in many applications, some studies indicate that deep learning algorithms are vulnerable in an adversarial environment. A small distortion on a sample leads to misclassification easily. Until now, the vulnerability issue of stacked autoencoder, which is one of the most popular deep learning algorithms, has not been investigated. In this paper, we firstly investigate the existing evasion attack to stacked autoencoder in an effort to understand whether, and to what extent, they can work efficiently. A robust learning algorithm which minimizes both its error and sensitivity is then proposed for stacked autoencoder. The sensitivity is defined as the change of the output due to a small fluctuation on the input. As the proposed algorithm considers not only accuracy but also stability, a more robust stacked autoencoder against evasion attack is expected. The performance of our methods is then evaluated and compared with conventional stacked autoencoder and denoising autoencoder experimentally in terms of accuracy, robustness and time complexity. Moreover, the experimental results also suggest that the proposed learning method is more robust than others when a training set is contaminated. [ABSTRACT FROM AUTHOR]

The fault detection problem for a class of networked industrial process with fading channel communication is studied in this paper. The dynamic of industrial process around a given operating point is first estimated based on subspace model identification (SMI) method. Then, a finite memory (FM) scheduling robust fault detection filter (FDF) strategy is proposed to generate residual signal for performing system monitoring over channel fading network. The main contributions lie in achieving the identification-oriented process monitoring via SMI and FDF on one hand, and enhancing the robustness of fault detection system against identification error and unreliable communication via FM and H ∞ performance optimization on the other hand. A practical simulation example is finally given to verify the effectiveness of the proposed fault detection methodology. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) has been increasingly popular in the field of transfer learning (TL) due to its simplicity, training speed and ease of use in online sequential learning process. This paper critically examines transfer learning algorithms formulated with ELM technique and provides state of the art knowledge to expedite the learning process ELM based TL algorithms. As this article discusses available ELM based TL algorithm in detail, it provides a holistic overview of current literature, serves as a starting point for new researchers in ELM based TL algorithms and facilitates identification of future research direction in concise manner. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hand tracking method which was inspired by the notion of the four dukkha: birth, aging, sickness and death (BASD) in Buddhism. Based on this philosophy, we formalize the hand tracking problem in the BASD framework, and apply it to hand track hand gestures in isolated sign language videos. The proposed BASD method is a novel nature-inspired computational intelligence method which is able to handle complex real-world tracking problem. The proposed BASD framework operates in a manner similar to a standard state-space model, but maintains multiple hypotheses and integrates hypothesis update and propagation mechanisms that resemble the effect of BASD. The survival of the hypothesis relies upon the strength, aging and sickness of existing hypotheses, and new hypotheses are birthed by the fittest pairs of parent hypotheses. These properties resolve the sample impoverishment problem of the particle filter. The estimated hand trajectories show promising results for the American sign language. [ABSTRACT FROM AUTHOR]

An evolving cascade neuro-fuzzy system and its online learning procedure are considered in this paper. The system is based on conventional Kohonen neurons. The proposed system solves a clustering task of non-stationary data streams under uncertainty conditions when data come in the form of a sequential stream in an online mode. A quality estimation process is defined by finding an optimal value of the used cluster validity index. [ABSTRACT FROM AUTHOR]

In this paper, a love-triangle model with nonlinear romantic sentimental interactions and four time delays is proposed. Regarding time delay as bifurcating parameter, the dynamical behaviors which include local asymptotical stability and Hopf bifurcation are studied in detail by analyzing the characteristic equation corresponding to the linearized system of a love-triangle model. When the delay passes through a sequence of critical values, we find that Hopf bifurcation occurs. Furthermore, stability and direction of bifurcating periodic solution are derived by applying the normal form theory and the center manifold theorem in our love-triangle model. Finally, an illustrative example is also used to support our theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the sampled-data control is applied to synchronize chaotic neural networks subject to actuator saturation. By employing a time-dependent Lyapunov functional that captures the characteristic information of actual sampling pattern, we derive a local stability condition for the synchronization error systems. By this condition, we design a sampled-data controller to regionally synchronize the drive neural networks and response neural networks subject to actuator saturation. Moreover, an optimization method is given to design the desired sampled-data controller such that the set of admissible initial conditions is maximized. A numerical example is given to demonstrate the effectiveness and merits of the proposed design technique. [ABSTRACT FROM AUTHOR]

Automated parameter search methods are commonly used to optimize neuron models. A more challenging task is to fit models of neural systems since the model response is determined by both intrinsic properties of neurons and the neural wiring and architecture of the network. Neural records of cells in the visual system are often analyzed in terms of the cell’s receptive field and its temporal response. This type of data requires a finer point-by-point comparison of response traces between the simulated output and the recorded data. To address these issues, we applied a genetic algorithm optimization in conjunction with a multiobjective fitness function and a population-based error metric. Two different models of the early stages in the visual system were fitted to electrophysiological recordings and results from a modeling study, respectively. The first one is a model of cone photoreceptors and horizontal cells that reproduces adaptation to the mean light intensity in the retina. A multiobjective fitness function based on the normalized root-mean-square error (NRMSE) and a shape error descriptor captures high-frequency oscillations in the impulse response to uniform white flashes. The second one is a large-scale model of the thalamocortical system that accounts for the slow rhythms observed during sleep. An error metric of the population neural activity is used in this case. We argue that the optimization framework proposed in this paper could serve as a useful tool for parameter fitting of neuron models and large-scale models in the visual system pathway. [ABSTRACT FROM AUTHOR]