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Inspired by the recurrent highway networks, we study the coupled networks as a modular behavior of the networks, which can better improve the performance of the system. For a neural network, the non-zero continuous attractors play an important role to decode the information. The emphasis of this paper is to study the conditions for the generation of non-zero continuous attractors in the coupled new system theoretically. By exploring the relationship between network parameters, the conditions for generating non-zero continuous attractors in the new model are successfully determined. In order to verify the correctness and validity of the proposed conclusions, the results of this paper are further verified by simulation.

In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple method in the context of solving the paraphrase generation task. If we use a sequential encoder-decoder model for generating paraphrase, we would like the generated paraphrase to be semantically close to the original sentence. One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far. This is ensured by using a sequential pair-wise discriminator that shares weights with the encoder that is trained with a suitable loss function. Our loss function penalizes paraphrase sentence embedding distances from being too large. This loss is used in combination with a sequential encoder-decoder network. We also validated our method by evaluating the obtained embeddings for a sentiment analysis task. The proposed method results in semantic embeddings and outperforms the state-of-the-art on the paraphrase generation and sentiment analysis task on standard datasets. These results are also shown to be statistically significant., Comment: This work is an extension of our COLING-2018 paper arXiv:1806.00807

This paper studies the blind super-resolution of real low-quality and low-resolution (LR) images. Existing convolutional network (CNN) based approaches usually learn a single image super-resolution (SISR) model for a specific downsampler (e.g., bicubic downsampling, blurring followed by downsampling). The learned model, however, is tailored to the specific downsampler and fails to super-resolve real LR images which are degraded in more sophisticated and diverse manners. Moreover, the ground-truth high-resolution (HR) of real LR images are generally unavailable. Instead of learning from unpaired real LR-HR images or a specific downsampler, this paper learns blind SR network from a realistic, parametric degradation model by considering blurring, noise, downsampling, and even JPEG compression. In contrast to direct blind reconstruction of HR image, the proposed model adopts a cascaded architecture for noise estimation, blurring estimation, and non-blind SR, which can be jointly end-to-end learned from training data and benefit generalization ability. By taking the bicubicly upscaled LR image as input to non-blind SR, the proposed method can present a single unified model for blind SR with any upscaling factors and varying degradation parameters. Experimental results show that the proposed method performs favorably on synthetic and real LR images.

Vehicle re-identification (re-ID) has numerous applications in real life, such as video surveillance, information retrieval, and public security. However, it suffers from the misalignment of vehicles, which is a critical problem caused by inaccurate detection and different views. This paper proposes a novel network named Part Alignment Network (PAN) which can properly handle the misalignment of vehicles. It is therefore adapted to the vehicle re-ID task, avoiding the use of any additional pre-processing steps such as the annotation of vehicle key points and part segmentation by hand. In PAN, cross-correlation is adopted to the alignment of vehicle parts. Then, an effective network architecture is designed to extract the discriminative aligned features. By combining complementary aligned features and original features, more robust feature representations are learned. To show the effectiveness of PAN, this paper conducts experiments on three vehicle re-ID databases (VD1, VD2, and VehicleID), on which it improves the current state-of-the-art performance.

In this study, the novel fully distributed asynchronous event-triggered protocols that use sampled state information are developed to address the leader-follower and leaderless consensus issues for the general linear multi-agent systems with physically interconnected network. By designing the state estimate of each follower agent, the protocols avoid the requirements for the continuous communication. Meanwhile, this paper proves that the designed protocols don’t exhibit the Zeno behavior in any finite time by giving a strictly positive lower-bound of the minimum inter-event interval. In contrast to the existing related papers, the salient features of this study are that both physically interconnected network and communication network of the multi-agent systems are considered and the developed protocols don’t require global network information, which reduce the conservativeness of previous systems modeling and protocols. Finally, two simulation results are provided to display the performance and effectiveness of the protocols.

The time-varying Lyapunov equation is an important problem that has been extensively employed in the engineering field and the Zeroing Neural Network (ZNN) is a powerful tool for solving such problem. However, unpredictable noises can potentially harm ZNN’s accuracy in practical situations. Thus, the comprehensive performance of the ZNN model requires both fast convergence rate and strong robustness, which are not easy to accomplish. In this paper, based on a new neural dynamic, a novel Noise-Tolerance Finite-time convergent ZNN (NTFZNN) model for solving the time-varying Lyapunov equations has been proposed. The NTFZNN model simultaneously converges in finite time and have stable residual error even under unbounded time-varying noises. Furthermore, the Simplified Finite-te convergent Activation Function (SFAF) with simpler structure is used in the NTFZNN model to reduce model complexity while retaining finite convergence time. Theoretical proofs and numerical simulations are provided in this paper to substantiate the NTFZNN model’s convergence and robustness performances, which are better than performances of the ordinary ZNN model and the Noise-Tolerance ZNN (NTZNN) model. Finally, simulation experiment of using the NTFZNN model to control a wheeled robot manipulator under perturbation validates the superior applicability of the NTFZNN model.

This paper focuses on the quasi-fixed time synchronization of memristive Cohen-Grossberg neural networks with reaction diffusion. First, a new definition of finite/fixed-time attraction set and the concept of quasi finite/fixed-time synchronization are proposed. Then, a quasi fixed-time synchronized lemma is given and an example is used to illustrate the correctness of the lemma. At the same time, the new results given in this article are compared with the existing ones under certain conditions, and the conclusions of this paper are less conservative in remark. In addition, quasi fixed-time synchronization theorem and full synchronization theorem are derived by designing different controllers, finally two examples are given to illustrate the validity of these theorems.

This paper studies the problem of adaptive output-feedback formation tracking control for networked uncertain nonholonomic mobile robots (NMRs) with different limited communication distances, while achieving connectivity preservation and collision avoidance. In robot control, equipping all the speed sensors not only increases the cost of the system but also risks losing accuracy and reliability. In this paper, it is assumed that the velocities of the nonholonomic mobile robots in this paper are unknown, they are estimated according to the output of the systems by an adaptive observer via a neural network. The designed dynamic surface based on nonlinear transformation errors can achieve the above three control objectives at the same time, avoiding the situation of using multiple potential functions to solve such problems, which will lead to conflicts in the selection of the design parameters. Compared with the existing references about formation tracking to maintain connectivity and avoid collisions, the main contribution of this paper is to design an observer of robot speed estimation and use only a neural network to estimate the unknown nonlinear term of the system itself. The unknown nonlinearity produced in the process of controller design does not need an additional neural network. Furthermore, the effectiveness of the proposed strategy is verified by simulation examples.

In the past few decades, numerous studies have demonstrated the effectiveness of diet modification in preventing and controlling different chronic diseases, bringing increased attention to the creation of nutritional recipes. Currently, the development of recipes is primarily based on personal experience rather than nutritional specifications. To this end, this paper first uses kernel canonical correlation analysis to demonstrate the relationship between nutrients in food and recipes. Based on this relationship, a new recipe expression method is put forward, which objectively reflects differing importance of an ingredient in differing recipes. Then, recipes are composed using an auto-encoder in deep learning, and a fusion model of two auto-encoders is proposed for better concocting recipes. This paper uses two machine learning methods, namely, non-negative matrix factorization and two-step regularized least squares, to form recipes. To tackle overfitting and instability in non-negative matrix factorization during the training of recipe model, we introduce the Frobenius norm to redefine the objective function and add non-smooth sparse matrices. Similar food has similar taste, but their nutrients might differ. This paper also considers nutrients as a kernel matrix of the two-step regularized least squares, which can effectively avoid the occurrence of different food combinations with similar taste. Experimental results show that developing recipes based on nutrients in food is feasible and effective in the context of machine learning.

This paper pays attention to the synchronization issue of complex-valued memristive neural networks (CVMNNs) that contain reaction–diffusion terms and Markovian jump parameters. To better meet the needs of some practical projects, the problem of synchronization investigated in this paper is defined in finite time and fixed time; namely, by designing a suitable controller, the synchronization error system can converge to zero in a finite/fixed time. Additionally, by combining Lyapunov–Krasovskii functional theory and algebraic inequality methods, a novel criterion of finite/fixed-time synchronization for proposed drive and response systems is derived. Note that the finite-time and fixed-time synchronization criteria are integrated into a unified theorem. Finally, two examples are provided, one is a simple numerical example to account for the feasibility of main results, the other realizes the application of this paper in establishing a spatiotemporal chaotic cryptosystem for image encryption, and the proposed cryptosystem has been illustrated that it has obvious advantages of large key space and high security by simulation results.

The prediction of short term power load owns a great influence on performance of the whole electric system. Raising the result of power load forecasting is always a research spot. This paper proposes a method combined Elman neural network (ENN) and the particle swarm optimization (PSO) for the short-term power load forecasting. Firstly, this paper introduces the principle of ENN and PSO algorithm respectively and analyzes performance of network influenced by parameters of ENN. Then the particle swarm optimization algorithm is applied to searching the optimal learning rate of ENN. To study the capability of the method proposed in this paper, the method is utilized for the short-term power load forecasting, which is suitable to be solved by ENN. Besides, a comparison experiment on this method (PSO–ENN) with general regression neural network (GRNN), the original ENN and the traditional back-propagation neural network (BPNN) is given out to illustrate effectiveness of PSO–ENN.

Accurate prediction of remaining useful life (RUL) for lithium-ion battery (LIB) plays a key role in increasing the reliability and safety of battery related industries and facilities. In this paper, RUL prediction of LIB is investigated by employing a hybrid data-driven method based support vector regression (SVR) and error compensation (EC). Firstly, two health indicators (HIs) are established by using capacity and discharging voltage difference of equal time interval (DVD), respectively. Secondly, the ensemble empirical mode decomposition (EEMD) is adopted to preprocess the obtained HIs, which is used to reduce the influence of capacity regeneration and noise. Especially, phase space reconstruction (PSR) with C–C technique is introduced to achieve optimal input sequence selection pattern, it has an important influence on the accuracy of SVR prediction. As an important innovation of the paper, the idea of EC is implemented by combining the predictions of both forecast error and RUL prediction with PSR-SVR. Last but not least, the genetic algorithm (GA) is utilized to optimize the key parameters of SVR so as to achieve more accurate RUL prediction. To verify the effectiveness of the proposed approach, the real data set of LIBs from National Aeronautics and Space Administration (NASA) is carried out, and the dominant is emphasized by comparison with other important methods.

In order to address such negative issues as GAN’s mediocre image quality, high-demand for training samples and computation resources, this paper proposes the collaborative Generative Adversarial Network with Visual perception and memory reasoning (ESA-CGAN). This not only makes use of the vision self-attention mechanism and objects salience model to analyze the global information and detailed features of objects, but also designs cross-correlation self-attention module so as to make a balance between the computation efficiency and computational on the one hand and the statistical efficiency and the ability to simulate remote dependencies on the other hand. Based on convolutional long-term and short-term memory network, this paper optimizes the attention feature map so as to highlight the features of objects themselves and improve their generative abilities. Meanwhile, a cooperative learning mechanism between generators is designed, which combines self-constructed generation model and pre-training generation model to form a generation model group. It not only improves effectively the generation ability of the model, improve the computing efficiency, but also restrains the collapse of the model from another angle. In fact, the model proposed here has completed numerical experiments on multiple common standard datasets and self-configuring datasets, and has made comparisons with several mainstream generation antagonistic network models in terms of the performance of image data augmentation. The experimental results demonstrate that the model has excellent simulation ability to enable itself to effectively realize the augmentation of data, thus making it highly applicable in the future.

In this paper, a novel control scheme is developed to solve an optimal containment control problem of unknown continuous-time multi-agent systems. Different from traditional adaptive dynamic programming (ADP) algorithms, this paper proposes an internal reinforcement ADP algorithm (IR-ADP), in which the internal reinforcement signals are added in order to facilitate the learning process. Then a distributed containment control law is designed for each agent with the internal reinforcement signal. The convergence of this IR-ADP algorithm and the stability of the closed-loop multi-agent system are analyzed theoretically. For the implementation of the optimal controllers, three neural networks (NNs), namely internal reinforcement NNs, critic NNs and actor NNs, are utilized to approximate the internal reinforcement signals, the performance indices and optimal control laws, respectively. Finally, some simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

In this paper, distributed consensus control protocols are given for linear multi-agent systems with input delay under direct communication graphs. Compared with the multi-agent systems under continuous communication, there are more challenges in the consensus problems of multi-agent systems under discontinuous communication. Furthermore, the consensus of multi-agent systems with control input delay and discontinuous communication are much more similar to the practical engineering situation, which further increases the difficulties of consensus control. Based on these discussions, two consensus problems, including leader following case and leaderless case, are studied in this paper. A prediction of the system’s state information over the delay time is approximated by the zero input solution of the agent system model. We also propose a new discontinuous communication mode, which combines event triggered mechanism and a uniform quantization method. Unlike the existing discontinuous communication method, our proposed control protocols ensure that each agent decides when to transport its state information to its neighbors such that continuous communication between any two agents is no longer needed. In addition, through the non-smooth analysis of the closed-loop systems, the uniformly bounded consensus results are guaranteed, and the bound of the consensus error can be adjusted by choosing appropriate parameters.

Implementing associative memory experiments with nanoscale memristors is an interesting subject, which can allow robots to mimic human thinking. This paper is concerned with a new emotion model of memristor-based neural network and its circuit implementation. The model has three inputs and two outputs, which can feel happy when it receives good news or feel sad when it receives bad news. Unknown news can be recognized through associative memory, which simulates human emotions. Associative learning and three kinds of forgetting process together make up the full-function emotion model. In addition, the Ag/AgInSbTe/Ta-based model closely related to the actual physical properties of memristors is used to design synaptic structures. The circuits of memristor-based neural networks are also simplified. Furthermore, the new emotion model is able to adjust the changing rate of emotion. The process reflects the fact that humans learn the same thing faster at the second time, compared with the first time. Finally, PSPICE is used to simulate all the circuits of the emotion model. The presented emotion model in this paper offers more possibilities for designing intelligent machines.

Deep learning technologies have been rapidly developing recently. They have shown excellent performances in many fields. However, deep learning networks have weak adaptability to small sample sizes. Usually, deep learning networks require tens of thousands of samples to avoid overfitting. In contrast, the kernel method can deal with small sample sizes. In this paper, we use only dozens of samples to train a successful classifier. Deep multiple kernel learning (DMKL) has emerged in recent years. It combines the ideas of deep learning and multiple kernel learning. Unfortunately, the DMKL network with a fixed structure cannot adapt to data of different dimensions and sizes. Therefore, in this paper, we propose a novel depth-width-scaling multiple kernel learning (DWS-MKL) algorithm. It has the ability to adjust the architecture according to the input data. We optimize the estimation of leave-one-out error by using the span bound instead of the dual objective function. Finally, we choose a large number of data sets from the UCI benchmark data sets for classification tasks. We also evaluate DWS-MKL on the MNIST data set. The experimental results show that different frameworks have different performances on the same dataset. Our DWS-MKL algorithm obtains better classification results than the state-of-the-art kernel learning algorithms, as determined by a thorough comparison. The encouraging results demonstrate that our method has the potential to improve generalization performance of the model.

This paper develops a robust adaptive neural practical fixed-time tracking control scheme for Euler-Lagrange systems (ELSs) with unknown dynamics and external disturbances under input saturations. A novel auxiliary dynamic system governed by a smooth piecewise continuous function is constructed to handle the input saturation effect, while promoting to achieve the fixed-time convergence of tracking errors. Moreover, the unknown dynamics of ELSs and the bound vector of unknown external disturbances are synthesized into a compounded uncertain vector in this paper. Here, adaptive neural networks with the ∊-modification updating laws are employed to only approximate the compounded uncertain vector, rather than each dynamic matrix of ELSs, such that the computational burden of the developed control scheme is significantly reduced. It is theoretically proven that the trajectory tracking is able to be achieved in a fixed time under the developed adaptive neural tracking control scheme, while all signals in the Euler-Lagrange closed-loop tracking control system are bounded. The simulation results on a 2-link robotic manipulator are included to illuminate the effectiveness of our developed tracking control scheme and superiority to a finite-time control scheme.

This paper focuses on state estimation problem for discrete-time high-order neural networks with time-varying delays. First, the delay-dependent global exponential stability criterion of the error system is derived. Then, the state observer is designed by using the generalized inverse theory of matrices. Last, two numerical examples are given to illustrate the validity of the theoretical results. The method proposed in this paper has two advantages: (i) it is directly based on the definitions of global exponential stability and Moore–Penrose inverse of matrix, which avoids the construction of Lyapunov–Krasovskii functional; (ii) the obtained stability criteria contain only several simple matrix inequalities, which are easier to solve. More valuable, this paper fills in the gaps in designing state observers for discrete-time high-order neural network models.

In this paper, we study multi-agent systems with asymmetric information structure. Due to limited channel capacity in communication network, the information in routing path suffers a transmission delay. Instead of the game theoretic setting, we formulate the problem as an online quadratic optimization problem subject to stochastic systems involving input delay. Since the probability statistics of system noise is unknown, the decision-maker can not utilize the traditional optimal control strategies. Motivated by online convex optimization theory, we introduce the notion of regret, which measures the cumulative performance difference between the optimal statistics known (offline) index value and the statistics unknown (online) index value. The contributions of this paper are twofold. First, utilizing the linear minimum mean square biased estimate, we derive a learning based control policy and then characterize its behavior. Second, under some basic assumptions, we further prove that the regret grows at a sub-linear rate and it is explicitly bounded by O ( ln T ) .

Adversarial networks have been used to learn transferable representations in many domain adaptation methods. However, there is no theoretical guarantee that two distributions are identical, even if the discriminator is fully confused. Therefore, a more elaborate domain adversarial method to better align distributions is desirable. In this paper, we propose two groups of multiple adversarial networks for domain-invariant and class-distinguishable feature learning: i) class-wise domain adversarial networks based on sample locations and ii) sample adversarial networks. We analyze the impact of the sample’s intra-class distribution on transfer learning and reveal that the distance between a sample and its cluster center affects the role of that sample in transfer learning. Domain adversarial learning is conducted separately on samples located near the cluster center (central samples) and samples located far away from cluster center (non-central samples) in order to align the distributions of the source domain and target domain better. However, separate domain adversarial learning on central and non-central samples ignores the relationship between them, because all these samples belong to the same class. We therefore propose a method called sample adversarial learning to convert the distribution of non-central samples to the distribution of central samples. In this way, the relationship between central samples and non-central samples is rebuilt. Sample adversarial learning solves the problem that arises in the sperate domain adversarial learning. Sample adversarial learning also enables us to obtain a class-distinguishable feature representation because of the reduction in intra-class distance. Experimental results show that our method extracts more transferable and class-distinguishable features than existing methods and achieves start-of-the-art results on several datasets. The significant contribution of this paper is to show how separate domain adversarial learning based on sample locations and sample adversarial learning together enhance positive transfer by maximally matching the multimodal structures underlying the data distributions across domains.

Person reidentification (ReID) refers to the task of verifying the identity of a pedestrian observed from nonoverlapping views in a surveillance camera network. It has recently been validated that reranking can achieve remarkable performance improvements in person ReID systems. However, current reranking approaches either require feedback from users or suffer from burdensome computational costs. In this paper, we propose to exploit a density-adaptive smooth kernel technique to achieve efficient and effective reranking. Specifically, we adopt a smooth kernel function to formulate the neighbor relationships among data samples with a density-adaptive parameter. Based on this new formulation, we present two simple yet effective reranking methods, termed \emph{inverse} density-adaptive kernel based reranking (inv-DAKR) and \emph{bidirectional} density-adaptive kernel based reranking (bi-DAKR), in which the local density information in the vicinity of each gallery sample is elegantly exploited. Moreover, we extend the proposed inv-DAKR and bi-DAKR methods to incorporate the available extra probe samples and demonstrate that when and why these extra probe samples are able to improve the local neighborhood and thus further refine the ranking results. Extensive experiments are conducted on six benchmark datasets, including: PRID450s, VIPeR, CUHK03, GRID, Market-1501 and Mars. The experimental results demonstrate that our proposals are effective and efficient., 39 pages, 18 figures and 12 tables. This paper is an extended version of our preliminary work on ICPR 2018

This paper addresses global Mittag-Leffler lag quasi-synchronization (GMLQS) of fractional-order memristor-based neural networks (FMNNs). Firstly, for a general class of fractional-order nonlinear systems with interval uncertainties, a kind of linear feedback pinning controller, which works by feeding back partial state errors to the partial controlled variables, is designed to achieve global Mittag-Leffler ultimate boundedness (GMUB). Correspondingly, a GMUB criterion in the form of linear matrix inequality (LMI) is derived with the aid of a Lyapunov function and a newly established fractional-order differential inequality. Then, the linear feedback pinning controller is employed for GMUB of the synchronization error system, which is equivalent to GMLQS of FMNNs. Since there exists the propagation delay between drive-response FMNNs and the synchronization error system can not be obtained straightforwardly, an identical equation about Caputo’s fractional derivatives is established to overcome this difficulty. Moreover, the detailed pinning control scheme design procedures for a prescribed GMLQS performance are presented, where the performance involves both ultimate error bound and transient behavior. In the control scheme, an auxiliary LMI and an optimization objective function are introduced, which can significantly reduce control cost. Finally, a numerical example is presented to show the feasibility of the control scheme. The results obtained in this paper have improved the existing criterion on quasi-synchronization of FMNNs, and will also provide a novel insight into pinning control of fractional-order nonlinear systems.

This paper investigates fault-tolerant tracking control problem for autonomous underwater vehicle (AUV) with rudders faults and ocean current disturbance. The adaptive dynamic programming (ADP) method is adopted to transform the fault-tolerant tracking control problem into an optimal control problem. Two neural-network estimators (NNEs) are designed to estimate rudders faults and ocean current disturbance respectively. The estimated rudders faults and the estimated ocean current disturbance are utilized to construct the performance index function. By using policy iteration (PI), critic neural network and action neural network are constructed to solve the Hamilton-Jacobi-Bellman (HJB) equation. The error tracking system of AUV is guaranteed to be uniformly ultimately bounded (UUB) based on the Lyapunov stability theorem. Simulation results are given to verify the effectiveness of the control scheme proposed in this paper.

The explosive growth of data leads researchers to waste time and energy to search for papers they need. Context-aware citation recommendation aims to solve this problem by analyzing a citation context and provides a list of recommended papers. In this paper, we propose a context-aware citation recommendation model based on end to end memory network. The model learns the representations of papers and citation contexts respectively based on bidirectional long short-term memory (Bi-LSTM). In particular, we jointly integrate author information and citation relationship in the distributed vector representations of citation contexts and papers. Then calculates the continuous relevance between them based on a computational multilayers memory network. We also conduct experiments on three real-world datasets to evaluate the performance of our model.

Face stands out as a preferable biometric trait for automatic human authentication as it is intuitive and non-intrusive. This paper investigates various feature-based automatic face recognition approaches in detail. High degree of freedom in head movement and human emotion leads a face recognition system to face critical challenges in terms of pose, illumination and expression. Human face also undergoes irreversible changes due to aging. These factors makes the process of face recognition non trivial and hard. This paper also provides a review of the facial recognition approaches individually dealing with these issues. Applications of face recognition in the forensic domain sometimes needs identification using a scanned facial image. The scenario is quite useful to get investigative leads. Important approaches for the same are also been discussed in the manuscript. Recent developments in the low-cost image capturing devices has flooded the facial image databases with a lot of images, at the same time availability of GPU based compute power has helped develop deep learning approaches to handle the face recognition at a very accurate and massive level. The same has also been surveyed and analyzed in the manuscript.

The property value assessment in the real estate market still remains as a challenges due to incomplete and insufficient information, as well as the lack of efficient algorithms. House attributes, such as size and number of bedrooms, are currently being employed to perform the estimation by professional appraisers and researchers. Numerous algorithms have been proposed; however, a better assessment performance is still expected by the market. Nowadays, there are more available relevant data from various sources in urban areas, which have a potential impact on the house value. In this paper, we propose to fuse urban data, i.e., metadata and imagery data, with house attributes to unveil the market value of the property in Philadelphia. Specifically, two deep neural networks, i.e., metadata fusion network and image appraiser, are proposed to extract the representations, i.e., expected levels, from metadata and street-view images, respectively. A boosted regression tree (BRT) is adapted to estimate the market values of houses with the fused metadata and expected levels. The experimental results with the data collected from the city of Philadelphia demonstrate the effectiveness of the proposed model. The research presented in this paper also provides the real estate industry a new reference to the property value assessment with the data fusion methodology.

In a localization environment based on wireless sensor networks, the non-line-of-sight (NLOS) propagation of wireless signal can greatly degrade the performance of the localization system. In this paper, a novel semidefinite-relaxation-based target localization method is presented by utilizing an adaptive threshold correction scheme. First, whether the measurement is in NLOS propagation is identified according to the prediction stage of extended Kalman filtering (EKF) algorithm. Then, the objective function and constraints are, respectively, constructed according to the measurement model of time of arrival (TOA) and received signal strength indicator (RSSI) ranging method. By employing the semidefinite relaxation method, the original problem is converted into the SDP problem, in which the corrected measurement information is obtained and thus the accuracy is improved. In addition, a correction method for NLOS propagation threshold is proposed by which an optimal threshold is obtained quickly. Different from the traditional simulation environment without the consideration of obstacles, in this paper, obstacles are set so as to make it closer to the engineering practice. Finally, the simulation results are given to show the superior performance of the proposed method.

Manual annotation of video segmentation datasets requires an immense amount of human effort, thus, reduction of human annotation costs is an active topic of research. While many papers deal with the propagation of masks through frames of a video, only a few results attempt to optimize annotation task selection. In this paper we present a deep learning based solution to the latter problem and train it using Reinforcement Learning. Our approach utilizes a modified version of the Dueling Deep Q-Network sharing weight parameters across the temporal axis of the video. This technique enables the trained agent to select annotation tasks from the whole video. We evaluate our annotation task selection method by means of a hierarchical supervoxel segmentation based mask propagation algorithm.

Face recognition recently has achieved remarkable success in many fields, especially in mobile payment, authentication, criminal investigation, and city management. However, face occlusion is still the key problem in person identification, such as in the field of anti-terrorism, criminal cases and public security. To solve this problem, an improved end-to-end deep generative adversarial network (named EyesGAN) has been proposed to synthesize human face from human eyes in this paper, which can be used as a potential scheme for masked face recognition. BicycleGAN is chosen as the baseline and effective improvements have been achieved. First, the self-attentional mechanism is introduced so that the improved model can more effectively learn about the internal mapping between human eyes and face. Second, the perceptual loss is applied to guide the model cyclic training and help with updating the network parameters so that the synthesized face can be of higher-similarity to the ground truth face. Third, EyesGAN has been designed by getting the utmost out of the performance of the perceptual loss and the self-attentional mechanism in GANs. A dataset of eyes-to-face synthesis has been reconstructed based on the public face datasets for training and testing. Finally, the faces synthesized by EyesGAN have been quantitatively and qualitatively compared with the results of existing methods. Extensive experiments demenstrate that our proposed method has performed better than the state-of-the-art methods in terms of Average Euclidean Distance, Average Cosine Similarity, Synthesis Accuracy Percentage, Frechet Inception Distance, and Baidu face recognition rate (the accuracy achieved 96.1 % on 615 test data of CelebA database). In this paper, the feasibility of synthesizing human face from human eyes has been explored, and the attention map shows that our network can predict other parts of the face from eyes.

Cross-modal person re-identification(Re-id) under infrared light and visible light (RGB-IR) is of great significance for modern video surveillance, especially nighttime surveillance. The existing research results in the single-mode person re-identification field have reached a high level. Cross-model person re-identification, however, is rather challenging for the tremendous cross-modality and intra-modality difference in addition to common issues such as lighting conditions, human posture, camera angle, and etc..The Cross-modal Multi-granularity Attention Network (CMGN) proposed by this paper enables network to learn the common features of different modalities and map them to the same feature space. Major contributions made by this paper includes:1) A new ”butterfly” attention module that can be used for cross-modal tasks is designed to constrain the network attention to common areas of different modes. And in the RegDB and SYSU-MM01 dataset reached the effect of State-of-the-art(SOA). 2) An end-to-end multi-granularity feature fusion network dedicated to processing cross-modal problems is proposed.

CAPTCHA, Completely Automated Public Turing test to tell Computers and Humans Apart, is widely used as a security mechanism to classify human and computer. This security mechanism is based on the Turing Test, which has been conceived to ensure network security. Usability is another fundamental issue, which can avoid human users proceeding tedious and time-consuming operation. CAPTCHA design should consider security and usability simultaneously. This paper provides a review on the development of CAPTCHA technologies for human and computer classification, along with their applications and instantiations. Different from previous CAPTCHA survey, this review discusses the CAPTCHA mechanism from usability and security aspects, therefore attacking (anti-classification) and defending (classification) technologies towards current CAPTCHA are both reviewed. Besides, recent emerging CAPTCHA and the attacking techniques are also introduced in this paper, such as game CAPTCHA, deep learning-based attacking, and etc.

Hashing methods have been widely applied to approximate nearest neighbor search in large-scale image retrieval due to its fast search speed and efficient storage space. In practice, most images are with multiple category-aware objects, i.e., multi-label images. This paper focuses on hash code learning for multi-label image retrieval. Most existing hashing methods directly extract one patch such as a downsized crop from each image as a training example, which ignores the multi-label characteristic of images and leads to suboptimal representations for multi-label images. Some researches have proved that each multi-label image follows a multi-instance assumption, where each image is represented as a bag of category-aware proposals (instances). However, existing multiple-instance learning methods use predefined statistical functions with limited learning capability to construct bag features, and they are designed for classification or pairwise-similarity preserving. Thus, directly applying existing multiple-instance learning methods into deep hashing framework still leads to suboptimal hash codes for retrieval. In this paper, we pose hashing learning for multi-label image retrieval as a problem of multiple-instance ranking learning. To solve this problem, we present an end-to-end deep hashing framework, referred to as Deep Multiple-Instance Ranking based Hashing (DMIRH). In DMIRH, we design a category-aware bag feature construction module, which jointly assigns the learned instances into categories and aggregates the selected instance features into a bag feature representation that can capture the multi-label information of each image. In addition, we propose a novel learning objective, which consists of an Inner Product distance based quantization loss to control the hash quality and a listwise ranking loss to preserve the ranking relationships. Experimental results on public benchmarks show the superiority of DMIRH over several state-of-the-art hashing methods.

This paper is mainly concerned with fixed-time synchronization control for a class of Cohen–Grossberg neural networks (CGNNs) with nonlinear coupling and time-varying delay based on synchronization dynamics. First, by using a derived sufficient condition of fixed-time synchronization (FTS) for nonlinear coupled CGNNs (NCCGNNs) with time-varying delay, a novel fixed-time synchronization controller with time-varying delay is designed. Second, by combining the synchronization dynamic viewpoint and the obtained fixed-time synchronization control rule, several useful fixed-time synchronization controllers (FTSCs) are derived. Third, comparisons with known conclusions are provided to show the innovation and feasibility of the proposed fixed-time synchronization control methods. Finally, two examples are given to verify the effectiveness of the theoretical results. Compared with related works, the advantages of this paper are as follows: (1) The designed novel FTSCs can scientifically and efficiently process the time-varying delay of the addressed coupled CGNNs; (2) The derived settling time of the FTS built on FTSCs can reflect the effect of the proposed system’s parameters on the synchronization convergence time.

Brain–machine interface (BMI) is a useful technology which creates a new way for disable people to communicate with the world, but experimenting with human brains is risky. Hence, a precise mathematical model of the information transmission in the process of limb movement is necessary to be established. In this paper, firstly, we improve the classical single-joint information transmission (SJIT) model through introducing several neuron models, and the improved model is closer to the true single-joint movements. Secondly, a closed-loop system with a Wiener filter-based decoder, an auxiliary controller based on model predictive control (MPC) and a network of Izhikevich neurons is formulated based on the improved model, and the used network of Izhikevich neurons is more time efficient than the existing one. Finally, in this closed-loop system, the intracortical micro-stimulation (ICMS) technology is introduced to feedback the information from the MPC controller in real time. The auxiliary controller assist the brain to control artificial arm by changing the frequency of stimulation current. In this way, the computational complexity of the optimization problem proposed in this paper is greatly reduced, and the closed-loop BMI system designed in this paper can well track the desired trajectory.

Siamese network is highly regarded in the visual object tracking filed because of its unique advantages of pairwise input and pairwise training. It can measure the similarity between two image patches, which coincides with the principle of the matching-based tracking algorithm. In this paper, a variant Siamese network based tracker is proposed to introduce attention module into traditional Siamese network, and relocate the object with some auxiliary relocation methods, when the proposed tracker runs under an untrusted state. Firstly, a novel attention shake layer is proposed to replace the max pooling layer in Siamese network. This layer could introduce and train two different kinds of attention modules at the same time, which means the proposed attention shake layer could also help to improve the expression power of Siamese network without increasing the depth of the network. Secondly, an auxiliary relocation branch is proposed to assist in object relocation and tracking. According to the prior assumptions of visual object tracking, some weights are involved in the auxiliary relocation branch, such as structure similarity weight, motion similarity weight, motion smoothness weight and object saliency weight. Thirdly, a novel response map based switch function is proposed to monitor the tracking process and control the effect of auxiliary relocation branch. Furthermore, in order to discuss the effect of pooling layer in Siamese network, 9 pooling and attention architectures are proposed and discussed in this paper. Some empirical results are shown in the experiment part. Comparing with the state-of-the-art trackers, the proposed tracker could achieve comparable performance in multiple benchmarks.

In this paper, we first established the fractional order memristive multidirectional associative memory neural networks (FMMAMNNs) model, and then considered its fixed-time synchronization control problem. On the basis of sliding model control and Lyapunov stability theorem, a fractional order sliding mode controller is constructed. By adding this controller to the response system, the error of the driver-response systems gradually converges to 0 in a fixed time. Compared with the previous researches, this paper considers a more complex model, and the proposed control theories can ensure that the setting time is only related to the model and controller, but not to the initial states of the system. Besides, the control theories are also applicable to the integer order models. Finally, two numerical simulations are given, the results show the validity of the theories.

It is well known that weighted sum of node states plays an essential role in function implementation of neural networks. Therefore, this paper proposes a new weighted sum synchronization model for memristive neural networks. Unlike the existing synchronization models of memristive neural networks which control each network node to reach synchronization, the proposed model treats the networks as dynamic entireties by weighted sum of node states and makes the entireties instead of each node reach expected synchronization. In this paper, weighted sum complete synchronization and quasi-synchronization are both investigated by designing feedback controller and aperiodically intermittent controller, respectively. Meanwhile, a flexible control scheme is designed for the proposed model by utilizing some switching parameters and can improve anti-interference ability of control system. By applying Lyapunov method and some differential inequalities, some effective criteria are derived to ensure the synchronizations of memristive neural networks. Moreover, the error level of the quasi-synchronization is given. Finally, numerical simulation examples are used to certify the effectiveness of the derived results.

This paper introduces a new procedure to train Minimal Learning Machines (MLM) for regression tasks. Besides that, we propose a new prediction process in MLM. A well-known drawback concerning the (original) MLM model formulation is the lack of sparseness.The most recent efforts on this problem strongly rely on the selection of reference points before training and prediction steps in MLM, all based on some supposition regarding the data. In the opposite direction, here, we explore another formulation of MLM in which we do not rely on any assumption regarding the data for prior selection. Instead, our proposal, named Lightweight Minimal Learning Machine (LW-MLM), builds a regularized system that imposes sparseness. We thrive in such a sparse criterion, not by selection but instead using a piece of weighted information into the model. We validate the contributions of this paper through four types of experiments to evaluate different aspects of our proposal: the prediction error performance, the goodness-of-fit of estimated vs. measured values, the norm values which are related to the sparsity, and finally, the prediction error in high dimensional settings. Based on the results, we show that LW-MLM is a valid alternative since achieved similar or higher accuracy rates against other variants being all seen as statistically equivalent.

This paper is concerned with event-triggered generalized dissipative filtering for delayed neural networks under aperiodic denial-of-service (DoS) attacks. Note that DoS attacks impede the wireless communications on measurement from time to time. This paper aims at designing resilient filters against DoS attacks to estimate neuronal states in the sense of dissipative. For this goal, a switched filter is introduced to cope with DoS attacks and unavailability of state information. In order to save precious communication resources, an event-triggered communication scheme is devised to transmit only necessary information to the filter. With such setting, the filtering error system is modeled as a switched system with time-delay. By employing the piecewise Lyapunov–Krasovskii functional approach and linear matrix inequality techniques, some criteria on the existence of suitable filters are presented against aperiodic DoS attacks while suitable filtering performance can be ensured. It should be mentioned that since a generalized dissipative performance index is introduced, several kinds of resilient event-based filtering issues, such as H∞ filtering, passive filtering, mixed H∞ and passive filtering, (Q, S, R)-dissipative filtering are solved in a unified framework in the presence of aperiodic DoS attacks. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

In this paper, the H∞ filtering problem is addressed for a class of nonlinear stochastic discrete-time systems subject to Round-Robin protocol, measurement outliers, randomly occurring parameter uncertainties and randomly occurring nonlinearities. First of all, the considered system is influenced by the parameter uncertainties, nonlinear dynamics and external disturbances. Next, mutually independent Bernoulli-distributed white sequences are employed to describe the phenomena of randomly occurring parameter uncertainties and nonlinearities, respectively. Besides, the Round-Robin protocol is introduced to prevent the occurrence of data collisions during the signal transmission. Moreover, a filter with adaptive saturation is constructed to weaken the impact of the measurement outliers. Through constructing a suitable Lyapunov function, sufficient conditions are established to ensure that the error system dynamics is exponentially mean-square stable and satisfies the H∞ performance. Subsequently, the filter gains are designed by solving a set of linear matrix inequalities. Finally, a simulation example is given to show the effectiveness of the filtering scheme proposed in this paper.

An adaptive dynamic programming (ADP) based event-triggered control method is established in this paper to solve the optimal control problem of unknown continuous-time nonlinear systems with input constraints. First, the unknown system is identified using two neural networks (NNs). Second, the threshold for event-triggering condition is designed, which guarantees the system stability. Then, a critic NN is employed to approximate the value function and the closed-loop system is proved to be uniformly ultimately bounded. Finally, the simulation results demonstrate the feasibility of the developed ADP method. The main contributions of this paper are that the developed ADP-based event-triggered control method avoids the Zeno phenomenon effectively and updates the weights of three NNs simultaneously in the process of implementation. Meanwhile, an improved critic NN weight updating criterion is adopted, which does not require an initial admissible control.

Intelligent transportation is an indispensable part of the smart city and the primary development direction of the future transportation systems. Vehicle detection and recognition, which is one of the most important aspects of intelligent transportation, plays a very important role in various areas of our daily life; one such important area is criminal investigation. In the fine-grained vehicle type detection and recognition, several difficult issues such as problems in data acquisition and tagging, dramatic variance in the data of different vehicle types, and challenges in identifying vehicles of the same brand with highly similar appearances remain unsolved. For the problems of data acquisition and tagging, this paper presents a strategy for automatic data acquisition and tagging based on object detection that can label the vehicle images efficiently while rapidly acquiring all types of fine-grained models. Considering the problem of data imbalance in the training process, this paper proposes a Faster-RCNN based data equalizing strategy (Faster-BRCNN), thereby improving the performance of object detection. In view of the severe information attenuation caused by the feature information transfer obstruct between layers in the traditional deep learning network, the lack of mutual dependency of these features, and the inability of the network to focus on the important region and characteristics, we propose an intensive dense attention network (DA-Net). Through its intensive connection and attention unit, we enhance the model's detection ability. The proposed method achieves mAP of 94.5% and 95.8% in the Stanford Cars and FZU Cars datasets, respectively, thereby verifying its effectiveness.

The Cognitive Packet Network (CPN) bases its routing decisions and flow control on the Random Neural Network (RNN) Reinforcement Learning algorithm; this paper proposes the addition of a Deep Learning (DL) Cluster management structure to the CPN for Quality of Service metrics (Delay Loss and Bandwidth), Cyber Security keys (User, Packet and Node) and Management decisions (QoS, Cyber and CEO). The RNN already models how neurons transmit information using positive and negative impulsive signals whereas the proposed additional Deep Learning structure emulates the way the brain learns and takes decisions; this paper presents a brain model as the combination of both learning algorithms, RNN and DL. The proposed model has been simulated under different network sizes and scenarios and it has been validated against the CPN itself without DL clusters. The simulation results are promising; the presented CPN with DL clusters as a mechanism to transmit, learn and make packet routing decisions is a step closer to emulate the way the brain transmits information, learns the environment and takes decisions.

Facial landmarks are highly correlated with each other since a certain landmark can be estimated by its neighboring landmarks. Most of the existing deep learning methods only use one fully-connected layer called shape prediction layer to estimate the locations of facial landmarks. In this paper, we propose a novel deep learning framework named Multi-Center Learning with multiple shape prediction layers for face alignment. In particular, each shape prediction layer emphasizes on the detection of a certain cluster of semantically relevant landmarks respectively. Challenging landmarks are focused firstly, and each cluster of landmarks is further optimized respectively. Moreover, to reduce the model complexity, we propose a model assembling method to integrate multiple shape prediction layers into one shape prediction layer. Extensive experiments demonstrate that our method is effective for handling complex occlusions and appearance variations with real-time performance. The code for our method is available at https://github.com/ZhiwenShao/MCNet-Extension., This paper has been accepted by Neurocomputing

This paper proposes a Neurocomputing application that reorders the Web results obtained from different Web Search Engines emulating the way our brain takes decisions. The proposed application is based on the Random Neural Network with Deep Learning Clusters that evaluates and adapts Web result relevance by associating independently each Deep Learning Cluster to a specific Web Search Engine. In addition, this paper presents a Deep Learning Cluster to perform as a Management Cluster that decides the final result relevance based on the inputs from each independent Deep Learning cluster. The performance of the proposed Management Cluster is evaluated when included as an additional layer to the Deep Learning Clusters. On average; the proposed Deep Learning cluster structure improves Smart Search performance.

This paper discusses a multi-player mixed-zero-sum (MZS) differential games with completely unknown dynamics. Based on off-policy integral reinforcement learning (IRL), a novel algorithm is proposed to obtain the optimal control. First, a policy iteration algorithm is put forward to obtain the optimal solution for deterministic system. Next, the case that the system dynamics is completely unknown is considered. And an IRL-based off-policy algorithm is presented. Meanwhile, the convergence of the presented algorithms is proved in this paper. At the end, the effectiveness of the proposed algorithm is shown by a simulation.

Feature selection plays an important role in reducing irrelevant and redundant features, while retaining the underlying semantics of selected ones. An effective feature selection method is expected to result in a significantly reduced subset of the original features without sacrificing the quality of problem-solving (e.g., classification). In this paper, a non-unique decision measure is proposed that captures the degree of a given feature subset being relevant to different categories. This helps to represent the uncertainty information in the boundary region of a granular model, such as rough sets or fuzzy-rough sets in an efficient manner. Based on this measure, the paper further introduce a differentiation entropy as an evaluator of feature subsets to implement a novel feature selection algorithm. The resulting feature selection method is capable of dealing with either nominal or real-valued data. Experimental results on both benchmark data sets and a real application problem demonstrate that the features selected by the proposed approach outperform those attained by state-of-the-art feature selection techniques, in terms of both the size of feature reduction and the classification accuracy.

The problem of event-triggered guaranteed cost consensus for nonlinear multi-agent systems with time delay and uncertain parameters is solved in this paper. First, a distributed event-triggered strategy is presented, by which control protocol is updated only at the triggering time instants, leading to significant saving of communication resources. Second, based on the Lyapunov method and the proposed event-triggered strategy, some criteria are obtained to ensure that guaranteed cost consensus can be achieved. Moreover, it is proved that the Zeno behavior is excluded under the proposed event-triggered scheme. Third, an algorithm is given to design suitable control gains in terms of linear matrix inequalities. Finally, the validity of the proposed results in this paper is verified via numerical simulation.

In this paper, the H ∞ state estimation problem of Markov jump memristive neural networks with fading channels is concerned by virtue of the hidden Markov model approach. The measurement transmission between the sensor and the filter is completed through fading channels which are described by a modified Rice fading model. The objective of the paper is to design a memristive filter such that, in the presence of fading channels, the effect of external disturbances on the error system is attenuated at a certain level and quantified by the H ∞ -norm in the mean square sense. By employing a mode-dependent Lyapunov-Krasovskii functional, some sufficient conditions are obtained to determine the gain matrices of the filter. Finally, a numerical example is provided to demonstrate the effectiveness of the main results.