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Paper cooperation network embodies expert topic similarity in an extent, thus, a novel method is proposed for expert topic analysis and extraction by combining paper cooperation network and topic model. In the method, we extract each paper’ author information and construct an expert cooperation network. At the same time, by means of LDA model, a probabilistic topic model is also built to analyze papers’ latent topics. Then, by making full use of the feature that adjacent nodes in the expert cooperation network share similar themes distribution, we makes a constraint on expert topic distribution in Gibbs sampling process of solving the probabilistic topic model. Experimental results on NIPS dataset show that the proposed method can effectively extract expert topics, and the expert paper cooperation network plays a very good supporting role on the extracting task.

Image recognition based on deep learning methods has gained remarkable achievements by feeding with abundant training data. Unfortunately, collecting a tremendous amount of annotated images is time-consuming and expensive, especially in grocery product recognition tasks. It is challenging to recognise grocery products accurately when the deep learning model is trained with insufficient data. This paper proposes multi-angle Generative Adversarial Networks (MAGAN), which can generate realistic training images with different angles for data augmentation. Mutual information is employed in the novel GAN to achieve the learning of angles in an unsupervised manner. This paper aims to create training images containing grocery products from different angles, thus improving grocery product recognition accuracy. We first enlarge the fruit dataset by using MAGAN and the state-of-the-art GAN variants. Then, we compare the top-1 accuracy results from CNN classifiers trained with different data augmentation methods. Finally, our experiments demonstrate that the MAGAN exceeds the existing GANs for grocery product recognition tasks, obtaining a significant increase in the accuracy.

Video-based person re-identification plays a critical role in intelligent video surveillance by learning temporal correlations from consecutive video frames. Most existing methods aim to solve the challenging variations of pose, occlusion, backgrounds and so on by using attention mechanism. They almost all draw attention to the occlusion and learn occlusion-invariant video representations by abandoning the occluded area or frames, while the other areas in these frames contain sufficient spatial information and temporal cues. To overcome these drawbacks, this paper proposes a comprehensive attention mechanism covering what, where, and when to pay attention in the discriminative spatial-temporal feature learning, namely What-Where-When Attention Network (W3AN). Concretely, W3AN designs a spatial attention module to focus on pedestrian identity and obvious attributes by the importance estimating layer (What and Where), and a temporal attention module to calculate the frame-level importance (when), which is embedded into a graph attention network to exploit temporal attention features rather than computing weighted average feature for video frames like existing methods. Moreover, the experiments on three widely-recognized datasets demonstrate the effectiveness of our proposed W3AN model and the discussion of major modules elaborates the contributions of this paper.

In this paper, we propose a control framework for intelligent surgical robots under the Remote Center of Motion (RCM). The goal of a surgical robot is to assist surgeons in performing complex surgeries. RCM constraint implies that the surgical tip attached to the end-effector of the surgical robot does not slide away from the point of the incision while performing surgery. Implementation of a control algorithm to comply with RCM constraints is a complicated task because of the nonlinear model of the surgical robots and stringent conditions of accuracy imposed by the patient’s safety. This paper proposes an optimization-driven approach to perform the surgical maneuver under RCM constraints. We then applied a bio-inspired optimization algorithm to solve the problem efficiently. For testing the performance of ZNNBAS, we used MATLAB to simulate a surgical procedure. A 7-DOF surgical robot (KUKA LBR IIWA 7) was used as a test bench for running the simulations. The simulation results show that the ZNNBAS is comparable with BAS, PSO, and GA and efficiently and robustly performed the task commanded maneuvers while enforcing the RCM constraints.

Dynamic networks are popularly used to describe networks that change with time. Although there have been a large number of research works on understanding dynamic networks using link prediction, node classification and community detection, there is rare work that is specially designed to address the challenge of big network size of dynamic networks. To this end, we study in this paper an emerging and challenging problem of network coarsening in dynamic networks. Network coarsening refers to a class of network “zoom-out” operations where node pairs and edges are grouped together for efficient analysis on big networks. However, existing network coarsening approaches can only handle static networks where network structure weights have been predefined before the coarsening calculation. Under the observation that big networks are highly dynamic and naturally change over time, we consider in this paper to embed information diffusion data which reflect the dynamics of networks for network coarsening. Specifically, we present a new Semi-NetCoarsen approach that jointly maximizes the likelihood of observing the information diffusion data and minimizes the network regularization with respect to the predefined network structural data. The learning function is convex and we use the accelerated proximal gradient algorithm to obtain the global optimal solution. We conduct experiments on two synthetic and five real-world data sets to validate the performance of the proposed method.

Machine reading comprehension (MRC) is a challenging task in natural language processing (NLP), which requires machine to determine the corresponding answer to a given passage and question. Whereas, there always exist unanswerable questions in the real world, which poses a new challenge to MRC tasks. Abundant research work has been carried out on the verification mechanism for the answerability of passage-question pairs. However, these researches only focus on its design and implementation, which has limitations in real-world scenarios. Thus, the method proposed in this paper not only verifies the answerability, but also validates and adjusts the predicted answer to obtain an elaborate answer span. Using powerful pre-trained model as encoder block, this paper explores a more comprehensive verification mechanism. Similar to how humans read passages and give answers, we propose a three-stage mechanism called ”Verification for an Elaborate Span” (V4ES): 1) sketchy reading that the model briefly browses the overall information of the passage and question, and then generates an initial answer; 2) intensive reading that it reads the passage and question again, judges the answerability of the question and gives an answer at this stage; 3) verification that it verifies these two answers produced at the previous two stages, and then gives the final prediction. Moreover, the proposed model is evaluated on two MRC challenge datasets: SQuAD2.0 and CMRC2018, and the experiment results show that our model has achieved great improvement compared with the ALBERT and BERT baselines. In conclusion, our proposed verification mechanism has demonstrated its effectiveness through a series of experiments and analysis.

This paper focuses on two related sub-tasks of aspect-based sentiment analysis, namely aspect-term extraction and aspect sentiment classification. The former aims to extract aspect-terms from given sentences and the latter aims to identify the sentiment polarity expressed on the extracted terms. Considering the practical application, researchers use more joint methods rather than pipeline methods. However, existing joint methods cannot model the interaction between aspect-terms and the sentence they belong to, or consider the relevance among the sentiments of different aspect-terms. In this paper, a novel end-to-end generative model based on encoder-decoder, namely Joint Term-Sentiment Generator (JTSG), is presented to generate all aspect term-polarity pairs. Specifically, a pre-trained model based encoder is used to encode the sentences, and specially, the decoder generates the start and end position to determine an aspect-term, rather than generate aspect-terms themselves. This new generative method contributes to avoid generating incomplete aspect-terms. Experimental results demonstrate that the proposed approach yields competitive performance on three benchmark datasets.

Object detection is one of the most important tasks involved in intelligent agriculture systems, especially in pest detection. This paper focuses on a most devastated agricultural disaster: grasshopper plagues. Grasshopper detection and monitoring is of paramount importance in preventing grasshopper plagues. This paper proposes a probabilistic faster R-CNN algorithm with stochastic region proposing, where a probabilistic region proposal network, an image classification network, and an object detection network are integrated to detect and locate grasshoppers. More specifically, in the proposed framework, the probabilistic region proposal network considers attributes (e.g. size, shape) of region proposals and the image classification network identifies the existence of grasshoppers while the object detection network scores recognition confidence for a region proposal. By integrating these three networks, the uncertainty can be passed from end to end, and the final confidence is obtained for each region proposal can be explicitly quantified. To enhance algorithm robustness, a stochastic region proposing algorithm is developed to screen region proposals rather than using a predetermined threshold. The proposed algorithm is validated by recently collected grasshopper datasets. The experimental results demonstrate that the proposed algorithm not only outperforms competing algorithms in terms of average precision (0.91), average missed rate (0.36), and maximum F1-score (0.9263), but also reduces the false positive rate of recognising the existence of grasshoppers in an open field.

Many methods achieve the visual object tracking task with deep learning technologies. As the deep features of different levels contain various semantic information and functions, this paper presents a multi-hierarchy feature aggregation approach to tackle the specific issues in the tracking task, which consists of two aspects. On one hand, this paper integrates the features captured by the offline and online classifiers at the score level, which constructs complementary roles of these classifiers to enhance the stability of classification. Besides, the proposed offline classifier is continuously optimized with different levels of features to reinforce classification constraints. On the other hand, we design a butterfly attention module to promote the capacity of multi-hierarchy feature aggregation in the regression network, which aims to fuse and strengthen the multi-scale features by attending to their spatial information. It can capture more spatial contexts by utilizing the self-attention mechanism during the fusion procedure, and preserve the hierarchy of the features during the strengthening process. Extensive experiments on four public datasets, i.e., VOT2018, OTB100, NFS and LaSOT datasets, demonstrate the effectiveness of the proposed methods.

Context and rationale Intrusion Detection, the ability to detect malware and other attacks, is a crucial aspect to ensure cybersecurity. So is the ability to identify this myriad of attacks. Objective Artificial Neural Networks (as well as other machine learning bio-inspired approaches) are an established and proven method of accurate classification. ANNs are extremely versatile – a wide range of setups can achieve significantly different classification results. The main objective and contribution of this paper is the evaluation of the way the hyperparameters can influence the final classification result. Method and results In this paper, a wide range of ANN setups is put to comparison. We have performed our experiments on two benchmark datasets, namely NSL-KDD and CICIDS2017. Conclusions The most effective arrangement achieves the multi-class classification accuracy of 99.909% on an established benchmark dataset.

Single-object tracking is regarded as a challenging task in computer vision, especially in complex spatio-temporal contexts. The changes in the environment and object deformation make it difficult to track. In the last 10 years, the application of correlation filters and deep learning enhances the performance of trackers to a large extent. This paper summarizes single-object tracking algorithms based on correlation filters and deep learning. Firstly, we explain the definition of single-object tracking and analyze the components of general object tracking algorithms. Secondly, the single-object tracking algorithms proposed in the past decade are summarized according to different categories. Finally, this paper summarizes the achievements and problems of existing algorithms by analyzing experimental results and discusses the development trends.

The problem of pursuing the data items of a large database whose distances to a query item are the least is known as Similarity Search (Nearest Neighbor Search) problem. There exist various algorithms to address this problem. Some of the well known algorithms are i) exact algorithms ii) approximation algorithms and iii) randomized algorithms. This paper has made study only on exact and approximation algorithms because randomized algorithm produces approximate results with some probability. Recently, there are several approximation algorithms are proposed by the researchers because this type of algorithms minimizes the problem of Curse of Dimensionality. This paper mainly has two major sections. In first section, various methods under exact and approximation algorithms are discussed with regard to storage, preprocessing and query time. In the second section, efficient algorithms for similarity search suitable for certain physiological characteristics based biometric systems are considered. Biometric system has five main steps viz acquisition of Image, pre-processing, extraction of features, matching and making final decision. In this paper, indexing algorithms for similarity search suitable for iris trait based on different features are discussed in detail. Since the nature of features are distinct and different in biometric traits, there does not exist a universal (one unique) solution which can apply to all traits of biometric systems. Various performance measures like Penetration Rate and Hit Rate are used to determine the correct recognition rate with top best match (rank-1 accuracy).

With the rapid development of deep learning technology and improvement in computing capability, deep learning has been widely used in the field of hyperspectral image (HSI) classification. In general, deep learning models often contain many trainable parameters and require a massive number of labeled samples to achieve optimal performance. However, in regard to HSI classification, a large number of labeled samples is generally difficult to acquire due to the difficulty and time-consuming nature of manual labeling. Therefore, many research works focus on building a deep learning model for HSI classification with few labeled samples. In this article, we concentrate on this topic and provide a systematic review of the relevant literature. Specifically, the contributions of this paper are twofold. First, the research progress of related methods is categorized according to the learning paradigm, including transfer learning, active learning and few-shot learning. Second, a number of experiments with various state-of-the-art approaches has been carried out, and the results are summarized to reveal the potential research directions. More importantly, it is notable that although there is a vast gap between deep learning models (that usually need sufficient labeled samples) and the HSI scenario with few labeled samples, the issues of small-sample sets can be well characterized by fusion of deep learning methods and related techniques, such as transfer learning and a lightweight model. For reproducibility, the source codes of the methods assessed in the paper can be found at https://github.com/ShuGuoJ/HSI-Classification.git.

Irregular time series data is becoming increasingly prevalent with the growth of multi-sensor systems as well as the continued use of unstructured manual data recording mechanisms. Irregular data and the resulting missing values severely limit the data's ability to be analysed and modelled for classification and forecasting tasks. Often, conventional methods used for handling time series data introduce bias and make strong assumptions on the underlying data generation process, which can lead to poor model predictions. Traditional machine learning and deep learning methods, although at the forefront of data modelling, are at best compromised by irregular time series data sets and fail to model the temporal irregularity of incomplete time series. Gated recurrent neural networks (RNN), such as LSTM and GRU, have had outstanding success in sequential modelling, and have been applied in many application fields, including natural language processing. These models have become an obvious choice for time series modelling and a promising tool for handling irregular time series data. RNNs have a unique ability to be adapted to make effective use of missing value patterns, time intervals and complex temporal dependencies in irregular univariate and multivariate time series data. In this paper, we provide a systematic review of recent studies in which gated recurrent neural networks have been successfully applied to irregular time series data for prediction tasks within several fields, including medical, human activity recognition, traffic monitoring and environmental monitoring. The review highlights the two common approaches for handling irregular time series data: missing value imputation at the data pre-processing stage and modification of algorithms to directly handle missing values in the learning process. Reviewed models are confined to those that can address issues with irregular time series data and does not cover the broader range of models that deal more generally with sequences and regular time series. This paper aims to present the most effective techniques emerging within this branch of research as well as to identify remaining challenges, so that researchers may build upon this platform of work towards further novel techniques for handling irregular time series data.

Road landmark inventory is becoming an important data product for the maintenance of transport infrastructures. Several commercial sensors are available which include synchronized optical cameras that allowto build 360° panoramic images of the surroundings of the vehicle used for road inspection. This paper is devoted to the analysis of such panorama images,specifically the area that contains themost relevant information. Road lane landmark detection is posed as a two class classification problem that may be solved bymachine learningapproaches, such as Random Forest (RF) and ensembles of Extreme Learning Machines (V-ELM). Besides model parameter selection, a central problem is the construction of a labeled training and validation datasetto cope with the highly uncontrolled conditions of image capture. Besides, human labor cost makes image data labeling a very expensive process. This paper proposes an open ended Active Learning (AL) approach involving a human oraclein the loop who provides the data labeling and can trigger the AL process when detection quality is degraded by the change in imaging conditions. The paper reports encouraging results over a collection of sample images selected from an industrial road landmark inventory operation. As an additional contribution, this paper assesses the ability of AL to overcomesome of the issues raised by highly class imbalanced datasets.

Depth estimation is a classic task in computer vision, which is of great significance for many applications such as augmented reality, target tracking and autonomous driving. Traditional monocular depth estimation methods are based on depth cues for depth prediction with strict requirements, e.g. shape-from-focus/ defocus methods require low depth of field on the scenes and images. Recently, a large body of deep learning methods have been proposed and has shown great promise in handling the traditional ill-posed problem. This paper aims to review the state-of-the-art development in deep learning-based monocular depth estimation. We give an overview of published papers between 2014 and 2020 in terms of training manners and task types. We firstly summarize the deep learning models for monocular depth estimation. Secondly, we categorize various deep learning-based methods in monocular depth estimation. Thirdly, we introduce the publicly available dataset and the evaluation metrics. And we also analysis the properties of these methods and compare their performance. Finally, we highlight the challenges in order to inform the future research directions.

With the advent of smart phones, the popularity of free Android applications has risen rapidly. This has led to malicious Android apps being involuntarily installed, which violate the user privacy or conduct attack. Malware detection on Android platforms therefore is a growing concern because of the undesirable similarity between malicious behavior and benign behavior, which can lead to slow detection, and allow compromises to persist for comparatively long periods of time in infected phones. The contributions of this paper are first a multiple dimensional, kernel feature-based framework and feature weight-based detection (WBD) designed to categorize and comprehend the characteristics of Android malware and benign apps. Furthermore, our software agent is orchestrated and implemented for the data collection and storage to scan thousands of benign and malicious apps automatically. We examine 112 kernel attributes of executing the task data structure in the Android system and evaluate the detection accuracy with a number of datasets of various dimensions. We find that memory- and signal-related features contribute to more precise classification than schedule-related and other descriptors of task states listed in our paper. Particularly, memory-related features provide fine-grain classification policies for preserving higher classification precision than the signal-related and others. Furthermore, we study and evaluate 80 newly infected attributes of the Android kernel task structure, prioritizing the 70 features of most significance based on dimensional reduction to optimize the efficiency of high-dimensional classification. Our second contribution is that our experiments demonstrate that, as compared to existing techniques with a short list of task structure features (16 or 32 features), our method can achieve 94%-98% accuracy and 2%–7% false positive rate, while detecting malware apps with reduced-dimensional features that adequately abbreviate online malware detections and advance offline malware inspections.

While meta learning approaches have achieved remarkable success, obtaining a stable and unbiased meta-learner remains a significant challenge, since the initial model of a meta-learner could be too biased towards existing tasks to adapt to new tasks. In order to avoid a biased meta-learner and improve its generalizability, this paper proposes a generic meta learning method that aims to learn an unbiased meta-learner towards a variety of tasks before its initial model is adapted to unseen tasks. Specifically, this paper presents a meta weight learning method for minimizing the inequality of performance across different training tasks. An end-to-end training approach is introduced for the proposed algorithm that allows for effectively learning weight and initializing the network model. Alternatively, a variety of measurement methods of weight is also designed to test the effectiveness of different weight learning methods on the improvement of model-agnostic meta-learning algorithm. The simulation results show that the proposed meta weight learning method not only outperforms state-of-the-art meta learning algorithms, but also is superior to other manually designed measurement methods of weight on discrete and continuous control problems.

Local Differential Privacy (LDP) can provide each user with strong privacy guarantees under untrusted data curators while ensuring accurate statistics derived from privatized data. Due to its powerfulness, LDP has been widely adopted to protect privacy in various tasks (e.g., heavy hitters discovery, probability estimation) and systems (e.g., Google Chrome, Apple iOS). In particular, ( ∊ , δ ) -LDP has been studied in related statistical tasks like private learning and hypothesis testing, but is mainly achieved by using Gaussian mechanism, leading to the limited data utility. In this paper, we investigate several novel mechanisms that achieve ( ∊ , δ ) -LDP with higher data utility in collecting and analyzing users’ data. Specifically, we first design two ( ∊ , δ ) -LDP algorithms for mean estimations on multi-dimensional numeric data, which can ensure higher accuracy than the optimal Gaussian mechanism. Then, we investigate different local protocols for frequency estimations on categorical attributes under ( ∊ , δ ) -LDP. Based on the proposed mechanisms, we further study on ( ∊ , δ ) -LDP-compliant stochastic gradient descent algorithms for machine learning models. Besides, the theoretical analysis of the error bound and the variance of the proposed algorithms are also presented in the paper. We have conducted extensive experiments on both real-world and synthetic datasets and demonstrated the high data utility of our proposed algorithms in the perspectives of simple data statistics tasks and complex machine learning tasks. The experimental results have shown that our proposed algorithms can effectively improve the data utility in different tasks while alleviating the privacy concerns of each individual.

The natural concept ontology structure of clothes has enabled easy management of large quantities of fashion images for online retailers and it is meaningful to study how to automatically recognize fashion images for both commercial promotion and academic research. In this paper, a new hierarchical approach is developed for large-scale fashion recognition. We first embed concept ontology into deep convolutional neural network (CNN) by adopting multiple deep CNN branches to learn node-specific features and classifiers explicitly. Then, we introduce a hierarchical knowledge distillation method to further improve the performance of fashion recognition. Finally, we employ the proposed approach for fashion recommendation. To deal with hierarchical deep learning constrains, we leverage back propagation to simultaneously refine the shared deep CNNs and the diverse CNN branches for relevant node features and classifiers by using our joint objective function. The main advantages of this paper lie in (1) providing an effective way for recognizing rich semantic explanations of fashion images without training large or multiple networks, and (2) saving the storage&time costs by learning personalized features and classifiers for each tree node. The experimental results on both our organized fashion dataset and the public DeepFashion dataset have verified the effectiveness and efficiency of the proposed approach on both hierarchical fashion recognition and within category fine-grained fashion recommendation.

Analyzing the runtime behaviors of Android apps is crucial for malware detection. In this paper, we attempt to learn the behavior level features of an app from function calls. The challenges of this task are twofold. First, the absence of function attributes hinders the understanding of app behaviors. Second, the graphical representation of function calls cannot be directly processed by classical machine learning algorithms. In this paper, we develop two methods to overcome these challenges. Based on function embedding, we first propose the concept of enhanced function call graphs (E-FCGs) to characterize app runtime behaviors. We then develop a Graph Convolutional Network (GCN) based algorithm to obtain vector representations of E-FCGs. Extensive experiments show that the features learned by our method can achieve surprisingly high detection performance on a variety of classifiers (e.g., LR, DT, SVM, KNN, RF, MLP and CNN), significantly outperforming the traditional static features.

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

In this paper, five evaluation indexes were determined for the evaluation of the node importance of the eco-industrial system, and a comprehensive evaluation method based on the network load was established by introducing the cascade effect model of complex network. Then, by combining the process of industrial production and the operation status of each raw material and production point, the paper analyzes the important sites by using the model principle of cascade effect in the actual production, and designs the optimization scheme on the basis of saving the cost of the enterprise.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

Facial expressions play an important role during communications, allowing information regarding the emotional state of an individual to be conveyed and inferred. Research suggests that automatic facial expression recognition is a promising avenue of enquiry in mental healthcare, as facial expressions can also reflect an individual's mental state. In order to develop user-friendly, low-cost and effective facial expression analysis systems for mental health care, this paper presents a novel deep convolution network based emotion analysis framework to support mental state detection and diagnosis. The proposed system is able to process facial images and interpret the temporal evolution of emotions through a new solution in which deep features are extracted from the Fully Connected Layer 6 of the AlexNet, with a standard Linear Discriminant Analysis Classifier exploited to obtain the final classification outcome. It is tested against 5 benchmarking databases, including JAFFE, KDEF,CK+, and databases with the images obtained ‘in the wild’ such as FER2013 and AffectNet. Compared with the other state-of-the-art methods, we observe that our method has overall higher accuracy of facial expression recognition. Additionally, when compared to the state-of-the-art deep learning algorithms such as Vgg16, GoogleNet, ResNet and AlexNet, the proposed method demonstrated better efficiency and has less device requirements. The experiments presented in this paper demonstrate that the proposed method outperforms the other methods in terms of accuracy and efficiency which suggests it could act as a smart, low-cost, user-friendly cognitive aid to detect, monitor, and diagnose the mental health of a patient through automatic facial expression analysis.

In the recent years, there have been remarkable improvements in the semantic segmentation based on deep convolutional neural networks. However, DCNN-based weakly supervised segmentation approaches are still inferior to the fully supervised manner. We observe that the performance gap mainly comes from the limitation of producing high-quality dense object localization clues from image-level labels. To mitigate this gap, in this paper, we are committed to finding more precise and complete pixel-level annotations from image-level tags. So this paper proposes a new iterative training framework for progressively refining pixel-wise labels and training the segmentation network. We first propose a new attention map generating method to locate more discriminative object regions. To find out less-discriminative regions and rectify wrong object location clues, the method fuses saliency map into the attention map to generate the initial pseudo pixel-level annotations. In the iteration training process, we train the segmentation network by treating pseudo pixel-level annotations as supervision. In order to correct the inaccurate labels of segmentation masks produced by current segmentation network, a superpixel-CRF refinement model is exploited to produce more accurate pixel-level annotations and use these annotations as supervision to train the segmentation network again. Our framework iterates between refining pixel-level annotations and optimizing the segmentation network. Experimental results demonstrate that our method significantly outperforms other previous weakly supervised semantic segmentation methods, and obtains the state-of-the-art performance, which are 64.7% mIoU score on PASCAL VOC 2012 test set and 26.3% mIoU score on MS COCO validation set.

This paper investigates network representation learning which involves network structures and labels. Most methods proposed so far try to utilize different kinds of network related data available in just one perfect model to learn the set of perfect embeddings, and then evaluate its performance comparing with other methods for downstream applications, such as node classification or link prediction. In this paper, we introduce ensemble learning to the study of network representation learning. In conventional scenario, ensemble methods train multiple individual learners and combine all their results about the same input to produce an improved one. Inspired by this, we try to train multiple individual models using network related data, and aggregate all the learned embeddings as the final network representations, to expect a performance boost for downstream applications. In order to learn good and diverse individual models, bootstrap sampling is used and different individual model structures are designed. Experimental results show that the idea of aggregating network embeddings really works, and can outperform existing excellent methods under specific experimental setups.

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task, which aims to predict sentiment polarities of given aspects or target terms in text. ABSA contains two subtasks: Aspect-Category Sentiment Analysis (ACSA) and Aspect-Term Sentiment Analysis (ATSA). Aspect embeddings have been extensively used for representing aspect-categories on ACSA task. Based on our observations, existing aspect embeddings cannot properly represent the relation between aspect-categories and aspect-terms. To address this limitation, this paper presents a learning method which trains aspect embeddings according to the relation between aspect-categories and aspect-terms. According to the cosine measure metric we proposed in this paper, the limitation is successfully alleviated in the aspect embeddings which are trained by our method. The trained aspect embeddings can be used as initialization in existing models to solve ACSA task. We conduct experiments on SemEval datasets for ACSA task, and the results indicate that our pre-trained aspect embeddings are capable of improving the performance of sentiment analysis.

The Bayesian linear regression is a useful tool for many scientific communities. This paper presents a novel algorithm for solving the Bayesian linear regression problem with Gaussian priors, which shares the same spirit as the gradient based methods. In addition, the standard scheme for this task, the Expectation Maximization (EM) algorithm, involves matrix inversions but our proposed algorithm is free of. Numerical experiments demonstrate that the proposed algorithm performs as well as the gradient based and EM algorithms in term of precision, but runs significantly faster than the gradient based and EM algorithms. Due to its matrix-inversion-free nature, the algorithm of this paper is a viable alternative to the competing methods available in the literature.

The Convolutional Neural Networks (CNNs), in domains like computer vision, mostly reduced the need for handcrafted features due to its ability to learn the problem-specific features from the raw input data. However, the selection of dataset-specific CNN architecture, which mostly performed by either experience or expertise is a time-consuming and error-prone process. To automate the process of learning a CNN architecture, this paper attempts at finding the relationship between Fully Connected (FC) layers with some of the characteristics of the datasets. The CNN architectures, and recently datasets also, are categorized as deep, shallow, wide, etc. This paper tries to formalize these terms along with answering the following questions. (i) What is the impact of deeper/shallow architectures on the performance of the CNN w.r.t. FC layers?, (ii) How the deeper/wider datasets influence the performance of CNN w.r.t. FC layers?, and (iii) Which kind of architecture (deeper/ shallower) is better suitable for which kind of (deeper/ wider) datasets. To address these findings, we have performed experiments with three CNN architectures having different depths. The experiments are conducted by varying the number of FC layers. We used four widely used datasets including CIFAR-10, CIFAR-100, Tiny ImageNet, and CRCHistoPhenotypes to justify our findings in the context of the image classification problem. The source code of this research is available at https://github.com/shabbeersh/Impact-of-FC-layers., This paper is accepted for publication in Neurocomputing Journal

Large amounts of training data in machine learning can keep the accuracy high to a certain extent, but the time costs are high because of the exorbitant amount of data and their dimensionality. Therefore, how to simultaneously select the most useful training data set and extract the main features of the samples, especially for image data, are essential problems that urgently need to be solved in the field of large-scale machine learning. Herein, a training sample selection method that is based on the fuzzy c-means clustering algorithm (FCM) is proposed for the problems. It first utilises condensed nearest neighbour (CNN) to make a preliminary selection of training samples. Then, it utilises the FCM to get the centres of the selected data, and, finally, it effectively condenses the sample using a compression parameter. Meanwhile, considering the critical influence of the sample features on the classification model, this paper selects the hierarchical extreme learning machine (H-ELM) model to better solve the classification task. Based on this, the paper presents the FCM-CNN-H-ELM framework for data classification, which combines FCM-Based CNN and H-ELM. The results of the experiments show that the proposed training sample selection method and classification framework can guarantee consistent, even higher, prediction results with a small number of training samples, and significantly reduce the training time.

In this paper, we have made an effort to investigate the general decay projective synchronization (GDPS) problem of a type of delayed memristor-based BAM neural networks. First, we introduced a type of novel nonlinear controller. Then, we derived some sufficient conditions ensuring the GDPS of considered networks by employing differential inclusion theory and using well-known Lyapunov functional method. Lastly, an example is given to show the correctness of obtained results. To the authors’ knowledge, the results derived in this paper are the only available ones on the projective synchronization of BAM neural networks, combining the three main factors, i.e., memristor, general decay and time-varying delays.

A seemingly unrelated regression (SUR) system simultaneously studies the groups of samples that are related with each other through the covariance of decision attributes. Each group of samples is studied by a regression equation, and the error terms of the regression equations are correlated in SUR system. Extreme Learning Machine (ELM) is a training method for single-hidden layer feedforward neural network, it is widely used in many machine learning domains due to the good generalization capability and fast training speed. Since a single ELM ignores the correlated information among different groups of samples, it fails to solve the SUR problem effectively. This ineffectiveness becomes more obvious with the correlation among equations going up. In order to overcome this problem, an extended ELM model is proposed in this paper, described as Seemingly Unrelated ELM (SUELM). SUELM simultaneously learns multiple ELMs by sufficiently using the correlated information among different groups of samples, thus it can solve the SUR problem effectively. In comparison with a single ELM, SUELM significantly improves the performance. Simulation results show that SUELM performs better than the single ELM with respect to mean square error and generalization ability, especially when significant correlations exist among different groups of samples. This paper provides an effective way for solving the SUR problem by adopting ELM as the learning model.

Virtualization in the data center network is used to overcome the resistance of Internet ossification. Node mapping in virtual network embedding (VNE) process is more important than link mapping because each node mapping has a lot of choices with high probability. Historical data always have valuable information which displays how to be mapped virtual networks with special features. Previous works use physical network historical information, and they do not pay attention to the characteristics of virtual networks during the embedding process. To address this problem, this paper proposes SoGVNE (Self-organized Green Virtual Network Embedding) which uses previous successful virtual node mapping information to train a self-organizing map (SOM) neural network. Then, it will respond to future requests quickly and with fewer resources. SoGVNE uses virtual networks historical data and is able to embed more than one virtual network concurrently. Using supervised SOM makes the SoGVNE aware of resources with renewable energy and uses them to manage energy consumption. This paper evaluates SoGVNE with reinforcement-learning, Monte-Carlo and Presto methods. The results indicate the high efficiency of the proposed method in learning the node mapping process and achieving high profit and acceptance ratio with low cost.

In online-learning, the data is incrementally received and the distributions from which it is drawn may keep changing over time. This phenomenon is widely known as concept drift. Such changes may affect the generalization of a learned model to future data. This problem may be exacerbated by the form of the drift itself changing over time. Quantitative measures to describe and analyze the concept drift have been proposed in previous work. A description composed from these measures is called a concept drift map. We believe that these maps could be useful for guiding how much knowledge in the old model should be forgotten. Therefore, this paper presents an adaptive online learning model that uses a concept drift map to regulate the forgetting factor of an extreme learning machine. Specifically, when a batch of new instances are labeled, the distribution of each class on each attribute is firstly estimated, and then it is compared with the distribution estimated in the previous batch to calculate the magnitude of concept drift, which is further used to regulate the forgetting factor and to update the learning model. Therefore, the novelty of this paper lies in that a quantitative distance metric between two distributions constructed on continuous attribute space is presented to construct concept drift map which can be further associated with the forgetting factor to make the learning model adapt the concept drift. Experimental results on several benchmark stream data sets show the proposed model is generally superior to several previous algorithms when classifying a variety of data streams subject to drift, indicating its effectiveness and feasibility.