Abstract:With the development of big data， data has become a major strategic resource for countries and its social impact is increasingly obvious. Thus， data science is proposed to explore and study basic scientific problems contained in big data. In this paper， the development of big data， the rise and connotation of data science are first introduced. Second， the research status of big data and data science is analyzed， and the application of data in various industries is discussed. Third， the big data proving ground that is constructed to explore laws and problems of data science is briefly described. Finally， in order to promote the development of data science， accelerate the transformation of the real world to the digital world， and realize the intelligent life， the key issues of data science and the new thinking in digital world are discussed.

Abstract:The internal network of the organization is not only faced with the threat of external attackers， but also faced with the insider threat including destruction of the organization network structure， internal information theft and various means of fraud. Because of the characteristics of concealment， destructiveness and diversification of attack methods， the insider threat poses a serious threat to the internal network. Therefore， it is very necessary to study the detection methods of insider threat. This paper analyzes the characteristics of insider threat and expounds the significance of studying the detection methods of insider threat. The existing insider threat detection methods are divided into three categories， namely， detection methods based on abnormal behavior， detection methods based on abnormal audit diary， and other detection methods. The current research status of each aspect is introduced respectively， and the progress of the research status of each aspect is summarized and analyzed.At last， the future research direction of insider threat detection methods is prospected.

Abstract:The evolution of massive multiple-input multiple-output （MIMO） techniques is an important support for further improving the performance of six-generation （6G） wireless communication systems. However， with the continuous expansion of large-scale antenna arrays， frequency division duplex （FDD） massive MIMO systems are facing severe challenges in acquiring downlink channel state information （CSI）. Deep learning has a powerful ability to learn and process high-dimensional data， which provides a potential solution to this challenge. In this paper， we survey FDD massive MIMO downlink CSI acquisition techniques based on deep learning， including CSI feedback and prediction techniques. Firstly， the theoretical frameworks of CSI feedback and prediction based on deep learning are presented. Then， the superior performance of relevant research results at home and abroad is analyzed， providing a reference scheme for solving the problem of acquiring downlink CSI in FDD massive MIMO systems towards 6G. Finally， unsolved open problems of FDD massive MIMO downlink CSI acquisition are discussed， followed by potential solutions correspondingly.

Abstract:Domain adaptation mainly deals with similar task decision across different data distributions. As an emerging branch of machine learning， domain adaptation has received much attention. With the rise of deep learning in recent years， the deep domain adaptation paradigm， as a combination of deep learning and traditional domain adaptation， has attracted more and more research. Although a variety of deep domain adaptation methods have been proposed， few systematic reviews have been published. To this end， this paper definitely reviews and analyzes the existing deep domain adaptation work and summarizes them to provide reference for relevant researchers. In conclusion， the main contributions of this work include the following aspects. Firstly， the background， concepts and application fields of domain adaptation are summarized. Secondly， according to whether the model training involves adversarial mechanism， we group the existing deep domain adaptation methods into two categories， such as deep adversarial domain adaptation and deep non-adversarial domain adaptation， and review and analyze them， respectively. Then， the benchmark datasets commonly used in the domain adaptation research are tabulated with profiles. Finally， the issues suffered in the existing deep domain adaptation work are summarized and analyzed， and future research directions are given.

Abstract:Aiming at the difference of speakers in speech emotion recognition task， calculate the first-order difference and second-order difference of spectral features to form three-channel feature sets and input the feature sets to the two-dimensional network. The convolutional neural network， bidirectional short and long memory network and attention mechanism were combined to establish a baseline model， and the deep residual shrinkage network was introduced to allocate channel weights in the two-dimensional network to further improve the accuracy of speech emotion recognition. In order to improve the learning effect of the model， two different information fusion mechanisms， feature layer fusion （Add and Concatenate） and decision layer fusion （Average and Maximum）， were adopted. The results show that ：（1） Add strategy in feature layer fusion is more effective； （2） The proposed model achieves 84.93% and 86.83% of unweighted average recall （UAR） in CASIA and EMO-DB databases respectively. Compared with the baseline model， the unweighted recall rates of CASIA and EMO-DB are increased by 5.3% and 6.2% respectively after introducing deep residual shrinkage network.

Abstract:The effectiveness of deep learning algorithms depends on a large amount of labeled data. The purpose of transfer learning is to use a dataset with known labels （source domain） to classify a dataset with unknown labels （target domain）， so the research of deep transfer learning has become a hotspot. For the problem of insufficient training data labels， a model of multi-scale domain adversarial network（MSDAN） based on multi-scale feature fusion is proposed. This method uses the idea of generating adversarial networks and multi-scale feature fusion to obtain the feature representation of the domain data and the target domain data in a high-dimensional feature space. The feature representation extracts common geometric features and common semantic features of the source domain data and the target domain data. The feature representation of the source domain data and the source domain label are input into the classifier for classification， and finally more advanced effect is obtained in the test of the target domain dataset.

Abstract:Perceptron is a binary classification model in the field of pattern recognition， which has the advantages of simplicity， linearity and high computational efficiency， and it is also the basis of many classifiers. However， the perceptron cannot express complex nonlinear mapping and is difficult to process nonlinear data. Aiming at the inherent defects of perceptron and combining the characteristics of granular computing， we propose a new perceptron classification model—Granular perceptron. According to the theory of granular computing， the granulation of samples on single feature forms granules， and the granulation on multiple features constructs a granular vector. Further， the granular perceptron model is defined， the granular perceptron strategy is designed， and the granular perceptron learning algorithm is proposed. In order to solve the optimal solution of the proposed model， the derivative form of the granular loss function is proved， and its gradient descent algorithm is designed. Finally， some experiments are carried out to compare on the convergence speed， nonlinear ability and classification accuracy. The results show that the proposed model has the ability of fast convergence and nonlinear data processing.

Abstract:Opinion leaders in social networks play an important role in the process of information dissemination. The traditional mining of opinion leaders is based on network structures and doesnot consider the role of a specific topic or event， and the current mining of opinion leaders based on topic is only based on a single network structure， without taking into account the multiple interactive relationships between nodes. This paper proposes a topic opinion leader mining method based on multi-relational networks （MRTRank）， which joins topic factors and a variety of interactive relationship between nodes. Through an attribute network representation learning algorithm， the similarity of different nodes in the multi-relationship network is obtained， and the transition probability matrix of nodes is formed. Finally， the top-k opinion leaders are obtained through the PageRank algorithm. Experimental results on real Twitter datasets verify that the proposed method is superior to traditional opinion leader mining algorithms.

Abstract:Aiming at the problem that the training samples for few-shot learning are too few， which leads to the weak expression of features， a novel dynamic routing prototypical network based on SLDA（DRP-SLDA） is proposed based on the supervised topic model（Supervised LDA， SLDA） and dynamic routing algorithm. The SLDA topic model is used to establish the semantic mapping between words and categories， enhance the category distribution characteristics of words， and obtain the semantic representation of samples from the perspective of word granularity. The dynamic routing prototypical network（DR-Proto） is presented. The network makes full use of the semantic relationship between samples by extracting cross features， and uses the dynamic routing algorithm to iteratively generate dynamic prototype with category representation， so as to solve the problem of feature expression. The experimental results show that the DRP-SLDA model can effectively extract the category distribution characteristics of words and dynamically obtain the dynamic prototype to increase the category identification， which can obviously improve the generalization ability of few-shot text classification.

Abstract:Traditional visual simultaneous localization and mapping （SLAM） algorithms rely on the scene rigidity assumption. However， when dynamic objects exist in the scene， the stability of the SLAM system will be affected and the accuracy of pose estimation will be reduced. Currently， most of the existing methods apply probability strategies and geometric constraints to reduce the impact caused by a small number of dynamic objects. But when the number of dynamic objects in the scene is high， these methods will fail. In order to deal with this problem， a novel algorithm is proposed in this paper. It combines the dynamic visual SLAM algorithm with the multi-target tracking algorithm. Firstly， a semantic instance segmentation network together with geometric constraints is introduced to assist the visual SLAM module to effectively separate the static feature points from the dynamic ones， and at the same time， it can also achieve the better multi-target tracking performance. Furthermore， the trajectory and velocity information of the moving objects can also be estimated， which can provide decision information for autonomous robots navigation. The experimental results on KITTI dataset show that the localization accuracy of the proposed algorithm is improved by about 28% compared with ORB-SLAM2 algorithm in dynamic environments.

Abstract:A locating method based on learning to rank is proposed to solve the location of urban facilities and introduce the features of human mobility to improve the effectiveness. First， representation vector is extracted with two stream autoencoders， fusing the features of human mobility with others. Then the plots are sorted based on representation vector of the candidate sets and the ranking network. Extensive experiments based on real multi-source dataset verify the effectiveness of the proposed locating method.

Abstract:With the rapid development of social networks and Internet， a large number of microblog short text stream data have been produced. Discovering hot topics from microblog text streams in time plays an important role in topic recommendation and public opinion monitoring. To solve the problem of sparse features of microblog， a feature extension-based hot topic detection （FE-HTD） method in microblog short text stream is proposed by using microblog comments to extend the features of microblog. To complete the feature extension of the microblog text， firstly， the comment text is selected by the influence of the comment users and the number of likes for comment text， and the feature words are extracted from the comment text by word co-occurrence and term frequency-inverse document frequency （TF-IDF） method. Then count the word pair speed， word pair acceleration and microblog text strength of the microblog short text stream. The burst feature is calculated by word pair acceleration and microblog text strength. Finally， the variable length window range of hot topic is determined according to the speed of the burst word pair， and the topic structure of hot topic in the window is obtained by clustering. In the experiment， the proposed algorithm is compared with the text-based topic detection （T-TD） method and the burst words-based topic detection （BW-TD） method. The results show that the accuracy of the proposed algorithm is 76.4%， and the recall rate is 78.7%，which are 10% higher than those of T-TD and BW-TD methods.

Abstract:The effect of classification learning is closely related to the distribution of limited training samples. Support vector data description （SVDD）， as a single boundary solution model， cannot well describe the actual distribution characteristics of the data， resulting in some target objects falling outside the hypersphere. To improve its classification ability， this paper proposes a granular computing-driven SVDD （GrC-SVDD） classification method to construct a multi-granularity levels attribute sets and the corresponding multi-granular hyperspheres. Firstly，the importance of the attribute within the current granularity level is calculated through the neighborhood self-information. Secondly， the best attribute set is then chosen to retrain the hyperspheres that did not achieve the purity criterion at the previous granularity level， and so on until all hyperspheres meet the conditions or the attributes are exhausted. The experimental section discusses the effect of parameters on classification performance and learns hyperparameters. The experimental results show that GrC-SVDD has better classification performance compared with SVDD and popular classification methods.

Abstract:Image prior is the key to solving ill-posed problems in image restoration. Since the extreme channels prior deblurring algorithm easily produces ringing artifacts and is unable to suppress noise when the image has significant noise，we take advantage of the total variation based method that can remove noise while preserving edge features， and propose an effective blind image denoising and deblurring model based on total variation before the extreme channels prior. First of all， we introduce the total variational model in the dark channel and the bright channel to protect the edge of the image and eliminating noise or ringing artifacts. Second， the half quadratic splitting technique is used to solve the non-convex problem of the model and estimate the clear image. Finally， the blur kernel of the image is estimated by the iterative multi-scale blind deconvolution. Experimental results show that the proposed model can effectively protect the edge details of the image and eliminate the ringing artifacts while suppressing the noise. Compared with the representative methods in recent years， the robustness， subjective visual effects and objective evaluation indexes of the model are significantly improved.

Abstract:In order to solve the problems of insufficient objective evaluation and lack of evaluation basis for the classification and identification of radar emitter signal， an improved gray correlation feature evaluation model combined with interval-valued intuitionistic fuzzy thought is constructed. The model introduces the dimension of signal-to-noise ratio （SNR） to examine the dynamic differences of data at different levels， describes feature information with interval data， and establishes an interval-valued intuitionistic fuzzy comprehensive decision matrix. Secondly， an optimization model that maximizes the total deviation between features is used to determine the weight of each indicator. Finally， based on the improved gray correlation framework， the ranking of feature schemes is achieved by combining with the approach to ideal points. The simulation results show that the proposed method can give the sorting identification feature evaluation and sorting results that are consistent with the actual situation， and is basically consistent with the analysis results by the unimproved gray correlation method， which verifies the feasibility and effectiveness of the proposed method.

Abstract:Feature selection is to choose a subset without containing redundant features， while keeping the classification performance of the data unchanged. Rough hypercuboid approaches can comprehensively evaluate the feature subsets from the three aspects of the relevance， dependency and significance of features， which have been used for feature selection successfully. However， calculating the combination of all feature subsets is NP-hard， and the results obtained by traditional forward search methods is locally optimal. Therefore， a new algorithm based on the rough hypercuboid approach is designed by integrating binary particle swarm optimization. The algorithm first introduces the feature relevance to generate a set of particles， then sets the improved objective function of the rough hypercuboid method as the optimization function， and finally finds the optimal feature subset by iterative optimization of binary particle swarm. By comparing with traditional rough hypercuboid methods and the rough set method based on particle swarm optimization， etc， experimental results demonstrate the proposed algorithm is able to acquire a feature subset with fewer features and higher classification performance.

Abstract:Communication spectral behavior analysis is critical to the improvement of communication situation awareness and electromagnetic reconnaissance capability in an electromagnetic countermeasure environment. With the development of artificial intelligence technology， communication spectral behavior analysis techniques have been gradually transferred from traditional methods based on feature extraction to intelligent methods based on deep learning technology. However， the insufficient and incomplete spectrum monitoring data in the electromagnetic countermeasure environment will hinder the deep network from feature learning. Moreover， the dynamic battlefield makes it even more challenging for real-time analysis. This paper categorizes the communication spectral behavior analysis technologies into three groups： Frequency behavior analysis， network topology recognition， and communication intention inference from researching objectives in the electromagnetic countermeasure environment. Furthermore， the inner relationship between the three categories is illustrated. Finally， the existing research and development venation are reviewed and prospected considering challenges.

Abstract:ZUC-256 is a stream cipher developed in China for 5G communication and post-quantum， which mainly includes the ZUC-256 stream cipher and the integrity algorithm （EIA3）. This paper designs two kinds of hardware structures of ZUC-256 stream cipher and an EIA3 algorithm structure based on ZUC-256. And then the designed structures are implemented based on PFGA， and their performance is compared. Comparison results show that： The two new ZUC-256 designs reach a throughput of 6.72 Gb/s， which is 45.24% faster than the current ZUC-256 design， and they uses fewer resources than the previous ZUC-128 design， reducing the area by 38.48% and 30.90%， respectively. And the EIA3 algorithm based on ZUC-256 can complete encryption of 128 bit data within 0.71 μs.

Abstract:In the indoor location based on Wi Fi location fingerprint， the received signal strength indicators （RSSI） collected by heterogeneous devices at the same location and time are different， which makes the offline fingerprint database incompatible with the online signals collected by different users， thus affecting the location accuracy. To solve this problem， this paper proposes a localization algorithm suitable to heterogeneous devices. In this method， the offline fingerprint database with stable signals is constructed through the selection of access point （AP）， and then the fingerprint database is standardized by procrustes analysis （PA） to eliminate the signal difference introduced by heterogeneous devices. In the online stage， the cosine similarity （CS） algorithm is used to obtain the position estimation of the target. The positioning performance of the proposed method is tested with four mobile phones in two typical indoor environments， and the factors affecting the positioning performance are analyzed. The experimental results show that the average positioning errors of the proposed method in the two indoor environments are 2.96 m and 2.29 m， which is 21.3% and 21.6% higher than those of the Weight K-nearest neighbor （WKNN） algorithm， respectively.

Abstract:To improve the operation mode of robots and improve the recognition accuracy of somatosensory interaction， a somatosensory interaction technique based on the limiting weighted skeleton node filtering is proposed. Firstly， the Kinect sensor is used to acquire the depth scene information， the obtained depth information is processed by the skeleton tracking technology to match the joints of the human body， and the 3D coordinates of the joints of the human body are established. Then the rotation angles of each joint are calculated in the form of space vector mapping， and the proposed limiting weighted filtering algorithm is used to reduce the influence of bone noise by limiting weighted filtering the acquired and calculated joint rotation angles. Finally， the rotation angle is converted into a control command， which is sent to the mechanical arm controller through the Bluetooth serial port， and the steering of the mechanical arm is controlled. Experimental results show that the method can realize the somatosensory interaction effect， and the recognition rate of the robot arm with the human arm movement is 96.3%， and the limiting weighted filtering algorithm can effectively reduce the influence of skeleton noise.