The accuracy of authorship identification based on deep neural network decreases significantly when faced with a large number of candidate authors. In order to improve the accuracy of authorship identification, a neural network consisting of fast text classification (fastText) and an attention layer was proposed, and it was combined with the continuous Part-Of-Speech (POS) n-gram features for authorship identification of Chinese novels. Compared with Text Convolutional Neural Network (TextCNN), Text Recurrent Neural Network (TextRNN), Long Short-Term Memory (LSTM) network and fastText, the experimental results show that the proposed model obtains the highest classification accuracy. Compared with the fastText model, the introduction of attention mechanism increases the accuracy corresponding to different POS n-gram features by 2.14 percentage points on average; meanwhile, the model retains the high-speed and efficiency of fastText, and the text features used by it can be applied to other languages.

The choices of Java locks are faced by the developers during parallel programming. To solve the problem of how to choose the appropriate lock mechanism to improve the program performance, a recommendation method named LockRec for developers of concurrent program to choose lock mechanism was proposed. Firstly, the program static analysis technology was used to analyze the use of lock mechanism in concurrent programs and determine the program feature attributes that affect the program performance. Then, the improved random forest algorithm was used to build a recommendation model of lock mechanism, so as to help the developers to choose the lock among synchronization lock, re-entrant lock, read-write lock, and stamped lock. Four existing machine learning datasets were selected to experiment with LockRec. The average accuracy of the proposed LockRec is 95.1%. In addition, the real-world concurrent programs were used to analyze the recommendation results of LockRec. The experimental results show that LockRec can effectively improve the execution efficiency of concurrent programs.

Concerning the problem that in the research of spatial crowdsourcing task allocation, the benefits of multiple participants and the global optimization of continuous task allocation are not considered, which leads to the problem of poor allocation effect, an online task allocation algorithm was proposed for the global optimization of tripartite comprehensive benefit. Firstly, the distribution of crowdsourcing objects (crowdsourcing tasks and workers) in the next time stamp was predicted based on online random forest and gated recurrent unit network. Then, a bipartite graph model was constructed based on the situation of crowdsourcing objects in the current time stamp. Finally, the optimal matching algorithm of weighted bipartite graph was used to complete the task allocation. The experimental results show that the proposed algorithm realize the global optimization of continuous task allocation. Compared with greedy algorithm, this algorithm improves the success rate of task allocation by 25.7%, the average comprehensive benefit by 32.2% and the average opportunity cost of workers by 37.8%; compared with random threshold algorithm, the algorithm improves the success rate of task allocation by 27.4%, the average comprehensive benefit by 34.7% and the average opportunity cost of workers by 40.2%.

The existing deep learning based infrared and visible image fusion methods have too many parameters and require large amounts of computing resources and memory. These methods cannot meet the deployment demand of resource constrained edge devices such as cell phones and embedded devices. In order to address these problems, a light-weight image fusion method based on SqueezeNet was proposed. SqueezeNet was used to extract image features, then the weight map was obtained by these features, and the weighted fusion was performed, finally the fused image was generated. By comparing with the ResNet50 method, it is found that the proposed method compresses the model size and network parameter amount to 1/21 and 1/204 respectively, and improves the running speed to 5 times while maintaining the quality of fused images. The experimental results demonstrate that the proposed method has better fusion effect compared to existing traditional methods as well as reduces the size of fusion model and accelerates the fusion speed.

Graphlet Degree Vector （GDV） is an important method for studying biological networks， and can reveal the correlation between nodes in biological networks and their local network structures. However， with the increasing number of automorphic orbits that need to be researched and the expanding biological network scale， the time complexity of the GDV method will increase exponentially. To resolve this problem， based on the existing serial GDV method， the parallelization of GDV method based on Message Passing Interface （MPI） was realized. Besides， the GDV method was improved and the parallel optimization of the optimized method was realized. The calculation process was optimized to solve the problem of double counting when searching for automorphic orbits of different nodes by the improved method， at the same time， the tasks were allocated reasonably combining with the load balancing strategy. Experimental results of simulated network data and real biological network data indicate that parallel GDV method and the improved parallel GDV method both obtain better parallel performance， they can be widely applied to different types of networks with different scales， and have good scalability. As a result， they can effectively maintain the high efficiency of searching for automorphic orbits in the network.

Movidius neural computing stick is a USB-based deep learning inference tool and a stand-alone artificial intelligence accelerator that provides dedicated deep neural network acceleration for a wide range of mobile and embedded vision devices. For the embedded application of deep learning, a near real-time pedestrian target detection method based on Movidius neural computing stick was realized. Firstly, the model size and calculation were adapted to the requirements of the embedded device by improving the RefineDet target detection network structure. Then, the model was retrained on the pedestrian detection dataset and deployed on the Raspberry Pi equipped with Movidius neural computing stick. Finally, the model was tested in the actual environment, and the algorithm achieved an average processing speed of 4 frames per second. Experimental results show that based on Movidius neural computing stick, the near real-time pedestrian detection task can be completed on the Raspberry Pi with limited computing resources.

Aiming at the problems of false positive and false negatives in data race detection, a novel static data race detection approach was proposed. Firstly, intra-thread and inter-thread function call graphs were automatically constructed via control flow analysis. Secondly, the information of variable-access events within thread were collected, and possible races were detected based on the defined data race conditions. Then, in order to improve the detection accuracy, alias variables and alias locks were analyzed to reduce false negatives and false positives, respectively. Finally, the sequential relationship between access events was abstracted through control flow analysis, and program slicing was used to determine the happens-before relationship of access events, thereby reducing false positives caused by ignoring thread interactions. A data race detection tool was implemented by Java and Soot framework based on this approach. In the experimentation, several benchmarks from JGF and IBM Contest benchmark suites, such as raytracer and airline, were selected for evaluation, and the results were compared with existing data race detection algorithm and tool (HB (Happens-Before) and RVPredict). The experimental results show that, compared with algorithm HB and tool RVPredict, total number of data races detected by the proposed approach are increased by 81% and 16% respectively, the accuracy of this approach for data race detection are respectively increased by 14% and 19%, which effectively avoids false negatives and false positives.

Aiming at the problem of high energy consumption caused by the increase of base station density in heterogeneous dense network, an energy-efficient method for micro base station deployment in heterogeneous networks was proposed. Firstly, the feasibility of micro base station positions was considered to mitigate the effects of environmental conditions. Then the optimization target value was weighed under different user distribution probability to enhance adaptability for different user distribution scenarios. Finally, an energy-efficient deployment algorithm for micro base stations was proposed by jointly optimizing the number, deployment position and power configuration of micro base stations. Simulation results show that the proposed method improves energy efficiency by up to 26% compared with the scheme which only optimizes the number and location of micro base stations. The experimental results demonstrate that the combined optimization method can improve the energy efficiency of the system compared with the deployment method without considering the power factor, and verifies the influence of the micro base station power on the energy efficiency of heterogeneous network.

With the deepening of Weibo research, credibility evaluation of Weibo users has become a research hotspot. Aiming at the problem of Weibo users' credibility evaluation, a user confidence analysis method based on association was proposed. Taking Sina Weibo as the research object, firstly, seven characteristics of the user from three aspects: user information, interactive information and behavior information were analyzed, and the user self-evaluation credibility was got by using Analytic Hierarchy Process (AHP). Then, by using the user self-evaluation as the base point, the user relationship network as the carrier, and the potential users' evaluation relationship among the users, was improved the PageRank algorithm, and the user credibility evaluation model called User-Rank was proposed. The proposed model was used to evaluate comprehensively credibility of users by other users in relational network. Experiments on large scale Weibo real data show that the proposed method can obtain good evaluation results of user credibility.

The Simultaneous Localization And Mapping (SLAM) is an essential skill for mobile robots exploring in unknown environments without external referencing systems. As the sparse map constructed by feature-based Visual SLAM (VSLAM) algorithm is not suitable for robot application, an efficient and compact map construction algorithm based on octree structure was proposed. First, according to the pose and depth data of the keyframes, the point cloud map of the scene corresponding to the image was constructed, and then the map was processed by the octree map technique, and a map suitable for the application of the robot was constructed. Comparing the proposed algorithm with RGB-Depth SLAM (RGB-D SLAM) algorithm, ElasticFusion algorithm and Oriented FAST and Rotated BRIEF SLAM (ORB-SLAM) algorithm on publicly available benchmark datasets, the results show that the proposed algorithm has high validity, accuracy and robustness. Finally, the autonomous mobile robot was built, and the improved VSLAM system was applied to the mobile robot. It can complete autonomous obstacle avoidance and 3D map construction in real-time, and solve the problem that the sparse map cannot be used for obstacle avoidance and navigation.

So far away, there is no accurate method for Chinese natural speech segmentation of syllables,which is meaningful in labeling speech with reference text instead of people. According to two hypotheses that time spans of Chinese syllables under the same pronunciation obey Gauss distribution and short-time energy valley exists between two adjacent syllables, Chinese speech segmentation method based on Gauss distribution of time spans of syllables was proposed. A simplified method based on distribution of energy valleys was given, which effectively reduced the time complexity of this speech segmentation method. The experimental results show that segmentation accuracy (mean square value of time spans between artificial labels and labels created by this method) achieve 10 ^-3 and computing times are less than 1 s in Matlab of PC.

Domain Name System (DNS) provides domain name resolution service, i.e., converting domain names to IP addresses. Malicious domain detection is mainly for discovering illegal activities and ensuring the normal operation of the domain name servers. Prior work on malicious domain name detection was summarized, and a new machine learning based malicious domain detection algorithm for exploiting multiple-dimensional features was further proposed. With respect to domain name lexical features, more fine-grained features were extracted, such as the conversion frequency of the numbers and letters and the maximum length of continuous letters. As for the network attribute features, more attentions were paid to the name servers, such as the quantity, and the degree of dispersion. The experimental results show that the accuracy, recall rate, F1 value of the proposed method reaches 99.8%, which means a better performance on malicious domain name detection.

In order to overcome the non-person entity's interference, the difficulties in selection of feature words and muti-person influence on target personal relation extraction, this paper proposed person judgment based on decision tree, relation feature word generation based on minimum set cover and statistical approach based on three-layer sentence pattern rules. In the first step, 18 features were extracted from attribute files of China Conference on Machine Learning (CCML) competition 2015, C4.5 decision was used as the classifier, then 98.2% of recall rate and 92.6% of precision rate were acquired. The results of this step were used as the next step's input. Next, the algorithm based on minimum set cover was used. The feature word set covers all the personal relations as the scale of feature word set is maintained at a proper level, which is used to identify the relation type in text headline. In the last step, a method based on statistics of three-layer sentence pattern rules was used to filter small proportion rules and specify the sentence pattern rules based on positive and negative proportions to judge whether the personal relation is correct or not. The experimental result shows the approach acquires 82.9% in recall rate and 74.4% in precision rate and 78.4% in F1-measure, so the proposed method can be applied to personal relation extraction from text headlines, which helps to construct personal relation knowledge graph.

Precise speech segmentation methods, which can also greatly improve the efficiency of corpus annotation works, are helpful in comparing voice with voice models in speech recognition. A new Chinese speech segmentation into syllables based on the feature of time-frequency-dimensional energy was proposed:firstly, silence frames were searched in traditional way; secondly, unvoiced frames were sought using the difference of energies in different frequencies; thirdly, the voiced frames and speech frames were looked for with the help of 0-1 energies in special frequency ranges; finally, syllable positions were given depending on the judgements above. The experimental results show that the proposed method whose syllable error is 0.0297 s and syllable deviation is 7.93% is superior to Merging-Based Syllable Detection Automaton (MBSDA) and method of Gauss fitting.

There are few works performed on the application and analysis of the Fork/Join framework at present. This paper refactored several benchmarks, including series, crypt, sparsematmult and sor, in the Java Grande Forum (JGF) benchmark suite by using Fork/Join framework. Taking the series benchmark as an example, detailed refactoring process was presented. In the experimentation, the execution time of each benchmark was evaluated with different threshold, and the execution time of Fork/Join framework was compared with that of multi-threaded version. Furthermore, the number of work-stealing operations was presented. The experimental results show that the execution time using Fork/Join framework can reduce 14.2% on average than that using multi-thread. When evaluating the series benchmark with data size sizeC and two threads, the execution time of Fork/Join framework is 40% lower than that with multi-thread. Programs using the Fork/Join framework can get better performance than that using multi-thread.

To improve the accuracy and efficiency of absolute gas emission prediction, a new algorithm based on Chaos Immune Particle Swarm Optimization (CIPSO) and General Regression Neural Network (GRNN) was proposed. In this algorithm, CIPSO was employed to dynamically optimize the smooth factor of GRNN to reduce the impact of artificial factors in GRNN model construction, and then the optimized network was adopted to establish gas emission prediction model. The simulation experiment results on gas emission data of a coal mine show that the model is of faster convergence and higher prediction accuracy than other prediction models based on BP and Elman neural network. It is proved that the proposed method is feasible and effective.

JOMP that is the OpenMP-like implementation in Java needs to be optimized, so a parallel framework, which can separate parallel logic and logic function, was proposed.The parallel framework was implemented by a parallel library named waxberry, and the parts which need to be processed parallelly were annotated and executed by using Aspect-Oriented Programming (AOP) and run-time reflection. AOP was used to separate parallel parts with core ones, and to weave them together. Run-time reflection was used to obtain the related information during the parallel execution. The library waxberry was evaluated using Java Grande Forum (JGF) benchmarks on a quad-core processor. The experimental results show that the waxberry can obtain good performance.

Firstly, the paper introduced the researches and basic methods of spatial data visualization technology, and analyzed two common kinds of methods, namely entity-based and region-based. A clustering-based spatial data visualization method was proposed, which firstly made a cluster analysis of spatial data and got the description parameters of the result through the use of spatial clustering algorithms represented by algorithm ASCDT (Adaptive Spatial Clustering algorithm based on Delaunay Triangulation). Secondly, it designed visual objects aimed at the cluster result by combining the basic visualization methods and the characteristics of the parameters. As a result, the mapping relationship was established. Finally, some issues that needed to be further studied and improved were discussed.

In view of the kinematic model of mobile robot, a tracking controller of global asymptotic stability was proposed. The design of tracking controller was divided into two parts: The first part designed the control law of angular velocity by using global fast terminal sliding mode in order to asymptotically stabilize the tracking error of the heading angle; the second part designed the control law of linear velocity by using the Lyapunov method in order to asymptotically stabilize the tracking error of the planar coordinate. By combining Lyapunov stability theorem and two control laws, the mobile robot can track the desired trajectory in a global asymptotic sense when the angular velocity and the linear velocity satisfy these control laws. The experimental results show that the mobile robot can track desired trajectory effectively. It is helpful for promoting the practical application.

Query interface schema matching is a key step in Deep Web data integration. Dual Correlated Mining (DCM) is able to make full use of association mining method to solve the problems of complex interface schema matching. There are some problems about DCM, such as inefficiency and inaccuracy in matching. Therefore, a new method based on matching degree and semantic similarity was presented in this paper to solve the problems. Firstly, the method used correlation matrix to save the association relationship among attributes; and then, matching degree was applied to calculate the degree of correlation between attributes; at last, semantic similarity was used to ensure the accuracy of final results. The experimental results on BAMM data sets of University of Illinois show that the proposed method has higher precision and efficiency than DCM and improved DCM, and indicate that the method can deal with the query interface schema matching problems very well.

The Web news grows in index tendency and disseminates rapidly, and the Web emergency news widely spreads on the Internet. While the traditional text classification is of low accuracy and efficiency, it is difficult to locate the emergency news and information of specific topics. The paper proposed a multiple-layer classification method for Web emergency news based on the rules and statistics. First, it extracted category keywords to form the library of rules. Second, the emergencies would be classified into four major categories by the rules, and then these major categories would be classified into small categories by the Bayesian classification method, thus a two-tier classification model based on rules and statistics was established. The experimental results show that the classification accuracy rate and the recall rate have reached over 90%, and the classification efficiency is generally higher than the traditional classification methods.

The traditional distance-based similarity calculation method was described. Concerning that the method of distance calculation does not contain sufficient semantic information, this paper proposed an improved method which used WordNet and edge weighting information between the concepts to measure the similarity. It considered the level of depth and density of concepts in corpus, i.e. the semantic richness of concept. Using this method, the authors can solve the semantic similarity calculation issues and make the calculation of similarity among concepts easy. The experimental results show that, the proposed method has a 0.9109 correlation with the benchmark data set-Rubenstein concept pairs. Compared with the classical method, the proposed method has higher accuracy.

With the rapid growth of bioinformatics (protein structure analysis, genome identification), social networks (links between entities), Web analysis (interlinkage structure analysis, content mining and Web log retrieval), as well as the complex structure of text information retrievals, mining graph data has become a hot research field in recent years. Some traditional data mining algorithms have been gradually extended to graph data, such as clustering, classification, and frequent pattern mining. In this paper, the authors presented several state-of-art mainstream techniques for mining graph data, and gave a comprehensive summary of their characteristics, practical significance, as well as real-life applications on mining graph data. Finally, several research directions on graph data, and particularly, uncertain graph data were pointed out.

Flooding-based broadcasting is the widely used mechanism in many current large-scale P2P networks,such as KaZaA and Gnutella model,which usually lead to serious communication cost problem.To solve these problems,an algorithm based on rumor propagation procedure of resource search was presented.Any source peer running this algorithm could achieve a relatively high probability of finding the resource while involving a relatively small fraction of the total number of peers.The simulation results show that this algorithm is excellent.

In the research on general purpose computation accelerated by graphics hardware, a computation model based on OPENGL was synthesized,the performance of the computation structure was tested and several ways to enhance computing performance were analyzed.Then a method of parallel 2-d DCT on GPU was presented.This method computes DCT on 8×8 pixel blocks by one pass rendering.Up to 4 color channels of a picture can be simultaneously performed during the computation.Experiment results indicate that performance of hardware accelerated DCT is hundreds of times faster than that of CPU implementation in our hardware conditions.