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At present, most link-prediction based models on anomaly detection in social networks lack the ability to consider the influence of abnormal nodes evolution. With the limitation of network scale and complexity, the detection efficiency of traditional models is generally low. To address these issues, this paper proposed an anomaly detection method based on the spatial scale coarse granulation and weighting mechanism on abnormal nodes. Firstly, the method introduced a cohesive community discovery algorithm, Louvain algorithm, to coarsely granulate process to streamline network. Subsequently, it identified abnormal nodes with different evolution behaviors in the processed network following the quantification of abnormal evolution process. Finally, it applied the link prediction method combined with a weighting mechanism of abnormal nodes for final abnormal detection. Compared with different LinkEvent-based strategy adjustment algorithms and NESO_ED method on three real social network data sets VAST, Email-EU ( dept1 and dept2 ) and Enron, the proposed method outperforms other state-of-the-art methods, can take into account both stability and sensitivity on anomaly detection tasks and more reasonably describe the network evolution process. [ABSTRACT FROM AUTHOR]

In order to fully mine nodes' features and better integrate these features simultaneously in the heterogeneous information network, this paper proposed a AMF L& GRec Firstly, AMFL & GRec used the Leader Rank algorithm to extract the target node' global sequence, and used a meta-path-bas ed heterogeneous information network embedding model to extract the node' local sequence,and used the skip-gram model to learn the node' global and local features. And then it used the self-attention mechanism to learn the preference of the target nodes' local and global features to obtain the feature representation of the target node in a single meta-path. Secondly, it used the self-attention mechanism to fuse the representation of the same node under different meta-paths to obtain the final feature representation. Finally, it utilized a multi-layer perceptron to achieve the task of rating prediction. This paper conducted a large number of experiments on two real datasets. The experimental results verify that the AMFL & GRec algorithm can not only capture the micro(local) structure of densely connected nodes, but also capture the global structure of the node in the network, and finally obtain nodes' overall (local +global) characteristics. At the same time, the experimental results also prove that the AMFL & GRec's rating prediction performance is better than the baselines. It proves that in the heterogeneous information network utilizing the self-attention mechanism to consider the nodes' preferences for local and global features and meta-paths can improve the accuracy of rating prediction. [ABSTRACT FROM AUTHOR]

In attribute network, the attribute information associated with nodes is essential to improve the performance of network embedding tasks. Nevertheless, network is a graph structure, in which nodes not only contain attribute information but also embrace the rich structural information. In order to make full use of the structural information, firstly, this paper defined influential node characteristics, spatial relationships, and constructed similarity matrix based on the definition of link prediction. Then it mapped correlation similarity vector associated with nodes in the binary group to the relationship space of the adjacency matrix, so as to maintain the node vector matrix structure information feature. Based on the definition of normalized graph Laplacian, it fused the attribute information and label feature and integrated the above three kinds of information into an optimization framework. Finally, it inferenced the projection matrix by calculating the local maximum value through a second order derivative. Experimental results indicate that the proposed algorithm can effectively utilize information of the adjacency structure with the binary group of nodes, and compared with other benchmark network embedding algorithms, it also can achieve better results on the node classification task. [ABSTRACT FROM AUTHOR]

Traditional graph regularization methods use Euclidean distance to measure the similarity of sample space, and can not accurately preserve the neighborhood information of complex data sets, which easily lead to the degradation of the generalization performance of the model in complex shape data and non convex data sets. This paper proposed an improved graph regularization algorithm, which used isometric feature mapping to preserve the neighborhood information of the sample space and help the model learn manifold. The simultaneous used of KL constraints further smoothed the external structure of the data representation, thereby capturing more sparse and advanced feature representations. Experimental results on MNIST and YaleB datasets show that compared with several popular feature extraction algorithms, the proposed algorithm can extract more meaningful and robust features. It has advantages in classification and clustering tasks, and also has better anti-interference capability. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the existing task allocation strategies, this paper proposed a multi-task allocation strategy based on deferred acceptance in mobile crowd sensing. Firstly, based on the different needs of platform and workers, this paper proposed a method to achieve the two-side matching of tasks and workers. Secondly, considering the urgency of the task, this paper designed a hybrid deferred acceptance algorithm. Finally, in order to verify the performance of the proposed strategy, it was compared with the two baseline task allocation methods. Experimental results show that the proposed task allocation strategy is effective in improving platform utility and worker satisfaction. [ABSTRACT FROM AUTHOR]

The detection speed of detection algorithms based on artificial design features is generally slow, and the detection accuracy needs to be improved, which can no longer meet the needs of today's industrial production. The detection technology based on deep learning cannot be deployed on resource-constrained devices because it requires a lot of computing and storage space. In response to these problems, this paper cited a channel pruning method to realize the lightweight of the YOL0v3 detection network, and obtained the pruning model Slim YOLOv3, and proposed to use Slim YOLOv3 for real-time detection tasks in industrial scenarios. The method enhanced the channel-level sparsity of the convolutional layer by applying L, regularization to the channel scaling factor, and pruned the feature channels with less information, and obtained a lightweight network model. Compared with the original model, SlimYOLOv3 gives a 60% reduction in model size and a 50% reduction in computing operations, the detection speed is 1. 7 times of the original model. It is more suitable for real-time detection of complex targets in intelligent industrial scenes. [ABSTRACT FROM AUTHOR]

Training text simplification model based on seq2seq requires large-scale parallel corpora. However, current task lacks large-scale and well-labeled parallel corpora. To address the above issues, this paper proposed an unsupervised text simplification algorithm that made the learning of the model only need simple and complex sentence datasets without labels. First, the method used denoising autoencoder to learn from simple sentence corpus and complex sentence corpus, respectively, to obtain a simple sentence autoencoder and a complex sentence autoencoder. Then, it combined the two autoencoders to form an initial text simplification model and a text complication model. Finally, it used back-translation to convert the unsupervised text simplification problem into a supervised problem, and iteratively optimized the text simplification model. Experiments on the standard dataset show that the method is superior to the existing unsupervised model on the general indicators BLEU and SARI, and the model has simplified effects at both the lexical and syntactic level. [ABSTRACT FROM AUTHOR]

Aiming at the problem of the discriminator' s poor classification accuracy on a small number of labeled samples and insufficient robustness to the local perturbation of manifolds in the generative adversarial network, this paper proposed a generative adversarial network based on variable loss and manifold regularization. The algorithm used a variable loss instead of the original discriminator to solve the adverse effect of the poorly trained classifier on the semi-supervised classification task. In addition, on the basis of variable loss of discriminator, it added manifold regular terms to improve the robustness of discriminator to local disturbance by punishing the variation of classification decision of discriminator on manifold. Using the quality of the generated samples and the semi-supervised classification accuracy as the evaluation criteria of the algorithm, it performed numerical experiments on the dataset SVHN and CIFAR-10. Comparing with other semi-supervised algorithms, the results show that the proposed algorithm can obtain higher quality generated samples and higher precision classification results with a small amount of labeled data. [ABSTRACT FROM AUTHOR]

Due to the roughness of cache replacement mechanism in Spark parallel computing framework, LRU algorithm does not consider the RDD data block reuse problem that easily results in high-reuse block to swap out of the cache and execution inefficiency. By optimizing the weight model and improving the replacement strategy, this paper put forward ERAC cache strategy, which could achieve efficient RDD autonomous cache and hierarchical replacement, included high-reuse self-caching algorithm and cache replacement hierarchical algorithm. Applied ERAC, RA and LRU in standard dataset of SNAP, experimental results show that ERAC can effective improve the memory utilization and task execution efficiency. [ABSTRACT FROM AUTHOR]

In traditional mobile cloud computing environment, task scheduling is often determined by random algorithm, adjusting working frequency by dynamic voltage regulation technology. Differentiating tasks exist in task integration, which often leads to many unreasonable task migration and heavy CPU load, and resulting in system damage and a large amount of energy consumption. For multi-workflow tasks, this paper proposed CCS algorithm, which included consolidation algorithm and multitask concurrency algorithm. By increasing the concurrency of transmission and execution between tasks, it could increase the probability of task set integration, could increase the rate of task processing, could reduce the response time of tasks, could increase the CPU usage, and control the CPU usage of host and kernel under the upper threshold. It could avoid CPU overload and optimize local algorithm according to multi-task concurrency, which adjusted task execution location, improved migration efficiency and avoided the limitations of random algorithm. Experimental results show that this algorithm can effectively improve system performance, avoid CPU overload problem, optimize energy consumption and workflow completion time. [ABSTRACT FROM AUTHOR]

In order to solve the shortcomings of current Hadoop cluster inherent scheduling distributed methods in heterogeneous resource environments, this paper proposed an adaptive scheduling algorithm NCAS based on the node capability. Firstly, NCAS algorithm calculated the scheduling factor based on node performance and task characteristics; Secondly, the scheduling factor determined the amount of data and the number of task slot that each node should be assigned; Finally, NCAS algorithm dispatched data and tasks more into fast nodes and less into slow nodes. Experimental results show that, compared with the traditional scheduling algorithms, NCAS algorithm can greatly reduce the number of speculative tasks, significantly reduce the job completion time, and improve the task execution efficiency. [ABSTRACT FROM AUTHOR]

In order to obtain good results when dealing with different data types or tasks, this paper designed a convolutional n eural network (CNN) architecture based on adaptive false nearest neighbors method. It applied the center moment to CNN pooling operation and used sparse filtering algorithm to realize the unsupervised training process. It also set the size of the convolution mask (convolution kernel ) of the CNN and the unit of convolution (CNN neural) . In addition, the architecture also used adaptive false nearest neighbors method to simplify tasks such as modeling and prediction. Experimental results confirm that the improved CNN architecture is less complex and can be trained faster and it is less prone to overfitting. [ABSTRACT FROM AUTHOR]