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Collecting and analyzing data from all devices to improve the efficiency of business processes is an important task of Industrial Internet of Things (IIoT). In the age of data explosion, extensive text data generated by the IIoT have given birth to a variety of text representation methods. The task of text representation is to convert the natural language to a form that computer can understand with retaining the original semantics. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in natural language. Combining the advantages of convolutional neural network (CNN) and variational autoencoder (VAE), this paper proposes an intelligent CNN-VAE text representation algorithm as an advanced learning method for social big data within next-generation IIoT, which help users identify the information collected by sensors and perform further processing. This method employs the convolution layer to capture the local features of the context and uses the variational technique to reconstruct feature space to make it conform to the normal distribution. In addition, the improved word2vec model based on topical word embedding (TWE) is utilized to add topical information to word vectors to distinguish polysemy. This paper takes the social big data as an example to illustrate the way of the proposed algorithm applied in the next-generation IIoT and utilizes Cnews dataset to verify the performance of proposed method with four evaluating metrics (i.e., recall, accuracy, precision, and F1-score). Experimental results indicate that the proposed method outperforms word2vec-avg and CNN-AE in K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classifiers and distinguishes polysemy effectively. [ABSTRACT FROM AUTHOR]

With the fast development of cloud computing and wide application of cloud storage, the energy efficiency of cloud storage system is drawing significant attention from researchers or specialists. For the typical Dynamo cloud storage system, we design a new policy instead of the consistent hashing policy which is a combination of consistent hashing and sequential policy. The basic idea of this policy is that it divides the nodes into groups and allows each other to be mirror modes so it can find the full coverage subset of data items easily. Also we use autoregressive-moving-average model to estimate the task numbers of servers so that when under low utilization period, certain numbers of servers can be turned off to save energy. Based on the model, we demonstrate that it can save up to 23.7 % energy and maintain load balancing of servers. Furthermore, we compare our policy with Heuristic which is a classical energy conservation policy for cloud storage system that is based on consistent hash table. And we find several advantages of our policy which include finding the minimum subset of full coverage as well as other aspects. [ABSTRACT FROM AUTHOR]