Feature weighting is used to alleviate the conditional independence assumption of Naïive Bayes text classifiers and consequently improve their generalization performance. Most traditional feature weighting algorithms use general feature weighting, which assigns the same weight to each feature for all classes. We focus on class-specific feature weighting approaches, which discriminatively assign each feature a specific weight for each class. This paper uses a statistical feature weighting technique and proposes a new class-specific deep feature weighting method for Multinomial Naïve Bayes text classifiers. In this deep feature weighting method, feature weights are not only incorporated into the classification formulas but they are also incorporated into the conditional probability estimates of Multinomial Naïve Bayes text classifiers. Experimental results for a large number of text classification datasets validate the effectiveness and efficiency of our method. INDEX TERMS Multinomial Naïve Bayes text classifiers, class-specific feature weighting, statistic, deep feature weighting. CHAOQUN LI received the Ph.D. degree from the

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MapReduce is a widely adopted computing framework for data-intensive applications running on clusters. This paper proposed an approach to exploit data parallelisms in XML processing using MapReduce in Hadoop. The authors' solution seamlessly integrates data storage, labeling, indexing, and parallel queries to process a massive amount of XML data. Specifically, the authors introduce an SDN labeling algorithm and a distributed hierarchical index using DHTs. More importantly, an advanced two-phase MapReduce solution are designed that is able to efficiently address the issues of labeling, indexing, and query processing on big XML data. The experimental results show the efficiency and effectiveness of the proposed parallel XML data approach using Hadoop.

Intelligent warehouses greatly improve management efficiency and reduce the management cost of enterprises. Now many enterprises are building their own intelligent warehouses. However, the current intelligent warehouse lacks the communication between digital space and physical space, and the efficiency of warehouse management still has a lot of room for improvement. The digital twin technology is a useful way to realize the fusion of virtual warehouse and physical warehouse and optimizes the activities. In order to realize the communication between digital space and physical space of the intelligent warehouse, eliminate information island and improve the efficiency of warehouse management, this paper proposed the digital twin system architecture of intelligent warehouse based on digital twin technology. At the same time, this paper analyzed the system application requirements, designed and constructed an intelligent warehouse digital twin system. Finally, the prototype system is developed with the warehouse of a garment enterprise as an example to verify the feasibility and effectiveness of the system.

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