Case reading comprehension is the specific application of machine reading comprehension in judicial field. Case reading comprehension is one of the important applications of judicial intelligence, which reads the judgment documents by computer and answers the related questions. At present, the mainstream method of machine reading comprehension is to use deep learning model to encode the text words and obtain vector representation of the text. The core problem of model construction is how to obtain the semantic representation of the text and how to match the questions with the context. Considering that syntactic information is helpful for model learning the sentence skeleton information and Chinese characters have potential semantic information, a case reading comprehension method that integrates syntactic guidance and character attention mechanism was proposed. By fusing the syntactic information and Chinese character information, the coding ability of the model for the case text was improved. Experimental results on the reading comprehension dataset of Law Research Cup 2019 show that compared with the baseline model, the proposed method has the Exact Match (EM) value increased by 0.816 and the F1 value improved by 1.809%.

There would be lack of useful information in added unlabeled samples during the iterations of co-training algorithm, meanwhile, the labels of the samples labeled by multiple classifiers may happen to be inconsistent, which would lead to accumulation of classification errors. To solve the above problems, a co-training algorithm combining improved density peak clustering and shared subspace was proposed. Firstly, the two base classifiers were obtained by the complementation of attribute sets. Secondly, an improved density peak clustering was performed based on the siphon balance rule. And beginning from the cluster centers, the unlabeled samples with high mutual neighbor degrees were selected in a progressive manner, then they were labeled by the two base classifiers. Finally, the final categories of the samples with inconsistent labels were determined by the shared subspace obtained by the multi-view non-negative matrix factorization algorithm. In the proposed algorithm, the unlabeled samples with better representation of spatial structure were selected by the improved density peak clustering and mutual neighbor degree, and the same sample labeled by different labels was revised via shared subspace, solving the low classification accuracy problem caused by sample misclassification. The algorithm was validated by comparisons in multiple experiments on 9 UCI datasets, and experimental results show that the proposed algorithm has the highest classification accuracy rate in 7 data sets, and the second highest classification accuracy rate in the other 2 data sets.

According to the characteristics of parallel BCH decoder, the multiplication of constant coefficient in finite field was realized by using XOR gates to reduce hardware complexity. The part of the error location polynomial was calculated, and then the remaining error location polynomial could be obtained using the theory of affine polynomial and Gray code. The proposed algorithm reduces the system resources occupied.Through timing simulation on Field Programmable Gate Array (FPGA)'s development software ISE10.1, the high-efficiency of the algorithm on time and space has got verified.

It has been proved that Physical Layer Network Coding (PLNC) can also improve the system throughput and spectral efficiency by taking the advantage of the broadcast nature of electromagnetic waves in wireless cooperative environments. In this paper, the basic idea of the PLNC was introduced and its benefit over traditional forward and straight-forward network coding under the two-way relay scenario was illustrated. Firstly, three types of physical layer network coding—Physical Network Coding over Finite Field (PNCF), Analog Network Coding (ANC) and Complex Field Network Coding (CFNC) were presented, the theory research development of the three kinds of PLNC were overviewed and new theory and technology related to it were introduced. Secondly, the application and implementation for the ANC scheme in the real wireless cooperative environments were overviewed. Finally, the opening issues and challenges for PLNC concerning both theory and implementation in near future were proposed．It is an important trend to improve the theory and implementation of PLNC, research the security of PLNC, and combine PLNC with other technologies, such as channel coding and modulation, relay choice, effective scheduling and resource allocation.

The method of differential analysis on collision attacks against MD5 was analyzed. Considering the increase of message blocks to be hashed via message pre-processing, a new method of modifying the step function of MD5 was proposed. The method can raise the resistibility of collision attack by preventing choosing a good differential path and limiting the use of message modification techniques. The efficiency of the improved algorithm is higher than that of method via message pre-processing.