As one of the basic tasks of natural language processing， new word identification provides theoretical support for the establishment of Chinese dictionary and analysis of word sentiment tendency. However， the current new word identification methods do not consider the homophonic neologism identification， resulting in low precision of homophonic neologism identification. To solve this problem， a Chinese homophonic neologism discovery method based on Pinyin similarity was proposed， and the precision of homophonic neologism identification was improved by introducing the phonetic comparison of new and old words in this method. Firstly， the text was preprocessed， the Average Mutual Information （AMI） was calculated to determine the degree of internal cohesion of candidate words， and the improved branch entropy was used to determine the boundaries of candidate new words. Then， the retained words were transformed into Chinese Pinyin with similar pronunciations and compared to the Chinese Pinyin of the old words in the Chinese dictionary， and the most similar results of comparisons would be retained. Finally， if a comparison result exceeded the threshold， the new word in the result was taken as the homophonic neologism， and its corresponding word was taken as the original word. Experimental results on self built Weibo datasets show that compared with BNshCNs （Blended Numeric and symbolic homophony Chinese Neologisms） and DSSCNN （similarity computing model based on Dependency Syntax and Semantics）， the proposed method has the precision， recall and F1 score improved by 0.51 and 5.27 percentage points， 2.91 and 6.31 percentage points， 1.75 and 5.81 percentage points respectively， indicating that the proposed method has better Chinese homophonic neologism identification effect.

Compared with ordinary networks， hypernetworks have complex tuple relationships， namely hyperedges. However， most existing network representation learning methods cannot capture the tuple relationships. To solve the above problem， a Heterogeneous hypernetwork Representation learning method with Hyperedge Constraint （HRHC） was proposed. Firstly， a method combining clique extension and star extension was introduced to transform the heterogeneous hypernetwork into the heterogeneous network. Then， the meta-path walk method that was aware of semantic relevance among the nodes was introduced to capture the semantic relationships among the heterogeneous nodes. Finally， the tuple relationships among the nodes were captured by means of the hyperedge constraint to obtain high-quality node representation vectors. Experimental results on three real-world datasets show that， for the link prediction task， the proposed method obtaines good results on drug， GPS and MovieLens datasets. For the hypernetwork reconstruction task， when the hyperedge reconstruction ratio is more than 0.6， the ACCuracy （ACC） of the proposed method is better than the suboptimal method Hyper2vec（biased 2nd order random walks in Hyper-networks）， and the average ACC of the proposed method outperforms the suboptimal method， that is heterogeneous hypernetwork representation learning method with hyperedge constraint based on incidence graph （HRHC-incidence graph） by 15.6 percentage points on GPS dataset.

In order to improve the explainability of Recommendation System （RS）， break the inherent limitations of recommendation system and enhance the user’s trust and satisfaction on recommender systems， a Deep Review Attention Neural Network （DRANN） model with enhanced explainability was proposed. Based on the potential relationships between users and items on text reviews， the rich semantic information in user reviews and item reviews was used to predict users’ interest preferences and sentiment tendencies by the proposed model. Firstly， a Text Convolutional Neural Network （TextCNN） was used to do shallow feature extraction for word vectors. Then， the attention mechanism was used to assign weights to comment data and filter invalid comment information. At the same time， the deep autoencoder module was constructed to reduce the dimension of high-dimensional sparse data， remove interference information， learn deep semantic representation， and enhance the explainability of recommendation model. Finally， the prediction score was obtained through the prediction layer. Experimental results on the four public data sets including Patio， Automotive， Musical Instrument （M?I） and Beauty show that DRANN model has the smallest Root Mean Square Error （RMSE） compared with Probabilistic Matrix Factorization （PMF）， Single Value Decomposition++ （SVD++）， Deep Cooperative Neural Network （DeepCoNN）， Tree-enhanced Embedding Model （TEM）， DeepCF （Deep Collaborative Filtering） and DER（Dynamic Explainable Recommender）， verifying its effectiveness in improving performance and the feasibility of the adopted explanation strategy.

Focused on the issue that real interaction targets cannot be matched with the virtual interaction targets one by one when providing passive haptics for redirected walking users in a vast Virtual Reality （VR） space， a method with two physical proxies acting as haptic proxies to provide haptic feedback for multiple virtual targets was proposed， in order to meet the user’s passive haptic needs alternately during the redirected walking process based on Artificial Potential Field （APF）. Aiming at the misalignment of virtual and real targets caused by the redirected walking algorithm itself and inaccurate calibration， the position and orientation of the virtual target were designed and haptic retargeting was introduced in the interaction stage. Simulation experimental results show that the design of the virtual target position and orientation can reduce the alignment error greatly. User experiments prove that haptic retargeting further improves the interaction accuracy and can bring users a richer and more immersive experience.

For the cutting stock problem of circular parts which is widely existed in many manufacturing industries， a new parallel genetic algorithm for cutting stock was proposed to maximize the material utilization within a reasonable computing time， namely Parallel Genetic Blanking Algorithm （PGBA）. In PGBA， the material utilization rate of cutting plan was used as the optimization objective function， and the multithread was used to perform the genetic manipulation on multiple subpopulations in parallel. Firstly， a specific individual coding method was designed based on the parallel genetic algorithm， and a heuristic method was used to generate the individuals of population to improve the search ability and efficiency of the algorithm and avoid the premature phenomena. Then， an approximate optimal cutting plan was searched out by adaptive genetic operations with better performance. Finally， the effectiveness of the algorithm was verified by various experiments. The results show that compared with the heuristic algorithm proposed in literature， PGBA takes longer computing time， but has the material utilization rate greatly improved， which can effectively improve the economic benefits of enterprises.

This paper presented a new Partial Differential Equation (PDE) model for fingerprint image restoration, which was an effective method for fingerprint image automatic inpainting. The existing solutions to image inpainting have some drawbacks: satisfactory inpainting results for fingerprint image cannot be provided by common image inpainting models, usually due to the lack of geometric information of the direction field; or because of the introduction of geometric information of the direction field, error results, such as different ridge lines connected to each other, will appear during inpainting process. The main principle of the presented model was to employ the orientation field to act as the constraint of the diffusion directions after comparing and analyzing the existing solutions, and the gray information could be propagated into the inpainting domain along a local fix orientation in the inpainting process, which characterized the orientation of the ridges. The presented model improved normal PDE models for fingerprint image inpainting. The numerical experimental results show that the proposed model is superior to common models at inpainting fingerprint images.

According to the research into how the users social characteristics and behavior properties influence the microblog fans network evolving, a new microblog fans network evolving model based on the attractive factor called SBPAF was proposed. The attractive factor for social characteristics and the attractive factor for behavior properties were defined. The nodes create new edges according to attractive factor preferential attachment principle and the two-step attachment principle. Besides, the dying out of the edges was also considered. The parameter in the model can be adjusted flexibly so that different microblog fans networks can be simulated. Finally, the mathematical analysis and computational experiments verify that SBPAF model is reasonable and available.

In recent years, with the advances in information communication and organizational ability, microblog has attracted the attention of scholars of all kinds. This paper reviewed the present study of microblog based on the information data analysis, and presented the concept of three components in microblog information transmission. Besides it summarized the main problems and methods in this field, generalized the domestic and foreign achievement. Finally, the trend for future work on the monitoring and management of microblog was discussed.

In order to overcome the low efficiency of traditional binary-tree anti-collision algorithms, an adaptive multi-tree anti-collision algorithm based on heuristic function was presented in the paper. By defining the heuristic function which was computed by the number of collision bits, the new algorithm can estimate the number of tags in the branch effectively. Because the new algorithm can adjust the number of searching fork in different branches and depths dynamically, it improves the searching efficiency. The theoretical analyses and simulation results show that the new algorithm overcomes the deficiency of traditional algorithms. For the large number of tags in particular, it can reduce the searching and recognition time and increase the throughput of Radio Frequency IDentification (RFID) system.

Dynamic Double-population Particle Swarm Optimization (DDPSO) algorithm was presented to solve the problem that the standard PSO algorithm easily fell into a locally optimized point, where the population was divided into two sub-populations varying with their own evolutionary learning strategies and exchanging between them. The algorithm had been applied to power system Unit Commitment (UC). The DDPSO particle consisted of a two-dimensional real number matrix representing the generation schedule. According to the proposed coding manner, the DDPSO algorithm could directly solve UC. Simulation results show that the proposed method performs better in term of precision and convergence property.