Aiming at the problems of the mismatch between aspect words and irrelevant context and the lack of grammatical level features in Aspect-Based Sentiment Analysis （ABSA） at current stage， an improved ABSA model integrating match-Long Short-Term Memory （mLSTM） and grammatical distances was proposed， namely mLSTM-GCN. Firstly， the correlation between the aspect word and the context was calculated word by word， and the obtained attention weight and the context representation were fused as the input of the mLSTM， so that the context representation with higher correlation with the aspect word was obtained. Then， the grammatical distance was introduced to obtain a context which was more grammatically related to the aspect word， so as to obtain more contextual features to guide the modeling of the aspect word， and obtain the aspect representation through the aspect masking layer. Finally， in order to exchange information， location weights， context representations and aspect representations were combined， thereby obtaining the features for sentiment analysis. Experimental results on Twitter， REST14 and LAP14 datasets show that compared with Aspect-Specific Graph Convolutional Network （ASGCN）， mLSTM-GCN has the accuracy improved by 1.32， 2.50 and 1.63 percentage points， respectively， and has the Macro-F1 score improved by 2.52， 2.19 and 1.64 percentage points， respectively. Therefore， mLSTM-GCN can effectively reduce the probability of mismatch between aspect words and irrelevant context， and improve the classification effect.

In order to overcome the shortcomings of traditional FireWorks Algorithm (FWA) in the process of optimization, such as the search range limited by explosion radius and the lack of effective interaction between particles, an Enhanced FireWork Algorithm with adaptive Merging strategy and Guidance operator (EFWA-GM) was proposed. Firstly, according to the position relationship between fireworks particles, the overlapping explosion ranges in the optimization space were adaptively merged. Secondly, by making full use of the position information of high-quality particles through layering the spark particles, the guiding operator was designed to guide the evolution of suboptimal particles, so as to improve the accuracy and convergence speed of the algorithm. Experimental results on 12 benchmark functions show that compared with Standard Particle Swarm Optimization (SPSO) algorithm, Enhanced FireWorks Algorithm (EFWA), Adaptive FireWorks Algorithm (AFWA), dynamic FireWorks Algorithm (dynFWA), and Guided FireWorks Algorithm (GFWA), the proposed EFWA-GM has better optimization performance in optimization accuracy and convergence speed, and obtains optimal solution accuracy on 9 benchmark functions.

A face hallucination method based on sparse representation and position prior was proposed, which can obtain the enlargement of a single low-resolution input. Some perspectives of compressed sensing were applied to the method. The high- and low-resolution over-complete atoms were classified according to different positions of face. The low-resolution face image inputs were approximated by the sparse linear combination of the over-complete atoms which were classified. The sparse coefficients were obtained to reconstruct the high-resolution data of certain position. According to their original positions, the generated patches were integrated into a global face. The experimental results illustrate that the proposed method can generate satisfying high-resolution face image using fewer atoms compared to other methods.

There are a large number of directed networks in the real world. According to a directional edge in the network, a cascading failure model was proposed which is suitable to describe the coupled map lattices with directed network. In this paper, using simulation methods, the cascading failures with BA (Barabási-Albert) scale free and ER (Erds-Rényi) random graph directed networks in this model was researched. Two attack strategies: deliberate attack and random attack were adopted in this fixed node number network, and relevant data were recorded. By analyzing the data, following conclusions can be made: 1) the cascading failures are much easier to occur in directed network than in undirected network; 2) when the networks are attacked, directed networks are more vulnerable than undirected networks; 3) in ER random graph networks, there is linear relationship among four thresholds with fault size increasing when network faults occur.