As a "killer" application in cloud computing, cloud gaming is leading the revolution of way of gaming. However, the high latency between the cloud and end devices hurts user experience. Aiming at the problem, a low latency cloud gaming system deployed on edge nodes, called Gaming@Edge, was proposed based on edge computing concept. To reduce the overhead of edge nodes for improving the concurrency, a cloud gaming running mechanism based on compressed graphics streaming, named GSGOD (Graphics Stream based Game-on-Demand), was implemented in Gaming@Edge system. The logic computing and rendering in the game running were separated and a computing fusion of edge nodes and end devices was built by GSGOD. Moreover, the network data transmission and latency were optimized through the mechanisms such as data caching, instruction pipeline processing and lazy object updating in GSGOD. The experimental results show that Gaming@Edge can reduce average network latency by 74% and increase concurrency of game instances by 4.3 times compared to traditional cloud gaming system.

Traditional Particle Filter (PF) algorithm based on Particle Swarm Optimization (PSOPF), which moves the moving particles to the high likelihood region, destroys the prediction distribution. When the likelihood function has many peaks, it has a large computation amount while filtering performance does not improved significantly. To solve this problem, a new PSOPF based on the Adjustment of the Likelihood (LA-PSOPF) was proposed. Under the premise of preserving the prediction distribution, the Particle Swarm Optimization (PSO) algorithm was used to adjust the likelihood distribution to increase the number of effective particles and improve the filtering performance. Meanwhile, a strategy of local optimization was introduced to scale down the swarm of PSO, reduce the amount of calculation and achieve the balance of accuracy and speed of estimation. The simulation results show that the proposed algorithm is better than PF and PSOPF when the measurement error is small and the likelihood function has many peaks, and the computing time is less than that of PSOPF.

Traditional machine learning faces a problem: when the training data and test data no longer obey the same distribution, the classifier trained by training data can't classify test data accurately. To solve this problem, according to the transfer learning principle, the features were weighted according to the improved distribution similarity of source domain and target domain's intersection features. The semantic similarity and Term Frequency-Inverse Class Frequency (TF-ICF) were used to weight non-intersection features in source domain. Lots of labeled source domain data and a little labeled target domain were used to obtain the required features for building text classifier quickly. The experimental results on test dataset 20Newsgroups and non-text dataset UCI show that feature transfer weighting algorithm based on distribution and TF-ICF can transfer and weight features rapidly while guaranteeing precision.

Aiming at the sentiment classification for Chinese consumption comments, a method called two-dimensional coordinate mapping for sentiment classification based on corpus was constructed. According to the Chinese language characteristics, firstly, a more pertinent searching method based on corpus was proposed. Secondly, the rules of extracting the Chinese subjective phrases were defined. Thirdly, the choosing optimal seed words algorithm of the specific field was constructed. Finally, the two-dimensional coordinate mapping algorithm was constructed, which mapped the comment in two-dimensional Cartesian coordinates through calculating the coordinate values of the comment and decided the semantic orientation of it. Experiments were conducted on 1200 comments of milk (half of them are positive or negative comments) in Amazon. In the experiments, word “henhao-lou” was chosen as the optimal seed word by using choosing optimal seed words algorithm, then the sentiment orientation of it was decided according to two-dimensional coordinate mapping algorithm. The average F-measure of the proposed algorithm reached more than 85%. The result shows that the proposed algorithm can classify the sentiment of Chinese consumption comments.

Since the positive comments may have the non-truly satisfied comments, a method which can truly reflect the sentiment of the consumers was constructed in order to decrease the non-truly satisfied comments from the positive comments. The research oriented to the consumption sentiment shows that the consumption sentiment vocabulary should be extracted at first. According to the consumption sentimental features, consumption sentiment got classified into seven classes and twenty-five subclasses, and the three-dimensional coordinate model was established. Afterwards, Protégé was used to build a consumption sentiment ontology library so that the consumption sentiment can be automatically classified by the three-dimensional coordinate vocabulary classification algorithm. Moreover, the consumption sentiment judging algorithm was applied to automatically judge consumer comments based on the completed library. Finally, compared with the positive comment ratio of Taobao, the F-measure can reach more than 95%. It can eliminate the non-truly satisfied comments from positive comments and reflect the consumer's real emotion.