With the rapid development of the hotel industry, the online hotel reservation system has become popular. How to let users quickly find the housing they need from massive housing information is the problem to be solved in the reservation system. Aiming at the cold start and data sparseness of users in the housing recommendation, the User Network Embedding Recommendation (UNER) method based on the network embedding method was proposed. Firstly, two kinds of user networks were constructed by the user's historical behavior data and tag information in the system. Then, the network was mapped into the low-dimensional vector space based on the network embedding method, and the vector representation of the user node was obtained and the user similarity matrix was calculated by the user vector. Finally, according to the matrix, the housing recommendation was performed for the user. The experimental data come from the hotel reservation system of "Shuidongxiangshe" in Guizhou. The experimental results show that compared with the user-based collaborative filtering algorithm, the proposed method has the comprehensive evaluation index (F1) increased by 20 percentage points and the Mean Average Precision (MAP) increased by 11 percentage points, reflecting the superiority of the method.

The traditional image labeling models often have two deficiencies; they only can model short-range contextual information in pixel-level of the image and have a complicated inference. To improve the precision of image labeling, the fully-connected Conditional Random Field (CRF) model was used; to simplify the inference of the model, the mean filed approximation based on Gaussian kd-tree for inference was proposed. To verify the effectiveness of the proposed algorithm, the experimental image datasets not only contained the standard picture library MSRC-9, but also contained MyDataset_1 (machine parts) and MyDataset_2 (office table) which made by authors. The precisions of the proposed method on those three datasets are 77.96%, 97.15% and 95.35% respectively, and the mean cost time of each picture is 2s. The results indicate that the fully-connected CRF model can improve the precision of image labeling by considering the contextual information of image and the mean field approximation using Gaussian kd-tree can raise the efficiency of inference.

It is often difficult to classify Electrocardiogram （ECG） waveform automatically due to high similarity. A new feature representation of ECG waveform was proposed — window-slope method. In this method, an ECG waveform was divided into different windows in a plane, and the slope of maximum and minimum amplitude in a window was extracted as feature information to perform similarity analysis. The experimental results show that the method can not only reduce the dimension, but also can enlarge the difference between different types of waveforms under distance-based classification. The classification accuracy and efficiency can be improved by using the method; meanwhile the sensitivity and specificity of classification can be stabilized at a higher level.

To cope with the variation of network conditions in scalable video streaming, a new Adaptive Media Playout (AMP) algorithm was proposed which predicates the risk of playout outage and buffer overflow and adjusts the frame rate in advance. The algorithm estimated the throughput of network and the lengths of frames in the video’s GOP structure for risk predication, realized adjustments in K steps for good smoothness and speed, and reduced quality loss of the video by exploiting the scalability of SVC stream. The simulation results show that the proposed algorithm outperforms the existing smooth and conventional AMP algorithms in outage suppressing, overflow processing and jitter performance.

To guarantee the QoS of WLAN(Wireless Local Area Network) after the network load increased,the technology of reserved contention window maintenance was applied to EDCA mechanism.When network was in low load,the performance was the same as the original access mechanism.As the load increased,it could greatly reduce the collision probability under stable condition.Therefore it can not only support QoS of the high priority traffic,but also improve the throughput of the whole network.

Several known extended rough set models were analyzed, a model based on weighted connection degree was proposed. In this model,the importance of each attribute was estimated before farther extension of rough set model in the incomplete information system. Finally, the experiment result shows that the model has good coincidence with user’s subjective request and objective reality.