With the rapid development of technologies such as modern network communication and social media, the networked big data is difficult to be applied due to the lack of efficient and available node representation. Network representation learning is widely concerned by transforming high-dimensional sparse network data into low-dimensional, compact and easy-to-apply node representation. However, the existing network embedding methods obtain the low-dimensional feature vectors of nodes and then use them as the inputs for other applications (such as node classification, community discovery, link prediction and visualization) for further analysis, without building models for specific applications, which makes it difficult to achieve satisfactory results. For the specific application of network community discovery, a deep auto-encoder clustering model that combines community structure optimization for low-dimensional feature representation of nodes was proposed, namely Community-Aware Deep Network Embedding (CADNE). Firstly, based on the deep auto-encoder model, the node low-dimensional representation was learned by maintaining the topological characteristics of the local and global links of the network, and then the low-dimensional representation of the nodes was further optimized by using the network clustering structure. In this method, the low-dimensional representations of the nodes and the indicator vectors of the communities that the nodes belong to were learnt at the same time, so that the low-dimensional representation of the nodes can not only maintain the topological characteristics of the original network structure, but also maintain the clustering characteristics of the nodes. Comparing with the existing classical network embedding methods, the results show that CADNE achieves the best clustering results on Citeseer and Cora datasets, and improves the accuracy by up to 0.525 on 20NewsGroup. In classification task, CADNE performs the best on Blogcatalog and Citeseer datasets and the performance on Blogcatalog is improved by up to 0.512 with 20% training samples. In the visualization comparison, CADNE molel can get a low-dimensional representation of nodes with clearer class boundary, which verifies that the proposed method has better low-dimensional representation ability of nodes.

Focusing on the limited pronunciation dictionary in Russian speech synthesis and speech recognition system, a Russian grapheme-to-phoneme algorithm based on Long Short-Term Memory (LSTM) sequence-to-sequence model was proposed, as well as a phonetic transcription system. Firstly, a new Russian phoneme set based on Speech Assessment Methods Phonetic Alphabet (SAMPA) was designed, making transcription results can reflect the stress position and vowel reduction of Russian words, and a 20 000-word Russian pronunciation dictionary was constructed according to the new phoneme set. Then, the proposed algorithm was implemented by using the TensorFlow framework, in which the Russian word was converted into a fixed-length vector by encoding LSTM, and then the vector was converted into the target pronunciation sequence by decoding LSTM. Finally, the Russian phonetic transcription system was designed and implemented. The experimental results on out-of-vocabulary test set show that the word correct rate reaches 74.8%, and the phoneme correct rate reaches 94.5%, which are higher than those of Phonetisaurus method. The system can effectively support the construction of the Russian pronunciation dictionary.

Aiming at the problem that there is redundancy when using the greedy algorithm to solve the minimum MultiPoint Relay (MPR) set in the traditional Optimized Link State Routing (OLSR) protocol, a Global_OP_MPR algorithm based on the improvement of overall situation was proposed. First, an improved OP_MPR algorithm based on the greedy algorithm was introduced, and this algorithm removed the redundancy by gradually optimizing MPR set, which could simply and efficiently obtain the minimum MPR set; then on the basis of OP_MPR algorithm, the algorithm of Global_OP_MPR added the overall factors into MPR selection criteria to introduce "overall optimization" instead of "local optimization", which could eventually obtain the minimum MPR set in the entire network. Simulations were conducted on the OPNET using Random Waypoint motion model. In the simulation, compared with the traditional OLSR protocol, the OLSR protocol combined with OP_MPR algorithm and Global_OP_MPR algorithm effectively reduced the number of MPR nodes in the entire network, and had less network load to bear Topology Control (TC) grouping number and lower network delay. The simulation results show that the proposed algorithms including OP_MPR and Global_OP_MPR can optimize the size of the MPR set and improve the network performance of the protocol. In addition, due to taking the overall factors into consideration, Global_OP_MPR algorithm achieves a better network performance.

Aiming at the continually increasing number of spams, an approach for spam filtering based on the use of Stacked Denoising Autoencoder (SDA) was proposed. Firstly, to get more abstract and robust feature representation of raw data, greedy layer-wise unsupervised algorithm was used to train the SDA by minimizing the construction error on unlabeled data set. Then a classifier was added on the top level of SDA. Next, the parameters of SDA were optimized with supervised algorithm by minimizing the classification error to obtain a optimal model on labeled data set. Lastly, experiments were performed on six different public corpora using the trained SDA. The performance of SDA algorithm was compared with Support Vector Machine (SVM), Bayes approach and Deep Belief Network (DBN), by using precision, recall, Matthews Correlation Coefficient (MCC) with more balanced performance measure as the experimental measures. The experimental results indicate that using SDA to filter spams has higher precision and more robustness. Since it not only acquires best average performance with all precision greater than 95%, but also gets close to prefect prediction with all MCC greater than 0.88.

The research on aggregate signcryption is mostly based on identity-based encryption to provide confidentiality and authentication, thus improving efficiency. But aggregate signcryption has the problem in certificate management and key escrow. Therefore, it needs to design new aggregate signcryption schemes, which not only solve the problem of certificate management and key escrow, but also guarantee the confidentiality and authentication of the scheme. This paper analyzed the main stream aggregate signcryption schemes and their development. Combined with the scheme of Zhang et al.(ZHANG L, ZHANG F T. A new certificateless aggregate signature scheme. Computer Communications, 2009,32(6)：1079-1085) and the needs mentioned above, this article designed a certificateless aggregate signcryption scheme, and proved its confidentiality and unforgeability based on the Bilinear Diffie-Hellman (BDH) problem and Computational Diffie-Hellman (CDH) problem. The experimental results show that the proposed scheme is more efficient and the amount of computation is equal or lower in comparison with the other schemes. What's more, the new scheme is publicly verifiable, and it eliminates the use of public key certificate and solves the problem in key escrow.