With the rapid development of online education platforms represented by Massive Open Online Courses （MOOC）， how to evaluate the large-scale subjective question assignments submitted by platform learners is a big challenge. Peer grading is the mainstream scheme for the challenge， which has been widely concerned by both academia and industry in recent years. Therefore， peer grading technologies for online education were survyed and analyzed. Firstly， the general process of peer grading was summarized. Secondly， the main research results of important peer grading activities， such as grader allocation， comment analysis， abnormal peer grading information detection and processing， true grade estimation of subjective question assignments， were explained. Thirdly， the peer grading functions of representative online education platforms and published teaching systems were compared. Finally， the future development trends of peer grading was summed up and prospected， thereby providing reference for people who are engaged in or intend to engage in peer grading research.

Focusing on the malicious cheating behaviors of Third Party Auditor （TPA） in cloud audit， a trusted cloud auditing scheme without bilinear pairings was proposed to support the correct judgment of the behaviors of TPA. Firstly， the pseudo-random bit generator was used to generate random challenge information， which ensured the reliability of the challenge information generated by TPA. Secondly， the hash value was added in the process of evidence generation to protect the privacy of user data effectively. Thirdly， in the process of evidence verification， the interactive process between users and TPA results was added. The data integrity was checked and whether TPA had completed the audit request truthfully or not was judged according to the above results. Finally， the scheme was extended to realize batch audit of multiple data. Security analysis shows that the proposed scheme can resist substitution attack and forgery attack， and can protect data privacy. Compared with Merkle-Hash-Tree based Without Bilinear PAiring （MHT-WiBPA） audit scheme， the proposed scheme has close time for verifying evidence， and the time for generating labels reduced by about 49.96%. Efficiency analysis shows that the proposed scheme can achieve lower computational cost and communication cost on the premise of ensuring the credibility of audit results.

Aiming at the low embedding capacity of Reversible Data Hiding （RDH） in encrypted videos， a high-capacity RDH scheme in encrypted videos based on histogram shifting was proposed. Firstly， 4×4 luminance intra-prediction mode and the sign bits of Motion Vector Difference （MVD） were encrypted by stream cipher， and then a two-dimensional histogram of MVD was constructed， and （0，0） symmetric histogram shifting algorithm was designed. Finally， （0，0） symmetric histogram shifting algorithm was carried out in the encrypted MVD domain to realize separable RDH in encrypted videos. Experimental results show that the embedding capacity of the proposed scheme is increased by 263.3% on average compared with the comparison schemes， the average Peak Signal-to-Noise Ratio （PSNR） of encrypted video is less than 15.956 dB， and the average PSNR of decrypted video with secret can reach more than 30 dB. The proposed scheme effectively improves the embedding capacity and is suitable for more types of video sequences.

Multi-scale Quantum Oscillator Harmonic Algorithm (MQHOA) has good characteristics of global convergence and adaptability. For analyzing the specific performance of MQHOA on solution precision and speed, the comparisons of theoretical models and experiments were completed among the MQHOA, the classic Quantum Particle Swarm Optimization (QPSO) algorithm adopting the quantum-behaved model and having been widely used, and the QPSO with Random Mean best position (QPSO-RM) through solving the integer nonlinear programming problems. In simulation experiments, MQHOA achieved 100% success rate in solving seven unconstrained integer nonlinear programming problems, and was faster than QPSO and QPSO-RM in most cases. MQHOA was a little slower than QPSO and QPSO-RM in solving the two constrained integer nonlinear programming problems, but could obtain 100% success rate which was higher than the latter. Through the comparison of the convergence process of QPSO, QPSO-RM, MQHOA was faster and earlier on converging to the global optimal solution. The experimental results show that MQHOA can effectively adapt to solving the integer programming problems, and can avoid falling into the local optimal solution so as to obtain the global optimal solution. MQHOA is better than the contrast algorithms of QPSO and QPSO-RM in accuracy and convergence rate.

For protecting the sensitive data on mobile operation system, a Taint-marking Based Access Control (TBAC) model was presented and a Taint-marking Information Flow Control (TIFC) framework was proposed. To improve fine-grained data sharing, labels were designed for each data. To support for least privilege characteristic, capacities were defined to each subject. To avoid accumulating of contamination, decontamination capacities of trust subjects were introduced. Compared with BLP, TBAC is more available, flexible and fine-grained. The results show TIFC is an effective, flexible and accurate framework in tracking and controlling the information flow at runtime, and TIFC solves the problem of covert channel caused by control flow during program execution.

Recently, the research on clothing simulation is becoming hotter. But the flexibility, sense of reality, real-time and integrity are always difficult to be unified. Therefore, a new dressing simulation system was designed concerning the automatic fitting of any human body and clothes. At first, the surface of Non-Uniform Rational B-Spline (NURBS) was used to complete deformable body modeling. Then, particles were reconstructed from the 3DMAX model and multi-type springs were created to complete arbitrary cloth modeling. Finally, Verlet integrator was adopted to complete dressing simulation, while a new simplification algorithm for cloth models and a new method for judging interior point with a triangle were implemented. The results show that the proposed modeling approach for body and clothes guarantees the diversity of dressing effect, and the model simplification and interior point judgment can increase the simulation performance by 30% or so, which ensures the real-time quality.

Considering the problem that the abnormal features have great similarity so that simple structure and high precision modeling cannot be achieved, a control chart pattern recognition method based on Kernel Principal Component Analysis (KPCA) and neural network was proposed. Firstly, the kernel method was used to translate the nonlinear feature into a higher dimensional linear feature space. Secondly this feature was projected to lower dimensional feature space. Finally the BP neural network classifier was introduced to identify the control chart pattern. This method was verified through stochastic simulation. The result demonstrates that the model can cluster each control chart pattern effectively and improve recognition accuracy.

Most training simulators of military power cannot give reasonable evaluation to the operation process of soldiers, because of being lack of scoring system. Therefore, in this paper, the scoring system for training simulator of military power based on the operating rule of actually weapon and the knowledge of large-scale system theory and expert system was analyzed. The operating rule of the military power and the key technologies of scoring system such as building up scoring rule, selecting scoring standard, selecting coefficient for the score deductions, calculation of the scoring system and program process of the scoring system were deeply analyzed in this paper. At last, a very useful scoring system for the training simulator of gas turbine generator was developed.

In order to increase the capacity of secret information in video steganography, a big-capacity video steganography algorithm was proposed based on motion vector phase and convolutional code. By studying the moving information of P frame and B frame in every Group of Pictures (GOP), the interlace switch was used to trade with the secret information at first, and then the different size of motion vector phase was used to represent different information to denote the basic generator matrix and the convolutional code was used to insert secret information. The experimental results show that the proposed algorithm not only has a big capacity of insert information, but also has a good imperceptibility and great robustness for secret information. It can achieve high capacity in video steganogrphy and maintain good video quality as well.

On the basis of BBS short group signature scheme, a new short group signature scheme based on the Strong Diffie-Hellman (SDH) assumptions and the decision linear assumption was constructed. The scheme was verified to satisfy the security demand of full anonymity and full traceability. Compared with the latest schemes security in the standard model, this scheme has shorter length of group signature and higher operation efficiency, and it allows new members to join in the group.