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.

In order to select more reasonable relay nodes for message transmission and improve the efficiency of message delivery in opportunistic networks, message forwarding utility was designed, a corresponding message copy forwarding algorithm was proposed. Firstly, based on the historical encounter information of nodes, the indirect encounter probability of nodes and the corresponding time-effectiveness were analyzed， then a time-effectiveness indicator was proposed to evaluate the encounter information value. Secondly, combined with the similarity of node motion, the problem of message repeated diffusion was analyzed, a deviation indicator of node movement was proposed to evaluate the possibility of message repeated diffusion. Simulation results show that compared with Epidemic, ProPHET, Maxprop, SAW (Spray And Wait) algorithms, the proposed algorithm has better performance in delivery success rate, overhead and delay.

According to the characteristics of tattoo images and the insufficient ability of the Convolutional Neural Network (CNN) to extract the image features in the full connection layer, a tattoo image detection algorithm based on three-channel CNN was proposed, and three aspects of improvement work were carried out. Firstly, the image preprocessing scheme was improved for the characteristics of tattoo images. Secondly, a CNN based on three-channel fully connected layer was designed to extracted and index the features. The spatial information extraction ability of different scales was enhanced effectively, and the efficient detection of tattoo images was realized. Finally, the generalization ability of the algorithm was verified by two data sets. The experimental results on the NIST data set show that the proposed preprocessing scheme has a 0.17 percentage points increase of total correct rate and a 0.29 percentage points increase of correct rate for tattoo images than Alex scheme. Under the proposed preprocessing scheme, the proposed algorithm has obvious advantages on the standard NIST tattoo image set. The correct rate of the proposed algorithm reaches 99.1%, which is higher than 96.3%, the optimal value published by NIST; and 98.8%, obtained by traditional CNN algorithm. There is also a performance improvement on the Flickr data set.

The traditional AVL (Adelson-Velskii and Landis) tree programming has been faced with the problem of too much code, complex process and high adjusting ratio. To solve these problems, a unified rebalancing method was developed and a generalized AVL (AVL-N) tree was defined. The unified rebalancing method automatically classifies the type of the unbalanced node in AVL tree and uses a new way to adjust the tree shape without using standard rotations. AVL-N tree with relaxed balance allows the height difference between the right sub-tree and left sub-tree doesn't exceed N(N ≥ 1). When insertions and deletions have been performed in AVL-N tree, the height difference between the right sub-tree and left sub-tree of some nodes may be higher than N. At that time the unified rebalancing would be applied to rearrange the unbalanced node's descendants. The simulation results indicate that the adjusting ratio of AVL-N tree reduced significantly with N increasing, it is less than 4% for N=5 and less than 0.1% for N=13. The adjusting ratio of AVL-N tree is far below other classic data structures, such as red-black tree, and allows for a greater degree of concurrency than the original proposal.

In order to solve the problem that network intrusion detection was weak in training speed, real-time process and high false positive rate when dealing with big data, a Dendritic Cell TWin Support Vector Machine (DCTWSVM) approach was proposed. The Dendritic Cell Algorithm (DCA) was firstly used for the basic intrusion detection, and then the TWin Support Vector Machine (TWSVM) was applied to optimize the first step detection outcome. The experiments were carried out for testing the performance of the approach. The experimental results show that DCTWSVM respectively improves the detection accuracy by 2.02%, 2.30%, and 5.44% compared with DCA, Support Vector Machine (SVM) and Back Propagation (BP) neural network, and reduces the false positive rate by 0.26%, 0.46%, and 0.90%. The training speed is approximately twice as the SVM, and the brief training time is another advantage. The results indicate that the DCTWSVM is suitable for the comprehensive intrusion detection environment and helpful to the real-time intrusion process.

In traditional Proxy Re-Encryption (PRE), a proxy is too powerful as it has the ability to re-encrypt all delegator's ciphertexts to delegatee once the re-encryption key is obtained; And for more than one delegatees, delegator needs to generate different re-encryption key for different delegatee, which wastes a lot of resources in the calculation process. To solve these problems, an identity-based conditional proxy broadcast re-encryption was introduced. The delegator generated a re-encryption key for some specified condition during the encryption, like that the re-encryption authority of the proxy was restricted to that condition only. Moreover, the delegator's ciphertexts could be re-broadcasted to ensure the important communication and save a lot of computation and communication cost. Finally, the theoretical analysis verified the security of the scheme.

Through the analysis and research of reliability problems in the existing workflow scheduling algorithm, the paper proposed a reliability-based workflow strategy concerning the problems in improving the reliability of the entire workflow by sacrificing efficiency or money in some algorithms. Combining the reliability of tasks in workflow and duplication ideology, and taking full consideration of priorities among tasks, this strategy lessened failure rate in transmitting procedure and meantime shortened transmit time, so it not only enhanced overall reliability but also reduced makespan. Through the experiment and analysis, the reliability of cloud workflow in this strategy, tested by different numbers of tasks and different Communication to Computation Ratios (CCR), was proved to be better than the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and its improved algorithm named SHEFTEX, including the superiority of the proposed algorithm over the HEFT in the completion time.

Recently Sun Jin,et al. proposed an dentity-based broadcast encryption scheme against selective opening attack, (SUN JIN, HU YU-PU. Identity-based broadcast encryption scheme against selective opening attack. Journal of Electronics and Information Technology, 2011, 33(12): 2929-2934) and it claimed that the scheme can fight against Selective-Opening Attack (SOA) and has constant-size key and ciphertext in the standard model without random tags. However, this paper proved that their proposal cannot work at all. Furthermore, the authors improved their scheme to be a correct one, and then proved its security in the standard model.

According to the research and analysis on the existing task scheduling strategy of scientific workflow under the cloud environment, a two-step task scheduling strategy was proposed. This strategy aimed at solving or alleviating the phenomenon of resource idle in Heterogeneous Earliest Finish Time (HEFT) algorithm and SHEFT algorithm. Along with the characteristics of cloud computing environment, it derives from the SHEFT algorithm. It can make the most use of the resources idle time and get the minimum makespan. The experiments and performance analysis for the scheduling strategy show that it has a significant improvement in the workflow makespan and resource utilization.

How to identify the most appropriate feature vector is the key of image texture recognition. Considering the characteristics of Contourlet transform, the image was transformed from the spatial domain to the frequency domain. The feature vectors of low-frequency subband, medium-frequency subband and high-frequency subband were extracted comprehensively and entered to Support Vector Machine (SVM) for classification. Brodatz database was used for simulation. The experimental results demonstrate that mean and variance of low frequency and the energy of high frequency are the optimal representation of the image texture. They are combined to make the recognition accuracy rate up to 98.75% and the vector dimension is lower.

To enhance the performance of Shuffled Frog Leaping Algorithm (SFLA) in solving optimization problems，this paper proposed an improved shuffled frog leaping algorithm. By adding mutation operator to the original algorithm, the improved algorithm regulated the scale of mutation operator via fuzzy controller, made a dynamic adjustment of mutation operator in the searching range of solution space with different phase and candidate solution distribution of evolution process. The simulation results of four typical functions of optimization problems show that the proposed algorithm can attain above twice improvement on accuracy, convergent speed and success rate, and it demonstrates a better optimization capability especially in solving the high dimensional complex optimization problem, in comparison with the basic shuffled frog leaping algorithm and the known improved algorithm.

Low operation speed and being only fit for high quality images are the disadvantages of incomplete tree-structured wavelet. To deal with this problem, a new algorithm was proposed. Firstly, pre-processment using tophat-bothat was done to clear noise and to enhance contrast degrees; then the feature consistency was extracted. If its value was high, only one part of the image would be used in incomplete tree-structured wavelet. Otherwise, the whole image would be used. Lastly, Double Probabilistic Neural Network (DPNN) ws adopted here to identify images. Brodatz database was used for simulation, and the algae images pictured at scene was used as an application of this method. Result shows that this algorithm is fast in feature extraction and identification, with especially good performance at low quality images.