In the field of image copy-move forgery detection， it is very challenging to locate the boundaries of tampered small objects accurately. Current deep learning-based methods locate forged regions by detecting similar content in images. However， these methods usually just transmit the final features extracted by the encoder to the decoder to generate the mask， and ignore more spatial information of forged regions contained in the high-resolution encoding features， resulting in inaccurate model output prediction results for the boundary identification of small objects. To address this problem， a detection network based on multi-scale feature extraction and fusion called SimiNet was proposed. Firstly， abundant features were extracted by the multi-scale feature extraction module. Secondly， a skip connection was added between the feature extraction module and the decoding module to bridge the gap between the encoding and decoding features， so as to identify the boundaries of small objects accurately. Finally， Log-Cosh Dice Loss function was used to take the place of cross entropy loss to reduce the impact of class-imbalance problem on detection results. Experimental results show that the F1 score of SimiNet on USCISI dataset reaches 72.54%， which is 3.39 percentage points higher than that of the suboptimal method CMSDNet （Copy-Move Similarity Detection Network）. It can be seen that SimiNet is more accurate for boundary identification of small objects and has better visualization.

For retrieving the encrypted data in cloud environment quickly， an efficient searchable encryption scheme for batch data processing scenarios was proposed. Firstly， two inverted indexes were built by the client， one file index was used to store the file-keyword mapping， another empty search index was used to store keyword-file mapping. Then， these two indexes were submitted to the cloud server. The search index_{was gradually updated and constructed according to the search tokens and file indexesduring the user’s search by the cloud， and the search results of the searched keywords were recorded by this search index. In this way， the search index construction time was shared to each retrieval process effectively and the storage space of search index was reduced. A set storage method based on key-value structure was adopted by the indexes， which supported the at-the-same-time merging and splitting of index， which means when adding and deleting files， the corresponding file index and search index were generated by the client according to the file set to be added or deleted， then the server merged or split the indexes， so that the files were able to be added and deleted in batch quickly. Testing results show that the proposed scheme greatly improves the updating efficiency of files and is suitable for batch data processing. Through leakage function， it is proved that the proposed scheme can meet the indistinguishability security standard against adaptive dynamic keyword selection attack.}

A new soft subspace clustering algorithm was proposed to address the optimization problem for the projected subspaces, which was generally not considered in most of the existing soft subspace clustering algorithms. Maximizing the deviation of feature weights was proposed as the sub-space optimization goal, and a quantitative formula was presented. Based on the above, a new optimization objective function was designed which aimed at minimizing the within-cluster compactness while optimizing the soft subspace associated with each cluster. A new expression for feature-weight computation was mathematically derived, with which the new clustering algorithm was defined based on the framework of the classical k-means. The experimental results show that the proposed method significantly reduces the probability of trapping in local optimum prematurely and improves the stability of clustering results. And it has good performance and clustering efficiency, which is suitable for high-dimensional data cluster analysis.

In the field of social influence propagation, social network as the media plays a fundamental role in interaction between social individuals and disseminating information or views. First, the definition of social influence and the essential attribute of social influences as the social relevance were discussed. Then, the independent cascade model and the linear threshold model were expounded, as well as greedy algorithm and heuristic algorithms which can confirm the influential people. Finally, the new trend of research on social influence, such as community-based influence maximization algorithm and research on the influence of multiple subjects and multiple behaviors were deeply analyzed.

In a periodic report Wireless Sensor Network (WSN), heavy data traffic very easily leads to serious transmission collisions. This paper proposed a distributed Time Division Multiple Access (TDMA) scheduling strategy, called DTSS, to construct an appropriate transmission schedule that avoided transmission collisions. DTSS took advantage of a distributed competitive algorithm to build the transmission schedule. Each node selected its next-hop forwarding node and competed for a transmission time slot with its contending nodes. After the construction of the schedule, the nodes sent and received the data according to the schedule. The simulation results confirm DTSS avoids transmission collisions, decreases the energy consumption of nodes and significantly improves the network lifetime.

To improve the prediction ability of image memorability, a method for automatically predicting the memorability of an image was proposed by using visual saliency entropy and improved Object Bank feature. The proposed method improved the traditional Object Bank feature and extracted the visual saliency entropy feature. Then a prediction model of image memorability was constructed by using Support Vector Regression (SVR). The experimental results show that the correlation coefficiency of the proposed method is three percentage higher than the state-of-the-art method. The proposed model can be used in image memorability prediction, image retrieval ranking and advertisement assessment analysis.

In order to protect Android platforms from the application-level privilege escalation attacks, this paper analyzed the XManDroid access control model, which has better ability on fighting these attacks, especially the collusion attack on the covert channel. To address the problem that XManDroid could not detect the multi-application and multi-permissions collusion attacks, this paper proposed an improved mandatory access control model which recorded the communication history of applications by building an IPC links colored diagram. At last, the test result on the prototype system show that the new model can solve the problem in the XManDroid well.

As surgeons do not have effective prediction on the the implant surgery for vocal cord paralysis, resulting in high rate of failure, the finite element method was used for preoperative simulation. Through Computed Tomography (CT) data of larynx, the 3D geometric model of vocal cords and glottis trachea was extracted by Mimics, and then imported into ANSYS-Fluent to simulate the vocal vibration mode and airflow dynamic coupling characteristics before and after implanted surgery. The experimental data and clinical statistics data were compared to prove the feasibility of the finite element analysis techniques for implant surgery simulation of vocal cord paralysis. The experimental result can provide support for the design of surgery program.

In order to overcome the defects of traditional rigid load balancing mechanism that it can not adapt to changing network environment, and to solve the problem of load balancing mechanisms in cloud environment that it can not take full advantage of elastic characteristics and Quality of Service (QoS) would be unstable, this paper proposed a new load balancing mechanism in cloud environment based on green computing resource pool strategy. It quantified the load according to the utilization rate of system resources and the quantization decided distribution of virtual machines. On the basis of the use of the virtual machines, resources would be recycled to improve resource utilization. The experimental results show that the response time stablizes around 2.5 seconds, the overall QoS has been obviously improved with the power consumption reduced, and the effectiveness of the mechanisms has been verified.

In order to meet the high requirements of real-time collision detection in increasingly complex virtual environment, a fast collision detection algorithm based on image space was proposed. It made efficiently use of the Graphics Processing Unit (GPU). Based on the hierarchical binary tree and the collision detection between Oriented Bounding Boxes (OBB), the algorithm could quickly eliminate disjoint bumps of the virtual scene. With the potential collision set, the efficiency of the algorithm has a significantly improvement on the basis of RECODE algorithm. The experimental results show that the algorithm achieves good results, and has a higher efficiency, especially in a highly complex virtual environment.

In order to solve the energy limitation problem on wireless sensor network nodes, this paper proposed a new adaptive update asynchronous MAC protocol — AU-MAC protocol. This protocol combined the sleep-work state switching mode, asynchronous mode with adaptive update to effectively extend the network life. AU-MAC protocol improved channel usage efficiency by making use of sender monitoring and receiver activating data transfer. And, it established a neighbor node information table and introduced adaptive updating mechanism, to reduce the free monitor. The functions of AU-MAC protocol had been estimated on NS2 network simulation platform. It shows that, AU-MAC protocol improves the energy efficiency at the basis of maintaining the same throughput and end-end transit delay.

Code function description extraction is the basic premise of functional integration. To address the problem of common low accuracy rate of extraction of code function, the mechanism of extracting code function description based on formal semantic of Clight was proposed, and it was realized by algorithm of Clight code function description. This mechanism was strictly based on Clight natural semantics inference rules, ignoring the details of code execution in the middle, only concerned with the memory states before and after implementation. And as a functional description of the code feature description, it improved the accuracy of functional extraction and the success rate of the key area in software development.

For preventing URL （Universal Resource Locator） attacks, this paper proposed a model based on artificial immunity. Applying the self-protection mechanism of biological immune system to network security, through deploying resource safely, avoiding guessing primary key, requesting valid check and business exception check, this model prevented URL attack in different levels while responding to valid request normally and made the applications acquire the secondary immunity. This paper also proposed and analyzed the key technology of implementing the model: the algorithm of avoiding guessing primary key and an enhanced static password algorithm based on random number. This model has been applied to two practical applications. The test results and the practical applications confirm the effectiveness of the model.

To support Quality of Service (QoS) requirements for different kinds of network in medium and high rate wireless sensor networks, a priority-based service differentiation and rate control strategy was proposed. The strategy gives high priority to the traffic with strong real-time and reliability requirement for service differentiation, calculates the rate difference using the factor ε, and adjusts the sending rate of its upstream node by using hop-by-hop method. The simulation results show the proposed strategy can guarantee high-priority real-time traffic with high throughput and low latency, and maintain the stability of the network throughput.

A cellular automaton model for one-lane highway traffic was proposed in this paper. Based on the classical NS model, the randomization parameter of a vehicle was calculated according to fuzzy control rules. Some basic qualitative results and the fundamental diagram of our new model were got through computer simulations. Compared with other existing single lane cellular automaton models, the experimental results show that our new model is able to reproduce some of the real macroscopic traffic characteristics such as hysteresis.