In order to improve the recommendation effect of social events in Event-based Social Network (EBSN), a personalized social event recommendation method combining historical behaviors and social relationships of users was proposed. Firstly, deep learning technology was used to build a user model from two aspects:the user's historical behaviors and the potential social relationships between users. Then, when modeling user preferences, the negative vector representation of user preferences was introduced, and the attention weight layer was used to assign different weights to different events in the user's historical behaviors and different friends in the user's social relationships according to different candidate recommendation events, at the same time, the various characteristics of events and groups were considered. Finally, a lot of experiments were carried out on the real datasets. Experimental results show that this personalized social event recommendation method is better than the comparative Deep User Modeling framework for Event Recommendation (DUMER) and DIN (Deep Interest Network) model combined with attention mechanism in terms of Hits Ratio (HR), Normalized Discounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR) evaluation indicators.

Aiming at the problem of reachable region search problem in urban computing, a method based on time segment tree was developed. In the method, a time segment tree structure was designed to store the local reachable regions, and a dynamic adaptive search algorithm was proposed, so as to improve the efficiency and accuracy of reachable region search. The method includes four steps. Firstly, the probability time weights of road segments were constructed on the basis of road speed distribution model and the trajectory data. Then, the short-term reachable regions were queried and stored by using the hierarchical skip list algorithm. After that, an efficient index structure for the hierarchical reachable region was built by the use of the time segment tree. Finally, the iterative search in the road network was carried out by using the time segment tree index, and the reachable region set was obtained. Extensive experiments were conducted on Beijing road network and taxi trajectory datasets. The results show that the proposed method improves the efficiency and accuracy by 18.6% and 25% respectively compared with the state-of-the-art Single-location reachability Query Maximum/minimum Bounding region search (SQMB) method.

Concerning the high time and space complexity caused by using high-dimensional tenuous vectors to represent network information in tenuous subgraph finding problem, a Tenuous subGraph Finding (TGF) algorithm based on network embedding was proposed. Firstly, the network structure was mapped to the low-dimensional space by the network embedding method in order to obtain the low-dimensional vector representation of nodes. Then, the tenuous subset finding problem in the vector space was defined, and the tenuous subgraph finding problem was transformed into the tenuous subset finding problem. Finally, the sample points with lowest local density were searched iteratively and expanded to figure out the largest tenuous subset satisfying the given conditions. Experimental results on Synthetic_1000 dataset show that, the search efficiency of TGF algorithm is 1 353 times that of Triangle and Edge Reduction Algorithm (TERA) and 4 times of that Weight of K-hop (WK) algorithm, and it achieves better results in k-line, k-triangle and k-density indexes

In terms of the problem that a safety-critical system will be paused, detected and resumed when security tools alert, and the delay between the occurrence and discovery of the false alarms (false positive or false negative) results in an effect on the availability of the guest Operating System (OS), a scheme based on virtualization was proposed. When a false alarm occurred, the operations of the suspicious application were quarantined correctly to avoid substantial system-wide damages. Then the operations of the suspicious application were logged and application inter-dependency information was generated according to its interactions with other applications. When the false alarm was determined, measures such as resuming the application's operations and killing the relevant applications according to the operation logs and inter-dependency information were taken so that the guest OS could reach the correct operating status quickly. The experimental results show that the scheme can reduce the overhead caused by rollback and recovery when a false alarm occurs. Compared to the situation without the proposed scheme, the overhead of handling the false alarm is reduced by 20%-50%. The proposed scheme can effectively reduce the effect of false alarm on the availability of clients, and can be applied in the cloud platform which provides services to safety-critical clients.

In recent years, physical and virtual machines are heavily threatened by malwares. Deploying traditional detection tools such as anti-virus softwares and firewalls on Infrastructure as a Service (IaaS) cloud faces the following problems:1) detection tools may be damaged or shut down by malwares; 2) the detection rate of a single tool is insufficient; 3) detection tools are easily bypassed; 4) it's difficult to deploy additional softwares in each virtual machine. A diversified malware detection framework was proposed to overcome these shortcomings. The framework leveraged virtualization technology to intercept some specific behavior of virtual machines at first. Then codes from virtual machines' memory were extracted dynamically. Finally, several anti-virus softwares were used to codetermine whether the extracted codes were malicious or not. The extraction and judgment processes were totally transparent to virtual machines. A prototype was implemented based on the Xen hypervisor and some experiments were done. The prototype has a malware detection rate of 85.7%, which is 14.3 percentage points higher than static anti-virus softwares. The experimental results show that the diversified malware detection framework on cloud platform can provide more effective protection to the security of virtual machines.

Joint trajectory tracking control and flexible vibration suppression techniques for a Free-Floating Flexible Space Robot (FFFSR) were discussed under parameter uncertainty and limited torque. A composite controller containing a slow control subsystem for joint trajectory tracking and a fast control subsystem for flexible vibration description were proposed using singular perturbation method. A model-free Fuzzy Radial Basis Function Neural Network (FRBFNN) adaptive tracking control strategy was applied in the slow subsystem. FRBFNN was adopted to support the estimation of velocity signals performed by the observer, the approximation of the unknown nonlinear functions of the observer as well as the controller. The fast subsystem adopted an Extended State Observer (ESO) to estimate coordinate derivatives of flexible modal and uncertain disturbance, which could hardly be measured, and used Linear Quadratic Regulator (LQR) method to suppress the flexible vibration. Numerical simulation results show that the composite controller can achieve stable joint trajectory tracking in 2.5 s, and the flexible vibration amplitude is restricted in ±1×10 ^-3 m, when the control torque is limited within ±20 N·m and ±10 N·m.

To combat the missing value problem in terrorism behavior data set, this paper proposed Compressed Context Space (CCS) method which is based on the idea of maximizing the dependence between the context vectors and actions. CCS relied on Hilbert-Schmidt independence criterion which evaluated the relationship between two variables according to their Hilbert-Schmidt norm. Theories have proven Hilbert-Schmidt norm can detect dependence. In order to detect the relevance well and maximum the dependence between the context features and actions, CCS should maximum Hilbert-Schmidt norm between the linearly mapped low-dimensional features and actions, which is able to reduce the effect of missing value problem. Combining CCS followed SVM (CCS) can produce effective classification. Experiments on MAROB show that the proposed CCS+SVM improves SVM, PCA+SVM, CCA+SVM and CONVEX by at least 1.5% and 1.0% for recall and F measure, and has competitive performance with the best results for precision and Area Under ROC Curve (AUC). The results show that CCS+SVM handles missing value problem well.

Due to the threats of Cross-Site Scripting (XSS) attack in Online Social Network (OSN), a approach combined classifiers and improved n-gram model was proposed to detect the malicious OSN webpages infected with XSS code. Firstly, similarity-based features and difference-based features were extracted to build classifiers and the improved n-gram model. After that, the classifiers and model were combined to detect malicious webpages in OSN. The experimental results show that compared with the traditional classifier detection methods, the proposed approach is more effective and the false positive rate is about 5%.

In the pointer forwarding schemes of Hierarchical Mobile IPv6 (HMIPv6), the influence of the distance between the Access Routers (ARs) on the handover performance has not been taken into consideration. To solve this problem, the optimization of intra-MAP (Mobile Anchor Point) handover in HMIPv6 based on pointer forwarding (OPF-HMIPv6) was proposed. The OPF-HMIPv6 compared the distance between ARs with the distance between AR and MAP firstly and gave priority to registering to MAP, rather than built a pointer chain immediately by registering to AR. The simulation results have shown that OPF-HMIPv6 can decrease the registration cost by 39% compared to HMIPv6 when the distance between AR and MAP is greater than the distance between ARs, which proves that the optimization reduces the overhead caused by the binding update and improves the efficiency of the intra-MAP handover.

Because of its unique advantage of information propagation, the online social network has been a popular social communication platform. In view of the characteristics of the form of information propagation and the dynamics theory of infectious diseases, this paper put forward the model of information propagation through online social network. The model considered the influence of different users' behaviors on the transmission mechanism, set up the evolution equations of different user nodes, simulated the process of information propagation, and analyzed the behavior characteristics of the different types of users and main factors that influenced the information propagation. The experimental results show that different types of users have special behavior rules in the process of information propagation, i.e., information cannot be transported endlessly, and be reached at a stationary state, and the larger the spread coefficient or immune coefficient is, the faster it reached the stationary state.

A reliability evaluation method was proposed to evaluate the reliability of Service-Oriented Architecture (SOA) before its realization. OWL-S (Ontology Web Language for Services), of which the formal semantics of the control structure was defined by Maude, was used to descript the information of software requirements and design. The operational profile of software was built up by distribution function. After this, how the information of operational profile and the architecture of software took part in reliability calculation was added in Maude. At last, the reliability of software could be achieved through rewriting with the supporting of Maude system. In addition, the Software Reliability Predict Tool (SRPT) was developed based on this method. The data flow, control flow, components as well as the operational profile and the architecture of software were considered in the impact on software reliability. According to the design of software, it can estimate the reliability before the accomplishment of software.

Blog is an important kind of network information resources, and the extraction of its comments is the basic work of public opinion analysis researches and of such work. The current mainstream blog comments extraction algorithms were summarized, and the application of page structure in information extraction was described. Using the characteristics of indicating phrases such as the "Home" when people understand Web pages, technology of extracting comments information was proposed by utilizing functional semantic units that they have clear semantics and functional indication. Many technologies involved in the extraction process were detailed such as page structure linearization, functional semantic units recognition, text distinguishment and comments extraction algorithm. Finally, the experimental results show that this technology can achieve better results in extraction of blog body and comments.