The high-level paper is one of the symbolic achievements of excellent scientific talents. Focusing on the "Web of Science (WOS)" hot research disciplines, on the basis of constructing the Neo4j semantic network graph of academic papers and mining active scientific research communities, the PageRank-based talent mining algorithm was used to realize the mining of outstanding scientific research talents in the scientific research communities. Firstly, the existing talent mining algorithms were studied and analyzed in detail. Secondly, combined with the WOS data, the PageRank-based talent mining algorithm was optimized and implemented by adding consideration factors such as the paper publication time factor, the author's order descending model, the influence of surrounding author nodes on this node, the number of citations of the paper. Finally, experiments and verifications were carried out based on the paper data of the communities of the hot discipline computer science in the past five years. The results show that community-based mining is more targeted, and can quickly find representative excellent and potential talents in various disciplines, and the improved algorithm is more effective and objective.

Aiming at the problem that System Wide Information Management (SWIM) system is affected by Distributed Denial of Service (DDoS) attacks in the application layer, a detection approach of SWIM application layer DDoS attack based on Hidden Semi-Markov Model (HSMM) was proposed. Firstly, an improved forward-backward algorithm was adopted, and HSMM was used to establish dynamic anomaly detection model to dynamically track the browsing behaviors of normal SWIM users. Then, normal detection interval was obtained by learning and predicting normal SWIM user behaviors. Finally, access packet size and request time interval were extracted as features for modeling, and the model was trained to realize anomaly detection. The experimental results show that the detection rate of the proposed approach is 99.95% and 91.89% in the case of attack 1 and attack 2 respectively. Compared with the HSMM constructed by fast forward-backward algorithm, the detection rate is improved by 0.9%. It can be seen that the proposed approach can effectively detect the application layer DDoS attacks of SWIM system.

Since the vehicles provided by current online car rental platforms are in the fixed price, there are some issues coming up such as unreasonable allocation of the vehicle resources, unreliable price that could not indicates the real market supply and demand timely, and generally low social welfare. Therefore, an auction based vehicle allocation and pricing mechanism for car rental was proposed. Firstly, a mathematical model and a social welfare maximization objective function were established by studying the model of online car rental issues. Secondly, based on the minimum cost and maximum flow algorithm, the optimal vehicle resource allocation algorithm was adopted among the rental vehicle allocation algorithms. Finally, in terms of the price calculation algorithms, a truthful VCG (Vickrey-Clarke-Groves) price algorithm was used to calculate the final price. As a result, compared with the traditional first-come-first-serving algorithms, the order success rate of the proposed scheme was increased by 20% to 30%, and the revenue was increased by about 30%. Theoretical analysis and experiment results show that the proposed mechanism has the advantages of optimizing vehicle allocation and flexible price strategy.

Concerning the loss of weak edges and texture details of the second-order Partial Differential Equation (PDE) amplification algorithm, an image enlargement algorithm was proposed based on improved complex diffusion adaptively coupled nonlocal transform domain model. By utilizing the advantage of accurate edge location of the complex diffusion model, the improved complex diffusion coupled impulse filter to enhance strong edges better; by modeling the sparse characteristics of the transform coefficients coming from three dimensional transformation of the image group composed of similar image blocks, the nonlocal transform domain model could make good use of the nonlocal information of the similar image blocks and had better processing effects on weak edges and texture details. Finally, the second-order derivation of the image obtained by the complex diffusion was used as the parameter to realize the adaptive coupling of the improved complex diffusion model and the nonlocal transform domain model. Compared with partial differential equation amplification algorithm, nonlocal transformation domain amplification algorithm and partial differential equation coupled space domain nonlocal model amplification algorithm, the proposed algorithm has better amplification effect on strong edges, weak edges and detail textures, the mean structural similarity measures of weak edges and texture detail images are higher than those of improved complex diffusion magnification algorithm and the nonlocal transform domain amplification algorithm. The proposed algorithm also confirms the validity of the coupling between the space domain model and the transform domain model, local model and nonlocal model.

A large-scale Discount {0-1} Knapsack Problem (D{0-1} KP) is difficult to solve with the deterministic algorithms, thus a differential crow search algorithm based on Lévy flight named LDECSA was proposed. Firstly, the coding problem about the second mathematical model of D{0-1} KP was solved by using mixed coding. Secondly, a New greedy Repair and Optimization Algorithm (NROA) was used to deal with the infeasible solution. Thirdly, in order to avoid the problems of local optimum and slow convergence, Lévy flight and differential strategy were introduced. Finally, the reasonable value of awareness probability and flight length were determined through experiments, the differential strategy was also chosen. The experimental results on four types of large-scale D{0-1} KP show that LDECSA is very suitable for solving large-scale D{0-1} KP with very satisfactory approximate solution.

In Discount {0-1} Knapsack Problem (D{0-1}KP), the weight coefficients and the value coefficients in a large range, are difficult to solve by deterministic algorithms. To solve this problem, a Chaotic Crow Search Algorithm based on Differential Evolution strategy (DECCSA) was proposed. Firstly, the initial crow population was generated by chaotic mapping. Secondly, mixed coding and Greedy Repair and Optimization Strategy (GROS) were used to solve the coding problem of D{0-1}KP. Finally, Difference Evolution (DE) strategy was introduced to improve the convergence rate of the algorithm. The experimental results on four large-scale D{0-1}KP instances show that DECCSA is better than Genetic Algorithm (GA), bacterial foraging optimization algorithm, and mutated bat algorithm, and it can get the optimal solution or approximate optimal solution. It's very suitable for solving D{0-1}KP.

Aiming at these problems of the current data service of Automatic Weather Stations (AWS), including data processing delay, slow interactive response, and low statistical efficiency, a new method based on Spark Streaming and HBase technologies was proposed and introduced to process massive streaming AWS data by integrating stream computing framework and distributed database system. Flume was used for data collection, and data processing was conducted by Spark Streaming and data were stored into HBase. In framework of Spark, two algorithms, one for writing streaming AWS data into HBase database, the other for realizing real-time statistical calculation of different observed AWS meteorological elements were designed. Finally, a stable and high-efficient system for real-time acquisition, processing, and statistics of AWS data was developed on Cloudera platform. Based on comparative analysis and running monitoring, performances of the system were confirmed, including low latency, high I/O efficiency, stable running status and excellent load balance. The experimental results show that the response time of Spark Streaming-based real-time operational processing for AWS data can reach to millisecond level, which includes paralleled data writing into HBase, HBase-based data query and statistics on different meteorological elements. The system can fully meet needs of operational applications to AWS data, and provides effective support to weather forecast.

Focusing on the issue that current real-time map matching algorithms are difficult to keep high efficiency and high accuracy simultaneously, an improved dynamic weighted real-time map matching algorithm was proposed. Firstly, considering the temporal, speed, heading and direction constraints of Global Positioning System (GPS) points and the topological structures of road network, a weighted model was constructed in the algorithm based on spatio-temporal analysis, which consisted of proximity weight, heading weight, direction weight and connectivity weight. Then according to the properties of GPS points, a dynamic weighted coefficient model was created. Lastly, the best matching road segment was selected according to the confidence level of current GPS point. The experiments were conducted on three city bus trajectories with length of 36 km in Chongqing. The average matching accuracy of the algorithm was 97.31% and the average matching delay of each GPS point was 17.9 ms. The experimental results show that compared with the contrast algorithms, the proposed algorithm has higher accuracy and efficiency, and has better performance in matching Y-junctions and parallel roads.

To deal with the problem of stance detection of Chinese social network reviews which lack theme or emotion features, a method of stance detection based on entity-emotion evolution Bayesian belief net was proposed in this paper. Firstly, three types of domain dependent entities, including noun, verb-object phrase and verb-noun compound attributive centered structure were extracted. The domain-related emotion features were extracted, and the variable correlation strength was used as a constraint on the learning of the network structure. Then the 2-dependence Bayesian network classifier was constructed to describe the dependence of entity, stance and emotion features. The stance of reviews was deducted from combination condition of entities and emotion features. Experiments were tested on Natural Language Processing & Chinese Computing 2016 (NLP&CC2016). The experimental results show that the average micro-F reaches 70.8%, and average precision of FAVOR and AGAINST increases by 4.1 percentage points and 3.1 percentage points over Bayesian network classification method with emotion features only respectively. The average micro-F on 5 target data sets of evaluation reaches 62.3%, which exceeds average level of the evaluation.

Since the deterministic algorithms are difficult to solve the Discounted {0-1} Knapsack Problem (D{0-1}KP) with large-scale and wide data range, a Mutated Double codes Binary Bat Algorithm (MDBBA) was proposed. Firstly, the coding problem of D{0-1} KP was solved by double coding. Secondly, the Greedy Repair and Optimization Algorithm (GROA) was applied to the individual fitness calculation of bats, and the algorithm was quickly and effectively solved. Then, the mutation strategy in Differential Evolution (DE) was selected to improve the global optimization ability. Finally, Lévy flight was carried out by the bat individual according to certain probability to enhance the ability of the algorithm to explore and jump out of local extrema. Simulation was tested on four large-scale instances. The result shows that MDBBA is very suitable for solving large-scale D {0-1} KP, which has better optimal value and mean value than FirEGA (First Genetic Algorithm) algorithm and Double Binary Bat Algorithm (DBBA), and MDBBA converges significantly faster than DBBA.

Focusing on the problem that PrePost algorithm needs to build complex Pre-order and Post-order Code tree (PPC-tree) and Node list (N-list), an improved frequent itemset mining algorithm based on the Interval list (I-list) was proposed. Firstly, data storage structure with more compression compared to Frequent Pattern tree (FP-tree), called Interval FP-tree (IFP-tree), was adopted, which mined frequent itemsets without iteratively establishing conditional tree. Secondly, the more concise method called I-list was used to replace the complex N-list in PrePost so as to improve mining speed. Finally, in the case of single branch path, some frequent itemsets were directly obtained by the method of combination. The experimental results prove the correctness of the proposed algorithm by getting the same results for the same dataset under same minimum supports, the proposed algorithm is superior to PrePost algorithm by about 10 percent in terms of time and space which has a good application in sparse database or intensive database.

To solve the problems in configuration network including compatibility of lower computer, data sharing and simplicity of presentation at client, a data acquisition model with king view supported by middleware was proposed. Based on configuration software with strong network connection and second development characteristic, taking general king view software as an example, the model was deeply analyzed with the theory of configuration software, the communication middleware was combined with the communication protocol, the device information and variable information of king view were defined, the display interface of king view was drawn, and the availability for the model was verified at last. The verification results show that, compared to the traditional configuration network model, the proposed model promotes the expandability and compatibility of configuration network data acquisition model, and it can be used for data sharing and variety show at client, and further accelerates the fusion of configuration and Internet of Things (IoT) technology.

To the shortage of theoretical support in the policy-making process of traffic guidance management, the research method of choice behavior with confinement mechanism of traffic information was proposed. From the perspective of human perception, the deep analysis of Multi-Source Traffic Information (MSTI) constraint rule was presented based on fuzzy clustering algorithm, then the road network environment was simulated by VISSIM and the traffic state pattern recognition model was established to simulate the mental activity of traveler under restriction of information. Then by means of Biogeme software, the choice model was constructed based on the behavior survey data, which was obtained in the road network example by using Stated Preference (SP) investigate method. Results show that the sanction of traffic information on travel behavior is very limited and the travelers prefer the preference path when traffic of this preference path is not very heavy, while this sanction enhances gradually and the path change behavior, which is influenced by the information, becomes more frequent when the preference path is more congested. The conclusions provided a new idea and reference for incomplete rational behavior research under the information environment, and also provided decision support for traffic management department.

Concerning the problem that pedestrians would be partially or seriously shaded by each other in video monitoring, this paper proposed a people counting algorithm based on human body skeleton feature. At first, the initial human skeleton was extracted by morphological skeleton extraction algorithm. Then the optimal skeleton feature was obtained by eliminating outliers and pseudo branches. Finally, this paper established a head detection response rule through analyzing the characteristics of skeleton in head areas to detect the head of pedestrian, and completed people counting by counting the heads of pedestrians. The experimental results show that the algorithm can solve the problems of partial and serious shading in video monitoring. For relatively sparse scene, the overall people counting accuracy rate of the algorithm is about 95%.

In view of the correction error problem which is caused by some factors such as dithering, the authors presented a new optical tilt correction method based on grating projection. The method was based on the analysis of each pixel of the data array in a sequence of fringe patterns having multiple frequencies, and setup model for pixel coordinates and pixel-slope. Then skew angles of fringes were calculated by trigonometry with the relationship between tilt angle and pixel-slope. At last, tilt correction was realized. The experimental results show that, the algorithm is capable of accurately detecting angle within the range [-90°,90°],accuracy is 99%. Compared with other algorithms such as Hough transform, the proposed algorithm improves precision and accuracy significantly.

Since traditional data dedupliation methods are of low time efficiency and detection accuracy, a Stepwise Clustering Data Elimination (SCDE) method was presented based on the features of Web information integration. Firstly the whole record set was divided into sub-sets using both key attributes division and the Canopy clustering technique, and then the similar records in each sub-set were accurately eliminated. A fuzzy entity matching strategy based on dynamic weight was proposed to accurately eliminate the duplicate records, which reduced the influence of missing attribute on record similarity calculation, and the name of company was especially treated to improve the matching accuracy. The results show that the method is superior to traditional algorithms in time efficiency and detection accuracy, and the precision is improved by 12.6%. The method is applied in forestry yellow page system and performs well.

In view of the characteristics of limited energy and dynamic change in Ad Hoc network, an identity-based group key agreement scheme was presented. The topology was in a structure composed by clusters, and allowed the synchronous execution of multi-party key agreement protocols based on pairings. The number of cluster members did not affect the key agreement, and it did not require interactivity during the key agreement. It provided the authentication and dynamics. In addition, the scheme was proved semantics secure under the Decisional Bilinear Diffie-Hellman (DBDH) problem. At last, compared with the previous schemes, the proposed scheme has advantages in terms of negotiation rounds and authentication.

To solve the multi-spot inventory sharing problem in an emergency system, emergency transportation strategy was studied in a system with random fuzzy demand in this paper through a multi-product and three-echelon emergency supply system. When the stockout happened, the nearest emergency lateral transshipment principle and partial inventory sharing strategy among the spots were permitted to satisfy the demand, and the model for the total cost expectation of random fuzzy demand was developed according to it, taking account of the service time constraints and the spots' storage space limitation. An advanced computing method combining Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm, called PSO-SA algorithm, was proposed to calculate the model, and the effects on the partial transshipment with the variation of the transshipment trigger inventory level, the per-item transshipment time and the inventory storage space were analyzed through a numerical example. The availability of the proposed algorithm and the model applicability were verified at last.

Networks coding is highly susceptible to pollution attacks，but such attacks cannot be prevented by the standard technology of signature. Based on the vector hash which is secure if the discrete logarithm problem is infeasible，an efficient signature scheme for securing two-source networks coding against pollution attacks was proposed. In this scheme, each source node signed the files with its own private key, the intermediate nodes, with the merge algorithm, produced linear combinations of vectors from different files. The intermediate nodes could verify the received signature solely by the public key. The security of signature scheme relies on the hardness of the Co-Deffie-Hellman problem. Under the random oracle model, the new scheme is proved to be secure against the source nodes and intermediate nodes attacks.

Authenticated key agreement protocol allows a group of users in an open network environment to identify each other and share a security session key. This article proposed a new scheme of ID-based authenticated multi-party key agreement based on McCullagh-Barreto scheme. Key seed was introduced to update temporary public/private key pairs. The new scheme is able to realize the authentication, improve the security, resist Reveal query attack and the key compromise impersonation attack successfully, and it has many properties such as non-key control and equal contribution.

A new computation model of the evolutionary algorithm applied with grid technology was proposed.It achieved the data classification by performing co-evolution of rules and rule sets.Taking advantages of grid computing,the new model enhances the power of the traditional complex data mining techniques and improves their performance.

The approach to the freeform surface self-organizing reconstruction for the dense 3D scattered data was discussed.Based on the self-organizing feature map neural network,a rectangle mesh reconstruction approach and the training algorithm were developed.The inherent topologic relations between the scattered points on the surface were learned by the self-organizing feature map neural network.The weight vectors of the neurons on the output layer of the neural network were used to approximate the scattered data points.By this approach,not only to approximate the scattered data points and the surface which is reconstructed by this method can be as base surface for further process,but also the experiment indicates that by this approach,the reconstruction of the surface and the reduce of the dense scattered data points are combined into the same process.The computer simulation result shows that this method is effective.