基于节点评估与最大类间方差的孤立森林异常值检测

针对孤立森林(isolation forest, iForest)无法有效检测局部异常值且异常值分数阈值难以精确设定的问题,提出一种基于节点评估(node evaluation, NE)与最大类间方差(Otsu)的iForest异常值检测方法。首先,在样本评估过程中将节点深度与相对质量同时引入评分机制,使算法对全局和局部异常值敏感;然后,为了准确设定分数阈值,采用Otsu自适应设定异常值分数阈值;最后,在不同数据集上验证所提方法的有效性。实验结果表明,该方法可以有效兼顾全局和局部异常值的检测,提高iForest检测异常值的准确性。

Terrain Rendering LOD Algorithm Based on Improved Restrictive Quadtree Segmentation and Variation Coefficient of Elevation

Aiming to deal with the difficult issues of terrain data model simplification and crack disposal,the paper proposed an improved level of detail(LOD)terrain rendering algorithm,in which a variation coefficient of elevation is introduced to express the undulation of topography.Then the coefficient is used to construct a node evaluation function in the terrain data model simplification step.Furthermore,an edge reduction strategy is combined with the improved restrictive quadtree segmentation to handle the crack problem.The experiment results demonstrated that the proposed method can reduce the amount of rendering triangles and enhance the rendering speed on the premise of ensuring the rendering effect compared with a traditional LOD algorithm.

TBDDoSA-MD:Trust-Based DDoS Misbehave Detection Approach in Software-defined Vehicular Network(SDVN)

Reliable vehicles are essential in vehicular networks for effective communication.Since vehicles in the network are dynamic,even a short span of misbehavior by a vehicle can disrupt the whole network which may lead to catastrophic consequences.In this paper,a Trust-Based Distributed DoS Misbehave Detection Approach(TBDDoSA-MD)is proposed to secure the Software-Defined Vehicular Network(SDVN).A malicious vehicle in this network performs DDoS misbehavior by attacking other vehicles in its neighborhood.It uses the jamming technique by sending unnecessary signals in the network,as a result,the network performance degrades.Attacked vehicles in that network will no longer meet the service requests from other vehicles.Therefore,in this paper,we proposed an approach to detect the DDoS misbehavior by using the trust values of the vehicles.Trust values are calculated based on direct trust and recommendations(indirect trust).These trust values help to decide whether a vehicle is legitimate or malicious.We simply discard the messages from malicious vehicles whereas the authenticity of the messages from legitimate vehicles is checked further before taking any action based on those messages.The performance of TBDDoSA-MD is evaluated in the Veins hybrid simulator,which uses OMNeT++and Simulation of Urban Mobility(SUMO).We compared the performance of TBDDoSA-MD with the recently proposed Trust-Based Framework(TBF)scheme using the following performance parameters such as detection accuracy,packet delivery ratio,detection time,and energy consumption.Simulation results show that the proposed work has a high detection accuracy of more than 90%while keeping the detection time as low as 30 s.

Rational Evaluation of Different Lymph Node Dissection.

To evaluate the effect and indication of diffferent lymph node dissection（D.D.D）,we performed radical operations for 371 cases of gastric cancer.The results showed that Dof Dlymph node dissection was enough for gastric cancer of Ia stage,

Signed PageRank on Online Rating Systems

The ratings in many user-object online rating systems can reflect whether users like or dislike the objects,and in some online rating systems,users can directly choose whether to like an object.So these systems can be represented by signed bipartite networks,but the original unsigned node evaluation algorithm cannot be directly used on the signed networks.This paper proposes the Signed Page Rank algorithm for signed bipartite networks to evaluate the object and user nodes at the same time.Based on the global information,the nodes can be sorted by the Signed Page Rank values in descending order,and the result is SR Ranking.The authors analyze the characteristics of top and bottom nodes of the real networks and find out that for objects,the SR Ranking can provide a more reasonable ranking which combines the degree and rating of node,and the algorithm also can help us to identify users with specific rating patterns.By discussing the location of negative edges and the sensitivity of object SR Ranking to negative edges,the authors also explore that the negative edges play an important role in the algorithm and explain that why the bad reviews are more important in real networks.