刑侦取证组证论

侦查破案与司法控罪都要以取证用证为基础。刑侦取证与心理现场有密切的关系 ,它们需要研究证据事实与证据材料的关系 ,研究涉嫌证据的特点与诉讼证据的组织。刑侦取证的目的是组织证据体系 ,终结侦查。这也需要研究证据体系的类型、组证原则和方法等相关问题。

刑侦取证组证论

侦查破案与司法控罪都要以取证用证为基础。刑侦取证与心理现场有密切的关系,它们需要研究证据事实与证据材料的关系,研究涉嫌证据的特点与诉讼证据的组织。刑侦取证的目的是组织证据体系,终结侦查,这也需要研究证据体系的类型、组证原则和方法等相关问题。

Bayesian network model for traffic flow estimation using prior link flows

In order to estimate traffic flow a Bayesian network BN model using prior link flows is proposed.This model sets link flows as parents of the origin-destination OD flows. Under normal distribution assumptions the model considers the level of total traffic flow the variability of link flows and the violation of the conservation law.Using prior link flows the prior distribution of all the variables is determined. By updating some observed link flows the posterior distribution is given.The variances of the posterior distribution normally decrease with the progressive update of the link flows. Based on the posterior distribution point estimations and the corresponding probability intervals are provided. To remove inconsistencies in OD matrices estimation and traffic assignment a combined BN and stochastic user equilibrium model is proposed in which the equilibrium solution is obtained through iterations.Results of the numerical example demonstrate the efficiency of the proposed BN model and the combined method.

Verified Solution for a Statically Determinate Truss Structure with Uncertain Node Locations

We consider a statically determinate structural truss problem where all of the physical model parameters are uncertain： not just the material values and applied loads, but also the positions of the nodes are assumed to be inexact but bounded and are represented by intervals. Such uncertainty may typically arise from imprecision during the process of manufacturing or construction, or round-off errors. In this case the application of the finite element method results in a system of linear equations with numerous interval parameters which cannot be solved conventionally. Applying a suitable variable substitution, an iteration method for the solution of a parametric system of linear equations is firstly employed to obtain initial bounds on the node displacements. Thereafter, an interval tightening （pruning） technique is applied, firstly on the element forces and secondly on the node displacements, in order to obtain tight guaranteed enclosures for the interval solutions for the forces and displacements.

Membership Proof and Verification in Authenticated Skip Lists Based on Heap

How to keep cloud data intact and available to users is a problem to be solved. Authenticated skip list is an important data structure used in cloud data integrity verification. How to get the membership proof of the element in authenticated skip list efficiently is an important part of authentication. Kaouthar Blibech and Alban Gabillon proposed a head proof and a tail proof algorithms for the membership proof of elements in the authenticated skip list. However, the proposed algorithms are uncorrelated each other and need plateau function. We propose a new algorithm for computing the membership proof for elements in the authenticated skip list by using two stacks, one is for storing traversal chain of leaf node, the other is for storing authentication path for the leaf. The proposed algorithm is simple and effective without needing plateau function. It can also be applicable for other similar binary hash trees.

CLASSIFIER FUSION BASED ON EVIDENCE THEORY AND ITS APPLICATION IN FACE RECOGNITION

A multiple classifier fusion approach based on evidence combination is proposed in this paper. The individual classifier is designed based on a refined Nearest Feature Line (NFL),which is called Center-based Nearest Neighbor (CNN). CNN retains the advantages of NFL while it has relatively low computational cost. Different member classifiers are trained based on different feature spaces respectively. Corresponding mass functions can be generated based on proposed mass function determination approach. The classification decision can be made based on the combined evidence and better classification performance can be expected. Experimental results on face recognition provided verify that the new approach is rational and effective.

A Location Privacy Preserving Solution to Resist Passive and Active Attacks in VANET

Existing location privacy- preserving methods, without a trusted third party, cannot resist conspiracy attacks and active attacks. This paper proposes a novel solution for location based service （LBS） in vehicular ad hoc network （VANET）. Firstly, the relationship among anonymity degree, expected company area and vehicle density is discussed. Then, a companion set F is set up by k neighbor vehicles. Based on secure multi-party computation, each vehicle in V can compute the centroid, not revealing its location to each other. The centroid as a cloaking location is sent to LBS provider （P） and P returns a point of interest （POI）. Due to a distributed secret sharing structure, P cannot obtain the positions of non-complicity vehicles by colluding with multiple internal vehicles. To detect fake data from dishonest vehicles, zero knowledge proof is adopted. Comparing with other related methods, our solution can resist passive and active attacks from internal and external nodes. It provides strong privacy protection for LBS in VANET.