Optimal paths planning in dynamic transportation networks with random link travel times

A theoretical study was conducted on finding optimal paths in transportation networks where link travel times were stochastic and time-dependent(STD). The methodology of relative robust optimization was applied as measures for comparing time-varying, random path travel times for a priori optimization. In accordance with the situation in real world, a stochastic consistent condition was provided for the STD networks and under this condition, a mathematical proof was given that the STD robust optimal path problem can be simplified into a minimum problem in specific time-dependent networks. A label setting algorithm was designed and tested to find travelers' robust optimal path in a sampled STD network with computation complexity of O(n2+n·m). The validity of the robust approach and the designed algorithm were confirmed in the computational tests. Compared with conventional probability approach, the proposed approach is simple and efficient, and also has a good application prospect in navigation system.

The robustness of contextuality and the contextuality cost of empirical models

In this paper, we introduce and discuss the robustness of contextuality（Ro C） R_C（e） and the contextuality cost C（e） of an empirical model e. The following properties of them are proved.（i） An empirical model e is contextual if and only if R_C（e） ＆gt; 0;（ii） the Ro C function R_C is convex, lower semi-continuous and un-increasing under an affine mapping on the set E M of all empirical models;（iii） e is non-contextual if and only if C（e） = 0;（iv） e is contextual if and only if C（e） ＆gt; 0;（v） e is strongly contextual if and only if C（e） = 1. Also, a relationship between RC（e） and C（e） is obtained. Lastly, the Ro C of three empirical models is computed and compared. Especially, the Ro C of the PR boxes is obtained and the supremum 0.5 is found for the Ro C of all no-signaling type（2, 2, 2） empirical models.

Novel Video Steganography Algorithm Based on Secret Sharing and Error-Correcting Code for H.264/AVC

In this paper, we analyze the video steganography technique, which is used to ensure national security and the confidentiality of the information of governmental agencies and enterprises. Videos may be used to transmit secrets and conduct covert communication. As such, we present an algorithm based on a secret sharing scheme and an Error-Correcting Code（ECC）, which combines Grey Relational Analysis（GRA） with a partition mode in video compression standard H.264/AVC. First, we process secret information by secret sharing, and then use an ECC to process the obtained information. Moreover, we choose the Discrete Cosine Transform（DCT） blocks using GRA, and then use rules to hide the pretreated information in DCT coefficients of the video frames. Experimental results indicate that our algorithm has good invisibility, better robustness, good anti-steganalysis ability, and little influence on the bit rate of the video carrier. In addition, the bit error rate is low after attacks such as noise, filtering,or frame loss in the simulation environment.