This paper describes an improved local search method for synthesizing arbitrary Multiple-Valued Logic (MVL) function. In our approach, the MVL function is mapped from its algebraic presentation (sum-of-products form) on a multiple-layered network based on the functional completeness property. The output of the network is evaluated based on two metrics of correctness and optimality. A local search embedded with chaotic dynamics is utilized to train the network in order to minimize the MVL functions. With the characteristics of pseudo-randomness, ergodicity and irregularity, both the search sequence and solution neighbourhood generated by chaotic variables enables the system to avoid local minimum settling and improves the solution quality. Simulation results based on 2-variable 4-valued MVL functions and some other large instances also show that the improved local search learning algorithm outperforms the traditional methods in terms of the correctness and the average number of product terms required to realize a given MVL function.

Software watermarking is a digital technique used to protect software by embedding some secret information as identification in order to discourage software piracy and unauthorized modification. Watermarking is still a relatively new field and has good potential in protecting software from privacy threats. However, there appears to be a security vulnerability in the watermark trigger behaviour, and has been frequently attacked. By tracing the watermark trigger behaviour, attackers can easily intrude into the software and locate and expose the watermark for modification. In order to address this problem, we propose an algorithm that obscures the watermark trigger behaviour by utilizing buffer overflow. The code of the watermark trigger behaviour is removed from the software product itself, making it more difficult for attackers to trace the software. Experiments show that the new algorithm has promising performance in terms of the imperceptibility of software watermark.