Informatica

Abstract data types constitute a central tool in computer science and play an important role in problem solving, knowledge representation, and programming. In this paper, formal and practical aspects of utilizing abstract data types (ADTs) are discussed in the context of logic programming when using the Prolog programming language. The approach is presented in the following stages: (a) First, alternative ways of implementing ADTs in terms of Prolog constructs are presented and partial encapsulation of ADTs in terms of grey boxes is demonstrated. (b) Next, complete encapsulation of ADTs in terms of black boxes is suggested in a way that strictly reflects the concept's formal computer science definition while taking into consideration the characteristics and constraints of the logic programming paradigm. (c) Finally, implications for instruction are discussed.

In this paper, a reliable information hiding scheme based on support vector machine and error correcting codes is proposed. To extract the hidden information bits from a possibly tampered watermarked image with a lower error probability, information hiding is modeled as a digital communication problem, and both the good generalization ability of support vector machine and the error correction code BCH are applied. Due to the good learning ability of support vector machine, it can learn the relationship between the hidden information and corresponding watermarked image; when the watermarked image is attacked by some intentional or unintentional attacks, the trained support vector machine can recover the right hidden information bits. The reliability of the proposed scheme has been tested under different attacks. The experimental results show that the embedded information bits are perceptually transparent and can successfully resist common image processing, jitter attack, and geometrical distortions. When the host image is heavily distorted, the hidden information can also be extracted recognizably, while most of existing methods are defeated. We expect this approach provide an alternative way for reliable information hiding by applying machine learning technologies.