Informatica

In this paper, we consider the problem of semi-supervised binary classification by Support Vector Machines (SVM). This problem is explored as an unconstrained and non-smooth optimization task when part of the available data is unlabelled. We apply non-smooth optimization techniques to classification where the objective function considered is non-convex and non-differentiable and so difficult to minimize. We explore and compare the properties of Simulated Annealing and of Simultaneous Perturbation Stochastic Approximation (SPSA) algorithms (SPSA with the Lipschitz Perturbation Operator, SPSA with the Uniform Perturbation Operator, Standard Finite Difference Approximation) for semi-supervised SVM classification. Numerical results are given, obtained by running the proposed methods on several standard test problems drawn from the binary classification literature. The performance of the classifiers were evaluated by analyzing Receiver Operating Characteristics (ROC).

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 new digital watermarking method based on vector quantization (VQ) is proposed. In contrast with conventional VQ‐based watermarking schemes, the mean of sub‐blocks is used to train the VQ codebook. In addition, the Anti‐Gray Coding (AGC) technique is employed to enhance the robustness of the proposed watermarking scheme. In this scheme, the secret keys are used to hide the associated information between the original image and the watermark. Then the set of secret keys will be registered to a trusted third party for future verification. Thus, the original image remains unchanged after the watermark is melted into the set of secret keys. Experimental results show that the watermark can survive various possible attacks. Besides that, the size of the secret keys can be reduced.