Fundamenta Informaticae

This paper presents a cancellation theorem for the preorders in van Glabbeek's linear time-branching time spectrum over BCCSP. Apart from having some intrinsic interest, the proven cancellation result plays a crucial role in the study of the cover equations, in the sense of Fokkink and Nain, that characterize the studied semantics. The techniques used in the proof of the cancellation theorem may also have some independent interest.

Embedding large amounts of secret data into a cover image with low distortion is an important research issue in steganography. The proposed scheme uses two clustering technologies - similar and dissimilar clustering - to enhance hiding capacity as provided by Du and Hsu's scheme. In general, the hiding capacity of our proposed scheme is higher than that of Du and Hsu's scheme, about 16 Kb for the same image quality. In addition, the image quality of our proposed scheme is more than provided by the schemes of either Du and Hsu's or Shie et al., about 2 dB. Moreover, the distortion for each test image in our proposed scheme is always less than those caused by Du and Hsu's and Shie et al.'s schemes. Experimental results confirm that the proposed scheme is significantly better than earlier works.

This paper proposes a large payload data embedding method for color images. Under the fast advancement of digital equipments, color images have become a common medium in many applications. A color image can be represented in different color systems, for instance, RGB and YUV color systems. The proposed method modifies the blue value of the color pixel in order to imply the secret data because the blue value is an insensitive color to human eyes. Furthermore, the number of secret bits that can be embedded into a cover pixel is dynamic so the proposed method tries to embed the secret data as many as possible. The proposed method can be applied to both RGB and YUV color systems. However, for anti-stegoanalysis reason, the RGB color system is suitable for the proposed high capacity data embedding method with lower image distortion. The experimental results show that the proposed method is superior to other methods we compared in terms of embedding capacity and visual quality.

We adapt the alias type technology to dealwith primitives supporting environment-awareness (that is, the ability to adapt the behavior of an object according to the capabilities of the environment). In particular, we propose a type and effect system for an imperative object-based calculus with a primitive for discriminating the presence or absence of the object's attributes. Both the shape of the environment which guarantees the correct execution of expressions and the effect of expression evaluation on the environment are specified via suitable aliasing constraints.

The abstraction inherent in most specifications and the need to specify nondeterministic programs are two well-known sources of nondeterminism in formal specifications. In this paper, we present a Z-based formalism by which one can specify bounded, unbounded, erratic, angelic, demonic, loose, strict, singular, and plural nondeterminism. To interpret our specifications, we use a constructive set theory, called CZ set theory, instead of the classical set theory Z. We have chosen CZ since it allows us to investigate the notion of nondeterminism from the formal program development point of view. In this way, we formally construct functional programs from Z specifications and then probe the effects of the initially specified nondeterminism on final programs. Our investigation shows that without specifying nondeterminism explicitly, the effects of the nondeterminism involved in initial specifications will not be preserved in final programs. We prove that using the new formalism, proposed by this paper, for writing nondeterministic specifications leads to programs that preserve the initially specified modalities of nondeterminism.

In recent years, classification learning for data streams has become an important and active research topic. A major challenge posed by data streams is that their underlying concepts can change over time, which requires current classifiers to be revised accordingly and timely. To detect concept change, a common method is to observe the online classification accuracy. If accuracy drops below some threshold value, a concept change is deemed to have taken place. An implicit assumption behind this methodology is that any drop in accuracy can be interpreted as a symptom of concept change. Unfortunately however, this assumption is often violated in the real world where data streams carry noise and missing values that can also introduce a significant reduction in classification accuracy. To compound this problem, traditional noise cleansing methods are not applicable to data streams. These methods normally need to scan data multiple times whereas learning in data streams can only afford one-pass scan because of data's high speed and huge volume. To solve these problems, this paper proposes a novel classification algorithm, Class Specific Fuzzy Decision Trees (CSFDT), which utilizes fuzzy logic to classify data streams. The base classifier of CSFDT is a binary fuzzy decision tree. Whenever the problem of concern contains q classes (q > 2), CSFDT learns one binary classifier for each class to distinguish instances of this class from instances of the remaining (q − 1) classes. The CSFDT's advantages are three folds. First, it offers an adaptive structure to effectively and efficiently handle concept change. Second, it is robust to noise. Third, it deals with missing values in an elegant way. As a result, accuracy drop can be safely attributed to concept change. Extensive evaluations are conducted to compare CSFDT with representative existing data stream classification algorithms on a large variety of data. Experimental results suggest that CSFDT provides a significant benefit to data stream classification in real-world scenarios where concept changes, noise and missing values coexist.

A robust thresholding technique is proposed in this paper for segmentation of brain MR images. It is based on the fuzzy thresholding techniques. Its aim is to threshold the gray level histogram of brain MR images by splitting the image histogram into multiple crisp subsets. The histogram of the given image is thresholded according to the similarity between gray levels. The similarity is assessed through a second order fuzzy measure such as fuzzy correlation, fuzzy entropy, and index of fuzziness. To calculate the second order fuzzy measure, a weighted co-occurrence matrix is presented, which extracts the local information more accurately. Two quantitative indices are introduced to determine the multiple thresholds of the given histogram. The effectiveness of the proposed algorithm, along with a comparisonwith standard thresholding techniques, is demonstrated on a set of brain MR images.

In this paper, geometric invariant texture analysis, which comprises of rotation, scale and translation (RST) invariance, is presented. Many types of moments and functions of moments have been utilized in RST invariant pattern recognition applications. However, use of moments for texture content-based image analysis is limited. Here, application of Zernike moment for geometric invariant texture analysis is studied. An algorithm is proposed to design fast and modified Zernike moments to extract a particular texture from image irrespective of its rotation scale change and translation. The algorithm is tested for its robustness in the presences of various noises like salt and paper, Gaussian and speckle noise. The operator is evaluated for computation of moments on different data sets of textures like Brodatz and Vistex. Comparison is done with traditional Zernike moments based techniques, Fast Fourier Transform (FFT)-Zernike moments based technique and proposed modified Zernike moment based technique.

In ASIACCS 2007, Liu et al proposed a certificateless signature scheme which is provably secure in the standard model. However, as we will show in this paper, the proposed scheme is insecure against a malicious-but-passive KGC attack. This implies that the malicious-but-passive KGC, which generates system parameters based on the information of the target user, can forge valid signatures for that signer without being detected. Furthermore, we propose an improved scheme that remedies the weakness of Liu et al's scheme. The improved scheme can be proven secure against malicious-but-passive KGC attack in the standard model.

This paper establishes an explicit embedding functor from the category of the proximity spaces into the category of nearness spaces. This is done by defining methods to constructively inducing a proximity structure with a given nearness structure and vis versa. Those induction methods provide useful tools to understand the inter-relations and to translate the result between the two types of spaces.