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Contents of Volume 20 (2010)

1/2010 2/2010

3/2010

[1] Editorial, 261-264.
[2] Zhiyong Zhang, Shiguo Lian, Qingqi Pei, Jiexin Pu (China): Fuzzy risk assessments on security policies for digital rights management, 265-284.
In multimedia consuming, Digital Rights Management (DRM) is the important means to confirm the benefits of both digital contents/services providers and consumers. To keep the DRM system running in order, risk management should be adopted, which identifies and assesses the DRM system's security level. Now, the legitimate sharing of copyrighted digital content is still an open issue, which faces severe risks of propertied assets circumvention and copyright infringements. In this paper, we try to highlight a multi-disciplinary method for all-around examinations on risks to digital assets in the contents sharing scenario. The method is a qualitative and quantitative fuzzy risk assessment, which is used for estimating a novel concept called Risk-Controlled Utility (RCU) in DRM. Then, we emphasize an application case of the emerging trusted computing policy, and analyze the influences of different content sharing modes. Finally, we address a business model with some simulation results. Comparison with other methods shows that the fusion of qualitative and quantitative styles cannot only evaluate the RCU with uncertain risk events effectively, but also provide accurate assessment data for the security policies of DRM.
[3] Xianglian Xue, Qiang Zhang, Xiaopeng Wei, Ling Guo, Qian Wang (China): A digital image encryption algorithm based on DNA sequence and multi-chaotic maps, 285-296.
This paper presented a new image encryption algorithm. The algorithm includes two steps: first, by using Cubic map and wavelet function to produce the 2D chaotic sequences to scramble the location of pixel points from the image, then using DNA sequence and chaotic sequence produced by Logistic chaotic map to disturb the gray of the pixel points from image. The experimental results and security analysis show that our algorithm can get good encryption effect, has widest secret key's space, strong sensitivity to secret key, and has the ability of resisting exhaustive attack and statistic attack.
[4] Sajedi H., Jamzad M. (Iran): Evolutionary rule generation for signature-based cover selection steganography, 297-316.
A novel approach for selecting proper cover images in steganography is presented in this paper. The proposed approach consists of two stages. The first stage is an evolutionary algorithm that extracts the signature of cover images against stego images in the form of fuzzy if-then rules. This algorithm is based on an iterative rule learning approach to construct an accurate fuzzy rule base. The rule base is generated in an incremental way by optimizing one fuzzy rule at a time using an evolutionary algorithm. In the second stage of the proposed approach, the fuzzy rules generated in the first stage are used for selecting suitable cover images for steganography. We applied our approach to some state-of-the-art steganography techniques and validated it using an image database. The results indicate that a secret message can be securely embedded in selected cover images. Therefore, we can apply the proposed evolutionary fuzzy algorithm, as an intelligent rule generation approach, to select the appropriate cover images from an image database and use them to have more secure steganography.
[5] Wanyu Deng, Lin Chen (China): Color image watermarking using regularized extreme learning machine, 317-330.
In this paper, a real-time watermarking scheme based on Regularized Extreme Learning Machine (RELM) is proposed. Using the information provided by the reference positions, RELM can be trained at the embedding procedure and watermark is adaptively embedded into the blue channel of the original image by considering the human visual system. Due to the extreme training speed (always hundreds of times faster than BP neural network and Support Vector Machine (SVM)) and good generalized performance, the trained RELM can exactly extract the watermark from the watermarked image against image processing attacks within very short time, and this makes this method applicable in real-time environment. Extensive experimental results illustrate that our technique outperforms Kutter's and Yu's method against simple and multiple attacks.
[6] Ebrahim G. A., Younis A. A. (Egypt, USA): A two-stage VoIP spam identification framework, 331-358.
Identifying a VoIP call as SPAM based on call characteristics is an important issue that has never been studied before. Most of the studies of VoIP SPAM impose the whole burden on the callee to judge SPAM calls. In other words, the accuracy of the identification process is totally based on the callee identifying the call as SPAM, which is questionable and not reliable. In this paper, a two-stage VoIP SPAM identification framework is introduced. The first stage is a pre-call identification process, which uses a set of parameters about the call that can be collected before allowing the call to go through. The second stage is a post-call identification process that uses other parameters that can be collected during/after the call. The first stage provides a pre-call evaluation score of the call, while the second stage further tunes this score. In the proposed framework, the decision of identifying VoIP SPAM calls is based on several uncertain parameters that represent meta-data of VoIP calls. These parameters include call duration, amount of exchanged information in each direction, and calling pattern. In this study, the potential set of parameters that can be used to identify VoIP SPAM are investigated. A set of rules is used in addition to any prior evaluation of the caller to provide the pre-call score. Then, a fuzzy-logic controller is developed to identify VoIP SPAM in the second stage. An augmented ongoing tuning strategy is adopted where callee feedback, if any, is taken into account to further tune the identification process. Simulation studies are carried out to demonstrate the effectiveness of the two-stage approach in identifying VoIP SPAM based on the proposed framework.
[7] Young Jae Lee, Ajith Abraham, Dong Hwa Kim (Korea, USA): 3D object recognition using octree model and fast search algorithm, 359-369.
This paper presents a new approach to 3D object recognition by using an Octree model library (OML) I, II and fast search algorithm. The fast search algorithm is used for finding the 4 pairs of feature points to estimate the viewing direction uses on effective two level database. The method is based on matching the object contour to the reference occluded shapes of 49, 118 viewing directions. The initially bestmatched viewing direction is calibrated by searching for the 4 pairs of feature points between the input image and the image projected along the estimated viewing direction. At this point, the input shape is recognized by matching it to the projected shape. The computational complexity of the proposed method is shown to be O(n^2) in the worst case, and that of the simple combinatorial method of O(m^4,n^2), where n and m denote the number of feature points of the 3D model object and the 2D object, respectively.
[8] Muda A. K., Shamsuddin S. M., Abraham A. (Malaysia, Norway): Improvement of authorship invarianceness for individuality representation in writer identification, 371-387.
Writer Identification (WI) is one of the areas in pattern recognition that have created a center of attention for many researchers to work in. Recently, its main focus is in forensics and biometric application, e.g. writing style can be used as biometric features for authenticating individuality uniqueness. Existing works in WI concentrate on feature extraction and classification task in order to identify the handwritten authorship. However, additional steps need to be performed in order to have a better representation of input prior to the classification task. Features extracted from the feature extraction task for a writer are in various representations, which degrades the classification performance. This paper will discuss this additional process that can transform the various representations into a better representation of individual features for Individuality of Handwriting, in order to improve the performance of identification in WI.
[9] Ahmadian K., Gavrilova M. (Canada): Transiently chaotic associative network for fingerprint image analysis, 389-403.
This paper presents a new technique for fingerprint image matching in biometric security applications, based on the hybrid of Neural Network and Delaunay Triangulation methodology. The Delaunay triangulation of the minutiae set is transformed to a set of points in the discretized space using duality. This translation results in a sampling method be acquiring which the system tolerates displacement and noise of the input image. Finally, Transiently Chaotic Associative Network (TCAN) is used to learn the obtained pattern. Experimental results show a significant improvement in the False Rejection Rate over both the traditional Delaunay Triangulation based approach and direct Neural Network application.
[10] Kwang-Baek Kim, Hae-Jung Lee, Doo Heon Song, Young Woon Woo (Korea): Extracting fascia and analysis of muscles from ultrasound images with FCM-based quantization technology, 405-416.
In this paper, we propose a novel method to measure the muscle thickness from ultrasound images of lumbar region in order to diagnose low back pain effectively. Images used in this study were obtained by muscle endurance tests for analyzing low back pain and muscle contraction patterns. We measure the thickness of the third muscle layer of lumbar spine, which is the most developed one in that region, and the measuring point is the center of ultrasound image. We apply Fuzzy C-Means (FCM) based quantization in extracting fascia from ultrasound images. FCM quantizer first analyzes the distribution of intensity from images and then makes clusters of similar intensity based on the distance from the center point. Our FCM based quantizer in conjunction with end-in search stretching algorithm overcomes the disadvantage of popular ART2 based quantizer that is weak in accuracy of extracting fascia when the distribution of the intensity is diverse. Based on our experiment, the proposed method is sufficiently competitive with that of human experts in measuring thickness of lumbar region muscles, thus it could be used as an auxiliary computer aided system for medical specialists.
[11] S. R. Kannan, S. Ramathilagam, R. Pandiyarajan, Shiguo Lian, A. Sathya (Taiwan, India, China): Improved fuzzy clustering segmentation for medical images, 417-426.
The purpose of this paper is to develop some effective robust fuzzy c-means methods for segmentation of Brain Medical Images and Dynamic Contrast-Enhanced Breast Magnetic Images (DCE-BMRI). Segmentation is a difficult task and challenging problem in the brain and breast medical images for diagnosing Breast and Brain cancer related diseases before the image goes for treatment plan. This paper presents three new effective fuzzy clustering techniques: Robust KFCM (Kernel Fuzzy C-Means) with spatial information, Effective Robust FCM based Kernel function, Modified fuzzy c-means algorithm with weight Bias Estimation. In experiments, the presented methods are compared with other reported methods. Experimental results on both breast and brain MR images show that the proposed algorithms have better performance than the standard algorithms. Thus, the proposed method is capable of dealing with the intensity in-homogeneities and noised image effectively.

2/2010

[1] Tanikić D., Manić M., Devedžić G., Ćojbašić Ž. (Serbia): Modelling of the temperature in the chip-forming zone using artificial intelligence techniques, 171-187.
Heat generation in the cutting zone occurs as a result of the work done in metal cutting. In this study, in order to measure the temperature generated in the chip-forming zone, numerous experiments were carried out for different cutting regimes. During these experiments, the chip's top temperature was measured using an infrared camera. Collected data were analyzed, and temperature dependence on various cutting regimes was formulated. After that, measured data were modelled using the various techniques: response surface methodology, various types of artificial neural networks and neuro-fuzzy system. The accuracy of the proposed models is presented as well as their suitability for the considered problem. Finally, the system for the adaptive control of the cutting temperature, based on the proposed models, is presented.
[2] Panchi Li, Kaoping Song, Erlong Yang (China): Model and algorithm of neural networks with quantum gated nodes, 189-206.
On the basis of analyzing the principles of the quantum rotation gates and quantum controlled-NOT gates, an improved design for CNOT gated quantum neural networks model is proposed and a smart algorithm for it is derived in our paper, based on the gradient descent algorithm. In the improved model, the input information is expressed by the qubits, which, as the control qubits after being rotated by the rotation gate, control the qubits in the hidden layer to reverse. The qubits in the hidden layer, as the control qubits after being rotated by the rotation gate, control the qubits in the output layer to reverse. The networks output is described by the probability amplitude of state |1> in the output layer. It has been shown in two application examples of pattern recognition and function approximation that the proposed model is superior to the standard error back-propagation networks with regard to their convergence rate, number of iterations, approximation ability, and robustness.
[3] Moallem P., Ayoughi S. A. (Iran): Improving backpropagation via an efficient combination of a saturation suppression method and momentum term, 207-222.
The gradient descent backpropagation (BP) algorithm that is widely used for training MLP neural networks can retard convergence due to certain features of the error surface like the local minimum and the flat spot. Common promoting methods, such as applying momentum term and using dynamic adaptation of learning rates, can enhance the performance of BP. However, saturation state of hidden layer neurons, which is the cause of some flat spots on the error surface, persists through such accelerating methods. In this paper, we propose a grading technique to gradually level off the potential flat spots into a sloping surface in a look-ahead mode; and thereby progressively renew saturated hidden neurons. We introduce symptoms indicating saturation state of hidden nodes. In order to suppress the saturation, we added a modifying term to the error function only when saturation is detected. In normal conditions, the improvement made to the learning process is adding a momentum term to the weight correction formula. We have recorded remarkable improvements in a selection of experiments.
[4] Temel T. (Turkey): A new digital cochlea model neuro-spike representation of auditory signals and its application to classification of bat-like biosonar echoes, 223-239.
For an improved neuro-spike representation of auditory signals within cochlea models, a new digital ARMA-type low-pass filter structure is proposed. It is compared to more conventional AR-type counterpart on a classification of biosonar echoes, in which echoes from various tree species insonified with a bat-like chirp call are converted to biologically plausible feature vectors. Next, parametric and non-parametric models of the class-conditional densities are built from the echo feature vectors. The models are deployed in single-shot and sequential-decision classification algorithms. The results indicate that the proposed ARMA filter structure offers an improved single-echo classification performance, which leads to faster sequential-decision making than its AR-type counterpart.
[5] Svítek M., Moos P., Votruba Z. (Czech Republic): Towards information circuits, 241-247.
A trial of analogies utilization among electrical, mechanical and information circuits is presented. The concepts of Information Power and significant proximity of the measure of information and knowledge could enable upgrading these analogies for solving important tasks from the area of Systems Engineering. This attempt seems to be attractive, as it could help in using the well-established and proved methodologies from the classical areas of electricity or mechanics.
[6] Faber J. (Czech Republic): Biofeedback and brain activity, 249-260.
Biofeedback is a treatment technique in which people are trained to improve their physiological functions by using different signals from their own bodies, e.g. from skin, heart (ECG), muscles (EMG), brain (EEG) etc. Psychotherapeutists use it to decrease intrapsychic tension in anxious and depressive patients and epileptics or learn to relax boys who suffered from attention deficit and hyperactivity disorders. The main system for consciousness (thalamocortical reverberation circuit) generates whole brain electromagnetic frequencies permanently (1-30 Hz = EEG activity). But we choose a specific frequency band, e.g. SMR (Sensory Motor Rhythm = 13-18 Hz) and these SMR episodes are rewarded by success in a simultaneously watched TV game. SMR is then repeated still more often and brings into electrogenesis and into psyche tendency its own property, which is motor inhibition and increasing attention. This is the aim of the therapeutical learning process.

1/2010

[1] Editorial, 1-2.
[2] Guest Editorial, 2-5.
[3] Kreinovich V., Perfilieva I. (USA, Czech Republic): A broad prospective on fuzzy transforms: From gauging accuracy of quantity estimates to gauging accuracy and resolution of measuring physical fields, 7-25.
Fuzzy transform is a new type of function transforms that has been successfully used in different applications. In this paper, we provide a broad prospective on fuzzy transform. Specifically, we show that fuzzy transform naturally appears when, in addition to measurement uncertainty, we also encounter another type of localization uncertainty: that the measured value may come not only from the desired location x, but also from the nearby locations.
[4] Kupka J., Tomanová I. (Czech Republic): Some extensions of mining of linguistic associations, 27-44.
This paper is a contribution to the theoretical foundations of data mining. More precisely, we contribute to a part of data mining allowing us to search for associations among attributes that can be expressed in the form of natural language sentences. The theoretical background and also a method for mining such associations was published recently in [V. Novák et al., Mining pure linguistic associations from numerical data, Int. Journal of Approximate Reasoning 48 (2008), 4 -- 22]. We elaborated other mathematical representations of the model presented in the mentioned paper in order to extend its applicability.
[5] Bohacik J. (Slovak Republic): Discovering fuzzy rules in databases with linguistic variable elimination, 45-61.
A group of fuzzy IF-THEN rules is belonging to one of the most popular, most effective, and user-friendliest knowledge representations. For this reason, extraction of these rules is becoming a more-and-more important part of the Data Mining stage in the Knowledge Discovery in Databases Process. In this paper, a direct algorithm for extracting fuzzy IF-THEN rules on the basis of linguistic variable elimination is described. The algorithm is implemented within a designed object-oriented software library Fuzzy Rule Miner. Besides the introduced algorithm, it implements two algorithms for fuzzy rule extraction based on using fuzzy decision trees of ID3 kind. An essential precondition for comparing the implemented algorithms and for verifying the legitimacy of the introduced algorithm is performance of experiments. The goal of experiments is to take in the behavior of algorithms on testing databases from the UCI Repository of Machine Learning Databases and to make comparisons of algorithms with one another. According to the conducted experiments, the introduced algorithm achieves high accuracy levels of discovered knowledge. The paper also contains a classification of rules and a specification of the Fuzzy Rule Discovery in Databases Process.
[6] Petrík M., Sarkoci P. (Czech Republic, Slovak Republic): Zero-reconstructible triangular norms as universal approximators, 63-67.
This paper is inspired by recent results [15, 16] which have shown that a multiplicative generator of a strict triangular norm can be reconstructed from the first partial derivatives of the triangular norm on the segment {0} x [0,1]. The strict triangular norms to which this method is applicable have been called zero-reconstructible triangular norms. This paper shows that every continuous triangular norm can be approximated (with an arbitrary precision) by a zero-reconstructible one, and thus substantiates the significance of this subclass of strict triangular norms.
[7] Jágr V., Komorníková M., Mesiar R. (Slovak Republic): Conditioning stable copulas, 69-79.
Copulas stable under univariate conditioning are studied. Limit approach to construction of conditioning stable copulas is introduced and illustrated. In the class of Archimedean copulas, Clayton copulas are shown to be the only conditioning stable copulas. Conditioning stable singular copulas are also discussed and examples of non-Archimedean absolutely continuous copulas which are conditioning stable are given.
[8] Bacigál T., Juráňová M., Mesiar R. (Slovak Republic): On some new constructions of Archimedean copulas and applications to fitting problems, 81-90.
Several constructions of additive generators of binary Archimedean copulas are introduced and discussed. Extension to general Archimedean copulas is also included. Applications to the fitting of copulas to real data are given and examplified.
[9] de Andrés R., García-Lapresta J. L. (Spain): An endogenous human resources selection model based on linguistic assessments, 91-111.
This paper proposes an endogenous human resources selection process by using linguistic information from a competency management perspective. We consider different sets of appraisers taking part in the evaluation process, having a different knowledge about the candidates that are being evaluated. Then, appraisers can express their assessments in different linguistic domains according to their knowledge. The proposed method converts each linguistic label into a fuzzy set on a common domain. Candidates are ranked by using different aggregation operators in order to allow the management team to make a final decision.
[10] Fuerst K. (Austria): Applying fuzzy models in rating systems, 113-124.
In the following paper, the use of fuzzy models in qualitative rating systems is analyzed in detail. The author works in an Austrian finance institution. There are at the moment two rating systems in use. The main purpose of such a rating system is to analyze company ratios to calculate a rating score, which is a measure for the financial situation and rigidity of a company. The first one is a solely hard fact rating system based on the Quicktest by Kralicek. The second one uses self-organizing maps and neural networks to calculate a rating classification and also offers the possibility to dispose personal appraisal in the calculation process.

The following work examines the application spectrum of fuzzy logic and fuzzy models in soft-fact rating systems.

We show that the use of fuzzy models in rating systems enables visualization of additional knowledge and offers the possibility to enhance the influence of a company's soft fact rating to the overall rating.
[11] Bebčáková I., Talašová J., Pavlačka O. (Czech Republic): Fuzzification of Choquet integral and its application in multiple criteria decision making, 125-137.
A common approach in the multiple criteria decision making is to obtain the overall evaluation by aggregating the partial evaluations. For this, a member of a large family of aggregation operators is used. Many of these operators commonly employed in decision making (weighted average, ordered weighted average, minimum, maximum, ...) can be used only when criteria are independent. On the other hand, the Choquet integral, a generalization of the aforementioned operators, can be used even when some interactions between criteria occur. We present a fuzzified Choquet integral capable of dealing not only with fuzzy partial evaluations (first level fuzzification), but also with fuzzy weights (second level fuzzification). We also provide an effective way to evaluate the fully fuzzified integral, which allows its straightforward application to decision making problems with inherent uncertainty.
[12] Galar M., Bustince H., Fernandez J., Sanz J., Beliakov G. (Spain, Australia): Fuzzy entropy from weak fuzzy subsethood measures, 139-158.
In this paper, we propose a new construction method for fuzzy and weak fuzzy subsethood measures based on the aggregation of implication operators. We study the desired properties of the implication operators in order to construct these measures. We also show the relationship between fuzzy entropy and weak fuzzy subsethood measures constructed by our method.
[13] Poláková R. (Czech Republic): A variant of competitive differential evolution algorithm with exponential crossover, 159-169.
The differential evolution (DE) algorithm is a powerful population-based stochastic technique to search for global optimum in the continuous search space. Success of DE algorithm strongly depends on choosing its parameters. The competition in differential evolution was shown to be an efficient instrument to avoid time-consuming process of tuning control parameters. A new variant of competitive DE algorithm, called BEBERAN, was proposed and tested on benchmark functions at four levels of the search space dimension. The BEBERAN was compared with the most promising competitive variant, DEBR18. BEBERAN, in contrast to DEBR18, includes in addition the exponential crossover.