Abstract: Content-Based Image Retrieval has been a major area
of research in recent years. Efficient image retrieval with high
precision would require an approach which combines usage of both
the color and texture features of the image. In this paper we propose
a method for enhancing the capabilities of texture based feature
extraction and further demonstrate the use of these enhanced texture
features in Texture-Based Color Image Retrieval.
Abstract: Color image segmentation plays an important role in
computer vision and image processing areas. In this paper, the
features of Volterra filter are utilized for color image segmentation.
The discrete Volterra filter exhibits both linear and nonlinear
characteristics. The linear part smoothes the image features in
uniform gray zones and is used for getting a gross representation of
objects of interest. The nonlinear term compensates for the blurring
due to the linear term and preserves the edges which are mainly used
to distinguish the various objects. The truncated quadratic Volterra
filters are mainly used for edge preserving along with Gaussian noise
cancellation. In our approach, the segmentation is based on K-means
clustering algorithm in HSI space. Both the hue and the intensity
components are fully utilized. For hue clustering, the special cyclic
property of the hue component is taken into consideration. The
experimental results show that the proposed technique segments the
color image while preserving significant features and removing noise
effects.
Abstract: The problem of frequent pattern discovery is defined
as the process of searching for patterns such as sets of features or items that appear in data frequently. Finding such frequent patterns
has become an important data mining task because it reveals associations, correlations, and many other interesting relationships
hidden in a database. Most of the proposed frequent pattern mining
algorithms have been implemented with imperative programming
languages. Such paradigm is inefficient when set of patterns is large
and the frequent pattern is long. We suggest a high-level declarative
style of programming apply to the problem of frequent pattern
discovery. We consider two languages: Haskell and Prolog. Our
intuitive idea is that the problem of finding frequent patterns should
be efficiently and concisely implemented via a declarative paradigm
since pattern matching is a fundamental feature supported by most
functional languages and Prolog. Our frequent pattern mining
implementation using the Haskell and Prolog languages confirms our
hypothesis about conciseness of the program. The comparative
performance studies on line-of-code, speed and memory usage of
declarative versus imperative programming have been reported in the
paper.
Abstract: This paper introduces a new method called ARPDC (Advanced Robust Parallel Distributed Compensation) for automatic control of nonlinear systems. This method improves a quality of robust control by interpolating of robust and optimal controller. The weight of each controller is determined by an original criteria function for model validity and disturbance appreciation. ARPDC method is based on nonlinear Takagi-Sugeno (T-S) fuzzy systems and Parallel Distributed Compensation (PDC) control scheme. The relaxed stability conditions of ARPDC control of nominal system have been derived. The advantages of presented method are demonstrated on the inverse pendulum benchmark problem. From comparison between three different controllers (robust, optimal and ARPDC) follows, that ARPDC control is almost optimal with the robustness close to the robust controller. The results indicate that ARPDC algorithm can be a good alternative not only for a robust control, but in some cases also to an adaptive control of nonlinear systems.
Abstract: The evolution of current modeling specifications gives rise to the problem of generating automated test cases from a variety of application tools. Past endeavours on behavioural testing of UML statecharts have not systematically leveraged the potential of existing graph theory for testing of objects. Therefore there exists a need for a simple, tool-independent, and effective method for automatic test generation. An architecture, codenamed ACUTE-J (Automated stateChart Unit Testing Engine for Java), for automating the unit test generation process is presented. A sequential approach for converting UML statechart diagrams to JUnit test classes is described, with the application of existing graph theory. Research byproducts such as a universal XML Schema and API for statechart-driven testing are also proposed. The result from a Java implementation of ACUTE-J is discussed in brief. The Chinese Postman algorithm is utilised as an illustration for a run-through of the ACUTE-J architecture.
Abstract: The COSvd Ciphers has been proposed by Filiol and others (2004). It is a strengthened version of COS stream cipher family denoted COSvd that has been adopted for at least one commercial standard. We propose a distinguish attack on this version, and prove that, it is distinguishable from a random stream. In the COSvd Cipher used one S-Box (10×8) on the final part of cipher. We focus on S-Box and use weakness this S-Box for distinguish attack. In addition, found a leak on HNLL that the sub s-boxes don-t select uniformly. We use this property for an Improve distinguish attack.
Abstract: Intelligent systems are required in order to quickly and accurately analyze enormous quantities of data in the Internet environment. In intelligent systems, information extracting processes can be divided into supervised learning and unsupervised learning. This paper investigates intelligent clustering by unsupervised learning. Intelligent clustering is the clustering system which determines the clustering model for data analysis and evaluates results by itself. This system can make a clustering model more rapidly, objectively and accurately than an analyzer. The methodology for the automatic clustering intelligent system is a multi-agent system that comprises a clustering agent and a cluster performance evaluation agent. An agent exchanges information about clusters with another agent and the system determines the optimal cluster number through this information. Experiments using data sets in the UCI Machine Repository are performed in order to prove the validity of the system.
Abstract: Most integrated inertial navigation systems (INS) and
global positioning systems (GPS) have been implemented using the
Kalman filtering technique with its drawbacks related to the need for
predefined INS error model and observability of at least four
satellites. Most recently, a method using a hybrid-adaptive network
based fuzzy inference system (ANFIS) has been proposed which is
trained during the availability of GPS signal to map the error
between the GPS and the INS. Then it will be used to predict the
error of the INS position components during GPS signal blockage.
This paper introduces a genetic optimization algorithm that is used to
update the ANFIS parameters with respect to the INS/GPS error
function used as the objective function to be minimized. The results
demonstrate the advantages of the genetically optimized ANFIS for
INS/GPS integration in comparison with conventional ANFIS
specially in the cases of satellites- outages. Coping with this problem
plays an important role in assessment of the fusion approach in land
navigation.
Abstract: Authentication plays a vital role in many secure
systems. Most of these systems require user to log in with his or her
secret password or pass phrase before entering it. This is to ensure all
the valuables information is kept confidential guaranteeing also its
integrity and availability. However, to achieve this goal, users are
required to memorize high entropy passwords or pass phrases.
Unfortunately, this sometimes causes difficulty for user to remember
meaningless strings of data. This paper presents a new scheme which
assigns a weight to each personal question given to the user in
revealing the encrypted secrets or password. Concentration of this
scheme is to offer fault tolerance to users by allowing them to forget
the specific password to a subset of questions and still recover the
secret and achieve successful authentication. Comparison on level of
security for weight-based and weightless secret recovery scheme is
also discussed. The paper concludes with the few areas that requires
more investigation in this research.
Abstract: Lacking an inherent “natural" dissimilarity measure
between objects in categorical dataset presents special difficulties in
clustering analysis. However, each categorical attributes from a given
dataset provides natural probability and information in the sense of
Shannon. In this paper, we proposed a novel method which
heuristically converts categorical attributes to numerical values by
exploiting such associated information. We conduct an experimental
study with real-life categorical dataset. The experiment demonstrates
the effectiveness of our approach.
Abstract: Measurement of the quality of image compression is important for image processing application. In this paper, we propose an objective image quality assessment to measure the quality of gray scale compressed image, which is correlation well with subjective quality measurement (MOS) and least time taken. The new objective image quality measurement is developed from a few fundamental of objective measurements to evaluate the compressed image quality based on JPEG and JPEG2000. The reliability between each fundamental objective measurement and subjective measurement (MOS) is found. From the experimental results, we found that the Maximum Difference measurement (MD) and a new proposed measurement, Structural Content Laplacian Mean Square Error (SCLMSE), are the suitable measurements that can be used to evaluate the quality of JPEG200 and JPEG compressed image, respectively. In addition, MD and SCLMSE measurements are scaled to make them equivalent to MOS, given the rate of compressed image quality from 1 to 5 (unacceptable to excellent quality).
Abstract: Automated discovery of hierarchical structures in
large data sets has been an active research area in the recent past.
This paper focuses on the issue of mining generalized rules with crisp
hierarchical structure using Genetic Programming (GP) approach to
knowledge discovery. The post-processing scheme presented in this
work uses flat rules as initial individuals of GP and discovers
hierarchical structure. Suitable genetic operators are proposed for the
suggested encoding. Based on the Subsumption Matrix(SM), an
appropriate fitness function is suggested. Finally, Hierarchical
Production Rules (HPRs) are generated from the discovered
hierarchy. Experimental results are presented to demonstrate the
performance of the proposed algorithm.
Abstract: Negation is useful in the majority of the real world applications. However, its introduction leads to semantic and canonical problems. We propose in this paper an approach based on stratification to deal with negation problems. This approach is based on an extension of predicates nets. It is characterized with two main contributions. The first concerns the management of the whole class of stratified programs. The second contribution is related to usual operations optimizations on stratified programs (maximal stratification, incremental updates ...).
Abstract: This paper introduces a novel approach to estimate the
clique potentials of Gibbs Markov random field (GMRF) models
using the Support Vector Machines (SVM) algorithm and the Mean
Field (MF) theory. The proposed approach is based on modeling the
potential function associated with each clique shape of the GMRF
model as a Gaussian-shaped kernel. In turn, the energy function of
the GMRF will be in the form of a weighted sum of Gaussian
kernels. This formulation of the GMRF model urges the use of the
SVM with the Mean Field theory applied for its learning for
estimating the energy function. The approach has been tested on
synthetic texture images and is shown to provide satisfactory results
in retrieving the synthesizing parameters.
Abstract: This paper describes the NEAR (Navigating Exhibitions, Annotations and Resources) panel, a novel interactive visualization technique designed to help people navigate and interpret groups of resources, exhibitions and annotations by revealing hidden relations such as similarities and references. NEAR is implemented on A•VI•RE, an extended online information repository. A•VI•RE supports a semi-structured collection of exhibitions containing various resources and annotations. Users are encouraged to contribute, share, annotate and interpret resources in the system by building their own exhibitions and annotations. However, it is hard to navigate smoothly and efficiently in A•VI•RE because of its high capacity and complexity. We present a visual panel that implements new navigation and communication approaches that support discovery of implied relations. By quickly scanning and interacting with NEAR, users can see not only implied relations but also potential connections among different data elements. NEAR was tested by several users in the A•VI•RE system and shown to be a supportive navigation tool. In the paper, we further analyze the design, report the evaluation and consider its usage in other applications.
Abstract: Frequent pattern discovery over data stream is a hard
problem because a continuously generated nature of stream does not
allow a revisit on each data element. Furthermore, pattern discovery
process must be fast to produce timely results. Based on these
requirements, we propose an approximate approach to tackle the
problem of discovering frequent patterns over continuous stream.
Our approximation algorithm is intended to be applied to process a
stream prior to the pattern discovery process. The results of
approximate frequent pattern discovery have been reported in the
paper.
Abstract: In this paper, a model for an information retrieval
system is proposed which takes into account that knowledge about
documents and information need of users are dynamic. Two
methods are combined, one qualitative or symbolic and the other
quantitative or numeric, which are deemed suitable for many
clustering contexts, data analysis, concept exploring and
knowledge discovery. These two methods may be classified as
inductive learning techniques. In this model, they are introduced to
build “long term" knowledge about past queries and concepts in a
collection of documents. The “long term" knowledge can guide
and assist the user to formulate an initial query and can be
exploited in the process of retrieving relevant information. The
different kinds of knowledge are organized in different points of
view. This may be considered an enrichment of the exploration
level which is coherent with the concept of document/query
structure.
Abstract: This paper aims to present a survey of object
recognition/classification methods based on image moments. We
review various types of moments (geometric moments, complex
moments) and moment-based invariants with respect to various
image degradations and distortions (rotation, scaling, affine
transform, image blurring, etc.) which can be used as shape
descriptors for classification. We explain a general theory how to
construct these invariants and show also a few of them in explicit
forms. We review efficient numerical algorithms that can be used
for moment computation and demonstrate practical examples of
using moment invariants in real applications.
Abstract: This research presents a system for post processing of
data that takes mined flat rules as input and discovers crisp as well as
fuzzy hierarchical structures using Learning Classifier System
approach. Learning Classifier System (LCS) is basically a machine
learning technique that combines evolutionary computing,
reinforcement learning, supervised or unsupervised learning and
heuristics to produce adaptive systems. A LCS learns by interacting
with an environment from which it receives feedback in the form of
numerical reward. Learning is achieved by trying to maximize the
amount of reward received. Crisp description for a concept usually
cannot represent human knowledge completely and practically. In the
proposed Learning Classifier System initial population is constructed
as a random collection of HPR–trees (related production rules) and
crisp / fuzzy hierarchies are evolved. A fuzzy subsumption relation is
suggested for the proposed system and based on Subsumption Matrix
(SM), a suitable fitness function is proposed. Suitable genetic
operators are proposed for the chosen chromosome representation
method. For implementing reinforcement a suitable reward and
punishment scheme is also proposed. Experimental results are
presented to demonstrate the performance of the proposed system.
Abstract: Discourse pronominal anaphora resolution must be part of any efficient information processing systems, since the reference of a pronoun is dependent on an antecedent located in the discourse. Contrary to knowledge-poor approaches, this paper shows that syntax-semantic relations are basic in pronominal anaphora resolution. The identification of quantified expressions to which pronouns can be anaphorically related provides further evidence that pronominal anaphora is based on domains of interpretation where asymmetric agreement holds.