Abstract: In this paper we analyze the application of a formal proof system to the discrete logarithm problem used in publickey cryptography. That means, we explore a computer verification of the ElGamal encryption scheme with the formal proof system Isabelle/HOL. More precisely, the functional correctness of this algorithm is formally verified with computer support. Besides, we present a formalization of the DSA signature scheme in the Isabelle/HOL system. We show that this scheme is correct what is a necessary condition for the usefulness of any cryptographic signature scheme.
Abstract: Today’s technology is heavily dependent on web applications. Web applications are being accepted by users at a very rapid pace. These have made our work efficient. These include webmail, online retail sale, online gaming, wikis, departure and arrival of trains and flights and list is very long. These are developed in different languages like PHP, Python, C#, ASP.NET and many more by using scripts such as HTML and JavaScript. Attackers develop tools and techniques to exploit web applications and legitimate websites. This has led to rise of web application security; which can be broadly classified into Declarative Security and Program Security. The most common attacks on the applications are by SQL Injection and XSS which give access to unauthorized users who totally damage or destroy the system. This paper presents a detailed literature description and analysis on Web Application Security, examples of attacks and steps to mitigate the vulnerabilities.
Abstract: Self-organizing map (SOM) is a well known data
reduction technique used in data mining. It can reveal structure in
data sets through data visualization that is otherwise hard to detect
from raw data alone. However, interpretation through visual
inspection is prone to errors and can be very tedious. There are
several techniques for the automatic detection of clusters of code
vectors found by SOM, but they generally do not take into account
the distribution of code vectors; this may lead to unsatisfactory
clustering and poor definition of cluster boundaries, particularly
where the density of data points is low. In this paper, we propose the
use of an adaptive heuristic particle swarm optimization (PSO)
algorithm for finding cluster boundaries directly from the code
vectors obtained from SOM. The application of our method to
several standard data sets demonstrates its feasibility. PSO algorithm
utilizes a so-called U-matrix of SOM to determine cluster boundaries;
the results of this novel automatic method compare very favorably to
boundary detection through traditional algorithms namely k-means
and hierarchical based approach which are normally used to interpret
the output of SOM.
Abstract: Kernel function, which allows the formulation of nonlinear variants of any algorithm that can be cast in terms of dot products, makes the Support Vector Machines (SVM) have been successfully applied in many fields, e.g. classification and regression. The importance of kernel has motivated many studies on its composition. It-s well-known that reproducing kernel (R.K) is a useful kernel function which possesses many properties, e.g. positive definiteness, reproducing property and composing complex R.K by simple operation. There are two popular ways to compute the R.K with explicit form. One is to construct and solve a specific differential equation with boundary value whose handicap is incapable of obtaining a unified form of R.K. The other is using a piecewise integral of the Green function associated with a differential operator L. The latter benefits the computation of a R.K with a unified explicit form and theoretical analysis, whereas there are relatively later studies and fewer practical computations. In this paper, a new algorithm for computing a R.K is presented. It can obtain the unified explicit form of R.K in general reproducing kernel Hilbert space. It avoids constructing and solving the complex differential equations manually and benefits an automatic, flexible and rigorous computation for more general RKHS. In order to validate that the R.K computed by the algorithm can be used in SVM well, some illustrative examples and a comparison between R.K and Gaussian kernel (RBF) in support vector regression are presented. The result shows that the performance of R.K is close or slightly superior to that of RBF.
Abstract: Medical images require special safety and confidentiality because critical judgment is done on the information provided by medical images. Transmission of medical image via internet or mobile phones demands strong security and copyright protection in telemedicine applications. Here, highly secured and robust watermarking technique is proposed for transmission of image data via internet and mobile phones. The Region of Interest (ROI) and Non Region of Interest (RONI) of medical image are separated. Only RONI is used for watermark embedding. This technique results in exact recovery of watermark with standard medical database images of size 512x512, giving 'correlation factor' equals to 1. The correlation factor for different attacks like noise addition, filtering, rotation and compression ranges from 0.90 to 0.95. The PSNR with weighting factor 0.02 is up to 48.53 dBs. The presented scheme is non blind and embeds hospital logo of 64x64 size.
Abstract: Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the network performance, set the rate of adjustments. This paper describes a proposed back propagation neural net classifier that performs cross validation for original Neural Network. In order to reduce the optimization of classification accuracy, training time. The feasibility the benefits of the proposed approach are demonstrated by means of five data sets like contact-lenses, cpu, weather symbolic, Weather, labor-nega-data. It is shown that , compared to exiting neural network, the training time is reduced by more than 10 times faster when the dataset is larger than CPU or the network has many hidden units while accuracy ('percent correct') was the same for all datasets but contact-lences, which is the only one with missing attributes. For contact-lences the accuracy with Proposed Neural Network was in average around 0.3 % less than with the original Neural Network. This algorithm is independent of specify data sets so that many ideas and solutions can be transferred to other classifier paradigms.
Abstract: The problem of mapping tasks onto a computational grid with the aim to minimize the power consumption and the makespan subject to the constraints of deadlines and architectural requirements is considered in this paper. To solve this problem, we propose a solution from cooperative game theory based on the concept of Nash Bargaining Solution. The proposed game theoretical technique is compared against several traditional techniques. The experimental results show that when the deadline constraints are tight, the proposed technique achieves superior performance and reports competitive performance relative to the optimal solution.
Abstract: In this paper, we first show a relationship between two
stabilizing controllers, which presents an extended feedback system
using two stabilizing controllers. Then, we apply this relationship to
the two-stage compensator design. In this paper, we consider singleinput
single-output plants. On the other hand, we do not assume the
coprime factorizability of the model. Thus, the results of this paper
are based on the factorization approach only, so that they can be
applied to numerous linear systems.
Abstract: We provide a supervised speech-independent voice recognition technique in this paper. In the feature extraction stage we propose a mel-cepstral based approach. Our feature vector classification method uses a special nonlinear metric, derived from the Hausdorff distance for sets, and a minimum mean distance classifier.
Abstract: This paper discusses a new, systematic approach to
the synthesis of a NP-hard class of non-regenerative Boolean
networks, described by FON[FOFF]={mi}[{Mi}], where for every
mj[Mj]∈{mi}[{Mi}], there exists another mk[Mk]∈{mi}[{Mi}], such
that their Hamming distance HD(mj, mk)=HD(Mj, Mk)=O(n), (where
'n' represents the number of distinct primary inputs). The method
automatically ensures exact minimization for certain important selfdual
functions with 2n-1 points in its one-set. The elements meant for
grouping are determined from a newly proposed weighted incidence
matrix. Then the binary value corresponding to the candidate pair is
correlated with the proposed binary value matrix to enable direct
synthesis. We recommend algebraic factorization operations as a post
processing step to enable reduction in literal count. The algorithm
can be implemented in any high level language and achieves best
cost optimization for the problem dealt with, irrespective of the
number of inputs. For other cases, the method is iterated to
subsequently reduce it to a problem of O(n-1), O(n-2),.... and then
solved. In addition, it leads to optimal results for problems exhibiting
higher degree of adjacency, with a different interpretation of the
heuristic, and the results are comparable with other methods.
In terms of literal cost, at the technology independent stage, the
circuits synthesized using our algorithm enabled net savings over
AOI (AND-OR-Invert) logic, AND-EXOR logic (EXOR Sum-of-
Products or ESOP forms) and AND-OR-EXOR logic by 45.57%,
41.78% and 41.78% respectively for the various problems.
Circuit level simulations were performed for a wide variety of
case studies at 3.3V and 2.5V supply to validate the performance of
the proposed method and the quality of the resulting synthesized
circuits at two different voltage corners. Power estimation was
carried out for a 0.35micron TSMC CMOS process technology. In
comparison with AOI logic, the proposed method enabled mean
savings in power by 42.46%. With respect to AND-EXOR logic, the
proposed method yielded power savings to the tune of 31.88%, while
in comparison with AND-OR-EXOR level networks; average power
savings of 33.23% was obtained.
Abstract: Fuzzy fingerprint vault is a recently developed cryptographic construct based on the polynomial reconstruction problem to secure critical data with the fingerprint data. However, the previous researches are not applicable to the fingerprint having a few minutiae since they use a fixed degree of the polynomial without considering the number of fingerprint minutiae. To solve this problem, we use an adaptive degree of the polynomial considering the number of minutiae extracted from each user. Also, we apply multiple polynomials to avoid the possible degradation of the security of a simple solution(i.e., using a low-degree polynomial). Based on the experimental results, our method can make the possible attack difficult 2192 times more than using a low-degree polynomial as well as verify the users having a few minutiae.
Abstract: Trust is essential for further and wider acceptance of
contemporary e-services. It was first addressed almost thirty years
ago in Trusted Computer System Evaluation Criteria standard by
the US DoD. But this and other proposed approaches of that
period were actually solving security. Roughly some ten years ago,
methodologies followed that addressed trust phenomenon at its core,
and they were based on Bayesian statistics and its derivatives, while
some approaches were based on game theory. However, trust is a
manifestation of judgment and reasoning processes. It has to be dealt
with in accordance with this fact and adequately supported in cyber
environment. On the basis of the results in the field of psychology
and our own findings, a methodology called qualitative algebra has
been developed, which deals with so far overlooked elements of trust
phenomenon. It complements existing methodologies and provides a
basis for a practical technical solution that supports management of
trust in contemporary computing environments. Such solution is also
presented at the end of this paper.
Abstract: Since the driving speed and control accuracy of
commercial optical disk are increasing significantly, it needs an
efficient controller to monitor the track seeking and following
operations of the servo system for achieving the desired data
extracting response. The nonlinear behaviors of the actuator and servo
system of the optical disk drive will influence the laser spot
positioning. Here, the model-free fuzzy control scheme is employed to
design the track seeking servo controller for a d.c. motor driving
optical disk drive system. In addition, the sliding model control
strategy is introduced into the fuzzy control structure to construct a
1-D adaptive fuzzy rule intelligent controller for simplifying the
implementation problem and improving the control performance. The
experimental results show that the steady state error of the track
seeking by using this fuzzy controller can maintain within the track
width (1.6 μm ). It can be used in the track seeking and track
following servo control operations.
Abstract: In this paper, all variables are supposed to be integer
and positive. In this modern method, objective function is assumed to
be maximized or minimized but constraints are always explained like
less or equal to. In this method, choosing a dual combination of ideal
nonequivalent and omitting one of variables. With continuing this
act, finally, having one nonequivalent with (n-m+1) unknown
quantities in which final nonequivalent, m is counter for constraints,
n is counter for variables of decision.
Abstract: Content-based Image Retrieval (CBIR) aims at searching image databases for specific images that are similar to a given query image based on matching of features derived from the image content. This paper focuses on a low-dimensional color based indexing technique for achieving efficient and effective retrieval performance. In our approach, the color features are extracted using the mean shift algorithm, a robust clustering technique. Then the cluster (region) mode is used as representative of the image in 3-D color space. The feature descriptor consists of the representative color of a region and is indexed using a spatial indexing method that uses *R -tree thus avoiding the high-dimensional indexing problems associated with the traditional color histogram. Alternatively, the images in the database are clustered based on region feature similarity using Euclidian distance. Only representative (centroids) features of these clusters are indexed using *R -tree thus improving the efficiency. For similarity retrieval, each representative color in the query image or region is used independently to find regions containing that color. The results of these methods are compared. A JAVA based query engine supporting query-by- example is built to retrieve images by color.
Abstract: This paper presents a new approach for automatic
document categorization. Exploiting the logical structure of the
document, our approach assigns a HTML document to one or more
categories (thesis, paper, call for papers, email, ...). Using a set of
training documents, our approach generates a set of rules used to
categorize new documents. The approach flexibility is carried out
with rule weight association representing your importance in the
discrimination between possible categories. This weight is
dynamically modified at each new document categorization. The
experimentation of the proposed approach provides satisfactory
results.
Abstract: This research focus on developing a new segmentation method for improving forecasting model which is call trend based segmentation method (TBSM). Generally, the piece-wise linear representation (PLR) can finds some of pair of trading points is well for time series data, but in the complicated stock environment it is not well for stock forecasting because of the stock has more trends of trading. If we consider the trends of trading in stock price for the trading signal which it will improve the precision of forecasting model. Therefore, a TBSM with SVR model used to detect the trading points for various stocks of Taiwanese and America under different trend tendencies. The experimental results show our trading system is more profitable and can be implemented in real time of stock market
Abstract: With the increasing number of on-chip components and the critical requirement for processing power, Chip Multiprocessor (CMP) has gained wide acceptance in both academia and industry during the last decade. However, the conventional bus-based onchip communication schemes suffer from very high communication delay and low scalability in large scale systems. Network-on-Chip (NoC) has been proposed to solve the bottleneck of parallel onchip communications by applying different network topologies which separate the communication phase from the computation phase. Observing that the memory bandwidth of the communication between on-chip components and off-chip memory has become a critical problem even in NoC based systems, in this paper, we propose a novel 3D NoC with on-chip Dynamic Random Access Memory (DRAM) in which different layers are dedicated to different functionalities such as processors, cache or memory. Results show that, by using our proposed architecture, average link utilization has reduced by 10.25% for SPLASH-2 workloads. Our proposed design costs 1.12% less execution cycles than the traditional design on average.
Abstract: This paper presents a constrained valley detection
algorithm. The intent is to find valleys in the map for the path planning
that enables a robot or a vehicle to move safely. The constraint to the
valley is a desired width and a desired depth to ensure the space for
movement when a vehicle passes through the valley. We propose an
algorithm to find valleys satisfying these 2 dimensional constraints.
The merit of our algorithm is that the pre-processing and the
post-processing are not necessary to eliminate undesired small valleys.
The algorithm is validated through simulation using digitized
elevation data.
Abstract: CIM is the standard formalism for modeling management
information developed by the Distributed Management Task
Force (DMTF) in the context of its WBEM proposal, designed to
provide a conceptual view of the managed environment. In this
paper, we propose the inclusion of formal knowledge representation
techniques, based on Description Logics (DLs) and the Web Ontology
Language (OWL), in CIM-based conceptual modeling, and then we
examine the benefits of such a decision. The proposal is specified
as a CIM metamodel level mapping to a highly expressive subset
of DLs capable of capturing all the semantics of the models. The
paper shows how the proposed mapping provides CIM diagrams with
precise semantics and can be used for automatic reasoning about the
management information models, as a design aid, by means of newgeneration
CASE tools, thanks to the use of state-of-the-art automatic
reasoning systems that support the proposed logic and use algorithms
that are sound and complete with respect to the semantics. Such a
CASE tool framework has been developed by the authors and its
architecture is also introduced. The proposed formalization is not
only useful at design time, but also at run time through the use of
rational autonomous agents, in response to a need recently recognized
by the DMTF.