Abstract: Crypto System Identification is one of the challenging tasks in Crypt analysis. The paper discusses the possibility of employing Neural Networks for identification of Cipher Systems from cipher texts. Cascade Correlation Neural Network and Back Propagation Network have been employed for identification of Cipher Systems. Very large collection of cipher texts were generated using a Block Cipher (Enhanced RC6) and a Stream Cipher (SEAL). Promising results were obtained in terms of accuracy using both the Neural Network models but it was observed that the Cascade Correlation Neural Network Model performed better compared to Back Propagation Network.
Abstract: When an assignable cause(s) manifests itself to a multivariate process and the process shifts to an out-of-control condition, a root-cause analysis should be initiated by quality engineers to identify and eliminate the assignable cause(s) affected the process. A root-cause analysis in a multivariate process is more complex compared to a univariate process. In the case of a process involved several correlated variables an effective root-cause analysis can be only experienced when it is possible to identify the required knowledge including the out-of-control condition, the change point, and the variable(s) responsible to the out-of-control condition, all simultaneously. Although literature addresses different schemes to monitor multivariate processes, one can find few scientific reports focused on all the required knowledge. To the best of the author’s knowledge this is the first time that a multi task model based on artificial neural network (ANN) is reported to monitor all the required knowledge at the same time for a multivariate process with more than two correlated quality characteristics. The performance of the proposed scheme is evaluated numerically when different step shifts affect the mean vector. Average run length is used to investigate the performance of the proposed multi task model. The simulated results indicate the multi task scheme performs all the required knowledge effectively.
Abstract: Human Resource (HR) applications can be used to
provide fair and consistent decisions, and to improve the
effectiveness of decision making processes. Besides that, among
the challenge for HR professionals is to manage organization
talents, especially to ensure the right person for the right job at the
right time. For that reason, in this article, we attempt to describe
the potential to implement one of the talent management tasks i.e.
identifying existing talent by predicting their performance as one of
HR application for talent management. This study suggests the
potential HR system architecture for talent forecasting by using
past experience knowledge known as Knowledge Discovery in
Database (KDD) or Data Mining. This article consists of three
main parts; the first part deals with the overview of HR
applications, the prediction techniques and application, the general
view of Data mining and the basic concept of talent management
in HRM. The second part is to understand the use of Data Mining
technique in order to solve one of the talent management tasks, and
the third part is to propose the potential HR system architecture for
talent forecasting.
Abstract: Despite extensive study on wireless sensor network
security, defending internal attacks and finding abnormal behaviour
of the sensor are still difficult and unsolved task. The conventional
cryptographic technique does not give the robust security or detection
process to save the network from internal attacker that cause by
abnormal behavior. The insider attacker or abnormally behaved
sensor identificationand location detection framework using false
massage detection and Time difference of Arrival (TDoA) is
presented in this paper. It has been shown that the new framework
can efficiently identify and detect the insider attacker location so that
the attacker can be reprogrammed or subside from the network to
save from internal attack.
Abstract: Document image processing has become an
increasingly important technology in the automation of office
documentation tasks. During document scanning, skew is inevitably
introduced into the incoming document image. Since the algorithm
for layout analysis and character recognition are generally very
sensitive to the page skew. Hence, skew detection and correction in
document images are the critical steps before layout analysis. In this
paper, a novel skew detection method is presented for binary
document images. The method considered the some selected
characters of the text which may be subjected to thinning and Hough
transform to estimate skew angle accurately. Several experiments
have been conducted on various types of documents such as
documents containing English Documents, Journals, Text-Book,
Different Languages and Document with different fonts, Documents
with different resolutions, to reveal the robustness of the proposed
method. The experimental results revealed that the proposed method
is accurate compared to the results of well-known existing methods.
Abstract: As embedded and portable systems were emerged power consumption of circuits had been major challenge. On the other hand latency as determines frequency of circuits is also vital task. Therefore, trade off between both of them will be desirable. Modulo 2n+1 adders are important part of the residue number system (RNS) based arithmetic units with the interesting moduli set (2n-1,2n, 2n+1). In this manuscript we have introduced novel binary representation to the design of modulo 2n+1 adder. VLSI realization of proposed architecture under 180 nm full static CMOS technology reveals its superiority in terms of area, power consumption and power-delay product (PDP) against several peer existing structures.
Abstract: This paper presents a solution for the behavioural animation of autonomous virtual agent navigation in virtual environments. We focus on using Dempster-Shafer-s Theory of Evidence in developing visual sensor for virtual agent. The role of the visual sensor is to capture the information about the virtual environment or identifie which part of an obstacle can be seen from the position of the virtual agent. This information is require for vitual agent to coordinate navigation in virtual environment. The virual agent uses fuzzy controller as a navigation system and Fuzzy α - level for the action selection method. The result clearly demonstrates the path produced is reasonably smooth even though there is some sharp turn and also still not diverted too far from the potential shortest path. This had indicated the benefit of our method, where more reliable and accurate paths produced during navigation task.
Abstract: This paper describes a segmentation algorithm based
on the cooperation of an optical flow estimation method with edge
detection and region growing procedures.
The proposed method has been developed as a pre-processing
stage to be used in methodologies and tools for video/image indexing
and retrieval by content. The addressed problem consists in
extracting whole objects from background for producing images of
single complete objects from videos or photos. The extracted images
are used for calculating the object visual features necessary for both
indexing and retrieval processes.
The first task of the algorithm exploits the cues from motion
analysis for moving area detection. Objects and background are then
refined using respectively edge detection and region growing
procedures. These tasks are iteratively performed until objects and
background are completely resolved.
The developed method has been applied to a variety of indoor and
outdoor scenes where objects of different type and shape are
represented on variously textured background.
Abstract: The ability to recognize humans and their activities by computer vision is a very important task, with many potential application. Study of human motion analysis is related to several research areas of computer vision such as the motion capture, detection, tracking and segmentation of people. In this paper, we describe a segmentation method for extracting human body contour in modified HLS color space. To estimate a background, the modified HLS color space is proposed, and the background features are estimated by using the HLS color components. Here, the large amount of human dataset, which was collected from DV cameras, is pre-processed. The human body and its contour is successfully extracted from the image sequences.
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: Given a parallel program to be executed on a heterogeneous
computing system, the overall execution time of the program
is determined by a schedule. In this paper, we analyze the worst-case
performance of the list scheduling algorithm for scheduling tasks
of a parallel program in a mixed-machine heterogeneous computing
system such that the total execution time of the program is minimized.
We prove tight lower and upper bounds for the worst-case
performance ratio of the list scheduling algorithm. We also examine
the average-case performance of the list scheduling algorithm. Our
experimental data reveal that the average-case performance of the list
scheduling algorithm is much better than the worst-case performance
and is very close to optimal, except for large systems with large
heterogeneity. Thus, the list scheduling algorithm is very useful in
real applications.
Abstract: The vast amount of information on the World Wide
Web is created and published by many different types of providers.
Unlike books and journals, most of this information is not subject to
editing or peer review by experts. This lack of quality control and the
explosion of web sites make the task of finding quality information
on the web especially critical. Meanwhile new facilities for
producing web pages such as Blogs make this issue more significant
because Blogs have simple content management tools enabling nonexperts
to build easily updatable web diaries or online journals. On
the other hand despite a decade of active research in information
quality (IQ) there is no framework for measuring information quality
on the Blogs yet. This paper presents a novel experimental
framework for ranking quality of information on the Weblog. The
results of data analysis revealed seven IQ dimensions for the Weblog.
For each dimension, variables and related coefficients were
calculated so that presented framework is able to assess IQ of
Weblogs automatically.
Abstract: Automated storage and retrieval systems (AS/RS)
become frequently used systems in warehouses. There has been a
transition from human based forklift applications to fast and safe
AS/RS applications in firm-s warehouse systems. In this study, basic
components and automation systems of the AS/RS are examined.
Proposed system's automation components and their tasks in the
system control algorithm were stated. According to this control
algorithm the control system structure was obtained.
Abstract: Pipeline infrastructures normally represent high cost of investment and the pipeline must be free from risks that could cause environmental hazard and potential threats to personnel safety. Pipeline integrity such monitoring and management become very crucial to provide unimpeded transportation and avoiding unnecessary production deferment. Thus proper cleaning and inspection is the key to safe and reliable pipeline operation and plays an important role in pipeline integrity management program and has become a standard industry procedure. In view of this, understanding the motion (dynamic behavior), prediction and control of the PIG speed is important in executing pigging operation as it offers significant benefits, such as estimating PIG arrival time at receiving station, planning for suitable pigging operation, and improves efficiency of pigging tasks. The objective of this paper is to review recent developments in speed control system of pipeline PIGs. The review carried out would serve as an industrial application in a form of quick reference of recent developments in pipeline PIG speed control system, and further initiate others to add-in/update the list in the future leading to knowledge based data, and would attract active interest of others to share their view points.
Abstract: Several combinations of the preprocessing algorithms,
feature selection techniques and classifiers can be applied to the data
classification tasks. This study introduces a new accurate classifier,
the proposed classifier consist from four components: Signal-to-
Noise as a feature selection technique, support vector machine,
Bayesian neural network and AdaBoost as an ensemble algorithm.
To verify the effectiveness of the proposed classifier, seven well
known classifiers are applied to four datasets. The experiments show
that using the suggested classifier enhances the classification rates for
all datasets.
Abstract: In the last years, the computers have increased their capacity of calculus and networks, for the interconnection of these machines. The networks have been improved until obtaining the actual high rates of data transferring. The programs that nowadays try to take advantage of these new technologies cannot be written using the traditional techniques of programming, since most of the algorithms were designed for being executed in an only processor,in a nonconcurrent form instead of being executed concurrently ina set of processors working and communicating through a network.This paper aims to present the ongoing development of a new system for the reconfiguration of grouping of computers, taking into account these new technologies.
Abstract: Proteomics is one of the largest areas of research for
bioinformatics and medical science. An ambitious goal of proteomics
is to elucidate the structure, interactions and functions of all proteins
within cells and organisms. Predicting Protein-Protein Interaction
(PPI) is one of the crucial and decisive problems in current research.
Genomic data offer a great opportunity and at the same time a lot of
challenges for the identification of these interactions. Many methods
have already been proposed in this regard. In case of in-silico
identification, most of the methods require both positive and negative
examples of protein interaction and the perfection of these examples
are very much crucial for the final prediction accuracy. Positive
examples are relatively easy to obtain from well known databases. But
the generation of negative examples is not a trivial task. Current PPI
identification methods generate negative examples based on some
assumptions, which are likely to affect their prediction accuracy.
Hence, if more reliable negative examples are used, the PPI prediction
methods may achieve even more accuracy. Focusing on this issue, a
graph based negative example generation method is proposed, which
is simple and more accurate than the existing approaches. An
interaction graph of the protein sequences is created. The basic
assumption is that the longer the shortest path between two
protein-sequences in the interaction graph, the less is the possibility of
their interaction. A well established PPI detection algorithm is
employed with our negative examples and in most cases it increases
the accuracy more than 10% in comparison with the negative pair
selection method in that paper.
Abstract: This paper is focused on issues of nonlinear dynamic process modeling and model-based predictive control of a fed-batch sugar crystallization process applying the concept of artificial neural networks as computational tools. The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. A feed forward neural network (FFNN) model of the process is first built as part of the controller structure to predict the process response over a specified (prediction) horizon. The predictions are supplied to an optimization procedure to determine the values of the control action over a specified (control) horizon that minimizes a predefined performance index. The control task is rather challenging due to the strong nonlinearity of the process dynamics and variations in the crystallization kinetics. However, the simulation results demonstrated smooth behavior of the control actions and satisfactory reference tracking.
Abstract: The main aim of this study was to examine whether
people understand indicative conditionals on the basis of syntactic
factors or on the basis of subjective conditional probability. The
second aim was to investigate whether the conditional probability of
q given p depends on the antecedent and consequent sizes or derives
from inductive processes leading to establish a link of plausible cooccurrence
between events semantically or experientially associated.
These competing hypotheses have been tested through a 3 x 2 x 2 x 2
mixed design involving the manipulation of four variables: type of
instructions (“Consider the following statement to be true", “Read the
following statement" and condition with no conditional statement);
antecedent size (high/low); consequent size (high/low); statement
probability (high/low). The first variable was between-subjects, the
others were within-subjects. The inferences investigated were Modus
Ponens and Modus Tollens. Ninety undergraduates of the Second
University of Naples, without any prior knowledge of logic or
conditional reasoning, participated in this study.
Results suggest that people understand conditionals in a syntactic
way rather than in a probabilistic way, even though the perception of
the conditional probability of q given p is at least partially involved in
the conditionals- comprehension. They also showed that, in presence
of a conditional syllogism, inferences are not affected by the
antecedent or consequent sizes. From a theoretical point of view these
findings suggest that it would be inappropriate to abandon the idea
that conditionals are naturally understood in a syntactic way for the
idea that they are understood in a probabilistic way.
Abstract: An important task in solving second order linear ordinary differential equations by the finite difference is to choose a suitable stepsize h. In this paper, by using the stochastic arithmetic, the CESTAC method and the CADNA library we present a procedure to estimate the optimal stepsize hopt, the stepsize which minimizes the global error consisting of truncation and round-off error.