Abstract: MATCH project [1] entitle the development of an
automatic diagnosis system that aims to support treatment of colon
cancer diseases by discovering mutations that occurs to tumour
suppressor genes (TSGs) and contributes to the development of
cancerous tumours. The constitution of the system is based on a)
colon cancer clinical data and b) biological information that will be
derived by data mining techniques from genomic and proteomic
sources The core mining module will consist of the popular, well
tested hybrid feature extraction methods, and new combined
algorithms, designed especially for the project. Elements of rough
sets, evolutionary computing, cluster analysis, self-organization maps
and association rules will be used to discover the annotations
between genes, and their influence on tumours [2]-[11].
The methods used to process the data have to address their high
complexity, potential inconsistency and problems of dealing with the
missing values. They must integrate all the useful information
necessary to solve the expert's question. For this purpose, the system
has to learn from data, or be able to interactively specify by a domain
specialist, the part of the knowledge structure it needs to answer a
given query. The program should also take into account the
importance/rank of the particular parts of data it analyses, and adjusts
the used algorithms accordingly.
Abstract: In today's day and age, one of the important topics in
information security is authentication. There are several alternatives
to text-based authentication of which includes Graphical Password
(GP) or Graphical User Authentication (GUA). These methods stems
from the fact that humans recognized and remembers images better
than alphanumerical text characters. This paper will focus on the
security aspect of GP algorithms and what most researchers have
been working on trying to define these security features and
attributes. The goal of this study is to develop a fuzzy decision model
that allows automatic selection of available GP algorithms by taking
into considerations the subjective judgments of the decision makers
who are more than 50 postgraduate students of computer science. The
approach that is being proposed is based on the Fuzzy Analytic
Hierarchy Process (FAHP) which determines the criteria weight as a
linear formula.
Abstract: We have developed a database for membrane protein functions, which has more than 3000 experimental data on functionally important amino acid residues in membrane proteins along with sequence, structure and literature information. Further, we have proposed different methods for identifying membrane proteins based on their functions: (i) discrimination of membrane transport proteins from other globular and membrane proteins and classifying them into channels/pores, electrochemical and active transporters, and (ii) β-signal for the insertion of mitochondrial β-barrel outer membrane proteins and potential targets. Our method showed an accuracy of 82% in discriminating transport proteins and 68% to classify them into three different transporters. In addition, we have identified a motif for targeting β-signal and potential candidates for mitochondrial β-barrel membrane proteins. Our methods can be used as effective tools for genome-wide annotations.
Abstract: Reducing the risk of information leaks is one of
the most important functions of identity management systems. To
achieve this purpose, Dey et al. have already proposed an account
management method for a federated login system using a blind
signature scheme. In order to ensure account anonymity for the
authentication provider, referred to as an IDP (identity provider),
a blind signature scheme is utilized to generate an authentication
token on an authentication service and the token is sent to an IDP.
However, there is a problem with the proposed system. Malicious
users can establish multiple accounts on an IDP by requesting such
accounts. As a measure to solve this problem, in this paper, the
authors propose an account checking method that is performed before
account generation.
Abstract: None of the processing models in the software
development has explained the software systems performance
evaluation and modeling; likewise, there exist uncertainty in the
information systems because of the natural essence of requirements,
and this may cause other challenges in the processing of software
development. By definition an extended version of UML (Fuzzy-
UML), the functional requirements of the software defined
uncertainly would be supported. In this study, the behavioral
description of uncertain information systems by the aid of fuzzy-state
diagram is crucial; moreover, the introduction of behavioral diagrams
role in F-UML is investigated in software performance modeling
process. To get the aim, a fuzzy sub-profile is used.
Abstract: To learn about China-s future energy demand, this paper first proposed GM(1,1) model group based on recursive solutions of parameters estimation, setting up a general solving-algorithm of the model group. This method avoided the problems occurred on the past researches that remodeling, loss of information and large amount of calculation. This paper established respectively all-data-GM(1,1), metabolic GM(1,1) and new information GM (1,1)model according to the historical data of energy consumption in China in the year 2005-2010 and the added data of 2011, then modeling, simulating and comparison of accuracies we got the optimal models and to predict. Results showed that the total energy demand of China will be 37.2221 billion tons of equivalent coal in 2012 and 39.7973 billion tons of equivalent coal in 2013, which are as the same as the overall planning of energy demand in The 12th Five-Year Plan.
Abstract: The ITE Project is a project that has 1800 km length
and across the Turkey's land through east to west. The project of
pipeline enters geographically from Iran to Doğubayazit (Turkey) in
the east, exits to Greece from Ipsala province of Turkey in the west.
This project is the one of the international projects in such scale that
provides the natural gas of Iran and Caspian Sea through the
European continent. In this investigation, some information will be
given about the methods used to verify the direction of the pipeline
and the technical properties of the results obtained. The cost of
project itself entirely depends on the direction of the pipeline which
would be as short as possible and the specifications of the land cover.
Production standards of 1/2000 scaled digital orthophoto and vectoral
maps as a results of the use of map production materials and methods
(such as high resolution satellite images, and digital aerial images
captured from digital aerial cameras), will also be given in this report.
According to Turkish national map production standards, TM
((Transversal Mercator, 3 degree) projection is used for large scale
map and UTM (Universal Transversal Mercator, 6 degree) is used for
small scale map production standards. Some information is also given
about the projection used in the ITE natural gas pipeline project.
Abstract: In the recent past Learning Classifier Systems have
been successfully used for data mining. Learning Classifier System
(LCS) is basically a machine learning technique which 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. All
LCSs models more or less, comprise four main components; a finite
population of condition–action rules, called classifiers; the
performance component, which governs the interaction with the
environment; the credit assignment component, which distributes the
reward received from the environment to the classifiers accountable
for the rewards obtained; the discovery component, which is
responsible for discovering better rules and improving existing ones
through a genetic algorithm. The concatenate of the production rules
in the LCS form the genotype, and therefore the GA should operate
on a population of classifier systems. This approach is known as the
'Pittsburgh' Classifier Systems. Other LCS that perform their GA at
the rule level within a population are known as 'Mitchigan' Classifier
Systems. The most predominant representation of the discovered
knowledge is the standard production rules (PRs) in the form of IF P
THEN D. The PRs, however, are unable to handle exceptions and do
not exhibit variable precision. The Censored Production Rules
(CPRs), an extension of PRs, were proposed by Michalski and
Winston that exhibit variable precision and supports an efficient
mechanism for handling exceptions. A CPR is an augmented
production rule of the form: IF P THEN D UNLESS C, where
Censor C is an exception to the rule. Such rules are employed in
situations, in which conditional statement IF P THEN D holds
frequently and the assertion C holds rarely. By using a rule of this
type we are free to ignore the exception conditions, when the
resources needed to establish its presence are tight or there is simply
no information available as to whether it holds or not. Thus, the IF P
THEN D part of CPR expresses important information, while the
UNLESS C part acts only as a switch and changes the polarity of D
to ~D. In this paper Pittsburgh style LCSs approach is used for
automated discovery of CPRs. An appropriate encoding scheme is
suggested to represent a chromosome consisting of fixed size set of
CPRs. Suitable genetic operators are designed for the set of CPRs
and individual CPRs and also appropriate fitness function is proposed
that incorporates basic constraints on CPR. Experimental results are
presented to demonstrate the performance of the proposed learning
classifier system.
Abstract: The technology usages of high speed Internet leads to
establish and start new era of online education. With the
advancement of the information technology and communication
systems new opportunities have been created. This leads universities
to have various online education channels to meet the demand of
different learners- needs. One of these channels is M-learning, which
can be used to improve the online education environment. With using
such mobile technology in learning both students and instructors can
easily access educational courses anytime from anywhere. The paper
first presents literature about mobile learning and to what extent this
approach can be utilized to enhance the overall learning system. It
provides a comparison between mobile learning and traditional elearning
showing the wide array of benefits of the new generation of
technology. The possible challenges and potential advantages of Mlearning
in the online education system are also discussed.
Abstract: Trust management and Reputation models are
becoming integral part of Internet based applications such as CSCW,
E-commerce and Grid Computing. Also the trust dimension is a
significant social structure and key to social relations within a
collaborative community. Collaborative Decision Making (CDM) is
a difficult task in the context of distributed environment (information
across different geographical locations) and multidisciplinary
decisions are involved such as Virtual Organization (VO). To aid
team decision making in VO, Decision Support System and social
network analysis approaches are integrated. In such situations social
learning helps an organization in terms of relationship, team
formation, partner selection etc. In this paper we focus on trust
learning. Trust learning is an important activity in terms of
information exchange, negotiation, collaboration and trust
assessment for cooperation among virtual team members. In this
paper we have proposed a reinforcement learning which enhances the
trust decision making capability of interacting agents during
collaboration in problem solving activity. Trust computational model
with learning that we present is adapted for best alternate selection of
new project in the organization. We verify our model in a multi-agent
simulation where the agents in the community learn to identify
trustworthy members, inconsistent behavior and conflicting behavior
of agents.
Abstract: This paper discusses the designing of knowledge
integration of clinical information extracted from distributed medical
ontologies in order to ameliorate a machine learning-based multilabel
coding assignment system. The proposed approach is
implemented using a decision tree technique of the machine learning
on the university hospital data for patients with Coronary Heart
Disease (CHD). The preliminary results obtained show a satisfactory
finding that the use of medical ontologies improves the overall
system performance.
Abstract: In this paper we present a method of abrupt cut detection with a novel logic of frames- comparison. Actual frame is compared with its motion estimated prediction instead of comparison with successive frame. Four different similarity metrics were employed to estimate the resemblance of compared frames. Obtained results were evaluated by standard used measures of test accuracy and compared with existing approach. Based on the results, we claim the proposed method is more effective and Pearson correlation coefficient obtained the best results among chosen similarity metrics.
Abstract: Information sharing and exchange, rather than
information processing, is what characterizes information
technology in the 21st century. Ontologies, as shared common
understanding, gain increasing attention, as they appear as the
most promising solution to enable information sharing both at
a semantic level and in a machine-processable way. Domain
Ontology-based modeling has been exploited to provide
shareability and information exchange among diversified,
heterogeneous applications of enterprises.
Contextual ontologies are “an explicit specification of
contextual conceptualization". That is: ontology is
characterized by concepts that have multiple representations
and they may exist in several contexts. Hence, contextual
ontologies are a set of concepts and relationships, which are
seen from different perspectives. Contextualization is to allow
for ontologies to be partitioned according to their contexts.
The need for contextual ontologies in enterprise modeling
has become crucial due to the nature of today's competitive
market. Information resources in enterprise is distributed and
diversified and is in need to be shared and communicated
locally through the intranet and globally though the internet.
This paper discusses the roles that ontologies play in an
enterprise modeling, and how ontologies assist in building a
conceptual model in order to provide communicative and
interoperable information systems. The issue of enterprise
modeling based on contextual domain ontology is also
investigated, and a framework is proposed for an enterprise
model that consists of various applications.
Abstract: The extraction of meaningful information from image
could be an alternative method for time series analysis. In this paper,
we propose a graphical analysis of time series grouped into table
with adjusted colour scale for numerical values. The advantages of
this method are also discussed. The proposed method is easy to
understand and is flexible to implement the standard methods of
pattern recognition and verification, especially for noisy
environmental data.
Abstract: This study examines the possibility to apply the theory of multidimensional accounting (momentum accounting) in a Brazilian Navy-s Services Provider Military Organization (Organização Militar Prestadora de Serviços - OMPS). In general, the core of the said theory is the fact that Accounting does not recognize the inertia of transactions occurring in an entity, and that occur repeatedly in some cases, regardless of the implementation of new actions by its managers. The study evaluates the possibility of greater use of information recorded in the financial statements of the unit of analysis, within the strategic decisions of the organization. As a research strategy, we adopted the case study. The results infer that it is possible to use the theory in the context of a multidimensional OMPS, promoting useful information for decision-making and thereby contributing to the strengthening of the necessary alignment of its administration with the current desires of the Brazilian society.
Abstract: Recent theorizations on the cognitive process of moral
judgment have focused on the role of intuitions and emotions, marking
a departure from previous emphasis on conscious, step-by-step
reasoning. My study investigated how being in a disgusted mood state
affects moral judgment.
Participants were induced to enter a disgusted mood state through
listening to disgusting sounds and reading disgusting descriptions.
Results shows that they, when compared to control who have not been
induced to feel disgust, are more likely to endorse actions that are
emotionally aversive but maximizes utilitarian return
The result is analyzed using the 'emotion-as-information' approach
to decision making. The result is consistent with the view that
emotions play an important role in determining moral judgment.
Abstract: Work stress causes the organizational work-life
imbalance of employees. Because of this imbalance, workers perform
with lower effort to finish assignments and thus an organization will
experience reduced productivity. In order to investigate the problem
of an organizational work-life imbalance, this qualitative case study
focuses on an organizational work-life imbalance among Thai
software developers in a German-owned company in Chiang Mai,
Thailand. In terms of knowledge management, fishbone diagram is
useful analysis tool to investigate the root causes of an organizational
work-life imbalance systematically in focus-group discussions.
Furthermore, fishbone diagram shows the relationship between
causes and effects clearly. It was found that an organizational worklife
imbalance among Thai software developers is influenced by
management team, work environment, and information tools used in
the company over time.
Abstract: Logistics is part of the supply chain processes that plans, implements, and controls the efficient and effective forward and reverse flow and storage of goods, services, and related information between the point of origin and the point of consumption in order to meet customer requirements. This research aims to investigate the current status and future direction of the use of Information Technology (IT) for logistics, focusing on Supply Chain Management (SCM) and E-Commerce adoption in Johor. Therefore, this research stresses on the type of technology being adopted, factors, benefits and barriers affecting the innovation in SCM and ECommerce technology adoption among Logistics Service Providers (LSP). A mailed questionnaire survey was conducted to collect data from 265 logistics companies in Johor. The research revealed that SCM technology adoption among LSP was higher as they had adopted SCM technology in various business processes while they perceived a high level of benefits from SCM adoption. Obviously, ECommerce technology adoption among LSP is relatively low.
Abstract: The objective of this paper is to use the Pfaffian
technique to construct different classes of exact Pfaffian solutions and
N-soliton solutions to some of the generalized integrable nonlinear
partial differential equations in (3+1) dimensions. In this paper, I will
show that the Pfaffian solutions to the nonlinear PDEs are nothing but
Pfaffian identities. Solitons are among the most beneficial solutions
for science and technology, from ocean waves to transmission of
information through optical fibers or energy transport along protein
molecules. The existence of multi-solitons, especially three-soliton
solutions, is essential for information technology: it makes possible
undisturbed simultaneous propagation of many pulses in both directions.
Abstract: In this study we investigate silica nanoparticle (SiO2- NP) effects on the structure and phase properties of supported lipid monolayers and bilayers, coupling surface pressure measurements, fluorescence microscopy and atomic force microscopy. SiO2-NPs typically in size range of 10nm to 100 nm in diameter are tested. Our results suggest first that lipid molecules organization depends to their nature. Secondly, lipid molecules in the vinicity of big aggregates nanoparticles organize in liquid condensed phase whereas small aggregates are localized in both fluid liquid-expanded (LE) and liquid-condenced (LC). We demonstrated also by atomic force microscopy that by measuring friction forces it is possible to get information as if nanoparticle aggregates are recovered or not by lipid monolayers and bilayers.