Abstract: In this paper, we propose novel algorithmic models
based on information fusion and feature transformation in crossmodal
subspace for different types of residue features extracted from
several intra-frame and inter-frame pixel sub-blocks in video
sequences for detecting digital video tampering or forgery. An
evaluation of proposed residue features – the noise residue features
and the quantization features, their transformation in cross-modal
subspace, and their multimodal fusion, for emulated copy-move
tamper scenario shows a significant improvement in tamper detection
accuracy as compared to single mode features without transformation
in cross-modal subspace.
Abstract: This paper explores the effectiveness of machine
learning techniques in detecting firms that issue fraudulent financial
statements (FFS) and deals with the identification of factors
associated to FFS. To this end, a number of experiments have been
conducted using representative learning algorithms, which were
trained using a data set of 164 fraud and non-fraud Greek firms in the
recent period 2001-2002. The decision of which particular method to
choose is a complicated problem. A good alternative to choosing
only one method is to create a hybrid forecasting system
incorporating a number of possible solution methods as components
(an ensemble of classifiers). For this purpose, we have implemented
a hybrid decision support system that combines the representative
algorithms using a stacking variant methodology and achieves better
performance than any examined simple and ensemble method. To
sum up, this study indicates that the investigation of financial
information can be used in the identification of FFS and underline the
importance of financial ratios.
Abstract: IEEE has recently incorporated CCMP protocol to provide robust security to IEEE 802.11 wireless LANs. It is found that CCMP has been designed with a weak nonce construction and transmission mechanism, which leads to the exposure of initial counter value. This weak construction of nonce renders the protocol vulnerable to attacks by intruders. This paper presents how the initial counter can be pre-computed by the intruder. This vulnerability of counter block value leads to pre-computation attack on the counter mode encryption of CCMP. The failure of the counter mode will result in the collapse of the whole security mechanism of 802.11 WLAN.
Abstract: Recent years have seen a growing trend towards the
integration of multiple information sources to support large-scale
prediction of protein-protein interaction (PPI) networks in model
organisms. Despite advances in computational approaches, the
combination of multiple “omic" datasets representing the same type
of data, e.g. different gene expression datasets, has not been
rigorously studied. Furthermore, there is a need to further investigate
the inference capability of powerful approaches, such as fullyconnected
Bayesian networks, in the context of the prediction of PPI
networks. This paper addresses these limitations by proposing a
Bayesian approach to integrate multiple datasets, some of which
encode the same type of “omic" data to support the identification of
PPI networks. The case study reported involved the combination of
three gene expression datasets relevant to human heart failure (HF).
In comparison with two traditional methods, Naive Bayesian and
maximum likelihood ratio approaches, the proposed technique can
accurately identify known PPI and can be applied to infer potentially
novel interactions.
Abstract: Mathematical programming has been applied to various
problems. For many actual problems, the assumption that the parameters
involved are deterministic known data is often unjustified. In
such cases, these data contain uncertainty and are thus represented
as random variables, since they represent information about the
future. Decision-making under uncertainty involves potential risk.
Stochastic programming is a commonly used method for optimization
under uncertainty. A stochastic programming problem with recourse
is referred to as a two-stage stochastic problem. In this study, we
consider a stochastic programming problem with simple integer
recourse in which the value of the recourse variable is restricted to a
multiple of a nonnegative integer. The algorithm of a dynamic slope
scaling procedure for solving this problem is developed by using a
property of the expected recourse function. Numerical experiments
demonstrate that the proposed algorithm is quite efficient. The
stochastic programming model defined in this paper is quite useful
for a variety of design and operational problems.
Abstract: To investigate the correspondence of theory and
practice, a successfully implemented Knowledge Management
System (KMS) is explored through the lens of Alavi and Leidner-s
proposed KMS framework for the analysis of an information system
in knowledge management (Framework-AISKM). The applied KMS
system was designed to manage curricular knowledge in a distributed
university environment. The motivation for the KMS is discussed
along with the types of knowledge necessary in an academic setting.
Elements of the KMS involved in all phases of capturing and
disseminating knowledge are described. As the KMS matures the
resulting data stores form the precursor to and the potential for
knowledge mining. The findings from this exploratory study indicate
substantial correspondence between the successful KMS and the
theory-based framework providing provisional confirmation for the
framework while suggesting factors that contributed to the system-s
success. Avenues for future work are described.
Abstract: In Knowledge Structure Graph, each course unit
represents a phase of learning activities. Both learning portfolios and
Knowledge Structure Graphs contain learning information of students
and let teachers know which content are difficulties and fails. The
study purposes "Dual Mode On-line Learning Diagnosis System" that
integrates two search methods: learning portfolio and knowledge
structure. Teachers can operate the proposed system and obtain the
information of specific students without any computer science
background. The teachers can find out failed students in advance and
provide remedial learning resources.
Abstract: Nowadays, Gene Ontology has been used widely by many researchers for biological data mining and information retrieval, integration of biological databases, finding genes, and incorporating knowledge in the Gene Ontology for gene clustering. However, the increase in size of the Gene Ontology has caused problems in maintaining and processing them. One way to obtain their accessibility is by clustering them into fragmented groups. Clustering the Gene Ontology is a difficult combinatorial problem and can be modeled as a graph partitioning problem. Additionally, deciding the number k of clusters to use is not easily perceived and is a hard algorithmic problem. Therefore, an approach for solving the automatic clustering of the Gene Ontology is proposed by incorporating cohesion-and-coupling metric into a hybrid algorithm consisting of a genetic algorithm and a split-and-merge algorithm. Experimental results and an example of modularized Gene Ontology in RDF/XML format are given to illustrate the effectiveness of the algorithm.
Abstract: Physical education (PE) is still neglected in schools
despite its academic, social, psychological, and health benefits.
Based on the assumption that Information and Communication
Technologies (ICTs) can contribute to the development of PE in
schools, this study aims to design a model of the factors affecting the
adoption of ICTs for PE in schools. The proposed model is based on
a sound theoretical framework. It was designed following a literature
review of technology adoption theories and of ICT adoption factors
for physical education. The technology adoption model that fitted to
the best all ICT adoption factors was then chosen as the basis for the
proposed model. It was found that the Unified Theory of Acceptance
and Use of Technology (UTAUT) is the most adequate theoretical
framework for the modeling of ICT adoption factors for physical
education.
Abstract: Mobile IP has been developed to provide the
continuous information network access to mobile users. In IP-based
mobile networks, location management is an important component of
mobility management. This management enables the system to track
the location of mobile node between consecutive communications. It
includes two important tasks- location update and call delivery.
Location update is associated with signaling load. Frequent updates
lead to degradation in the overall performance of the network and the
underutilization of the resources. It is, therefore, required to devise
the mechanism to minimize the update rate. Mobile IPv6 (MIPv6)
and Hierarchical MIPv6 (HMIPv6) have been the potential
candidates for deployments in mobile IP networks for mobility
management. HMIPv6 through studies has been shown with better
performance as compared to MIPv6. It reduces the signaling
overhead traffic by making registration process local. In this paper,
we present performance analysis of MIPv6 and HMIPv6 using an
analytical model. Location update cost function is formulated based
on fluid flow mobility model. The impact of cell residence time, cell
residence probability and user-s mobility is investigated. Numerical
results are obtained and presented in graphical form. It is shown that
HMIPv6 outperforms MIPv6 for high mobility users only and for low
mobility users; performance of both the schemes is almost equivalent
to each other.
Abstract: In this paper, we present a new algorithm for clustering data in large datasets using image processing approaches. First the dataset is mapped into a binary image plane. The synthesized image is then processed utilizing efficient image processing techniques to cluster the data in the dataset. Henceforth, the algorithm avoids exhaustive search to identify clusters. The algorithm considers only a small set of the data that contains critical boundary information sufficient to identify contained clusters. Compared to available data clustering techniques, the proposed algorithm produces similar quality results and outperforms them in execution time and storage requirements.
Abstract: Recently, X. Ge and J. Qian investigated some relations between higher mathematics scores and calculus scores (resp. linear algebra scores, probability statistics scores) for Chinese university students. Based on rough-set theory, they established an information system S = (U,CuD,V, f). In this information system, higher mathematics score was taken as a decision attribute and calculus score, linear algebra score, probability statistics score were taken as condition attributes. They investigated importance of each condition attribute with respective to decision attribute and strength of each condition attribute supporting decision attribute. In this paper, we give further investigations for this issue. Based on the above information system S = (U, CU D, V, f), we analyze the decision rules between condition and decision granules. For each x E U, we obtain support (resp. strength, certainty factor, coverage factor) of the decision rule C —>x D, where C —>x D is the decision rule induced by x in S = (U, CU D, V, f). Results of this paper gives new analysis of on higher mathematics scores for Chinese university students, which can further lead Chinese university students to raise higher mathematics scores in Chinese graduate student entrance examination.
Abstract: In networks, mainly small and medium-sized businesses benefit from the knowledge, experiences and solutions offered by experts from industry and science or from the exchange with practitioners. Associations which focus, among other things, on networking, information and knowledge transfer and which are interested in supporting such cooperations are especially well suited to provide such networks and the appropriate web platforms. Using METORA as an example – a project developed and run by the Federal Association for Information Economy, Telecommunications and New Media e.V. (BITKOM) for the Federal Ministry of Economics and Technology (BMWi) – This paper will discuss how associations and other network organizations can achieve this task and what conditions they have to consider.
Abstract: The main objective of this paper is to contribute the
existing knowledge transfer and IT Outsourcing literature
specifically in the context of Malaysia by reviewing the current
practices of e-government IT outsourcing in Malaysia including the
issues and challenges faced by the public agencies in transferring the
knowledge during the engagement. This paper discusses various
factors and different theoretical model of knowledge transfer starting
from the traditional model to the recent model suggested by the
scholars. The present paper attempts to align organizational
knowledge from the knowledge-based view (KBV) and
organizational learning (OL) lens. This review could help shape the
direction of both future theoretical and empirical studies on inter-firm
knowledge transfer specifically on how KBV and OL perspectives
could play significant role in explaining the complex relationships
between the client and vendor in inter-firm knowledge transfer and
the role of organizational management information system and
Transactive Memory System (TMS) to facilitate the organizational
knowledge transferring process. Conclusion is drawn and further
research is suggested.
Abstract: The trends of design and development of information systems have undergone a variety of ongoing phases and stages. These variations have been evolved due to brisk changes in user requirements and business needs. To meet these requirements and needs, a flexible and agile business solution was required to come up with the latest business trends and styles. Another obstacle in agility of information systems was typically different treatment of same diseases of two patients: business processes and information services. After the emergence of information technology, the business processes and information systems have become counterparts. But these two business halves have been treated under totally different standards. There is need to streamline the boundaries of these both pillars that are equally sharing information system's burdens and liabilities. In last decade, the object orientation has evolved into one of the major solutions for modern business needs and now, SOA is the solution to shift business on ranks of electronic platform. BPM is another modern business solution that assists to regularize optimization of business processes. This paper discusses how object orientation can be conformed to incorporate or embed SOA in BPM for improved information systems.
Abstract: Every organization is continually subject to new damages and threats which can be resulted from their operations or their goal accomplishment. Methods of providing the security of space and applied tools have been widely changed with increasing application and development of information technology (IT). From this viewpoint, information security management systems were evolved to construct and prevent reiterating the experienced methods. In general, the correct response in information security management systems requires correct decision making, which in turn requires the comprehensive effort of managers and everyone involved in each plan or decision making. Obviously, all aspects of work or decision are not defined in all decision making conditions; therefore, the possible or certain risks should be considered when making decisions. This is the subject of risk management and it can influence the decisions. Investigation of different approaches in the field of risk management demonstrates their progress from quantitative to qualitative methods with a process approach.
Abstract: Knowledge sharing in general and the contextual
access to knowledge in particular, still represent a key challenge in
the knowledge management framework. Researchers on semantic
web and human machine interface study techniques to enhance this
access. For instance, in semantic web, the information retrieval is
based on domain ontology. In human machine interface, keeping
track of user's activity provides some elements of the context that can
guide the access to information. We suggest an approach based on
these two key guidelines, whilst avoiding some of their weaknesses.
The approach permits a representation of both the context and the
design rationale of a project for an efficient access to knowledge. In
fact, the method consists of an information retrieval environment
that, in the one hand, can infer knowledge, modeled as a semantic
network, and on the other hand, is based on the context and the
objectives of a specific activity (the design). The environment we
defined can also be used to gather similar project elements in order to
build classifications of tasks, problems, arguments, etc. produced in a
company. These classifications can show the evolution of design
strategies in the company.
Abstract: Results of Chilean wine classification based on the
information provided by an electronic nose are reported in this paper.
The classification scheme consists of two parts; in the first stage,
Principal Component Analysis is used as feature extraction method to
reduce the dimensionality of the original information. Then, Radial
Basis Functions Neural Networks is used as pattern recognition
technique to perform the classification. The objective of this study is
to classify different Cabernet Sauvignon, Merlot and Carménère wine
samples from different years, valleys and vineyards of Chile.
Abstract: The aim of this study was to investigate the
environmental conservation behavior of the Applied Health Science
students of Suranaree University of Technology, a green and clean
university. The sample group was 184 Applied Health Science
students (medical, nursing, and public health). A questionnaire was
used to collect information.
The result of the study found that the students had more negative
than positive behaviors towards energy, water, and forest
conservation. This result can be used as basic information for
designing long-term behavior modification activities or research
projects on environmental conservation. Thus Applied Health
Science students will be encouraged to be conscious and also be a
good example of environmental conservation behavior.
Abstract: Web-based technologies have created numerous
opportunities for electronic word-of-mouth (eWOM) communication.
There are many factors that affect customer adoption and decisionmaking
process. However, only a few researches focus on some
factors such as the membership time of forum and propensity to trust.
Using a discrete-time event simulation to simulate a diffusion model
along with a consumer decision model, the study shows the effect of
each factor on adoption of opinions on on-line discussion forum. The
purpose of this study is to examine the effect of factor affecting
information adoption and decision making process. The model is
constructed to test quantitative aspects of each factor. The simulation
study shows the membership time and the propensity to trust has an
effect on information adoption and purchasing decision. The result of
simulation shows that the longer the membership time in the
communities and the higher propensity to trust could lead to the
higher demand rates because consumers find it easier and faster to
trust the person in the community and then adopt the eWOM. Other
implications for both researchers and practitioners are provided.