Abstract: Our globalizing world has become almost a small
village and everyone can access any information at any time.
Everyone lets each other know who does whatever in which place.
We can learn which social events occur in which place in the world.
From the perspective of education, the course notes that a lecturer use
in lessons in a university in any state of America can be examined by
a student studying in a city of Africa or the Far East. This dizzying
communication we have mentioned happened thanks to fast
developments in computer and internet technologies. While these
developments occur in the world, Turkey that has a very large young
population and whose electronic infrastructure rapidly improves has
also been affected by these developments. Nowadays, mobile devices
have become common and thus, it causes to increase data traffic in
social networks. This study was carried out on students in the
different age groups in Selcuk University Vocational School of
Technical Sciences, the Department of Computer Technology.
Students’ opinions about the use of internet and social media were
obtained. The features such as using the Internet and social media
skills, purposes, operating frequency, accessing facilities and tools,
social life and effects on vocational education and so forth were
explored. The positive effects and negative effects of both internet
and social media use on the students in this department and findings
are evaluated from different perspectives and results are obtained. In
addition, relations and differences were found out statistically.
Abstract: In order to protect data privacy, image with sensitive or
private information needs to be encrypted before being outsourced to
the cloud. However, this causes difficulties in image retrieval and data
management. A secure image retrieval method based on orthogonal
decomposition is proposed in the paper. The image is divided into two
different components, for which encryption and feature extraction are
executed separately. As a result, cloud server can extract features from
an encrypted image directly and compare them with the features of the
queried images, so that the user can thus obtain the image. Different
from other methods, the proposed method has no special requirements
to encryption algorithms. Experimental results prove that the proposed
method can achieve better security and better retrieval precision.
Abstract: This paper discusses the value theory in cultural
heritage and the value theory in environmental economics. Two
economic views of the value theory are compared, within the field of
cultural heritage maintenance and within the field of the environment.
The main aims are to find common features in these two differently
structured theories under the layer of differently defined terms as well
as really differing features of these two approaches; to clear the
confusion which stems from different terminology as in fact these
terms capture the same aspects of reality; and to show possible
inspiration these two perspectives can offer one another. Another aim
is to present these two value systems in one value framework. First,
important moments of the value theory from the economic
perspective are presented, leading to the marginal revolution of (not
only) the Austrian School. Then the theory of value within cultural
heritage and environmental economics are explored. Finally,
individual approaches are compared and their potential mutual
inspiration searched for.
Abstract: Incineration of municipal solid waste (MSW) is one of
the key scopes in the global clean energy strategy. A computational
fluid dynamics (CFD) model was established in order to reveal these
features of the combustion process in a fixed porous bed of MSW.
Transporting equations and process rate equations of the waste bed
were modeled and set up to describe the incineration process,
according to the local thermal conditions and waste property
characters. Gas phase turbulence was modeled using k-ε turbulent
model and the particle phase was modeled using the kinetic theory of
granular flow. The heterogeneous reaction rates were determined
using Arrhenius eddy dissipation and the Arrhenius-diffusion
reaction rates. The effects of primary air flow rate and temperature in
the burning process of simulated MSW are investigated
experimentally and numerically. The simulation results in bed are
accordant with experimental data well. The model provides detailed
information on burning processes in the fixed bed, which is otherwise
very difficult to obtain by conventional experimental techniques.
Abstract: Data mining idea is mounting rapidly in admiration
and also in their popularity. The foremost aspire of data mining
method is to extract data from a huge data set into several forms that
could be comprehended for additional use. The data mining is a
technology that contains with rich potential resources which could be
supportive for industries and businesses that pay attention to collect
the necessary information of the data to discover their customer’s
performances. For extracting data there are several methods are
available such as Classification, Clustering, Association,
Discovering, and Visualization… etc., which has its individual and
diverse algorithms towards the effort to fit an appropriate model to
the data. STATISTICA mostly deals with excessive groups of data
that imposes vast rigorous computational constraints. These results
trials challenge cause the emergence of powerful STATISTICA Data
Mining technologies. In this survey an overview of the STATISTICA
software is illustrated along with their significant features.
Abstract: This article discusses ways to implement a
differentiated approach to developing academic motivation for
mathematical studies which relies on defining the primary structural
characteristics of motivation. The following characteristics are
considered: features of realization of cognitive activity, meaningmaking
characteristics, level of generalization and consistency of
knowledge acquired by personal experience. The assessment of the
present level of individual student understanding of each component
of academic motivation is the basis for defining the relevant
educational strategy for its further development.
Abstract: Adolescents with Autism Spectrum Disorders (ASD)
often experience social-communication difficulties that negatively
impact their social interactions with typical peers. However, unlike
other age and disability groups, there is little intervention research to
inform best practice for these students. One evidence-based strategy
for younger students with ASD is peer-mediated intervention (PMI).
PMI may be particularly promising for use with adolescents, as peers
are readily available and are natural experts for encouraging authentic
high school conversations. This paper provides a review of previous
research that evaluated the use of PMI to improve the socialcommunication
skills of students with ASD. Specific intervention
features associated with positive student outcomes are identified and
recommendations for future research are provided. Adolescents with
ASD are targeted due the critical importance of social conversation at
the high school level.
Abstract: Array-based gene expression analysis is a powerful
tool to profile expression of genes and to generate information on
therapeutic effects of new anti-cancer compounds. Anti-apoptotic
effect of thymoquinone was studied in MCF7 breast cancer cell line
using gene expression profiling with cDNA microarray. The purity
and yield of RNA samples were determined using RNeasyPlus Mini
kit. The Agilent RNA 6000 NanoLabChip kit evaluated the quantity
of the RNA samples. AffinityScript RT oligo-dT promoter primer
was used to generate cDNA strands. T7 RNA polymerase was used to
convert cDNA to cRNA. The cRNA samples and human universal
reference RNA were labelled with Cy-3-CTP and Cy-5-CTP,
respectively. Feature Extraction and GeneSpring softwares analysed
the data. The single experiment analysis revealed involvement of 64
pathways with up-regulated genes and 78 pathways with downregulated
genes. The MAPK and p38-MAPK pathways were
inhibited due to the up-regulation of PTPRR gene. The inhibition of
p38-MAPK suggested up-regulation of TGF-ß pathway. Inhibition of
p38-MAPK caused up-regulation of TP53 and down-regulation of
Bcl2 genes indicating involvement of intrinsic apoptotic pathway.
Down-regulation of CARD16 gene as an adaptor molecule regulated
CASP1 and suggested necrosis-like programmed cell death and
involvement of caspase in apoptosis. Furthermore, down-regulation
of GPCR, EGF-EGFR signalling pathways suggested reduction of
ER. Involvement of AhR pathway which control cytochrome P450
and glucuronidation pathways showed metabolism of Thymoquinone.
The findings showed differential expression of several genes in
apoptosis pathways with thymoquinone treatment in estrogen
receptor-positive breast cancer cells.
Abstract: The paper describes the OAS role in dispute
resolution. The authors make an attempt to identify a general pattern
of the OAS activities within the peaceful settlement of interstate
conflicts, in the beginning of 21st century, as well as to analyze some
features of Honduras–Belize, Nicaragua–Honduras, Honduras–El
Salvador, Costa-Rica–Nicaragua, Colombia–Ecuador cases.
Abstract: Nowadays, huge amount of multimedia repositories
make the browsing, retrieval and delivery of video contents very slow
and even difficult tasks. Video summarization has been proposed to
improve faster browsing of large video collections and more efficient
content indexing and access. In this paper, we focus on approaches to
video summarization. The video summaries can be generated in many
different forms. However, two fundamentals ways to generate
summaries are static and dynamic. We present different techniques
for each mode in the literature and describe some features used for
generating video summaries. We conclude with perspective for
further research.
Abstract: Offering a Product-Service System (PSS) is a
well-accepted strategy that companies may adopt to provide a set of
systemic solutions to customers. PSSs were initially provided in a
simple form but now take diversified and complex forms involving
multiple services, products and technologies. With the growing
interest in the PSS, frameworks for the PSS development have been
introduced by many researchers. However, most of the existing
frameworks fail to examine various relations existing in a complex
PSS. Since designing a complex PSS involves full integration of
multiple products and services, it is essential to identify not only
product-service relations but also product-product/ service-service
relations. It is also equally important to specify how they are related
for better understanding of the system. Moreover, as customers tend to
view their purchase from a more holistic perspective, a PSS should be
developed based on the whole system’s requirements, rather than
focusing only on the product requirements or service requirements.
Thus, we propose a framework to develop a complex PSS that is
coordinated fully with the requirements of both worlds. Specifically,
our approach adopts a multi-domain matrix (MDM). A MDM
identifies not only inter-domain relations but also intra-domain
relations so that it helps to design a PSS that includes highly desired
and closely related core functions/ features. Also, various dependency
types and rating schemes proposed in our approach would help the
integration process.
Abstract: Pulmonary Function Tests are important non-invasive
diagnostic tests to assess respiratory impairments and provides
quantifiable measures of lung function. Spirometry is the most
frequently used measure of lung function and plays an essential role
in the diagnosis and management of pulmonary diseases. However,
the test requires considerable patient effort and cooperation,
markedly related to the age of patients resulting in incomplete data
sets. This paper presents, a nonlinear model built using Multivariate
adaptive regression splines and Random forest regression model to
predict the missing spirometric features. Random forest based feature
selection is used to enhance both the generalization capability and the
model interpretability. In the present study, flow-volume data are
recorded for N= 198 subjects. The ranked order of feature importance
index calculated by the random forests model shows that the
spirometric features FVC, FEF25, PEF, FEF25-75, FEF50 and the
demographic parameter height are the important descriptors. A
comparison of performance assessment of both models prove that, the
prediction ability of MARS with the `top two ranked features namely
the FVC and FEF25 is higher, yielding a model fit of R2= 0.96 and
R2= 0.99 for normal and abnormal subjects. The Root Mean Square
Error analysis of the RF model and the MARS model also shows that
the latter is capable of predicting the missing values of FEV1 with a
notably lower error value of 0.0191 (normal subjects) and 0.0106
(abnormal subjects) with the aforementioned input features. It is
concluded that combining feature selection with a prediction model
provides a minimum subset of predominant features to train the
model, as well as yielding better prediction performance. This
analysis can assist clinicians with a intelligence support system in the
medical diagnosis and improvement of clinical care.
Abstract: MSMEs are regarded as the sunrise sector of the
Indian economy in view of its large potential for growth and likely
socio economic impact specifically on employment and income
generation. In today’s competitive business environment, global
competition forces companies to continuously seek ways of
improving their products and services. The pressure on organizations
to adapt to new technologies and external threats requires
resourcefulness, creativity and innovation. Market has become more
open, competitive and customers more demanding. Without
continuous technology innovation, no organization can ever remain
competitive. Innovations reflect a critical way in which organizations
respond to either technological or market challenges. The need of the
market is to deliver high quality products through continuous
changing in features in product, improve existing products, reduce
their cost, and improve employee skills, training, technology
infrastructure and financial policies. Therefore, the key factor of
organization’s ability to change is innovation. The study presents a
detailed review of literature on the role of technology innovation in
improving manufacturing performance of industries.
Abstract: Electroencephalogram (EEG) is a noninvasive
technique that registers signals originating from the firing of neurons
in the brain. The Emotiv EEG Neuroheadset is a consumer product
comprised of 14 EEG channels and was used to record the reactions
of the neurons within the brain to two forms of stimuli in 10
participants. These stimuli consisted of auditory and visual formats
that provided directions of ‘right’ or ‘left.’ Participants were
instructed to raise their right or left arm in accordance with the
instruction given. A scenario in OpenViBE was generated to both
stimulate the participants while recording their data. In OpenViBE,
the Graz Motor BCI Stimulator algorithm was configured to govern
the duration and number of visual stimuli. Utilizing EEGLAB under
the cross platform MATLAB®, the electrodes most stimulated during
the study were defined. Data outputs from EEGLAB were analyzed
using IBM SPSS Statistics® Version 20. This aided in determining
the electrodes to use in the development of a brain-machine interface
(BMI) using real-time EEG signals from the Emotiv EEG
Neuroheadset. Signal processing and feature extraction were
accomplished via the Simulink® signal processing toolbox. An
Arduino™ Duemilanove microcontroller was used to link the Emotiv
EEG Neuroheadset and the right and left Mecha TE™ Hands.
Abstract: This paper presents two techniques, local feature
extraction using image spectrum and low frequency spectrum
modelling using GMM to capture the underlying statistical
information to improve the performance of face recognition
system. Local spectrum features are extracted using overlap sub
block window that are mapped on the face image. For each of this
block, spatial domain is transformed to frequency domain using
DFT. A low frequency coefficient is preserved by discarding high
frequency coefficients by applying rectangular mask on the
spectrum of the facial image. Low frequency information is non-
Gaussian in the feature space and by using combination of several
Gaussian functions that has different statistical properties, the best
feature representation can be modelled using probability density
function. The recognition process is performed using maximum
likelihood value computed using pre-calculated GMM components.
The method is tested using FERET datasets and is able to achieved
92% recognition rates.
Abstract: Over the past few years, the online multimedia
collection has grown at a fast pace. Several companies showed
interest to study the different ways to organise the amount of audio
information without the need of human intervention to generate
metadata. In the past few years, many applications have emerged on
the market which are capable of identifying a piece of music in a
short time. Different audio effects and degradation make it much
harder to identify the unknown piece. In this paper, an audio
fingerprinting system which makes use of a non-parametric based
algorithm is presented. Parametric analysis is also performed using
Gaussian Mixture Models (GMMs). The feature extraction methods
employed are the Mel Spectrum Coefficients and the MPEG-7 basic
descriptors. Bin numbers replaced the extracted feature coefficients
during the non-parametric modelling. The results show that nonparametric
analysis offer potential results as the ones mentioned in
the literature.
Abstract: Due to the rapid increase of Internet, web opinion
sources dynamically emerge which is useful for both potential
customers and product manufacturers for prediction and decision
purposes. These are the user generated contents written in natural
languages and are unstructured-free-texts scheme. Therefore, opinion
mining techniques become popular to automatically process customer
reviews for extracting product features and user opinions expressed
over them. Since customer reviews may contain both opinionated and
factual sentences, a supervised machine learning technique applies
for subjectivity classification to improve the mining performance. In
this paper, we dedicate our work is the task of opinion
summarization. Therefore, product feature and opinion extraction is
critical to opinion summarization, because its effectiveness
significantly affects the identification of semantic relationships. The
polarity and numeric score of all the features are determined by
Senti-WordNet Lexicon. The problem of opinion summarization
refers how to relate the opinion words with respect to a certain
feature. Probabilistic based model of supervised learning will
improve the result that is more flexible and effective.
Abstract: This study aims to analyze ceramic employees’
occupational health and safety training expectations. To that general
objective, the study tries to examine whether occupational health and
safety training expectations of ceramic employees meaningfully
differentiate depending on demographic features and professional,
social and economic conditions. For this purpose, a questionnaire was
developed by the researcher. The research data were collected
through this questionnaire called “Questionnaire of Occupational
Health and Safety Training Expectation” (QSOHSTE). QSOHSTE
was applied to 125 ceramic employees working in Kütahya, Turkey.
Data obtained from questionnaire were analyzed via SPSS 21.
The findings, obtained from the study, revealed that employees’
agreement level to occupational health and safety training expectation
statements is generally high-level. The findings reveal that employees
expect professional interest such as increased development and
investment, preventive measures for accidents, interventions to
evaluate the working conditions, establishment of safe working
environments and sustainment of adequate equipment for
occupational health and safety training process.
Besides these findings, employees’ agreement level to
occupational health and safety training expectation statements also
varies in terms of educational level, professional seniority, income
level and perception of economic condition.
Abstract: This paper proposes a rotational invariant texture
feature based on the roughness property of the image for psoriasis
image analysis. In this work, we have applied this feature for image
classification and segmentation. The fuzzy concept is employed to
overcome the imprecision of roughness. Since the psoriasis lesion is
modeled by a rough surface, the feature is extended for calculating
the Psoriasis Area Severity Index value. For classification and
segmentation, the Nearest Neighbor algorithm is applied. We have
obtained promising results for identifying affected lesions by using
the roughness index and severity level estimation.
Abstract: The aim of this work is to build a model based on
tissue characterization that is able to discriminate pathological and
non-pathological regions from three-phasic CT images. With our
research and based on a feature selection in different phases, we are
trying to design a neural network system with an optimal neuron
number in a hidden layer. Our approach consists of three steps:
feature selection, feature reduction, and classification. For each
region of interest (ROI), 6 distinct sets of texture features are
extracted such as: first order histogram parameters, absolute gradient,
run-length matrix, co-occurrence matrix, autoregressive model, and
wavelet, for a total of 270 texture features. When analyzing more
phases, we show that the injection of liquid cause changes to the high
relevant features in each region. Our results demonstrate that for
detecting HCC tumor phase 3 is the best one in most of the features
that we apply to the classification algorithm. The percentage of
detection between pathology and healthy classes, according to our
method, relates to first order histogram parameters with accuracy of
85% in phase 1, 95% in phase 2, and 95% in phase 3.