Abstract: One major issue that is regularly cited as a block to
the widespread use of online assessments in eLearning, is that of the
authentication of the student and the level of confidence that an
assessor can have that the assessment was actually completed by that
student. Currently, this issue is either ignored, in which case
confidence in the assessment and any ensuing qualification is
damaged, or else assessments are conducted at central, controlled
locations at specified times, losing the benefits of the distributed
nature of the learning programme. Particularly as we move towards
constructivist models of learning, with intentions towards achieving
heutagogic learning environments, the benefits of a properly
managed online assessment system are clear. Here we discuss some
of the approaches that could be adopted to address these issues,
looking at the use of existing security and biometric techniques,
combined with some novel behavioural elements. These approaches
offer the opportunity to validate the student on accessing an
assessment, on submission, and also during the actual production of
the assessment. These techniques are currently under development in
the DECADE project, and future work will evaluate and report their
use..
Abstract: This questionnaire-based study, aimed to measure and
compare the awareness of English reading strategies among EFL
learners at Bangkok University (BU) classified by their gender, field
of study, and English learning experience. Proportional stratified
random sampling was employed to formulate a sample of 380 BU
students. The data were statistically analyzed in terms of the mean
and standard deviation. t-Test analysis was used to find differences in
awareness of reading strategies between two groups (-male and
female- /-science and social-science students). In addition, one-way
analysis of variance (ANOVA) was used to compare reading strategy
awareness among BU students with different lengths of English
learning experience. The results of this study indicated that the
overall awareness of reading strategies of EFL learners at BU was at
a high level (ðÑ = 3.60) and that there was no statistically significant
difference between males and females, and among students who have
different lengths of English learning experience at the significance
level of 0.05. However, significant differences among students
coming from different fields of study were found at the same level of
significance.
Abstract: Grid computing is a group of clusters connected over
high-speed networks that involves coordinating and sharing
computational power, data storage and network resources operating
across dynamic and geographically dispersed locations. Resource
management and job scheduling are critical tasks in grid computing.
Resource selection becomes challenging due to heterogeneity and
dynamic availability of resources. Job scheduling is a NP-complete
problem and different heuristics may be used to reach an optimal or
near optimal solution. This paper proposes a model for resource and
job scheduling in dynamic grid environment. The main focus is to
maximize the resource utilization and minimize processing time of
jobs. Grid resource selection strategy is based on Max Heap Tree
(MHT) that best suits for large scale application and root node of
MHT is selected for job submission. Job grouping concept is used to
maximize resource utilization for scheduling of jobs in grid
computing. Proposed resource selection model and job grouping
concept are used to enhance scalability, robustness, efficiency and
load balancing ability of the grid.
Abstract: Nanophotocatalysts such as titanium (TiO2), zinc (ZnO), and iron (Fe2O3) oxides can be used in organic pollutants oxidation, and in many other applications. But among the challenges for technological application (scale-up) of the nanotechnology scientific developments two aspects are still little explored: research on environmental risk of the nanomaterials preparation methods, and the study of nanomaterials properties and/or performance variability. The environmental analysis was performed for six different methods of ZnO nanoparticles synthesis, and showed that it is possible to identify the more environmentally compatible process even at laboratory scale research. The obtained ZnO nanoparticles were tested as photocatalysts, and increased the degradation rate of the Rhodamine B dye up to 30 times.
Abstract: “Web of Trust" is one of the recognized goals for
Web 2.0. It aims to make it possible for the people to take
responsibility for what they publish on the web, including
organizations, businesses and individual users. These objectives,
among others, drive most of the technologies and protocols recently
standardized by the governing bodies. One of the great advantages of
Web infrastructure is decentralization of publication. The primary
motivation behind Web 2.0 is to assist the people to add contents for
Collective Intelligence (CI) while providing mechanisms to link
content with people for evaluations and accountability of
information. Such structure of contents will interconnect users and
contents so that users can use contents to find participants and vice
versa. This paper proposes conceptual information storage and
linking model, based on decentralized information structure, that
links contents and people together. The model uses FOAF, Atom,
RDF and RDFS and can be used as a blueprint to develop Web 2.0
applications for any e-domain. However, primary target for this
paper is online trust evaluation domain. The proposed model targets
to assist the individuals to establish “Web of Trust" in online trust
domain.
Abstract: Participation in sporting activities can lead to injury.
Sport injuries have been widely studied in many sports including the
more extreme categories of aquatic board sports. Kitesurfing is a
relatively new water surface action sport, and has not yet been
widely studied in terms of injuries and stress on the body. The aim of
this study was to get information about which injuries that are most
common among kitesurfing participants, where they occur, and their
causes. Injuries were studied using an international open web
questionnaire (n=206).
The results showed that many respondents reported injuries, in
total 251 injuries to knee (24%), ankle (17%), trunk (16%) and
shoulders (10%), often sustained while doing jumps and tricks
(40%). Among the reported injuries were joint injuries (n=101),
muscle/tendon damages (n=47), wounds and cuts (n=36) and bone
fractures (n=28). Also environmental factors and equipment can
influence the risk of injury, or the extent of injury in a hazardous
situation. Conclusively, the information from this retrospective study
supports earlier studies in terms of prevalence and site of injuries.
Suggestively, this information should be used for to build a
foundation of knowledge about the sport for development of
applications for physical training and product development.
Abstract: In this paper, we proposed a method for detecting consistency violation between UML state machine diagrams and communication diagrams using Alloy. Using input language of Alloy, the proposed method expresses system behaviors described by state machine diagrams, message sequences described by communication diagrams, and a consistency property. As a result of application for an example system, we confirmed that consistency violation could be detected using Alloy correctly.
Abstract: The aim of this article is to explain how features of attacks could be extracted from the packets. It also explains how vectors could be built and then applied to the input of any analysis stage. For analyzing, the work deploys the Feedforward-Back propagation neural network to act as misuse intrusion detection system. It uses ten types if attacks as example for training and testing the neural network. It explains how the packets are analyzed to extract features. The work shows how selecting the right features, building correct vectors and how correct identification of the training methods with nodes- number in hidden layer of any neural network affecting the accuracy of system. In addition, the work shows how to get values of optimal weights and use them to initialize the Artificial Neural Network.
Abstract: In general, image-based 3D scenes can now be found in many popular vision systems, computer games and virtual reality tours. So, It is important to segment ROI (region of interest) from input scenes as a preprocessing step for geometric stricture detection in 3D scene. In this paper, we propose a method for segmenting ROI based on tensor voting and Dirichlet process mixture model. In particular, to estimate geometric structure information for 3D scene from a single outdoor image, we apply the tensor voting and Dirichlet process mixture model to a image segmentation. The tensor voting is used based on the fact that homogeneous region in an image are usually close together on a smooth region and therefore the tokens corresponding to centers of these regions have high saliency values. The proposed approach is a novel nonparametric Bayesian segmentation method using Gaussian Dirichlet process mixture model to automatically segment various natural scenes. Finally, our method can label regions of the input image into coarse categories: “ground", “sky", and “vertical" for 3D application. The experimental results show that our method successfully segments coarse regions in many complex natural scene images for 3D.
Abstract: The use of hard and brittle material has become
increasingly more extensive in recent years. Therefore processing of
these materials for the parts fabrication has become a challenging
problem. However, it is time-consuming to machine the hard brittle
materials with the traditional metal-cutting technique that uses
abrasive wheels. In addition, the tool would suffer excessive wear as
well. However, if ultrasonic energy is applied to the machining
process and coupled with the use of hard abrasive grits, hard and
brittle materials can be effectively machined. Ultrasonic machining
process is mostly used for the brittle materials. The present research
work has developed models using finite element approach to predict
the mechanical stresses sand strains produced in the tool during
ultrasonic machining process. Also the flow behavior of abrasive
slurry coming out of the nozzle has been studied for simulation using
ANSYS CFX module. The different abrasives of different grit sizes
have been used for the experimentation work.
Abstract: This paper shows a simple and effective approach to
the design and implementation of Industrial Information Systems
(IIS) oriented to control the characteristics of each individual product manufactured in a production line and also their manufacturing conditions. The particular products considered in this work are large steel strips that are coiled just after their manufacturing. However, the approach is directly applicable to coiled strips in other industries, like
paper, textile, aluminum, etc. These IIS provide very detailed information of each manufactured product, which complement the general information managed by the ERP system of the production line. In spite of the high importance of this type of IIS to guarantee and improve the quality of the products manufactured in many industries, there are very few works about them in the technical literature. For this reason, this paper represents an important contribution to the development of this type of IIS, providing guidelines for their design, implementation and exploitation.
Abstract: In this paper we describe a computer-aided diagnosis (CAD) system for automated detection of pulmonary nodules in computed-tomography (CT) images. After extracting the pulmonary parenchyma using a combination of image processing techniques, a region growing method is applied to detect nodules based on 3D geometric features. We applied the CAD system to CT scans collected in a screening program for lung cancer detection. Each scan consists of a sequence of about 300 slices stored in DICOM (Digital Imaging and Communications in Medicine) format. All malignant nodules were detected and a low false-positive detection rate was achieved.
Abstract: The biomarker for colorectal cancer (CRC) is CEACAM-6 antigen (C6AG). Therefore, this study aims to develop a novel, simple and low-cost CEACAM-6 antigen immumosensor (C6AG-IMS), based on electrical impedance measurement, for precise determination of C6AG. A low-cost screen-printed graphite electrode was constructed and used as the sensor, with CEACAM-6 antibody (C6AB) immobilized on it. The procedures of sensor fabrication and antibody immobilization are simple and low-cost. Measurement of the electrical impedance at a definite frequency ranges (0.43 – 1.26 MHz) showed that the C6AG-IMS has an excellent linear (r2>0.9) response range (8.125 – 65 pg/mL), covering the normal physiological and pathological ranges of blood C6AG levels. Also, the C6AG-IMS has excellent reliability and validity, with the intraclass correlation coefficient being 0.97. In conclusion, a novel, simple, low-cost and reliable C6AG-IMS was designed and developed, being able to accurately determine blood C6AG levels in the range of pathological and normal physiological regions. The C6AG-IMS can provide a point-of-care and immediate screening results to the user at home.
Abstract: The concurrent era is characterised by strengthened interactions among financial markets and increased capital mobility globally. In this frames we examine the effects the international financial integration process has on the European bond markets. We perform a comparative study of the interactions of the European and international bond markets and exploit Cointegration analysis results on the elimination of stochastic trends and the decomposition of the underlying long run equilibria and short run causal relations. Our investigation provides evidence on the relation between the European integration process and that of globalisation, viewed through the bond markets- sector. Additionally the structural formulation applied, offers significant implications of the findings. All in all our analysis offers a number of answers on crucial queries towards the European bond markets integration process.
Abstract: Text categorization (the assignment of texts in natural language into predefined categories) is an important and extensively studied problem in Machine Learning. Currently, popular techniques developed to deal with this task include many preprocessing and learning algorithms, many of which in turn require tuning nontrivial internal parameters. Although partial studies are available, many authors fail to report values of the parameters they use in their experiments, or reasons why these values were used instead of others. The goal of this work then is to create a more thorough comparison of preprocessing parameters and their mutual influence, and report interesting observations and results.
Abstract: A novel thermo-sensitive superabsorbent hydrogel
with salt- and pH-responsiveness properties was obtained by grafting
of mixtures of acrylic acid (AA) and N-isopropylacrylamide
(NIPAM) monomers onto kappa-carrageenan, kC, using ammonium
persulfate (APS) as a free radical initiator in the presence of
methylene bisacrylamide (MBA) as a crosslinker. Infrared
spectroscopy was carried out to confirm the chemical structure of the
hydrogel. Moreover, morphology of the samples was examined by
scanning electron microscopy (SEM). The effect of MBA
concentration and AA/NIPAM weight ratio on the water absorbency
capacity has been investigated. The swelling variations of hydrogels
were explained according to swelling theory based on the hydrogel
chemical structure. The hydrogels exhibited salt-sensitivity and
cation exchange properties. The temperature- and pH-reversibility
properties of the hydrogels make the intelligent polymers as good
candidates for considering as potential carriers for bioactive agents,
e.g. drugs.
Abstract: Rule Discovery is an important technique for mining knowledge from large databases. Use of objective measures for discovering interesting rules lead to another data mining problem, although of reduced complexity. Data mining researchers have studied subjective measures of interestingness to reduce the volume of discovered rules to ultimately improve the overall efficiency of KDD process. In this paper we study novelty of the discovered rules as a subjective measure of interestingness. We propose a hybrid approach that uses objective and subjective measures to quantify novelty of the discovered rules in terms of their deviations from the known rules. We analyze the types of deviation that can arise between two rules and categorize the discovered rules according to the user specified threshold. We implement the proposed framework and experiment with some public datasets. The experimental results are quite promising.
Abstract: Metal stamping die design is a complex, experiencebased
and time-consuming task. Various artificial intelligence (AI)
techniques are being used by worldwide researchers for stamping die
design to reduce complexity, dependence on human expertise and
time taken in design process as well as to improve design efficiency.
In this paper a comprehensive review of applications of AI
techniques in manufacturability evaluation of sheet metal parts, die
design and process planning of metal stamping die is presented.
Further the salient features of major research work published in the
area of metal stamping are presented in tabular form and scope of
future research work is identified.
Abstract: An advanced Monte Carlo simulation method, called Subset Simulation (SS) for the time-dependent reliability prediction for underground pipelines has been presented in this paper. The SS can provide better resolution for low failure probability level with efficient investigating of rare failure events which are commonly encountered in pipeline engineering applications. In SS method, random samples leading to progressive failure are generated efficiently and used for computing probabilistic performance by statistical variables. SS gains its efficiency as small probability event as a product of a sequence of intermediate events with larger conditional probabilities. The efficiency of SS has been demonstrated by numerical studies and attention in this work is devoted to scrutinise the robustness of the SS application in pipe reliability assessment. It is hoped that the development work can promote the use of SS tools for uncertainty propagation in the decision-making process of underground pipelines network reliability prediction.
Abstract: Transesterification of candlenut (aleurites moluccana)
oil with methanol using potassium hydroxide as catalyst was
studied. The objective of the present investigation was to produce
the methyl ester for use as biodiesel. The operation variables
employed were methanol to oil molar ratio (3:1 – 9:1), catalyst
concentration (0.50 – 1.5 %) and temperature (303 – 343K). Oil
volume of 150 mL, reaction time of 75 min were fixed as common
parameters in all the experiments. The concentration of methyl ester
was evaluated by mass balance of free glycerol formed which was
analyzed by using periodic acid. The optimal triglyceride conversion
was attained by using methanol to oil ratio of 6:1, potassium
hydroxide as catalyst was of 1%, at room temperature. Methyl ester
formed was characterized by its density, viscosity, cloud and pour
points. The biodiesel properties had properties similar to those of
diesel oil, except for the viscosity that was higher.