Abstract: Information and Communication Technologies (ICT) in mathematical education is a very active field of research and innovation, where learning is understood to be meaningful and grasping multiple linked representation rather than rote memorization, a great amount of literature offering a wide range of theories, learning approaches, methodologies and interpretations, are generally stressing the potentialities for teaching and learning using ICT. Despite the utilization of new learning approaches with ICT, students experience difficulties in learning concepts relevant to understanding mathematics, much remains unclear about the relationship between the computer environment, the activities it might support, and the knowledge that might emerge from such activities. Many questions that might arise in this regard: to what extent does the use of ICT help students in the process of understanding and solving tasks or problems? Is it possible to identify what aspects or features of students' mathematical learning can be enhanced by the use of technology? This paper will highlight the interest of the integration of information and communication technologies (ICT) into the teaching and learning of mathematics (quadratic functions), it aims to investigate the effect of four instructional methods on students- mathematical understanding and problem solving. Quantitative and qualitative methods are used to report about 43 students in middle school. Results showed that mathematical thinking and problem solving evolves as students engage with ICT activities and learn cooperatively.
Abstract: Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved.
Abstract: This paper presents an information retrieval model on
XML documents based on tree matching. Queries and documents are
represented by extended trees. An extended tree is built starting from
the original tree, with additional weighted virtual links between each
node and its indirect descendants allowing to directly reach each
descendant. Therefore only one level separates between each node
and its indirect descendants. This allows to compare the user query
and the document with flexibility and with respect to the structural
constraints of the query. The content of each node is very important to
decide weither a document element is relevant or not, thus the content
should be taken into account in the retrieval process. We separate
between the structure-based and the content-based retrieval processes.
The content-based score of each node is commonly based on the
well-known Tf × Idf criteria. In this paper, we compare between
this criteria and another one we call Tf × Ief. The comparison
is based on some experiments into a dataset provided by INEX1 to
show the effectiveness of our approach on one hand and those of
both weighting functions on the other.
Abstract: The early diagnostic decision making in industrial processes is absolutely necessary to produce high quality final products. It helps to provide early warning for a special event in a process, and finding its assignable cause can be obtained. This work presents a hybrid diagnostic schmes for batch processes. Nonlinear representation of raw process data is combined with classification tree techniques. The nonlinear kernel-based dimension reduction is executed for nonlinear classification decision boundaries for fault classes. In order to enhance diagnosis performance for batch processes, filtering of the data is performed to get rid of the irrelevant information of the process data. For the diagnosis performance of several representation, filtering, and future observation estimation methods, four diagnostic schemes are evaluated. In this work, the performance of the presented diagnosis schemes is demonstrated using batch process data.
Abstract: Cultural stories are political. They register cultural
phenomena and their relations with the world and society in term of
their existence, function, characteristics by using different context.
This paper will provide a new way of rethinking which will help us
to rethink the relationship between fiction and politics. It discusses
the theme of human rights and it shows the relevance between art and
politics by studying the civil society through a literary framework.
Reasons to establish a relationship between fiction and politics are
the relevant themes and universal issues among the two disciplines.
Both disciplines are sets of views and ideas formulated by the human
mind to explain political or cultural phenomenon. Other reasons are
the complexity and depth of the author-s vision, and the need to
explain the violations of human rights in a more active structure
which can relate to emotional and social existence.
Abstract: Due to the fact that in the new century customers tend
to express globally increasing demands, networks of interconnected
businesses have been established in societies and the management of
such networks seems to be a major key through gaining competitive
advantages. Supply chain management encompasses such managerial
activities. Within a supply chain, a critical role is played by quality.
QFD is a widely-utilized tool which serves the purpose of not only
bringing quality to the ultimate provision of products or service
packages required by the end customer or the retailer, but it can also
initiate us into a satisfactory relationship with our initial customer;
that is the wholesaler. However, the wholesalers- cooperation is
considerably based on the capabilities that are heavily dependent on
their locations and existing circumstances. Therefore, it is undeniable
that for all companies each wholesaler possesses a specific
importance ratio which can heavily influence the figures calculated in
the House of Quality in QFD. Moreover, due to the competitiveness
of the marketplace today, it-s been widely recognized that
consumers- expression of demands has been highly volatile in
periods of production. Apparently, such instability and proneness to
change has been very tangibly noticed and taking it into account
during the analysis of HOQ is widely influential and doubtlessly
required. For a more reliable outcome in such matters, this article
demonstrates the application viability of Analytic Network Process
for considering the wholesalers- reputation and simultaneously
introduces a mortality coefficient for the reliability and stability of
the consumers- expressed demands in course of time. Following to
this, the paper provides further elaboration on the relevant
contributory factors and approaches through the calculation of such
coefficients. In the end, the article concludes that an empirical
application is needed to achieve broader validity.
Abstract: Firstly, research and development on RFID focuses on
manufacturing and retail sectors, because it can improve supply chain
efficiency. But, now a variety of field is considered the next research
area for Radio Frequency Identification (RFID). Although RFID is
infancy, RFID technology has great potential in power industry to
significantly reduce cost, and improve quality of power supply. To
complement the limitation of RFID, we adopt the WSN (Wireless
Sensor Network) technology. However, relevant experience is limited,
the challenge will be to derive requirement from business practice and
to determine whether it is possible or not. To explore this issue, we
conduct a case study on implementing power facility management
system using RFID/WSN in Korea Electric Power Corporation
(KEPCO). In this paper we describe requirement from power industry.
And we introduce design and implementation of the test bed.
Abstract: Indices summarizing community structure are used to
evaluate fundamental community ecology, species interaction,
biogeographical factors, and environmental stress. Some of these
indices are insensitive to gross community changes induced by
contaminants of pollution. Diversity indices and similarity indices are
reviewed considering their ecological application, both theoretical
and practical. For some useful indices, empirical equations are given
to calculate the expected maximum value of the indices to which the
observed values can be related at any combination of sample sizes at
the experimental sites. This paper examines the effects of sample size
and diversity on the expected values of diversity indices and
similarity indices, using various formulae. It has been shown that all
indices are strongly affected by sample size and diversity. In some
indices, this influence is greater than the others and an attempt has
been made to deal with these influences.
Abstract: Web applications have become very complex and
crucial, especially when combined with areas such as CRM
(Customer Relationship Management) and BPR (Business Process
Reengineering), the scientific community has focused attention to
Web applications design, development, analysis, and testing, by
studying and proposing methodologies and tools. This paper
proposes an approach to automatic multi-dimensional concern
mining for Web Applications, based on concepts analysis, impact
analysis, and token-based concern identification. This approach lets
the user to analyse and traverse Web software relevant to a particular
concern (concept, goal, purpose, etc.) via multi-dimensional
separation of concerns, to document, understand and test Web
applications. This technique was developed in the context of WAAT
(Web Applications Analysis and Testing) project. A semi-automatic
tool to support this technique is currently under development.
Abstract: Recently the use of data mining to scientific bibliographic data bases has been implemented to analyze the pathways of the knowledge or the core scientific relevances of a laureated novel or a country. This specific case of data mining has been named citation mining, and it is the integration of citation bibliometrics and text mining. In this paper we present an improved WEB implementation of statistical physics algorithms to perform the text mining component of citation mining. In particular we use an entropic like distance between the compression of text as an indicator of the similarity between them. Finally, we have included the recently proposed index h to characterize the scientific production. We have used this web implementation to identify users, applications and impact of the Mexican scientific institutions located in the State of Morelos.
Abstract: The paper outlines the relevance of computational
geometry within the design and production process of architecture.
Based on two case studies, the digital chain - from the initial formfinding
to the final realization of spatial concepts - is discussed in
relation to geometric principles. The association with the fascinating
complexity that can be found in nature and its underlying geometry
was the starting point for both projects presented in the paper. The
translation of abstract geometric principles into a three-dimensional
digital design model – realized in Rhinoceros – was followed by a
process of transformation and optimization of the initial shape that
integrated aesthetic, spatial and structural qualities as well as aspects
of material properties and conditions of production.
Abstract: Term Extraction, a key data preparation step in Text
Mining, extracts the terms, i.e. relevant collocation of words,
attached to specific concepts (e.g. genetic-algorithms and decisiontrees
are terms associated to the concept “Machine Learning" ). In
this paper, the task of extracting interesting collocations is achieved
through a supervised learning algorithm, exploiting a few
collocations manually labelled as interesting/not interesting. From
these examples, the ROGER algorithm learns a numerical function,
inducing some ranking on the collocations. This ranking is optimized
using genetic algorithms, maximizing the trade-off between the false
positive and true positive rates (Area Under the ROC curve). This
approach uses a particular representation for the word collocations,
namely the vector of values corresponding to the standard statistical
interestingness measures attached to this collocation. As this
representation is general (over corpora and natural languages),
generality tests were performed by experimenting the ranking
function learned from an English corpus in Biology, onto a French
corpus of Curriculum Vitae, and vice versa, showing a good
robustness of the approaches compared to the state-of-the-art Support
Vector Machine (SVM).
Abstract: Twist drills are geometrical complex tools and thus various researchers have adopted different mathematical and experimental approaches for their simulation. The present paper acknowledges the increasing use of modern CAD systems and using the API (Application Programming Interface) of a CAD system, drilling simulations are carried out. The developed DRILL3D software routine, creates parametrically controlled tool geometries and using different cutting conditions, achieves the generation of solid models for all the relevant data involved (drilling tool, cut workpiece, undeformed chip). The final data derived, consist a platform for further direct simulations regarding the determination of cutting forces, tool wear, drilling optimizations etc.
Abstract: Logistics outsourcing is a growing trend and measuring its performance, a challenge. It must be consistent with the objectives set for logistics outsourcing, but we have found no objective-based performance measurement system. We have conducted a comprehensive review of the specialist literature to cover this gap, which has led us to identify and define these objectives. The outcome is that we have obtained a list of the most relevant objectives and their descriptions. This will enable us to analyse in a future study whether the indicators used for measuring logistics outsourcing performance are consistent with the objectives pursued with the outsourcing. If this is not the case, a proposal will be made for a set of financial and operational indicators to measure performance in logistics outsourcing that take the goals being pursued into account.
Abstract: The paper deals with quality labels used in the food products market, especially with labels of quality, labels of origin, and labels of organic farming. The aim of the paper is to identify perception of these labels by consumers in the Czech Republic. The first part refers to the definition and specification of food quality labels that are relevant in the Czech Republic. The second part includes the discussion of marketing research results. Data were collected with personal questioning method. Empirical findings on 150 respondents are related to consumer awareness and perception of national and European food quality labels used in the Czech Republic, attitudes to purchases of labelled products, and interest in information regarding the labels. Statistical methods, in the concrete Pearson´s chi-square test of independence, coefficient of contingency, and coefficient of association are used to determinate if significant differences do exist among selected demographic categories of Czech consumers.
Abstract: Effective knowledge support relies on providing
operation-relevant knowledge to workers promptly and accurately. A
knowledge flow represents an individual-s or a group-s
knowledge-needs and referencing behavior of codified knowledge
during operation performance. The flow has been utilized to facilitate
organizational knowledge support by illustrating workers-
knowledge-needs systematically and precisely. However,
conventional knowledge-flow models cannot work well in cooperative
teams, which team members usually have diverse knowledge-needs in
terms of roles. The reason is that those models only provide one single
view to all participants and do not reflect individual knowledge-needs
in flows. Hence, we propose a role-based knowledge-flow view model
in this work. The model builds knowledge-flow views (or virtual
knowledge flows) by creating appropriate virtual knowledge nodes
and generalizing knowledge concepts to required concept levels. The
customized views could represent individual role-s knowledge-needs
in teamwork context. The novel model indicates knowledge-needs in
condensed representation from a roles perspective and enhances the
efficiency of cooperative knowledge support in organizations.
Abstract: Knowledge is attributed to human whose problemsolving
behavior is subjective and complex. In today-s knowledge
economy, the need to manage knowledge produced by a community
of actors cannot be overemphasized. This is due to the fact that
actors possess some level of tacit knowledge which is generally
difficult to articulate. Problem-solving requires searching and sharing
of knowledge among a group of actors in a particular context.
Knowledge expressed within the context of a problem resolution
must be capitalized for future reuse. In this paper, an approach that
permits dynamic capitalization of relevant and reliable actors-
knowledge in solving decision problem following Economic
Intelligence process is proposed. Knowledge annotation method and
temporal attributes are used for handling the complexity in the
communication among actors and in contextualizing expressed
knowledge. A prototype is built to demonstrate the functionalities of
a collaborative Knowledge Management system based on this
approach. It is tested with sample cases and the result showed that
dynamic capitalization leads to knowledge validation hence
increasing reliability of captured knowledge for reuse. The system
can be adapted to various domains.
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: Autism Spectrum Disorder (ASD) is a pervasive developmental disorder which affects individuals with varying degrees of impairment. Currently, there has been ample research done in serious game for autism children. Although serious games are traditionally associated with software developments, developing them in the autism field involves studying the associated technology and paying attention to aspects related to interaction with the game. Serious Games for autism cover matters related to education, therapy for communication, psychomotor treatment and social behavior enhancement. In this paper, a systematic review sets out the lines of development and research currently being conducted into serious games which pursue some form of benefit in the field of autism. This paper includes a literature review of relevant serious game developments since in year 2007 and examines new trends.
Abstract: In the automotive industry test drives are being conducted
during the development of new vehicle models or as a part of
quality assurance of series-production vehicles. The communication
on the in-vehicle network, data from external sensors, or internal
data from the electronic control units is recorded by automotive
data loggers during the test drives. The recordings are used for fault
analysis. Since the resulting data volume is tremendous, manually
analysing each recording in great detail is not feasible.
This paper proposes to use machine learning to support domainexperts
by preventing them from contemplating irrelevant data and
rather pointing them to the relevant parts in the recordings. The
underlying idea is to learn the normal behaviour from available
recordings, i.e. a training set, and then to autonomously detect
unexpected deviations and report them as anomalies.
The one-class support vector machine “support vector data description”
is utilised to calculate distances of feature vectors. SVDDSUBSEQ
is proposed as a novel approach, allowing to classify subsequences
in multivariate time series data. The approach allows to
detect unexpected faults without modelling effort as is shown with
experimental results on recordings from test drives.