Abstract: The article investigates how 14- to 15- year-olds build informal conceptions of inferential statistics as they engage in a modelling process and build their own computer simulations with dynamic statistical software. This study proposes four primary phases of informal inferential reasoning for the students in the statistical modeling and simulation process. Findings show shifts in the conceptual structures across the four phases and point to the potential of all of these phases for fostering the development of students- robust knowledge of the logic of inference when using computer based simulations to model and investigate statistical questions.
Abstract: The fuzzy technique is an operator introduced in order
to simulate at a mathematical level the compensatory behavior in
process of decision making or subjective evaluation. The following
paper introduces such operators on hand of computer vision
application.
In this paper a novel method based on fuzzy logic reasoning
strategy is proposed for edge detection in digital images without
determining the threshold value. The proposed approach begins by
segmenting the images into regions using floating 3x3 binary matrix.
The edge pixels are mapped to a range of values distinct from each
other. The robustness of the proposed method results for different
captured images are compared to those obtained with the linear Sobel
operator. It is gave a permanent effect in the lines smoothness and
straightness for the straight lines and good roundness for the curved
lines. In the same time the corners get sharper and can be defined
easily.
Abstract: This paper discusses a qualitative simulator QRiOM
that uses Qualitative Reasoning (QR) technique, and a process-based
ontology to model, simulate and explain the behaviour of selected
organic reactions. Learning organic reactions requires the application
of domain knowledge at intuitive level, which is difficult to be
programmed using traditional approach. The main objective of
QRiOM is to help learners gain a better understanding of the
fundamental organic reaction concepts, and to improve their
conceptual comprehension on the subject by analyzing the multiple
forms of explanation generated by the software. This paper focuses
on the generation of explanation based on causal theories to explicate
various phenomena in the chemistry subject. QRiOM has been tested
with three classes problems related to organic chemistry, with
encouraging results. This paper also presents the results of
preliminary evaluation of QRiOM that reveal its explanation
capability and usefulness.
Abstract: In this paper, we focus on the use of knowledge bases
in two different application areas – control of systems with unknown
or strongly nonlinear models (i.e. hardly controllable by the classical
methods), and robot motion planning in eight directions. The first
one deals with fuzzy logic and the paper presents approaches for
setting and aggregating the rules of a knowledge base. Te second one
is concentrated on a case-based reasoning strategy for finding the
path in a planar scene with obstacles.
Abstract: In this paper, perceptions of actors on changes in
crop productivity, quantity and quality of water, and determinants of
their perception are analyzed using descriptive statistics and ordered
logit model. Data collected from 297 Ethiopian farmers and 103
agricultural professionals from December 2009 to January 2010 are
employed. Results show that the majority of the farmers and
professionals recognized decline in water resources, reasoning
climate changes and soil erosion as some of the causes. However,
there is a variation in views on changes in productivity. The
household asset, education level, age and geographical positions are
found to affect farmers- perception on changes in crop productivity.
But, the study underlines that there is no evidence that farmers-
economic status, age, or education level affects recognition of
degradation of water resources. Thus, more focus shall be given on
providing them different coping mechanisms and alternative
resource conserving technologies than educating about the
problems.
Abstract: Representing objects in a dynamic domain is essential
in commonsense reasoning under some circumstances. Classical logics
and their nonmonotonic consequences, however, are usually not
able to deal with reasoning with dynamic domains due to the fact that
every constant in the logical language denotes some existing object
in the static domain. In this paper, we explore a logical formalization
which allows us to represent nonexisting objects in commonsense
reasoning. A formal system named N-theory is proposed for this
purpose and its possible application in computer security is briefly
discussed.
Abstract: This paper applies Bayesian Networks to support
information extraction from unstructured, ungrammatical, and
incoherent data sources for semantic annotation. A tool has been
developed that combines ontologies, machine learning, and
information extraction and probabilistic reasoning techniques to
support the extraction process. Data acquisition is performed with the
aid of knowledge specified in the form of ontology. Due to the
variable size of information available on different data sources, it is
often the case that the extracted data contains missing values for
certain variables of interest. It is desirable in such situations to
predict the missing values. The methodology, presented in this paper,
first learns a Bayesian network from the training data and then uses it
to predict missing data and to resolve conflicts. Experiments have
been conducted to analyze the performance of the presented
methodology. The results look promising as the methodology
achieves high degree of precision and recall for information
extraction and reasonably good accuracy for predicting missing
values.
Abstract: The Integrated Performance Modelling Environment
(IPME) is a powerful simulation engine for task simulation and
performance analysis. However, it has no high level cognition such
as memory and reasoning for complex simulation. This article
introduces a knowledge representation and reasoning scheme that can
accommodate uncertainty in simulations of military personnel with
IPME. This approach demonstrates how advanced reasoning models
that support similarity-based associative process, rule-based abstract
process, multiple reasoning methods and real-time interaction can be
integrated with conventional task network modelling to provide
greater functionality and flexibility when modelling operator
performance.
Abstract: The emergence of mobile application services and App
Store has led to the explosive growth of user innovation, which users
voluntarily contribute to. User innovation communities where end
users freely reveal innovative ideas and needs with other community
members are becoming increasingly influential in this area. However,
user-s ideas in user innovation community are not enough to be new
service opportunity, because some of them can already developed as
existing services in App Store. Moreover, the existing services similar
to new service opportunity can be significant references to apply
analogy to develop service concept. In response, this research
proposes Case-Based Reasoning approach to matching the user needs
and existing services, identifying unmet opportunistic user needs, and
retrieving similar services with opportunity. Due to its intuitive and
transparent algorithm, users related to App Store innovation
communities can easily employ Case-Based Reasoning based
approach to their innovation.