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The management of intelligence-assisted finite element analysis technology
Artificial Intelligence (AI) approaches to Finite Element Analysis (FEA), have had tentative degrees of success over the last few years and some authors have argued that effective FEA can help in the manufacture reliability and safety aspects of engineered artefacts. The author of this paper reviews how such AI techniques have been applied and in this light, the author then uses a Fuzzy Cognitive Mapping (FCM), to develop a framework for the management of intelligence-assisted FEA
Inhibition and young children's performance on the Tower of London task
Young children, when performing problem solving tasks, show a tendency to break task rules and produce incomplete solutions. We propose that this tendency can be explained by understanding problem solving within the context of the development of âexecutive functionsâ â general cognitive control functions, which serve to regulate the operation of the cognitive system. This proposal is supported by the construction of two computational models that simulate separately the performance of 3â4 year old and 5â6 year old children on the Tower of London planning task. We seek in particular to capture the emerging role of inhibition in the older group. The basic framework within which the models are developed is derived from Fox and Dasâ Domino model [Fox, J., & Das, S. (2000). Safe and sound: Artificial intelligence in hazardous applications. Cambridge, MA: MIT Press] and Norman and Shalliceâs [Norman, D.A., & Shallice, T. (1986). Attention to action: Willed and automatic control of behaviour. In R. Davidson, G. Schwartz, & D. Shapiro (Eds.), Consciousness and Self Regulation (Vol. 4). New York: Plenum] theory of willed and automatic action. Two strategies and a simple perceptual bias are implemented within the models and comparisons between model and child performance reveal a good fit for the key dependent measures (number of rule breaks and percentage of incomplete solutions) of the two groups
Automatic Test Generation for Space
The European Space Agency (ESA) uses an engine to perform tests in the Ground
Segment infrastructure, specially the Operational Simulator. This engine uses
many different tools to ensure the development of regression testing
infrastructure and these tests perform black-box testing to the C++ simulator
implementation. VST (VisionSpace Technologies) is one of the companies that
provides these services to ESA and they need a tool to infer automatically
tests from the existing C++ code, instead of writing manually scripts to
perform tests. With this motivation in mind, this paper explores automatic
testing approaches and tools in order to propose a system that satisfies VST
needs
A Machine Learning Based Analytical Framework for Semantic Annotation Requirements
The Semantic Web is an extension of the current web in which information is
given well-defined meaning. The perspective of Semantic Web is to promote the
quality and intelligence of the current web by changing its contents into
machine understandable form. Therefore, semantic level information is one of
the cornerstones of the Semantic Web. The process of adding semantic metadata
to web resources is called Semantic Annotation. There are many obstacles
against the Semantic Annotation, such as multilinguality, scalability, and
issues which are related to diversity and inconsistency in content of different
web pages. Due to the wide range of domains and the dynamic environments that
the Semantic Annotation systems must be performed on, the problem of automating
annotation process is one of the significant challenges in this domain. To
overcome this problem, different machine learning approaches such as supervised
learning, unsupervised learning and more recent ones like, semi-supervised
learning and active learning have been utilized. In this paper we present an
inclusive layered classification of Semantic Annotation challenges and discuss
the most important issues in this field. Also, we review and analyze machine
learning applications for solving semantic annotation problems. For this goal,
the article tries to closely study and categorize related researches for better
understanding and to reach a framework that can map machine learning techniques
into the Semantic Annotation challenges and requirements
Does modularity undermine the proâemotion consensus?
There is a growing consensus that emotions contribute positively to human practical rationality. While arguments that defend this position often appeal to the modularity of emotion-generation mechanisms, these arguments are also susceptible to the criticism, e.g. by Jones (2006), that emotional modularity supports pessimism about the prospects of emotions contributing positively to practical rationality here and now. This paper aims to respond to this criticism by demonstrating how models of emotion processing can accommodate the sorts of cognitive influence required to make the pro-emotion position plausible whilst exhibiting key elements of modularity
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