This paper presents SRST a tool designed to support knowledge management and collaboration activities of the student-supervisor relationship that arise during the development of a graduate thesis. In universities, gra...
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ISBN:
(纸本)142440164X
This paper presents SRST a tool designed to support knowledge management and collaboration activities of the student-supervisor relationship that arise during the development of a graduate thesis. In universities, graduate students account for the greatest part of the research work force, and are usually in close collaboration with their supervisors. Most student-supervisor interaction can be characterized as a form of knowledge management. SRST unites these concepts in a single and original tool and deals with four of the elements related to a thesis: "ideas", "tasks", "discussions", and "documents".
In this paper we consider a process which complements the learning process for building personal knowledge through the exchange of knowledge chains. This approach consists in the partial automatization of the process ...
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ISBN:
(纸本)142440164X
In this paper we consider a process which complements the learning process for building personal knowledge through the exchange of knowledge chains. This approach consists in the partial automatization of the process of creating knowledge chains, through the use of the technology from agents, ontologies and data mining. The agents will monitor all media used by the learner, and will classify its content using an ontology. From there, we want to create and recommend a chain to the learner. This point became important when we observed that the learners weren't motivated to create their chains, which, normally, takes a lot of effort.
Integration of goal-driven, top-down attention and image-driven, bottom-up attention is crucial for visual search. Yet, previous research has mostly focused on models that are purely top-down or bottom-up. Here, we pr...
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Integration of goal-driven, top-down attention and image-driven, bottom-up attention is crucial for visual search. Yet, previous research has mostly focused on models that are purely top-down or bottom-up. Here, we propose a new model that combines both. The bottom-up component computes the visual salience of scene locations in different feature maps extracted at multiple spatial scales. The topdown component uses accumulated statistical knowledge of the visual features of the desired search target and background clutter, to optimally tune the bottom-up maps such that target detection speed is maximized. Testing on 750 artificial and natural scenes shows that the model’s predictions are consistent with a large body of available literature on human psychophysics of visual search. These results suggest that our model may provide good approximation of how humans combine bottom-up and top-down cues such as to optimize target detection speed.
Decentralized projects, where several organizations come together to work on a joint project, are quickly becoming the norm, due to increased networking and distribution. Subcontracting and consulting lead to the crea...
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Efficient evaluation of spatial queries is an important issue in spatial database. Among spatial operations, spatial join is very useful, intersection being the most common predicate. However, the exact intersection t...
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ISBN:
(纸本)1595935290
Efficient evaluation of spatial queries is an important issue in spatial database. Among spatial operations, spatial join is very useful, intersection being the most common predicate. However, the exact intersection test of two spatial objects is the most time-consuming and I/O-consuming step in processing spatial joins. On the other hand, the use of approximations can reduce the need for examining the exact geometry of spatial objects in order to find the intersecting ones. This work proposes a new raster approximation (Three-Color Raster Signature - 3CRS) for representing different data types (polygons, polylines and points), and to be used as filter in the second step of the Multi-Step Query Processor. We have also executed experimental tests over real datasets, the results having demonstrated the effectiveness of our approach.
Established statistical representations of data clusters employ up to second order statistics including mean, variance, and covariance. Strategies for merging clusters have been largely based on intra-and inter-cluste...
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Established statistical representations of data clusters employ up to second order statistics including mean, variance, and covariance. Strategies for merging clusters have been largely based on intra-and inter-cluster distance measures. The distance concept allows an intuitive interpretation, but it is not designed to merge from the viewpoint of probability distributions. We suggest an alternative strategy to compare clusters based on higher order statistics to capture the underlying probability distributions. Higher order statistics, such as multivariate skewness and kurtosis, enable a more accurate description of the shape of a cluster. Although the original definitions of kurtosis and skewness do require simultaneous involvement of all data points, our finding shows that their estimation can be decomposed into combinations of the cross moments of subsets of data. This decomposable property makes it possible to apply skewness and kurtosis to data stream clustering, where historical data are not accessible. We utilize tests for normality based on skewness and kurtosis to discover cluster pairs that can be merged to produce a less complex normal cluster even if they have different means or covariance structures.
Research centers and universities are knowledge-intensive institutions, where the knowledge creation and distribution are constant - and this knowledge should be managed. In spite of it, scientific work had been known...
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This paper presents a distributed data mining technique based on a multiagent environment, called SMAMDD (multiagent system for distributed data mining), which uses model integration. Model integration consists in the...
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This paper presents a distributed data mining technique based on a multiagent environment, called SMAMDD (multiagent system for distributed data mining), which uses model integration. Model integration consists in the amalgamation of local models into a global, consistent one. In each subset, agents perform mining tasks locally and, afterwards, results are merged into a global model. In order to achieve that, agents cooperate by exchanging messages, aiming to improve the process of knowledge discover generating accurate results. The multiagent system for distributed data mining proposed in this paper has been compared with classical machine learning algorithms which are based on model integration as well, simulating a distributed environment. The results obtained show that SMAMDD can produce highly accurate data models
Commercial polyurethane foams with a monomodal pore size distribution were used to produce LZSA glass ceramic foams by the polymeric sponge method. A suspension containing LZSA glass ceramic, bentonite and sodium sili...
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ISBN:
(纸本)0470080507
Commercial polyurethane foams with a monomodal pore size distribution were used to produce LZSA glass ceramic foams by the polymeric sponge method. A suspension containing LZSA glass ceramic, bentonite and sodium silicate, was prepared in water and isopropanol media to impregnate the polymeric foams by dip coating. The suspension was characterized by Theological measurements. The effect of the solvent on the microstructure and physical properties on the LZSA foams was also evaluated. The cellular microstructure of the glass-ceramic foams was characterized by scanning electron microscopy (SEM) and micro-computer X-ray tomography (μ-CT). The LZSA foam prepared with isopropanol suspension exhibited higher mechanical strength under compression than those prepared with water.
This paper describes the development of a Multi-Electrode Array (MEA) with Guided Network for Cell-to-Cell Communication Transduction using a standard integrated circuit (IC) fabrication process. Unlike conventional e...
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