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See detailA Method for Architectural Inclusive Design: the Case of Users Experiencing Down Syndrome
Schelings, Clémentine ULiege; Elsen, Catherine ULiege

in International Journal on Advances in Life Sciences (2017), 9(3&4), 151-162

This paper develops an in-situ methodology to help architects insure better inclusion of people with Down syndrome all along preliminary phases of the architectural design process, and eventually to the ... [more ▼]

This paper develops an in-situ methodology to help architects insure better inclusion of people with Down syndrome all along preliminary phases of the architectural design process, and eventually to the designed space. This methodology first offers architects some design keys in regard of how people with Down syndrome interact with two types of spaces: their personal dwellings and some completely unknown spaces. The methodology then unfolds towards more pro-active inclusion of the participants thanks to playful expression of their feelings and perceptions. This paper discusses how this methodology relates to inclusive and universal principles, useful to design smart environments, be they ICT-enabled or not. This paper closes on prevalent models of disability in architecture and how they articulate with the model of “architectural handicap”. [less ▲]

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See detailPOINT CLOUD CLASSIFICATION OF TESSERAE FROM TERRESTRIAL LASER DATA COMBINED WITH DENSE IMAGE MATCHING FOR ARCHAEOLOGICAL INFORMATION EXTRACTION
Poux, Florent ULiege; Neuville, Romain ULiege; Hallot, Pierre ULiege et al

in International Journal on Advances in Life Sciences (2017, August 16), IV-2/W2

Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically ... [more ▼]

Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor’s biased data, each tessera in the high-density point cloud from the 3D captured complex mosaics of Germigny-des-prés (France) is segmented via a colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface and outline polygon of each tessera is generated by a RANSAC plane extraction and convex hull fitting. Knowledge is then used to classify every tesserae based on their size, surface, shape, material properties and their neighbour’s class. The detection and semantic enrichment method shows promising results of 94% correct semantization, a first step toward the creation of an archaeological smart point cloud. [less ▲]

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See detailGender differences in variability and extreme scores in an international context
Baye, Ariane ULiege; Monseur, Christian ULiege

in International Journal on Advances in Life Sciences (2016), 4(1),

This study examines gender differences in the variability of student performance in reading, mathematics and science. Twelve databases from IEA and PISA were used to analyze gender differences within an ... [more ▼]

This study examines gender differences in the variability of student performance in reading, mathematics and science. Twelve databases from IEA and PISA were used to analyze gender differences within an international perspective from 1995 to 2015. Effect sizes and variance ratios were computed. The main results are as follows. (1) Gender differences vary by content area, students’ educational levels, and students’ proficiency levels. The gender differences at the extreme tails of the distribution are often more substantial than the gender differences at the mean. (2) Exploring the extreme tails of the distributions shows that the situation of the weakest males in reading is a real matter of concern. In mathematics and science, males are more frequently among the highest performing students. (3) The “greater male variability hypothesis” is confirmed. [less ▲]

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