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Università della Svizzera italiana

E2ML : Educational Environment Modeling Language

Botturi, Luca

In: Proceedings of EDMEDIA 2003, Honolulu, Hawaii, USA, 2003, vol. 1, p. 304

E2ML is a modeling language spe cifically developed for the design of educational environments. It models the goals, requirement and design of the teaching and learning activity. Its core parts are a verbal statement and visual mapping of goals and a visual UML-like representation of the learning activity. E2ML can be profitably integrated with Instructional Design models in order to enhance the...

Università della Svizzera italiana

Maps as Learning Tools : the SWISSLING Solution

Armani, Jacopo ; Botturi, Luca ; Rocci, Andrea

In: 4th International Conference on New Educational Environments (ICNEE) 02, Lugano, Switzerland, 2002, no. 2.1, p. 7

Università della Svizzera italiana

Knowledge as relationship and e-learning

Botturi, Luca

In: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (ELEARN), 2002, vol. 1, p. 144

The shift to new paradigms in education pushed by new media has initiated a critical re-thinking of the way we talk of and practice teaching and learning which is still open. Some concepts of the Western educational tradition can be helpful in finding a new way of integra ting IT in learning. This paper propose to consider knowledge as a relationship and shows how this provides helpful insights...

Università della Svizzera italiana

Visualizing learning goals with the Quail Model

Botturi, Luca

In: Australasian Journal of Educational Technologies - AJET, 2004, vol. 20, p. 248

This paper introduces the Quail Model, a device for the classification and visualization of learning goals. The model is a communication tool that can smoothen the discussion within a course design team, support shared understanding and improve decision-making. Its theoretical background mingles contributions from Instructional Design (Bloom, Gagné, Merrill) with the insights of an author of...

Université de Fribourg

Callosal connections of dorsal versus ventral premotor areas in the macaque monkey: a multiple retrograde tracing study

Boussaoud, Driss ; Tanné-Gariépy, Judith ; Wannier, Thierry ; Rouiller, Eric M.

In: BMC Neuroscience, 2005, vol. 6, p. 67

Background: The lateral premotor cortex plays a crucial role in visually guided limb movements. It is divided into two main regions, the dorsal (PMd) and ventral (PMv) areas, which are in turn subdivided into functionally and anatomically distinct rostral (PMd-r and PMv-r) and caudal (PMd-c and PMv-c) sub-regions. We analyzed the callosal inputs to these premotor subdivisions following 23...

Université de Neuchâtel

Real-time visual attention on a massively parallel SIMD architecture

Ouerhani, Nabil ; Hügli, Heinz

In: Real-Time Imaging, 2003, vol. 9(3), p. 189-196

Visual attention is the ability to rapidly detect the visually salient parts of a given scene on which higher level vision tasks, such as object recognition, can focus. Found in biological vision, this mechanism represents a fundamental tool for computer vision. This paper reports the first real-time implementation of the complete visual attention mechanism on a compact and low-power...

Université de Neuchâtel

Empirical Validation of the Saliency-based Model of Visual Attention

Ouerhani, Nabil ; Hügli, Heinz ; Müri, René ; Von Wartburg, Roman

In: Electronic Letters on Computer Vision and Image Analysis, 2003, vol. 3(1), p. 13-23

Visual attention is the ability of the human vision system to detect salient parts of the scene, on which higher vision tasks, such as recognition, can focus. In human vision, it is believed that visual attention is intimately linked to the eye movements and that the fixation points correspond to the location of the salient scene parts. In computer vision, the paradigm of visual attention has...

Université de Neuchâtel

Visual attention-based robot self-localization

Ouerhani, Nabil ; Bur, Alexandre ; Hügli, Heinz

In: In Proceeding of European Conference on Mobile Robotics, 2005, p. 8-13

This paper reports a landmark-based localization method relying on visual attention. In a learning phase, the multi-cue, multi-scale saliency-based model of visual attention is used to automatically acquire robust visual landmarks that are integrated into a topological map of the navigation environment. During navigation, the same visual attention model detects the most salient visual features...