59,026 research outputs found
Improving the viability of mental models held by novice programmers
Recent research has found that many novice programmers often hold non-viable mental models of basic programming concepts such as assignment and object reference. This paper proposes a constructivist-based teaching model, integrating a cognitive conflict strategy with program visualization, with the aim of improving novice programmers’ mental models. The results of a preliminary empirical study suggest that, for the relatively straightforward concept of assignment, tight integration of program visualization with a cognitive conflict event that highlights a student’s inappropriate understanding can help improve students’ non-viable mental models. 14 out of 18 participants who held non-viable mental models of the assignment process successfully changed their model to be viable as a result of the proposed teaching model
Facial Action Unit Detection Using Attention and Relation Learning
Attention mechanism has recently attracted increasing attentions in the field
of facial action unit (AU) detection. By finding the region of interest of each
AU with the attention mechanism, AU-related local features can be captured.
Most of the existing attention based AU detection works use prior knowledge to
predefine fixed attentions or refine the predefined attentions within a small
range, which limits their capacity to model various AUs. In this paper, we
propose an end-to-end deep learning based attention and relation learning
framework for AU detection with only AU labels, which has not been explored
before. In particular, multi-scale features shared by each AU are learned
firstly, and then both channel-wise and spatial attentions are adaptively
learned to select and extract AU-related local features. Moreover, pixel-level
relations for AUs are further captured to refine spatial attentions so as to
extract more relevant local features. Without changing the network
architecture, our framework can be easily extended for AU intensity estimation.
Extensive experiments show that our framework (i) soundly outperforms the
state-of-the-art methods for both AU detection and AU intensity estimation on
the challenging BP4D, DISFA, FERA 2015 and BP4D+ benchmarks, (ii) can
adaptively capture the correlated regions of each AU, and (iii) also works well
under severe occlusions and large poses.Comment: This paper is accepted by IEEE Transactions on Affective Computin
Fast training of self organizing maps for the visual exploration of molecular compounds
Visual exploration of scientific data in life science
area is a growing research field due to the large amount of
available data. The Kohonen’s Self Organizing Map (SOM) is
a widely used tool for visualization of multidimensional data.
In this paper we present a fast learning algorithm for SOMs
that uses a simulated annealing method to adapt the learning
parameters. The algorithm has been adopted in a data analysis
framework for the generation of similarity maps. Such maps
provide an effective tool for the visual exploration of large and
multi-dimensional input spaces. The approach has been applied
to data generated during the High Throughput Screening
of molecular compounds; the generated maps allow a visual
exploration of molecules with similar topological properties.
The experimental analysis on real world data from the
National Cancer Institute shows the speed up of the proposed
SOM training process in comparison to a traditional approach.
The resulting visual landscape groups molecules with similar
chemical properties in densely connected regions
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