47,796 research outputs found
Handwriting styles: benchmarks and evaluation metrics
Evaluating the style of handwriting generation is a challenging problem,
since it is not well defined. It is a key component in order to develop in
developing systems with more personalized experiences with humans. In this
paper, we propose baseline benchmarks, in order to set anchors to estimate the
relative quality of different handwriting style methods. This will be done
using deep learning techniques, which have shown remarkable results in
different machine learning tasks, learning classification, regression, and most
relevant to our work, generating temporal sequences. We discuss the challenges
associated with evaluating our methods, which is related to evaluation of
generative models in general. We then propose evaluation metrics, which we find
relevant to this problem, and we discuss how we evaluate the evaluation
metrics. In this study, we use IRON-OFF dataset. To the best of our knowledge,
there is no work done before in generating handwriting (either in terms of
methodology or the performance metrics), our in exploring styles using this
dataset.Comment: Submitted to IEEE International Workshop on Deep and Transfer
Learning (DTL 2018
Describing Videos by Exploiting Temporal Structure
Recent progress in using recurrent neural networks (RNNs) for image
description has motivated the exploration of their application for video
description. However, while images are static, working with videos requires
modeling their dynamic temporal structure and then properly integrating that
information into a natural language description. In this context, we propose an
approach that successfully takes into account both the local and global
temporal structure of videos to produce descriptions. First, our approach
incorporates a spatial temporal 3-D convolutional neural network (3-D CNN)
representation of the short temporal dynamics. The 3-D CNN representation is
trained on video action recognition tasks, so as to produce a representation
that is tuned to human motion and behavior. Second we propose a temporal
attention mechanism that allows to go beyond local temporal modeling and learns
to automatically select the most relevant temporal segments given the
text-generating RNN. Our approach exceeds the current state-of-art for both
BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on
a new, larger and more challenging dataset of paired video and natural language
descriptions.Comment: Accepted to ICCV15. This version comes with code release and
supplementary materia
Organizational knowledge transfer through creation, mobilization and diffusion: A case analysis of InTouch within Schlumberger
There is a paucity of theory for the effective management of knowledge transfer within large organisations. Practitioners continue to rely upon ‘experimental’ approaches to address the problem. This research attempts to reduce the gap between theory and application, thereby improving conceptual clarity for the transfer of knowledge.
The paper, through an in-depth case analysis conducted within Schlumberger, studies the adoption of an intranet-based knowledge management (KM) system (called InTouch) to support, strategically align and transfer knowledge resources.
The investigation was undertaken through the adoption of a robust methodological approach (abductive strategy) incorporating the role of technology as an enabler of knowledge management application. Consequently, the study addressed the important question of translating theoretical benefits of KM into practical reality.
The research formulates a set of theoretical propositions which are seen as key to the development of an effective knowledge based infrastructure. The findings identify 30 generic attributes that are essential to the creation, mobilisation and diffusion of organisational knowledge.
The research makes a significant contribution to identifying a theoretical and empirically based agenda for successful intranet-based KM which will be of benefit to both the academic and practitioner communities. The paper also highlights and proposes important areas for further research
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