Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Gagnon, S.
Talent management of transdisciplinary roles in digital projects: Designing a business technology management body of knowledge Article d'actes
Dans: 27th Annual Americas Conference on Information Systems, AMCIS 2021, Association for Information Systems, 2021, ISBN: 978-1-73363-258-4.
Résumé | Liens | BibTeX | Étiquettes: Body of knowledge, Business technology, Business technology management, Digital transformation, Industrial management, IT profession, IT professional, Iterative methods, Project designing, Talent management, Technology managements
@inproceedings{gagnon_talent_2021,
title = {Talent management of transdisciplinary roles in digital projects: Designing a business technology management body of knowledge},
author = {S. Gagnon},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118645646&partnerID=40&md5=01b6df6228f6ae49dd8c96f13db6aac6},
isbn = {978-1-73363-258-4},
year = {2021},
date = {2021-01-01},
booktitle = {27th Annual Americas Conference on Information Systems, AMCIS 2021},
publisher = {Association for Information Systems},
abstract = {The acceleration of Digital Transformation has led to rapidly evolving transdisciplinary IS-IT professional roles operating across technology and business units. Digital projects require new tasks and skillsets, staffing requirements, and career progressions continuously updated by organizations of all sectors. IT Talent Management (TM) requires a more integrative and adaptive competency framework to help guide IT professionals in becoming new digital leaders. Accordingly, a unified Body of Knowledge (BOK) is developed to improve TM accuracy, breadth, and flexibility. Entitled Business Technology Management (BTM), it serves as a common language to integrate professional standards and match talents to projects. The first iteration results are presented such as the BTM BOK meta-model, methodology, and development tools. The impacts on digital project leadership practices and ontology-driven design methods are outlined. © AMCIS 2021.},
keywords = {Body of knowledge, Business technology, Business technology management, Digital transformation, Industrial management, IT profession, IT professional, Iterative methods, Project designing, Talent management, Technology managements},
pubstate = {published},
tppubtype = {inproceedings}
}
The acceleration of Digital Transformation has led to rapidly evolving transdisciplinary IS-IT professional roles operating across technology and business units. Digital projects require new tasks and skillsets, staffing requirements, and career progressions continuously updated by organizations of all sectors. IT Talent Management (TM) requires a more integrative and adaptive competency framework to help guide IT professionals in becoming new digital leaders. Accordingly, a unified Body of Knowledge (BOK) is developed to improve TM accuracy, breadth, and flexibility. Entitled Business Technology Management (BTM), it serves as a common language to integrate professional standards and match talents to projects. The first iteration results are presented such as the BTM BOK meta-model, methodology, and development tools. The impacts on digital project leadership practices and ontology-driven design methods are outlined. © AMCIS 2021.
2.
Filali, I.; Allili, M. S.; Benblidia, N.
Multi-scale salient object detection using graph ranking and global–local saliency refinement Article de journal
Dans: Signal Processing: Image Communication, vol. 47, p. 380–401, 2016, ISSN: 09235965, (Publisher: Elsevier B.V.).
Résumé | Liens | BibTeX | Étiquettes: Algorithms, Boundary information, Decision trees, Feature relevance, Iterative methods, Multi-layer graphs, Object detection, Object recognition, Random forests, Salient object detection
@article{filali_multi-scale_2016,
title = {Multi-scale salient object detection using graph ranking and global–local saliency refinement},
author = {I. Filali and M. S. Allili and N. Benblidia},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982091007&doi=10.1016%2fj.image.2016.07.007&partnerID=40&md5=60dabe68b5cff4b5d00216d6a632e1cd},
doi = {10.1016/j.image.2016.07.007},
issn = {09235965},
year = {2016},
date = {2016-01-01},
journal = {Signal Processing: Image Communication},
volume = {47},
pages = {380–401},
abstract = {We propose an algorithm for salient object detection (SOD) based on multi-scale graph ranking and iterative local–global object refinement. Starting from a set of multi-scale image decompositions using superpixels, we propose an objective function which is optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. This step aims at roughly estimating the location and extent of salient objects in the image. We then enhance the object saliency through an iterative process employing random forests and local boundary refinement using color, texture and edge information. We also use a feature weighting scheme to ensure optimal object/background discrimination. Our algorithm yields very accurate saliency maps for SOD while maintaining a reasonable computational time. Experiments on several standard datasets have shown that our approach outperforms several recent methods dealing with SOD. © 2016 Elsevier B.V.},
note = {Publisher: Elsevier B.V.},
keywords = {Algorithms, Boundary information, Decision trees, Feature relevance, Iterative methods, Multi-layer graphs, Object detection, Object recognition, Random forests, Salient object detection},
pubstate = {published},
tppubtype = {article}
}
We propose an algorithm for salient object detection (SOD) based on multi-scale graph ranking and iterative local–global object refinement. Starting from a set of multi-scale image decompositions using superpixels, we propose an objective function which is optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. This step aims at roughly estimating the location and extent of salient objects in the image. We then enhance the object saliency through an iterative process employing random forests and local boundary refinement using color, texture and edge information. We also use a feature weighting scheme to ensure optimal object/background discrimination. Our algorithm yields very accurate saliency maps for SOD while maintaining a reasonable computational time. Experiments on several standard datasets have shown that our approach outperforms several recent methods dealing with SOD. © 2016 Elsevier B.V.