245 research outputs found
Towards Designing Spatial Robots that are Architecturally Motivated
While robots are increasingly integrated into the built environment, little
is known how their qualities can meaningfully influence our spaces to
facilitate enjoyable and agreeable interaction, rather than robotic settings
that are driven by functional goals. Motivated by the premise that future
robots should be aware of architectural sensitivities, we developed a set of
exploratory studies that combine methods from both architectural and
interaction design. While we empirically discovered that dynamically moving
spatial elements, which we coin as spatial robots, can indeed create unique
life-sized affordances that encourage or resist human activities, we also
encountered many unforeseen design challenges originated from how ordinary
users and experts perceived spatial robots. This discussion thus could inform
similar design studies in the areas of human-building architecture (HBI) or
responsive and interactive architecture
Heuristic Algorithms for Solving Bounded Diameter Minimum Spanning Tree Problem and Its Application to Genetic Algorithm Development
Selection of Exercises to Improve the Effectiveness of Physical Education under the Project of Elective Sports Cockpitching for Female Students K2023 Hanoi Metropolitan University
The interview results have selected 36 exercises belonging to 5 exercise groups to improve the effectiveness of physical education according to the project for female Hanoi Metropolitan University students studying the optional sport of shuttlecock, including Group 1: Exercises to develop strength quickly has 7 exercises; Group 2: Strength development exercises have 6 exercises; Group 3: Exercises to develop endurance has 5 exercises; Group 4: Exercises to develop flexibility and coordination has 5 exercises; Group 5: Exercises with the bridge has 13 exercises with an agreement level of > 70% or more and an average score of > 3.41 - 4.20 points.
 
LES RĂLES DE L'ORGANISATION PAYSANNE ET DE L'ACTION COLLECTIVE POUR LE RENFORCEMENT DES FILIĂRES DE COMMERCIALISATION DES PRODUITS DE «SPĂCIALITĂ LOCALE» LE CAS DU LONGANE «LONG» DE LA PROVINCE DE HUNGYEN AU VIETNAME
N° ISBN - 978-2-7380-1284-5International audienceLe Vietnam est actuellement engagĂ© dans un processus d'intĂ©gration Ă©conomique internationale issue notamment de son adhĂ©sion rĂ©cente Ă l'OMC. Ce processus inclut une ouverture croissante du secteur agro-alimentaire domestique Ă la concurrence des produits importĂ©s. Les exploitations agricoles familiales sont particuliĂšrement fragilisĂ©es par ce nouveau contexte, en raison de le petite taille et du morcellement des superficies cultivables. Les moyens de renforcer la compĂ©titivitĂ© des produits issues de l'agriculture familiale constitue ainsi une des prioritĂ©s pour les recherches vietnamiennes en Ă©conomie agricole. Au Vietnam, le longane âLongâ produit dans la province de Hungyen est un produit de spĂ©cialitĂ© locale, c'est-Ă -dire dont la qualitĂ© spĂ©cifique est reconnue par une partie des consommateurs. NĂ©anmoins, le manque d'action collective entre les exploitations agricoles familiales fragilisent les performances de cette filiĂšre face aux longanes des autres rĂ©gions du Vietnam et les longanes importĂ©s. Cet article prĂ©sente les expĂ©riences d'appui pour le renforcement de la filiĂšre du longane âLongâ de la province de Hungyen au Vietnam vers le dĂ©veloppement de l'indication gĂ©ographique. GrĂące Ă l'appui de GTZ (German Technical Cooperation) et de l'IPSARD (Institut de politique et de stratĂ©gie pour l'agriculture et le dĂ©veloppement rural), la coopĂ©rative de longane Long Hongnam, qui consiste en une organisation de producteurs et de commerçants, a Ă©tĂ© mise sur pied en 2006. La coopĂ©rative a permis la mise en place des actions collectives suivantes: l'application d'un itinĂ©raire technique de production amĂ©liorĂ© incluant le respect de certaines bonnes pratiques agricoles locales (Good agricultural practices ou GAP), et la mise sur pied d'un espace de dialogue avec les commerçants. Grace Ă ce dispositif, les producteurs ont pu augmenter leur prix du vente, amĂ©liorer l'homogĂ©nĂ©itĂ© de la qualitĂ© des produits, et amĂ©liorer leur revenu. La durabilitĂ© de ce dispositif est discutĂ©e. L'article fait le bilan des forces et faiblesses de ces strategies de soutien a l'action collective
ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution
Existing 3D instance segmentation methods are predominated by the bottom-up
design -- manually fine-tuned algorithm to group points into clusters followed
by a refinement network. However, by relying on the quality of the clusters,
these methods generate susceptible results when (1) nearby objects with the
same semantic class are packed together, or (2) large objects with loosely
connected regions. To address these limitations, we introduce ISBNet, a novel
cluster-free method that represents instances as kernels and decodes instance
masks via dynamic convolution. To efficiently generate high-recall and
discriminative kernels, we propose a simple strategy named Instance-aware
Farthest Point Sampling to sample candidates and leverage the local aggregation
layer inspired by PointNet++ to encode candidate features. Moreover, we show
that predicting and leveraging the 3D axis-aligned bounding boxes in the
dynamic convolution further boosts performance. Our method set new
state-of-the-art results on ScanNetV2 (55.9), S3DIS (60.8), and STPLS3D (49.2)
in terms of AP and retains fast inference time (237ms per scene on ScanNetV2).
The source code and trained models are available at
https://github.com/VinAIResearch/ISBNet.Comment: Accepted to CVPR 202
GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo Labelers
Instance segmentation on 3D point clouds (3DIS) is a longstanding challenge
in computer vision, where state-of-the-art methods are mainly based on full
supervision. As annotating ground truth dense instance masks is tedious and
expensive, solving 3DIS with weak supervision has become more practical. In
this paper, we propose GaPro, a new instance segmentation for 3D point clouds
using axis-aligned 3D bounding box supervision. Our two-step approach involves
generating pseudo labels from box annotations and training a 3DIS network with
the resulting labels. Additionally, we employ the self-training strategy to
improve the performance of our method further. We devise an effective Gaussian
Process to generate pseudo instance masks from the bounding boxes and resolve
ambiguities when they overlap, resulting in pseudo instance masks with their
uncertainty values. Our experiments show that GaPro outperforms previous weakly
supervised 3D instance segmentation methods and has competitive performance
compared to state-of-the-art fully supervised ones. Furthermore, we demonstrate
the robustness of our approach, where we can adapt various state-of-the-art
fully supervised methods to the weak supervision task by using our pseudo
labels for training. The source code and trained models are available at
https://github.com/VinAIResearch/GaPro.Comment: Accepted to ICCV 202
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