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

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. &nbsp

LES RÔLES DE L'ORGANISATION PAYSANNE ET DE L'ACTION COLLECTIVE POUR LE RENFORCEMENT DES FILIÈRES DE COMMERCIALISATION DES PRODUITS DE «SPÉCIALITÉ LOCALE» LE CAS DU LONGANE «LONG» DE LA PROVINCE DE HUNGYEN AU VIETNAME

N° ISBN - 978-2-7380-1284-5International audienceLe Vietnam est actuellement engagé dans un processus d'intégration économique internationale issue notamment de son adhésion récente à l'OMC. Ce processus inclut une ouverture croissante du secteur agro-alimentaire domestique à la concurrence des produits importés. Les exploitations agricoles familiales sont particulièrement fragilisées par ce nouveau contexte, en raison de le petite taille et du morcellement des superficies cultivables. Les moyens de renforcer la compétitivité des produits issues de l'agriculture familiale constitue ainsi une des priorités pour les recherches vietnamiennes en économie agricole. Au Vietnam, le longane “Long” produit dans la province de Hungyen est un produit de spécialité locale, c'est-à-dire dont la qualité spécifique est reconnue par une partie des consommateurs. Néanmoins, le manque d'action collective entre les exploitations agricoles familiales fragilisent les performances de cette filière face aux longanes des autres régions du Vietnam et les longanes importés. Cet article présente les expériences d'appui pour le renforcement de la filière du longane “Long” de la province de Hungyen au Vietnam vers le développement de l'indication géographique. Grâce à l'appui de GTZ (German Technical Cooperation) et de l'IPSARD (Institut de politique et de stratégie pour l'agriculture et le développement rural), la coopérative de longane Long Hongnam, qui consiste en une organisation de producteurs et de commerçants, a été mise sur pied en 2006. La coopérative a permis la mise en place des actions collectives suivantes: l'application d'un itinéraire technique de production amélioré 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 à ce dispositif, les producteurs ont pu augmenter leur prix du vente, améliorer l'homogénéité de la qualité des produits, et améliorer leur revenu. La durabilité de ce dispositif est discutée. 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