Archives audiovisuelles & Humanités numériques : exemple d’une documentation multimédia en ligne consacrée au Vietnam

International audienceDans le cadre du séminaire du Quai Branly « Autour de l’image », organisé par l’INALCO les 6 et 7 février 2014, Elisabeth de Pablo et Thi Thanh Tam DO ont présenté une communication intitulé : "Archives audiovisuelles & Humanités numériques : exemple d’une documentation multimédia en ligne". Cette communication a comme but de présenter l’ensemble des étapes de la réalisation d’une documentation audiovisuelle portant sur le Vietnam et mis en ligne petit à petit sur le site ARC-Archives Rencontre des Cultures, l’un des sites pilote du projet Campus-AAR. Cette réalisation a notamment été motivée par les années croisées France-Vietnam 2013-2014.Après avoir rappelé brièvement le contexte, Elisabeth de Pablo et Thi Thanh Tam DO présentent, dans une première partie, les objectifs généraux du dossier mis en place suite à deux constats importants : la méconnaissance générale de la culture vietnamienne liée aux stéréotypes persistants dans notre société et la très forte demande de connaissance de la culture vietnamienne par la jeune génération de vietnamien née en France. Dès lors, il semblait évident d’essayer de créer des partenariats, de solliciter différents acteurs, tous capables de nous donner à voir une autre image du Vietnam.La deuxième partie de l’exposé est centrée sur la réalisation de la documentation multimédia elle-même : collecte de données, publication et republication de celles-ci, articulation au sein de différents dossiers et chapitres interactifs, création de documents annexes venant enrichir les dossiers, etc.Une troisième partie illustre par des exemples concrets deux aspects de la documentation : un dossier thématique portant sur la littérature vietnamienne contemporaine et un dossier pédagogique portant sur la gastronomie vietnamienne.Enfin, la conclusion porte sur les différentes perspectives liées à ce projet

Few-Shot Object Detection via Synthetic Features with Optimal Transport

Few-shot object detection aims to simultaneously localize and classify the objects in an image with limited training samples. However, most existing few-shot object detection methods focus on extracting the features of a few samples of novel classes that lack diversity. Hence, they may not be sufficient to capture the data distribution. To address that limitation, in this paper, we propose a novel approach in which we train a generator to generate synthetic data for novel classes. Still, directly training a generator on the novel class is not effective due to the lack of novel data. To overcome that issue, we leverage the large-scale dataset of base classes. Our overarching goal is to train a generator that captures the data variations of the base dataset. We then transform the captured variations into novel classes by generating synthetic data with the trained generator. To encourage the generator to capture data variations on base classes, we propose to train the generator with an optimal transport loss that minimizes the optimal transport distance between the distributions of real and synthetic data. Extensive experiments on two benchmark datasets demonstrate that the proposed method outperforms the state of the art. Source code will be available

Ecpoc: an evolutionary computation-based proof of criteria consensus protocol

Recently, blockchain technology has been applied in many domains in our life. Blockchain networks typically utilize a consensus protocol to achieve consistency among network nodes in a decentralized environment. Delegated Proof of Stake (DPoS) is a popular mechanism adopted in many networks such as BitShares, EOS, and Cardano because of its speed and scalability advantages. However, votes that come from nodes on a DPoS network tend to support a set of specific nodes that have a greater chance of becoming block producers after voting rounds. Therefore, only a small group of nodes can be selected to become block producers. To address this issue, we propose a new protocol called Evolutionary Computation-based Proof of Criteria (ECPoC), which uses ten criteria to evaluate and select a new block procedure in each round. Next, a set of optimal weights used for maximizing the network’s decentralization level is identified through the use of evolutionary computation algorithms. The experimental results show that our consensus significantly enhances the degree of decentralization in the selection process of witness nodes compared to DPoS. As a result, ECPoC facilitates fairness between nodes and creates momentum for blockchain network developmen

Privacy-Preserving Schema Reuse

As the number of schema repositories grows rapidly and several web-based platforms exist to support publishing schemas, \emph{schema reuse} becomes a new trend. Schema reuse is a methodology that allows users to create new schemas by copying and adapting existing ones. This methodology supports to reduce not only the effort of designing new schemas but also the heterogeneity between them. One of the biggest barriers of schema reuse is about privacy concerns that discourage schema owners from contributing their schemas. Addressing this problem, we develop a framework that enables privacy-preserving schema reuse. Our framework supports the contributors to define their own protection policies in the form of \emph{privacy constraints}. Instead of showing original schemas, the framework returns an \emph{anonymized schema} with maximal \emph{utility} while satisfying these privacy constraints. To validate our approach, we empirically show the efficiency of different heuristics, the correctness of the proposed utility function, the computation time, as well as the trade-off between utility and privacy

Tag-based Paper Retrieval: Minimizing User Effort with Diversity Awareness

As the number of scientific papers getting published is likely to soar, most of modern paper management systems (e.g. ScienceWise, Mendeley, CiteULike) support tag-based retrieval. In that, each paper is associated with a set of \emph{tags}, allowing user to search for relevant papers by formulating tag-based queries against the system. One of the most critical issues in tag-based retrieval is that user often has difficulties in precisely formulating his information need. Addressing this issue, our paper tackles the problem of automatically suggesting new tags for user when he formulates a query. The set of tags are selected in such a way that resolves query ambiguity in two aspects: \emph{informativeness} and \emph{diversity}. While the former reduces user effort in finding the desired papers, the latter enhances the variety of information shown to user. Through studying theoretical properties of this problem, we propose a heuristic-based algorithm with several salient performance guarantees. We also demonstrate the efficiency of our approach through extensive experimentation using real-world datasets

Towards Enabling Schema Reuse with Privacy Constraints

As the number of schema repositories grows rapidly and several web-based platforms exist to support publishing schemas, \emph{schema reuse} becomes a new trend. Schema reuse is a methodology that allows users to create new schemas by copying and adapting existing ones. This methodology supports to reduce not only the effort of designing new schemas but also the heterogeneity between them. One of the biggest barriers of schema reuse is privacy concerns that discourage the participants from contributing their schemas. Addressing this problem, we develop a framework that enables privacy-preserving schema reuse. To this end, our framework supports users to define their own protection policies in the form of \emph{privacy constraints}. Instead of showing original schemas, the framework returns an \emph{anonymized schema} with maximal \emph{utility} while satisfying these privacy constraints. To validate our approach, we empirically show the efficiency of different heuristics, the correctness of the proposed utility function, the computation time, as well as the trade-off between utility and privacy

MirrorNet: Bio-Inspired Camouflaged Object Segmentation

Camouflaged objects are generally difficult to be detected in their natural environment even for human beings. In this paper, we propose a novel bio-inspired network, named the MirrorNet, that leverages both instance segmentation and mirror stream for the camouflaged object segmentation. Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the mirror stream corresponding with the original image and its flipped image, respectively. The output from the mirror stream is then fused into the main stream's result for the final camouflage map to boost up the segmentation accuracy. Extensive experiments conducted on the public CAMO dataset demonstrate the effectiveness of our proposed network. Our proposed method achieves 89% in accuracy, outperforming the state-of-the-arts. Project Page: https://sites.google.com/view/ltnghia/research/camoComment: Under Revie

Design and simulation of automotive radar for autonomous vehicles

Modern automobile technology is pushing towards maximizing road safety, connected vehicles, autonomous vehicles, etc. Automotive RADAR is core sensor technology used for ADAS (Advanced Driver Assistance Technology), ACC (Adaptive Cruise Control), AEB (Automatic Emergency Braking System), traffic assistance, parking aid, and obstacle/pedestrian detection. Despite being inexpensive, RADAR technology provides robust results in harsh conditions such as harsh weather, extreme temperature, darkness, etc. However, the performance of these systems depends on the position of the RADAR and its characteristics like frequency, beamwidth, and bandwidths. Moreover, the characterization of varied materials like layers of paint, polish, primer, or layer of rainwater needs to be analyzed. This performance can be predicted through real-time simulation using advanced FEM software like Altair FEKO&WinProp. These simulations can provide valuable insight into the performance of the system, allowing engineers to optimize the system for specific use cases. For example, simulation can be used to determine the optimal parameters of the RADAR system for a given application. This information can then be used to design and build a physical model or prototype that is optimized for the desired performance. These simulations play a prominent role in determining appropriate data collection and sensor fusion, which reduces the cost and time required for the development of a physical model or prototype. The continued growth and demand for advanced safety features in vehicles further highlight the importance of RADAR technology in modern automobile technology. By accurately characterizing the environment and simulating the system's behavior in real time, engineers can optimize RADAR systems for specific use cases, contributing to safer and more efficient driving experience

A High-Quality Genome Assembly of Striped Catfish (Pangasianodon hypophthalmus) Based on Highly Accurate Long-Read HiFi Sequencing Data

The HiFi sequencing technology yields highly accurate long-read data with accuracies greater than 99.9% that can be used to improve results for complex applications such as genome assembly. Our study presents a high-quality chromosome-scale genome assembly of striped catfish (Pangasianodon hypophthalmus), a commercially important species cultured mainly in Vietnam, integrating HiFi reads and Hi-C data. A 788.4 Mb genome containing 381 scaffolds with an N50 length of 21.8 Mb has been obtained from HiFi reads. These scaffolds have been further ordered and clustered into 30 chromosome groups, ranging from 1.4 to 57.6 Mb, based on Hi-C data. The present updated assembly has a contig N50 of 14.7 Mb, representing a 245-fold and 4.2-fold improvement over the previous Illumina and Illumina-Nanopore-Hi-C based version, respectively. In addition, the proportion of repeat elements and BUSCO genes identified in our genome is remarkably higher than in the two previously released striped catfish genomes. These results highlight the power of using HiFi reads to assemble the highly repetitive regions and to improve the quality of genome assembly. The updated, high-quality genome assembled in this work will provide a valuable genomic resource for future population genetics, conservation biology and selective breeding studies of striped catfish