Publications

2022

• Robust and Scalable Content-and-Structure Indexing
  
  • Wellenzohn Kevin
  • Böhlen Michael H.
  • Helmer Sven
  • Pietri Antoine
  • Zacchiroli Stefano
  The VLDB Journal, Springer, 2022. Frequent queries on semi-structured hierarchical data are Content-and-Structure (CAS) queries that filter data items based on their location in the hierarchical structure and their value for some attribute. We propose the Robust and Scalable Content-and-Structure (RSCAS) index to efficiently answer CAS queries on big semi-structured data. To get an index that is robust against queries with varying selectivities we introduce a novel dynamic interleaving that merges the path and value dimensions of composite keys in a balanced manner. We store interleaved keys in our triebased RSCAS index, which efficiently supports a wide range of CAS queries, including queries with wildcards and descendant axes. We implement RSCAS as a log-structured merge (LSM) tree to scale it to data-intensive applications with a high insertion rate. We illustrate RSCAS's robustness and scalability by indexing data from the Software Heritage (SWH) archive, which is the world's largest, publiclyavailable source code archive.
• A new family of polyphase sequences with low correlation
  
  • Gu Zhi
  • Zhou Zhengchun
  • Mesnager Sihem
  • Parampalli Udaya
  Cryptography and Communications - Discrete Structures, Boolean Functions and Sequences, Springer, 2022, 14 (1), pp.135-144. (10.1007/s12095-021-00522-x)
  DOI : 10.1007/s12095-021-00522-x
• Preimages of p −Linearized Polynomials over ${\mathbb {F}}_{p}$
  
  • Kim Kwang Ho
  • Mesnager Sihem
  • Choe Jong Hyok
  • Lee Dok Nam
  Cryptography and Communications - Discrete Structures, Boolean Functions and Sequences, Springer, 2022, 14 (1), pp.75-86. (10.1007/s12095-021-00514-x)
  DOI : 10.1007/s12095-021-00514-x
• Empirical Risk Minimization under Random Censorship
  
  • Ausset Guillaume
  • Clémençon Stéphan
  • Portier François
  Journal of Machine Learning Research, Microtome Publishing, 2022, 23 (1), pp.168–226. We consider the classic supervised learning problem where a continuous non-negative random label Y (e.g. a random duration) is to be predicted based upon observing a random vector X valued in R d with d ≥ 1 by means of a regression rule with minimum least square error. In various applications, ranging from industrial quality control to public health through credit risk analysis for instance, training observations can be right censored, meaning that, rather than on independent copies of (X, Y), statistical learning relies on a collection of n ≥ 1 independent realizations of the triplet (X, min{Y, C}, δ), where C is a nonnegative random variable with unknown distribution, modelling censoring and δ = I{Y ≤ C} indicates whether the duration is right censored or not. As ignoring censoring in the risk computation may clearly lead to a severe underestimation of the target duration and jeopardize prediction, we consider a plug-in estimate of the true risk based on a Kaplan-Meier estimator of the conditional survival function of the censoring C given X, referred to as Beran risk, in order to perform empirical risk minimization. It is established, under mild conditions, that the learning rate of minimizers of this biased/weighted empirical risk functional is of order O P (log(n)/n) when ignoring model bias issues inherent to plug-in estimation, as can be attained in absence of censoring. Beyond theoretical results, numerical experiments are presented in order to illustrate the relevance of the approach developed. (10.5555/3586589.3586594)
  DOI : 10.5555/3586589.3586594
• Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments
  
  • Du Yicheng
  • Nugraha Aditya Arie
  • Sekiguchi Kouhei
  • Bando Yoshiaki
  • Fontaine Mathieu
  • Yoshii Kazuyoshi
  , 2022. This paper describes noisy speech recognition for an augmented reality headset that helps verbal communication with in real multiparty conversational environments. A major approach that has actively been studied in simulated environments is to sequentially perform speech enhancement and automatic speech recognition (ASR) based on deep neural networks (DNNs) trained in a supervised manner. In our task, however, such a pretrained system fails to work due to the mismatch between the training and test conditions and the head movements of the user. To enhance only the utterances of a target speaker, we use beamforming based on a DNN-based speech mask estimator that can adaptively extract the speech components corresponding to a head-relative particular direction. We propose a semi-supervised adaptation method that jointly updates the mask estimator and the ASR model at run-time using clean speech signals with ground-truth transcriptions and noisy speech signals with highly-confident estimated transcriptions. Comparative experiments using the state-of-theart distant speech recognition system show that the proposed method significantly improves the ASR performance.
• Online Unsupervised Domain Adaptation for Person Re-identification
  
  • Rami Hamza
  • Ospici Matthieu
  • Lathuilière Stéphane
  , 2022. Unsupervised domain adaptation for person re-identification (Person Re-ID) is the task of transferring the learned knowledge on the labeled source domain to the unlabeled target domain. Most of the recent papers that address this problem adopt an offline training setting. More precisely, the training of the Re-ID model is done assuming that we have access to the complete training target domain data set. In this paper, we argue that the target domain generally consists of a stream of data in a practical real-world application, where data is continuously increasing from the different network's cameras. The Re-ID solutions are also constrained by confidentiality regulations stating that the collected data can be stored for only a limited period, hence the model can no longer get access to previously seen target images. Therefore, we present a new yet practical online setting for Unsupervised Domain Adaptation for person Re-ID with two main constraints: Online Adaptation and Privacy Protection. We then adapt and evaluate the state-of-the-art UDA algorithms on this new online setting using the well-known Market-1501, Duke, and MSMT17 benchmarks.