DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment

Quantization is one of the most applied Deep Neural Network (DNN) compression strategies, when deploying a trained DNN model on an embedded system or a cell phone. This is owing to its simplicity and adaptability to a wide range of applications and circumstances, as opposed to specific Artificial Intelligence (AI) accelerators and compilers that are often designed only for certain specific hardware (e.g., Google Coral Edge TPU). With the growing demand for quantization, ensuring the reliability of this strategy is becoming a critical challenge. Traditional testing methods, which gather more and more genuine data for better assessment, are often not practical because of the large size of the input space and the high similarity between the original DNN and its quantized counterpart. As a result, advanced assessment strategies have become of paramount importance. In this paper, we present DiverGet, a search-based testing framework for quantization assessment. DiverGet defines a space of metamorphic relations that simulate naturally-occurring distortions on the inputs. Then, it optimally explores these relations to reveal the disagreements among DNNs of different arithmetic precision. We evaluate the performance of DiverGet on state-of-the-art DNNs applied to hyperspectral remote sensing images. We chose the remote sensing DNNs as they're being increasingly deployed at the edge (e.g., high-lift drones) in critical domains like climate change research and astronomy. Our results show that DiverGet successfully challenges the robustness of established quantization techniques against naturally-occurring shifted data, and outperforms its most recent concurrent, DiffChaser, with a success rate that is (on average) four times higher.Comment: Accepted for publication in The Empirical Software Engineering Journal (EMSE

The development of an innovative computer-based Integrated Water Resources Management System for water resources analyses

International audienceThe European IWRMS (Integrated Water Resources Management System) project is dedicated to developing a toolset for a sustainable use and distribution of water resources in souther African countries. This system integrates various scientific components: remote sensing, information systems, database management, and hydrological modelling. This paper is mainly related to the remote sensing contribution of the project. Two points are discussed: land cover classification and the spatio-temporal processing of remote sensing data to extract hydrological parameters

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

An approach using wavelet transform for land cover changes detection on remote sensing data

International audienceThe paper addresses a new application of time-frequency representation in classification of non-stationary signals. It defines an approach to characterize curves describing the temporal reflectance profiles related to land-cover behavior. These curves are obtained from processing multi-resolution remote sensing data and then projected on a representation space improving discrimination between different land cover types. This approach can be applied in a scenario dedicated to the detection of land use changes caused by erosion

An approach using wavelet transform for land cover changes detection on remote sensing data

International audienceThe paper addresses a new application of time-frequency representation in classification of non-stationary signals. It defines an approach to characterize curves describing the temporal reflectance profiles related to land-cover behavior. These curves are obtained from processing multi-resolution remote sensing data and then projected on a representation space improving discrimination between different land cover types. This approach can be applied in a scenario dedicated to the detection of land use changes caused by erosion

An approach using low resolution remote sensing data to detect land cover changes

International audienceThe paper addresses land cover monitoring at large scale. It investigates an approach for extracting information from remote sensing data and evaluating the possibility of doing automated analysis to detect land use changes. A multi-resolution remote sensing data process is performed to obtain curves describing the temporal reflectance profiles related to land cover behaviour. Then a time-frequency transformation is used to extract relevant features characterizing these profiles. The features are then projected on a representation space improving discrimination between different land cover types. This approach can applied in a scenario dedicated to the detection of land use changes caused by erosion

Image processing for sequence of oceanographic images

International audienceThis paper is concerned with three main problems of image processing occuring on temporal sequences of satellite oceanographic images: approximate localization of the interesting structures on the images; segmentation or determination of the boundary of the structures; and temporal tracking of these boundaries to illustrate their evolution. In this paper, application is done on vortices and results are displayed all over the paper. For this purpose, we propose a new method for optical flow computation and interpretation and a geometric modelling of the structures. Oceanographic images obtained from environmental satellite platforms present a new challenge in computer science. The huge amount of data collected each day and the need for characterizing some specific structures on these images for oceanographic monitoring justify our approach for the detection and tracking of vortices on oceanographic images

Detection and Tracking of Vortices on Oceanographic Images

This paper deals with the problem of automatic interpretation of oceanographic images for vortices detection, modelization and tracking

An operational approach to monitor vegetation using remote sensing

International audienceThis paper addresses vegetation monitoring in European agricultural areas using Earth Observation satellites. Due to the small size of typical European fields, two complementary sensors are used, SPOT and NOAA-AVHRR, bringing the spatial and the temporal information respectively. A sub-pixel analysis of NOAA data using one SPOT image is performed to characterize fields with high spatial and temporal resolutions. To be used in an operational context, the method must have realistic data requirements. We define an operational scenario making use of only one SPOT image per site and a one year NOAA sequence, covering a large part of Europe. We first proceed to an unsupervised segmentation of the SPOT image; the NOAA data analysis on test sites provides the temporal evolution of vegetation; then, identification of fields is performed by minimizing a cost function measuring the similarity between the global reflectance observed on NOAA pixels and the reflectance computed from corresponding regions at SPOT resolutio

A Remote Sensing Data Fusion Approach to Monitor Agricultural Areas

International audienceDescribes a fusion process between two different data sources, one providing an accurate spatial information, the other providing time series with a much coarser spatial scale. It is applied in the following remote sensing context: the forecast of cereals production, which is a challenging application of the new generation of Earth observation satellites. These two data types are required since agronomical models must be fed with a daily sampling of cereals reflectances, and since in Europe, fields have a relatively small size. SPOT-XS is wed to provide spatial information at the parcels level, a meso-scale sensor (here, NOAA-AVHRR), which outputs images of large areas every day, provides the temporal information. The combination of these two data sources makes it possible to daily estimate reflectances of main cultivations at the parcels level. The selected approach is as follows: a preliminary learning stage provides the reflectances of each type of cultivation; then operational scenarios are defined to apply the learning information in order to estimate statistics on large areas: using only one SPOT-XS image and meso-scale daily images, a fusion scheme makes it possible to obtain land use identification at high spatial resolution with its temporal behavior