High throughput DNA sequencing is now the main workforce for most genomics applications. They have already started to impact research and clinical use. Genome sequencing is now becoming a part of preventive and personalized medicine to identify, genetic mutations for rare disease diagnosis, or to determine cancer subtypes to guide treatment options. Currently genomics data are processed in energy-hungry bioinformatics centers, which necessitate to transfer data via the internet, consuming substantial amounts of energy and wasting of time. There is thus a need for fast, energy-efficient, and cost-efficient technologies to significantly reduce cost, computation time, and power consumption. In this project we aim to leverage the emerging processing-in-memory technologies to enable such powerful edge computing. We will focus on co-designing algorithms and data structures commonly used in genomics together with PIM to obtain the highest benefit in cost, energy, and time saving.

The objective of the SargAlert project is to significantly improve the forecasts of the strandings of the invasive algal species Sargassum in the tropical Atlantic Ocean, in the Caribbean Sea and on the Brazilian coast. The synergy between satellite data / ocean transport modeling / in-situ measurements will be used for that purpose. SargAlert will provide alert bulletins to end-users such as territorial authority, tourism, fishers. The challenges that will be addressed by SargAlert are as follows: - detection and monitoring of at different time (hour to daily) and spatial (20 m to 5 km) scales using a multi-sensor satellite data analysis (Low Earth and GEOstationary orbits), - improvement of Sargassum stranding forecasts by combining physical transport models with artificial intelligence approaches, - validation of satellite data products and forecast models using in-situ measurements, - production of alert bulletins to address societal issues. The innovative developments of the project will enable an integrative approach of the Sargassum stranding issues: synergy between satellite data, understanding of Sargassum spatio-temporal distribution, transport forecast. Improvements of ocean modeling of dynamics will benefit societal authorities to better respond to the risks induced by the more frequent and intense Sargassum blooms in the Atlantic Ocean. The operational Sargassum forecast center will thus have all required inputs to provide reliable forecasts in near real time. This federative and interdisciplinary project includes complementary partners from academic laboratories, including a human science team (AEM, IRISA, LATMOS, LC2S, LIS, Marbec, MIO, UFPE/UFRPE), from an operational forecast center (Météo-France) and from a national satellite data center (AERIS/ICARE).

This project aims to propose a declarative language dedicated to cryptanalytic problems in symmetric key cryptography using constraint programming (CP) to simplify the representation of attacks, to improve existing attacks and to build new cryptographic primitives that withstand these attacks. We also want to compare the different tools that can be used to solve these problems: SAT and MILP where the constraints are homogeneous and CP where the heterogeneous constraints can allow a more complex treatment. One of the challenges of this project will be to define global constraints dedicated to the case of symmetric cryptography. Concerning constraint programming, this project will define new dedicated global constraints, will improve the underlying filtering and solution search algorithms and will propose dedicated explanations generated automatically.

In this project we aim to study the foundations of processing large-scale, noisy string data. Our goal is to understand the limit of computations, and to provide new ultra-efficient algorithms and data structures for processing such data, inspired by approaches in hashing and high-dimensional geometry. We will focus on three research directions: streaming pattern matching, probabilistic text indexing, and sketching-based sting comparison. Algorithms and data structures on strings have traditionally been exploited in such fields as Bioinformatics, Information Retrieval, and Digital Security, and we expect our project to have a significant impact on these fields.

Crowdsourcing platforms offer the unprecedented opportunity to connect easily on-demand task providers, or taskers, and on-demand task solvers, or workers, locally or world-wide, for paid or voluntary work, and for various kinds of tasks. By facilitating the accurate search of specific workers, otherwise unavailable, they have the potential to reduce costs as well as to accelerate and even democratize innovation. Their growing importance has made them unavoidable actors of the 21st century economy. However, abusive behaviors from crowdsourcing platforms against taskers or workers are frequently reported in the news or on dedicated websites, whether performed willingly or not, putting them at the epicenter of a burning societal debate. Real-life examples of such abusive behaviors range from strong concerns about private information accesses and uses (see, e.g., the privacy scandals due to illegitimate accesses to the location data of a well-known drivers-riders company – https://tinyurl.com/wp-priv) to blatant denials of workers' independence (see, e.g., the complaints of micro-task workers or of drivers about the strong work control and monitoring imposed by their respective platforms – https://tinyurl.com/wsj-ind and https://tinyurl.com/trans-ind). This fuels the growing concern of individuals, overshadowing the possible benefits that crowdsourcing processes can bring to societies. In addition to obvious legal and ethical reasons, protecting both taskers and workers – i.e., the two sides of a crowdsourcing platform – from the platform itself, is thus crucial for establishing sound trust foundations. The goal of the CROWDGUARD project is to design sound protection measures of the taskers and workers from threats coming from the platform, while still enabling the latter to perform efficient and accurate tasks assignments. In CROWDGUARD, we advocate for an approach that uses confidentiality and privacy guarantees as building blocks for preventing a large variety of abusive behaviors. First, the enforcement of privacy and confidentiality guarantees directly prevents the first kind of abuse that we consider, i.e., the abusive usage of the personal or confidential information that taskers and workers disclose to the platform for the assignment of tasks. Second, through their obfuscation abilities, privacy and confidentiality guarantees carry the promise, in an extended form, to be also efficient for preventing a large variety of abusive behaviors (e.g, non-discrimination, or workers' independence). The CROWDGUARD project will specify relevant use-cases, extracted from real-life situations and illustrating the need to protect the crowd from various abusive behaviors from the platform. The project will propose secure distributed algorithms for allowing workers (resp. taskers) to collaboratively compute a privacy-preserving version of their profiles (resp. a confidentiality-preserving version of their tasks) which will then be sent to the platform. The resulting tasks and profiles will enable highly efficient and accurate crowdsourcing processes while being protected by sound confidentiality and privacy guarantees. CROWDGUARD will also identify and formalize the possible abusive behaviors that the platform may perform, and propose sound models/algorithms to prevent them. Finally, the project will develop a prototype that will be used for evaluating the efficiency of the techniques proposed. The main scientific outcomes of CROWDGUARD will advance the state-of-the-art on sound models and algorithms for the definition and prevention of abusive behaviors from crowdsourcing platforms. They will enable the development of respectful crowdsourcing processes by private companies or associations.