The Quickref cohort

By Didier Verna


In ELS 2024, the 17th european lisp symposium

Abstract The internal architecture of Declt, our reference manual generator for Common Lisp libraries, is currently evolving towards a three-stage pipeline in which the information gathered for documentation purposes is first reified into a formalized set of object-oriented data structures. A side-effect of this evolution is the ability to dump that information for other purposes than documentation. We demonstrate this ability applied to the complete Quicklisp ecosystem. The resulting “cohort” includes more than half a million programmatic definitions, and can be used to gain insight into the morphology of Common Lisp software.

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Weakly supervised training for hologram verification in identity documents

By Glen Pouliquen, Guillaume Chiron, Joseph Chazalon, Thierry Géraud, Ahmad Montaser Awal


In The 18th international conference on document analysis and recognition (ICDAR 2024)

Abstract We propose a method to remotely verify the authenticity of Optically Variable Devices (OVDs), often referred to as “holograms”, in identity documents. Our method processes video clips captured with smartphones under common lighting conditions, and is evaluated on two public datasets: MIDV-HOLO and MIDV-2020. Thanks to a weakly-supervised training, we optimize a feature extraction and decision pipeline which achieves a new leading performance on MIDV-HOLO, while maintaining a high recall on documents from MIDV-2020 used as attack samples.

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An end-to-end approach for the detection of phishing attacks

By Badis Hammi, Tristan Billot, Danyil Bazain, Nicolas Binand, Maxime Jaen, Chems Mitta, Nour El Madhoun


In Advanced information networking and applications (AINA))

Abstract The main approaches/implementations used to counteract phishing attacks involve the use of crowd-sourced blacklists. However, blacklists come with several drawbacks. In this paper, we present a comprehensive approach for the detection of phishing attacks. Our approach uses our own detection engine which relies on Graph Neural Networks to leverage the hyperlink structure of the websites to analyze. Additionally, we offer a turnkey implementation to the end-users in the form of a Mozilla Firefox plugin.

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Automatic vectorization of historical maps: A benchmark

Abstract Shape vectorization is a key stage of the digitization of large-scale historical maps, especially city maps that exhibit complex and valuable details. Having access to digitized buildings, building blocks, street networks and other geographic content opens numerous new approaches for historical studies such as change tracking, morphological analysis and density estimations. In the context of the digitization of Paris atlases created in the 19th and early 20th centuries, we have designed a supervised pipeline that reliably extract closed shapes from historical maps.

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Unsupervised discovery of interpretable visual concepts

Abstract Providing interpretability of deep-learning models to non-experts, while fundamental for a responsible real-world usage, is challenging. Attribution maps from xAI techniques, such as Integrated Gradients, are a typical example of a visualization technique containing a high level of information, but with difficult interpretation. In this paper, we propose two methods, Maximum Activation Groups Extraction (MAGE) and Multiscale Interpretable Visualization (Ms-IV), to explain the model’s decision, enhancing global interpretability. MAGE finds, for a given CNN, combinations of features which, globally, form a semantic meaning, that we call concepts.

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A CP-based automatic tool for instantiating truncated differential characteristics

By François Delobel, Patrick Derbez, Arthur Gontier, Loïc Rouquette, Christine Solnon


In Progress in cryptology – INDOCRYPT 2023

Abstract An important criteria to assert the security of a cryptographic primitive is its resistance against differential cryptanalysis. For word-oriented primitives, a common technique to determine the number of rounds required to ensure the immunity against differential distinguishers is to consider truncated differential characteristics and to count the number of active S-boxes. Doing so allows to provide an upper bound on the probability of the best differential characteristic with a reduced computational cost.

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Closure and decision properties for higher-dimensional automata

By Amazigh Amrane, Hugo Bazille, Uli Fahrenberg, Krzysztof Ziemiański


In 20th international colloquium on theoretical aspects of computing (ICTAC’23)


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Performance evaluation of container management tasks in OS-level virtualization platforms

By Pedro Melo, Lucas Gama, Jamilson Dantas, David Beserra, Jean Araujo


In 31th IEEE international conference on enabling technologies: Infrastructure for collaborative enterprises (WETICE)

Abstract Cloud computing is a method for accessing and managing computing resources over the internet, providing flexibility, scalability, and cost-efficiency. Cloud computing relies more and more on OS-level virtualization tools such as Docker and Podman, enabling users to create and run containers, which are widely used for application management. Given its significance in cloud infrastructures, it is crucial to have a better understanding of OS-level virtualization performance, especially in tasks related to container management (ex: creation, destruction).

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