Publications

2024

• Momentum‐preserving inversion alleviation for elastic material simulation
  
  • Jeong Heejo
  • Kim Seung‐wook
  • Lee JaeHyun
  • Um Kiwon
  • Kee Min Hyung
  • Han JungHyun
  Computer Animation and Virtual Worlds, Wiley, 2024, 35 (3), pp.e2249. Abstract This paper proposes a novel method that enhances the optimization‐based elastic body solver. The proposed method tackles the element inversion problem, which is prevalent in the prediction‐projection approach for numerical simulation of elastic bodies. At the prediction stage, our method alleviates inversions such that the subsequent projection solver can benefit in stability and efficiency. To prevent excessive suppression of predicted inertial motion when alleviating, we introduce a velocity decomposition method and adapt only the non‐rigid motion while preserving the rigid motion, that is, linear and angular momenta. Thanks to the respected inertial motion in the prediction stage, our method produces lively motions while keeping the entire simulation more stable. The experiments demonstrate that our alleviation method successfully stabilizes the simulation and improves the efficiency particularly when large deformations hamper the solver. (10.1002/cav.2249)
  DOI : 10.1002/cav.2249
• A Parser-Based Data Collector for Intrusion Detection
  
  • Quétel Grégor
  • Alata Eric
  • Gimenez Pierre-François
  • Pautet Laurent
  • Robert Thomas
  , 2024, pp.1-2. Intrusion detection systems often struggle to identify attacks directed at applications. A contributing factor is the various syntactical forms these attacks can take. This paper introduces a methodology to design and adapt applicative data collectors (DCs) to software projects by integrating them into the application's parsers. This data collector aims to enhance applications' security by providing semantic information to intrusion detection mechanisms.
• Minimax optimal seriation in polynomial time
  
  • Issartel Yann
  • Giraud Christophe
  • Verzelen Nicolas
  , 2024. We consider the statistical seriation problem, where the statistician seeks to recover a hidden ordering from a noisy observation of a permuted Robinson matrix. In this paper, we tightly characterize the minimax rate for this problem of matrix reordering when the Robinson matrix is bi-Lipschitz, and we also provide a polynomial time algorithm achieving this rate; thereby answering two open questions of [Giraud et al., 2021]. Our analysis further extends to broader classes of similarity matrices.
• WebGraph: The Next Generation (Is in Rust)
  
  • Fontana Tommaso
  • Vigna Sebastiano
  • Zacchiroli Stefano
  , 2024. We report the results of a yearlong effort to port the WebGraph framework from Java to Rust. For two decades WebGraph has been instrumental in the analysis and distribution of large graphs for the research community of TheWebConf, but the intrinsic limitations of the Java Virtual Machine had become a bottleneck for very large use cases, such as the Software Heritage Merkle graph with its half a trillion arcs. As part of this clean-slate implementation of WebGraph in Rust, we developed a few ancillary projects bringing to the Rust ecosystem some missing features of independent interest, such as easy, consistent and zero-cost memory mapping of data structures. WebGraph in Rust offers impressive performance improvements over the previous implementation, enabling open-source graph analytics on very large datasets on top of a modern system programming language. (10.1145/3589335.3651581)
  DOI : 10.1145/3589335.3651581
• AI is Entering Regulated Territory: Understanding the Supervisors' Perspective for Model Justifiability in Financial Crime Detection
  
  • Bertrand Astrid
  • Eagan James R
  • Maxwell Winston
  • Brand Joshua
  , 2024, pp.Article No.: 480, Pages 1 - 21. Artificial intelligence (AI) has the potential to bring significant benefits to highly regulated industries such as healthcare or banking. Adoption, however, remains low. AI's entry into complex sociotechno-legal systems raises issues of transparency, specifically for regulators. However, the perspective of supervisors, regulators who monitor compliance with applicable financial regulations, has rarely been studied. This paper focuses on understanding the needs of supervisors in anti-money laundering (AML) to better inform the design of AI justifications and explanations in highly regulated fields. Through scenario-based workshops with 13 supervisors and 6 banking professionals, we outline the auditing practices and sociotechnical context of the supervisor. By combining the workshops' insights with an analysis of compliance requirements, we identify the AML obligations that conflict with AI opacity. We then formulate seven needs that supervisors have for model justifiability. We discuss the role of explanations as reliable evidence on which to base justifications. (10.1145/3613904.3642326)
  DOI : 10.1145/3613904.3642326
• Separating common from salient patterns with Contrastive Representation Learning
  
  • Louiset Robin
  • Duchesnay Edouard
  • Grigis Antoine
  • Gori Pietro
  , 2024. Contrastive Analysis is a sub-field of Representation Learning that aims at separating common factors of variation between two datasets, a background (i.e., healthy subjects) and a target (i.e., diseased subjects), from the salient factors of variation, only present in the target dataset. Despite their relevance, current models based on Variational Auto-Encoders have shown poor performance in learning semantically-expressive representations. On the other hand, Contrastive Representation Learning has shown tremendous performance leaps in various applications (classification, clustering, etc.). In this work, we propose to leverage the ability of Contrastive Learning to learn semantically expressive representations well adapted for Contrastive Analysis. We reformulate it under the lens of the InfoMax Principle and identify two Mutual Information terms to maximize and one to minimize. We decompose the first two terms into an Alignment and a Uniformity term, as commonly done in Contrastive Learning. Then, we motivate a novel Mutual Information minimization strategy to prevent information leakage between common and salient distributions. We validate our method, called SepCLR, on three visual datasets and three medical datasets, specifically conceived to assess the pattern separation capability in Contrastive Analysis.
• Random walks on simplicial complexes
  
  • Bonis Thomas
  • Decreusefond Laurent
  • Chi Tran Viet
  • Zhang Iris Zhihan
  , 2024. The notion of Laplacian of a graph can be generalized to simplicial complexes and hypergraphs, and contains information on the topology of these structures. Even for a graph, the consideration of associated simplicial complexes is interesting to understand its shape. Whereas the Laplacian of a graph has a simple probabilistic interpretation as the generator of a continuous time Markov chain on the graph, things are not so direct when considering simplicial complexes. We define here new Markov chains on simplicial complexes. For a given order~$k$, the state space is the set of $k$-cycles that are chains of $k$-simplexes with null boundary. This new framework is a natural generalization of the canonical Markov chains on graphs. We show that the generator of our Markov chain is the upper Laplacian defined in the context of algebraic topology for discrete structure. We establish several key properties of this new process: in particular, when the number of vertices is finite, the Markov chain is positive recurrent. This result is not trivial, since the cycles can loop over themselves an unbounded number of times. We study the diffusive limits when the simplicial complexes under scrutiny are a sequence of ever refining triangulations of the flat torus. Using the analogy between singular and Hodge homologies, we express this limit as valued in the set of currents. The proof of tightness and the identification of the limiting martingale problem make use of the flat norm and carefully controls of the error terms in the convergence of the generator. Uniqueness of the solution to the martingale problem is left open. An application to hole detection is carried.
• Strategic Reasoning under Capacity-constrained Agents
  
  • Ballot Gabriel
  • Malvone Vadim
  • Leneutre Jean
  • Laarouchi Youssef
  , 2024, pp.123--131. (10.5555/3635637.3662859)
  DOI : 10.5555/3635637.3662859
• Obstruction Alternating-time Temporal Logic: A Strategic Logic to Reason about Dynamic Models
  
  • Catta Davide
  • Leneutre Jean
  • Malvone Vadim
  • Murano Aniello
  , 2024. (10.5555/3635637.3662875)
  DOI : 10.5555/3635637.3662875
• Investigation of Intensity Noise in an Interband Cascade Laser Epitaxially Grown on Silicon and Designed for High-speed Applications
  
  • Kim Hyunah
  • Didier Pierre
  • Zaminga Sara
  • Huang Heming
  • Díaz-Thomas Daniel Andrés
  • Baranov A. N.
  • Rodriguez Jean-Baptiste
  • Tournié Eric
  • Knötig Hedwig
  • Schwarz Benedikt
  • Cerutti Laurent
  • Spitz Olivier
  • Grillot Frédéric
  , 2024. The high-speed parameters of an interband cascade laser grown on silicon are analyzed through the prism of relative intensity noise. The evolution of the relaxation frequency allows deriving a modulation bandwidth in the GHz range.
• Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
  
  • Ahmad Tamim El
  • Brogat-Motte Luc
  • Laforgue Pierre
  • d'Alché-Buc Florence
  , 2024, 238. Surrogate kernel-based methods offer a flexible solution to structured output prediction by leveraging the kernel trick in both input and output spaces. In contrast to energy-based models, they avoid to pay the cost of inference during training, while enjoying statistical guarantees. However, without approximation, these approaches are condemned to be used only on a limited amount of training data. In this paper, we propose to equip surrogate kernel methods with approximations based on sketching, seen as low rank projections of feature maps both on input and output feature maps. We showcase the approach on Input Output Kernel ridge Regression (or Kernel Dependency Estimation) and provide excess risk bounds that can be in turn directly plugged on the final predictive model. An analysis of the complexity in time and memory show that sketching the input kernel mostly reduces training time while sketching the output kernel allows to reduce the inference time. Furthermore, we show that Gaussian and sub-Gaussian sketches are admissible sketches in the sense that they induce projection operators ensuring a small excess risk. Experiments on different tasks consolidate our findings.
• Double InfoGAN for Contrastive Analysis
  
  • Carton Florence
  • Louiset Robin
  • Gori Pietro
  , 2024. Contrastive Analysis (CA) deals with the discovery of what is common and what is distinctive of a target domain compared to a background one. This is of great interest in many applications, such as medical imaging. Current state-of-the-art (SOTA) methods are latent variable models based on VAE (CA-VAEs). However, they all either ignore important constraints or they don't enforce fundamental assumptions. This may lead to suboptimal solutions where distinctive factors are mistaken for common ones (or viceversa). Furthermore, the generated images have a rather poor quality, typical of VAEs, decreasing their interpretability and usefulness. Here, we propose Double InfoGAN, the first GAN based method for CA that leverages the high-quality synthesis of GAN and the separation power of InfoGAN. Experimental results on four visual datasets, from simple synthetic examples to complex medical images, show that the proposed method outperforms SOTA CA-VAEs in terms of latent separation and image quality. Datasets and code are available online.
• AirCon: Over-the-Air Consensus for Wireless Blockchain Networks
  
  • Xie Xin
  • Hua Cunqing
  • Hong Jianan
  • Gu Pengwenlong
  • Xu Wenchao
  IEEE Transactions on Mobile Computing, Institute of Electrical and Electronics Engineers, 2024, 23 (5), pp.4566-4582. (10.1109/TMC.2023.3292898)
  DOI : 10.1109/TMC.2023.3292898
• Deliverable D1 - Technical Report NF-PERSEUS 2023
  
  • Abdel Nour Charbel
  • Adjih Cédric
  • Amis Karine
  • Begaud Xavier
  • Crussière Matthieu
  • Durant Antoine
  • Di Renzo Marco
  • Douillard Catherine
  • El Hassani Hajar
  • Farah Joumana
  • Fijalkow Inbar
  • Gaillot Davy
  • Gorce Jean-Marie
  • Goursaud Claire
  • Guillaud M.
  • Le Ruyet Didier
  • Asma Mabrouk
  • Paganini Pascal
  • Pham Dang-Kièn Germain
  • Prabhu Balakrishna
  • Rekaya Ben Othman Ghaya
  • Simon Eric Pierre
  • Zayani Rafik
  , 2024, pp.1-86. This is deliverable D1 "Technical Report NF-PERSEUS 2023". This document provides an overview of the progress of the studies undertaken in NF-PERSEUS project and their progress by the end of 2023. Chapter 1 delves into the advantages of cell-free architectures and their pivotal role in shaping the future of 6G wireless networks. Cell-free architectures, which move away from the traditional cellular network model, offer several compelling benefits that make them a promising solution for the next generation of mobile communication. Chapter 2 describes the main use cases identified for the NF-PERSEUS project and their KPI, providing an inventory of use case specifications, representative deployment scenarios and technical requirements. It presents also the MAMIMOSA sounder which will be employed to perform massive MIMO channel measurments for the considered NF-PERSEUS scenarios. Chapter 3 examines and discusses relevant reference scenarios based on RIS. This analysis sheds light on the technical challenges involved and the potential performance that can be achieved. Chapter 4 provides an in-depth analysis of the performance metrics used to evaluate cell-free network architectures. It presents the studies and research activities undertaken within Work Package 3 (WP3) and summarizes the progress made by the end of 2023. It presents some potential PHY layer solutions studied in the framework of NF-PERSEUS project. These solutions comprise advanced precoding/combing schemes and multi-carrier waveforms which are adequate for cell-free architectures. Chapter 5 provides an overview of the progress of the studies undertaken in WP4 and their progress by the end of 2023. WP4 deals with radio resource management and aims at introducing novel multi-user access schemes and resource allocation algorithms dedicated to distributed antenna systems, with an emphasis on achieving power and spectrum efficient massive access in scalable B5G sub-7GHz networks. Chapter 6 presents an overview of electromagnetically consistent communication models for Reconfigurable Intelligent Surfaces (RISs). These models aim to describe the communication mechanisms and performance characteristics of RISs, which are emerging technologies that can dynamically control the propagation of electromagnetic waves. The chapter then goes on to showcase preliminary results obtained based on a communication model for RISs that is grounded in multiport network theory. Multiport network theory provides a framework for analyzing the electromagnetic behavior of complex systems, which is particularly relevant for understanding the operation and capabilities of RISs. Chapter 7 summarizes the studies undertaken in WP2 related to Radio-Frequency Front-End Modules, Reconfigurable antennas and RIS aspects of the project NF-PERSEUS. Specifically, it provides details about the design and manufacturing of Hybrid PA architecture, design and manufacturing of highly efficient miniature and reconfigurable antennas using in particular agile metamaterials and/or biosourced technologies. Chapter 8 gives the conclusion.
• Generative models for complex visual content
  
  • Careil Marlène
  , 2024. In this thesis, we explore generative image models with a focus on improving the compositionality of objects as well as enabling better user controllability. Semantic image synthesis is a specific type of conditional generative task that enables to tackle both problems. It consists of synthesizing images conditioned on semantic segmentation maps which indicate per-pixel class information. We aim to develop methods that alleviate the need to train on large-scale annotated datasets. Toward this goal, we make the following contributions. Firstly, in OCO-GAN, we design a generative adversarial model that shares backbones for unconditional and semantic image synthesis and is jointly trained for both tasks. When little unconditional or labeled data are available, we show thatwe leverage synergy between the two tasks which benefit from each other. Secondly, in CAT for Class Affinity Transfer, we design a few-shot transfer method for semantic image synthesis applicable to diffusion models and Generative Adversarial Networks. It leverages a pretrained semantic image synthesis model trained on a large source dataset and finetunes it on a small target dataset. This method exploits class affinity between source and target classes to provide prior knowledge to the model when finetuning on the target dataset. Thirdly, we tackle a more challenging scenario that is training-free and is a more flexible version of semantic image synthesis in ZestGuide. We replace the per-pixel class label conditioning with a free-form text conditioning. We build upon large pretrained text-to-image diffusion models to develop a method that adapts the generation process to take into account this spatial conditioning. Finally, in PerCo, for Perceptual Compression, we explore how to use generative models for the task of compression. Similar to semantic image synthesis, we condition a generative model on discrete representation, but in this case, the discrete representation is learned and constrained to have a fixed low bitrate. We target image compression at extremely low bitrates to recover realistic images with satisfying semantic preservation. After introducing the context and related work on generative image models on which this thesis builds upon, we present each of the four contributions in detail, with a chapter dedicated to each of them. Finally, we will conclude this document with a discussion of the limitations of the presented work and perspectives for future research.
• Variational Perspective on Fair Edge Prediction
  
  • Gourru Antoine
  • Laclau Charlotte
  • Choudhary Manvi
  • Largeron Christine
  , 2024, 14641, pp.93-104. Algorithmic fairness has been of great interest in the machine learning community and more recently in the graph context. In this paper, we address the problem of dyadic fairness where the task at hand is edge prediction, and the population of interest (nodes) is divided into a protected and a non-protected group, e.g. men and women. The goal is then to ensure that there should be no statistically significant difference in the prediction outcomes between the two groups, after accounting for any relevant factors that may impact the outcome. To proceed, we design a novel loss based on the variational information bottleneck principle to learn individual node representation while controlling a given level of dyadic fairness. The optimization of the loss is done with a Graph Neural Network. Experiments carried out on several real-world datasets confirmed the capacity of the proposed method, to maintain high accuracy on the edge prediction task while significantly reducing potential bias. (10.1007/978-3-031-58547-0_8)
  DOI : 10.1007/978-3-031-58547-0_8
• Self-supervised learning of deep despeckling networks with MERLIN: ensuring the statistical independence of the real and imaginary parts
  
  • Dalsasso Emanuele
  • Brigui Frédéric
  • Denis Loïc
  • Abergel Rémy
  • Tupin Florence
  , 2024. Due to the wide variety of sensors, with different spatial resolutions, operating frequency bands, as well as acquisition modes (Stripmap, Spotlight, TOPS...), despeckling neural networks trained on a given type of SAR images do not perform well on other kinds of images. By considerably simplifying the building of training sets and directly including images from the sensor and acquisition mode of interest, self-supervised learning is a very appealing solution. This paper analyses the preprocessing requirements of the MERLIN strategy that assumes statistical independence of the real and imaginary parts of single look complex SAR images to perform the self-supervised training.
• Learning a versatile representation of SAR data for regression and segmentation by leveraging self-supervised despeckling with MERLIN
  
  • Dalsasso Emanuele
  • Rambour Clément
  • Denis Loïc
  • Tupin Florence
  , 2024. Synthetic Aperture Radar (SAR) images are abundantly available, yet labels are often missing. Thus, training a neural network in a fully supervised manner is arduous. In this work, we leverage MERLIN, a self-supervised despeckling algorithm, to learn a mapping of SAR images into a representation space shared among despeckling, segmentation and regression. Our experiments demonstrate that the joint training of a neural network for these three tasks reduces considerably the need for labeled data to solve the supervised tasks.
• Artificial Intelligence Methods to Assist the Diagnosis of Pancreatic Diseases in Radiology
  
  • Vétil Rebeca
  , 2024. With its increasing incidence and its five- year survival rate (9%), pancreatic cancer could be- come the third leading cause of cancer-related deaths by 2025. These figures are primarily attributed to late diagnoses, which limit therapeutic options. This the- sis aims to assist radiologists in diagnosing pancrea- tic cancer through artificial intelligence (AI) tools that would facilitate early diagnosis. Several methods have been developed. First, a method for the automatic segmentation of the pancreas on portal CT scans was developed. To deal with the specific anatomy of the pancreas, which is characterized by an elonga- ted shape and subtle extremities easily missed, the proposed method relied on local sensitivity adjust- ments using geometrical priors. Then, the thesis tack- led the detection of pancreatic lesions and main pan- creatic duct (MPD) dilatation, both crucial indicators of pancreatic cancer. The proposed method started with the segmentation of the pancreas, the lesion and the MPD. Then, quantitative features were extracted from the segmentations and leveraged to predict the presence of a lesion and the dilatation of the MPD. The method was evaluated on an external test cohort comprising hundreds of patients. Continuing towards early diagnosis, two strategies were explored to de- tect secondary signs of pancreatic cancer. The first approach leveraged large databases of healthy pan- creases to learn a normative model of healthy pan- creatic shapes, facilitating the identification of anoma- lies. To this end, volumetric segmentation masks were embedded into a common probabilistic shape space, enabling zero-shot and few-shot abnormal shape de- tection. The second approach leveraged two types of radiomics: deep learning radiomics (DLR), extracted by deep neural networks, and hand-crafted radiomics (HCR), derived from predefined formulas. The propo- sed method sought to extract non-redundant DLR that would complement the information contained in the HCR. Results showed that this method effectively de- tected four secondary signs of pancreatic cancer: ab- normal shape, atrophy, senility, and fat replacement. To develop these methods, a database of 2800 exa- minations has been created, making it one of the lar- gest for AI research on pancreatic cancer.
• BallMerge: High-quality Fast Surface Reconstruction via Voronoi Balls
  
  • Parakkat Amal Dev
  • Ohrhallinger Stefan
  • Eisemann Elmar
  • Memari Pooran
  Computer Graphics Forum, Wiley, 2024. We introduce a Delaunay-based algorithm for reconstructing the underlying surface of a given set of unstructured points in 3D. The implementation is very simple, and it is designed to work in a parameter-free manner. The solution builds upon the fact that in the continuous case, a closed surface separates the set of maximal empty balls (medial balls) into an interior and exterior. Based on discrete input samples, our reconstructed surface consists of the interface between Voronoi balls, which approximate the interior and exterior medial balls. An initial set of Voronoi balls is iteratively processed, merging Voronoi-ball pairs if they fulfil an overlapping error criterion. Our complete open-source reconstruction pipeline performs up to two quick linear-time passes on the Delaunay complex to output the surface, making it an order of magnitude faster than the state of the art while being competitive in memory usage and often superior in quality. We propose two variants (local and global), which are carefully designed to target two different reconstruction scenarios for watertight surfaces from accurate or noisy samples, as well as real-world scanned data sets, exhibiting noise, outliers, and large areas of missing data. The results of the global variant are, by definition, watertight, suitable for numerical analysis and various applications (e.g., 3D printing). Compared to classical Delaunay-based reconstruction techniques, our method is highly stable and robust to noise and outliers, evidenced via various experiments, including on real-world data with challenges such as scan shadows, outliers, and noise, even without additional preprocessing.
• Driller: An Intuitive Interface for Designing Tangled and Nested Shapes
  
  • Butler Tara
  • Guehl Pascal
  • Parakkat Amal Dev
  • Cani Marie-Paule
  , 2024. The ability to represent not only isolated shapes but also shapes that interact is essential in various fields, from design to biology or anatomy. In this paper, we propose an intuitive interface to control and edit complex shape arrangements. Using a set of pre-defined shapes that may intersect, our ''Driller'' interface allows users to trigger their local deformation so that they rest on each other, become tangled, or even nest within each other. Driller provides an intuitive way to specify the relative depth of different shapes beneath user-selected points of interest by setting their local depth ordering perpendicularly to the camera's viewpoint. Deformations are then automatically generated by locally propagating these ordering constraints. In addition to being part of the final arrangement, some of the shapes can be used as deformers, which can be later deleted to help sculpt the target shapes. We implemented this solution within a sketch-based modeling system designed for novice users. (10.2312/egs.20241025)
  DOI : 10.2312/egs.20241025
• Non Sum-Separable Energy Systems Consideration for Equilibrium Propagation
  
  • Kiraz Zulal
  • Pham Dang-Kièn Germain
  • Desgreys Patricia
  , 2024. Implementing Stochastic Gradient Descent (SGD)-based algorithms in analog neural networks, particularly with programmable resistors, introduces significant challenges, especially in managing the complexity of weight updates during the backward pass. Therefore, embracing hardware-friendly deep learning algorithms becomes essential for unlocking the full capabilities of neuromorphic computing architectures, ensuring they can efficiently support advanced computational tasks. The Equilibrium Propagation (EqProp) algorithm was introduced as a hardware-friendly algorithm that offers a promising approach for training analog neural networks by estimating error gradients without needing a separate computational circuit. While the EqProp gradient estimation method has been widely used, it often simplifies the analysis, potentially leading to incomplete or inaccurate expressions of a network’s true learning dynamics. In this talk, we will discuss the EqProp algorithm and introduce a gradient of conventional electrical power that aims to provide a more accurate representation of the energy function in analog neural circuits as energy-based models.
• Recent Advances in Power Amplifier Behavioral Modeling and Digital Predistortion using Neural Networks
  
  • Pham Dang-Kièn Germain
  , 2024. In this talk, we explore the integration of neural networks in improving power amplifier efficiency and linearity through advanced behavioral modeling and digital predistortion (DPD). The basics of power amplifiers, their common design challenges, and traditional behavioral modeling techniques are discussed. The presentation delves into the fundamentals of DPD, its objectives, and conventional methods before introducing neural networks' advantages and various architectures, such as Feedforward, Recurrent, and Convolutional Neural Networks, for behavioral modeling and DPD. Practical implementation challenges, such as data requirements, model integration, and computational resources, are quickly reviewed. Recent research highlights present some common structures in the literature.
• Fully Dynamic Attribute-Based Signatures for Circuits from Codes
  
  • Ling San
  • Nguyen Khoa
  • Phan Duong Hieu
  • Tang Khai Hanh
  • Wang Huaxiong
  • Xu Yanhong
  , 2024, 14601, pp.37-73. Attribute-Based Signature (ABS), introduced by Maji et al. (CT-RSA’11), is an advanced privacy-preserving signature primitive that has gained a lot of attention. Research on ABS can be categorized into three main themes: expanding the expressiveness of signing policies, enabling new functionalities, and providing more diversity in terms of computational assumptions. We contribute to the development of ABS in all three dimensions, by providing a fully dynamic ABS scheme for arbitrary circuits from codes. The scheme is the first ABS from code-based assumptions and also the first ABS system offering the full dynamicity functionality (i.e., attributes can be enrolled and revoked simultaneously). Moreover, the scheme features much shorter signature size than a lattice-based counterpart proposed by El Kaafarani and Katsumata (PKC’18). In the construction process, we put forward a new theoretical abstraction of Stern-like zero-knowledge (ZK) protocols, which are the major tools for privacy-preserving cryptography from codes. Our main insight here actually lies in the questions we ask about the fundamental principles of Stern-like protocols that have remained unchallenged since their conception by Stern at CRYPTO’93. We demonstrate that these long-established principles are not essential, and then provide a refined framework generalizing existing Stern-like techniques and enabling enhanced constructions. (10.1007/978-3-031-57718-5_2)
  DOI : 10.1007/978-3-031-57718-5_2
• Mathematical Morphology Applied to Feature Extraction in Music Spectrograms
  
  • Romero-García Gonzalo
  • Bloch Isabelle
  • Agón Carlos
  , 2024, 14605, pp.431-442. Mathematical Morphology has proven to be a powerful tool for extracting geometric information from greyscale images. In this paper, we demonstrate its application to spectrograms (two-dimensional greyscale images of sound) of music excerpts. The sounds of musical instruments exhibit particular shapes when represented as a spectrogram. These shapes are determined by the sound characteristics. In general, musical sounds contain three different components: the attack component, appearing as vertical lines; the sustain component, appearing as horizontal lines; and the stochastic component, appearing as a landscape of hills and holes. In this paper we propose a pipeline of morphological operators to separate these three components. This separation allows us to build a new sound similar to the input one. (10.1007/978-3-031-57793-2_33)
  DOI : 10.1007/978-3-031-57793-2_33