Publications

2022

• Software protections for artificial neural networks
  
  • Guiga Linda
  , 2022. In a context where Neural Networks (NNs) are very present in our daily lives, be it through smartphones, face and biometrics recognition or even in the medical field, their security is of the utmost importance. If such models leak information, not only could it imperil the privacy of sensitive data, but it could also infringe on intellectual property.Selecting the correct architecture and training the corresponding parameters is time-consuming -- it can take months -- and requires large computational resources. This is why an NN constitutes intellectual property. Moreover, once a malicious user knows the architecture and/or the parameters, multiple attacks can be carried out, such as adversarial ones. Adversarial attackers craft a malicious datapoint by adding a small noise to the original input, such that the noise is undetectable to the human eye but fools the model. Such attacks could be the basis of impersonations. Membership attacks, which aim at leaking information about the training dataset, are also facilitated by the knowledge of a model. More generally, when a malicious user has access to a model, she also has access to the manifold of the model's outputs, making it easier for her to fool the model.Protecting NNs is therefore paramount. However, since 2016, they have been the target of increasingly powerful reverse-engineering attacks. Mathematical reverse-engineering attacks solve equations or study a model's internal structure to reveal its parameters. On the other hand, side-channel attacks exploit leaks in a model's implementation -- such as in the cache or power consumption -- to uncover the parameters and architecture. In this thesis, we seek to protect NN models by changing their internal structure and their software implementation.To this aim, we propose four novel countermeasures. In the first three, we consider a gray-box context where the attacker has partial access to the model, and we leverage parasitic models to counter three types of attacks.We first tackle mathematical attacks that recover a model's parameters based on its internal structure. We propose to add one -- or multiple -- parasitic Convolutional Neural Networks (CNNs) at various locations in the base model and measure the incurred change in the structure by observing the modification in generated adversarial samples.However, the previous method does not thwart side-channel attacks that extract the parameters through the analysis of power or electromagnetic consumption. To mitigate such attacks, we propose to add dynamism to the previous protocol. Instead of considering one -- or several -- fixed parasite(s), we incorporate different parasites at each run, at the entrance of the base model. This enables us to hide a model's input, necessary for precise weight extraction. We show the impact of this dynamic incorporation through two simulated attacks.Along the way, we observe that parasitic models affect adversarial examples. Our third contribution is derived from this, as we suggest a novel method to mitigate adversarial attacks. To this effect, we dynamically incorporate another type of parasite: autoencoders. We demonstrate the efficiency of this countermeasure against common adversarial attacks.In a second part, we focus on a black-box context where the attacker knows neither the architecture nor the parameters. Architecture extraction attacks rely on the sequential execution of NNs. The fourth and last contribution we present in this thesis consists in reordering neuron computations. We propose to compute neuron values by blocks in a depth-first fashion, and add randomness to this execution. We prove that this new way of carrying out CNN computations prevents a potential attacker from recovering a small enough set of possible architectures for the initial model.
• Hollow core photonic crystal fibers for generation of photon pairs or triplets
  
  • Zaquine Isabelle
  , 2022.
• Design optimization of flexible analog-to-feature converter for smart sensors
  
  • Manokhin Mikhail
  • Chollet Paul
  • Desgreys Patricia
  , 2022. Analog-to-Feature (A2F) conversion is an attractive alternative for the significant reduction of energy consumed by wireless communication systems during data transfer from sensor to aggregator. This work illustrates the simulation results of anomaly detection in ECG signals via A2F conversion based on Non-Uniform Wavelet Sampling (NUWS). Moreover, another application is envisioned to show the genericity of the proposed converter and determine its parameters for a circuit design.
• Colmade: Collaborative Masking in Auditable Decryption for BFV-based Homomorphic Encryption
  
  • Ibarrondo Alberto
  • Chabanne Hervé
  • Despiegel Vincent
  • Önen Melek
  , 2022, pp.129-139. This paper proposes a novel collaborative decryption protocol for the Brakerski-Fan-Vercauteren (BFV) homomorphic encryption scheme in a multiparty distributed setting, and puts it to use in designing a leakage-resilient biometric identification solution. Allowing the computation of standard homomorphic operations over encrypted data, our protocol reveals only one least significant bit (LSB) of a scalar/vectorized result resorting to a pool of N parties. By employing additively shared masking, our solution preserves the privacy of all the remaining bits in the result as long as one party remains honest. We formalize the protocol, prove it secure in several adversarial models, implement it on top of the open-source library Lattigo and showcase its applicability as part of a biometric access control scenario (10.1145/3531536.3532952)
  DOI : 10.1145/3531536.3532952
• Detecting Laser Fault Injection Attacks via Time-to-Digital Converter Sensors
  
  • Ebrahimabadi Mohammad
  • Mehjabin Suhee Sanjana
  • Viera Raphael
  • Guilley Sylvain
  • Danger Jean-Luc
  • Dutertre Jean-Max
  • Karimi Naghmeh
  , 2022, pp.97-100. Fault Injection Attacks (FIA) have received a lot of attention in recent years. An adversary launches such an attack to abusively take control over the system or to leak sensitive data. Laser illumination has been considered as an effective technique to launch FIA. The laser-based FIAs are mainly used when the adversary opts to target a specific location in the target circuit. However, thanks to the miniaturization of transistors and moving towards smaller feature size, even small laser spots may illuminate more than one gate; making the attack more detectable when the circuitries are equipped with embedded fault detection mechanisms such as digital sensors. In this paper, we use time-to-digital convertors, aka digital sensors, to detect the laser shots. We show that by embedding these digital sensors in the target circuitry, the IR drop caused by the laser illumination can be sensed with a high accuracy. An alarm will be raised when the fault is detected. The simulation results show the high accuracy of the proposed scheme in detecting laser-based FIAs (10.1109/HOST54066.2022.9840318)
  DOI : 10.1109/HOST54066.2022.9840318
• Locally Decodable Slepian-Wolf Compression
  
  • Vatedka Shashank
  • Chandar Venkat
  • Tchamkerten Aslan
  , 2022, pp.1430-1435. This paper investigates the Slepian-Wolf distributed compression of two sources X n and Y n with the additional property that any pair (X i , Y i ) should reliably be decoded by probing a small number d of compressed bits. We show that for certain source distributions, the error probability of any such local decoder is lower bounded by 2 –O(d) , in the worst case over index i, whenever one of the sources is compressed below its entropy. Unlike the single-source setup, it is thus impossible to simultaneously achieve constant local decodability d and vanishing local decoding error probability as n increases. We also provide a compression scheme with a local decoder that almost achieves the above lower bound. (10.1109/ISIT50566.2022.9834371)
  DOI : 10.1109/ISIT50566.2022.9834371
• Signaling for MISO Channels Under First-and Second-Moment Constraints
  
  • Ma Shuai
  • Moser Stefan M
  • Wang Ligong
  • Wigger Michèle M
  , 2022. Consider a multiple-input single-output system, where the nonnegative, peak-limited inputs X1,. .. , Xn T ∈ [0, A] are subject to first-and second-moment sum-constraints on all antennas. The paper characterizes all probability distributions that can be induced for the "channel image," which is given by the inner product of the input vector with a given channel vector. Key to this result is the description of input vectors that achieve a given deterministic channel image with the smallest energy, where "energy" of an input vector refers to a weighted sum of its one-and two-norms. Minimum-energy input vectors have an interesting structure: depending on the desired channel image, some of the weakest antennas are silenced, and the remaining antennas are chosen according to a shifted and amplitudeconstrained beamforming rule.
• Coding for Sensing: An Improved Scheme for Integrated Sensing and Communication over MACs
  
  • Ahmadipour Mehrasa
  • Wigger Michèle
  • Kobayashi Mari
  , 2022. A memoryless state-dependent multiple-access channel (MAC) is considered, where two transmitters wish to convey their messages to a single receiver while simultaneously sensing (estimating) the respective states via generalized feedbacks. For this channel, an improved inner bound is provided on the fundamental rate-distortions tradeoff which characterizes the communication rates the transmitters can achieve while simultaneously ensuring that their state-estimates satisfy desired distortion criteria. The new inner bound is based on a scheme where each transmitter codes over the generalized feedback so as to improve the state estimation at the other transmitter. This is in contrast to the schemes proposed for point-to-point and broadcast channels where coding is used only for the transmission of messages and the optimal estimators operate on a symbol-bysymbol basis on the sequences of channel inputs and feedback outputs.
• Benefits of rate-sharing for distributed hypothesis testing
  
  • Hamad Mustapha
  • Sarkiss Mireille
  • Wigger Michèle
  , 2022, pp.1-6. We study distributed binary hypothesis testing with a single sensor and two remote decision centers that are also equipped with local sensors. The communication between the sensor and the two decision centers takes place over three links: a shared link to both centers and an individual link to each of the two centers. All communication links are subject to expected rate constraints. This paper characterizes the optimal exponents region of the type-II error for given type-I error thresholds at the two decision centers and further simplifies the expressions in the special case of having only the single shared link. The exponents region illustrates a gain under expected rate constraints compared to equivalent maximum rate constraints. Moreover, it exhibits a tradeoff between the exponents achieved at the two centers. (10.1109/ISIT50566.2022.9834807)
  DOI : 10.1109/ISIT50566.2022.9834807
• Attacking masked cryptographic implementations: Information-theoretic bounds
  
  • Cheng Wei
  • Liu Yi
  • Guilley Sylvain
  • Rioul Olivier
  , 2022. Measuring the information leakage is critical for evaluating the practical security of cryptographic devices against side-channel analysis. Information-theoretic measures can be used (along with Fano’s inequality) to derive upper bounds on the success rate of any possible attack in terms of the number of side-channel measurements. Equivalently, this gives lower bounds on the number of queries for a given success probability of attack. In this paper, we consider cryptographic implementations protected by (first-order) masking schemes, and derive several information-theoretic bounds on the efficiency of any (second- order) attack. The obtained bounds are generic in that they do not depend on a specific attack but only on the leakage and masking models, through the mutual information between side- channel measurements and the secret key. Numerical evaluations confirm that our bounds reflect the practical performance of optimal maximum likelihood attacks.
• Visualization Empowerment: How to Teach and Learn Data Visualization
  
  • Bach Benjamin
  • Huron Samuel
  • Hinrichs Uta
  • Carpendale Sheelagh
  , 2022. The concept of visualisation literacy encompasses the ability to read, write, and create visualiza- tions of data using digital or physical representations and is becoming an important asset for a data- literate, informed, and critical society. While many useful textbooks, blogs, and courses exist about data visualization—created by both academics and practitioners—little is known about 1) how learning processes in the context of visualization unfold and 2) what are the best practices to engage and to teach the theory and practice of data visualization to diverse audiences, ranging from children to adults, from novices to advances, from students to professionals, and including different domain backgrounds. Hence, the aim of this Dagstuhl Seminar is to collect, discuss, and systematize knowledge around the education and teaching of data visualization to empower people making effective and unbiased use of this powerful medium. To that end, we aim to: • Provide a cohesive overview of the state-of-the-art in visualization literacy (materials, skills, evaluation, etc.) and compile a comprehensive handbook for academics, teachers, and practitioners; • Collect and systematize learning activities to inform teaching visualization across a wide range of education scenarios in the form of a teaching activities cook-book. • Discuss open challenges and outline future research agendas to improve visualization literacy and education. Besides those outcomes, we aim to facilitate interdisciplinary research collaborations among attendees; researchers, practitioners, and educators from a wide range of background including data visualization, education, and data science.
• DoF of a Cooperative X-Channel with an Application to Distributed Computing
  
  • Bi Yue
  • Ciblat Philippe
  • Wigger Michèle
  • Wu Yue
  , 2022, pp.566-571. We consider a cooperative X-channel with K transmitters (TXs) and K receivers (Rxs) where Txs and Rxs are gathered into groups of size r respectively. Txs belonging to the same group cooperate to jointly transmit a message to each of the K − r Rxs in all other groups, and each Rx individually decodes all its intended messages. By introducing a new interference alignment (IA) scheme, we prove that when K/r is an integer the Sum Degrees of Freedom (Sum-DoF) of this channel is lower bounded by 2r if K/r ∈ {2, 3} and by K(K−r)−r 2 2K−3r if K/r ≥ 4. We also prove that the Sum-DoF is upper bounded by K(K−r) 2K−3r. The proposed IA scheme finds application in a wireless distributed MapReduce framework, where it improves the normalized data delivery time (NDT) compared to the state of the art. (10.1109/ISIT50566.2022.9834584)
  DOI : 10.1109/ISIT50566.2022.9834584
• A generic and adaptive approach to explainable AI in autonomic systems : the case of the smart home
  
  • Houzé Etienne
  , 2022. Smart homes are Cyber-Physical Systems where various components cooperate to fulfill high-level goals such as user comfort or safety. These autonomic systems can adapt at runtime without requiring human intervention. This adaptation is hard to understand for the occupant, which can hinder the adoption of smart home systems. Since the mid 2010s, explainable AI has been a topic of interest, aiming to open the black box of complex AI models. The difficulty to explain autonomic systems does not come from the intrinsic complexity of their components, but rather from their self-adaptation capability which leads changes of configuration, logic or goals at runtime. In addition, the diversity of smart home devices makes the task harder. To tackle this challenge, we propose to add an explanatory system to the existing smart home autonomic system, whose task is to observe the various controllers and devices to generate explanations. We define six goals for such a system. 1) To generate contrastive explanations in unexpected or unwanted situations. 2) To generate a shallow reasoning, whose different elements are causaly closely related to each other. 3) To be transparent, i.e. to expose its entire reasoning and which components are involved. 4) To be self-aware, integrating its reflective knowledge into the explanation. 5) To be generic and able to adapt to diverse components and system architectures. 6) To preserve privacy and favor locality of reasoning. Our proposed solution is an explanatory system in which a central component, name the ``Spotlight'', implements an algorithm named D-CAS. This algorithm identifies three elements in an explanatory process: conflict detection via observation interpretation, conflict propagation via abductive inference and simulation of possible consequences. All three steps are performed locally, by Local Explanatory Components which are sequentially interrogated by the Spotlight. Each Local Component is paired to an autonomic device or controller and act as an expert in the related knowledge domain. This organization enables the addition of new components, integrating their knowledge into the general system without need for reconfiguration. We illustrate this architecture and algorithm in a proof-of-concept demonstrator that generates explanations in typical use cases. We design Local Explanatory Components to be generic platforms that can be specialized by the addition of modules with predefined interfaces. This modularity enables the integration of various techniques for abduction, interpretation and simulation. Our system aims to handle unusual situations in which data may be scarce, making past occurrence-based abduction methods inoperable. We propose a novel approach: to estimate events memorability and use them as relevant hypotheses to a surprising phenomenon. Our high-level approach to explainability aims to be generic and paves the way towards systems integrating more advanced modules, guaranteeing smart home explainability. The overall method can also be used for other Cyber-Physical Systems.
• Smoothed Separable Nonnegative Matrix Factorization
  
  • Nadisic Nicolas
  • Gillis Nicolas
  • Kervazo Christophe
  , 2022. Given a set of data points belonging to the convex hull of a set of vertices, a key problem in data analysis and machine learning is to estimate these vertices in the presence of noise. Many algorithms have been developed under the assumption that there is at least one nearby data point to each vertex; two of the most widely used ones are vertex component analysis (VCA) and the successive projection algorithm (SPA). This assumption is known as the pure-pixel assumption in blind hyperspectral unmixing, and as the separability assumption in nonnegative matrix factorization. More recently, Bhattacharyya and Kannan (ACM-SIAM Symposium on Discrete Algorithms, 2020) proposed an algorithm for learning a latent simplex (ALLS) that relies on the assumption that there is more than one nearby data point for each vertex. In that scenario, ALLS is probalistically more robust to noise than algorithms based on the separability assumption. In this paper, inspired by ALLS, we propose smoothed VCA (SVCA) and smoothed SPA (SSPA) that generalize VCA and SPA by assuming the presence of several nearby data points to each vertex. We illustrate the effectiveness of SVCA and SSPA over VCA, SPA and ALLS on synthetic data sets, and on the unmixing of hyperspectral images.
• A Novel Approach for Doherty PA Design using a Compact L-C Combiner
  
  • Bachi Joe
  • Serhan Ayssar
  • Pham Dang-Kien Germain
  • Parat Damien
  • Reynier Pascal
  • Desgreys Patricia
  • Giry Alexandre
  IEEE Transactions on Circuits and Systems II: Express Briefs, Institute of Electrical and Electronics Engineers, 2022, pp.5. (10.1109/TCSII.2022.3185174)
  DOI : 10.1109/TCSII.2022.3185174
• Extraction des positions continues des cibles dans les signaux RSO
  
  • Kervazo Christophe
  • Ladjal Saïd
  , 2022. En imagerie radar à synthèse d'ouverture, les images considérées sont la somme d'un fond diffus et de cibles très énergétiques. Dans cet article, nous proposons une méthode permettant d'estimer de manière continue la position des cibles dans de tels signaux. Celle-ci se fonde sur une écriture du problème sous la forme d'un problème d'optimisation du type Continuous Basis Pursuit, agrémentée par un choix automatique des hyperparamètres de régularisation. Cette méthode est ensuite complétée par un post-traitement, permettant d'extraire les cibles du signal d'origine et de n'en conserver que le fond. Les deux méthodes sont validées sur données simulées.
• Active and passive metasurfaces : methodology for the design of a low profile, beam-steerable, multiband, and wideband antenna
  
  • Gonçalves Licursi de Mello Rafael
  , 2022. Metasurfaces are artificial engineered materials that can be combined with traditional microwave components in ground-breaking solutions. The research on the use of metasurfaces in the roles of antenna reflector and/or superstrate considerably increased mainly from the beginning of the 2020s, because of their innovative functionalities in line with the ultimate Telecommunication trends. In this thesis, methodologies for the use of passive and active metasurfaces in the design of antennas are presented. A first methodology which exploits both the near-perfect electric conductor (PEC) and near-perfect magnetic conductor (PMC) behaviors of a dual-band artificial magnetic conductor (AMC) is used to design a low-profile, multiband, directive antenna. This methodology is validated with a prototype suitable for the European standards of 4G/5G and Wi-Fi 2.4/5/6E, operating within the following bands: 2.40–2.70 GHz, 3.40–3.80 GHz, 5.17–5.83 GHz, and 5.93–6.45 GHz. Additionally, a methodology to handle the Fabry-Pérot mechanism in an antenna composed of a grooved rounded-edge bow-tie, a passive dual-band AMC, and an active multiband Huygens metasurface is presented. This methodology is validated with the design of a multiband, directive, low-profile, antenna that performs an independent beam-steering in only one of the operating frequency bands. Through the controlling of the bias voltages over four columns of varactors in the reconfigurable, multiband Huygens metasurface, the beam may be dynamically steered in ±51°, in a continuous manner, in a frequency range lying inside the European 5G frequency range (from 3.50 to 3.65 GHz. All at once, the radiation patterns concerning the 4G and Wi-Fi 2.4/5/6E keep practically unaffected.
• Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences
  
  • Guyet Thomas
  • Zhang Wenbin
  • Bifet Albert
  , 2022, LNCS -13350, pp.460-472. The need to analyze information from streams arises in a variety of applications. One of its fundamental research directions is to mine sequential patterns over data streams. Current studies mine series of items based on the presence of the pattern in transactions but pay no attention to the series of itemsets and their multiple occurrences. The pattern over a window of itemsets stream and their multiple occurrences, however, provides additional capability to recognize the essential characteristics of the patterns and the inter-relationships among them that are unidentifiable by the existing presence-based studies. In this paper, we study such a new sequential pattern mining problem and propose a corresponding sequential miner with novel strategies to prune the search space efficiently. Experiments on both real and synthetic data show the utility of our approach. (10.1007/978-3-031-08751-6_33)
  DOI : 10.1007/978-3-031-08751-6_33
• Polysemy in Spoken Conversations and Written Texts
  
  • Soler Aina Garí
  • Labeau Matthieu
  • Clavel Chloé
  , 2022. Our discourses are full of potential lexical ambiguities, due in part to the pervasive use of words having multiple senses. Sometimes, one word may even be used in more than one sense throughout a text. But, to what extent is this true for different kinds of texts? Does the use of polysemous words change when a discourse involves two people, or when speakers have time to plan what to say? We investigate these questions by comparing the polysemy level of texts of different nature, with a focus on spontaneous spoken dialogs; unlike previous work which examines solely scripted, written, monolog-like data. We compare multiple metrics that presuppose different conceptualizations of text polysemy, i.e., they consider the observed or the potential number of senses of words, or their sense distribution in a discourse. We show that the polysemy level of texts varies greatly depending on the kind of text considered, with dialog and spoken discourses having generally a higher polysemy level than written monologs. Additionally, our results emphasize the need for relaxing the popular "one sense per discourse" hypothesis.
• Using a Knowledge Base to Automatically Annotate Speech Corpora and to Identify Sociolinguistic Variation
  
  • Wu Yaru
  • Suchanek Fabian
  • Vasilescu Ioana
  • Lamel Lori
  • Adda-Decker Martine
  , 2022, pp.1054-1360. Speech characteristics vary from speaker to speaker. While some variation phenomena are due to the overall communication setting, others are due to diastratic factors such as gender, provenance, age, and social background. The analysis of these factors, although relevant for both linguistic and speech technology communities, is hampered by the need to annotate existing corpora or to recruit, categorise, and record volunteers as a function of targeted profiles. This paper presents a methodology that uses a knowledge base to provide speaker-specific information. This can facilitate the enrichment of existing corpora with new annotations extracted from the knowledge base. The method also helps the large scale analysis by automatically extracting instances of speech variation to correlate with diastratic features. We apply our method to an over 120-hour corpus of broadcast speech in French and investigate variation patterns linked to reduction phenomena and/or specific to connected speech such as disfluencies. We find significant differences in speech rate, the use of filler words, and the rate of non-canonical realisations of frequent segments as a function of different professional categories and age groups.
• EZCAT: an Easy Conversation Annotation Tool
  
  • Guibon Gaël
  • Lefeuvre Luce
  • Labeau Matthieu
  • Clavel Chloé
  , 2022. Users generate content constantly, leading to new data requiring annotation. Among this data, textual conversations are created every day and come with some specificities: they are mostly private through instant messaging applications, requiring the conversational context to be labeled. These specificities led to several annotation tools dedicated to conversation, and mostly dedicated to dialogue tasks, requiring complex annotation schemata, not always customizable and not taking into account conversation-level labels. In this paper, we present EZCAT, an easy-to-use interface to annotate conversations in a two-level configurable schema, leveraging message-level labels and conversation-level labels at once. Our interface is characterized by the voluntary absence of a server and accounts management, enhancing its availability to anyone, and the control over data, which is crucial to confidential conversations. We also present our first usage of EZCAT along with our annotation schema we used to annotate confidential customer service conversations. EZCAT is freely available at https://gguibon.github.io/ezcat.
• Security bootstrapping for Internet of Things
  
  • Khalfaoui Sameh
  , 2022. The demand for internet of Things (IoT) services is increasing exponentially, and a large number of devices are being deployed. However, these devices can represent a serious threat to the security of the deployment network and a potential entry-point when exploited by the adversaries. Thus, there is an imminent need to perform a secure association approach of the IoT objects before being rendered operational on the network of the user. This procedure is referred to as secure bootstrapping, and it primarily guarantees the confidentiality and the integrity of the data exchanges between the user and the devices. Secondly, this process provides an assurance on the identity and the origin of these objects.Due to scalability limitations, the first phase of the bootstrapping process cannot be efficiently conducted using pre-shared security knowledge such as digital certificates. This step is referred to as secure device pairing, and it ensures the establishment of a secure communication channel between the use and the object. The pairing phase uses a symmetric key agreement protocol that is suitable to the resource-constrained nature of these devices. The use of auxiliary channels has been proposed as a way to authenticate the key exchange, but they require a relatively long time and an extensive user involvement to transfer the authentication bits. However, the context-based schemes use the ambient environment to extract a common secret without an extensive user intervention under the requirement of having a secure perimeter during the extraction phase, which is considered a strong security assumption. The second phase of the bootstrapping process is referred to as secure device enrollment, and it aims at avoiding the associating of a malicious IoT object by authenticating its identity. The use of hardware security elements, such as the Physical Unclonable Function (PUF), has been introduced as a promising solution that is suitable for the resource-constraint nature of these devices. A growing number of PUF architectures has been demonstrated mathematically clonable through Machine Learning (ML) modeling techniques. The use of PUF ML models has been recently proposed to authenticate the IoT objects. Nonetheless, the leakage scenario of the PUF model to an adversary due to an insider threat within the organization is not supported by the existing solutions. Hence, the security of these PUF model-based enrollment proposals can be compromised.In this thesis, we study the secure bootstrapping process of resource-constrained devices and we introduce two security schemes:- A hybrid ad-hoc pairing protocol, called COOB, that efficiently combines a state-of-the-art fast context-based scheme with the use of an auxiliary channel. This protocol exploits a nonce exponentiation of the Diffie-Hellman public keys to achieve the temporary secrecy goal needed for the key agreement. Our method provides security even against an attacker that can violate the safe zone requirement, which is not supported by the existing contextual schemes. This security improvement has been formally validated in the symbolic model using the TAMARIN prover.- An enrollment solution that exploits a ML PUF model in the authentication process, called Water-PUF. Our enrollment scheme is based on a specifically designed black-box watermarking technique for PUF models with a binary output response. This procedure prevents an adversary from relying on the watermarked model in question or another derivative model to bypass the authentication. Therefore, any leakage of the watermarked PUF model that is used for the enrollment does not affect the correctness of the protocol. The Water-PUF design is validated by a number of simulations against numerous watermark suppression attacks to assess the robustness of our proposal.
• 40 ans de musiques hip-hop en France
  
  • Hammou Karim
  • Sonnette-Manouguian Marie
  • Aterianus-Owanga Alice
  • Barone Stefano
  • Becquet Vincent
  • Déon Maxence
  • Eloy Florence
  • Guillard Séverin
  • Kneubühler Marine
  • Legon Tomas
  • Molinero Stéphanie
  • Tatchim Nicanor
  • Carinos Emmanuelle
  , 2022, pp.244 p.. Passées en quatre décennies du statut de pratiques artistiques confidentielles à celui de courant esthétique majeur, les musiques hip-hop portent des innovations fondées sur de nouvelles techniques instrumentales (Djing, sampling, etc.) et vocales (interprétations rappées, ragga, human beatboxing, etc.). En tant que musiques de producteurs, elles sont à l’avant-garde des mutations que connaissent les industries musicales sous l’effet de l’informatisation puis de la numérisation de la création. En France comme dans de nombreux autres pays, leur essor a contribué à diversifier les musiques populaires et à en renouveler les pratiques. Pourtant, la valorisation culturelle de ces œuvres, situées au carrefour de tensions sociales vives, demeure contestée. Première synthèse des connaissances sur les musiques hip-hop, cet ouvrage mêle résultats d’enquêtes inédites et état de la recherche en sciences sociales. Il révèle l’ampleur des transformations politiques, économiques, sociologiques, géographiques et esthétiques auxquelles elles participent. Ont contribué à cet ouvrage : Alice Aterianus-Owanga, Stefano Barone, Vincent Becquet, Emmanuelle Carinos Vasquez, Maxence Déon, Florence Eloy, Séverin Guillard, Karim Hammou, Marine Kneubühler, Tomas Legon, Stéphanie Molinero, Marie Sonnette-Manouguian et Nicanor Tatchim. (10.3917/deps.hammo.2022.01)
  DOI : 10.3917/deps.hammo.2022.01
• Opinions in Interactions : New Annotations of the SEMAINE Database
  
  • Barrière Valentin
  • Clavel Chloé
  • Essid Slim
  , 2022. In this paper, we present the process we used in order to collect new annotations of opinions over the multimodal corpus SEMAINE composed of dyadic interactions. The dataset had already been annotated continuously in two affective dimensions related to the emotions: Valence and Arousal. We annotated the part of SEMAINE called Solid SAL composed of 79 interactions between a user and an operator playing the role of a virtual agent designed to engage a person in a sustained, emotionally colored conversation. We aligned the audio at the word level using the available high-quality manual transcriptions. The annotated dataset contains 5627 speech turns for a total of 73,944 words, corresponding to 6 hours 20 minutes of dyadic interactions. Each interaction has been labeled by three annotators at the speech turn level following a three-step process. This method allows us to obtain a precise annotation regarding the opinion of a speaker. We obtain thus a dataset dense in opinions, with more than 48% of the annotated speech turns containing at least one opinion. We then propose a new baseline for the detection of opinions in interactions improving slightly a state of the art model with RoBERTa embeddings. The obtained results on the database are promising with a F1-score at 0.72.
• R on Raspberry Pi: The "RaspberryPiR" package for collecting and analysing streaming sensor data
  
  • Zhu Alex
  • Lafaye de Micheaux Pierre
  • Mozharovskyi Pavlo
  • Navarro Fabien
  , 2022. Raspberry Pi is a powerful, popular, low-cost minicomputer with the ability to collect physical environmental data from sensors and circuits, such as temperature, luminosity, gas concentration, images and infrared radiation level. Our new R package RaspberryPiR can store sensor data using sensor controlling modules on the Pi (GPIO pins) into shared memory. The data analysis can then be done in a streaming manner, using various streaming statistical and machine learning algorithms. In its current implementation, our package is compatible with the following sensors: DHT11 Temperature and Humidity Sensor, Photo Resistor, MQ2 Gas Sensor and Raspberry Pi Camera Module V2, which already allows for numerous streaming applications. We review and suggest implementation of a set of existing statistics tools for windowed data streams, such as Control Charts and Tukey Region. These can help visualizing data streams collected using our package. To summarize, our package simplifies the process of collecting data streams from surroundings using a Raspberry Pi. This permits scientists, statisticians, data scientists and practitioners to be in control of their environmental research and data project without the need of understanding complexity of data storage and electric circuits on the Raspberry Pi. https://github.com/alexzhu1998/RaspberryPiR