Publications

2020

• EVALUATION OF A STOCHASTIC REVERBERATION MODEL BASED ON THE IMAGE SOURCE PRINCIPLE
  
  • Aknin Achille
  • Dupré Théophile
  • Badeau Roland
  , 2020. Various audio signal processing applications, such as source separation and dereverberation, require an accurate mathematical modeling of the input audio data. In the literature, many works have focused on source signal modeling, while the reverberation model is often kept very simplistic. This paper aims to investigate a stochastic room impulse response model presented in a previous article: this model is first adapted to discrete time, then we propose a parametric estimation algorithm, that we evaluate experimentally. Our results show that this algorithm is able to efficiently estimate the model parameters, in various experimental settings (various signal-to-noise ratios and absorption coefficients of the room walls).
• The Big Picture of Delay-PUF Dependability
  
  • Schaub Alexander
  • Danger Jean-Luc
  • Rioul Olivier
  • Guilley Sylvain
  , 2020. Physically Unclonable Functions (PUFs) allow to generate bitstrings for applications such as device identification, authentication, or key management. For real-world deployment, the industry has stringent requirements on reliability. In addition, as it greatly impacts the security of the whole application chain, the randomness produced by the PUF cannot be compromised. These two requirements are captured by the notions of dynamic randomness-to be minimized in order to improve reliability-and static randomness-to be maximized to increase security. In this paper, we illustrate the whole methodology on a delay-PUF called the loop-PUF. To meet the above requirements on dynamic and static randomness, the PUF's behavior should be modeled and validated; such activities are described in the international standard ISO/IEC 20897. Modeling consists in establishing a stochastic model of the PUF, to predict bit error rates due to dynamic noise, and entropies of the static noise. The model is then verified, its parameters estimated, based on measures in representative environmental conditions.
• Knowledge base curation using constraints
  
  • Pellissier-Tanon Thomas
  , 2020. Knowledge bases are huge collections of primarily encyclopedic facts.They are widely used in entity recognition, structured search, question answering, and other tasks.These knowledge bases have to be curated, and this is a crucial but costly task.In this thesis, we are concerned with curating knowledge bases automatically using constraints.Our first contribution aims at discovering constraints automatically. We improve standard rule mining approaches by using (in-)completeness meta-information. We show that this information can increase the quality of the learned rules significantly. Our second contribution is the creation of a knowledge base, YAGO 4, where we statically enforce a set of constraints by removing the facts that do not comply with them. Our last contribution is a method to correct constraint violations automatically.Our method uses the edit history of the knowledge base to see how users corrected violations in the past, in order to propose corrections for the present.
• First analysis of strong optical feedback on a packaged Fabry-Perot quantum cascade laser emitting at 4 µm
  
  • Durupt Lauréline
  • Spitz Olivier
  • Grillot Frédéric
  , 2020.
• Revisiting Shared Data Protection Against Key Exposure
  
  • Kapusta Katarzyna
  • Memmi Gerard
  • Rambaud Matthieu
  , 2020. This paper puts a new light on secure data storage inside distributed systems. Specifically, it revisits computational secret sharing in a situation where the encryption key is exposed to an attacker. It comes with several contributions: First, it defines a security model for encryption schemes, where we ask for additional resilience against exposure of the encryption key. Precisely we ask for (1) indistinguishability of plaintexts under full ciphertext knowledge, (2) indistinguishability for an adversary who learns: the encryption key, plus all but one share of the ciphertext. (2) relaxes the "all-or-nothing" property to a more realistic setting, where the ciphertext is transformed into a number of shares, such that the adversary can't access one of them. (1) asks that, unless the user's key is disclosed, noone else than the user can retrieve information about the plaintext. Second, it introduces a new computationally secure encryption-then-sharing scheme, that protects the data in the previously defined attacker model. It consists in data encryption followed by a linear transformation of the ciphertext, then its fragmentation into shares, along with secret sharing of the randomness used for encryption. The computational overhead in addition to data encryption is reduced by half with respect to state of the art. Third, it provides for the first time cryptographic proofs in this context of key exposure. It emphasizes that the security of our scheme relies only on a simple cryptanalysis resilience assumption for blockciphers in public key mode: indistinguishability from random, of the sequence of diferentials of a random value. Fourth, it provides an alternative scheme relying on the more theoretical random permutation model. It consists in encrypting with sponge functions in duplex mode then, as before, secret-sharing the randomness. (10.1145/3320269.3372198)
  DOI : 10.1145/3320269.3372198
• Constant-Delay Enumeration for Nondeterministic Document Spanners
  
  • Amarilli Antoine
  • Bourhis Pierre
  • Mengel Stefan
  • Niewerth Matthias
  SIGMOD record, ACM, 2020, 49 (1), pp.25-32. One of the classical tasks in information extraction is to extract subparts of texts through regular expressions. In the database theory literature, this approach has been generalized and formalized as document spanners. In this model, extraction is performed by evaluating a particular kind of automata, called a sequential variable-set automaton (VA). The efficiency of this task is then measured in the context of enumeration algorithms: we first run a preprocessing phase computing a compact representation of the answers, and second we produce the results one after the other with a short time between consecutive answers, called the delay of the enumeration. Our goal is to have an algorithm that is tractable in combined complexity, i.e., in the sizes of the input document and the VA, while ensuring the best possible data complexity bounds in the input document size, i.e., a constant delay that does not depend on the document. We present such an algorithm for a variant of VAs called extended sequential VAs and give an experimental evaluation of this algorithm. (10.1145/3422648.3422655)
  DOI : 10.1145/3422648.3422655
• Identifying the "Right" Level of Explanation in a Given Situation
  
  • Beaudouin Valérie
  • Bloch Isabelle
  • Bounie David
  • Clémençon Stéphan
  • d'Alché-Buc Florence
  • Eagan James R
  • Maxwell Winston
  • Mozharovskyi Pavlo
  • Parekh Jayneel
  , 2020, 2659, pp.63-66. We present a framework for defining the "right" level of AI explainability based on technical, legal and economic considerations. Our approach involves three logical steps: First, define the main con-textual factors, such as who is the audience of the explanation, the operational context, the level of harm that the system could cause, and the legal/regulatory framework. This step will help characterize the operational and legal needs for explanation, and the corresponding social benefits. Second, examine the technical tools available, including post-hoc approaches (input perturbation, saliency maps...) and hybrid AI approaches. Third, as function of the first two steps, choose the right levels of global and local explanation outputs, taking into the account the costs involved. We identify seven kinds of costs and emphasize that explanations are socially useful only when total social benefits exceed costs. (10.2139/ssrn.3604924)
  DOI : 10.2139/ssrn.3604924
• Scalable Detection of Amplification Timing Anomalies for the Superscalar TriCore Architecture
  
  • Binder Benjamin
  • Asavoae Mihail
  • Brandner Florian
  • Ben Hedia Belgacem
  • Jan Mathieu
  , 2020.
• Improved CRL Distribution point (ICRLDP) for Cooperative Intelligent Transportation Systems (C-ITS)
  
  • Christian Yves
  • Adja Elloh Yves Christian
  • Serhrouchni Ahmed
  , 2020. The Cooperative Intelligent Transportation Systems (C-ITS) are already part of our daily life, and their adoption is exponentially increasing, especially with the rise of smart cities concept. However, the security of these infrastructures remains a critical and significant challenge to meet. The Public key infrastructure (PKI) using certificates is the most popular solution to address security issues. The vehicles are identified by a lot of pseudonyms certificates, which must be revoked when the vehicle becomes misbehaving or faulty. The use of multiple certificates introduces new critical problems, like the certificate revocation issue. The revocation management is critical for a PKI, even worse in vehicular communications, where there are many certificates to revoke. All nodes of a network must be aware of all pairs' revocation status as soon as possible to prevent the revoked node from unauthorized activities in the network. The revocation is still an open challenge that is starting to attract a lot of attention from researchers. In this paper, we propose a new scalable and reliable approach called improved certificate distribution point system (ICRLDP), which aims to disseminate vehicle revocation information in a distributed way. Our plan proposes a trade-off between vehicle privacy and security.
• Les débits en 5G: mythes et réalité, présentation pour l'ARCEP
  
  • Coupechoux Marceau
  , 2020.
• On the Capacity of MIMO Optical Wireless Channels
  
  • Li Longguang
  • Moser Stefan M
  • Wang Ligong
  • Wigger Michèle
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2020, 66 (9), pp.5660 - 5682. This paper studies the capacity of a general multiple input multiple-output (MIMO) free-space optical intensity channel under a per-input-antenna peak-power constraint and a total average-power constraint over all input antennas. The focus is on the scenario with more transmit than receive antennas. In this scenario, different input vectors can yield identical distributions at the output, when they result in the same image vector under multiplication by the channel matrix. We first determine the most energy-efficient input vectors that attain each of these image vectors. Based on this, we derive an equivalent capacity expression in terms of the image vector, and establish new lower and upper bounds on the capacity of this channel. The bounds match when the signal-to-noise ratio (SNR) tends to infinity, establishing the high-SNR asymptotic capacity. We also characterize the low-SNR slope of the capacity of this channel. (10.1109/TIT.2020.2979716)
  DOI : 10.1109/TIT.2020.2979716
• Sculptissimo
  
  • Zayana Karim
  Au fil des maths, APMEP, 2020. Quand les sciences s'invitent dans l'atelier du sculpteur... Deuxième art après l'architecture mais avant la peinture, la sculpture désigne étymologiquement l'art de tailler la pierre. Restrictive, cette définition a depuis évolué, incluant des techniques aussi variées que sont le modelage, l'assemblage, l'embossage ou la soudure. Ce faisant, elle s'ouvrait à d'autres matériaux : argile, plâtre, cire, métal, bois, verre, résines. Aujourd'hui tout à la fois plasticien, paysagiste, menuisier, potier, verrier, forgeron, le sculpteur se joue de la matière. Dans ses trois états il l'étire, la chauffe, l'ajoute ou la retire. Tout en faisant des sciences. En témoignent les outils et les procédés dont quelques observations, de simples activités récréatives ou une sortie au musée nous révèlent combien les mathématiques y sont esthétiques et indispensables.
• Detecting Failures and Attacks via Digital Sensors
  
  • Anik Md Toufiq Hasan
  • Danger Jean-Luc
  • Guilley Sylvain
  • Karimi Naghmeh
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, IEEE, 2020, pp.1-1. (10.1109/TCAD.2020.3020921)
  DOI : 10.1109/TCAD.2020.3020921
• The carry propagation of the successor function
  
  • Berthe Valerie
  • Frougny Christiane
  • Rigo Michel
  • Sakarovitch Jacques
  Advances in Applied Mathematics, Elsevier, 2020, 120, pp.102062. Given any numeration system, we call carry propagation at a number N the number of digits that are changed when going from the representation of N to the one of N+1, and amortized carry propagation the limit of the mean of the carry propagations at the first N integers, when N tends to infinity, if this limit exists. In the case of the usual base p numeration system, it can be shown that the limit indeed exists and is equal to P / (P - 1). We recover a similar value for those numeration systems we consider and for which the limit exists. We address the problem of the existence of the amortized carry propagation in non-standard numeration systems of various kinds: abstract numeration systems, rational base numeration systems, greedy numeration systems and beta-numeration. We tackle the problem with three different types of techniques: combinatorial, algebraic, and ergodic. For each kind of numeration systems that we consider, the relevant method allows for establishing sufficient conditions for the existence of the carry propagation and examples show that these conditions are close to being necessary conditions. (10.1016/j.aam.2020.102062)
  DOI : 10.1016/j.aam.2020.102062
• A Programmable Mode-Locked Fiber Laser Using Phase-Only Pulse Shaping and the Genetic Algorithm
  
  • Karar Abdullah
  • Ghandour Raymond
  • Mahariq Ibrahim
  • Alboon Shadi
  • Maaz Issam
  • Neji Bilel
  • Barakat Julien Moussa H.
  Photonics, MDPI, 2020, 7 (3), pp.69. (10.3390/photonics7030069)
  DOI : 10.3390/photonics7030069
• Local Decode and Update for Big Data Compression
  
  • Vatedka Shashank
  • Tchamkerten Aslan
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2020. This paper investigates data compression that simultaneously allows local decoding and local update. The main result is a universal compression scheme for memoryless sources with the following features. The rate can be made arbitrarily close to the entropy of the underlying source, contiguous fragments of the source can be recovered or updated by probing or modifying a number of codeword bits that is on average linear in the size of the fragment, and the overall encoding and decoding complexity is quasilinear in the blocklength of the source. In particular, the local decoding or update of a single message symbol can be performed by probing or modifying a constant number of codeword bits. This latter part improves over previous best known results for which local decodability or update efficiency grows logarithmically with blocklength. (10.1109/TIT.2020.2999909)
  DOI : 10.1109/TIT.2020.2999909
• Effect of p-doping on the intensity noise of epitaxial quantum dot lasers on silicon
  
  • Duan Jianan
  • Zhou Yueguang
  • Dong Bozhang
  • Huang Heming
  • Norman Justin C
  • Jung Daehwan
  • Zhang Zeyu
  • Wang Cheng
  • Bowers John E
  • Grillot Frederic
  Optics Letters, Optical Society of America - OSA Publishing, 2020.
• A Statistical Estimation of 5G Massive MIMO’s Exposure using Stochastic Geometry
  
  • Hajj Maarouf Al
  • Wang Shanshan
  • Doncker Philippe De
  • Oestges Claude
  • Wiart Joe
  , 2020, pp.1-3. This paper aims to estimate the exposure in 5G massive MIMO networks using a stochastic geometric approach. The paper investigates the effect of beamforming, and the effect of multi-user massive MIMO on the exposure of a 5G network. The massive MIMO antenna pattern distribution is obtained by fitting the radiation pattern obtained by running a large amount of channel simulations on NYUSIM. The distribution is then implemented into an analytical framework based on stochastic geometry, so we can obtain a close form expression of the downlink exposure. (10.23919/URSIGASS49373.2020.9232290)
  DOI : 10.23919/URSIGASS49373.2020.9232290
• XMobiSensePlus: An updated application for the assessment of human exposure to RF-EMFs
  
  • Mazloum T.
  • Danjou Amn.
  • Schuz J.
  • Bories S.
  • Huss A.
  • Conil E.
  • Deltour I.
  • Wiart J.
  , 2020, pp.1-2. Calling via Voice over IP using apps such as Skype, Viber and WhatsApp is common nowadays, but in traffic records, these calls are included with all the other data transfers. Voice over IP calls may lead to different exposure levels for people than other data usage since the position in which the phone is held could be different than for other data usage such as web surfing. Telecom ParisTech has modified the XMobiSense app, previously used in several epidemiological settings, to record details of data transfers. The app updates are crucial in order to adapt to the stunningly rapid evolution of the Android operating system (10.23919/URSIGASS49373.2020.9232310)
  DOI : 10.23919/URSIGASS49373.2020.9232310
• Assessment of EMF Exposure from Urban Sensor Measurements by Using Artificial Neural Network
  
  • Wang Shanshan
  • Wiart Joe
  , 2020, pp.1-3. This paper studies the electromagnetic field (EMF) exposure emitted by base stations (BSs) from cellular networks in the urban city environment. We reconstruct the EMF exposure by using artificial neural network (ANN) based on data measured by sensors. We take consideration of spatial locations of real BSs in 14th district of Paris, time variation and antenna orientation. And most importantly, we propose a new path loss model to capture the Light-of-Sight (LoS) and None-Light-of-Sight (NLoS) effects caused by complicated and varying blockages in urban cities. By applying the ANN, we are able to reconstruct EMF exposure for the locations of interest with R2 up to 0.767. And we provide results under different splitting percentage of training and testing data. (10.23919/URSIGASS49373.2020.9232299)
  DOI : 10.23919/URSIGASS49373.2020.9232299
• Mathematical Morphology and Artificial Intelligence
  
  • Bloch Isabelle
  • Blusseau Samy
  • Pino Pérez Ramon
  , 2020.
• HRI-RNN: A User-Robot Dynamics-Oriented RNN for Engagement Decrease Detection
  
  • Atamna Asma
  • Clavel Chloé
  , 2020. Natural and fluid human-robot interaction (HRI) systems rely on the robot's ability to accurately assess the user's engagement in the interaction. Current HRI systems for engagement analysis , and more broadly emotion recognition, only consider user data while discarding robot data which, in many cases, affects the user state. We present a novel recurrent neural architecture for online detection of user engagement decrease in a spontaneous HRI setting that exploits the robot data. Our architecture models the user as a distinct party in the conversation and uses the robot data as contextual information to help assess engagement. We evaluate our approach on a real-world highly imbalanced data set, where we observe up to 2.13% increase in F1 score compared to a standard gated recurrent unit (GRU).
• Compressed k-Nearest Neighbors Ensembles for Evolving Data Streams
  
  • Bahri Maroua
  • Maniu Silviu
  • Bifet Albert
  • de Mello Rodrigo Fernandes
  • Tziortziotis Nikolaos
  , 2020, pp.961-968. The unbounded and multidimensional nature, the evolution of data distributions with time, and the requirement of singlepass algorithms comprise the main challenges of data stream classification, which makes it impossible to infer learning models in the same manner as for batch scenarios. Data dimensionality reduction arises as a key factor to transform and select only the most relevant features from those streams in order to reduce algorithm space and time demands. In that context, Compressed Sensing (CS) encodes an input signal into lower-dimensional space, guaranteeing its reconstruction up to some distortion factor. This paper employs CS on data streams as a pre-processing step to support a k-Nearest Neighbors (kNN) classification algorithm, one of the most often used algorithms in the data stream mining area-all this while ensuring the key properties of CS hold. Based on topological properties, we show that our classification algorithm also preserves the neighborhood (withing an factor) of kNN after reducing the stream dimensionality with CS. As a consequence, end-users can set an acceptable error margin while performing such projections for kNN. For further improvements, we incorporate this method into an ensemble classifier, Leveraging Bagging, by combining a set of different CS matrices which increases the diversity inside the ensemble. An extensive set of experiments is performed on various datasets, and the results were compared against those yielded by current state-of-the-art approaches, confirming the good performance of our approaches. (10.3233/FAIA200189)
  DOI : 10.3233/FAIA200189
• DrumGAN: Synthesis of drum sounds with timbral feature conditioning using Generative Adversarial Networks
  
  • Nistal Hurlé Javier
  • Lattner Stefan
  • Richard Gael
  , 2020. Synthetic creation of drum sounds (e.g., in drum machines)is commonly performed using analog or digital synthesis,allowing a musician to sculpt the desired timbre modify-ing various parameters. Typically, such parameters controllow-level features of the sound and often have no musicalmeaning or perceptual correspondence. With the rise ofDeep Learning, data-driven processing of audio emergesas an alternative to traditional signal processing. This newparadigm allows controlling the synthesis process throughlearned high-level features or by conditioning a modelon musically relevant information. In this paper, we ap-ply a Generative Adversarial Network to the task of au-dio synthesis of drum sounds. By conditioning the modelon perceptual features computed with a publicly availablefeature-extractor, intuitive control is gained over the gen-eration process. The experiments are carried out on a largecollection of kick, snare, and cymbal sounds. We showthat, compared to a specific prior work based on a U-Netarchitecture, our approach considerably improves the qual-ity of the generated drum samples, and that the conditionalinput indeed shapes the perceptual characteristics of thesounds. Also, we provide audio examples and release thecode for reproducibility.1
• Efficient Scheduling of FPGAs for Cloud Data Center Infrastructures
  
  • Bertolino Matteo
  • Enrici Andrea
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2020. In modern cloud data centers, reconfigurable devices can be directly connected to a data center's network. This configuration enables FPGAs to be rented for acceleration of data-intensive workloads. In this context, novel scheduling solutions are needed to maximize the utilization (profitability) of FPGAs, e.g., reduce latency and resource fragmentation. Algorithms that schedule groups of tasks (clusters, packs), rather than individual tasks (list scheduling), well match the functioning of FPGAs. Here, groups of tasks that execute together are interposed by hardware reconfigurations. In this paper, we propose a heuristic based on a novel method for grouping tasks. These are gathered around a high-latency task that hides the latency of remaining tasks within the same group. We evaluated our solution on a benchmark of almost 30000 random workloads, synthesized from realistic designs (i.e., topology, resource occupancy). For this testbench, on average, our heuristic produces optimum makespan solutions in 71.3% of the cases. It produces solutions for moderately constrained systems (i.e., the deadline falls within 10% of the optimum makespan) in 88.1% of the cases.