Miracle-dz/UniFAR-FTUs huggingface dataset:
A Sequence Learning Method for Domain-Specific Entity Linking

Recent collective Entity Linking studies usually promote global coherence of all the mapped entities in the same document by using semantic embeddings and graph-based approaches. Although graph-based approaches are shown to achieve remarkable results, they are computationally expensive for general datasets. Also, semantic embeddings only indicate relatedness between entity pairs without considering sequences. In this paper, we address these problems by introducing a two-fold neural model. First, we match easy mention-entity pairs and using the domain information of this pair to filter candidate entities of closer mentions. Second, we resolve more ambiguous pairs using bidirectional Long Short-Term Memory and CRF models for the entity disambiguation. Our proposed system outperforms state-of-the-art systems on the generated domain-specific evaluation dataset.
Une approche hybride pour la détection d'influenceurs dans les médias sociaux

Résumé : Un influenceur est une personne ayant les capacités d’imposer des attitudes, des comportements, des opinions à un public. La détection de tels individus est d’un grand intérêt, par exemple pour les campagnes commerciales qui cherchent à identifier les acteurs les plus influents pour promouvoir une marque ou un produit. Dans cet article, nous nous focalisons sur la détection d’influenceurs dans les réseaux sociaux qui s’appuie généralement sur une structure de graphe dont les nœuds sont les utilisateurs et les arcs orientés sont leurs interactions. Au-delà de la structure du graphe, nous expérimentons l’impact du contenu textuel des messages sur l’influence. En considérant des critères linguistiques hypothétiques (tels que l’argumentation d’un utilisateur, l’accord/désaccord entre utilisateurs) en plus des critères numériques classiques (nombre de réponses, taille du message, nombre de relations, etc.), nous montrons par le biais d’un système d’apprentissage que certains critères linguistiques sont pertinents pour la tâche de détection d’influenceurs. Mots-clés : Détection d’influenceurs, traitement automatique du langage naturel, système d’apprentissage
A comparative study of pivot selection strategies for unsupervised cross-domain sentiment classification

Abstract Selecting pivot features that connect a source domain to a target domain is an important first step in unsupervised domain adaptation (UDA). Although different strategies such as the frequency of a feature in a domain, mutual (or pointwise mutual) information have been proposed in prior work in domain adaptation (DA) for selecting pivots, a comparative study into (a) how the pivots selected using existing strategies differ, and (b) how the pivot selection strategy affects the performance of a target DA task remain unknown. In this paper, we perform a comparative study covering different strategies that use both labelled (available for the source domain only) as well as unlabelled (available for both the source and target domains) data for selecting pivots for UDA. Our experiments show that in most cases pivot selection strategies that use labelled data outperform their unlabelled counterparts, emphasising the importance of the source domain labelled data for UDA. Moreover, pointwise mutual information and frequency-based pivot selection strategies obtain the best performances in two state-of-the-art UDA methods.
ADEL : une méthode adaptative de désambiguïsation d'entités nommées

Résumé : Nous identifions quatre critères principaux pouvant causer de sérieuses difficultés lorsqu’il s’agit de développer un système de désambiguïsation d’entités nommées : i) la nature du document à analyser (tweet, sous-titre d’une vidéo, article de presse) ; ii) le nombre de types possibles qui sont utilisés pour catégoriser une mention (Personne, Lieu, Date, Rôle) ; iii) la base de connaissances utilisée pour désambiguïser les mentions extraites (DBpedia, Wikidata, Musicbrainz) ; iv) la langue utilisée dans le document. Dans cet article, nous présentons ADEL, une approche innovante basée sur une architecture hybride pour un système de désambiguïsation d’entités nommées combinant des méthodes du domaine du Traitement Automatique du Langage Naturel (TALN), de la recherche d’information et du Web Sémantique. En particulier, nous proposons une approche modulaire afin d’être aussi indépendants que possible du texte analysé et de la base de connaissance utilisée. Notre évaluation montre que ce système obtient des résultats comparables ou meilleurs que l’état de l’art sur cinq jeux de données : OKE2015, OKE2016, NEEL2014, NEEL2015 et NEEL2016. Mots-clés : Désambiguïsation d’entités nommées, reconnaissance d’entités nommées, extraction d’information
An Eclectic View on Numerical Methods for PDEs: Presentation of the Special Issue “Advanced Numerical Methods: Recent Developments, Analysis and Applications”

The design and analysis of numerical methods for partial differential equations (PDEs) is a field of constant growth. It seems that the more we develop numerical analysis for PDEs, the more we face questions and problems that require additional developments – maybe a consequence of an increased knowledge that gives the tools to tackle questions previously inaccessible and of the growing place of scientific computing in Physics and Engineering. The aspects currently taken into account when dealing with numerical schemes for PDEs include: the need for geometrical flexibility, as the meshes available for certain applications may comprise cells with complicated shapes, possibly degenerate faces or bad aspect-ratios; the capacity to handle complex models, with non-linearities, couplings, strong heterogeneities, weakly regular data or even singularities; the respect of physical properties of the model, such as conservation of energy or mass, and proper asymptotic behaviour. The above mentioned challenges are ubiquitous in practical problems arising from Engineering, Life Sciences, and Industry. Such challenges require both the design of new schemes, and the development of a rigorous mathematical analysis of old and new schemes to ensure that they properly handle these questions with innovative tools made available by the most advanced research in mathematics – benchmarking is essential to assess the quality of a scheme, but it cannot cover all situations encountered in applications. The analysis of advanced numerical methods and their application to problems arising in the field of Engineering and Computer Science also identifies the key elements that make schemes robust and efficient, and clarifies relationships between the various methods. These themes, among others, were at the core of two main events of the program on Numerical Methods for PDEs , organised at the Institute Henri Poincaré (Paris, France) at the end of 2016 by D. A. Di Pietro, A. Ern and L. Formaggia. The first of these events was an introductory school on current topics in numerical methods
Fault tolerant sliding mode control for T-S fuzzy stochastic time-delay system via a novel sliding mode observer approach

ABSTRACT In this paper, a fault estimation and fault-tolerant control problem for a class of T-S fuzzy stochastic time-delay systems with actuator and sensor faults is investigated. A novel sliding mode observer is proposed, which can simultaneously estimate the system states, actuator and sensor faults with good accuracy. Based on the state and actuator fault estimation, a new sliding mode control scheme is developed, which can effectively eliminate the influence of actuator fault. Sufficient conditions for the existence of the proposed observer and fault-tolerant sliding mode controller are provided in terms of linear matrix inequality, and moreover, the reachability of the sliding mode surface can be guaranteed under the proposed control scheme. The propose sliding mode observer and fault-tolerant sliding mode controller can overcome the restrictive assumption that the input matrix of all local modes is the same. Finally, a numerical example is provided to verify the effectiveness of the proposed sliding mode observer and fault-tolerant sliding mode control technique.
Flexible associativity for DRAM caches

Applications continue to increase their capacity requirements. Die-stacked DRAM caches have been proposed as a solution to filter as many accesses as possible to main memory. DRAM caches have been predominantly studied in the scenario where the next level of memory is off-chip DRAM. With similar access times for both of these levels of the memory hierarchy, the focus has been optimizing access latency, rather than increasing the hit rate. Emerging non-volatile memory (NVM) technologies are proposed as an affordable solution to replace DRAM. These technologies come with a significantly higher access latency than an already slow DRAM. Thus, architects must reconsider design choices for DRAM caches. It is no longer reasonable to compromise on hit rate, and it is still necessary to mitigate access latency. In this work, we revisit the idea of associativity for DRAM caches. We propose Flexible Associativity (FlexA), a low-cost technique that allows the DRAM cache to behave as a low-access latency direct-mapped design for most accesses, but provides associativity when needed to optimize hit rate. We evaluate (FlexA) on a wide variety of workloads as well as in the presence of different main memory latencies. FlexA achieves an average speedup of 13% compare to the state-of-the-art work.
Estimating Depth-Salient Edges and Its Application to Stereoscopic Image Quality Assessment

The human visual system pays attention to salient regions while perceiving an image. When viewing a stereoscopic 3-D (S3D) image, we hypothesize that while most of the contribution to saliency is provided by the 2-D image, a small but significant contribution is provided by the depth component. Further, we claim that only a subset of image edges contribute to depth perception while viewing an S3D image. In this paper, we propose a systematic approach for depth saliency estimation, called salient edges with respect to depth perception (SED) which localizes the depth-salient edges in an S3D image. We demonstrate the utility of SED in full reference stereoscopic image quality assessment. We consider gradient magnitude and inter-gradient maps for predicting structural similarity. A coarse quality map is estimated first by comparing the 2-D saliency and gradient maps of reference and test stereo pairs. We average this quality map to estimate luminance quality and refine this quality map using SED maps for evaluating depth quality. Finally, we combine this luminance and depth quality to obtain an overall stereo image quality. We perform a comprehensive evaluation of our metric on seven publicly available S3D IQA databases. The proposed metric shows competitive performance on all seven databases with state-of-the-art performance on three of them.
Particle Filtering for Stochastic Navier-Stokes Signal Observed with Linear Additive Noise

We consider a non-linear filtering problem, whereby the signal obeys the stochastic Navier-Stokes equations and is observed through a linear mapping with additive noise. The setup is relevant to data assimilation for numerical weather prediction and climate modelling, where similar models are used for unknown ocean or wind velocities. We present a particle filtering methodology that uses likelihood informed importance proposals, adaptive tempering, and a small number of appropriate Markov Chain Monte Carlo steps. We provide a detailed design for each of these steps and show in our numerical examples that they are all crucial in terms of achieving good performance and efficiency.
Network Intrusion Detection System for Jamming Attack in LoRaWAN Join Procedure

LoRaWAN is a Low Power Wide Area Network (LPWAN) protocol designed to allow low power battery operated nodes to communicate with each other. Though LoRaWAN provides end-to-end security, however vulnerabilities exist in the security mechanism of LoRaWAN join procedure. A jammer can be used to launch a denial of service (DOS) attack by permanently disconnecting the LoRa end nodes from the LoRaWAN network. In this paper, we propose a novel LoRaWAN based Intrusion Detection System (LIDS) for jamming attacks. A real experimental testbed is developed and deployed and LIDS is trained on real join request data. We propose two LIDS algorithms based on Kullback Leibler Divergence (KLD) and Hamming distance (HD). The algorithms are extensively tested on realworld dataset. Receiver Operating Characteristic (ROC) based performance evaluations show that KLD and HD can achieve detection rates as high as 98% and 88% respectively with 5% false positive rate.
A Fog Computing-Oriented, Highly Scalable IoT Framework for Monitoring Public Educational Buildings

We present here an IoT-based platform that provides an integrated solution for real-time monitoring and management of educational buildings at a national scale. The proposed system follows the Fog Computing paradigm so that sensor data processing takes place at the edge devices of the network. In this way, the system significantly reduces the network traffic across the network core layers. The architecture and implementation of the system are presented in details in relation to existing use-case scenaria. The performance of the prototype architecture is evaluated in a real-world environment using a range of edge devices available in a pilot deployment spanning across 18 school buildings. The evaluation indicates that existing resources are sufficient to accommodate traffic that can increase up to 5 times higher from the existing one even in sites where low-end devices (e.g., such as Raspberry Pi) are available. The results provide evidence that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture.
Saccadic Predictive Vision Model with a Fovea

We propose a model that emulates saccades, the rapid movements of the eye, called the Error Saccade Model, based on the prediction error of the Predictive Vision Model (PVM). The Error Saccade Model carries out movements of the model's field of view to regions with the highest prediction error. Comparisons of the Error Saccade Model on Predictive Vision Models with and without a fovea show that a fovea-like structure in the input level of the PVM improves the Error Saccade Model's ability to pursue detailed objects in its view. We hypothesize that the improvement is due to poorer resolution in the periphery causing higher prediction error when an object passes, triggering a saccade to the next location.
Research topics related to real-time communications over 5G networks

In this article we describe the discussion and conclusions of the "Roundtable on Real-Time Communications Research: 5G and Real-Time Communications --- Topics for Research" held at the Illinois Institute of Technology's Real-Time Communications Conference and Expo, co-located with the IPT-Comm Conference, October 5--8, 2015.
NEGR1 and FGFR2 cooperatively regulate cortical development and core behaviours related to autism disorders in mice

See Contreras and Hippenmeyer (doi:10.1093/brain/awy218) for a scientific commentary on this article. Autism spectrum disorders (ASDs) are complex conditions with diverse aetiologies. Szczurkowska et al. demonstrate that two ASD-related molecules – FGFR2 and Negr1 – physically interact to act on the same downstream pathway, and regulate cortical development and ASD-relevant behaviours in mice. Identifying common mechanisms in ASDs may reveal targets for pharmacological intervention.
Long-Term Ensemble Learning for Cross-Season Visual Place Classification

This paper addresses the problem of cross-season visual place classification (VPC) from the novel perspective of long-term map learning. Our goal is to enable transfer learning efficiently from one season to the next, at a small constant cost, and without wasting the robot’s available long-term-memory by memorizing very large amounts of training data. To achieve a good tradeoff between generalization and specialization abilities, we employ an ensemble of deep convolutional neural network (DCN) classifiers and consider the task of scheduling (when and which classifiers to retrain), given a previous season’s DCN classifiers as the sole prior knowledge. We present a unified framework for retraining scheduling and we discuss practical implementation strategies. Furthermore, we address the task of partitioning a robot’s workspace into places to define place classes in an unsupervised manner, as opposed to using uniform partitioning, so as to maximize VPC performance. Experiments using the publicly available NCLT dataset revealed that retraining scheduling of a DCN classifier ensemble is crucial in achieving a good balance between generalization and specialization. Additionally, it was found that the performance is significantly improved when using planned scheduling.
A Novel Bayesian Filtering Based Algorithm for RSSI-Based Indoor Localization

Indoor localization can provide a number of different services such as location-aware advertisement, indoor navigation and automating different appliances based on the user location. A number of different techniques such as time-difference- of-arrival, angle-of-arrival, time-of-flight, and received signal strength indicator (RSSI) have been used to provide Location Based Services (LBS). RSSI is one of the widely used methods as it is cost efficient and easy to implement. However, RSSI's performance is limited by multipath fading and indoor noise. Particle Filter (PF) is an accurate Bayesian Filtering algorithm that can improve the performance of RSSI-based indoor localization. However, PF is not able to satisfy the high accuracy requirement (possibly 10cm) of indoor localization. In this paper, we present Particle Filter-Extended Kalman Filter (PFEKF) cascaded algorithm that combines PF and EKF in series to reduce the impact of multipath effects and noise on the RSSI. Our experimental results show that PFEKF improves the localization accuracy by 31.3% and 33.9% in 3D and 2D environments respectively when compared with using only a PF.
A Block Preconditioned Harmonic Projection Method for Large-Scale Nonlinear Eigenvalue Problems

We propose a block preconditioned harmonic projection (BPHP) method for solving large-scale nonlinear eigenproblems of the form $T(\lambda)v = 0$. Similar to classical preconditioned eigensolvers such as the locally optimal block preconditioned conjugate gradient method and preconditioned Lanczos, BPHP aims at computing a few eigenvalues of the nonlinear problem close to a specified shift, using preconditioners that enhance the local spectrum, without the need for exact solution of large shifted linear systems. We explore the development of search subspaces, stabilized preconditioning, nonlinear harmonic Rayleigh--Ritz projections, thick restart, and soft deflation capable of resolving linearly dependent eigenvectors. Numerical experiments show that BPHP with a good preconditioner is storage efficient, and it exhibits robust convergence. A moving-window-style partial deflation enables BPHP to reliably compute a large number of eigenvalues.
Microshapes: Efficient Querying of 3D Object Collections based on Local Shape

Content-based querying of 3D object collections has the intrinsic difﬁculty of creating the query object and previous approaches have concentrated in producing global simpliﬁcations such as sketches. In contrast, in this paper, the concept of querying 3D object collections based on local shape is introduced. Microshapes are very promising in terms of generality and applicability and is based on a variation of the spin image descriptor. This descriptor uses intersection counts to determine the presence of boundaries in the support volume. These boundaries can be used to recognise local shape similarity. Queries based on this descriptor are general, easy to specify and robust to geometric clutter
Polyhedral voronoi diagrams for additive manufacturing

A critical advantage of additive manufacturing is its ability to fabricate complex small-scale structures. These microstructures can be understood as a metamaterial: they exist at a much smaller scale than the volume they fill, and are collectively responsible for an average elastic behavior different from that of the base printing material making the fabricated object lighter and/or flexible along specific directions. In addition, the average behavior can be graded spatially by progressively modifying the micro structure geometry. The definition of a microstructure is a careful trade-off between the geometric requirements of manufacturing and the properties one seeks to obtain within a shape: in our case a wide range of elastic behaviors. Most existing microstructures are designed for stereolithography (SLA) and laser sintering (SLS) processes. The requirements are however different than those of continuous deposition systems such as fused filament fabrication (FFF), for which there is currently a lack of microstructures enabling graded elastic behaviors. In this work we introduce a novel type of microstructures that strictly enforce all the requirements of FFF-like processes: continuity, self-support and overhang angles. They offer a range of orthotropic elastic responses that can be graded spatially. This allows to fabricate parts usually reserved to the most advanced technologies on widely available inexpensive printers that also benefit from a continuously expanding range of materials.
Retransmission Policy with Frequency Hopping for Ultra-Reliable and Low-Latency Communications

In this work, we study the benefit of retransmission to ultra-reliable and low-latency communications (URLLC). We consider a retransmission policy that employs frequency hopping to improve the retransmission success probability for the packets suffering deep fading. We investigate how to satisfy the quality-of-service (QoS) with retransmission by taking downlink transmission as an example. End-to-end (E2E) delay components, including transmission delay, queueing delay and backhaul latency, and packet loss components, including transmission error probability and E2E delay violation probability are considered. Resource allocation for the retransmission policy is optimized. Numerical results show that the requirement on transmission error probability can be significantly relaxed with retransmission, which does not compromise the E2E delay requirement owing to the short packets in URLLC. Simulation results indicate that more users can be supported with QoS guarantee compared to a counterpart system without retransmission, and the performance gain increases with the times of retransmissions.