Miracle-dz/UniFAR-FTUs huggingface dataset:
La simulation relaxée de graphes pour la recherche de motifs

Résumé. La recherche de motifs de graphe est l’une des opérations principales de la recherche des correspondances d’une requête dans un graphe donné. Dans ce contexte, trouver des solutions garantissant l’optimalité en termes de précision et de temps de calcul est un problème de recherche difficile et d’actualité. Différents modèles ainsi que leurs algorithmes appropriés ont été proposés pour la recherche de motifs dans les graphes de données. Cependant, l’inconvénient majeur est leur limitation à trouver des réponses significatives entraînant le problème des réponses vides. Dans cet article nous introduisons un nouveau modèle pour la recherche de motifs de graphe permettant un certain type d’assouplissement de requêtes afin d’éviter ce problème. Ensuite nous développons un algorithme efficace basé sur des techniques d’optimisation pour trouver les k-meilleurs réponses selon notre modèle. Nos expérimentations sur quatre ensembles de données réelles démontrent l’efficacité de notre approche.
Propagation d'événements dans un graphe économique

Les modeles de diffusion dans les reseaux sociaux sont beaucoup etudies ces dernieres annees. Les etudes concernent notamment les diffusions de maladies et de rumeurs dans les reseaux sociaux ou de risques financiers dans les reseaux bancaires. Nous proposons dans cet article de repondre au probleme de diffusion des evenements au sein de reseaux economico-sociaux. En particulier, nous proposons d’etudier un nouveau probleme de diffusion appele Influence Classification Problem (ICP) dont l’objectif est de classifier automatiquement quels noeuds sont impactes pour un evenement donne. Nous proposons egalement deux modeles de propagation bases sur un seuil calcule en fonction des attributs du graphe et de l’evenement. Nous testons nos modeles sur deux evenements connus : l’ouragan Katrina et l’acquisition de Monsanto par Bayer.
Similarité par recouvrement de séquences pour la fouille de données séquentielles et textuelles

Résumé. Nous introduisons la notion de similarité par recouvrement de séquences pour estimer la similarité entre une séquence et un ensemble de séquences. Nous en dérivons une pseudo-distance qui s’apparente aux distances d’édition de type Levenshtein pour comparer des paires de séquences. La complexité algorithmique associée à cette semi-métrique peut-être ramenée à O(n · log(n)) en utilisant des arbres de suffixes. Nous introduisons un nouveau modèle discriminant dédié à la classification de données textuelles dont la complexité algorithmique ne dépend pas de la taille de l’ensemble d’apprentissage, mais uniquement du nombre de classes et de la longueur des séquences. L’étude expérimentale préliminaire présentée s’appuie sur deux benchmaks : le premier concerne des séquences de nucléotides, le second une tâche de classification de textes. Les résultats obtenus positionnent l’approche proposée au niveau de l’état de l’art (incluant les approches "deep learning") sur les tâches considérées. , avec des temps de calcul et un nombre de méta-paramètres avantageux.
A new DOA estimation approach for QPSK Alamouti STBC using ICA technique‐based PSO algorithm

Alamouti space time block code (STBC) has been a revolutionary technology in multiple‐input multiple‐output (MIMO) wireless communication since it provides full transmission diversity. To reduce a multi‐path effect and a consumed power, the dynamic beam‐forming technique should be used to enable antennas focusing on a particular area. The aim of this paper is how to reduce the computational complexities of independent component analysis (ICA) and speed up the algorithm used in estimating the direction of arrival (DOA) angles. First, we derive a simple formula to reduce the number of unknown DOA to be one only. Then, real‐imaginary (Re‐Im) decomposition for MIMO system is used to reduce the computational complexities of ICA algorithm. The novel criteria used in this paper is that the kurtosis measuring for the extracted source will be minimum at one of the unknown values of DOA angles. Finally, particle swarm optimization (PSO) will be used as an effective tool to locate the DOA angle positions that minimize the kurtosis measuring.
Supply Chain Decisions Considering Heterogeneous Consumer Greenness Preference and Reservation Utilities

This article focuses on how the prices set by supply chains and the product greenness level changes when there exists a difference for consumers in both their greenness preference and their reservation utility for the common product with minimal greenness, based on a two-dimensional model which is built and the market is partitioned into four groups. In this study, the authors use the Stackelberg game model to analyze the decisions of a two-stage supply chain, providing environmentally friendly products affected by a consumer greenness preference which is represented by the willingness-to-pay (WTP) for product greenness. The authors found that manufacturers may lower the product greenness level with the decrease of the valuation of consumer's WTP for product greenness, but he may prefer keeping the same product greenness, he will even improve it, when there is a reduction in reservation utility for the traditional product. Moreover, this article shows that there is different impact for different combinations of both WTP for product greenness and product greenness level (different market segmentations) on price decisions of the manufacturer and retailer. In consideration of the asymmetric information about consumer's utility and willingness to pay between manufacturer and retailer, the authors introduce the bargaining power into the study, and then they conclude that during the different market segmentations, the wholesale price and retail price go down as a retailer strengthens his bargaining power, and increasing sales volume can improve profit to make up for a loss in retail price.
Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction

Accurate prediction of the seawater intrusion extent is necessary for many applications, such as groundwater management or protection of coastal aquifers from water quality deterioration. However, most applications require a large number of simulations usually at the expense of prediction accuracy. In this study, the Gaussian process regression method is investigated as a potential surrogate model for the computationally expensive variable density model. Gaussian process regression is a nonparametric kernel-based probabilistic model able to handle complex relations between input and output. In this study, the extent of seawater intrusion is represented by the location of the 0.5 kg/m3 iso-chlore at the bottom of the aquifer (seawater intrusion toe). The initial position of the toe, expressed as the distance of the specific line from a number of observation points across the coastline, along with the pumping rates are the surrogate model inputs, whereas the final position of the toe constitutes the output variable set. The training sample of the surrogate model consists of 4000 variable density simulations, which differ not only in the pumping rate pattern but also in the initial concentration distribution. The Latin hypercube sampling method is used to obtain the pumping rate patterns. For comparison purposes, a number of widely used regression methods are employed, specifically regression trees and Support Vector Machine regression (linear and nonlinear). A Bayesian optimization method is applied to all the regressors, to maximize their efficiency in the prediction of seawater intrusion. The final results indicate that the Gaussian process regression method, albeit more time consuming, proved to be more efficient in terms of the mean absolute error (MAE), the root mean square error (RMSE), and the coefficient of determination (R2).
Fouille de données pour l’analyse du comportement complexe de systèmes photovoltaïque-thermiques en vrai grandeur et in situ intégrés aux bâtiments

– Dans le contexte des bâtiments et des systemes innovants a tres haute performances energetiques leur monitoring en condition reelles de fonctionnement devient quasiment indispensable afin de pouvoir beneficier d'un controler les performances reelle et de beneficier d'un retour d'experience. Comme le suivi d'une installation s'etend sur des periodes longues afin de mesurer la tenue ou la degradation des performances et qu'un un nombre important de capteurs est implique, il est interessant de considerer les methodes dites de « fouille de donnees » adaptees aux grands volumes de donnees. Comme cas d'etude, objet de nos recherches sur les systemes solaires, nous nous interessons aux analyses des donnees issues des prototypes du projet RESSOURCES soutenu par l'ADEME. Les techniques d'analyse ont ete developpees en interdisciplinarite entre les domaines du traitement de data de masse (Big Data) et de l'energetique des systemes solaires. Les composants etudies sont de type double-peau photovoltaique (PV) ventilee et font partie de ces innovations face au modele reglementaire actuellement fonde sur une enveloppe « statique » isolee et etanche. De meme que pour le rafraichissement des composants photovoltaiques (PV), la lame d'air situee derriere la peau PV peut contribuer a la performance thermique du bâtiment : en ete servant de barriere thermique avec un ecoulement regi par la convection naturelle (effet cheminee), et en hiver comme dispositif de prechauffage a partir d'une ventilation mecanique pour la recuperation de chaleur. En integrant plusieurs fonctions ainsi que les criteres esthetiques, les geometries des composants double-peau PV different d'un bâtiment a l'autre. Tenant en plus compte de la complexite du milieu urbain, des phenomenes physiques inter-relies regissant leur comportement, la prediction et l'evaluation des performances reelles de cette variete de configurations constituent un veritable defi difficile a surmonter. Il est donc essentiel d'exploiter de facon ordonnancee les resultats experimentaux issus des systemes PV integres au bâtiment en vraie grandeur et in situ pour estimer l'apport de tels concepts et pour elaborer des modeles numeriques fiables et robustes permettant de predire leurs performances (thermique et electrique). Cet article concerne l'analyse du comportement des 3 premiers prototypes du projet, operationnels depuis 2012 et fonctionnant en mode de ventilation naturelle. La classification de donnees en termes des conditions environnementales ou de comportement du systeme est realisee a partir d'algorithmes tels que le clustering, et l'arbre de decision. Cette approche permet de distinguer les periodes de comportement normal, les defaillances et les defauts d'instrumentation.
Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory

Preventive maintenance activities require a tool to be offline for long hour in order to perform the prescribed maintenance activities. Although preventive maintenance is crucial to ensure operational reliability and efficiency of the tool, long hour of preventive maintenance activities increases the cycle time of the semiconductor fabrication foundry (Fab). Therefore, this activity is usually performed when the incoming Work-in-Progress to the equipment is forecasted to be low. The current statistical forecasting approach has low accuracy because it lacks the ability to capture the time-dependent behavior of the Work-in-Progress. In this paper, we present a forecasting model that utilizes machine learning method to forecast the incoming Work-In-Progress. Specifically, our proposed model uses LSTM to forecast multistep ahead incoming Work-in-Progress prediction to an equipment group. The proposed model's prediction results were compared with the results of the current statistical forecasting method of the Fab. The experimental results demonstrated that the proposed model performed better than the statistical forecasting method in both hit rate and Pearson's correlation coefficient, r.
Recognition System for Euro and Mexican Banknotes Based on Deep Learning with Real Scene Images

This article presents a robust and efficient system for euro and Mexican banknote recognition. A high banknote recognition and classification rate was achieved using neural networks and deep learning with real scene images taken with both sunlight and artificial light. Without extracting characteristics by hand, the convolutional neural networks was fed with raw images. Analysis and experiments were carried out on banknotes based on key features, such as; watermarks, portraits on the bills, bill value written in words and numbers, and the complete banknotes. It was concluded that both the color information and some regions of the banknotes, as well as the banknote denomination written in words and numbers and the complete banknote, is the appropriate information to achieve a high rate of banknote classification and recognition. The experimental results show that the proposed approach is promising with quite remarkable results; it performs an efficient and robust classification using real scene images taken with both sunlight and artificial light and is invariant to banknote rotation and translation. A high recognition rate was achieved for Mexican banknotes and for euros. At present, the results contained herein are an improvement over thosereported in the state of the art.
Multimedia Social Network Authorization Scheme of Comparison-based Encryption

In many Ciphertext-Policy Attributed Based Encryption (CP-ABE) schemes, the level of attributes is ignored; while the comparison based attribute encryption scheme is not ﬂexible enough. In this paper, an encryption scheme based on comparative attributes is proposed. In this scheme, users can’t only make more granular and ﬂexi-ble access control policies based on the level of attributes, but also support more diverse forms of access control policy. At the same time, in order to solve the computational pressure of the user terminal, a third-party proxy is added to the solution to assist the user to decrypt the ciphertext. Through the comparative and experimental data analysis, the scheme can be better applied to multimedia social networks.
Using multidimensional scaling and PLS-SEM to assess the relationships between personality traits, problematic internet use, and fear of missing out

ABSTRACT Informed by the Compensatory Internet Use theory, this study was aimed at revealing possible links between adults’ personality traits, Fear of Missing Out (FoMO), and Problematic Internet Use (PIU). Given the exploratory nature of this study, two statistical techniques were used: Smallest Space Analysis (SSA, a variant of multidimensional scaling), and Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were gathered from 359 adults, by three questionnaires: Fear of Missing Out scale (FoMOs), Short Problematic Internet Use Test (SPIUT), and the Big Five personality trait questionnaire. The SSA results yielded a radex projection. The projection has confirmed the five-factorial theoretical structure of the Big Five personality traits. In the SSA projection, the PIU was found in the extraversion area, the FoMO in the neuroticism area, and both PIU and FoMO factors were in close proximity to each other. Based on the SSA map and the theoretical framework, PLS-SEM was deployed to discover possible links between the research factors. According to the results, the FoMO variable had a mediating role, linking neuroticism to PIU. Limitations, conclusions, and directions for further research are discussed.
Datos normativos de la Escala de Procrastinación Académica en estudiantes de psicología de Lima

This paper synthesizes the normative data obtention (scales) of Academic Procrastination Scale (APS) in Lima psychology students. Seven hundred seventeen students participated (74.3% women) aged between 17 and 54 years (M = 22.65, SD = 6.49). Confirmatory factor analysis conducted on APS indicates that the data adjust to the structure of two dimensions, and reliability coefficients used (? and H) have high magnitudes. APS scores are not adjusted to normality, so the scales were developed using percentiles. The results are discussed and guidelines aresuggested to use the standards
Prédiction de liens dans les graphes de connaissances avec les concepts de plus proches voisins

La nature ouverte des graphes de connaissances implique souvent qu'ils soient incomplets. La prediction de liens consiste a inferer de nouveaux liens entre entites sur la base des liens existants. La plupart des approches exis-tantes s'appuient sur l'apprentissage de vecteurs de traits latents pour l'encodage des entites et des relations. En general cependant, les traits latents ne sont pas facilement interpretables. Les approches a base de regles sont interpretables mais un ensemble de regles different doit etre appris pour chaque relation. Nous proposons une nouvelle approche qui n'a pas besoin de phase d'apprentissage et qui peut fournir des explications intelligibles pour chaque inference. Elle repose sur le calcul de Concepts de plus proches voisins (Concepts of Nearest Neighbours, CNN) pour identifier des entites similaires fondees sur des motifs de graphe communs. La theorie de Dempster-Shafer est ensuite utilisee pour tirer des inferences a partir des CNN. Nous evaluons notre approche sur FB15k-237, un benchmark classique en prediction de liens, ou elle obtient de meilleures performances que les approches existantes.
A prediction‐based and power‐aware virtual machine allocation algorithm in three‐tier cloud data centers

With the increasing popularity of cloud computing services, the more number of cloud data centers are constructed over the globe. This makes the power consumption of cloud data center elements as a big challenge. Hereby, several software and hardware approaches have been proposed to handle this issue. However, this problem has not been optimally solved yet. In this paper, we propose an online cloud resource management with live migration of virtual machines (VMs) to reduce power consumption. To do so, a prediction‐based and power‐aware virtual machine allocation algorithm is proposed. Also, we present a three‐tier framework for energy‐efficient resource management in cloud data centers. Experimental results indicate that the proposed solution reduces the power consumption; at the same time, service‐level agreement violation (SLAV) is also improved.
Regional Enlarged Observability of Fractional Differential Equations with Riemann - Liouville Time Derivatives

We introduce the concept of regional enlarged observability for fractional evolution differential equations involving Riemann–Liouville derivatives. The Hilbert Uniqueness Method (HUM) is used to reconstruct the initial state between two prescribed functions, in an interested subregion of the whole domain, without the knowledge of the state.
A Decision-Making Framework for Inventory Positioning in an Omnichannel Business Environment

Omnichannel is an evolving business model that has been gaining increased popularity among retailers. This business model allows firms to use a variety of channels to interact with their customers and fulfill their orders. Customers can order online and pick up later in the store, or they can choose to have the products delivered from a nearby store. Due to the complexity of fulfilling customer orders via omnichannel models, positioning inventory is a key challenge in supporting this type of business model. This article presents a framework for assisting companies in deciding under what condition to centralize or decentralize their inventory to fulfill customer orders without disrupting the shopping experience.
Poincaré Wasserstein Autoencoder

This work presents a reformulation of the recently proposed Wasserstein autoencoder framework on a non-Euclidean manifold, the Poincare ball model of the hyperbolic space. By assuming the latent space to be hyperbolic, we can use its intrinsic hierarchy to impose structure on the learned latent space representations. We demonstrate the model in the visual domain to analyze some of its properties and show competitive results on a graph link prediction task.
Calcul d'une politique déterministe dans un MDP avec récompenses imprécises

Résumé. Pour beaucoup d’applications réelles nécessitant une prise de décision séquentielle dans un cadre incertain, on utilise un processus de décision Markovien avec récompenses inconnues (IRMDP) en calculant naturellement des politiques stochastiques. Une politique stochastique n’est pas facilement interprétable pour l’utilisateur final. Celui-ci a souvent besoin d’une politique déterministe et compréhensible. Pour mieux motiver l’utilisation d’une procédure exacte pour trouver une politique déterministe, nous montrons quelques cas où l’idée intuitive d’utiliser une politique déterministe obtenue après une «déterminisation» (arrondi) de la politique stochastique optimale donne une politique déterministe différente de la politique optimale.
Performance modeling and optimization of multiple‐objective cross‐layer design in multi‐flow ad‐hoc networks

In ad‐hoc wireless networks, to achieve good performance, multiple parameters need to be optimized jointly. However, existing literature lacks a design framework that investigates the synchronic impact of several parameters on overall system performance. Among several design parameters, energy conservation, end‐to‐end delay minimization, and improved throughput are considered most important for efficient operation of these networks. In this paper, we propose a novel scheme for multiple‐objective cross‐layer optimization capable of optimizing all these performance objectives simultaneously for reliable, energy‐efficient, and timely transmission of continuous media information across the network. The three global criteria considered for optimization are incorporated in a single programming problem via linear scalarization. Besides, we employ standard convex optimization method and Lagrangian technique to solve the proposed problem to seek optimality. Extensive simulation results are generated accounting for several topologies with multiple concurrent flows in the network. These results are used to validate the analytical results and demonstrate the efficiency of the proposed optimization model. Efficiency of the model is verified by finding the set of Pareto‐optimal solutions plotted in three‐dimensional objective space. These solution points constituting the Pareto front are used as the best possible balance points among maximum throughput, maximum residual energy, and least network delay. Finally, to emphasize the effectiveness and supremacy of our proposed multiple‐objective cross‐layer design scheme, we compare it with the conventional multiple‐objective genetic algorithm. Simulation results demonstrate that our method provides significant performance gain over the genetic algorithm approach in terms of the above specified three objectives.
Une approche basée graphes pour la détection de zones fonctionnelles urbaines

Dans cet article, nous proposons une methode pour l'identification de zones fonctionnelles, utilisant la detection de communautes dans un graphe de mobilite. Les sommets du graphe correspondent a des unites spatiales, issues du decoupage d'une ville suivant le reseau routier. Les aretes relient des sommets entres lesquels des deplacements sont observes et sont ponderees en fonction du nombre de deplacements et de la distance entre sommets. Notre approche optimise la modularite sur ce reseau pour assurer que les zones fonctionnelles obtenues maximisent les interactions spatiales en leur sein. De plus, nous uti-lisons les points d'interets pour maintenir une heterogeneite suffisante dans les zones detectees. Nous avons mene des experimentations avec des trajectoires de taxi et des points d'interets de la ville de Porto, afin de montrer la capacite de notre approche a identifier les zones fonctionnelles.