Miracle-dz/UniFAR-FTUs huggingface dataset:
Religion and humane entrepreneurship: Insights for research, policy, and practice

ABSTRACT Our research examines the impact of religion on the humane orientation of entrepreneurs. Specifically, we studied whether and how humane entrepreneurship (HumEnt) is guided by the entrepreneur’s religious background. We conducted qualitative semistructured interviews with 16 entrepreneurs across four of India’s major religions: Hinduism, Islam, Jainism, and Sikhism. We found that HumEnt matters in entrepreneurial ventures and across industries. Further, we noted deep connections between HumEnt and religion, with entrepreneurs indicating a pervasive influence of religion on the multiple dimensions of the HumEnt construct. Yet, in terms of religion’s impact on the three specific HumEnt dimensions, we also noticed significant differences across the four religious groups.
Inference for the neighbourhood inequality index

ABSTRACT The neighborhood inequality (NI) index measures aspects of spatial inequality in the distribution of incomes within a city. It is a population average of the normalized income gap between each individual’s income (observed at a given location in the city) and the incomes of the neighbours located within a certain distance range. The approach overcomes the modifiable areal units problem affecting local inequality measures. This paper provides minimum bounds for the NI index standard error and shows that unbiased estimators can be identified under fairly common hypothesis in spatial statistics. Results from a Monte Carlo study support the relevance of the approximations. Rich income data are then used to infer about trends of NI in Chicago, IL, over the last 35 years.
Dogs follow human misleading suggestions more often when the informant has a false belief

We investigated whether dogs (Canis familiaris) distinguish between human true (TB) and false beliefs (FB). In three experiments with a pre-registered change of location task, dogs (n = 260) could retrieve food from one of two opaque buckets after witnessing a misleading suggestion by a human informant (the ‘communicator’) who held either a TB or a FB about the location of food. Dogs in both the TB and FB group witnessed the initial hiding of food, its subsequent displacement by a second experimenter, and finally, the misleading suggestion to the empty bucket by the communicator. On average, dogs chose the suggested container significantly more often in the FB group than in the TB group and hence were sensitive to the experimental manipulation. Terriers were the only group of breeds that behaved like human infants and apes tested in previous studies with a similar paradigm, by following the communicator's suggestion more often in the TB than in the FB group. We discuss the results in terms of processing of goals and beliefs. Overall, we provide evidence that pet dogs distinguish between TB and FB scenarios, suggesting that the mechanisms underlying sensitivity to others' beliefs have not evolved uniquely in the primate lineage.
Variable tropical moisture and food availability underlie mixed winter space-use strategies in a migratory songbird

Identifying environmental correlates driving space-use strategies can be critical for predicting population dynamics; however, such information can be difficult to attain for small mobile species such as migratory songbirds. We combined radio-telemetry and high-resolution GPS tracking to examine space-use strategies under different moisture gradients for wood thrush (Hylocichla mustelina). We explored the role moisture plays in driving food abundance and, in turn, space-use strategies at a wintering site in Belize across 3 years. Individuals occupying drier habitats experienced lower food abundance and poorer body condition. Using data from our radio-tracked study population and GPS tracking from across five breeding populations, we detected low rates of overwinter site persistence across the wood thrush wintering range. Contrary to expectations, individuals in wetter habitats were more likely to engage in permanent mid-winter relocations, up to 148 km. We suggest facultative movements are instead a condition-dependent strategy that enables wintering wood thrush to locate alternative habitat as food availability declines throughout the dry season. Increased aridity is predicted across the wintering range of wood thrush, and future research should delve deeper into understanding how moisture impacts within and between season space-use dynamics and its ultimate impact on the population dynamics of this declining species.
Etat de l’art en compression multi-phrases pour la synthèse de documents (State-of-the-art of multi-sentence compression for document summarization)

La compression multi-phrases est utilisée dans différentes tâches de résumé (microblogs, opinions, réunions ou articles de presse). Leur objectif est de proposer une reformulation compressée et grammaticalement correcte des phrases sources tout en gardant les faits principaux. Dans cet article, nous présentons l’état de l’art de la compression multi-phrases en mettant en avant les différents corpus et outils à disposition. Nous axons notre analyse principalement sur la qualité grammaticale et informative plus que sur le taux de compression.
Mesure de similarité textuelle pour l’évaluation automatique de copies d’étudiants (Textual similarity measurement for automatic evaluation of students’ answers)

Cet article décrit la participation de l’équipe Nantalco à la tâche 2 du Défi Fouille de Textes 2021 (DEFT) : évaluation automatique de copies d’après une référence existante. Nous avons utilisé principalement des traits basés sur la similarité cosinus des deux vecteurs représentant la similarité textuelle entre des réponses d’étudiant et la référence. Plusieurs types de vecteurs ont été utilisés (vecteur d’occurrences de mots, vecteur tf-idf, embeddings non contextualisés de fastText, embeddings contextualisés de CamemBERT et enfin Sentence Embeddings Multilingues ajustés sur des corpus multilingues). La meilleure performance du concours sur cette tâche a été de 0.682 (précision) et celle de notre équipe 0.639. Cette performance a été obtenue avec les Sentence Embeddings Multilingues alors que celle des embeddings non ajustés ne s’est élevée qu’à 0.55, suggérant que de récents modèles de langues pré-entraînés doivent être fine-tunés afin d’avoir des embeddings adéquats au niveau phrastique.
Caractérisation des relations sémantiques entre termes multi-mots fondée sur l’analogie (Semantic relations recognition between multi-word terms by means of analogy )

La terminologie d’un domaine rend compte de la structure du domaine grâce aux relations entre ses termes. Dans cet article, nous nous intéressons à la caractérisation des relations terminologiques qui existent entre termes multi-mots (MWT) dans les espaces vectoriels distributionnels. Nous avons constitué un jeu de données composé de MWT en français du domaine de l’environnement, reliés par des relations sémantiques lexicales. Nous présentons une expérience dans laquelle ces relations sémantiques entre MWT sont caractérisées au moyen de l’analogie. Les résultats obtenus permettent d’envisager un processus automatique pour aider à la structuration des terminologies.
Une étude des avis en ligne : généralisabilité d’un modèle d’évaluation (A Study of Online Reviews : Generalizability of the Evaluation Model)

Ce travail se situe dans la continuité de nos travaux antérieurs proposant le modèle d’évaluation portant sur des avis en ligne sur des restaurants. Le modèle est composé de quatre catégories : l’opinion (positive, négative, mixte), la suggestion, l’intention et la description. Cet article vise à tester la généralisabilité du modèle en l’appliquant sur deux corpus supplémentaires : un corpus relevant d’un autre domaine (celui de l’hôtellerie) et un corpus écrit dans une autre langue (le coréen). Nous avons présenté l’annotation manuelle et la détection automatique de ces catégories en nous appuyant sur différents modèles de l’apprentissage de surface (SVM) et l’apprentissage profond (LSTM).
Plongements Interprétables pour la Détection de Biais Cachés (Interpretable Embeddings for Hidden Biases Detection)

De nombreuses tâches sémantiques en TAL font usage de données collectées de manière semiautomatique, ce qui est souvent source d’artefacts indésirables qui peuvent affecter négativement les modèles entraînés sur celles-ci. Avec l’évolution plus récente vers des modèles à usage générique pré-entraînés plus complexes, et moins interprétables, ces biais peuvent conduire à l’intégration de corrélations indésirables dans des applications utilisateurs. Récemment, quelques méthodes ont été proposées pour entraîner des plongements de mots avec une meilleure interprétabilité. Nous proposons une méthode simple qui exploite ces représentations pour détecter de manière préventive des corrélations lexicales faciles à apprendre, dans divers jeux de données. Nous évaluons à cette fin quelques modèles de plongements interprétables populaires pour l’anglais, en utilisant à la fois une évaluation intrinsèque, et un ensemble de tâches sémantiques en aval, et nous utilisons la qualité interprétable des plongements afin de diagnostiquer des biais potentiels dans les jeux de données associés.
Un modèle Transformer Génératif Pré-entrainé pour le

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_ (French) We introduce a French adaptation from the well-known GPT model)

Nous proposons une adaptation en français du fameux modèle Generative Pre-trained Transformer (GPT). Ce dernier appartient à la catégorie des architectures transformers qui ont significativement transformé les méthodes de traitement automatique du langage. Ces architectures sont en particulier pré-entraînées sur des tâches auto-supervisées et sont ainsi spécifiques pour une langue donnée. Si certaines sont disponibles en français, la plupart se déclinent avant tout en anglais. GPT est particulièrement efficace pour les tâches de génération de texte. Par ailleurs, il est possible de l’appliquer à de nombreux cas d’usages. Ses propriétés génératives singulières permettent de l’utiliser dans des conditions originales comme l’apprentissage sans exemple qui ne suppose aucune mise à jour des poids du modèle, ou modification de l’architecture.
Vers la production automatique de sous-titres adaptés à l’affichage (Towards automatic adapted monolingual captioning)

Une façon de réaliser un sous-titrage automatique monolingue est d’associer un système de reconnaissance de parole avec un modèle de traduction de la transcription vers les sous-titres. La tâche de « traduction » est délicate dans la mesure où elle doit opérer une simplification et une compression du texte, respecter des normes liées à l’affichage, tout en composant avec les erreurs issues de la reconnaissance vocale. Une difficulté supplémentaire est la relative rareté des corpus mettant en parallèle transcription automatique et sous-titres sont relativement rares. Nous décrivons ici un nouveau corpus en cours de constitution et nous expérimentons l’utilisation de méthodes de contrôle plus ou moins direct de la longueur des phrases engendrées, afin d’améliorer leur qualité du point de vue linguistique et normatif.
Analyse en dépendances du français avec des plongements contextualisés (French dependency parsing with contextualized embeddings)

Cet article présente un analyseur syntaxique en dépendances pour le français qui se compare favorablement à l’état de l’art sur la plupart des corpus de référence. L’analyseur s’appuie sur de riches représentations lexicales issues notamment de BERT et de FASTTEXT. On remarque que les représentations lexicales produites par FLAUBERT ont un caractère auto-suffisant pour réaliser la tâche d’analyse syntaxique de manière optimale.
GECko+: a Grammatical and Discourse Error Correction Tool

GECko+ : a Grammatical and Discourse Error Correction Tool We introduce GECko+, a web-based writing assistance tool for English that corrects errors both at the sentence and at the discourse level. It is based on two state-of-the-art models for grammar error correction and sentence ordering. GECko+ is available online as a web application that implements a pipeline combining the two models.
Is youth unemployment in EU countries structural?

Abstract This paper’s main aim is to observe and confirm youth unemployment as a structural phenomenon in certain EU countries, including Slovenia. An innovative contribution is that it complements the prevailing economistic discourse with a sociological one. In the introduction, a brief history and overview of youth unemployment is presented. Slovenia is only briefly mentioned as having one of the relatively longest-lasting youth unemployment rates in the EU. In sections 2 and 3, approaches from economics and sociology are relied on while discussing three ‘types’ of EU countries with regard to different structural unemployment rates. From a sociological view, the longterm nature of youth unemployment is described, together with its impact on the social structure and (possible) socially destructive and economically destabilizing consequences. In section 4, analysis of “every-day life” indicators, namely, young people’s perceptions of work and life, reveals some surprising facts that depart from previous findings. In short: young people are more satisfied with their work and lives than older generations.
Ambiguous Sentence Processing in Translation

Abstract The goal of our research was to explore the possible online co-activation of both the target language (TL) syntactic structure representation and TL attachment strategies in translation, and to look over a possible interaction between both syntactic properties. To this purpose, Spanish (L1) – English (L2) bilinguals were instructed to read complex noun phrases with an ambiguous relative clause in Spanish to either repeat them in Spanish or translate them into English. The final word of the sentences and the syntactic congruency between the source language (SL) and TL syntactic structure were manipulated. The results revealed co-activation of both TL syntactic properties: participants interpreted sentences more accordingly to the TL preferred strategy (low attachment) in the reading for translation task, read congruent sentences faster, and used the TL preferred interpretation strategy in the congruent condition of the sentences more. These results indicated TL activation at different syntactic levels during comprehension of the SL in translation.
Manus manum lavat: media clients and servers cooperating with common media client/server data

The newly rectified CTA standard --- Common Media Client Data (CMCD) --- allows content providers to get insights into the performance of their large-scale streaming operations. Its sister standard --- Common Media Server Data (CMSD) --- is in the works and will allow servers to send hints to other servers and clients. The CMCD/CMSD combo is the long-awaited upgrade to HTTP adaptive streaming systems.
Research and Realization of Crop Instance Segmentation Based on YOLACT

Abstract: The invention realizes a computer vision-based system for improving crop picking accuracy and controlling automatic path-finding of robotic arm motion. Combined with the work of YOLACT (You Only Look At CoefficienTs) on Resnet-101, the traditional two-stage instance segmentation model is modified into a one-stage model, 64 robust masks are generated from the deepest layer, and the prediction coefficients are output according to the four-layer characteristic pyramid network in turn. The mask coefficients of different layers are given different weights to improve the mask accuracy. The plant is positioned, and a mask is generated to cover the crop closely pixel by pixel. The center point and contour coordinates of the crop are also outputted. Then, A* algorithm and its heuristic function are used to conduct piecewise shortest path planning using these coordinates in turn and generate corresponding G code statements to control the movement of the mechanical arm. Finally, the mechanical claw is combined with the center point and contour coordinates of the crop to pick crops.
User as a Super Admin: Giving the End-Users Full Control to Manage Access to Their Data in Social Media Networks

With the huge expansion of social media networks, they become an important part of users’ daily life everywhere, where users have large amount of data stored in these networks. these networks provide users with the tools and settings to control their privacy and security. Even though these settings give the users control over how to manage access to their data, they remain according to the specifications of the network providers rather than the user himself/herself. Role-based Access Control (RBAC) is a method used widely in managing user authorizations and restrictions in computer systems. In this paper we suggest an RBAC system called Grant, that provides fine-grained access control to user data. This system is also pluggable and can be used with any existing system or social network. Grant system gives the end user the full control to manage access to their data and create customs roles based on their needs. The system has a user interface to enable users to define roles and permissions over their resources inside social media networks. We show that our system Grant, can be used to create custom RBAC policies easily, and it can be used with other systems.
A Development of Personality Recognition Model from Conversation Voice in Call Center Context

A call center is an important communication channel between a business and its customers. The call center staffs are responsible for resolving customer problems and fulfilling their needs. It is undeniable that customer satisfaction can be increased if personalized services relating to their characteristics such as personality are provided. Researches are suggesting that a person's personality can be recognized from his/her conversational voice. Thus, a machine learning model that recognizes each customer's personality from one's conversational voice in a call center would enable the cell center to give that one appropriate response. This study focuses on developing a personality recognition model to predict each MPI (Maudsley Personality Inventory) personality dimension from each conversational voice. The MPI personality dimension includes E-scale (representing extraversion and introversion) and N-scale (representing neuroticism and stability). Audio files of conversational voice were collected from 92 volunteers instructed to make conversation in the simulated call center context. Logistic regression, LinearSVC, Random forest, and Artificial neural networks were used in the modeling process. The result shows that the model generated by using Artificial neural networks has the best performance on predicting the E-scale. The model has the positive predictive value (Introversion prediction) and the negative predictive value (Extraversion prediction) equal to 0.71 and 0.75, respectively. No model shows satisfying performance on neuroticism and stability prediction. This study shows a piece of evidence that extraversion and introversion in MPI, which have implications in businesses, can be effectively recognized from each person's conversational voice made through call centers. The model can be beneficial in many business applications such as call center management, personalized product offering, and personalized advertisement.
Improving the Robustness of a Convolutional Neural Network with Out-of-Distribution Data Fine-Tuning and Image Preprocessing

Deep convolutional neural networks trained on readily available datasets are often susceptible to decreases in performance when executing tasks on new data from a different domain. Making models generalize well on data in a new domain is the task of domain adaptation. Recently, a simple method, known as Out-of-Distribution Image Detector for Neural Networks (ODIN), was proposed for identifying out-of-distribution (OOD) images in a dataset. This paper proposes fine-tuning an image classifier model using OOD images detected in an ideal training set to improve the model's ability to classify real-life images. This work aims to investigate the effectiveness of such a technique, as well as image preprocessing methods like background removal and image cropping, at increasing the robustness of a ResNet50V2 baseline image classifier in the context of a multi-class classification task. It was observed that fine-tuning with OOD images identified by ODIN consistently increased the model's performance and that a combination of cropping images and fine-tuning with OOD images resulted in the greatest increase in the model's performance.
Isolating namespace and performance in key-value SSDs for multi-tenant environments

Key-value SSDs (KVSSDs) implement the storage engine of a key-value store such as log-structured merge-tree (LSM-tree) inside the SSD. However, recent LSM-tree based KVSSDs cannot be used directly in a multi-tenant environment. LSMtree-based KVSSDs are not designed with isolation in mind in terms of namespaces and performance, leading to incorrect data access between concurrent users and poor read performance. In this paper, we propose Iso-KVSSD, a LSM-tree based KVSSD for multi-tenancy by supporting namespace and performance isolation. The Iso-KVSSD performs access control based on the user's namespace and constructs per-namespace dedicated LSM-trees for users. We implement the Iso-KVSSD on Cosmos+ OpenSSD in a Linux environment and evaluate performance with Put() and Get() workloads by varying the number of tenants. Our extensive evaluation results showed that Iso-KVSSD has negligible write performance overhead and an average 2.9 times higher read throughput than a baseline that manages one global shared LSM tree between users.
UI Interactions to Invoke Viewing Experience and Their Effects

A user interaction scheme for a user-interface to extract emotional experience of art appreciation as knowledge is proposed and evaluated. The interaction was designed based on theories supported in the field of art appreciation education to invoke behaviors to enhance the experience. This study examines whether the behavior invoked by the interaction affects the quality of the viewing experience. The results of the experiment confirmed that some of the behaviors invoked by the interaction affect the quality of the experience. The results suggest that when designing user-interfaces to produce quality experiences, it is necessary to pay attention to the behavior leading up to the generation of the impressions that constitute the experience.
Deep-learning Based Prediction of Virtual Non-contrast CT Images

In this paper, we present a method, based on deep learning, for prediction of non-contrast CT image from a single contrast image. For training of this image-to-image translation task, virtual contrast and virtual non-contrast (VNC) images were created from spectral CT data by Philips IntelliSpace Portal (ISP) software. Virtual version of conventional CT (cCT) images and VNC images allows to train paired supervised image-to-image translation models. Two different schemes were tested to train the Convolutional Neural Network (CNN) with U-Net architecture, using standard training with L1/L2 loss as well as training via conditional Generative Adversarial Network (cGAN) with an additional Wasserstein modification (WcGAN). Qualitatively, the proposed method achieves similar results to the original VNC images. However, quantitatively, the trained CNN provides a slightly smaller density reduction in some tissues. Non-contrast image can be predicted from a single conventional CT image, without the need for pre- and post-contrast scan or without a spectral CT scan.