Miracle-dz/UniFAR-FTUs huggingface dataset:
Employment protection and country ' s attractiveness : a more ambiguous relationship than is usually assumed

This paper argues that being constrained by a strict employment protection legislation (EPL) is not as terrible for domestic firms as is usually assumed. Indeed, the markets concerned with international trade are oligopolistic. In such markets, a firm located in a country with a strict EPL suffers from a lack of production flexibility. However, because of this, that same firm may become a Stackelberg leader, which may turn out to be beneficial. Strategic interactions, therefore, cause the profitability of firms to become disconnected from their ability to adjust to demand fluctuations. Moreover, although they are unable to adjust their total output, firms located in the country with a strict EPL can allocate their sales across the different markets. This allocation flexibility, therefore, causes the adaptation of firms to demand fluctuations to be disconnected from their level of production flexibility. Unfortunately, allocation flexibility weakens the strategic advantage related to quantity commitment. The analysis of the impact of EPL on the attractiveness of the country then becomes more complex than generally alleged. However, this paper shows that, for a wide range of values of model parameters, a strict EPL does not deter investments. The author is grateful to two anonymous referees for very valuable comments. Citation: Sophie Lecostey, (2020) ''Employment protection and country's attractiveness: a more ambiguous relationship than is usually assumed'', Economics Bulletin, Volume 40, Issue 4, pages 2734-2740 Contact: Sophie Lecostey sophie.lecostey@unicaen.fr. Submitted: March 20, 2020. Published: October 12, 2020.
Content Selection for Explanation Requests in Customer-Care Domain

This paper describes a content selection module for the generation of explanations in a dialogue system designed for customer care domain. First we describe the construction of a corpus of a dialogues containing explanation requests from customers to a virtual agent of a telco, and second we study and formalize the importance of a specific information content for the generated message. In particular, we adapt the notions of importance and relevance in the case of schematic knowledge bases.
Video‐triggered EEG‐emotion public databases and current methods: A survey

Emotions, formed in the process of perceiving external environment, directly affect human daily life, such as social interaction, work efficiency, physical wellness, and mental health. In recent decades, emotion recognition has become a promising research direction with significant application values. Taking the advantages of electroencephalogram (EEG) signals (i.e., high time resolution) and video‐based external emotion evoking (i.e., rich media information), video‐triggered emotion recognition with EEG signals has been proven as a useful tool to conduct emotion‐related studies in a laboratory environment, which provides constructive technical supports for establishing real‐time emotion interaction systems. In this paper, we will focus on video‐triggered EEG‐based emotion recognition and present a systematical introduction of the current available video‐triggered EEG‐based emotion databases with the corresponding analysis methods. First, current video‐triggered EEG databases for emotion recognition (e.g., DEAP, MAHNOB‐HCI, SEED series databases) will be presented with full details. Then, the commonly used EEG feature extraction, feature selection, and modeling methods in video‐triggered EEG‐based emotion recognition will be systematically summarized and a brief review of current situation about video‐triggered EEG‐based emotion studies will be provided. Finally, the limitations and possible prospects of the existing video‐triggered EEG‐emotion databases will be fully discussed.
Resilience in Children With Speech and Language Disorders

Speech and language impairment (SLI) or developmental language disorder (DLD) is highly likely to affect children’s well-being. Children with SLI/DLD are much more likely than typically developing children to have academic and social learning difficulties and to experience mental health issues (anxiety and depression; Feeney et al., 2012; McGregor, 2020; St. Clair et al., 2011). However, not all children with speech and language disorders experience negative outcomes. Some have a better sense of well-being and appear more resilient in the face of adversity. Researchers reported that children defined well-being in terms of feeling good about one’s self, the absence of psychological distress, the presence of positive affective states (e.g., happiness and contentment), and integrating sadness with happiness (Fattore et al., 2006). Resilience is defined as a process of adaptation when exposed to adverse conditions and has been conceptualized in terms of risk and protective factors (Ungar, 2015). Resilience is determined both by the individual’s capacity and by the capacity of their social context (e.g., home, school, and social contexts). This social conceptualization of resilience considers risk factors to be at both the individual level and the societal level (Thomas, 2004). Within a social model of disability framework, risks to the well-being of children with SLI/ DLD may arise from a range of factors, such as potential negative feelings associated with having a communication disability and difficulties related to social inclusion. Because children can have positive outcomes despite adversity, it is important to identify protective factors that help children to overcome adversity. The authors note that research about quality of life in children with SLI/DLD focuses on individual factors rather than social and environmental factors and tells us little about how children cope with potential stressors (de Chavez et al., 2005). Article 12 of the United Nations Convention on the Rights of the Child (United Nations, 1989) states A newsletter dedicated to speech & language in school-age children
Evaluating Playfulness in Children

Kasari, C. , Freeman, S. , & Paparella, T. (2006). Joint attention and symbolic play in young children with autism: A randomized controlled intervention study. Journal of Child Psychology and Psychiatry, 47(6), 611–620. https://doi. org/10.1111/j.1469-7610.2005. 01567.x Kent, C. , Cordier, R. , Joosten, A. , Wilkes-Gillan, S. , Bundy, A. C. , & Speyer, R. (2020). A systematic review and metaanalysis of interventions to improve play skills in children with Autism Spectrum Disorder. Review Journal of Autism and Developmental Disorders, 7, 91–118. https://doi. org/10.1007/s40489-019-00181-y Merrell, K. W. (2002). School Social Behavior Scales User’s Guide. Paul H. Brookes. Piers, E. V. , & Herzberg, D. S. (2002). Piers-Harris 2: PiersHarris children’s self-concept scale. Western Psychological Services. Skard, G. , & Bundy, A. (2008). Test of playfulness. In L. D. Parham & L. S Fazio (eds.), Play in occupational therapy for children (2nd ed., pp. 71–93). Mosby. Williams, K. T. (2007). Expressive Vocabulary Test, Second Edition (EVT-2) (2nd ed.). AGS Publishing.
Segregation and Life Satisfaction

Our aim is to cast light on socioeconomic residential segregation effects on life satisfaction (LS). In order to test our hypothesis, we use survey data from Chile (Casen) for the years 2011 and 2013. We use the Duncan Index to measure segregation based on income at the municipality level for 324 municipalities. LS is obtained from the CASEN survey, which considers a question about self-reported well-being. Segregation’s impact upon LS is not clear at first glance. On one hand, there is evidence telling that segregation’s consequences are negative due to the spatial concentration of poverty and all the woes related to it. On the other hand, segregation would have positive effects because people may feel stress, unhappiness, and alienation when comparing themselves to better-off households. Additionally, there is previous evidence regarding the fact that people prefer to neighbor people of a similar socioeconomic background. Hence, an empirical test is needed. In order to implement it, we should deal with two problems, first, the survey limited statistical significance at the municipal level, hence we use the small area estimation (SAE) methodology to improve the estimations’ statistic properties, and second, the double causality between segregation and LS; to deal with the latter, we include lagged LS as a regressor. Our findings indicate that socioeconomic segregation has a positive effect on LS. This result is robust to different econometric specifications.
A Physical Method of Fabricating Hollow Polymer Spheres Directly from Oil/Water Emulsions of Solutions of Polymers†

Summary: A new physical method of fabricating hollowspheres from different polymers has been developed. In thismethod, emulsions were prepared by mixing organic solu-tionsofpolystyrene,poly( D , L -lactide-co-glycolide)(PLGA),and bacterial poly(3-hydroxybutyrate-co-3-hydroxyvalerate)(PHBV), water, and surfactants. The evaporation of solventsat room temperature caused a phase separation that eventu-ally yielded hollow spheres. Molecular weights, concentra-tions of polymers, and the natures of surfactant and solventwere important aspects of hollow sphere formation andstructure.Amechanismfortheformationofhollowspheresisproposedbasedonobservationsmadeusinganopticalmicro-scope equipped with a digital camcorder and using scanningelectron microscopy images of hollow spheres obtainedunder different conditions.A scanning electron microscopy image of a broken smallerhollowspherepreparedusinga7wt. -%polystyrenesolution(diameter of the sphere "10 micrometers). Macromol. Rapid Commun. 2004, 25, 1643–1649 DOI:10.1002/marc.200400203 ! 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Transfer Detection of YOLO to Focus CNN's Attention on Nude Regions for Adult Content Detection

Video pornography and nudity detection aim to detect and classify people in videos into nude or normal for censorship purposes. Recent literature has demonstrated pornography detection utilising the convolutional neural network (CNN) to extract features directly from the whole frames and support vector machine (SVM) to classify the extracted features into two categories. However, existing methods were not able to detect the small-scale content of pornography and nudity in frames with diverse backgrounds. This limitation has led to a high false-negative rate (FNR) and misclassification of nude frames as normal ones. In order to address this matter, this paper explores the limitation of the existing convolutional-only approaches focusing the visual attention of CNN on the expected nude regions inside the frames to reduce the FNR. The You Only Look Once (YOLO) object detector was transferred to the pornography and nudity detection application to detect persons as regions of interest (ROIs), which were applied to CNN and SVM for nude/normal classification. Several experiments were conducted to compare the performance of various CNNs and classifiers using our proposed dataset. It was found that ResNet101 with random forest outperformed other models concerning the F1-score of 90.03% and accuracy of 87.75%. Furthermore, an ablation study was performed to demonstrate the impact of adding the YOLO before the CNN. YOLO–CNN was shown to outperform CNN-only in terms of accuracy, which was increased from 85.5% to 89.5%. Additionally, a new benchmark dataset with challenging content, including various human sizes and backgrounds, was proposed.
Inverse-exponential correlation bounds and extremely rigid matrices from a new derandomized XOR lemma

In this work we prove that there is a function f ∈ E NP such that, for every sufficiently large n and d = √n/logn, fn (f restricted to n-bit inputs) cannot be (1/2 + 2−d)-approximated by F2-polynomials of degree d. We also observe that a minor improvement (e.g., improving d to n1/2+ε for any ε > 0) over our result would imply E NP cannot be computed by depth-3 AC0-circuits of 2n1/2 + ε size, which is a notoriously hard open question in complexity theory. Using the same proof techniques, we are also able to construct extremely rigid matrices over F2 in P NP. More specifically, we show that for every constant ε ∈ (0,1), there is a P NP algorithm which on input 1n outputs an n× n F2-matrix Hn satisfying RHn(2log1 − ε n) ≥ (1/2 − exp(−log2/3 · ε n) ) · n2, for every sufficiently large n. This improves the recent P NP constructions of rigid matrices in [Alman and Chen, FOCS 2019] and [Bhangale et al., FOCS 2020], which only give Ω(n2) rigidity. The key ingredient in the proof of our new results is a new derandomized XOR lemma based on approximate linear sums, which roughly says that given an n-input function f which cannot be 0.99-approximated by certain linear sum of s many functions in F within ℓ1-distance, one can construct a new function Ampf with O(n) input bits, which cannot be (1/2+sΩ(1))-approximated by F-functions. Taking F to be a function collection containing low-degree F2-polynomials or low-rank F2-matrices, our results are then obtained by first using the algorithmic method to construct a function which is weakly hard against linear sums of F in the above sense, and then applying the derandomized XOR lemma to f. We obtain our new derandomized XOR lemma by giving a generalization of the famous hardcore lemma by Impagliazzo. Our generalization in some sense constructs a non-Boolean hardcore of a weakly hard function f with respect to F-functions, from the weak inapproximability of f by any linear sum of F with bounded ℓp-norm. This generalization recovers the original hardcore lemma by considering the ℓ∞-norm. Surprisingly, when we switch to the ℓ1-norm, we immediately rediscover Levin’s proof of Yao’s XOR Lemma. That is, these first two proofs of Yao’s XOR Lemma can be unified with our new perspective. For proving the correlation bounds, our new derandomized XOR lemma indeed works with the ℓ4/3-norm.
Application of Artificial Intelligence (AI) for Sustainable Highway and Road System

There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.
An Ensemble of Global and Local-Attention Based Convolutional Neural Networks for COVID-19 Diagnosis on Chest X-ray Images

The recent Coronavirus Disease 2019 (COVID-19) pandemic has put a tremendous burden on global health systems. Medical practitioners are under great pressure for reliable screening of suspected cases employing adjunct diagnostic tools to standard point-of-care testing methodology. Chest X-rays (CXRs) are appearing as a prospective diagnostic tool with easy-to-acquire, low-cost and less cross-contamination risk features. Artificial intelligence (AI)-attributed CXR evaluation has shown great potential for distinguishing COVID-19-induced pneumonia from other associated clinical instances. However, one of the associated challenges with diagnostic imaging-based modeling is incorrect feature attribution, which leads the model to learn misguiding disease patterns, causing wrong predictions. Here, we demonstrate an effective deep learning-based methodology to mitigate the problem, thereby allowing the classification algorithm to learn from relevant features. The proposed deep-learning framework consists of an ensemble of convolutional neural network (CNN) models focusing on both global and local pathological features from CXR lung images, while the latter is extracted using a multi-instance learning scheme and a local attention mechanism. An inspection of a series of backbone CNN models using global and local features, and an ensemble of both features, trained from high-quality CXR images of 1311 patients, further augmented for achieving the symmetry in class distribution, to localize lung pathological features followed by the classification of COVID-19 and other related pneumonia, shows that a DenseNet161 architecture outperforms all other models, as evaluated on an independent test set of 159 patients with confirmed cases. Specifically, an ensemble of DenseNet161 models with global and local attention-based features achieve an average balanced accuracy of 91.2%, average precision of 92.4%, and F1-score of 91.9% in a multi-label classification framework comprising COVID-19, pneumonia, and control classes. The DenseNet161 ensembles were also found to be statistically significant from all other models in a comprehensive statistical analysis. The current study demonstrated that the proposed deep learning-based algorithm can accurately identify the COVID-19-related pneumonia in CXR images, along with differentiating non-COVID-19-associated pneumonia with high specificity, by effectively alleviating the incorrect feature attribution problem, and exploiting an enhanced feature descriptor.
Data for Beryllium-Hydrogen Charge Exchange in One and Two Centres Models, Relevant for Tokamak Plasmas

Data on the cross section and kinetic rate of charge exchange (CX) between the bare beryllium nucleus, the ion Be(+4) and the neutral hydrogen atom are of great interest for visible-range high-resolution spectroscopy in the ITER tokamak because beryllium is intended as the material for the first wall in the main chamber. Here an analysis of available data is presented, and the data needs are formulated. Besides the active probe signal produced by the CX of the diagnostic hydrogen neutral beam with impurity ions in plasma, a passive signal produced by the CX of impurity ions with cold edge plasma is also important, as it shows in observation data from the JET (Joint European Torus) tokamak with an ITER-like beryllium wall. Data in the range of a few eV/amu to ~100 eV/amu (amu stands for the atomic mass unit) needed for simulations of level populations for principal and orbital quantum numbers in the emitting beryllium ions Be(+3) can be obtained with the help of two-dimensional kinetic codes. The lack of literature data, especially for data resolved in orbital quantum numbers, has instigated us to make numerical calculations with the ARSENY code. A comparison of the results obtained for the one-centre Coulomb problem using an analytic approach and for the two-centre problem using numerical simulations is presented.
Fluctuating Asymmetry and Developmental Instability, a Guide to Best Practice

Best practices in studies of developmental instability, as measured by fluctuating asymmetry, have developed over the past 60 years. Unfortunately, they are haphazardly applied in many of the papers submitted for review. Most often, research designs suffer from lack of randomization, inadequate replication, poor attention to size scaling, lack of attention to measurement error, and unrecognized mixtures of additive and multiplicative errors. Here, I summarize a set of best practices, especially in studies that examine the effects of environmental stress on fluctuating asymmetry.
PACR: Position-Aware Protocol for Connectivity Restoration in Mobile Sensor Networks

Wireless Sensor Networks (WSNs) have gained global attention in recent times due to their vast applications in various fields. These networks can face the disruption of data transmission due to sensor node failures when placed in harsh, inaccessible, and adverse environments such as battlefields or monitoring in enemy territory. The specific tasks performed by the collaboration among the sensor nodes in WSNs by internode connectivity may be terminated. Besides this, due to the failure of sensor nodes, the area covered by the network may be limited, which can cause damage to the objectives for such a network, as there might be an unaware danger in the lost area. Connectivity is a big problem in mobile WSNs due to the mobility of nodes. Researchers have developed a lot of algorithms that are capable enough for connectivity problems, but they do not emphasize the loss of coverage. We try to fill these gaps by proposing the new hybrid algorithm PACR (Position-Aware protocol for Connectivity Restoration). The concept behind PACR is the same as a person who writes his will before death on a deathbed. In the same way, when the sensor energy is below the threshold, it is converted into a recovery coordinator and generates a recovery plan. This accelerates the recovery by decreasing the time needed for failure identification. For the recovery process, the neighbor’s nodes do not travel to the exact position of the failed node. Instead, they just move to the distance where they can build communication links with other nodes. This greatly prolongs the network lifetime. The simulation results show that PACR outperforms other techniques present in the literature.
A Change of Scale Formula for Wiener Integrals about the First Variation on the Product Abstract Wiener Space

We shall prove the existence of the Wiener integral and the analytic Wiener and Feynman integral and we obtain those relationships and later, we prove the change of scale formula for the Wiener integral about the first variation of a function defined on the product abstract Wiener space. Later, we obtain those relationships in the Fresnel class as it’s corollaries.
Modal Logics with Transitive Closure: Completeness, Decidability, Filtration

We give a suﬃcient condition for Kripke completeness of modal logics that have the transitive closure modality. More precisely, we show that if a modal logic admits what we call deﬁnable ﬁltration , then its enrichment with the transitive closure modality (and the corresponding axioms) is Kripke complete; in addition, the resulting logic has the ﬁnite model property and admits deﬁnable ﬁltration, too. This argument can be iterated, and as an application we obtain the ﬁnite model property for PDL -like expansions of multimodal logics that admit deﬁnable ﬁltration.