Miracle-dz/UniFAR-FTUs huggingface dataset:
Childbearing , and Female Labor Market Participation : Evidence from Lebanon

The objective of this paper is the investigation of the determinants of labor market participation for a sample of young Lebanese women. It was observed that female labor market participation in Lebanon is extremely low. While the paper reproduces the standard result that education is an important determinant of female labor market participation, it finds that, once one uses an instrumental variables estimator to control for the endogeneity of fertility, the correlation between labor market and childbearing completely disappears. After showing that education alone cannot explain the low labor market participation of Lebanese women, the paper tests the role of cultural factors and examines whether religion plays a role in determining labor market participation. Contrary to what is often believed, no significant relationship was found between religion and female participation. لمعلا قوس في ةأرلما ةكراشمو لافطلأا بانجإ ،ميلعتلا : نانبل نم عئاقوو هلدأ
Foreign Direct Investment in Tunisia in the Context of the Free Trade Agreement with the European Union

The aim of this study is to determine whether Tunisia could expect an increase in Foreign Direct Investment (FDI) flows in response to the establishment of a Free Trade Area with the European Union (EU). While the necessary conditions to stimulate the flow of FDI have received considerable attention on behalf of economists in recent years, the relationship between trade policy and FDI has not been the subject of in-depth studies. It is theoretically well established that the effect of regional integration on FDI flows is ambiguous. The partnership agreement between Tunisia and the EU has the potential of playing a catalytic role in increasing the openness of the Tunisian economy and attracting FDI. Empirical analysis shows that the investment potential created by the partnership agreement as well as the growth of FDI inflows to Tunisia are conditioned by the existence of important capital productivity. Even if liberalization is extended to agricultural products and services, Tunisia couldn’t expect important welfare gains if there is an absence of vital increase of capital productivity. ةيقافتا ءوض في سوت في ةرشابلما ةيبنجلأا تارامثتسلاا يبورولأا داتحلاا عم ةرلحا ةراجتلا يقنشم طسابلادبع دممح
The Market Notices Published by the Italian Stock Exchange: A Machine Learning Approach for the Selection of the Relevant Ones

Bank of Italy data managers check the market notices published daily by the Italian Stock Exchange (Borsa Italiana) and select those of interest to update the Bank of Italy's Securities Database. This activity is time-consuming and prone to errors should a data manager overlook a relevant notice. In this paper we describe the implementation of a supervised model to automatically select the market notices. The model outperforms the manual approach used by data managers and can therefore be implemented in the regular process to update the Securities Database.
On the Nile Fan's Wave Power Potential and Controlling Factors Integrating Spectral and Geostatistical Techniques

Wave energy is foreseen to contribute to the 20 % renewable energy supply of Egypt’s electricity budget to meet burgeoning energy demand. This research analyses the Nile Fan wave energy forcasted from numerical modeling for 2020 of the Copernicus Marine Environment Monitoring Service (CMEMS) database, hourly sampled at 0.042° as the finest remotely-sensed data available. Wave power spatial distribution is analyzed using data from 259 points proposed as Wave Energy Converters (WECs). Spectral analyses techniques were appraised for disclosing the frequency and power return periods of the significant wave height and peak periods and to understand the similarity among selected WECs of varied conditions. K-means clustering was used to distinguish energy classes with large inter-class variances. The obtained wave power resources ( av . 5.32 kW/m; maximum of 112.9 kW/m; annual mean of 46.96 MW/m) are among the largest found in the Mediterranean Sea. More than 98 % of the wave power variance is controlled, in decreasing order of influence, by the depth, distance to shore, significant wave height, wave peak period, and the wave principal direction. Deepest water and near shore classes attained the largest wave power. Detailed environmental factors induced the wave shape and power is concluded.
Effect of Market Information on Bidder Attrition in Online Auction Markets

Information generated in online markets can affect both buyers’ and sellers’ expectations and therefore their choices. In this research, we investigate the effect of market information, generated in online auction markets, on buyers’ expectations and choices. To clear large inventories, sellers often conduct many auctions selling identical items over time, which creates an online auction market where competition dynamics spill over from one to auction to another. In these markets, bidders can participate in many auctions over a period of time, observe market information (supply, demand, and competition), and gain experience to increase their payoffs. We observe that despite having an opportunity to compete and win in future auctions, many bidders stop participating in these auction markets. We argue that observed market information affects their choices. We explore how bidders form expectations about market supply, demand, and competition based on information from two sources—market design parameters and behavior of market participants. By employing a hierarchical Bayesian latent attrition model, we empirically detect and investigate the effect of bidders’ expected market supply, demand, and competition on their attrition in these markets. Our study shows that the effect of market information on attrition is nuanced by bidders’ value heterogeneity. Through the lenses of behavioral economics theories, we show that the attrition behavior of high-value bidders is completely opposite that of low-value bidders. We discuss the practical implications of our findings.
Soteria: Privacy-Preserving Machine Learning for Apache Spark

—Privacy and security are prime obstacles to the wider adoption of machine learning services offered by cloud computing providers. Namely, trusting users’ sensitive data to a third-party infrastructure, vulnerable to both external and internal malicious attackers, restricts many companies from leveraging the scalability and ﬂexibility offered by cloud services. We propose S OTERIA , a system for distributed privacy-preserving machine learning that combines the Apache Spark system, and its machine learning library (MLlib), with the conﬁ-dentiality features provided by Trusted Execution Environments ( e.g. , Intel SGX). S OTERIA supports two main designs, each offering speciﬁc guarantees in terms of security and performance. The ﬁrst encapsulates most of the computation done by Apache Spark on a secure enclave, thus offering stronger security. The second ﬁne-tunes the Spark operations that must be done at the secure enclave to reduce the needed trusted computing base, and consequently the performance overhead, at the cost of an increased attack surface. An extensive evaluation of S OTERIA , with classiﬁcation, regression, dimensionality reduction, and clustering algorithms, shows that our system outperforms state-of-the-art solutions, reducing their performance overhead by up to 41%. Moreover, we show that privacy-preserving machine learning is achievable while providing strong security guarantees.
DEEP NEURAL NETWORK BASED CLASSIFICATION MODEL FOR FEATURE EXTRACTION

Background: Computer aided diagnosis model developed for the detection of lung cancer can leads to higher survival rate. In recent days, Deep Learning (DL) models finds successful for various applications in medical domain. Materials and Methods: In this paper, an effective feature extraction based lung cancer classification model has been presented. The proposed model makes use of Hybrid Feature Extraction (HFE) with Deep Neural Network (DNN) based classification models. The proposed HFE-DNN model undergoes pre-processing to enhance the image quality. Then, feature extraction process is carried out by the use of HFE technique followed by DNN based data classification. The outcome of the HFE-DNN technique can assist doctors in the diagnosis process of lung cancer. Results and Conclusion: An extensive simulation results takes place on the benchmark HFE-DNN model and the results confirmed its superior characteristics over the existing methods. The simulation outcome pointed out that the proposed HFE-DNN model has offered effective classification outcome with the maximum accuracy of 85.79%, sensitivity of 79.55% and specificity of 89.03%.
Tarım Ekonomisi Dergisi Turkish Agricultural Economics

Purpose: This study aims at analyzing the behaviors of women producers in producers' markets and obtain results that will help the decision and policy makers in local governments. Des gn/Methodology/Approach: The content-text analyses done with the Maxqda data analysis software on the answers to the face-to-face interviews, the changes in the producer women's lives, feelings and perceptions have been analyzed. F nd ngs: According to the results of the study, it has been concluded that the women who are economically powered feel better about themselves and their self-confidence improve. In conclusion, this study emphasizes the importance of producer women's places in the labor market. Or g nal ty/Value: The importance of producer women's places in the labor market and changes in women's lives are identified.
Sports broadcasting and multiple object tracking with deep learning methods

Since less than a decade ago, deep learning techniques started to dominate many different fields, revolutionizing the possibilities of artificial intelligence. Seeing their potential, industrial sectors started to invest in applying such technologies as key components of the company strategy. This thesis has been developed in an industrial context, in AutomaticTV. The main focus along this period has been the transfer of knowledge and know-how between academia and industry, development of tools to exploit this knowledge, the exploration of new techniques for future challenges, and, from an academic research perspective, contributions to the multiple object tracking problem. The first part of the thesis is devoted to the introduction of deep learning technologies to AutomaticTV, a company dedicated to automatic sports analysis and broadcasting, and the development of tools and tasks that surround the application. The second part of this thesis introduces the contributions to the multiple object tracking challenge. We present TrajE, a trajectory estimator based on mixture density networks and beam search, used to boost the performance of existing multiple object trackers, and introduce an occlusion reconstruction step using the estimated trajectory information. By adding TrajE to an existing multiple object tracker, we boost its performance by 6.3, 1.8 points in MOTA and IDF1 scores respectively, becoming the new state of the art in the MOTChallenge dataset. Des de fa menys de deu anys, les tècniques basades en “deep learning” van començar a dominar molts camps diferents, revolucionant les possibilitats de la intel·ligència artificial. Veient el seu potencial, l'industria va començar a invertir per aplicar aquestes tecnologies com a components clau per l’estratègia de l’empresa. Aquesta tesi ha estat desenvolupada en el context d’AutomaticTV. Durant aquest període, el focus principal ha estat la transferència de coneixement entre l’acadèmia i la indústria, el desenvolupament d’eines per explotar aquest coneixement, l’exploració de noves tècniques per futurs reptes i, des d’una perspectiva de recerca acadèmica, contribucions al problema del seguiment de múltiples objectes “multiple object tracking”. La primera part d’aquesta tesi es centra en la introducció de tecnologies basades en deep learning a AutomaticTV, una empresa dedicada a l’automatització de l’anàlisi i retransmissió d’esports, i al desenvolupament de les eines que ho envolten. La segona part d’aquesta tesi presenta les contribucions fetes al repte del seguiment de múltiples objectes. Presentem TrajE, un estimador de trajectòries basat en “mixture density networks” i “beam search”, utilitzat per millorar les prestacions de tècniques existents pel seguiment de múltiples objectes, i introduïm un pas de reconstrucció d’oclusions fent servir la trajectòria estimada. Afegint TrajE a un “multiple object tracker”, CenterTrack, fem que les seves prestacions millorin un 6.3 i 1.8 punts en puntuacions de MOTA i IDF1 respectivament, esdevinguent el nou estat de la qüestió al dataset de MOTChallenge.
Understanding foreign currency borrowing by firms: Evidence from India

The Indian capital controls permit foreign currency borrowing (FCB) for firms with low credit risk, and by 2013, there was a stock of borrowing of $132 billion. Through a combination of home bias and capital controls, FCB is the preserve of large and internationally active firms with low financing constraints. We establish a quasi-experimental design through which the causal impact of FCB upon future growth in capital, labour, exports and output is assessed. The two doublings of exchange rate flexibility in India, in 2003 and 2007, appear to have avoided moral hazard: the firms which have undertaken FCB have successfully achieved modest gains in capital stock and output. ∗The support of the International Growth Centre and NIPFP-DEA Research Programme is gratefully acknowledged. Akhil Dua, Dhananjay Ghei, Apoorva Gupta, Shekhar Hari Kumar and Pramod Sinha provided outstanding research assistance. The opinions expressed and remaining shortcomings are our responsibility alone.
Sleep Disruption in a Mouse Model of Medial Temporal Lobe Epilepsy

While not typically a recognized comorbidity of epilepsy, sleep disorders are common in epilepsy, particularly medial temporal lobe epilepsy (MTLE). However, consistent and severe sleep disruptions have a potentially cascading effect in the severity of seizure control, as well as other comorbidities of TLE. The present study uses the intra-amygdala kainic acid (IAKA) mouse model of TLE to develop a basis for novel sleep-focused treatments. Using a custom 3-D printed headset equipped with electroencephalogram (EEG) electrodes, electromyogram (EMG) electrodes, and a guide cannula, mice were injected with kainic acid targeting the left basolateral amygdala and were recorded continuously throughout the duration of the experiment. Preliminary results show that IAKA mice (with seizures) spent more time in wake and less time in non-rapid eye movement (NREM) and rapid eye movement (REM) sleep over the experiment’s final 72 hours, compared to controls. Furthermore, a weak positive correlation was identified between percent of time spent in wake and amount of seizures. Histological data showed amygdala and hippocampal damage in mice with seizures. Introduction Temporal lobe epilepsy (TLE) is the most prevalent and drug-resistant form of adult epilepsy (Zenteno et al. 2012), with the bulk of these patients having medial TLE (MTLE). Even among patients with seizures regulated by medications, side-effects are common. About a third of patients with TLE do not respond well to anti-epileptic drugs (Granata et al. 2009, Kwan & Brodie 2000) and only approximately 32-75% of patients experience seizure freedom after surgical treatment of their epilepsy (Téllez-Zeteno et al. 2010). The epileptogenic focus in MTLE lies in the hippocampus and surrounding structures, and the progressive pathology is correlated with memory disorder in humans, possibly related to various comorbidities such as
Modeling and Training Strategies for Language Recognition Systems

Automatic speech recognition is complementary to language recognition. The language recognition systems exploit this complementarity by using frame-level bottleneck features extracted from neural networks trained with a phone recognition task. Recent methods apply frame-level bottleneck features extracted from an end-to-end sequence-to-sequence speech recognition model. In this work, we study an integrated approach of the training of the speech recognition feature extractor and language recognition modules. We show that for both classical phone recognition and end-to-end sequence-to-sequence features, sequential training of the two modules is not the optimal strategy. The feature extractor can be improved by supervision with the language identiﬁcation loss, either in a ﬁne-tuning step or in a multi-task training framework. Besides, we no-tice that end-to-end sequence-to-sequence bottleneck features are on par with classical phone recognition bottleneck features without requiring a forced alignment of the signal with target tokens. However, for sequence-to-sequence, the architecture of the model seems to play an important role; the Conformer architectures leads to much better results than the conventional stacked DNNs approach; and can even be trained directly with the LID module in an end-to-end approach.
A simple model for predicting the hurricane radius of maximum wind from outer size NOT PUBLISHED. Submitted for peer review

The radius of maximum wind ( 𝑅 𝑚𝑎𝑥 ) in a hurricane governs the footprint of hazards, particularly damaging wind and rainfall. However, 𝑅 𝑚𝑎𝑥 is noisy to observe directly and is poorly resolved in reanalyses and climate models. In contrast, outer wind radii are much less sensitive to such issues. Here we present a simple empirical model for predicting 𝑅 𝑚𝑎𝑥 from the radius of 34-kt wind ( 𝑅 17 . 5 𝑚𝑠 ) that only requires as input quantities that are routinely estimated operationally: maximum wind speed, 𝑅 17 . 5 𝑚𝑠 , and latitude. The form of the empirical model takes advantage of our physical understanding of hurricane radial structure and is trained on the Extended Best Track database from the North Atlantic; results are similar for the TC-OBS database. The physics reduces the relationship between the two radii to a dependence on two physical parameters, while the observational data enables an optimal estimate of the quantitative dependence on those parameters. The model performs substantially better than existing operational methods for estimating 𝑅 𝑚𝑎𝑥 . The model reproduces the observed statistical increase in 𝑅 𝑚𝑎𝑥 with latitude and demonstrates that this increase is driven by the increase in 𝑅 17 . 5 𝑚𝑠 with latitude. Overall, the model oﬀers a simple and fast ﬁrst-order prediction of 𝑅 𝑚𝑎𝑥 that can be used operationally and in risk models.
Welfare Services from a Minority Perspective. Explaining Satisfaction with Language-Based Welfare Services among Swedish-Speaking Finns

This article explores perceptions of welfare services from the perspective of a linguistic minority, the Finland-Swedes. Since Finland has an extensive universal welfare system and is an officially bilingual country, the relatively small Swedish-speaking numerical minority has equal rights to welfare provision in their mother tongue. This provides an analytically promising case, as expectations toward the Finnish welfare system are high, while delivering quality language-based welfare services to minorities can be challenging. We hypothesize that attitudes regarding language-based welfare service are guided by either rational self-interest, i.e . language skills, or ingroup identity. Our analyses are based on a survey among a random sample of Swedish-speaking Finns in the winter of 2018-2019. We found that the attitudes toward language-based welfare services were guided by both self-interest and identity, but their impact depended on whether respondents were asked about their specific evaluation of, or general desire for, language-based welfare services.
Review of Communication Technologies for Electric Vehicle Charging Management and Coordination

Recently, electric vehicles (EVs) have been introduced as an alternative method of transportation to help mitigate environmental issues, such as carbon emissions and fuel consumption, caused by conventional transportation systems. The implementation of effective EV charging systems is essential to motivate mass adoption of EVs. Accordingly, fast and reliable communications between the charging systems and the EVs are vital for efficient management of the charging process. Different radio access technologies (RATs) are discussed in the literature to enable communication between the highly mobile EVs and the charging subsystems, to collect and exchange information such as state of charge (SoC), users’ locations, and charging decisions between the different network entities. This information can be used to coordinate charging plans and select the optimal routes for moving vehicles. This paper presents a survey of existing literature on vehicular communications for EV charging coordination and management. The communication requirements and feasible communication technologies for vehicular communication are first discussed in details. A review of the physical layer security strategies is then presented and the role of the different RATs in EV charging coordination and management is described and studied.
Review on Emerging Zoonoses and Factors in the Emergence of Infectious Diseases

In global health, critical challenges have arisen from infectious diseases, including the emergence of infectious diseases. Emerging infectious diseases can be defined as infections that have newly appeared in a population or have existed but are rapidly increasing in incidence or geographic range. Among recent examples are HIV/AIDS, hantavirus pulmonary syndrome, Lyme disease, and hemolytic uremic syndrome. Specific factors precipitating disease emergence can be identified in virtually all cases. These include ecological, environmental, or demographic factors that place people at increased contact with a previously unfamiliar microbe or its natural host or promote dissemination. These factors are increasing in prevalence; this increase, together with the ongoing evolution of viral and microbial variants and selection for drug resistance, suggests that infections will continue to emerge and probably increase and emphasizes the urgent need for effective surveillance and control.
The effects of teaching self-regulated learning strategy on students' academic delay of gratification

The purpose of this study was to examine the effects of teaching self-regulated learning strategy on students’ academic delay of gratification. The sample size consisted of 50 student’s high school, 25 each in experimental and control groups, and the method used was of a random cluster multiple-stage sampling type. The measurement tool consisted of academic delay of gratification scale Bembenutty and Karabenick (1998) that was executed as a pre-test in both groups. After which, only the experimental group was administered teaching in self-regulated learning strategies for 15 sessions. After the training, a post-test using the aforementioned scale was taken from both groups. Data was analysed by calculating the analysis of covariance (ANCOVA). The results of the study indicated that the teaching of self-regulation learning strategies has had a significant effect on students’ academic delay of gratification.