Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbon and nitrogen uptake in above-and below-ground biomass of cereal crops in the integrated farming system

A significant reduction in greenhouse gas (GHG) emissions, as well as technologies that ensure removal of CO 2 from the atmosphere, are necessary to achieve the set goals for the transition to carbon neutrality. During the crop growth cycle, a significant amount of biomass is produced, and carbon (C) and nitrogen (N) are captured both by the harvested crop removed from the field and by residues left on the field. The trials were conducted to find out patterns between crop and residues while trying to figure out the amount of captured C and N. In this study data of the most widely grown cereal crops in Latvia are summarized. The data are representative, obtained in different agroclimatic conditions, they vary both by species and variety, by year and fertilizers applied. The mean amount of biomass from cereal crops left on the field was 1,070.9 g m -2 DM, besides, 906.7 g m -2 of that was made up of above-ground (AG) residues and 164.2 g m -2 of below-ground (BG) residues. On average, 471.8 g m -2 C and 14.3 g m -2 N were captured, including: 411.2 g m -2 C and 12.9 g m -2 N by AG residues; 60.7 g m -2 C and 1.4 g m -2 N by BG residues. Regularities between grain yield and residues were found, however, they were not very strong. The dataset should be enlarged to reduce uncertainty. As the data calculated from crop have a greater uncertainty, the GHG inventory should be calculated according to the average AG and BG biomass, which provide more accurate data.
A Regime-Switching Lognormal Model of Philippine Stock Exchange Index (PSEi) Returns

In this study, we model the returns on the Philippine Stock Exchange Index (PSEi) using a regime-switching lognormal (RSLN) model. Monthly data on the index were obtained from a Technistock 1 terminal, over the period 1987 to 2014. We used maximum likelihood estimation to estimate the parameters of the model. We also presented the results of modeling the returns using a simple lognormal model in comparison with the RSLN model. Finally, we performed Monte Carlo simulations to project expected levels of the PSEi for the immediate future
Sklar's theorem via regularization techniques

Sklar’s theorem establishes the connection between a jointd{dimensional distribution function and its univariate marginals. Its proof is straightforward when all the marginals are continuous. The hard part is the extension to the case when at least one of the marginals has a discrete component. We present a new proof of this extension based on some analytical regularization techniques (i.e., molliers) and on the compactness (with respect to the L 1 norm) of the class of copulas.
Markov processes with product-form stationary distribution

We consider a continuous time Markov process (X,L) , where X jumps between a finite number of states and L is a piecewise linear process with state space R d . The process L represents an “inert drift” or “reinforcement. ” We find sufficient and necessary conditions for the process (X,L) to have a stationary distribution of the product form, such that the marginal distribution of L is Gaussian. We present a number of conjectures for processes with a similar structure but with continuous state spaces.
Ordonnancement, assignation et transformations dynamiques de graphe simultanés pour projeter efficacement des applications sur CGRAs

Porter une application sur une architecture reconfigurable a gros grain est une tâche complexe qui reste encore souvent realisee entierement ou partiellement manuellement. Cet article presente un flot original de synthese automatise base sur des etapes d'ordonnancement et d'assignation simultanees. L'approche proposee parcourt en sens inverse les noeuds du modele formel extrait a partir du code de l'application compile pour le transformer dynamiquement uniquement si necessaire. Les resultats des experiences montrent que l'approche proposee permet une meilleure exploration de l'espace de solution et obtient la meilleure latence dans 90% des cas.
Impact of COVID-19 Pandemic on the Development of Childrens Executive Functions Implications for School-Based Interventions

The pandemic of COVID-19 has had a significant impact on children’s lives. Worldwide, there has been evidence of a decline in children's mental health, well-being, and quality of life. Few studies, however, have reported the pandemic's impact on children's cognitive functioning. Executive Functions (EFs) are a set of high-order cognitive functions involved in behavior and emotions self-regulation. EFs are powerful predictors of school performance, child’s well-being and health. During crisis and disaster, EFs are critical resources to cope with unusual and complex situations, and to find novel solutions to problems. Because of EFs’ extensive growth, there are many time periods during which experience has the greatest effect on brain development. Therefore, EFs are among the cognitive systems that are most vulnerable to environmental stress. As a result, children's emotional distress has been related to concentration and memory problems, difficulties managing impulses and emotions, and difficulties in planning ahead. Recent research has found executive dysfunction in children and adolescents during pandemic. Adopting an ecological contextual model, this study aimed to identify factors associated to pandemic that may affect children's performance and development of EFs, as well as, to propose a set of evidence-based strategies for teachers to assist children manage stress and promote EFs during crisis. Peer-reviewed academic articles, books, and web resources published between 2010 and 2023 were chosen for review in this study. As a result, child stress, family stress, school closure, changes in child-teacher interactions, and unhealthy habits are highlighted as potential factors influencing child EF development. In response to the Sustainable Development Goals agenda, a set of strategies was developed that may be easily included into the school curriculum. Creating school-based interventions to develop students' EFs is one approach to improve children's crisis-response resources.
Confidence Intervals for Randomized Quasi-Monte Carlo Estimators

Randomized Quasi-Monte Carlo (RQMC) methods provide unbiased estimators whose variance often converges at a faster rate than standard Monte Carlo as a function of the sample size. However, computing valid confidence intervals is challenging because the observations from a single randomization are dependent and the central limit theorem does not ordinarily apply. A natural solution is to replicate the RQMC process independently a small number of times to estimate the variance and use a standard confidence interval based on a normal or Student t distribution. We investigate the standard Student t approach and two bootstrap methods for getting nonparametric confidence intervals for the mean using a modest number of replicates. Our main conclusion is that intervals based on the Student t distribution are more reliable than even the bootstrap t method on the integration problems arising from RQMC.
Classification of Arabic Social Media Texts Based on a Deep Learning Multi-Tasks Model

The proliferation of social networking sites and their user base has led to an exponential increase in the amount of data generated on a daily basis. Textual content is one type of data that is commonly found on these platforms, and it has been shown to have a signi ﬁ cant impact on decision-making processes at the individual, group, and national levels. One of the most important and largest part of this data are the texts that express human intentions, feelings and condition. Understanding these texts is one of the biggest challenges that facing data analysis. It is the backbone for understanding people, their orientations, and making decisions in many cases and thus predicting their behavior. In this paper, a model was proposed for understanding texts that written by people on social media platforms, and hence knowing people ' s attitudes within speci ﬁ c topics, the emotion of those people, positivity, negativity, and neutrality. Also, it extracts emotion of those people. In this context, the system solves many tasks in natural language processing therefore it uses many techniques including topic classi ﬁ er, sentiment analyzer, sarcasm detector and emotion classi ﬁ er. CNN-BiLSTM was used for topic classi ﬁ er, sentiment analyzer, sarcasm detector, and emotion classi ﬁ er where (f-measure, accuracy) were (97,97.58) %, (84,86) %, (95,97) %, and (82,81.6) % respectively.
DESIGN & VALIDATION OF A FLIGHT CENTRIC WORKLOAD MODEL INCLUDING ATC TASK CHANGE & CONSIDERING INFLUENCING FACTORS

Airspace capacity is limited by the ATCO s’ control resources, which depend on their workload. For a sectorized airspace, a variety of workload models for distributing air traffic equally among controllers exist but approaches for a sectorless airspace are absent. This paper is the first to establish a workload model for Flight Centric ATC by adapting a workload model for sectorized airspace
Effect of Implementing Educational Program for Pregnant Women about Childbirth Preparation on Coping with Labor Process

: Pregnant woman during the last trimester face many challenges physically, and psychologically that can interfere with her ability to cope with and to be well prepared for labor process. Preparation for childbirth (PFC) is an educational approach to labor and delivery that can help woman to be informed about pregnancy, birth, postpartum, parenting, breastfeeding, sexuality, changing relationships, and family. There is contradictory evidence on the effect of antenatal education on self-efficacy during labor process and more research is required. Aim of the study
Maximum allowed angular errors for positioning mirrors in Advanced Virgo OMC

We study in this paper the propagation of an optical beam inside the Output Mode Cleaner (OMC) cavity in order to test the tolerancing of construction specifications. The main goal of this problem is to check if the beam stay inside polished mirrors during the propagation assuming random angular errors for mirror positions. The diameters of mirrors, assume to be 5mm, and the maximum allowed angular error, which we would determine, constitute the set of two relevant parameters in this problem if we exclude configuration parameters of the OMC. To solve the problem we will progress in two steps, first we will establish the equations of the beam propagation inside the cavity using the geometrical optics and second we will do the numerical resolution with a C++ code of those equations and find the maximum allowed angular error. More details on http://lucdigallo.free. fr/spip.php? article34&lang=en
This paper is included in the Proceedings of the 32nd USENIX Security Symposium.

Aimbots, which assist players to kill opponents in First-Person Shooter (FPS) games, pose a significant threat to the game industry. Although there has been significant research effort to automatically detect aimbots, existing works suffer from either high server-side overhead or low detection accuracy. In this paper, we present a novel aimbot detection design and implementation that we refer to as BotScreen, which is a client-side aimbot detection solution for a popular FPS game, Counter-Strike: Global Offensive (CS:GO). BotScreen is the first in detecting aimbots in a distributed fashion, thereby minimizing the server-side overhead. It also leverages a novel deep learning model to precisely detect abnormal behaviors caused by using aimbots. We demonstrate the effectiveness of BotScreen in terms of both accuracy and performance on CS:GO. We make our tool as well as our dataset publicly available to support open science.
Problematic use of social media: the phenomenon of popularity, consequences and risk factors

: Social media has revolutionized the way people communicate with each other, the whole world is now available on the screen of a smartphone. People who seemed too distant until yesterday are now available in seconds via a network connection. Smartphone and social media overcome the limitations of remoteness by introducing relationships between people into a completely different dimension. They were supposed to serve mainly as facilitating communication with loved ones, but with time they started to create the surrounding reality. The aim of the article is to present the issues related to the problematic use of social media. The most popular social network sites (SNS) will be presented first, along with their functions, number of users and the reasons why people use them so often. Then, issues related to problematic use of social media will be presented, such as: symptoms, questionnaire methods and consequences. Finally, the author discusses the risk factors of excessive of social media.
Aiding Adaptive Co-management in Irrigation

Shared governance of water flows and infrastructure poses a critical challenge for institutional design by water users and state agencies. Programs for participatory irrigation management (PIM) and irrigation management transfer (IMT) are often insufficient to achieve equitable water distribution and adequate infrastructure maintenance. If future responses to local and global challenges such as water scarcity and agricultural transformation only repeat past approaches, then they are likely to result in familiar frustrations and disappointments. Insights into potential solutions can be derived from understanding irrigation waterscapes as complex adaptive systems and from analysis of “Samaritan’s dilemmas” and other social dilemmas affecting aid. While participatory design and construction have often proved possible and devolution has been feasible under some conditions, continuing cooperation between water users and state agencies is essential to the performance of many irrigation systems. Many systems remain trapped in vicious cycles of deferred maintenance, degradation, poor performance, and inefficient rehabilitation. Perverse incentives discourage timely maintenance and local resource mobilization. Experiences from PIM and IMT programs illustrate some of the difficulties of deliberate social engineering, and suggest the need to focus on improving institutions for joint problem-solving in water distribution and infrastructure maintenance.
Board Characteristics and Environmental Disclosure of Quoted Oil and Gas Firms in Nigeria: The Moderating Role of Firm Size

This study investigated the relationship between board characteristics and environmental disclosure of quoted oil and gas firms in Nigeria: The moderating role of firm size with its specific objectives such as to determine the relationship between board independence and environmental disclosure. The research design adopted was ex-post facto design while, the population and the sample size for the study is the 12 quoted oil and gas companies in the Nigerian Stock Exchange (NSE). Secondary data were used in this study and data were analyzed using both descriptive, inferential statistics and Pearson Correlation Coefficient Statistical tool complementarily with the aid of Statistical Package for Social Sciences version 23.0 to test the null hypotheses. The findings of the study reveal that board independence has a negative relationship with environmental disclosure. The findings of the study further indicate that firm size significantly moderates the relationship between board characteristics and environmental disclosure. Based on the findings, the study recommends that independence should be assessed by weighing all the relevant factors that may compromise independence while the classification of directors as independent or otherwise in the integrated report should be done on the basis of assessment. Finally, increase in total asset is required as firm size was identified as a moderator variable between board characteristics and environmental disclosure.
Air pollution and birth weight: Evidence from extremely polluted places*

I document a surprising resilience to air pollution in the world’s most polluted places, at least as measured by birth outcomes. Average birth weight and the incidence of low birth weight in some of the most polluted cities in the world, and in highly polluted US counties in the 1970s, are essentially identical to the contemporary US. This is puzzling since quasi-experimental studies find large negative impacts of air pollution on fetal health. I discuss several possible explanations. *Email: maxim.massenkoff@nps.edu. Jalaluddin Khan provided excellent research assistance. I thank to Gantuya Dorj for help understanding Ulaanbaatar birth data. For helpful feedback and comments, I thank Janet Currie, Stefano DellaVigna, Nathan Franz, Jacob Marcus, David Molitor, Joshua Rosenberg, Evan Rose, Aartik Sarma, Dean Spears, Alex Tabarrok, Christine Tsang, and Reed Walker. All errors are my own.
Short: Deep Learning Approach to Skeletal Performance Evaluation of Physical Therapy Exercises

At-home exercising strongly predicts physical therapy patient out-comes, underscoring the need for analyzing patient behaviors at-home via remote patient monitoring. Contemporary methods for remote patient monitoring rely on specialized sensors, i.e., Inertial Measurement Units, RGB-Depth cameras, motion capture systems, or stereo vision which are costly and not scalable to all physical therapy patients. Here, we observe a lack of literature using only a monocular RGB camera. In this paper, we demonstrate a skeletal feedback model for at-home exercises using only video acquired from a smartphone camera. We propose models for (i) Patient Performance Evaluation - which classifies the correctness of exercises, and (ii) Guidance - which identifies why the exercise went wrong so the patient can correct themselves. We use these models on our dataset of four common physical therapy exercises labeled by a physical therapist. Our results demonstrate the feasibility of using skeletal data from state-of-the-art 3D human pose estimation models for physical rehabilitation exercise evaluation and guidance. Thus, we enable remote patient monitoring and guidance from a single camera - making it highly cost-effective and scalable.
Adaptive signals recovery by convex optimization

We consider the problem of denoising a signal observed in Gaussian noise.In this problem, classical linear estimators are quasi-optimal provided that the set of possible signals is convex, compact, and known a priori. However, when the set is unspecified, designing an estimator which does not ``know'' the underlying structure of a signal yet has favorable theoretical guarantees of statistical performance remains a challenging problem. In this thesis, we study a new family of estimators for statistical recovery of signals satisfying certain time-invariance properties. Such signals are characterized by their harmonic structure, which is usually unknown in practice. We propose new estimators which are capable to exploit the unknown harmonic structure of a signal to reconstruct. We demonstrate that these estimators admit theoretical performance guarantees, in the form of oracle inequalities, in a variety of settings.We provide efficient algorithmic implementations of these estimators via first-order optimization algorithm with non-Euclidean geometry, and evaluate them on synthetic data, as well as some real-world signals and images.
XÂY DỰNG HỆ THỐNG TỰ ĐỘNG GIẢI ĐÁP THẮC MẮC VỀ QUY ĐỊNH HỌC TẬP TẠI TRƯỜNG ĐẠI HỌC CÔNG NGHIỆP BẰNG KỸ THUẬT HỌC SÂU

Hiện nay, đối với trường đại học có quy mô lớn như Đại học Công nghiệp TPHCM, số lượng quy định, quy chế, thông báo rất lớn và cập nhật thường xuyên dẫn đến việc tìm hiểu và nắm bắt nội dung trở nên khó khăn. Trong bài báo, chúng tôi xây dựng hệ thống tự động trả lời câu hỏi dựa trên nội dung của các file văn bản bằng kỹ thuật deep learning. Hệ thống trích chọn thông tin từ câu hỏi, đưa vào là các từ khoá và trả về đoạn văn bản liên quan bằng thuật toán BM25. Ứng với đoạn văn bản có độ liên quan cao nhất, mô hình deep learning được huấn luyện để trích xuất ra câu trả lời tương ứng. Mô hình được huấn luyện dựa trên bộ dữ liệu huấn luyện với 10000 và bộ dữ liệu test 1600 cặp câu hỏi và câu trả lời tương ứng từ các đoạn văn bản được lấy từ các thông báo, quy định, quy chế của nhà trường. Chúng tôi tinh chỉnh các mô hình deep learning để huấn luyện và đánh giá, dựa trên hiệu quả và độ chính xác để lựa chọn mô hình tối ưu nhất. Kết quả độ chính xác đạt được theo F1-score của mô hình BERT là 73.93%, RoBERTa là 75.59% PhoBERT là 45.13% và DistilBERT là 72.95%. Mô hình RoBERTa được lựa chọn với tốc độ huấn luyện và độ chính xác cao nhất và được triển khai lên hệ thống để đánh giá kết quả.
A Disturbance Observer Based Sliding Mode Control for a Class of Underactuated Robotic System With Mismatched Uncertainties

In this note, a high-order disturbance observer for a class of underactuated robotic systems with mismatched uncertainties is proposed. To improve its estimation performance, a choice method of optimal gain matrices of the high-order disturbance observer is obtained as the solution of an optimization problem. A high-order disturbance observer based sliding mode control method is also designed for this class of underactuated robotic system with mismatched disturbance. The stabilities of both the disturbance observer and the controlled closed-loop system are proved by the Lyapunov theorem. Simulations on a benchmark Acrobot system demonstrate the efficiency of proposed approaches.
Using of Sentinel-2 images for automation of the forest succession detection

ABSTRACT The study was performed for the part of the administrative district Milicz. The authors analysed the parcels where the changes in land use, compared to the cadastral data, were found. The areas of interest were the parcels, where agricultural use was abandoned and the forest succession progressed. This paper investigates the possibility of applying satellite images Sentinel-2A for the automation of land use/land cover change detection, mainly in the aspect of monitoring uncontrolled forest succession. The results of the supervised classification of images Sentinel-2A were referred to the results of the traditionally applied manual vectorization of aerial orthophotomap. The difference for area covered by trees or shrub was 3.85% of the analysed parcels area. Analysing the results for each parcel in which the process of succession occurred, the mean difference is on average 2.25% for one parcel. The mean difference in the absolute value of the total area of participation in individual land use plots was about 1.54% of the analysed area.
Implementing Classical Hadamard Transform Algorithm by Continuous Variable Cluster State

Measurement-based one-way quantum computation, which uses cluster states as resources, provides an efficient model to perform computation. However, few of the continuous variable (CV) quantum algorithms and classical algorithms based on one-way quantum computation were proposed. In this work, we propose a method to implement the classical Hadamard transform algorithm utilizing the CV cluster state. Compared with classical computation, only half operations are required when it is operated in the one-way CV quantum computer. As an example, we present a concrete scheme of four-mode classical Hadamard transform algorithm with a four-partite CV cluster state. This method connects the quantum computer and the classical algorithms, which shows the feasibility of running classical algorithms in a quantum computer efficiently.