Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimizing the Maximum Risk of Currency Conversion for a Company Buying Abroad

Purpose: The relevance of the study is due to the fact that the task of determining the moment of transition from one currency to another arises for each organization, which is not limited to work in the domestic market. This article is aimed at solving the problem of performing the operation of required sale (exchange) of the available currency to pay for goods to a foreign supplier. Design/Methodology/Approach: There are two days to complete the exchange, which must be completed in no more than two steps. In the literature, the leading approach to solving this problem is the prediction of the exchange rate. Findings: The solution proposed in this article is based on Savage's criteria (regret metric). We minimize the maximum risk of unprofitable exchange. This allows you to prevent large deviations from the minimum possible amount of sale of the available currency necessary to purchase a given amount in another currency. Practical Implications: The article presents the prerequisites for the emergence of the task of determining the moment of transition from one currency to another, reveals the specifics of this task in Russian companies, identifies the main tools for solving problems of this type, justifies the use of the proposed approach to solve this problem. Originality/Value: Article submissions are of practical value for managers of companies trading with constant foreign partners, especially under the condition that the operations of converting a significant sum fulfil often, but not daily.
Appraising the Performance of Chief Executive Officers of Banking Institutions in Auckland

The financial crunch of 2008/9 occasioning the collapse of companies and financial institutions worldwide, sparked calls for transparency and accountability in the dealings of corporate bodies. It engendered more interests on issues concerning corporate governance among researchers, academicians and practitioners. It is worth noting that Chief Executive Officers (CEOs) are usually appointed and entrusted with the responsibility of managing corporate affairs on behalf of shareholders. Dual interests consequently emerge following the appointment of a CEO comprising the interest of shareholders and that of the CEO with a probability that the self-interests of CEO might run counter to the general interest of shareholders. As a result, many firms are actively incorporating into their corporate governance provisions, strategies to control executives’ performance. A strategic tool used in influencing the performance of chief executives towards the desired goals of an organisation is performance appraisal, yet there is little consideration for executive appraisal in the academic literature. This study seeks to complement the dearth of scholarly conversation on executive appraisal using a qualitative single case study approach drawing on an interview with a Chief Executive Officer of an international bank based in New Zealand. Employing stakeholder theory as a theoretical lens, the findings suggest that appraising CEOs can be instrumental in building good relationships, sustain organisation’s performance and enhance their career advancement.
Symbiotic organisms search algorithm based solution to optimize both real power loss and voltage stability limit of an electrical energy system

2016) Abstract. This paper presents a novel symbiotic organisms search (SOS) algorithm to optimize both real power loss (RPL) and voltage stability limit (VSL) of a transmission network by controlling the variables such as unified power flow controller (UPFC) location, UPFC series injected voltage magnitude and phase angle and transformer taps simultaneously. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained multi objective, multi variable optimization problem with a fitness function integrating both RPL and VSL. The symbiotic organisms search (SOS) algorithm is a nature inspired optimization method based on the biological interactions between the organisms in ecosystem. The advantage of SOS algorithm is that it requires a few control parameters compared to other meta-heuristic algorithms. The proposed SOS algorithm is applied for solving optimum control variables for both single objective and multi-objective optimization problems and tested on New England 39 bus test system. In the single objective optimization problem only RPL minimization is considered. The simulation results of the proposed algorithm have been compared with the results of the algorithms like interior point successive linear programming (IPSLP) and bacteria foraging algorithm (BFA) reported in the literature. The comparison results confirm the efficacy and superiority of the proposed method in optimizing both single and multi objective Symbiotic organisms search algorithm based solution to optimize both real power loss.
Hierarchical Statistical Models for Large Spatial Data in Uncertainty Quantification and Data Fusion

Modeling of spatial data often encounters computational bottleneck for large datasets and changeof-support effect for data at different resolutions. There is a rich literature on how to tackle these two problems but few gives a comprehensive solution for solving them together. This dissertation aims to develop hierarchical models that can alleviate those two problems together in uncertainty quantification and data fusion. For uncertainty quantification, a fully Bayesian hierarchical model combined with the nearest neighbor Gaussian process is proposed to produce consistent parameter inferences at different resolutions for a large spatial surface. Simulation studies demonstrate the ability of the proposed model to provide consistent parameter inferences at different resolutions with only a fraction of the computing time of the traditional method. This method is then applied to a real surface data. For data fusion, we propose a hierarchical model that can fuse two or more large spatial datasets with the exponential family of distributions. The “change-of-support” problem is handled along with the computational bottleneck by using a spatial random effect model for the underlying process. Through simulated and real data illustrations, the proposed data fusion method is demonstrated to possess predictive advantage over the univariate-process modeling approach by borrowing strength across processes.
A method to evaluate the impact of user training from the perspective of information literacy

This paper reports on a qualitative research study that aimed at determining the impact of user training on the information literacy level of users of the Capes Portal of EJournals, focusing mainly on the methodological aspects of the study. Initially, the paper presents the Capes Portal of E-Journals, a digital library of scientific journals, and the actions promoted by the UFMG University Library to promote access to and use of the Portal’s information resources. A brief presentation of fundamental concepts for the study follows. The methodology was mostly qualitative and used tests, interviews and observation. Data collection was conducted in two stages: pre-instruction and postinstruction. 14 students who enrolled voluntarily in a training program offered by the university library to the UFMG community formed the sample, in the first stage - preinstruction. After the pre-instruction test, the students went through a training session. In the second stage of the research, post-instruction test and interviews, seven of the 14 students present at the initial stage participated. Data was collected through tests and observation with computer screen recording in the pre-instruction stage and, two weeks after, in the post-instruction stage. In the post-instruction stage interviews were also used. Data was obtained and analyzed based on the second standard of the ACRL Information Literacy Competency Standards for Higher Education. Results led to the conclusion that participation in training sessions can positively influence the development and achievement of information literacy competencies related to the second standard of the Information Literacy Competency Standards for Higher Education (ACRL, 2000).
Techniques d'optimisation pour la détection et ré-identification de personnes dans un réseau de caméras

Cette these traite de la detection et de la re-identification de personnes dans un environnement instrumente par un reseau de cameras a champ disjoint. Elle est a la confluence des communautes Recherche Operationnelle et Vision car elle s'appuie sur des techniques d'optimisation combinatoire pour formaliser de nouvelles modalites de vision par ordinateur. Dans ce contexte, un detecteur visuel de personnes, base sur la programmation lineaire en nombres entiers, est tout d'abord propose. Son originalite est de prendre en compte le cout de traitement et non uniquement les performances de detection. Ce detecteur est evalue et compare aux detecteurs de la litterature les plus performants. Ces experimentations menees sur deux bases de donnees publiques mettent clairement en evidence l'interet de notre detecteur en terme de cout de traitement avec garantie de performance de detection. La seconde partie de la these porte sur la modalite de re-identification de personnes. L'originalite de notre approche, denommee D-NCR (pour Directed Network Consistent Re-identification), est de prendre explicitement en compte les temps minimum de transit des personnes dans le reseau de cameras et sa topologie pour ameliorer la performance de la re-identification. On montre que ce probleme s'apparente a une recherche de chemins disjoints particuliers a profit maximum dans un graphe oriente. Un programme lineaire en nombres entiers est propose pour sa modelisation et resolution. Les evaluations realisees sur une base publique d'images sont prometteuses et montrent le potentiel de cette approche.
Adaptive Sigma-Point Kalman Filtering for Wind Turbine State and Process Noise Estimation

This contribution investigates different noise adaptive Kalman filtering techniques with regard to their usability for wind turbine application. Since advanced model-based control schemes arise as promising alternative for standard industrial control, the necessity for robust and adaptive state estimation techniques has simultaneously emerged as an important topic. The comparison of the implemented adaptation rules shows that the master-slave adaptive filters are very flexible, numerically efficient and easy to implement. Maximum likelihood estimation based methods are more robust but show less flexibility and fewer design parameters to influence the filter performance. The simulation study shows that adaptive filters are beneficial since they solve two typical problems involved with static Kalman filter design: First, filter parameter adaptation compensates incorrect assumptions of noise statistics. Secondly, adaptation rules prevent poor filter performance for systems with time-varying statistical properties.
Cuantificación de la respuesta del caqui al riego y fertilización para el establecimiento de pautas de manejo eficientes y sostenibles

Los requerimientos hidricos y nutricionales del caqui asi como la respuesta de esta especie a las variaciones en su disponibilidad no son frecuentes en la literatura cientifica. Ello es debido, entre otros motivos, a que este cultivo ha sido particularmente importante en zonas de Corea y China donde el regimen de lluvias es abundante y por tanto el riego no es esencial. Sin embargo, en los ultimos 15 anos el cultivo del caqui ha ganado importancia en algunas zonas radicalmente diferentes a aquellas, como la cuenca mediterranea, gracias, entre otras causas, a la presencia de la variedad autoctona "Rojo Brillante", muy productiva, con un fruto de gran calidad y cuya astringencia es sencilla de eliminar (Llacer y Badenes, 2009). Estas areas, sin embargo, se caracterizan por un deficit hidrico casi estructural, por lo que el riego se revela como una practica determinante de la productividad del cultivo. Pero ademas, un manejo eficiente de la fertirrigacion no es solo deseable, sino en muchos casos obligatorio dada la hipersensibilidad a la salinidad de esta especie cuando se encuentra injertada sobre D. Lotus (Visconti y col. 2017), el portainjertos mayoritario. En este texto se pretende realizar una sintetica y practica revision sobre la informacion hoy en dia disponible sobre los requerimientos hidricos y nutricionales del caqui asi como su respuesta de esta especie a las restricciones de agua. El cultivo del caqui esta ganando relevancia en muchas zonas semiaridas del mundo. Es el caso del sudeste espanol, donde el regimen pluviometrico es incapaz de suplir las necesidades hidricas de los cultivos y donde, ademas, el riego por goteo se impone como medio de aportar a las plantas agua y nutrientes. En esta revision se pretende resumir la informacion existente en la literatura cientifica sobre el riego y la nutricion de esta especie, como tambien sobre la respuesta del cultivo al estres hidrico. De todo ello se concluye que el caqui es particularmente exigente en agua durante la epoca estival, momento en el que el coeficiente de cultivo (Kc) alcanza valores proximos a 1. Por otra parte, las tecnicas de riego deficitario controlado (RDC) tambien se han ensayado en este cultivo, revelandose como una herramienta util para reducir la caida fisiologica del fruto e influir sobre la produccion final. Asimismo, una restriccion tardia favorece un ligero adelanto de la maduracion del fruto, permitiendo avanzar la recoleccion, si bien a cambio de un menor calibre. En este articulo tambien se pretende dar referencias respecto de las necesidades nutricionales de variedades astringentes y no astringentes. Por ultimo, se plantea de manera critica la necesidad de profundizar en el estudio de las relaciones hidricas de esta especie asi como en las necesidades nutricionales en condiciones de limitacion del recurso.
A meshfree isosurface computation method for boundary element methods

Isosurfaces are an appropriate approach to visualize scalar fields or the absolute value of vector fields in three dimensions. The nodes of the corresponding isosurface mesh are determined using an efficient and accurate isovalue search method. Then, these nodes are typically connected by triangular elements, which are obtained with the help of an adapted advancing front algorithm. An important prerequisite of an isovalue search method is that volume data of the examined field is available in total space. That means, the field values are precomputed in the nodes of an auxiliary post-processing volume mesh or a novel meshfree method is developed that enables both efficient computations of field values in arbitrary points and fast determination of domains with a defined range of field values. If the first approach is applied, a classical isovalue search method is to use an octree scheme to find relevant volume elements, which are intersected by the isosurface. Finally, the surface elements of the isosurface are constructed based on the intersection points of the isosurface with the volume elements. In that case, the accuracy and the computational costs are mainly influenced by the density of the post-processing volume mesh. In contrast, an innovative coupling of established isovalue search methods, fast boundary element method (BEM) techniques, and advancing front meshing algorithms is here presented to compute isosurfaces with high accuracy only using the original BEM model. This novel meshfree method enables very accurate isovalue search methods along with nearly arbitrarily adjustable resolution of the computed isosurface. Furthermore, refinements of the isosurface are also possible, for instance in dependency of the current viewing position. The main idea to realize this meshfree method is to directly combine an octree-based isovalue search method with the octree-based fast multipole method (FMM).
Using Neural Networks to Forecast the Implied Volatility: the Case of S&P100XEO

Currently the most popular method of estimating volatility is the implied volatility. It is calculated using the Black-Scholes option price formula, and is considered by traders to be a significant factor in signaling price movements in the underlying market. A trader is able to establish the proper strategic position in anticipation of changes in market trends if she/he could accurately forecast future volatility. There is an abundance of ways to compute the volatility. For two decades neural networks has been developed to forecast future volatility, using past volatilities and other options market factors. In this article a network is created for this purpose whose performance demonstrates the value of neural networks as a predictive tool in volatility analysis.
Forecasting monthly energy production of small hydropower plants in ungauged basins using grey model and improved seasonal index

A first-order one-variable grey model (GM(1,1)) is combined with improved seasonal index (ISI) to forecast monthly energy production for small hydropower plants (SHPs) in an ungauged basin, in which the ISI is used to weaken the seasonality of input data for the GM(1,1) model. The ISI is calculated by a hybrid model combining K-means clustering technique and ratio-to-moving-average method, which can adapt to different inflow scenarios. Based on the similar hydrological and meteorological conditions of large hydropower plants (LHPs) and SHPs in the same basin, a reference LHP is identified and its local inflow data, instead of the limited available data of SHPs, is used to calculate the ISI. Case study results for the Yangbi and Yingjiang counties in Yunnan Province, China are evaluated against observed data. Compared with the original GM(1,1) model, the GM(1,1) model combined with traditional seasonal index (TSI-GM(1,1)), and the linear regression model, the proposed ISI-GM(1,1) model gives the best performance, suggesting that it is a feasible way to forecast monthly energy production for SHPs in data-sparse areas.
Subjective Evaluation of Correlation of Melody Length and Melodic Similarity Based on a Music Theory

This paper studies to measure similarity of melodies based on Implication-Realization Model (IRM), a music theory that abstracts music and then expresses music through symbol sequences based on information constituting the music such as pitch, rhythm, and rests and so on. The model employed in this paper is an extended one that is much more appropriate to measuring similarity of melodies. Compared with the symbols of the original IRM, the extended model employs finer grained symbols by simply distinguishing up and down of interval directions and by dividing each most symbols of the original IRM into two extended symbols. Based on such a fundamental framework of measuring similarity of melodies by an extended IRM, the major focus of this paper is to claim that the similarity measure studied in this paper has a certain correlation with subjective human judgments on melodic similarities. Furthermore, this paper examines the correlation between melody length and subjective human judgments on melodic similarities. It is quite remarkable to note the result of this analysis: i.e., the smaller the melody length is, the more similar the pair of melodies is judged to be by human subjects, even though their similarities based on the extended IRM are measured to be almost in the same range. Thus, we conclude that it is necessary to invent a much finer-grained similarity measure which is designed to be more sensitive to melody length and to be coincide with the results of subjective human judgments. 
Code-switching in emergent grammars: Verb marking in bilingual children’s speech [Koodivahetus arenevas grammatikas: verbi markeerimine kakskeelsete laste kõnes]

Artiklis vaadeldakse kahe 3–7aastase eesti ja inglise keelt raakiva kakskeelse lapse tegusonade koodivahetust. Verbid osalevad tavaliselt nimisonadest harvemini koodivahetuses ja seda eeldatavasti mitme teguri tottu: keskne roll argumentstruktuuris, vahem spetsiiifiline semantika ning morfoloogiline komplekssus. Vaadeldavad andmed holmavad erinevaid morfoloogilisi votteid ning sisaldavad ka naiteid, mis raagivad vastu hupoteesile, et vaid infiniitsed verbid kannavad koodivahetuse puhul teise keele morfeeme. Need naited toetavad vaidet, et laste koodivahetus ei allu samadele piirangutele nagu taiskasvanute koodivahetus.
Design Options for the Assessment of Head Start Quality Enhancements: Final Report. Volume I.

Head Start, the largest federally funded preschool program, provides comprehensive services to economically disadvantaged children and their families so that children can enter kindergarten ready to succeed in school. Performance standards include requirements for the intensity and quality of a broad range of services for children and families. This report discusses design options for potential future evaluations of Head Start quality enhancements. It describes the goals and activities associated with each of the three stages of research through the use of specific examples of potential Head Start Quality enhancements.
Institutional Investor Distraction and Earnings Management

Abstract In this study, we explore the implications of institutional investor distraction for earnings management. Our identification approach relies on a firm-level measure of institutional investor distraction that exploits exogenous attention-grabbing shocks to unrelated parts of institutional investors' portfolios. We find that firms with distracted institutional shareholders engage more in both accrual-based and real earnings management. Further analyses show that the association between investor distraction and earnings management is stronger in firms with low analyst coverage and weak board monitoring, as well as in firms where managing earnings upward allows meeting or just beating their earnings target. Collectively, our results suggest that managers exploit the loosening in monitoring intensity resulting from investor distraction by engaging in earnings management. Even in the presence of institutional investors with superior monitoring abilities, limited attention may induce insufficient monitoring of earnings management practices.
Enhanced critical feature representation for fuzzy-matching for lithography hotspot detection

The detection of problematic low-fidelity lithographic design patterns in the physical verification phase is considered to be one of the greatest challenges and important techniques nowadays in the manufacturing process. There are lots of contributions to detect the lithographic hotspots, which mostly rely on using machine learning (ML) and/or pattern matching (PM) approaches. The fuzzy PM was presented due to the limitations in ML approach, where inappropriate layout feature representation impacts the number of false alarms, and the PM approach, which lacks prediction or identifying two similar patterns. However, the tricky step in fuzzy PM techniques is to define a similarity measurement between two patterns. This paper proposes a new feature representation for fuzzy matching for lithography hotspot detection. The technique enhances the modified-transitive-closure-graph (MTCG) by adding specific do-not-care (DC) regions to filter out unwanted polygons. This technique is capable of reaching 88% success rates, by 10% increase in success rates compared to the conventional MTCG, with no impact on total run-time. Building on the uniqueness of MTCGs for any tiled pattern, a new similaritydetection technique is also introduced that detects hotspots of similar shapes with acceptable defined tolerance. The proposed technique, besides MTCG, is able to reach 97.044% success rate with only 1.0287% increase in total run-time.