Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of European integration processes on the investment potential and institutional maturity of rural communities

The purpose of the study is to assess the impact of European integration processes in Ukraine on the development of investment potential and institutional maturity of rural communities. As a result of the analysis of strategic documents of all rural communities of Ivano-Frankivsk, Lviv and Rivne regions, a low level of institutional capacity to attract investments was established. Based on the analysis of the strategies, programs, and investment passports of 79 united territorial communities, it is found that only 39.2% of them have approved development strategies, and 45% of communities have socio-economic development plans. To assess the challenges and threats arising during the process of implementation of the EU-Ukraine
Effects of Nutrient Management Scenarios on Marine Food Webs: A Pan-European Assessment in Support of the Marine Strategy Framework Directive

Eutrophication is one of the most important anthropogenic pressures impacting coastal seas. In Europe, several legislations and management measures have been implemented to halt nutrient overloading in marine ecosystems. This study evaluates the impact of freshwater nutrient control measures on higher trophic levels (HTL) in European marine ecosystems following descriptors and criteria as defined by the Marine Strategy Framework Directive (MSFD). We used a novel pan-European marine modeling ensemble of fourteen HTL models, covering almost all the EU seas, under two nutrient management scenarios. Results from our projections suggest that the proposed nutrient reduction measures may not have a significant impact on the structure and function of European marine ecosystems. Among the assessed criteria, the spawning stock biomass of commercially important fish stocks and the biomass of small pelagic fishes would be the most impacted, albeit with values lower than 2.5%. For the other criteria/indicators, such as species diversity and trophic level indicators, the impact was lower. The Black Sea and the North-East Atlantic were the most negatively impacted regions, while the Baltic Sea was the only region showing signs of improvement. Coastal and shelf areas were more sensitive to environmental changes than large regional and sub-regional ecosystems that also include open seas. This is the first pan-European multi-model comparison study used to assess the impacts of land-based measures on marine and coastal European ecosystems through a set of selected ecological indicators. Since anthropogenic pressures are expanding apace in the marine environment and policy makers need to use rapid and effective policy measures for fast-changing environments, this modeling framework is an essential asset in supporting and guiding EU policy needs and decisions.
ANALYTICAL METHOD FOR DETERMINING ANTROPOMETRIC PARAMETERS IN BODY MASS REDUCTION PROCESS IN THE ASPECT OF HEALTH SAFETY

In this study, for the first time we developed a mathematical model showing that the reduction in parameters such as total body mass, total fat mass, visceral fat mass, waist circumference, hip circumference and body mass index (BMI) can be described using exponential equations. Constants kt, kf, kfv, kBMI, kp, kb that occur in particular equations characterize individual features related to these anthropometric parameters. The presented model was verified using experimental data obtained by analysis of body composition. These individuals were overweight in the initial moment of the experiment and eliminated or limited easily digestible products containing sugar from their diet. We showed that there is a critical time, when each individual achieved normal values of body mass index, fat mass, visceral fat mass, waist circumference, hip circumference, and waist-hip ratio. This critical time ranged between 35 and 46 weeks.
Issues in Information Systems

The paper presents a specific approach to designing the curriculum and syllabus of a newly formed course for study programmes in information technology, protection and other fields. The topic is risk assessment that is introduced in the course and which is a common element in various fields. The content creation approach is the result of the strong relationship and cooperation between the higher education institution and the overall industrial and nonindustrial sectors of the region.
Gender Inequality, Economic Performance & Human Development in India: An Application of Bound Test Approach to Co- Integration

This paper attempts to analyze short term and long term effects of gender inequality on economic growth and human development of India using annual time series data for the period of 2000-2017. The study employed Auto-Regressive Distributed Lag (ARDL) model with GDP per Capita and Human Development Index (HDI) as dependent variable and composite Gender Inequality Index, investment and trade openness as independent variables. The empirical results show that the gender inequality has significant and negative impact on economic growth and human well-being of India in the short run as well as long run. Thus gender equality is not merely an issue of social relevance but also an economic necessity.
National Grid Connected 3-Phase Inverter based on Photovoltaic Solar System

In this paper, a national grid-connected photovoltaic (PV) system is proposed. It extracts the maximum power point (MPP) using three-incremental-steps perturb and observe (TISP&O) maximum power point tracking (MPPT) method. It improves the classic P&O by using three incremental duty ratio (ΔD) instead of a single one in the conventional P and O MPPT method. Therefore, the system's performance is improved to a higher speed and less power fluctuation around the MPP. The Boost converter controls the MPPT and then is connected to a three-phase voltage source inverter (VSI). This type of inverter needs a high and constant input voltage. A second-order low pass (LC) filter is connected to the output of VSI to reduce the total harmonic distortion (THD) of the output current. The LC filter is then connected to a step-up transformer to push up the low VSI output voltage to the high grid voltage level. The control strategy is based on the rotating reference frame (dq reference frame) and the grid phase shift angle extracted using a phase-locked loop (PLL) technique. The designed PV system supplies only active power with zero reactive power to the utility grid. The system is simulated using MATLAB / Simulink software.
ANOMALY DETECTION IN SYSTEM LOG FILES USING MACHINE LEARNING ALGORITHMS ZAHEDEH ZAMANIAN FACULTY OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY UNIVERSITY OF MALAYA

In recent years due to rapid growth of information technology and easy access to computers, digital devices and internet, security management and investigating malicious activity have been main concern of organization and governments. People who are greatest asset of organization, they may also be the greatest threat due to their access to highly confidential information and their knowledge of the organizational systems. Insider threat activity has huge impact on business. Therefore, there is a need for methods to detect insider threats inside an organization. Log files are great source of information which can help to detect, understand and predict these kinds of threats. However, the sheer size of log files generated by systems makes human log analysis impractical. Moreover, log files have a lot of irrelevant and redundant features that act as noise. Also, log files are heterogenous and cannot fed them directly in machine learning algorithms. Furthermore, many of the companies use the signature-based detection method which is not capable of capturing more advanced attackers that use unfamiliar attacks methods. This study uses machine learning method to detect anomalies in system log files. This study uses synthetic CERT Insider Threat v6.2 dataset that includes five different domains of file, logon/logoff, http, device and email. This study generates 200 features from raw system log files that can be fed to machine learning. This study uses principal component analysis (PCA) as a feature extraction method to extract 117 independent and discriminative features with 95% of variance. This study applies unsupervised Isolation Forest and One Class SVM as ML algorithms to detect anomalies. Isolation Forest area under curve (AUC) successfully achieved 96.6% with applying PCA Un ive rsi ty of Ma lay
Meta-Algorithms untuk Meningkatkan Kinerja Klasifikasi dalam Keberhasilan Telemarketing Perbankan Meta-Algorithms for Improving Classification Performance in the Success of Banking

Many banks and insurance companies interact with their customers using telemarketing services in order to further increase bank revenue and achieve targets. Tight competition in the banking industry makes the bank's sales force pursue the target especially towards the end of the year. The perpetrators of telemarketing or commonly called telemarketers in banking are tasked with contacting potential customers which are done regularly through telephone lines. However, not all customers get positive responses, often telemarketing meets antipathy from customers before the communication process is carried out. The proposed approach for finding potential customers is by way of classifying customer data using machine learning. The use of a single learning algorithm allows to be developed using meta-algorithms so that it will maximize the performance of classifications in predicting target customers or customers in accordance with company needs so that marketing objectives are achieved. Based on the research results obtained, pre-processing actions and the use of meta-algorithms such as bagging make a good contribution to the learning process of the decision tree model on telemarketing data with the best accuracy results of 98.7%.
© 2020 Peilun Zhang AUTOMATED FIXING OF WRONG ASSUMPTIONS ON UNDERDETERMINED SPECIFICATIONS BY

Software specifications assist in the implementation of software by stating expected software behavior. Many software specifications are deterministic, i.e., the software produces the same output for the same input. However, some specifications are underdetermined, meaning that the software may produce different outputs given the same input. Underdetermined specifications are not uncommon because they can offer some advantages over deterministic ones. For instance, underdetermined specifications can allow for optimization as developers can be more flexible when conducting speed optimization. We encounter potential problems when deterministic implementations produce different outputs. For example, even though the Java standard library does not specify the order of elements returned by method getDeclaredFields, prevailing implementations, like Oracle JDK 1.8, return fields in the order as they are listed in the class file (which is itself the order in which they are in the source file when using the standard javac compiler). There is no reason to expect the implementation will not change because the specifications allow flexibility, and popular vendors have historically changed the implementations of several widely used library methods. Unfortunately, software library users may write code that relies on a specific implementation rather than on the specification, e.g., assuming mistakenly that the order of elements cannot change in the future. If users write software tests that involve methods with underdetermined specifications, those tests can therefore produce unexpectedly non-deterministic outputs, meaning that tests can intermittently fail or pass without changing the production and test code (but changing the library code). Prior work proposed the NonDex approach to proactively detect such wrong assumptions in the production and test code. The goal of this thesis is to propose automated code changes that help resolve these issues by either making the output deterministic or making the test assertion order-agnostic. We present a novel approach, called DexFix, to fix wrong assumptions on underdetermined software specifications in an automated way. To demonstrate these efforts, we run the NonDex tool on 200 open-source Java projects and detect 275 tests that fail due to wrong assumptions. We find that the majority of failures are based on HashMap/HashSet class iterations and the getDeclaredFields method. We provide several new automated fix strategies that can fix these violations in both the production and test code, which are implemented in the DexFix tool. Our experiments show that DexFix proposes fixes for 101 tests from our 275 tests. We have reported fixes for 84 tests to the developers as GitHub pull requests: 57 have been merged, with only 2 rejected, and the remaining are pending.
WAV: Voice Access to Web Information for Masses

– One of the main reasons for a large section of the world population to be left out of the internet revolution is, limited or no access to a computer due to economic, educational, cultural and age factors. Enabling masses to extract information from the web via voice will bring the Internet revolution to additional billions of people. In this paper, we describe a system called WAV (Web Access via Voice), that is a step in this direction. Departing from the traditional approaches of manually building a VoiceXML based site, the WAV system uses information from existing web sites to serve the user. Challenges to overcome include extracting contextually relevantinformation from the user and also from the pages returned by websites, reducing amount of information relayed to user over phone and maintaining the context of the conversation for easy renement based on feedback from the user. Our prototype system not only shows successful integration of many dierent technologies such as automatic speech recognition, scripts for web navigation, text to speech conversion, but also introduces a novel way of extracting information from web via voice in a programmatic manner. We describe initial solutions developed to tackle above challenges and demonstrate the feasibility of the system by describing proto type implementations on two popular web sites in India.
Information-based stochastic volatility model using the extended Kalman filter

This study explores the use of non-linear filtering in an information-based stochastic volatility model. The stochastic differential equations in the model incorporate information which is the sum of true information about the future cash flows and noise which distorts the true information. It is assumed that there exists a market information process consisting of a combination of these two sources of information. The volatility process in the model is non-linear and gaussian which makes the use of the extended Kalman filter approach ideal. The study first obtains a state space model and the extended Kalman filter is then used to model volatility based on it. The modelled volatility is used to determine the extent to which the price of the information-based stochastic volatility model is affected by fluctuations in volatility. Using simulated data, the study finds that there’s a general increase in volatility as the price obtained from the model increases.
Deep Learning for Text Style Transfer: A Survey

Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. It has a long history in the field of natural language processing, and recently has re-gained significant attention thanks to the promising performance brought by deep neural models. In this article, we present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017. We discuss the task formulation, existing datasets and subtasks, evaluation, as well as the rich methodologies in the presence of parallel and non-parallel data. We also provide discussions on a variety of important topics regarding the future development of this task.1
Delimitation of tribes in the subfamily Leptanillinae (Hymenoptera: Formicidae), with a description of the male of Protanilla lini Terayama, 2009

The subfamily Leptanillinae Emery, 1910 (Hymeno ptera: Formicidae) is a clade of cryptic subterranean ants, which is restricted to the tropics and warm temperate regions of the Old World. Due to acquisition bias against the minute and hypogaeic workers, most known leptanilline specimens are male, with four genera described solely from males. The sexes have been associated in only two out of 69 described species, meaning that redundant naming of taxa is likely. Herein, the male of Protanilla lini Terayama, 2009 is associated with corresponding workers collected on Okinawa-jima, Japan, by means of genome-scale data, allowing the first published description of male ants belonging to the Anomalomyrmini Taylor, 1990, one of the two established tribes within the Leptanillinae. The first male-based diagnoses of these tribes are provided, based on a phylogeny of the Leptanillinae inferred from ultra-conserved elements using maximum-likelihood and Bayesian inference, along with a dichotomous key to all described male-based species within the Leptanillinae and to undescribed male morphospecies sequenced in this study. With molecular data enabling the association of separately collected sexes and phylogenomic inference contextualizing morphological observations, the parallel taxonomy that afflicts this enigmatic group of ants can begin to be resolved.
Virtual Tools Aid in Transportation: Usability Test of an Application for Cyclists from UFMA

The chaotic panorama of large cities regarding locomotion is well known and one of the major infrastructure problems in Brazil. The exacerbated appreciation of automobiles ends up overloading cities' road network and, together with a deficient public transport system, entails long hours of traffic jams, stress, loss of quality of life and loss to the economy resulting from the sacrificed production cost, which is the impact of long journeys from home to work, exceeding R$ 111 billion in all Brazilian territory (FIRJAN, 2015a).
A Feasible Operative Treatment Strategy for Trauma Patient with Difficulties in Closing the Abdomen during Open Abdomen Management (OAM) Following Damage Control Surgery-Secondary Publication

Damage control surgery (DCS) is standard treatment strategy for severe trauma cases accompanied with deadly triad (bloody vicious cycles) improving survival rate. Appropriate implement of DCS has decreased the mortality, however, serious complications such as ventral hernia (VH), enteroatmospheric fistula (EAF), the cases which is unable to close the abdominal wall have been increased in proportion to increase of abbreviated surgery and open abdomen management (OAM) [1]. Damage control resuscitation (DCR) has reduced the number of cases requiring DCS and mortality, and improved the rate of fascial closure, but there are still some cases which unable to close the abdominal wall closure despite appropriate DCR [2]. In this article, we report 1) temporary abdominal closure method following DCS, 2) key points of OAM for definitive abdominal closure following DCS, 3)various definitive abdominal closure methods for difficult cases of abdominal closure following DCS, 4) surgical treatment guideline in our trauma center for definitive abdominal closure in the OAM following DCS.
Materialization Optimizations for Feature Selection Workloads

There is an arms race in the data management industry to support statistical analytics. Feature selection, the process of selecting a feature set that will be used to build a statistical model, is widely regarded as the most critical step of statistical analytics. Thus, we argue that managing the feature selection process is a pressing data management challenge. We study this challenge by describing a feature selection language and a supporting prototype system that builds on top of current industrial R-integration layers. From our interactions with analysts, we learned that feature selection is an interactive human-in-the-loop process, which means that feature selection workloads are rife with reuse opportunities. Thus, we study how to materialize portions of this computation using not only classical database materialization optimizations but also methods that have not previously been used in database optimization, including structural decomposition methods (like QR factorization) and warmstart. These new methods have no analogue in traditional SQL systems, but they may be interesting for array and scientific database applications. On a diverse set of datasets and programs, we find that traditional database-style approaches that ignore these new opportunities are more than two orders of magnitude slower than an optimal plan in this new trade-off space across multiple R backends. Furthermore, we show that it is possible to build a simple cost-based optimizer to automatically select a near-optimal execution plan for feature selection.
University of Southern Denmark Evans syndrome in children below 13 years of age-A nationwide population-based cohort

Evans syndrome is defined by autoimmune haemolytic anaemia and immune thrombocytopenia occurring in the same patient. Although known to be rare the frequency and prognosis of Evans syndrome in children is unknown, and only few registry-based studies are available. The epidemiology and prognosis of Evans syndrome in patients above 13 years of age has recently been investigated. In this age group both incidence and prevalence of Evans syndrome increased during the study period and median survival was just 7.2 years. Using Danish health registries and the same approach, we identified 21 children below 13 years of age with Evans syndrome during 1981–2015. Patients with Evans syndrome were age–and sex matched with children both from the general population, and with patients with either autoimmune haemolytic anaemia or immune thrombocytopenia. The incidence of Evans syndrome ranged between 0.5 and 1.2/1,000,000 person-years. Prevalence was 6.7 and 19.3/1,000,000 in 1990 and 2015 respectively. Hazard ratio for death was 22 fold higher for children with ES compared to matched children from general population, and was also elevated compared to children with autoimmune haemolytic anaemia or immune thrombocytopenia. We conclude that pediatric ES is very rare and associated with elevated mortality. However, despite the nationwide study and a long and complete follow-up, results are imprecise due to the rarity of this disorder.
Elliptic curves over $\mathbb{F}_p$ and determinants of Legendre matrices

Abstract. Determinants with Legendre symbol entries have close relations with character sums and elliptic curves over finite fields. In recent years, Sun [16], Krachun and his cooperators [11] studied this topic. In this paper, we confirm some conjectures posed by Sun and investigate some related topics. For instance, given any integers c, d with d 6= 0 and c − 4d 6= 0, we show that there are infinitely many odd primes p such that
Epsom Salt Hot Water and Plain Hot Water Application: Reduce Knee Joint Pain Updated Review

Epsom salt is a pure, time tested mineral compound with many uses ranging from creating at-home spas to soothing achy muscles and reduces pain. [3] • Epsom salt compress will be prepared by adding 30 g of Epsom salts to 1 L of boiling water (the temperature of the boiling water is 125°F as tolerated by the client) creating a hot compress by dipping a clean washcloth in the boiling water, wringing it out, applying for 20 min over the joint in which pain will present, twice a day for 10 days. [4]