Miracle-dz/UniFAR-FTUs huggingface dataset:
Development Of Class Teacher's Pedagogic Competency In The Implementation Of Learning Guidelines In Extraordinary Elementary School (EES)

This research starts from the fact that learning nuanced guidance in Extraordinary Elementary Schools (EES) is still not much done so the purpose of this study is to find data about the competence of classroom teachers in implementing guidance nuanced learning in EES. This study uses a quantitative approach with descriptive methods with the number of respondents as many as 34 teachers. Areas that are indicators of the competence of classroom teachers in the implementation of instructional nuances in EES are developing learning activities that educate, and understand and develop the potential of students. The results showed that in developing learning activities that educate most are in the category of less competent, a small portion competent, very competent and the rest are not competent. The ability to understand and develop the potential of students shows that most are in the competent category and the rest are in the category of less and not competent. Based on the data obtained in the results of the study indicate that the competence of EES classroom teachers in developing learning activities that educate, and understand and develop the potential of students has not shown maximum competence so that they still need development assistance using various techniques, methods, strategies, and appropriate approaches so that it can have implications effectively in organizing learning nuanced guidance in EES.
Frisbee: A Suite for Benchmarking Systems Recovery

With failures being unavoidable, a system's ability to recover from failures quickly is a critical factor in the overall availability of the system. Although many systems exhibit self-healing properties, their behavior in the presence of failures is poorly understood. This is primarily due to the shortcomings of existing benchmarks, which cannot generate failures. For a more accurate systems evaluation, we argue that it is essential to create new suites that treat failures as first-class citizens. We present Frisbee, a benchmark suite and evaluation methodology for comparing the recovery behavior of highly available systems. Frisbee is built for the Kubernetes environment, leveraging several valuable tools in its stack, including Chaos tools for fault injection, Prometheus for distributed monitoring, and Grafana for visualization. We discuss a set of design requirements and present an initial prototype that makes faultloads as easy to run and characterize as traditional performance workloads. Furthermore, we define a core set of failure patterns against which systems can be compared.
The Price of Meltdown and Spectre: Energy Overhead of Mitigations at Operating System Level

The Meltdown and Spectre hardware vulnerabilities shocked hardware manufacturers and system users upon discovery. Numerous attack vectors and mitigations have been developed and deployed. However, due to the deep entanglement in core CPU components they will be an important topic for years. Although the performance overhead of software mitigations has been examined closely, the energy overhead has experienced little attention---even though the energy demand is a critical cost factor in data centres. This work contributes a fine-grained energy-overhead analysis of Meltdown/Spectre software mitigations, which reveals application-specific energy overheads of up to 72 %. We further compare energy overheads to execution time overheads. Additionally, we examine subsystem-specific effects (i.e., CPU, memory, I/O, network/interprocess communication) and develop a model that predicts energy overheads for applications.
TagVet: Vetting Malware Tags using Explainable Machine Learning

When managing large malware collections, it is common practice to use short tags for grouping and organizing samples. For example, collected malware is often tagged according to its origin, family, functionality, or clustering. While these simple tags are essential for keeping abreast of the rapid malware development, they can become disconnected from the actual behavior of the samples and, in the worst case, mislead the analyst. In particular, if tags are automatically assigned, it is often unclear whether they indeed align with the malware functionality. In this paper, we propose a method for vetting tags in malware collections. Our method builds on recent techniques of explainable machine learning, which enable us to automatically link tags to behavioral patterns observed during dynamic analysis. To this end, we train a neural network to classify different tags and trace back its decision to individual system calls and arguments. We empirically evaluate our method on tags for malware functionality, families, and clusterings. Our results demonstrate the utility of this approach and pinpoint interesting relations of malware tags in practice.
A Survey on the Verification of Adversarial Data Planes in Software-Defined Networks

As network policies are becoming increasingly nuanced and complex, so too are the mechanisms required to ensure that the network is functioning as intended. In particular, since the dawn of software-defined networking and the shift towards high-level descriptions of intended network policy, traditional tools such as ping and traceroute have been insufficient to test that complex data plane configurations have been correctly implemented. As a result, novel data plane verification solutions have been proposed that use formal methods to ensure that network policies are adhered to and that the data plane is free of bugs. While the number of these verification solutions continues to grow, only a few are equipped to verify the data plane when a malicious adversary is present. As research continues to expand the remit of data plane functionality, these solutions may become key to securing an increasingly valuable attack target. In this survey, we review the work that has been dedicated to preventing and detecting attacks on data planes in software-defined networks and discuss some of the unsolved problems in this field that must be addressed in future adversarial verification solutions.
The New Family's Level of Understanding In ADHD Children At Early Age In Surakarta

This study aims to describe the level of new family knowledge about Attention Deficit Hyperactivity Disorder (ADHD) in Surakarta. This study uses a descriptive quantitative research design and data collection using a questionnaire. The sampling technique used is purposive sampling by determining the criteria in new families to be selected as respondents and obtained as many as 68 respondents. The results of the study of 68 respondents found 37 people (54,4%) had less knowledge about ADHD, on the early detection indicator 9 people (13,2%) had good knowledge, on the early intervention indicator 55 people (80,9%) had knowledge less and in the attittude indicator 38 people (55,9%) have less knowledge. Conclusions that can be drawn that the level of new family knowledge about ADHD is still lacking, it is hoped that the results of this study can be used as input to increase efforts in the form of health education for new families who have early childhood about ADHD so that the negative effects of ADHD behavior can be detected and addressed earlier.
The Development of Massive Open Online Course (MOOC) Based on ADDIE Model for Basic Natural Sciences Course in College

Distance learning is not limited to space and time so that the goal of independent learning can be achieved. Supporting platforms for distance learning in Indonesia are still lacking, especially for basic natural sciences. The aims of this research is to develop interactive learning for basic natural science using massive open online course (MOOC). The sampling method used is the purposive sampling and the instrument used in the form of the questionnaire. Data collection were collected and analyzed descriptively using Statistical Packages for Social Science (SPSS) 26.0 version. Based on the analysis, the reliability value of the instrument is 0.994 and shows that the reliability of the instrument is at a high level. The findings show that the use of MOOC can increase achievement (3.65), interest (3.74) and student learning styles (3.65) and make the learning process more interesting. In addition, the findings show that the use of the MOOC application can help students in improving the performance and achievement of students in learning several courses and thus can be an alternative to diversifying the teaching and learning process in college.
Crystal structure of 3,3′,3″-((1E,1′E,1″E)-((nitrilotris(ethane-2,1-diyl))tris(azaneylylidene)) tris(methaneylylidene))tris(4-hydroxy-1-naphthaldehyde) monohydrate, C42H36N4O6·H2O

Abstract C42H38N4O7, triclinic, P1‾$P\bar{1}$ (no. 2), a = 11.8563(6) Å, b = 12.6263(6) Å, c = 13.4492(6) Å, α = 113.563(2)°, β = 96.740(2)°, γ = 104.429(2)°, V = 1733.07(15) Å3, Z = 2, Rgt(F) = 0.0470, wRref(F2) = 0.1248, T = 301.72 K.
EFFECT OF LINEAR GROWTH RATE AND CHANGE IN BODY MASS INDEX IN CHILDHOOD AND ADOLESCENCE ON BLOOD PRESSURE IN AFRO-CARIBBEAN YOUTH: THE VULNERABLE WINDOWS COHORT STUDY

Background: Faster growth velocity during childhood may increase blood pressure (BP) in adults, but there are little data from African-origin populations. We evaluated the effect of postnatal linear growth (increase in height) and change in body mass index (BMI) from birth to adolescence on systolic and diastolic blood pressure (SBP and DBP) in Afro-Caribbean youth. Methods: We used data from the Vulnerable Windows Birth Cohort Study in Jamaica. Children were followed from birth, with periodic anthropometric measurements. BP measurements started at age 1-year and every six months thereafter. Analyses used BP measurements (mmHg) from age 15-21 years. Linear growth and change in BMI measurements were calculated for: early infancy (0-6 months), late infancy (6 months - 2 years), early childhood (2-8 years), and later childhood (8-15 years). Conditional analyses were used to compute growth rates (as z-scores). Linear mixed models were used to estimate the effect of growth rates on BP. Results: Analyses included 365 individuals (162 males, 203 females) with mean age 16.7 years. In multivariable models, after adjustment for age, sex, birth length, gestational age and BMI at age 15 years, faster linear growth for early infancy ({beta}=1.06, p=0.010) was significantly associated with higher SBP. For change in BMI, after adjustment for age, sex, gestational age, height and SES at birth, significant associations of higher SBP were seen for greater increase in BMI in late infancy ({beta}=1.41, p=0.001), early childhood ({beta}=1.22, p=0.001) and later childhood ({beta}=0.74, p=0.035). Faster post-natal linear growth had no significant associations with DBP, but greater increase in BMI for each of the late infancy to late childhood periods was significantly associated with higher DBP. When both growth rates were modeled together, rate of change of BMI and faster linear growth in early infancy retained significance for SBP, but only change in BMI retained significance for DBP. Conclusion: Faster linear growth and greater rate of increase in BMI were associated with higher SBP and DBP in Afro-Caribbean youth, but the BMI effect was stronger.
A DEEP LEARNING APPROACH FOR SEMANTIC SEGMENTATION IN BRAIN TUMOR IMAGES

: In medical technology, Magnetic Resonance Imaging (MRI) has been used widely for detection of tumors and diagnosing various abnormalities in tissues. In scientific research, a major role has been played by the active development in the computerized segmentation of medical image. Based on fast decision making, the doctors can take required treatment easily. In the information technology, the segmentation of brain tumor is a key point. By analysing the radiation therapy treatment, tumor growth, computer-based surgery, developing the growth models of tumor, and treatment responses, the segmentation of brain tumor is motivated. For segmentation of brain tumor, a deep learning-based framework is presented. To achieve the robust performance through a majority rule, deep learning based semantic segmentation architecture is used for segmentation of tumor that can improve the performance effectively.
Fatty Acid Composition of Grape Seed Oil as Affected by Grape Variety and Extraction Solvent

Grape seeds are valuable by-product of grapes from wine industry. In addition to health promoting polyphenols, grape seeds contain significant amount of oil. In this study, grape seed oils (GSOs) were extracted from the seeds of two Muscadine cultivars and two Cabernet cultivars using hexane and chloroform-methanol. The oil yields and total polyphenol contents of the crude oil were quantified. The fatty acid composition of each GSO sample was determined by GC. Results show that Muscadine Nobel seeds had lowest total oil yield regardless the extraction solvents. Major fatty acids in GSOs were linoleic (C18:2), oleic (C18:1), palmitic (C16:0) and stearic acids (C18:0). Hexane extracted oils showed higher C16:0, C18:0, C18:2, but lower C18:1 contents than chloroform-methanol extracted oils. Total unsaturated fatty acid contents were 83.34 85.93 for hexane extracted oil 85.71 88.84% for chloroform-methanol extracted oils, respectively, depending on the grape cultivars. Linoleic acid dominated the unsaturated fatty acids in all samples. The high unsaturated fatty acid content makes GSO a good choice for food products that need to be in the liquid form at temperature. Total polyphenol contents of GSO also varied with extraction solvents and is cultivar dependent. The results indicate that fatty composition of different grape seed varies only slightly, but is influenced significantly by extraction solvent due to the polarity difference of different fatty acid. Therefore, when comparing fatty acid composition and antioxidant content of oils from different materials, it is important to make sure that all oil samples are extracted by same solvent and under same extraction condition.
Assessment of heavy metal levels in blood of metal fabricating factory workers in Nnewi, Nigeria

Introduction of Study: Heavy metal contamination or toxicity poses serious deleterious effects to human health. Materials and Methods: This is a cross sectional study designed to assess the heavy metal levels in blood of metal fabricating factory workers in Nnewi. A total of 39 apparently healthy individuals in the exposed group (metal fabricating factory workers) aged between 19 and 56 years and 79 control individuals (comprising of 39 control individuals from Nnewi (N) and 40 control individuals from Elele (E) respectively) aged between 18 and 44 years were recruited for the study. Demographic data and anthropometric indices (BMI) of participating individuals were obtained using structured questionnaire and thereafter, 10ml of venous blood sample was collected from each individual for the evaluation of heavy metal levels (Pb, Ni, Cu, Zn, As and Se) using atomic absorption spectroscopy (AAS). Results: This showed that these workers had an average length of service (LOS) of 11.92±2.77 years with no significant difference in body mass index (BMI) of factory workers compared with control N and E individuals (p>0.05) respectively. Ni, As and Pb levels and Cu, Zn and Se levels were significantly elevated and reduced respectively in factory workers than in controls (p<0.05). Metal levels differed significantly with both age and length of service (LOS) in exposed worker compared with control individuals (p<0.05). However, no significant statistical correlations were found between metal levels in factory workers and age as well as LOS (p>0.05). Conclusion: This study showed elevated levels of Ni, As and Pb and decreased levels of Cu, Zn and Se in the blood of metal fabricating factory workers and this demands urgent attention.
LC-ESI-QTOF-MS/MS characterization of phenolic compounds from Prosopis farcta (Banks & Sol.) J.F.Macbr. and their potential antioxidant activities.

Prosopis farcta (Banks & Sol.) J.F. Macbr. is an emerging medicinal plant containing a diverse array of phytochemicals, including protein, fat, carbohydrate, fibre, alkaloids, fatty acids, glycosides, and polyphenols, with strong antioxidant potential. However, the screening and characterization of phenolic compounds in P. farcta is limited. This study is conducted to determine the polyphenol contents and their antioxidant activity in P. farcta leaves samples via liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry (LC-ESI-QTOF-MS/MS) and high-performance liquid chromatography-photodiode array (HPLC-PDA). Total phenolic content (TPC), total flavonoid content (TFC), and total tannins content (TTC) were determined for polyphenol estimation. The antioxidant properties were measured by total antioxidant capacity (TAC), 2,2'-Diphenyl-2-picrylhydrazyl (DPPH), Ferric Reducing Antioxidant Power (FRAP), and 2,2"²-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS). LC-ESI-QTOF-MS/MS was used to identify and characterize 47 phenolic compounds, which mainly included phenolic acids (13), flavonoids (28), other polyphenols (4), lignans (1), and stilbenes (1). According to HPLC-PDA quantification, chlorogenic acid (9.78 ± 2.15 mg/g dw) was the most abundant phenolic acid, while the main flavonoids included catechin (12.73 ± 1.29 mg/g dw) and kaempferol (7.93 ± 1.47 mg/g dw). The study demonstrated the significance of P. farcta as a rich source of phenolic compounds with antioxidant capacity that can be widely used in food, beverage, feed, and pharmaceutical applications.
Students’ Perception and Expectation towards Pharmacy Education: A Qualitative Study of Pharmacy Students in a Developing Country

Background: Pharmacy education is a vital element sustaining the supply of pharmacists to meet the needs in the social and health care sectors. Understanding the perception and expectation of pharmacy students towards their study is pivotal to the maintenance of public health services in a country. Objectives: This study uses China as a sample of developing countries and seeks implications for the future development of pharmacy education. Methods: A total of 20 undergraduate students majoring in pharmacy at a university in the Guangdong province participated in in-depth individual interviews. Content analysis was adopted. Results: Four main themes were identified: intention of pursuing pharmacy education, need satisfaction by pharmacy education, implementation of pharmacy education and passion for a pharmacy practice. Conclusion: The poor motivation of pharmacy students and the low social status of pharmacists are some of the factors that may need to be addressed so as to enhance pharmacy education in developing countries in which the orientation of pharmacy education as well as the social status of pharmacists are similar to that in China.
Evaluation of an optimized bush fire propagation model with large-scale fire experiments

Thirty-one bush fire experiments are used to assess the predictive capability of a bush fire spread model. This model has been optimized by a deterministic method of parameter calibration. The experiments used were carried out each year from 2014 to 2017 in a forest–savannah transition zone. The characteristics of the herbaceous stratum as well as the meteorological and topographical data are well documented. The characteristics of the fire have also been measured to understand the behaviour of the fire in a Guinean savannah. The predicted rate of fire spread and fire contours gave results in good accordance with those of the experiments. Résumé. Trente et une (31) expériences de feu de brousse ont été utilisées pour évaluer la capacité prédictive d’un modèle de propagation de feux de brousse. Ce modèle a été optimisé par une méthode déterministe de calibrage des paramètres. Les expériences utilisées ont été réalisées chaque année de 2014 à 2017 dans une zone de transition forêt savane. Les caractéristiques de la strate herbacée ainsi que les données météorologiques et topographiques ont été bien documentées. Les caractéristiques du feu ont été également mesurées pour comprendre le comportement du feu dans une savane Guinéenne. Les vitesses de propagation et les contours de feux prédits ont donné des résultats en bon accord avec ceux des expériences.
A Study on the Desired Target Signal Estimation using MUSIC and LCMV Beamforming Algorithm in Wireless Coherent Channel

In this paper, we studied to direction of arrival (DoA) estimation to use DoA and optimum weight algorithms in coherent interference channels. The DoA algorithm have been considerable attention in signal processing with coherent signals and a limited number of snapshots in a noise and an interference environment. This paper is a proposed method for the desired signal estimation using MUSIC algorithm and adaptive beamforming to compare classical subspace techniques. Also, the proposed method is combined the updated weight value with LCMV beamforming algorithm in adaptive antenna array system for direction of arrival estimation of desired signal. The proposed algorithm can be used with combination to MUSIC algorithm, linearly constrained minimum variance beamforming (LCMV) and the weight value method to accurately desired signal estimation. Through simulation, we compare the proposed method with classical direction of in order to desired signals estimation. We show that the propose method has achieved good resolution performance better that classical direction arrival estimation algorithm. The simulation results show the effectiveness of the proposed method.
Efﬁcient Web-Based Parallel Sparse Matrix-Vector Multiplication

Sparse Matrix-Vector Multiplication (SpMV) is a computational kernel of a wide range of scientific computations, including popular targets like machine learning. Web-based implementations simplify access to such computations, but are limited by the performance of web-based programming languages. In this work we describe a scalable and highly optimized parallel SpMV implementation based on WebAssembly. Our design builds on a stack of optimizations, exploiting different sparse storage formats as well as novel matrix properties and machine-level performance characteristics. We perform exhaustive experiments with 2000 real-life sparse matrices to evaluate the effect of our optimizations and performance relative to a static, C baseline. Using our stack of optimizations, we achieve similar performance to the Intel MKL C library, showing that a web-based design can offer performance competitive with even highly tuned and well established native implementations.
Factors Associated with the Work-Related Burnout of Residential Employees: An Examination of Perfectionism and Coping

Employees in residential facilities face emotionally stressful situations with youth who often become physically aggressive. Exposure to physical aggression and a stressful work environment can lead to burnout in employees who work in youth residential facilities. Previous research suggests that perfectionism and coping styles are related to burnout. However, there is a gap in the literature related to the relationships between perfectionism, coping, and burnout in residential employees. Therefore, this study sought to explore the relationships among adaptive perfectionism, avoidant coping styles, and work-related burnout in (n = 69) residential employees. The results indicated that both avoidant coping styles and adaptive perfectionism were significant predictors of work-related burnout. Implications of the study’s findings are discussed for human services employees who work in residential settings.