Miracle-dz/UniFAR-FTUs huggingface dataset:
An Examination of Standardized Language, Mathematics, and Reading Achievement Testing Results when Compared for Middle School Aged Student Leaders and Non-Student Leaders

The evolution of the middle school concept, according to Forte and Schurr (2003), with its own identity and its subsequent implementation, has proven to be an ideal or preferred model for educating students. Knowles and Brown (2000) assert the importance of middle level teachers’ ability to nurture their students’ academic, emotional, personal, and social development during the often “chaotic” years of transescence and early adolescence. This study examined and explored sixty-four randomly selected seventh grade middle school students’ earned scores on their annual standardized achievement testing, particularly the Terra Nova II Multiple Assessments subtests in the areas of Language, Mathematics, and Reading. The investigator wanted to determine if demonstrating and applying leadership abilities, competencies, qualities, skills, and traits positively influenced the seventh grade middle school students’ earned achievement? Additionally, regardless of the seventh grade middle school students’ intelligence quotient (IQ), did their demonstrating leadership abilities, competencies, qualities, skills, and traits positively influence their earned scores on the annual standardized achievement testing, particularly the Terra Nova II Multiple Assessments subtests in the areas of Language, Mathematics, and Reading? The statistical analyses included an analysis of variance (ANOVA), specifically a T test; in addition, an F test was subsequently calculated to determine any significant interactions.
The Virtual Fields Method for Extracting Constitutive Parameters From Full‐Field Measurements: a Review

ABSTRACT: The virtual fields method has been developed for extracting constitutive parameters from full‐field measurements provided by optical non‐contact techniques for instance. It is based on the principle of virtual work written with some particular virtual fields. This paper can be regarded as a general review summarising some 15 years of developments of this method. The main aspects of the method are first recalled in the case of both linear and non‐linear constitutive equations. They are then illustrated by some recent relevant examples. Some studies underway as well as relevant issues to be addressed in the near future are eventually discussed.
The effectiveness of emotional schema therapy on emotional schemas of female victims of child abuse and neglect

Introduction: Development of emotional schemas is regarded as one of the long term effects of child abuse and neglect. Therefore, the present study aimed to investigate the effect of Emotional Schema Therapy on emotional schemas of female victims of child abuse and neglect. Methods: In this quasi-experimental design, 30 women, who had the history of child abuse and neglect (according to the scores obtained in the child abuse and neglect questionnaire) and met entry criteria, were selected and then assigned into the experimental and control groups randomly (n=15). The experimental group received a 13-session intervention based on Emotional Schema Therapy and the control group was on a waiting list. The applied instruments consisted of child abuse and neglect questionnaire, Leahy Emotional Schema Scale (LESS) and Depression Anxiety Stress Scales (DASS-21). Moreover, the study data were analyzed by the analysis of covariance. Results: The study results demonstrated that Emotional Schema Therapy has decreased the emotional schemas of “Guilt” and “Simplistic View of Emotions” and increased “Acceptance of Emotions” in both posttest and follow-up stages. This effectiveness was confirmed via increase of emotional schema of “Consensus” in posttest stage and “ Emotional Validation and Comprehensibility” in the follow-up stage. Conclusion: The study findings proposed that Emotional Schema Therapy can be regarded as an effective intervention in order to modify some dimensions of emotional schemas in women with child abuse and neglect history.
The Performance of Ross 308 and Arbor Acres Broiler Breeder and their Commercial Broiler in Iraq

The broiler industry in Iraq depends on importing or producing hatching eggs by using many commercial broiler breeder, such as Hubbard, Lohmann, Ross 308 and Arbor Acres, to supply hatcheries within the broiler industry in Iraq. The aim of this study is evaluating the performance of the female line of Ross 308 and Arbor Acres broiler breeder, and the performance of its broiler progeny. The chicks of the two genetic groups were reared up from hatch to 35 days of age, and the process was replicated for three hatches. The results showed a significant difference in egg weight between Ross 308 and Arbor Acres female lines (77.68 and 69.62 g. respectively) and a significant difference in albumin weight and shell thickness, while there are no significant differences between the genetic groups in body weight, egg production and number of eggs per hen. There is a significant superiority of Ross 308 compared with Arbor Acres in protein and triglyceride concentrations in blood plasma. There are no significant differences between Ross 308 and Arbor Acres broiler in all growth traits. Keywords : Broiler performance, Hatching eggs, Ross 308, Arbor Acres.
Studies upon genetic and environmental factors affecting the cholesterol content of cow milk. II. Effect of silage type offered

The aim of the study was to determine the effect of the type of silage (wilted grass vs. whole maize plants) offered to high-yielding dairy cows on cholesterol content of their milk. Silage type did not affect the cholesterol level as expressed either in mg/100 ml milk or as mg/g milk fat. However, the significant relationships were identified between the cholesterol content of milk and stage of lactation, milk somatic cell count, daily milk yield, fat content of milk and the amount of fat yielded daily.
Laboratory experiments on mesoscale vortices colliding with an island chain

[1] The present laboratory study investigates the behavior of a self-propagating barotropic cyclonic vortex colliding perpendicularly with aligned circular cylinders and determines the condition for a vortex to bifurcate and split into multiple vortices and/or to generate dipoles downstream of the cylinders. During the experiments, four parameters were varied: G, the gap width between the cylinders; d, the diameter of the incident vortex; Ydis, a parameter expressing the initial vortex positions; and Disl, the total length of the “middle” island. It has been observed that as long as 0.1 < G/d ≤ 0.4, the vortex fluid was funneled between two cylinders at one of the gaps and a dipole generally formed, much like water ejected from a circular nozzle generates a dipole ring. After the dipole formed, the cyclonic part of the dipole became dominant and self propagated away from the cylinders. Furthermore, in some experiments having 0.2 < Disl/d ≤ 0.5, after a weak dipole formed, the remnant of the original vortex moved zonally “south. ” When the remnant of the vortex came in contact with a new cylinder, fluid peeled off the outer edge of the vortex and a so-called “streamer” went around the cylinder in a counterclockwise direction. Under the right conditions, this fluid formed a new cyclonic vortex in the wake of the cylinder, causing bifurcation of the original vortex into two vortices, as observed in previous studies. In general, independently of the configurations and Ydis, the number of cyclonic vortices downstream of the cylinders was one, either originating from the dipole or generated by the bifurcation of the original vortex. The vortex center position, radius, and circulation, before and after the interaction, were computed from its velocity field. It was found that for 0.1 < G/d ≤ 0.4, intense vortices experienced greater amplitude loss than weak vortices. The formation of both a dominant cyclone and an anticyclone (i.e., a dipole) downstream of the aligned cylinders, representing an island chain, is in agreement with recent oceanic observations of North Brazil Current (NBC) rings interacting with the Lesser Antilles in the Eastern Caribbean Sea. Since the passages of the Lesser Antilles have values of 0.07 ≤ G/d ≤ 0.3, the oceanic observations might be explained by the experimental results reported in this paper.
Causes and Effects of Role Ambiguity as Perceived by Human Resource Management Professionals in Zimbabwe

Abstract The study sought to investigate the causes and effects of role ambiguity as perceived by human resource management practitioners. A quantitative design was used. One hundred and thirty (61 male, 69 female) participants responded to a questionnaire on causes and effects of role ambiguity. Descriptive statistics were used to analyse data. Five causes of role ambiguity were identified. These were size and complexity of the organisation, rapid rate of change, restrictions on employees’ authority, vaguely defined tasks and task interdependence. The perceived consequences of role ambiguity were interpersonal tensions, poor self-image, low job satisfaction, decrease in affectivity, decrease in propensity to leave and extreme behaviour. Recommendations to r educe role ambiguity were made.
Fast SemiDirect Monocular

We propose a semi-direct monocular visual odometry algorithm that is precise, robust, and faster than current state-of-the-art methods. The semi-direct approach eliminates the need of costly feature extraction and robust matching techniques for motion estimation. Our algorithm operates directly on pixel intensities, which results in subpixel precision at high frame-rates. A probabilistic mapping method that explicitly models outlier measurements is used to estimate 3D points, which results in fewer outliers and more reliable points. Precise and high frame-rate motion estimation brings increased robustness in scenes of little, repetitive, and high-frequency texture. The algorithm is applied to micro-aerial-vehicle stateestimation in GPS-denied environments and runs at 55 frames per second on the onboard embedded computer and at more than 300 frames per second on a consumer laptop. We call our approach SVO (Semi-direct Visual Odometry) and release our implementation as open-source software.
Infrared astronomical characteristics of the Roque de los Muchachos Observatory: precipitable water vapour statistics

We present measurements of the atmospheric water vapour content above the Roque de los Muchachos Observatory (ORM) obtained using the Global Positioning System (GPS). The GPS measurements have been evaluated by comparison with 940-nm radiometer observations. A statistical analysis of the GPS measurements points to the ORM as an observing site with suitable conditions for infrared observations, with a median column of precipitable water vapour (PWV) of 3.8 mm. PWV presents a clear seasonal behaviour, with winter and spring being the best seasons for infrared observations. The percentage of nights showing PWV values less than 3 mm is over 60 per cent in February, March and April. We have also estimated the temporal variability of water vapour content at the ORM. We present a summary of PWV statistical results at different astronomical sites, noting that these values are not directly comparable as a result of the differences in the techniques used to recorded the data.
MLPerf Inference Benchmark

Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and five orders of magnitude in performance; they range from embedded devices to data-center solutions. Fueling the hardware are a dozen or more software frameworks and libraries. The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf Inference answers that call. In this paper, we present our benchmarking method for evaluating ML inference systems. Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf prescribes a set of rules and best practices to ensure comparability across systems with wildly differing architectures. The first call for submissions garnered more than 600 reproducible inference-performance measurements from 14 organizations, representing over 30 systems that showcase a wide range of capabilities. The submissions attest to the benchmark’s flexibility and adaptability.
Electron beam–plasma interaction and ion-acoustic solitary waves in plasmas with a superthermal electron component

The study of non-Maxwellian plasmas is crucial to the understanding of space and astrophysical plasma dynamics. In this paper, we investigate the existence of arbitrary amplitude ion-acoustic solitary waves in an unmagnetized plasma consisting of ions and excess superthermal electrons (modelled by a kappa-type distribution), which is penetrated by an electron beam. A kappa (κ-) type distribution is assumed for the background electrons. A (Sagdeev-type) pseudopotential formalism is employed to derive an energy-balance like equation. The range of allowed values of the soliton speed (Mach number), wherein solitary waves may exist, is determined. The Mach number range (allowed soliton speed values) becomes narrower under the combined effect of the electron beam and of the superthermal electrons, and may even be reduced to nil (predicting no solitary wave existence) for high enough beam density and low enough κ (significant superthermality). For fixed values of all other parameters (Mach number, electron beam-to-ion density ratio and electron beam velocity), both soliton amplitude and (electric potential perturbation) profile steepness increase as κ decreases. The combined occurrence of small-amplitude negative potential structures and larger amplitude positive ones is pointed out, while the dependence of either type on the plasma parameters is investigated.
Balance functions of charged hadron pairs $(π,K,p)\otimes (π,K,p)$ in Pb--Pb collisions at $\sqrt{s_{_{\rm NN}}} =$2.76 TeV

We present the first balance function (BF) measurement of charged hadron pairs $(\pi,K,p)\otimes (\pi,K,p)$ in Pb--Pb collisions at $\sqrt{s_{_{\rm NN}}} =$2.76 TeV. The BF measurements are carried out as two-dimensional (2D) differential correlators vs. the relative rapidity ($\Delta y$) and azimuthal angle ($\Delta\varphi$) of hadron pairs, and studied as a function of collision centrality. While the BF azimuthal widths of all pairs substantially decrease from peripheral to central collisions, the longitudinal widths exhibit mixed behaviors: BF of $\pi\pi$ and cross-species pairs narrow in more central collisions whereas those of $KK$ and $pp$ are found invariant with collision centrality. This dichotomy is qualitatively consistent with the presence of strong radial flow effects and the existence of two waves of quark production in relativistic heavy ion collisions. We additionally present first measurements of the BF integrals and find that hadron pairing probabilities are very different from single hadron ratios and feature minimal collision centrality dependence. Overall, the results presented provide new and challenging constraints for theoretical models of hadron production and transport in relativistic heavy ion collisions.
Semantic Image Completion and Enhancement using Deep Learning

In real life applications, certain images utilized are corrupted in which the image pixels are damaged or missing, which increases the complexity of computer vision tasks. In this paper, a deep learning architecture is proposed to deal with image completion and enhancement. Generative Adversarial Networks (GAN), has been turned out to be helpful in picture completion tasks. Therefore, in GANs, Wasserstein GAN architecture is used for image completion which creates the coarse patches to filling the missing region in the distorted picture, and the enhancement network will additionally refine the resultant pictures utilizing residual learning procedures and hence give better complete pictures for computer vision applications. Experimental outcomes show that the proposed approach improves the Peak Signal to Noise ratio and Structural Similarity Index values by 2.45% and 4% respectively, when compared to the recently reported data.
Kolmogorovian versus Non-Kolmogorovian Probabilities in Contextual Theories

Most scholars maintain that quantum mechanics (QM) is a contextual theory and that quantum probability does not allow for an epistemic (ignorance) interpretation. By inquiring possible connections between contextuality and non-classical probabilities we show that a class TμMP of theories can be selected in which probabilities are introduced as classical averages of Kolmogorovian probabilities over sets of (microscopic) contexts, which endows them with an epistemic interpretation. The conditions characterizing TμMP are compatible with classical mechanics (CM), statistical mechanics (SM), and QM, hence we assume that these theories belong to TμMP. In the case of CM and SM, this assumption is irrelevant, as all of the notions introduced in them as members of TμMP reduce to standard notions. In the case of QM, it leads to interpret quantum probability as a derived notion in a Kolmogorovian framework, explains why it is non-Kolmogorovian, and provides it with an epistemic interpretation. These results were anticipated in a previous paper, but they are obtained here in a general framework without referring to individual objects, which shows that they hold, even if only a minimal (statistical) interpretation of QM is adopted in order to avoid the problems following from the standard quantum theory of measurement.
Intriguing Properties of Adversarial ML Attacks in the Problem Space

Recent research efforts on adversarial ML have investigated problem-space attacks, focusing on the generation of real evasive objects in domains where, unlike images, there is no clear inverse mapping to the feature space (e.g., software). However, the design, comparison, and real-world implications of problem-space attacks remain underexplored.This paper makes two major contributions. First, we propose a novel formalization for adversarial ML evasion attacks in the problem-space, which includes the definition of a comprehensive set of constraints on available transformations, preserved semantics, robustness to preprocessing, and plausibility. We shed light on the relationship between feature space and problem space, and we introduce the concept of side-effect features as the byproduct of the inverse feature-mapping problem. This enables us to define and prove necessary and sufficient conditions for the existence of problem-space attacks. We further demonstrate the expressive power of our formalization by using it to describe several attacks from related literature across different domains.Second, building on our formalization, we propose a novel problem-space attack on Android malware that overcomes past limitations. Experiments on a dataset with 170K Android apps from 2017 and 2018 show the practical feasibility of evading a state-of-the-art malware classifier along with its hardened version. Our results demonstrate that "adversarial-malware as a service" is a realistic threat, as we automatically generate thousands of realistic and inconspicuous adversarial applications at scale, where on average it takes only a few minutes to generate an adversarial app. Yet, out of the 1600+ papers on adversarial ML published in the past six years, roughly 40 focus on malware [15]—and many remain only in the feature space.Our formalization of problem-space attacks paves the way to more principled research in this domain. We responsibly release the code and dataset of our novel attack to other researchers, to encourage future work on defenses in the problem space.
Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications

In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this review article, we highlight the imperative role of machine learning algorithms in enabling efficient and accurate segmentation in the field of medical imaging. We specifically focus on several key studies pertaining to the application of machine learning methods to biomedical image segmentation. We review classical machine learning algorithms such as Markov random fields, k-means clustering, random forest, etc. Although such classical learning models are often less accurate compared to the deep-learning techniques, they are often more sample efficient and have a less complex structure. We also review different deep-learning architectures, such as the artificial neural networks (ANNs), the convolutional neural networks (CNNs), and the recurrent neural networks (RNNs), and present the segmentation results attained by those learning models that were published in the past 3 yr. We highlight the successes and limitations of each machine learning paradigm. In addition, we discuss several challenges related to the training of different machine learning models, and we present some heuristics to address those challenges.
Unsupervised Cross-lingual Representation Learning at Scale

This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6% average accuracy on XNLI, +13% average F1 score on MLQA, and +2.4% F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7% in XNLI accuracy for Swahili and 11.4% for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available.