Miracle-dz/UniFAR-FTUs huggingface dataset:
Hate speech spreader detection using contextualized word embeddings

he paper presents a method of hate speech spreaders recognition developed for the task of “Profiling Hate Speech Spreaders on Twitter” at the PAN@CLEF Conference 2021. Hate speech is an increasing problem nowadays. Due to the spread of the internet and the rise of social media, people with hostile views towards certain groups post hateful messages on social media resources. In this paper, we present a model to detect hate speech spreaders based on their Twitter posts. We aggregate contextualized embeddings of single tweets to form a vector representation for every user and employ classification methods to find users spreading hate speech. We analyze different embedding models based on BERT architecture for the problem. The submitted model achieves 67% in terms of accuracy for the English part of the dataset and 83% for the Spanish part of the dataset.
IDENTIFICATION OF LIVER PATIENTS USING SUPERVISED LEARNING: A COMPARATIVE ANALYSIS

: Liver Failure is a serious condition and it affects the patient’s life time. Disease identification is the most crucial task for treating any disease. Liver disease can be inherited genetically or caused by a variety of subjects that damage the liver. Machine learning technique is broadly used in various grounds of science and technology. They have been giving out meaningful information. It also explores in creation and study of algorithms which can learn from data. Data mining in healthcare is an evolving field of high importance for providing diagnosis and a deeper understanding of medical data. To build an effective disease management strategy, large amount of data should be analyzed for the early detection of the disease, assessment of the severity and early prediction of adverse events. This will impede the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. The aim of this research paper is to present the state-of-the-art of the machine learning methodologies applied for the prediction of liver failure. The main objective of this research work is to find the best classification algorithm in terms of precision, accuracy, specificity and sensitivity. Therefore, the present investigation was done to determine the relative performance of four classification algorithms namely, Support Vector machine (SVM), Logistic Regression, Random Forest and Decision Tree algorithm based on the available downloaded Indian Liver Patient Dataset(ILPD
Low-cost algorithm for some bearing estimation methods in presence of separable nuisance parameters

The reduction of complexity of some multidimensional optimisation algorithms of direction of arrival (DOA) estimation with an array of sensor is studied. It is shown that upon a condition of factorisation of the steering vector, the criterion can be concentrated with respect to DOA, leading to a more simple 1D procedure.
Usability elements as benchmarking criteria for enterprise architecture methodologies

An Enterprise Architecture (EA) serves as a blueprint to deal with the complexity within an organisation. Information Technology architects implement EA by utilizing the EA methodologies and frameworks. Although there are many methodologies and frameworks in enterprise architecture, each of them does not completely address all enterprise issues. The qualitative characteristics of the EA methodologies should be evaluated before implementing the EA in the organization, otherwise the organization might fail to achieve the organizational goals, cause confusion in the business process, and waste a lot of resources. One of the qualitative characteristics is usability, which should be evaluated in EA frameworks as an essential element of the whole system. The purpose of this study is to detail out the usability elements in EA methodologies.
How Facebook’s Advertising Algorithms Can Discriminate By Race and Ethnicity

Over the last 5 years, Facebook has faced repeated criticism and lawsuits over the potential for discrimination on its ad platform. In July 2020, advocacy groups focused on issues of misinformation and civil rights organized a high-profile boycott of Facebook’s advertising platform that successfully pressured more than a thousand major corporations to stop advertising on Facebook for the month. Facebook responded by releasing a civil rights audit and announcing the removal of its much-criticized multicultural affinity groups, such as “African American (US)”, “Asian American (US)”, and “Hispanic (US – All)”, as ad targeting options. This study examines whether Facebook has gone far enough to prevent discrimination on its advertising platform. I collected data on Facebook’s ad platform in January 2020 and in January 2021. I compared the racial and ethnic breakdown of the old multicultural affinity groups with the similar-sounding cultural interest groups “African-American Culture”, “Asian American Culture”, and “Hispanic American Culture,” which were still usable by advertisers in 2021. I also used a set theory approach to study racial and ethnic biases in Facebook’s Lookalike Audience and Special Ad Audience algorithms in both time periods. Results summary: I found that in 2021 Facebook’s “African-American Culture” ad targeting option produced audiences that contained 75% fewer White users than the old “African American (US)” option removed in the previous year. Facebook’s tools to help advertisers find users similar to their existing customers exhibited bias toward including more African-Americans or Whites depending on which racial group was dominant in an advertiser’s customer list. This was true for the Lookalike Audience tool as well as the Special
52.4L: Late‐News Paper: a‐Si:H TFT Active‐Matrix Phosphorescent OLED Pixel

We report a two‐transistor amorphous silicon (a‐Si:H) thin film transistor (TFT) active‐matrix organic light emitting diode (OLED) pixel using high‐efficiency phosphorescent OLED devices. The gate to source voltage (VGS) for the OLED drive transistor is less than 8 V for a pixel brightness of 100 cd/m2 and less than 12 V for 400 cd/m2. The low drive voltage is important for good TFT stability and display uniformity.
Restoring single-molecule localizations with wavefront sensing adaptive optics for deep-tissue super-resolution imaging

Specimen-induced aberration has been one of the major factors limiting the imaging depth in single-molecule localization microscopy (SMLM). In this study, we measured the wavefront of intrinsic reflectance signal at the fluorescence emission wavelength to construct a time-gated reflection matrix and find complex tissue aberration without resorting to fluorescence detection. Physically correcting the identified aberration via wavefront shaping with a liquid-crystal spatial light modulator (SLM) enables super-resolution imaging even when the aberration is too severe for initiating localization processes. We demonstrate the correction of complex tissue aberration, the root-mean-square (RMS) wavefront distortion of which is more than twice the 1 rad limit presented in previous studies; this leads to the recovery of single molecules by 77 times increased localization number. We visualised dendritic spines in mouse brain tissues and early myelination processes in a whole zebrafish at up to 102 μm depth with 28-39 nm localization precision. The proposed approach can expand the application range of SMLM to thick samples that cause the loss of localization points owing to severe aberration.
A Mechanical Analysis of the Grab Starting Technique in Swimming

The purpose of this study was to identify the mechanical characteristics of the hands-between-the-feet grab starting technique which contribute to a faster start. Twenty-four high school swimmers performed four trials of a grab start followed by a glide to a distance of 9 m. The results suggested that to obtain a faster start, swimmers should (a) move the center of mass fast in the forward direction while the feet are in contact with the starting block, (b) maximize the force exerted through the feet in the backward direction, and (c) maximize the force exerted through the hands against the starting block in the forward and upward direction.
Combination of nano-CuO/silica sol preservative with various post-treatments to improve the compressive strength, water resistance, and thermal stability of wood

A nano-CuO/silica sol wood preservative was obtained by dispersing CuO nanoparticles in propylene glycol and silica sol. Scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction analysis, thermogravimetric analysis, and compressive tests were conducted to investigate the effects of different post-treatments, i.e., steaming at 100 °C and freezing at -30 °C, on the variations in microstructure, mechanical, physical, and thermal stability properties of the preservative-impregnated wood. The results revealed that the mechanical properties, water resistance, and thermal stability of the impregnated specimens were greatly ameliorated. The steaming treatment resulted in a more uniform and dense distribution of the preservative in the blocks. The steaming treatment performed better in terms of enhancing the compressive strength of the specimens, while the freezing treatment was more effective in improving the thermal stability of the specimens. Both the steaming and freezing treatments can considerably improve the water resistance of the specimens. The different post-treatments retain the basic properties of the wood; however, they differ in the improved wood properties and provide a basis for their selection in the industrial production of nano-preservatives.
Analytical determination of characteristic frequencies and equivalent circuit parameters of a piezoelectric bimorph

Piezoelectric structures are nowadays used in many different applications. A better understanding of the influence of material properties and geometrical design on the performance of these structures helps to develop piezoelectric structures specifically designed for their application. Different equivalent circuits have been introduced in the literature to investigate the behaviour of piezoelectric transducers. The model parameters are usually determined from measurements covering the characteristic frequencies of the piezoelectric transducer. This article introduces an analytical technique for calculating the mechanical and electrical equivalent system parameters and characteristic frequencies based on material properties and geometry for a cantilever bimorph structure. The model is validated by measurements using a cantilever bimorph and fits the experimental results better than previous models. The model gives a full set of piezoelectric transducer parameters and is therefore well suited for further theoretical investigations of piezoelectric transducers for different applications. The results also show that even small manufacturing tolerances have a considerable effect on the system parameters and characteristic frequencies. This might lead to intolerable deviations, especially in dynamic applications and should be avoided by careful design and production.
The Effects of Temperature and Median Age on Infection Rates of Human-To-Human Transmission of SARS-Cov-2 Virus: Preliminary Findings from a Global Study

Aims: A preliminary epidemiological study, for COVID-19, for the period January to 31 March 2020, was undertaken for the six major global regions, covering 173 infected countries. Factors that were considered to affect the human-to-human transmission of the SARS-CoV-2 virus: the effect of average monthly country temperature and median age, were investigated. Methods: Documented case infections were accessed from the WHO website . Temperature, median age, COVID-19 testing data were obtained from relevant sites. Standard statistical data analysis was used to investigate any relationships. Results: The optimum temperature range for viral infection with SARS-CoV-2 appears to be ± -2,71°C up to 10,50˚C based on about 91% of the total infections occurring at this range. Transmission does appear to occur at a fairly wide temperature range of ± -26˚C up to +33°C. Relatively more cases seem to occur in human subjects at median age of 40,1 ± 5,0 years (56,6%), as noted for Europe, compared to younger subjects of age 20,1 ± 4,3 (0,5%), for Africa. Conclusions: The virus appears to be fairly stable, and of varying transmission, at extreme conditions of low and high temperature. There may be a seasonal behavior regarding its occurrence in line with a recent finding. Keywords: Enveloped virus; SARS-CoV-2; COVID-19; temperature; median age; Case Infection Rate (CIR); seasonal
Fake News Classification in Machine Learning with Different Word Representations

Text classiﬁcation has been effectively applied in a variety of domains, one of which is the detection of fake news. Working with a classiﬁcation framework is an important approach for detecting fake news. One of the most signiﬁcant steps in converting text to numbers in a classiﬁcation framework is feature extraction. In this paper, we compare the effectiveness of several feature extraction approaches such as bag of words, TF-IDF, and one-hot encoding. For the experiment, we measured the accuracy of the classiﬁcation and evaluated the best/worst classiﬁer in three techniques using three fake news detection data sets and six machine learning classiﬁers. Following our tests, we discovered that employing a bag of words, also known as CountVectorizer, and the TF-IDF approach in text classiﬁcation for selected data outperforms one-hot encoding. Despite the fact that logistic regression and support vector machine both produce valid results by using bag of words and TF-IDF, random forest classiﬁer is the only algorithm that consistently produces accurate results in all three feature extraction methods. The accuracy of support vector machine in one-hot encoding was the lowest even though the algorithm produced substantial results in the other two extraction procedures
CLASS-II KNOX genes coordinate spatial and temporal patterns of the tomato ripening

Ripening is a complex developmental change of a mature organ, the fruit. In plants like a tomato, it involves softening, pigmentation, and biosynthesis of metabolites beneficial for the human diet. Examination of the transcriptional changes towards ripening suggests that redundant uncharacterized factors may be involved in the coordination of the ripening switch. Previous studies have demonstrated that Arabidopsis CLASS-II KNOX genes play a significant role in controlling the maturation of siliques and their transition to senescence. Here we examined the combined role of all four tomato CLASS-II KNOX genes in the maturation and ripening of fleshy fruits using an artificial microRNA targeting them simultaneously. As expected, the knockdown plants (35S::amiR-TKN-CL-II) exhibited leaves with increased complexity, reminiscent of the leaf phenotype of plants overexpressing CLASS-I KNOX, which antagonize CLASS-II KNOX gene functions. The fruits of 35S::amiR-TKN-CL-II plants were notably smaller than the control. While their internal gel/placenta tissue softened and accumulated the typical pigmentation, the pericarp color break took place ten days later than control, and eventually, it turned yellow instead of red. Additionally, the pericarp of 35S::amiR-TKN-CL-II fruits remained significantly firmer than control even after three weeks of shelf storage. Strikingly, the 35S::amiR-TKN-CL-II fruits showed early ethylene release and respiration peak, but these were correlated only with liquefaction and pigmentation of the internal tissues. Our findings suggest that CLASS-II KNOX genes are required to coordinate the spatial and temporal patterns of tomato fruit ripening. One sentence summary Tomato CLASS-II KNOX genes play antagonistic roles in the regulation of ripening at the internal fruit domains and pericarp.
Ultra‐Wideband Slot Antenna With Frequency Band‐Stop Operation

In this article, a novel ultra‐wideband (UWB) slot antenna with frequency band‐stop performance is designed and manufactured. The fabricated antenna has the frequency band of 3.01 to more than 11.07 GHz with a rejection band around 5–6 GHz. Good VSWR and radiation pattern characteristics are obtained in the frequency band of interest. Simulated and measured results are presented to validate the usefulness of the proposed antenna structure for UWB applications. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:2020–2023, 2013
Synchronization methods for communication with chaos over band-limited channels

SUMMARY Methods of chaos synchronization applied to communication with chaotic signals over band-limited channels are studied numerically and experimentally. The "rst group of methods employs a "lter in the receiver which is designed to compensate channel distortions of the transmitted signal. The second approach does not use the compensation but relies on the design of chaotic oscillators which shapes the spectrum of chaotic waveforms sent through the channel. Copyright ( 1999 John Wiley & Sons, Ltd. Recent interest in chaos synchronization is supported, in part, by the hope to build a common framework for the design of self-synchronizing analog, digital, and hybrid (analog}digital) chaotic scramblers which can be used for private communication. Several methods of scrambling of an information signal with analog chaotic signal at the transmitter and extracting the message from chaos at the receiver have been proposed. In one class of these methods, the information signal m(t) is added to the chaotic output x % (t) generated by a chaotic oscillator whose dynamics do not depend on m(t). The mixture x % (t)#m(t) is transmitted to the receiver where it is used as a driving signal for response system with matched parameters. Various implementations of the response systems were proposed, see for example References 1}6 and references therein. The common shortcoming of such methods of communication is that the driving signal which is &distorted’ by the message m(t), does not perfectly "t the response system. As a result, recovered message m 3 (t) will always contain some traces of chaotic waveforms no matter how perfectly the parameters of the decoder (response system) match those of the encoder (driving system). The amplitude of the chaotic contamination is small only if m(t) is small as compared to the chaotic signal x(t). Ad i ! erent approach to the problem of chaotic encoding and decoding was suggested in a number of papers.7~13 The main idea of this approach is that information signal m(t) is injected into the feedback loop of the chaotic encoder. As a result the distorted chaotic signal x % (t)#m(t) returns back to the generator and in#uences the dynamics of the encoder. Now when the signal x % (t)#m(t) is applied to the decoder, it generates oscillations of the response system which are identical to the oscillations in the encoding generator. The message m(t) can be recovered using the open feedback loop of the response system. In this case (in the absence of channel distortions), after initial transients, the information can be restored exactly. Since in this
HOM-free linear accelerating structure for e/sup +/e/sup -/ linear collider at C-band

The HOM-free linear acceleration structure using the choke mode cavity (damped cavity) is now under design for the e/sup +/e/sup -/ linear collider project at C-band frequency (5712 MHz). Since this structure shows a powerful damping effect on most or all HOMs, there is no multibunch problem due to long range wakefields. The structure will be equipped with microwave absorbers in each cell and also the in-line dummy load in the last few cells. The straightness tolerance for a 1.8 m long structure is looser than 30 /spl mu/m for the 25% emittance dilution limit, which can be achieved by standard machining and braising techniques. Since it has good vacuum pumping conductance through annular gaps in each cell, instabilities due to the interaction of the beam with the residual-gas and ions can be minimized.
Correntropy-Based Kernel Learning for Nonlinear System Identification with Unknown Noise: An Industrial Case Study

Abstract One significant challenge in nonlinear system identification development for industrial processes is that the modeling samples often contain outliers and unknown noise. In this paper, a novel Correntropy-based Kernel Learning (CKL) method is proposed for identification of nonlinear systems with such uncertainty. Without resort to unnecessary efforts, the CKL identification method can reduce the effects of outliers by the use of a robust nonlinear estimator that maximizes correntropy. The superiority of the proposed CKL method is demonstrated through identification of an industrial process in Taiwan. The benefit of its more accurate and reliable performance indicates that CKL is promising in practice for identification of nonlinear systems with unknown noise.
Kalman filter based synchronisation methods

Single- and three-phase synchronisation methods based on optimum filtering theory are proposed. These methods are based mainly on the Kalman filter and are therefore termed Kalman filter-phase locked loop. They explicitly include in the problem formulation the presence of harmonics, voltage unbalance, measurement noise, transients and frequency deviation. Such perturbations degrade the performance of many synchronisation structures presented in literature. The formulation presented here makes the synchronisation signals less sensitive to these perturbations. It is also shown that the proposed methods can be helpful by also providing the amplitude, instantaneous phase and frequency of grid voltages that can be useful for the analysis of power quality. Furthermore, the Kalman filter provides a way of obtaining the best compromise between transient response and measurement noise rejection for the synchronisation signals. The paper sets out the development of the proposed methods together with the choice of tuning parameters and their physical meaning. Simulations and experimental results using a DSP TMS320F2812 are presented to show the effectiveness of the proposed schemes.
The effect of four weeks high-intensity interval training (HIIT) on the content of AKT1, mTOR, P70S6K1, 4EBP1 proteins in the Left ventricular muscle tissue of the heart obese rats with type 2 diabetic

Purpose: The pathway for a Rapamycin target in mammals (mTORC1) is one of the important pathways for protein synthesis in the heart, and diabetes can lead to disorder of this pathway. The aim of this study was to investigate the effect of four weeks high-intensity interval training (HIIT) on the content of AKT1, mTOR, P70S6K1, 4EBP1 proteins in the left ventricular muscle tissue of the heart obese rats with type 2 diabetic. Methods: In this experimental study, 16 Sprague-Dawley male rats (with mean weight of 300 ± 20 gr) were selected and after induction of diabetes by STZ and nicotinamide was randomly assigned into two groups: diabetic training and diabetic control. The experimental group performed HIIT training for four weeks’ accordance with the training program for four weeks, while the control group did not have any training program. Independent t-test was used to analyze the data. Results: There was no significant difference change the total form content of the AKT1 proteins (p=0.56), mTOR (p=0.50), P70S6K1 (p=0.99) and 4E-BP1 (p=0.32) in the training group compared to the control group; Also, the HIIT training did not significantly changed the phosphorylated form content of AKT1ser473 (p=0.25), mTORser2448 (p=0.74), P70S6K1Thr389 (p=0.37) and 4E-BP1Thr37/46 (p=0.38) proteins in the training group compared to the control group. Conclusion: HIIT training has not significantly changed the total and phosphorylated form content of the AKT1, mTOR, P70S6K1, 4EBP1 proteins in the left ventricle of heart tissue obese rats with type 2 diabetic.