Reviewerly/exHarmony huggingface dataset:
APPLICATION OF MACHINE LEARNING ALGORITHMS FOR PROCESSING COMMENTS FROM THE YOUTUBE VIDEO HOSTING UNDER TRAINING VIDEOS

Purpose. Detecting toxic comments on YouTube video hosting under training videos by classifying unstructured text using a combination of machine learning methods. Methodology. To work with the specified type of data, machine learning methods were used for cleaning, normalizing, and presenting textual data in a form acceptable for processing on a computer. Directly to classify comments as “toxic”, we used a logistic regression classifier, a linear support vector classification method without and with a learning method – stochastic gradient descent, a random forest classifier and a gradient enhancement classifier. In order to assess the work of the classifiers, the methods of calculating the matrix of errors, accuracy, completeness and F-measure were used. For a more generalized assessment, a cross-validation method was used. Python programming language. Findings. Based on the assessment indicators, the most optimal methods were selected – support vector machine (Linear SVM), without and with the training method using stochastic gradient descent. The described technologies can be used to analyze the textual comments under any training videos to detect toxic reviews. Also, the approach can be useful for identifying unwanted or even aggressive information on social networks or services where reviews are provided. Originality. It consists in a combination of methods for preprocessing a specific type of text, taking into account such features as the possibility of having a timecode, emoji, links, and the like, as well as in the adaptation of classification methods of machine learning for the analysis of Russian-language comments. Practical value. It is about optimizing (simplification) the comment analysis process. The need for this processing is due to the growing volumes of text data, especially in the field of education through quarantine conditions and the transition to distance learning. The volume of educational Internet content already needs to automate the processing and analysis of feedback, over time this need will only grow.
Nonparametric estimation of marginal distributions for unordered pairs

In this article, we consider the estimation of the marginal distributions for pairs of data are recorded, with unobserved order in each pair. New estimators are proposed and their asymptotic properties are established, by proving a Glivenko-Cantelli theorem and a functional central limit result. Results from a simulation study are included and we illustrate the applicability of the method on the homologous chromosomes data.
Help ID fraud suspects seen using victim's credit card stolen from a car prowl

King County Sheriff's detectives are asking for your help to identify the couple shown above.
Low Cost Hardware Architecture of Fast Lifting Wavelet Transform for Image Compression

In this work, the researchers have given a low-cost, multiplier-less design with latest DWT (2D lifting technology) for high-speed dual-Z scans. A single dimension parallele row, column processors and five transposing registers are the suggested architecture. Furthermore, a 4N timeline buffer is used to process 2D DWT images with NxN resolution. Flipping architecture is intended to decrease the critical path, replacing multipliers with shifting and adding logic. To reduce transposition and latency buffers, dual Z scanning technology is introduced. The proposed architecture is better for similar performance requirements than the existing hardware architectures. Verilog is defined as the suggested Design Register Transfer Logic (RTL) and is synthesized with Xilinx ISE 14.5. When synthesized with a better hardware efficiency for Xilinx Spartan 6 series field programmable gate array, the suggested architecture works at a frequency of 140.47 MHz.
Group Abnormal Behaviour Detection Algorithm Based on Global Optical Flow

Abnormal behaviour detection algorithm needs to conduct behaviour analysis on the basis of continuous video inclination tracking, and the robustness of the algorithm is reduced for the occlusion of moving targets, the occlusion of the environment, and the movement of targets with the same colour. For this reason, the optical flow information between RGB (red, green, and blue) images and video frames is used as the input of the network in view of group behaviour. Then, the direction, velocity, acceleration, and energy of the crowd were weighted and fused into a global optical flow descriptor. At the same time, the crowd trajectory map is extracted from the original image of a single frame. Following, in order to realize the detection of large displacement moving target and solve the problem that the traditional optical flow algorithm is only suitable for the detection of displacement moving target, a video abnormal behaviour detection algorithm based on the double-flow convolutional neural network is proposed. The network uses two network branches to learn spatial dimension information and temporal dimension information, respectively, and uses short- and long-time neural network to model the dependency relationship between long-time video frames, so as to obtain the final behaviour classification results. Simulation test results show that the proposed method can achieve good recognition effect on multiple datasets, and the performance of abnormal behaviour detection can be significantly improved by using interframe motion information.
The Influence of Task Based Approach toward Students’ Speaking Ability at the Tenth Grade of SMAN 4 Kota Serang

The objective of this research was to find out the influence of task based approach toward students’ speaking ability at the tenth grade of SMAN 4 Kota Serang. The researcher used quantitative research by applying true experimental design for conducting the research. The population of this research was the tenth grade students of SMAN 4 Kota Serang. The sample of this research was chosen based on cluster random sampling. The groups were put into experimental and control group. The researcher used content validity and inter-rater reliability in order to make the instruments was valid and reliable. In testing the hypothesis the researcher used t-test as formulas to know the influence of task based approach toward students’ speaking ability at the tenth grade of SMAN 4 Kota Serang. The calculation shows the result of t_count is higher than t_table (5.38≥ 2.00) with the significance 0.05 (5%). It can be concluded that there is an influence of task based approach toward students’ speaking ability at the tenth grade of SMAN 4 Kota Serang. It is suggested that task based can be used by English teacher to help students improve their speaking ability.
Research Guides: Electronic Lab Notebooks (ELN): WashU ELN - LabArchives

An electronic laboratory notebook (ELN) is a software tool that replicates a traditional paper lab notebook.
Digital Image Visual Quality Enhancement Using Histogram Equalization Techniques with Proposed Linear Perception Neural Network Method

Image improvement is one among the issues in low visual quality and low enhancement level of image histogram method. Image improvement completely different methods like histogram leveling, multipoint histogram equalizations and picture component dependent distinction protecting, but of this technique are not up to marks. Projected linear perception network methodology for image improvement that features an additional strong result for distinction improvement with brightness preservation. Image element mutuality linear perceptron network supported curvelet transform and perceptron network. Curvelet transform image transform into multi-resolution mode. it's a realize element distinction of pixel for the dependency of characteristic and matrix work as a weight vector for perceptron network and so the perceptron network is in work to alter the load of input image or values. Image mutuality linear perceptron network for distinction improvement has applied on several pictures and compared the results of our proposed methodology with various image improvement methods like histogram leveling. Absolute mean brightness error (MBE) is used to measure the degree of brightness preservation. Low AMBE and also called is best, Peak signal to noise quantitative relation (PSNR) is used to measure the degree of distinction improvement, larger PSNR and also called is best. In experiment image secure encoding improvement technique using histogram leveling with proposed methodology supported the MBE low and PSNR High. Image secure cryptography improvement have found that proposed methodology is best than existing methodology (HEM).Keywords: Contrast Enhancement, Image Histogram, Histogram Equalization, Brightness Preserving, AMBE, PSNR, HEM.
Sentiment Prediction Accuracy of Amazon Fine Food Review using TFIDF and LightGBM models

Abstract. Changes in the pattern of society in meeting their needs develop as the times progress from conventional to digital. This makes service providers need to change business work patterns towards digitizing buying and selling transactions. Service providers serve consumer needs digitally and maintain optimal service patterns. One of the efforts to maintain optimal service is through community response to services, both positive and negative. The community response can be analyzed using sentiment analysis. This study focuses on the analysis of the accuracy of sentiment predictions on the Amazon fine food review dataset, which was taken as many as 20,000 data samples. The analysis was carried out in various stages, namely dataset collection, data preprocessing, TF-IDF, and LightGBM. The test results used TF-IDF and LightGBM with TF-IDF parameter settings of 1 to 2 grams and LightGBM parameter settings with a max_depth of 50. Num_leaves used were 40 and the learning rate was 0.1 on the Amazon Review dataset which took 20,000 samples. The analysis carried out resulted in a predictive level of sentiment accuracy above 90%, reaching 93.2%.
STATE OF IMPLEMENTATION OF ECOLOGICAL TRAINING OF FUTURE TEACHERS OF NATURAL SCIENCES

The article considers the problems of ecological training of future teachers of natural sciences in higher education institutions. In order to study the educational environment effectiveness on the process of formation of ecological competency of future teachers of natural sciences, an analysis of educational professional programs for their filling ecological content and conducted teachers and students survey. The main criterion of analysis was the presence of ecological disciplines (subjects of theoretical and applied ecology, methodology of ecological education and upbringing) and disciplines that determine the theoretical foundations of ecological culture and students' worldview of in the educational content. Based on the study results, promising guidelines for improving the effectiveness of students' ecological training of the first and second levels of higher education in the specialty “Secondary Education” of natural profile has been defined. In most universities that train teachers of natural sciences, the environmental component needs to be strengthened in the content of educational professional programs. Most ecological disciplines are optional, so we suggest including at least 50 % of the environmental component in the mandatory block. Due to the intersectional nature of ecology as a science, it is possible to further involve the potential of educational content in the formation of students' ecological competency. Such approaches should be used when designing training and internship programs, in which students will have the opportunity to gain experience in environmentally sound behavior and activities. Important in the students' ecological training is their participation in the work of environmental NGOs, environmental movements, volunteering, which allows to implement the acquired knowledge in practice and contributes to the formation of ecological culture and worldview of future teachers of natural sciences.
An Evasion Attack against Stacked Capsule Autoencoder

Capsule network is a type of neural network that uses the spatial relationship between features to classify images. By capturing the poses and relative positions between features, its ability to recognize affine transformation is improved, and it surpasses traditional convolutional neural networks (CNNs) when handling translation, rotation and scaling. The Stacked Capsule Autoencoder (SCAE) is the state-of-the-art capsule network. The SCAE encodes an image as capsules, each of which contains poses of features and their correlations. The encoded contents are then input into the downstream classifier to predict the categories of the images. Existing research mainly focuses on the security of capsule networks with dynamic routing or EM routing, and little attention has been given to the security and robustness of the SCAE. In this paper, we propose an evasion attack against the SCAE. After a perturbation is generated based on the output of the object capsules in the model, it is added to an image to reduce the contribution of the object capsules related to the original category of the image so that the perturbed image will be misclassified. We evaluate the attack using an image classification experiment, and the experimental results indicate that the attack can achieve high success rates and stealthiness. It confirms that the SCAE has a security vulnerability whereby it is possible to craft adversarial samples without changing the original structure of the image to fool the classifiers. We hope that our work will make the community aware of the threat of this attack and raise the attention given to the SCAE's security.
Sparse convolutional context-aware multiple instance learning for whole slide image classification.

Whole slide microscopic slides display many cues about the underlying tissue guiding diagnostic and the choice of therapy for many diseases. However, their enormous size often in gigapixels hampers the use of traditional neural network architectures. To tackle this issue, multiple instance learning (MIL) classifies bags of patches instead of whole slide images. Most MIL strategies consider that patches are independent and identically distributed. Our approach presents a paradigm shift through the integration of spatial information of patches with a sparse-input convolutional-based MIL strategy. The formulated framework is generic, flexible, scalable and is the first to introduce contextual dependencies between decisions taken at the patch level. It achieved state-of-the-art performance in pan-cancer subtype classification. The code of this work will be made available.
THE EFFECT OF SQ3R METHOD ON THE EIGHTH-GRADE STUDENTS READING SKILL IN RECOUNT TEXT AT SMP AL-ITQON BALARAJA

Reading is one of the most important skills which should be mastered by the students. Teacher, as an educator is required to help students to acquire their reading skills by selecting appropriate techniques to accelerate their reading process especially in the vocabulary and grammar knowledge of students. The objective of this research was to find out whether there is a significant effect of students reading skill in recount text by using the SQ3R method. This research used a quasi-experimental method with experimental design. The population in this research was the eighth-grade students of SMP AL – ITQON Balaraja academic year 2019/2020, the total number of populations in this research was 90 students which distributed in eighth grade students. It was conducted in two classes, they were Class VIII C (32 students) as the experimental class and Class VIII B (32 students) as the control class. So, the total number of samples taken in this research was 64 students from the eighth grade. To collect the data, the researcher used the test as the research instrument in which analyzed by the t-test. The formula by using SPSS v.22. The result was calculated by independent sample t-test showed that sig. (2-tailed) was lower than significance level (0.00 < 0.05). it indicated that the null hypothesis (H 0 ) was rejected or the alternative hypothesis (H a ) was accepted. It can be concluded that there is a significant difference effect who taught by using SQ3R method on students reading skill in recount text at eighth-grade of SMP AL ITQON Balaraja.
El audiocuento como recurso para trabajar los valores morales en los niños de transición del centro educativo rural Balsamina del municipio de San Calixto

The main objective of this research is to determine the usefulness of children's audio stories in the strengthening of moral values ​​in boys and girls between 5 and 6 years of the rural educational center Balsamina, headquarters of the hill, of the Municipality of San Calixto The approach used is qualitative focused on the meaning and significance of social actions. The study sample is non-probabilistic with 15 participants between 5 and 6 years of age corresponding to the degree of transition of the educational institution. The instrumentsthat were used were: Matrix for the classification of audio stories and a bibliographic matrix. As a result of the research process, it is contemplated to propose a series of audio stories, by traditional and non-traditional authors, in order to work in the classroom with actions that allow to strengthen in the little ones, values ​​such as: honesty, tolerance, generosity, respect, humility , and responsibility.The research allows to conclude that regarding the usefulness of audio stories, the ease of use stands out, together with the fact that they transmit ideas and messages that influence people with different intentions in a relatively short period of time. The benefits that the use of stories in audio format brings for children are varied and important, of which active listening, verbal expression, and raising the reading level of students stands out.
ANALISA DAM BREAK BENDUNGAN TANJU DI KABUPATEN DOMPU

Besides being useful for meeting various needs, a dam also has a very big potential for danger.  In order to know the impact of the collapse of the Tanju Dam, it is necessary to make an approach through a dam failure analysis or Dam Break analysis. The collapse of the Tanju Dam was simulated by a scenario due to overtopping and piping using the HEC-RAS v.5 application. The maximum possible rainfall or probable maximum precipitation (PMP) by the Hersfield method is 143.16 mm and the maximum design flood discharge or probable maximum flood (PMF) is 485.44 m3 / s. Based on the results of runoff modeling, the collapse of the tanju dam due to overtopping occurred within a period of 4.83 hours with a peak discharge due to the collapse of the dam of 8674.2 m3 / s and with a total inundation area of 703.52 hectares, where for Dompu Regency there are 7 villages affected in total an area of 464.38 ha, while the inundation area of 239.14 hectares in Bima Regency and the time it takes to get to the estuary is 7 hours. Based on the results of modeling runoff due to piping with a peak discharge of 8582.1 m3 / s, the total inundation area is 697.35 hectares, where for Dompu Regency there are 7 affected villages with a total area of 458.90 hectares, while the inundation area is 238.45 ha in Bima Regency.
Towards Low-burden Responses to Open Questions in VR.

Subjective self-reports in VR user studies is a burdening and often tedious task for the participants. To minimize the disruption with the ongoing experience VR research has started to administer the surveying directly inside the virtual environments. However, due to the tedious nature of text-entry in VR, most VR surveying tools focus on closed questions with predetermined responses, while open questions with free-text responses remain unexplored. This neglects a crucial part of UX research. To provide guidance on suitable self-reporting methods for open questions in VR user studies, this position paper presents a comparative study with three text-entry methods in VR and outlines future directions towards low-burden qualitative responding.
Good distribution modelling with the R package good

Although models for count data with over-dispersion have been widely considered in the literature, models for under-dispersion -- the opposite phenomenon -- have received less attention as it is only relatively common in particular research fields such as biodosimetry and ecology. The Good distribution is a flexible alternative for modelling count data showing either over-dispersion or under-dispersion, although no R packages are still available to the best of our knowledge. We aim to present in the following the R package good that computes the standard probabilistic functions (i.e., probability density function, cumulative distribution function, and quantile function) and generates random samples from a population following a Good distribution. The package also considers a function for Good regression, including covariates in a similar way to that of the standard glm function. We finally show the use of such a package with some real-world data examples addressing both over-dispersion and especially under-dispersion.
Image Dehazing from Depth Estimation using Convolutional Neural Networks

Image dehazing is a process that removes haze effects from images. In this paper, we propose a new paradigm that uses depth information to generate the transmission map for image dehazing. Other methods for image dehazing estimate the transmission map directly. We propose the generation of transmission map indirectly from the depth map using the properties of an atmospheric scattering model to obtain a dehazed image. Depth information can be estimated using established methods that employ convolutional neural networks (CNNs) based on deep learning. Experimental results show that more sophisticated transmission maps can be generated from depth maps without any post-processing. The proposed method can provide improved subjective image dehazing results without any darkening effects compared to previous methods.
Proposing an Optimized Cost-Effective Data Carrier Mechanism for Smart Contracts and Transactions in Blockchain with ACO Algorithm

In this article, a new optimal cost-effective data carrier architecture for smart contracts in the blockchain-enabled IoT environment proposed. This method requires communication with external off-chain data. Three steps consist of this model such as Mission Manager, Task Publisher, and Worker which formulated with the ACO algorithm. Based on this method, some of the best solutions for filtering smart contract events and decoding event logs to fit different requirements are presented adequately. The proposed system is designed to minimize contract deployment costs and monitor contract events without subscribing to any filter at the Ethereum node. In the evaluation, we show that it will save about 21USD deployment costs for average by our data carrier system.