Reviewerly/exHarmony huggingface dataset:
Automatic Detection of Online Hate Speech Against Women Using Voting Classifier

Freedom of expression found on social media has various pros and cons. Gender-Based Violence (GBV) is also a major issue in social media. As a part of GBV, hate speech against women is on the rise on all social media. There are some lapses available in the stand-alone classifiers in detecting such speech, and the performance of ensemble classifiers is much better. Also, many research works have focused on common hate speech datasets. Hate speech against women has been used in very few research activities. But such hate speech is very dangerous. As a result, this research employs, Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and Long Short-Term Memory (LSTM) to compute metrics and performances and then use those algorithms to create a voting classifier to develop a more accurate model for detecting hate speech against at women. Two phases were used in this study. RF, LR, DT, and LSTM were used as foundation stand-alone classifiers in the first phase of the ensemble procedure. In Phase Two, the weights of the second-level classifier were estimated using first-level classifiers. Hate speech against women was detected using an open-source #MeToo dataset that was utilized for training and testing by the researchers. The dataset is publicly available on GitHub which was uploaded by Nazmus Sakib. This dataset consists of 278,765 #MeToo movement posts on social media. It clearly shows that the proposed voting classifier model has the highest values in all metrics including accuracy (89%). When we check the strongly positive classification, the proposed model has performed well in precision (0.90), recall (0.91), and F-measures (0.90) and it can calculate strong positive hate speech more efficiently than other stand-alone classifiers. This voting model takes more time to train since it has multiple models inside. By training it for more epochs, we can further increase accuracy.
An Empirical Analysis of Causal Relationships Between Agricultural Labour and Agricultural GDP in Libya

The agricultural sector is the second largest sector contributing to the economy of Libya; yet empirical studies focusing on Libyan agricultural sector appear to be limited. Hence, the paper aims to investigate the causal relationship between agricultural labour and agricultural GDP in Libya over the study period of 2000-2019. The method employed in this study is the time-series technique which consists of the unit–root tests, cointegration and the Granger causality test. The results of this study show that there is a cointegration between variables as per the Johansen Co integration test, while there is a uni-directional causal relationship running from agricultural GDP to agricultural labour in the long and short- run. Nevertheless, agricultural labour does not cause agricultural GDP in the long and short run. The findings imply that the Libyan government should focus more on the integration of technology advancement in the agricultural sector.
Taylor’s way of continuity random variable

The way of Taylor’s series expansion is a kind of general operate and which are popularity in theory approximate in continuity functions . In fact , about the continuity random variable because of the vary of continuous , so it can use the way of continuity functions . this paper will be use the Taylor’s series expansion use in continuity random variable.
Boosting Few-shot Learning by Self-calibration in Feature Space

Few-shot learning aims at adapting models to a novel task with extremely few labeled samples. Fine-tuning the models pre-trained on a base dataset has been recently demonstrated to be an effective approach. However, a dilemma emerges as whether to modify the parameters of the feature extractor. This is because tuning a vast number of parameters based on only a handful of samples tends to induce overfitting, while fixing the parameters leads to inherent bias in the extracted features since the novel classes are unseen for the pre-trained feature extractor. To alleviate this issue, we novelly reformulate fine-tuning as calibrating the biased features of novel samples conditioned on a fixed feature extractor through an auxiliary network. Technically, a self-calibration framework is proposed to construct improved image-level features by progressively performing local alignment based on a self-supervised Transformer. Extensive experiments demonstrate that the proposed method vastly outperforms the state-of-the-art methods.
Unlocking the Potential of Disentangled Representation for Robust Media Understanding

It has been argued that for AI to fundamentally understand the world around us, it must learn to identify and disentangle the underlying explanatory factors hidden in the observed environment of low-level sensory data. In this talk, I will first provide an overview of the recent developments in disentangled representation learning and identify some major trends. I will then present some applications of this powerful concept for robust media processing and understanding in tasks such as image restoration, super-resolution, classification, person re-ID, depth estimation, etc. I will also discuss some future directions.
Studying Early Decision Making with Progressive Bar Charts

We conduct a user study to quantify and compare user performance for a value comparison task using four bar chart designs, where the bars show the mean values of data loaded progressively and updated every second (progressive bar charts). Progressive visualization divides different stages of the visualization pipeline—data loading, processing, and visualization—into iterative animated steps to limit the latency when loading large amounts of data. An animated visualization appearing quickly, unfolding, and getting more accurate with time, enables users to make early decisions. However, intermediate mean estimates are computed only on partial data and may not have time to converge to the true means, potentially misleading users and resulting in incorrect decisions. To address this issue, we propose two new designs visualizing the history of values in progressive bar charts, in addition to the use of confidence intervals. We comparatively study four progressive bar chart designs: with/without confidence intervals, and using near-history representation with/without confidence intervals, on three realistic data distributions. We evaluate user performance based on the percentage of correct answers (accuracy), response time, and user confidence. Our results show that, overall, users can make early and accurate decisions with 92% accuracy using only 18% of the data, regardless of the design. We find that our proposed bar chart design with only near-history is comparable to bar charts with only confidence intervals in performance, and the qualitative feedback we received indicates a preference for designs with history.
Finite mixtures of multivariate skew Student’s t distributions with independent logistic skewing functions

This paper extends the multivariate skew t distributions with independent logistic skewing functions (MSTIL) introduced in Kwong and Nadarajah (Methodology and Computing in Applied Probability 24 (2022) 1669–1691) to finite mixture models (FM-MSTIL). A stochastic EM-type algorithm is proposed for fitting the FM-MSTIL, and a divisive hierarchical algorithm is proposed for initialisations and model selections. We show that the model can outperform other finite mixture models in the literature for some simulated data sets. The performance of the FM-MSTIL in cluster analysis is also investigated. We show that the FM-MSTIL-R, a nested version of the FM-MSTIL, performs well for automatic gating tasks on some flow cytometry data sets in the FlowCap-I challenge. The FM-MSTIL-R achieved a better overall score than all other competing algorithms in the original challenge. An efficient implementation of the FM-MSTIL is available as an R package in GitHub.
Method for Determining the Digital Twin Structure of Distributed Open Sources

For the material of this article, open sources are publicly available information posted on the Internet for repeated and free use in the form of machine-readable systematized data, in a format that allows their automated processing. Any of these sources is considered as a structure with iterative nesting of content, formalized by a number of stable rules. The mathematical description of the rules for the formulation of open source content objects constitutes a knowledge base containing statistics of various properties and mappings that bring such content closer to its digital counterpart, regardless of its nature. The digital twin, in this case, can be represented as a set of local systems with feedback subsystems connected by categorical relations at the level of functorial dependence. The conducted research has established the possibility of forming the structures of the digital twin of an open source, which makes it possible to increase the importance of the analysis of the qualifications of open sources. Turning to the same type of content, it becomes necessary to formalize the structures of connections between its objects, which can be described in the language of graph theory and as virtual objects of category theory. The formalized open source assumes preliminary iterations on the graph structure of the main content objects, as well as objects related to them. The subject area is an open source, which has some property of stable connections between the objects of research.
LIN-MM: Multiplexed Message Authentication Code for Local Interconnect Network message authentication in road vehicles

The automotive market is profitable for cyberattacks with the constant shift toward interconnected vehicles. Electronic Control Units (ECUs) installed on cars often operate in a critical and hostile environment. Hence, both carmakers and governments have supported initiatives to mitigate risks and threats belonging to the automotive domain. The Local Interconnect Network (LIN) is one of the most used communication protocols in the automotive field. Today’s LIN buses have just a few light security mechanisms to assure integrity through Message Authentication Codes (MAC). However, several limitations with strong constraints make applying those techniques to LIN networks challenging, leaving several vehicles still unprotected. This paper presents LIN Multiplexed MAC (LIN-MM), a new approach for exploiting signal modulation to multiplex MAC data with standard LIN communication. LIN-MM allows for transmitting MAC payloads, maintaining full-back compatibility with all versions of the standard LIN protocol.
Design of intelligent garbage sorting control system based on vision

Aiming at the pain points of low efficiency of garbage classification, low intelligence, large personnel investment, and lack of supervision in most communities, an intelligent garbage classification control system is designed based on vision and STM32 controller, including voice broadcast module, multi-sensor Collaboration module, wireless network access module, visual recognition module, remote control and diagnosis module, based on the authorized patents, the odor filter module was developed, and the automatic bucket changing function module was redesigned, which improved the efficiency of garbage classification and reduced manpower and material resources. Input costs. The test results show that the control system can effectively improve the work efficiency of sanitation personnel, the operation process is simplified and humanized, and it is suitable for promotion and application in various occasions, and has good market prospects and economic benefits.
A Machine Learning-Based Microseismic Event Location and Wave Velocity Prediction

ABSTRACT: Human-induced seismicity from underground mining can have a huge effect on the safety, productivity, and operating costs of mines. Hence, the mining industry demands an accurate predictor for microseismic source locations in order to mitigate unexpected catastrophes such as rockbursts. The aim of the research is to use machine learning models to predict real-time seismic wave velocities in the constantly degrading underground mine environment. In underground mining, the rockmasses and voids are constantly changing in quality, geometry, and content with mining. Data for this study is generated by a research team at the laboratory of the School of Mining and Geosciences at Nazarbayev University. The research on the application of machine learning is also conducted at the School of Engineering and Digital Sciences in the same university. The main findings of the data analysis were that due to the heterogeneity and constant change of the mining environment, the seismic wave velocity is not constant, hence the traditional methods of calculating the event source location using a constant velocity model in the seismic monitoring systems will often lead to inaccurate results. The accuracy of determining seismic event source locations depends on the seismic wave velocity assumed in the algorithm for the event source location. Thus, the ability to predict real-time seismic wave velocity allows the better location of seismic events. Several machine learning approaches have been explored for the prediction of seismic wave velocities under various conditions. Linear Regression models were trained from the data obtained in the laboratory to predict the wave velocity. The deep learning approach was also used to achieve higher precision, in particular, Deep Artificial Neural Networks (ANN) have been applied. Compared with Linear Regression which could only learn linear relationships between the features and the target, Deep ANN could learn complex relations due to the activation function in each layer.
Will TikTok Go Viral in Scholarly Publishing?

Is Jennifer really going to write an article about TikTok and scholarly publishing? Why, yes, I am. I joined TikTok so you don’t have to. Unless, of course, you want to. Either way, grab some popcorn and a cold bev, curl up in a comfy spot, and let’s get started. I set out to write this column considering TikTok from a serious point of view. We all know TikTok as the place where catchy songs and fun dances are showcased 24 hours a day. Aside from the fun aspect of TikTok, though, I was truly curious to learn how TikTok is affecting the academic world. I asked this question as I considered what I would write: “Will TikTok go viral in scholarly publishing?” My prediction is YES, TikTok will go viral in scholarly publishing, particularly with researchers. It is a way to make complicated principles accessible to anyone, easy to understand, and entertaining. (And yes, TikTok is a serious time waster.) CAUTION TO ALL READERS: TikTok is not for the faint of heart. It is beyond fascinating and is guaranteed to suck you in with its boundless resources of… well… everything. Since its launch in 2016, TikTok has been growing exponentially. Make sure you have time to get absorbed (euphemism for hours of distraction) in this labyrinth of information overload. Remember when the World Wide Web used to be called the Information Superhighway? Well, TikTok is the Information Rocket Ship that will take you to worlds you didn’t even know […]
User Experience Perception Algorithm for Long and Short Videos Based on Multiple Nonlinear Regression

The low correlation of evaluation indices from the current user perception evaluation model and the neglect of the nonlinear relationship between diversified indices and user experience in different duration videos result in low user perception accuracy of long and short videos. To address these issues, we propose a user experience perception algorithm for long and short videos based on multiple nonlinear regression (LSMNR). First, to improve the efficiency and accuracy of modeling, the algorithm involves preprocessing of video data in edge servers and subdivides the videos based on their duration and popularity. Then, we introduce a new multidimensional quantitative evaluation index that fits the user’s subjective experience and further analyze the influence between multiple evaluation indices (video lag, black screen, etc.) and user quality of experience (QoE) for different video types. Moreover, the characteristics of the data in the multiple evaluation indices are extracted; user subjective evaluation experiments are designed using the video quality expert group (VQEG) standard; and sample and test databases were established. Finally, the optimal model parameters were trained by applying the nonlinear least square method and support vector machine (SVM) to fit and cross-verify the sample data. Our simulation results revealed that the Pearson correlation coefficient of the proposed LSMNR algorithm acquires a value of 0.9810. Compared with algorithms based on multinomial linear regression (MLR), linear SVM, and neural network (NN), the perceptual accuracy of the proposed algorithm is improved by at least 4.0%, and it is applicable to a wider range of video types.
Structure and Content of Monitoring of Professional Trajectories of Graduates of Pedagogical Classes

The article is devoted to the problems of studying the professional trajectories of graduates of pedagogical classes. The need for research in the monitoring mode is determined by the importance of identifying internal and external factors that influence the choice of pedagogical classes by graduates of professions and vocational education at different levels, as well as conditions that determine the productivity of their development of the main professional educational program. The results of the first stage of the study described in the article allowed us to conclude that despite the motivation of students of pedagogical classes, a wide range of pedagogical technologies used, a significant part of graduates make a choice not in favor of the pedagogical profession. This circumstance determines the need for a prolonged assessment of the productivity of pedagogical classes in the aspect of analyzing the content of the educational process. The conducted research allows us to identify ways to improve the effectiveness of the system of pre-professional training of a future teacher.
The impact of digital and internet technologies on language development

The article is devoted to studying the development of languages under the influence of computer technology. The article aims to determine the impact of digitalization and other factors on the development of Ukrainian and English languages. The study's central hypothesis is that digital technology has significant language development. To empirically confirm this hypothesis, a survey of Internet users was organized, who expressed their opinions about the use of new words (words of foreign origin and slang words) in the process of communication. The study results showed that digital technology impacts language development, but users' vocabulary is expanding following the scope of digital technology. In particular, young people, even having a good knowledge of English, do not know business slang, while the mature generation has almost no knowledge of youth slang originating from entertainment. This study has practical significance for philologists and digital technology professionals who are looking for accessible methods of communication by expanding the speech vocabulary with words of computer origin.
Measuring Effects of Spatial Visualization and Domain On Visualization Task Performance: A Comparative Study

Understanding one's audience is foundational to creating high impact visualization designs. However, individual differences and cognitive abilities influence interactions with information visualization. Different user needs and abilities suggest that an individual's background could influence cognitive performance and interactions with visuals in a systematic way. This study builds on current research in domain-specific visualization and cognition to address if domain and spatial visualization ability combine to affect performance on information visualization tasks. We measure spatial visualization and visual task performance between those with tertiary education and professional profile in business, law & political science, and math & computer science. We conducted an online study with 90 participants using an established psychometric test to assess spatial visualization ability, and bar chart layouts rotated along Cartesian and polar coordinates to assess performance on spatially rotated data. Accuracy and response times varied with domain across chart types and task difficulty. We found that accuracy and time correlate with spatial visualization level, and education in math & computer science can indicate higher spatial visualization. Additionally, we found that motivational differences between domains could contribute to increased levels of accuracy. Our findings indicate discipline not only affects user needs and interactions with data visualization, but also cognitive traits. Our results can advance inclusive practices in visualization design and add to knowledge in domain-specific visual research that can empower designers across disciplines to create effective visualizations.
Enabling Factors That Lead Educational Middle Leaders to an Effective Professional Performance

Effective school leadership is made of senior and middle leaders who work effectively and collaboratively. More than ever, in the 21st century, there has been recognition that schools need knowledgeable, skilled, proactive and committed leaders, along with great teachers, to ensure outstanding education at all levels of schooling. The scope of this paper lies within
FAKTOR-FAKTOR YANG MEMPENGARUHI TINGKAT PEMAHAMAN AKUNTANSI PADA MAHASISWA PROGRAM STUDI AKUNTANSI

This study aims to determine the factors that influence the level of accounting understanding of students in the accounting study program. The population used in this study amounted to 264 students with a total sample of 176 students. The sampling technique used the slovin formula. The variables studied include the competence of lecturers, emotional intelligence and learning styles. The method of data collection is by distributing questionnaires. Methods of data analysis using descriptive analysis and linear regression. The results showed that simultaneously lecturer competence, emotional intelligence and learning style had an effect on the level of accounting understanding of students in the accounting program. Partially, lecturer competence, emotional intelligence and learning style each have a significant positive effect on the level of accounting understanding of students in the accounting program.
The Freirean Legacy in Innovative Educational Practices: Maker Culture, Active Methodologies, Digital Technologies and Transdisciplinarity

Currently, the challenges of contemporaneity impose a new social contract for a fairer, more democratic and inclusive society. This idea implies a new vision of education and, consequently, a re(think) and innovation of school curricular practices with the purpose of contributing to the education of all citizens in this new era. In this context, we
Supplemental Material: Strategies for effective unmanned aerial vehicle use in geological field studies based on cognitive science principles

&lt;p&gt;Item 1: Field-Based UAV Protocol for Geoscientists. Used in the Mecca Hills with field geologists at three locations. Provides goals, instructions to the geologist, and questions asked of them before, during, and after their flights. Item 2: AV Geoscience User Survey. Administered to expert geoscientists attending a UAV operators group town hall at the 2019 American Geophysical Union (AGU) meeting. &lt;/p&gt;
Exploring the Challenges of Academic Fatigue and Strategies to Enhance Retention for the Determined Ones (TDOs) in Online Learning

The daily activities of educational institutions globally were affected by the Covid-19 Pandemic in 2020. The abrupt transition from on-campus to distance learning gave students and educators limited time to prepare for such a massive shift in teaching and learning. Students with disabilities (dubbed TDO students in the UAE), were negatively impact because their normal