Reviewerly/exHarmony huggingface dataset:
Come siamo diventati populisti: la lingua politica italiana della «terza Repubblica»

Vulgar, dishonest, incompetent: in recent years Italian politicians and their speeches have been described as such by journalists, linguists and political scientists. The turning point seems to date back to the three-year period from 2007 to 2009, when the political landscape in Italy appears to have taken on the most vivid colours of populism. These years saw the emergence of political movements and forces very different from each other, but which had some common characteristics: direct appeal to the people, intolerance for parliamentary representation rules, and a close relationship between supporters and a charismatic leader. In this paper the author wonders whether this representation of political communication reflects real new features or if it is rather the result of some longer processes in the history of Italian politics and its language. To answer this question, the author proposes a synthetic profile of political language in Italy over the last 10–15 years and focuses on its evolution and current trends, using written or transcribed texts and a corpus of parliamentary speeches from the last legislatures.
Tic Tac Toe Effectiveness in Teaching English Vocabulary for SD Dana Punia Singaraja Fifth Graders

Vocabulary has become an essential element for students to communicate with others. This research intended to analyze students’ vocabulary mastery after being exposed to the Tic Tac Toe game as a learning medium. The total number of the class is 17 students of fifth-grade students of SD Dana Punia Singaraja in the 2021/2022 academic year. This research used quasi-experimental research in one group pre-test post-test design. The researcher took the accurate data during six meetings of implementation. Tic Tac Toe game used as a strategy with the familiar nine grid of Tic Tac Toe board. It used a vocabulary test with multiple-choice as an instrument. The vocabulary mastery was improved from 65.00 to 79.12 in the mean score. Besides, Kolmogorov-Smirnov and Levelene Statistic test results revealed that the test result was normally distributed and homogeny. Furthermore, the effect size was showing this game has a strong effect. As a result, Tic Tac Toe game had significantly affect students’ English vocabulary mastery.
Concepts

It’s time to dive deeper into CockroachDB! This chapter explores some of CockroachDB’s building blocks, including data types, indexes, and geo-partitioning.
Simultaneously obstacle avoidance and kinematic control for redundant manipulators based on improve danger field

Redundant robots are widely used because of their superior flexibility. However, due to the physical constraints and the influence of uncertain obstacles in the workspace, it brings great challenges to the high-precision kinematic control of redundant robots. This paper presents a real-time obstacle avoidance and high-precision kinematic control method for redundant robot based on improved danger field method. An improved danger field description is established to improve the adaptability of the system to dynamic obstacles by introducing the speed forward-looking information between robot and obstacles. The kinematic control strategy with finite time convergence is designed. The dynamic programming type problem description is established through two terminal inequality constraint modeling of joint angle and velocity. Then a recursive solver is designed to solve the nonlinear HJB problem, and the control command is obtained in real time. The stability of the closed-loop system is analyzed. Simulation results show that the proposed method has better obstacle avoidance performance than traditional dangerous field, and has better advantages in avoiding physical constraints.
Emotion Aware Reinforcement Network for Visual Storytelling

Visual storytelling is the task of generating a sequence of human-like sentences (i.e. story) for an ordered stream of images. Unlike traditional image captioning, the story contains not only factual descriptions but also concepts and objects that do not explicitly appear in the input images. Recent works utilize either end-to-end or multi-stage frameworks to produce more relevant and coherent stories but usually ignore latent emotional information. In this work, to generate an affective story, we propose an Emotion Aware Reinforcement Network for VIsual StoryTelling (EARN-VIST). Specifically in our network, lexicon-based attention is leveraged to encourage the model to pay more attention to the emotional words. Then we apply two emotional consistency reinforcement learning rewards using an emotion classifier and commonsense transformer respectively to find the gap between generated story and human-labeled story so as to refine the generation process. Experimental results on the VIST dataset and human evaluation demonstrate that our model outperforms most of the cutting-edge models across multiple evaluation metrics.
Development online models for automatic speech recognition systems with a low data level

Speech recognition is a rapidly growing field in machine learning. Conventional automatic speech recognition systems were built based on independent components, that is an acoustic model, a language model and a vocabulary, which were tuned and trained separately. The acoustic model is used to predict the context-dependent states of phonemes, and the language model and lexicon determine the most possible sequences of spoken phrases. The development of deep learning technologies has contributed to the improvement of other scientific areas, which includes speech recognition. Today, the most popular speech recognition systems are systems based on an end-to-end (E2E) structure, which trains the components of a traditional model simultaneously without isolating individual elements, representing the system as a single neural network. The E2E structure represents the system as one whole element, in contrast to the traditional one, which has several independent elements. The E2E system provides a direct mapping of acoustic signals in a sequence of labels without intermediate states, without the need for post-processing at the output, which makes it easy to implement. Today, the popular models are those that directly output the sequence of words based on the input sound in real-time, which are online end-to-end models. This article provides a detailed overview of popular online-based models for E2E systems such as RNN-T, Neural Transducer (NT) and Monotonic Chunkwise Attention (MoChA). It should be emphasized that online models for Kazakh speech recognition have not been developed at the moment. For low-resource languages, like the Kazakh language, the above models have not been studied. Thus, systems based on these models have been trained to recognize Kazakh speech. The results obtained showed that all three models work well for recognizing Kazakh speech without the use of external additions.
Bootstrapped Neuro-Simulation for Damage Recovery in Complex Robots

Bootstrapped Neuro-Simulation is a technique in which a Neural Network Simulator, used for the evaluation of controllers during the Evolutionary Robotics process, is trained concurrently with the evolution of the controllers themselves. This removes the need for creating a simulator before commencing controller evolution and results in a simulator that is not only tailored specifically to the robot being used, but also to the task that the robot is expected to perform. This paper demonstrates that Bootstrapped Neuro-Simulation can also be used for damage recovery since the Neural Network Simulator adapts to physical changes to the robot and enables the evolution of controllers that utilize the undamaged components of the robot. Limbs of a hexapod robot are disabled to simulate damage in the experiments described in this paper. Various adaptations to the Bootstrapped Neuro-Simulation algorithm are investigated in simulation. A real-world robot is used to demonstrate the successful recovery from damage and to illustrate situations where the adaptations were found to be beneficial.
A note on the differential spectrum of a class of power functions

Differential uniformity is a significant concept in cryptography as it quantifies the degree of security of S-boxes with respect to differential attacks. Power functions of the form <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F(x)={x}^{d}$</tex> with low differential uniformity have been extensively studied in the past decades due to their strong resistance to differential attacks and low implementation cost in hardware. In a recent paper, all of us, along with Tu and Jiang gave an affirmative answer to the conjecture about the differential uniformity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F(x)=x^{d}$</tex> over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{2^{4n}}$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> is a positive integer and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d=2^{3n}+2^{2n}+2^{n}-1$</tex> , proposed by Budaghyan, Calderini, Carlet, Davidova and Kaleyski (IEEE Trans. Inf. Theory, 68(5), 3389–3403, 2022). In this paper, we show an alternative proof of the conjecture. Also, we completely determine the differential spectrum of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F(x)$</tex> .
The Impact of Electronic Soft Skills on the Quality of Tourism Services during the Corona Pandemic (A Field Study on 5-star Hotels on the Syrian Coast)

The Corona pandemic has greatly affected the tourism sector, and most hotels around the world have transformed to electronic work. Administrations must possess electronic soft skills in order to move. The study objective: clarifying the concept of electronic soft skills in hotels; defining the dimensions and concepts of electronic soft skills and methods of measuring them; and measuring the quality of the provided services and illustrating the impact of soft skills on them. The descriptive analytical approach was relied on in the study, and a questionnaire was designed and distributed to the Syrian coast hotel workers. The data were analyzed by using SPSS and SMARTPLS programs, and the results were as follows: There is a statistically significant impact of electronic soft skills and its diminutions (communication skills, electronic leadership skill, skill of electronic decision-making, electronic time management skill) on the quality of services provided in the Syrian coast 5-star hotels during the Corona pandemic.
Analisis Sentimen terhadap Opini Feminisme Menggunakan Metode Naive Bayes

The perspective of the development of feminism centered on women around the world who wants to be free from pressure, oppression and inequality from men, continues to this day. Various public opinions about feminism are now contained in various social media. Long debates about criticism and support for feminism in equalizing women's position both in terms of intellect, and the role of women in making decisions. This research was conducted with the aim of looking at public sentiment based on opinions circulating on social media. Hashtags or hash tags related to feminism from social media are the main data that will be used to analyze public opinion sentiment about feminism and 600 data are obtained about feminism. The data obtained were separated into positive, negative and neutral opinions for analysis using Naïve Bayes (NB). The results of using the Naïve Bayes method obtained a recall value of 84%, precision 94% and Fi-Score of 86% with an accuracy of 88%. Through this research, the results of classification using the nave Bayes method in analyzing sentiment against feminist opinions have good performance.
Data Mining menggunakan Metode Rough Set dalam Memprediksi Tingkat Penjualan Peralatan Komputer

Data mining is a job in the form of collecting and using data to get a rule, pattern, or relationship in large data. The output of this data mining can be used to facilitate future decision-making. The purpose of this study is to predict the level of sales of computer equipment to make it easier for sellers to meet consumer needs. The data processed in this study include several factors which will later be included in the Roughtset method. The method used is a Rough set. Factors include the name of goods, warranty, price, and level of sales. These factors will later be grouped in the Equivalence Class, where the same attribute values ​​will be grouped. Then proceed to the next stage, namely the Discernibility Matrix which contains a collection of condition attributes that have different condition values. After that, proceed to the Discernibility Matrix Modulo D stage where the columns in the matrix are filled with a collection of condition attributes that have different conditions and different decision values. The next stage is Reduction, which is how to get the condition attributes used to get output in the form of knowledge. The last stage is knowledge which is the result of the reduction obtained. Then the results of the rough set application will be entered into the Rosetta application. The results obtained using the rough set method on 10 samples of computer equipment sales data, obtained 17 new rules or knowledge that can be used as guidelines in decision making to identify the level of computer sales.
Predicting News Article Popularity with Multi Layer Perceptron Algorithm

Nowadays, news media seems to have been digitized. One of them is printed news which has now turned into online news. The increasing use of social media has made people interested in reading news online. News needs to attract readers with their headlines. Various online news media businesses want to know the future demand of readers, as well as whether the released news can reach more readers so that the news becomes popular. Therefore, with the increasing interest in online news today, this paper will analyze the performance of the Neural Network Algorithm and other artificial intelligence techniques in predicting the popularity of news articles that can help the media to know whether their news will become popular. The news article popularity prediction system can increase its revenue if there are advertisements in the news. The test results show that the accuracy of the Multi Layer Perceptron is 76% and Random Forest gives an accuracy of 70%.
A marker collaborating model for entity and relation extraction

Recognizing entities and extracting their relations are significant tasks for information extraction. Many recent works focus on leveraging entity markers for better span (pair) representations, which have achieved promising performance. Nevertheless, existing works have two shortcomings: (1) previous markers are randomly embedded into distributed representations, which ignores semantic information relevant to the targeted tokens that we want to highlight. (2) most works simply implant markers into a sentence, which is weak to encode the interrelation between multiple tokens. In this work, we propose a marker collaborating model for entity and relation extraction, which is composed of two modules. The first module is semantic marker module, where two kinds of semantic markers are proposed to leverage semantic features from the PLMs. The second module is collaborative marker attention module, where a marker-oriented directional attention is proposed to model the interaction between multiple tokens. Our model yields SOTA performance on three standard benchmarks (ACE04, ACE05 and SciERC).
Education on the Relationship between Body Mass Index and Balanced Nutrition for School-Age Children Through Video Learning (Case Study of Integrated Islamic Elementary School Daarul Fikri grade 6, Cihanjuang Village)

This study aims to determine the level of students' understanding through learning videos regarding the relationship between Body Mass Index (BMI) education and balanced nutrition in 6th grade elementary school-aged children at integrated Islamic elementary school Daarul Fikri, Cihanjuang Village. Quantitative research method with pre-experimental design type one group pretest-posttest. The research was conducted on 20 6th grade students of integrated Islamic elementary school Daarul Fikri. The research was conducted in 3 stages, namely (i) pre-test; (ii) education through learning videos; and (iii) post-test. The results showed the average value of the pretest was 67.25 and the average posttest was 79.25. The comparison of pretest and posttest showed an increase in students' knowledge after taking action through learning videos. The result of calculating the average N-Gain value is 0.34 (34%) which shows that education through learning videos regarding the relationship between Body Mass Index (BMI) and balanced nutrition is not effective. This is because the students' pre-test scores are quite good. As for the results of the calculation of the t-test, it is known that T count (-6.564) &lt; T table (1.729) means that the posttest scores of students increase but are not significant. With the completion of this study, it is hoped that more school-age children will understand the relationship between Body Mass Index and balanced nutrition. That we can identify the nutritional status that will affect the health of our bodies.
Weighted Pooling from Salient Regions for Place Recognition

We address the visual place recognition problem by identifying salient regions in images. The core problem of visual place recognition is how to generate robust image representations by reducing the interference caused by viewpoint changes, appearance changes, and dynamic object occlusions. We propose a novel method of weighted pooling from salient regions, which highlights more discriminative regions that positively contribute to the similarity measure while suppressing task-irrelevant local region features. Detailed experiments have been conducted on benchmark datasets with different appearances and viewpoint variations to verify the effectiveness of our proposed method. The results show that our proposed method outperforms the state-of-the-art. Finally, we visualize the learned weight mask to gain insight into the discriminative regions learned by the network.
Haze accounting and earnings from e-activities of commercial banks: The contemporary drift

The exploration was carried out on Haze Accounting and earnings from E-activities of commercial banks: The Contemporary drift. The principal drive was to examine the effect of data loss, hackers, and computer fraud on the profitability of commercial banks with proven facts from of commercial banks operating in Nigeria. Primary source of data was employed using questionnaire instrument, ordinary least square multiple regression statistical technique was adopted in the analysis of the data. The findings of the analysis exposed that data loss, hackers, computer fraud affected the earnings of the investigated commercial bank in Nigeria. The study suggested that the commercial banks should deployed vigorous and healthy emergencies and eventualities methods and actions against customer data loss, management should deployed pro-active application that will check hacking into the computer system. The investigation also suggested that commercial banks should intensify efforts to computerize their operations to enhance efficiency and quality of service delivery.
Towards Undeceived: Fake Reviews Detection Models Comparison

Product reviews are an important reference for customers shopping online. But with weak censorship on the Internet, many businesses trying to outsource people to publish fake reviews in exchange for the free use of the product or cash incentives. This could be harmful for developing a healthy e-commerce environment. In this paper, we compared five different classification models and find in general the Random Forest performs the best among all models, accuracy can be roughly 99%.
Video Alignment of Lecture Notes Missing Corners

During the Covid-19 pandemic, generating content for live online lectures was challenging for many instructors who were not used to teaching remotely. In this paper, our major goal is to capture educational material for online classes to be efficiently delivered to students with minimal technological requirements using a smart device. For effective delivery of material, the main focus in this work is to capture the paper where the lecture content is written or drawn. However, video alignment becomes challenging since i) some of the corners of the paper may not be visible, ii) the background of text is predominantly light colored, and iii) the front text is composed of small letters aligned over the background. We propose a novel method for integrating content printed or written on a paper by computing paper corners analytically and using a machine learning algorithm. These image corners are used for image alignment and then image stitching. The image transformation is computed locally for missing areas and applied globally to maintain the legibility of the text. Our experiments quantitatively show that detecting corners analytically and using machine learning algorithms enable image stitching effectively.
PSYCHOLOGICAL DIFFICULTIES IN REALIZATION OF SUCCESSION OF PRE-SCHOOL AND PRIMARY GENERAL EDUCATION ON IMPLEMENTATION OF FEDERAL STATE EDUCATIONAL STANDARD OF PRE-SCHOOL EDUCATION OF NEW GENERATION

The article dwells upon the problem of succession of pre-school and primary school; analysis of psychological difficulties; organizational and pedagogical conditions that influence on quality of work of educational organizations on implementation of FSES for pre-school and general education in the whole.
Unsupervised Extractive News Articles Summarization leveraging Statistical, Topic-Modelling and Graph-Based Approaches

Due to the presence of large amounts of data and its exponential level generation, the manual approach of summarization takes more time, is biased, and needs linguistic professional experts. To avoid these substantial issues or to generate a succinct summary report, automatic text summarization is very much important. Three different approaches namely the statistical approach such as Term Frequency Inverse Document Frequency(TF-IDF), the topic modeling approach such as Latent Semantic Analysis (LSA), and graph-based approaches such as TextRank were applied to generate a concise summary for the benchmark the British Broadcasting Corporation (BBC) news articles summarization dataset. The domain-specific implementations of each approach in the five domains of the dataset and domain-agnostic prospects were explored in the paper while drawing various insights. The generated summaries were evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) framework, leveraging precision, recall, and f-measure metrics. The approaches were not only able to achieve a commendable ROUGE score but also outperform the previous works on the dataset