Reviewerly/exHarmony huggingface dataset:
Letter Detection : An Empirical Comparative Study of Different ML Classifier and Feature Extraction

Work and communication activities are inextricably linked. Letters are an example of a communication medium that is still widely utilized. When it comes to significant job, however, simply an official letter is required. Official and private letters must be distinguished and classified. Different feature extraction methods, such as the count-vectorizer and TF-IDF vectorizer, are employed to transmit the detection of this official and personal letter. To categorize letters by type, various machine learning (ML) techniques are employed. Nave Bayes, Support vector machine, and AdaBoost are the algorithms. The accuracy measurements used in this study include accuracy scores, F1-mean, recall, and precision. The best working algorithm is Naïve Bayes for two vectorizer methods used, with an accuracy value of 98%.
Study on the Discourse Optimization of English Classroom Teachers from the Perspective of Subject Core Literacy

Subject core literacy is the key goal of English learning in the new era. As a medium of language and education, classroom discourse contains rich humanistic functions and plays an important role in the cultivation of core literacy. However, there are still many problems in the usage of classroom discourse, such as excessive quantity, mechanical repetition, single form of questioning and feedback, etc. These questions are not conducive to the development of the core quality of English subject. Therefore, this paper analyzes in detail the promotion effect of English teachers’ discourse with humanistic functions on students' language ability, cultural awareness, thinking quality and learning ability, and puts forward optimization suggestions on teacher consciousness, content extraction, question design and classroom feedback. This research will be used to improve the quality of classroom teaching discourse and provides some constructive ideas and methods for the cultivation of core literacy of English subject.
Contact Tracing in the Management of the Second Wave of SARS-Cov 2 Infection in Children: How Useful is it?

Background: Individuation and management of the contacts of confirmed COVID-19 infection cases allow quickly identifying and isolating any secondary cases and interrupt the transmission chain of the infection.Contact tracing is a very useful way to value the spread of the pandemic from SARS-Cov 2, however there are no paediatric data that have measured the effectiveness of this method.Methods: From 30 October 2020 to 30 April 2021, during the second wave of the Covid pandemic, 19 primary care pediatricians reported suspected cases of SAR Cov 2 to the Public Health Service of the Piacenza district via a digital platform relating to a total pediatric population of about 17,500 children.All reported cases underwent a molecular PCR test as soon as possible and in any case within 48 hours.At the same time, Public Hygiene Service implemented contact tracing in the same pediatric population in all children who were in close contact with positive cases.Results: On the basis of clinical suspicion reported on the database by primary care paediatricians, 1306 subjects underwent a molecular PCR nose pharyngeal swab for Covid-19 and 107 of them tested positive (8.2% of reports, 0.6% of the monitored population).In the same period, the Public Hygiene Service identified another 2582-suspected cases through contact-tracing and 1137 of them resulted positive for Covid-19 (44% of reports, 6.4% of the monitored population).Contact tracing has been about nine times more sensitive than the clinical criteria for identifying positive subjects in paediatric age, during the second wave of the SARS-Cov 2 pandemic (OR = 8.8, 7.1-10.9). Conclusions:Our experience confirm the effectiveness of contact tracing for the managment of the SAR Cov 2 pandemic .It was about nine times more sensitive than the clinical criteria for identifying positive subjects in pediatric age, often asymptomatic, during the second wave of the Covid pandemic.
Creating Purchase Intention through Social Media: The use of AR enabled Social Media Filters

Since the advent of humans on this planet, communication has been a key for transferring ideas, information, and knowledge. Communication has been identified as a crucial element in the advancement of human civilization (Heintz &amp; Scott-Phillips, 2023). However, communication channels have been used differently in different times, places, and situations. The impact of communication is significantly affected by the channels employed to convey information (Murphy et al., 1997). The efficiency of information delivery and reception is contingent upon the mode of communication employed (Ren &amp; Zhao, 2023). The effectiveness of a message in terms of its clarity, relevance, and accuracy can exhibit significant variability based on the mode of transmission (Miller, 2014). An appropriate selection of communication channels helps convey the desired message and elicit the desired response (Shiva et al., 2021). Communication channels have evolved drastically during the last two decades (Kaufman et al., 2015).
PENGARUH PENGGUNAAN MEDIA DREAM BOOK TERHADAP KREATIVITAS MAHASISWA

This research is motivated by the lack of creativity of students in making media when learning is implemented and the lack of use of media in teaching practices by students during PLP activities in elementary schools. This study aims to determine student creativity by applying Dream Book media. The research method used is quantitative research methods. The population used in this study were 4th semester students consisting of 6 classes with purposive sampling technique. The independent variable is Media Dream book while the dependent variable is student creativity. Hypothesis testing was carried out using the t test (Independent Sample T-Test), which was previously also tested for normality tests and homogeneity tests. The results of this study get a sig. 2-tailed of 0.000 where &amp;lt; than 0.05. So it can be concluded that there is an effect of using dream book media on the creativity of fourth semester students of class F PGSD.
Analisi onomasiologica e motivazionale delle denominazioni dialettali italiane di ‘pomo d’Adamo’

This study aims to identify and analyse the main lexotypes for the notion ‘Adam’s apple’ in Italian dialects. The comparative analysis of data collected by the Linguistic and Ethnographic Atlas of Italy and Southern Switzerland and the Italian Linguistic Atlas reveals a fair variety of expressions which in most cases are distributed in continuous and homogeneous areas. Generally, according to the main denomination models that distinguish the parts of the human body, these nouns are the result of redetermination processes or metaphorical associations, especially with the plant world. On the motivational level the most productive category refers to the roundness and protrusion of the Adam’s apple, with interesting crossings with cultural references of biblical and popular origin.
Online Educational Community as a Platform for Online Teaching of Python Programming Language to Schoolchildren

The issues of teaching schoolchildren of online communities programming in the Python environment using learning management systems (LMS) and Internet services: Google Docs and Google Colabratory are considered.
Selective Binarization based Architecture Design Methodology for Resource-constrained Computation of Deep Neural Networks

In this paper, we introduced a novel selective binarization based architecture design methodology for the compute-intensive deep neural networks (DNNs) implementation on memory-constrained platforms with insignificant compromise in accuracy. To demonstrate the advantages of our proposed architecture design methodology, we performed a detailed layer-wise performance analysis of a DNN with the help of metrics like memory savings and accuracies. Subsequently, we validated the proposed design methodology by implementing it on the resource-constrained AMD-Xilinx Kintex-7 field-programmable gate array (FPGA) chip for the considered DNN. A thorough analysis of our architecture design methodology results shows a significant memory savings of about 93% or compression of 15.7× with less than a 1% marginal reduction in accuracy.
A Scientific Study for Breast Cancer Detection Using Various Machine Learning Algorithms

A noncommunicable chronic illness, cancer impacts people of all ages, races, nationalities, and socioeconomic statuses indistinctly. Having this diagnosis confirmed illness is usually concerning for the patient. Because it is extremely difficult to predict. A significant spot in the ranking is taken by breast cancer, particularly in women. However, there is a very good possibility of a cure if it is discovered quickly. The cancer of the breast is among the most common type of cancer in women. Mammography also is one of the most successful methods for the early diagnosis and detection of breast cancer lowers the death rate. Mammograms, which are digital images of the breast, are used to identify the beginning signs of breast cancer. These radiographic images improve diagnostic precision while decreasing human error in cyst detection and diagnosis time. This study provides a scholarly analysis of machine learning techniques for cancer in breast detection and classification. In this research, we investigate how different machine-learning approaches affect the automation of breast imaging categorization. This study gathers examples that show how the machine learning approach is used to address various issues that have been identified through various analytical scientific investigations. This work aims to gather and compare the various screening techniques, classifiers, and their efficacy. Along with details about the kind of dataset used, the author has stated a Research Gap analysis. Numerous authors have used various machine learning algorithms on this common dataset, but the outcomes of the algorithms vary. The author has discussed these various algorithms and analyzed the findings in tabulation format.
Lightweight Jitter Suppression Algorithm for Medical Endoscopes

Digital video stabilization technology is an important branch of shake-suppression technology, which is widely used in medical and military fields with its advantages of high accuracy and portability. In order to reduce the low-frequency jitter caused by doctors when shooting and recording with handheld medical microscopes, this project investigates a low-cost jitter suppression algorithm for medical microscopes. We also design digital video stabilization technology algorithms based on OpenCV and C++ programs to achieve effective suppression and elimination of slight jitter in medical microscope videos. Experiments show that the algorithm in this paper provides a significant solution to the problem of slight jitter in medical microscope video, improves the efficiency of doctors in testing, and provides a technical guarantee for the practical application of medical microscope.
Pengembangan Media Pembelajaran E-Komik Berbasis Problem Based Learning (PBL) Terhadap Kemampuan Pemecahan Masalah

The purpose of this study was to develop Problem Based Learning (PBL) based E-Comic learning media on Problem Solving Ability that is valid, practical, and effective on student learning outcomes in statistics material in class XII at SMA Negeri 1 Sungai Laur. The method used is the ADDIE method. The subjects of this study were 29 class XII students of SMA Negeri 1 Sungai Laur, 3 material and media experts. The instruments used in this study were in the form of material expert validation sheets and media expert validation sheets, teacher response questionnaires, student response questionnaires and post-test questions based on the results of the research that had been carried out obtained the results of validation of E-Comic learning media based on Problem Based Learning (PBL). ) Regarding Problem Solving Ability has a validity level of 91.22% with very valid criteria, the practicality level of learning media is 89.60% with very practical criteria and the level of effectiveness of learning media is 93.10% with very effective criteria.
Interdisciplinary multilingual environment as facilitator of managing educational processes

The article examines the impact of the growing flow of information and the introduction of digital technologies on transformations in the field of education. The author notes that the ability to find and analyze the necessary information contributes to the formation of professional skills, along with the ability to comprehensively and systematically approach the solution of emerging problems using modern IT technologies. It is noted that these aspects have become dominant in modern education. The author sees opportunities for realizing the new goals in the integration of interdisciplinary relations and profile context. The combination of various disciplines with their own specifics and terminological base creates a special educational environment in which the sublanguages of specialties, the language of mathematical symbols and foreign language terms interact. The article shows how this kind of interdisciplinary multilingualism facilitates managing educational processes. The hypothesis that creating the multilingual environment is highly productive in building an individual learning trajectory for students with different levels of academic achievement via educational scaffolding is put forward.
Zero-Knowledge Predicates for Hashing to Prime: Theory and Applications

Hashing to prime in zero-knowledge takes as input a committed secret $$a$$ -bit input x and outputs a committed secret $${(k+1)}$$ -bit prime number $$p_x$$ . In this work we are considering zero-knowledge arguments that establish that the corresponding computations are done correctly and that the resulting commitment is indeed on the prime number derived from x. Depending on the number of zero-knowledge argument rounds k and the number of primality bases t used to establish primality, we obtain a soundness error probability of at most $$2^{-k}+ 2^{-t}$$ . While the corresponding ZK arguments have already been established in earlier work [22], we explore the use of hash-to-prime ZK arguments to enable dynamic encodings for a selection of cryptographic schemes, namely prime-based accumulator [2, 10], attribute-based credential [7], and graph signature schemes [19, 21].
Privacy-Preserving Traceable Encrypted Traffic Inspection in Blockchain-based Industrial IoT

Blockchain-based Industrial Internet-of-Things (IIoT) integrates the blockchain technology into the traditional IIoT infrastructure to provide secure and collaborative services. In IIoT, the traffic is usually encrypted using cipher suite (SSL/TLS) for secure communication, which makes it hard for middleboxes to detect malicious activity in the traffic. To address this problem, secure middleboxes that directly perform encrypted traffic inspection have been presented. Recently, a new privacy-preserving deep packet inspection (DPI) system on middlebox for IoT scenarios was proposed, but it suffered from the following two limitations: (i) no support for fast token detection; (ii) no support for tracing abnormal sources. To address the two limitations, we propose BTDPI, a privacy-preserving traceable DPI system that efficiently performs inspection over encrypted traffic in Blockchain-based IIoT. Technically, we adopt a two-layer filter architecture to improve the efficiency of detection, and moreover introduce a new online-offline certificateless aggregate signature with smart contract to design an identity traceability mechanism. The experiment result shows that BTDPI runs 26.7× faster for token detection with 3,000 tokens and 3,000 rules than the state-of-the-art work.
Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models

Knowledge distillation (KD), best known as an effective method for model compression, aims at transferring the knowledge of a bigger network (teacher) to a much smaller network (student). Conventional KD methods usually employ the teacher model trained in a supervised manner, where output labels are treated only as targets. Extending this supervised scheme further, we introduce a new type of teacher model for connectionist temporal classification (CTC)-based sequence models, namely Oracle Teacher, that leverages both the source inputs and the output labels as the teacher model's input. Since the Oracle Teacher learns a more accurate CTC alignment by referring to the target information, it can provide the student with more optimal guidance. One potential risk for the proposed approach is a trivial solution that the model's output directly copies the target input. Based on a many-to-one mapping property of the CTC algorithm, we present a training strategy that can effectively prevent the trivial solution and thus enables utilizing both source and target inputs for model training. Extensive experiments are conducted on two sequence learning tasks: speech recognition and scene text recognition. From the experimental results, we empirically show that the proposed model improves the students across these tasks while achieving a considerable speed-up in the teacher model's training time.
Exploring the Potential of Mixed Reality as a Support Tool for Industrial Training and Production Processes: A Case Study Utilizing Microsoft HoloLens

As processes become increasingly complex within companies, limitations in employees’ abilities and skills can reduce their work potential. To address this, traditional learning processes have shifted towards technology-assisted learning, utilizing virtual reality, augmented reality, and mixed reality as support tools to ensure the evolution of operators’ skills. The objective of this study was to test the use of mixed reality as a support tool in a sector of an assembly line by utilizing Microsoft HoloLens 2 glasses on a didactic bench in the process of assembling a pneumatic cylinder. The data collected by the smart glasses was used to develop a report on productive times and analyze the data to implement changes in the assembly process to achieve better results. Overall, this study highlights the potential of mixed reality as a support tool in the learning and control of operator tasks in an industrial context, improving the learning experience and sometimes even eliminating the need for a separate learning phase. The data collected also allowed for the development of reports on production times and stock control, further showcasing the benefits of utilizing mixed reality technology in the workplace.
Exploring OpenStack for Scalable and Cost-Effective Virtualization in Education

The technological landscape of cloud computing has transformed significantly with the advent of virtualization, allowing for the provision of computing resources over the internet. The leading cloud computing software providers offer this new concept, and various service providers currently exist, providing options for virtualizing services on-premises as well. OpenStack presents an open-source alternative for building virtual local or cloud setups that support petabytes of data, unlimited scale, and configurable networking. The tool’s features make it an ideal solution for large scale virtualization, reducing maintenance costs and optimizing hardware resource utilization, particularly in education and government sectors. This paper presents an overview of the OpenStack software, with a focus on constructing a scalable hosting architecture tailored to educational settings. We discuss functional and architectural details that are essential to implementing unique cloud computing models for virtualization purposes. We describe an experimental virtualization setup that was implemented within an educational scenario, along with a guideline for configuring OpenStack. Overall, this study offers insights into the potential of OpenStack for virtualizing large-scale educational setups, paving the way for cost-effective and efficient resource utilization.
Digital Competence And Intercultural Praxis Of Rural Secondary Education Teachers

In the multicultural and interconnected society, digital technology has become an essential pedagogical tool for teachers in rural areas, whose fundamental task is to integrate digital technology into the educational context. This study analyzes and evaluates the self-perception of teachers about their digital competence and the relationship with their performance in the intercultural environment of rural secondary education. The research approach was quantitative, non-experimental descriptive-correlational design, with a sample of 192 rural secondary education teachers from Huánuco-Peru, using the survey technique through the questionnaire instrument, validated by expert judgment. The results indicate that the digital competence and intercultural vision of teachers in rural areas is direct, on average more than 58% assume to have developed their digital competence at a high or very high level, while more than 71% have a positive perception about intercultural education; the Spearman correlation coefficient between the two variables is 0.75. It is concluded that there is a direct and complementary relationship between the development of digital competence and the interculturality of teachers, which leads to an efficient management of the teaching-learning process.
Cryptoverse and its Unflinching Cog of Fickleness

Investing in crypto currencies is highly risky and speculative because it is not backed by any economic fundamentals.This paper is an attempt to check if the volatility in cryptocurrency market is influenced by the volatile behaviour of three other markets, namely, Indian stock market, Volatility index and movement in the Oil prices.The time period of study ranges from February 28, 2017 to March 31, 2022 with daily frequency of data.A break point has also been identified to study the sub period movements.ARCH -GARCH family of models have been used to gauge the volatility transmissions.It is found that crude oil market and volatility index market spills the volatility in the cryptocurrency market but stock market does not play a significant role in transmitting the volatility.This is evident from the recent times when a huge fall in crypto currencies have been experienced with jig jag movements in the stock market behaviour.
STRATEGI PENERIMAAN SISWA BARU PADA SEKOLAH INKLUSI DI SCHOOL OF HUMAN JATISAMPURNA BEKASI

This research discusses the strategy of admitting new students at the School of Human (SOH), which is an inclusive school. The purpose of this research is to be able to describe the process of admitting new students to the school where the concept uses a pulse, meaning that all students can enter and attend school at SOH as long as there is still a new student enrollment quota. This research method is descriptive qualitative which presents in full the stages of admitting new students using the pulse method at SOH. The collection of data used is by interviews and documentation related to the acceptance of new students. Data analysis techniques using research data triangulation. In the research on the strategy of admitting new students to SOH without discriminating, it means that all students can become SOH students as long as there is an available quota. As an inclusive school, SOH implements three important stages in admitting new students, namely initial observation which contains general registration, further observation for special students, and class determination using the Multiple Intelligences Research (MIR) method.