Reviewerly/exHarmony huggingface dataset:
Analysis of Research Trends in the Sociology of Education from 1990 to 2014

The objective of this study is to investigate the principal aspects of the research trends on the development of Korean educational sociology from 1990 to 2014.This is mostly based on 567 papers on educational sociology in order to outline the prospective task that academic development should undertake.According to the study, the most intensively studied area in Korean educational sociology is educational systems and social justice with 181 papers (31.42 %).It is followed by studies on society's influence on the education in 141 papers (24.47 %).The most intensively studied specific area concerns the issue of education and equality in 69 papers (11.97 %), followed by the policy on teachers and educational reforms in 60 papers (10.41 %).The task that this study presents is as follows.First, diversifying the analytical framework when analyzing study trends is necessary.Second, to validate the trends in the study educational sociology in Korea, mainly on the basis of the results of this study, it also becomes necessary to extensively study how educational sociology permeates the broader society, doctoral and master's degree theses, and books on educational sociology that have been studied so far.It would also be meaningful to compare and analyze the research trends in the sociology of education with those of other sociology journals.
A Traffic Splitting Algorithm for Load Balancing in Tor

As the most popular anonymous communication system, Tor provides anonymous protection for users by sending their messages through a series of relays. Due to the use of the bandwidth-weighted path selection algorithm, many more users choose routers with high bandwidth as relays. This will cause the utilization of high bandwidth routers to be much higher than that of low bandwidth routers, which will bring congestion risk. The Quality of Service (QoS) is difficult to guarantee for users who need delay-sensitive services such as web browsing and instant messaging. To reduce the average load of routers and improve the network throughput, we propose a circuit construction method with multiple parallel middle relays and conduct a dynamic load allocation method. The experiment demonstrates that our proposed method can provide better load balancing. Compared with other multipath anonymous communication networks, our proposed method can provide better anonymity.
Penerapan Algoritma FP Growth pada Penjualan Produk Distro Raden Madura

The transaction data for the purchase of the distro's cost of selling is quite stable but its sales are less than the sales target, so it is necessary to manage sales data. The focus of this research is focused on the management of purchase transaction data that will be used as a dataset. The management uses the FP-Growth algorithm which takes from the item set, from the algorithm it gets a buying pattern that will become information for making a decision. Based on the results of the study, the authors conclude that the pattern of purchases by consumers gets a pattern of sales, namely, consumers by buying 1 item of T-shirts from distributions will buy back one item of T-shirts from 26 different distributions. consumers by buying 2 items of t-shirts distributions will buy back 1 item of t-shirts from different distributions as many as 72 patterns, and consumers by buying 3 items of t-shirts distributions will buy back 1 item of t-shirts with different distributions of 25 patterns.
Teachers’ Perceptions on the Literacy Skills of 5th Grade Primary School Students During Covid-19 Pandemic in Mandailing Natal Regency

This study aims to discover the literacy skills of 5th grade primary school students during covid-19 outbreak in Mandailing Natal Regency based on teachers’ perceptions. Descriptive qualitative approach was applied with a simple quantitative support in form of percentages. The respondents in this study were 23 primary school teachers of fifth year class from 23 different primary schools located in 23 districs in Mandailing Natal Regency. To obtain the data, a semi structured interview was conducted adapted from four literacy skills consisting of listening, speaking, reading and writing. The obtained data subsequently were analyzed by thematic analysis based on specific separated themes. The findings demonstrated four results; (1) in listening skill, dominantly, 11 (48%) teachers thought that students’ literacy level is average and for higher level of listening 11 (48%) teachers shared low. (2) in speaking, 11 (48%) teachers stated average and for higher level, 13 (57%) teachers assumed low. (3) in reading, 12 (52%) teachers noted average, and (4) in writing, 11 (48%) teachers perceived average and for higher level, (52%) 12 of teacher-respondents responded that students’ literacy skills were average. Based on these results, it is highly expected to be worthwhile for teachers, lecturers, academician, stakeholders and public community to anticipate and to develop the students’ literacy development by supporting and facilitating the students’ fundamental needs such as children books, complete and comfortable school library, providing reading corner and ensuring teachers’ mastery on digital literacy platforms amidst covid-19 pandemic and in the coming years, as well.
Membandingkan Tingkat Efisiensi Metode Tsukamoto dan Sugeno untuk kasus Pneumonia

There are three methods that can be used to implement the Fuzzy Inference System (FIS) , namely Tsukamoto, Mandani and Sugeno. Each of the three methods has its own characteristics and advantages. Several third studies used this method to compare the efficiency level of different cases. This study also aims to see the most efficient method by comparing the two FIS methods, namely Tsukamoto and Sugeno, based on medical cases from previous studies that have tested the validity of the results from pulmonary specialists. The data used are the same data as previous studies, namely regarding the diagnosis of pneumonia. The analytical method used is Mean Absolute Percantage Error (MAPE) to get the accuracy value of how close a measurement result is to the actual number. Based on the cases tested, the key from the Sugeno method resulted in a smaller MAPE than Tsukamoto, namely 3.15%, which means that the Sugeno method results closer to the pneumonia score/actual value.
Excerpts From the <i>New Dictionary of Cybersecurity, 2036</i>

The New Dictionary of Cybersecurity, 2036 is an invaluable resource for high school students enrolled in a cybersecurity education course.
Deep Learning based Emotion Analysis and Recognition for Movie Review

Abstract: In the present world of AI-based systems, image processing is a prominent emerging method. One of the topics covered by artificial intelligence machine learning techniques is emotion recognition. Emotion Recognition is the process of analyzing facial expressions, body posture, gestures, and voice to interpret a person's thinking and current mental state. This allows one to determine whether or not the individual is interested in the continuing action. The Audience Feedback Analysis' fundamental functionality Using Emotion Recognition allows you to collect and analyze every individual's facial expression during or after a seminar, discussion, lecture, or keynote, and provide an analysis of the average emotional state of the audience. The major goal of the proposed system is to offer the event organizer or administrator with accurate and unbiased feedback by analyzing the expressions of the crowd during the duration of the event or at a specific time interval. Keywords: Convolutional Neural Network(CNN), Emotion recognition, Deep-Learning, ReLU.
Assessment of Groundwater in Al-Lith Region

There are many random wells in the city of Al-Lith, especially in the east of the city of Al-Lith in (the study area).There are no desalination companies in the area, and there are no data showing that the wells are potable & non-potable, and there is no information about the groundwater level in the area.In the beginning, the locations of the targeted wells were determined to conduct the study on, and then the samples were collected from the 20 identified sites in the east of the city of Al-Lith, from 20 wells, and the depth of each well was also determined.According to the results, the aim is to give idea for the water wells whether potable or non-potable.
Advantages and Disadvantages of High School Students’ Mobile Learning Based on Mobile Phones: Taking Erongo Region as An Example

There has been an unprecedented growth in the number of mobile devices in recent years. This spread and prevalence of mobile devices has promoted the popularity of mobile applications. This has instigated the need to understand the usage pattern of mobile devices particularly among high school teachers and learners. This paper reports on the results of a survey of one hundred and twenty (120) high school learners and twenty four (24) high school teachers in the Erongo region of Namibia drawn from three different high schools. This research is conducted in order to find out the usage pattern of mobile devices and to ascertain the possibility of its adoption in Namibian High Schools. This study is expected to establish an understanding of the usage patterns basedon its benefit for pedagogy.
BENEFITS OF VR IN EDUCATION// International Journal of Educational and Scientific Research 1(14), 2022/ Chief Editor D. Sidorkin /OEAPS OU - Tallinn, Estonia (Open European Academy of Public Sciences). Marseille, France 30.04.2022: OEAPS OU., 2022 - P. 63-68.

Education is the base for a thriving society, and the transfer of knowledge has been a top priority for civilizations since the very beginning. People are constantly looking for ways to make knowledge transfer more easily, more quickly, and more effectively. In the era of digital devices, we have an opportunity to enable better learning with technology. Virtual Reality (VR) seems to be the natural next step for the evolution of education. In this article, I’ll show how virtual reality can transform the way educational content is delivered.
Automated Audio Captioning with Epochal Difficult Captions for Curriculum Learning

In this paper, we propose an algorithm, Epochal Difficult Captions, to supplement the training of any model for the Automated Audio Captioning task. Epochal Difficult Captions is an elegant evolution to the keyword estimation task that previous work have used to train the encoder of the AAC model. Epochal Difficult Captions modifies the target captions based on a curriculum and a difficulty level determined as a function of current epoch. Epochal Difficult Captions can be used with any model architecture and is a lightweight function that does not increase training time. We test our results on three systems and show that using Epochal Difficult Captions consistently improves performance
The Impact Of Deprivation During The Covid-19 Pandemic On Values Of Students

The article considers social deprivation as a logical consequence of various restrictions for students, especially during lockdowns, during the COVID-19 pandemic.The social deprivation provoked by the pandemic differs in certain features.For example, during this period, online education of students was considered not as a new form of digital education, but as a challenge -stressor social deprivation during the pandemic.The subject of the study was the study of the influence of social deprivation on the value orientations of medical university students.Medical university students during the pandemic are the most vulnerable part of university students, as they experience a double load of stressors, as citizens of the country and as future medical workers, who may have to fight for the lives of their patients in extreme conditions.The research was conducted using a questionnaire using the Google platform.The study involved 301 respondents -students of a medical university.The analysis of the results confirmed that under the influence of social deprivation during the pandemic, there were significant changes in such value orientations as the value perception of freedom, prognostic values, value attitude to family, relatives, friends, value attitude to oneself.Almost half of the students have changed their ethical consciousness.The results of the study confirmed the impact of social deprivation during the COVID-19 pandemic on the value orientations of medical university students.
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The Relationship between Indonesian Language Learning and the Environment

Learning Indonesian language and literature is one of the learning priorities in schools. Learning Indonesian language and literature in the 2013 curriculum requires students to have a balanced attitude, knowledge and skills. Students are expected to be able to master four aspects of language skills, namely writing, listening, speaking, and reading. Learning Indonesian language and literature is the basis of all existing learning. Language and reasoning skills or logic in students are obtained in the process of learning Indonesian language and literature. In addition, learning Indonesian language and literature is able to improve students' literacy skills, namely language skills which include proficiency in receptive skills, proficiency in analytical skills and, proficiency in productive skills. Indonesian language learning is closely related to the surrounding environmental conditions. The environment has a great influence on children's language development. Environment-based language learning aims to make students participate in protecting the environment.
Differentially Private Decoding in Large Language Models

Recent large-scale natural language processing (NLP) systems use a pre-trained Large Language Model (LLM) on massive and diverse corpora as a headstart. In practice, the pre-trained model is adapted to a wide array of tasks via fine-tuning on task-specific datasets. LLMs, while effective, have been shown to memorize instances of training data thereby potentially revealing private information processed during pre-training. The potential leakage might further propagate to the downstream tasks for which LLMs are fine-tuned. On the other hand, privacy-preserving algorithms usually involve retraining from scratch, which is prohibitively expensive for LLMs. In this work, we propose a simple, easy to interpret, and computationally lightweight perturbation mechanism to be applied to an already trained model at the decoding stage. Our perturbation mechanism is model-agnostic and can be used in conjunction with any LLM. We provide theoretical analysis showing that the proposed mechanism is differentially private, and experimental results showing a privacy-utility trade-off.
The methodology of practical botany classes in distance learning during the COVID-19 pandemic

The forced emergency widespread transition to distance learning during the COVID 19 pandemic period required the urgent development of new tools for working in the digital environment, adaptation to new methods of communication in the system teacher - student. The use of digital tools and technologies in the implementation of bachelors studies in the context of the digitalization of higher education and the introduction of online education complicated the teachers activity in designing the learning process. There was a lack of research devoted to the study and description of teaching Biology practice (including Botany) in the online format, there were no teaching materials for organizing practical work with students. In connection with the situation, it was necessary to search for new ways to implement various types of activities in the distance format, search for new ways of communication in the system teacher - student, and test modern pedagogical technologies. Since the autumn of 2020, many universities, including P.P. Ershov Ishim Pedagogical Institute (Branch) of Tyumen State University, have been successfully using the Microsoft Teams platform. The following methods were used during the study: theoretical and comparative analysis of literature, systematization and synthesis, collection and evaluation of the results of the completed tasks (reports on working with cases, test results and questionnaires). The results of the individual task - a report on working with case illustrations showed that 47% of students were rated satisfactory and 53% of students - good. The qualitative progression, according to the test results, was 53%. The results of the survey Reflexive Screen made it possible to identify the positive effect, obtained from the lesson held in the online format (100% of students were interested in the lesson). The paper describes in detail the methods for online teaching Botany at the lesson, conducted in the winter of 2021, and analyzes it.
Activating hidden non-Markovianity with the assistance of quantum SWITCH

There are certain dynamics which although are non-Markovian but never show information backflow. We show that if two such dynamical maps are considered in a scenario where the order of application of these two dynamical maps are not definite, then the effective channel can manifest information backflow. In particular, we use quantum SWITCH to activate such a channel. In contrast, activation of those channels are not possible even if one uses many copies of such channels in series or parallel action. We then investigate the actual cause and dynamics behind the quantum SWITCH experiment and find out that after the action of quantum SWITCH both the CP (Complete Positive)- divisiblity and P (Positive)- divisibility of the channel breaks down, triggering the information backflow. Our study elucidate the advantage of quantum SWITCH by investigating its dynamical behaviour.
Komparasi QoS Load Balancing Pada 4 Line Internet dengan Metode PCC, ECMP dan NTH

The ability to communicate on the internet who do not know the distance and time are the key requirements for improving the work on the internet. To improve performance access to the internet is required solutions as the best of the internet network that can be relied on, so that companies use more than one ISP (Internet Service Provider) to maintain the quality of the internet access with the goal to avoid the interruption of internet access and have backup internet access network if you experience down resulting in disruption of the company's performance in carrying out their duties. One of the contributions of information technology to overcome the merging problems two or more-line internet is a technique of load balancing. This study aims to determine the comparison of QoS load balancing on a 4 line by the method of PCC, ECMP and NTH on real tool MikroTik rb2011UiAS-2HnD. The test method uses speedtest.net from the results it will be seen ping, speed download, speed date, as well as monitoring from the side of the router MikroTik. Stages of research using the method of NDLC (Network Development Life Cycle), namely analysis, design, simulation, prototype, implementation, monitoring and management. With the usefulness of this research is to know the performance resulting from each method of load balancing that is PCC, ECMP and NTH with 4-line internet on MikroTik
Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks

The convergence of GD and SGD when training mildly parameterized neural networks starting from random initialization is studied. For a broad range of models and loss functions, including the most commonly used square loss and cross entropy loss, we prove an ``early stage convergence'' result. We show that the loss is decreased by a significant amount in the early stage of the training, and this decrease is fast. Furthurmore, for exponential type loss functions, and under some assumptions on the training data, we show global convergence of GD. Instead of relying on extreme over-parameterization, our study is based on a microscopic analysis of the activation patterns for the neurons, which helps us derive more powerful lower bounds for the gradient. The results on activation patterns, which we call ``neuron partition'', help build intuitions for understanding the behavior of neural networks' training dynamics, and may be of independent interest.
The Presence and Roles of University Museums in Indonesia

The study of museums in almost all leading universities in Indonesia continues to develop from time to time. This research aims to explore the arguments of the campus community about the founding factors and their opinions about the presence and roles of university museums. The Airlangga University History and Culture Museum and the UGM Mandala Majapahit Museum are the main references for this research. A phenomenological approach with in-depth interviews, observations and relevant literature studies is used in this study. Research findings reveal that several factors of the university museums presence are due to the initiatives from university leaders. University museums serve as historical reminders, fulfill educational and research needs, and instill the identity of campus communities. In relation to the role of university museums, the campus community believes that its role is similar to libraries in supporting learning and research activities and even as community service. Considering the importance of university museums, both university leaders and the education ministry authorities are required to provide optimal support, especially by providing official regulations to support the function of museums as part of information institutions at universities.
UniInst: Unique Representation for End-to-End Instance Segmentation

Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e.g., multiple boxes, grids, and anchor points) are inferred for one instance, which leads to multiple duplicated predictions. Thus, mainstream methods usually rely on a hand-designed non-maximum suppression (NMS) post-processing step to select the optimal prediction result, which hinders end-to-end training. To address this issue, we propose a box-free and NMS-free end-to-end instance segmentation framework, termed UniInst, that yields only one unique representation for each instance. Specifically, we design an instance-aware one-to-one assignment scheme, namely Only Yield One Representation (OYOR), which dynamically assigns one unique representation to each instance according to the matching quality between predictions and ground truths. Then, a novel prediction re-ranking strategy is elegantly integrated into the framework to address the misalignment between the classification score and the mask quality, enabling the learned representation to be more discriminative. With these techniques, our UniInst, the first FCN-based box-free and NMS-free instance segmentation framework, achieves competitive performance, e.g., 39.0 mask AP using ResNet-50-FPN and 40.2 mask AP using ResNet-101-FPN, against mainstream methods on COCO test-dev 2017. Moreover, the proposed instance-aware method is robust to occlusion scenes, outperforming common baselines by remarkable mask AP on the heavily-occluded OCHuman benchmark. Code is available at https://github.com/b03505036/UniInst.