Reviewerly/exHarmony huggingface dataset:
Evaluating Bayes Error Estimators on Read-World Datasets with FeeBee

The Bayes error rate (BER) is a fundamental concept in machine learning that quantifies the best possible accuracy any classifier can achieve on a fixed probability distribution. Despite years of research on building estimators of lower and upper bounds for the BER, these were usually compared only on synthetic datasets with known probability distributions, leaving two key questions unanswered: (1) How well do they perform on real-world datasets?, and (2) How practical are they? Answering these is not trivial. Apart from the obvious challenge of an unknown BER for real-world datasets, there are two main aspects any BER estimator needs to overcome in order to be applicable in real-world settings: (1) the computational and sample complexity, and (2) the sensitivity and selection of hyper-parameters. In this work, we propose FeeBee, the first principled framework for analyzing and comparing BER estimators on any modern real-world dataset with unknown probability distribution. We achieve this by injecting a controlled amount of label noise and performing multiple evaluations on a series of different noise levels, supported by a theoretical result which allows drawing conclusions about the evolution of the BER. By implementing and analyzing 7 multi-class BER estimators on 6 commonly used datasets of the computer vision and NLP domains, FeeBee allows a thorough study of these estimators, clearly identifying strengths and weaknesses of each, whilst being easily deployable on any future BER estimator.
Assessing Competencies of Academic Staff in University from Point of View of Students: Local Aspect

Identified are criteria for assessing the competence level of the academic staff of the local university, formulated by students of the Rostov State University of Economics. It is argued that university students are endowed with a certain experience of interaction with the teaching staff and the relative objectivity of the assessment of both the experience itself and those components of the educational process that must correspond to the professional competencies of each lectures. The relevance of the issue is justified by the author’s research conducted in the social network “VK”, where group conversations were created with the deep involvement of students of the participating groups. During the 2020–2021 academic year, students were asked to evaluate the professional competencies of the teacher (the author of the article) according to certain criteria, but without categorical reference to the specified ones, i.e. students could independently formulate assessment criteria and the level of competence within a separate discipline, knowledge of teaching techniques, information and computer technologies, software products, podcasts, tools of social networks and messengers, etc. The key focus was on information and computer technologies and their implementation in the educational process. The study involved 107 students of different groups, bachelor’s and master’s degrees. The results were summarized and discussed among university colleagues and 4th-year graduates of the Rostov State University of Economics, Rostov-on-Don.
The Use of Conjunctions in Buginese Compound Sentences in Junior High School Students

Abstract. This study aims to describe the use of conjunctions in buginese  compound sentences of eighth grade students of the Soppeng State Junior High School. And describe errors in compound sentences of eighth grade students of State Junior High School one Soppeng in Bugis language. This research is a qualitative research. The data in this study are conjunctions in Bugis compound sentences obtained from eighth grade students of the Soppeng State Junior High School. Data collection techniques in this study were carried out by tests. The data analysis technique was carried out in three stages, namely, (1) classifying all conjunctions based on their types, (2) analyzing the use of conjunctions, (3) drawing conclusions from the results of the analysis. On the accuracy of use and forms of errors in the use of conjunctions in equivalent compound sentences include (1) compound sentences equivalent to addition, (2) compound sentences equivalent to resistance, (3) compound sentences equivalent to choice. (1) compound sentences with subject density, (2) compound sentences with object density, (3) compound sentences with predicate density, (4) compound sentences with adverb density. Furthermore, (5) the accuracy of the use and forms of errors in the use of conjunctions in mixed compound sentences. After finding the accuracy of the use and the form of errors in the use of conjunctions in Bugis compound sentences, the researcher can conclude that students' understanding of the use of conjunctions in compound sentences is still low or not capable. Keywords : conjunctions, compound sentences.
Comment Sections and the Ethical Demands of Democracy

The decision of some online news platforms to eliminate comment sections is both understandable and frustrating. It is understandable as one does not have to read far into comment sections to see d...
KLASIFIKASI PENYAKIT PARU BERDASARKAN CITRA X-RAY THORAX MENGGUNAKAN METODE FRAKTAL BOX COUNTING

The lungs are important and vital organs that are easily infected. Lung examination can be done through examination of x-ray images, but the clinical diagnosis of the results of x-ray images is difficult. In making it easier to analyze the diagnosis of the results of the x-ray image, this research carried out the process of classifying x-ray images based on the type of disease.. The image that was processed was 120 (one hundred and twenty) chest x-ray images by segmenting the lung area. From this lung area, then canny edge detection is done to take spots from lung disease. From smart fractal edge detection values are obtained, which will be calculated using the box calculation method, so classification can be done. The results of the experiment using the nearest k-neighbor method (K-NN), where the results with the greatest accuracy are shown by the value K = 5 which is 79.65% and the lowest accuracy at the value K = 7 is 71.28%.
Machine learning algorithm for information extraction from gynaecological domain in Tamil

Information Extraction is a significant task in Natural Language Processing. It is the process of extracting useful information from unstructured text. Information extraction helps in most of the recent NLP applications like scientific research, financial investigation, business intelligence, media monitoring, healthcare records management, agriculture, and pharmacy research. There are several information extraction research approaches using many techniques from English dataset. As a multi lingual country India, it is actually challenging task to extract information from text in Indian language. Such research work has been done in the following domain data travel, food, agriculture, weather casting, social media, marketing and bio- medical. In this research work the relevant information extracted from Gynaecology related text data in Tamil language. The combination of machine learning based classification model and ontology representation is used extract the useful information. Auto filling IE framework is designed to extract the appropriate information in a structured format. The user query will be pre-processed and converted into entities to check from the classified data using ontological representation by using machine learning based classification model naive bayes classification. From the ontological representation entity based relation extraction will be performed to fill the IE framework. The proposed IE framework given good results in extracting relevant information based on user query. It was analyzed for more than 57 user queries regarding gynaecological issues. The 75% of accuracy obtained for the correctness in user queries.
Models of using immersive teaching technologies in the practical activity of a teacher of informatics

Problem and goal. Virtual, augmented mixed reality and augmented virtuality are becoming integral attributes of the immersive educational environment, disposed to continuous learning and comprehensive development. The relevance of the study of models of using immersive technologies in the activities of a computer science teacher is beyond doubt, because they are the real embodiment of new methodological ideas and approaches. Methodology. In the course of the research, the analysis of models of immersive learning technologies for the practical activities of a computer science teacher was carried out. On its basis, a set of computer tools and equipment has been proposed that allows introducing immersive technologies into educational practice. Results. Methodological recommendations on the use of virtual, augmented and mixed reality technologies both in informatics lessons and after school hours are presented. The key topics of the computer science program, in which it is advisable to use immersive technologies, have been identified. Methodological approaches to the transformation of informatics teaching in the context of digitalization of education have been formulated. Conclusion. The results of the study indicate that immersive learning technologies can be successfully applied both in informatics lessons and outside the classroom. They not only contribute to the immersion of students in an interactive environment, but also increase the interest, motivation and quality of their knowledge. Lessons using immersive technologies open up new opportunities for a computer science teacher for professional growth, methodological and subject self-improvement.
Exploring Pair-Wise NMT for Indian Languages

In this paper, we address the task of improving pair-wise machine translation for specific low resource Indian languages. Multilingual NMT models have demonstrated a reasonable amount of effectiveness on resource-poor languages. In this work, we show that the performance of these models can be significantly improved upon by using back-translation through a filtered back-translation process and subsequent fine-tuning on the limited pair-wise language corpora. The analysis in this paper suggests that this method can significantly improve a multilingual model's performance over its baseline, yielding state-of-the-art results for various Indian languages.
Analisa Unjuk Kerja Software Defined Radio (SDR) dengan Teknik Quadrature Amplitude Modulation (QAM)

The development of wireless communication technology require technology (software and hardware) that can keep up with changing standards. SDR platform is a software-based wireless technology that can keep up with technological developments without changing hardware. WARP is one of SDR platforms developed by American Rice University. This research to determine the performance of the SDR with the QAM technique. The performance system can be seen from BER for different distance between transmitter and receiver and different level QAM. The research was carried in two enviroment (indoor and outdoor) with LOS propagation. The results show that the value of BER 4-QAM in the two enviroment is the smallest than another levels. The value of BER 4-QAM at indoor is 0.0023 and outdoors is 0 for a distance of 6 meters, so the performance of outdoor is better than indoor. SDR performance can be improved using QAM techniques.
A Novel Picture Steganographic Approach For Hiding Text In Color Images Through HSI Color Model

Picture “Steganography” is the most common way of installing message in pictures with the end goal that its reality can't be identified by “Human Visual System (HVS)” and is known distinctly to sender and collector. The paper presents a novel methodology for picture “Steganography” utilizing “Hue-Saturation-Intensity (HSI)” shading space dependent on “Least Significant Bit (LSB)”. The proposed strategy changes the picture from “RGB” shading space to “Hue-Saturation-Intensity (HSI)” shading space and afterward implants privileged information inside the “Intensity Plane (I-Plane)” and changes it back to “RGB” shading model subsequent to inserting. The said strategy is assessed by both abstract and Objective Analysis. Tentatively it is tracked down that the proposed strategy have bigger “Peak Signal-to Noise Ratio (PSNR)” values, great impalpability and numerous security levels which shows its prevalence as thought about over a few existing techniques.
중학생의 읽기 능력 향상을 위한 독서교육

This study aims to study the ways to improve reading skills of middle school students through suitable reading education. Modern society is information age, in which knowledge and information are overflowing. It is a society that requires the ability to select proper information and utilize it. Although various information is obtained through various media through the spread of computers and the development of the Internet through industrialization, reading ability is the fundamental ability for selecting and utilizing this information. As a result, this study focuses on reading education in order to improve the reading skills of middle school students. Reading ability refers to the ability to read and understand various passages and reading education can play an important role in improving reading ability. This paper presents a plan for making reading everyday habit through the method of reading education tailored to the interests and right level of middle school students. It also suggests a plan for proper reading education for improving reading ability of them. This research figures out that reading education is essential for middle school students to improve their reading skills. Various methods of reading education contributes developing creative and critical problem-solving skills.
Teachers, students using Zoom for e-learning see alarming behavior on platform

A number of virtual classroom hacking incidents that have occurred since the new school year began, leaving some parents concerned.
Enhancing Secure Coding Assistant System with Design by Contract and Programming Logic

The system titled Secure Coding Assistant was developed to automate early detection for a subset of the Java secure coding rules specified by the SEI CERT at the Carnegie Mellon University. This system can help Java programmers significantly reduce security vulnerabilities in their code caused by the violations of secure coding rules. Since other software defects can also lead to security vulnerabilities, efforts have been taken to extend Secure Coding Assistant aiming at empowering programmers to detect, locate and remove code errors during coding time. This paper presents an enhancement to Secure Coding Assistant by a combination of Design by Contract and Programming Logic. Java programmers using this system are advised to provide their design contracts, i.e., logic assertions, for program structures of methods, if-then-else statements and while-loop statements. The design contracts defined by programmers can be automatically checked at the time of their program execution. To further facilitate the process of detecting and locating of code errors, using the programmers-defined design contracts, sub-design contracts can be automatically generated by the system based on the inference rules for the if-then-else statement and the while-loop statement in programming logic. The sub-design contracts generated by the system can also be automatically checked at dynamic time. In addition, based on the assignment axiom and the inference rule for the sequence statement in programming logic, the weakest pre-conditions of certain assignment sequences can be automatically generated from the post-conditions of the sequences, enabling programmers to statically analyze the correctness of the corresponding design contracts they specify. With the enhancement presented, Secure Coding Assistant can assist programmers for the early detections of not only secure coding rule violations but also errors in code. These early detections are performed in unison with the coding process to pursue software security.
Main characteristics and prospects of big data technologies application

The article is devoted to an overview of the Big Data technology and its features. The main characteristics are given that make it possible to distinguish this technology from others, the principles of working with it, which allow the analysis to be carried out as efficiently as possible. The necessity of using and the prospects of using Big Data technologies is substantiated, the results of using this technology are considered. The analysis of existing software hardware used for analysis and processing of big data, such as MapReduce and NoSQL, is carried out, their advantages and features are highlighted.
Vulnerability and risk assessment for operating system (OS) with framework STRIDE: comparison between VulnOS and Vulnix

&lt;p&gt;&lt;span lang="EN-US"&gt;The rapid development of information technology has made security become extremely. Apart from easy access, there are also threats to vulnerabilities, with the number of cyber-attacks in 2019 showed a total of 1,494,281 around the world issued by the &lt;/span&gt;&lt;span lang="EN-US"&gt;national cyber and crypto agency (BSSN) honeynet project. Thus, vulnerability analysis should be conducted to prepare worst case scenario by anticipating with proper strategy for responding the attacks. Actually, vulnerability is a system or design weakness that is used when an intruder executes commands, accesses unauthorized data, and carries out denial of service attacks. The study was performed using the AlienVault software as the vulnerability assessment. The results were analysed by the formula of risk estimation equal to the number of vulnerability found related to the threat. Meanwhile, threat is obtained from analysis of sample walkthroughs, as a reference for frequent exploitation. The risk estimation result indicate the 73 (seventy three) for the highest score of 5 (five) type risks identified while later on, it is used for re-analyzing based on the spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of prvilege (STRIDE) framework that indicated the network function does not accommodate the existing types of risk namely spoofing.&lt;/span&gt;&lt;/p&gt;
Web image search engine based on LSH index and CNN Resnet50

To implement a good Content Based Image Retrieval (CBIR) system, it is essential to adopt efficient search methods. One way to achieve this results is by exploiting approximate search techniques. In fact, when we deal with very large collections of data, using an exact search method makes the system very slow. In this project, we adopt the Locality Sensitive Hashing (LSH) index to implement a CBIR system that allows us to perform fast similarity search on deep features. Specifically, we exploit transfer learning techniques to extract deep features from images; this phase is done using two famous Convolutional Neural Networks (CNNs) as features extractors: Resnet50 and Resnet50v2, both pre-trained on ImageNet. Then we try out several fully connected deep neural networks, built on top of both of the previously mentioned CNNs in order to fine-tuned them on our dataset. In both of previous cases, we index the features within our LSH index implementation and within a sequential scan, to better understand how much the introduction of the index affects the results. Finally, we carry out a performance analysis: we evaluate the relevance of the result set, computing the mAP (mean Average Precision) value obtained during the different experiments with respect to the number of done comparison and varying the hyper-parameter values of the LSH index.
STaCK: Sentence Ordering with Temporal Commonsense Knowledge

Sentence order prediction is the task of finding the correct order of sentences in a randomly ordered document.Correctly ordering the sentences requires an understanding of coherence with respect to the chronological sequence of events described in the text.Documentlevel contextual understanding and commonsense knowledge centered around these events are often essential in uncovering this coherence and predicting the exact chronological order.In this paper, we introduce STaCK -a framework based on graph neural networks and temporal commonsense knowledge to model global information and predict the relative order of sentences.Our graph network accumulates temporal evidence using knowledge of 'past' and 'future' and formulates sentence ordering as a constrained edge classification problem.We report results on five different datasets, and empirically show that the proposed method is naturally suitable for order prediction, thus demonstrating the role of temporal commonsense knowledge.The implementation of this work is available at: https://github.com/declare-lab/sentence-ordering.
GENDER DIFFERENCES IN READING STRATEGIES AMONG VOCATIONAL HIGH SCHOOL STUDENTS

Second language learners use different reading strategies so they can comprehend a text. Nevertheless, generally recognizing the value of reading strategies is that some work on whether or not ESL male and female have similar or different strategic reading habits has different results. This research was conducted to determine what reading strategies are reported to be used by male and female students when they are taught using three-phase approach and whether the use of such reading strategies varies between male and female or not. Researcher performed this study in quantitative approach in survey design. The system is questionnaire. The researcher was conducted as the subject in SMKN 1 JOMBANG, with 10 th grade students. The result of the study showed that both male and female student actively applied some reading strategies in their reading process. Male and female have similarity in their preferences in using reading strategy, they also have differences in using some reading strategy.
Re-energizing Domain Discriminator with Sample Relabeling for Adversarial Domain Adaptation

Many unsupervised domain adaptation (UDA) methods exploit domain adversarial training to align the features to reduce domain gap, where a feature extractor is trained to fool a domain discriminator in order to have aligned feature distributions. The discrimination capability of the domain classifier w.r.t the increasingly aligned feature distributions deteriorates as training goes on, thus cannot effectively further drive the training of feature extractor. In this work, we propose an efficient optimization strategy named Re-enforceable Adversarial Domain Adaptation (RADA) which aims to re-energize the domain discriminator during the training by using dynamic domain labels. Particularly, we relabel the well aligned target domain samples as source domain samples on the fly. Such relabeling makes the less separable distributions more separable, and thus leads to a more powerful domain classifier w.r.t. the new data distributions, which in turn further drives feature alignment. Extensive experiments on multiple UDA benchmarks demonstrate the effectiveness and superiority of our RADA.