Reviewerly/exHarmony huggingface dataset:
Privacy in Public Archives: Managing Personally Identifiable Information in Special Collections

Archivists aim to make research and manuscripts accessible to the public. However, accessibility becomes tricky when donors or institutions enforce limitations. Sometimes limitations need to be enforced, especially when dealing with sensitive information such as personally identifiable information (PII), unpublished works, and student records. Redactions and restrictions may be necessary in these situations, but archives find this difficult to accomplish because of the size of the collections, results from previous accession practices, and the lack of staff and resources. The Special Collections department at Edith Garland Dupré Library, University of Louisiana at Lafayette is addressing this problem and has put forward methods to confront its backlog of PII while staying true to its accessibility mission. This article examines the challenges of handling PII in physical archival materials, the impact of More Product, Less Process (MPLP) on sensitive information, and how the Special Collections department, while using some MPLP processing methods, adopted a slightly more meticulous and efficient approach to protect privacy while still providing access.
A Theory-Driven Self-Labeling Refinement Method for Contrastive  Representation Learning

For an image query, unsupervised contrastive learning labels crops of the same image as positives, and other image crops as negatives. Although intuitive, such a native label assignment strategy cannot reveal the underlying semantic similarity between a query and its positives and negatives, and impairs performance, since some negatives are semantically similar to the query or even share the same semantic class as the query. In this work, we first prove that for contrastive learning, inaccurate label assignment heavily impairs its generalization for semantic instance discrimination, while accurate labels benefit its generalization. Inspired by this theory, we propose a novel self-labeling refinement approach for contrastive learning. It improves the label quality via two complementary modules: (i) self-labeling refinery (SLR) to generate accurate labels and (ii) momentum mixup (MM) to enhance similarity between query and its positive. SLR uses a positive of a query to estimate semantic similarity between a query and its positive and negatives, and combines estimated similarity with vanilla label assignment in contrastive learning to iteratively generate more accurate and informative soft labels. We theoretically show that our SLR can exactly recover the true semantic labels of label-corrupted data, and supervises networks to achieve zero prediction error on classification tasks. MM randomly combines queries and positives to increase semantic similarity between the generated virtual queries and their positives so as to improves label accuracy. Experimental results on CIFAR10, ImageNet, VOC and COCO show the effectiveness of our method. PyTorch code and model will be released online.
Looping Through Multidimensional Arrays

Using for loops with multidimensional arrays can be a bit tricky, but essentially they are the same as loops created with simple arrays; you just have to have a loop for each dimension nested within another.
Adding Pre-Training and Feedback Enhances the Benefits of Adaptive Flashcard Training

Adaptive training is tailored instruction designed to target an individual’s strengths and weaknesses, and it has been shown to improve learning outcomes and efficiency in a variety of domains, including flashcard training. The goal of this experiment was to determine whether the addition of other instructional strategies, such as enhanced feedback and pre-training, would further boost the learning benefits of an adaptive flashcard trainer. Thirty-nine college students learned to identify U.S. Navy ships using either a base version of the flashcard trainer, a version that included enhanced feedback, or a version that included pre-training and enhanced feedback. Results showed that the pre-training and enhanced feedback group significantly outperformed the other groups on delayed retention and transfer tests and showed higher learning efficiency. Implications for adaptive training and the Cognitive Theory of Multimedia Learning are discussed.
Simulation Inquisition of Noise Dissolving Algorithm Hinge on TTSD filter for RIIN Situation

This primary aim of this philosopher paper investigates the efficacy of the noise dissolving algorithm hinge on TTSD (Triple Threshold Statistical Detection) filter that has been originated since 2018 is one of the highest efficacy for dissolving RIIN (Random-Intensity Impulse Noise), exclusively at dense distribution. As a results, there are three essential contributions: the exhaustive explanation of the TTSD filter algorithm and its computation examples, the calculation simulation of noise apprehension correctness and overall comparative simulation of noise dissolving effectiveness. For TTSD filter, three malleable offsets that are the complementary requirement are employed in the TTSD filter that can adequately resolve the limitation of the antecedent noise dissolving algorithms. The first malleable offset is calculated for determining the noise characteristic of all elements by using the mathematical verification. Next, the second malleable offset is calculated for determining the another noise characteristic by using the normal distribution mathematical verification (the average value and standard deviation value). Later, the third malleable offset is calculated for determining the another noise characteristic by using the quartile mathematical verification (median value). In the simulation inquisition, the bountiful standard portraits that are desecrated by RIIN (Random Intensity Impulse Noise) with many dense distributions are experimented by noise dissolving algorithm hinge on TTSD in both noise segregation and noise dissolving perspective.
Real-Time Detection of Sign Language using Deep Learning

Sign language is one of the more experienced and regular types of correspondence language, however, since most are unfamiliar with sign communication and interpreters are undeniably challenging to stop by we have concocted a constant technique that uses neural organization for fingerspelling based American gesture-based communication. It is a profoundly visual-spatial, semantically complete language. It is typically the first language and the main means of communication for deaf individuals. Sign Language is communication that relies on hand signs, gestures, and expressions, generally used in the deaf community. But people outside of the deaf community find it hard or almost impossible to speak with deaf people. They need to rely on an interpreter which may both be expensive and hurt the privacy of the people trying to communicate. This paper proposes a method that makes use of a Convolutional Neural Network to identify and recognize hand signs which are captured in real-time through a webcam. Since there is no universal sign language. The model was specifically trained on American Sign Language alphabets.
A Model for Protecting Personal Information using Blockchain

Users have lost control and ownership of their personal information in Cyberspace. A user’s personal information is scattered across many (company) databases in Cyberspace, which introduces many security risks. The user usually has no idea where his/her personal data is stored in Cyberspace, and has very little, if any, control over consolidating or deleting such data. The purpose of the research presented in this paper is to explore an alternative approach to return the ownership and control of their data to the real owners. This will allow for the secured ownership and control of personal information by the real owner. This new approach will use the Blockchain technology, together with Smart Contracts, to put the owner in charge of his/her personal data, and ensure the control and secure access to such data. In this paper, we will present a model, called the “SUUS CHAIN” model, to manage the ownership of personal data.
A Study on the Virtual Remote Input-Output Model for IoT Simulation Learning

In our technology-driven world, various methods for teaching in an educational venue or in a simulated environment have been suggested especially for computer and coding education. In particular, IoT related education has been made possible owing to the industrial developments that have occurred in various fields since the Fourth Industrial Revolution. The proposed model allows various IoT systems to be indirectly built; it provides an inexpensive learning method by applying a simulation system in a 3D environment. The model is implemented on Virtual Remote IO based on the Arduino platform, thereby reducing the cost of building an education system. In addition various education-related content can be provided to learners through such an indirectly developed system. Test code was written to check the consistency of an operation between the real system and the virtual system.
La terminologia della catalogazione e la sua traduzione: aspetti linguistici, etici e professionali

The article analyses some critical issues related to the translation of the terminology of cataloguing, and the impact this has on professional language and practice, focusing in particular on some of the choices made in various editions of ISBD standards, in their Italian translations and, subsequently, in the Italian cataloguing rules (REICAT).The article emphasizes the need to combine expertise in the historical evolution of modern cataloguing rules with a high level of linguistic competence, so as to avoid unclear or overly generic language choices; at the same time, the article underlines that international committees should carefully take into account national ‘localisms’, which are considered ‘valuable clues’ for the detection of flaws and limits in the source texts.
USE OF MICROPROCESSOR-BASED DEBUG MODULES FOR THE DEVELOPMENT OF GPS (GPRS) AND GSM COMMUNICATION DEVICES

Abstract This report is devoted to an overview of the technical characteristics and the specifics of the use of modern debugging microprocessor modules for the development of devices for geolocation of objects and data transmission over a mobile communication channel. Keywords: microcontroller system, RASPBERRY PI, arduino ide, GSM(GPS-GPRS)-module.
CLASSROOM MANAGEMENT STRATEGY OF TEACHING ENGLISH PRONUNCIATION TO YOUNG LEARNERS AT INTENSIVE ENGLISH COURSE BANDA ACEH

This research aims: (1) to find out how the teachers of Intensive English Course prepares classroom management strategy of teaching and learning process of pronunciation instruction, (2) to find out how the teachers of Intensive English Course conduct classroom management in teaching and learning process of pronunciation instruction, and (3) to find out how the teachers of Intensive English Course evaluates the result on classroom management in teaching and learning process of pronunciation instruction. This study belongs to qualitative research because only describes the situation and condition of applying pronunciation instruction at the course. Since the number of subjects of this researcher is only two teachers thus it should be taken as a sample and it is named data source. The instruments that are used to collect data are observation, interview, and documentation. The data gotten is analyzed by narration procedure and discussed. The result shows that in preparing classroom management strategy, the teachers prepare the program of year, syllabus, and program package. Next, in conducting classroom management strategy, the teachers apply student-centered approach. Besides, the teachers use three strategies in teaching and learning process namely: group discussion, role play, and drills. Next, in evaluating the result of teaching and learning process, the teachers evaluate students by using two kinds of tests namely on going process test and final test. The tests are written tests and conducted individually. Key Words: Classroom Management Strategy, Pronunciation, Teaching Learning Process
Research paths between ubiquity, distance and presence in digital learning environments after distance learning experiences in the COVID emergency

The inclusive potential of technologies - potential that is not intended to be given as such, but as a result of the synergy between technological tools and methodological systems - has been, in common perception, overturned, affirming a dangerous equation Technologies = Distance, which brings with it the equally dangerous corollary Distance = Exclusion. On the contrary, several experimental paths, well before the lockdown and the massive experiences of Distance Learning during the COVID emergency (DaD), explored the possibility that technologies work, in a methodologically oriented educational framework, from driving to inclusive educational processes and constitute the means to enhance participation in traditional settings and at the same time extend educational settings in extreme contexts of physical or digital isolation
THE IMPLEMENTATION OF SCREENCAST O’MATIC VIDEO IN TEACHING LISTENING SKILL

This study aims to know the implementation of Screencast O’matic video in teaching learning listening class, the researcher was chosen video screencast O’matic as media to deliver the material to the students. This study was descriptive qualitative research and conducted on Jun 21th – 22th, 2020. The participants of this research were the English teacher and the students of X IPA 1 at SMAS Islam YKHS Sepulu. The data were obtained by observing the students, the teacher, and the implementation of Screencast O’matic video in the listening class by making a transcript and screenshot of the result. The research finding indicates that the students like Screencast O’matic video because help them to know and learned about how to summarize, find the main idea from the video narrative, get a new vocabulary, how to speak like a native speaker and so on. The result of the observation indicates that the implementation of Screencast O’matic video in listening class makes the students more enthusiastic about the material and they can deliver what they have been heard from the video using their own words. Thus, Screencast O’matic video can be used as one of the alternative media for teaching students’ listening skill in a fun way.
Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation?

Exposure bias has been regarded as a central problem for auto-regressive language models (LM). It claims that teacher forcing would cause the test-time generation to be incrementally distorted due to the training-generation discrepancy. Although a lot of algorithms have been proposed to avoid teacher forcing and therefore alleviate exposure bias, there is little work showing how serious the exposure bias problem actually is. In this work, we focus on the task of open-ended language generation, propose metrics to quantify the impact of exposure bias in the aspects of quality, diversity, and consistency. Our key intuition is that if we feed ground-truth data prefixes (instead of prefixes generated by the model itself) into the model and ask it to continue the generation, the performance should become much better because the training-generation discrepancy in the prefix is removed. Both automatic and human evaluations are conducted in our experiments. On the contrary to the popular belief in exposure bias, we find that the the distortion induced by the prefix discrepancy is limited, and does not seem to be incremental during the generation. Moreover, our analysis reveals an interesting self-recovery ability of the LM, which we hypothesize to be countering the harmful effects from exposure bias.
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT

Differently from the traditional statistical MT that decomposes the translation task into distinct separately learned components, neural machine translation uses a single neural network to model the entire translation process. Despite neural machine translation being de-facto standard, it is still not clear how NMT models acquire different competences over the course of training, and how this mirrors the different models in traditional SMT. In this work, we look at the competences related to three core SMT components and find that during training, NMT first focuses on learning target-side language modeling, then improves translation quality approaching word-by-word translation, and finally learns more complicated reordering patterns. We show that this behavior holds for several models and language pairs. Additionally, we explain how such an understanding of the training process can be useful in practice and, as an example, show how it can be used to improve vanilla non-autoregressive neural machine translation by guiding teacher model selection.
Calibrate your listeners! Robust communication-based training for pragmatic speakers

To be good conversational partners, natural language processing (NLP) systems should be trained to produce contextually useful utterances. Prior work has investigated training NLP systems with communication-based objectives, where a neural listener stands in as a communication partner. However, these systems commonly suffer from semantic drift where the learned language diverges radically from natural language. We propose a method that uses a population of neural listeners to regularize speaker training. We first show that language drift originates from the poor uncertainty calibration of a neural listener, which makes high-certainty predictions on novel sentences. We explore ensemble- and dropout-based populations of listeners and find that the former results in better uncertainty quantification. We evaluate both population-based objectives on reference games, and show that the ensemble method with better calibration enables the speaker to generate pragmatic utterances while scaling to a large vocabulary and generalizing to new games and listeners.
The effect of teacher certification on changes in pedagogical practice: a documentary analysis

Purpose: Based onthe literature, conceptual aspects of innovations in teaching practices were identified in order to understand and reflect onthe transcendence of the formative processes, and confirmthe achievement of teaching competences. Methodology: a documentary review was conductedto identify concepts about innovation and transformation of educational practices, as well as the possible impact of teacher training and certificationon the quality of learning. Main results: Innovation in teachingimplies adoptingdifferent ways of workingby incorporating new components into the teacher’s practice, and the papersanalyzed showthat there is a clear tendency to incorporate ICT as an innovative element. Likewise, apermanent transformation of these practices canonly beachieved through reflection and performance evaluation processes, provided thatfeedback is taken into account. Discussion: Present and future educational demands require the training of teachers to use tools of the digital age and transform their practice, which is possible only through thorough reflection that allowsteachers toconsolidate theirmethodologies and reinforcetheir own learning. Teachingperformance evaluation should focus on the teacher’s actualperformance, retrievingvaluable information about their performance and at the same time providingfeedback to promote reflection. No studies were found that relate the effectiveness in the results of learning linkedto teacher certification.
Why Do Better Loss Functions Lead to Less Transferable Features

Previous work has proposed many new loss functions and regularizers that improve test accuracy on image classification tasks. However, it is not clear whether these loss functions learn better representations for downstream tasks. This paper studies how the choice of training objective affects the transferability of the hidden representations of convolutional neural networks trained on ImageNet. We show that many objectives lead to statistically significant improvements in ImageNet accuracy over vanilla softmax cross-entropy, but the resulting fixed feature extractors transfer substantially worse to downstream tasks, and the choice of loss has little effect when networks are fully fine-tuned on the new tasks. Using centered kernel alignment to measure similarity between hidden representations of networks, we find that differences among loss functions are apparent only in the last few layers of the network. We delve deeper into representations of the penultimate layer, finding that different objectives and hyperparameter combinations lead to dramatically different levels of class separation. Representations with higher class separation obtain higher accuracy on the original task, but their features are less useful for downstream tasks. Our results suggest there exists a trade-off between learning invariant features for the original task and features relevant for transfer tasks.
Feature Sport News, Before and During Pandemic Covid 19

The Covid-19 pandemic has affected the sports news industry, one of which is on featured sports news.This study aims to analyze the differences in the characteristic of feature sport news in ten categories given.A descriptive qualitative research method were implemented with three primary data namely documentation, interview and observation.Of 57 feature sport news, before (August 4, 2019 -November 29, 2019) and during the pandemic (February 29, 2020 -May 29, 2020) were analyzed.The results of this study shows that there is limited difference in quality but a high difference of quantity in feature sport news before and during pandemic.This difference is due to the decrease of sports event during pandemic which result on minimum hard news.Thus, the journalist fill the gap on sports rubric by writing more feature news.
БУЛАЖАК БИОЛОГИЯ УКИТУВЧИЛАРИДА БАДИИЙ-ЭСТЕТИК КОМПЕТЕНТЛИКНИ РИВОЖЛАНТИРИШНИНГ ПЕДАГОГИК-ПСИХОЛОГИК ХУСУСИЯТЛАРИ

ARTISTIC-AESTHETICS FOR FUTURE BIOLOGY TEACHERS IN THIS ARTICLEPEDAGOGICAL-PSYCHOLOGICAL CHARACTERISTICS OF COMPETENCE DEVELOPMENT.
Dual-frame demodulation based on an advanced vector normalization technology

A set of sine and cosine components are constructed through the addition and subtraction of dual-frame interferograms. We propose a method based on advanced vector normalization technology to achieve dual-frame fringe pattern phase extraction.
Perhaps PTLMs should go to School – A Task to Assess Open Book and Closed Book QA

Our goal is to deliver a new task and leaderboard to stimulate research on question answering and pre-trained language models (PTLMs) to understand a significant instructional document, e.g., an introductory college textbook or a manual. PTLMs have shown great success in many question-answering tasks, given significant supervised training, but much less so in zero-shot settings. We propose a new task that includes two college-level introductory texts in the social sciences (American Government 2e) and humanities (U.S. History), hundreds of true/false statements based on review questions written by the textbook authors, validation/development tests based on the first eight chapters of the textbooks, blind tests based on the remaining textbook chapters, and baseline results given state-of-the-art PTLMs. Since the questions are balanced, random performance should be ~50%. T5, fine-tuned with BoolQ achieves the same performance, suggesting that the textbook’s content is not pre-represented in the PTLM. Taking the exam closed book, but having read the textbook (i.e., adding the textbook to T5’s pre-training), yields at best minor improvement (56%), suggesting that the PTLM may not have “understood” the textbook (or perhaps misunderstood the questions). Performance is better (~60%) when the exam is taken open-book (i.e., allowing the machine to automatically retrieve a paragraph and use it to answer the question).