Reviewerly/exHarmony huggingface dataset:
Cultural Turn In Education: Perspective Model And The Realities Of Life

The article is devoted to the analysis of cultural education. The article reveals the features of cultural studies as a special sphere of scientific knowledge. Cultural turn in education is considered as a possibility of formation of skills of the XXI century. The stages of formation and development of cultural education in modern Russia are defined. The conclusion about perspective development ways of culturological education is made on the analysis basis. Russian science has established research practices of analysis of socio-cultural reality. Russian education has experience in implementing a culturological approach in both a broad and a narrowly specific sense. There is a need for an interdisciplinary approach to the study of social processes and the introduction of research results into educational practices, but cultural education is not among the priorities. Possible scenarios of a "cultural turn" in education are described as optimistic, realistic or pessimistic. The method of discursive analysis was chosen as a leading method of research that allows revealing the dependence of educational strategies and practices on the requirements of the industrial revolution 4.0. The methods of comparative analysis, systematization and classification were used to describe the actual practices of cultural education.
Digital Repository Training: Role of the Professional Librarian in Technical Services Interest Group Midwinter 2020 Report

The ALCTS’s Role of the Professional Librarian in Technical Services Interest Group (RPLTS IG) hosted three presentations at the ALA’s 2020 Midwinter Meeting in Philadelphia, PA. The well attended ...
The Great Indian Currency Scramble: Digital Money

An hour before dawn on November 8, 2016—a Tuesday—Sarvesh Kumar woke abruptly from what had been restless sleep. As he propped himself up on his charpoy, he told me, his eyes adjusted to the darkness of his tiny room, tucked away in a slum near New Delhi’s prosperous Vasant Kunj neighborhood. It was, he recalls, pitch black. Kumar’s inability to see enhanced his other senses. His ears picked up a chorus of mournful howls from the stray dogs roaming the streets some distance away. A few seconds later, he began to take account of a gentle rumble of snoring. Bandhana, his wife, was still asleep beside him. The next stimulation to hit him, he said, was that of smell: they had saved leftovers from dinner in a small dish, placed on the floor in one corner of the bedroom. (There was no kitchen, no fridge, no cupboards.) The smell of stale dal and starchy rice lingered in the air, mingling with the stench of urine from the latrine right outside. The apartment’s only toilet had no working flush, just a mug and a bucket with which to pour water. At night, Kumar’s father—who slept in the next room—would get up to urinate in the dark, sitting on his haunches and peeing all over the cramped toilet floor. Kumar was only twenty-two years old but looked much older. He was developing a hunch. His gray shirt and trousers—both made of the same rough, fraying cotton—seemed to fall over his limbs like oversized bags. Life was wearing him down. Kumar was working sixteen-hour shifts driving a three-wheeled auto-rickshaw, seven days a week. Ever since he had gotten married and brought Bandhana to join him in the city, his expenses had ballooned. His father, meanwhile, was working less and less, his health and eyesight beginning to fail him. Asha Ram Kumar worked as a chauffeur for a family nearby in Vasant Kunj. He had recently bumped their car into a lamppost. Any day now, Kumar told me, his father would lose his job.
XAI Language Tutor - A XAI-based Language Learning Chatbot using Ontology and Transfer Learning Techniques

In this paper, we proposed a XAI-based Language Learning Chatbot (namely XAI Language Tutor) by using ontology and transfer learning techniques. To facilitate three levels of language learning, XAI Language Tutor consists of three levels for systematically English learning, which includes: 1) phonetics level for speech recognition and pronunciation correction; 2) semantic level for specific domain conversation, and 3) simulation of “free-style conversation” in English - the highest level of language chatbot communication as “free-style conversation agent”. In terms of academic contribution, we implement the ontology graph to explain the performance of free-style conversation, following the concept of XAI (Explainable Artificial Intelligence) to visualize the connections of neural network in bionics, and explain the output sentence from language model. From implementation perspective, our XAI Language Tutor agent integrated the mini-program in WeChat as front-end, and fine-tuned GPT-2 model of transfer learning as back-end to interpret the responses by ontology graph.
Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only

Health departments have been deploying text classification systems for the early detection of foodborne illness complaints in social media documents such as Yelp restaurant reviews.Current systems have been successfully applied for documents in English and, as a result, a promising direction is to increase coverage and recall by considering documents in additional languages, such as Spanish or Chinese.Training previous systems for more languages, however, would be expensive, as it would require the manual annotation of many documents for each new target language.To address this challenge, we consider cross-lingual learning and train multilingual classifiers using only the annotations for English-language reviews.Recent zero-shot approaches based on pre-trained multi-lingual BERT (mBERT) have been shown to effectively align languages for aspects such as sentiment.Interestingly, we show that those approaches are less effective for capturing the nuances of foodborne illness, our public health application of interest.To improve performance without extra annotations, we create artificial training documents in the target language through machine translation and train mBERT jointly for the source (English) and target language.Furthermore, we show that translating labeled documents to multiple languages leads to additional performance improvements for some target languages.We demonstrate the benefits of our approach through extensive experiments with Yelp restaurant reviews in seven languages.Our classifiers identify foodborne illness complaints in multilingual reviews from the Yelp Challenge dataset, which highlights the potential of our general approach for deployment in health departments.
Background and Occlusion Defenses Against Adversarial Examples and Adversarial Patches

Author(s): McCoyd, Michael Joseph | Advisor(s): Wagner, David A | Abstract: Machine learning is increasingly used to make sense of our world in areas from spam detection, recommendation systems, to image classification. However, in each, it is vulnerable to adversarial manipulation. Within adversarial machine learning, we examine image classification attacks and defenses. We construct spoofs of face detection, and we create defenses against two attacks on image classification: normal and patch adversarial example attacks.We examine the Viola-Jones 2D face detection algorithm to study whether images can be created that humans do not notice as faces, yet the algorithm detects as faces. We show that it is possible to construct images that Viola-Jones recognizes as containing faces, yet no human would consider a face. Moreover, we show that it is possible to construct images that fool facial detection even after the images are printed and then photographed.Adversarial examples allow crafted attacks against deep neural network classification of images. The attack changes the computer classification of an image without changing how humans classify it.We propose a defense of expanding the training set with a single, large, and diverse class of background images, striving to ‘fill’ around the borders of the classification boundary. We find that our defense aids the detection of simple attacks on EMNIST, but not advanced attacks. We discuss several limitations of our examination.An attacker limited to changing just a small patch of an image can still deceive deep learning image classification. We propose a defense against such patch attacks based on multiple partial occlusions of the image such that a few occlusions each completely hide the patch. We provide certified accuracy for CIFAR-10, Fashion MNIST and MNIST, with a tunable tradeoff between the false-positive rate and certified accuracy. For CIFAR-10 and a 5 × 5 patch, we can provide certified accuracy for 43.8% of images, at the cost of only 1.6% in clean image accuracy compared to the architecture we defend or a cost of 0.1% compared to our training of that architecture, including a 0.2% false-positive rate.
Assistance the Packaging of Literacy Learning Materials in Digital Formats for Elementary School Teachers in Kendari City

The activity of assisting the packaging of literacy learning materials in digital format for elementary school teachers in partner schools in this community partnership program, namely: SDN 85 Kendari and SD IT Al-Kalam Kendari becomes very important, bearing in mind the two schools have implemented a school literacy program in which one of the activities is integrating the activity of reading and writing at the beginning of learning for 15-20 minutes. The main objective of the assisting activity is to improve teacher skills in packaging varied learning materials, especially those with the digital format needed in literacy activities on learning themes in elementary schools. The method used in this community partnership program was the assistance of packaging literacy learning materials in digital format for use in learning. The implementation of the community partnership program in the form of mentoring produced a product in the form of digital format literacy learning materials whose effectiveness would be evaluated through the implementation in the implementation of learning, especially in the literacy activity segment at the beginning of the core activity. The evaluation was based on the reflection of the teachers who used this product after activities were carried out. The main obstacle found in this mentoring activity was the inadequacy of the ability of some teachers to operate the main supporting applications (such as power points) as well as the low ability to find digital learning resources on the pages that have been shown at the time of mentoring
Libraries + Learning Commons: Database Training Resources: Home

List below are resources that can be used for introductory and additional training of the databases that Niagara College subscribes to.
Technologies In The Implementation Of Paradigm Of The Future Teacher Training

The article presents the use of information and communication technologies in organizing the methodological training of a future teacher to develop his or her readiness for project activities. The design paradigm in the student’s methodological preparation is implemented from the position of competency-based, personality-oriented and subject-activity approaches. Readiness for project activities includes the ability to identify information resources, to search for data and establish meaningful relationships between them, to develop a methodological project plot, to isolate and describe the situation, to analyze the situation structurally, to identify and justify alternative solutions with an assessment of each of them, to formulate results and conclusions from the decision-making process. This readiness is assessed by the results of the future teacher's implementation of a methodological project at the information planning, practical and reflective-evaluative stages. The main research methods are the project method, the survey method and questionnaires. The implementation of the project method using network services in the training of the future teacher of mathematics and computer science is presented. The following capabilities of services in the implementation of methodological projects are indicated: the choice of Internet resources for solving problems, the expansion of the space and training resources, the development of interdisciplinary relationships. In the readiness structure, personal, theoretical and practical components are highlighted.
Advancing Seq2seq with Joint Paraphrase Learning

We address the problem of model generalization for sequence to sequence (seq2seq) architectures.We propose going beyond data augmentation via paraphrase-optimized multi-task learning and observe that it is useful in correctly handling unseen sentential paraphrases as inputs.Our models greatly outperform SOTA seq2seq models for semantic parsing on diverse domains (Overnight -up to 3.2% and emrQA -7%) and Nematus (Sennrich et al., 2017), the winning solution for WMT 2017, for Czech to English translation (CzENG 1.6 -1.5 BLEU).* Work done while So Yeon Min was a student at MIT. 1 AllenNLP's competitive BiDAF (Seo et al., 2017) reading comprehension model is not always capable at handling this: https://demo.allennlp.org/reading-comprehension/ODE5ODc2
Online Learning Platform Effectiveness in West Papua Campus During the Covid-19 Pandemic Period

The corona virus disease outbreak or more commonly known as Covid-19 is sweeping the world. The Emergency Period for the Spread of the Corona Virus Disease (Covid 19) imposes an online learning from home (BDR) rule that applies to all levels of education which is intended to provide a meaningful learning experience Regarding these rules, West Papua itself has issued a circular letter from the Head of the West Papua Provincial Education Office, Barnabas Dowansiba, which states that all learning activities must be carried out from home. student internet quota, including the unpreparedness of students and lecturers in conducting online learning itself. Therefore, the use of online platforms can vary from campus to campus, all of which lead to the objective of obtaining effective learning during the Covid-19 pandemic. The purpose of this study is to find out which online platforms were effective during the Covid-19 pandemic. Sampling using purposive sampling technique. The results of this study indicate that the recommended online platform is a zoom meeting on the grounds that it is easier to use
ANALYSIS OF THE INTEREST RATE AT THE DEPOSITS AND LOANS OF POPULATION HOUSEHOLDS PRACTICED BY CREDIT INSTITUTIONS IN ROMANIA

The paper presents an analysis of the interest rate, knowing the fact that it has an important role in the operation of the banking system, being the main element according to which the interest is calculated. The interest rates charged by credit institutions on deposits and loans granted to households are analyzed. The saving process has beneficial effects in terms of the development of the bank's activity because it is directly related to the volume of resources attracted. It is also demonstrated that lending is the main financing flow of a country's economy, the types of credit being diverse adapting to market requirements.
Effects of Combined Exercise on Serotonin and Dopamine Levels in Middle-aged Women

This research was conducted in middle-aged women to find out how 12 weeks exercise program can affect on serotonin and dopamine. For this, 12 middle-aged women living in Y city were divided into 2 groups which were 6 for control group and 6 for combined exercise group and they were asked to do combined exercise for 70 minutes a day, three times a week for 12 weeks and the result was as follows. The 12weeks-combined exercise program helped serotonin level in middle-aged women to be increased statistically significantly and dopamine to be decreased significantly. There was interaction effects according to time and group in both of these variables (p≤.05). Collectively, 12 weeks-combined exercise program significantly increased serotonin and decreased dopamine in middle-aged women. It is believed that further research has to examine what the effects of long-term regular exercise are serotonin and dopamine objectively in a larger number of subjects and research on mechanism, which can explain clearly the connection between serotonin and dopamine, and on development of various combined exercise program which can help physical strength and mental health should be done.
Linked Data for Smart Homes: Comparing RDF and Labeled Property Graphs

The need to integrate siloed data in the built environment led to a gaining interest in semantic web technologies in the Architecture, Engineering and Construction (AEC) sector. Especially for smart home developments, the integration of information about the building, users and Internet of Things (IoT) devices could be valuable. The Resource Description Framework (RDF) is the standard model for the semantic web, however, labeled property graphs (LPG) also proved to be effective in linking data. This research used the Open Smart Home Dataset and a dataset representing a kitchen to compare the two graph models both qualitatively and quantitatively. The comparison shows that native labeled property graphs are less complex and outperform the atomic RDF in complex graph traversals. However, RDF shows qualitative advantages for multi-domain and multi-stakeholder environments, such as the use of ontologies and HTTP URIs, making it a more stable interoperability format.
Measuring syntactic complexity in learner Finnish

In the study of complexity, accuracy and fluency (CAF), syntactic complexity can be measured by a multitude of measures. Traditionally, the measures are quantitative and they use production units such as words, clauses, T-units, and sentences. Despite the vast number of measures available, many studies have used only one or two of them, or parallel ones tapping the same component of complexity. The present study explores syntactic complexity using seven frequently used quantitative complexity measures to gauge different facets of complexity in written learner Finnish. The data of the study consist of texts written by adult and adolescent language learners, and they cover proficiency levels from beginner (A1) to advanced learner (C2) in the Common European Framework of Reference (CEFR). According to the results, changes in the measures are not linear from one proficiency level to the next. The results also show that while all the selected measures catch some statistically significant differences between proficiency levels in adult language learner texts, only four measures do so in adolescent language learner texts. The results also suggest that the measures are sensitive to task type.
Toward Outcome Based Education (OBE): An Impact of Active Learning Approach for 'Computer Organization' Course

In recent years, there is an incredible development in technology, which brought innovative changes in the educational sector. Active learning approaches are vital in teaching learning process such as content delivery, assessment, monitoring, etc. This paper discussed about active learning activities as simulation tool, buzz activity through animated video, and poster presentation. In addition to this, we checked students' performance and impact to gain knowledge by considering result analysis and course outcome attainment of two consecutive years for �computer organization� course. Individual students learning performance and learning interest increased due to implementation of different active learning tools during classes.
THE ANALYSIS OF GRAMMATICAL ERROR IN WRITING RECOUNT TEXT USING SURFACE TAXONOMY STRATEGY

The objectives of the study are to know the most common grammatical errors made by the tenth grade students of SMK Muhammadiyah kota Cirebon in recount text writing, and to identify the causes of the errors. The sample of this research is 18 students. The method which is used in this research is descriptive analysis. Firstly, students were given essay test about recount text. Afterwards, the data were collected, identified, and then classified based on grammatical classification. The result of the study showed that verb tense is the most grammatical error made by the students. Based on the analysis of the grammatical errors, four types of errors were found. The numbers of errors of omission are 18 errors with the percentage of 41.86%. Then the second common error is misformation with total errors 16 errors or 37.20%. The third error is addition with 5 errors or 11.62%. And the last is misordering with 4 errors or 9.30%. It can be concluded that most students of SMK Muhammadiyah kota Cirebon got wrong in omission and misformation. And the sources of errors, two types of errors affected the occurrence of studentsâ€™ errors. These two errors are as follows. Intralingual error was found 16 errors or 8.42% and interlingual error was found 3 errors or 1.57%. Based on the result, it is figured out that the students were creative in constructing English sentences and still have difficulties in learning grammatical structures in writing process.
Analysis of user preferred youtube videos

In current era of big data, a wide variety of high-volume data having different veracity can be easily collected or generated at a high velocity. Social network data, as well as audio and video in social media and social networking sites, are examples of big data. Embedded in these big data are valuable information and knowledge. Previously unknown and useful information and knowledge from these big data, some big data solutions are in demand. In this project, we explore the big data for detecting various results from YouTube video data so that the user- preferred YouTube viewing can be recognized and the analysis of user- preferred YouTube videos can then be enhanced.
Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks

Transfer learning is a popular technique to learn a task using less training data and fewer compute resources.However, selecting the correct source model for transfer learning is a challenging task.We demonstrate a novel predictive method that determines which existing source model would minimize error for transfer learning to a given target.This technique does not require learning for prediction, and avoids computational costs of trial-and-error.We have evaluated this technique on nine datasets across diverse domains, including newswire, user forums, air flight booking, cybersecurity news, etc.We show that it performs better than existing techniques such as fine-tuning over vanilla BERT, or curriculum learning over the largest dataset on top of BERT, resulting in average F 1 score gains in excess of 3%.Moreover, our technique consistently selects the best model using fewer tries.
On the Evaluation of Machine Translation n-best Lists

The standard machine translation evaluation framework measures the single-best output of machine translation systems.There are, however, many situations where n-best lists are needed, yet there is no established way of evaluating them.This paper establishes a framework for addressing n-best evaluation by outlining three different questions one could consider when determining how one would define a 'good' n-best list and proposing evaluation measures for each question.The first and principal contribution is an evaluation measure that characterizes the translation quality of an entire n-best list by asking whether many of the valid translations are placed near the top of the list.The second is a measure that uses gold translations with preference annotations to ask to what degree systems can produce ranked lists in preference order.The third is a measure that rewards partial matches, evaluating the closeness of the many items in an n-best list to a set of many valid references.These three perspectives make clear that having access to many references can be useful when n-best evaluation is the goal.