Reviewerly/exHarmony huggingface dataset:
Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data

While multilingual pretrained language models (LMs) fine-tuned on a single language have shown substantial cross-lingual task transfer capabilities, there is still a wide performance gap in semantic parsing tasks when target language supervision is available. In this paper, we propose a novel Translate-and-Fill (TaF) method to produce silver training data for a multilingual semantic parser. This method simplifies the popular Translate-Align-Project (TAP) pipeline and consists of a sequence-to-sequence filler model that constructs a full parse conditioned on an utterance and a view of the same parse. Our filler is trained on English data only but can accurately complete instances in other languages (i.e., translations of the English training utterances), in a zero-shot fashion. Experimental results on three multilingual semantic parsing datasets show that data augmentation with TaF reaches accuracies competitive with similar systems which rely on traditional alignment techniques.
Implementation of GA with Position Based Crossover PX Technique for Size Optimization of BDD Mapped Adder Circuits

Binary Decision Diagrams or BDD are data structure used to represent single and multi-output digital circuits. BDD mapped adder circuits are used to represent different adder functions in a digital system. Optimization of adder circuits are done by optimizing the corresponding BDDs. In this work the optimization of BDD Mapped adder circuits are proposed by using genetic algorithm with position-based crossover-PX technique. The main feature of position-based crossover technique is that it is suitable for order-based solution formation. We compared our result with other existing variable order method available in BDD manipulation tool BuDDy-2.4. The result is obtained for Full Adder circuits of 1 to 8-bit size. Experimental results show the improvement of the proposed work over other techniques. The result is quite significant for large circuits i.e. full adder circuit having larger bit size.
PENERAPAN PENDEKATAN CTL UNTUK MENINGKATKAN KETERAMPILAN MENULIS KARANGAN DESKRIPSI SISWA SD

This research has motivated by the low skills of descriptive essay writing on the pre-cycle staged showed of completeness only 34% been experiencing by 5th grade Primary School students. The problem is caused by the learning process in the pre-cycle stage where the learning approach applied by the teacher does not provide opportunities for students to improve their descriptive essay writing skills. The purpose of this Classroom Action Research (CAR) is to describe how is the implementation of Contextual Teaching and Learning approach in increasing the descriptive essay writing skills on 5th class of elementary students which cover planning, implementation, and developing the study outcome of the students. The research was done using class action method that adapted from Kemmis and Mc. Taggart model, which done in two cycles. Data collection on this research was using the observation, field notes and test. Observation sheet, field notes, and test questions were used as the research instruments. Based on the previous research on class action, it can be concluded that the implementation of Contextual Teaching and Learning approach could increase the descriptive essay writing skills of 5th grade students in Primary School.
A Brief Survey on Various Technologies Involved in Cloud Computing Security

In the past decade, big technical advances have appeared which can bring more comfort not only in the corporate sector but at the personal level of everyday life activities. The growth and deployment of cloud computing technologies by either private or public sectors were important. Recently it became apparent to many organizations and businesses that their workloads were moved to the cloud. However, protection for cloud providers focused on Internet connectivity is a major problem, leaving it vulnerable to numerous attacks. Although cloud storage protection mechanisms are being introduced in recent years. However, cloud protection remains a major concern. This survey paper tackles this problem by recent technology that enables confidentiality conscious outsourcing of the data to public cloud storage and analysis of sensitive data. In specific, as an advancement, we explore outsourced data strategies focused on data splitting, anonymization and cryptographic methods. We then compare these approaches for operations assisted by accuracy, overheads, masked outsourced data and data processing implications. Finally, we recognize excellent solutions to these cloud security issues.
Systematic Literature Study on User Profiles in Smart Homes

Smart homes are becoming a part of our lives, gradually and consistently. Smart homes offer improved Quality of Living. Automation is the primary force behind this progression. In case of smart homes User Profiles play a vital part in it. User profiles are responsible for customization of devices and services offered by a smart home. This paper aims at the study of existing approaches and techniques for profiling in smart homes as stated in the scientific literature for the duration 2004 to 2019. A total of 26 studies have been selected to answer the research questions framed for this review. The approaches and frameworks are hereby documented to find emerging trends and the future scope for the research community
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. Document-level 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. The implementation of this work is available at: https://github.com/declare-lab/sentence-ordering.
GRADUATES’ EXPECTATION IN RELATION TO THE BIOMEDICINE COURSE AND THEIR INSERTION IN THE LABOR MARKET

INTRODUCTION: The Biomedical Scientist is prepared for the exercise of numerous activities related to the area, today there are 35 qualifications duly established and regulated by the Federal Council of Biomedicine (CFB) ensuring this performance. Since then the course has expanded and broken national boundaries, so there is also the course in Bahia, especially in the extreme Baiano. MATERIALS AND METHODS: This is a study of perception, carried out with field research with the use of data survey through questionnaires applied to undergraduate students in the year before the SARS-Cov-2 Pandemic. RESULTS AND DISCUSSION: The answers of 63 students were evaluated, and separated according to each question, 33% of the students have the knowledge that for the profession there is still no defined wage cap, needing more regulations with the CFB, 86% of the students did not know that they can act in the Strategy and Health of the Family (ESF), in the vaccination area and acupuncture, at the time to the research 64% of the sample did not exercise paid activity, what facilitates the non-obligatory internship that according to the questioned ones should begin in the 3rd semester. CONCLUSION: Thus, through the study of the students' knowledge of the course, it is clear that health care becomes more complex and the biomedicine student should be trained in a broader way, having contact with discussions in other areas.
DEVELOPMENT OF THE INNOVATIVE POTENTIAL OF UNIVERSITY TEACHERS IN ADDITIONAL PROFESSIONAL EDUCATION

The article considers the theoretical foundations of the creation and understanding of the effectiveness of the development of innovative potential of teachers of universities with a personal point of view. The result of the innovation process in education is the application of innovations in theory and practice. Innovation in the education system is the introduction of innovations for the purpose of education, the transformation of new approaches and practices and forms of education, the introduction and dissemination of new pedagogical systems, the creation, development and implementation of new school management technologies. to meet the new requirements. The concept of an innovative educational process as a system of primary values characterizes it as spiritual production, and its product is not only the acquisition of new knowledge and values, but also the disclosure of the true strengths and abilities of students. Innovations in education are based on socially significant values: people, life, work, home land. This is considered as an indispensable condition for creating an effective model for the development of innovation potential of teachers of universities at the expense of using innovations in additional professional education. The basic concept of "innovative potential of university teachers" is defined. The purpose of this article is to consider the theoretical aspects of the development of innovative potential of teachers in the system of additional professional education.
THE EFFECTIVENESS OF USING CARTOON VIDEO IN TEACHING ENGLISH VOCABULARY AT FIFTH GRADE OF MI NAJATUS SALIKIN

ABSTRACTFitri, Mahdina Lutfiani. Student Registered Number 17203163071. 2020. The Effectiveness of Using Cartoon Video in Teaching English Vocabulary at Fifth Grade of MI Najatus Salikin. Thesis. English Education Department. Faculty of Tarbiyah and Teacher Training. State Islamic Institute (IAIN) of Tulungagung. Advisor: Dr. Sukarsono, M.Pd.Keyword: Effectiveness, Cartoon Video, VocabularyVocabulary is one of the language elements that are very important for learning English. Without an extensive vocabulary and strategies for acquiring new vocabulary, students cannot achieve one of the skill of English well. However, the students get difficulties in memorizing new vocabulary. Therefore, the researcher found cartoon video as an effective media to teach English vocabulary. The formulation of the research problem was: Is there any significant different scores of students’ vocabulary mastery before and after taught by using cartoon video?. The objective of this study is to identify whether there is significant different scores of students’ vocabulary mastery before and after by using cartoon video. Research method: 1) the research design in this study was pre-experimental design with quantitative approach, 2) the population of this study was the students of the fifth grade at MI Najatus Salikin, 3) the sample was fifth grade students which consisted of 28 students, 4) the reseach instrument in this study is test, which consists of pre-test and post-test, 5) the data analysis used Paired Sample T-test through SPSS 16. The result showed that the students’ score before being taught using cartoon video was 66.79. While, the students’ score after being taught by using cartoon video was 77.86. Then, the result of paired sample t-test showed that the significant value is lower than the level of significant (0.000 < 0.05). It means that Ha which stated that there is significant different scores of students’ vocabulary mastery before and after taught by using cartoon video for the fifth grade of MI Najatus Salikin was accepted. Whereas, H0 which stated that there is no significant different scores of students’ vocabulary mastery before and after taught by using cartoon video for the fifth grade of MI Najatus Salikin was rejected. In conclusion, cartoon video is effective used as an alternative media to teach vocabulary to the students in the fifth grade of MI Najatus Salikin.
A Strong Baseline for Query Efficient Attacks in a Black Box Setting.

Existing black box search methods have achieved high success rate in generating adversarial attacks against NLP models. However, such search methods are inefficient as they do not consider the amount of queries required to generate adversarial attacks. Also, prior attacks do not maintain a consistent search space while comparing different search methods. In this paper, we propose a query efficient attack strategy to generate plausible adversarial examples on text classification and entailment tasks. Our attack jointly leverages attention mechanism and locality sensitive hashing (LSH) to reduce the query count. We demonstrate the efficacy of our approach by comparing our attack with four baselines across three different search spaces. Further, we benchmark our results across the same search space used in prior attacks. In comparison to attacks proposed, on an average, we are able to reduce the query count by 75% across all datasets and target models. We also demonstrate that our attack achieves a higher success rate when compared to prior attacks in a limited query setting.
Image Super Resolution Using Generative Adversarial Networks and non-Paired Strategy

The quality of images obtained from video surveillance systems is a decisive aspect when performing investigations at the Forensic Science. Features such as scars, tattoos, and skin marks are great examples of details that allow to consolidate an investigation at certain scenarios in which there is the necessity to identify individuals captured in a video or image footage. However, the low quality of images could affect the results of the investigations. In this sense, this work proposes the study of a computational model to address the problem of increasing the resolution of Low-Resolution (LR) images, also known as the problem of super-resolution of images. The main idea is to train a Generative Adversarial Network (GAN) so that it can be able to enhance low-quality images. The hypothesis is that a variant model of a GAN, named Super-Resolution Generative Adversarial Network (SRGAN), is capable to produce High-Resolution (HR) images from LR ones. The proposed methodology is based on experimental research with the aid of the hypothetical deductive method, where two well-recognised state of art methods were used, which proposes the use of convolutional neural networks and deep learning. For the model validation, were conducted four different experiments: two to avail the capacity of the GAN to produce images with enhanced resolution and two other experiments to evaluate the quality of the results produced by the SRGAN. The quantitative results of our experiments are promising, with performances that are similar to those obtained by state-of-the-art approaches. Moreover, the qualitative results based on performing a visual analysis of the images produced by our approach suggest a interesting performance in terms of visual quality.
Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence

Topic model evaluation, like evaluation of other unsupervised methods, can be contentious. However, the field has coalesced around automated estimates of topic coherence, which rely on the frequency of word co-occurrences in a reference corpus. Recent models relying on neural components surpass classical topic models according to these metrics. At the same time, unlike classical models, the practice of neural topic model evaluation suffers from a validation gap: automatic coherence for neural models has not been validated using human experimentation. In addition, as we show via a meta-analysis of topic modeling literature, there is a substantial standardization gap in the use of automated topic modeling benchmarks. We address both the standardization gap and the validation gap. Using two of the most widely used topic model evaluation datasets, we assess a dominant classical model and two state-of-the-art neural models in a systematic, clearly documented, reproducible way. We use automatic coherence along with the two most widely accepted human judgment tasks, namely, topic rating and word intrusion. Automated evaluation will declare one model significantly different from another when corresponding human evaluations do not, calling into question the validity of fully automatic evaluations independent of human judgments.
어휘 역사 정보의 현황과 개선 방안

The Urimalsaem contains the historical information of about 5,000 lexical items. They are based on the materials as a part of the 21st Century Sejong Project, which has constructed from 2002. The historical information in the Urimalsaem, however, have several issues. The information is provided only in modern entries and is especially careless about semantic changes. In giving examples, the modern and archaic entries are asymmetric and one can find hardly semantic differences from the examples. The information about related words is not well-constructed and there are not a few cases that the historical information is not described well due to the exclusion of Old Korean materials from the reference. In order to resolve these issues, it is necessary to collect the corpora of historical materials maximally and to fine them for high-quality data. Also the corpora of historical materials need to be open so that users of the Urimalsaem can search and correct errors from them, correct and supplement the historical informations of lexical items furthermore.
Evaluating Transferability of BERT Models on Uralic Languages.

Transformer-based language models such as BERT have outperformed previous models on a large number of English benchmarks, but their evaluation is often limited to English or a small number of well-resourced languages. In this work, we evaluate monolingual, multilingual, and randomly initialized language models from the BERT family on a variety of Uralic languages including Estonian, Finnish, Hungarian, Erzya, Moksha, Karelian, Livvi, Komi Permyak, Komi Zyrian, Northern Sami, and Skolt Sami. When monolingual models are available (currently only et, fi, hu), these perform better on their native language, but in general they transfer worse than multilingual models or models of genetically unrelated languages that share the same character set. Remarkably, straightforward transfer of high-resource models, even without special efforts toward hyperparameter optimization, yields what appear to be state of the art POS and NER tools for the minority Uralic languages where there is sufficient data for finetuning.
Improving Conditional Coverage via Orthogonal Quantile Regression

We develop a method to generate prediction intervals that have a user-specified coverage level across all regions of feature-space, a property called conditional coverage. A typical approach to this task is to estimate the conditional quantiles with quantile regression -- it is well-known that this leads to correct coverage in the large-sample limit, although it may not be accurate in finite samples. We find in experiments that traditional quantile regression can have poor conditional coverage. To remedy this, we modify the loss function to promote independence between the size of the intervals and the indicator of a miscoverage event. For the true conditional quantiles, these two quantities are independent (orthogonal), so the modified loss function continues to be valid. Moreover, we empirically show that the modified loss function leads to improved conditional coverage, as evaluated by several metrics. We also introduce two new metrics that check conditional coverage by looking at the strength of the dependence between the interval size and the indicator of miscoverage.
Projected GANs Converge Faster

Generative Adversarial Networks (GANs) produce high-quality images but arechallenging to train. They need careful regularization, vast amounts ofcompute, and expensive hyper-parameter sweeps. We make significant headway onthese issues by projecting generated and real samples into a fixed, pretrainedfeature space. Motivated by the finding that the discriminator cannot fullyexploit features from deeper layers of the pretrained model, we propose a moreeffective strategy that mixes features across channels and resolutions. OurProjected GAN improves image quality, sample efficiency, and convergence speed.It is further compatible with resolutions of up to one Megapixel and advancesthe state-of-the-art Fr\'echet Inception Distance (FID) on twenty-two benchmarkdatasets. Importantly, Projected GANs match the previously lowest FIDs up to 40times faster, cutting the wall-clock time from 5 days to less than 3 hoursgiven the same computational resources.
Cloud Based Privacy Preserving Dynamic Intermediate Datasets

Design an encryption of privacy preserving and scheduling of intermediate datasets in cloud. Implemenation of encryption is done as follows: Identification of intermediate datasets that needs to be encrypted. Based on frequent pattern mining the least frequent intermediate datasets are encrypted. Perform column level encryption to the sensitive information. Predicting the data based on inference analysis would not be possible. So that the data will be secure when compared to the existing system.
SURVEY OF STUDENTS’ ATTITUDE TOWARD ONLINE LEARNING AT THE SECOND YEAR AT ALAUDDIN STATE ISLAMIC UNIVERSITY OF MAKASSAR

The aim of this study to examining the attitudes of students toward online learning at Alauddin State Islamic University of Makassar. Over the past several decades, there has been a growing interest in learning the English language in colleges and universities throughout Makassar.  However, the real reasons for college students choosing English as a major do not seem to be clear.  The researcher felt the need to investigate such reasons as well as describe the attitudes that students have towards online learning English and how these attitudes might impact the outcomes of the learning process.  The purpose of this paper is to determine what attitudes college students at Alauddin State Islamic University of Makassar have towards online learning English as a foreign language.  The 30 students involved in this project were from Alauddin State Islamic University of Makassar The students were asked to explain why they wished to become English teachers.  Notes were taken during these interviews and the responses were recorded.  A student survey was designed and distributed to all 30 students at the college and detailed instructions were given.  The students’ reasons for studying online English revealed a wide array of attitudes and motivations.  Surprisingly, almost half of the students exhibited positive attitudes towards online learning English and entering the teaching profession.  Other students demonstrated more positive attitudes. Keywords: Language attitudes, college students, English as a foreign language, attitude components, attitude functions.
미용종사자의 공유리더십과 조직신뢰가 커뮤니케이션에 미치는 영향

In this study, we aim to explore the direction of communication that increases organizational trust through shared leadership within beauty organizations, supplementing existing leadership research while providing opportunities for further research, and research on leadership and organizational communication methods suitable for current beauty management environment. The subjects of this study were 584 copies of beauty care workers in Seoul and Daejeon were finally used as analysis data. As a results, this study examined the relationship between shared leadership, organizational trust, and communication of beauty workers, and found that both shared leadership and organizational trust are related to communication, and we cannot help but emphasize the importance of leadership, trust and communication to improve management performance. Based on this study, it is believed that if beauty managers directly apply it to organizational members, they will set more clear goals and produce better results.