Reviewerly/exHarmony huggingface dataset:
Validitas Pengembangan Mobile Learning Berbasis Moodle di Sekolah Menengah Kejuruan Pariwisata Aisyiyah Pada Mata Pelajaran Pengetahuan Bahan Makanan

The use of learning media in Aisyiyah Tourism Vocational School is still dominant in using printed books and less using mobile-based learning media, so it is felt to be less effective in learning and makes students passive. So the development of Moodle-based Mobile Learning media is necessary for the learning process. The purpose of this research is the development of Moodle-based Mobile Learning in Aisyiyah Tourism Vocational School in the subject of valid Food Ingredients Knowledge. The development method is R&amp;D for education, with the Four-D development model. Data retrieval was carried out by giving instruments to validators of media experts and material experts, data analysis using Aikens V, the research location was Aisyiyah Tourism Vocational School, West Sumatra, class X-cooking class students. The results of research on Moodle-based Mobile Learning learning media as interactive media from product validity tests based on media validation an average of 0.92 is declared "Valid" and material validation is declared "Valid" an average of 0.87, so Moodle-based Mobile Learning learning media is very feasible to be applied in the learning process at the Aisyiyah Tourism Vocational School.The use of learning media in Aisyiyah Tourism Vocational School is still dominant in using printed books and less using mobile-based learning media, so it is felt to be less effective in learning and makes students passive. So the development of Moodle-based Mobile Learning media is necessary for the learning process. The purpose of this research is the development of Moodle-based Mobile Learning in Aisyiyah Tourism Vocational School in the subject of valid Food Ingredients Knowledge. The development method is R&amp;D for education, with the Four-D development model. Data retrieval was carried out by giving instruments to validators of media experts and material experts, data analysis using Aikens V, the research location was Aisyiyah Tourism Vocational School, West Sumatra, class X-cooking class students. The results of research on Moodle-based Mobile Learning learning media as interactive media from product validity tests based on media validation an average of 0.92 is declared "Valid" and material validation is declared "Valid" an average of 0.87, so Moodle-based Mobile Learning learning media is very feasible to be applied in the learning process at the Aisyiyah Tourism Vocational School.
한국어교육실습 교안 작성 지도를 위한 기초 연구

Journal of the International Network for Korean Language and Culture 17-2, 1-28, In this study, primary research was conducted on how to teach lesson planning to Korean practicum classes. As suggested by the Korean language education academic community, the principles of drafting teaching plans were synthesized. The lesson plans of preliminary teachers of the credit bank system were analyzed for deficiencies. The lesson plans were divided into three categories: 1. Total Composition; 2. Teacher Language, and; 3. Interaction. Twenty-one specific items were prepared to analyze the lesson plans, and two appraisers analyzed 129 plans compiled by credit bank students. A Cohen's Kappa coefficient was performed using SPSS 25 to verify the reliability of the appraisers. An analysis of the results indicated that preliminary teachers were the most challenged with homework assignments, error correction, feedback, and presentations. Based on the problems revealed by each item, a specific teaching method for lesson planning was suggested.(Catholic University)
Exponential Weights Algorithms for Selective Learning

We study the selective learning problem introduced by Qiao and Valiant (2019), in which the learner observes $n$ labeled data points one at a time. At a time of its choosing, the learner selects a window length $w$ and a model $\hat\ell$ from the model class $\mathcal{L}$, and then labels the next $w$ data points using $\hat\ell$. The excess risk incurred by the learner is defined as the difference between the average loss of $\hat\ell$ over those $w$ data points and the smallest possible average loss among all models in $\mathcal{L}$ over those $w$ data points. We give an improved algorithm, termed the hybrid exponential weights algorithm, that achieves an expected excess risk of $O((\log\log|\mathcal{L}| + \log\log n)/\log n)$. This result gives a doubly exponential improvement in the dependence on $|\mathcal{L}|$ over the best known bound of $O(\sqrt{|\mathcal{L}|/\log n})$. We complement the positive result with an almost matching lower bound, which suggests the worst-case optimality of the algorithm. We also study a more restrictive family of learning algorithms that are bounded-recall in the sense that when a prediction window of length $w$ is chosen, the learner's decision only depends on the most recent $w$ data points. We analyze an exponential weights variant of the ERM algorithm in Qiao and Valiant (2019). This new algorithm achieves an expected excess risk of $O(\sqrt{\log |\mathcal{L}|/\log n})$, which is shown to be nearly optimal among all bounded-recall learners. Our analysis builds on a generalized version of the selective mean prediction problem in Drucker (2013); Qiao and Valiant (2019), which may be of independent interest.
Anonymous Symmetric-Key Communication.

We study anonymity of probabilistic encryption (pE) and probabilistic authenticated encryption (pAE). We start by providing concise game-based security definitions capturing anonymity for both pE and pAE, and then show that the commonly used notion of indistinguishability from random ciphertexts (IND$) indeed implies the anonymity notions for both pE and pAE. This is in contrast to a recent work of Chan and Rogaway (Asiacrypt 2019), where it is shown that IND$-secure nonce-based authenticated encryption can only achieve anonymity if a sophisticated transformation is applied. Moreover, we also show that the Encrypt-then-MAC paradigm is anonymity-preserving, in the sense that if both the underlying probabilistic MAC (pMAC) and pE schemes are anonymous, then also the resulting pAE scheme is. Finally, we provide a composable treatment of anonymity using the constructive cryptography framework of Maurer and Renner (ICS 2011). We introduce adequate abstractions modeling various kinds of anonymous communication channels for many senders and one receiver in the presence of an active man-in-the-middle adversary. Then we show that the game-based notions indeed are anonymity-preserving, in the sense that they imply constructions between such anonymous channels, thus generating authenticity and/or confidentiality as expected, but crucially retaining anonymity if present.
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional  Pixel Synthesis

High-resolution satellite imagery has proven useful for a broad range of tasks, including measurement of global human population, local economic livelihoods, and biodiversity, among many others. Unfortunately, high-resolution imagery is both infrequently collected and expensive to purchase, making it hard to efficiently and effectively scale these downstream tasks over both time and space. We propose a new conditional pixel synthesis model that uses abundant, low-cost, low-resolution imagery to generate accurate high-resolution imagery at locations and times in which it is unavailable. We show that our model attains photo-realistic sample quality and outperforms competing baselines on a key downstream task -- object counting -- particularly in geographic locations where conditions on the ground are changing rapidly.
Cluster approach to the formation of mor qualities in adolescents

The article discusses a cluster approach to the formation of moral qualities in adolescents. Particular attention is paid to the development and substantiation of the role of the educational cluster, as well as the project method in the formation of the system of spiritual and moral education. The necessity of modernizing the educational system, understanding the importance of cooperation and creating certain conditions on the basis of an innovative infrastructure (cluster), as well as the obligation to take into account the cultural characteristics of the subjects of the educational process are substantiated.
Constant-Overhead Unconditionally Secure Multiparty Computation over Binary Fields.

We study the communication complexity of unconditionally secure multiparty computation (MPC) protocols in the honest majority setting. Despite tremendous efforts in achieving efficient protocols for binary fields under computational assumptions, there are no efficient unconditional MPC protocols in this setting. In particular, there are no n-party protocols with constant overhead admitting communication complexity of O(n) bits per gate. Cascudo, Cramer, Xing and Yuan (CRYPTO 2018) were the first ones to achieve such an overhead in the amortized setting by evaluating \(O(\log n)\) copies of the same circuit in the binary field in parallel. In this work, we construct the first unconditional MPC protocol secure against a malicious adversary in the honest majority setting evaluating just a single boolean circuit with amortized communication complexity of O(n) bits per gate.
문학교육은 저자성의 변화를 어떻게 수용할 것인가

This study examined the plagiarism debate surrounding Shin Kyung-sook''s Legend and Park Min-gyu''s Last Fan Club of Sammi Superstars. The former had some plagiarized sentences from Mishima Yukio’s Ukuk; however, the entire work cannot be viewed as plagiarism. Kyung-sook’s Legend was influenced by Mishima Yukio, but the author failed to acknowledge this influence. Park Min-gyu''s Last Fan Club of Sammi Superstars embodied his own ideas using Internet posts, for which Min-gyu expressed his gratitude to the bulletin board creator. Given that it is difficult to determine the original author or origin of Internet humor or stories, we cannot accuse Park of plagiarism.On examining these plagiarism issues, it is possible to confirm three authorships: modern authorship where the author is the only creator of his work, and medieval authorship where the author is the annotator of canonical texts, and postmodern authorship where the author creates new texts by freely assembling various preceding texts. The three aforementioned principles coexist with Legend and Last Fan Club of Sammi Superstars.To date, literature education has emphasized the role of learners as good readers. Given the changing nature of authorship, it is necessary to emphasize the ability of learners to actively produce their own texts. In order to link the learner''s reading of a literary work with their ability to produce their own texts, this study uses the phrase “reinforcing the reader''s authorship,” which can be realized through production-oriented and creation-oriented literature education.
Analysis and implementation of the bi-polar slope one algorithm with the content base filtering method in producing culinary place recommendations in kuningan regency

Abstract The recommendation system is an analysis model to obtain recommendations that use filtering techniques to produce recommendations as desired. One of the prediction algorithms is Bipolar Slope One which is a development of the Slope One algorithm to predict an event. Previous studies have shown that the results of the recommendations on the Bipolar Slope One algorithm show a mismatch between the type of recommended item and the type of item that has been rated by the active user, because the rating prediction process in the Bipolar Slope One algorithm does not pay attention to the type or content of the item, but pay more attention to the similarity of rating patterns. This study uses the Content-Based Filtering method to form a profile based on the attributes of an item which will make an assessment based on the analysis of the similarity of the user profile with the vector space model. Application users get information about which culinary delights have an item profile that is expected by him. Culinary players get information about what item profile I expect by the User.
An introduction to R

Modern datasets have grown substantially in the past few decades. As such, computer programs are necessary to execute even simple calculations. In this chapter, we get introduced to R, a statistical program and language that we will use in this book. R has functionality across Macintosh, Windows, and Linux systems with minimal differences in the code and interface. The software can ultimately be used for a variety of functions, including basic mathematical calculations, defining vectors of character and numeric values, and doing mathematical operations on these numeric vectors. These vectors can then be turned into data frames, the main storage form of the data we often find in our analyses.
Everything You Ever Said

This practice-based research consisted of 3 artworks in a group exhibition (The Word, Roxy Gallery, May 2021), an interview with professional podcasters, and a 30 minute public discourse about the work (16th May, 2021). The focus of the research was upon the public understanding of cutting edge natural language processing. Everything You Ever Said is a playful and participatory experience in which well-known speeches, stories, songs and letters are given new meaning by a computer program. The program allows you to take any piece of text and add/subtract 'concepts', with fascinating consequences. At its heart is a model of the entire English language, trained upon enormous data dumps of (your) everyday online conversations--entirely legally and without your permission. Everything you ever said provides an intuition for how it is that computers can predict what we'll say next; and the way that they 'understand' the meaning behind our words.
Technical Reports Compilation: Detecting the Fire Drill anti-pattern  using Source Code and issue-tracking data

Detecting the presence of project management anti-patterns (AP) currently requires experts on the matter and is an expensive endeavor. Worse, experts may introduce their individual subjectivity or bias. Using the Fire Drill AP, we first introduce a novel way to translate descriptions into detectable AP that are comprised of arbitrary metrics and events such as logged time or maintenance activities, which are mined from the underlying source code or issue-tracking data, thus making the description objective as it becomes data-based. Secondly, we demonstrate a novel method to quantify and score the deviations of real-world projects to data-based AP descriptions. Using nine real-world projects that exhibit a Fire Drill to some degree, we show how to further enhance the translated AP. The ground truth in these projects was extracted from two individual experts and consensus was found between them. Our evaluation spans three kinds of pattern, where the first is purely derived from description, the second type is enhanced by data, and the third kind is derived from data only. The Fire Drill AP as translated from description only for either, source code- or issue-tracking-based detection, shows weak potential of confidently detecting the presence of the anti-pattern in a project. Enriching the AP with data from real-world projects significantly improves detection. Using patterns derived from data only leads to almost perfect correlations of the scores with the ground truth. Some APs share symptoms with the Fire Drill AP, and we conclude that the presence of similar patterns is most certainly detectable. Furthermore, any pattern that can be characteristically modeled using the proposed approach is potentially well detectable.
Caption to Voice Bot for Assistive Vision

Over the last few years, with the rapid development of artificial intelligence, the generation of the caption of images has progressively caught the considerable interest of several artificial intelligence research groups and has become a fascinating and tedious mission. A large component of scene comprehension, which encompasses the knowledge of computer vision and natural language processing, is image caption, which automatically produces natural language explanations according to the content observed in an image. The applications of such an image caption are substantial and noteworthy. The prime intention of the project is to build an object detection and captioning module that produces captions from the features extracted from the input images fed to the module in the form of audio and interface it with a virtual text reader, a read-aloud technology. Additionally, both these features can be accomplished using live images. The module as a whole helps the visually impaired identify objects and their positions.
Efficient Classification with Adaptive KNN

In this paper, we propose an adaptive kNN method for classification, in which different k are selected for different test samples. Our selection rule is easy to implement since it is completely adaptive and does not require any knowledge of the underlying distribution. The convergence rate of the risk of this classifier to the Bayes risk is shown to be minimax optimal for various settings. Moreover, under some special assumptions, the convergence rate is especially fast and does not decay with the increase of dimensionality.
Learning stochastic object models from medical imaging measurements by use of advanced AmbientGANs.

In order to objectively assess new medical imaging technologies via computer-simulations, it is important to account for all sources of variability that contribute to image data. One important source of variability that can significantly limit observer performance is associated with the variability in the ensemble of objects to-be-imaged. This source of variability can be described by stochastic object models (SOMs), which are generative models that can be employed to sample from a distribution of to-be-virtually-imaged objects. It is generally desirable to establish SOMs from experimental imaging measurements acquired by use of a well-characterized imaging system, but this task has remained challenging. Deep generative neural networks, such as generative adversarial networks (GANs) hold potential for such tasks. To establish SOMs from imaging measurements, an AmbientGAN has been proposed that augments a GAN with a measurement operator. However, the original AmbientGAN could not immediately benefit from modern training procedures and GAN architectures, which limited its ability to be applied to realistically sized medical image data. To circumvent this, in this work, a modified AmbientGAN training strategy is proposed that is suitable for modern progressive or multi-resolution training approaches such as employed in the Progressive Growing of GANs and Style-based GANs. AmbientGANs established by use of the proposed training procedure are systematically validated in a controlled way by use of computer-simulated measurement data corresponding to a stylized imaging system. Finally, emulated single-coil experimental magnetic resonance imaging data are employed to demonstrate the methods under less stylized conditions.
Sentiment Analysis of College Reviews using Machine Learning

Sentiment analysis is the process of detecting positive or negative or neutral sentiment in text. It’s often used by businesses to detect sentiment in social data, gauge brand reputation, to understand it and make better. Sentiment analysis models focused on polarity (positive, negative and neutral) and even intentions (interested or not interested). Depending on how we wish want to interpret feedback and queries, we will define and tailor your categories to meet your sentiment analysis needs. This paper focuses the reviews of various colleges which are an important form of opinion mining. The essential objective of this paper is to classify every sentence’s semantic orientation (e.g. positive, negative and neutral) of the reviews. It is a very useful analysis since we could possibly determine opinion about various colleges
New Length Dependent Algorithm for Maximum Satisfiability Problem

In this paper, we study the computational complexity of the Maximum Satisfiability problem in terms of the length L of a given formula. We present an algorithm with running time O(1.0927^L), hence, improving the previously known best upper bound O(1.1058^L) developed more than 20 years ago by Bansal and Raman. Theoretically speaking, our algorithm increases the length of solvable formulas by 13.3% (compare this to the recent breakthrough result for Maximum Satisfiability problem with respect to the number of clauses by Xu et al. in 2019 giving a 7.5% improvement). Besides, we propose a significantly simpler algorithm with running time O(1.1049^L). The algorithm outperforms Bansal's and Raman's algorithm in simplicity and running time.
The Motion Detection on Video Surveillance by Using Background Subtraction

Video surveillance has been widely used for monitoring incidents at facilities and public places. Surveillance systems that are used in general are still using people as video surveillance. The surveillance system has been developed with a semi-automatic and automatic system. Video surveillance in this study aims to detect motion in the room by estimating static cameras and static backgrounds. Object detection starts by reading the video file and the number of video frames. The background subtraction method is used to find differences between frames by reducing the current frame to the background frame. The difference in pixels that appear in the current frame shows the pixel location of a motion. Through threshold operations in the range 0.2 to 0.3, binary images of motion can be produced better. Noise problems that arise are resolved using morphological operations in the form of holes or points. The results of research on videos with a size of 1,280 x 720 pixels and FPS 25 can detect motions marked with bounding boxes.
A Proposal of Blended-Learning Activities for Learning English in the BA Program at CIEX:

This study was developed at Centro de Idiomas Extranjeros “Ignacio Manuel Altamirano”, a private language institution that offers language courses (English, German and French), as well as a Bachelor in English Letters and a Master’s degree in Language Teaching and Applied Linguistics. The study was developed in the BA program and the main research objective was to create blended-learning activities which enhance learners’ abilities in the four macro language skills through the institutional online platform. This article presents the literature review of the most relevant terms, theories and models related to blended language learning. The study of the theory was done considering the most relevant tendencies in the topics and subtopics of the thesis, describing, comparing and contrasting the theorists’ points of view. The methodology of the research, the main results and the general conclusions will be presented in another article since the main purpose of this paper is to present the theory which is the basis of the study.
한·중 중·한 법령 번역에서 법률용어 선택 전략

This study explores the translation of the most error-prone legal terms in the translation of statutes between Chinese and Korean. Korean and Chinese belong to the same Chinese character culture, so they share common features that facilitate translations between the two languages, but the same features may cause many errors in translation. There are many Chinese characters that cannot be translated on a one-on-one basis due to legal system and cultural differences, and there are many similar words that are very limited in their use due to their legal meanings. In addition, there are gaps in the legal systems between the two countries, so this study attempts to look into how to translate these legal terms. The study will contribute to the development of reference materials and guidelines for educating or translating legal terms.