Reviewerly/exHarmony huggingface dataset:
ENGLISH DEPARTMENT STUDENTS’ LEARNING AUTONOMY IN IMPROVING LISTENING COMPREHENSION AT UNIVERSITAS ISLAM NEGERI ANTASARI BANJARMASIN

This research is aimed to find out the description of student activities while doing autonomy learning in improving listening comprehension at Antasari Islamic State University. This research is conducted in the middle of the second semester of batch 2020 using descriptive qualitative research.In conducting the research, to collect the data the researcher only using interviews to gain the data. The researcher interviewed 15 students of 130 students from all classes in the second semester of batch 2020. The researcher interviewed the students to get an overview of how they do learner autonomy on their own and using it to describe how students learning autonomy is.Throughout the interview, the researcher found the students’ learning autonomy in improving listening comprehension and their perceptions about it. Each student has their own way to study listening and motivations to study autonomy. However, the researcher found some similarities in their learner autonomy, like their motivations, want to improve their listening skill, and the material that they used was mostly taken from the internet and YouTube. The researcher also found students’ perceptions are divided into two parts, good perception, and bad perception. Therefore, the finding of this research is expected to the students could describe how their autonomy learning in improving listening comprehension and what their perception about learning autonomy.
Revisiting Prioritized Experience Replay: A Value Perspective

Experience replay enables off-policy reinforcement learning (RL) agents to utilize past experiences to maximize the cumulative reward. Prioritized experience replay that weighs experiences by the magnitude of their temporal-difference error ($|\text{TD}|$) significantly improves the learning efficiency. But how $|\text{TD}|$ is related to the importance of experience is not well understood. We address this problem from an economic perspective, by linking $|\text{TD}|$ to value of experience, which is defined as the value added to the cumulative reward by accessing the experience. We theoretically show the value metrics of experience are upper-bounded by $|\text{TD}|$ for Q-learning. Furthermore, we successfully extend our theoretical framework to maximum-entropy RL by deriving the lower and upper bounds of these value metrics for soft Q-learning, which turn out to be the product of $|\text{TD}|$ and "on-policyness" of the experiences. Our framework links two important quantities in RL: $|\text{TD}|$ and value of experience. We empirically show that the bounds hold in practice, and experience replay using the upper bound as priority improves maximum-entropy RL in Atari games.
Clear possibilities and subtle problems of Russian academic journals development model

The findings of the analysis of 85 academic journals in Russian Science Citation Index (RSCI) being indexed as for August 1, 2020, are presented. The analysis was based on surveys and scientomtrical indicators on eLIBRARY.RU platform; the periodicals’ websites; Russian Academy’s of Sciences (RAS) statements on RSCI policy and expert assessment of the journals; experience of the editorial boards of 5 peer reviewed academic journals found by The Northern Arctic Federal University named after M. V. Lomonosov. The possibilities and problems of integrating journals into the global scientific space are discussed.
Game Edukasi Museum Adityawarman Berbasis Android

Museum is a vehicle that has a strategic role in strengthening people's identity. One of the museums that exhibits historical relics is the State Museum of West Sumatra Province, known as the Adityarwan Museum. The Adityawarman Museum is the most important cultural museum in West Sumatra. The museum functions as a place to store and preserve historical objects, such as: Minangkabau cultural heritage, Mentawai cultural heritage and other archipelago cultural heritage. The use of smartphones, iPads, playbooks, tablet PCs and the like is more widely used today for various reasons and choices than PCs (Personal Computers) at home. In addition, there are many game features that are not only for entertainment, but there are already many games to hone thinking and logic that can educate children. Android is an operating system for Linux-based mobile devices which includes an operating system, middleware, and applications. Android provides an open platform for developers to create their applications. Android is the operating system that powers more than a billion smartphones and tablets
A Tabular Data Analysis Solution to help Improve the Quality of Data Relating to the COVID-19 Pandemic

Tabular structures (e.g. Excel, CSV) are often used to represent and store information. Unfortunately, user error can result in the valuable and diverse data stored in such structures being lost or overwritten. This can lead to major problems, depending on how and why the data is intended to be re-used. We propose a visual solution to help users analyse and detect problems in tabular data.
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling

Transformer-based models are widely used in natural language processing (NLP). Central to the transformer model is the self-attention mechanism, which captures the interactions of token pairs in the input sequences and depends quadratically on the sequence length. Training such models on longer sequences is expensive. In this paper, we show that a Bernoulli sampling attention mechanism based on Locality Sensitive Hashing (LSH), decreases the quadratic complexity of such models to linear. We bypass the quadratic cost by considering self-attention as a sum of individual tokens associated with Bernoulli random variables that can, in principle, be sampled at once by a single hash (although in practice, this number may be a small constant). This leads to an efficient sampling scheme to estimate self-attention which relies on specific modifications of LSH (to enable deployment on GPU architectures). We evaluate our algorithm on the GLUE benchmark with standard 512 sequence length where we see favorable performance relative to a standard pretrained Transformer. On the Long Range Arena (LRA) benchmark, for evaluating performance on long sequences, our method achieves results consistent with softmax self-attention but with sizable speed-ups and memory savings and often outperforms other efficient self-attention methods. Our code is available at https://github.com/mlpen/YOSO
Ordering-Based Causal Discovery with Reinforcement Learning

It is a long-standing question to discover causal relations among a set of variables in many empirical sciences. Recently, Reinforcement Learning (RL) has achieved promising results in causal discovery from observational data. However, searching the space of directed graphs and enforcing acyclicity by implicit penalties tend to be inefficient and restrict the existing RL-based method to small scale problems. In this work, we propose a novel RL-based approach for causal discovery, by incorporating RL into the ordering-based paradigm. Specifically, we formulate the ordering search problem as a multi-step Markov decision process, implement the ordering generating process with an encoder-decoder architecture, and finally use RL to optimize the proposed model based on the reward mechanisms designed for~each ordering. A generated ordering would then be processed using variable selection to obtain the final causal graph. We analyze the consistency and computational complexity of the proposed method, and empirically show that a pretrained model can be exploited to accelerate training. Experimental results on both synthetic and real data sets shows that the proposed method achieves a much improved performance over existing RL-based method.
Epistemic risk-sensitive reinforcement learning

We develop a framework for risk-sensitive behaviour in reinforcement learning (RL) due to uncertainty about the environment dynamics by leveraging utility-based definitions of risk sensitivity. In this framework, the preference for risk can be tuned by varying the utility function, for which we develop dynamic programming (DP) and policy gradient-based algorithms. The risk-averse behavior is compared with the behavior of risk-neutral policy in environments with epistemic risk.
예비 어린이 영어교사들을 위한 영어뮤지컬 수업모형 개발

The purpose of this study is for the childhood English education department to prepare a systematic way of teaching English musical courses that primarily focus on students. To this end, a survey was conducted on 17 students who enrolled in the children’s English musical education class, which is a 3rd year major subject for the first semester of 2019. Through in-depth interviews, the students’ perceptions, pros and cons, improvements of the subject were examined. As a result of this study, students felt the classes should be organized as follows. First, classes should be organized in a way which theory, creativity and practice are equally balanced. Second, a musical based on English fairy tales should be used to teach English expressions that match the children’s level. Third, students should be familiar with teaching methods by watching multiple musicals. The last steps are for them to produce, and present English musicals. The result of this study should be practical for professors who are planning to teach children’s English musical to provide a different prospective to future children’s English teachers. They will not only improve their English but also develop creativity and emotional development as well as social skills. (Kwangju Women’s University)
Emotion Classification via Social Media using Machine LearningAlgorithm

Social media analysis is a special area of assessment while the basic concept is to identify customercommunication. The current function of Emotion approval on Twitter apparently relies on word usage and directseparation in the word model package. An important question in our understanding is whether we will improve theiroverall performance usingAI statistics. Mythical Counting of the Pride Status Profile (POMS) speaks to the statusquo of 12 shows using 65 descriptive words with a combination of Ekman's classes and Plutchik's Emotions such as,anger, sadness, fatigue, strength, difficulty, frustration, joy, frustration, fear, anxiety, fear , panic and anticipation.These Feelings organize with the help of bags based on the names and statistics of LSI. Commitment function toincorporate AI calculations into the Emotion system, providing minimal use without human anonymity. SequentialMinimal Optimization calculations shoot when database testing with the help of a large amount of data preparation.Rate POMS exhibition and Sequential Minimal Optimization calculations on the Twitter API. The result shows withAssist of Sentiment Emojis by assent to utilize a tweet object.
The Effectiveness Project Work Teaching Techniques Of Sakubun Teaching Collaboration With Kaiwa

This research is focused on collaborative learning between sakubun (writing) and kaiwa (speaking) subjects. In the subjects of sakubun and kaiwa, the teacher must look for the right teaching method or technique so that the teaching and learning process is not monotonous and boring for learners. In sakubun courses, the teacher must have thoroughness in terms of examining the results of student writing and having insight into vocabulary and culture. Whereas in speaking courses (kaiwa), the teacher must have creativity in preparing the teaching material and techniques because of the teaching that all learners are required to actively speak. The ultimate goal of teaching Japanese is so that learners are able to communicate their ideas or ideas using Japanese both verbally and in writing.The methods and techniques used in learning are thematic methods with the technique of teaching project work. The purpose of this study is to provide a renewal in the teaching pattern of the sacred and kaiwa so that it can improve the effectiveness of teaching and the ability of students in writing and speaking is expected to increase.    Collaborating writing (sakubun) and speaking (kaiwa) courses is one of the treatments carried out by the author to create a learning environment that is not boring. From the results of data processing, it can be concluded that collaboration between teaching writing and teachingspeaks using project work techniques is quite effective to improve student achievement. in addition to teaching using project work techniques, learners also get a pleasant learning experience.
SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation

For model privacy, local model parameters in federated learning shall be obfuscated before sent to the remote aggregator. This technique is referred to as \emph{secure aggregation}. However, secure aggregation makes model poisoning attacks such backdooring more convenient considering that existing anomaly detection methods mostly require access to plaintext local models. This paper proposes SAFELearning which supports backdoor detection for secure aggregation. We achieve this through two new primitives - \emph{oblivious random grouping (ORG)} and \emph{partial parameter disclosure (PPD)}. ORG partitions participants into one-time random subgroups with group configurations oblivious to participants; PPD allows secure partial disclosure of aggregated subgroup models for anomaly detection without leaking individual model privacy. SAFELearning can significantly reduce backdoor model accuracy without jeopardizing the main task accuracy under common backdoor strategies. Extensive experiments show SAFELearning is robust against malicious and faulty participants, whilst being more efficient than the state-of-art secure aggregation protocol in terms of both communication and computation costs.
Extraction of relation from natural language texts using statistical and linguistic methods.

The work is devoted to the automated extraction of knowledge from unstructured text with the aim of their application in factextraction, the formation and replenishment of a thesaurus, analysis of document consistency. To extract and structure knowledge, methods of statistical and linguistic analysis are used.
Do físico ao digital: o acervo histórico do legislativo Bragantino

The advancement of the digital world and the implementation of document management policies allowed the expansion of the democratization of access to information, especially those of a public scope. Historical collections, books and documents that until then could only be consulted in person can now be accessed through digital libraries on the internet. Therefore, this article aims to present the development of the website of the Digital Collection of the city of Bragança Paulista.
From Matching Logic To Parallel Imperative Language Verification

Program verification is to develop the program's proof system, and to prove the proof system soundness with respect to a trusted operational semantics of the program. However, many practical program verifiers are not based on operational semantics and can't seriously validate the program. Matching logic is proposed to make program verification based on operational semantics. In this paper, following Grigore Ro{ş}u 's work, we consider matching logic for parallel imperative language(PIMP). According to our investigation, this paper is the first study on matching logic for PIMP. In our matching logic, we redefine "interference-free" to character parallel rule and prove the soundness of matching logic to the operational semantics of PIMP. We also link PIMP's operational semantics and PIMP's verification formally by constructing a matching logic verifier for PIMP which executes rewriting logic semantics symbolically on configuration patterns and is sound and complete to matching logic for PIMP. That is our matching logic verifier for PIMP is sound to the operational semantics of PIMP. Finally, we also verify the matching logic verifier through an example which is a standard problem in parallel programming.
Development of Bandung City Transport Optimization Application with Best-Path Planning Algorithm on Android Platform: Development of Bandung City Transport Optimization Application with Best-Path Planning Algorithm on Android Platform

City transport is often used by some people who travel without using their private vehicle. Usually, city transportation is empowered by the city government to encourage people to prioritize public transportation as the main choice for traveling. However, there are several problems such as the difficulty in knowing what city transportation routes can be used to reach a destination in the city, especially the city of Bandung. From the problems that arise, it requires information media that can be a virtual guide in optimizing the use of city transportation. This information media is needed to stimulate the public to always return to using city transportation as the primary means of transportation for travel. Smartphones with the Android operating system are one of the information media that can be used because of the very good development and growth of users in society, this can be seen from a survey conducted on one site, namely StatCounter which shows Android users are getting better in 2014. Therefore The development of this application for optimizing the use of city transportation is expected to be able to provide good information in determining the use of public transportation. On the other hand, the Best - Path Planning method is empowered to support the search for city transportation and decision making for the use of city transportation.
Deep Online Fused Video Stabilization

We present a deep neural network (DNN) that uses both sensor data (gyroscope) and image content (optical flow) to stabilize videos through unsupervised learning. The network fuses optical flow with real/virtual camera pose histories into a joint motion representation. Next, the LSTM block infers the new virtual camera pose, and this virtual pose is used to generate a warping grid that stabilizes the frame. Novel relative motion representation as well as a multi-stage training process are presented to optimize our model without any supervision. To the best of our knowledge, this is the first DNN solution that adopts both sensor data and image for stabilization. We validate the proposed framework through ablation studies and demonstrated the proposed method outperforms the state-of-art alternative solutions via quantitative evaluations and a user study.
НАСЛЕДОВАНИЕ СИНГУЛЯРНЫХ ВЕКТОРОВ ПРИ ПОПОЛНЕНИИ МАТРИЦЫ СТОЛБЦОМ

Let the matrix A1 be obtained from the matrix A by adding a column to it on the right. The possibility of inheritance of singular numbers and the corresponding singular vectors when passing from matrix A to matrix A1 is investigated. The singular value decompositions of the matrix A are based on the scalar and vector properties of the square symmetric matrices ATA and AAT. The article deals with the singular value decomposition of the matrix A, which has more rows than columns, and the decomposition is based on the analysis of the ATA matrix.
Reconstructing Actions To Explain Deep Reinforcement Learning

Feature attribution has been a foundational building block for explaining the input feature importance in supervised learning with Deep Neural Network (DNNs), but face new challenges when applied to deep Reinforcement Learning (RL).We propose a new approach to explaining deep RL actions by defining a class of \emph{action reconstruction} functions that mimic the behavior of a network in deep RL. This approach allows us to answer more complex explainability questions than direct application of DNN attribution methods, which we adapt to \emph{behavior-level attributions} in building our action reconstructions. It also allows us to define \emph{agreement}, a metric for quantitatively evaluating the explainability of our methods. Our experiments on a variety of Atari games suggest that perturbation-based attribution methods are significantly more suitable in reconstructing actions to explain the deep RL agent than alternative attribution methods, and show greater \emph{agreement} than existing explainability work utilizing attention. We further show that action reconstruction allows us to demonstrate how a deep agent learns to play Pac-Man game.
Improving Calibration through the Relationship with Adversarial Robustness

Neural networks lack adversarial robustness, i.e., they are vulnerable to adversarial examples that through small perturbations to inputs cause incorrect predictions. Further, trust is undermined when models give miscalibrated predictions, i.e., the predicted probability is not a good indicator of how much we should trust our model. In this paper, we study the connection between adversarial robustness and calibration and find that the inputs for which the model is sensitive to small perturbations (are easily attacked) are more likely to have poorly calibrated predictions. Based on this insight, we examine if calibration can be improved by addressing those adversarially unrobust inputs. To this end, we propose Adversarial Robustness based Adaptive Label Smoothing (AR-AdaLS) that integrates the correlations of adversarial robustness and calibration into training by adaptively softening labels for an example based on how easily it can be attacked by an adversary. We find that our method, taking the adversarial robustness of the in-distribution data into consideration, leads to better calibration over the model even under distributional shifts. In addition, AR-AdaLS can also be applied to an ensemble model to further improve model calibration.
Multiple Targets Tracking with Compact High-Frequency Surface Wave Radar

Compact High-frequency Surface Wave Radar (HFSWR) can detect ship targets, which has the characteristics of high Doppler velocity accuracy. However, limited by the wavelength and system of the radar, the radial range and azimuth detection accuracy of HFSWR is low. A multi-target tracking method by compact HFSWR is proposed in this paper. The track initiation and track association are performed in polar coordinates and target tracking is performed in Cartesian coordinates. According to radar detection characteristics and ship motion characteristics, the M/N logic method is used for track initiation, the minimum cost association algorithm is used for track association. Finally, the conversion measurement Kalman filter is used for tracking. Finally, a simulation experiment was carried out using the measured Compact HFSWR data and compared with the track of the Automatic Ship Identification System (AIS). The experimental results show that the proposed tracking process can effectively realize the multi-target tracking of the high frequency surface wave radar at sea.
A method of laser point temperature detection based on CNN

There has been a direct relationship between the temperature of the laser point and the quality of the casting in the process of the 3D printing. In the paper, a method based on convolutional neural network (CNN) was proposed to estimate the temperature of the laser point. The collected temperature data used were trained by the deep-learning method. A new structure of the model was proposed on the part of the CNN model, which improved from original LeNet. The process of the prediction for the testing set was carried out through the new model. The unknown temperature in the testing set can be estimated. The experimental result illustrates that the proposed method is satisfactory.