Reviewerly/exHarmony huggingface dataset:
Problem Encountered in Teaching Reading for Junior High School Students

The aim of this study is to explore the problem in teaching reading for junior high school students. One of junior high school in Surakarta was chosen as the research object of this case study. Eight students in grade eighth and a teacher are chosen as the sample, because the sample used in this study was purposive sampling. The data collection techniques are interview, observation, and document analysis. This study finds that: 1) the problems found by the teacher, i.e. demotivated students and unappealing English text book material. By using authentic materials, the teacher can help improve students’ motivation, provide various materials, and conduct many differentactivities in teaching reading. Thus, the teacher learned how to be creative in teaching. 2) the teacher uses authentic material to create lively atmosphere in the classroom so that the students are more interested to learn reading and make the students excited so that they want to discover things such as new vocabulary, religious values, and moralmessages in the text. 3) through the use of authentic materials, students’ vocabulary mastery is improving.
Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks

Neuromorphic sensing and computing hold a promise for highly energy-efficient and high-bandwidth-sensor processing. A major challenge for neuromorphic computing is that learning algorithms for traditional artificial neural networks (ANNs) do not transfer directly to spiking neural networks (SNNs) due to the discrete spikes and more complex neuronal dynamics. As a consequence, SNNs have not yet been successfully applied to complex, large-scale tasks. In this article, we focus on the self-supervised learning problem of optical flow estimation from event-based camera inputs, and investigate the changes that are necessary to the state-of-the-art ANN training pipeline in order to successfully tackle it with SNNs. More specifically, we first modify the input event representation to encode a much smaller time slice with minimal explicit temporal information. Consequently, we make the network's neuronal dynamics and recurrent connections responsible for integrating information over time. Moreover, we reformulate the self-supervised loss function for event-based optical flow to improve its convexity. We perform experiments with various types of recurrent ANNs and SNNs using the proposed pipeline. Concerning SNNs, we investigate the effects of elements such as parameter initialization and optimization, surrogate gradient shape, and adaptive neuronal mechanisms. We find that initialization and surrogate gradient width play a crucial part in enabling learning with sparse inputs, while the inclusion of adaptivity and learnable neuronal parameters can improve performance. We show that the performance of the proposed ANNs and SNNs are on par with that of the current state-of-the-art ANNs trained in a self-supervised manner.
Does Putting a Linguist in the Loop Improve NLU Data Collection

Many crowdsourced NLP datasets contain systematic artifacts that are identified only after data collection is complete. Earlier identification of these issues should make it easier to create high-quality training and evaluation data. We attempt this by evaluating protocols in which expert linguists work ‘in the loop’ during data collection to identify and address these issues by adjusting task instructions and incentives. Using natural language inference as a test case, we compare three data collection protocols: (i) a baseline protocol with no linguist involvement, (ii) a linguist-in-the-loop intervention with iteratively-updated constraints on the writing task, and (iii) an extension that adds direct interaction between linguists and crowdworkers via a chatroom. We find that linguist involvement does not lead to increased accuracy on out-of-domain test sets compared to baseline, and adding a chatroom has no effect on the data. Linguist involvement does, however, lead to more challenging evaluation data and higher accuracy on some challenge sets, demonstrating the benefits of integrating expert analysis during data collection.
Cognitive Computing-Powered, NLP-Based, Autonomous Question-Answering System

In today’s time, we have to deal with an enormous amount of digital data accessible over the web to get our answers. It is a monotonous task which pioneers the requirement of such question-answering (Q-A) systems that can offer accurate responses to user queries. In this chapter, we are going to recommend “QAEdu”, a cognitive computing-based Q-A system in the field of education, a novel way to system approach, and social agent-based Q-A systems. It blends the power of (i) NLP (Natural Language Processing), (ii) Cognitive Computing, (iii) Knowledge-Base, (iv) Recommendation System, and (v) Confidence Value system. Here, NLP techniques offer the power of deep understanding of user questions asked in natural language form. Cognitive computing provides the tools and techniques to synergize an assortment of technologies like speech recognition, knowledge-based construction techniques, computer vision, and much more, along with the creation of a robust knowledge base. Recommendation system provides answer suggestion to the students, and the Confidence Value finds the best answer from the knowledge base. Thus, for the practical implementation of the proposed architecture, we have chosen the education domain and will be teaching electromagnetic concepts for the edification of students.
A Workflow for Offline Model-Free Robotic Reinforcement Learning

Offline reinforcement learning (RL) enables learning control policies by utilizing only prior experience, without any online interaction. This can allow robots to acquire generalizable skills from large and diverse datasets, without any costly or unsafe online data collection. Despite recent algorithmic advances in offline RL, applying these methods to real-world problems has proven challenging. Although offline RL methods can learn from prior data, there is no clear and well-understood process for making various design choices, from model architecture to algorithm hyperparameters, without actually evaluating the learned policies online. In this paper, our aim is to develop a practical workflow for using offline RL analogous to the relatively well-understood workflows for supervised learning problems. To this end, we devise a set of metrics and conditions that can be tracked over the course of offline training, and can inform the practitioner about how the algorithm and model architecture should be adjusted to improve final performance. Our workflow is derived from a conceptual understanding of the behavior of conservative offline RL algorithms and cross-validation in supervised learning. We demonstrate the efficacy of this workflow in producing effective policies without any online tuning, both in several simulated robotic learning scenarios and for three tasks on two distinct real robots, focusing on learning manipulation skills with raw image observations with sparse binary rewards. Explanatory video and additional results can be found at sites.google.com/view/offline-rl-workflow
Direct Computation of Robot Inverse Kinematic Transformations Using Hopfield Neural Network

Computation of inverse kinematic transformations is an important problem in the field of robotics as it must be solved in real-time in order to position the end-effector at the desired position in a given task-space. However, it is a difficult problem for it involves the determination whether or not at least one mathematical set of robot joint angle values exists that will produce the desired coordinate configuration. The mathematical solutions should be checked against the physical constraints associated with the manipulator. The advent of artificial neural networks has made it possible to obtain general learning schemes which can be used to arrive at feasible solutions to inverse kinematics problem in a constrained environment independent of a robotic structure. The intent of this paper is to use the Hopfield neural network for the direct computation of inverse kinematic transformations of two- and three-linked robots, thereby avoiding the off-line training of neural networks.
МЕТОДИКА ПРЕПОДАВАНИЯ ТЕМЫ «КУБИЧЕСКИЕ УРАВНЕНИЯ» В УНИВЕРСИТЕТАХ СРЕДСТВАМИ ЭЛЕКТРОННОГО ОБУЧЕНИЯ

Introduction. Electronic teaching is a new and useful event in education. By this reason, there is a need in searching of all aspects of electronic teaching. The main amount of investigations must be devoted to define essential difference between two methods of teaching: the first of them is a traditional teaching, and second is an electronic teaching. The useful way of comparing these methods of teaching consists of studying some model problems of teaching in various brunches of the mathe matics by electronic method of education. The theme of cubic equations is one of key problems of teaching in algebra because this question historically was one of corner stones of algebra. It was beginning of introducing new methods in all brunches of mathematics. Purpose. The aim of our investigations is to show that sides of teaching that show how we can use the excellence of electronic teaching for most deep studying of basic methods of algebra, acquiring abilities in applying of methods of algebra for solving of concrete problems of informatics. Methods. The way of reaching these purposes is in combining of deep studying of theoretical background with construction of problem’s algorithm and preparing of program in appropriate language. Simultaneously, the methods of solution which is accessible in Internet can be used as an additional source for checking of got results. Results. The students after of complete solving of problems will better understand not only the considered question but also the main methodology of study of problems by using of electronic education methods. Originality. The research of the put problem is original in posing and solving of concrete questions connected with the teaching of the theme “cubic equations”. It is separated the main idea allowing use the theoretic result to obtain all three solutions of cubic equation using minimal number of operations. Conclusion. The electronic teaching must use excellent methods of teaching in algebra that cannot be reachable in traditional education. The questions of teaching for some themes which lie in common area of interests in different disciplines such as algebra and informatics acquires specific feature, be studied simultaneously. The main thesis of the work is as follows: simultaneous studying of theoretical and practical questions of teaching in algebra is useful for better learning of the material.
Dual Convolutional Neural Networks for Crowd Counting

The core of crowd counting methods based on convolutional neural networks (CNN) is to construct a CNN model that can effectively mapping the crowd image to the density map, and then accurately estimate the number of crowd from the density map. The conventional CNN based methods only consider the mapping from crowd images to density maps without taking that from density maps to crowd maps and the impact of the mapping on model performance. A dual convolutional neural networks (DualCNN) is proposed to improve the accuracy of the mapping from crowd maps to density maps. DualCNN contains two mapping sub-models:1) a convolutional neural model that mapping a crowd image to a density map; 2) a dual convolutional neural model that mapping a density map to a crowd image. In the learning process, through the mutual influence of the two sub-models, the performance of the convolutional neural model on crowd counting is improved.Experimental results on the datasets of UCF_CC_50 and ShangHaitech show that the presented method can effectively improve the counting performance of CNN. Especially in the UCF_CC_50 data set, DualCNN reduces the MAE of MCNN and CSRNet by 15.6% and 15.8%, and MSE by 18.1% and 28.8%, respectively.
Research and Practice on the Reform of Innovative English Teaching Mode in Higher Vocational Colleges

This article introduces the background of the reform of higher vocational English teaching mode, points out the problems that need to be solved in the reform, and focuses on the measures taken and the practical results achieved in the reform. Through research and practice, the embryonic form of vocational English blended teaching mode has been formed, and it has also served as a reference and demonstration for the intelligent teaching reform of other courses.
A Computationally Efficient Method for Learning Exponential Family Distributions

We consider the question of learning the natural parameters of a $k$ parameter minimal exponential family from i.i.d. samples in a computationally and statistically efficient manner. We focus on the setting where the support as well as the natural parameters are appropriately bounded. While the traditional maximum likelihood estimator for this class of exponential family is consistent, asymptotically normal, and asymptotically efficient, evaluating it is computationally hard. In this work, we propose a computationally efficient estimator that is consistent as well as asymptotically normal under mild conditions. We provide finite sample guarantees to achieve an ($\ell_2$) error of $\alpha$ in the parameter estimation with sample complexity $O(\mathrm{poly}(k/\alpha))$ and computational complexity ${O}(\mathrm{poly}(k/\alpha))$. To establish these results, we show that, at the population level, our method can be viewed as the maximum likelihood estimation of a re-parameterized distribution belonging to the same class of exponential family.
Pengembangan Literasi Digital Guru dan Siswa Melalui Program Kampus Mengajar di SMPN 19 Mataram

The basic literacy that must be possessed by someone to be able to compete in the 21st century is literacy, numeric literacy, scientific literacy, digital literacy, financial literacy, and cultural and civic literacy. This community service activity aims to develop digital literacy for teachers and students through the Teaching Campus program at SMPN 19 Mataram. The Teaching Campus Program is one of the implementations of the Independent Campus Policy which is beneficial for students. This community service activity at SMPN 19 Mataram was carried out from August 4, 2021 to September 25, 2021. The method of activity consisted of four stages each week, namely: planning weekly activities with the team, carrying out weekly activity plans, analyzing weekly activities, making improvement plans and follow-up. The form of digital literacy development activities is that students help teachers and students in implementing technology, information and communication (ICT) based learning. Students help teachers and students in optimizing the use of various digital applications, namely WhatsApp, Zoom Meeting, Google Meet, Google Drive, and Google Form. Students also assist teachers in preparing teaching materials in the form of power point media (ppt) and learning videos. Thus, the Campus Teaching program is beneficial for students to gain hands-on experience in the field of becoming professional teachers, teachers and students also gain information and skills in using various digital technologies as a provision to face global competition in the 21st century
Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph Embeddings

Recent knowledge graph embedding (KGE) models based on hyperbolic geometry have shown great potential in a low-dimensional embedding space. However, the necessity of hyperbolic space in KGE is still questionable, because the calculation based on hyperbolic geometry is much more complicated than Euclidean operations. In this paper, based on the state-of-the-art hyperbolic-based model RotH, we develop two lightweight Euclidean-based models, called RotL and Rot2L. The RotL model simplifies the hyperbolic operations while keeping the flexible normalization effect. Utilizing a novel two-layer stacked transformation and based on RotL, the Rot2L model obtains an improved representation capability, yet costs fewer parameters and calculations than RotH. The experiments on link prediction show that Rot2L achieves the state-of-the-art performance on two widely-used datasets in low-dimensional knowledge graph embeddings. Furthermore, RotL achieves similar performance as RotH but only requires half of the training time.
Introduction to Data Science: Data analysis and Prediction Algorithms with R

Journal of the Royal Statistical Society: Series A (Statistics in Society)Early View BOOK REVIEW Introduction to data science: Data analysis and prediction algorithms with R , Irizarry, Rafael A. Chapman and Hall/CRC, 2019, 743 pages. USD \$92.63 (hardback). ISBN 1119282012 Li-Pang Chen, Li-Pang Chen lchen723@nccu.edu.tw Department of Statistics, National Chengchi University, Taipei City, TaiwanSearch for more papers by this author Li-Pang Chen, Li-Pang Chen lchen723@nccu.edu.tw Department of Statistics, National Chengchi University, Taipei City, TaiwanSearch for more papers by this author First published: 16 November 2021 https://doi.org/10.1111/rssa.12781Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Don't Search for a Search Method - Simple Heuristics Suffice for Adversarial Text Attacks.

Recently more attention has been given to adversarial attacks on neural networks for natural language processing (NLP). A central research topic has been the investigation of search algorithms and search constraints, accompanied by benchmark algorithms and tasks. We implement an algorithm inspired by zeroth order optimization-based attacks and compare with the benchmark results in the TextAttack framework. Surprisingly, we find that optimization-based methods do not yield any improvement in a constrained setup and slightly benefit from approximate gradient information only in unconstrained setups where search spaces are larger. In contrast, simple heuristics exploiting nearest neighbors without querying the target function yield substantial success rates in constrained setups, and nearly full success rate in unconstrained setups, at an order of magnitude fewer queries. We conclude from these results that current TextAttack benchmark tasks are too easy and constraints are too strict, preventing meaningful research on black-box adversarial text attacks.
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning

Traditionally, federated learning (FL) aims to train a single global model while collaboratively using multiple clients and a server. Two natural challenges that FL algorithms face are heterogeneity in data across clients and collaboration of clients with {\em diverse resources}. In this work, we introduce a \textit{quantized} and \textit{personalized} FL algorithm QuPeD that facilitates collective (personalized model compression) training via \textit{knowledge distillation} (KD) among clients who have access to heterogeneous data and resources. For personalization, we allow clients to learn \textit{compressed personalized models} with different quantization parameters and model dimensions/structures. Towards this, first we propose an algorithm for learning quantized models through a relaxed optimization problem, where quantization values are also optimized over. When each client participating in the (federated) learning process has different requirements for the compressed model (both in model dimension and precision), we formulate a compressed personalization framework by introducing knowledge distillation loss for local client objectives collaborating through a global model. We develop an alternating proximal gradient update for solving this compressed personalization problem, and analyze its convergence properties. Numerically, we validate that QuPeD outperforms competing personalized FL methods, FedAvg, and local training of clients in various heterogeneous settings.
Residual2Vec: Debiasing graph embedding with random graphs

Graph embedding maps a graph into a convenient vector-space representation for graph analysis and machine learning applications. Many graph embedding methods hinge on a sampling of context nodes based on random walks. However, random walks can be a biased sampler due to the structural properties of graphs. Most notably, random walks are biased by the degree of each node, where a node is sampled proportionally to its degree. The implication of such biases has not been clear, particularly in the context of graph representation learning. Here, we investigate the impact of the random walks' bias on graph embedding and propose residual2vec, a general graph embedding method that can debias various structural biases in graphs by using random graphs. We demonstrate that this debiasing not only improves link prediction and clustering performance but also allows us to explicitly model salient structural properties in graph embedding.
Willingness to Read in Class and Outside Class, Reading Anxiety, and Comprehension of Korean EFL College Students

The purpose of this study is twofold: to examine whether and to what extent reading anxiety is associated with Korean EFL college students’ willingness to read (WTR) in and outside class; and to investigate whether and to what extent WTR inside, and outside class are related to reading comprehension in English. Participants included 108 undergraduate students. Simple regression and standard multiple regression using SPSS reveal two major findings: First, Korean college students are more affected with anxiety when they are willing to try read in English outside class than inside; second, students who willingly try to read English outside class are better at reading comprehension than those who are more willing to read inside class. The results of the study suggest that instructors need to be aware of EFL students
Blockchain Proliferation in this Digital Epoch

In this digital epoch, the block chain is the correct choice to solve many digital societal problems. Block chain proliferations are just not constricted to digital currencies like bitcoin. Different attributes like immutability, decentralized, no need for third parties, and automatic access policy realization using smart contracts, etc. made block chain as a quintessential framework to solve manifold problems in our society. In this paper, we made a look over on block chain proliferation in different applications and finally we analyze to understand it’s suitability in this digital epoch
Label-Enhanced Reading Comprehension Model

In the existing extractive reading comprehension models,only the boundary of answers is utilized as the supervision signal and the labeling processed by human is ignored.Consequently,learned models are prone to learn the superficial features and the generalization performance is degraded.In this paper,a label-enhanced reading comprehension model is proposed to imitate human activity.The answer-bearing sentence,the content and the boundary of the answer are learned simultaneously.The answer-bearing sentence and the content of the answer can be derived from the boundary of the answer and these three types of labels are regarded as supervision signals.The model is trained by multitask learning.During prediction,the probabilities from three predictions are merged to determine the answer,and thus the generalization performance is improved.Experiments on SQuAD dataset demonstrate the effectiveness of LE-Reader model.