Reviewerly/exHarmony huggingface dataset:
챗봇을 통한 스마트러닝 한국어교육의 학습 효과: 학습자 인식을 중심으로

With the development of Artificial Intelligence (AI) and its sharp growth in demand, AI chatbot has spread to other fields, including the field of language education on AI-based smart learning. This empirical study aims to review the learning effects of Korean education using AI chatbots in smart learning. In addition, the perceived learning effects of Korean as second language (KSL) learners were examined. Additionally, as the learning style of the learners could influence the learning effect, this study also compared the differences in perceived learning effect according to the students’ learning style (visual, auditory, and hands-on). The participants of this study used a Korean language education chatbot application and answered a survey on the learning effect, namely the earning content, learning flow, learning anxiety, learning efficacy, and learning satisfaction. The results showed that KSL learners considered Korean language education through chatbots was as effective as existing Korean language education. The chatbot also received positive evaluation in terms of decreasing learning anxiety and increasing learning efficacy. Furthermore, the study revealed that among the different learning styles, visual learners showed a significant reduction in learning anxiety.
A Sparsity Algorithm with Applications to Corporate Credit Rating.

In Artificial Intelligence, interpreting the results of a Machine Learning technique often termed as a is a difficult task. A counterfactual explanation of a particular black box attempts to find the smallest change to the input values that modifies the prediction to a particular output, other than the original one. In this work we formulate the problem of finding a counterfactual explanation as an optimization problem. We propose a new which solves the optimization problem, while also maximizing the sparsity of the counterfactual explanation. We apply the sparsity algorithm to provide a simple suggestion to publicly traded companies in order to improve their credit ratings. We validate the sparsity algorithm with a synthetically generated dataset and we further apply it to quarterly financial statements from companies in financial, healthcare and IT sectors of the US market. We provide evidence that the counterfactual explanation can capture the nature of the real statement features that changed between the current quarter and the following quarter when ratings improved. The empirical results show that the higher the rating of a company the greater the effort required to further improve credit rating.
IMPLEMENTASI DAN EFEKTIFITAS PENGGUNAAN MOBILE LEARNING TERHADAP HASIL BELAJAR MAHASISWA PROGRAM STUDI SISTEM INFORMASI STIKOM UYELINDO KUPANG

This research was made aiming to implement and improve the effectiveness of learning media through mobile-based mobile learning for all courses in Uyelindo Kupang STIKOM information system students. The research target to be achieved is an additional learning media that is interesting and easy to use so that it can increase student interest and student achievement. This research will discuss how to design, develop and evaluate the effectiveness of using Mobile Learning applications. This application is made so that students can easily access a course material using the Mobile Learning application by Smartphone and also to increase the effectiveness of conducting online lectures at the Uyelindo Kupang STIKOM information system study program. To measure the effectiveness of the use of mobile learning it is necessary to test among them, from the results of the Kolmogorov-Smirnov test using SPSS 16 it is found that the research subjects are normally distributed and based on the results of the Paired Samples Test t-test it is known that the significant value is 0,000&gt; 0,05. While t-count = -11,142 and for t-table at a significant level ? = 5% (0.05) with degrees of freedom (df) n-1 or 50-1 = 49. Two-sided testing (significance = 0.025) results are obtained for t-table = 2 , 00958 with a significant level of 0.025. In the result t-count &lt; t-table (-11.142 &lt;2, 00958) then H0 is rejected and Ha is accepted. So that the use of mobile learning media can improve student learning outcomes at the Uyelindo Kupang STIKOM information system.
SWITCHED RELUCTANCE MOTOR CONTROL BASED ON ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)

This paper gives some studies on the model of Switched Resistor Motor (SRM). The adaptive neuro-fuzzy inference system (ANFIS) is used to model the inductance and moment of the SRM. Then, the PI Controller is applied to model the inductance and flux bond of the SRM. Comparing these two types of modeling method, it is clear that although the PI Controller method can do online research, it is not as accurate as ANFIS.
IMPROVING STUDENTS’ PARTICIPATION, COMPREHENSION AND FOCUS DURING THE LEARNING PROCESS WITH THE EXPLICIT USE OF INTERACTIVE AND MOBILE LEARNING IN AN EFL CLASSROOM

IMPROVING STUDENTS’ PARTICIPATION, COMPREHENSION AND FOCUS DURING THE LEARNING PROCESS WITH THE EXPLICIT USE OF INTERACTIVE AND MOBILE LEARNING IN AN EFL CLASSROOM
PKSpell: Data-Driven Pitch Spelling and Key Signature Estimation

We present PKSpell: a data-driven approach for the joint estimation of pitch spelling and key signatures from MIDI files. Both elements are fundamental for the production of a full-fledged musical score and facilitate many MIR tasks such as harmonic analysis, section identification, melodic similarity, and search in a digital music library. We design a deep recurrent neural network model that only requires information readily available in all kinds of MIDI files, including performances, or other symbolic encodings. We release a model trained on the ASAP dataset. Our system can be used with these pre-trained parameters and is easy to integrate into a MIR pipeline. We also propose a data augmentation procedure that helps retraining on small datasets. PKSpell achieves strong key signature estimation performance on a challenging dataset. Most importantly, this model establishes a new state-of-the-art performance on the MuseData pitch spelling dataset without retraining.
Standardization of Light Emitting Diode Feedback on Metered Solar Home Systems

Light Emitting Diodes (LEDs) are used in a variety of devices ranging from watches to solar home systems, as status indicators or communicative tools.  However, the communicative output varies across devices of similar or different functions. This variation and the sense that there is a wide array of expressions that can be employed, makes it hard for users of the devices to deduce the feedback easily. To address this gap, this paper discusses the effectiveness of a standardized light-based interface for metered solar home systems, which has been redesigned using a structured Human Computer Interaction (HCI) design process. The study makes use of the UFuRT (User, Function, Representation and Task analyses) framework to evaluate the use of these devices ethnographically and contextually among solar home-system owners in the Rift Valley region of Kenya. To understand how solar home systems users utilize multi-communicative LEDs, 111 solar home system owners participated in in-person interviews. The results indicate that, on average, 51% of solar-home system owners cannot correctly interpret or do not know the LED feedback of the device they own. The quantitative and qualitative data collected from the in-person sessions was used to design a simulated standardized interface of a metered solar home system. The effectiveness of this newly designed interface was measured by interviewing another set of 43 solar home system users. The results indicate that, on average, up to 63% of solar system owners correctly interpret the LED feedback mechanisms of a standardized design. Further, up to 86% of solar system owners correctly interpret specific feedback mechanisms of a standardized design.  With these findings, the study concludes that using a HCI framework to standardize the interface design of LED-touting devices increases the expressivity and user understanding of feedback relayed by these devices.
한국의 중국학 관련 박사학위논문 현황 분석

This paper aims to understand the development of Chinese studies education and the characteristics of Chinese studies in Korea by analyzing the current status of doctoral dissertations related to Chinese studies in Korea. To this end, it was analyzed focusing on the year of publication, the university of thesis acquisition, the field of major, the university of regional origin, and the language of thesis preparation. In particular, we would like to study the characteristics of Ph. D. thesis related to Chinese studies through various current analysis. The results of the study are first, changes in China-linked research. Second, various changes in Chinese studies major. Third, revitalize the major universities in Seoul. Fourth, polarization of the Chinese study area. Finally, it was shown as diversity in the language of writing doctoral dissertations. In the future, we will be able to draw advantages, disadvantages, or characteristics of each other by comparing comparative methods for the study of Chinese studies in Korea. In addition, we hope to establish a research problem for ‘Chinese Studies’ using interdisciplinary research or comprehensive research methods, and to use theories and techniques from various academic fields to identify research problems.
mmPose-NLP: A Natural Language Processing Approach to Precise Skeletal Pose Estimation using mmWave Radars

In this paper we presented mmPose-NLP, a novel Natural Language Processing (NLP) inspired Sequence-to-Sequence (Seq2Seq) skeletal key-point estimator using millimeter-wave (mmWave) radar data. To the best of the author's knowledge, this is the first method to precisely estimate upto 25 skeletal key-points using mmWave radar data alone. Skeletal pose estimation is critical in several applications ranging from autonomous vehicles, traffic monitoring, patient monitoring, gait analysis, to defense security forensics, and aid both preventative and actionable decision making. The use of mmWave radars for this task, over traditionally employed optical sensors, provide several advantages, primarily its operational robustness to scene lighting and adverse weather conditions, where optical sensor performance degrade significantly. The mmWave radar point-cloud (PCL) data is first voxelized (analogous to tokenization in NLP) and $N$ frames of the voxelized radar data (analogous to a text paragraph in NLP) is subjected to the proposed mmPose-NLP architecture, where the voxel indices of the 25 skeletal key-points (analogous to keyword extraction in NLP) are predicted. The voxel indices are converted back to real world 3-D coordinates using the voxel dictionary used during the tokenization process. Mean Absolute Error (MAE) metrics were used to measure the accuracy of the proposed system against the ground truth, with the proposed mmPose-NLP offering <3 cm localization errors in the depth, horizontal and vertical axes. The effect of the number of input frames vs performance/accuracy was also studied for N = {1,2,..,10}. A comprehensive methodology, results, discussions and limitations are presented in this paper. All the source codes and results are made available on GitHub for furthering research and development in this critical yet emerging domain of skeletal key-point estimation using mmWave radars.
EXPERIENCE OF ONLINE LEARNING DURING THE PANDEMIC AND ITS EFFECT ON PRESERVICE TEACHERS’ FUTURE TEACHING

A. Margaliot1, D. Gorev2 1Hakibbutzim College of Education Technology and Arts (ISRAEL) 2Achva Academic College (ISRAEL)
A Transductive Maximum Margin Classifier for Few-Shot Learning

Few-shot learning aims to train a classifier that can generalize well when just a small number of labeled examples per class are given. We introduce a transductive maximum margin classifier for few-shot learning (FS-TMMC). The basic idea of the classical maximum margin classifier is to solve an optimal prediction function so that the training data can be correctly classified by the resulting classifer with the largest geometric margin. In few-shot learning, it is challenging to find such classifiers with good generalization ability due to the insufficiency of training data in the support set. FS-TMMC leverages the unlabeled query examples to adjust the separating hyperplane of the maximum margin classifier such that the prediction function is optimal on both the support and query sets. Furthermore, we use an efficient and effective quasi-Newton algorithm, the L-BFGS method for optimization. Experimental results on three standard few-shot learning benchmarks including miniImagenet, tieredImagenet and CUB show that our method achieves state-of-the-art performance.
A Lower Bound on Determinantal Complexity.

The determinantal complexity of a polynomial P ∈ F[x1,..., xn] over a field F is the dimension of the smallest matrix M whose entries are affine functions in F[x1,...,xn] such that P = Det(M). We prove that the determinantal complexity of the polynomial Σni=1 xni is at least 1.5n − 3. For every n-variate polynomial of degree d, the determinantal complexity is trivially at least d, and it is a long standing open problem to prove a lower bound which is super linear in max{n, d}. Our result is the first lower bound for any explicit polynomial which is bigger by a constant factor than max{n, d}, and improves upon the prior best bound of n + 1, proved by Alper, Bogart and Velasco [2] for the same polynomial.
Implementation &amp; Comparative Analysis of RSA, Caesar Cipher and Playfair Cipher

With the growing use of the Internet, and more people being connected with it, the security of the data becomes a major concern. It is necessary that the data can only be accessed by the intended receiver and no person in the middle makes alterations to it. This is achieved by encryption of the data using cryptography. This paper presents a comparative analysis of RSA (Rivest-Shamir-Adleman), Caesar Cipher and Playfair Cipher cryptographic techniques. This paper also presents a comparative analysis of Symmetric Key Cryptography and Asymmetric Key Cryptography. Also, this paper includes the basic working of the above-mentioned techniques along with their implementation in C language over Visual Studio Code 1.49.3.
RANCANGAN SISTEM INFORMASI MANAJEMEN BERBASIS WEB PADA PT OZ FASTENER

PT OZ FASTENER is one of the companies that in manufacturing, with sales worldwide. Since 1992, PT OZ FASTENER's control system has experienced problems because: The system works is still traditional, so the data stored is not very accurate. Therefore, this study designed a system for the company. In the current information age, one of the predominant troubles is how to process data to produce useful data and make it easier for information users to use it. The approach used is the SDLC waterfall model, which includes analysis, design, coding, and testing. The end result of this research is a project management information system that makes it simpler for employees to manage data. Based on those questions, this have a look at aims to design a management system for PT OZ  FASTENER for employee easier to manage the data
Federated Stochastic Gradient Langevin Dynamics

Stochastic gradient MCMC methods, such as stochastic gradient Langevin dynamics (SGLD), employ fast but noisy gradient estimates to enable large-scale posterior sampling. Although we can easily extend SGLD to distributed settings, it suffers from two issues when applied to federated non-IID data. First, the variance of these estimates increases significantly. Second, delaying communication causes the Markov chains to diverge from the true posterior even for very simple models. To alleviate both these problems, we propose conducive gradients, a simple mechanism that combines local likelihood approximations to correct gradient updates. Notably, conducive gradients are easy to compute, and since we only calculate the approximations once, they incur negligible overhead. We apply conducive gradients to distributed stochastic gradient Langevin dynamics (DSGLD) and call the resulting method federated stochastic gradient Langevin dynamics (FSGLD). We demonstrate that our approach can handle delayed communication rounds, converging to the target posterior in cases where DSGLD fails. We also show that FSGLD outperforms DSGLD for non-IID federated data with experiments on metric learning and neural networks.
Differential Privacy in Distributed Environments

Differential privacy is a mathematically rigorous definition of privacy tailored to statistical analysis of large datasets. Differentially private algorithms are equipped with a parameter which controls the formal measure of privacy loss. All algorithms have utility/privacy tradeoffs, and the goal of algorithmic research in differential privacy is to optimize this tradeoff.
Identifying Hijacked Reviews

Fake reviews and review manipulation are growing problems on online marketplaces globally.Review Hijacking is a new review manipulation tactic in which unethical sellers "hijack" an existing product page (usually one with many positive reviews), then update the product details like title, photo, and description with those of an entirely different product.With the earlier reviews still attached, the new item appears well-reviewed.However, there are no public datasets of review hijacking and little is known in the literature about this tactic.Hence, this paper proposes a three-part study: (i) we propose a framework to generate synthetically labeled data for review hijacking by swapping products and reviews; (ii) then, we evaluate the potential of both a Twin LSTM network and BERT sequence pair classifier to distinguish legitimate reviews from hijacked ones using this data; and (iii) we then deploy the best performing model on a collection of 31K products (with 6.5 M reviews) in the original data, where we find 100s of previously unknown examples of review hijacking.
중국의 물류표준화 현황과 한중 협력방안에 관한 연구

Logistics standardization not only stands for logistics industry competitiveness, but also it is core competitive power. And Logistics industry makes smooth the exchange of goods, service and information in improving economic efficiency. Logistics cooperation between Korea and China becomes important issue because it has strategic meaning for two countries’ benefit. Korea and China should reform logistics standardization and cooperate each other. This study based on analysing the China’s logistics standardization policy, and comparison of ywo countries’ logistics standardization, explored the alternatives of its cooperation between Korea and China in the new perspective.
TEACHING EFFECTIVELY IN A MOBILE LEARNING ENVIRONMENT AND HOW TO MEASURE EFFECTIVENESS

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 1280-1284ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.0313Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
Low Pass Filter for Anti-aliasing in Temporal Action Localization

In temporal action localization methods, temporal downsampling operations are widely used to extract proposal features, but they often lead to the aliasing problem, due to lacking consideration of sampling rates. This paper aims to verify the existence of aliasing in TAL methods and investigate utilizing low pass filters to solve this problem by inhibiting the high-frequency band. However, the high-frequency band usually contains large amounts of specific information, which is important for model inference. Therefore, it is necessary to make a tradeoff between anti-aliasing and reserving high-frequency information. To acquire optimal performance, this paper learns different cutoff frequencies for different instances dynamically. This design can be plugged into most existing temporal modeling programs requiring only one additional cutoff frequency parameter. Integrating low pass filters to the downsampling operations significantly improves the detection performance and achieves comparable results on THUMOS'14, ActivityNet~1.3, and Charades datasets. Experiments demonstrate that anti-aliasing with low pass filters in TAL is advantageous and efficient.
Delegated Proof of Reputation: a novel Blockchain consensus

Consensus mechanism is the heart of any blockchain network. Many projects have proposed alternative protocols to improve restricted scalability of Proof of Work originated since Bitcoin. As an improvement of Delegated Proof of Stake, in this paper, we introduce a novel consensus, namely, Delegated Proof of Reputation, which is scalable, secure with an acceptable decentralization. Our innovative idea is replacing pure coinstaking by a reputation ranking system essentially based on ranking theories (PageRank, NCDawareRank and HodgeRank).
EKULIAH WEBSITE FOR TEACHING WRITING DESCRIPTIVE TEXT DURING THE COVID-19 PANDEMIC SITUATION

The teaching and learning process during the pandemic requires adjustments and the use of adequate learning alternatives in providing material to students in brave classes. This study aims to determine the effectiveness of using the ekuliah website as a learning medium for third semester students in the D-III Accounting study program at Politeknik Harapan Bersama in studying descriptive text material during the COVID-19 pandemic situation. This research is a true experimental study that uses two class groups as research objects. Pre-test was given to both class groups in order to determine the initial competencies possessed by students. The two class groups were then taught descriptive text material with different treatments. The treatment was carried out using the conventional method for the comparison class group and the use of the ekuliah website media for the experimental class group. After the post-test was held at the end of the treatment, the test results experienced a significant increase in the average score for the comparison class group of 2.4 points (mean of pre-test score was 72.6 and post-test was 75) and 6.9 points (mean of pre-test score was 76 and post-test was 82.9) for the experimental class. It can be said that learning descriptive text material for third semester students of the D-III Accounting Study Program at Politeknik Harapan Bersama using the ekuliah website is effective in improving learning outcomes.
Water Consumption Prediction of Semarang Water Utilities using Support Vector Regression Radial Basic Function Kernel Method

People have various needs that can’t be released considering their role as living things. The diversity of human needs requires planning for the future, one of which is the provision of water supply because water needs are increasing. Estimation models can be done using the Support Vector Regression (SVR) method. SVR is a development of the Support Vector Machine for regression cases. SVR has four kernels that are commonly used, and in this study, the kernel used is the Radial Basis Function Kernel because RBF is considered capable of maintaining good predictive accuracy. The purpose of this study is to apply the SVR method to predict water consumption with the Radial Basis Function kernel by getting the best SVR parameter and knowing the error value generated from the SVR method. The data used in this study is Semarang Water Utilities’ (PDAM) water consumption data from January 2013 to March 2018. The SVR method test results obtained the best parameters are lambda (λ) = 10, sigma (σ) = 0.001, cLR = 0.01, C (Complexity) = 0.01, epsilon (ɛ) = 0.00000001, with the number of iterations = 1000, produces the lowest Mean Absolute Percentage Error (MAPE) is 1.751%.