Reviewerly/exHarmony huggingface dataset:
A Real-Time Tracking Algorithm for Human Following Mobile Robot using 3D Sensor

Detecting and tracking a particular person are considered the main tasks of a mobile robot. In this paper, we propose a real-time mobile robot system using 3D Kinect sensor for automatically detecting, tracking, and following humans. This method is based on depth information, skeleton, and color of humans from 3D camera. Firstly, the depth image is taken from 3D Kinect to segment the individual region. After that, we calculate the body length, shoulder length, and arm length in combination with the color of target’s clothes to gather as the material for the tracking task. Finally, the mobile robot which is controlled by voice command can recognize and follow the single target person. The effectiveness and robustness of the proposed method are evaluated in comparison with the method based on single skeleton or color of objective. Moreover, our proposed method can identify the target again when it disappears and appears again in the frame. All experiments are implemented with the support of model designed to integrate 3D Kinect camera on a wheeled mobile robot. The velocity and direction of the wheeled mobile robot are controlled by a proportional-integral-derivative controller to keep a constant velocity all the time. The experiment result is shown that the proposed system has worked effectively, stably, and flexibly and the success rate is more than 90%.
A basic study on the development of Korean vocabulary textbooks for Vietnamese learners: Focusing on demands analysis

본 연구의 목적은 한국어 어휘 교재에 대해 한국어를 배우는 베트남인 학습자의 요구를 분석하여 그 분석 결과를 바탕으로 한국어 어휘 교재 개발 방안을 제시한다. 이를 하기 위해 베트남 한국어 학습자 168명을 대상으로 기초 연구로 조사를 진행하고 요구분석은 다음에 몇 가지의 내용을 중심으로 실시한다. 1) 베트남 한국어 학습자의 특성을 확인함, 2) 어휘 학습의 어려운 점들을 파악함, 3) 교재 구성에 대한 요구 사항을 확인함. 조사 결과를 분석한 후 베트남인 학습자를 대상으로 한 한국어 어휘 교재 개발 방안을 다음과 같이 제시한다. 첫째, 대학생과 젊은 특히 18세부터 25세까지 베트남인 초급 학습자를 위한 한국어 어휘 교재를 개발할 필요가 있다. 둘째, 새 단어 제시한 후 번역 제공, 의미, 예문, 관련어, 연습 문제 등을 제공하고 한국어 어휘의 특성에 대한 설명을 교재 앞부분과 어휘의 의미 설명 부분에 충분히 제공할 것은 바람직하다. 셋째, 어휘 교재는 주제별 구성해야 하고 학습자들의 관심을 끌고 쉽게 배울 수 있는 교재를 만들기 위해 그림(삽화)을 추가해야 한다. 게다가 어휘 교재의 출판 형태로 책이 가장 선호하는 형태이지만 개방형 질문에 앱으로 출판하는 형태도 원하는 응답자가 많았으므로 듣기 파일이나 앱을 제공할 것은 바람직하다. 넷째, 어휘 설명 부분에 한국어능력시험(TOPIK) 에 대한 단어의 중요도나 빈도 표시할 뿐만 아니라 예문 부분에 문장 단위로 일상생활에서 자주 쓰는 단어를 활용할 필요가 있다.The purpose of this paper is to analyze the needs of Vietnamese learners who learn Korean with Korean vocabulary textbooks, and based on the analysis results suggesting a future Korean vocabulary textbook development plan. A basic study was conducted on 168 Vietnamese Korean learners, and an analysis of learner s demand was conducted focusing on the followings: 1) Identifying the characteristics of Vietnamese Korean learners, 2) Identifying the difficulty in learning vocabulary, 3) Confirming the requirements for the content of the textbook. After analyzing the results of the survey, a plan for developing Korean vocabulary textbooks for Vietnamese learners was presented as follows. First, it is necessary to develop Korean vocabulary textbooks for university students and juniors, especially Vietnamese beginner learners whose ages are from 18 to 25. Second, it is desirable to provide translation, meaning, example sentences, related words, practical questions, etc., after presenting a new word, and to provide an explanation of the characteristics of Korean vocabulary m the beginning of the textbook and in the explanation of the meaning of the vocabulary. Third, vocabulary textbooks should be organized by topics; and pictures (illustrations) should be added to create a textbook that attracts learners attention and is easy to learn. In addition, although books are the most preferred form of publication for vocabulary teaching materials, many respondents also wanted to publish as an app to open-ended questions, so it is desirable to provide a listening file or an app. Fourth, it is necessary to not only indicate the importance or frequency of words for the Test of Proficiency in Korean (TOPIK) in the vocabulary explanation part, but also use the words frequently used in daily life in the example part.
Analysis of COVID-19 Information Based on Social Media Big Data Classification Using the K-Means Data Mining Method

This Covid-19 began to infect almost all countries in early 2020, including in Indonesia, Covid-19 spread widely throughout the world and was declared as a global pandemic by the World Health Organization (WHO). In the current era of Big Data, large amounts of data have been generated and collected from a variety of rich data sources. Big Data is useful information and valuable knowledge. In this study, the method that will be used for data analysis is the K-Means algorithm with orange tools as a tool to display the results of data classification. One of the information that can be generated is Sentiment Analysis. The purpose of this research was to determining the information such as public sentiment on social media towards government policies in handling COVID-19. In this research, 2000 tweets were used. The keyword used related to government policies are sourced from several online media. The tools used to analyze this twitter data is using Orange Application. The selected keywords are covid19, lockdown, PSBB, and isolation. This keyword is used as a reference to retrieve tweet data from twitter. From each of these keywords, a sentiment classification process will be carried out automatically so that data or tweets are obtained and grouped into positive, negative and neutral sentiment classes. From the result of research conducted, public sentiment on social media towards government policies in handling this virus outbreak tends to be positive.
A modified authentication approach for cloud computing in e-healthcare systems

The opportunity to access on-demand, unbounded computation and storage resources has increasingly motivated users to move their health records from local data centers to the cloud environment. This change can reduce the costs associated with the management of data sharing, communication overhead, and improving Quality of Service (QoS). Processing, storing, hosting, and archiving data related to E-Healthcare systems without physical access and control can exacerbate authentication and access control issues in this new environment. E-Healthcare systems require authentication services that are strong enough to ensure data confidentiality and privacy, and flexible to meet the needs of health professionals and patients. Therefore, convincing users to move sensitive medical records to the cloud environment requires implementing secure and strong authentication and access control methods to protect the data. This chapter proposes a new authentication approach that preserves both authentication and access control in cloud-based e-Health systems. In each access request, based on the maximum rights of the user, the minimum access is extracted. The focus of this work is the authentication process. We propose an authentication approach for e-Healthcare systems based on cloud computing. Its main characteristics are: flexibility of configuration for the authentication mechanisms and the use of a robust system for recording events. We analyze our scheme with respect to data confidentiality and resistance to common attacks on the network. Experimental results show that the proposed method tolerates a high number of concurrent authentication requests with a reasonable response time. In this chapter we deal with the engineering requirements of the security system and the details of its implementation. We also discuss the efficacy and ease of use of different authentication methods.
Covid fuels the rise of hand biometrics

Hand biometric authentication has come to the fore during the Covid crisis, as a hygienic, contactless way to ID people. But what's the future for hand recognition post-pandemic, and how does it stack up against rival biometric modes? We speak to Bernard Garcia, CEO of hand identity specialist nVIAsoft.
A Study on the Application of Text Classification Algorithms in University Archive Management

With the rapid development of higher education in China, the archives of universities have been managing more and more archives, and many universities have been equipped with archives management software to manage archives by computer. However, the mainstream archives management software for universities does not have the function of automatically classifying archival documents. In order to reduce the workload of university archivists, this paper improves some steps in the traditional Chinese text classification algorithm according to the characteristics of archives managed by universities, based on the characteristics of university archives such as periodicity, repetitiveness and knowledge, and also drawing on the experience of archivists in manually classifying It also explores an automatic text classification method suitable for university archives, based on the characteristics of university archives, such as periodicity, repetition and knowledge, while drawing on the classification method in which archivists can determine the category of archives by simply observing their proper title name and responsible person information when classifying them manually.
Machine Learning for Understanding Lab Earthquake Prediction and Precursors&amp;#160;

&amp;lt;p&amp;gt;I summarize a broad suite of laboratory data sets showing that stick-slip failure events &amp;amp;#8211;lab earthquakes&amp;amp;#8211; are commonly preceded by both measurable changes in fault zone properties and acoustic emission (AE) events that foretell catastrophic failure. &amp;amp;#160;These works show that both types of data can be used to predict labquakes with machine learning (ML) methods and deep learning (DL) approaches.&amp;amp;#160; The first works used continuous measurements of AE to predict the timing of labquakes and the fault zone shear stress. Subsequent studies showed that catalogs of AE events could also predict labquakes and that ML approaches could also predict stress drop, peak fault slip velocity and the duration of failure. Recently, DL has been used to predict and autoregressively forecast labquakes and fault zone shear stress. Consistent with previous works, we see that seismic b-value begins to decrease as faults unlock and start to creep.&amp;amp;#160; This provides a sensible connection between the ML-based predictions, fault zone elastic properties, and the physics of failure.&amp;amp;#160; In the lab, AE events represent a form of foreshock and, not surprisingly, the rate of foreshock activity correlates with fault slip rate and its acceleration toward failure.&amp;amp;#160; Our work shows precursory changes in wave speed prior to labquakes, consistent with many well known past studies, but the early studies did not provide a method to predict impending failure.&amp;amp;#160; ML and DL predicts with fidelity the time of impending failure and other aspects of it. This suggests the possibility of physics-based models for prediction. We are working to connect ML prediction of labquakes with the evolution of fault zone elastic properties, frictional contact mechanics and constitutive laws.&amp;amp;#160; A central goal is to learn from lab earthquake prediction to improve forecasts of earthquake precursors and tectonic faulting.&amp;lt;/p&amp;gt;
THE EFFECTIVENESS OF USING WHATSAPP GROUP TO IMPROVE GRAMMAR ABILITY IN REPORT TEXT OF THE TENTH GRADE STUDENTS AT SMA N 1 PRAMBANAN IN THE ACADEMIC YEAR OF 2019/2020

This research discusses whether there was any significant difference or not in the improvement of students’ grammar ability achievement in report text between students who were taught by using WhatsApp group and those who were not taught by using it. The population of the study were all of the tenth graders of SMA N 1 Prambanan in the academic year of 2019/2020. While, the samples were the students of X IPS 1 class as the experimental group, and the students of X IPS 2 class as the control group. Data collecting techniques used by the researcher were try out, pre-test, and post-test. Whereas, grammar test and questionnaire were used as the research instruments. In analysing the data, it was applied descriptive analysis and inferential analysis. The results showed that the mean increase in the experimental class was higher than that in the control class. Moreover, the independent sample t-test result revealed that the value of Sig. (2-tailed) was smaller than 0,05. Therefore, WhatsApp group was effective to be used as a media to teach grammar in report text at the tenth grade students of SMA N 1 Prambanan in the academic year of 2019/2020. Keywords: WhatsApp Group, Grammar, Report Text
Deep Learning and Sentinel-2 data for artisanal mine detection in a semi-desert area

&amp;lt;p&amp;gt;In sub-Saharan Africa, artisanal and small-scale mining (ASM) represents a source of subsistence for a significant number of individuals. While 40 million people officially work in ASM across 80 countries, more than 150 million rely indirectly upon ASM. However, because ASM is often illegal, and uncontrolled, the materials employed in the excavation process are highly dangerous for the environment, as well as for the people involved in the mining activities. One of the most important aspects regarding ASM is their localization, which currently is missing in most of the African regions. ASM inventories are crucial for the planning of safety and environmental remediation interventions. Furthermore, the past location of ASM could be used to predict the spatial probability of the creation of newborn mines. To this end, we propose a Deep Learning (DL) based approach able to exploit Sentinel-2 open-source data and a non-complete small-size mine inventory to accomplish this task. The area chosen for this study lies in northern Burkina Faso, Africa. The area is chosen for its peculiar semi-desert environment which, in addition to being a per se challenging mapping environment, presents a wide spatial variability. Moreover, given the high level of danger involved in field mapping, it is fundamental to develop reliable remote sensing-based methods able to detect ASM. Performance comparison of two convolutional neural networks (CNNs) architectures is provided, along with an in-depth analysis of the predictions when dealing both with dry and rainy seasons. Models&amp;amp;#8217; predictions are compared against an inventory obtained by manual mapping of Sentinel-2 tiles, with the help of multitemporal interpretation of Google Earth imagery. The findings show that this approach can detect ASM in semi-desertic areas starting with a few samples at a low cost in terms of both human and financial resources.&amp;lt;/p&amp;gt;
Comparing prediction algorithms in FTIR-based chemometric analysis to predict soil variables within centuries-old charcoal rich Technosols

&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;&amp;lt;span&amp;gt;As an alternative to the costly wet chemical analysis of soil, Machine Learning (ML) algorithms can be applied for the quantification of soil properties through prediction models. In this study, we evaluate the performances of the Random Forest (RF) algorithm and Partial Least Square Regression (PLSR), in the prediction of soil variables, including CEC, pH, total contents of&amp;amp;#160;&amp;lt;/span&amp;gt;&amp;lt;/strong&amp;gt;&amp;lt;strong&amp;gt;&amp;lt;span&amp;gt;C and N as wells as other elements (Al, Fe, Ca, Mn, Mg, K, and Na) based on FTIR-spectra of Relict Charcoal Hearth (RCH) soils and reference forest soils (Non-RCH). We investigate the effect of high quantities of charcoal in the soil on the prediction models. Preliminary results suggest that there is no significant difference in the results of prediction models for total N, C, and Fe contents, while the accuracy of PLSR in the prediction of pH, Mg, and Ca, and RF performance in prediction of pH and C decreased for RCH soils. Both algorithms demonstrate higher accuracies in the prediction of Al within the RCH soils. Prediction of CEC, Na, K, Mg (RF) within the RCH soils, Al (PLSR) for Non-RCH soils, and Mn and Ca (RF) for both soil types resulted in lower quality of predictions.&amp;amp;#160;&amp;lt;/span&amp;gt;&amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;&amp;lt;span&amp;gt;It can be inferred from the results that the performance of FTIR-based prediction models can be affected by the presence of charcoal in soil due to the nature of spectral features reflecting the soil composition. The presence of charcoal in soil likely alters the absorption interference and peak overlaps, which can result in lower accuracy of the prediction models. In addition to the accuracy of the prediction models, we evaluate the reliability of the weighted wavenumbers (as important variables) in each prediction, which provides information about the correlation of spectral features and chemical properties. It can be studied through the Variable Importance Plot in RF and Variable Importance on Projection through PLSR (VIPs), which show high potential for studying soil composition and metal distribution in mineral and organic soil fractions despite the observed weaknesses in weighing wavenumbers in predictions. Therefore, we assess and compare the quality of information gained regarding soil chemical properties from the algorithms besides a sole quantification of soil parameters. Furthermore, we applied the developed prediction models on a large number (n &amp;gt; 600) of FTIR-spectra of RCH soils to investigate the practical application of the models and thereby compare spectral derived chemical properties of the studied Technosols and reference forest soils.&amp;lt;/span&amp;gt;&amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt;
CONTENT ANALYSIS OF SPEAKING MATERIALS IN ENGLISH TEXTBOOK “PATHWAY TO ENGLISH” FOR THE TENTH GRADE STUDENTS OF SENIOR HIGH SCHOOL

This paper aimed to find out the degrees of linguistic supports, content-based and affective supports, skill supports, and diversity and flexibility of speaking materials in “Pathway to English” for the tenth-grade students of senior high school. This was a descriptive research type with content analysis. The English textbook “Pathway to English” was published by Erlangga. The theory of Dat Bao was used to analyze the data. Based on the analysis, the result of linguistic support shows 36.36 % which means “Poor”. The result of content-based and affective support shows 54.54 % which means “Sufficient”. The result of skill support shows 45.45 % which means “Poor”. Diversity and flexibility show the result 45.45 % which is “Poor”. It is concluded that the speaking materials are not fully supported inspiration, imagination, creativity, and cultural sensibilities to satisfy learners with moments of inspiration, imagination, creativity, and cultural sensibilities, lack of spoken language characteristics, communication function and strategies, and a variety of speaking materials. It implies that the materials should be elaborated to fulfill those criteria proposed by Bao.
Numerical Analysis in Entanglement Swapping Protocols

Entanglement has recently been one of the most essential elements in the development of various quantum technologies. In fact, a swapping protocol was introduced to create a long-distance entanglement from multiple shorter ones. Extending the previous work, this paper provides a more detailed numerical analysis to help create long-distance entanglement out of the two non-maximal three-level states. Specifically, it shows that while the protocol does not always yield optimal results, namely, the weaker link, there is a substantial number of states that yield an optimal result. Moreover, we discuss the numerical approach in showing the existence of states that provide a result close to the optimal outcome, which may be useful in realizing the long-distance entanglement used in quantum technology.
AI-enhanced Integrated Alert System for effective Disaster Management

&amp;lt;p&amp;gt;Effective communication systems supported by Information and Communication Technologies (ICTs) are integral and important components for ensuring comprehensive disaster management. Continuous warning monitoring, prediction, dissemination, and response coordination along with public engagement by utilizing the capabilities of emerging technologies including Artificial Intelligence (AI) can assist in building resilience and ensuring Disaster Risk Reduction. Thus, for effective disaster management, an Integrated Alert System is proposed which encapsulates all concerned disaster management authorities, alert forecasting and disseminating agencies under a single umbrella for alerting the targeted public through various communication channels. Enhancing the capabilities of the system through AI, its integral part includes the data-driven citizen-centric Decision Support System which can help disaster managers by performing complete impact assessment of disaster events through configuration of decision models developed by learning inter-relationships of different parameters. The system needs to be capable of identification of possible communication means to address community outreach, prediction of scope of alert, providing influence of alert message on targeted vulnerable population, performing crowdsourced data analysis, evaluating disaster impact through threat maps and dashboards, and thereby, providing complete analysis of the disaster event in all phases of disaster management. The system aims to address challenges including limited communication channels utilization and audience reach, language differences, and lack of ground information in decision making posed by current systems by utilizing the latest state of art technologies.&amp;lt;/p&amp;gt;
Quantum coherence bounds the distributed discords

Establishing quantum correlations between two remote parties by sending an information carrier is an essential step of many protocols in quantum information processing. We obtain trade-off relations between discords and coherence within a bipartite system. Then we study the distribution of coherence in a bipartite quantum state by using the relations of relative entropy and mutual information. We show that the increase of the relative entropy of discord between two remote parties is bounded by the nonclassical correlations quantified by the relative entropy of coherence between the carrier and two remote parties, providing an optimal protocol for discord distribution and showing that quantum correlations are the essential resource for such tasks.
Student Attendance Analysis and Statistics Platform based on Capture Recognition Technology

With the development of face recognition technology and HD camera, it is possible to use face recognition to realize the classroom attendance statistics. The traditional way of classroom attendance statics needs teachers to roll call according to the list of students, but face recognition can not only save the time of class, but also lighten the burden of statistics attendance of school. This paper realizes the face recognition system of attendance analysis and statistics platform, and it needs cameras and a main computer. In software, under the development environment of VS2017, it relies on OPENCV, arcsoft face recognition SDK and My -SQL database realizes a real-time video stream face recognition system with C++ programming. The system captures the student face by the camera installed in the classroom, then using SDK extracts the face feature and compares with the feature in the database. When the compare value is over the set value, the system will output the corresponding students' information and completion time to the TXT file.