Reviewerly/exHarmony huggingface dataset:
Senior high school students’ learning styles and experiences in the emergency remote teaching

Nowadays Mexican senior high school institutions are promoting temporally emergency remote teaching. Even though the instructional shift is just temporal, it is advisable to implement technology in education, consider student needs, learning styles, and promote student autonomy in the online learning experiences, Moreover, to include the teacher, the social, and the cognitive presences in the community of inquiry. This qualitative study aimed to analyze senior high school students' online learning experiences in relation to their learning styles. The sampling process was voluntary and consists of 37 participants. The results show a predominance of theorist students, followed by the pragmatists, and finally, activists. Favorable learning experiences involved teachers using appropriate strategies and materials to promote learning. Negative online learning experiences were due to students' lack of understanding and interaction, as well as teachers' lack of explanation, reinforcement, and feedback, which reflect the lack of cognitive and teacher presence and omission of elements in online learning experiences that address the particularities of students regarding their learning styles.
A university crisis provoked by an online class: a communications case study on a social media turbulence

In 2020, a Latin American university with a Catholic ethos suffered a crisis when the content of an online class on bioethics regarding homosexuality was posted on Twitter. This communications case study – in which names have been altered to protect confidentiality – describes the objective and subjective circumstances of the case, how the problem evolved, how different stakeholders reacted, and what the University did in order to cope with the crisis. It also narrates what the University did in the post-crisis period to prevent something similar from happening again. The study concludes with a brief analysis of the main takeaways that this case could offer to University authorities and, in general, to managers and communicators in Church-related institutions.
Time Machines; An exploration of Roman period copper-alloy objects in an Estonian tarand cemetery, using pXRF

This paper presents a new qualitative method for the use of pXRF in archaeological research.A bulk, multi-elemental approach applies a non-destructive survey technique to the copperalloy objects recovered in a Roman period tarand cemetery, in north-east Estonia.The aim is to explore the chronological development of the cemetery by comparing the objects and their find locations against historically known changes in alloy composition.Then a more focused destructive analysis is undertaken from a selected group of bracelets commonly found in these northerly cemeteries, but also in greater numbers in the Roman provinces.The results revealed strong correlations between alloy classification and find location.Furthermore, the quantitative (destructive) analysis of a single bracelet has added to the debate about the nature of long-distance contact between the people of north-eastern Estonia, the southern Baltic and the distant Roman frontier.It also raises the possibility that these people were placing Roman produced items into their cemeteries in the decades before the traditionally accepted start of the Roman Iron Age.This suggests that a new assessment for its beginning is called for, one that aligns the earliest imported Roman items to the first half of the 1st century AD.
Local Government Use Website and Twitters as Communication and Interaction Tools

Website and Twitter Local Government is one government implementation to optimize public services to increase efficiency, effectiveness, transparency, and government administration accountability. This paper aims to determine the quality of all city governments' websites and Twitter in West Java province, Indonesia. The research method used is descriptive interpretive by using scoring analysis techniques, namely the author gives a score for each element of the site assessment indicator based on several theories to find the weight of the score and the quality level of the website, and uses the N Vivo 12 Plus application to assess the Twitter of the city government in the province of West Java. The results showed that of all city government websites in the province of West Java, there were 4 (four) quality websites, 4 (four) websites that were of sufficient quality, and 1 (one) website was less qualified, so that the overall city government website was overall, namely 44.44% of high quality, 44.44% of sufficient quality, and 11.11% of low rate with quality status occupied by the City Government of Bandung, Bogor, Cimahi, and Cirebon; In the assessment of Twitter activity as a medium of communication and interaction with city governments throughout West Java, every city government has made efforts to fulfill public information and communication facilities as well as electronic services via Twitter. However, the implementation is less than optimal. So it is always suggested to improve the innovation and creativity of website and twitter management, monitor and evaluate and survey community satisfaction regularly to enhance the quality of the Website and Twitter of the city government.
User-Level Membership Inference for Federated Learning in Wireless Network Environment

With the rise of privacy concerns in traditional centralized machine learning services, federated learning, which incorporates multiple participants to train a global model across their localized training data, has lately received significant attention in both industry and academia. Bringing federated learning into a wireless network scenario is a great move. The combination of them inspires tremendous power and spawns a number of promising applications. Recent researches reveal the inherent vulnerabilities of the various learning modes for the membership inference attacks that the adversary could infer whether a given data record belongs to the model’s training set. Although the state-of-the-art techniques could successfully deduce the membership information from the centralized machine learning models, it is still challenging to infer the member data at a more confined level, the user level. It is exciting that the common wireless monitor technique in the wireless network environment just provides a good ground for fine-grained membership inference. In this paper, we novelly propose and define a concept of user-level inference attack in federated learning. Specifically, we first give a comprehensive analysis of active and targeted membership inference attacks in the context of federated learning. Then, by considering a more complicated scenario that the adversary can only passively observe the updating models from different iterations, we incorporate the generative adversarial networks into our method, which can enrich the training set for the final membership inference model. In the end, we comprehensively research and implement inferences launched by adversaries of different roles, which makes the attack scenario complete and realistic. The extensive experimental results demonstrate the effectiveness of our proposed attacking approach in the case of single label and multilabel.
Analysis of hospital reviews through sentiment analysis: An approach to aid patients in the times of COVID-19 pandemic.

The COVID pandemic had over-stressed our healthcare system This has affected the lives of both COVID and non-COVID patients, in the worst possible way The patients are facing difficulty in getting proper medical care in time The reason being the already stressed situation of hospitals and lack of proper information in the general population The lack of information is amplified in the environment of fear and panic in the pandemic We aim to use sentiment analysis of hospital reviews to provide relevant and important information about the operating conditions and current status of hospitals to the general public Sentiment analysis applied to patient's reviews to quantify the direction and/or magnitude of the emotive content Patient comments are segregated into different sections and analysis is done on these sections to quantify the positive or negative aspects of the reviews This will allow us to give an overall rating to the hospital-based on key parameters that will help people to understand the hospital's current condition The results established a strong relationship between the online reviews and the overall recommendation percentage of the hospitals This information provides great value to the patients by allowing them to compare and select the best option The information is more reliable and robust due to its dynamic nature © 2021 CEUR-WS All rights reserved
ESP Material for Maritime Affairs and Fisheries School in Indonesia

Identifying English for Specific Purposes material for A Specific Student was the process of selecting, adjusting, and evaluating English material based on the special needs of students. Need analysis is needed to identify ESP learning material. The purpose of this research was to identify the English for Specific Purpose (ESP) Materials need by students in learning the English Language for Students in Maritime Affairs and Fisheries School using mix method. Through the Cluster Random Sampling, thirty-six students of fisheries product processing technology department level II at Sekolah Usaha Perikanan Menengah (SUPM) Negeri Sorong became the sample of this research. Guttman scale questionnaires and interviews were used to collect the Data. Questionnaires were given to students to find out the goals, necessities, want, and lack of students. To do the triangulation method, the interview was conducted to deeper explanation gather from the Questionnaires’ answer. The results showed that ESP Material need by the students in the fisheries product processing technology department was English learning material that related to fisheries product processing technology in order students can use the language when having a job. While the ESP topics that students were expected to learn are about the duties of a captain, special terms in shipping, special terms of fisheries, ways to operate machinery, conversations about fisheries, ways to bind fish, special terms for a cook and others English materials are correlated with fisheries product processing technology department.
Fusion in Dissimilarity Space Between RGB D and Skeleton for Person Re Identification

Person re-identification (Re-id) is one of the important tools of video surveillance systems, which aims to recognize an individual across the multiple disjoint sensors of a camera network. Despite the recent advances on RGB camera-based person re-identification methods under normal lighting conditions, Re-id researchers fail to take advantages of modern RGB-D sensor-based additional information (e.g. depth and skeleton information). When traditional RGB-based cameras fail to capture the video under poor illumination conditions, RGB-D sensor-based additional information can be advantageous to tackle these constraints. This work takes depth images and skeleton joint points as additional information along with RGB appearance cues and proposes a person re-identification method. We combine 4-channel RGB-D image features with skeleton information using score-level fusion strategy in dissimilarity space to increase re-identification accuracy. Moreover, our propose method overcomes the illumination problem because we use illumination invariant depth image and skeleton information. We carried out rigorous experiments on two publicly available RGBD-ID re-identification datasets and proved the use of combined features of 4-channel RGB-D images and skeleton information boost up the rank 1 recognition accuracy.
Uncertain estimation-based motion-planning algorithms for mobile robots

Collision-free motion planning for mobile agents is a challenging task, especially when the robot has to move towards a target position in a dynamic environment. The main aim of this paper is to introduce motion-planning algorithms using the changing uncertainties of the sensor-based data of obstacles. Two main algorithms are presented in this work. The first is based on the well-known velocity obstacle motion-planning method. In this method, collision-free motion must be achieved by the algorithm using a cost-function-based optimisation method. The second algorithm is an extension of the often-used artificial potential field. For this study, it is assumed that some of the obstacle data (e.g. the positions of static obstacles) are already known at the beginning of the algorithm (e.g. from a map of the enviroment), but other information (e.g. the velocity vectors of moving obstacles) must be measured using sensors. The algorithms are tested in simulations and compared in different situations.
Simplest non-additive measures of quantum resources

Given an arbitrary state $\rho$ and some figure of merit ${\cal E}(\rho)$, it is usually a hard problem to determine the value of ${\cal E}(\rho^{\otimes N})$. One noticeable exception is the case of additive measures, for which we simply have ${\cal E}(\rho^{\otimes N}) = Ne$, with $e\equiv {\cal E}(\rho)$. In this work we study measures that can be described by ${\cal E}(\rho^{\otimes N}) =E(e;N) \ne Ne$, that is, measures for which the amount of resources of $N$ copies is still determined by the single real variable $e$, but in a nonlinear way. If, in addition, the measures are analytic around $e=0$, recurrence relations can be found for the Maclaurin coefficients of $E$ for larger $N$. As an example, we show that the $\ell_1$-norm of coherence is a nontrivial case of such a behavior.
Exploration of Sensor-Based Activity Recognition Based on Time Series Feature Extraction

Sensor-based human activity recognition (HAR) has gained its momentum and become an active research topic due to the advance of machine learning (ML) algorithms and ubiquitous sensing devices in our daily life. Recent research trend in ML algorithms for HAR is deep learning-based approaches that have already developed state-of-the-art learning models in various tasks. However, complex deep learning models may not be the best choice when it comes to data sufficiency problems and model transparency. Exploratory data analysis (EDA) can benefit feature extraction, which is an important step in a machine learning pipeline. In this study, to explore sensor-based HAR, a widely used HAR dataset is adopted to examine the effectiveness of time series feature extraction together with conventional machine learning models. Experimental results show that EDA can be beneficial for obtaining data insights and determining better features for HAR classification.
THE RELATIONSHIP BETWEEN STUDENTS' READING ATTITUDE WITH THE RESULT OF READING COMPREHENSION

Reading is a very important activity to get abundant information and add new knowledge. As students, reading activities have a very dominant role because they are required to be more scientific and rational in giving opinions or providing information both in oral and written. Besides, students' attitudes towards reading also have an important role. This study focused on their English reading comprehension and attitudes towards reading in English. This study aims to determine whether there is a significant relationship between students' attitudes with the result of reading comprehension. Data was collected by distributing questionnaires and reading comprehension tests for students of STKIP PGRI Jombang in the English Education Study Program. The Pearson Coefficient Product Moment is used to determine whether there is a significant relationship between students' attitudes with the result of reading comprehension. From the results found from the SPSS 16 application with a sample of 63 students in the 2019 class, there is a correlation of 0.478. These result indicates that students' reading attitudes are positively related to their reading comprehension. Thus, students must continue to read to improve their English reading skill.
A unified schedule policy of distributed machine learning framework for CPU-GPU cluster

With the widespread using of GPU hardware facilities, more and more distributed machine learning applications have begun to use CPU-GPU hybrid cluster resources to improve the efficiency of algorithms. However, the existing distributed machine learning scheduling framework either only considers task scheduling on CPU resources or only considers task scheduling on GPU resources. Even considering the difference between CPU and GPU resources, it is difficult to improve the resource usage of the entire system. In other words, the key challenge in using CPU-GPU clusters for distributed machine learning jobs is how to efficiently schedule tasks in the job. In the full paper, we propose a CPU-GPU hybrid cluster schedule framework in detail. First, according to the different characteristics of the computing power of the CPU and the computing power of the GPU, the data is divided into data fragments of different sizes to adapt to CPU and GPU computing resources. Second, the paper introduces the task scheduling method under the CPU-GPU hybrid. Finally, the proposed method is verified at the end of the paper. After our verification for K-Means, using the CPU-GPU hybrid computing framework can increase the performance of K-Means by about 1.5 times. As the number of GPUs increases, the performance of K-Means can be significantly improved.
Pengaruh Pembelajaran Matematika Menggunakan Model Learning Cycle Terhadap Hasil Belajar Siswa Pokok Bahasan Barisan Dan Deret Kelas XI IPA SMAN 1 Kerinci

The purpose of writing this research is to determine the learning outcomes of mathematics learning on the subject matter of the language of sequences and series by using the learning cycle learning model before using the learning cycle learning model (Lecture learning model). This study uses quantitative research methods with an experimental approach. The population of this research is all students of class XI IPA using purposive sampling technique, the class that will be sampled in this study is seen from the level of ability possessed by each student in understanding the subject matter of sequences and series, where the sample is 25 control class students, and 25 experimental class students. There are three stages of data collection techniques, namely the preparation stage, the implementation stage and the final stage. The analysis of the instrument test carried out in this experimental study used a test of validity and reliability, a test of the level of difficulty of the test items, and a test of discriminating power. While the technique to analyze the data using normality test, homogeneity test, and hypothesis testing using t-test. The results of this study indicate that there are differences in student learning outcomes on the subject matter of sequences and series before using the Learning Cycle learning model and after using the Learning Cycle learning model. This can be seen by using the t-test part of the t-test for Equality of Means, the sig value is known. (2 tailed) of 0.000 &lt; 0.05.
The Digital Transformation of Assessment: Challenges and Opportunities

In the education sector, the digitization of the learning process has been a topical issue for several decades. It includes various components of the learning process, including assessment, which is an integral part of the learning. Innovative educators have been using technology in assessment for several years, but since March 2020, all educators have been forced to use it. Some educators started using technology without sufficient skills and adequate training, so the forced transformation was not always successful. In most cases, educators learn by doing, as well as developing skills and finding the best technologies for assessment. The aim of this research is to identify opportunities and challenges in the application of technologies in assessment. To achieve this goal, the results of a survey of educators (n = 181) and interviews, both individual and group (26 respondents), were evaluated. The main results are related to the findings of new ideas in assessment, and an exploration of the possibility of providing more frequent and timely feedback. At the same time, there is the challenge of developing needed technological skills, as well as coping with an increase in the amount of work and time required to adequately prepare for the assignments. Having knowledge concerning digital assessment can complement an educator’s practice. It gives an insight into a wider study – the post-doctoral research project “Models of Assessment in the Digital Learning Environment (MADLE)”.
Assessment of future teachers' satisfaction with the conditions for the implementation of Russian-Chinese joint educational programs

The relevance of the research is due to the need to increase the competitiveness of joint educational programs of pedagogical orientation in the system of Russian-Chinese relations in the field of higher education. The student satisfaction indicator not only indicates the attractiveness/unattractiveness of the educational program, but also allows you to identify the most vulnerable places in the organization of the educational process. The purpose of the study is to identify ways to improve the conditions for the implementation of Russian-Chinese joint educational programs of pedagogical orientation. Materials and methods. The study applied a descriptive method to conducting a survey of students studying under the bachelor's degree majors "Preschool Education", "Music Education", "Fine Arts" at the Moscow Institute of Arts of the Weinan Pedagogical University (Province Shanxi). 198 graduates participated in the questionnaire, accounting for 77% of the total number of graduates from the Moscow Institute of Arts. The study was conducted using the author's questionnaire, compiled taking into account the practice of assessing student satisfaction at Moscow Pedagogical State University and the recommendations of the Ministry of Education of the People's Republic of China on the evaluation of joint Chinese-foreign educational programs. Results. In the study, the assessment of students' satisfaction with learning was carried out in five directions: satisfaction with the quality and content of training, satisfaction with the organization of the educational process, satisfaction with the material and technical support of the educational process, satisfaction with the organization of research activities, satisfaction with the socio-psychological climate at the institute. The study found that students' satisfaction with the organization of the educational process (68.2%) and its material and technical support (66.92%) was the highest, compared with their publishing activities in research work (60.4%). Despite the impossibility of implementing programs under the "3+1" scheme, due to the COVID-19 pandemic, the internal motivation of students during online training in the "4+0" mode did not decrease, which led to the preservation of the contingent of students throughout the entire period of program implementation. Discussion and conclusion. The research results have practical significance for managers, employees and teachers of pedagogical universities participating in the implementation of Chinese-foreign joint educational programs, as well as for representatives of the scientific community dealing with the problems of ensuring the quality of such programs. Further research of students' satisfaction with studying under Russian-Chinese joint educational programs for teacher training can be aimed at analyzing the quality of implementation of such programs at the level of academic degrees of students of pedagogical universities.