Reviewerly/exHarmony huggingface dataset:
Characterization of Distant and Moderate Earthquakes with Inexpensive MEMS Sensors: Application to the Mw 6.3, 29th December 2020, Petrinja Event

In this work, we evaluate the suitability of a new MEMS sensor prototype, called ASX1000 (ADEL s.r.l., Modena, Italy), for the monitoring of distant and moderate seismic events. This device is an inexpensive capacitive accelerometer with a relatively low level of instrumental noise; it can record both local and far seismic events. An experimental network built with ASX1000 MEMS, located in northern Italy, was able to record the Mw 6.3 Petrinja earthquake that occurred in December 2020; it had an epicentral distance of more than 350 km. We retrieved the strong motion parameters (PGA, pseudo-absolute velocity, and pseudo-absolute spectral acceleration) from the acceleration time histories recorded by the MEMS sensors. The obtained parameters were compared with the ones obtained by the closer high-quality seismometers, belonging to the INGV National Seismic Network. The comparison to the highest-quality sensors confirms a reasonable agreement of the inferred parameters. This work suggests that—in the near future—MEMS sensors could be adopted to integrate the existing seismic network. A denser coverage of sensors can sample more accurately the seismic wavefield, taking into account the large spatial variability of local geology and the relative differences in seismic response.
A Culturological Approach To The Formation Of Educational Material In Physical Education

One of the goals of physics education at a university is the formation of a culture of intellectual activity as an epistemological aspect of culture.To achieve it, the content of a physics course at the level of an academic subject is proposed to be formed based on a didactic unit that determines the technology of constructing the content of education based on a culturological approach: the selection of educational material and its structuring.The paper introduces the concept and proposes the structure of a complex didactic unit of the content of physics education at a university, containing components of three levels of generalization: philosophical and fundamental physical ideas, methods of description, fundamental physical theories.The agreement between these components is determined by a system of methodological principles, which includes a system of fundamental physical principles and a system of general scientific methodological principles of cognition.The advantage of the proposed didactic unit over other tools for systematizing and structuring educational material in physics is that it is part of a general multilevel systemic approach to the formation of the content of physics education.The proposed approach determines the factors of selection of educational material, theoretical and methodological foundations for creating the structure of a subject of study and, accordingly, approaches to the presentation of the material.The paper presents a fragment of the theoretical part of this study.
ECHO Cyber-Skills Framework as a Cyber-Skills Education and Training Tool in Health and Medical Tourism

The ECHO Horizon 2020 Project develops a European cybersecurity ecosystem. One of its assets is the ECHO Cyber-Skills Framework (ECSF). This work in progress paper aims to improve cybersecurity education and training in the healthcare industry including health and medical tourism. First, this paper finds out how ECSF will benefit the healthcare sector regarding cyber-skills and awareness in order to create a more secure information technology (IT) environment when it comes to healthcare. Based on these findings, the paper proposes a strategy to adopt ECSF in order to improve the existing state of IT security and increase worker and management awareness and understanding. Finally, the paper looks at ECSF’s possibilities to be a tool for education and training in health and medical tourism.
Jointly sparse least square support vector machine

Least square support vector machine (LS-SVM) is extended from support vector machine (SVM) for binary classification problems. However, it may suffer from the small sample size (SSS) problem when the sample size is much smaller than the number of features. Motivated by the dimensionality reduction and feature selection methods, we introduce L2,1-norm into LS-SVM to design a novel classification algorithm called jointly sparse LS-SVM (JS-LSSVM). JS-LSSVM minimizes the L2,1-norm regularization on the projection matrix with orthogonal constraint, which is used to project the samples into an optimal low-dimensional subspace, where the derived LS-SVM can obtain the best performance. This projection matrix releases the least square problem in primal space and allows us to select features with joint sparsity. Besides, we propose an iterative algorithm to solve the optimization problem, which guarantees the convergence of JS-LSSVM. The experiments also show the superior performance of JS-LSSVM on many datasets. The proposed method has at least 1% improvement to the conventional methods.
The Effect of Data Augmentation in Deep Learning Approach for Peripheral Blood Leukocyte Recognition

Data augmentation is reported as a useful technique to generate a large amount of image datasets from a small image dataset. The aim of this study is to clarify the effect of data augmentation for leukocyte recognition with deep learning. We performed three different data augmentation methods (rotation, scaling, and distortion) as pretreatment on the original images. The subjects of clinical assessment were 51 healthy persons. The thin-layer blood smears were prepared from peripheral blood and stained with MG. The effect of data augmentation with rotation was the only significant effective technique in AI model generation for leukocyte recognition. On contrast, the effect of data augmentation with image distortion or image scaling was poor, and accuracy improvement was limited to specific leukocyte categories. Although data augmentation is one effective method for high accuracy in AI training, we consider that a highly effective method should be selected.
The Implementation of Reading Assessment Method

English, which is still considered a foreign language in Indonesia, has an impact on the teaching and learning process that is not optimal. This refers to the low proficiency in English, especially those of the Akademi Maritim Nusantara (AMN) Cilacap students. Good reading skills are one of the skills that AMN Cilacap students must possess. This is in line with the mission of the Ministry of Education and Culture of the Republic of Indonesia which makes reading and writing literacy the first position of the 6 (six) literacy abilities that must be possessed. The measurement of reading ability can be done in various ways. This study aims to see what methods are used by English language lecturers in assessing the reading ability of AMN Cilacap students. Also, the researcher wanted to see what methods the lecturer used most often. Researchers conducted a small survey of 2 (two) lecturers who taught English courses from 2 (two) different study programs, namely Marine Engineering and Port Management. Researchers distributed 2 (two) kinds of questionnaires which were then supported by interviews with each lecturer. The results show that there are 6 (six) methods used by the two lecturers in assessing reading skills. The Perceptive Task method is the method most often used. Even so, in choosing the right assessment method, the lecturer must also understand the conditions and level of understanding possessed by students.
Representation and Normalization of Complex Interventions for Evidence Computing

Complex interventions are ubiquitous in healthcare. A lack of computational representations and information extraction solutions for complex interventions hinders accurate and efficient evidence synthesis. In this study, we manually annotated and analyzed 3,447 intervention snippets from 261 randomized clinical trial (RCT) abstracts and developed a compositional representation for complex interventions, which captures the spatial, temporal and Boolean relations between intervention components, along with an intervention normalization pipeline that automates three tasks: (i) treatment entity extraction; (ii) intervention component relation extraction; and (iii) attribute extraction and association. 361 intervention snippets from 29 unseen abstracts were included to report on the performance of the evaluation. The average F-measure was 0.74 for treatment entity extraction on an exact match and 0.82 for attribute extraction. The F-measure for relation extraction of multi-component complex interventions was 0.90. 93% of extracted attributes were correctly attributed to corresponding treatment entities.
Development Animated Dubbing Video as Learning Media in English Speaking at Senior High School

This research was motivated by the results of the needs analysis at MAN 1 Blitar. Researcher found several problems faced by students in the process of learning English. This study aims to determine the opinion of the validators, the response of teacher and students to the learning media that has been developed in the form of Video Dubbing Animation. The research method used is Research and Development (R&amp;D) using the ADDIE model by Dick and Carry. The media that has been developed consists of two videos, one video to explain the material and one dubbing video as a student assignment. The material presented in this product is a narrative text focused on speaking skills. Data collection techniques were carried out using interviews and surveys. The instruments used are interview guidelines and questionnaires using a Likert scale. Based on the results of validation carried out by material experts and media expert, then the results of teacher and student responses, the percentage of results obtained were 80%, 94%, 86% and 66.7% with a total score of 81.7% where the score reached the criteria of "Very Good or Very Valid or Very Usable”. So, it can be concluded that the animation video dubbing learning media is very well used in the English learning process.
Secure Data Publishing of Private Trajectory in Edge Computing of IoT

Secure data publishing of private trajectory is a typical application scene in the Internet of Things (IoT). Protecting users’ privacy while publishing data has always been a long-term challenge. In recent years, the mainstream method is to combine the Markov model and differential privacy (DP) mechanism to build a private trajectory generation model and publishes the generated synthetic trajectory data instead of the original data. However, Markov cannot effectively model the long-term trajectory data spatio-temporal correlation, and the DP noise results in the low availability of the synthetic data. To protect users’ privacy and improve the availability of synthetic trajectory data, we propose a trajectory generation model with differential privacy and deep learning (DTG). In DTG, we design a private hierarchical adaptive grid method. It divides the geospatial region into several subregions according to the density of positions to realize the discretization of coordinates of the trajectory data. Second, GRU is used to capture the temporal features of the trajectory sequence for good availability, and we generate synthetic trajectory data by predicting the next position. Third, we adopt the optimizer perturbation method in gradient descent to protect the privacy of model parameters. Finally, we experimentally compare DTG with the state-of-the-art approaches in trajectory generation on actual trajectory data T-Drive, Portotaxi, and Swedishtaxi. The result demonstrates that DTG has a better performance in generating synthetic trajectories under four error metrics.
Pedagogical Specialties Students' Pedagogical Support For Work In Summer Health Camps

In our opinion, great teacher A.S. Makarenko's legacy has not only not been thoroughly studied but also exceedingly distorted under the influence of Soviet ideology.These created several myths that formed the basis of a definite canonical scheme damaging and continuing to harm the grounds of future teachers' professional training.First of all, this is a focus in line with "collectivist" pedagogy to create "small groups" based on the formation of a specific activity type or pastime (communication) for team building.At the same time, we can define a school class or a student group as a "small group," and educational activity is the "cohesion "factor, which becomes a foundation, being individualistic in nature.The teachers' attention was drawn to this fact, pointing out the limited possibilities of a unified school team formation.Simultaneously, teachers emphasized education, based on a broad life practice, including work, without which the education process can't exist.As we can see from the current perspective, without meaningful education, the simple assimilation of knowledge creates a human-consumer, which contributes to the separation of people and acts as a factor of social differentiation.The purposeful education process, based on socially significant work, leads to the creative person who has an active life position and is an innovative and spiritual-moral person capable of productive social and pedagogical activity.
VANT: A Visual Analytics System for Refining Parallel Corpora in Neural Machine Translation

The quality of parallel corpora used to train a Neural Machine Translation (NMT) model can critically influence the model's performance. Various approaches for refining parallel corpora have been introduced, but there is still much room for improvements, such as enhancing the efficiency and the quality of refinement. We introduce VANT, a novel visual analytics system for refining parallel corpora used in training an NMT model. Our system helps users to readily detect and filter noisy parallel corpora by (1) aiding the quality estimation of individual sentence pairs within the corpora by providing diverse quality metrics (e.g., cosine similarity, BLEU, length ratio) and (2) allowing users to visually examine and manage the corpora based on the pre-computed metrics scores. Our system's effectiveness and usefulness are demonstrated through a qualitative user study with eight participants, including four domain experts with real-world datasets.
Heavy Metal Contamination of Groundwater: the Example of Boreholes Around the Avindo-Sony Sugar Factory

Water is very essential and always gets contaminated when the industrial effluents are released into the environment untreated. The concentration levels of heavy metals in underground water in residential areas around Sony Sugar factory was determine. Water from different boreholes around the company were collected twice a month and tested for heavy metals (Zn, Cr, Cu, Cd, Pb, Co and Ni). The tests were carried out in the University of Eldoret chemistry laboratories using AAS. The concentration of Pb was the highest among the metals studied ranging between 3.843mg/L -4.098mg/L. This range exceeds the World health organisation recommended limits for humanitarian and general life habitat. Fe concentrations from the samples were ranging from 2.066 -2.843mg/L. This concentration is lying within the desirable limits as recommended by WHO, thus does not cause any health risks to the environment. Zn and Cu concentration were all within the recommended concentration levels. With the exception of Pb which had high concentration levels. The concentration of other heavy metals around the company's underground water were found to be within the tolerable limits and does not impose any health risk to the environment. To reduce the concentration of lead, the company should implement the use of unleaded fuel to reduce environmental pollution. Health screening should be carried out regularly on the workers and farmers around the company to check for some symptoms and effect of heavy metals particularly lead.
Constructing the Neighborhood Structure of VNS Based on Binomial Distribution for Solving QUBO Problems

The quadratic unconstrained binary optimization (QUBO) problem is categorized as an NP-hard combinatorial optimization problem. The variable neighborhood search (VNS) algorithm is one of the leading algorithms used to solve QUBO problems. As neighborhood structure change is the central concept in the VNS algorithm, the design of the neighborhood structure is crucial. This paper presents a modified VNS algorithm called “B-VNS”, which can be used to solve QUBO problems. A binomial trial was used to construct the neighborhood structure, and this was used with the aim of reducing computation time. The B-VNS and VNS algorithms were tested on standard QUBO problems from Glover and Beasley, on standard max-cut problems from Helmberg–Rendl, and on those proposed by Burer, Monteiro, and Zhang. Finally, Mann–Whitney tests were conducted using α=0.05, to statistically compare the performance of the two algorithms. It was shown that the B-VNS and VNS algorithms are able to provide good solutions, but the B-VNS algorithm runs substantially faster. Furthermore, the B-VNS algorithm performed the best in all of the max-cut problems, regardless of problem size, and it performed the best in QUBO problems, with sizes less than 500. The results suggest that the use of binomial distribution, to construct the neighborhood structure, has the potential for further development.
Strategic Innovation, Digital Dexterity and Service Quality of Selected Quoted Deposit Money Banks in Nigeria

A company's approach for enhancing service quality through digitalization of services aims to produce digital-based services that respond to consumers' increasingly digital requirements. Similarly, the banking industry keeps innovating to improve the quality of its services. Adoption, execution, and proper use of innovation and digitalization in financial services are techniques that can address issues that arise in terms of increasing banking service quality. However, in recent years, difficulties such as bank service reliability, reduced service responsiveness, a lack of empathy, and insufficient service assurance have been identified as significant factors negatively influencing bank service quality. Despite several research on strategic innovation and service quality in developed economies, few studies have been conducted in Nigeria. Hence, this study examined the effect of strategic innovation and digital dexterity on service quality of selected Quoted Deposit Money Banks in Nigeria. Survey research design was adopted. The population was 6,975,037 corporate customers and 4,873 employees of eight systemically important deposit money banks operating in Lagos, Nigeria. A sample size of 501 corporate customers and 464 bank employees was determined using Raosoft calculator. Convenience sampling technique was adopted in selection of bank corporate customers, while stratified random sampling technique was adopted for the bank employees. A validated questionnaire was used to collect data. Cronbach’s alpha reliability coefficients for the constructs ranged from 0.79 to 0.92. The response rate was 95.4%. Data were analyzed using descriptive and inferential statistics. Findings revealed that strategic alignment (β = .872, t = 8.611, p = .000), strategic foresight (β = 0.941, t = 10.295, p = .000), organizational readiness (β = 0.983, t = 11.656, p = .000), and organizational responsiveness (β = 1.046, t = 15.263, p = .000) have positive and significant effect on service quality. While organizational pro-activeness (β = -.035, t = -.517, p = .605) and organizational flexibility (β = -.099, t = -1.573, p = .116) have negative and insignificant effect on service quality of the selected Quoted Deposit Money Banks in Nigeria. The study concluded that strategic innovation dimensions and digital dexterity dimensions had significant effect on service quality of the selected Quoted Deposit Money Banks in Nigeria. The study therefore recommends that bank management should insist and practice continued strategic innovation and digital dexterity to improve the overall service quality of their Banks.
PENERAPAN MODEL PEMBELAJARAN TEAM GAMES TOURNAMENT (TGT) UNTUK MENINGKATKAN HASIL BELAJAR PADA MATA PELAJARAN IPA DI SDN INPRES DALAKA

Low learning outcomes are a manifestation of various problems that arise from learning activities. Low learning outcomes in science subjects are indicated by results that are below the Minimum Completeness Criteria (KKM). Based on the results of observations, the method used is in the form of lectures, discussions and practicals, but the results are not optimal. Students tend to be passive, only a few are actively participating in learning. Based on these problems, the author made efforts to improve the science learning outcomes of the fifth grade students of SDN Inpres Dalaka for the 2020/2021 academic year by applying the TGT learning model. This research is a quasi-experimental research. The purpose of this research is to improve learning outcomes in science subjects. Based on the results of the analysis, it was found that learning science by applying the TGT method could improve science learning outcomes. This is indicated by the difference in pretest and posttest scores. Where the posttest results are higher than the pretest results. This increase is an implication of increasing student learning activities. Students feel more enthusiastic about participating in learning.
Customer Engagement with VR-enabled Tourism Activities at Cultural Heritage Sites

Despite the tremendous amount of academic interest in virtual reality (VR), the existing conceptual models that presented VR attributes as antecedents of users’ acceptance and attitudes, failed to examine the power of VR technologies in promoting the alternative attractiveness of virtual tourism compared to traditional onsite visitation. This is of great significance to the solution of over-tourism issues frequently seen in the today’s tourism destinations, especially world-famous cultural heritage sites. Therefore, the present study fills the literature gap and empirically validates a conceptual framework that shows how visitors’ engagement with VR-enabled tourism activities could lead to an enhanced alternative attractiveness of virtual tourism, and their pro-cultural behaviors. A total of 571 valid responses from US visitors that have adopted VR-enabled tourism activities were collected to confirm the role of four VR attributes, i.e., immersion, vividness, presence, and enjoyment, in motivating users’ engagement behaviors with VR tourism activities. The study findings also testified that visitors who engaged with VR tourism activities are likely to adopt pro-cultural behaviors in the future and assessed the alternative attractiveness of VR tourism against the traditional onsite visitation. The findings will provide valuable implications for policymakers who aspire to preserve the cultural heritage sites while promoting cultural heritage destinations through advanced VR technologies.