Reviewerly/exHarmony huggingface dataset:
SPEAKING TEACHING STRATEGIES BY SECONDARY SCHOOL ENGLISH TEACHER

The research aims to find out the strategies used by teachers in teaching speaking to students in secondary school and teachers’ perception about the method. This study involved ten English teachers at several secondary schools in North Sumatra. To collect the data, researchers use qualitative descriptive methods by distributed questionnaires as research instruments using the google form platform. This research was conducted to find and identify the teachers’ strategies in teaching speaking. Research indicates that the strategies used by the English teacher were information gap, communicative language by real life situation, brain storming, picture storming, discussion, story-telling, role-play, daily integration in learning process, speech and games. Whereas the teacher's response to the strategy produces a positive attitude because their perception answers that the strategy they used was satisfying and effective to help students to improve their speaking skills. The results of the study also reveal that the teachers’ strategies are essential to improving students' willingness to study at school. Thus the teacher strategy is the key to success in teaching. The results of this study provide a full description of teaching practice, and the assessment employed by the teacher. From this research, it is suggested that teachers use speaking teaching strategies by paying attention to steps to perform the method.
Translation of Medical Texts with Ensembling and Knowledge Distillation

Knowledge distillation describes a way to train a student network to perform better by learning from a more powerful teacher network. We show that the performance of a teacher consisting of an ensemble of 6 models can be achieved with the help of a student consisting of one model, which leads to significantly faster decoding and a large increase in the quality of the model. In our approach to distilling knowledge, we translate the full training dataset using the teacher model to use the translations as additional knowledge for the student network. For the translation of biomedical texts containing a large number of specific nested named entities, the byte-pair encoding approach was chosen, which also improved the quality of the model.
Organizational and Pedagogical Model of Professional Formation of Future Teachers in the Process of Introducing them to Mentoring Activities

The article examines the modern features of the professional development of future teachers at the university at the bachelor’s degree level. An attempt is made to theoretically substantiate the need to develop a new model of professional formation of future teachers. It suggests that one of the effective means contributing to the development of professionally significant qualities of pedagogical university students may be their involvement in mentoring activities. The purpose of the article is to present the model of professional formation of future teachers developed by the author through introduction to mentoring at the bachelor’s level. Research methodology and methods. The methodological basis of the study was competence-based, subject-activity and acmeological approaches. Leading research methods: modeling, pedagogical experiment, questionnaire, interpretation of experimental data. The content and organizational and pedagogical conditions for the implementation of the proposed model are characterized. The results of a pilot experiment on its implementation in the practice of educational activities on the example of student groups are presented. Conclusion. In conclusion, conclusions are drawn about the expediency of continuing research in this direction after adjusting the content aspects of the model of professional formation of future teachers by introducing them to mentoring activities at the undergraduate level.
A Study on Forecasting the Producer Price of Farmed Korean Rockfish Using Deep Learning Models －Focusing on LSTM(Long-short Term Memory) and GRU(Gated Recurrent Units) Models－

This study is aimed to forecast the producer price of aquaculture seafood using LSTM(Long-short Term Memory) and GRU(Gated Recurrent Units) models, a type of deep learning model. Since the producer price is directly related to aquacultural farm’s profitability, accurate forecasting of the producer price is essential to establish an effective production and management plan for aquaculture and stabilize supply and demand of farmed seafood. Korean Rockfish(Sebastes schlegelii) is a commercially important fish species that ranks second in domestic farmed fish production and is the main breed of fish farms in Tongyeong region. As the volatility of the producer price of Korean Rockfish has recently intensified, the importance of forecasting the producer price is increasing. In the analysis, total 19 variables were used, including producer prices in other regions, production-related variables, consumption-related variables, alternative farmed fish variables, water temperature, and COVID-19 dummy variables. Total 189 monthly data from October 2006 to June 2022 were used. In this study, the producer price of farmed Korean Rockfish was forecasted by LSTM and GRU models, a type of RNN(Recurrent Neural Network) model specialized for time series forecasting and the accuracy of models was compared with MAPE(Mean Absolute Percent Error). Results showed that MAPEs of LSTM and GRU were 4.66% and 6.27%, respectively. In addition, when comparing the accuracy by the number of independent variables, it was found that the accuracy of the multivariate LSTM model was better than that of the univariate LSTM model.
On the Issue of Diagnostics of the Level of Formation of Logical Universal Learning Activities

The development of logical universal learning activities (ULAs) is one of the global aims of subject-based learning at school. In accordance with the standards of general education, special attention is paid to the formation of these ULAs. But at the same time the analysis of the demo version of the All-Russian test on mathematics for 4th grade students shows that the means of checking the formation of logical ULAs are poorly represented. Thus, the development of tools for diagnostics and development of logical skills of elementary school students remains an important research task. The purpose of our study is, on the one hand, to develop an approach to the use of specific analysis of the results of students’ solutions of logical problems with simple mathematical content in order to diagnose the level of formation of some types of logical ULAs. On the other hand, development of recommendations for organizing solutions of these problems, aimed at students’ conscious solution of logical ULAs. Methodology. From methodological point of view our research is based on psychological and pedagogical theories of the development of various thinking components, including empirical approaches to the formation and evaluation of mental actions in the process of learning mathematics. Results. In the article the results of the experiment on approbation of the problems with simple mathematical content developed by the authors, aimed at diagnosing the formation of some types of logical skills are represented. An original approach to the analysis of the results of the solution of problems for revealing the level of formation of certain types of logical ULAs is presented. According to our study most of the students are not able to perform abstracting, analyzing, synthesizing, comparing, generalizing, and specifying general logical actions in a stable way. Recommendations for organizing mathematics teaching aimed at the development of logical ULA are given. In conclusion the specifics of the developed approach to diagnostics of the level of logical ULA are described.
Artificial Neural Network Model and Its Application in Signal Processing

The human brain is a powerful image and pattern recognition processor, and its basic processing element is neurons. Synapses are weighted interconnections between neurons, allowing learning and communication between neurons. Artificial neural network (ANN) is an information processing system established by simulating the structure and logical thinking mode of human brain. The uniqueness of ANN is that it is nonlinear and trained to complete processing tasks in a way similar to human brain learning. It is particularly suitable for processing signals sent by various sensors, signals sent by communication devices, and other signals that are difficult to identify. This paper introduces the origin, types and research progress of neural networks, and summarizes the application research progress of neural networks in the field of signal processing. This paper introduces the origin, types and research progress of ANN, and summarizes the application research progress of ANN in the field of signal processing.
SicHash - Small Irregular Cuckoo Tables for Perfect Hashing

A Perfect Hash Function (PHF) is a hash function that has no collisions on a given input set. PHFs can be used for space efficient storage of data in an array, or for determining a compact representative of each object in the set. In this paper, we present the PHF construction algorithm SicHash - Small Irregular Cuckoo Tables for Perfect Hashing. At its core, SicHash uses a known technique: it places objects in a cuckoo hash table and then stores the final hash function choice of each object in a retrieval data structure. We combine the idea with irregular cuckoo hashing, where different objects can have a different number of hash functions. Additionally, we use many small tables that we overload beyond their asymptotic maximum load factor. The most space efficient competitors often use brute force methods to determine the PHFs. SicHash provides a more direct construction algorithm that only rarely needs to re-compute parts. Our implementation improves the state of the art in terms of space usage versus construction time for a wide range of configurations. For some configurations, SicHash is up to 4.3 times faster than the next best competitor. At the same time, it provides very fast queries.
QKD parameter estimation by two-universal hashing

This paper proposes and proves security of a QKD protocol which uses two-universal hashing instead of random sampling to estimate the number of bit flip and phase flip errors. This protocol dramatically outperforms previous QKD protocols for small block sizes. More generally, for the two-universal hashing QKD protocol, the difference between asymptotic and finite key rate decreases with the number <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> of qubits as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>c</mml:mi><mml:msup><mml:mi>n</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>&amp;#x2212;</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:math>, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>c</mml:mi></mml:math> depends on the security parameter. For comparison, the same difference decreases no faster than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>c</mml:mi><mml:mo>&amp;#x2032;</mml:mo></mml:msup><mml:msup><mml:mi>n</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>&amp;#x2212;</mml:mo><mml:mn>1</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> for an optimized protocol that uses random sampling and has the same asymptotic rate, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>c</mml:mi><mml:mo>&amp;#x2032;</mml:mo></mml:msup></mml:math> depends on the security parameter and the error rate.
Franklin Institute Awards Call for Nominations – 2024 Bower Science Award

This study addresses the accuracy of stochastic simulations performed in Two-Dimensional Stochastic Neural Fields (2D-SNFs) with the infinite signal transmission speed and in the presence of external stimuli input. The numerical method in use belongs to the family of Galerkin-kind spectral approximations to Two-Dimensional Stochastic Neural Field Equations (2D-SNFEs). It translates the partial integro-differential fashion of such models into a large system of ordinary Stochastic Differential Equations (SDEs). Eventually, these SDEs are integrated approximately by the Euler–Maruyama scheme of the strong convergence order 0.5. In this paper, we devise a different-order approximate solution to the SNFE models at hand and look at the difference of such stochastic simulations on average for evaluating the consistency of the Euler–Maruyama-based numerical solution derived. The error committed in the 2D-SNFE-numerical-integration-scheme under study becomes available in our research. The other issue of particular attention and interest is hidden state reconstructions rooted in the 2D-SNFE approximations and incomplete noisy measurements of the membrane potential fulfilled at some user-assigned space positions and time instants. This statement leads to high-dimensional prediction and filtering problems to be solved. Here, we implement the Extended Kalman Filtering (EKF) approach, but accommodate it to our 2D-SNFE-oriented data assimilation scheme of huge size because of the two-dimensional manner of the stochastic process models in use. A sound performance of the newly-devised hidden state estimation technique is observed and exposed on a challenging 2D-SNFE example of computational neuroscience in Matlab.
Revolutionary Digital Sensor Platform

CAN-MD is a new digital sensor communication platform from Dytran that has a variety of benefits that can help test cell operators monitor and protect the health of their key assets
A Study on the Integrated Management of National Road Information - Case of Road Register -

도로법 제10조에서는 도로를 고속국도, 일반국도, 특별·광역시도, 지방도 등 7가지로 구분하고 있다. 도로법 제56조에서 도로관리청은 소관도로에 대해 도로대장을 작성하도록 하고 있다. 도로대장은 도로 및 시설물을 관리하는 공적 장부임에도 불구하고 일반국도에 국한되어 전국 도로정보 관리를 위해서는 관리 범위의 확대가 필요하다. 1990년대 종이도면의 전산화를 중심으로 추진된 도로대장은 자율주행차 등 현 시대의 요구사항을 반영하지 못하고 있다. 본 연구에서는 도로정보를 통합관리 할 수 있는 방안을 제시하였으며, 이를 토대로 국가, 산업, 민간 등 다분야에서 활용할 수 있을 것으로 기대된다. In Article 10 of the Road Act, roads are classified into seven categories: high-speed national roads, general national roads, special and metropolitan roads, and local roads. In Article 56 of the Road Act, the Road Management Office requires a road ledger to be prepared for the roads under its jurisdiction. Although the road ledger is a public ledger that manages roads and facilities, it is limited to general national roads, and it is necessary to expand the scope of management for the management of road information nationwide. The road ledger, which was promoted in the 1990s around the computerization of paper drawings, does not reflect the requirements of the present era such as self-driving cars. In this study, a method for integrated management of road information was proposed, and based on this, it is expected that it can be used in various fields such as the country, industry, and the private sector.
AR FLEXES ITS MUSCLES

‘Evolution, not revolution’ has been the watchword for augmented reality (AR) in industry thus far, but this is now starting to change, as Andrew Wade reports.
Shaping the Trajectory of Blockchain in Healthcare

In 2020, 55% of organizations viewed blockchain as a strategic priority. Though blockchain investments and use cases in healthcare are increasing, attention is needed on how this technology will impact patients. This panel discusses the implications of blockchain for society, its potential to improve healthcare, and the possibilities for shaping a positive future.
Optimization of Data Sets and Improvement of Methods for Calculating the Soil Brightness Index

Summary This paper considers the possibility and briefly describes the process of creating a soil brightness index for the territory of Italy. The difficulty of creating such datasets is that it is necessary to use the entire time series of satellite images. The soil brightness index is calculated for bare soil, the complex task of masking is described in this paper. The authors suggest how the calculations can be simplified and briefly describe the creation of the dataset.
The Effectiveness of Project-Based Learning on Basic Writing Classes in College

Objectives This study aims to explore the effectiveness of PBL on basic writing courses in college with an introduction to a case study of PBL writing classes as an alternative to improve creative problem-solving ability with writing skills.&#x0D; Methods For 243 university students in Gyenggi-do, PBL classes were operated for 7 weeks out of 15 weeks of basis writing courses non-face-to-face and face-to-face in the 2nd semester of 2021 and the 1st semester of 2022, respectively. With the detailed report of the class cases that carried out the two projects, the students' reflection journals, self-evaluation and peer evaluation, and the survey on the effectiveness of PBL classes, including the final lecture evaluation, were analyzed quantitatively and qualitatively.&#x0D; Results As a result, almost all the students reported that they became to have a positive attitude toward writing through empirical learning by establishing an intimate cooperative relationship, recognizing problems to be solved in the context of their lives, and deriving solutions by connecting theory and practice while working on the projects. They also showed very high satisfaction with the PBL classes.&#x0D; Conclusions As an alternative to lecture-style classes that can make students familiar with passive learning participation, PBL classes need to be highly considered because PBL is key to creating an organic learning community that fosters problem-solving and communication skills, which can be attained through various scaffolding strategies and multimedia tools that promote self-directed learning, communication, and collaboration.
A super‐resolution proof of concept in a cycloidal coded aperture miniature mass spectrometer

Higher resolution in fieldable mass spectrometers (MS) is desirable in space flight applications to enable resolving isobaric interferences at m/z < 60 u. Resolution in portable cycloidal MS coupled with array detectors could be improved by reducing the slit width and/or by reducing the width of the detector pixels. However, these solutions are expensive and can result in reduced sensitivity. In this paper, we demonstrate high-resolution spectral reconstruction in a cycloidal coded aperture miniature mass spectrometer (C-CAMMS) without changing the slit or detector pixel sizes using a class of signal processing techniques called super-resolution (SR).We developed an SR reconstruction algorithm using a sampling SR approach whereby a set of spatially shifted low-resolution measurements are reconstructed into a higher-resolution spectrum. This algorithm was applied to experimental data collected using the C-CAMMS prototype. It was then applied to synthetic data with additive noise, system response variation, and spatial shift nonuniformity to investigate the source of reconstruction artifacts in the experimental data.Experimental results using two ½ pixel shifted spectra resulted in a resolution of ¾ pixel full width at half maximum (FWHM) at m/z = 28 u. This resolution is equivalent to 0.013 u, six times better than the resolution previously published at m/z = 28 for N2+ using C-CAMMS. However, the reconstructed spectra exhibited some artifacts. The results of the synthetic data study indicate that the artifacts are most likely caused by the system response variation.This paper demonstrates super-resolution spectral reconstruction in C-CAMMS without changing the slit or detector pixel sizes using a sampling SR approach. With improvements, this technique could be used to resolve isobaric interferences in a portable cycloidal MS for space flight applications.
THE EFFECTIVENESS OF USING ENGLISH POP SONGS TOWARD STUDENTS’ LISTENING COMPREHENSION ACHIEVEMENT AT THE SEVENTH GRADE OF SMP NEGERI 38 PALEMBANG

The objectives of this research were to know (1) whether there was any significant improvement in students’ listening comprehension achievement after being taught by using English pop songs at the seventh grade of SMP Negeri 38 Palembang; (2) whether there was any significant mean difference in students’ listening comprehension achievement between the students who are taught by using English pop songs and those who are not. The research design of this research was a quasi-experimental design. The population of this research was all the seventh-grade students of SMP Negeri 38 Palembang. Sample of this research was class 7.1 as the control group, which consisted of 34 students, and class 7.2 as the experimental group, which consisted of 34 students. Technique of collecting the data used in this research was pre-test and post-test. Then, the data were analyzed by using paired sample t-test and independent sample t-test through SPSS v.25 programs. The result of paired sample t-test in the experimental group showed that t-obtained was 19,380 &gt; t-table (df = 33) at the significance level of 0,05 was 2,034. It means that there was a significant improvement in students' listening comprehension achievement after being taught by using English pop songs. Furthermore, the result of the independent sample t-test showed that t-obtained was 7,858 &gt; t-table (df = 66) at the significance level of 0,05 was 1,996. It means that there was a significant mean difference between the experimental group and the control group. The research findings showed that teaching listening using English pop songs was effective in improving students’ listening comprehension achievement at the seventh grade of SMP Negeri 38 Palembang.
On-board Artificial Intelligence for Failure Detection and Safe Trajectory Generation

The use of autonomous flight vehicles has recently increased due to their versatility and capability of carrying out different type of missions in a wide range of flight conditions. However, the operational safety of these systems can easily be affected if abnormal flight conditions are not timely detected, compromising the nominal bounds of design of the system's flight envelop. This paper aims to provide a methodology for modeling and protection of autonomous vehicle trajectories under normal and abnormal flight conditions. An artificial immune system (AIS) framework is combined with the multi-goal Rapidly-Exploring Random Tree (RRT*) path planning algorithm to generate safe trajectories based on a reduced flight envelop. A high-fidelity model of a fixed-wing unmanned aerial vehicle is used to demonstrate the capabilities of the approach by timely generating safe trajectories as an alternative to original paths.
Prolegomenons of Research Education

Research education is a key institution in the knowledge society and a tool for its construction and development. Its full-fledged formation is the main task in our days that requires disclosure of its underlying beginnings. This work summarizes findings of the author's research studies over two decades that makes him possi­ble to the problem in its essence. Identified and disclosed prolegomenons of re­search education are the following: the relation between learning and truth, the generativity of learning, the fundamental principles of generative didactics, the socio-cognitive structure of knowledge society. It is shown that the attitude to truth determines the essence of research education, where research is taken as a search for truth, is the constitutive basis of research-type behavior and prede­termines the transformative framework of learning. Origins of the “generative learning” concept are studied, its differences from adaptive learning are ana­lyzed, and its role is defined as a fundamental condition of research education opportunity. Cognitive flexibility, cognitive generativity, sociocultural interac­tion of learning are presented as fundamental principles of generative didactics.