Reviewerly/exHarmony huggingface dataset:
APPLICATION OF THE STEAM APPROACH TO INCREASE LEARNING OUTCOMES FOR THEME 4 STUDENTS OF CLASS IVB SD NEGERI 0411 PASAR UJUNG BATU T.P 2020/2021

The background of this research is the low student learning outcomes which are marked by the number of student scores who have not reached the KKM, this is because the method used by the teacher does not attract the attention of students. The purpose of this study was to determine the improvement of learning outcomes in theme 4 through the STEAM approach to the fourth grade students of SD Negeri 0411 Pasar Ujung Batu. This research is classroom action research which consists of 2 cycles, namely cycle I and cycle II. The data sources in this study were the fourth grade students of SD Negeri 0411 Pasar Ujung Batu, totaling 25 students in the 2020/2021 school year and the sample was determined by the entire population. The data are the results of the initial test, the results of the final test. Data analysis techniques through quantitative and qualitative approaches. The results of the initial test in this study obtained a percentage (34.7%) in the first cycle (60.9%) and (100%) in the second cycle. It was concluded that the STEAM approach could improve the learning outcomes of fourth graders at SD Negeri 0411 Pasar Ujung Batu on theme 4
Using Flashcards to Improve Students’ Vocabulary Mastery at the Eleventh Grade Students of SMAN 10 Makassar

This research applied classroom action research which aimed to see the implementation of flash cards to improve students’ vocabulary mastery. The subjects of this research were the eleventh grade students of SMAN 10 Makassar. The stage of this research started from planning, action, observation and then reflection. The result of this research quantitatively showed that in the pre cycle there was only 1 student (3,44%) who pass the KKM, in the first cycle there were 5 students (17,24%) who pass the KKM.In the second cycle there were 26 students (89,65%) who pass the KKM. The average score of students from pre cycle, first cycle and the second cycle is always increasing which means that there is an improvement of students’ vocabulary mastery. Qualitatively, the students gained experience and input related to vocabulary mastery. Besides that, the implementation of flash card was very interesting for students because they can learn in fun way. In conclusion, the implementation of role-play and interactive activities can gradually improve students’ vocabulary mastery.
A Guide to Standardizing Digital Calibration and Measurement Capabilities

Demand for transparent digital measurement data exchange and processing continues to grow in the metrology industry. Automated data processing, however, requires metadata to adequately describe and identify the information content. In metrology, the measurand description comprises the fundamental metadata that associates meaning with measurement data. Measurement information exists in many metrological documents, including calibration certificates and instrument specifications, but in the international quality infrastructure (IQI), CMC1 descriptions define all approved and accredited measurands. These CMCs appear atthe IQI’s apex in the BIPM2 KCDB3 and throughout the IQI in official accreditation scopes and unaccredited measurement capability statements. In the current state of the art, metrology experts develop CMCs primarily as free-form text that often lacks full information, requires subject-matter experts to interpret correctly, hinders machine processing, and does not lend itself to contextual or semantic search on CMC technical characteristics of interest: uncertainty, range, or even the specific measurand. This paper describes a measurand taxonomy structure suitable for use as CMC metadata and provides a procedure and examples for developing the metadata taxons to standardize CMCs in human- and machine-readable formats.
Coreference Resolution: Are the Eliminated Spans Totally Worthless?

Up to date, various neural-based methods have been proposed for joint mention span detection and coreference resolution. However, existing studies on coreference resolution mainly depend on mention representations, while the rest spans in the text are largely ignored and directly eliminated. In this paper, we aim at investigating whether those eliminated spans are totally worthless, or to what extent they can help improve the performance of coreference resolution. To achieve this goal, we propose to refine the representation of mentions with global spans including these eliminated ones leveraged. On this basis, we further introduce an additional loss term in this work to encourage the diversity between different entity clusters. Experimental results on the document-level CoNLL-2012 Shared Task English dataset show that the eliminated spans are indeed useful and our proposed approaches show promising results in coreference resolution.
Research on dark field image processing algorithm based on matlab

In order to overcome the large error and poor repeatability of traditional dark field image processing, a dark field spectrum analysis algorithm is proposed. This algorithm uses Matlab as the platform to accurately identify gold nanoparticles and calculate the brightness through image expansion and other methods in a dark field environment. At the same time, it takes into account issues such as spectral standardization and removal of contaminants and impurities, and then reconstructs the single-particle scattering peak and records its spectrum characteristic. The experimental results of gold nanorod detection show that this algorithm is more flexible and accurate in identifying particles than the traditional method based on brightness binarization. The particle recognition rate can reach 91.33%, and the accuracy of particle fitting has increased by 15.59%. This algorithm can also detect the spectral characteristics of particles before and after the combination in batches. The data results are consistent with the theoretical values, and the data processing effect is good. It provides a more flexible, fast and accurate new algorithm for the field of dark field image processing.
Forensic land surveying: accuracy data testing through a computer-graphic model

The work is devoted to one of the aspects related to the resolution of disputes over the boundaries of land plots. Recently, the number of these disputes has been steadily growing, which confirms the relevance of the chosen topic. Border disputes are currently resolved only through the courts, almost always with a forensic land surveying. The aspect considered in the article concerns the accuracy of constructing models of the objects of surveying, since software products used do not have documentary evidence of the accuracy of the created models. Therefore, experts need to independently test the models for accuracy. Moreover, such tests make it possible to reveal expert errors at the modeling stage. The paper proposes the authors' method of testing for the accuracy of a computer-graphic model of objects, studied by a forensic land surveying.
A computational hybrid proposal for imaging the stress field through a three-wavelength phase stepping

Dynamical photoelasticity studies are affected by requiring multiple acquisitions under optical elements motion. In consequence, this paper introduces a computational hybrid technique of three-wavelength phase stepping using only one-color image to recover the stress field.
P-475 Socioeconomic and clinical factors associated with death and hospitalization of health professionals affected by COVID-19 in Brazil, 2020

<h3>Introduction</h3> The new coronavirus pandemic affected the lives of millions of people across the planet in 2020. Brazil, in just a few months, became the epicenter of the pandemic in Latin America. This, due to the absence of vaccines and the ineffective isolation measures adopted. In this context, health professionals stand out as a group with greater exposure and risk of being affected by COVID-19. <h3>Objective</h3> To analyze clinical and sociodemographic characteristics associated with death and hospitalization of health professionals due to COVID-19, in addition to calculating the incidence rates per occupation. <h3>Methods</h3> We conducted a cross-sectional observational study that used secondary data from the State of Espírito Santo Health Department. COVID-19 cases in healthcare professionals were recorded between February 27 and August 17, 2020 in Espírito Santo, Brazil. Cases with a lack of information were excluded. To compare the sociodemographic and clinical characteristics of health professionals with the clinical outcomes (cure or death) and the need for hospitalization, a bivariate analysis was performed using the <i>X</i><sup>2</sup> tests of independence or Fisher's exact test. Data analysis was performed using the SPSS 20.0 software. <h3>Results</h3> 75.6% (n = 9,191) of the cases were female; the general lethality rate was 0.27% and the general hospitalization rate was 1.0%. The clinical outcome of death and the occurrence of hospitalization were associated with: male gender, age greater than or equal to 50 years, higher education, fever, difficulty breathing, cough, cardiac comorbidity, diabetes and obesity (p &lt;0.05). Only the occurrence of hospitalization was associated with: case reported in the metropolitan region of Vitória-ES, runny nose, sore throat, headache and renal comorbidity (p &lt;0.05). The occupation with the highest incidence rate was nurses (16,053 cases/100,000 nurses). <h3>Conclusion</h3> The study demonstrated a high frequency of cases in females, low general lethality and high incidence in nurses.
Modular Approach Towards Implementation of Foundational Image Processing Operations on FPGA

This paper puts forth the implementation of a few of the foundational image processing operations such as Grayscale conversion, Sobel edge detection, and fixed threshold image segmentation, using an integration-based modular approach on traditional Spartan-7-based Field Programmable Gate Array (FPGA). While there exist many implementations for these basic image processing operations, which are mostly hard-coded, the uniqueness of our work is demonstrated through our simplified and flexible approach. Our approach offers complete flexibility and control at every stage of the design as well as perfectly timed clock domains which are necessary for a stable FPGA design along with easier troubleshooting at each step.Deshpande, Anuja Agrawal, Pankaj Dahikar, Pradeep
ОБЗОР МУЛЬТИАГЕНТНЫХ СИСТЕМ В УМНЫХ ГОРОДАХ

Cities face a new challenge related to their populations; for the first time in history, a large part of the population now lives in a megacity. Based on the current situation, the city should implement new solutions and become a Smart city, presenting its citizens with systems that meet the requirements related to security, health, resources, government, education and other urban everyday systems. The purpose of this article is to investigate the smart city model using multi - agent systems that provide city intelligence as the basic infrastructure for determining the model that can be reproduced and exported to create a smart city.
Preparation for the XXV Youth Balkaniade in Mathematics 2021

The paper contains the team selection tests for the team of Bulgaria at the Junior Balkan Math Olympiad 2021 as well as an overview of the team results. The included materials can be useful for the math contest preparation classes in Junior high school.
A Step-Size Adaptive Hill-Climbing Algorithm for Local Search

This paper proposed a step-size adaptive local search (SSALS) algorithm. SSALS is designed for single individual search, and can also be used in multiple individuals search when combined with population-based algorithms. In SSALS, each dimension possesses a basic step size. For each iteration, one or more dimensions are randomly selected to mutate. The basic step size of these dimensions will be adjusted automatically based on the fitness of the new position. The SSALS was compared with five state-of-the-art algorithms by low- and high-dimensional test experiments. A set of 28 benchmark functions in the CEC’2013 test suite on real-parameter optimization is used for low-dimensional experiments, and for high-dimensional experiments, a set of 15 benchmark functions in the CEC’2013 test suite on large-scale global optimization is utilized. The experimental results indicate that the performance of SSALS is significantly higher than the other algorithms, especially in high-dimensional optimization.
Economic Development Ideas of Regional Convergence Industry Based on Artificial Intelligence Multimedia Background

Today, with the development of the times and the advancement of science and technology, informatization has gradually become an important standard for measuring the state of national development in the course of national development. The artificial intelligence multimedia technology has been developed under the background of informationization and has gradually become an important means to achieve national development and regional economic progress. The impact often depends not only on the economic attributes of the industry but also on the economic attributes of urban planning. Countries and regions with high levels of regional integration and industrial economic development have begun to inject economic knowledge and methods into regional integration and industrial economic processes, which are used to solve or alleviate problems and contradictions in the process of urban and rural construction and development. It can be seen that industrial economy and regional integration are not only a spatial phenomenon but also an economic phenomenon. Based on the needs of the development of urban planning discipline theory and the era of national industrial economic integration, this paper puts forward the research methods and ideas of this paper and combines current multimedia backgrounds and their development trends to analyze key innovations and research frameworks, incorporating the main theoretical research elements into the concept of “combination” of traditional Chinese philosophy, aiming at the main problems and contradictions in the process of China’s industrial economic development, respectively, the cultural economy and industrial economy, and the integration of public economy and environmental economy, land economy, and housing economy, a new urbanization development strategy of population economy and regional economic competition.
Picking up the pieces: Sex differences in mechanisms of curve tracing.

This study examined potential sex differences in the application of models of curve tracing, namely the pixel-by-pixel model, the bipartite model, and the zoom lens model. The purpose of this study was therefore to determine whether sex differences existed in terms of reliance on a particular model or whether the results of each sex could be best explained by one model. This was done by examining the combined data obtained by Voyer and MacPherson (2020), consisting of 420 participants, with 194 men and 226 women. We examined only the curve-tracing task data from that study and compared the fit of the different models as well as a possible interaction with sex of participants on the proportion of correct responses and response time. Overall, sex was a significant factor, with men showing better average accuracy and faster performance than women. On accuracy, we found that the pixel-by-pixel model provided the best fit for women, whereas the zoom lens model produced the best fit for men. On response time, the zoom model was the best predictor of response time for both sexes. The discussion elaborates on an account of these findings and on how our results might generalize to other visual-spatial tasks where a performance advantage for men is found. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Machine learning approach for prediction of status of rechargeable batteries used in deep ocean moored buoy system using in-situ parameters

Battery packs are the only source of power storage of critical systems operated remotely at deep ocean location. The complete system operates only with the power available from the battery during the low sunlight condition. In this paper, the batteries used in oceanographic buoy systems operated in deep ocean regions far away from the mainland, types, backup mechanism along with the energy demand are briefed. Implementation of artificial neural network technique for understanding the status of battery systems are explained. An RMSE value of 0.29 is obtained between the predicted and actual value of battery voltage. This method will effectively indicate the status of the battery system of the buoy system and hence will provide decision-makers an idea on the operation and endurance of critical deep ocean buoy systems.
Towards Hyperparameter-free Policy Selection for Offline Reinforcement  Learning

How to select between policies and value functions produced by different training algorithms in offline reinforcement learning (RL) -- which is crucial for hyperpa-rameter tuning -- is an important open question. Existing approaches based on off-policy evaluation (OPE) often require additional function approximation and hence hyperparameters, creating a chicken-and-egg situation. In this paper, we design hyperparameter-free algorithms for policy selection based on BVFT [XJ21], a recent theoretical advance in value-function selection, and demonstrate their effectiveness in discrete-action benchmarks such as Atari. To address performance degradation due to poor critics in continuous-action domains, we further combine BVFT with OPE to get the best of both worlds, and obtain a hyperparameter-tuning method for Q-function based OPE with theoretical guarantees as a side product.
When size does not matter: compositional data analysis in marketing research

Compositional Data analysis (CoDa) is the standard statistical methodology when data contain information about the relative importance of parts of a whole. Many research questions in marketing have to do with distribution of a whole (e.g., market share, product portfolio, spending distribution), or with relative importance (e.g., advertising content or style, preferred product attributes). CoDa solves the statistical problems that arise when treating compositional data with classical statistical methods and focuses on research questions about relative importance. In a costumer opinion platform the dominant types of reviews matter more than the number of reviews. We show how to apply the most common CoDa tools (visualization and linear models), by means of real data from an electronic word-of-mouth platform: are hotel characteristics affecting the share of valuation categories (e.g., from terrible to excellent reviews), or is it related to other compositions (e.g., by type of travelers)?
Beetle Antennae Search Strategy Improved Grey Wolf Optimization Algorithm

In view of the problem of being easy to fall into local optimization when the gray wolf optimization algorithm handles complex optimization problems, this thesis starts from the two angles of perspectives of gray wolf search strategy and nonlinear control strategy to propose an improved gray wolf optimization algorithm based on beetle antennae search strategy (BIGWO). Firstly, it gives the leading wolves the ability of free exploration and communication by combining with the beetle antennae search strategy, which helps the improvement of the global search performance of the algorithm. Besides, it carries out the design of a nonlinear control strategy to balance the exploration and development of the algorithm. Then, the optimization test is carried out for 10 benchmark functions to verify the optimization ability of the algorithm. Finally, it makes the application of the algorithm into the actual optimization problem of vehicle planning task allocation with capacity constraints, so as to verify the ability of the algorithm to solve practical problems. According to the experimental results, it indicates that BIGWO algorithm has better optimization accuracy, stability and convergence speed, which is both suitable for the optimization of characteristic function and the practical optimization problems.
Welcome to the Eighth International Workshop on Evolving Security and Privacy Requirements Engineering (ESPRE’21)

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
25 MHz repetition rate pilot-tone-assisted Gaussian-modulated continuous-variable quantum key distribution with a local local oscillator

We experimentally demonstrate a 25 MHz repetition rate pilot-tone-assisted Gaussian-modulated continuous-variable quantum key distribution (CVQKD) with a local local oscillator (LLO) by employing the frequency-multiplexing and polarization-multiplexing techniques. In the proposed LLO-CVQKD system, the strong pilot tone and weak quantum signal can be separated in different frequency and polarization domain for avoiding the crosstalk between them and guaranteeing different detection requirements of low-noise for quantum signal and high-saturation limitation for pilot tone. Moreover, the dominant phase noise over 25 km optical fiber channel can be compensated in real time for supporting high secure key rate of 1.61 Mbps in the asymptotic regime.
An Efficient Segmentation of Biomedical Images using Optimized Level Set Method via Enhanced Possibilistic FCM Clustering

The important aspect to deal with medical images is image-segmentation. Image-segmentation is the way to remove the vicinity of attentiveness using different image segments. This segmentation helps to analyze the representation of the image more efficiently. The segmentation of the image in medical image analysis is taken as a challenging task because it is concerned with numerous clinical applications. To have a segmentation of colonoscopy images, cardiac vascular, knee image and MRI (Magnetic Resonance Imaging) brain images', a new approach in this paper is put forward by combining the particle swarm optimization (PSO) and enhanced possibilistic Fuzzy C-Means algorithm. The proposed algorithm proceeds in two stages: In the first stage, optimum pixel values are calculating automatically from the input medical images by using the PSO algorithm. These optimum pixels are acted as a random cluster centres for enhanced Possibilistic Fuzzy C-Means (EP-FCM) clustering algorithm. This process improves the clustering efficiency because of optimum random clusters are choosing instead of normal cluster centres. Segments obtained in the pre-processing are incoherent and highly noisy in clustered results. Therefore, Post-processing is necessary to effectively reduced the noise and boundary leakages (outliers’). In the Second Stage, it is necessary to refine the segmentation results in the pre-processing stage by using the level-set method for robustness. In this paper, tested proposed segmentation algorithm on medical images such as MRI brain and CT Colonoscopy images. The performance of the algorithm is proven to be outstanding with the best accuracy and has dealt with noise effect, boundary leakages effectively.
A practical implementation of the signature scheme falcon suited for memory constrained device

Falcon is the only NTRU-based signature scheme submitted for the NIST standardization process of post-quantum cryptography. Compared to other signature schemes, Falcon has achieve very small public keys and signatures. The signature generation and verification procedures are very fast. The limitations are that both the key generation procedure and the fast Fourier sampling are non-trivial to understand and delicate to implement. This work aims to present an optimized implementation of Falcon suited for memory constrained devices. We managed to reduce the dynamic memory consumption of Falcon by 37% in comparison to the reference implementation.. Besides, our implementation requires only 334.7 ms for signing and 6.16ms for verification.