Reviewerly/exHarmony huggingface dataset:
EFEKTIVITAS PEMBELAJARAN BERBASIS DISCOVERY LEARNING PADA MATERI KEGIATAN EKONOMI KELAS V DI SEKOLAH DASAR

This study aims to determine the effectiveness of the Discovery learning model on social studies learning of grade V students of SDN 79 Palembang. The samples in this study were VA class students as experimental research and VC class students as control classes. The number of samples in this study was 30 students in each class. The type of research used in this study is experimental research with the lecture method. Data collection techniques are carried out using tests, observation and documentation. The instrument used in the social studies learning test is in the form of a description test. Data analysis techniques using t-tests. The results of the hypothesis test then suggest that the sigmifikan (2-taileid) value is 0.000 because the significant value is more than 0.025, then H0 is rejected that means Ha is enmitted, so it can be implied that there is a moideil Eifeiktivity of Discoiveiry Leiarning learning in the mathematics of eikoinoimi activity in social studies teaching.
FOURIER SERIES APPLICATION FOR MODELING “CHOCOLATE” KEYWORD SEARCH TRENDS IN GOOGLE TRENDS DATA

In some cases of regression modeling, it is very common to find a repeating pattern. To model this, of course, the approach used must be in accordance with the characteristics of the data. The Fourier series is one of the proposed approaches, because it has advantages in modeling relationship patterns that tend to repeat, such as cosine sine waves. The Fourier series is a subset of nonparametric regression, which has good flexibility in modeling. In this study, the Fourier series approach was applied to model search trend data for the keyword "Chocolate" sourced from Google Trends. Generalized Cross-Validation (GCV) is used as model evaluation criteria. Based on the results of the analysis, the best Fourier series nonparametric regression model is obtained with the number of oscillations of five, which is indicated by the minimum GCV value.
Earthquake Footprints for Predicting Events

This paper considers the problem of predicting earthquakes. It uses a small amount of information to create a descriptive key that can be used as a footprint to describe an event. A frequency grid clusters events that occurred at the same time and then the algorithm measures the history of these events over preceding days, in particular the gaps when the events did not occur. The clusters with the time gaps can be used to create the footprint description and results suggest that seismic events can in fact be traced using this, and subsequently recognised again, if the same conditions occur. Greek and USA datasets have been looked at and the prediction accuracy can be 70% or better. The author therefore suggests that this is an interesting method that deserves attention.
Analisis tingkat pemahaman keselamatan berenang pada pengunjung kolam renang

This study was conducted to determine how high the level of understanding of swimming safety in swimming pool visitors in Surakarta City. The purpose of this study was to determine the level of understanding of swimming safety in swimming pool visitors in Surakarta City. This research is a quantitative research with data analysis with percentage sescriptive. Data collection using questionnaires. This study focused the population on pool visitors in Surakarta City. This sampling technique is incidental, which is a sampling technique based on chance, that is, anyone who incidentally meets the researcher. The sample of this study was 24 people. The results of this study are described in percentage, namely the level of knowledge of swimming pool visitors about swimming safety in Surakarta City is categorized into 3 categories, including the "low" category as many as 3 people or 12.5%, for the "medium" category as many as 4 people or 16.7%, while for the "high" category as many as 17 people or 70.8%. Overall more than 50% of pool visitors in Surakarta City have a high level of understanding about swimming safety.
Inferring the Target of Self-defeating Humour in Internet Memes: A Pragmatic Analysis

Nowadays, people spend hours on social media conversing and sharing information through jokes and references made online. Digital memes are one of the most favourable means of internet communication, which thrives on humour. The main purpose of humour is to entertain and make people laugh. So, to avoid hurting others by joking at the expense of others, many people turn to self-mockery. One type of self-mockery is self-defeating humour in which the speaker targets himself/herself in a “poor me” fashion. By using a mixed approach of qualitative and quantitative analysis, this research investigated how internet users interpret the target of self-defeating humour in memes. This analysis used the relevance theory of Sperber and Wilson (1995). On this account, this research addressed the process of decoding and inferential enrichment, and lexical pragmatic adjustment. It is said that making oneself the target of a joke is safer and less sensitive than targeting other people. After analysing the inferences of 100 netizens that were drawn from five self-defeating memes, it has been concluded that targeting oneself could be as sensitive as targeting others.
BAYESIAN ANALYSIS OF TOBIT QUANTILE REGRESSION WITH ADAPTIVE LASSO PENALTY IN HOUSEHOLD EXPENDITURE FOR CIGARETTE CONSUMPTION

Tobit Quantile Regression with Adaptive Lasso Penalty is a quantile regression model on censored data that adds Lasso's adaptive penalty to its parameter estimation. The estimation of the regression parameters is solved by Bayesian analysis. Parameters are assumed to follow a certain distribution called the prior distribution. Using the sample information along with the prior distribution, the conditional posterior distribution is searched using the Box-Tiao rule. Computational solutions are solved by the MCMC Gibbs Sampling algorithm. Gibbs Sampling can generate samples based on the conditional posterior distribution of each parameter in order to obtain a posterior joint distribution. Tobit Quantile Regression with Adaptive Lasso Penalty was applied to data on Household Expenditure for Cigarette Consumption in 2011. As a comparison for data analysis, Tobit Quantile Regression was used. The results of data analysis show that the Tobit Quantile Regression model with Adaptive Lasso Penalty is better than the Tobit Quantile Regression.
Kebijakan Konservasi Air Suci dalam Menjaga Kelangsungan Agama Hindu di Desa Mas Kecamatan Ubud Kabupaten Gianyar

Beji Temple in the context of conveying the socio-religious life of the Balinese people has a vital and multifunctional role. As the main source of holy water for the people of Bali, Beji Temple must be conserved to ensure that the existence of this spring is maintained. In the process of protecting this holy water source, it is important to carry out the holy water conservation policy program in Mas Village, Ubud District, Gianyar Regency.
MENINGKATKAN HASIL BELAJAR DENGAN PENGGUNAAN STRATEGI MAKE A MATCH DI SEKOLAH DASAR

This action research is motivated by the teaching and learning process of teachers less able to create active and fun learning, the use of methods that are still conventional results in the learning process in the classroom making students feel bored and seem inactive and student learning outcomes below KKM. Classroom action research is the method used in this study. The research subjects were students of class V B MI Al Huda consisting of 37 students. The instruments used were observation, tests and documentation. The results of this study indicate that the implementation of social studies subjects, learning with the make a match strategy can improve student learning activities and outcomes. This can be proven from the results of each cycle designed. In cycle I only 40.54% (15 students completed) with an average of 56.29, cycle II amounted to 56.76% (21 students completed) average score 65.27 and cycle III amounted to 94.60% (35 students completed) with an average of 77.02
Generations’ Perception Towards the Interaction with AI

Acknowledging the ongoing trend and increasing implementation of different forms of AI that simulate human behavior for various tasks in a human-like manner, the focus of the present research is set to determine the attitude of consumers towards interaction with AI from a more personal point of view, rather than from an operational perspective. The present research mainly focuses on differences between two age groups of respondents: aged over and under 30. In order to obtain insights about generations’ perceptions towards the interaction with AI, a primary data analysis has been conducted. The questionnaire contains 31 items and is set to test the following nine aspects regarding consumer-AI interactions: performance expectancy, effort expectancy, social influence, hedonic motivation, anthropomorphism, trust, anxiety, willingness to accept AI and objection to use AI. The results show that in the case of AI, there are only average significant differences between the studied age groups, the two groups being at the moment more alike than different in their perception towards AI. As AI advances at a rapid pace, practical implications consist in indicating relevant AI aspects to be studied for further human-AI interaction related research.
University students’ and educators’ perceptions on the use of digital and social media platforms: A sentiment analysis and a multi-country review

Due to Covid-19, an inevitable restructuring of higher education teaching and learning pedagogies ensuring the continuous and effective learning of students is deemed important. Despite such vitality, a prevalent disparity worldwide on the usages and gains of digital and social media integration is still noticeable. Following a Scoping Literature Review and using the Atlas.ti software for a Grounded Theory qualitative analysis, this study aims to ascertain the significance of digital and social media tools during and after the Covid-19 pandemic. The study explains the common challenges and opportunities both students and educators faced in thirty countries. Drawing on the sentiment analysis of these stakeholders, results indicate that despite the acceleration of digital education into a flexible, and student-centered didactic approach, various barriers in effectively fulfilling online learning still exist. Findings also revealed the lack of, and therefore need for, proper teaching and learning material and strategies suitable for digital education.
Distributed accelerated Nash equilibrium learning for two-subnetwork zero-sum game with bilinear coupling

This paper proposes a distributed accelerated first-order continuous-time algorithm for O(1/t 2 ) convergence to Nash equilibria in a class of two-subnetwork zero-sum games with bilinear couplings.First-order methods, which only use subgradients of functions, are frequently used in distributed/parallel algorithms for solving large-scale and big-data problems due to their simple structures.However, in the worst cases, first-order methods for two-subnetwork zero-sum games often have an asymptotic or O(1/t) convergence.In contrast to existing timeinvariant first-order methods, this paper designs a distributed accelerated algorithm by combining saddle-point dynamics and time-varying derivative feedback techniques.If the parameters of the proposed algorithm are suitable, the algorithm owns O(1/t 2 ) convergence in terms of the duality gap function without any uniform or strong convexity requirement.Numerical simulations show the efficacy of the algorithm.
Generalized synchronization-based partial topology identification of complex networks

In this paper, partial topology identification of complex networks is investigated based on synchronization method.We construct the response networks consisting of nodes withsimplerdynamics than that in the drive networks.By constructing Lyapunov function, sufficient conditions are derived to guaranteepartial topology identificationby designing suitable controllers and parameters update laws.Several numerical examples are provided to illustrate the effectiveness of the theoretical results.
Research on Algorithm and Technology of Computer Intelligent Image Recognition

The research of computer intelligent image recognition algorithm and technology is the research field of the development of computer intelligent image recognition algorithm. The main purpose of this research field is to develop efficient algorithms for computer intelligent image recognition, which can be used in various applications, such as face detection, object tracking and video surveillance. In order to achieve these goals, there are different methods available, such as neural network, support vector machine (SVM), genetic algorithm and so on. What are the advantages of computer intelligent image recognition algorithm and technology research? The main advantage related to this research area is that it helps to improve Carl's efficiency
A Comprehensive Data Driven Approach on Crop Yield and Fertilizer Efficiency

India’s economy, which is mostly dependent on agricultural production growth and agroindustry goods, is an agricultural nation. A significant field of research for agricultural production analysis is data mining. Every farmer wants to know how much harvest he may anticipate. Examine a number of relevant factors, such as the location and the pH level used to calculate the soil’s alkalinity. Moreover, the proportion of nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). Location is utilized in conjunction with the usage of third-party apps like APIs to identify factors such as weather and temperature, soil type, nutrient value, the quantity of rainfall, and soil composition. All of these parameters will be reviewed, and the data will be trained to develop a model using several efficient machine-learning techniques. The system incorporates a model to give the user precise recommendations regarding the right fertilizer ratio based on field atmospheric and soil data, which improves crop output and increases farmer revenue.
Collaborative collection development: a MedPrint case report

In response to several of Texas' largest medical libraries being forced to discard all serial print holdings, the Texas A&M University System and University of Texas System's Joint Library Facility (JLF) staff worked to help provide a solution to save and store these resources. This process fire-started a comprehensive effort by JLF staff to contact the National Library of Medicine (NLM) and devise a blueprint that would be used to help save and preserve all serial medical resources listed in NLM's medical retention program.In an unprecedented approach, the Texas A&M JLF staff launched efforts to collect and preserve the complete holdings range of all NLM MedPrint periodical runs. This case report details the planning and steps JLF staff took to accomplish this feat; highlights important matters of consideration for the medical community which heavily relies upon continuous access to MedPrint materials; and provides insight on the apparent preservation vulnerabilities these materials increasingly face in an environment where digitization may create a false sense of security.By May 2021, JLF had collected complete title runs up to year 2000 for 202 of the 254 MedPrint titles, which consists of more than twelve thousand volumes. These efforts proved particularly beneficial in the wake of the COVID-19 pandemic, which forced NLM to halt ILL processing from their print collection. During this time, JLF was uniquely positioned to meet and respond to the historic high number of medical literature ILL requests it received during this time.
High resolution gamma-ray spectrometry logging applied in the interpretation of sedimentary processes and environments of the Itararé Group (Permo-Carboniferous), Paraná Basin, Santa Catarina state

Outcrop gamma-ray spectrometric logging in the upper strata of the Campo Mourão Formation and lower strata of the Taciba Formation were used to investigate: (i) the influence of lithological variation on gamma-ray patterns; (ii) its potential to discriminate and identify lithotypes; and (iii) sedimentary processes and related environments of these Permo-Carboniferous strata. The gamma-ray logs were subdivided into three gamma-ray units, according to the total count, concentrations and contents of K, eU and eTh. These gamma-ray units were associated to three depositional units: Glaciolacustrine system with thin-bedded turbidites in the Campo Mourão Formation; and two submarine fans systems (channels, overbanks and lobes) with thick-bedded turbidites systems in the Taciba Formation. Gamma-ray spectrometry allowed distinguishing 'common' shales from black shales, which are often similar in the faciological description in fieldwork. Likewise, carbonate levels were identified as a function of the decrease in gamma-ray concentrations in black shales of the Campo Mourão Formation. The relationship between the radiometric data and the grain size indicated that, in the field, the lithological heterogeneity has a great influence on the ability of the gamma-ray spectrometer to distinguish lithotypes. The eTh/K and eTh/eU ratios were used to infer sedimentary processes and related environments. Through the eTh/eU ratio, a reducing environment, responsible for the precipitation of authigenic uranium in low sedimentation rate deposicional system, was evidenced, discriminating depositional units from different environments.
ỨNG DỤNG PHƯƠNG PHÁP HỌC ĐẢO NGƯỢC VÀO GIẢNG DẠY GIÁO DỤC HỌC ĐẠI CƯƠNG - PHƯƠNG PHÁP GIẢNG DẠY TIẾNG NHẬT

Recently, many Japanese language training institutes have been established to meet the rapid increase in Japanese language learners. The majority of instructors at these institutes are recent graduates with a Japanese language degree. However, many Japanese classes are run by those who studied or worked in Japan for a short period of time without any pedagogical qualifications. In order to provide a high quality human resource, the Department of Japanese Studies developed a Japanese Language Pedagogy curriculum in the academic year 2021-2022. General Education Studies is a unit taught at the early stage of this curriculum; therefore, research on how to both impart fundamental knowledge about teaching Japanese and spark learners’ interests in teaching should be conducted. Flipped learning is a pedagogical approach that has been effectively adopted in many developed countries worldwide. This study uses the methods of analysis and synthesis to investigate the effectiveness of the application of flipped learning method in teaching the General Education Studies unit for 3rd year students of Japanese Language Pedagogy at the Department of Japanese Studies, Hanoi University. The findings show that the participants were more active, more creative, more critical as well as more eager to self-study during the learning process.
N ASSESSING THE EFFECTIVENESS OF HYBRID SOLAR COLLECTORS SCHEME INIRAQ'S ENVIRONMENT

An evaluation of the performance of the Iraqi environments in terms of electrical, thermal and exergy efficiency is introduced in this study. The research is carried out in May 2022, in the Baghdad metropolis. The extraction process of heat from the photovoltaic units which arises from the coolant liquid mass flow rate deem as an essential point. The experimental studies were implemented by absorbing heat energy behind from the photovoltaic cell's surface in insulated conditions and using a cooled water unit. The results indicated that at a mass inflow rate of 0.2 kg/sec, the maximum average total efficiency of the system was recorded 22%. As a result, it is advised that to reduce the payback interval, it is possible to design efficient solar photovoltaic–thermal systems to promote the whole system's efficiency and lower the payback interval.
The 'uberization of policing'? How police negotiate and operationalise predictive policing technology

&lt;p&gt;Predictive policing generally refers to police work that utilises strategies, algorithmic technologies, and big data to generate near-future predictions about the people and places deemed likely to be involved in or experience crime. Claimed benefits of predictive policing centre on the technology’s ability to enable pre-emptive police work by automating police decisions. The goal is that officers will rely on computer software and smartphone applications to instruct them about where and who to police just as Uber drivers rely on similar technologies to instruct them about where to pick up passengers. Unfortunately, little is known about the experiences of the in-field users of predictive technologies. This article helps fill this gap by addressing the under researched area of how police officers engage with predictive technologies. As such, data is presented that outlines the findings of a qualitative study with UK police organisations involved in designing and trialing predictive policing software. Research findings show that many police officers have a detailed awareness of the limitations of predictive technologies, specifically those brought about by errors and biases in input data. This awareness has led many officers to develop a sceptical attitude towards predictive technologies and, in a few cases, these officers have expressed a reluctance to use predictive technologies. Based on these findings, this paper argues that claims about predictive software’s ability to neutralise the subjectivity of police work overlooks the ongoing struggles of the police officer to assert their agency and mediate the extent to which predictions will be trusted and utilised.&lt;/p&gt;
Eliminating Vertical Fixed Pattern Noise in CMOS-Based Endoscopic Images using Modified Dark Frame Subtraction

Complementary Metal Oxide Semiconductor (CMOS) image sensors are widely used in medical endoscopy because of their small size, low power consumption, and high sensitivity. However, they can suffer from vertical fixed pattern noise (VFPN), which appears as a series of vertical stripes in images and can obscure fine details, reducing image quality. Dark frame subtraction is a common technique used to eliminate VFPN, but it may result in poor image quality if the sensor parameters, such as gain and black level clamp (BLC), are not considered. This paper proposes a modified dark frame subtraction method that includes a simple preprocessing step to minimize VFPN by taking into account the impact of sensor gain and BLC. To evaluate the proposed method, it was tested in a simulation and real-time set up and its performance is compared to that of the actual dark frame subtraction. The results show that the modified method significantly improves noise reduction and enhances image quality, making it more suitable for endoscopic applications. By considering the effects of sensor parameters on VFPN, the proposed method offers a more effective solution for eliminating VFPN in CMOS image sensors used in medical endoscopy. This method can help clinicians more accurately diagnose medical conditions by providing clearer images with reduced noise and improved quality.