Reviewerly/exHarmony huggingface dataset:
Sparse Blossom: correcting a million errors per core second with minimum-weight matching

In this work, we introduce a fast implementation of the minimum-weight perfect matching (MWPM) decoder, the most widely used decoder for several important families of quantum error correcting codes, including surface codes. Our algorithm, which we call sparse blossom, is a variant of the blossom algorithm which directly solves the decoding problem relevant to quantum error correction. Sparse blossom avoids the need for all-to-all Dijkstra searches, common amongst MWPM decoder implementations. For 0.1% circuit-level depolarising noise, sparse blossom processes syndrome data in both $X$ and $Z$ bases of distance-17 surface code circuits in less than one microsecond per round of syndrome extraction on a single core, which matches the rate at which syndrome data is generated by superconducting quantum computers. Our implementation is open-source, and has been released in version 2 of the PyMatching library.
From Private to Public: Benchmarking GANs in the Context of Private Time Series Classification

Deep learning has proven to be successful in various domains and for different tasks. However, when it comes to private data several restrictions are making it difficult to use deep learning approaches in these application fields. Recent approaches try to generate data privately instead of applying a privacy-preserving mechanism directly, on top of the classifier. The solution is to create public data from private data in a manner that preserves the privacy of the data. In this work, two very prominent GAN-based architectures were evaluated in the context of private time series classification. In contrast to previous work, mostly limited to the image domain, the scope of this benchmark was the time series domain. The experiments show that especially GSWGAN performs well across a variety of public datasets outperforming the competitor DPWGAN. An analysis of the generated datasets further validates the superiority of GSWGAN in the context of time series generation.
Sparse Gaussian Processes with Spherical Harmonic Features Revisited

We revisit the Gaussian process model with spherical harmonic features and study connections between the associated RKHS, its eigenstructure and deep models. Based on this, we introduce a new class of kernels which correspond to deep models of continuous depth. In our formulation, depth can be estimated as a kernel hyper-parameter by optimizing the evidence lower bound. Further, we introduce sparseness in the eigenbasis by variational learning of the spherical harmonic phases. This enables scaling to larger input dimensions than previously, while also allowing for learning of high frequency variations. We validate our approach on machine learning benchmark datasets.
Testability Refactoring in Pull Requests: Patterns and Trends

To create unit tests, it may be necessary to refactor the production code, e.g. by widening access to specific methods or by decomposing classes into smaller units that are easier to test independently. We report on an extensive study to understand such composite refactoring procedures for the purpose of improving testability. We collected and studied 346,841 java pull requests from 621 GitHub projects. First, we compared the atomic refactorings in two populations: pull requests with changed test-pairs (i.e. with co-changes in production and test code and thus potentially including testability refactoring) and pull requests without test-pairs. We found significantly more atomic refactorings in test-pairs pull requests, such as Change Variable Type Operation or Change Parameter Type. Second, we manually analyzed the code changes of 200 pull requests, where developers explicitly mention the terms "testability" or "refactor + test". We identified ten composite refactoring procedures for the purpose of testability, which we call testability refactoring patterns. Third, we manually analyzed additional 524 test-pairs pull requests: both randomly selected and where we assumed to find testability refactorings, e.g. in pull requests about dependency or concurrency issues. About 25% of all analyzed pull requests actually included testability refactoring patterns. The most frequent were extract a method for override or for invocation, widen access to a method for invocation, and extract a class for invocation. We also report on frequent atomic refactorings which co-occur with the patterns and discuss the implications of our findings for research, practice, and education
Examining the Relationship Between Teachers' Levels of Digital Literacy and Their Attitudes Towards Distance Education

The primary aim of this study was to investigate the relationship between teachers' levels of digital literacy and their attitudes towards distance education. Teachers' perceptions of distance education and their digital literacy levels were also compared in terms of gender, major, age, professional experience and the time they spend online daily. The sample of this survey study consisted of 919 teachers with 10 different majors working in state schools in the 2020-2021 academic year. The data were gathered through the digital literacy scale and the distance education attitude scale. In data analysis, regression and Pearson correlation, independent samples t-test, one-way ANOVA and Post Hoc tests were employed. The results showed that the teachers had a good level of digital literacy and their attitudes towards distance education were at moderate level. Significant differences were observed in their attitudes towards distance education and digital literacy levels in terms of gender, age, major and the time they spend online daily. In addition, a positive and moderately significant relationship was found between the teachers' levels of digital literacy and their attitudes towards distance education, and it was concluded that their digital literacy levels were a statistically significant predictor of their attitudes towards distance education attitude.
Signal transmission optimization moving-vector classification Mpeg standard video coding

The rise of useful multimedia applications has sparked a rush to perfect video compression methods. Over the last decade, there has been a lot of interest in using motion-compensated frame interpolation (MCFI) methods to boost video frame rate during playback in both the academic and consumer electronics sectors. Interpolation refers to the process of defining the values of a function at places that are not represented by any of the examples. It accomplishes this process by fitting a constant function across the discrete input samples. We based our methodology on the fact that color cameras function similarly to the human eye. We have been used to black and white photographs, yet the real world is full of color. Grayscale images were the sole option until the advent of color monitors and cameras. Energy assessment calls for photo capture using a camera. Light's electromagnetic waves provide the observable energy in this scheme. For motion-compensated video vector frame interpolation or frame rate up-conversion and Phase frame interpolation Method, the authors present a novel approach with low vector dispensation method in the conclusion. Using optical flow or other conventional methods, it is necessary to have precise pixel correspondences between pictures in order to compute interpolated frames in a video series. We provide a practical replacement by capitalizing on advances in phase-based approaches, which use the phase shift of individual pixels to express motion. In challenging interpolation conditions, such as major appearance changes, flow-based algorithms frequently create severe visual distortions, yet our solution fails gently. Our approach is particularly useful for retiming and interpolating frames in high-resolution video shot at a high frame rate. Estimating motion for the purpose of improving the quality of the decompressed video requires more research on the nature of the compression problem. a novel way to estimating motion that is analogous to full-search 3ss,4ss, block-matching, and parametric techniques. The primary focus will be research into both error-free compression and the development of novel motion estimation algorithms. The results of the research indicate that the suggested system has the potential to increase visual superiority and is also robust, especially when applied to video sequences with quick movements and challenging portions. By combining phase based and conventional Lagrangian, which is utilized to get better quality results and deal with variations in light more gracefully.
An introduction about Cryptocurrencies

The researchers provide a brief definition of bitcoin in this paper along with a quick introduction to cryptocurrencies. The study looks at the role cryptocurrencies play in modern technology and how bitcoin has evolved in relation to spot contracts.
When to Use Large Language Model: Upper Bound Analysis of BM25 Algorithms in Reading Comprehension Task

Large language model (LLM) is a representation of a major advancement in AI, and has been used in multiple natural language processing tasks. Nevertheless, in different business scenarios, LLM requires fine-tuning by engineers to achieve satisfactory performance, and the cost of achieving target performance and fine-tuning may not match. Based on the Baidu STI dataset, we study the upper bound of the performance that classical information retrieval methods can achieve under a specific business, and compare it with the cost and performance of the participating team based on LLM. This paper gives an insight into the potential of classical computational linguistics algorithms, and which can help decision-makers make reasonable choices for LLM and low-cost methods in business R&amp;amp;D.
SIGNAL TRANSMISSION OPTIMIZATION MOVING-VECTOR CLASSIFICATION MPEG STANDARD VIDEO CODING

The rise of useful multimedia applications has sparked a rush to perfect video compression methods. Over the last decade, there has been a lot of interest in using motion-compensated frame interpolation (MCFI) methods to boost video frame rate during playback in both the academic and consumer electronics sectors. Interpolation refers to the process of defining the values of a function at places that are not represented by any of the examples. It accomplishes this process by fitting a constant function across the discrete input samples. We based our methodology on the fact that color cameras function similarly to the human eye. We have been used to black and white photographs, yet the real world is full of color. Grayscale images were the sole option until the advent of color monitors and cameras. Energy assessment calls for photo capture using a camera. Light's electromagnetic waves provide the observable energy in this scheme. For motion-compensated video vector frame interpolation or frame rate up-conversion and also Phase frame interpolation Method, the authors present a novel approach with low vector dispensation method in the conclusion. Using optical flow or other conventional methods, it is necessary to have precise pixel correspondences between pictures in order to compute interpolated frames in a video series. We provide a practical replacement by capitalizing on advances in phase-based approaches, which use the phase shift of individual pixels to express motion. In challenging interpolation conditions, such as major appearance changes, flow-based algorithms frequently create severe visual distortions, yet our solution fails gently. Our approach is particularly useful for retiming and interpolating frames in high-resolution video shot at a high frame rate. Estimating motion for the purpose of improving the quality of the decompressed video requires more research on the nature of the compression problem. a novel way to estimating motion that is analogous to full-search 3ss,4ss, block-matching, and parametric techniques. The primary focus will be research into both error-free compression and the development of novel motion estimation algorithms. The results of the research indicate that the suggested system has the potential to increase visual superiority and is also robust, especially when applied to video sequences with quick movements and challenging portions. By combining phase-based and conventional Lagrangian, which is utilized to get better quality results and deal with variations in light more gracefully.
ARtistic FURNITURE

Abstract - Augmented reality (AR) technology has potential to revolutionize the way we interact with and visualize the world around us. One area where this technology has great potential is in the field of furniture design and home decor. The goal of the Augmented Reality Furniture project is to develop a platform which allows users to virtually preview and interact with furniture in their own home environments using AR technology. Using a smartphone or other AR-enabled device, users will be able to see how different furniture pieces will look and fit in their own living spaces before making a purchase. The project will involve the use of 3D models of a variety of furniture items, as well as the development of AR software that can accurately place and scale these models in the user's environment. The platform will also include a user-friendly interface for browsing and selecting different furniture items, as well as tools for customizing and arranging the items in the virtual space. [4] Key Words: Augmented-reality, Furniture, AR core, AR Application, 3D model, AR technology.
Supplementary Table 1 from Strong Evidence of a Genetic Determinant for Mammographic Density, a Major Risk Factor for Breast Cancer

Supplementary Table 1 from Strong Evidence of a Genetic Determinant for Mammographic Density, a Major Risk Factor for Breast Cancer
IMPLEMENTATION OF THE COMMUNITY SERVICE PROGRAM IN IMPROVING STUDENTS' ENGLISH LITERACY AND NUMERACY SKILLS AT SDN 091409 SARIMATONDANG

The Community Service Program (PKM) aims to improve students' English literacy and numeracy skills and develop a culture of English literacy and numeracy within SD Negeri 091409 Sarimatondang. This form of community service activity is carried out in 3 states of activity: Literacy and Numeracy Learning Activities in English "English Day," Additional Lesson Hours Activities, and Repair of library archives. This activity begins February 6 – 25, 2023. The subjects of this community service are all grade 1 – 6 students at SD Negeri 091409 Sarimatondang. The method of implementing this activity consists of several stages: Preparation, Implementation, and Evaluation. The results of community service obtained are the success of increasing students' literacy, and numeracy abilities, and students have been able to implement it as a new culture in the school environment.
Relationship between SW · AI Education in High School and University

This paper investigates the relationship between the information curriculum conducted in high school and the basic artificial intelligence curriculum from the second semester of 2021 and the required SW and AI courses currently being conducted at universities.Through the results, I would like to propose the direction of the university's SW•AI education content.First of all, the educational contents of the two subjects, 'Artificial Intelligence Basic' and 'Information' revised in 2015, are summarized as of the first semester of 2022.In addition, we would like to conduct qualitative research on the contents of the class by conducting expert interviews with professors who have lectured on the contents of SW•AI education.Through this study, the contents of 'Information' and 'Artificial Intelligence Basic' education learned in high school and the contents of liberal arts subjects in college are analyzed and the direction of SW•AI education contents is proposed.
The Effect of Pre-service Teachers' Digital Literacy Attitude on Digital Literacy Competency

The purpose of this study is to analyze the effect of digital literacy attitude on digital literacy competency for preservice teachers.The data of 81 pre-service teachers who faithfully responded to the questionnaire were used for analysis, targeting 161 pre-service teachers who are enrolled in the A graduate school of education located in the metropolitan area.The results of descriptive statistics, correlation analysis, and regression analysis are as follows.First, the average score of digital literacy attitude was higher than the average score of digital literacy competency.Second, there was a positive correlation between digital literacy attitude and digital literacy competency, and among the sub-variables of digital literacy attitude, self-regulation showed the highest correlation with digital literacy competency.Third, among the sub-variables of digital literacy, the relative influence was high in the order of self-control, self-efficacy, and participation.Based on these results, specific efforts and implications for improving digital literacy attitudes for pre-service teachers were discussed.
Learning Styles-Based Personalized Recommender System Design: Focusing on AI-Embedded Language Learning Applications

Although educational mobile applications (apps) integrated with artificial intelligence (AI) functions are expanding recently, it becomes more difficult for learners to find and consume mobile apps suitable for them amidst the flood of resources.Focusing on AI-embedded language learning apps, this study aims to design and evaluate the effect of a personalized recommender system based on learning styles.The recommender system proposed in this study was developed through three stages based on the learning style theory by Felder and Silverman.In the first step 'database construction', we selected 16 language apps with AI functions, and collected data on learning styles and learners' satisfaction with apps from 40 adult learners.In the second step 'recommendation mechanism development', we used the collaborative filtering method to cluster learning styles and to derive the recommendation mechanism.In the last step 'performance evaluation', we calculated the mean absolute error (MAE) between the app satisfaction data of actual learners and the predicted satisfaction scores.The results indicated that the learning styles-based recommendation mechanism proposed in this study showed a prediction accuracy rate of 83.33% for learners' app satisfaction.The contribution of this study is that it proposes a new recommendation method based on the learning styles in relation to the personalized recommendation system for education, which has been lacking in research.
Analysis of Relationship between Avoidant Attachment, Growth Mindset, and College Environment Adaptation of College Students

This study investigates the relationship between avoidant attachment, growth mindset, and adaptation to college environments of college students to verify the mediating effect of growth mindset.For this study, a survey was conducted among 260 students enrolled in colleges in Seoul and nearby capital area were surveyed.The collected data were analyzed using the SPSS 25.0 program.As a result, there was no gender difference in avoidant attachment, growth mindset, and adaptation to college environments, but there were significant differences depending on their grades and majors.Second, significant correlations between avoidant attachment, growth mindset, and adaptation to college environments were observed.Avoidant attachment had a significant effect on growth mindset and adaptation to college environments.Lastly, growth mindset was found to partially mediate the relationship between avoidant attachment and adaptation to college environments.These results suggest that college students' avoidant attachment not only directly affects adaptation to college environments, but also indirectly affects it through growth mindset.This result of this study indicates that the growth mindset is a buffering factor for avoidant attachment.This study suggests the possibility of mediating both adult avoidance attachment and adaptation to college environments of college students in terms of growth mindset, which derives from willingness to change their abilities and personalities.
Influencing Factors on Counseling Intention for Obesity Treatment

This study explored the mean differnces in obesity risk knowledge (ORK) level and willingness to communicate about health (WTCH) between obese and non-obese people.Specifically, this study verified the relationship between obesity ORK, WTCH, and the intention to seek counseling from doctors.The results revealed a significant difference in WTCH.There was no significant difference in the level of ORK.For obese people's intention to seek counseling from doctors, it was found that disease status, the WTCH and ORK had significant effects on the counseling intention for dietary treatment.The result showed that being of age in their 20s and 30s, disease status, WTCH were the significant predictors on their counseling intention for excercise treatment.Regarding the counseling intention for behavioral treatment, only disease status and WTCH were analyzed as significant predictors.This study suggests to enhance the level of WTCH and ORK in order to increase obese people's counseling intention.
Forecasting of Future Technology Based on the Law of Technology Evolution and Scenario Planning : Focused on Telemedicine &amp; Digital Health Service

After the COVID-19 outbreak, expectations and interest in telemedicine and digital healthcare are increasing in Korea.As a regulated industry, telemedicine has many impacts and uncertainties, which has a hard time forecasting in future technologies and designing R&D roadmaps.Scenario planning is widely used as a thinking tool to make good decisions in the uncertain future.However, it has a fatal disadvantage that concreteness cannot be guaranteed in future technology prospects.In this study, after deriving scenarios, TRIZ's law of technology evolution was applied to provide specific forecasts for possible future technologies for each scenario.As a result, a total of four scenarios were derived depending on whether or not telemedicine would be legalized, and it was possible to increase the detailed specificity for each scenario.It is expected that the framework of future technology prospects in this study will provide meaningful insights into the future prospects for promising technologies and establishment of R&D strategies.
Local Community Adaptation to Flood Disaster In Soppeng District

Every year floods occur in Soppeng regency. The high rainfall and land conversion become the main cause of the flood. It affects the society both in their life and their welfare. The community has carried out various responses and adaptation strategies to deal with the risk of future floods. Thus, this study aims to determine the local adaptation strategy of the community in dealing with flood disasters in Soppeng Regency. The method in this study was carried out using in-depth interviews and observations in flooded areas. The community participation comes from socially inductive research that produces descriptive results. The result of this research shows that the lack of coordination between the community, government, and private parties (NGOs) leads to higher vulnerability. In addition, the adaptation model carried out by the community begins at the household level, especially the house-building model in the form of a stilt house. It consists of two parts namely, the lower and the upper. The lower part is usually used to deviate goods, agricultural materials, and vehicles, while the upper part is used as a place to rest and carry out daily routines such as cooking, chatting with neighbors, receiving guests, and so on. The act of adaptation in groups is by raising the position of the house to the second floor, building a small embankment, and moving household appliances to a higher place.