Reviewerly/exHarmony huggingface dataset:
A study on the sample extraction for a quality inspection tool and operator training

Recently, almost all the quality inspection work in the manufacturing industry is automated. However, there are many products for which inspection work cannot be automated. Since the tip of a rotating tool (Diamond bar) for dental care is attached with diamond particles, all parts are slightly different. In this study, we will develop a tool to train the inspection work and to replace manual inspection. As basic research, we checked the training effect by training tool and constructed a parts model using neural network and convolutional neural network. This model was evaluated using the training result.
Applying Conversation Method and Self-Confidence and Its Effect to Learning Achievement

English in elementary schools in the implementation of learning is still not effective until now. Actually, there are many problems related to the implementation of the development of English learning. In fact, the development of learning English itself is very beneficial for elementary school students, so they can improve their abilities. The research method used in this classroom action research is an experimental method with a Factorial Group Design (FGD) design with 2 categories. The data analysis technique in this study used two way ANOVA, with the help of computers, namely: SPSS for Windows version 24 program. The results of the study can be concluded that there are differences in English learning outcomes between learning by conversation methods and conventional methods for grade 4 students. Madrasah Ibtidaiyah Nu Sumber Agung. Then, there is no difference in English learning outcomes between grade 4 students of Madrasah Ibtidaiyah Nu Sumber Agung with high self-confidence and low self-confidence. And the last, there is no interaction of methods with self-confidence on learning achievement in English.
Genre Familiarity Correlation-Based Recommender Algorithm for New User Cold Start Problem

The advancement of web services paved the way to the accumulation of a tremendous amount of information into the world wide web. The huge pile of information makes it hard for the user to get the required information at the right time. Therefore, to get the right item, recommender systems are emphasized. Recommender algorithms generally act on the user information to render recommendations. In this scenario, when a new user enters the system, it fails in rendering recommendation due to unavailability of user information, resulting in a new user problem. So, in this paper, a movie recommender algorithm is constructed to address the prevailing new user cold start problem by utilizing only movie genres. Unlike other techniques, in the proposed work, familiarity of each movie genre is considered to compute the genre significance value. Based on genre significance value, genre similarity is correlated to render recommendations to a new user. The evaluation of the proposed recommender algorithm on real-world datasets shows that the algorithm performs better than the other similar approaches.
Comparative Analysis of Heart Attack Readmission Using Machine Learning

A Hospital readmission happens when a patient who has been passed on from a clinical office is readmitted inside a given time span. Readmission rates are overall being utilized as a consistency benchmark for success structures and appropriately factor in clinical thought study. If all else fails, a higher readmission rate recommends that treatment during earlier hospitalizations was unsuccessful. According to the Patient Security and Reasonable Idea Act (ACA) of 2010, which repels success structures with higher-than-anticipated readmission rates through the Emergency office Readmission Reduction Program, clinical focus readmission rates were authoritatively connected with partition choices for the Organizations for Government clinical thought and Medicaid Associations (CMS) first. Second Phenomenal infringement have happened since the origin of this penalty. Programmes that have been started to diminish clinical focus readmissions. These practices join the Local Idea Move Program, Oneself principle at Home Show Program, and the Packaged Remuneration for Care Update Initiative. Spontaneous readmissions an issue with the underlying consideration patients got. At the point when your medical clinic or medical services office has a high readmissions rate, it can demonstrate an assortment of issues in both therapy viability and nature of care. High rates likewise sway emergency clinic profitability and the accessibility of beds for new patients. Lessening emergency clinic readmissions is central for bringing down operational overheads. It is additionally a decent pointer of how different measures are functioning when utilized related to starting treatment KPIs. A for the most part used quality degree of emergency office treatment for explicit conditions adds to post-release, clinical adequacy, and recovery issues. This paper presents a comparative analysis between various machine learning algorithms for predicting heart attack readmissions. Various research papers were consulted to perform the analysis. Those procedures are KNN, Decision Trees(ID3),Gaussian Innocent Bayes, Logistic Regression, Random Timberlands and Backing Vector Machines (SVM).
Computing and Assessment of Discrete Angle Positions for Optimizing the Solar Energy Harvesting for Urban Sustainable Development

This paper proposes the computation and assessment of optimal tilt and azimuth angles for a receiving surface, using a mathematical model developed at the University of Tomsk, Russia. The model was validated and analyzed for the Nuevo León State, Northeast Mexico, utilizing a set of metrics, comparing against satellite data from NASA. A point of interest in the city of Monterrey was analyzed to identify orientation patterns throughout the year for an optimal solar energy gathering. The aim is providing the best orientation tilt angles for photovoltaic or solar thermal panels without tracking systems. In addition, this analysis is proposed as a tool to achieve optimal performance in sustainable urban development in the region. Based on the findings, a set of optimal tilt and azimuth surface angles are proposed for the analyzed coordinates. The aim is to identify the optimal performance to obtain the maximum solar irradiation possible over the year for solar projects in the region. The results show that the model can be used as a tool to accelerate decision making in the design of solar harvesting surfaces and allows the design of discrete tracking systems with an increase in solar energy harvesting above 5% annually.
Quizizz as Fun Multiplying Learning Media in English Lecturing Process

Indonesia has a great opportunity to use technological developments in the current 4th industrial revolution. The opportunity is based on the fact that from the total population of Indonesia, more than half of them are active internet users. The largest group of internet users in Indonesia is at the age of students, these groups of students are in the middle to college student level. To occupy the need for technology-based learning, it is necessary to modify and have some innovations to various learning tools in learning activities. This qualitative descriptive research will discuss the use of technology-based learning media with Quizizz as an application in English learning lecturing process. This study involved students of the 1st level of accounting study program who had passed English Class at the Universitas Islam Kadiri-Kediri (UNISKA). In general, the results of the study indicate that English lecturing process using the Quizizz is a fun and effective activity so that it is expected to continue to be used in further lecturing activities.
Application Analysis of Image Enhancement Method in Deep Learning Image Recognition Scene

As the most important form of unstructured data in the era of big data, image has many advantages, such as large amount of information and easy to be accepted by everyone. In contrast to sound and text, images contain a lot of potentially rich information and are presented in the most direct way. At present, medical image is widely used in clinical examination, and has become an important means to assist doctors in diagnosis, disease evaluation and treatment. In recent decades, the development of computer technology has greatly improved the imaging speed and resolution of medical imaging equipment. However, the medical image is different from the ordinary image in daily life, which has the characteristics of multimodal, uncertainty and data fuzziness. This paper studies deep learning medical image recognition based on image enhancement.
An Open-Source Library of 2D-GMM-HMM Based on Kaldi Toolkit and Its Application to Handwritten Chinese Character Recognition

As an open source toolkit based on 1D-HMM framework, Kaldi toolkit is widely used in many signal processing tasks. However, when dealing with complex spatial structures, e.g. in image related tasks, 2D-HMM is more suitable since it allows free transition between hidden states in both horizontal and vertical directions. Although 2D-HMM framework has been proposed for years, there is still a lack of efficient open source toolkit for further research due to its complexity. In this paper we present a highly efficient code library of 2D-GMM-HMM based on Kaldi toolkit with implementation details. As a demonstration of its effectiveness, we apply 2D-GMM-HMM to handwritten Chinese character recognition (HCCR) task. The experiments on a 50-class HCCR task have proved that the 2D-GMM-HMM system has obvious advantages over the 1D-GMM-HMM system in terms of recognition accuracy and modeling precision. Moreover, the visual analysis shows that 2D-GMM-HMM can well segment the Chinese characters into basic components such as radicals via the hidden states in both horizontal and vertical directions while 1D-GMM-HMM can only conduct the segmentation in the horizontal direction. The project code of 2D-GMM-HMM library and its recipe on HCCR is publicly available at https://github.com/jfma-USTC/2DHMM.
A Quasi-Experiment to Investigating the Impact of the Strategy Design Pattern on Maintainability

GoF Design Patterns (DPs) are optimal solutions for several recurring problems in software development. As maintenance accounts for a massive amount of development costs, understanding the impact of DPs on maintainability is key to decide whether their benefits outweigh their costs. To test the hypothesis that the Strategy DP impacts maintainability, we carried out a quasi-experiment with 19 final-year undergraduate students. Participants performed enhancement tasks in two identical versions of Java classes that differed only in that a version implemented the Strategy DP whereas another version did not. As recommended in the literature, we used effectiveness as a representative proxy of maintainability and correctness as a measure of effectiveness. We found a statistically significant correctness change between treatments. Moreover, the effect size shows that a participant is 47.7 percentage points more likely to succeed when the Strategy DP is not used. Conversely, a participant is 5.5 times more likely to fail when the DP is introduced. Unlike most related studies, our findings suggest that the Strategy DP may reduce software maintainability although these findings need to be confirmed by independent and extended replications.
Detection of Moving Objects in a Metro Rail CCTV Video Using YOLO Object Detection Models

Most of the people in India give less care to safety rules. As we all know passengers are not allowed to cross the yellow line in metro stations, there arises a need for an alert system. The proposed system focuses on the detection of moving objects from stationary surveillance cameras in metro stations and alerts when an object crosses the yellow line. For the detection of an object, YOLO object detection models are used. To make it simple, pre-trained models are used for object detection in this project. The processing steps of motion detection for video surveillance include detection of motion, classification and object detection, behavior understanding, and activity recognition. The system works in real time with more accuracy than previous models available. The color detection method is used for detecting the yellow lines in the metro stations and since the camera is static, line detection is performed only on the first frame which further reduces the time of detection of objects. An emergency alert is given to the authorities/ passengers in the form of sound signals once the object crosses the yellow line in the stations.
Compiling Quantamorphisms for the IBM Q Experience

Based on the connection between the categorical derivation of classical programs from specifications and a category-theoretic approach to quantum information, this paper contributes to extending the laws of classical program algebra to quantum programming. This aims at building <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">correct-by-construction</i> quantum circuits to be deployed on quantum devices such as those available through the IBM Q Experience. Reversibility is ensured by minimal complements. Such complementation is extended inductively to encompass catamorphisms on lists (vulgo folds), giving rise to the corresponding recursion scheme in reversible computation. The same idea is then applied to the setting of quantum programming, where computation is expressed by unitary transformations. This yields the notion of ‘quantamorphism’, a structural form of quantum recursion implementing cycles and folds on lists with quantum control flow. By Kleisli correspondence, quantamorphisms can be written as monadic functional programs with quantum parameters. This enables the use of Haskell, a monadic functional programming language, to perform the experimental work. Such calculated quantum programs prepared in Haskell are pushed through Quipper and the Qiskit interface to IBM Q quantum devices. The generated quantum circuits – often quite large – exhibit the predicted behaviour. However, running them on real quantum devices naturally incurs a significant amount of errors. As quantum technology is rapidly evolving, an increase in reliability is likely in the future, allowing for our programs to run more accurately.
New Machine Learning Method for Integrated Subsurface Modeling

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]New Machine Learning Method for Integrated Subsurface ModelingAuthorsWenPaniDCarlosTorres-VerdinMichaelPyrczSee all authors Wen PaniDCorresponding Author• Submitting AuthorUniversity of Texas at AustiniDhttps://orcid.org/0000-0002-0076-6992view email addressThe email was not providedcopy email addressCarlos Torres-VerdinUniversity of Texas at Austinview email addressThe email was not providedcopy email addressMichael PyrczUniversity of Texas at Austinview email addressThe email was not providedcopy email address
Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data

When deploying machine learning models in high-stakes real-world environments such as health care, it is crucial to accurately assess the uncertainty concerning a model's prediction on abnormal inputs. However, there is a scarcity of literature analyzing this problem on medical data, especially on mixed-type tabular data such as Electronic Health Records. We close this gap by presenting a series of tests including a large variety of contemporary uncertainty estimation techniques, in order to determine whether they are able to identify out-of-distribution (OOD) patients. In contrast to previous work, we design tests on realistic and clinically relevant OOD groups, and run experiments on real-world medical data. We find that almost all techniques fail to achieve convincing results, partly disagreeing with earlier findings.
Animation Media on Human Digestive System Material for Fifth-Grade Elementary School Students

The lack of understanding of the learning material has an impact on the results of student learning. Teachers only depend on a single source, textbooks, so it is pretty tricky for students to learn abstract material. In addition, teachers in teaching and learning activities only use conventional methods to focus on the teacher, not the students. This study aims to develop animated video learning media on the human digestive system material for fifth-grade elementary school. Media development in research uses the ADDIE model, which includes the Analyze, Design, Development, Implementation, and Evaluation stages. The data collection method in this study used a questionnaire method and a rating scale instrument—the data analysis technique used qualitative descriptive statistical data analysis and quantitative descriptive statistics. The study result showed the calculation result of the average validity test score; the result is that the percentage obtained in terms of the material as a whole is 95.71%. The percentage of learning material experts is 90-100 with outstanding qualifications and does not need to be revised. Calculating the percentage of the validity test in terms of the overall design is 94.61%. The percentage of learning design experts is 90-100 with outstanding qualifications and does not need to be revised. From the average score in terms of media experts, it was stated that the learning media had outstanding qualifications with 92.93%. Meanwhile, from the practitioner test, it was stated that the learning media had outstanding qualifications with an overall percentage of 100%. So, animated video learning media is feasible to use in learning. This research implies that animated video media can help students in the learning process.
Anonymous Whistleblowing over Authenticated Channels

The goal of anonymous whistleblowing is to publicly disclose a message while at the same time hiding the identity of the sender in a way that even if suspected of being the sender, this cannot be proven. While many solutions to this problem have been proposed over the years, they all require some form of interaction with trusted or non-colluding parties. In this work, we ask whether this is fundamentally inherent. We put forth the notion of anonymous transfer as a primitive allowing to solve this problem without relying on any participating trusted parties. We initiate the theoretical study of this question, and derive negative and positive results on the existence of such a protocol. We refute the feasibility of asymptotically secure anonymous transfer, where the message will be received with overwhelming probability while at the same time the identity of the sender remains hidden with overwhelming probability. On the other hand, resorting to fine-grained cryptography, we provide a heuristic instantiation (assuming ideal obfuscation) which guarantees that the message will be correctly received with overwhelming probability and the identity of the sender leaks with vanishing probability. Our results provide strong foundations for the study of the possibility of anonymous communications through authenticated channels, an intriguing goal which we believe to be of fundamental interest.
Teacher Challenge and Tech Issues in Online Schools

While the study on distance learning and its challenges has proliferated, this study describes how teachers confide in the challenges of teaching when schools close and tech issues during the pandemic.Drawing upon the source of data, including an online survey via telephone interviews and Google meet online video conferencing.This case study examines how (1) Online teaching during COVID-19 school closure; and (2) Teacher responses related to challenges and tech issues during online schooling.Teacher challenges and tech issues within each indicator explored sudden changes that make teachers and students learn to adapt to online school.The findings show that the relationship between teachers and technology become interconnected in a dynamic and complex way when implemented in a pandemic situation.It provides pedagogical implications about teachers need technology support, training, and teacher beliefs to integrate pedagogy and technology in the teaching and learning process.
Convolutional neural network-based face recognition using non-subsampled shearlet transform and histogram of local feature descriptors

&lt;span lang="EN-US"&gt;Face recognition has been using in a variety of applications like preventing retail crime, unlocking phones, smart advertising, finding missing persons, and protecting law enforcement. However, the ability of face recognition techniques reduces substantially because of changes in pose, illumination, and expressions of the individual. In this paper, a novel face recognition approach based on a non-subsampled shearlet transform (NSST), histogram-based local feature descriptors, and a convolutional neural network (CNN) is proposed. Initially, the Viola-Jones algorithm is used for face detection and then the extracted face region is preprocessed by image resizing operation. Then, NSST decomposes the input image into a low and high-frequency component image. The local feature descriptors such as local phase quantization (LPQ), pyramid of histogram of oriented gradients (PHOG), and the proposed CNN are used for extracting features from the low-frequency component of the NSST decomposition. The extracted features are fused to generate the feature vector and classified using support vector machine (SVM). The efficiency of the suggested method is tested on face databases like Olivetti Research Laboratory (ORL), Yale, and &lt;/span&gt;&lt;span lang="IN"&gt;J&lt;/span&gt;&lt;span lang="EN-US"&gt;apanese female facial expression&lt;/span&gt;&lt;span lang="IN"&gt;(&lt;/span&gt;&lt;span lang="EN-US"&gt;JAFFE&lt;/span&gt;&lt;span lang="IN"&gt;)&lt;/span&gt;&lt;span lang="EN-US"&gt;. The experimental outcomes reveal that the suggested face recognition method outperforms some of the state-of-the-art recognition approaches.&lt;/span&gt;
How to Sell NFTs Without Really Trying

Something is happening and we don’t know what it is. Suddenly last summer, the internet went nuts for non-fungible tokens or NFTs. In a matter of months, NFT sales swelled from a sleepy slough of the blockchain to a thundering cataract that shows no sign of slaking. Special NFTs sell for millions of dollars, and some are even securitized. It’s a big business that’s only getting bigger.But no one seems to know why. Objectively, NFTs are useless, meaningless, and worthless. So why are people willing to pay millions of dollars for them, even begging for the opportunity? Maybe it doesn’t matter. If the market says NFTs are valuable, who are we to doubt it? Still, I’m curious. Why are people buying NFTs, and what accounts for their value?