Reviewerly/exHarmony huggingface dataset:
Deep Learning Mammography Classification with a Small Set of Data

Breast cancer is one of the leading causes of mortality among women. In addition, 1 in 8 women and 1 in 833 men will be diagnosed with breast cancer in 2022. The detection of breast cancer can not only lower treatment costs but also increase survival rates. Due to increased cancer awareness, more women are undergoing breast cancer screening, leading to more cases being diagnosed worldwide, but doctors' ability to analyze these images is limited. As a result, they get overloaded leading to misinterpretations. The advent of computer-aided diagnosis (CAD) minimized man's involvement and achieved good results. CAD helps medical doctors automatically detect and analyze abnormalities found in the breast. Such abnormalities may be benign or malignant tumors.The goal of this study is to evaluate the effectiveness of using seven layers to classify breast cancer as either benign or malignant using mammograms.The open-source MIAS dataset of 322 images was used for our study, of which 207 were normal images and 115 were abnormal images. The proposed CNN model convolves an image into seven layers that extract features from the input images, and these features are used to classify breast cancer as malignant or benign.The proposed CNN used a limited data set and achieved the best result compared to previous work. The method achieved results with a 0.39% loss, 99.89% accuracy, 99.85% precision, 99.89% recall, 99.87% F1-score, and an area under the curve noted to be 100.0%.CNN uses a small amount of data to determine abnormalities; the method will assist a medical doctor in determining whether or not a specific patient has cancer.
Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset

While recent pre-trained transformer-based models can perform named entity recognition (NER) with great accuracy, their limited range remains an issue when applied to long documents such as whole novels. To alleviate this issue, a solution is to retrieve relevant context at the document level. Unfortunately, the lack of supervision for such a task means one has to settle for unsupervised approaches. Instead, we propose to generate a synthetic context retrieval training dataset using Alpaca, an instruction-tuned large language model (LLM). Using this dataset, we train a neural context retriever based on a BERT model that is able to find relevant context for NER. We show that our method outperforms several retrieval baselines for the NER task on an English literary dataset composed of the first chapter of 40 books.
PoseVEC: Authoring Adaptive Pose-aware Effects using Visual Programming and Demonstrations

Pose-aware visual effects where graphics assets and animations are rendered reactively to the human pose have become increasingly popular, appearing on mobile devices, the web, or even head-mounted displays like AR glasses. Yet, creating such effects still remains difficult for novices. In a traditional video editing workflow, a creator could utilize keyframes to create expressive but non-adaptive results which cannot be reused for other videos. Alternatively, programming-based approaches allow users to develop interactive effects, but are cumbersome for users to quickly express their creative intents. In this work, we propose a lightweight visual programming workflow for authoring adaptive and expressive pose effects. By combining a programming by demonstration paradigm with visual programming, we simplify three key tasks in the authoring process: creating pose triggers, designing animation parameters, and rendering. We evaluated our system with a qualitative user study and a replicated example study, finding that all participants can create effects efficiently.
Human Motion Abnormal Posture Monitoring Method based on Intelligent Bracelet

In order to overcome the problem of low classification accuracy and monitoring accuracy of sample data in traditional abnormal posture monitoring methods, a new human motion abnormal posture monitoring method based on Intelligent bracelet is proposed in this paper. Firstly, the structure of smart Bracelet sensor is analyzed, and Gabor transform method is used to collect human motion data. Secondly, utilizing the data acquisition findings, the formation of the abnormal posture monitoring model for human motion is established, and the outcome of the model represents the monitoring result of abnormal postures in human motion. Finally, a comparative verification experiment is carried out. The experimental results show that compared with the traditional monitoring methods, the sample data classification accuracy of this method is higher, and it can accurately monitor the abnormal posture of human movement.
Research on Software Development Based on Low-Code Technology

Because low code needs to write a small amount of code, it is basically built with visualization tools. Therefore, the developed platform can greatly reduce the threshold for developers to build a system and shorten the system development cycle. The purpose of this article is to research software development based on low code technology. Based on the analysis of the principles of low-code software development, the mechanism of low-code programming is briefly designed, and the B/S architecture technology is studied. The whole system adopts the three-tier architecture as the overall framework for development. At the same time, the system generation model is divided into detailed functional modules, and a general functional design is carried out. Finally, the system implementation and related tests are carried out according to the detailed design of the system. The platform implements two subsystems: the community-side elderly information management system and the human resource management system. The construction time of the community-side elderly information management system using low-code technology is 16 days less than the construction time of the traditional method. The test results meet the requirements in the demand analysis and reach the online standard.
Mapping Capability of Hyperspectral Information on Dominant Arctic Vegetation Species Using Terrestrial Hyperspectral Imagery

Hyperspectral remote sensing serves as a valuable tool to monitor the rapid changes in the Arctic vegetation ecosystem induced by global warming. Nevertheless, studies on mapping Arctic vegetation using hyperspectral information have been limited due to data acquisition challenges in the Arctic region. To overcome this, we collected hyperspectral images measured in the Adventdalen Valley, Svalbard. We constructed labeled datasets by extracting pixel data from ground-based hyperspectral images and evaluated the classification performance of three machine learning-based classifiers, random forest (RF), support vector machine (SVM), and one-dimensional convolutional neural network (1D-CNN). The 1D-CNN exhibited the highest statistical accuracies of 0.97 and 0.93 for two independent test sets and generated the most precise vegetation map. Furthermore, we developed a spectral library using mean spectra from the labeled datasets, which serves as a fundamental reference for understanding the spectral characteristics of Arctic vegetation. Our approaches and findings have broad applicability across various remote sensing datasets, facilitating the effective monitoring of changes in Arctic vegetation as a consequence of climate change.
Prospective Comparison of SURF and Binary Keypoint Descriptors for Fast Hyperspectral Remote Sensing Registration

Image registration is a crucial process that involves determining the geometric transformation required to align multiple images. It plays a vital role in various remote sensing image processing tasks that involve analyzing changes among images. To enable real-time response, it is essential to have computationally efficient registration algorithms, especially when dealing with large datasets as is the case of hyperspectral images. This article presents a comparative analysis of two descriptors used to characterize local features of images prior to their matching and registration. The objective is to analyze whether the LATCH binary keypoint descriptor, which produces compact descriptors, provides similar results to the gradient-based SURF descriptor in terms of execution time and registration precision. To obtain the best computational performance, multithreaded implementations using OpenMP have been proposed. LATCH has proven to be 7× faster and as reliable as SURF in terms of accuracy on scale differences of up to 1.2×.
Challenges and Lessons Learned for Conducting Usability Evaluations in Applied Military Research Environments

Conducting usability evaluations in applied military environments is notably different from traditional usability testing, as such environments pose restrictions on when, where, and how testing is conducted. In addition, specialized experience needed from military personnel pose challenges to recruitment and obtaining sufficient time with representative end users. This paper will discuss some challenges and lessons learned for consideration before, during, and after usability assessment data collection. Main recommendations include: recruiting experts qualified to provide feedback that aligns with the purpose of the evaluation; developing a flexible research protocol to allow for the maximum participation possible from any and all involved; and conducting domain research to supplement the researchers’ own knowledge base to minimize reliance on SMEs. Finally, it is important to carefully document not only results, but knowledge and experience gained from specific efforts in order to assist future researchers in conducting work with similar systems.
Semantic web ecosystem based on CVE (NVD, CPE), CWE and CAPEC

CVE (NVD, CPE), CWE and CAPEC are databases in the Cybersecurity area sponsored and maintained by the US government. These are lists (databases) organized in taxonomies where it is appropriate. They contain information about known vulnerabilities, weaknesses and attacks. CVE (NVD, CPE), CWE and CAPEC are the corner stone in many cybersecurity tools.The usage of traditional database systems for the tasks in the cybersecurity require extended knowledge and skills in querying for identification of vulnerabilities, weaknesses and attacks. CVE (NVD, CPE), CWE and CAPEC contain hidden facts and relationships (knowledge) buried in the data. This knowledge can be effectively accessed by the Semantic web tools.The paper presents an approach for transition to the Semantic web of above-mentioned databases. The approach is presented in illustrative way. This means without duplication with information about the contents available for CVE (NVD, CPE), CWE and CAPEC.
Penerapan Data Mining Menggunakan Algoritma K-Means Untuk Analisa Penjualan Parfume

Abstract?IM Perfume Rantauprapat store is a shop that offers various types of perfume scents under the IM brand. Even though it provides a wide range of choices, not all types of perfume sell quickly, some are in demand and some are less desirable. Data on sales, purchases and expenses at the store is irregular, so that the data only functions as an archive without being used for developing marketing strategies. The data that has been collected should be used as a decision-making system to solve business problems. To achieve this, the authors designed a data mining application in this study with the hope of providing maximum and effective results in analyzing perfume sales at the IM Parfume Rantauprapat store. The application of Data Mining with the K-Means Algorithm is proven to provide the best analysis and be a solution in developing the perfume business. Through clustering modeling with the K-Means algorithm and by dividing the number of clusters into 3, rapidminer succeeded in forming three clusters, where cluster 1 consisted of 9 products, cluster 2 had 3 products, and cluster 3 had 13 products out of a total of 25 product items observed
Sistem Pendukung Keputusan Penerapan Metode OCRA dalam Seleksi Penerima Bantuan Penduduk Miskin dengan Pembobotan ROC

The research conducted was the selection of recipients of assistance to the poor in Perk Village. Old Country in the district. District Downstream Labuhan Batu, namely BLT-DD or Village Fund Direct Cash Assistance. The process of distributing aid to the poor is still done conventionally in the selection of recipients of assistance to the poor, so the process of distributing funds is very long. This study applies a Decision Support System (SPK) which is used as a system that can facilitate the selection process for recipients of assistance to the poor. In the selection process for recipients of assistance, the poor must meet 5 criteria, namely Chronic Disease, Income, Occupation, Number of Dependents, and House Status. Thus, a system that can make decisions is needed, namely SPK by applying the ROC and OCRA methods to get the weight value of each criterion and the preference value of the best alternative, namely alternative A4 on behalf of Suadi with a preference value of 1.3757
Analisis Metode Backpropagation Dalam Memprediksi Jumlah Produksi Daging Kambing di Indonesia

A science that has always developed rapidly until now is artificial neural networks. A computational science that works like the human nervous system is an artificial neural network. Artificial neural networks with the backpropagation method can make a prediction on data. In this article, a prediction will be made on the amount of goat production in Indonesia. Goats are one of the livestock that can produce nutritious meat. The lack of goat meat will cause the price of goat meat to rise. Producing enough goat meat helps stabilize the price of meat, but if goat meat production is less than demand, it will lead to price increases. Therefore, looking at the problems above, this study aims to predict goat meat so that in the future it can know how much goat meat must be predicted by processing data first and then being used as input in predicting the amount of goat meat production. Prediction is one way to estimate future demand. Avoiding the lack of meat availability, by predicting the amount of goat meat produced in such a way that there is no scarcity of goat meat and fluctuations in the price of goat meat in the market. Basic methods and data are required to make predictions. In this study, data was obtained from BPS Indonesia in the livestock section using data from 2001-2021 as training data and 2002-2022 as test data. The method applied in this article is the backpropagation algorithm. This article applies 5 network architectures implemented in the mathlab application. The architecture used in this article is 20-25-1 with a Mean Squred Error testing 0.00447765, in 20-30-1 architecture produces Mean Squred Error 0.00300466, in 20-35-1 architecture produces 0.00426823, in 20-37-1 architecture produces 0.00357757. Based on the best architecture produced in this study, the 20-15-1 architecture with 90% accuracy with a Mean Squared Error testing 0.00262384 at epoch 27915 Iterations. Thus it can be concluded that the backpropagation algorithm can provide good accuracy in the prediction process. With this research, the livestock industry can utilize it as one of the materials to predict goat meat in the future
Shadow-Imaging-Based Triangulation Approach for Tool Deflection Measurement

As incrementally formed sheets show large geometric deviations resulting from the deflection of the forming tool, an in-process measurement of the tool tip position is required. In order to cover a measuring volume of 2.0 m × 1.0 m × 0.2 m and to achieve measuring uncertainties of less than 50 µm, a multi-sensor system based on triangulation is realized. Each shadow imaging sensor in the multi-sensor system evaluates the direction vector to an LED attached to the tool, and the three-dimensional position of the LED is then determined from the combination of two sensors. Experimental results show that the angle of view from the sensor to the LED limits both the measurement range and the measurement uncertainty. The measurement uncertainty is dominated by systematic deviations, but these can be compensated, so that the measurement uncertainty required for measuring the tool tip position in the ISF is achieved.
On Validating a Generic Video-Based Blink Detection System for Cognitive Load Detection

This work aims to validate using a video-based blink detection system for detecting changes in cognitive load. Participants completed a cognitive task with increasing levels of difficulty. Blink rate was recorded via both our video-based system and a scientific-grade eye-tracker. Results showed no differences in the blink rates recorded by the two systems. However, while strong evidence was found that the blink rate recorded through the eye-tracker increased under greater cognitive task demand, no such pattern was observed for the output from the video-based system. Our findings show that the overall performance of the video-based system was comparable with that of the eye-tracker's, but its sensitivity in detecting changes in cognitive load was inferior to that of its counterpart's.
Developing COVID-19 digital comic by using flip pdf professional as a learning media for tenth grade of senior high school

The development of learning media in the form of COVID-19 digital comics using Flip PDF Professional is based on the background that the learning media used by SMA Negeri 2 Sekampung teachers are still in the form of textbooks and powerpoints, and there are still many students who do not understand the virus and educational material related to COVID-19, so it is necessary to develop a learning media innovation in the form of COVID-19 digital comics to make learning fun and not boring by combining viral material. Comics were chosen because its presentations are interesting, simple and have a storyline which contains complex messages and information. It can be presented briefly and equipped with communicative language that is dialogic and easy to understand by readers. This study aims to develop a COVID-19 digital comic learning media for tenth grade senior high school students. The method used in this study is research and development with the ADDIE development model which consists of five stages, namely analyze, design, development, implementation and evaluation. The results of product validation developed have been declared "very feasible" to be tested by validators, namely material expert validators and media experts, while the results of product trials developed have been declared "very good" by teachers and students, so that learning media in the form of digital comics can be used as an alternative learning media in the classroom.
Time of day and chronotype in the assessment of cognitive functions

AMA Wiłkość-Dębczyńska M, Liberacka-Dwojak M. Time of day and chronotype in the assessment of cognitive functions. Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii. 2023. doi:10.5114/ppn.2023.132032. APA Wiłkość-Dębczyńska, M., & Liberacka-Dwojak, M. (2023). Time of day and chronotype in the assessment of cognitive functions. Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii. https://doi.org/10.5114/ppn.2023.132032 Chicago Wiłkość-Dębczyńska, Monika, and Magdalena Liberacka-Dwojak. 2023. "Time of day and chronotype in the assessment of cognitive functions". Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii. doi:10.5114/ppn.2023.132032. Harvard Wiłkość-Dębczyńska, M., and Liberacka-Dwojak, M. (2023). Time of day and chronotype in the assessment of cognitive functions. Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii. https://doi.org/10.5114/ppn.2023.132032 MLA Wiłkość-Dębczyńska, Monika et al. "Time of day and chronotype in the assessment of cognitive functions." Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii, 2023. doi:10.5114/ppn.2023.132032. Vancouver Wiłkość-Dębczyńska M, Liberacka-Dwojak M. Time of day and chronotype in the assessment of cognitive functions. Advances in Psychiatry and Neurology/Postępy Psychiatrii i Neurologii. 2023. doi:10.5114/ppn.2023.132032.
Analysis of a Second-Order Factor Model of Attitude Toward Mathematics

Based on a multidimensional definition of attitude, this study tested a second-order factor model of attitude toward mathematics using data from 1960 7th-grade students. This study found that students’ attitudes toward mathematics are identified with cognitive, affective, and behavioral factors. This study confirmed three hypotheses: the cognitive component of attitude toward mathematics is explained by students’ confidence in learning mathematics and their beliefs about the usefulness and importance of mathematics; the affective component of attitude toward mathematics is explained by their liking of mathematics and mathematics anxiety; and the behavioral component of attitude toward mathematics is explained by learner behaviors toward mathematics and the time they spent on mathematics at home. Moreover, the final model obtained through factor analysis revealed strong relationships between the three components of attitude and attitude toward mathematics.
THE METHODOLOGY OF USING THE EUROPEAN OPEN SCIENCE CLOUD IN THE PROCESS OF EDUCATION AND PROFESSIONAL DEVELOPMENT OF TEACHERS

The statement of basic materials. The structure of the methodology for using the European Open Science Cloud in the process of teaching and professional development of teachers consists of the following components: target (goal, target group), content, technological (methods, forms and means of training) and effective. An approximate plan of training sessions is given. The main steps of conducting the master class “Organization of training using European Open Science Cloud” are described.
Professional Development of Excellent Physics Teachers

Excellent teacher is a representative in the teacher group and is the decisive force to boost innovation and development of education and teaching. The level of teachers’ professional development can be characterized by the theory of teacher professional development status. The quality of teaching and research papers published by teachers can directly reflect the professional development level of teachers. Based on the bibliometric analysis of CNKI in 2010-2019, the situation of teaching and research papers published by middle school physics teachers in Shandong Province in the past ten years was analyzed, and visual analysis was performed using Citespace software. The excellent senior high school physics teachers’ professional growth actuality is surveyed by the self-made “Questionnaire of Excellent Senior High School Physics Teachers’ Professional Growth”, and by the survey, the total status and the professional growth of excellent senior high school physics teachers could be grasped.