Reviewerly/exHarmony huggingface dataset:
Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning

Abstract In this study, we show a new way for a small unmanned aerial vehicle (UAV) to move around on its own in the plantations of the tree using a single camera only. To avoid running into trees, a control plan was put into place. The detection model looks at the image heights of the trees it finds to figure out how far away they are from the UAV. It then looks at the widths of the image between the trees without any obstacles to finding the largest space. The purpose of this research is to investigate how virtual reality (VR) may improve student engagement and outcomes in the classroom. The emotional consequences of virtual reality on learning, such as motivation and enjoyment, are also explored, making this fascinating research. To investigate virtual reality’s potential as a creative and immersive tool for boosting educational experiences, the study adopts a controlled experimental method. This study’s most significant contributions are the empirical evidence it provides for the efficacy of virtual reality in education, the illumination of the impact VR has on various aspects of learning, and the recommendations it offers to educators on how to make the most of VR in the classroom.
Common Errors and Mistakes 

Sometimes, one gets some error messages or warnings when executing code (write and run a script) or warnings in R.
Using R as a Calculator

To interact with R, commands are typed into the console at the command prompt. If there is no error message, R will do something as instructed in the typed command on the R prompt ( $${>}$$ )R prompt.
Data Mode and Data Structure

As it has already been discussed that R is an object-oriented language and an object in R can be anything such as constants, data structures, functions, and graphs, these objects can be assigned to a variable.
Obtaining and Installing R Language

This chapter will provide a comprehensive overview of the fundamental steps involved in downloading, installing, and updating the R software.
Important Packages

R packages are extensions to the R programming language, and these are collections of R functions, complied code, sample data, and documentation in a standard format.
Functions and R Resources 

The format of a function is that a function name is followed by a set of parentheses that contains the arguments.
R Language: Introduction

This chapter aims to provide an understanding of the R language and the R Foundation.
Classifier-Based Approach for Continuous User Authentication Using Keystroke Dynamics

Authentication at one time is vulnerable to many cyberattacks and is extremely vulnerable to various attacks at user, system, or network level. As a result, users may lose confidential information. Traditionally, authentication is done using password and other means like one-time password. But these traditional methods do authentication only once; after logging in through these, no security will be provided afterward. In scenarios such as a system is left logged in an authenticated session, an intruder can impersonate a legitimate user to gain access into the system. Keystroke is an important factor while evaluating transcript typing tests. Based on how the particular user types particular text, one can extract some features and use it afterward to check whether legitimate user is accessing or impostor is accessing. In this paper, keystroke dynamics is employed to perform continuous authentication.
Engineering Education Program Initiatives at Metaverse School of Engineering in The University of Tokyo

In the fall of 2022, Metaverse School of Engineering is launched by the Graduate School of Engineering in the University of Tokyo. This aims to realize a society in which all people can acquire the latest information and practical engineering skills to realize their dreams, and is a place for education in the field of engineering using digital technology. The University of Tokyo’s Graduate School of Engineering and Faculty of Engineering will play a central role in promoting the development of DX human resources through industry-academia collaboration.
Similarity Segmentation Approach for Sensor-Based Activity Recognition

The fixed sliding window is the commonly used technique for signal segmentation in human activity recognition (HAR). However, the fixed sliding window may not produce optimal segmentation because human activities have varying durations, especially for transitional activities (TAs). This is because a large window size may contain activity signals belonging to different activities, and a small window size may split the activity signal into multiple windows. Furthermore, the fixed sliding window does not consider the relationship between adjacent windows, which may affect the performance of the HAR model. In this study, we propose a similarity segmentation approach (SSA) that exploits the temporal structure of the activity signal within the window segmentation process. Specifically, the proposed approach segments each window into subwindows and extracts the inner features by measuring the similarity between them. The inner features are used to measure the dissimilarity between the adjacent windows. The proposed approach is able to distinguish between transitional and nontransitional windows, which achieves more effective segmentation and classification processes. Two public datasets are used for the evaluation. The experimental results show that the proposed approach can distinguish TAs from basic activities (BAs) at 97.65% accuracy, which enhanced the accuracy of TAs recognition compared to the fixed sliding window by 33.41%. Also, our approach achieved accuracy for activity recognition of 92.71% and 86.65% for both datasets, respectively, which exceeds the fixed sliding window by 2.29% and 3.93% for both datasets, respectively. These results are significant and exceed the accuracy of the state-of-the-art models.
Random Sparse Approximation for a Fast and Scalable Gaussian Process

For machine learning algorithms the availability of huge data offers ample opportunity to learn and infer educated generalizations. However, for gaussian process the size of the data presents a challenge for their wider application in the areas of big data domain. Various approaches have been suggested to ensure scalability and computational efficiency. Such as, the kernel approximation and the variational inference are few notable mentions. This paper proposes a random sparse Gaussian approximation method based on a stochastic column sampling. It employs frequency analysis to select subsets of points that would generalize the observed data. Then, applies sparsity and sampling without replacement strategy when building the model. The predictive performance of the model is evaluated using the Variational Gaussian Process (VGA) as a benchmark. We run a Monte Carlo type model building and evaluation scheme using the mean square error (MSE) and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score as quality metrics. An ensemble of models were trained and evaluated for different sampling sizes under the same setting. The experiments have shown that the RSGA, on average is 10 times faster and offer better predictive performance compared to VGA. In addition, the RSGA offers a robust response to changes in kernel type compared to the VGA. Hence, for a fast optimal kernel estimation and big data analysis, the RSGA can give an alternative route to model building and inference.
Computational analysis of peripheral blood smears detects disease-associated cytomorphologies

Abstract Many hematological diseases are characterized by altered abundance and morphology of blood cells and their progenitors. Myelodysplastic syndromes (MDS), for example, are a group of blood cancers characterised by cytopenias, dysplasia of hematopoietic cells and blast expansion. Examination of peripheral blood slides (PBS) in MDS often reveals changes such as abnormal granulocyte lobulation or granularity and altered red blood cell (RBC) morphology; however, some of these features are shared with conditions such as haematinic deficiency anemias. Definitive diagnosis of MDS requires expert cytomorphology analysis of bone marrow smears and complementary information such as blood counts, karyotype and molecular genetics testing. Here, we present Haemorasis, a computational method that detects and characterizes white blood cells (WBC) and RBC in PBS. Applied to over 300 individuals with different conditions ( SF3B1 -mutant and SF3B1 -wildtype MDS, megaloblastic anemia, and iron deficiency anemia), Haemorasis detected over half a million WBC and millions of RBC and characterized their morphology. These large sets of cell morphologies can be used in diagnosis and disease subtyping, while identifying novel associations between computational morphotypes and disease. We find that hypolobulated neutrophils and large RBC are characteristic of SF3B1 -mutant MDS. Additionally, while prevalent in both iron deficiency and megaloblastic anemia, hyperlobulated neutrophils are larger in the latter. By integrating cytomorphological features using machine learning, Haemorasis was able to distinguish SF3B1 -mutant MDS from other MDS using cytomorphology and blood counts alone, with high predictive performance. We validate our findings externally, showing that they generalize to other centers and scanners. Collectively, our work reveals the potential for the large-scale incorporation of automated cytomorphology into routine diagnostic workflows.
[Preoperative Virtual Reality Simulation Regarding the Appropriate Port Location for Thoracoscopic Surgery for the Mediastinal or Chest Wall Lesions].

In thoracoscopic surgery for mediastinal or chest wall lesions the suitable position of ports( trocars) are required depending on the position of a target lesion in a particular patient. We have therefore developed a virtual reality (VR) simulation system using the specific data of each individual patient. The model data generation system, PASS-GEN, is customized for thoracic surgery. The chest wall and organs around the tumor are extracted from DICOM image data of computed tomography (CT) scan, and three-dimensional (3D) virtual images are constructed. Rehearsal of ports insertion is carried out by locating the scope and the forceps anywhere on the chest wall on PC monitor. The constructed VR images clearly show three dimensional relationships between the target and surrounding structures. This system also simulates circumstances where on the chest wall a thoracoscope and tools should be inserted for the better view and more comfortable manipulation. Particularly in mediastinal or chest wall surgery VR simulation is more practical because those structures would be less transformed during operation.
Challenges and Issues on Extracting Named Entities from Oncology Clinical Notes

This article aims to describe the annotation process of a multi-institutional corpus of clinical texts in the oncology specialty and to train models for the Recognition of Named Entities. We use the annotated corpus to train models with different amounts of data and compare the model result with the amount of data used in training. The training of the models was done from the fine-tuning of the Bidirectional Encoder Representations from Transformers adapted to the medical-biological domain of the Portuguese language (BioBERTpt). To compare model behavior with increasing training data, models were trained with incremental amounts of data. As a result, we found that models trained with smaller but fully revised datasets performed better than models trained with larger datasets with little revision.
Korean irony corpus construction

Language and Information 27.1, 19-36. This research presents a Korean irony corpus composed of 2,689 documents which are collected from multi-sources including Tweets, movie reviews and sports news comments. The documents of the initial stage are reviewed to check whether they truly convey ironic meaning, utilizing 40 linguistic features which are presumed to trigger irony. 1,385 documents have been confirmed as irony containing ones which are further analyzed and manually annotated with 4 different labels, “I”, “IS”, “IR” and “ISR”. The labels not only represent different types of irony, but also show that irony is superordinate to sarcasm by including “I”(rony) in every label. In addition to the irony documents, non-irony documents are included in the final corpus to balance the dataset. The resulting corpus leads to follow-up studies in which more data will be accumulated and the increased corpus will be augmented and refined with more features. Eventually, it is expected to contribute to the improvement of performance in automatically identifying irony from Korean text.
Learnable DoG convolutional filters for microcalcification detection

Difference of Gaussians (DoG) convolutional filters are one of the earliest image processing methods employed for detecting microcalcifications on mammogram images before machine and deep learning methods became widespread. DoG is a blob enhancement filter that consists in subtracting one Gaussian-smoothed version of an image from another less Gaussian-smoothed version of the same image. Smoothing with a Gaussian kernel suppresses high-frequency spatial information, thus DoG can be regarded as a band-pass filter. However, due to their small size and overimposed breast tissue, microcalcifications vary greatly in contrast-to-noise ratio and sharpness. This makes it difficult to find a single DoG configuration that enhances all microcalcifications. In this work, we propose a convolutional network, named DoG-MCNet, where the first layer automatically learns a bank of DoG filters parameterized by their associated standard deviations. We experimentally show that when employed for microcalcification detection, our DoG layer acts as a learnable bank of band-pass preprocessing filters and improves detection performance by 4.86% AUFROC over baseline MCNet and 1.53% AUFROC over state-of-the-art multicontext ensemble of CNNs.
On the Problem of a Physics Teacher Readiness for Professional and Pedagogical Activities in the Conditions of Higher Education Digitalization

The current state of the readiness of a physics teacher for professional and pedagogical activities in the context of the rapid growth of information technologies and the development of electronic information opportunities among university students are analyzed in the article. A review of scientific and methodological literature is being carried out on the concept of “readiness for professional and pedagogical activity” the concept of readiness of professional and pedagogical activity is being specified in relation to a physics teacher who implements educational programs of specialization in universities based on the use of modern electronic information technologies. The article concludes that modern digitalization, against the background of huge opportunities for creativity and the manifestation of pedagogical skill in creating electronic educational resources in physics, creates colossal problems that can be solved if the physics teacher is ready to work in digital mode. An example of methodological developments in physics created by physics teachers for training students based on the use of modern information technologies is also given.
Carbon-Neutral Bitcoin for Nation States

Sovereign adoption of bitcoin, whether as legal tender or in treasury reserves, increases the profitability of energy-intensive bitcoin mining, creating significant carbon emissions. This paper explores methods for adopting bitcoin while mitigating or eliminating associated carbon emissions. We survey three solutions: regulation/taxation, carbon offsetting, and finally, state-directed or state-supported carbon-neutral mining, arguing for the advantages of the latter. We then compare two ways of executing this last approach: (1) the state must mine all its bitcoin holdings; (2) the state must mine the same percentage of mining as its percentage of all bitcoin holdings. We show that (2) is a superior method, and that a nation state can adopt bitcoin in a carbon-neutral manner with a relatively small investment in carbon-neutral mining. At present levels of bitcoin mining and bitcoin pricing, an annual allocation of around 1% of the state’s bitcoin holdings towards mining will suffice, and may generate a positive return. El Salvador is used throughout as a case study, and we make specific suggestions for how much El Salvador should mine to achieve carbon neutrality with respect to their bitcoin holdings.
Chatbot Architecture for a Footwear E-Commerce Scenario

The amount of information accessible to businesses grows as more individuals get access to the internet. This information can be used by businesses to make corporate decisions that can highly affect them. E-commerce is the result of businesses starting to export their operations to the internet as a result of taking advantage of this reality. To help a footwear e-commerce platform keep up with the current demands of the market in terms of accessibility and customer service, a chatbot was created to help on this request. To build this chatbot, datasets were created leading to Intent Classification and Named-Entity Recognition pre-trained models, utilizing Bert-Large, getting fine-tuned with those datasets. These models achieved a F1-score of 0.90 and 0.88 respectively in their tasks. The Sentiment Analysis functionality of TextBlob was also utilized to help the chatbot comprehend user’s text polarity to reply appropriately.
How to Overcome Cognitive Biases that Skew Your Perception and Lead to Bigotry

People often act irrationally, and one example of this is bigotry. For instance, the claim that "all obese people are lazy" can be easily disproved with facts. We should aim to eradicate all kinds of prejudice, which are harmful and rooted in irrationality. It is not enough to focus on one type of discrimination and neglect the others. This paper argues that cognitive bias is a key cause of nonsensical beliefs, including prejudice. People who use logic will understand the foolishness of any form of bias. The paper also discusses the main cognitive biases that lead to bigotry and how to overcome them.
Coaching technologies in socionomy specialists' professional self-development in supporting families with children having special needs

The aim of the article was to evaluate the effectiveness of coaching technologies in training social workers to work with families that have children with special needs. The research involved the following methods: formative experiment, where the Professional Coaching technology is implemented as an influence; tests as tools to determine the level of theoretical knowledge, Job Satisfaction Test, Ontogenetic Reflection Self-Assessment Test; observation to determine the specialists’ communication skills level; statistical data processing. The research involved two samples - experimental and control, which included social workers. Social Competence Model in working with families that have children with special needs was developed, which consists of motivational-value, cognitive, communicative-activity and reflective criteria. The dominance of medium levels of social workers’ social competence in working with families that have children with special needs was found. The effectiveness of the developed coaching system is proved. It was found that the formative influence is more effective for the cognitive and reflexive criteria than for motivational-value and communicative-activity criteria. The obtained results can be guidelines for further improvement of the developed Professional Coaching system. Further research prospects involve studying the impact of coaching on the self-development of not only social workers but also other socionomy specialists.
Error Analysis and Enlightenment in Junior High School English Autonomous Writing

English writing is one of the language output skills, which is the embodiment of the comprehensive use of English language ability of English learners. It is also the focus and difficulty of teachers in English teaching. Based on the theory of error analysis, this paper analyzes the common types of mistakes in junior middle school students’ English writing and their causes, and expounds how teachers improve junior middle school writing teaching for students’ common writing mistakes in order to help improve students’ English writing level.
Phishing Website Prediction Using Gradient Boosting Classifier

Abstract: Phishing is a widespread tactic used to trick gullible people into disclosing their personal information by using bogus websites. Phishing website URLs are designed to steal personal data, including user names, passwords, and online financial activities. Phishers employ websites that resemble those genuine websites both aesthetically and linguistically. Utilizing antiphishing methods to identify phishing is necessary to stop the rapid advancement of phishing techniques as a result of advancing technology. A strong tool for thwarting phishing assaults is machine learning. Attackers frequently use phishing because it is simpler to fool a victim into clicking a malicious link that looks authentic than to try to get past a computer's security measures. The malicious links within the message body are intended to appear to go to the spoofed company utilising that company's logos and other genuine information. In the method that is being presented, machine learning is used to create a revolutionary approach for detecting phishing websites. Gradient Boosting Classifier is the model we utilised in our suggested strategy to identify phishing websites based on aspects of URL significance. By extracting and comparing different characteristics between legitimate and phishing URLs, the suggested method uses gradient boosting classifier to identify phishing URLs. The studies' findings demonstrate that the suggested approach successfully identifies legitimate websites from bogus ones in real time.
PENINGKATAN PRESTASI BELAJAR MATEMATIKA PECAHAN SEDERHANA DENGAN MENGGUNAKAN PENDEKATAN REALISTIK PADA SISWA KELAS III MI RIYADLUL ULUM BANGIL

The success of a teaching and learning process in the realm of education in schools is heavily influenced by several determinants. The factors in question are for example teachers, students, curriculum, social environment, and others. From the several findings found by the author in the teaching and learning process in the classroom, especially in fractional material, the learning outcomes of students are not good, where the results are quite low. This happens a lot to grade III MI Riyadlul Ulum Bangil, where there are students who find it difficult to understand how to solve simple fraction assignments. The type of research used in this research is Classroom Action Research (CAR), where the improvement of this research is through a cycle of two cycles. Based on the implementation of classroom action research in class III, MI Riyadlul Ulum Bangil can be concluded that using a realistic approach can increase the level of interest in student learning in the implementation of learning that is more lively and students are more active. From the results of class action in cycle I, there were criteria for increasing success in cycle I, a score of less than 70 or incomplete, there were 6 students (35%) and those who scored above 70 or said to have completed, there were 13 students (65%). However, after taking action in cycle II in cycle II, there were no students who scored below 70 and those who scored 70 were 6 students (25%) and students who scored above 70 were 13 students (75%).