Reviewerly/exHarmony huggingface dataset:
Cross-domain Federated Object Detection

Detection models trained by one party (including server) may face severe performance degradation when distributed to other users (clients). Federated learning can enable multi-party collaborative learning without leaking client data. In this paper, we focus on a special cross-domain scenario in which the server has large-scale labeled data and multiple clients only have a small amount of labeled data; meanwhile, there exist differences in data distributions among the clients. In this case, traditional federated learning methods can't help a client learn both the global knowledge of all participants and its own unique knowledge. To make up for this limitation, we propose a cross-domain federated object detection framework, named FedOD. The proposed framework first performs the federated training to obtain a public global aggregated model through multi-teacher distillation, and sends the aggregated model back to each client for fine-tuning its personalized local model. After a few rounds of communication, on each client we can perform weighted ensemble inference on the public global model and the personalized local model. We establish a federated object detection dataset which has significant background differences and instance differences based on multiple public autonomous driving datasets, and then conduct extensive experiments on the dataset. The experimental results validate the effectiveness of the proposed method.
To teach logic to prospective mathematics teachers (Part III)

Introduction. The article is the third part of the author’s article under the same title published in previous issues of this journal. It discusses the problem of forming the logical competencies of future mathematics teachers both at the undergraduate level and at the master’s level. Theoretical analysis. In this case, logic is considered in three aspects – classical Aristotelian logic, modern mathematical logic and its application to Aristotelian logic, non-classical logics. This part of the article proposes a number of measures related to the formation of the logical and didactic training content for future mathematics teachers at the undergraduate and graduate levels in order to make this training a system-forming factor in the entire system of their training. At the same time, training at the master’s level must meet the principle of continuity and become a natural continuation of training at the undergraduate level. Conclusions. Such an approach will significantly increase the level of the logical and general professional competence of mathematics teachers who graduate from pedagogical and classical universities. Such teachers will be able to apply logical knowledge and skills in their pedagogical activities to educate logically and mathematically competent students at different levels of general education capable of further self-development and creative activity.
Exposing and addressing the fragility of neural networks in digital  pathology

Neural networks have achieved impressive results in many medical imaging tasks but often perform substantially worse on out-of-distribution datasets originating from different medical centres or patient cohorts. Evaluating this lack of ability to generalise and address the underlying problem are the two main challenges in developing neural networks intended for clinical practice. In this study, we develop a new method for evaluating neural network models' ability to generalise by generating a large number of distribution-shifted datasets, which can be used to thoroughly investigate their robustness to variability encountered in clinical practice. Compared to external validation, \textit{shifted evaluation} can provide explanations for why neural networks fail on a given dataset, thus offering guidance on how to improve model robustness. With shifted evaluation, we demonstrate that neural networks, trained with state-of-the-art methods, are highly fragile to even small distribution shifts from training data, and in some cases lose all discrimination ability. To address this fragility, we develop an augmentation strategy, explicitly designed to increase neural networks' robustness to distribution shifts. \texttt{StrongAugment} is evaluated with large-scale, heterogeneous histopathology data including five training datasets from two tissue types, 274 distribution-shifted datasets and 20 external datasets from four countries. Neural networks trained with \texttt{StrongAugment} retain similar performance on all datasets, even with distribution shifts where networks trained with current state-of-the-art methods lose all discrimination ability. We recommend using strong augmentation and shifted evaluation to train and evaluate all neural networks intended for clinical practice.
PENGAJARAN BAHASA INGGRIS MELALUI PROGRAM KEMAH SASTRA SMA KABUPATEN BURU

Learning English has an important role in developing students' talents for it as a form of applying creative teaching methods so that the subjects taught must be understood by students. This Community Service, namely the “Literature Camp” activity, aims to develop knowledge and interest in language and literature for high school students in Namlea city to be more creative in their work. In addition, through this activity students can start themselves in carrying out various activities that are beneficial to the community. This activity is also a place to show students' creativity in the field of literature. The literature camp was held in Lala Village, Lilialy District, Buru Regency for three days, in 2020. The first day the participants were gathered in the middle of the field to be divided into groups in this case providing education to students about learning English. Service Results Literature camp activities provide the expected results. Through literary camp activities, students' understanding and knowledge of language and literature can be increased. In addition, the participants of the literary camp can develop an interest and talent in literary arts.
CBMIR: Content Based Medical Image Retrieval Using Hybrid Texture Feature Extraction Method

Due to the revolution of digital era in the medical domain at various hospitals across the world, the online users on the internet access have been increased. So the amount of collections of digitized medical images has grown rapidly and continuously. As well it is ratting significant to mention that the images are globally used by radiologists, professors in medical colleges and Lab technicians, etc. These Images are increasingly applied to communicate information about patient history. In this context, there is a necessity to develop appropriate systems to manage these medical images in storage and retrieval for diagnosis of the patient information. Another big issue is the convolution of image data and that can be interpreted in different ways. In order to manipulate these data and establish policies to its content is very tedious job. This will raise another big question. These issues motivated the researchers to give more focus on the image retrieval area whose goal is trying to solve those problems to provide an efficient retrieval system to the user community. In this perspective, this work has been proposed to facilitate radiologists, professors in medical colleges, lab technicians, and all other medical image user communities for their purpose for easy access from the remote location.
Bauman Moscow State Technical University — a forge of personnel for the Decade of Science and Technology

Russian President Vladimir Putin signed a decree on strengthening the role of science and technology in solving the most important tasks of the development of society and the country. Taking into account the results achieved during the Year of Science and Technology in the Russian Federation in 2021, the President decided to declare 2022–2031 the Decade of science and Technology in the Russian Federation. As one of the well-known technical universities is ready to solve such important tasks, the rector of Bauman Moscow State Technical University M.V. Gordin told the editor-in-chief about the development, plans and construction of a new campus.
Single magnon excited states of a Heisenberg spin-chain using a quantum  computer

Excited states of spin-chains play an important role in condensed matter physics. We present a method of calculating the single magnon excited states of the Heisenberg spin-chain that can be efficiently implemented on a quantum processor for small spin chains. Our method involves finding the stationary points of the energy vs wavenumber curve. We implement our method for 4-site and 8-site Heisenberg Hamiltonians using numerical techniques as well as using an IBM quantum processor. Finally, we give an insight into the circuit complexity and scaling of our proposed method.
Managing the parameters of the «Student - technical learning tool» model

The control of the model parameters aims to refine the results of modeling the system "Student – technical means of education", taking into account the impact on the model parameters of the intensity of the student's work, the lack of educational information and other stress factors. From the point of view of the student's resistance to stress factors, their personal characteristics can be represented by the following four groups: stress-resistant type; labile type; inert type; psychoasthenic type. It can be assumed that for each type of student's personality, which determines the nature of his behavior and changes in the effectiveness of his activities, there is a well-defined family of characteristics that reflect the dependence of the accuracy of the tasks performed by the student on the intensity of his work.
Smart Maneuvering of Security Camera

One of the ongoing demanding research problems in computer vision is visual surveillance in dynamic situations, particularly for humans and automobiles. It is a critical technology in the battle against terrorist attacks, crime, public health and safety, and effective traffic management. The endeavour entails the creation of an effective visual surveillance system for use in complex contexts. Detecting directional movement from a video is critical for target tracking, object categorization, activity recognition, and behaviour understanding in video surveillance. The first relevant phase of data is detecting object tracking in streaming video, and background subtraction is a typical method for foreground segmentation. Different backdrop subtraction approaches are simulated in this work to tackle the issues of lighting variance, background clutter, shadows, and concealment.
Accountable Proxy Re-Encryption for Secure Data Sharing

The COVID 19 pandemic is a humanitarian emergency that poses an enormous threat to society and has impacted various social media platforms and journalism. News and social media has become an immensely popular platform for consumption of information. The wide spread of fake news could inflict damages on social media platform. And hence, the need of Fake News Detection it the present scenario is inevitable. In this paper, we survey the recent literature about different approaches to detect fake news over the Internet. In particular, firstly I have discussed about fake news and the various terms related to it that have been considered in the literature. Further I have highlighted various publicly available datasets and various online tools that are available and cam debunk Fake News in real time. Hence, I have described fake news detection methods based on two broader areas i.e., it’s content and the social context. Finally, I have curated a comparison of various techniques that are used to detect fake news.
ENGLISH BLENDED LEARNING: AN ANALYSIS OF INDONESIAN STUDENTS’ PERCEPTION

ABSTRACTThe study attempted to analyze the students of Tadulako University's perspective toward blended learning instruction in English teaching. The study employed a quantitative approach using a longitudinal survey design. 48 English department students in the fourth and sixth semesters were selected as the sample of the research using a simple random sampling technique. The data were gained through the questionnaire distributed online and the interview in the face-to-face technique. Data have been analyzed descriptively and inferentially using SPSS Program version 21. The results showed that blended learning through Moodle-based Instruction and face-to-face learning in the classroom was a modern way of learning, convenient to learn English anywhere and anytime. According to most of the respondents, blended learning is the way to enhance their self-directed learning. They can understand the subject well through blended learning, and students feel satisfied using blended learning in English Teaching. It could be concluded that blended learning is helpful to apply in English teaching. The results contributed theoretical and practical information and references to practitioners and researchers in this field of study. ABSTRAK Penelitian ini mencoba untuk menganalisis perspektif mahasiswa Universitas Tadulako terhadap pengajaran blended learning dalam pengajaran bahasa Inggris. Penelitian ini menggunakan pendekatan kuantitatif dengan menggunakan desain survei longitudinal. 48 mahasiswa jurusan Bahasa Inggris semester empat dan enam dipilih sebagai sampel penelitian dengan menggunakan teknik sampel acak secara sederhana. Data diperoleh melalui kuesioner yang disebarkan secara daring dan wawancara dengan teknik tatap muka. Data dianalisis secara deskriptif dan inferensial menggunakan Program SPSS versi 21. Hasil penelitian menunjukkan bahwa blended learning melalui Pembelajaran Berbasis Moodle dan pembelajaran tatap muka di kelas menurut mahasiswa merupakan cara belajar yang modern, nyaman untuk belajar bahasa Inggris dimana saja dan kapan saja. Blended learning menurut sebagian besar responden, adalah cara untuk meningkatkan self-directed mereka dalam belajar, mereka dapat memahami materi pembelajaran dengan baik melalui blended learning, dan siswa merasa puas menggunakan blended learning dalam Pengajaran Bahasa Inggris. Dapat disimpulkan bahwa blended learning berguna untuk diterapkan dalam pengajaran bahasa Inggris. Hasil penelitian ini memberikan kontribusi informasi dan referensi secara teoritis dan praktis kepada para praktisi dan peneliti selanjutnya dalam bidang studi ini.
The Canadian Statistical Sciences Institute 2003–2022

Abstract This article describes the founding and growth of the Canadian Statistical Sciences Institute (CANSSI), starting from its early roots and continuing through to establishment as a mature research enterprise. The goal is to present a historical record of events and activities that were important in the development of CANSSI.
Single-image deraining network based on multistage feature fusion

Single-image deraining is a classical problem in the field of low-level computer tasks. Most of the recent state-of-the-art image rain removal methods are trained on synthetic images, which have the problems of incomplete rain removal on real images and inability to process complex rain conditions. Based on these limitations, we propose a single-image deraining network based on multistage feature fusion (SIDNMFF). The network performs rain removal in four stages, with the first three stages using an improved encoder–decoder subnetwork for feature fusion to extract global and local information from the image, resulting in more detailed texture information of the obtained image. In the last stage, the network fuses the feature information extracted in the first three stages, performs feature extraction on the original resolution image, and finally outputs the final result of multistage deraining. The proposed method conducts a large number of comparative experiments on synthetic and real datasets as well as experiments on real datasets using no-reference metrics and the target detection method as the evaluation basis. Experimental results confirm that the proposed method achieves a satisfactory rain removal effect and outperforms the other methods.
Software Architecture and Design Patterns for Efficient Development of a Software Dedicated to Euler Graph Study

Implementation of object-oriented software is difficult, and object-oriented software design with reusable modules is even more complicated. Accomplishment musts to be suitable to subject, still too generic sufficient to be fit to settle further requisites. In proceeding of development an interactive educative software, design constitutes probably the most meaningful factor that can affect its quality. Efficiently, now there are many design patterns whose application can supply an abscission of a generic issue. Design solutions of competent architects are settled by design methods. This chapter presents an interactive educative software that can be used in study of Eulerian graphs. Development of the interactive educative software is refined by usage of "Decorator" structural pattern and “Strategy” behavioural pattern, but also the use of MVP architecture. Design of educative software is reached through distinctive UML diagrams signifying the phases of analysis, design and implementation, and implementation is realised through C# programming language.&nbsp
“Perceptual Decision-Making” and Neuroscience: A Brief Anatomy of a Pseudoscience

A significant swathe of the activity labelled “neuroscience” today concerns “perceptual decision-making.” The term putatively refers to a real-world process whose features may be revealed via experiment. But the experiments in question artificially and severely constrain, a priori, the form and content of the “data,” ensuring that it is binary, categorical, and forms a rough sigmoid when correct “decisions” are plotted against stimuli treated as varying along a single, arbitrary dimension. These by-design sigmoidal datasets are then interpreted as imperfect reflections of corresponding binary, categorical, unidimensional processes in the brain. The job of the practitioner is to recover the “true” sigmoid hiding in the data - the true sigmoid being whichever of a number of standard sigmoids the practitioner chooses to fit, post hoc, with the help of free parameters. This procedure, self-insulated from real-world pushback, lacks methodological, functional and empirical validity, and its associated ad hoc assumptions cannot be reconciled with the most basic mental facts. Results have low to no replicability and reproducibility. The paradigm has been extended to individual neurons using p-hacking-type exploration of data.
Neural network of multivariate Bernstein operators with positive integer parameter <i>m</i>

This paper defined and studied the family of neural network operators (NN) with a positive integer parameter m, by using the multivariate m-Bernstein operators and some sigmoidal functions. The pointwise and uniform approximation theorems are given for this NN for continuous functions on . Also, the order of this NN is studied by using the absolute moment of order v for functions belonging to Lipschitz space in .
GOS: A Genetic OverSampling Algorithm for Classification of Quranic Verses

Imbalanced classes problem is a problem in many datasets in real applications, where one class “minority class” contain few numbers of samples and the other “majority class” contain many numbers of samples. It is difficult to build a training model to classify the imbalanced classes correctly due to tending the accuracy of classification to the majority class. In this paper, a new technique is called "GOS: a Genetic OverSampling algorithm", is proposed using a genetic algorithm. A genetic algorithm is applied to oversample the imbalanced datasets and to improve the performance of imbalanced classification. This improvement is achieved due to adjusting the locations of samples in the minority class in the optimal places. According to the experimental results obtained, the GOS algorithm outperformed other techniques used widely in the imbalanced classification field.
FOLDER SYSTEM ORGANIZER USING WEB DEVELOPMENT

This paper gives an overview of the web technologies. The general idea of the new version of the HTML5, CSS and JS and other tools which is presented in this paper is the formal specifications and the establishment of uniform solutions for technologies and functionalities which have been for the computer users for the easily access of the files storage so that they can make their folder and files easily and by using one extension. The extension use the support of the node js. The structure and styling will be done in the HTML and CSS respectively. The complex features like adding the file or folder in the project will be done by JavaScript(JS). The main aim of this paper is to make have one extension which that will make the folders of the individual types of the files like documents, excels, pdfs, videos etc. The extension will automatically will make the folder and the files will go in that folder according to their type automatically. At last, I can conclude you saying that File System organizer will be very helpful and will make our work easy. Keywords— local storage, organizing files
A Q Methodological Analysis of Types of Freshmen’s Perception on College Life Adjustment During the COVID-19 Pandemic

Objectives The purpose of this study was to investigate the freshmen experiences and perceptions on college life while adjusting to the COVID-19 pandemic.&#x0D; Methods The P-sample consisted of 33 full-time freshmen enrolled in the department of social welfare. Using Q-methodology, these students categorized 34 Q-sample statements from October 1 to 20.&#x0D; Results Analyzing the scores from the 34 statements using Q factor analysis through QUANL-PC program, freshmen perceptions on college life adjustment were classified into three types: (a) showing approval of online learning but preferring for face to face learning and having low academic achievement, (b) showing adaptive online learning but pursuing face to face learning and having moderate academic achievement, (c) showing adaptive online learning and having high academic achievement.&#x0D; Conclusions Result of this study suggests that actual online learning is needed to better evaluate the benefits, challenges and useful strategies of successful students and to revised in order to help freshmen to successfully adjust to college life, improve academic performance and develop employee competencies.&#x0D; &#x0D;
Design and Development of Covid-19 Pandemic Situation-based Contactless Automated Teller Machine Operations

The Automated Teller Machine (ATM) in the bank enables the institutional financial customer(s) (FIC) physical money (hard cash) handling operations. The Covid-19 pandemic (C19P) introduced social distancing (SD) to safeguard human lives from the virus infection. The life-threatening situation worldwide encourages all system operations in touchless or contactless mode while maintaining SD to prevent the spreading of Corona. The ATM operations were not restricted in this pandemic situation with contactless operations. This work relates the design and development of such a system to introduce the Artificial Intelligence Subsystems (AI-SS) for the existing ATM units. The AI-SS uses additional sensors and actuators, and supporting algorithmic routines are proposed to enhance the existing ATMs operations. This article presents the integra- tion of the mobile application with the existing ATMs and proposes AI-SSs, to enable touchless ATM (CL-ATM) operations, also known as C19PCL-ATM.