Reviewerly/exHarmony huggingface dataset:
“Brosset Collection”: The History of Early Ac-quisitions of Japanese Manuscripts and Woodblock-Printed Books by the Imperial St. Petersburg Academy of Sciences and by the Asiatic Museum (1791–1864)

The article presents the results of a study of the history of early acquisitions of Japanese manuscripts and woodblock-printed books to the Imperial St. Petersburg Academy of Sciences and by the Asiatic Museum from 1791 to 1864, previously known under the collective name of the Brosset collection. The main sources for the study were the catalogs of Chinese and Japanese books by I. Busse (1798), P. Kamensky and S. Lipovtsev (1818), M.F. Brosset (1840, additions until 1864), as well as lists of some acquisitions by the Asiatic Museum and by the Academy of Sciences housed at the Archive of Orientalists of the IOM, RAS, and at the St. Petersburg branch of the Archive of the Russian Academy of Sciences. The method of work involved comparing the titles of books mentioned in each of the catalogs or lists with the Japanese manuscripts and woodblock-printed books stored today at the IOM, RAS. As a result, it was found that the Brosset Collection was made up of acquisitions from at least nine donors, including valuable collections by Daikokuya Kodayu, J.A. Stutzer, the Russian-American Company, P.L. Schilling and K.I. Maksimovich.
The Influence of School Self-Reliance Projects in Enhancing Students’ Academic Performance in Morogoro Municipality

The study on the influence of school self-reliance projects in enhancing students’ academic performance was conducted in Morogoro Municipality. The study was guided by the following research objective; to explore the uses of income generated from the available school self-reliance project in private secondary schools in Morogoro Municipality. The study used a sample of 50 respondents and the data were collected through questionnaires and interviews and were analysed thematically and by using descriptive statistics. Data were extracted from primary and secondary sources. The study revealed the following; the dominant income generation projects which were identified in the field were; farming (vegetables), brick making, livestock keeping, school shop and canteen. These were the dominant self-reliance projects which are used by the school management to produce extra income within school compounds. The study also revealed that from 2017 to 2020, St. Peter’s Junior Seminary generated 10,456,400 million Tz shillings from income generation projects, Kitungwa Secondary School generated 163,245,000 million Tz shillings while Kigurunyembe secondary school generated 5,370,250 million Tz shillings from the available income generation projects. The study revealed that most of the income generated is used to pay for water and electricity bills for the advantage of students and teachers, conduct remedial classes, provide lunch for teachers, buy school facilities, maintenance of school buildings and employ teachers and non-teachers. The study recommends that School-based income generation projects are very important as they help heads of schools to produce additional funds for improving quality education provision in secondary schools rather than depending on school fees, loans and grants from donors who are sometimes not reliable. Through such activities, heads of schools are in the position of ensuring a constant supply of teaching and learning materials, construction, and maintenance of physical infrastructures such as the library, laboratories, dormitories, dining and assembly halls, and staff houses and motivating students, teachers, and non-teachers to do their work up to their best
Penerapan Metode Vikor untuk Menentukan Pemberian Dana Mekaar Plus pada PNM Kota Binjai

&#x0D; PT Permodalan Nasional Madani or PNM, is here as a solution to improve welfare through access to capital, assistance and capacity building programs for business actors. Along with business development, in 2016, PNM launched a capital loan service for underprivileged women who are ultra micro business actors through the Fostering a Prosperous Family Economy (PNM Mekaar) program. For this reason, PNM must be more careful and considerate in determining the provision of Mekaar Plus loans. In order not to make mistakes and cause disappointment in the future. So it is necessary to build a system that can be used as a determining and alternative system in determining how to provide Mekaar Plus funds by using a decision support system. This system will be able to make decisions quickly and precisely according to predetermined criteria. So that in the process of giving the blooming funds it can be done more effectively and can reduce the occurrence of errors in the decision-making process. There are many methods used in the decision-making process. One of the methods used in this research is the Vise Kriterijumska Optimizajica I Kompromisno Resenje (VIKOR) method.&#x0D; Keywords: Decision Support System, PNM, Mekaar, VIKOR
Data Mining Pengelompokan Pembayaran Listrik Menggunakan Metode Clustering

&#x0D; Electricity is a basic need that greatly influences all community and government activities. Almost all people from the upper to the lower classes, both in the fields of education, government, economics and social, really need a source of electrical energy as a means of supporting activities and productivity. PLN (Persero) is a State-Owned Enterprise (BUMN) that provides services to the community in the provision of electricity. The K-Means algorithm is one of the clustering technique algorithms that starts with random selection, which is the number of clusters that you want to form from the data to be clustered, namely electricity payments. the system made from k-means displays the results of the clustering of electricity payments, namely the pattern of electricity payments whose clusters are up, fixed and down. By utilizing a number of data owned by the agency, it can be grouped using data mining technology. The use of data mining techniques in classifying electricity payments is expected to be useful to simplify the process of searching for system data, which previously was still manual&#x0D; Keywords: Data Mining, K-Means, Electricity
Korelasi Antara Motivasi Belajar Dan Minat Baca Dengan Prestasi Siswa Menggunakan Metode Apriori: Studi Kasus SMP Negeri 11 Binjai

The quality of education can be achieved by measuring how big the level of success of learning outcomes and achievements obtained by each student. Students who have high achievement are students who have learning motivation and broad knowledge base. By reading, students are expected to have the ability to absorb various knowledge which is mostly conveyed through writing.To get a relationship or correlation between the relationship of one variable with another variable of course required a method in the process of completion. There is a method used by several previous researchers, namely using the association rule method, which is a data mining technique to find associative rules between a combination of items. The algorithm used is a priori which is a step for the process of finding frequent-itemsets by iterating over the data. Where the itemset is the set of items that are in the set processed by the system, while the frequent-itemset shows the itemset that has an occurrence frequency of more than a predetermined minimum value.The objectives of this research are: 1) Build a system that can correlate between learning motivation and interest in reading with student achievement. 2) To find out the minimum support and maximum confidence and the variation between learning motivation and interest in reading and student achievement. 3) To obtain the best rules and produce the latest information.&#x0D; Keywords: Reading Interest, Correlation, Apriori, Data Mining
SURMOUNTING THE BRIM OF MODULAR LEARNING MODALITY: A PHENOMENOLOGICAL STUDY

Pandemic makes a vast change wherein everything outside and even inside everyones home is in calamity. The closure of educational institutions such as the school in which learning is a shift into a new platform of learning. Modular learning modality mode is used where all instructional materials are printed. It is one way to continue a students education in-house without risking their health. Parents serve as the teacher and guide their children in learning while they do house chores and jobs to provide needs. A qualitative phenomenological approach was used to investigate and provide revealing looks and perspectives on the said issues of the parents. The study aims to discover and understand the lived experiences and perspectives of the childrens parents who are surmounting the brim of modular learning. In this study, 14 parents were selected. Particularly those parents that have elementary students will be our participants. The researcher gathered information from the participants through in-depth interviews. This research attempts to explore parents perspectives in surmounting the brim of modular learning, examine its outcomes, and investigate how parents deal with the issue. There are six themes revealed in this study. Interestingly, findings revealed that parents have a hard time in the implementation of modular learning modality. Therefore, parents lived experiences in modular learning modality should be considered and understood for them to look for an outlet to express and hear their voice on facing this new learning as they work and teach children at the same time. Modular learning parents play an important role in a students development and progress. KEYWORD: parents lived experiences, modular learning modality, handling children in teaching modules, Philippines
The Exploring Factors that Teachers View as Hindering Quality in Teaching and Learning at a TVET College

The overarching purpose of this study was to explore teacher views on some issues that hinder quality in teaching and learning at a TVET college. This is explored by considering challenges that face teachers in providing quality teaching and learning. A total of twenty-four lecturers participated in the study. The study adopts a mixed methods approach and enacts Karl Maton` Legitimation Code Theory (LCT) as its analytical framework. Data were collected by means of interviews, questionnaires, and document analysis. While a symbiotic relationship is expected to exist between expertise and decision-making structure in the technical and vocational education and training (TVET), there is a dichotomous gap in the two domains, which in turn compromises the quality of teaching and learning. &#x0D;
Review on Recent Arabic Information Retrieval Techniques

Information retrieval is an important field that aims to provide a relevant document to a user information need, expressed through a query. Arabic is a challenging language that gained much attention recently in the information retrieval domain. To overcome the problems related to its complexity, many studies and techniques have been presented, most of them were conducted to solve the stemming problem. This paper presents an overview of the Arabic information retrieval process, including various text processing techniques, ranking approaches, evaluation measures, and some important information retrieval models. The paper finally presents some recent related studies and approaches in different Arabic information retrieval fields.
Activation Function: Absolute Function, One Function Behaves more Individualized

&lt;p&gt;Inspire by nature world mode, a activation function is proposed. It is absolute function.Through test on mnist dataset and fully-connected neural network and convolutional neural network, some conclusions are put forward. The line of accuracy of absolute function is a little shaken that is different from the line of accuracy of relu and leaky relu. The absolute function can keep the negative parts as equal as the positive parts, so the individualization is more active than relu and leaky relu function. In order to generalization, the individualization is the reason of shake, the accuracy may be good in some set and may be worse in some set. The absolute function is less likely to be over-fitting. The batch size is small, the individualization is clear, vice versa. Through test on mnist and autoencoder, It is that the leaky relu function can do classification task well, while the absolute function can do generation task well. Because the classification task need more universality and generation task need more individualization. The pleasure irritation and painful irritation is not only the magnitude differences, but also the sign differences, so the negative part should keep as a part. Stimulation which happens frequently is low value, it is showed around zero in figure 1 . Stimulation which happens accidentally is high value, it is showed far away from zero in figure 1. So the high value is the big stimulation, which is individualization.&lt;/p&gt;
Improvement of the Sensor Capability of the NAO Robot by the Integration of a Laser Rangefinder

This paper focuses on integrating a laser rangefinder system with an anthropomorphic robot (NAO6—Aldebaran, United Robotics Group) to improve its sensory and operational capabilities, as part of a larger project concerning the use of these systems in “assisted living” activities. This additional sensor enables the robot to reconstruct its surroundings by integrating new information with that identified by the on-board sensors. Thus, it can identify more objects in a scene and detect any obstacles along its navigation path. This feature will improve the efficiency of navigation algorithms, increasing movement competence in environments where people live and work. Indeed, these environments are characterized by details and specificities within a range of distances that best suit the new robot design. The paper presents a laser finder integration project that consists of two different parts, which are as follows: the former, the mechanical part, provided the NAO robot’s head; the latter, the software, provided the robot with proper software drivers to enable integration of the new sensor with its acquisition system. Some experimental results in an actual environment are presented.
Sanal evrende Eğitim varlıklarını yönetmek için NFT tabanlı model

Blockchain has become a trend in the last few years due to its financial impact correlated to Cryptocurrency. Blockchain is a distributed, immutable, and decentralized database to store transactions which are done by users without the need for a trusted Third-party. Similar to cryptocurrency, tokens are objects that Represent digital or physical assets over a Blockchain network. Token market value has been dramatically increasing in the last few years due to The breakthrough that was made in preserving intellectual property And the usage in many other fields like tourism, education, and Art. Non- Fungible tokens (NFT) Are unique and exchangeable tokens because they are different in type and value due to their scarcity. Using NFT to manage the educational assets will be an effective way to protect the intellectual property and the educational assets from fraud. In this paper, we propose a NFT-based framework to manage the educational assets on the Metaverse that authenticate the ownership of the assets using Blockchain technology. We also explain the minting process we used to convert the assets into NFT in the framework.
Hibrit Yapay Zeka Tabanlı Meme Kanseri Teşhisi

According to the data of 2020, it is seen that 1 of every eight cancers diagnosed worldwide and the 5th among cancers that cause death is breast cancer. Cancer can spread to different organs and reach an incurable stage in patients who are not diagnosed and treated at the right time. Therefore, reducing the time taken for breast cancer diagnosis and reducing mortality rates are of great importance for accurate and early diagnosis of the disease. This study aims to improve the accuracy of cancer detection by using various machine learning algorithms and methods for artificial intelligence-based breast cancer diagnosis. By using ultrasonography images taken from 780 people, image information processed with statistical parameters was extracted. Artificial intelligence-based breast cancer detection was performed by applying three different machine learning algorithms and the hybrid machine learning algorithm designed as a combination of these algorithms on the extracted data set. In this way, early detection of cancerous cells will be carried out without creating advanced risks for the individual, and treatment will be possible.
Specifying and Verifying Higher-order Rust Iterators

Abstract In Rust, programs are often written using iterators , but these pose problems for verification: they are non-deterministic , infinite , and often higher-order , effectful and built using adapters . We present a general framework for specifying and reasoning with Rust iterators in first-order logic. Our approach is capable of addressing the challenges set out above, which we demonstrate by verifying real Rust iterators, including a higher-order, effectful . Using the Creusot verification platform, we evaluate our framework on clients of iterators, showing it leads to efficient verification of complex functional properties.
Tensor Calculus in Digital Colorimetry

Any object can have many implementations in the form of digital images and any digital image can be processed many times increasing or decreasing accuracy and reliability. Digital colorimetry faces the need to work out issues of ensuring accuracy, metrological traceability and reliability. The purpose of this work was to generalize approaches to the description of multidimensional quantized spaces and show the possibilities of their adaptation to digital colorimetry. This approach will minimize the private and global risks in measurements. For color identification digital colorimetry uses standard color models and spaces. Most of them are empirical and are improved during the transition from standard to real observation conditions taking into account the phenomena of vision and the age of observers. From the point of view of measurement, a digital image can be represented by a combinatorial model of an information and measurement channel with the appearance of the phenomenon of a color covariance hypercube requiring a significant amount of memory for data storage and processing. The transition from the covariance hypercube to high-dimensional matrices and tensors of the first, second and higher ranks provides the prospect of optimizing the color parameters of a digital image by the criterion of information entropy. Tensor calculus provides opportunities for expanding the dynamic range in color measurements describing multidimensional vector fields and quantized spaces with indexing tensors and decomposing them into matrices of low orders. The proposed complex approach based on tensor calculus. According to this approach the color space is a set of directed vector fields undergoing sampling, quantization and coding operations. Also it is a dynamic open system exchanging information with the environment at a given level and to identify color with specified levels of accuracy, reliability, uncertainty and entropy.
Leukaemia Identification based on Texture Analysis of Microscopic Peripheral Blood Images using Feed-Forward Neural Network

ABSTRACT&#x0D; Leukaemia is very dangerous because it includes liquid tumour that it cannot be seen physically and is difficult to detect. Alternative detection of Leukaemia using microscopy can be processed using a computing system. Leukemia disease can be detected by microscopic examination. Microscopic test results can be processed using machine learning for classification systems. The classification system can be obtained using Feed-Forward Neural Network. Extreme Learning Machine (ELM) is a neural network that has a feedforward structure with a single hidden layer. ELM chooses the input weight and hidden neuron bias at random to minimize training time based on the Moore Penrose Pseudoinverse theory. The classification of Leukaemia is based on microscopic peripheral blood images using ELM. The classification stages consist of pre-processing, feature extraction using GLRLM, and classification using ELM. This system is used to classify Leukaemia into three classes, that is acute lymphoblastic Leukaemia, chronic lymphoblastic Leukaemia, and not Leukaemia. The best results were obtained in ten hidden nodes with an accuracy of 100%, a precision of 100%, a withdrawal of 100%.
Life Prediction of the Gear Transmission System with Multicharacteristics

This study obtains and predicts multifault data in the key transmission and connection systems with gears. Model building is based on the multikernel extreme learning machine with the method of maximum correlation kurtosis deconvolution and variational mode decomposition. To this end, the realization form of the life prediction is first studied by enhancing the low-frequency signal. Then, the larger correlation coefficient is selected as the sensitive feature parameter aiming at mapping to a feature space by the randomly initialized hidden layer in the learning machine, and the weight value of output layer is obtained using the least square method. A case study on the fault diagnosis of gear transmission system is conducted in the end to illustrate the proposed approach.
The timeliness of COVID-19 testing and tracing in eight public health regions in the Netherlands

Abstract Background Testing and Contact Tracing (TCT) was a core strategy in the fight against the spread of SARS-CoV-2. However, little is known about the real-world effectiveness of TCT for COVID-19. Because time is an important conditional factor, we aim to study timeliness of TCT in the Netherlands, and its determinants. Methods We used routine COVID-19 TCT registry data from all individuals who tested positive for SARS-CoV-2 at 8 Dutch regional public health services from 1-6-2020 to 28-2-2021 (N = 338,066). We calculated median time intervals of TCT stages. Factors associated with the time between test result and completion of TCT, categorised as ≤ 3 days and &amp;gt;3 days, were assessed using logistic regression adjusting for region, testing site, and laboratory. Potential determinants were: gender, age, country of birth, number of close contacts, working in health-care or education, TCT manpower, and the Oxford Covid-19 Government Response Tracker (OxCGRT). Results The median time from symptom onset to TCT completion was 6 days (IQR:3-10). Median times between TCT stages were 1 day (IQR:0-3) for symptom onset to test request, 1 day, (IQR:0-1) for test request to sample collection, 1 day, (IQR:1-1) for sample collection to test result, and 2 days (IQR:1-5) for test result to TCT completion. In 31.7% of tests, time between test result and TCT completion was &amp;gt;3 days. This delay was associated with being older (65+), whereas being younger (0-14), a higher OxCGRT, scaling down TCT, and a higher number of TCT employees were associated with a shorter interval. Conclusions Over fifty percent of interval times from symptom onset to TCT completion exceeded the median SARS-CoV-2 incubation period of 5 days. There seems to be little room for improvement on the side of the index case, but there are some implications for logistics such as increasing TCT manpower, and better integration of digital systems. Key messages