Reviewerly/exHarmony huggingface dataset:
Regional Geological Disasters Emergency Management System Monitored by Big Data Platform

In order to deal with the hazards caused by geological disasters in time, an emergency management system is proposed based on association rule data mining. With the support of a big data platform, a regional geological disaster emergency management system is built based on monitoring data. In the result analysis, the association rule algorithm demonstrates high computing power in the test, which can filter the data with strong association rules. In addition, the big data platform can allow data visualization, which has good data storage capacity and disaster early warning capacity. In the simulation test of the emergency management system, it was found that the system is feasible in theory. When it is applied to the actual disaster emergency management, it wasfound that, in the face of geological disasters, the processing speed of relevant departments increased by 59.4%, and the allocation of personnel and materials wasmore reasonable. The above results show that the big data platform monitoring data can improve the regional geological disasters emergency management capacity and ensure the safety of people’s lives and property.
System asymptotic stability analysis of a kind of complex production processes based on multi-dimensional moving pattern

Abstract This paper presents a new method to study the system stability of a kind of complex production processes. The system dynamics is described by multi-dimensional pattern class variables instead of state variables or output variables. The system asymptotic stability is defined by system matrix and output class partition. Firstly, “pattern moving space” is constructed by a new maximum entropy clustering method. Multi-dimensional pattern class variables measured by pattern class centers are defined in this new space. Then the nonlinear state space model of the relevant control system is derived and established through two-step prediction model. On the basis of the original stability in Euclidean space, the multi-dimensional pattern moving state space is defined and Lyapunov stability is given in this space. Finally, experimental results show the effectiveness of the proposed conditions.
FSP: Towards Flexible Synchronous Parallel Frameworks for Distributed Machine Learning

Myriad of machine learning (ML) algorithms refine model parameters iteratively. Existing synchronous data-parallel frameworks can accelerate training with convergence guarantees. However, the pre-assigned workload-based synchronous design still poses great challenges, since fast workers must wait for slow, straggling ones, especially in a heterogeneous computing cluster. Asynchronous alternatives can bypass this performance bottleneck, but at expense of potentially losing convergence guarantees. This article proposes a new time-based flexible synchronous parallel framework (FSP). It provides strict convergence analysis by consistently updating parameters, as well as significant cost reduction by completely unleashing the power of fast workers. It identifies the optimal synchronization frequency, by online balancing costs of parameters’ update and benefits brought by their freshness. Besides the basic goal of keeping all workers fully CPU-utilized, FSP also aims to keep data spread over the cluster fully utilized, so that they can contribute to convergence with equal opportunities. These proposals are all implemented in a prototype system Flegel, with additional engineering optimizations for further performance enhancement and programming facilitation. Experiments demonstrate that Flegel significantly outperforms recent studies.
A New Electronic Instrumentation Approach for the Acquisition of Microscopic Blood Smear Images for the Automatic Diagnosis of Anemia

Anemia is a blood abnormality that affects the quantity and quality of red blood cells in the human body.This sometimes banal sign spares no continent and no social stratum.This anomaly is generally appreciated through biological analyzes of patients' blood.These analyzes, which boil down to the knowledge of hemato-metric constants, cannot by themselves allow the characterization of certain forms of anemia in the sense that most anemia are related to the morphology and color of red blood cells.Our work in this paper is to perform blood smears on patients and perform a morphological and colorimetric analysis of red blood cells on these smears.This approach allowed us to highlight on each erythrocyte morphological and colorimetric descriptors to accurately identify the types of anemia by image processing methods.This identification is performed in an automated environment to allow pathologists to respond quickly to anemia-related emergencies and also improve the treatment to be conducted.This automation required the implementation of a new approach to electronic instrumentation and the acquisition of microscopic blood smear images for the automatic and rapid diagnosis of anemia.
Development of Islamic Text-Based Reading Materials with a Genre-Based Approach

This study aims to develop Islamic text-based reading materials with a Genre-Based Approach as teaching materials for compulsory subjects for Islamic University students. The study used a Research and Development approach, where the data were collected through questionnaires and interviews. Qualitative data were analyzed using interactive analysis and quantitative data analysis used percentages. The research participants consisted of lecturers and 50 students from the Islamic Education Department. The data were collected through questionnaires and interviews. The result of this study indicated that designing Islamic text-based reading teaching materials with a genre-based approach is exceedingly needed. The content of the book consists of the following components: cover, preface, table of content, explanation text, recount text, descriptive text, narrative text, discussion text, hortatory text, news item text, references, and about the writer. Furthermore, the student's responses to the teaching materials designed were 0% disagreed, 13% less agreed, 52% agreed, and 35% strongly agreed. The average score is 3.22 and, if converted on a table with a scale of 4, includes it in a very good category. Therefore, an Islamic text-based reading material with a genre-based approach is worth applying by the students of the Islamic Education Department.&#x0D;
Clustering Sales Patterns of Best Selling and Less Selling Products at El Jhon Bengkulu Stores Using the K-Medoid Method

The clustering application of the best-selling and less-selling product sales patterns at the El Jhon Bengkulu Store can provide information on the best-selling and less-selling product sales patterns as a basis for consideration in product inventory management at the El John Store. Based on 40 total product sales and inventory data in April 2022, the results obtained that 9 data entered cluster C1 (22.5% best selling products) and 31 data entered C2 cluster (77.25% less selling products). Based on the results of the tests that have been carried out, the functionality of the product inventory is running well as expected, and can provide information on the results of product data clustering into 2 groups, namely Best Selling and Less Selling
Development of Electronic Pocket Books For Immune System Material To Increase Students' Learning Motivation

The teaching and learning process certainly requires interesting media and does not tend to be boring. For this reason, it is necessary to use media that can attract students' interest by using electronic pocket book media. This study aims to determine the quality of electronic pocket books and determine the effectiveness of electronic pocket book media. This development research uses the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. The analysis stage is an analysis of the needs of students. The design stage is the making of an electronic pocket book media design. The Development stage is an assessment of the feasibility of experts, namely material experts, media experts and teachers at SMA PGRI 1 Bogor. The Implementation stage is an assessment of the feasibility of the media and the Evaluation stage is a measurement of development achievement. The results showed the feasibility level of electronic pocketbook media, according to material experts, electronic pocketbooks were included in the appropriate category, while according to high school biology teachers and students, electronic pocketbooks were included in the very feasible category. The results of the analysis of student learning motivation before and after using the media were obtained, this showed a significant difference between before and after using the media, so it can be concluded that the electronic pocket book learning media can increase students' learning motivation.
Library Website Redesign in the Time of COVID

In November 2019, Binghamton University Libraries initiated a website redesign project. Our goal was to create a user-centered, data-informed website with refreshed content and upgraded functionality. Originally, our redesign plan included in-person card-sorting activities, focus groups, and usability studies, but when the Libraries went remote in March 2020 due to the COVID-19 pandemic, we had to quickly reassess and adapt our processes and workflows. In this article, we will discuss how we completed this significant project remotely by relying on effective project management, communication, teamwork, and flexibility.
Implementasi Sistem Otomasi Order Mentoring Pada Marketplace Mentoring Platform Nusademy

The COVID-19 pandemic has caused the acceleration of digitalization to occur throughout the world in various aspects of life, one of which is in the world of education. The openness of access to learning resources has not been the right solution for humans to learn independently. There are details in the learning process that require the guidance of a teacher called a mentor. In line with this, PT Nusademy Solusi Indonesia (Nusademy) built a marketplace mentoring platform that allows learning to take place in a network between users and mentors. Nusademy provides an opportunity for users to able to choose learning topics and mentors according to their needs. In it, there are steps for mentoring orders, which are currently still being processed manually by the admin every time there is a transaction. So, we need a system that can bridge users with mentors to share benefits without having to depend on admin availability. Python is a programming language that has characteristics suitable for autonomous system development and integration with third-party services. This study aims to create, implement, and test an automated order mentoring system on the Nusademy Web Application which is integrated with the Xendit payment application, the Google Calendar scheduling application, and the Google Hangouts video-conferencing application. The automation system provides automatic services in billing, checking payment status, communication processes between users and admins, communication processes between admins and mentors, creating virtual mentoring rooms, and notification of mentoring agenda reminders. The system is launched on a cloud computing-based server which can enable automatic capacity scaling based on user requests using Google Cloud Run. Testing of the order mentoring automation system is carried out by considering aspects of system capability, system reliability, level of effectiveness, user experience, and business analysis.
Machine Learning for Feeling Analysis in Twitter Communications: A Case Study in HEYDRU!, Perú

At present, sentiment analysis has become a trend; above all, in digital product development companies, as it is essential for rapid and automatic analysis. Sentiment analysis deals with emotions with the help of software, and it is playing an unavoidable role in workplaces. The constant growth of social networks, especially the Twitter social network, has made the ability to understand and comprehend users or clients take a greater scope regarding their needs; and therefore, increase the complexity of analysis of this social network, causing excessive expenses in time, personnel and money. This work presents a solution through the application of Machine Learning (ML) for sentiment analysis and thus improve analysis, execution time and customer satisfaction. The scope of this research is limited to using the Support Vector Machine (SVM) supervised learning technique for the intended analysis. The model derives from the ML technique making use of cross validation. The applied methodology is the CRISP-ML(Q) Methodology. The results show that the use of ML allows efficient sentiment analysis in Twitter communications.
The Influence of Learning Attitudes and Motivation on Students' Learning Achievement

The goal of this study was to see how learning attitudes and motivation affected student learning success at MAN 1 Batanghari. This is a quantitative study using a survey approach. This study's population included students from classes X and XI, with a sample size of 52 students. All respondents completed three types of instruments based on learning attitudes, motivation, and success in the learning process. The findings indicate that 1) learning attitudes have a significant impact on learning success. The better the learning attitude of students, the more successful the student learning process at MAN 1 Batang Hari will be;Â 2) Motivation has a significant impact on student learning success. The better the students' motivation, the better their success in the student learning process at MAN 1 Batang Hari; and 3) learning attitudes and motivation both have a significant effect on student learning process success. Whereas the better the learning attitude and motivation, the better the student learning process success.
Practicing Contextual Teaching and Learning Approach to Enhance Students’ Higher Order Thinking Skill on Writing Ability

Contextual Teaching and Learning (CTL), a learning approach which connects learning materials with real life applications within the context of students’ lives, encourages students to find the meaning of what they have learned on their own and the lecturer only act as a guide or facilitator, so the students are motivated to think critically. This study aims to describe the implementation of CTL approach to enhance students’ higher order thinking skill (HOTS) in writing ability of STKIP PGRI Sidoarjo, which has been found in preliminary observations to be lacking in this aspect. This quantitative research uses a quasi-experimental design through a pretest-posttest pattern. The research sample was 22 students of 2021 A class (Experimental Class) and 23 students of 2021 B class (Control Class). The data were collected through observation, test, and questionnaires. The higher order thinking skill includes: understanding problems (24,20%), making assumptions (28,17%), explaining in depth (25,13%), solving problems (23,25 %), and making conclusions (20,63%). From the table of t-test results, the results (α) = 0.004. It can be stated that (α) &lt; 0.05, so it can be concluded that there is a significant difference in the students' higher order thinking skill in writing ability by using contextual teaching and learning approach. The findings revealed that the contextual teaching and learning approach was successful to enhance students’ higher order thinking skill in writing ability. Moreover, it is recommended that CTL and higher order thinking skill have significant influence toward writing ability.
Suppressing Decoherence in Quantum State Transfer with Unitary Operations

Decoherence is the fundamental obstacle limiting the performance of quantum information processing devices. The problem of transmitting a quantum state (known or unknown) from one place to another is of great interest in this context. In this work, by following the recent theoretical proposal, we study an application of quantum state-dependent pre- and post-processing unitary operations for protecting the given (multi-qubit) quantum state against the effect of decoherence acting on all qubits. We observe the increase in the fidelity of the output quantum state both in a quantum emulation experiment, where all protecting unitaries are perfect, and in a real experiment with a cloud-accessible quantum processor, where protecting unitaries themselves are affected by the noise. We expect the considered approach to be useful for analyzing capabilities of quantum information processing devices in transmitting known quantum states. We also demonstrate the applicability of the developed approach for suppressing decoherence in the process of distributing a two-qubit state over remote physical qubits of a quantum processor.
Recognition of Handwritten English and Digits Using Stroke Features and MLP

This study aims to develop a handwritten English character recognition model by training a compact multilayer perceptron (MLP) neural network with text strokes. The primary data for model training was stroke features, including stroke orientation and density. Feature extraction was used to achieve sampling and data reduction. Training time was significantly reduced by using data reduction, where each iteration ran about 10 times faster than that using LeNet-5. With less training time, the model can make faster predictions. This model is suitable for edge computing and real-time prediction, even for Internet of Things (IoT) platforms not supported by graphics processing unit (GPU). This study used one split of the extended MNIST (EMNIST) dataset, i.e., EMNIST-Balanced, which consists of 47 classes containing English digits. According to literature related to EMNIST, the classification accuracy with this dataset was 78.02%. In comparison, this study achieved an accuracy of 87.49%
Evaluation of artificial neural networks for the detection of esophagus tumor cells in microscopic hyperspectral images

Microscopic analysis of histological slides of cancer tissue samples is standardly performed under white light microscopy. Researchers demonstrated the potential of artificial intelligence (AI) methods for the automatic identification of tumor cells. Hyperspectral imaging (HSI) combined with AI approaches can improve the accuracy, reliability, and time of the analysis. In this work, a HSI camera was coupled with a standard microscope to acquire microscopic hyperspectral (HS) images of stained histological slides of esophagus cancer tissue of 95 patients. The HS images were analyzed with deep learning algorithms to discriminate healthy cells (squamous epithelium) and tumors (stroma tumor and esophagus adenocarcinoma EAC). Five models were considered: a 2D CNN, a 2D CNN preserving the spatial relationship between spectral layers, a 3D CNN, a pre-trained 3D CNN and a recurrent neural network (RNN). They were evaluated using a leave-one-patient-out cross-validation. The predicted two classes were visualized with false colors. The RNN obtained the highest quantitative results with an accuracy of 0.791, an AUC of 0.79 and a computing time of 7.57 s per 10,000 patches. The best visual result was obtained on two selected HS images with the 2D CNN model. The performance of the automatic classification was higher on tissue which has not been treated with previous neoadjuvant therapy. The combination of HSI with deep learning method is promising for the automatic analysis of histological slides for cancer diagnosis.
Performance Analysis of Different Filters For Gibbs Phenomenon Minimization: A Comparative Study

When the approximated function is smooth enough, spectral methods have superior resolution; however, the approximation is plagued by dynamic Gibbs oscillations if the function has discontinuities within the given domain. Using the filtering process, it is possible to recover spectral accuracy for nonsmooth functions. This paper proposes a filter function for filtering the expansion coefficients. To perform the approximation, filtered Chebyshev spectral method has been considered in conjunction with proposed filter function. To elaborate the efficiency of proposed filter, illustrations have been taken for piecewise continuous functions. The comparison analysis with pre-existing filters function is also made. The error analysis is provided through mean squared difference error and maximum error.
Voltage prognosis of PEMFC estimated using Multi-Reservoir Bidirectional Echo State Network

One of the key points in the development of fuel cell technologies is to increase their lifetime. For this, it is necessary to be able to detect a degraded state in order to correct it, but also to be able to estimate the future state, which can facilitate predictive tasks such as control and maintenance. Echo State Networks are good candidates to estimate the future performance of proton exchange membrane fuel cells. They are part of the reservoir computing family and process time series using a neural reservoir. This paper proposes a method to predict the future voltage of a low-temperature proton exchange membrane operating in a steady state. For this purpose, several bidirectional reservoirs are used in parallel which improves the captured dynamics but also reduces the reservoir optimization effort which is currently one of the limitations in the development of Echo State Networks. The forecasting performance is quantified in three different ways: First, to simulate the temporal evolution of a system, the network is trained by varying the size of the training base. This allowed observing that the network can correctly capture the degradation dynamics while having an adaptation capacity when new data are added. Secondly, a comparison with a unidirectional ESN shows that bidirectional reservoirs will more easily capture non-periodic recovery and noise phenomena. Lastly, a comparison with 3 stacked bidirectional long-term memories shows that the proposed approach provided similar results while facilitating the optimization due to its low number of parameters to be set.
THE EFFECTS OF DIGITAL CURRENCY (ENAIRA) ADOPTION ON NIGERIA ECONOMY

This study examined the determinants of eNaira adoption in Nigeria, also examined the effects of eNaira adoption on economy in Nigeria. The utilized data were randomly collected from banks staff, economists, and Nigerians through questionnaires. These were rigorously analyzed through MANOVA, correlation, chi-square and Crombach’s Alpha statistics reliability. It is concluded that eNaira will have positive significant effect on economy in Nigeria in terms of employment generation, economic stability, easy facilitation of transaction, security of money, direct welfare disbursements enablement, Revenue and tax collection Increment, cost of processing cash reduction, resilient payment system supports, Central Bank currency usability improvement, and economic activities enhancement. It will also serve as medium of exchange, secured store of value, and stable unit of account. It is recommended that CBDC should establish validation scheme (centralized or decentralized) to prevent double spending or identity theft of eNaira code. Also, there is need to educate people about the eNaira by the government on the importance of this new development. Citizens must enlightened to understand the difference between cash deposits’ digital representation in bank accounts and eNaira in digital wallets. There must also be a programme which should be organized by CBN to deepen the enlightenment of eNaira in the country.