Reviewerly/exHarmony huggingface dataset:
A 3D discrete memristive chaotic map and its application in image encryption

In recent years, researchers proposed many discrete memristive models. And the performance of chaotic map can be improved by using discrete memristor. In this paper, a kind of discrete chaotic map is studied. First, the map is cascaded with memristor to generate a new discrete memristor chaotic map. The dynamic behavior of discrete memristor chaotic map is analyzed. Numerical simulations demonstrate that the proposed map has complex dynamics, like hyperchaos and coexisting attractors. Then, based on the proposed memristive map and DNA coding, an image encryption algorithm is designed and its security and robustness are analyzed. Experimental results show that the algorithm can effectively resist plaintext attacks and has good robustness.
Estimation of Distribution Algorithm with Discrete Hopfield Neural Network for GRAN3SAT Analysis

The Discrete Hopfield Neural Network introduces a G-Type Random 3 Satisfiability logic structure, which can improve the flexibility of the logic structure and meet the requirements of all combinatorial problems. Usually, Exhaustive Search (ES) is regarded as the basic learning algorithm to search the fitness of neurons. To improve the efficiency of the learning algorithm. In this paper, we introduce the Estimation of Distribution Algorithm (EDA) as a learning algorithm for the model. To study the learning mechanism of EDA to improve search efficiency, this study focuses on the impact of EDA on the model under different proportions of literals and evaluates the performance of the model at different phases through evaluation indicators. Analyze the effect of EDA on the synaptic weights and the global solution. From the discussion, it can be found that compared with ES, EDA has a larger search space at the same efficiency, which makes the probability of obtaining satisfactory weights higher, and the proportion of global solutions obtained is higher. Higher proportions of positive literals help to improve the model performance.
A Deep Learning App for Counterfeit Banknote Detection in the WAEMU

In West Africa, counterfeit CFA banknotes impact the economic growth of states.Because of this scourge, we are witnessing a decline in the purchasing power of the population.However, some hardware kits for detecting counterfeit CFA banknotes are available on the market.These kits are expensive for the actors of the informal sector.For them, these kits are not easily portable and also frequently break down.In this work, we propose an approach based on deep learning for the detection of counterfeit CFA banknotes through an Android application.Furthermore, the proposed approach is a first one in the WAEMU in the field of research for the CFA banknote forgery detection.We use an image dataset of over 4000 genuine and counterfeit ten-thousand CFA banknote for our model training.The images of the banknotes are taken by a smartphone camera.We use the convolutional neural network Alexnet for banknote classification.The accuracy of the training model reaches 99.7% for the detection of counterfeit CFA banknotes.
Two Way Indian Sign Language Translator using LSTM and NLP

Abstract: Sign language is the efficacious medium that connects silent people and the world, and there are many significant existing sign languages across the globe. A lot of research is done to compromise the borders of difficulty in the communication between silent and normal person and most of them are based on ASL (American Sign Language). The aspiration for bridging the gap between silent people and normal people in terms of communication using ISL (Indian Sign Language) led to unfold this project. It functions as a two-way sign translator that is the conversion of sign to text and contrariwise. It recognizes various poses as well as gestures and returns appropriate results. The designed translator predicts the sign with an accuracy of 88 percent in real-time and was trained to recognize 15 actions using LSTM and MediaPipe. The text to sign translator works up to paragraph level using NLP.
A study on the meaning of the learning experience in the literacy program accredited elementary education of the female elderly learner who experienced life crisis events: Focusing on psychological changes

This study is conducted to explore the meaning of the learning experiences of the female elderly learner who experienced life crisis events and participated in the literacy program accredited elementary education. For the purpose, the life history research method was employed. The participant overcame the great crisis of life that she faced in the latter half of her lives by learning in the literacy program, while reporting a significant change in her mind compared to before learning. In addition to a sense of confidence and achievement, it is said that unlike before, her mind has softened and her has become more tolerant of others than before. Based on the above research results, the following implications were derived. First, the literacy program process involves more than academic accreditation. The participant’s learning was further motivated by the needs of escaping from the crisis situation, which led to her perceived joy of learning and life. Second, the participant gained a sense of accomplishment and confidence by receiving support from teachers and fellow learners. Third, the participant newly established her attitude toward life for the rest of her lives while attending literacy schools. Through introspection and rumination on the learning experience, which is the process of reinterpreting or reinterpreting the meaning of the learning experience and converting it into action (Mezirow, 2000), an opportunity for a positive change in consciousness is created. Based on the research results, it is necessary to re-prove the relationship between social support and psychological aspects as well as the relationship between learning experience, perception and attitude change for the rest of life with a more systematic and structured study in future follow-up studies. Also, the need to expand the budget and support policies for literacy education was suggested
Aether signs contract with Saudi Aramco Technologies

Handwritten Mathematical Expression Recognition (HMER) is essential to online education and scientific research. However, discerning the length and characters of handwritten mathematical expressions remains a formidable challenge. In this study, we propose an encoder–decoder architecture incorporating a novel Adaptive momentum-based Regularized Gated Recurrent Unit (AdamR-GRU) to solve this problem. It incorporates a novel gate within the GRU framework to maintain input histories, enabling extended memory retention and faster convergence. To assess the performance of AdamR-GRU, we conducted a series of comprehensive experiments on the CROHME 2014, 2016, and 2019 datasets. The results indicate that the AdamR-GRU model exhibits competitive performance with existing state-of-the-art methods regarding Expression Recognition Rate (exprate). Moreover, our model achieves state-of-the-art accuracy concerning 1, 2, and 3 symbol-level errors (top 1, 2, 3 error accuracy) on the CROHME 2014 and 2016 datasets. For the CROHME 2019 dataset, the model attains state-of-the-art performance in top 2 and 3 error accuracy. AdamR-GRU demonstrates reduced computational complexity and memory usage compared to alternative contemporary models.
Global catalyst fertilizers market report 2023: Rising decline in arable land &amp; increasing fertilizer consumption bolsters growth

There are many annotation methods for the English language based on adapting an argumentation model according to the study domain. However, as far as research has been done, there are no annotation methods for detecting argumentative content in Spanish, not only due to the complexity of identifying the evidence but also because of the lack of data available for this task. The research aims to present and evaluate an annotation method consisting of an adapted argumentation model, an annotation guide, and an annotation process based on Twitter data analysis. The Inter Annotator Agreement (IAA) study achieves 0.63 Fleiss Kappa for Argument/Non-Argument tagging, 0.35 Fleiss Kappa for Argument Component tagging, and 0.53 Fleiss Kappa for Non-Argument Component tagging, while the best Cohen's kappa (k) index achieved, was 0.73, 0.52 and 0.75 respectively. The results' assessment highlights the need to include linguistic segmentation rules for the second annotation task. It is crucial to use discourse markers for the claim and evidence detection. For the first annotation task, it determined that if the prevalence index and the bias index are very low, the prevalence index predominates over the bias index because k increases (0.52<=k<=0.72); likewise, for the third annotation task, when the observed agreement index is almost perfect (0.92) the value of k increases (k=0.75) despite a high prevalence index and a low bias index. The annotated corpus with a Fleiss Kappa >= 0.60, agreement and disagreement tables, and confusion matrices code are available on Mendeley Data Repository.
ORGANIZATION OF DISTANCE AND MIXED EDUCATION AT HIGHER EDUCATIONAL ESTABLISHMENTS BY USING CLOUD SERVICES

The article examines the theoretical and practical aspects of the application of cloud services in the process of IT training of students of higher education institutions during distance and mixed learning. In recent years, the national education system has faced a number of problems related to the organization of the educational process in the conditions of quarantine restrictions, military operations and the need to ensure the quality and availability of higher education. In particular, among the main activities of the teaching staff is the search for and effective use of modern technologies, methods and means of organizing distance and mixed forms of education. In today's realities, the distance and, later, the mixed form of education have become an integral part of the organization of the educational process of higher education institutions in Ukraine. Regardless of the specifics of the training of higher education applicants, training is, to varying degrees, based on the use of remote technologies and cloud services. There is a sufficient number of educational materials management systems (LCMS Moodle, ATutor, NEO LMS, etc.) and cloud services (Google Classroom, Microsoft Teams, etc.) to support and organize the educational process. Among a large number of various systems and services, it is advisable to pay attention to the Microsoft Teams cloud service, as it is integrated with other Microsoft tools. An analysis of the functionality of the Microsoft Teams cloud service, which is the "core" of the virtual learning environment, was carried out. The advantages of its use are highlighted and the expediency of introducing cloud services into the educational process is substantiated. General approaches to the use of the Microsoft Teams cloud service are proposed, in particular, the organization and division of academic groups, methods of communication between participants in the educational process, methods of presenting methodical and educational materials, methods of evaluating the results of educational activities. According to the results of the research, it can be stated that the use of cloud services during distance and mixed learning can fully ensure the assimilation of the theoretical and practical aspects of computer science disciplines, regardless of certain problems in the self-organization of students. In our opinion, it is caused by the lack of direct student-teacher communication, which is obvious in the case of distance education.
Correlation between anxiety and critical reading ability of Indonesian English as a Foreign Language students

This study investigates the possible relationship between reading anxiety and the critical reading ability of tertiary EFL students majoring in English education at a state university in West Sumatra, Indonesia. Thirty-two students were selected utilizing convenience sampling as the research sample. By employing a correlational approach, the instrument used for this analysis was a TOEFL test consisting of 30 questions to measure students' ability to read critically. In addition, a questionnaire was also developed based on the theory of experts composed of 20 statements used to measure students' reading anxiety. Pearson product-moment correlation was used to investigate the relationship between reading anxiety and students' critical reading ability. The study found that 1) most students experience anxiety at the intermediate level, 2) the level of students' critical reading ability is at the average level, and 3) there is a significant relationship between students' reading anxiety and critical reading ability. The results imply that the reading anxiety students experience correlates to their scores in critical reading, leading to a significant effect of reading anxiety on students' critical reading skills.
PELATIHAN PENYUSUNAN INSTRUMEN PENILAIAN BERBASIS E-MODUL INTERAKTIF BAGI GURU DAN SISWA MI MUHAMMADIYAH SREBEGAN CEPER KLATEN

This Community Service activity was carried out with the aim of increasing the competence of teachers to develop an interactive E-module-based assessment instrument and the use of interactive E-modules for MI Muhammadiyah Srebegan Ceper Klaten students. The Community Service activity was carried out by the Education and Basic Education Research and Evaluation Study Program in collaboration with MI Muhammadiyah Srebegan Ceper Klaten on Saturday, July 23, 2022. The activity was carried out offline at MI Muhammadiyah Srebegan. The participants of the activity were teachers and students of MI Muhammadiyah. The implementation of the activity begins with the presentation of material by each of the 2 resource persons, followed by an experience sharing session by students of the UST Research and Evaluation Study Program and Basic Education who have worked as teachers in elementary schools, then discussions and questions and answers, and continued with the practice of preparing learning tools. independently. The results of this activity are that the activity runs smoothly, quite a lot of benefits are obtained by the participants, including getting additional knowledge related to good learning tools, as a material for self-introspection to correct previously prepared learning tools, teachers are able to practice directly in compiling assessment instruments based on E-modules are interactive and students are able to use interactive E-modules.
A Study on Understanding and Exploring Basic Directions for Education Methods in Church Schools Suitable for the Next Generation

The purpose of this study is to examine the overall reality of church education in the educational field of church schools and to explore the basic direction of education methods suitable for the next generation. In order to achieve this purpose, this study looked at the overall appearance of church education based on the meaning and importance of education in church schools, the reality of church schools, and the difficulties and solutions of church education. In addition, based on domestic research trends on educational methods in church schools, various educational methods in church schools, and concerns about newly introduced media and educational methods, the educational methods conducted in church schools were examined. Through this examination, the study proposes three main basic education directions suitable for the next generation. First, the educational method of the church school should act as a way of putting the essence and purpose of the church school in the first place, and advance in the direction based on the Bible. Second, the teaching method of the church school should focus on the student. Third, the educational method of church schools must go beyond the existing formal paradigm and move toward reform. In this study, the direction of church education and education methods for the next generation was explored. And based on this, in this study, the direction for the correct use of new edutech, which is gradually expanding the scope of application due to COVID-19, was sought.
Law by Algorithm

Abstract— This review article offers a critical analysis of Horst Eidenmüller and Gerhard Wagner’s Law by Algorithm by focusing on four major sets of issues that are covered in this important work: (i) separate legal personality for artificial intelligence (AI) systems; (ii) the exploitation and protection of consumers; (iii) liability; and (iv) online dispute resolution. On separate legal personality, it is shown that neither unbundled products nor difficulties in proving that the systems resulted in damage or losses necessarily justify giving legal personality to AI systems. On consumer protection, it is argued that exploitation of consumers can be regulated by consumer protection legislation provided that reforms are made to remove enforcement hurdles. On liability, the issues arising from product liability legislation and problems associated with proving causation are critically examined. On online dispute resolution, smart contracts and self-driving contracts are distinguished, and a distinction is drawn between AI-assisted and AI-substitutionary adjudication.
Ensemble recurrent neural network with whale optimization algorithm-based DNA sequence classification for medical applications

The modern data-driven era has facilitated the gathering of large quantities of biomedical and clinical data. The deoxyribonucleic acid gene expression datasets have become a vital focus for the research community because of their capability to detect pathogens via ‘biomarkers’ or particular modifications in the gene sequence which portray a specific pathogen. Metaheuristic-related feature selection (FS) efficiently filters out only the pertinent genes out of large feature sets to lessen the data storage and computation requirements. This paper embraces the whale optimization algorithm for the FS issue in HD microarray data for the effectual propagation of candidate solutions to reach global optima over sufficient iterations. The chosen data are classified by employing an ensemble recurrent network (ERNN) that retains the amalgamation of long short-term memory, bidirectional long short-term memory, and gated recurrent units. Analysis of this proposed ERNN methodology would be performed by correlating with diverse advanced methodologies, and thus, the ERNN attains 99.59% precision and 99.59% accuracy.
Analysis of the digital transfer framework of power grid engineering based on the integration of QR code and blockchain

The digital transfer of power grid engineering is faced with problems of multiple links, multiple objects and multiple data. During the transfer process, various resources are difficult to locate, correlate and trace, and the data of each link is repeated, which consumes a lot of manpower. In this study, we propose a two-dimensional code for power grid engineering based on QR technology, establish a unified basic identity library for basic elements of power grid engineering such as personnel, units, projects, equipment, and atlases, and realize the organic correlation of various objects of power grid engineering and the information penetration of various power grid majors. Research and build a code management and control technology platform for power grid engineering, take power grid engineering management as the main line, combine State Grid chain(Blockchain) to realize the whole process of code generation, authorization, code use, and cancellation of power grid engineering code on the chain, digitally empower grid management of power grid engineering, and accelerate the digital transformation of power grids.
Synthesizing reversible functions via bounded model checking

We consider the problem of synthesizing optimized circuits for reversible functions using NOT, CNOT and Toffoli gates (NCT), which has important applications in circuit design and quantum cryptography. By defining the state space and state transitions from NCT gates, we transform the synthesis problem into a reachability problem of finding the shortest path connecting the identity function and the targeted function that is to be synthesized. Using Bounded Model Checking (BMC), we propose an algorithm for synthesizing the optimized circuit for reversible functions. For the synthesis of linear reversible functions, we design another method by directly parametrizing the linear invertible matrix. Our methods can produce optimal synthesis of reversible circuits when the number of bits is small, and also can be used as a subroutine of heuristic methods for large bits. Experimental results show the effectiveness of our methods.
Design of fast and sparse accelerator for deep learning model based on FPGA

At present, there have been many studies to design various CNN hardware accelerators to accelerate the inference of deep neural network models. The FPGA-based CNN reasoning accelerator can provide sufficient computing power support with flexible data accuracy, lower energy consumption and lower application cost, and has received a lot of attention in the application field of IoT terminal devices with limited computing power and energy consumption. Widespread concern. However, although the current FPGA-based CNN accelerator has greatly improved the speed of model reasoning through various methods, most of the methods cannot be effectively applied to actual terminal scenarios due to limitations in memory and energy consumption. In response to this situation, we designed an acceleration framework that takes into account both inference acceleration and energy consumption. Aiming at the limitation of computing power in the terminal environment, optimize a large number of multiplication operations in the convolution operation that consumes the most computing power in the CNN inference stage, by using local cache and matrix transformation formulas, and skipping pairings by zero values in the calculation process the model inference operation is further accelerated while reducing energy consumption. The experimental results show that compared with the current advanced neural network accelerator, not only the computing power has been significantly improved, but also the energy efficiency ratio has achieved better results. Moreover, this method can not only be implemented in FPGA, but also be migrated to other embedded terminals.
Unpaired strong-light image augmentation network with illumination curve

Images are a way for humans to get information, and they are ubiquitous in our daily lives. Compared to other media, we tend to get information through images. Most current image enhancement methods are based on Retinex theory. However, this method can easily lead to the loss of image information. More importantly, the theory is based on the assumption that light changes smoothly. And this assumption is very unfriendly to bright images. To solve this problem, an unpaired enhancement network for strong light image is proposed. The network consists of illumination curve, illumination loss function and illumination attenuation network. Experiments show that our network can obviously improve the image contrast caused by strong light environment, and significantly improve the quality of strong light image.