Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Smart Text Reader System for People with Vision Impairment

This paper proposes a smart text reader system which is designed to help people with vision impairment. The Microsoft Azure Cognitive Services Technology is used in this system for recognition of print or written character by taking input image and using computer programming. This technology converts the image of printed or handwritten characters to machine-coded characters. These files are then converted to acoustic outcome, which is speech by employing Microsoft Azure Cognitive Services and Google Text-to-Speech (gTTS) Converter. The transformation of the image into text file is performed by employing Microsoft Azure Cognitive Services which further utilizes the Computer Vision Client, Cognitive Services Credentials, Operation Status Codes, json and gTTS library, and Python programming language. The Microsoft Azure Cognitive Services further processes the character files using its library and Python programming language and that is how the acoustic outcome speech is attained.
Auto Organizer: A Machine Learning-Based Tool for Automatic Organization of Files

In this digitalization era where technologies are growing up day by day to provide easy and user-friendly solutions for various problems. In this way, we often download a bunch of files over the network or create a bunch of files and directories in the system to perform various tasks. But sometimes, it becomes difficult to find a single file in a bunch. In this paper, an artificial intelligence-based tool called Auto Organizer is proposed that focuses on providing a solution for users by automatic organization of all types of files including Portable Document Format (PDF), documents, images, and audio files. Different deep learning algorithms were trained to classify PDF, documents, images, and audio files into several categories. Total twenty classification categories are added in this tool. Special features such as sorting the directories, file name recommendation, and file save path recommendation were added with user friendly and interactive graphical user interface. The proposed tool is compared with some existing work and found the tool significantly better.
On complex trinomial roots distribution

The paper deals with a certain trinomial with two strictly complex coefficients.The root locus technique is utilized to obtain a distribution of roots with respect to a unit circle in the complex plane.A number of roots inside the unit disk is described in a relation with the two parameter values.Several appropriate graphs illustrate the trinomial roots distribution in particular cases.
Improved Tomographic Estimates by Specialized Neural Networks

Characterization of quantum objects, being them states, processes, or measurements, complemented by previous knowledge about them is a valuable approach, especially as it leads to routine procedures for real-life components. To this end, Machine Learning algorithms have demonstrated to successfully operate in presence of noise, especially for estimating specific physical parameters. Here we show that a neural network (NN) can improve the tomographic estimate of parameters by including a convolutional stage. We applied our technique to quantum process tomography for the characterization of several quantum channels. We demonstrate that a stable and reliable operation is achievable by training the network only with simulated data. The obtained results show the viability of this approach as an effective tool based on a completely new paradigm for the employment of NNs operating on classical data produced by quantum systems.
Nonlinear Activation-Free Contextual Attention Network for Polyp Segmentation

The accurate segmentation of colorectal polyps is of great significance for the diagnosis and treatment of colorectal cancer. However, the segmentation of colorectal polyps faces complex problems such as low contrast in the peripheral region of salient images, blurred borders, and diverse shapes. In addition, the number of traditional UNet network parameters is large and the segmentation effect is average. To overcome these problems, an innovative nonlinear activation-free uncertainty contextual attention network is proposed in this paper. Based on the UNet network, an encoder and a decoder are added to predict the saliency map of each module in the bottom-up flow and pass it to the next module. We use Res2Net as the backbone network to extract image features, enhance image features through simple parallel axial channel attention, and obtain high-level features with global semantics and low-level features with edge details. At the same time, a nonlinear n on-activation network is introduced, which can reduce the complexity between blocks, thereby further enhancing image feature extraction. This work conducted experiments on five commonly used polyp segmentation datasets, and the experimental evaluation metrics from the mean intersection over union, mean Dice coefficient, and mean absolute error were all improved, which can show that our method has certain advantages over existing methods in terms of segmentation performance and generalization performance.
Measurement of missing video frames in NPP control room monitoring system using Kalman filter

Using the Kalman filtering technique, we propose a novel method for estimating the missing video frames to monitor the activities inside the control room of a nuclear power plant (NPP). The purpose of this study is to reinforce the existing security and safety procedures in the control room of an NPP. The NPP control room serves as the nervous system of the plant, with instrumentation and control systems used to monitor and control critical plant parameters. Because the safety and security of the NPP control room are critical, it must be monitored closely by security cameras in order to assess and reduce the onset of any incidents and accidents that could adversely impact the safety of the NPP. However, for a variety of technical and administrative reasons, continuous monitoring may be interrupted. Because of the interruption, one or more frames of the video may be distorted or missing, making it difficult to identify the activity during this time period. This could endanger overall safety. The demonstrated Kalman filter model estimates the value of the missing frame pixel-by-pixel using information from the frame that occurred in the video sequence before it and the frame that will occur in the video sequence after it. The results of the experiment provide evidence of the effectiveness of the algorithm.
An IoT-Based Vending Machine Using Blockchain for Enhanced Security

The Internet of Things (IoT) is an established technology changing the way of our daily lives. However, as its usage increases, the threat of utilizing them wrongly also increases. Blockchain technology, which provides strong security, is used to protect IoT devices from being hacked, helping solve the drawbacks of an IoT system. The automated vending machine works by distributing a variety of vended goods and helps provide disintermediation. The vending machine's primary goal is to speed up client service while minimizing human touch. In this paper, the development of a system for cashless payment is described. This helps vending companies with problems like cash handling, fake or slug currency, encashment, and change. The infrastructure of IoT vending machines, which is based on the blockchain network, is discussed thoroughly. Finally, a model has been provided for the security of the internet of things using blockchain.
Dynamic Collaborative Learning Based on Recommender Systems and Emergent Collective Intelligence in Online Learning Communities

The presence of interaction and mutual support functionalities offered by online social networks has increased in e-learning systems. Current collaborative learning platforms have witnessed the emergence of many resources, relationships and interactions provided by social information systems, leading to an information overload. Indeed, this phenomenon has made users unable to cope with information overload. As a solution, we aim to recommend relevant resources based on the interaction between users. We present a multilayer, graph-based recommender system that enables pedagogical resources to be recommended by relying on the connections between individuals in collaborative online learning communities. The multilayer, graph-based recommender system harnesses emergent collective intelligence in online learning communities. Our proposal describes dynamic collaborative learning that uses a recommender system based on emerging semantic graphs by investigating collective intelligence within online learning communities. Our findings reveal relevant performance results of the multilayer, graph-based recommender system of pedagogical resources. Future studies on network analysis aim to improve the performance of recommendations.
10 Years of Research at Cadi Ayyad University: Pedagogical Innovation to Cross Borders in Education

This introductory chapter presents an overview of all contributions that summarize a decade of scientific research around science education at Cadi Ayyad University (UCA). Several practical cases have been presented and discussed in real situations in our classes, and the results have been taken into consideration. All studies have been quantified with data and reflect various issues that Moroccan universities were facing, as well as massification, digital transformation, COVID-19 pandemic, development of AI, traditional and active learning and teaching, new tools for assessment, introduction of educational technology in teaching and development of ICT in classes and technological progress, among others. These scientific contributions are a field of pedagogical innovation in which PhD students at UCA are conducting research. This work aims to inspire educators, PhD students and all stakeholders with the models we developed at the university, putting various research ideas together to promote the quality of higher education.
Automatic Detection of Counterfeit and Genuine News Article

In the new years, the issue of fake news has developed a lot quicker. Web-based media has incomprehensibly changed its span and effect overall. On the one hand, it is modest and effectively accessible with fast portion of data draws more consideration of individuals to peruse news from it. Then again, it gives wide spread of fake news, which are only off-base data to mislead individuals or per users. Accordingly, Counterfeit News Location assumes an essential part to keep up vigorous on the Web and online media. In this paper, machine learning strategies are utilized to identify the believability of information dependent on the content substance. Furthermore, after that an examination is made to show that calculation utilized here is more solid and compelling as far as distinguishing a wide range of information. The algorithms applied in the work are Logistic Regression, decision tree, Gradient Boosting Classifier, and Random Forest Classifier. The outcomes were discovered to be promising. Here, we present a model which will precisely anticipate the validity of information. Our model outflanks existing model designs.
Specific Pose Detection Through Efficient Neural Network

Human pose detection is a process of detecting human body points in images or videos. For the past few years, it has gained the attention of the computer vision community. Some of the achievements such as single pose detection and top-down network were efficient and reliable but suffered some problems. This technology has a very vast scope and is applicable in areas such as medical industry, defense industry, public safety, personal monitoring, and mentoring but due to the limitation of technology and algorithm, implementation has become a big hassle. In this work, we bring a real-time network-based pose estimation system that utilizes CPU processing and generates the normalized body points concerning the frame. It demonstrates how these points can be used to calculate the different body part angles as well as their lengths and by using these calculations, classifying the pose into a category to detect and output the result in the desired format. Our project outperformed the current system in terms of efficiency, cost, and processing speed.
Beat Frequency Detection on Boolean Chaotic Oscillator for True Random Number Generation on FPGA

This work proposes a new True Random Number Generator (TRNG) design on Intel Cyclone II FPGA for cryptographic key generation applications. Boolean Chaotic Oscillator (BCO) has been utilised in this work to generate true randomness where the entropy of TRNG has been harvested through beat frequency detection technique. 50 MHz of the operating clock has been used for true random number generation. Linear Feedback Shift Register (LFSR) has been adopted as a corrector function to enhance the randomness of TRNG. The entropy source requires only 9 Logic Elements (LEs) and the proposed TRNG architecture consumed 783 logic elements. Throughput has been achieved as 27.306666 Mbps. Entropy, hamming distance calculation and NIST 800–22 analyses have been performed to validate the design. Further, true randomness of the proposed TRNG has been verified through restart experiment.
Chaos Game Optimization Algorithm with Crossover Operator for Solving Constraint Engineering Optimization Problems

Metaheuristic algorithms are intelligence optimization techniques that showed satisfactory performance in dealing with real-world constrained optimization problems. One of the most recent metaheuristic methods is Chaos Game Optimization (CGO), which has a high ability in solving various types of optimization problems. CGO is a robust metaheuristic method that is developed according to some rules in chaos theory in which fractals self-similar configuration issue is employed by chaos game methodology. This chapter introduces the Crossover based Chaos Game Optimization (CrCGO) algorithm by embedding the crossover operator in the search process of the CGO algorithm. A total number of 30 benchmark constrained engineering optimization problems are used to study the capability of CrCGO in dealing with challenging optimization problems. In addition, the results of the developed algorithm is compared with a variety range of optimization methods, including six popular, and six newly developed methods from the literature. Besides, two well-known non-parametric statistical methods, Friedman and Wilcoxon signed-rank, are utilized to analyze the performance of the developed method, and results shows the better capability of the developed method in dealing with most of the selected complex optimization problems.
The Analysis of Teaching Writing Descriptive Text at the Seven Grade of Junior High School

This study aims to identify and describe the learning process and the problems faced by teachers in teaching writing descriptive texts in the seventh grade of SMP Negeri 2 Candi. The method in this study is a qualitative descriptive method. In collecting data, researchers used interviews and document studies. The results showed that the learning process of writing descriptive text by the teacher in class seven grade of SMP Negeri 2 Candi consisted of preliminary activities, core activities, and closing activities. The teaching process carried out by the teacher is still effective. The teacher has planned the learning process, selected materials, prepared activities and roles, and evaluated learning. Then, the problem of the teacher in the teaching process of writing descriptive text comes from external factors, namely the students. students have problems in developing their ideas in writing, they have a lack of vocabulary and students have difficulties in grammar.
The Influence of the Use of Media Learning Raya (Cultural Variety) Based on Articulate Storyline on Interest in Learning Social Sciences for Grade IV Elementary School Students

This study aims to describe the effect of the Articulate Storyline Application based RAYA (Cultural Variety) Learning Media on the interest in learning social studies for fourth graders at SDN Balongmacekan, and to find out how much influence the application of the interactive articulate storyline-based RAYA (Cultural Variety) learning media has on learning interest. Social Studies students of class IV SDN Balongmacekan. Analysis of research data using the t-test (one-tail test). Uses experimental research methods and one-group pretest-posttest design. The data collection technique used a student's social studies learning interest questionnaire. The sampling technique used a total sample, and fourteen student was the fourth-grade of SDN Balongmacekan. The results showed of the t-test with a tcount of 32.56. If viewed from the t table value with a significance level of 5% (0.05), the value is 1.761, and it can be said that the value of tcount &gt; ttable is 32.56 &gt; 1.761. This shows an influence on applying articulate storyline-based RAYA (Cultural Variety) learning media on the interest in learning social studies for fourth-grade students at SDN Balongmacekan. While the results of the analysis using eta square show that the value of eta square is 0.987 &gt; 0.14, this shows that there is a significant influence on the application of interactive media-based RAYA (Cultural Variety) learning media on the interest in learning social studies for fourth-grade students at SDN Balongmacekan.
The Effect of Question Box Assisted Talking Stick Learning Method on Student Learning Outcomes in Class III Civics Learning at Elementary School

The purpose of this study was to determine the effect of the Question Box-assisted Talking Stick method on Civics Class III learning at SDN Kedondong 2 Tulangan. This becomes an obstacle in the learning process, because learning is still teacher-centered so that it affects student learning outcomes. Therefore, to overcome these problems, it is necessary to change the learning methods that are usually used by teachers when teaching so that there is an increase in learning outcomes, namely through the use of the Talking Stick method with the help of the Question Box. Analysis of research data using paired sample t-test. This type of research is a pre-experimental design with data collection techniques using pre-test and post-test questions. The results showed a significant increase in results with the use of the Talking Stick method with the help of the Question Box. This can be seen from the results of the t-hit test of -21,273 and significantly 0.308, it is stated that there is an effect of the Talking Stick method with the help of Question Box on student learning outcomes.
Application of the Project Citizen Learning Model in Developing Social Intelligence Elementary School Students in Grade 5 and 6

The purpose of this study was to analyze the application of the Project Citizen Learning Model and social intelligence to the 5th and 6th graders of SD Muhammadiyah 01 Sidoarjo. The method used is descriptive qualitative. The result is that the steps in this model have been implemented by the teacher. Starting from the beginning of learning, teachers and students identify problems and seek information about public policies that exist in the community by discussing, groups will present their work and portfolios that have been made, reflecting on the learning experience at the end of the lesson. Based on the results of the questionnaire data obtained 89% of students are confident in making new friends, 97.8%. Students easily communicate with friends, 93.5% are able to communicate with other people including school workers, 79.7% help friends complete assignments, 90% are able to do school assignments well, 100% of students answer the mandate to take care of safekeeping items. With this Learning Model, not only cognitive abilities are obtained by students but the development of students' social intelligence is evident from the results of questionnaires distributed by researchers.
Imam Al-Ghazali's Perspective Moral Education

This article aims to find ethical education in Imam Al-Ghazali's attitude, so that the knowledge of the ethical school is more diverse and more comprehensive. The study technique used is a literature study, the information of which is obtained from a literature review with theoretical and philosophical techniques. Imam Al-Ghazali's moral perspective which is applied to Islamic education today can foster good and well-done values.
Contactless Payment Security

The introductory part of the chpater refers to the expansion of digital payments as a consequence of the spread of coronavirus and measures to protect the population. A detailed but not too broad definition of cryptocurrencies also describes the legal background, comparing digital money with traditional monetary obligations and classic money. In the field of use of Bitcoin, the pros and cons are accompanied by standard payment cards – functionality and risks, including potential frauds with payment cards. Through the instant payment solution, the authors come to digitalization, and various forms of abuse in practice – from charity scams and crypto heists to the Russian-Ukrainian war, which seemed to take away the coronavirus and flood the world with the virus of aggression and despair. Although the battle against the virus took place on the ground at the time this work began, and war-torn Ukraine stole the focus, civilians from all parts of the world appear to be the biggest victims of virtual violence.
Evaluation of alteration zones around Parmagasu copper indication, Kuh Zar, Damghan, Iran, using ASTER satellite data

Parmagasu copper indication is located 100 km southeast of Damghan city, Iran. Copper, gold, Pb, and Fe mineralizations have occurred in the region, in the Eocene trachyandesite-andesite porphyry host rocks. This study attempts to identify alteration zones and their expansions to comprehend the origin of mineralizations and their distribution pattern in the area. The phyllic alteration zone is surrounded by more extensive propylitic alteration. The argillic zone is less widespread than the other alteration zones and overlaps with the phyllic zone. The Band Ratio, Principal Component Analysis, Independent Component Analysis, and Spectral Angle Mapper remarkably identified the alteration zones among the employed methodologies. The validity of the results was proved by field observations, microscopic investigations, and the computed overall accuracy for the outcomes of supervised classification. This investigation confirms the importance of faults in the occurrence of alteration in the Kuh Zar area.
Cloud Resource Scheduling Method based on Markov Process and the Cuckoo Search

Abstract With the rapid development of cloud computing, the computing reliability of cloud servers has become an important research area. To improve the load balancing capability of cloud servers, an autonomous adaptive architecture cloud computing framework based on software-defined networking and network function virtualization were proposed. And a cloud resource scheduling method based on the Markov process model and cuckoo search algorithm to realize the load balancing of cloud nodes was designed. The cloud platform verified by the experiment shows that the method can effectively guarantee the service reliability of cloud computing.