Reviewerly/exHarmony huggingface dataset:
Introducing deep learning in medical diagnosis

In various fields, DL, which is a subcategory of artificial intelligence, has made an impact. Medical imaging is among the most significant application fields for DL, in which DL algorithms could be used for the classification of the dataset and prediction of datasets. The discriminative and generative structures of DL are discussed in detail in this chapter. Also discussed is the significance of CNN and its variants in the treatment of neurodegenerative disorders. The use of GPU-automated machines for DL allows medical professionals to analyze images. CNN and its variants, FAST-RCNN, are used to track the progression of disease symptoms in humans. In other words, various CNN algorithms are used to model and predict the possibility of new illness progression due to the patient&apos;s current clinical illness state. FAST-RCNN is also used to classify new brain disorders based on the patient&apos;s gait features, and various CNN techniques are also used to detect the development of diseases with high accuracy.
A prioritized potential framework for combined computing technologies: IoT, Machine Learning, and blockchain

In today&apos;s scenario, every organization is dependent on the use of computers for automating business and work. The data generated by the organization is huge and has great significance in analyzing the processes. It can be rightfully called as the lifeblood of any organization. The world of computing requires three technologies: Internet of Things (IoT), machine learning (ML), and blockchain. The confluence of these technologies is inevitable in the coming future due to the benefits which are involved in future applications. There is barely any activity today which is cannot apply the use of these three technologies e.g., healthcare, automation, education, etc. IoT can be defined as interconnection of various autonomous devices which are capable of communicating with each other. IoT requires an intermittent Internet connection and address for every device. The user of these devices can remotely monitor and manage by retrieving the information on a handheld device similar to the cellphone. This way, the devices are connected 24/7 and continuously generating data. Challenges of IoT involve the following: security, connectivity problems, and huge data. The devices can be made capable of taking intelligent decisions by incorporating Artificial Intelligence (AI) and ML technology. ML is a sub-branch of AI. It has got huge potential to detect the patterns and anomalies in the data which is generated by the wireless sensor nodes in IoT. The advanced decision-making process of ML has already influenced our daily routines, for example: banking, healthcare, gaming, transportation, and space exploration. Challenges faced by ML are the following: security, centralized architecture, and resource limitations. To cover the security aspect of these two technologies, blockchain technology is the perfect answer. It is a decentralized peer-to-peer network which stores the records and transactions in blocks which cannot be altered. This technology secures the communication by eliminating the need for any trusted third party. The blocks are stored in such a manner which makes it impossible to hack or tamper the data by taking control of device or capturing the records. This chapter will present a comprehensive and quantitative analysis of the existing research and how these technologies can be a transformative impact for access to information by the users. The convergence of blockchain, ML, and IoT will provide scalable, secure high-level intellectual functioning that will be the new paradigm of digital information. This book chapter presents futuristic potential of convergence of three technologies and elaborate discussion of the past researches.
Clustering crowdsourced healthcare data from drones using Big Data analytics

Nowadays, a vast amount of data is considered for statistical analytical tools. Big Data analytics have generated an exponentially increasing variety of medical data from IoT devices such as drones. Today, the epidemiological data collection pattern in disease monitoring is gathering with drones. This includes organized, semi-structured and unstructured data, leading to drone data trawling. This chapter provides big data to increase healthcare quality by applying effective machine learning (ML) strategies for segregation and drones-compilation data. The process intends to resolve the drone&apos;s use of trawled data and provide real-time analyses of the data. The ML algorithm implemented the Apache Spark core for smoother segregated streaming from various crowd sources. Three drones were configured for the experiment. The principle will increase the accuracy of health care forecasts based on the investigation findings. This result showed that relative to hierarchical clustering and density-dependent clustering, the K-means cluster has the highest smoothness rate of real-time segregating data.
Promoting perceived English reading self-efficacy of underserved students using an out-of-class extensive reading module

Perceived English reading self-efficacy is one of the essential factors that facilitate development of English language ability as it motivates learners to read and learn. This qualitative study explored the outcomes of the implementation of an out-of-class extensive reading module to enhance perceived English reading self-efficacy of lower secondary students in a slum area in Bangkok, Thailand. The pre- and post-perceived reading self-efficacy tests, reading logs, teacher observation notes, and interview protocols were used to collect data in the study. The findings revealed that out-of-class extensive reading activities could help promote students' perceived reading self-efficacy as it suited the specific characteristics of underserved students and motivated them to read. Thus, the out-of-class extensive reading module could be utilised to enhance perceived reading self-efficacy, which, in turn, could help develop reading ability of underserved students.
[Cries in Spanish]

The memetic circulation of Mexican actress, Itatí Cantoral, while in character for her iconic, evil role of Soraya Montenegro within the telenovela Maria la del Barrio (Maria of the Slums) demonstrates how Mexican Spanish-language content can reinvent itself decades later within US Latina/o/x popular culture. The dramatic protagonist of Maria la del Barrio, Soraya Montenegro, began her reign in Spanish-language television but now, thanks to memes modeled on her then telenovela character, enjoys widespread social media recognition within bilingual (Spanish-English) and English-dominant corners of the Internet. This chapter analyzes the rich intertextuality embedded in the series of memes generated by Internet users that feature Soraya Montenegro with comedic subtitles that regularly communicate emotions and attitudes rather than translated text. These memes originated on Tumblr and quickly spawned into a series of memes, courtesy of user-friendly meme generators. The text is always presented as bracketed or within paratheses shown as: “INSERT in Spanish.” Yet, these widely circulated series of memes produced during the mid-aughts clearly communicated the linguistic, media user experiences of US Latina/o/xs. We argue that these memes allow Latina/o/x users to engage in socially constructed public discourses (Shifman 2014) about language and media use. These memes invite Latina/o/x users familiar with the emotive genre of telenovelas and proficient in Mexican and US media and language play to reimagine how we remember Soraya Montenegro.
A Study of the Development of a Fuzzy Inference System Using Image Watermarking

The primary purpose of this research is to develop a non-blind image watermarking system that is both robust and unnoticeable using wavelet transforms and fuzzy inference. The emphasis is on developing discrete wavelet transform, human visual system, and fuzzy inference system-based picture watermarking that makes optimal use of human vision constraints. The purpose of this research is to develop a novel technique for embedding perfect measure watermark data into the host image without creating perceptual damage. The methodology is to combine discrete wavelet transforms, the human visual system, and fuzzy inference to enable the utilisation of human vision limits in a viable manner. Additionally, the idea is to insert a significant logo as a watermark rather than the more traditional pseudorandom paired grouping for robust image watermarking. The purpose of this project is to develop image watermarking using the discrete wavelet transform. The objective is to investigate wavelet basis functions that are ideal for picture watermarking and to empirically estimate the strength of the watermark in order to inject the maximum amount of watermark information into the host image without degrading its perceptual quality. To model and execute various assaults on watermarked photos in order to degrade their quality in order to assess performance for resilience. Additionally, performance is evaluated in the presence of the maximum potential degradation of the watermarked image in order to define effective robustness requirements. To evaluate the performance of suggested approaches based on DWT, DWT-HVS, and DWT-HVS-FIS image watermarking, as well as to compare the proposed approach's performance to that of existing approaches utilising performance parameters.
Machine Learning Could Revolutionize Mineral Exploration

Using a global data set of zircon trace elements, new research demonstrates the power of machine learning algorithms to accurately identify and locate porphyry copper deposits.
RANCANG BANGUN SISTEM INFORMASI ADMINISTRASI PEMBAYARAN SISWA BERBASIS WEB DI SMK KESEHATAN MANDALA BHAKTI SURAKARTA

Mandala Bhakti Health Vocational School Surakarta is a vocational school that focuses on the health sector and has two majors, namely nursing and pharmacy. The use of computers at the Mandala Bhakti Health Vocational School in Surakarta has begun to be applied to help work in the field of school administration, especially the student payment administration section which serves all student payments including medical tests, tuition fees, practicum, competency tests, re-registration, and registration.However, the administrative process for student payments still takes quite a long time because students must first write a payment slip and wait for a payment receipt from the treasurer which is written manually and the recording process must be done twice, namely per day and class. In addition, the passbooks and receipts that are used by students to see the payments that have been made, are mostly lost and damaged, making students not know what bills to pay. Therefore, it is necessary to have a Student Payment Administration Information System that can speed up the payment process, simplify the reporting process, and grant access rights for students to view payment history and bill payments.This Student Payment Administration Information System was created based on a website using visual studio code software (text editor), MySQL database management system (DBMS), Xampp, and the PHP programming language. Methods of data collection are done using interviews, observation, and literature study. While in its construction, the author uses the RAD (Rapid Application Development) development method which consists of requirements analysis, modeling analysis, modeling design, and construction.Testing the student payment administration information system uses black box testing which shows that the system is functioning properly without any errors. User testing was carried out using a questionnaire which showed the results that for the admin, treasurer, and principal access rights questionnaire the highest score was the 'agree' option with a percentage of 60%, while for the student access rights questionnaire, the highest score was the 'agree' option. with a percentage of 55%. This shows that the student payment administration information system is following the needs of its users
Working with Integrations for Power Apps and Dynamics 365

Integration is very important in all projects. You can integrate Power Apps using Microsoft native apps or third-party systems using the low-code, less-code concept. In this chapter, you learn how to integrate native and third-party apps with model-driven Power Apps and Dynamics 365 Apps.
ANALISIS SENTIMEN DATA ULASAN APLIKASI RUANGGURU PADA SITUS GOOGLE PLAY MENGGUNAKAN ALGORITMA NAÏVE BAYES CLASSIFIER DENGAN NORMALISASI KATA LEVENSHTEIN DISTANCE

One form of technological development in education is the increasing number of online based learning. More than that, during this period of Covid-19 pandemic distance education was tried by the government that requires learning are done online. The online learning application that is the implementation of this technological development continues to show its existence. Many non-formal educational companies are available, one of which is the Ruangguru, getting a nickname as a number one learning application requires the Ruangguru to continue and improve the performance. Users of the Ruangguru application can communicate a response to Ruangguru through the review feature available on the google play site. The reviews that have been written can be analyzed how the user sentiment is whether positive or negative using Multinomial Naïve Bayes. This method is used because it is easy to use with simple structures and gives high accuracy values. The model will be selected using 10-fold cross validation method to get the model with the best accuracy. The normalization phase of words was also perfected using Levenshtein Distance method that was proven to add accuracy value. Performance result using Multinomial Naïve Bayes by adding Levenshtein Distance method to fix the words gives an average accuracy value of 88,20% with the 8th fold as the fold with the best accuracy value of 94%.
Review Paper on Cyber Security and Types of Cyber Attacks

Cyber attacks have become increasingly dangerous and quite common in world of internet.so the demand of cyber security has been increased to safeguard the information personal data and computer network .now a days everything has been computerized and transaction are done online thus economic reimbursement are increasing due to cyber attacks as the technological evolution comes the progress of cyber-crime increases thus develops the new types of attacks are reported throughout the study the exact number of attack type determination is quite difficult however the most common cyber attacks are described in this article .the aim of this study is to survey and convincing manner review the strength and weakness presented in the field of cyber security. the article aim is to analyze various type of attacks existing to understand the state of being exposed to the possibility of being attacked. The main goal is to handle cyber attacks.
Co-creating FAIR Data for Healthy and Resilient Urban Development

Cities across the world are facing complex environmental, demographic and socio-economic changes and have been additionally burdened with climatic impacts. These issues are even more evident in developing countries as the speed of their urban growth often outpaces adequate planning. Knowledge and awareness of health have increased significantly in the last half a century, yet the pace and profundity of change in urban environments have been impeding continuous health improvement. In many cases, they have been jeopardizing existing human and environmental health conditions. COVID-19 and climate change provide compelling examples.
Developing a Digital Learning Platform for Adolescents in Vulnerable Communities in the City of Medellin, Colombia. A Tool to Tackle COVID-19 Confinement Challenges

COVID-19-related implications have created an additional struggle and stress in high-numbered families from vulnerable communities, due to the lockdown measures adopted by local and national governments.
Cloud Network Communication Performance Improvement Using a Stochastic Bandwidth Allocation and Swarm Optimization Algorithm

Cloud computing is fulfilling the dream of providing the highest resource utilization in the IT world. Many commercial applications also offer a wide range of shaded services. Owing to rapid increase in the usage of the internet, rapid access has also been the factor to the prosperity of the industry. Different services are built within the virtualized resources of cloud computing that allow us to perform the abstraction of the underlying resources. It offers flexibility, high capacity, and an ecological service-oriented mindset. In this work, algorithm development solves resource utilization, performance degradation, and availability issues. This problem requires an almost unlimited number of tasks assigned to cloud resources that are solved in polynomial time. The proposed Stochastic Bandwidth Allocation with Swam Optimization (SBA-SO) framework for dynamic network-based resource scheduling algorithms provides scalability and reliability by providing better resource utilization and minimum response times during migration. User requests arrive at the data center as bandwidth, response time and throughput quality check different parameters based on the request. Additive-polling load analysis is introduced to estimate each cloud datacenter load to categorize overload server and under load cloud servers. Each cloud resource monitors the usage and updates into the cloud index table. When the cloud suddenly increases the load, it searches other load servers to allocate to the user. The Swam Optimization method using a memetic hyper-heuristic algorithm is used to construct the network topology and establish the communication between servers. Optimization algorithms are also suitable for solving individual problems and can be used for managing cloud computing resources and scheduling resources.
Integrating and Evaluating Helpful Features in Interactive Computer Assisted Learning Courseware for Low Achieving Children

With the aim to provide a learning content that is digitally delivered to assist low achieving children, this paper proposes some helpful features in an interactive courseware. The low achieving children usually encounter reading problem. Therefore, common courseware with textual instructions are not suitable for them. Accordingly, this paper discusses an Interactive Computer Assisted Learning courseware for Low Achievers known as Bijak Matematik. The application consolidates the well-defined components and elements that induce positive interaction supported with helpful object behavior features in the learning courseware. This study went through a threestage research process, which are i) design, ii) development and iii) user experience testing. Having design and develop the application, user experience testing was conducted with 30 participants from five primary schools. Result justifies that the prototype (iCAL4LA-Bijak Matematik) is significantly very helpful to the targeted users.
Material Property Measurement of 3D Printed Carbon Fiber Composite Using a Digital Image Correlation Method

Three-dimensional (3D) printing of carbon fiber composite is a promising manufacturing process for aerospace and other industry fields because it is fast and easy to implement and requires less labor. Knowledge of the material properties of the 3D printed composite is important for applications to aerospace and other industrial structures. In this study, a digital image correlation (DIC) technique is employed to measure the material properties of 3D printed composites. This is a non-contact measurement method, and able to measure the full-field displacement and strain of the structure. This study provides a suitable testing method for determining the material properties of 3D printed continuous fiber composite materials according to ASTM standards using the DIC method.
Awareness of crypto currency among adults

Crypto currency, an encrypted, peer-to-peer network for facilitating digital barter, is a technology developed eight years ago. Bitcoin, the first and most popular crypto currency, is paving the way as a disruptive technology to long standing and unchanged financial payment systems that have been in place for many decades. While crypto currencies are not likely to replace traditional fiat currency, they could change the way Internet-connected global markets interact with each other, clearing away barriers surrounding normative national currencies and exchange rates. Technology advances at a rapid rate, and the success of a given technology is almost solely dictated by the market upon which it seeks to improve. Crypto currencies may revolutionize digital trade markets by creating a free-flowing trading system without fees. Present study aims to understand the awareness of Crypto Currency among adults with reference to Kerala.
The Integration of the Teacher-centered View and the Student-centered View in the Era of Artificial Intelligence

In the era of artificial intelligence, the differences between the Teacher-centered View and the Student-centered View are gradually widening, especially in three aspects which are educational purposes, teaching methods and students' attitudes.Due to the limitations of both views that are difficult to ignore, this paper advocates to integrate them together and strive to maximize and optimize the teaching effect.
You can’t suggest that?!

In this article, we study correction of spelling errors, specifically on how the spelling errors are made and how can we model them computationally in order to fix them.The article describes two different approaches to generating spelling correction suggestions for three Uralic languages: Estonian, North Sámi and South Sámi.The first approach of modelling spelling errors is rule-based, where experts write rules that describe the kind of errors are made, and these are compiled into finite-state automaton that models the errors.The second is data-based, where we show a machine learning algorithm a corpus of errors that humans have made, and it creates a neural network that can model the errors.Both approaches require collection of error corpora and understanding its contents; therefore we also describe the actual errors we have seen in detail.We find that while both approaches create error correction systems, with current resources the expert-build systems are still more reliable.
Mii *eai leat gal vuollánan – Vi *ha neimen ikke gitt opp

Machine learning is the dominating paradigm in natural language processing nowadays. It requires vast amounts of manually annotated or synthetically generated text data. In the GiellaLT infrastructure, on the other hand, we have worked with rule-based methods, where the linguistis have full control over the development the tools. In this article we uncover the myth of machine learning being cheaper than a rule-based approach by showing how much work there is behind data generation, either via corpus annotation or creating tools that automatically mark-up the corpus. Earlier we have shown that the correction of grammatical errors, in particular compound errors, benefit from hybrid methods. Agreement errors, on the other other hand, are to a higher degree dependent on the larger grammatical context. Our experiments show that machine learning methods for this error type, even when supplemented by rule-based methods generating massive data, can not compete with the state-of-the-art rule-based approach.