Reviewerly/exHarmony huggingface dataset:
The Educational Game "City Language" Development with Elements of Artificial Intelligence

The educational game "City Language" development with elements of artificial intelligence is designed to teach a contingent of any age English (English, American) in a game format. The application is a strategy where the user has to build his city while learning the language at the same time. The result of working with the application will be mastering the language at the C1 level and obtaining a confirmation certificate. The distinctive quality of the product is an intelligent thesaurus construction technology that corresponds to the conceptual apparatus of the user.
A Comprehensive Examination of Cloud-Enabled Frameworks for Power Management Systems: Current State, Challenges, and Forward-Looking Guidance in Big Data Analytics

A Comprehensive Examination of Cloud-Enabled Frameworks for Power Management Systems: Current State, Challenges, and Forward-Looking Guidance in Big Data Analytics” examines cloud-enabled power management system frameworks in depth. This article provides a thorough evaluation of cloud-enabled power management system frameworks. This article examines the present state of power management systems from a range of perspectives. It emphasizes the importance of shifting away from traditional, locally focused methodologies and toward big data analytics-driven, cloud-based solutions. In this study, we look at the wide-ranging implications that cloud-enabled frameworks have had on a variety of sectors, including smart grids and data centers. It is hard to exaggerate the significance of these frameworks in handling the exponential expansion of data while also improving efficiency, scalability, and adaptability. This research acknowledges the relevance of data security and privacy, the necessity for interoperability and standardization in this rapidly expanding environment, and the difficulty in managing the huge volumes of data created by contemporary energy systems. This is achieved while stressing the numerous benefits of cloud-hosted data centers and intelligent grids. The paper also underlines how advantageous cloud-based data centers and smart grids are to their respective users. The essay proposes potential solutions to these difficulties in the form of future recommendations.
Comparison of two architectures for text-independent verification after character-unaware text segmentation

This paper compares the performance of two popular CNN architectures, ResNet-50 and MobileNetV2, fine-tuned for text-independent writer verification. The used benchmark is IAM dataset. The further contributions are an easy and fast sub-region cropping for robust model training, and a biometrics-oriented performance evaluation. The preliminary results are encouraging.
Extractive Document Summarization with Advanced Deep Reinforcement Learning

Due to the exponential increase in the amount of information available on the internet, there has been an increasing interest in automatic text summarization in recent years. The goal of extractive text summarization is to generate a condensed version of a document while keeping its important points. It involves selecting the most relevant sentences from the document and arranging them in a coherent and concise manner. Using Deep Q-Networks (DQN), we provide a novel method for extracting text summarization in this research. In our suggested method, each phrase in the text is given a score depending on its importance to the summary, and the process of summarizing is seen as a sentence ranking problem. The DQN-based model is trained to learn the ranking function, where the reward signal is determined by the extent to which the resulting summary resembles the ground truth summary. We conduct experiments on the CNN/DailyMail dataset, which is a widely-accepted benchmark in this field, and compare our model with state-of-the-art extractive summarization systems and demonstrate that our approach outperforms them in terms of both automatic metrics and human evaluation. Our study highlights the potential of DQN-based models to significantly enhance the quality of extractive text summarization. Our findings suggest that our proposed approach provides a promising avenue for future research in the field of extractive text summarization.
An Analogical Study Between Lateritic Index and an Innovative Index to Enhance Detection of Bauxite in Ranchi and Lohardaga in Jharkhand, India using Sentinel 2A Data

The primary objective of this study is to determine and assess the accessibility of bauxite in Jharkhand, which involves some specific regions such as Ranchi and Lohardaga. The data used in this study was generated by the Sentinel 2A satellite from ESA Copernicus and altered using SNAP 9.0 in the mentioned regions. In this context, spectral signatures of lateritic bauxite specimens were examined on GIS software using the spectral characteristics of lateritic bauxite. In this spectral signatures and central resolution of Sentinel 2A was preferred and was made use of which vegetative spectral signatures were visible i.e. VNIR-SWIR (visible-near-infrared and short wave infrared) domain. Understanding the distinctions between the spectral characteristics of bauxites and laterites is made easier by analysing the spectral signatures of lateritic bauxite samples. Based on these variations, simple pixel values of pictures were used after applying formulation in SNAP 9.0 After uploading the image in SNAP 9.0, the image was further underwent operations and later output was generated in the form of band 11 and band 12 image. The SNAP 9.0 mapping method is used to map the presence of bauxite deposits along the research region, and the occurrence is draped over the DEM of the study area. The pictorial representation of availability of bauxite suggests the size of the bauxite mineral resources. The statistical program SPSS was used to assess the data, and it was found that the statistically significant difference between the two indices was 0.005 which follows (p≤0.05) with a 95% Confidence interval. The standard error difference is around 0.1555, whereas the average mean difference is almost 1.20311. The area of Research study Ranchi and Lohardaga of Jharkhand displays a distinct bauxite resource zone, according to a research that was conducted utilising data and the aforementioned approach with suitable regard for the results generated. Consequently, SNAP 9.0 used Sentinel 2A from ESA Copernicus data to identify bauxite.
Design And Implementation of Two Axis Sun Follower PV System

The general population is interested in developing techniques for utilizing sources of clean energy due to known as simultaneous hazards to fossil fuel exhaustion along with warming temperatures. among the many declaring avenues for renewable power was solar power. A solar energy system's ability to produce energy can be significantly increased by sun tracking devices. In order to offer reliable system reliability, this article offers an innovative layout for a multi-axis tracking photovoltaic (PV) array that makes use of feedback regulation theory, a five-quadrant light-depending resistor (LDR) detector, and straightforward electrical circuits. The put forward that is suggested achieves tracking of the sun by means of a special hybrid-axis AC drive and an independent photovoltaic inverter. A technological breakthrough with a straightforward and efficient development is controlling its deployment. To confirm the viability of the plan, a resized-down laboratory model is also built. Scientific proof supports the solar system's tracker's efficacy. To sum up, the findings in this research could be useful names for uses for sunlight in future generations.
Monkeypox and other Skin Conditions Detection Utilizing CNN with VGG16 Transfered Language

The upcoming multi country monkeypox outbreak in 2022 becomes a worldwide health problem. Following Covid19, the monkeypox virus has issued a fresh warning that it might become an across-the-world epidemic; interaction with infected animals or people spreads the virus. Monkeypox has been correlated to Measles and Chickenpox. Monkeypox can be differentiated from other skin diseases by its etiology. These viruses are all different infectious diseases that are spread via the air and have similar symptoms. The most effective approach to avoid these is through vaccination, but no vaccine is available to prevent monkeypox infection. The study provides an image categorization to separate signs of monkeypox, chickenpox, and Measles sickness. In image-based diagnostics, such as identifying cancerous cells, tumor cellular identification, and COVID-19 patient detection, machine learning (ML) has shown significant potential. The image categorization method used by the researchers was deep learning (DL) utilizing Convolutional Neural Network (CNN) architecture and VGG-16. Transfer learning is a technique that enables a model learned on one dataset to be applied to another, allowing the model to translate information from the initial data set to the newly acquired data. This strategy is recommended by experts because neural networks are exceptionally good at learning comparable images and accurately predicting new data. As a consequence, at epoch value is 15; the VGG-16 has a high accuracy of 92%. However, it might be high if images are more widely available. As a result, a dataset is urgently required to include images of monkeypox afflicted patients.
Comprehensive Analysis of Cloud Security: Issues &amp; Challenges

Cloud Computing is a profitable and tensile interface for delivering Information Technology initiation. However, the perception of cloud security requires significant advancements for widespread enterprise adoption. This research aims to understand key factors in the cloud, such as privileged user access, compliance, data positioning, isolation, recovery, cost considerations, and security issues like data breaches and denial of service attacks. It explores emerging technologies and advancements to address vulnerabilities and enhance cloud security. The research paper presents a comprehensive framework covering cloud security, current risks, industry significance, research challenges, and hypothesized future advances.
Internet of Things and Blockchain Data Supplier for Intelligent Applications

Dependence is increasing in direct proportion to how user-friendly intelligent applications are as technologies are used in a wider range of areas. Increasingly individuals are using these novel technologies every day in efficient ways, according to statistics. There are numerous Internet of Things (loT)-oriented intelligent refrigerator applications that enable self-monitoring of supplies however, the devices are unable to accomplish the optimal operational duration and data security. Thus, a new architecture is established in this study that combines a highly complex software architecture using hardware-level device connectivity. The utility of blockchain as a data supplier in loT intelligent applications is discussed in this study. Blockchain technology's primary goal is to enable anonymous transactions between users via a peer-to-peer system and a decentralized distributed ledger (DL). Through the integration of loT technologies and intelligent mobile device applications, it has been intended to develop a novel smart refrigerator platform with the capacity for autonomous self-purchasing and checking. This research's main objective is to do away with all third-party verification and substitute cryptography evidence for the reliance on a centralized authority for transaction verification. Although cryptocurrencies are the focus of the maj ority of blockchain applications, the technology is also useful in a wide range of other industries, including banking, automation, distributed storage of data, etc. The blockchain can indeed be utilized to construct decentralized apps and facilitate data accessibility and exchange on a considerably greater scale than the typical user-server framework implementations currently in usage by establishing a public, decentralized system. This research offers a proof of conceptual approach for field phones to hold and share data employing a DL constructed on the IOTA tangles, along with a technique for data access that may be utilized in loT and decentralized applications.
Implementing Blockchain Based Security in EHR Using Ganache

A blockchain-based electronic health record (EHR) system has been proposed to manage patient data and maintain computerized records across different healthcare institutions. The main objective of this system is to provide immediate access to patient health information while addressing concerns about privacy and security. It is developed on the Ethereum network using programming languages, tools, and technologies such as Ganache, Solidity, and web3.js. The system uses smart contracts to provide security and privacy, and transactions are verified and propagated to the entire decentralized network. The system includes a cryptocurrency wallet (MetaMask) with a centralized privacy system that allows authorized parties to access and secure records quickly. The proposed system aims to achieve several objectives such as allowing users to receive the same data simultaneously, increasing efficiency, building trust, and reducing barriers. It secures data storage by restricting user access and enables the secure transfer of patient medical records. The solution allows for better transparency and ownership of sensitive data, which can be recorded and safeguarded via blockchain technology, therefore assisting the healthcare sector. This paper describes a medical record system and a new protocol that can be used quickly and safely, paving the way for more secure and efficient healthcare systems. Overall, the proposed system offers potential solutions to the challenges associated with traditional EHR systems and has the potential to revolutionize the healthcare industry.
Performances of Various Aspect-Oriented Systems with their Significant Quality Attributes

Aspect-oriented system gives a system for utilizing the crosscutting concerns. This paper found a few procedures that break down the reusability of Object-Oriented (OO) programming frameworks in setting of Aspect-Oriented (AO) frameworks. The paradigm of Aspect-Oriented Software Development (AOSD) focuses to modularize the worries of crosscutting application. Object-situated and customary methodology arranged approaches can't modularize such worries. A viewpoint situated method can dispose of these worries prompts rise the reusability and practicality of the framework. This paper presents the idea of reusability for AO frameworks. AO frameworks have quality appraisal of huge properties like viability, reusability, intricacy, coupling and attachment. This effort gives the connection amid a portion of the quality credits of AO framework. These relationships generally clarify the varieties of attachment with deference for variability and intricacy. Additional, this article portrays the use of attachment metric as a pointer for examining the alterability and intricacy of AO framework. Such marker is additionally used to assess the reusability of AO framework.
A Voting Enabled Predictive Approach for Hate Speech Detection

In today's digital environment, hate speech, which is defined as disparaging and discriminating communication based on personal characteristics, presents a big difficulty. Hate crimes and the rising amount of such content on social media platforms are two examples of how it is having an impact. Large volumes of textual data require manual analysis and categorization, which is tedious and subject to prejudice. Machine learning (ML) technologies have the ability to automate hate speech identification with increased objectivity and accuracy in order to overcome these constraints. This article intends to give a comparative analysis of various ML models for the identification of hate speech. The proliferation of such content online and its negative repercussions on people and society are explored, as is the necessity for automated hate speech recognition. This paper intends to support the creation of efficient hate speech detection systems by performing a comparative analysis of ML models. Random forest records the best performance with higher accuracy and low response delay period for hate speech detection. The results will help enhance automated text classification algorithms and, in the end, promote a safer and more welcoming online environment by illuminating the benefits and drawbacks of various approaches.
Sustainable and Secure Renewable Energy Trading System Based on Digital Twin, Blockchain and Game Theory Integration

Renewable energy significantly reduces carbon emis- sions, combats climate change, and paves the way for a sustainable future for generations to come. In alignment with other countries' efforts towards climate change mitigation and sustainability, Denmark is committed to achieving a 70% reduction in emissions by 2030 and ultimately reaching zero emissions by 2050 within its borders. While solar and wind power are the primary sources of renewable energy, their intermittent nature poses challenges when energy demand is high. Consequently, energy storage emerges as a critical concern. To address this, sector coupling and an efficient energy trading system are pivotal, as they interconnect energy- consuming sectors with power-producing sectors to maximize the efficient utilization of renewable energy. This paper delves into the potential benefits of integrating digital twin and blockchain technology into renewable energy trading and sector coupling.
STUDENTS’ PERCEPTION IN LEARNING VOCABULARY MASTERY THROUGH DUOLINGO

Education is a systematic and structured process aimed at acquiring knowledge and developing abilities. The acquisition of English language proficiency necessitates the utilization of various methodologies and strategies, as it is a skill-oriented form of education. Technological advancements have significantly impacted contemporary education. The utilization of technology is a contemporary approach in addressing the obstacles encountered in the process of acquiring proficiency in the English language. In the realm of English language acquisition, Duolingo continually strives to reinvent language learning methods through its mobile phone platform. Therefore, the objective of this research was to identify of Student’s perception in learning vocabulary through Duolingo Apps to the 1st semester of management class at STIE WIRA BHAKTI MAKASSAR University batch 2023 who were currently or had previously used Duolingo. Using questionnaires to collect data in a qualitative descriptive, the findings showed that students' perceptions of learning vocabulary through Duolingo are overwhelmingly favorable, with an average score of 92,37%. To sum up, the use Duolingo was very effective in learning vocabulary because the application is simple to use, entertaining, fun, and free of advertisements.
PENINGKATAN KETERAMPILAN MENULIS KARANGAN MELALUI MEDIA GAMBAR SERI PADA SISWA KELAS IV SD TAHUN AJARAN 2022/2023

Essay writing skills, especially descriptive text essays, are still relatively low for grade IV students at SD. This problem requires a solution so that students' essay writing skills will increase. The purpose of this study is to determine the planning, implementation, and improvement of essay writing skills through the use of picture series media. The subjects of this study were 24 grade IV students at SD. The study used a class action research design by Kemmis and Mc Taggart. Data was collected through tests, observations, and documentation and then analyzed using descriptive statistical techniques. The results showed that students' ability to write essays, especially descriptive texts, increased by using picture series. After learning using picture series students can describe objects completely and clearly then in writing descriptive text students can also write them using good grammar and use correct punctuation and spelling. From the results of the assessment it was found that in the pre-cycle the average value obtained was 67.26 and was included in the criteria of being quite capable. Then in cycle I there was an increase of 11.31 so that the average value obtained was 78.57 and included in the capable criterion. In cycle II it increased again by 11.01 so that in cycle II the average value obtained was 89.58 and it was included in the very capable criteria.
Unlocking the Value of Media Libraries With AI and ML

In the dynamic landscape of the media and entertainment industry, the quest for efficiency and optimization is perpetual. As we stand on the cusp of a new era, AI and ML technologies promise to revolutionize the way we handle, process, and monetize media content. The challenges are evident, but so are the solutions, provided we know where to look.
What Electronic Image Noise Signatures Can Tell Us About Image Linearity and Camera Encoding

Electronic image noise is often categorized as an artifact of image capture which typically requires correction. There are several sources of electronic image noise that contribute to the overall total noise in an image. Some of the noise from specific sources, known as systematic noise, can be corrected for in-camera, but noise due to certain sources, like photon noise, persists after capture and increases with exposure. We study this relationship between exposure and noise and discover that this relationship reveals information about the linearity, or non-linearity of the image encoding.
Population and Economic Growth in Tanzania

The debate on the relationship between population growth and economic growth has been enduring and varies across countries. While the first theory on population and economic growth states that population growth stimulates economic growth, the second theory views population growth as a factor that adversely affects economic growth. The third school of thought posits that population growth is a neutral factor in economic growth, and is determined outside standard growth models. Given this situation, there was a need to establish the relationship between economic growth and population growth in Tanzania. The study employed the Vector Auto-regression (VAR) estimation technique, and used annual time series data from 1971 to 2017. The results indicate that economic shocks due to population growth are positive and negative. This could be attributed to unstable expenditure on healthcare by the government in the improvement of child survival through various government programmes. The study concludes that, in Tanzania, population growth promotes economic growth and, subsequently, economic development. The study recommends that the government should ensure that the economy grows at a higher rate than population growth. This will ensure that the increasing demand for services arising from population growth is met. Having a large, healthier, and better-educated workforce will only bear economic fruit if the extra workers can find jobs.