Reviewerly/exHarmony huggingface dataset:
APPLICATION OF DATA MINING USING NAIVE BAYES ALGORITHM FOR CLASSIFICATION OF EMPLOYEE CONTRACT EXTENSION AT PT TORINI JAYA ABADI

An employment contract is an agreement made verbally or in writing between an employee and an employer, the working status of contract employees in a company is an important matter and needs to be considered in order to achieve company goals. PT Torini Jaya Abadi is a company that uses a work contract system. Every year there must be employees who are protected or their work contracts are terminated. In terms of determining whether an employee is strained or terminates his work contract, there are often difficulties in determining this. This is done to create a system that can help determine the extension of an employee's work contract by classifying it into the labels Extended and Disconnected which have 18 Variables for the process of assessing employees who will have problems or termination of employment contract. This system uses the Naïve Bayes method and is built using the Bootstrap programming language and MySQL database. The results of this study are a system that helps PT Torini Jaya Abadi in extending employee work contracts.&#x0D; &#x0D; Keywords : Employment Contract, Naïve Bayes algoritm, PHP MySQL
HSVI Can Solve Zero-Sum Partially Observable Stochastic Games

State-of-the-art methods for solving 2-player zero-sum imperfect information games rely on linear programming or regret minimization, though not on dynamic programming (DP) or heuristic search (HS), while the latter are often at the core of state-of-the-art solvers for other sequential decision-making problems. In partially observable or collaborative settings (e.g., POMDPs and Dec-POMDPs), DP and HS require introducing an appropriate statistic that induces a fully observable problem as well as bounding (convex) approximators of the optimal value function. This approach has succeeded in some subclasses of 2-player zero-sum partially observable stochastic games (zs-POSGs) as well, but how to apply it in the general case still remains an open question. We answer it by (i) rigorously defining an equivalent game to work with, (ii) proving mathematical properties of the optimal value function that allow deriving bounds that come with solution strategies, (iii) proposing for the first time an HSVI-like solver that provably converges to an $$\epsilon $$ -optimal solution in finite time, and (iv) empirically analyzing it. This opens the door to a novel family of promising approaches complementing those relying on linear programming or iterative methods.
PROJECT-BASED LEARNING OF ELT STUDENTS ON ESSAY WRITING

Essay writing has always been an important skill for students. However, this skill could be more comfortable for them. Therefore, an effective technique is needed to help students to cope with the problem. Project-based learning (PjBL)is one of the learning models which can make learners interested and motivated in writing. The study employs expose-facto with mixed data types. This research investigated 23 English students’ activities, results, and their responses in essay writing using a PjBL model. We employed three instruments; observation, questionnaire, and test. Qualitative descriptive techniques analyzed the data. The results showed that (1) the implementation of the project-based learning model was classified as good and successful. It can be seen from the student's learning outcomes and the successful implementation of the steps that have been determined. (2) The students’ responses to the project-based learning model have been classified as positive since the students explained effective ways of learning essay writing. The students’ learning outcomes show better achievements in learning essay writing. Thus, learning essay writing can be concluded. It implies that implementing the PjBL model in ELT students yields good vibes in students’ learning activities.
ONLINE PROJECT-BASED LEARNING IN COVID-19 PANDEMIC: EFL PRE-SERVICE TEACHERâ€™ STORY

The study explored the experiences of a pre-service EFL teacher when participating in online project-based learning activities in critical listening class during the Covid-19 pandemic. The study aimed to investigate students' skills that can be developed through online project-based learning other than language skills and a female pre-service EFL teacher's perspectives on the benefits and challenges of using online project-based learning in English as a foreign language online class. The study employed a narrative inquiry model. The semi-structured interview and narrative frame were employed to collect the data. The result revealed that not only develop critical listening but such skills as technical skills in using technology for learning; self-learning; problem-solving; collaboration and communication with peers; critical and creative thinking; and time management outside the classroom can also be increased after joining online classes that implement project-based learning by using various learning platforms. In addition, implementing online project-based learning also provides several benefits and challenges for the participant
Exploring the Cultural Significance of Ecommerce Websites Through Semiotic Analysis: A Literature Review

Semiotic analysis serves as a tool for comprehending the ways in which individuals interpret and comprehend their surroundings by scrutinizing the interplay between signs, symbols, and their connotations. The efficacy of digital business platforms is heavily reliant on the creation of well-crafted, meticulously structured websites that facilitate effective communication with targeted customers. E-commerce websites extensively employ images and texts to enhance product awareness and cultural significance. Scholars contend that the utilization of e-commerce websites will expand if designers integrate the cultural norms of different user subsets. Thus, this study aimed to examine this issue through a literature review of semiotic analysis applied to e-commerce websites.
North Sumatera Athletes Hotel with Ecological Practices Implementation

The North Sumatra Athlete Hotel’s design with Ecological Architecture approach is intended to meet the need for supporting facilities in accommodating athletes who will compete in the upcoming XXI / 2024 national sports event (PON). This is a North Sumatra Province self-preparation form as one of the hosts in accommodating this event. The building design includes supporting facilities, outdoor and indoor sports facilities, a restaurant, and a convention centre. In terms of function, hotel building design and facilities are open to athletes and the public. In terms of the design theme, the theme approach is carried out through the application of concepts in the form of natural ventilation, natural lighting, shading with using vegetation, overhangs, balconies, and secondary skin which are applied through consideration of the orientation of the building to the incoming sunlight.
Students’ and lecturers’ perceptions towards e-learning in higher education

This research determines the perceptions of students and lecturers on using e-learning at a private university in Batam. A mixed methods approach was employed to find out these perceptions. Researchers further collected the data through open and closed questionnaires from 243 students (2019-2022 school year) and 19 lecturers who were actively teaching and using e-learning. After obtaining the data, the questionnaire results were calculated using SPSS software version 25 and transcribed qualitatively. The results show that students and lecturers had positive perceptions towards e-learning. This is measured by the knowledge, adaptation, benefits, and attitudes of students and lecturers in using e-learning. However, several obstacles were faced by them, such as time management, difficulties in accessing the e-learning website, the user interface (UI) display is quite complicated, unsupported internet signal, and others. It can be concluded that both lecturers and students have good ability in using e-learning at higher education levels.
A Study on Effectiveness of Motivational on Employee with Special Reference to Shiash Info Solutions Pvt Ltd, Chennai.

The title of the project report’s ‘A study on Effectiveness of Motivational on employee’. The project work has been carried out at Shiash info solutions Pvt ltd in Chennai for the potential fulfillment of the MBA program under Anna University, Chennai. Organizational culture is the accepted norms and values that are associated with a particular company. These norms are seen as distinctive to a particular organization. Organizational culture can be tackled in three real ms such as behavior &amp; artifacts, values &amp; assumptions and beliefs. The term behavior denotes the visible elements of culture prevailing with a certain organizational dress code, the way employees use technology in their task etc. Their search design us edit this study is descriptive research design. Data from 50 people were collected as population study. Data was collected by survey method through structured questionnaire with close-ended questions. The primary data was obtained through questionnaire directly from the employees. The secondary data was collected through internal source and external source, company records, documents, journals &amp;websites. The collected data has been edited, classified and tabulated. The statistical tool used in this study is Percentage analysis, Chi-square test, and Weighted Average method.
Load Distribution in Software Defined Networks for Enabling Big Data Tasks Scheduling

Benefited from its reliability and convenience, biometric identification has become one of the most popular authentication technologies. Due to the sensitivity of biometric data, various privacy-preserving biometric identification protocols have been proposed. However, the low computational efficiency or the security vulnerabilities of these protocols limit their wide deployment in practice. To further improve the efficiency and enhance the security, in this project, propose two new privacy-preserving biometric identification outsourcing protocols. One mainly utilizes the efficient Householder transformation and permutation technique to realize the high-efficiency intention under the known candidate attack model. The other initializes a novel random split technique and combines it with the invertible linear transformation to achieve a higher security requirement under the known-plaintext attack model. Also, we argue the security of our proposed two protocols with a strict theoretical analysis and, by comparing them with the prior existing works, comprehensively evaluate their efficiency.
Efficient Privacy-Preserving Biometric Identification in Cloud Computing

Benefited from its reliability and convenience, biometric identification has become one of the most popular authentication technologies. Due to the sensitivity of biometric data, various privacy-preserving biometric identification protocols have been proposed. However, the low computational efficiency or the security vulnerabilities of these protocols limit their wide deployment in practice. To further improve the efficiency and enhance the security, in this project, propose two new privacy-preserving biometric identification outsourcing protocols. One mainly utilizes the efficient Householder transformation and permutation technique to realize the high-efficiency intention under the known candidate attack model. The other initializes a novel random split technique and combines it with the invertible linear transformation to achieve a higher security requirement under the known-plaintext attack model. Also, argue the security of our proposed two protocols with a strict theoretical analysis and, by comparing them with the prior existing works, comprehensively evaluate their efficiency.
Indian Paper Currency Recognition Framework for Blind and Visually Impaired People using Deep Learning Model

Currency recognition has always been a troublesome task for blind and visually impaired people (BVIP). Most of the current Indian legal tenders resemble in size, thus making the identification process more difficult. Automated paper currency recognition system, issues such as folded or partial views, uneven illumination, and background clutter make it non-trivial and challenging. This Paper proposes deep learning model for assisting BVIP in recognizing the Indian paper currency denomination. This Paper exploits the latent embeddings of variational auto encoders combined with IPCRNet. Two encoders are trained on currency images to generate two feature maps. The IPCRNet is based on dense connection, Multi-Dilation, and Depth-wise separable convolution layers. Contextual Block (CB) in the backend utilizing the dense connection and dilation scheme in depth-wise separable convolutional layers .The novel contextual part utilizes a depthwise separable convolution for reducing network computations .The multi-dilation scheme offers an enlarged receptive field without increasing the parameters.
Reviewing the Objectives and Goals for Sustainable Small and Medium Enterprises in Nigeria

SMEs are often cited as the part of national dispensations that require the goodwill and kind interventions of governments. This is especially in circumstances where the law does not allow government’s direct control of citizens’ socio economic powers. This study describes how the Nigerian government navigates issues relating to SME’s via her lofty ideals and actions for a better set up. The government policies and promises of ideals are as influenced by the country’s political history, obligations and existing unfavorable socio-economic experience.
Decentralized Management of Medical Test Results Utilizing Blockchain, Smart Contracts, and NFTs

In today’s medical landscape, the effective management and availability of diagnostic data, including current and historical medical tests, play a critical role in inform-ing physicians’ therapeutic decisions. However, the conventional centralized storage system presents a significant impediment, particularly when patients switch healthcare providers. Given the sensitive nature of medical data, retrieving this information from a different healthcare facility can be fraught with challenges. While decentralized storage models using blockchain and smart contracts have been suggested as potential solutions, these methodologies often expose sensitive personal information due to the inherently open nature of data on the blockchain. Addressing these challenges, we present an innovative approach integrating Non-Fungible Tokens (NFTs) to facilitate the creation and sharing of medical document sets based on test results within a medical environment. This novel approach effectively balances data accessibility and security, introducing four key contributions: (a) We introduce a mechanism for sharing medical test results while preserving data privacy. (b) We offer a model for generating certified, NFT-based document sets that encapsulate these results.(c) We provide a proof-of-concept reflecting the proposed model’s functionality and (d) We deploy this proof-of-concept across four EVM-supported platforms—BNB Smart Chain, Fantom, Polygon, and Celo—to identify the most compatible platform for our proposed model. Our work underscores the potential of blockchain, smart contracts, and NFTs to revolutionize medical data management, demonstrating a practical solution to the challenges posed by centralized storage systems.
An Automated Medical Image Segmentation Framework using Deep Learning and Variational Autoencoders with Conditional Neural Networks

It is a highly difficult challenge to achieve correlation between images by reliable image authentication and this is essential for numerous therapeutic activities like combining images, creating tissue atlases and tracking the development of the tumors. The separation of healthcare data utilizing deep learning variational autoencoders and conditional neural networks is presented in this research as a paradigm. One of the essential jobs in machine vision is the partitioning of an image. Due to the requirement for low-level spatial data, this assignment is more challenging compared to other vision-related challenges. By utilizing VAEs' capacity to develop hidden representations and combining CNNs in a conditioned environment, the algorithm generates accurate and efficient results for the segmentation. Moreover, to learn the representation of latent space from labelled clinical images, the VAE is trained as part of the system that is suggested. After that, the representations that were learned and real categorizations are used to develop the conditional neural network. Furthermore, the model that has been trained is utilized to accurately separate the areas that are important in brand-new medical images during the inferential stage. Thus, the experimental findings on several healthcare imaging databases show the enhanced precision of segmentation of the structure, highlighting its ability to enhance automated diagnosis and treatment. Henceforth, the suggested Deep Learning and Variational Auto Encoders with Conditional Neural Networks (DL-VAE-CNN) are employed to solve the pixel-level problem of classification that plagues the earlier investigations.
The &amp;#8220;Accessible&amp;#8221; in FAIR (data).

In a previous post, I looked at the Findability of FAIR data in common chemistry journals. Here I move on to the next letter, the A = Accessible. The attributes of A[1] include: (meta)data are retrievable by their identifier using a standardized communication protocol. the protocol is open, free and universally implementable.&amp;nbsp; the protocol allows for an authentication and authorization procedure.
Regional Economic Development Trend Prediction Method Based on Digital Twins and Time Series Network

At present, the interpretation of regional economic development (RED) has changed from a simple evaluation of economic growth to a focus on economic growth and the optimization of economic structure, the improvement of economic relations, and the change of institutional innovation. This article uses the RED trend as the research object and constructs the RED index to conduct the theoretical analysis. Then this paper uses the attention mechanism based on digital twins and the time series network model to verify the actual data. Finally, the regional economy is predicted according to the theoretical model. The specific research work mainly includes the following aspects: 1) This paper introduced the development status of research on time series networks and economic forecasting at home and abroad. 2) This paper introduces the basic principles and structures of long and short-term memory (LSTM) and convolutional neural network (CNN), constructs an improved CNN-LSTM model combined with the attention mechanism, and then constructs a regional economic prediction index system. 3) The best parameters of the model are selected through experiments, and the trained model is used for simulation experiment prediction. The results show that the CNN-LSTM model based on the attention mechanism proposed in this paper has high accuracy in predicting regional economies.
SENTIMENT ANALYSIS OF POST-COVID-19 INFLATION BASED ON TWITTER USING THE K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE CLASSIFICATION METHODS

The COVID-19 pandemic caused a crisis in global economic growth. The impact of injuries due to the COVID-19 pandemic has also caused price increases and an increase in the inflation rate. Inflation is a price increase caused by a certain factor so that it has an impact on the prices of nearby goods which increase the circulation of money in society to increase. Many people expressed their various opinions or criticisms of the post-COVID-19 price increase policy on social media, one of which was via Twitter. Sentiment analysis was carried out to see how public sentiment is towards the price increase policy after the COVID-19 pandemic, and these sentiments are combined into multiclasses, namely positive, negative and neutral sentiments. So that this sentiment can later be used as material for evaluation regarding the post-COVID-19 price increase policy. This study aims to see and compare the accuracy of the two classification methods, namely K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) in the sentiment classification process. The data used was 5989 tweets with the keywords ""Stuffets Go Up Post-Pandemic", "Fuel Goes Up", "Inflation 2022", "Covid19 Inflation", "Inflation Post-Pandemic" with a data collection period from August to October 2022. The data obtained then enter the text preprocessing stage before later entering the classification stage. The results obtained after carrying out the classification using the K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) methods show that the Support Vector Machine (SVM) method has a higher accuracy of 79%, while the K-Nearest Neighbor (K -NN) has an accuracy of 54%.
Analisis Peran Pojok Baca terhadap Literasi Siswa di SDN 238 Palembang

This research is to analyze the Role of Reading Corner on Student Literacy at SD Negeri 238 Palembang. The subjects used were grade 2 students. This study used a qualitative descriptive method of observation and documentation. The results of this study indicate that the Reading Corner has a very important role in the development of Student Literacy. Reading is an important activity in the teaching and learning process, because reading is the ability to transfer knowledge through text or reading material. Reading corner activities are carried out as an effort to increase students' interest in reading which is applied as a habit and aims to develop a culture of literacy in schools through reading corner activities. Through the Reading Corner, students can add extensive knowledge and insight through reading in the Reading Corner. The Reading Corner is placed in the corner of each class, which contains several fairy tale books or story books, etc. Students can read in the Reading Corner during their free time.
Pengembangan Bahan Ajar Untuk Meningkatkan Kemampuan Literasi Finansial Siswa SD

In the 21st century, one of the life skills that students must master is financial literacy. However, the facts on the ground show that financial literacy material has not yet been included in learning in elementary schools. This study aims to develop teaching materials to improve the financial literacy skills of elementary school students. Based on the analysis of the social studies curriculum in grade IV elementary schools, there is no editorial related to financial literacy. One solution is the development of teaching materials. This teaching material was developed using the Design and Development method with the Richey Klein model. This model consists of planning, production, and evaluation stages. Data collection techniques used are literature study and expert validation. The research instruments needed are AMP sheets, teaching material structure sheets, and questionnaire sheets.