Reviewerly/exHarmony huggingface dataset:
ANALISIS KESALAHAN PENULISAN HURUF KAPITAL DAN PENGGUNAAN TANDA BACA PADA KARANGAN DESKRIPSI

The problem in this study is that composing is often described as an activity that has not taken place as expected. Not a few students often experience difficulties in dealing with writing descriptions. Writing is expressing thoughts through writing. Errors in writing capital letters and using inappropriate punctuation often occur in student writing. Data collection activities in this study used observation techniques (observing) with note-taking techniques. The data collection method used in this research is documentation. Teacher and student interview data techniques. Test technique. Data analysis techniques use the Miles and Huberman concepts which include (1) data collection, (2) data reduction, (3) data presentation (4) conclusions. In this study, the data was in written form, in the form of errors in capital letters, punctuation and descriptive essays. Based on the results of the study, it was found that there were errors in the use of capital letters (A), full stop punctuation (.), (2) errors in the use of comma punctuation (,). In writing, one must understand the rules of writing, one of which is standard writing and the use of PUEBI, so that writing is in accordance with good and correct rules in writing capital letters, punctuation.
English teachers’ perspectives on the implementation of extensive reading

There needs to be more literature regarding English teachers' perspectives on the actual practice of extensive reading in the classroom in Indonesia. Therefore, a study aimed to investigate the implementation of extensive reading based on the teachers' perspectives. This study employed a qualitative research method as the research design. The participants were two senior high school English teachers and two junior high school English teachers. Interviews were conducted as the data collection and transcribed for the data analysis. The results showed that all English teachers had acknowledged and were well-informed of the benefits of extensive reading. However, half of them projected their positive beliefs through implementing extensive reading in their teaching practice, but the other half did not. The obstacles found in applying extensive reading were the students' need for more interest in reading, their lack of vocabulary mastery, and opposing views toward reading. Therefore, this insight into the study's results has enriched the literature, and the stakeholder of the curriculum should provide support regarding the teachers' concern about implementing extensive reading.&#x0D; Keywords: perspective, implementation, extensive reading
Sistem Penilaian Kinerja Karyawan PT Bank Syariah Indonesia Berbasis Website Menggunakan Algoritma Naive Bayes

This study focuses on developing an employee performance appraisal system that aims to evaluate employee performance at PT Bank Syariah Indonesia within the criteria set by the company aimed at classifying employee appraisals that can assist companies in classifying good or bad performance on the value of the employee. This method uses the nave Bayes classifier, a website-based employee performance appraisal system. The system will take the value of the criteria that have been set by the company such as: the value of neatness, loyalty, compliance, discipline, productivity and thoroughness. Based on the results of taking the value of these criteria, the system will issue an output that decides the classification of employees is classified as good or not good. Thus the system will determine whether the employee is good or bad.
Wild Animals Detection Based on YOLOv5

Wild animal monitoring is of great significance to population discovery and research on animal behavior and habits. Early wild animal monitoring mainly relied on human effort, which is time-consuming and contains safety risks. In recent years, with the continuous development of pattern recognition techniques, automated wildlife detection algorithms based on image content analysis have also made progress thanks to these achievements. However, due to the complexity of field scenes, the recognition accuracy and robustness of existing methods can not meet the practical application requirements. Based on considerations, we suggest a field animal detection method based on YOLOv5, which aims to localize and recognize the wild animal. We analyzed the change of recognition accuracy for different scenes in detail, especially for the scene containing multiple targets, small targets or occluded targets. We have used a large number of experiments to verify the feasibility of this method.
False Comment Detection Based on BERT and Long Short-Term Memory

In the beginning and ending of an article of the rapid development of e-commerce, almost all e-commerce websites support consumers to rate and comment on the workmanship, express delivery, and price of products. Publishing many messages and comments on the network platform has become a popular form of the Internet. Nowadays, online paid Internet platforms are also in the process of continuous evolution. The comments written are more and more authentic and highly misleading. It is a waste of time and easy to be confused to distinguish by the naked eye alone. This article manually annotates a valid dataset containing 4000 snack product reviews and proposes a false review detection model based on Bidirectional Encoder Representation from Transformers (BERT) and Long Short-Term Memory (LSTM). The experimental results show that the accuracy of the BERT and LSTM false comment detection model and BERT false comment detection model is above 50% and effective. The BERT and LSTM model is slightly better than the BERT model regarding loss rate and accuracy. The experiments were scientific and effective, and the data provided by the two experimental models are of the reference value.
Exploring the Advancements and Challenges of Automated Machine Learning

Automl,a rapidly growing field which is aiming to apply the machine to solve problems that human cant easily deal with . This includes tasks such as feature selection, model selection, and hyperparameter tuning. One of the many advantages about Auto Ml is that it can greatly shorten the cost of researchs and resources cost by applying machine learning to a problem. This makes it accessible to a wider range of users, including those without a background in computer science or statistics.In spite of some advantages of AutoML, many challenges are waiting to be addressed. The main challenge is that it is often challenging to ensure that the models generated by AutoML are of high quality and generalize well to new data. Another challenge is that AutoML can be computationally expensive, which can make it infeasible for some problems. Overall, AutoML has the potential to revolutionize the way we apply machine learning to real-world problems, but it is important to be aware of its limitations and challenges.
Feature Matching Search Mechanism Over Encrypted Cloud Data

Abstract: In recent years, there has been a lot of research done on encrypted search technologies. Creating indexes with separate keywords has led to high storage costs and poor search accuracy, which has made the issue urgently need to be solved as more and more data is being stored in the cloud. So, for encrypted cloud data, we provide a novel feature matching ranked search method (FMRSM) in this study. This approach builds indexes using the feature score algorithm (FSA), which enables multiple keywords to be mapped to an index dimension after being taken from a page as a feature. As a result, index storage costs may be decreased and encryption effectiveness can be increased. Furthermore, the FMRSM generates trapdoor processes using a matching score algorithm (MSA). The matching score algorithm is able to produce results with improved ranking accuracy because to FSA, which allows it to rank the search results according to the type of match and the amount of matching terms. A thorough investigation shows that our mechanism is more practical and efficient
Eyes on dementia: an overview of the interplay between eye movements and cognitive decline

The economic and disease burden of dementia is forecasted to continue increasing. Considering its cognitive effects, timely diagnosis is important in developing a stage-based treatment plan and gathering data to support advocacy efforts and plan healthcare and social services. Eye-tracking technology has emerged as an efficient diagnostic tool in clinical practice and experimental studies. This review aimed to comprehensively analyze various aspects of eye-tracking technology, including pupillometry parameters, eye movements, eye-tracking devices, and neuropsychological tools. We conducted a systematic review retrieving articles published in the last ten years from six databases. Our results provide a complex overview for each included form of dementia/cognitive decline in terms of patient characteristics (age, sex-disaggregated by included pathologies), inclusion and exclusion criteria, devices, and neuropsychological tools. We also summarized findings on fixation stability tasks, saccadic evaluation, pupillometry, scene perception, object recognition, spatial memory, eye-tracking video tasks, and visual search. The eye-tracking method has become more common in cognitive assessments.
Optimasi Pengendalian Persediaan dengan Metode Reorder Point dalam Pengembangan Aplikasi Kontrol Stok Berbasis Web

Stock control is a process of keeping track of owned items, their location, and their movements in and out of storage. It helps businesses to manage their inventory efficiently and meet consumer demand while reducing the cost of storing goods. However, online shops like Omah Mode often struggle to meet the demand of consumers due to the lack of a stock control system, resulting in penalties from the marketplace. To solve this problem, a web-based stock control application was designed using the reorder point method. This method determines the minimum stock of goods that should be in the warehouse and the right time to order goods with low stock from suppliers. The purpose of this research is to develop an inventory control system that displays the minimum stock of goods needed in the warehouse. The study used the SDLC Prototype method, consisting of needs analysis, prototype making, prototype evaluation, system coding, system testing, system evaluation, and system use. The results showed that the system created has an 85.71% accuracy rate based on a comparison of manual calculations and system calculations of 28 sample items.
Rekomendasi Teknis Antar Muka Aspek Keselamatan dan Keamanan Sistem Instrumentasi dan Kendali PLTN di Indonesia

Instrumentation and control (I&amp;C) systems are crucial in ensuring the safe operation of power reactors. New and modern nuclear facilities commonly use digital I&amp;C systems. However, the application of digital technologies within I&amp;C systems has made these systems vulnerable to cyber-attack. Literature study shows that cyber-attack is one of the threats that could occur at any time in the lifetime of the I&amp;C system. Indonesia regulates cyber security and interface of safety and security through BAPETEN Chairman Regulation (BCR) number 1 of 2009 on Nuclear Installation Physical Protection and BCR number 6 of 2012 on Design Provision of Computer-Based Important Safety System on Power Reactor. The provision related to interface safety and security against cyber-attack has a significant gap compared to IAEA Requirements and Internationals practice. Therefore, this paper tries to identify technical recommendations for BCR number 1 of 2009 and BCR number 6 of 2012 related to the interface of safety and security and cyber security in the Nuclear Power Plant (NPP) I&amp;C system using IAEA requirements and international practice. The paper concludes that some technical recommendations must be added to BCR 1 of 2009 and BCR 6 of 2012. These recommendations are required in establishing cyber security program, integrating a cyber-security program with physical protection system, implementing control networks with reduced interconnectivity, implementing defense-in-depth as an essential concept in designing the NPP I&amp;C system, developing a highly integrated protection system platform that utilizes FPG, and integrating passive safety system into the safety system. Keywords: cyber-attack, safety and security interfaces, instrumentation and control system
Learning Intention-Aware Policies in Deep Reinforcement Learning

Abstract Deep reinforcement learning (DRL) provides an agent with an optimal policy so as to maximize the cumulative rewards. The policy defined in DRL mainly depends on the state, historical memory, and policy model parameters. However, we humans usually take actions according to our own intentions, such as moving fast or slow, besides the elements included in the traditional policy models. In order to make the action-choosing mechanism more similar to humans and make the agent to select actions that incorporate intentions, we propose an intention-aware policy learning method in this letter To formalize this process, we first define an intention-aware policy by incorporating the intention information into the policy model, which is learned by maximizing the cumulative rewards with the mutual information (MI) between the intention and the action. Then we derive an approximation of the MI objective that can be optimized efficiently. Finally, we demonstrate the effectiveness of the intention-aware policy in the classical MuJoCo control task and the multigoal continuous chain walking task.
PREDICTIVE CONTROL BY NEURAL NETWORK OF HOT-WATER FLOOR HEATING SYSTEM WITH SHEET PHASE CHANGE MATERIAL INTO THE FLOOR OF RC APARTMENT HOUSE.

Assuming the use of excess daytime electricity, we built and verified a model that can predict, using neural network, how to control a floor heating system with PCM installed during the daytime to reduce nighttime electricity consumption while still providing a comfortable temperature range. The results confirmed the effectiveness of PCM in reducing floor surface temperature and room temperature, and showed that it can be predicted more accurately than multiple regression analysis by ANN. In addition, it was suggested that excess electricity during the daytime could be utilized to reduce CO2 emissions.
What We Do in the Shadows: How does Experiencing Cybercrime Affect Response Actions &amp; Protective Practices?

Despite the increasing prevalence of cybercrimes, there is limited understanding of the response strategies and protective (privacy, security) practices of individuals who have experienced at least one attempted or successful cybercrime incident. To address this gap, we conduct an online survey across an N=605 sample from the UK, eliciting firsthand accounts of cybercrime experiences, actions taken in response to such incidents, as well as their level of engagement with security and privacy (SP) methods and technologies. We identify the occurrence of 8 distinct types of cybercrime and 9 corresponding response strategies amongst 319 cybercrime reports, encompassing IT and protective themes. We depict the association between the experience of cybercrime and the utilisation of response strategies through visual representation. In parallel with IT-related or protective SP theme response strategies, we find that the type of cybercrime experienced (e.g., malware) and the frequency of experiences (e.g., multiple cybercrimes or repeated experiences of the same cybercrime type) influence the engagement with SP technologies. This paper provides empirical insights into experiences of different types of cybercrimes, including their multiple occurrences, and (protective) actions. We also provide recommendations for research and practical implementation, emphasising the importance of addressing the specific needs of individuals who have experienced multiple or repeat cybercrimes. Our paper suggests the adoption of a de-responsibiling cyber-strategy that complements individual guardianship — while promoting infrastructure that caters to the diverse range of users.
Implementation of Multiple Linear Regression to Estimate Profit on Sales of Screen Printing Equipment

Traditional marketing strategies are no longer practical to implement because the process requires more costs and time to disseminate information which is much longer. Data Mining is a science that discusses knowledge from previous data to estimate the amount of production in the future. Data mining is a term used to find hidden knowledge in databases. “Data mining is a semi-automatic process using statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify valuable and helpful information in large databases. It is necessary to solve the problem by using one of the five methods in the field of Data Mining, namely the Multiple Linear Regression method, where this method will analyze the variables that have an influence and can make estimates. Multiple Linear Regression Is a method that can be used to analyze data and obtain meaningful conclusions about a relationship between one variable and another. This relationship is generally expressed by a mathematical equation expressing the relationship between the independent and dependent variables in the form of a simple equation
Purely Image-Based Dynamic Impedance Control of a Fully Actuated Aerial Vehicle

Existing works on the impedance control of aerial vehicles are generally based on the assumption of known translational velocity. However, velocity estimation can be unreliable in many cases. To solve this problem, we propose a concurrent velocity observation and impedance control framework to perform contact-based interaction tasks of aerial vehicles. This framework is cost-optimized since it only uses monocular vision and inertial measurement unit (IMU) measurements, without using global navigation satellite system (GNSS) modules, optical flow, and force sensors. Specifically, we propose a novel observer-based impedance model (VOIM) and a dynamic image-based visual servoing (DIBVS) controller to achieve the desired interaction behavior. Rigorous input-to-state stability analysis of the closed-loop system is subsequently conducted, proving the effectiveness of the method under unknown environment position and stiffness, along with the robustness to any positive estimate of the scale factor of visual features. Experiments demonstrate the performance of the proposed method by implementing an aerial drawing task and a point-contact impedance-based force regulation task. Furthermore, the robustness under vision system uncertainties is tested, highlighting the potential of application in unstructured environments.
The Design and Implementation of Online Examination Hall Allocation System for Students in Kaduna Polytechnic, Kaduna

The purpose of examination hall allocation process is to determine the venue of students who are candidates of examination in any given institutions and also be able to give first-hand information about any student who sat for the exam. The aim of the project is to design and implement online examination hall allocation system for students in Kaduna Polytechnic, Kaduna. The new system will be achieved using Structured System Analysis and Design Methodology. The software required to design the new system includes Adobe Dreamweaver, the programming language that will be used comprises of Hypertext Mark-up Language, JavaScript’s, Cascading Style Sheet and Hypertext Preprocessor (PHP), the website will be tested on a local web server “XAMPP” and Structured Query Language is used for the Database design. The project work covered all the necessary details involved in designing the Online exam hall allocation system. The web-based application is aimed at allocating halls only to students and keeping record in a database. The methodology focused on the study area, system design analysis, review of proposed system and architecture of the system. Other method of information generation adopted includes relevant literatures (such as textbooks.) and surfing the Internet, the concerned with presentation of the implementation, the different views of the scheduling on computer system. Therefore, this project if implemented will replace the manual method of exam hall allocation in department of computer science and enhance the process of hall allocation also, would address the problems of the existing system paper based outlined earlier.
Comparative Analysis of Hidden Markov Model and Bidirectional Long Short-Term Memory for POS Tagging in Eastern Armenian

This scientific article introduces a novel POS tagging system specifically developed for the Eastern Armenian language. The primary objective of the study is to conduct a comparative analysis of two well-established methods for part-of-speech (POS) tagging: the hidden Markov model (HMM) coupled with the Viterbi algorithm, and an artificial neural network in the form of a recurrent neural network. The study places particular emphasis on the Eastern Armenian language and employs the ArmSpeech-POS Eastern Armenian part-of-speech tagged corpus for conducting comprehensive experiments and evaluations. POS tagging is a fundamental task in natural language processing (NLP) that involves assigning grammatical tags to words in a given text. Accurate POS tagging is crucial for various NLP applications, including machine translation, information retrieval, and sentiment analysis. The Viterbi algorithm is a well-established probabilistic method that utilizes a hidden Markov model (HMM) to determine the most likely sequence of POS tags. On the other hand, RNNs, a type of deep learning model, can capture complex patterns and dependencies in sequential data. Experimental results indicate that both methods achieve reasonable accuracy in POS tagging for Eastern Armenian. However, the RNN outperforms the Viterbi algorithm, exhibiting higher accuracy rates. This can be attributed to the RNN’s ability to capture long-range dependencies and learn intricate linguistic patterns. The article concludes by discussing the implications of the study’s findings and potential areas for further research. It emphasizes the significance of accurate POS tagging for improving NLP applications in Eastern Armenian and suggests exploring advanced neural network architectures and incorporating linguistic features to enhance POS tagging performance.
Introduction to Serverless

Cloud computing has transformed the way we store, process, and manage data. In this chapter, we will cover the basics of cloud computing, including its evolution from traditional IT, its key advantages, and the next generation of cloud technologies. We will explore serverless architectures, BaaS (Backend as a Service) and FaaS (Function as a Service), discussing their benefits and weaknesses. We will also introduce AWS Amplify, a platform for building web and mobile applications with AWS services, and guide you through setting it up locally and configuring the Amplify CLI with AWS.
On pre-service STEM teachers' training in Ukraine

The essence, purpose, tasks, approaches, principles, content, forms and methods of methodical training of future teachers of Physics, Mathematics and Computer Science, as well as Masters in Educational and Pedagogical Sciences are revealed. Methodical training of future teachers is considered as one that should provide a synthesis of subject, psychological, pedagogical and methodological knowledge, skills, abilities and personal qualities of applicants for Higher Pedagogical Education. As the analysis of the experience of the Faculty of Physics and Mathematics of H.S. Skovoroda Kharkiv National Pedagogical University, implementation of the developed concept of methodical training of future teachers allows to provide preconditions for creation of a trajectory of their further professional development focused on their personal and professional growth, successful professional self-realization. Key words: future teachers; Physics; Mathematics; Computer Science; Educology; methodical training.