Reviewerly/exHarmony huggingface dataset:
Analisis Website Tapanuli Tengah Menggunakan Metode Open Web Application Security Project Zap (Owasp Zap)

Data security on the website is very important to prevent misuse of data or information on the website. Due to the rapid advancement of technology, many irresponsible persons who are often called hackers or hackers steal data. The author is interested in learning more about the security of the Central Tapanuli website (TAPTENG) as a result of this research. In this section, the author checks the security of the Tapanuli Tengah website using the OWASP ZAP method to assist in determining the actions that need to be taken to mitigate the vulnerability. There are several stages of OWASP that are carried out including Information Gathering, Session Management Testing, Data Validation Testing, and Webservices Testing. From the overall research results detected on the 192.187.99.170 website, the results obtained were 22079 instances with the threat name Timestamp Disclosure - Unix with the threat level at the Low level, which means it is at a low level.
Rancangan Pembuatan API Website Data Tanaman Obat Dan Langka Kabupaten Kediri

The design of a website for information on medicinal and rare plants in Kediri Regency aims to provide convenience in obtaining information easily and quickly. The design of this website uses the Software Development Life Cycle (SLDC) Waterfall method with the stages of identification, analysis, design, implementation and testing. The web-based Application Programming Interface (API) uses Node.js as a runtime environment for the JavaScript programming language using the Express.js framework. The Application Programming Interface (API) is used as a liaison between the client and server, testing the endpoint Application Programming Interface (API) using Postman tools by displaying the results of the test in the form of data in JSON (JavaScript Object Notation) format which is used to store and transfer data
An In-depth Study on the Teaching Reform of the Course "Career Development and Employment Guidance for College Students" in Vocational Colleges in the Information Age

According to survey data released by MyCOS Corporation, the basic work abilities of college students when they leave school are worrying. Although vocational guidance courses for college students have Incorporated into the professional teaching plan, but currently there are obvious shortcomings in the teaching of this course. Vocational guidance education for college students should transform its concept from focusing on cultivating 'students' quality education. To implement vocational quality education that integrates the cultivation of "students", "professionals", and "social individuals"，the course 'Career Development and Employment Guidance for College Students' should be constructed to a career core systematic curriculum system, including the cultivation of mental abilities, which implements a comprehensive teaching throughout the entire stage of vocational education.
KESALAHAN PENGGUNAAN ADVERBIA再 ZÀI DAN又 YÒU PADA MAHASISWA PROGRAM STUDI BAHASA DAN BUDAYA TIONGKOK FAKULTAS ILMU BUDAYA UNIVERSITAS PADJADJARAN

This research aimed to determine the misuse of the adverbs再 zài and 又yòu on the Chinese Language and Culture at Faculty of Cultural Science Universitas Padjadjaran. Chinese language learners. This research applied qualitative approach with data collection techniques using the test techniques through test instruments. Result shows that the error rate of using the adverbs再 zài and 又yòu reached 30%,with four types of errors; omission ,addition, misselection and misordering. The factors that influenced these misuse are: the interlingual factor or the factor from the mother tongue, the intralingual factor from the students' understanding levels and students' vocabulary mastery, and the last factor is the performance error factor.
Investigation and Analysis of the Current Situation of Information Technology Classroom Example Teaching in High School

In the current information technology classroom teaching in high school, teachers use more traditional example teaching methods and simple example teaching resources. In order to enhancing the use of example teaching and enrich example teaching resources, questionnaires and interviews employs in this paper. This paper studies the current situation of classroom example teaching, student example learning demand analysis, teacher example teaching current situation and demand analysis, and puts forward specific optimization path on the basis of this, in order to provide reference for the improvement of example teaching.
Identifikasi Deteksi Tepi Pada Pola Wajah Menerapkan Metode Sobel, Roberts dan Prewitt

The face is a part of the body that is generally useful as a recognition tool. Faces that are visible to the naked eye look similar but each person's face has a different pattern. Facial pattern is one of the factors that can show the characteristics of a person's face that can affect the characteristics of that person's race. Several researchers suggested facial types based on facial morphological indices. To find out the difference in the pattern of the human face, image processing can be done with edge detection. Edge (edge) is the edge of the surface of an object. The edge of an image is a different boundary between two areas in an image object. Edge detection is the process of finding the outline of an image object. The purpose of detecting edges is to classify objects in the image and useful for analyzing subsequent images. In detecting images on facial patterns, among others, by applying the Sobel, Roberts and Prewitt methods. From the detection results, it can be concluded that the Sobel method produces image edges that show a clearer pattern and are in accordance with the original image
Rancang Bangun Sistem Rekomendasi Produk Sepatu pada Toko Online Menggunakan Metode User-Base Collaborative Filtering

When shopping at an online store, customers are always confused by the number of products displayed in the catalog. As a solution, customers see testimonials and other user ratings to ensure that the product to be purchased is in accordance with what is displayed in the product catalog. User-base collaborative filtering is a recommendation method that uses rating data from users to generate recommendations. To create a recommendation system using this method, there are three steps that must be taken, namely, first calculating the rating distance between users using the Euclidean distance equation, second calculating the similarity between active users and other users based on Euclidean distance calculations, and third calculating predictions. rating, which is to transfer the similarity value to the user rating value and then add up all the multiplication values. The final result of the prediction calculation is the five highest recommendation values which will then be processed by the system in such a way as to produce recommendations for shoes products that are displayed to active users as product recommendations. With this recommendation system, it is hoped that it will further increase consumer convenience when shopping at online stores
Pembangkitan Kunci Beaufort Cipher Dengan Teknik Blum-blum Shub pada Pengamanan Citra Digital

Data security is not only due to the complexity of the algorithms used, but one of the most important factors is the security of the keys used. One of the most popular data that needs to be backed up today is digital images. However, these days, the algorithms used to back up data are often attacked. This is because the structure of the key formations used is still weak. One of the data security algorithms that form a simple key is the Beaufort cipher. It relies on the duplication of key characters to form the key for each protected element. Key generators based on blum-blum shub technology can generate very random and difficult-to-guess keys, so the strength of the keys you use can enhance the resilience of protected data. Keys generated based on blum-blum shub technology will later be used in the process of encrypting and decrypting pixel values in digital images.
Pemanfaatan Web Gis Dalam Sistem Informasi Pemasaran Rumah Kredit BNI Jombang

Bank Negara Indonesia (Persero) Tbk or BNI is one of the oldest commercial banks in the history of the State of Indonesia. This bank was established on July 5, 1949 as the central bank and in 1968, BNI was designated as “Bank Negara Indonesia 1946”, and its status became a State-Owned Commercial Bank. The house is one of the most important needs for the community. The difficult economic conditions and the many demands that must be met by the community make the bank as a place for people to own a house on credit, especially for people with middle to lower economic levels. Buying a house on credit among the public is a very attractive option. The purpose of marketing is to understand the wants and needs of consumers so that the product or service can sell itself. In marketing their products, a marketer must be good at reading the market situation now and in the future. This means that marketers must be responsive to what consumers want and need. Therefore, marketing is needed. If the bank still acts passively, it is possible that the banking service product will not be recognized by the public. These marketing activities are proposed to maintain the existence of banking in the hearts of customers or prospective customers. Therefore, from marketing activities, customers or other prospective customers can obtain accurate information about housing provided by a BNI Jombang bank
MIRROR (LEFT-RECURSIVE) GRAY CODE

Here we consider a version of the Binary Reflected Gray Code (BRGC, or Gray code for short), called the mirror Gray code – in parallel and in comparison to the Gray code. We discuss some sources where the two codes are not distinguished and the reasons why. We present our arguments for treating the two codes as distinct, starting with a definition of the mirror Gray code and showing its main properties. Among the most important of these is the relationship between the two codes – the codewords of the mirror Gray code are “left-right mirror images” of those in the Gray code and vice versa. Other arguments we show are the differences in the algorithms for generating the codewords. We also present another approach that involves representing the codewords by integers (their serial numbers), generating and examining the two codes by using their sequences of serial numbers. For this purpose, we use the bijections that relate the pairs of combinatorial objects: binary vectors in a certain order and the corresponding integer sequences and subsets. In this way, we get different algorithms, sequences, and functions for successor and predecessor, for ranking and unranking at the mirror Gray code. We believe that this article provides at least one more perspective, as well as expands and enriches the knowledge of the Gray code.
Understanding the impact of requirements evolution and reaction on evolution of software: a survey and comparison

In software systems, the continuous changing of requirements, known as requirements evolution, is considered one of the significant issues. Requirements' evolution denotes the postـdeployment changes in the requirements. This article reviews the most related requirements evolution approaches. Different approaches have been presented in modelling requirements evolution, managing requirements evolution, and relevant analysis techniques, like inconsistency detection and change impact analysis. The relevant approaches of requirements evolution can be generally classified into the impact of evolution and reaction on evolution. The article also has given a comparison among those approaches. The approaches that have been surveyed in this article exhibited many limitations. These limitations need to be addressed and coped with for the approaches to be more effective in managing the evolution of software requirements. One of the solutions to these limitations is to develop an approach that addresses the reasoning behind software requirements evolution.
Variety and Mainstays of the R Developer Community

The thriving developer community has a significant impact on the widespread use of R software. To better understand this community, we conducted a study analyzing all R packages available on CRAN. We identified the most popular topics of R packages by text mining the package descriptions. Additionally, using network centrality measures, we discovered the important packages in the package dependency network and influential developers in the global R community. Our analysis showed that among the 20 topics identified in the topic model, *Data Import, Export, and Wrangling*, as well as *Data Visualization, Result Presentation, and Interactive Web Applications*, were particularly popular among influential packages and developers. These findings provide valuable insights into the R community.
C443: An R-package to see a forest for the trees

Classification trees, well-known for their ease of interpretation, are a widely used tool to solve statistical learning problems. However, researchers often end up with a forest rather than an individual classification tree, which implies a major cost due to the loss of the transparency of individual trees. Therefore, an important challenge is to enjoy the benefits of forests without paying this cost. In this paper, we propose the R-package C443. The C443 methodology simplifies a forest into one or a few condensed summary trees, to gain insight into its central tendency and heterogeneity. This is done by clustering the trees in the forest based on similarities between them, and on post-processing the clustering output. We will elaborate upon the implementation of the methodology in the package, and will illustrate its use with three examples.
bayesassurance: An R Package for Calculating Sample Size and Bayesian Assurance

In this paper, we present bayesassurance, an [R]{.sans-serif} package designed for computing Bayesian assurance criteria which can be used to determine sample size in Bayesian inference setting. The functions included in the [R]{.sans-serif} package offer a two-stage framework using design priors to specify the population from which the data will be collected and analysis priors to fit a Bayesian model. We also demonstrate that frequentist sample size calculations are exactly reproduced as special cases of evaluating Bayesian assurance functions using appropriately specified priors.
Early Identification of Deforestation using Anomaly Detection

Research involving anomaly detection in image streams has seen growth through the years, given the proliferation of high-quality image data in various applications. One such application that is in urgent need of attention is deforestation. Detecting anomalies in this context, however, remains challenging due to the irregular and low-probability nature of deforestation events. This study introduces two anomaly detection frameworks utilizing machine learning and deep learning for the early detection of deforestation activities in image streams. Furthermore, Explainable AI was used to explain the black box models of the deep learning-based anomaly detection framework. The class imbalance problem, the inter-dependency between the images with time, the lack of available labelled images, a data-driven anomalous threshold, and the trade-off of accuracy while increasing interpretability in the black box optimization methods are some key aspects considered in the model-building process. Our novel framework for anomaly detection in image streams underwent rigorous evaluation using a range of datasets that included synthetic and real-world data, notably datasets related to Amazon’s forest coverage. The objective of this evaluation was to detect occurrences of deforestation in the Amazon. Several metrics were used to evaluate the performance of the proposed framework.
Learning Application for Educational and Skills Development of Primary Children

This research uses interactive web-based learning tools to improve primary school children's language skills, mathematical competency, and critical thinking ability. The study attempts to bridge the gap between linguistic and numerical literacy while also including critical thinking aspects into the learning process, resulting in a full and interesting educational experience. The importance of this study stems from its dedication to comprehensive growth through the integration of technology and education. The technique integrates auditory, visual, and interactive components to make language and mathematical skill development successful and pleasant by providing a dynamic platform that accommodates varied learning styles. The project's methodology entails rigorous data gathering, the building of strong neural network models for linguistic and arithmetic skills, and the use of facial expression detection technologies to test critical thinking skills. Through various data sources, these models are taught to recognize and improve handwriting, numbers, and mathematical problem-solving. The study yields favorable results in all three categories. Significant increases in letter and numerical recognition, vocabulary enhancement, and mathematical competency have been demonstrated via interactive games aimed to promote language and mathematics ability. Furthermore, the use of facial expression detection technologies in educational games analyses and improves the critical thinking skills of primary school children. Finally, this study pioneers a strategy that demonstrates the potential of interactive web-based learning apps to improve linguistic, mathematics, and critical thinking skills. The findings reflect a huge step forward towards a more integrated and effective learning environment, with technology aiding an all-encompassing education. The research emphasizes the need to integrate technology and education to provide kids with language, numeracy, and critical thinking abilities that will prepare them for future problems.
Detecting Tabnabbing Attacks Via An RL-Based Agent

Tabnabbing attacks exploit user behavior in web browsers, deceiving users by altering content in inactive tabs to appear legitimate, leading to data disclosure or unintended actions. This research evaluates the effectiveness of Reinforcement Learning (RL) in detecting Tabnabbing attacks at the web browser level, presenting a proactive defense mechanism against this cyber threat. The study began with a literature review to find the top 5 critical features of Tabnabbing attacks and were extracted using a publicly available dataset from “Phishpedia”. Data preprocessing is conducted to handle missing and incorrect data, resulting in a refined dataset. The RL agent is designed using the Deep Q-Network (DQN) algorithm, which effectively handles high-dimensional state spaces. The evaluation of the RL agent demonstrates promising results. However, there is room for improvement requiring further research and model tuning.
Time Analysis Side Channeling Attack in Symmetric Key Cryptography

Protecting sensitive data in the field of symmetric key cryptography requires strong security against side-channel attacks. The confidentiality and integrity of cryptographic systems are seriously threatened by side-channel attacks, which take advantage of accidental information loss through a variety of physical channels. This research provides a comprehensive analysis of side-channel attacks and suggests an analysis method to highlight the vulnerability of a given symmetric algorithm. The relevance of side-channel attacks and their impact on cryptographic solutions are first discussed in the study. Following that, it concentrates on a well-known side-channel attack category which is Time Analysis attack. The simulation of this attack will be primarily tested on DES (Data Encryption Standard) algorithm. This research contributes to the present day attempts to strengthen the security of symmetric key cryptography by providing a thorough analysis of side-channel attacks.
The Application of Convolutional Neural Network in the Context of Tamil Handwritten Character Recognition

This research paper presents an in-depth investigation into the application of Convolutional Neural Networks (CNNs) for Tamil handwritten character recognition. We explore existing research, methodologies, and cutting-edge techniques, showcasing CNNs’ effectiveness in achieving a remarkable 95% accuracy. Our dataset comprises 247 Tamil characters and 18 North Indian characters, accommodating diverse writing styles. We tailor CNN architectures for Tamil characters, implement advanced preprocessing, data augmentation, and training methods to enhance model performance. Our paper tackles challenges posed by accessible datasets, offering remedies for data scarcity, class imbalance, and writing style variations. Our distinct contribution lies in achieving 95% accuracy across 247 Tamil characters and 18 North Indian characters, demonstrating CNNs’ potential for document processing, language preservation, and automation in Tamil-speaking regions. This work advances the field by introducing novel techniques, a comprehensive dataset, and strategic insights, serving as a significant step forward in Tamil character recognition.
Multiple Objective Optimization Based Dietary Recommender System

In contemporary society, the profound impact of an individual’s diet on their health underscores the critical importance of dietary choices in maintaining overall wellbeing. Amidst the challenges posed by modern, hectic lifestyles, the manual tracking of meals becomes a cumbersome task. This paper addresses this issue through the development of a comprehensive meal recommender system. The envisioned system aims to automatically analyze and offer a nutritional breakdown of meals, alleviating the burden on individuals to manually track their dietary intake. It goes beyond conventional solutions by providing personalized recommendations that not only satisfy daily nutritional requirements but also cater to user preferences and promote meal diversity. This research endeavors to contribute to the enhancement of customers’ overall well-being by leveraging the capabilities of an advanced recommendation system. The paper outlines the design and implementation of this system, highlighting its potential to revolutionize how individuals manage their diets in contemporary, fast-paced lifestyles.
FPGA Latch Primitive based Efficient True Random Number Generators

In cryptography, true random number generators are essential and have a crucial role. The study provides an innovative and effective way for producing true random numbers on FPGAs by taking advantage of the metastability of latches and the phase noises of ring oscillators. The proposed noise sources are based on Latch primitives provided by the FPGA vendor. Since it uses less Look-up tables (LUTs), using that kind of resource for noise sources is area-efficient. Another key benefit of the suggested true random numbers generator is that it just needs a limited number of primitives and doesn’t require any FPGA-specific configuration or constraint before implementation. The proposed TRNGs are validated on three different FPGA parts (XC7Z015, XC7Z020, XC7Z045). Without any post-processing, they pass all AIS-20/31 tests, NIST 800-22 tests, NIST 800-90B tests, and NIST 800-22 tests for all the FPGA ICs. Hence, the proposed TRNGs have the best area-speed efficiency values compared to the other TRNG implementations given in the literature.