Reviewerly/exHarmony huggingface dataset:
درجة امتلاك معلمي اللغة الإنجليزية في المرحلة الثانوية للكفايات المهنية اللازمة لتطبيق التعلم الرقمي من وجهة نظرهم ومشرفيهم بمحافظة الزلفي Title of Thesis: The Degree of Secondary School EFL Teachers' Possession of Professional Competencies Necessary for Implementing Digital Learning, from the Perspectives of EFL Teachers and EFL Superintendents in the Zulfi Governorate

هدفت الدراسة الحالية إلى التعرف على مدى امتلاك معلمي اللغة الإنجليزية في المرحلة الثانوية للكفايات المهنية اللازمة لتطبيق التعلم الرقمي، والمعوقات التي تواجههم وسبل التغلب عليها من وجهة نظرهم ومشرفيهم، ويتكون مجتمع الدراسة من جميع معلمي اللغة الإنجليزية بالمرحلة الثانوية بمحافظة (الزلفي) بالمملكة العربية السعودية لعام 1443هـ، وتشتمل الدراسة على كامل أفراد المجتمع وعددهم (114) معلم/ة ومشرف/ة، وتتبنى الدراسة المنهج الوصفي المسحي كمنهج للدراسة وتستخدم الاستبانة كأداة للدراسة. وقد كانت أبرز نتائج الدراسة ما يلي: أن "مدى امتلاك معلمي اللغة الإنجليزية في المرحلة الثانوية للكفايات المهنية اللازمة لتطبيق التعلم الرقمي" جاءت بدرجة (عالية) من وجهة نظر أفراد مجتمع الدراسة. أن "المعوقات التي تواجه المعلمين وسبل التغلب عليها" جاءت بدرجة (عالية) من وجهة نظر أفراد مجتمع الدراسة. وكانت أبرز توصيات الدراسة: ضرورة المواءمة بين الاحتياجات التدريبية والبرامج التدريبية المقدمة لمعلمي اللغة الإنجليزية لتطبيق التعلم الرقمي. وضرورة تنمية الاتجاهات الإيجابية لدى المعلمين نحو تطبيق التعلم الرقمي. وضرورة تقوية شبكات الاتصال بالإنترنت داخل المدارس لتسهيل وصول المعلمين لبرامج تطبيق التعلم الرقمي The present study aims to investigate the extent to which secondary school English teachers possess the professional competencies necessary to implement digital learning, and the obstacles they encounter and ways to overcome them, from their perspectives and those of their supervisors. The population of the study consists of all secondary school English teachers in Az Zulfi Governorate in Saudi Arabia in 1443 H. The study sample includes the entire community, numbered at (114) teachers and supervisors. The study adopts the descriptive survey approach as the research methodology and uses the questionnaire as the research instrument. Findings of the study include the following: "The extent to which secondary school English teachers possess the professional competencies necessary to implement digital learning" was rated (high) from the perspectives of sample members. "The obstacles that teachers face and ways to overcome them" was rated (high). Recommendations of the study include the following. Recommendations of the study include the following: The necessity of aligning between training needs and training programs provides to English teachers for implementing digital learning. The necessity of developing positive attitudes among teachers toward digital learning. The necessity of strengthening internet networks at schools in order to facilitate teachers' access to programs of implementing digital learning
DenseDINO: Boosting Dense Self-Supervised Learning with Token-Based Point-Level Consistency

In this paper, we propose a simple yet effective transformer framework for self-supervised learning called DenseDINO to learn dense visual representations. To exploit the spatial information that the dense prediction tasks require but neglected by the existing self-supervised transformers, we introduce point-level supervision across views in a novel token-based way. Specifically, DenseDINO introduces some extra input tokens called reference tokens to match the point-level features with the position prior. With the reference token, the model could maintain spatial consistency and deal with multi-object complex scene images, thus generalizing better on dense prediction tasks. Compared with the vanilla DINO, our approach obtains competitive performance when evaluated on classification in ImageNet and achieves a large margin (+7.2% mIoU) improvement in semantic segmentation on PascalVOC under the linear probing protocol for segmentation.
Towards Autonomous Testing Agents via Conversational Large Language Models

Software testing is an important part of the development cycle, yet it requires specialized expertise and substantial developer effort to adequately test software. Recent discoveries of the capabilities of large language models (LLMs) suggest that they can be used as automated testing assistants, and thus provide helpful information and even drive the testing process. To highlight the potential of this technology, we present a taxonomy of LLM-based testing agents based on their level of autonomy, and describe how a greater level of autonomy can benefit developers in practice. An example use of LLMs as a testing assistant is provided to demonstrate how a conversational framework for testing can help developers. This also highlights how the often criticized hallucination of LLMs can be beneficial for testing. We identify other tangible benefits that LLM-driven testing agents can bestow, and also discuss potential limitations.
Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squares

We propose a new algorithm for the problem of recovering data that adheres to multiple, heterogeneous low-dimensional structures from linear observations. Focusing on data matrices that are simultaneously row-sparse and low-rank, we propose and analyze an iteratively reweighted least squares (IRLS) algorithm that is able to leverage both structures. In particular, it optimizes a combination of non-convex surrogates for row-sparsity and rank, a balancing of which is built into the algorithm. We prove locally quadratic convergence of the iterates to a simultaneously structured data matrix in a regime of minimal sample complexity (up to constants and a logarithmic factor), which is known to be impossible for a combination of convex surrogates. In experiments, we show that the IRLS method exhibits favorable empirical convergence, identifying simultaneously row-sparse and low-rank matrices from fewer measurements than state-of-the-art methods. Code is available at https://github.com/ckuemmerle/simirls.
Computational Linguistics Approach

This article aims to explain the field of computational linguistics and its specializations، its interest in computational modeling of natural language، and the study of computational approaches appropriate to linguistic questions. In general، computational linguistics draws on linguistics، computer science، artificial intelligence، mathematics، logic، philosophy، cognitive science، cognitive psychology، psycholinguistics، anthropology، and neuroscience، among others.
Comparative Analysis of Algorithms for Mining Frequent Patterns

Abstract: In the computerized world, everything is moving online, and data comes in different shapes and sizes and is collected in different ways. By using data mining, frequent pattern in the databases can be identified, and it can be used in numerous applications. Finding frequent patterns in huge databases is important because it reveals important information that cannot be found through simple data surfing. To find common patterns, a variety of methods are utilized, each of which performs differently. Apriori and FP Growth are the fundamental algorithms employed in frequent pattern mining. The functioning and experimental results of various algorithms are compared in this study, and their benefits and drawbacks are discussed.
KARNA - An Application for Babies/Toddlers with Hearing Impairment and their Parents

Abstract: KARNA is an application for babies/toddlers who suffer from hearing impairment from before birth or after birth. Hearing impairment can have adverse effects on the lives of such babies and their families. This application will provide sign language guide for toddlers and their parents, speech therapy for toddlers along with a community for experience sharing for parents and also complete information regarding hearing impairment and the problems faced by these kids, solutions, new technologies available for new born babies and toddlers who have hearing impairments.
Image Caption Generator Using Attention Based Neural Networks

Abstract: Image caption generation is a method used to create sentences that describe the scene depicted in a given image. The process includes identifying objects within the image, carrying out various operations, and identifying the most important features of the image. Once the system has identified this information, it generates the most relevant and concise description of the image, which is both grammatically and semantically correct. With the progress in deep-learning techniques, algorithms are able to generate text in the form of natural sentences that can effectively describe an image. However, replicating the natural human ability to comprehend image content and produce descriptive text is a difficult task for machines. The uses of image captioning are vast and of great significance, as it involves creating succinct captions utilizing a variety of techniques such as Natural Language Processing (NLP), Computer Vision(CV), and Deep Learning (DL) techniques. The current study presents a system that employs an attention mechanism, in addition to an encoder and a decoder, to generate captions. It utilizes a pretrained CNN, Inception V3, to extract features from the image and a RNN, GRU, to produce a relevant caption. The attention mechanism used in this model is Bahdanau attention, and the Flickr-8Kdataset is utilized for training the model. The results demonstrate the model's capability to understand images and generate text in a reasonable manner.
Dynamic Inertia Weight Particle Swarm Optimization for Anomaly Detection: A Case of Precision Irrigation

Anomaly-based Intrusion Detection System (IDS) is a type of IDS that detects abnormal behaviors by analyzing system activity and network traffic. Anomaly-based IDS works by establishing a baseline of normal behavior for a system or a network. However, these types of systems are less used compared to signature-based IDS for one primary challenge: How to define this normal behavior baseline? The answer to this question is complicated, since it involves not only analyzing or learning from historical data, but requires and understanding of the business domain the system is implemented in. The present study proposes a novel approach to constructing an unsupervised data classifier that combines both Particle Swarm Optimization (PSO) and clustering techniques for anomaly detection. The primary objective of this methodology is to surmount the limitations that conventional clustering algorithms suffer from, such as their inability to identify non-linear patterns within the data, susceptibility to initial conditions, and difficulty in overcoming the problem of local optima. The concept of particle systems is discussed by examining their origins, search strategies, and convergence mechanisms. We use a variant of the Particle Swarm Optimization called Dynamic Inertia Weight-Particle Swarm optimization (DIW-PSO) for our clustering process, and we elaborate on the reasoning behind this decision. Subsequently, we describe the labeling algorithm used for the resulting clusters and we explain the process for identifying anomalous clusters. We have demonstrated the effectiveness of our method by applying it to an intelligent irrigation control system for cotton plants. The results show that our classifier was able to accurately detect abnormal patterns that deviated from the optimal water requirements and growth conditions of the plants.
Development of Bigbooks for Contextual Teaching and Learning (CTL) Social Studies (Araling Panlipunan) 7 in Zone 2, Division of Zambales

A descriptive study investigated the effect on students’ academic performance of developed Big Book as an instructional material for contextual teaching and learning Social Studies (Araling Panlipunan) 7 at public schools in Zone 2, Division of Zambales. The study was conducted during the school year 2022-2023 among Social Studies teachers. Findings revealed that the developed Big Book for Contextualized Teaching and Learning in Social Studies 7- Histiory of Asia is useful in terms of Practicality. The academic performance of the students in Social Studies 7-Histiory of Asia during the 1st Quarter was Developing while Approaching Proficiency during the 2nd Quarter after the utilization of Big Book for contextualized teaching and learning. The ANOVA computation result shows a no significant difference on the appraised level of usefulness of Big Book for Contextualized Teaching and Learning in Social Studies 7-Histiory of Asia. The t-Test computation result reveals a significant difference between the students’ academic performances in the First and Second Quarters Social Studies 7-History of Asia. The Pearson r computation result manifested a very high positive relationship between the students’ second quarter academic performance and the usefulness of Big Book in Social Studies 7 History of Asia. An enhanced Big Book for Contextualized Teaching and Learning in Social Studies 7- Histiory of Asia was developed based on the result of review and appraisal of the material. It was recommended to present the enhanced Big Book in Social Studies 7 to the Department Heads, School Heads/Principals, and Education Specialist/Curriculum Planners of the Division of Zambales for more review or appraisal and utilization of the improvised instructional material in secondary schools in the Division of Zambales.
RSA Prime Factorization on IBM Qiskit

The advancement of quantum computing in recent years poses severe threats to the RSA public-key cryptosystem. The RSA cryptosystem fundamentally relies its security on the computational hardness of number theory problems: prime factorization (integer factoring). Shor’s quantum factoring algorithm could theoretically answer the computational problem in polynomial time. This paper contributes to the experiment and demonstration of Shor’s quantum factoring algorithm for RSA prime factorization using IBM Qiskit. The performance of the quantum program is evaluated based on user time and the success probability. The results show that a more significant public modulus N in the RSA public key improves factorization’s computational hardness, requiring more quantum bits to solve. A further enhancement on implementing Shor’s oracle function is essential in increasing success probability and reducing the number of shots required.
Hiding Sensitive Medical Data Using Simple and Pre-Large Rain Optimization Algorithm through Data Removal for E-Health System

Privacy has become a significant factor of e-Health system in the area of data mining termed as Privacy preserving data mining (PPDM) as it can uncover underlying rules and hide sensitive data for data sanitization. Various algorithms and heuristics have been studied to hide sensitive data using transaction removal. However, they are facing challenges to attain the reasonable side effects. Thus, rain optimization algorithm (ROA) based sensitive data hiding techniques is proposed in this paper. Using this algorithm, suitable transactions to be removed are selected. Besides, in this work, ROA based two frameworks are designed for data sanitization that are simple ROA to remove transaction (sROA2RT) and pre-large ROA to remove transaction (pROA2RT). In this algorithm, fitness is evaluated based on four side effects such as hiding failure, artificial cost, missing cost and dissimilarity of database. The proposed frameworks are evaluated using three e-Health datasets. Compared to previous frameworks, the proposed frameworks attain reasonable side effects.
Webtoon-Based Anticorruption Childrens Story Development

The rise of corruption cases illustrates that Indonesia is experiencing a crisis of morals and trust. Seeing these conditions, efforts to eradicate anti-corruption are needed. One of them is through preventive efforts in the form of anti-corruption education from an early age. This research aims to 1) develop the webtoon CANTIK, 2) find out the feasibility of the webtoon CANTIK, and 3) find out the effectiveness of the webtoon CANTIK to introduce anti-corruption moral values to children. The research method used is research and development (RD) which consists of 4 stages: define, design, development, and dissemination. The content contained in CANTIK contains simple forms of corruption and anti-corruption values. The first series describes a form of bribery corruption with anti-corruption values: honesty, responsibility, independence, and courage. The second series describes the form of corruption of the time with anti-corruption values: discipline, care and justice. The third series describes the form of greed corruption with anti-corruption values: simplicity, hard work, and responsibility.
Similarity Intelligence: Similarity Based Reasoning, Computing, and Analytics

Similarity has been playing an important role in computer science, artificial intelligence (AI) and data science. However, similarity intelligence has been ignored in these disciplines. Similarity intelligence is a process of discovering intelligence through similarity. This article will explore similarity intelligence, similarity-based reasoning, similarity computing and analytics. More specifically, this article looks at the similarity as an intelligence and its impact on a few areas in the real world. It explores similarity intelligence accompanying experience-based intelligence, knowledge-based intelligence, and data-based intelligence to play an important role in computer science, AI, and data science. This article explores similarity-based reasoning (SBR) and proposes three similarity-based inference rules. It then examines similarity computing and analytics, and a multiagent SBR system. The main contributions of this article are: 1) Similarity intelligence is discovered from experience-based intelligence consisting of data-based intelligence and knowledge-based intelligence. 2) Similarity-based reasoning, computing and analytics can be used to create similarity intelligence. The proposed approach will facilitate research and development of similarity intelligence, similarity computing and analytics, machine learning and case-based reasoning.
FOSTERING AND EMPOWERING DIGITAL BAZNAS

In an era of increasingly rapid technology, its impact has been felt in various circles, including in the management of zakat. This information system aims to facilitate data management, starting from entering information about zakat payers and recipients, calculating zakat calculations to making zakat fund reports at the Palembang City Baznas. Previously, the Palembang City Baznas only used simple computer calculations using Microsoft Excel and Word. Because of that, an information system is needed that can regulate the management process in managing zakat. The method used is direct analysis to BAZNAS locations and design of interfaces, rules, and design thinking. This research produced a hybrid-based BAZNAS Information System for Palembang City. This system is expected to increase efficiency and accuracy in processing zakat data and facilitate the process of making reports for the Palembang City Baznas
Lightweight digital imaging and communications in medicine image encryption for IoT system

Diagnosis in healthcare systems relies heavily on the use of medical images. Images such as X-rays, ultrasounds, computed tomography (CT) scans, magnetic resonance imaging (MRIs), and other scans of the brain and other internal organs of patients include private and personal information. However, these images are vulnerable to unauthorized users who unlawfully use them for non-diagnostic reasons due to the lack of security in communication routes and the gaps in the storage systems of hospitals or medical centers. Image encryption is a prominent technique used to protect medical images from unauthorized access in addition to enhancing the security of communication networks. In this paper, researchers offer a lightweight cryptosystem for the secure encryption of medical images that makes use of the present block cipher and a five-dimensional chaotic map. More than 25 images from the open science framework (OSF) public database of patients with coronavirus disease 2019 (COVID-19) were used to evaluate the proposed system. DICOM stands for “digital imaging and communications in medicine”. The efficiency of the proposed system is proved in terms of adjacent pixels’ correlation analysis, National Institute of Standards and Technology (NIST) analysis, mean square error, information entropy, unified average changing intensity, peak-to-signal noise ratio, entropy, and structure similarity index image.
Analisis Sistem Pengendalian Internal Gaji dan Upah Pada Pengadilan Tata Usaha Negara Palembang

Abstract : The Palembang State Administrative Court (PTUN) is one of the mainstays of regulatory conditions in Indonesia in its ability to complement the power of law, especially to decide issues that occur in state organizations and organizations or government businesses within the scope of South Sumatra, the Bangka Belitung Islands Region. The Palembang State Administrative Count in managing data is quite good but the way when it is going to follow transactions into an application, especially regarding the management of employee salary and wage data still looks quite complicated and difficult to operate except by the person who holds the application system. Therefore the author makes an application that is easier to operate, so that it can help when inputting salary transactions. This study uses the waterfall method and the modeling language UML (unified Modeling Language), xampp, PHP Rad classic and database.
Experimental of vectorizer and classifier for scrapped social media data

In this study, we used several classifiers and vectorizers to see their effect on processing social media data. In this study, the classifiers used were random forest, logistic regression, Bernoulli Naive Bayes (NB), and support vector clustering (SVC). Random forests are used to reduce spatial complexity, and also to minimize errors. Logistic regression is a method with a statistical model whose basic form uses a logistic function to represent the binary dependent variable. Then, the Naive Bayes function uses binary elements and SVC which has so far given good results rivals other guided learning. Our tests use social media data. Based on the tests that have been carried out on classifier variations and vectorizer variations, it was found that the best classifier is a linear regression algorithm based on predictive adaptive compared to the random forest method based on decision trees, probability-based Bernoulli NB and SVC which work by clustering. Meanwhile, from the test results on the count vectorizer, term frequency-inverse document frequency (TFIDF), and hashing, the best accuracy is achieved on the TFIDF vectorizer. In this case, it means that the TFIDF vectorizer has a better value in presenting word feature dimensions.
Pedagogical conditions of education of ecological culture in high school students

The article describes the pedagogical conditions of education on ecological culture in high school students. This research article focuses on the pedagogical conditions required for the education of ecological culture in high school students. The study employed a mixed-methods approach, including a literature review, a survey, and interviews with teachers and students. The study found that there are several pedagogical conditions necessary for the effective education of ecological culture in high school students, including the use of interactive teaching methods, practical tasks and projects, interdisciplinary and integrated approaches, and the integration of modern technology and social media in teaching and learning. The study also highlights the importance of teachers' professional development, the involvement of parents and local community, and the creation of a positive and supportive learning environment.
An Innovative LiDAR Substitute in Nighttime Environments: Unsupervised Depth Estimation from Single Thermal Images

An Innovative LiDAR Substitute in Nighttime Environments:Unsupervised Depth Estimation from Single Thermal Images