Reviewerly/exHarmony huggingface dataset:
PENINGKATAN HASIL BELAJAR SISWA MELALUI MODEL PEMBELAJARAN PICTURE AND PICTURE PADA MATERI KENAMPAKAN ALAM MATA PELAJARAN IPS, KELAS IV SD NEGERI 1 ULU SEMONG

Based on the existing formative results, student learning outcomes on natural appearance material are low, because the completeness of learning outcomes only reaches 30% of the 19 fourth grade students. The results of reflection and discussion with colleagues and supervisors, it is necessary to have learning that is focused on the visualization of learning material. Therefore, it is necessary to make efforts to improve student learning outcomes through the implementation of Classroom Action Research in class IV SD Negeri 1 Ulu Semong with 2 cycles and one of the actions taken is to use the picture and picture learning model. Data was collected through instrument tests for learning outcomes and observation sheets to observe learning activities which were then analyzed by reducing and classifying data. At the stage of implementing more active actions it is shown, student learning activities are tinkering with the images presented. Besides that, students are also more enthusiastic about asking about the pictures given, which indicates increasing student curiosity. At the end of the second cycle, the data showed that the students' mastery had increased from 32% in the pre-cycle to 89% in the second cycle. Thus it can be interpreted that student learning outcomes of natural appearance can be improved by using picture and picture learning models.
Vulnerabilities of \(ex^2\) - \(y^2\)\(\phi\) (N)= z Using Modulus of the Form N= \(p^r\)\(q^s\)

The technical details of RSA works on the idea that it is easy to generate the modulus by multiplying two sufficiently large prime numbers together, but factorizing that number back into the original prime numbers is extremely difficult. Suppose that \(N=p^r q^s\) are RSA modulus, where \(p\) and \(q\) are product of two large unknown of unbalance primes for \(2 \leq s&lt;r\). The paper proves that using an approximation of \(\phi(N) \approx\) \(N-N^{\frac{r+8-1}{2 r}}\left(\lambda^{\frac{1-8}{2 r}}+\lambda^{\frac{-8}{2 r}}\right)+N^{\frac{r+8-2}{2 T}} \lambda^{\frac{1-8}{2 r}}\), private keys \(\frac{x^2}{y^2}\) can be found from the convergents of the continued fractions expansion of\[\left|\frac{e}{N-N^{\frac{r+8-1}{2 r}}\left(\lambda^{\frac{1-8}{2 r}}+\lambda^{\frac{-8}{2 r}}\right)+N^{\frac{r+8-2}{2 r}} \lambda^{\frac{1-8}{2 r}}}-\frac{y^2}{x^2}\right|&lt;\frac{1}{2 x^4}\] which leads to the factorization of the moduli \(N=p^r q^s\) into unbalance prime factors p and q in polynomial time. The second part of this reseach report further, how to generalized two system of equations of the form \(e_ux^2\) - \(y^2_u\phi(N_u)\) = \(z_u\) and \(e_ux^2_u\) - \(y^2\phi(N_u)\) = \(z_u\) using simultaneous Diophantine approximation method and LLL algorithm to and the values of the unknown integers \(x,y_u\),\(\phi(N_u)\) and \(x_u\),y,\(\phi(N_u)\) respectively, which yeild to successful factorization of k moduli \(N_u=p^r_uq^s_u\) for u = 1,2, ... k in polynomial time.
Virtual Assistant With Sign Language

This topic is all about developing a successful Sign language recognition and translation system which is helpful for people with certain disabilities who can’t use present voice enabled Virtual Assistants. Communication is important with people as it helps us understand them and gain knowledge. In case of Deaf &amp; mute people, this becomes a problem. It is solved by creating a virtual assistant built to recognize hand gestures and translate them in normal language to perform tasks ordered by the user. This certainly helps other normal people to successfully communicate with deaf &amp; mute people Without worrying about any misunderstanding at the listener’s end. There are more than 300 sign languages used by various cultural groups worldwide. In this article, we provide a technique for generating a sign language dataset using a camera, followed by the training of a TensorFlow model using transfer learning to produce a real-time sign language recognition system. Despite the small amount of the dataset, the system still performs well.
PENGEMBANGAN MEDIA FLIPCHART TERHADAP KETERAMPILAN BERBICARA SISWA SDN BEBERAN 1

This study aims to develop flipchart media on the speaking skills of students at SDN Beberan 1. This research is to determine the feasibility of the developed flipchart media and to find out the students responses to the flipchart media. The subjects of this study were 26 students of class IV at SDN Beberan 1. This type of research refers to the Borg and Gall model modified by Sugiyono. This model consists of six stages, namely potential problems, data collection, product design, design validation, design revision, and product testing. The instruments used in this study were questionnaire sheets for media validation by lecturers and student questionnaire sheets. Based on the analysis of the data, there were results obtained from the quality of the flipchart media developed, including the very decent category with a percentage of 84% from two media experts, the appropriate category with a percentage of 74% from two linguists, and the very feasible category with a percentage of 86% from two material experts. Student responses to flipchart media were 87.7% in the very good category. Thus, it can be concluded that flipchart media is very feasible and can be used in learning the speaking skills of students in class IV.
A Reusable Model-agnostic Framework for Faithfully Explainable Recommendation and System Scrutability

State-of-the-art industrial-level recommender system applications mostly adopt complicated model structures such as deep neural networks. While this helps with the model performance, the lack of system explainability caused by these nearly blackbox models also raises concerns and potentially weakens the users’ trust in the system. Existing work on explainable recommendation mostly focuses on designing interpretable model structures to generate model-intrinsic explanations. However, most of them have complex structures, and it is difficult to directly apply these designs onto existing recommendation applications due to the effectiveness and efficiency concerns. However, while there have been some studies on explaining recommendation models without knowing their internal structures (i.e., model-agnostic explanations), these methods have been criticized for not reflecting the actual reasoning process of the recommendation model or, in other words, faithfulness . How to develop model-agnostic explanation methods and evaluate them in terms of faithfulness is mostly unknown. In this work, we propose a reusable evaluation pipeline for model-agnostic explainable recommendation. Our pipeline evaluates the quality of model-agnostic explanation from the perspectives of faithfulness and scrutability. We further propose a model-agnostic explanation framework for recommendation and verify it with the proposed evaluation pipeline. Extensive experiments on public datasets demonstrate that our model-agnostic framework is able to generate explanations that are faithful to the recommendation model. We additionally provide quantitative and qualitative study to show that our explanation framework could enhance the scrutability of blackbox recommendation model. With proper modification, our evaluation pipeline and model-agnostic explanation framework could be easily migrated to existing applications. Through this work, we hope to encourage the community to focus more on faithfulness evaluation of explainable recommender systems.
Comparative Analysis of Cross-Platform Frameworks

The use of frameworks for cross-platform development, whether native or hybrid, is a fundamental part of the field of programming, facilitating in one way or another to software developers to save time, reduce compilation errors, and optimize resources, among other features that denote some advantage between one tool and another. For this reason, it was decided to perform a comparative analysis between the cross-platform application development frameworks Flutter, React Native, Ionic, and Xamarin, using relevant features of each tool as parameters. For this purpose, research was conducted through a literature review of research articles, theses, and books obtained using the Google Scholar search engine, and consequently, parameters were determined that allowed comparing the mentioned frameworks. As a case study, a simple web page was developed in the Flutter framework with the help of the Visual Studio Code environment, to experiment with the characteristics of the mentioned framework directly and briefly. It is concluded that the frameworks for multiplatform development are useful tools that optimize time and maintenance by not requiring writing code to create a computer application on different platforms, benefiting developers and companies focused on software development.
Solving Finite-Horizon Discounted Non-Stationary MDPS

Abstract Research background Markov Decision Processes ( MDPs ) are a powerful framework for modeling many real-world problems with finite-horizons that maximize the reward given a sequence of actions. Although many problems such as investment and financial market problems where the value of a reward decreases exponentially with time, require the introduction of interest rates. Purpose This study investigates non-stationary finite-horizon MDPs with a discount factor to account for fluctuations in rewards over time. Research methodology To consider the fluctuations of rewards with time, the authors define new nonstationary finite-horizon MDPs with a discount factor. First, the existence of an optimal policy for the proposed finite-horizon discounted MDPs is proven . Next, a new Discounted Backward Induction ( DBI ) algorithm is presented to find it. To enhance the value of their proposal, a financial model is used as an example of a finite-horizon discounted MDP and an adaptive DBI algorithm is used to solve it. Results The proposed method calculates the optimal values of the investment to maximize its expected total return with consideration of the time value of money. Novelty No existing studies have before examined dynamic finite-horizon problems that account for temporal fluctuations in rewards.
Monte Carlo SHALSTAB: A probabilistic-based SHALSTAB Analysis

ABSTRACT
Face Mask Detection Using Machine Learning

Novel coronavirus has ushered in a new normal where social distancing and wearing face masks play a key role in limiting the spread of the virus. I’m here. This can lead to a serious problem of spreading infection. Therefore, people should be informed to wear face masks to avoid such situations. Humans can be more and more affected by Corona by not wearing protective clothing, so they cannot be involved in this process. This can pose a serious problem of spread of infection. Here lies the need for artificial intelligence (AI), the main topic of our project. Our project uses image processing and AI technology to identify people in public places, whether they wear masks or not, and alert authorities. It uses object recognition algorithms to identify people with and without face masks. This is reflected in the number of people using face masks and those not. The Internet of Things (IOT) is used to send alerts. Notifications are sent to appropriate people via mobile notifications and email. Status is determined based on a count of mask wearers and non-wearers. Depending on the situation, we will notify you with a buzzer or LED.
AMP: Total Variation Reduction for Lossless Compression via Approximate Median-based Preconditioning

With the increasing scale of cloud computing applications of next-generation embedded systems, a major challenge that domain scientists are facing is how to efficiently store and analyze the vast volume of output data. Compression can reduce the amount of data that needs to be transferred and stored. However, most of the large datasets are in floating-point format, which exhibits high entropy. As a result, existing lossless compressors can not provide enough performance for such applications. To address this problem, we propose a total variation reduction method for improving the compression ratio of lossless compressors (namely, FPC + and FPZIP + ), which employs a median-based hyperplane to precondition the data. In particular, we first try to exploit the space-filling curve (SFC), a well-known technique to preserve data locality for a multi-dimensional dataset. We show and explain why a raw SFC, such as Hilbert and Z-order curves, cannot improve the compression ratio. Then, we explore the opportunity and theoretical feasibility of the proposed total variation reduction based algorithm. The experiment results show the effectiveness of the proposed method. The compression ratios are improved up to 48.2% (20.6% on average) for FPZIP and 42.4% (18.4% on average) for FPC. Moreover, through observing the time composition of the proposed method, it is found that the median finding holds a high percentage of the execution time. Hence, we further introduce an approximate median finding algorithm, providing a linear-time overhead reduction scheme. The experiment results clearly demonstrate that this algorithm reduces execution time by an average of 56.7% and 40.7% compared to FPC + and FPZIP + , respectively.
Transfer learning model for the motion detection of sports players

Recognizing and analyzing moving targets is an important research subject since computer vision is employed in so many facets of our daily lives, including intelligent robotics, video surveillance, medical education, sporting events, and the maintenance of our national defense. This is because it may be difficult to properly analyse and keep up with moving materials. The various training postures of an athlete are explored in this study through the examination of a weightlifting video. This article was written to assist coaches in their efforts to improve the performance of their athletes in their respective sports. A technique for extracting essential poses from sports films has been proposed. The classification of different subjects of interest serves as the foundation for this technique. Because of its inadequate edge detection method, the current motion identification system does a bad job of detecting athletes, which is one of the reasons why it does a poor job of identifying motion in general. This flaw is one of the reasons why the system isn’t very strong at detecting athletes. The following was one of the factors that contributed to this outcome: in truth, the situation is currently in this state. The result of the newly developed system outperforms the prior system in terms of tracking recognition accuracy and convergence speed. The system was put to the test. The findings of the system’s study served as the foundation for this decision. Finally, the findings of the categorization reveal that the selection approach tries to separate fundamental postures.
Does Digitalization Affects Work-Life Flexibility? A Perspective of MNCs in Malaysian Working Environment

MNCs advances in digitalization have changed the way one work and the way they live their lives. The long-term goal of this study is to better understands, organize and restructure each MNCs digitalized knowledge system parallel to their employees’ work-life flexibility. This chapter aims to investigate the impact of digitalization and knowledge absorptive capacity and employees’ work-life flexibility in MNCs industry. Previous research has primarily relied on organizational science that have explored digital innovation in specific industry and the did not treat work-life flexibility as pre-requisite in order for an employee to achieve wellbeing or performance. Besides that, this study treats digitalization as an example of external disruptive technology that influences knowledge transfer from MNCs subsidiaries to contract manufacturing in a post-Covid environment where it has changed the way instructions, methods, and ideas transferred across these organizations and its effects to employee’s work-life flexibility. Previous research fails to fully recognize the unique and distinct characteristics of digitalization and their formative and transformative effects in terms of MNCs operational logics and to achieve work-life flexibility. Therefore, this study is investigating the relationship of digitalized knowledge transfer between MNCs’ subsidiaries and contract manufacturers and employee work-life flexibility in the context of learning intent on the effectiveness and efficiency of knowledge transfer among the technical team across organizations.
Design and Implementation of Home Online Store System Based on PHP Framework

The online store system is a kind of software used to open stores on the Internet, generally B/S architecture.Commonly known as online store system, e-commerce system and shopping system.With the help of online store system, small and medium-sized enterprises and individuals can quickly and efficiently build e-commerce platforms suitable for their own enterprises or individuals and carry out e-commerce activities.This paper designs and implements a home online store system based on Web architecture, through which users can search and view home goods, add shopping carts, and add orders to purchase; Administrators can manage user orders.The front end of the system is based on HTML5 technology and uses Element UI for layout.The developed web page can adapt to different resolutions.The back end is developed based on Think PHP framework.This paper discusses the system development process from the key stages of software development, such as function design, data model design, effect realization, etc.Finally, through experimental testing, the system has good usability, users can easily search and purchase goods, and get feedback in time.
CONVERSION OF TEXT IMAGE TO AUDIO FOR VISUALLY IMPAIRED PEOPLE USING CNN ALGORITHM

The integration of text-to-speech (TTS) and optical character recognition (OCR) is revolutionizing various applications, particularly for individuals with visual impairments.This powerful framework enables seamless interaction between users and computers through speech.By leveraging OCR technology, a text-to-speech program scans images and accurately converts more than 38 languages and numerical characters into spoken words.The project comprises two essential modules: a speech processing module and an image processing module.Previous approaches such as the pointy method, linked element method, feel-based method, and mathematical morphology method have been employed, but they do have limitations when it comes to accuracy in assessment, rating, and review.Captured text associated with photographs, sourced from magazines, newspapers, and banners, has become increasingly valuable in conveying shared information, enhancing accessibility, and driving innovation in areas such as employment, earnings, efficiency, and problem-solving.The project incorporates a voice processing module and a photo processing module, utilizing various methods including the pointy approach, connected factor approach, texture-based approach, and mathematical morphology technique.However, these methods also face limitations in terms of accuracy for assessment, rating, and evaluation.Advancements in technology, particularly in machine learning algorithms and artificial intelligence, play a vital role in developing cutting-edge smart systems worldwide.Within this context, an automated strategy for text detection and recognition in natural scenes using an optical character recognition (OCR) function is gaining prominence.Additionally, a suitable speech synthesizer is employed to pronounce the extracted text.A prototype of the proposed system has been developed and its functionality confirmed through an experimental setup.These findings underscore the system's underlying principles and demonstrate its potential in creating advanced assistive technology for visually impaired individuals.
Simulation of Projective Non-Abelian Anyons for Quantum Computation

Abstract
Analisis Implementasi Kesiapan Pembelajaran Online Berbasis Colaborative Learning Menggunakan Model E-Learning Readiness (ELR) pada UIN Raden Fatah Palembang

Online Learning or what is often referred to as e-learning, a learning method that is carried out through electronic media networks. E-learning has become an alternative material for problems in the field of education, both as an additional learning medium or a complement to the learning process, the learning process carried out in the scope of several places such as campuses, schools or additional tutoring usually still uses the old method, where teaching materials are delivered through face-to-face, both verbally and non-verbally, the use of technology in the world of education, be it campuses, schools or other tutors, should be used as a whole, e-learning technology is present as a means of supporting education at this time where e-learning present to bring a new color in changing the education system. Collaborative learning does not prioritize a system of competition between students. Students who have the ability to help more underprivileged students, and vice versa, students who feel less capable ask for help from capable students, so that an atmosphere of mutual learning is created. This study aims to analyze the readiness of implementation and strategy and focuses on the application of e-learning at Raden Fatah State Islamic University, Palembang. By using the readiness model, the researcher found e-learning at the Raden Fatah State Islamic University in Palembang. that the application of e-learning at the Raden Fatah State Islamic University of Palembang is ready, based on the research results the availability of human resources can support the existing system, the university also provides several computers to be used while studying, and lecturers have basic skills in operating computers to use e -learning so this can make it easier for lecturers to provide material to students.Keywords : E-learning, readiness, collaborative elearning.
AgAsk: an agent to help answer farmer’s questions from scientific documents

Abstract Decisions in agriculture are increasingly data-driven. However, valuable agricultural knowledge is often locked away in free-text reports, manuals and journal articles. Specialised search systems are needed that can mine agricultural information to provide relevant answers to users’ questions. This paper presents AgAsk—an agent able to answer natural language agriculture questions by mining scientific documents. We carefully survey and analyse farmers’ information needs. On the basis of these needs, we release an information retrieval test collection comprising real questions, a large collection of scientific documents split in passages, and ground truth relevance assessments indicating which passages are relevant to each question. We implement and evaluate a number of information retrieval models to answer farmers questions, including two state-of-the-art neural ranking models. We show that neural rankers are highly effective at matching passages to questions in this context. Finally, we propose a deployment architecture for AgAsk that includes a client based on the Telegram messaging platform and retrieval model deployed on commodity hardware. The test collection we provide is intended to stimulate more research in methods to match natural language to answers in scientific documents. While the retrieval models were evaluated in the agriculture domain, they are generalisable and of interest to others working on similar problems. The test collection is available at: https://github.com/ielab/agvaluate .