Reviewerly/exHarmony huggingface dataset:
Formally Verifying Optimizations with Block Simulations

CompCert (ACM Software System Award 2021) is the first industrial-strength compiler with a mechanically checked proof of correctness. Yet, CompCert remains a moderately optimizing C compiler. Indeed, some optimizations of “gcc ‍-O1” such as Lazy Code Motion (LCM) or Strength Reduction (SR) were still missing: developing these efficient optimizations together with their formal proofs remained a challenge. Cyril Six et al. have developed efficient formally verified translation validators for certifying the results of superblock schedulers and peephole optimizations. We revisit and generalize their approach into a framework (integrated into CompCert) able to validate many more optimizations: an enhanced superblock scheduler, but also Dead Code Elimination (DCE), Constant Propagation (CP), and more noticeably, LCM and SR. In contrast to other approaches to translation validation, we co-design our untrusted optimizations and their validators. Our optimizations provide hints, in the forms of invariants or CFG morphisms , that help keep the formally verified validators both simple and efficient. Such designs seem applicable beyond CompCert.
Is Information Extraction Solved by ChatGPT? An Analysis of Performance, Evaluation Criteria, Robustness and Errors

ChatGPT has stimulated the research boom in the field of large language models. In this paper, we assess the capabilities of ChatGPT from four perspectives including Performance, Evaluation Criteria, Robustness and Error Types. Specifically, we first evaluate ChatGPT's performance on 17 datasets with 14 IE sub-tasks under the zero-shot, few-shot and chain-of-thought scenarios, and find a huge performance gap between ChatGPT and SOTA results. Next, we rethink this gap and propose a soft-matching strategy for evaluation to more accurately reflect ChatGPT's performance. Then, we analyze the robustness of ChatGPT on 14 IE sub-tasks, and find that: 1) ChatGPT rarely outputs invalid responses; 2) Irrelevant context and long-tail target types greatly affect ChatGPT's performance; 3) ChatGPT cannot understand well the subject-object relationships in RE task. Finally, we analyze the errors of ChatGPT, and find that "unannotated spans" is the most dominant error type. This raises concerns about the quality of annotated data, and indicates the possibility of annotating data with ChatGPT. The data and code are released at Github site.
Deep learning symmetries and their Lie groups, algebras, and subalgebras from first principles

Abstract We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset. We use fully connected neural networks to model the symmetry transformations and the corresponding generators. The constructed loss functions ensure that the applied transformations are symmetries and the corresponding set of generators forms a closed (sub)algebra. Our procedure is validated with several examples illustrating different types of conserved quantities preserved by symmetry. In the process of deriving the full set of symmetries, we analyze the complete subgroup structure of the rotation groups SO (2), SO (3), and SO (4), and of the Lorentz group <?CDATA $SO(1,3)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"><mml:mi>S</mml:mi><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>3</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:math> . Other examples include squeeze mapping, piecewise discontinuous labels, and SO (10), demonstrating that our method is completely general, with many possible applications in physics and data science. Our study also opens the door for using a machine learning approach in the mathematical study of Lie groups and their properties.
GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP

ChatGPT's emergence heralds a transformative phase in NLP, particularly demonstrated through its excellent performance on many English benchmarks. However, the model's efficacy across diverse linguistic contexts remains largely uncharted territory. This work aims to bridge this knowledge gap, with a primary focus on assessing ChatGPT's capabilities on Arabic languages and dialectal varieties. Our comprehensive study conducts a large-scale automated and human evaluation of ChatGPT, encompassing 44 distinct language understanding and generation tasks on over 60 different datasets. To our knowledge, this marks the first extensive performance analysis of ChatGPT's deployment in Arabic NLP. Our findings indicate that, despite its remarkable performance in English, ChatGPT is consistently surpassed by smaller models that have undergone finetuning on Arabic. We further undertake a meticulous comparison of ChatGPT and GPT-4's Modern Standard Arabic (MSA) and Dialectal Arabic (DA), unveiling the relative shortcomings of both models in handling Arabic dialects compared to MSA. Although we further explore and confirm the utility of employing GPT-4 as a potential alternative for human evaluation, our work adds to a growing body of research underscoring the limitations of ChatGPT.
Development of a Video Surveillance System for Motion Detection and Object Recognition

This article explores the development of a video surveillance system that utilizes cuttingedge technology to analyze the video stream in real-time, identify motion, and recognize objects within the video stream. The functionality of this system enables it to provide a high level of accuracy in identifying objects, even in low-light conditions or with low-resolution cameras. The software system has been designed as a user-friendly desktop application with the latest technologies and features that will ensure its relevance and easy maintenance in the future. To ensure that the developed desktop application meets common optimization requirements, extensive testing has been conducted to evaluate its resource usage. The resulting system is an efficient and reliable tool for monitoring and detecting movement in various locations, providing enhanced security measures and public safety.
A Geometric Interpretation of the Multivariate Gaussian Distribution and its Entropy and Mutual Information

The fundamental objective is to study the application of multivariate sets of data in Gaussian distribution. This paper examines broad measurements of structure for both Gaussian and non-Gaussian distributions, which shows that they can be described in terms of the infor-mation-theoretic between the given covariance matrix and correlated random variables (in terms of relative entropy). In order to develop the multivariate Gaussian distribution with entropy and mutual information, several significant methodologies are presented through the discussion supported by illustrations, both technically and statistically. The content obtained allows readers to better perceive concepts, comprehend techniques, and properly execute software programs for future study on the topic&amp;#039;s science and implementations. It also helps readers grasp the themes&amp;#039; fundamental concepts. Involving the relative entropy and mutual information as well as the potential correlated covariance analysis based on differential equations, a wide range of information is addressed, including basic to application concerns.
Smart Cities Using Machine Learning and Intelligent Applications

The goal of smart cities is to properly manage to expand urbanization, Reduce energy usage, Enhance the economic and quality of life of the locals while also preserving the environment conditions, and improve people's ability to use and adapt modern technology used in information and communication efficiently (ICT). Information and communication technology (ICT) is central to the concept of smart cities because it facilitates the development of policies, decision-making, implementation, and, eventually, the provision of useful services. This review's main objective is to examine how machine learning, deep reinforcement learning (DRL), and artificial intelligence are advancing smart cities. The previous strategies are effectively utilized to provide the best policies possible for a number of challenging issues relating to smart cities. The uses of the earlier methods are thoroughly discussed in this survey. in intelligent transportation systems (ITSs), Cybersecurity, as well as smart grid energy efficiency (SGs), ensuring the best 5G and beyond 5G (B5G) networking service, as well as a smart city with a smart health system, by effectively using unmanned aerial vehicles (UAVs). Finally, we list a number of the above-mentioned research problems and potential future directions approaches can be extremely helpful in bringing the idea of a smart city to life.
Dental Service System into Blockchain Environment

A platform that enables users to schedule appointments and connect with dentists is called the Dental Services System. The bulk of appointment slot orders are placed through more traditional channels, such phone calls, texts, or clinic entrances, prior to obtaining treatment. When staff members are unable to alter their status or take too long to provide information, schedule a time, or complete an assignment, this might be troublesome. Blockchain technology is a distributed ledger system that makes use of mathematics, algorithms, encryption, and financial factors. Blockchain's high-security design makes it safe, and the immutability of the data stored there helps to increase public confidence. Data storage databases that use blockchain technology and include security features that permit the exploitation of exposed user data are the main subject of the study. The blockchain may be integrated into a dental service system because of its excellence. The goal of this implementation of blockchain technology into a Dental Service System is to guarantee complete confidentiality while enabling authorized users to quickly create and get permanent records when paired with an application layer. The goal of this project is to create a tool that allows users to schedule appointments while utilizing a blockchain for safe data storage. In the end, this application will facilitate user appointment scheduling while limiting third parties' access to user data.
Pelatihan Microsoft Excel bagi Guru-guru di SD Negeri 3 Bareng Lor Klaten

SD Negeri 3 Bareng Lor Klaten is an elementary school located in the North Klaten area. Adequate facilities are needed in presenting the right learning facilities for students. The right resources will help in the teaching and learning process in an institution, especially in school units. One of the resources to be aware of is the "Teacher" itself. The quality of a teacher will greatly affect the quality of teaching and learning in a school. Fun and capable teachers will be able to increase students' interest in participating in learning at school. The ability of a teacher to analyze students is very necessary in the teaching and learning process. One of the tools that can be used is to use Microsoft Excel in analyzing statistically about student data or school data. Microsoft Excel is a program or application that is part of the Microsoft Office installation package, functioning to process numbers using a spreadsheet consisting of rows and columns to execute commands. One of the benefits that can be utilized by teachers from SD Negeri 03 Bareng Lor Klaten schools when gaining knowledge about advance excel is that it can provide interesting visualizations for teachers and students at school. Based on the above conditions, a Microsoft excel training is needed which is intended for teachers who are at SD Negeri 3 Bareng Lor Klaten. The expected goal is that teachers can have more mastery with the use of Microsoft excel so that they can provide more attractive visual exposure to students or parents. The methods used in the training are face-to-face methods, interviews, and discussions. The material given during the training is an introduction to excel, data management, creating tables and graphs, exercises of basic formulas and formulas, filtering and sorting data. Microsoft Excel training for teachers at SD Negeri 3 Bareng Lor Klaten can be carried out properly.
Preferences for printed books versus E−books among university students in a Middle Eastern country

<h2>Abstract</h2><h3>Background</h3> Learning styles vary among the students at the university level which has changed from traditional to more technology pervaded strategies involving digital gadgets. The Academic libraries are being challenged to upgrade from old fashioned hard copy resources to digital libraries including electronic books. <h3>Purpose</h3> The main purpose of the study is to assess the preferences of using printed books versus e-books. <h3>Methods</h3> A descriptive cross sectional survey design was used to collect the data. A total of 607 students were included in the study. Data collected was analyzed using descriptive and inferential statistics. <h3>Results</h3> Results showed that 86.8% of the students were in undergraduate program with 48.9% students in the second year of the program, 95.6% in the age group of 17–26 years and 59.5% of them being females. The study concluded that 74.6% of students prefer e-books in terms of easy to carry and 80.6% of them spent more than 1 h reading from e-books, while 66.7% of the students preferred printed books due to the ease in studying and 67.9% favored as it is easy to make notes. However, 54% of them felt studying from the digital copies difficult. <h3>Conclusion</h3> The study concludes that the students prefer e-books as they spent more time reading from e-books and are easy to carry; while traditional printed copy is comfortable to use and easy to take notes and study for exams. <h3>Implications for practice</h3> As many changes are happening in the instructional design strategies with the introduction of hybrid methods of teaching and learning, the findings of the study will guide the stake holders and educational policy makers to design novel and modernized educational design that has a psychological, and social impact among the students.
A Comprehensive Overview of Handwritten Recognition Techniques: A Survey

Deep learning and deep neural networks, particularly ConvolutionalNeural Networks (CNNs), are rapidly growing areas of machine learning and are currently the primary tools used for image analysis and classification applications.Handwriting recognition involves using computer algorithms and software to interpret and recognize handwritten text and drawings and has various applications such as automated handwriting analysis, document digitization, and handwriting-based user interfaces.Many deep learning models have been applied in the field of handwriting recognition and various datasets have been used to evaluate new computer vision techniques.This article provides an overview of the current state-of-the-art approaches and contributions to handwriting recognition using different datasets.Furthermore, the paper explains the most commonly used algorithms for recognizing handwritten characters, words, and numbers.Compares them based on their accuracy.This study covered different aspects and methods of machine learning and Deep Learning (DL) for handwritten recognition that showed different achievements for each.
convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancer

Generally, human epidermal growth factor 2 (HER2) breast cancer is more aggressive than other kinds of breast cancer. Currently, HER2 breast cancer is detected using variety of medical test which are most expensive. Therefore, the aim of this study was to develop a computational model named convoHER2 for detecting HER2 breast cancer with image data using convolution neural network (CNN). Hematoxylin and eosin (H&amp;E) and immunohistochemical (IHC) stained images were used as raw data from the Bayesian information criterion (BIC) benchmark dataset. This dataset consists of 4873 images of H&amp;E and IHC. Among all images of the dataset, 3896 and 977 images were applied to train and test the convoHER2 model, respectively. All images of this dataset are resized due to high resolution of the image for forming better detection performance of convoHER2 model. Moreover, the dataset is classified into four different labels (0+, 1+, 2+, 3+) for identifying the grade of detected HER2 breast cancer. The convoHER2 model is able to detect HER2 cancer and its grade with accuracy 85% and 88% using H&amp;E images and IHC images, respectively. The outcomes of this study determined that the HER2 cancer detecting rates of the convoHER2 model are much enough to provide better diagnosis to the patient for recovering their HER2 breast cancer in future.
Single Step Ahead Assessment of Solar Irradiation Using Ann Model Based on Various Combination Of Meterological Parameters

Solar energy is a valuable resource on earth but the availability of solar resources relies on meteorological variables. In this paper, forecasting models using the artificial neural network are developed by changing the input meteorological variables from five to seven. The two years data are used to train the model whereas the testing is performed using one year data on different seasons following single step ahead. The input parameters are relative humidity, pressure, temperature, solar zenith angle, wind speed, wind direction and perceptible water. Three artificial neural network models (ANN-I5, ANN-I6, ANN-I7) are developed to estimate the global horizontal irradiation and performance of all models are measured on the basis of Mean Absolute Percentage Error (MAPE), Relative Root Mean Square Error (RRMSE) and Correlation Coefficient (R2). Result indicates that ANN-I7 shown better performance as comparison to other developed models. The average MAPE and RRMSE of ANN models such as ANN-I7, ANN-I6, ANN-I5 are 14.52%, 16.53%, 18.97% and 20.74%, 22.28%, 24.43% respectively. The ANN-I7 haiving an input meteorological parameters relative humidity, pressure, temperature, solar zenith angle, wind speed, wind direction and perceptible water showed good accuracy as comparison to other two developed models. This study indicates that accuracy of solar irradiation forecasting depends on meteorological parameters.
Stochastic metrology and the empirical distribution

We study the problem of parameter estimation in time series stemming from general stochastic processes, where the outcomes may exhibit arbitrary temporal correlations. In particular, we address the question of how much Fisher information is lost if the stochastic process is compressed into a single histogram, known as the empirical distribution. As we show, the answer is non-trivial due to the correlations between outcomes. We derive practical formulas for the resulting Fisher information for various scenarios, from generic stationary processes to discrete-time Markov chains to continuous-time classical master equations. The results are illustrated with several examples.
New Digital Culture Metaverse Preparation Digital Society for Virtual Ecosystem

The advancement of digital culture in human life presents a new life that can be lived virtually. The phenomenon of metaverse becomes a new concept to combine the physical and digital worlds in society. The development of this metaverse will present many new opportunities in the future, especially in virtual work. The characteristics of this metaverse result in automation, telework, and also management algorithms, this can have the effect of replacing the work done by humans. It will also appear new job in the metaverse world certainly requires new skills in society, this requires the preparation of an integrated and digital-based educational role. This research is descriptive of skill development and education in the virtual metaverse world, with a qualitative method based on an analysis of the six pillars of the virtual ecosystem (avatar, content creation, virtual economy, social acceptability, security and privacy, trust and accountability). The result of this research is a future job in virtual ecosystem sustainability scheme, by linking the sustainability design dimension – virtual ecosystem – future job and skills need for society. Indonesian people need to adapt to advances in digital technology with academic development that is ready with digital technology
A Case Study on Enhancing the Expertise of Artificial Intelligence Education for Pre-Service Teachers

Artificial intelligence, a key technological tool in the era of the 4th Industrial Revolution, is being actively applied to education beyond industry, economy, and culture. In the future society, understanding and using artificial intelligence technology will be considered an essential skill for everyone, and the professionalism of AI education for pre-service teachers nurturing future talents is also important. This study verifies the effectiveness of pre-service teachers after undergoing a 15-week extension program consisting of AI Literacy Education (10 weeks), AI-Linked Subjet Education (3 weeks), and Micro-teaching (2 weeks) which was designed to enhance the AI ​​education professionalism of pre-service teachers. The AI Literacy Education section consisted of understanding AI, AI ethics, and basic programming, and the education was conducted through both experience and practice. After that, the pre-service teachers analyzed the curriculum of the 5th and 6th grades of an elementary school, and developed a subject-related education program using AI tools in three stages. Using the developed program, micro-teaching classes were conducted twice. The study results are as follows. First, there was a change in the positive perception of AI (p&amp;lt;.05). Second, the understanding of AI educational technology tools was improved and the recognition of the usefulness of developing a curriculum-related program was expanded. Third, the importance of teacher competency development was learned through the micro-teaching, and the students&amp;#039; understanding of the learning process was expanded.
Virtual Reality STEM Trails: Exploring Math Trails with STEM Education Approach in a Virtual World

This study explores how STEM-based math trails may be conducted virtually. We used the STEM Education approach, math trails concept, and virtual reality technology to develop a town-based STEM learning environment. In this project, a virtual reality mobile app was produced that can be used with Cardboard VR. Teachers build virtual trails with mathematics tasks related to municipal landmarks. Students utilize a virtual reality program on their phones to explore the trail. They employ the mathematical modeling cycle to solve real-world STEM problems. These activities can be done anywhere, anytime using cutting-edge technology. Experiments and deployment in several contexts are needed.