Reviewerly/exHarmony huggingface dataset:
Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context

Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_{2}$ emissions. Nonetheless, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. While the majority of prior research has centered on employing purely time series-based methodologies for solar forecasting, only a limited number of studies have taken into account factors such as cloud cover or the surrounding physical context. In this paper, we put forth a deep learning architecture designed to harness spatio-temporal context using satellite data, to attain highly accurate \textit{day-ahead} time-series forecasting for any given station, with a particular emphasis on forecasting Global Horizontal Irradiance (GHI). We also suggest a methodology to extract a distribution for each time step prediction, which can serve as a very valuable measure of uncertainty attached to the forecast. When evaluating models, we propose a testing scheme in which we separate particularly difficult examples from easy ones, in order to capture the model performances in crucial situations, which in the case of this study are the days suffering from varying cloudy conditions. Furthermore, we present a new multi-modal dataset gathering satellite imagery over a large zone and time series for solar irradiance and other related physical variables from multiple geographically diverse solar stations. Our approach exhibits robust performance in solar irradiance forecasting, including zero-shot generalization tests at unobserved solar stations, and holds great promise in promoting the effective integration of solar power into the grid.
Analyzing piece-plotted artifacts just got simpler: A good solution to the wrong problem?

This paper presents SEAHORS, an R shiny application available as an R package, dedicated to the intra-site spatial analysis of piece-plotted archaeological remains. This open-source script generates 2D and 3D scatter and density plots for archaeological objects located with cartesian coordinates. Many different GIS software already exist for this, but they mostly require specific skills and training to be used and are rarely designed for the particular needs of archaeological applications. The goal of SEAHORS is to make the two and three-dimensional intra-site spatial exploration of archaeological data as user-friendly as possible, in order to give the opportunity to researchers not familiar with GIS and R software to utilise such approaches. SEAHORS has an easily accessible interface and can import data from text and Excel files (.csv and .xls/xlsx respectively) without preformatting. The application includes functions to concatenate columns and to merge databases, for example when spatial data (XYZ coordinates) and analytical data (e.g. taxonomical attribution of faunal remains, typo-technological attributes of artifacts, etc.) are stored in separate files. SEAHORS can generate five types of plots: 3D, 2D and density plots, as well as 2D plots by slices (i.e. subdivisions according to a third dimension) and 2D plots with a modification of the angle of projection (i.e. to explore spatial organization without the constraints of the field grid orientation). SEAHORS has visualization tools with several sorting and formatting options (color, size, etc.) applicable to coordinates and all possible analytical variables (i.e. levels, spits, identified species, taphonomical alterations, etc.). Orthophotos can also be imported and directly used in the program. The application also allows the grouping of objects into new variables by selecting items on the interactive 2D plots. We present an overview of the application's functions by using the case study of the Cassenade Paleolithic site (France).
EDUCATION OF CULTURE OF INTERACTION WITH INTERNET ENVIRONMENT AMONG STUDENTS OF UNIVERSITIES OF THE MINISTRY OF INTERNAL AFFAIRS OF RUSSIA

ПЕДАГОГИЧЕСКИЕ НАУКИ (5.8.1, 5.8.2, 5.8.3, 5.8.7) УДК 378.1
Learning Transformer Programs

Recent research in mechanistic interpretability has attempted to reverse-engineer Transformer models by carefully inspecting network weights and activations. However, these approaches require considerable manual effort and still fall short of providing complete, faithful descriptions of the underlying algorithms. In this work, we introduce a procedure for training Transformers that are mechanistically interpretable by design. We build on RASP [Weiss et al., 2021], a programming language that can be compiled into Transformer weights. Instead of compiling human-written programs into Transformers, we design a modified Transformer that can be trained using gradient-based optimization and then automatically converted into a discrete, human-readable program. We refer to these models as Transformer Programs. To validate our approach, we learn Transformer Programs for a variety of problems, including an in-context learning task, a suite of algorithmic problems (e.g. sorting, recognizing Dyck languages), and NLP tasks including named entity recognition and text classification. The Transformer Programs can automatically find reasonable solutions, performing on par with standard Transformers of comparable size; and, more importantly, they are easy to interpret. To demonstrate these advantages, we convert Transformers into Python programs and use off-the-shelf code analysis tools to debug model errors and identify the "circuits" used to solve different sub-problems. We hope that Transformer Programs open a new path toward the goal of intrinsically interpretable machine learning.
Exploring the Role of Virtual Reality in STEM Distance Education and Remote Outreach Opportunities

Distance education is essential to modern education; it is therefore crucial to evaluate pedagogical techniques that provide exceptional and equitable education to remote students.Major challenges of online learning include social isolation, feelings of disconnection, and elevated distraction, resulting in lower engagement, motivation, and performance.The present study utilized virtual reality (VR) to remotely connect rural high school students to graduate student mentors to learn human anatomy.Qualitative data assessed student and mentor motivation, engagement, satisfaction, and overall perceptions while utilizing VR compared to traditional online methods.Quantitative data assessed changes in student critical thinking ability throughout the semester.Results indicated increased motivation, engagement, and satisfaction while learning in VR compared to traditional online methods.Focus group interviews further revealed that participants viewed VR as uniquely valuable for applying knowledge and intuitively understanding spatial relationships.Although modality (VR vs online) did not have a significant effect on critical thinking ability between individual units, further analysis suggested that VR may improve student critical thinking skills longitudinally.Research on the implementation of VR in remote education is in its early stages, but there is a growing need to investigate the effectiveness of immersive technologies in overcoming barriers to distance learning.
Student Literacy Skills on Academic Performance as the Implication of Online Learning to University Students

The students’ literacy skills and some external factors that affect the student’s academic achievements demand a considerable improvement in practice, making it an undeniable context within which to examine the implementation of the literacy strategy in teaching. This study looked into how students’ literacy skills changed after they used an online learning system, as well as what their primary literacy skills were. This study used a survey approach and aimed to collect the data from students about their literacy skills. There are three aspects which are described and found, they are the aspects of explaining scientific phenomena (with the total score of 8.59 points and an average score of competency as 2.86 points, which is in a good category), identifying scientific issues (the total score of each indicator was 9.18 points and the average score of the competency was 3.06, which categorized as a good category) and interpreting and using scientific evidence (the total score was 8.93 points and the average score of the competency was 2.97 which categorized as a good category). It is feasible to conclude that every aspect of the information literacy skill of University of Borneo Tarakan students’ in their academic activities was fairly good. The score in each indicator of competencies were varied, but still, show a stagnant value and serve as evidence for the assessment of students’ information literacy skills.&#x0D; Keywords: literacy skills, academic performance, scientific
Security Challenges Facing Blockchain Based-IoV Network: A Systematic Review

The Internet of Vehicles (IoV) is an innovative concept aimed at addressing the critical problem of traffic congestion.IoV applications are part of a connected network that collects relevant data from various smart sensors installed in connected vehicles.This information is freely and easily exchanged between vehicles, which leads to improved traffic management and a reduction in traffic accidents.As the IoV technology continues to grow, the amount of data collected will increase, presenting new challenges for data privacy and security.The use of blockchain technology has been proposed as a solution, as its decentralized and distributed architecture has been proven reliable with cryptocurrencies such as Bitcoin.However, studies have shown that blockchain alone may not be sufficient to address privacy and security concerns, and there are currently no tools available to evaluate its performance in an IoV simulation environment.This research aims to provide a comprehensive review of the challenges associated with the implementation of blockchain technology in the IoV context.
A Proposed Approach for Motif Finding Problem Solved on Heterogeneous Cluster with Best Scheduling Algorithm

The Motif Finding Problem (MFP) is the problem of finding patterns in sequences of DNA.This paper discusses and presents an enhanced scheduling approach to solve the motif problem on the Heterogeneous Cluster by making a comparison between exact algorithms.The method that was followed is to analyze several exact algorithms, compare them within specific points to measure, and improve performance by comparing the number of devices and peripheral units used in every situation and running time in every method.Our experimental results show that the use of the scheduling approach that use different algorithms on Heterogeneous Cluster make a significant difference in the speed of completing the problem and in a shorter record time with less resources, and that this proposed approach is more effective than the traditional method of distributing tasks to solve the motif problem.
A General Framework for Uncertainty Quantification via Neural SDE-RNN

Uncertainty quantification is a critical yet unsolved challenge for deep learning, especially for the time series imputation with irregularly sampled measurements. To tackle this problem, we propose a novel framework based on the principles of recurrent neural networks and neural stochastic differential equations for reconciling irregularly sampled measurements. We impute measurements at any arbitrary timescale and quantify the uncertainty in the imputations in a principled manner. Specifically, we derive analytical expressions for quantifying and propagating the epistemic and aleatoric uncertainty across time instants. Our experiments on the IEEE 37 bus test distribution system reveal that our framework can outperform state-of-the-art uncertainty quantification approaches for time-series data imputations.
Case Study Analysis of the Use of Cloud Computing for Assessing Big Data Risks

Risks associated with adopting big data and cloud computing and exposing sensitive information must be evaluated as usage of these technologies continues to rise rapidly within businesses. Also, the company needs to investigate the potential consequences of cyber security threats, considering the severity of those risks. There has been no comparative analysis of the risk assessment methods available to businesses in various nations. Thus, the researcher in this study asked forty people from four countries (Canada, Jordan, South Africa (SA), and the United Kingdom (UK)) questions on the risk assessment procedures at their respective organizations using semi-structured interviews. After compiling and analyzing the data, it became clear that Canada and the UK were the frontrunners in adopting big data and cloud computing. It also demonstrated that Jordan and SA are in the early phases of an evolving adoptive relationship. Recommendations are made to strengthen the organization's standing in light of the different risk assessment frameworks used in each country.
The Impact of Quizlet on Vocabulary Improvement: A Case Study in Binh Duong Province Secondary Schools

This study aims to investigate the impact of using Quizlet in improving English vocabulary for secondary school students, and to explore teachers’ and students’ attitudes toward the application of Quizlet in teaching and learning English vocabulary. The sample includes 300 students in grade 8 and 15 teachers in charge of these students in Binh Duong province. Mixed methods were utilized to obtain the data, including semi-structured interviews, questionnaires, and vocabulary tests. The results indicated that Quizlet gradually improved the students' vocabulary, and also showed that both teachers and students have favorable opinions about educating and studying English vocabulary via Quizlet. Recommendations were made to support and help the participants optimize their impact while using Quizlet.
Klusterisasi Penyandang Masalah Kesejahteraan Sosial (PMKS) Di Kabupaten Bojonegoro Menggunakan Algoritma K-Medoids

Persons with Social Welfare Problems (PMKS) are individuals, community groups, or families who cannot adequately and properly meet their economic, physical, mental, and social needs, both spiritually and physically, because of an obstacle, difficulty, or disturbance.This study aimed to classify sub-districts in Bojonegoro Regency based on the level of social welfare problems using the K-Medoids Clustering (PAM) Analysis method.There are 2 clusters formed with an Average Silhouette of 0.73.Cluster 1 is a sub-district group with common social welfare problems, and Cluster 2 is a sub-district group with high social welfare problems.Each silhouette value of the cluster is 0.74 and 0.70 with the specifications of a well-formed and strong structure.
IMPROVED REAL-TIME HOUSE FIRE DETECTION SYSTEM PERFORMANCE WITH IMAGE CLASSIFICATION USING MOBILENETV2 MODEL

The problem with the Ardunio microcontroller-based fire detection system with fire and smoke sensors is the detection distance. For example, in another research, it was stated that the maximum distance for fire detection on two pieces of paper that were burned was 140 cm. This means that if the fire point is at a farther distance, the system cannot detect a fire early, of course, this will be problematic if used in a wider room. Based on these problems, a system is needed that can detect fires in large rooms. A method that can be used is detection using image classification. MobileNetV2 is a real-time model for classifying or detecting an object in an image. In this study, the model was built using real-time based on the TensorFlow and Keras libraries. The system will use a laptop with an Nvidia GeForce MX130 GPU, a 48MP resolution smartphone camera, and the OpenCV library for the image classification process, as well as Telegram for sending fire notifications via the Re-quests library. The test results obtained on burnt 80/90 motorcycle tires, the most optimal detection distance is 7 meters with an accuracy of 99.91%. While testing on two sheets of paper that are burned, the most optimal detection distance is 3 meters with an accuracy of 99.75%. The average response time obtained varies greatly from 74.5 ms to 117.1 ms, which depends on the internet network connection.
Super-Resolution of Brain MRI via U-Net Architecture

This paper proposes a U-Net-based deep learning architecture for the task of super-resolution of lower resolution brain magnetic resonance images (MRI).The proposed system, called MRI-Net, is designed to learn the mapping between lowresolution and high-resolution MRI images.The system is trained using 50-800 2D MRI scans, depending on the architecture, and is evaluated using peak signal-to-noise ratio (PSNR) metrics on 10 randomly selected images.The proposed U-Net architecture outperforms current state-of-the-art networks in terms of PSNR when evaluated with a 3 x 3 resolution downsampling index.The system's ability to super-resolve MRI scans has the potential to enable physicians to detect pathologies better and perform a wider range of applications.The symmetrical downsampling pipeline used in this study allows for generically representing low-resolution MRI scans to highlight proof of concept for the U-Net-based approach.The system is implemented on PyTorch 1.9.0 with NVIDIA GPU processing to speed up training time.U-Net is a promising tool for medical applications in MRI, which can provide accurate and highquality images for better diagnoses and treatment plans.The proposed approach has the potential to reduce the costs associated with high-resolution MRI scans by providing a solution for enhancing the image quality of low-resolution scans.
Confidence Intervals for Error Rates in 1:1 Matching Tasks: Critical Statistical Analysis and Recommendations

Matching algorithms are commonly used to predict matches between items in a collection. For example, in 1:1 face verification, a matching algorithm predicts whether two face images depict the same person. Accurately assessing the uncertainty of the error rates of such algorithms can be challenging when data are dependent and error rates are low, two aspects that have been often overlooked in the literature. In this work, we review methods for constructing confidence intervals for error rates in 1:1 matching tasks. We derive and examine the statistical properties of these methods, demonstrating how coverage and interval width vary with sample size, error rates, and degree of data dependence on both analysis and experiments with synthetic and real-world datasets. Based on our findings, we provide recommendations for best practices for constructing confidence intervals for error rates in 1:1 matching tasks.
Analysis of the Application of the Teams Games Tournament (TGT) Learning Model in Primary School

Learning is a process of interaction that occurs between teachers and students, there are goals set by the teachers based on the conditions of students and schools. Learning objectives are activities to be achieved by students. One of the things that can affect student’s success in the learning process is the selection of learning models. In order to get optimal learning outcomes, the quality of learning must be improved. Quality improvement can start with the use of approaches, methods, models, and other activities that encourage students to be active participants in the learning process. Cooperative learning can support the problems that occur because there are activities that require cooperation. Cooperative learning has many types, one of which is the Teams Games Tournament (TGT). Several studies have discussed the use of the TGT type of learning model and have proven to be able to solve some of the problems that occur. The purpose of this paper is to review several articles that have been selected and then grouped based on the similarities that exist between them. In addition, this writing is done to find out what causes the need to use the TGT model in learning.&#x0D; Keywords: TGT, result of learning, student activity
Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation

Very large language models (LLMs) perform extremely well on a spectrum of NLP tasks in a zero-shot setting. However, little is known about their performance on human-level NLP problems which rely on understanding psychological concepts, such as assessing personality traits. In this work, we investigate the zero-shot ability of GPT-3 to estimate the Big 5 personality traits from users' social media posts. Through a set of systematic experiments, we find that zero-shot GPT-3 performance is somewhat close to an existing pre-trained SotA for broad classification upon injecting knowledge about the trait in the prompts. However, when prompted to provide fine-grained classification, its performance drops to close to a simple most frequent class (MFC) baseline. We further analyze where GPT-3 performs better, as well as worse, than a pretrained lexical model, illustrating systematic errors that suggest ways to improve LLMs on human-level NLP tasks.
Usability and Security of Knowledge-based Authentication Systems: A State-of-the-Art Review

Knowledge-based passwords are still the most dominant authentication method for securing digital platforms and services, in spite of the emergence of alternative systems such as token-based and biometric systems.This method has remained the most popular one mostly because of its usability, compatibility, affordability of implementation, and user familiarity.However, the main challenge of knowledge-based password schemes lies in creating passwords that provide a balance between memorability and security.This research aimed to compare various knowledgebased schemes in order to establish a strategy that provided high memorability and resilience to most cyberattacks.The overview of this research identifies areas of knowledge-based passwords for further research and enhances the methodology that helps to offer insight into usable, secure, and sustainable authentication approaches.Future work has been recommended to explore the major features and drawbacks of recognition-based textual passwords because this method provides the usability and security benefits of graphical passwords with the familiarity of textual passwords.
World's first practical power flow algorithm operating on today's noisy quantum computers

Power flow is an indispensable foundation for power system analytics. Under the deep penetration of renewables, modern power system analytics often becomes intractable because it needs to run an enormous amount of power flow analyses to quantify the impact of uncertainties. Unlike classical power flow methods that scale polynomially with the system size, quantum computing enables using logarithmically-scaled number of qubits to solve linear equations in power flow analysis. Thus, quantum power flow (QPF) provides a promising direction to make today's intractable power system analytics tractable. However, a major obstacle to the development of a practical quantum power flow algorithm lies in the fact that today's mainstream quantum computers are still noisy-intermediate-scale quantum (NISQ) devices whose capability is restricted by the limited number of qubits and considerable noises. To bridge this gap, Stony Brook University establishes a variational quantum power flow that allows for practical and noise-resilient power flow analysis on today's NISQ devices.