Reviewerly/exHarmony huggingface dataset:
Application of Hough Transformation Method for Value Analysis Rupiah Coins

Digital image processing is one of the uses of computer technology used to solve problems regarding image processing or imagery so that it is easy to process. The process of applying image processing in this study aims to detect the value analysis of Rupiah coins by applying the Hough Transformation method, with the hough transformation method is expected to detect the nominal of coins because this method is an image transformation technique that can be used to isolate an object in the image by finding its boundaries. From research that has been conducted 10 images of coins on nominal inputs in the form of rupiah coin coins with a nominal value of 100, 200, 500, and 1000 rupiah obtained an accuracy rate of 90%. This means that there are 8 test data that get a true positive (TP) information, meaning that the data is successful, 1 false positive test data (FP) means that the data is not expected because it only gets a nominal result of the etapi coin not being parameterized in the circle of the coin and 1 test data gets a false negative (FN) description, meaning that the test result is wrong in assessing the nominal of the coin due to the image resolution factor that is too small.
Analyzing the Traffic of MANETs using Graph Neural Networks

Graph Neural Networks (GNNs) have been taking role in many areas, thanks to their expressive power on graph-structured data. On the other hand, Mobile Ad-Hoc Networks (MANETs) are gaining attention as network technologies have been taken to the 5G level. However, there is no study that evaluates the efficiency of GNNs on MANETs. In this study, we aim to fill this absence by implementing a MANET dataset in a popular GNN framework, i.e., PyTorch Geometric; and show how GNNs can be utilized to analyze the traffic of MANETs. We operate an edge prediction task on the dataset with GraphSAGE (SAG) model, where SAG model tries to predict whether there is a link between two nodes. We construe several evaluation metrics to measure the performance and efficiency of GNNs on MANETs. SAG model showed 82.1 accuracy on average in the experiments.
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off

A default assumption in reinforcement learning (RL) and optimal control is that observations arrive at discrete time points on a fixed clock cycle. Yet, many applications involve continuous-time systems where the time discretization, in principle, can be managed. The impact of time discretization on RL methods has not been fully characterized in existing theory, but a more detailed analysis of its effect could reveal opportunities for improving data-efficiency. We address this gap by analyzing Monte-Carlo policy evaluation for LQR systems and uncover a fundamental trade-off between approximation and statistical error in value estimation. Importantly, these two errors behave differently to time discretization, leading to an optimal choice of temporal resolution for a given data budget. These findings show that managing the temporal resolution can provably improve policy evaluation efficiency in LQR systems with finite data. Empirically, we demonstrate the trade-off in numerical simulations of LQR instances and standard RL benchmarks for non-linear continuous control.
Content characterization for live video compression optimization

&lt;p&gt;Compressing video sequences with different content complexity results in different bitrates for the same quality level or in different quality levels for the same bitrate; for instance it is well known that content with high spatial complexity and/or high motion requires high bitrates for compression with adequate quality. To address this, per-title optimization is used recently (e.g., by Netflix) to generate appropriate rate-quality representations for different Video on demand (VoD) content to be streamed via adaptive video streaming. However, this cannot be adopted for live video streaming as it requires encoding (multiple times) each video content. Spatial Information (SI) and Temporal Information (TI) have been often used as indicators of video complexity, for instance for preparing and describing content for video quality assessment tests, and for rate-distortion modeling. However, it has been questioned recently if different metrics could lead to a better estimation of ”compressibility” of video. In this paper we compare existing and proposed metrics in terms of their ability to estimate ”compressibility”. This supports quality-rate estimation and the possibility to create appropriate ”quality ladders” (different quality representations) for adaptive live video streaming. We observe in particular that metrics related to the variance of pixel values provide a good estimation of compressiblity for the considered datasets. &lt;/p&gt;
Media Literacy in Peru: Reflections and comparisons on a 10-year journey

This paper compares the results of a media literacy test taken in 2009 and 2019 at three schools in Lima, Peru. To understand the study’s results in this 10-year comparison, the context surrounding the study is examined, including Peru’s approach to national standards and curriculum and the Center for Media Literacy’s frameworks for media literacy. Although technology, especially social media, advanced dramatically during this 10-year period, students continued to demonstrate that they doubt the media. However, the paradigm that has prevailed in Peru has been to provide technology to schools instead of promoting media literacy in the curriculum or in teacher training, ignoring Latin American tradition of educommunication. After initial promising results for students’ understanding Core Concepts of media literacy, later results in 2019 show that these Concepts were not retained, nor was teacher training reinforced. The combination of more teacher training and incorporating media literacy into the national curriculum can be powerful drivers for introducing media literacy into the education system of Peru. This article, based on survey results from three Lima-based private schools, contends its importance and urgence, over all in the context of global Covid-19, which has forced school lockdowns for almost two years, increasing Peruvian children’s exposure and interaction with media.
Learners’ Home-Based Learning Activities and Academic Achievement in Modular Learning

The new normal education requires learners to be independent in the learning process without face-to-face instruction. This study determined the home-based learning activities in relation to the academic achievement of Grade 6 learners in Plaridel North District, Municipality of Plaridel, Misamis Occidental. The descriptive-correlational design was used in the study. There were 135 students who served as respondents, selected through purposive and convenient sampling techniques. The researcher-made Home-Based Learning Activities Questionnaire was used as a research instrument in determining the learners’ learning activities at home. Documentary analysis was made to determine the learners' academic achievement. Mean, Standard Deviation, Pearson Product-Moment Correlation Coefficient, and Regression Analysis were used as statistical tools in analyzing the data gathered. Results revealed that the respondents had a very high engagement in home-based learning activities, and their academic achievement was generally very satisfactory. The level of engagement in home-based learning activities was highly influential to the learners' academic achievement. The efforts extended for modular distance learning can determine what learners can achieve academically.
Two-sample test based on Self-Organizing Maps

Machine-learning classifiers can be leveraged as a two-sample statistical test. Suppose each sample is assigned a different label and that a classifier can obtain a better-than-chance result discriminating them. In this case, we can infer that both samples originate from different populations. However, many types of models, such as neural networks, behave as a black-box for the user: they can reject that both samples originate from the same population, but they do not offer insight into how both samples differ. Self-Organizing Maps are a dimensionality reduction initially devised as a data visualization tool that displays emergent properties, being also useful for classification tasks. Since they can be used as classifiers, they can be used also as a two-sample statistical test. But since their original purpose is visualization, they can also offer insights.
Foundation models in brief: A historical, socio-technical focus

Foundation models can be disruptive for future AI development by scaling up deep learning in terms of model size and training data's breadth and size. These models achieve state-of-the-art performance (often through further adaptation) on a variety of tasks in domains such as natural language processing and computer vision. Foundational models exhibit a novel {emergent behavior}: {In-context learning} enables users to provide a query and a few examples from which a model derives an answer without being trained on such queries. Additionally, {homogenization} of models might replace a myriad of task-specific models with fewer very large models controlled by few corporations leading to a shift in power and control over AI. This paper provides a short introduction to foundation models. It contributes by crafting a crisp distinction between foundation models and prior deep learning models, providing a history of machine learning leading to foundation models, elaborating more on socio-technical aspects, i.e., organizational issues and end-user interaction, and a discussion of future research.
The Effect of Using Autonomous Learning Model by Duolingo-Assisted to Improve Students’ Vocabulary Mastery at VIII Class of SMPN 3 Buton Tengah

The aim of this study was to find out whether the use of the Autonomous Learning Model by Duolingo-assisted has an effect on increasing the vocabulary mastery of the eighth grade students of SMPN 3 Buteng. Purposive sampling was used to select 29 students in VIII grade class as sample of this research. This study applied a pre-experimental method using one class pretest and posttest. To collect data and information, researchers used tests, distributed questionnaires, and conducted interviews. From the analysis result, the researcher found that the students' average score increased from 60.94 to 86.83. Hypothesis analysis at a significant level of 0.05 showed that the value of Tcount was higher than the value of Ttable (14,662&gt;2,048) so that H0 was rejected and H1 was accepted. This means that the treatment given to students, namely the Autonomous Learning Model assisted by the Duolingo application has an effect to increase vocabulary. Autonomous Learning assisted by the Duolingo application is very appropriate to use in teaching vocabulary because with this learning model students were encouraged to be more independent and responsible in learning English without others help. Duolingo as a learning media made students more interested in learning English because this application is equipped with complete features that make it easier for students to learn vocabulary.
Penerapan Laporan Keuangan Berdasarkan SAK EMKM Berbasis Microsoft Excel pada Kanein Food di Jatirasa, Bekasi

Abstract: Purpose: This study aims to help Kanein Food prepare financial reports based on SAK EMKM using an excel-based system, so that the preparation of financial reports becomes more effective. Method: The type of research used in this research is qualitative research. Source of data used in this research is primary data. Result: This study show that the preparation of financial reports prior to implementing the system at Kanein Food was still done manually and the recording was still general in nature. After implementing the system, the preparation of financial reports becomes more administrative and tidy, the preparation of financial reports also saves time because the results of financial reports appear automatically. Based on the results of testing the effectiveness of the system, the test produces an average value of 8 which means it is effective. It can be concluded that this system makes the preparation of financial reports at Kanein Food more effective and efficient. Limitation: Tthe number of respondents is only business owners and one employee who are both trained objects until they are proficient in operating the Microsoft Excel financial reporting application with contributions between the assistant and the mentor experiencing positive benefits in applying the Excel accounting software and can be sustainable. Keywords: 1. SAK EMKM Financial reports 2. Excel Accounting and UMKM
La Realidad Virtual en bibliotecas: estrategias de inclusión de una tecnología emergente

Virtual Reality is an emerging technology that is gaining relevance in popular culture. Its contents allow for greater involvement between viewers and media due to its immersive capabilities. The possibilities offered by its use justify the inclusion of this technology in the collections offered by libraries. In this article, we justify its adoption, present strategies for its use and future lines of work. In order for libraries to lead the adoption of Virtual Reality, the development of specific literacy, Virtual Reality equipment lending and the creation of specialized collections are proposed. Existing resources for the development of each of these three pillars necessary to implement Virtual Reality in libraries are presented along with the procedures that could be adopted for its proper use and operation.
Perancangan Sistem Informasi Pembelajaran Ekstrakulikuler Kepramukaan Pada SMK Fatahillah Cileungsi Berbasis Android

This study aims to introduce scouting learning to students. The benefits of this research can increase students insight into scouting material with android application, so that learning is more efficient. Qualitative subjective research method with data colletion techniques observational documentation (observation), interview souces directly meet scout coaches and students. The research location is at SMK Fatahillah Cileungsi, Kp. Central, Cileungsi District, Bogor Regency. The result of this study increase insight into the material knowledge of scouting both to the author and user of the application later.
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics

Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional integration methods. Here, methods (1) and (2) relied on Long-Short-Term Memory (LSTM) architecture, with method (3) relying on convolutional neural networks. Pure ML methods to solve (nonlinear) PDEs are represented by Physics-Informed Neural network (PINN) methods, which could be combined with attention mechanism to address discontinuous solutions. Both LSTM and attention architectures, together with modern and generalized classic optimizers to include stochasticity for DL networks, are extensively reviewed. Kernel machines, including Gaussian processes, are provided to sufficient depth for more advanced works such as shallow networks with infinite width. Not only addressing experts, readers are assumed familiar with computational mechanics, but not with DL, whose concepts and applications are built up from the basics, aiming at bringing first-time learners quickly to the forefront of research. History and limitations of AI are recounted and discussed, with particular attention at pointing out misstatements or misconceptions of the classics, even in well-known references. Positioning and pointing control of a large-deformable beam is given as an example.
A DECISION SUPPORT SYSTEM PROPOSAL ON THE USAGE OF EXTENDED REALITY SDKS IN AEC DISCIPLINES

Technology is employed in the fields of architecture, engineering, and construction (AEC) for characteristics like producing visual representations and offering assistance during the building phase. Both users and creators of these tools are able to immediately take advantage of the technology's potential as well as create a variety of workarounds for its drawbacks. Both viewpoints will be looked at in this study with regard to mobile extended reality SDKs (software development kit). By excluding the articles that did not provide the relevant information, this research concentrates solely on the papers that discuss the technological aspects of the SDK that were used, the opportunities the SDK offers, and/or the flaws of the SDK. The study's main objective is to compare the technological contributions made by the SDKs employed in the scope of the examined literature to the AEC disciplines and to the contexts in which such contributions are made. Through applications in literature research, the study aims to highlight the contributions of mobile extended reality SDKs to the fields of architecture, engineering, and construction. An entry-level developer can use the SDKs in accordance with his work by using the comparison diagrams, produced in this study, to see the relationships and comparisons between them, as well as to build a framework for what uses should be made in which domains. The technological capabilities and constraints of SDKs have an impact on how research is designed. Making relationality diagrams on the SDK to use and the effects it will have throughout the research phase is also crucial. As a result of the research, SDKs permit flexible uses in a variety of sectors, and their use also financially and logistically supports literature studies.
The Organization of Library Services for the Readers in the Context of the History of the Library at the Count Bezborodko’s Nizhyn Law Lyceum

The services provided by the libraries at the institutions of higher education in Ukraine are characterized by considerable structural and content changes, the analysis of which is one of the most important tasks in the investigations of university libraries. It is necessary to learn historical experience which greatly influences modern university library and defines main directions of its activities, providing services for readers in particular, In order to realize all the changes in the library activity and reveal its main tasks, it is necessary to learn historical experience which significantly influences present day university library and determines the main directions of its activity, concerning, in particular, the needs of readers. The article highlights the problems in the organization of the library structure, establishing and functioning of additional libraries, namely, a library for students, pupils, a gymnasium, in the context of the history of the Count Bezborodko’s Law Lyceum (1840-1875). The contribution of the administration of the educational establishment to the development of the library is investigated. The aim of the investigation is to reveal the characteristic features, peculiarities, and tendencies in providing library services in the historical period of the library development at the Count Bezborodko’s Law Lyceum. The ways of improvement which facilitated differentiated
Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs

Graph neural networks (GNNs), as the de-facto model class for representation learning on graphs, are built upon the multi-layer perceptrons (MLP) architecture with additional message passing layers to allow features to flow across nodes. While conventional wisdom commonly attributes the success of GNNs to their advanced expressivity, we conjecture that this is not the main cause of GNNs' superiority in node-level prediction tasks. This paper pinpoints the major source of GNNs' performance gain to their intrinsic generalization capability, by introducing an intermediate model class dubbed as P(ropagational)MLP, which is identical to standard MLP in training, but then adopts GNN's architecture in testing. Intriguingly, we observe that PMLPs consistently perform on par with (or even exceed) their GNN counterparts, while being much more efficient in training. This finding sheds new insights into understanding the learning behavior of GNNs, and can be used as an analytic tool for dissecting various GNN-related research problems. As an initial step to analyze the inherent generalizability of GNNs, we show the essential difference between MLP and PMLP at infinite-width limit lies in the NTK feature map in the post-training stage. Moreover, by examining their extrapolation behavior, we find that though many GNNs and their PMLP counterparts cannot extrapolate non-linear functions for extremely out-of-distribution samples, they have greater potential to generalize to testing samples near the training data range as natural advantages of GNN architectures.
New sufficient degree conditions for an $r$-uniform hypergraph to be $k$-edge-connected

An $r$-uniform hypergraphic sequence (i.e., $r$-graphic sequence) $d=(d_1, d_2,\cdots,d_n)$ is said to be forcibly $k$-edge-connected if every realization of $d$ is $k$-edge-connected. In this paper, we obtain a strongest sufficient degree condition for $d$ to be $k$-edge-connected for all $k\ge 1$ and a strongest sufficient degree condition for $d$ to be super edge-connected. As a corollary, we give the minimum degree condition for $d$ to be maximally edge-connected. We also obtain another sufficient degree condition for $d$ to be $k$-edge-connected.
Intelligent Vehicle-road Integrated Traffic Safety System Based on Beidou

With the rapid development of science and technology, people's travel mode has become more and more diversified, and the tasks of solving the road traffic safety problems are also heavier.In view of the safety problems, the relevant departments have taken some measures, and a lot of scientific research products are being put into use, but the situation is still not optimistic.In order to cultivate people's safety awareness and create a safe and beautiful road, our team designed a set of intelligent vehicle-road integrated traffic safety system based on Beidou.This system not only considers the safety of motor vehicles, but also gives full consideration to non-motor vehicles, so that safety is everywhere on the road.At the same time, the vehicles and the roads are closely linked, reflecting the characteristics of "integration".The system has the following functions: for motor vehicles, it can use pressure sensors and gyroscopes for collision sensing, then it timely alarms after a traffic accident.With the ultrasonic and Beidou host, it can measure the range and velocity, which prompts vehicles to control the distance in a safe range.For non-motor vehicles, after the gyroscope detecting the vehicle rollover, it can carry out emergency treatment.The function of range and velocity measurement can also be reflected accordingly in the non-motor vehicle.For road safety, there are pressure sensors installed in the mountains on both sides of the road, so that once there are precursors of landslides or debris flows, the warning of sensors can be timely, and the damage caused by disasters can be reduced as much as possible.At the sharp turn of the winding mountain road, pressure sensors on both sides can sense the passing of vehicles and buzz to prevent collisions.Considering comprehensively, this system can effectively avoid many road traffic accidents and reduce the harm of some accidents as far as possible.It has practical significance for road traffic safety.