Reviewerly/exHarmony huggingface dataset:
Recognition of Tamil handwritten characters using Scrabble GAN

Recognition of hand-or machine-printed documents is now a crucial component of applications. A commonly used method for converting a printed or handwritten file into its equivalent text format is Optical Character Recognition (OCR). Handwriting recognition is a major research area in pattern recognition and attracts extensive research in OCR. In Southern part of India, Tamil is the primary language with the longest continuous literary heritage. OCR is used to extract characters from scanned digital input images and convert them into formats that can be edited by machines. It is quite challenging to recognize handwritten Tamil characters due to variations in font size, orientation angle and writing styles, bigger category sets, and misunderstanding over the similarities between handwritten characters. In this study, we suggest a deep learning-based technique for handwritten Tamil character recognition. Text documents printed on paper require time-consuming character correction and reprinting. Here, a model incorporating scrabble GAN with CNN is used to increase the dataset. New training instances are created from the old instances using Scrabble Generative Adversarial and Networks augmentation to improve the accuracy of detecting the Tamil handwritten characters. The primary goal of the research work is to expand the handwritten characters and recognize them. Hyperparameters such as kernel size, activation function, padding, step size and layer count are adjusted to enhance CNN-based handwriting recognition. In order to distinguish between characters, CNN evaluates their differences in shapes and features.. Dataset consists of nearly 6,000 images and the 80% of the images are used as training images and 20 % of the images used as testing images and obtained the accuracy of 96.23%.
Basic Study About Large-Scale Acoustic Analysis of Violin Based on Domain Decomposition

Recent advances in numerical computation technology have popularized wave acoustic analysis, which is used in the design of musical instruments. The goal of this study is to shorten the production time and reduce the cost of musical instruments by predicting the sound behavior of a violin as the object of analysis. The analysis model was created based on CT scan data of a Stradivarius violin, and the open software ADVENTURE_Sound was used as the analysis tool. ADVENTURE_Sound is characterized by its ability to perform detailed analysis with tens to hundreds of millions of degrees-of-freedom meshes and its high parallel efficiency even in massively parallel computing environments consisting of tens of thousands of processors. In conclusion, we confirmed that a steady-state analysis of a high-definition violin model is successfully performed with more than tens of millions of elements.
A Data Enhancement Strategy for Multi-Agent Cooperative Hunting based on Deep Reinforcement Learning

Cooperative hunting is a typical and significant scene to study multi-agent behaviors, where conventional control strategies are difficult to cope with, due to its high dimensionality of state space and locality of communication. Reinforcement learning provides a framework and a set of tools for this issue by trial-and-error interactions with the environment. Though promising, it often requires a large number of empirical sample data to learn effective hunting strategies, leading to low sample efficiency, understood as the training episodes required for the agent to learn effective behavior strategies. To improve the sampling efficiency, we propose a data enhancement strategy integrated in the execution (CTDE) training framework to train the multi-agent system. The data enhancement strategy is based on a state transfer dynamics model to generate additional predicted data, which we called dynamic prediction model, combined with the empirical data by interacting with the environment, for higher sample efficiency. The simulation results on the Webots platform show that our method outperforms some state-of-the-art methods, such as MAPPO, with high data sample efficiency.
A Study on the Learning Agility of Medical Institution Workers Focused on E-learning

This study was conducted to study learning agility using e-learning experience for medical institution workers. A total of 149 medical institution workers were classified into doctor, nurse, healthcare personnels, hospital administrative personnel, and etc, by occupation and surveyed by questionnaire. As a result of the study, medical institution workers had the highest open sensing among the four sub-factors of learning agility, and hospital administrative personnel showed high in open sensing, strategic exploring, experimental applying, and critical reflecting among the sub-factors except etc. In addition, it was found experimental applying factors among learning agility had a significant effect on learning flow and learning satisfaction. The results of this study are expected to be basic data for research on the learning agility of medical institution workers, and in further studies, it is believed the advanced learning agility of medical institution workers should be conducted for each occupation.
The Introduction and Implications of Teacher AI Education Policy in China

Now that global attention is paid to AI education, China is aggressively implementing AIeducation for teachers at the government level. China which induced AI education early to theregular curriculum is producing teachers through teacher training and re-education courses. Thisstudy investigated the introduction and current status of teacher AI education in China mainlywith both nonconfidential and confidential documents and analyzed the characteristics of it. Inthat process, this researcher examined problems related with teacher supply and demand andthe policy of flexibility in different school levels and also analyzed feasibility and problemsassociated with the placement of applicants for teacher training and the plan to appointnonregular teachers. Korea requires a systematic approach to expanding AI education andtraining related teachers from now on. To reduce problems that may occur in it, this authorintended to derive implications from the cases in China regarding the sufficient input ofbudgets, the matter of political use, teachers’ voluntary participation and support from theMinistry of Education, or connection with career education. This study was written mainlythrough literature review, but expert interviews were done, too, partly.
Agent-Based Autonomous System Verification

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Verifiable Autonomous Systems

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Design development and execution of Smart Contract : An Overview

With the advent of decentralised cryptocurrencies such as Bitcoin and Ethereum, blockchain technology has sparked intense interest among businesses and developers. One notable application of blockchain is the development of smart contracts, which are code scripts that function on a blockchain network and are automatically executed whenever the prescribed requisites are met without the assistance of a reliable third party. As a result, smart contracts have been gaining momentum rapidly with various applications in fields including pharmaceuticals, trade finance, supply chain management, etc. This paper overviews the fundamental framework supporting blockchain platforms and programming languages. This helped create smart contracts with a detailed study on creating a smart test contract running on the Ethereum blockchain, written in Solidity programming language and executed in the Remix IDE.
Smart Library using Arduino

Academic Institute’s heart is the library. Libraries old concepts are rapidly changing nowadays. Librarians used to use card catalogues and manually assign library work. The concept of libraries was that they were primarily used as book storage facilities. However, this concept is evolving and giving way to global libraries powered by computers. The library can only be managed with the help of automation. In our project, we hope to automate the library in the most cost-effective manner possible. As a result, it would be advantageous for small-scale libraries that cannot afford expensive automated software. We used an Arduino and a barcode system that was relatively inexpensive.
Implementation of 3-Level Security System Using Image Grid Based Authentication System

We now know that the majority of computer security relies on passwords to verify and authenticate users. There are numerous proposed authentication methods, and the majority of them still have flaws. Some of them, like voice recognition, are based on the user’s physical and behavioral characteristics, while others, like textual and graphical passwords, are based on the user’s knowledge. However, these plans are still insufficiently secure and make it simple for hackers to steal the data. In addition, users frequently use simple passwords that are simple for an attacker to guess. As a result, something more user-friendly and secure is required to meet this demand. Level 2 authentication and a one-time password that is automatically generated and sent to the authentic user via email at Level 3 authentication. Additionally, the IBA System’s unique image set has been used with great diligence to thwart Shoulder, Tempest, and Brute-Force attacks on the client side. The three-level security system’s main goals are to make web applications safer, keep unauthorized users from accessing them, and make them easier to use. Hopefully, the system will be able to provide the user with a more secure environment free of unauthorized access.
尤度推定手法を用いた超音波漏洩箇所推定システムに関する基礎研究

In the decommissioning process of the Fukushima Daiichi Nuclear Power Plant of the Tokyo Electric Power Company, Incorporated (TEPCO), it is necessary to investigate the leakage area of coolant and so on. We have focused on ultrasonic measurement as an investigation method to supplement optical measurement methods such as cameras.In this study, we have developed and verified a measurement system using ultrasonic velocity distribution measurement and ultrasonic environmental measurement to measure the velocity distribution in a wide area and to estimate the leakage point in unknown underwater environment, aiming to apply the measurement method to a robot for the investigation in the reactor during the decommissioning work process.
形状設計タスクにおける深層生成モデルとPhysics Guided GAN

When designing a part of machines, it is desired to generate shapes that satisfies performance requirements. For such an aim, deep generative models are used. Generative adversarial network (GAN), variational autoencoders (VAE), and VAEGAN are usually employed. In the present study, we compare those three generative models, and explain the necessity of physics guided generative models.
Reform of Business Administration Teaching Mode Based on Blockchain Technology

Blockchain technology is another subversive technology that has a far-reaching impact on the teaching mode of business administration after big data, Mu class and virtual reality. The essence of blockchain technology is a new information organization structure. Its core technologies include decentralization, distributed ledger, smart contract, hash algorithm, digital signature and timestamp. It has the advantages of reliability, credibility, security and efficiency. At present, there are some problems in the teaching mode of business administration, such as scattered teaching resources, conflict of training subjects and single teaching evaluation method. The application of blockchain technology helps to promote the reform and innovation of business administration teaching in terms of educational resource supply, multi-agent collaborative education and learning achievement evaluation, so as to create a more open, efficient and safe business administration teaching system.
Elemental sound

A team from Indiana University has used a technique called data sonification to convert the visible light given off by elements such as helium and iron into audio.
A Review on Applications of Computer Vision

Humans and animals acquire most of their information through visual systems. The sense of sight in the machine is provided by computer vision technology. Nowadays, vision-based technologies are playing a vital role in automating various processes. With the improvement in computation, powers, and algorithms computer vision performs different tasks such as object detection, human and animal action recognition, and image classification. The application of computer vision is growing continuously in various industries. In this paper, we summarize computer vision implementation in popular fields such as astronomy, medical science, the food industry, the manufacturing industry, and autonomous vehicles. We provide the different computer vision techniques and their performance in various areas. Finally, a brief overview is presented on future development.
Clembench: Using Game Play to Evaluate Chat-Optimized Language Models as Conversational Agents

Recent work has proposed a methodology for the systematic evaluation of "Situated Language Understanding Agents"-agents that operate in rich linguistic and non-linguistic contexts-through testing them in carefully constructed interactive settings. Other recent work has argued that Large Language Models (LLMs), if suitably set up, can be understood as (simulators of) such agents. A connection suggests itself, which this paper explores: Can LLMs be evaluated meaningfully by exposing them to constrained game-like settings that are built to challenge specific capabilities? As a proof of concept, this paper investigates five interaction settings, showing that current chat-optimised LLMs are, to an extent, capable to follow game-play instructions. Both this capability and the quality of the game play, measured by how well the objectives of the different games are met, follows the development cycle, with newer models performing better. The metrics even for the comparatively simple example games are far from being saturated, suggesting that the proposed instrument will remain to have diagnostic value. Our general framework for implementing and evaluating games with LLMs is available at https://github.com/clembench .
Development of E-Modules Based On Mobile Learning Applications to Improve Students' Critical Thinking Skills in Science Subject

Developing an E-module based on a mobile learning application is used to improve the critical thinking skills of junior high school science students. Objective: This research aims to develop products in e-modules based on mobile learning applications that are valid, practical, and effective. Methods: The research method used is the ADDIE development model, which includes five stages: analyzing, designing, developing, implementing, and evaluating. The data analysis techniques used are validation sheets for validity tests and observation sheets for learning implementation for practicality. And the N-gain test and student questionnaire to test effectiveness. Results: Based on the results of the data analysis conducted, e-modules based on mobile learning applications have obtained valid results with average validation scores from the three validators, namely 83%, e-modules based on mobile learning in learning are considered very practical as teaching materials in science learning in junior high school because they get an average score of 94%, e-modules based on mobile learning applications are also considered to have been effective and feasible to use as teaching materials during science learning in junior high school because they have used e-modules based on mobile learning applications, students' thinking skills increase with grades N-gain with a high score of 0.77 and student response with a score of 74% with a good category. Novelty: Previous researchers have never done this research, which develops e-modules based on mobile learning applications to improve students' critical thinking skills.
Mapping alteration zones in the Southern section of Yulong copper belt, Tibet using multi-source remote sensing data

Methods for extracting mineralized alteration information using remote sensing images have recently become both efficient and cost-effective. Technology involving the extraction of alteration information based on multi-spectral data has been widely practiced and effectively verified. In recent years, research on fine mineral extraction methods based on hyperspectral data has also been rapidly developing. The Yulong copper belt is a porphyry copper belt located in China with high prospects for mineralization. However, most previous studies focused on the northern section of the Yulong copper belt, with limited exploration of the southern section. In this study, alteration information of the southern section of the Yulong copper belt was extracted from remote sensing data from Landsat-8 OLI, ASTER, and ZY1-02D, and the prospecting potential of this area was evaluated. Principal component analysis was used to extract iron oxide and hydroxyl alteration from Landsat-8 data, in addition to Al hydroxyl and propylitic alterations from ASTER data. Considering the challenge of the extraction of too many pseudo-anomalies using traditional methods, the mixture-tuned matched filtering (MTMF) method was used to more accurately extract iron oxide alterations. Regarding hyperspectral data, the minimum noise fraction and pure pixel index algorithms were used to extract white mica and carbonatite endmembers. The MTMF method was also used for alteration mapping, which took advantage of sub-pixel abundance mapping to finely divide the white mica and carbonatite alterations into five classes. The extraction results of multi-source remote sensing data were then compared and analyzed to avoid occasional single-image extraction results, which confirmed the superiority of the hyperspectral remote sensing and MTMF methods. Combined with field verification, the mineralization alteration information coincided with the spatial location of the Secuo, Mamupu, and Jicuo deposits, which confirmed the accuracy of alteration information extraction. The results of this study confirmed the application potential of remote sensing alteration information extraction in the field of mineral resource exploration. The results have important reference significance for further geological prospecting and exploration in the southern section of the Yulong copper belt.
Transportation Service Report Information System Using Prototype Method

CV. Femarse Inti Mulia, a company engaged in the distribution of products, still processes reporting data, especially payments for transportation services, manually using Microsoft Excel. Problems arising from the old system are errors in the calculation of total wages for transport services for each pen, as well as the occurrence of differences in recording by the warehouse and the finance department. In addition, when using Microsoft Excel formulas, typographical errors can occur, causing calculations to be different than they should be. In light of these problems CV. PT. Femarse Inti Mulia needs an information system for daily transportation service wages reports that is capable of calculating daily transportation service wages, storing this data securely, and managing daily transportation service wages reports easily, quickly, accurately, and in a well-organized manner. The use of information systems for daily wage reports is the right solution to solve existing problems. With the prototype software development method and primary data collection, it makes it easier to design the daily wage report information system.&#x0D;
Application Of Sales Forecasting Using The Least Square Method In Web-Based Information Systems

Technology has become an important role in life, causing the role of computers to be indispensable in various aspects. The presence of technology today is not only in the field of technology but computational methods are also developing. The use of the internet in the aspect of E-commerce (electronic commerce) also plays an important role in the business process cycle. Sale is a major aspect of supporting survival in an industry. Because the high level of sales in an industry/service can compensate let alone provide benefits for the industry. The LEAST Square method is used as an analytical tool for forecasting / predicting sales at the ABDS Store Pekanbaru store. The level of accuracy of the calculation will have an impact on the availability of stock in the store. This method is often used in finding the best parameters of a mathematical model that describes observational data. By using the least squares method, it is expected that the resulting mathematical model can provide a better description of the observation data.
Seeing with Fresh Eyes: Meaning, Space, Data, Truth

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsJen ChristiansenJen Christiansen is a senior graphics editor at Scientific American. jen@jenchristiansen.com
Development of Contextual-Based Reading Skills Teaching Modules

This study aims to develop a contextual p-based reading teaching module for Semester 3 Indonesian Language and Literature Education Study Program (PBSI) UKI Toraja students. The research used a modification of the Borg and Gall model which includes: (1) Conducting preliminary research (presurvey), (2) Planning, (3) Developing the type/form of the initial product, (4) Conducting field trials, (5) Revising the main product, (6) Main field trials (more extensive), (7) Revision to operational products, (8) Operational field tests (feasibility tests), (9) Revisions to the final product (final revision) and, (10) disseminate and implement the product—data collection using questionnaires and observations. The data were analyzed in a descriptive quantitative manner. The study's results obtained the feasibility of the reading teaching module reached 3.92 (very valid). The responses of lecturers and students obtained 97.5% and 96.5% (efficient). The results of the observation of the implementation of learning obtained a percentage of 93.5% (very effective). The conclusion of the research is the development of reading teaching modules based on contextual approaches feasible and practical used by 3rd-semester students of the Indonesian Language and Literature Education Study Program, Indonesian Christian University of Toraja.
Dicke Superradiance Requires Interactions beyond Nearest Neighbors

Photon-mediated interactions within an excited ensemble of emitters can result in Dicke superradiance, where the emission rate is greatly enhanced, manifesting as a high-intensity burst at short times. The superradiant burst is most commonly observed in systems with long-range interactions between the emitters, although the minimal interaction range remains unknown. Here, we put forward a new theoretical method to bound the maximum emission rate by upper bounding the spectral radius of an auxiliary Hamiltonian. We harness this tool to prove that for an arbitrary ordered array with only nearest-neighbor interactions in all dimensions, a superradiant burst is not physically observable. We show that Dicke superradiance requires minimally the inclusion of next-nearest-neighbor interactions. For exponentially decaying interactions, the critical coupling is found to be asymptotically independent of the number of emitters in all dimensions, thereby defining the threshold interaction range where the collective enhancement balances out the decoherence effects. Our findings provide key physical insights to the understanding of collective decay in many-body quantum systems, and the designing of superradiant emission in physical systems for applications such as energy harvesting and quantum sensing.