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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Wang, Ge Sun, Kaiwen Atabey, Ayça Pothong, Kruakae Lin, Grace C. Zhao, Jun Yip, Jason Department of Computer Science University of Oxford United Kingdom School of Information University of Michigan United States School of Law Centre for Data Culture & Society University of Edinburgh United Kingdom Department of Media and Communications The London School of Economics and Political Science 5Rights Foundation United Kingdom Scheller Teacher Education Program The Education Arcade The Massachusetts Institute of Technology United States Information School University of Washington United States

ISBN: (纸本)9781450394222

AI systems and related algorithms are starting to play a variety of roles in the digital ecosystems of children - being embedded in the connected toys, smart home IoT technologies, apps, and services they interact with on a daily basis. Going forward, AI systems will, in all likelihood, become even more pervasive in children's applications simply due to their sheer usefulness in creating compelling, adaptive, and personal user experiences. Yet, understanding the ways that AI-driven systems used by children operate, and how AI could be designed to better anticipate and respond to children's diverse requirements is still a new and emerging area of investigation. Our goals of this workshop are to (1) extend the current critically constructive dialogue around the meaning of child-centred AI design and (2) explore ways to operationalise such child-centred AI design in practice, and finally (3) further expand and foster a community for those who are interested in designing and developing child-centred AI systems. © 2023 Owner/Author.

关键词： Automation

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An Analysis Air Traffic Prediction During a Pandemic

An Analysis Air Traffic Prediction During a Pandemic

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Darmeli Nasution Herman Mawengkang Fahmi Muhammad Zarlis Departmen Of Computer Science and Information Technology Doctoral Program Universitas Sumatera Utara Medan Indonesia Departmen Of Math and Science Universitas Sumatera Utara Medan Indonesia Departmen Of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Departmen Of Computer Science and Information Technology Universitas Bina Nusantara Jakarta Indonesia

ISBN: (纸本)9781665453967

Air transportation during the covid-19 pandemic experienced a very drastic decline. The decrease in the number of passengers was caused by national and international restrictions. The troublesome administration makes passengers discouraged from traveling using Air transportation. Based on the National Statistics Agency, air transportation experienced a decline from early 2020 to 2021. This study focuses on air traffic predictions, namely the number of aircraft passengers during the COVID-19 pandemic at Indonesia's main airports, namely Kuala Namu, Sukarno Hatta, and Juanda airports., Ngurah Rai and Hasanuddin. The method used to predict the number of airplane passengers during a pandemic is the backpropagation algorithm using the Fletcher Reeves method.

关键词： COVID-19 Analytical models Airplanes Pandemics Atmospheric modeling Backpropagation algorithms Predictive models

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Auto Digit Selection for Most Significant Digit First Multiplication

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IEEE Embedded Systems Letters 2025年

作者： Gorgin, Saeid Jaberipur, Ghassem Lee, Jeong-A Shin, Seokjoo Kim, Jungrae Computer Engineering Iranian Research Organization for Science and Technology Tehran Iran Semiconductor Systems Engineering Department College of Information and Communication Engineering SungKyunKwan University Korea Republic of Brain Pool Program for Chosun University Gwangju Korea Republic of Shahid Beheshti University Tehran Iran Chosun University Department of Computer Engineering Gwangju Korea Republic of

Performance in modern embedded systems, particularly those executing computation-intensive signal/image processing and machine learning algorithms, is critically dependent on the efficiency of multiplication operations. Serial multiplication offers key advantages in these resource-constrained environments, including low memory usage and minimal interconnect complexity. The most significant digit first (MSDF) serial multiplication scheme further enhances efficiency by enabling early termination and supporting variable precision. It is ideal for embedded applications where power and performance are critical. This paper proposes an auto-selection scheme for MSDF multiplication, where product digits are generated as a byproduct of residual maintenance, leading to speed, cost, and power efficiency improvements. We demonstrate the implementation of serial-serial and serial-parallel multipliers, where the multiplier operand in the latter is fully available throughout the computation. Simulation and synthesis results, compared to the most relevant previous works, show reductions in delay (16% and 20%), area consumption (11% and 23%), and power dissipation (7% and 20%), making our approach well-suited for embedded systems requiring efficient, low-power multiplication hardware. © 2009-2012 IEEE.

关键词： Left-to-Right Arithmetic Machine Learning Algorithms MSDF Multiplication Online Arithmetic

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Long Short Term Memory (LSTM) Improvement Accuracy Using FastText and Glove for Language Translation Batak - Indonesian

Long Short Term Memory (LSTM) Improvement Accuracy Using Fas...

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technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Atikah Rahayu Mandasari Erna Budhiarti Nababan Herman Mawengkang Master of Informatics Program Universitas Sumatera Utara Medan Indonesia Faculty Of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

This research discusses the improvement of Long Short-Term Memory (LSTM) model accuracy in translating Batak to Indonesian by utilizing FastText and GloVe embeddings. Traditional LSTM models often struggle with the complexity and richness of underrepresented languages like Batak. By integrating FastText and GloVe, which provide word vectors with deeper semantic meanings, the translation quality can be significantly enhanced. Experimental results show that this approach offers a substantial performance improvement compared to conventional LSTM models, producing more accurate and contextual translations. These findings have the potential to be applied in broader multilingual translation systems and support the preservation and digital accessibility of minority languages.

关键词： Long short term memory Training Vectors Accuracy Machine translation Vocabulary Predictive models Numerical models Decoding Context modeling

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Design of an Assisted Learning System Based on ChatGPT 23

Design of an Assisted Learning System Based on ChatGPT

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Proceedings of the 2023 6th Artificial Intelligence and Cloud Computing Conference

作者： Chia-Chuan Tsao Yen-Hung Lin Chien-Hsing Chou Kuan-Ning Chang Ping-Hsuan Han Department of Mathematics and Information Education National Taipei University of Education Taiwan Department of Electrical and Computer Engineering Tamkang University Taiwan Doctoral Program in Design National Taipei University of Technology Taiwan Department of Interaction Design National Taipei University of Technology Taiwan

ISBN: (纸本)9798400716225

This paper describes a system that utilizes ChatGPT as an auxiliary learning tool to assist students in overcoming learning difficulties. By providing ChatGPT with relevant domain-specific knowledge in advance, the system enhances the accuracy of responses. Additionally, the interface design facilitates students in posing high-quality questions, thereby obtaining precise answers. This paper aims to use this system design to help students address learning challenges, increase motivation for learning, and reduce the cost of developing domain-specific chatbots, thus enabling its application in a broader range of educational fields.

关键词： ChatGPT chatbot computer-assisted instruction question-answering system teaching and learning

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Exploring Personal Knowledge Ecologies: Dealing with Digital Platform Asymmetries

Exploring Personal Knowledge Ecologies: Dealing with Digital...

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International Conference on computer Supported Cooperative Work in Design

作者： Marcos Antonio De Almeida Jano Moreira De Souza António Correia Daniel Schneider Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The rise of artificial intelligence agents and information technology platforms governed by data-driven algorithms has had profound impacts on the way knowledge work is managed and carried out. Concerning the novel uses of digital work platforms, the arising tensions might suggest that the socio-technical design frameworks adopted by major tech firms are becoming less viable and in some cases ineffective. We propose a conceptual architecture to support a socio-technical framework for exploring Personal Knowledge Ecologies in the context of the digital knowledge economy. This framework aims to increase the degree of individual freedom for the interoperation of these knowledge information infrastructures.

关键词： Knowledge engineering Sociotechnical systems computer architecture Ecology Software Heterogeneous networks Complexity theory

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A Finite Difference Method for a Normalized Time-Fractional Black–Scholes Equation

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International Journal of Applied and Computational Mathematics 2025年第3期11卷 1-17页

作者： Nam, Yunjae Wang, Jian Lee, Chaeyoung Choi, Yongho Bang, Minjoon Li, Zhengang Kim, Junseok Program in Actuarial Science and Financial Engineering Korea University Seoul 02841 South Korea School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing 210044 China Department of Mathematics Kyonggi University Suwon 16227 South Korea Computer and Information Engineering Daegu University Gyeongsan 38453 South Korea Department of Mathematics Korea University Seoul 02841 South Korea

In this article, we propose a normalized time-fractional Black–Scholes (TFBS) equation. The proposed model uses a normalized time-fractional derivative which has a distinctive feature wherein a weight function possesses the property that its integral with respect to time is always equal to one. This feature ensures a well-balanced integration over time and provides a fair comparison between different fractional orders. An implicit finite difference method is used for the numerical solution of the TFBS equation. We perform several standard numerical tests to examine the effect of the fractional parameter on the option pricing. These experiments are designed to investigate how variations in the fractional parameter influence pricing outcomes and provide valuable insights into the model’s behavior under different conditions. The computational results demonstrate the model’s potential for capturing more intricate market dynamics, which makes the proposed model a promising tool for financial analysis. In the appendix, we provide a computer program that implements the numerical methods discussed in the paper for interested readers. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2025.

关键词： Implicit finite difference method Memory effect Normalized time-fractional Black–Scholes equation

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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Detection and Recognition of Vehicle Components Using Image Processing and Deep Learning Techniques

Detection and Recognition of Vehicle Components Using Image ...

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Research, Invention, and Innovation Congress (RI2C)

作者： Mahasak Ketcham Thittaporn Ganokratanaa Patiyuth Pramkeaw Phumraphi Boonthai Narumol Chumuang Department of Information Technology Management King Mongkut's University of Technology North Bangkok Bangkok Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Bangkok Thailand Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Department Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand

This research study presents an algorithmic approach for the detection and identification of key components on license plates, including numerical digits, province codes, vehicle colors, and vehicle makes and types. The methodology integrates image processing techniques and deep learning methodologies to achieve comprehensive image analysis. The algorithm incorporates image preprocessing techniques to optimize the input images, followed by a deep neural network trained on a large annotated dataset. The algorithm utilizes image segmentation and character recognition techniques to detect and classify numerical digits on license plates. Similarly, it employs segmentation and classification processes for identifying province codes, vehicle colors, makes, and types. Extensive training ensures the algorithm's accuracy and robustness under varying conditions. The proposed algorithm demonstrates the potential for accurate license plate component detection and identification, with applications in law enforcement and traffic monitoring systems.

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Predicting Oil and Gas Pipe Wall Thickness Using Machine Learning

Predicting Oil and Gas Pipe Wall Thickness Using Machine Lea...

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2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

作者： Mohammed, Abdulrahim Hewahi, Nabil College of Science University of Bahrain Big Data Science and Analytics Program Sakhir Bahrain College of Information Technology University of Bahrain Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781665416566

Pipeline networks are crucial for process industries transportation and business operation. These vital elements, however, are highly exposed to material degradation due to corrosion that seriously impedes operational profitability, reliability, and safety. Industries usually monitor pipeline corrosion rate by gauging pipe wall thickness. Predicting pipe wall thickness helps industries to identify locations with high corrosion rate at an early stage and take necessary actions. The aim of this research is to predict pipe wall thickness at different locations using different machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (KNN), fully connected Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM). The data processed and analyzed in this study included six different process variables as well as pipe wall thickness at three different locations in a downstream oil and gas industry. The study findings demonstrated that LSTM with test Root Mean Squared Errors (RMSEs) of 0.019 mm, 0.015 mm, and 0.017 mm for the three selected locations, outperformed the other methods applied and successfully identified the pipe wall thickness pattern. © 2021 IEEE.

关键词： Long short-term memory

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