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Proceedings of the 41st International Conference on Machine Learning

作者： Yexin Zhang Chenyi Zhang Cong Fang Liwei Wang Tongyang Li School of Electronics Engineering and Computer Science Peking University China Computer Science Department Stanford University National Key Lab of General Artificial Intelligence School of Intelligence Science and Technology and Institute for Artificial Intelligence Peking University Center on Frontiers of Computing Studies and School of Computer Science Peking University China

Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, etc. In this paper, we initiate the study of solving finite-sum optimization problems by quantum computing. Specifically, let $f_1,\ldots,f_n:\mathbb{R}^d\to\mathbb{R}$ be ℓ-smooth convex functions and ψ: ℝd → ℝ be a µ-strongly convex proximal function. The goal is to find an ε-optimal point for $F(\mathbf{x})=\frac{1}{n}\sum_{i=1}^n f_i(\mathbf{x})+\psi(\mathbf{x})$. We give a quantum algorithm with complexity $\tilde{O}\big(n+\sqrt{d}+\sqrt{\ell/\mu}\big(n^{1/3}d^{1/3}+n^{-2/3}d^{5/6}\big)\big)$ improving the classical tight bound $\tilde{\Theta}\big(n+\sqrt{n\ell/\mu}\big)$. We also prove a quantum lower bound $\tilde{\Omega}(n+n^{3/4}(\ell/\mu)^{1/4})$ when d is large enough. Both our quantum upper and lower bounds can extend to the cases where ψ is not necessarily strongly convex, or each fi is Lipschitz but not necessarily smooth. In addition, when F is nonconvex, our quantum algorithm can find an ε-critial point using Õ(n + ℓ(d1/3n1/3 + √d)/ε2) queries.

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Smart Commuting: Exploring Machine Learning Approaches to Understanding the Metro Rail Transit System

Smart Commuting: Exploring Machine Learning Approaches to Un...

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2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

作者： Empino, Jayrald Junsay, Jean Allyson Verzon, Mary Grace Abisado, Mideth Huyo-A, Shekinah Lor Sampedro, Gabriel Avelino Technological University of the Philippines Department of Computer Science Manila Philippines National University College of Computing and Information Technologies Manila Philippines Philippine Coding Camp Research and Development Center Manila Philippines University of the Philippines Open University Faculty of Information and Communication Studies Laguna Philippines

ISBN: (纸本)9798350320213

The Metro Rail Transit Line 3 (MRT3) has been a mode of transportation for many commuters since its inception last 1999. Each day, more than thousands of passengers are recorded by the department of Transportation (DOTr) to ride the MRT3 and predicting the number of passengers per day can be quite difficult. The ridership of the MRT3 varies daily due to factors such as holidays, working days, and even sudden technical issues. Commuters do not know how many other commuters will board on a certain day and this could lead to difficulty in planning a convenient journey to a destination. Currently, the DOTr relies on historical data plotted on spreadsheets and this can be quite difficult to analyze. In this research, a time series forecasting of daily ridership predicts the future ridership in a specific station on certain days is proposed. The proposed prediction method runs on Azure AutoML to train different models that can give accurate data and uses the ridership data from DOTr. The trained models used include Gradient Boosting, Extreme Random Trees, and Light GBM - these models have the best accuracy. © 2023 IEEE.

关键词： Extreme Random Trees Gradient Boosting Light GBM Time Series Forecasting

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EyeRis: visual Image Recognition using Machine Learning for the visually-Impaired

EyeRis: Visual Image Recognition using Machine Learning for ...

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2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

作者： Eugenio, Alliah May Patulot, Marian Jowie Seguiro, Lykha Jane Tuazon, Angelica Nicole Huyo-A, Shekinah Lor Abisado, Mideth Sampedro, Gabriel Avelino Technological University of the Philippines Department of Computer Science Manila Philippines Research and Development Center Philippine Coding Camp Manila Philippines National University College of Computing and Information Technologies Manila Philippines University of the Philippines Open University Faculty of Information and Communication Studies Laguna Philippines

ISBN: (纸本)9798350320213

visually impaired people struggle daily and have difficulty recognizing and distinguishing objects around them. Thus, they mainly depend on supervision from other people to assist them. Since smartphones have become a necessity in this modern world, the researchers formulated a solution to help the visually impaired through a machine learning-based mobile application for object recognition. Nowadays, software applications can provide accurate findings in picture classification and processing processes thanks to machine learning techniques and algorithms. In this study, the researchers use a convolutional neural network (CNN)-based system on TensorFlow Lite to create a mobile version of a visual information system employing a machine learning strategy and deep learning framework. The main objectives of the smartphone application, EyeRis, is to recognize and categorize items in real-time and to separate photographs from the user-selected scenarios. Results were analyzed and contrasted based on the app's obtained recognition accuracy data. It demonstrated the utility of CNN as a model for image recognition algorithms. © 2023 IEEE.

关键词： Convolutional Neural Networks Machine Learning visual Image Recognition

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Hierarchical games with restrictions on the amount of information transmitted 14

Hierarchical games with restrictions on the amount of inform...

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14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Ereshko, Felix Gorelov, Mikhail Federal Research Center 'Computer Science and Control' Russian Academy of Sciences Information and Computing Systems Department Moscow Russia

ISBN: (纸本)9781665412308

The paper examines a new class of models, the common feature of which is the presence of a limited volume of information exchanges between players. It is shown that taking such restrictions into account allows one to significantly expand the class of situations that can be adequately described using the games in normal form. Possible ways of formalizing the concept of 'amount of information' are discussed, returning to the constructions by A.N. Kolmogorov. A new concept of the maximal guaranteed result is discussed. The application of the outlined ideas in the information theory of hierarchical systems is considered. © 2021 IEEE.

关键词： Hierarchical systems

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Analysis of the Russian Agricultural Research Organizations' Digital Footprint 14

Analysis of the Russian Agricultural Research Organizations'...

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14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Medennikov, Viktor Flerov, Yuri Federal Research Center 'Computer Science and Control Russian Academy of Sciences Information and Computing Systems Department Moscow Russia

ISBN: (纸本)9781665412308

This work analyzes domestic agricultural research organizations' ratings shaped by applying methodologies of generating such ratings with consideration to active, passive, and semiactive digital footprints in the internet space. A mathematical description of the methodologies is provided. The benchmarking analysis of the resulting ratings is presented. © 2021 IEEE.

关键词： Agriculture

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Building a Large Japanese Web Corpus for Large Language Models

arXiv

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arXiv 2024年

作者： Okazaki, Naoaki Hattori, Kakeru Shota, Hirai Iida, Hiroki Ohi, Masanari Fujii, Kazuki Nakamura, Taishi Loem, Mengsay Yokota, Rio Mizuki, Sakae Department of Computer Science School of Computing Tokyo Institute of Technology Japan Global Scientific Information and Computing Center Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8550 Japan

Open Japanese large language models (LLMs) have been trained on the Japanese portions of corpora such as CC-100, mC4, and OSCAR. However, these corpora were not created for the quality of Japanese texts. This study builds a large Japanese web corpus by extracting and refining text from the Common Crawl archive (21 snapshots of approximately 63.4 billion pages crawled between 2020 and 2023). This corpus consists of approximately 312.1 billion characters (approximately 173 million pages), which is the largest of all available training corpora for Japanese LLMs, surpassing CC-100 (approximately 25.8 billion characters), mC4 (approximately 239.7 billion characters) and OSCAR 23.10 (approximately 74 billion characters). To confirm the quality of the corpus, we performed continual pre-training on Llama 2 7B, 13B, 70B, Mistral 7B v0.1, and Mixtral 8x7B Instruct as base LLMs and gained consistent (6.6-8.1 points) improvements on Japanese benchmark datasets. We also demonstrate that the improvement on Llama 2 13B brought from the presented corpus was the largest among those from other existing corpora. © 2024, CC BY.

关键词： Benchmarking

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Exploring Delay Tolerant Network: A Simulation Approach 4

Exploring Delay Tolerant Network: A Simulation Approach

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Salam, Mohammad Abdus Deniz, Arif Furkan Aghadinuno, Chinwe Kassouf-Short, Robert Hylton, Alan G. Chicago State University Department of Computing Information and Mathematical Sciences and Technology ChicagoIL60628 United States Southern University and A&M College Department of Computer Science Baton RougeLA United States Southern University and A&M College Department of Electrical Engineering Baton RougeLA United States NASA Glenn Research Center ClevelandOH United States NASA Goddard Space Flight Center GreenbeltMD United States

ISBN: (纸本)9798350395914

This paper provides an in-depth exploration of Delay Tolerant Networks (DTNs), focusing particularly on the development and functionality of High Delay Tolerant Networks (HDTNs). The distinction between DTNs and HDTNs is illuminated, emphasizing the greater resilience and performance of HDTNs. The role and significance of HDTN simulators were thoroughly explained, explaining their essential role in modeling, and predicting the behavior of these advanced networks. A detailed walkthrough is offered on the installation, configuration, and simulation process of the HDTN Simulator, making this study a valuable resource for practitioners in the field. The paper also incorporates the use of DTNSim to further extend the simulation capabilities of HDTNs. The research also delves into the results and network topology obtained from the simulation, offering valuable insights into the practical applications and implications of HDTNs. The findings contribute to a growing body of knowledge surrounding the design, implementation, and management of high delay tolerant networks, thereby aiding future developments in this critical aspect of network engineering. © 2024 IEEE.

关键词： Delay tolerant networks

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Analysis of Panoramic Images using Deep Learning For Dental Disease Identification 3

Analysis of Panoramic Images using Deep Learning For Dental ...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Mallick, Mohamed Midhun Kumar, A. Govindaraju, Sakthi Kumar, A. Suresh Kandasamy, Manivel Anitha, P. Rathinam Group of Institutions Coimbaotore India Indian Biological Data Center Center for Biotechnology Database Engineer Faridabab India Galogotias University School of Computing Science and Engineering Delhi NCR India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Karnavati University Unitedworld School of Computational Intelligence Gujarat India Oasys Institute of Technology Trichy India

ISBN: (纸本)9781665462167

Dental caries is a distinct disease strategy caused by the metabolic action of dental biofilm, which continually demineralizes lacquer and dentine. Its commonly known as tooth rot, is one of the most prevalent and long- lasting dental illnesses, affecting people at all stages of their lives. There are a few different sorts of dental x-rays, each of which records a different perspective on dental living systems for caries detection and has the highest symptomatic accuracy, whereas panoramic radiography has the lowest Despite this, Bitewing has a few downsides, such as patient unease. Because of the lack of knowledge among administrators, Bitewing imaging frequently results in increased patient radiation exposure as a result of the need for image retakes. On the other hand, panoramic x-ray scans, also known as Orthopantomogram (OPG), are commonly utilized to capture the whole mouth with a small amount of radiation. OPG has a low radiation content, is simple to use, takes less time, and provides exceptional patient comfort. In this way, when compared to intraoral frameworks, pediatric, impaired, and senior people would profit greatly from panoramic imaging frameworks. This research work explores panoramic photos to detect accurate dental disease using deep learning. As a result, most patients can benefit from panoramic imaging, which covers a large portion of the maxillofacial region, including all of the teeth. © 2023 IEEE.

关键词： Image enhancement

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AI-based Framework for Discriminating Human-authored and AI-generated Text

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年

作者： Wani, Mudasir Ahmad Abd El-Latif, Ahmed A. Elaffendi, Mohammad Hussain, Amir Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Department of Mathematics and Computer Science Faculty of Science Shebin El-Koom32511 Egypt Edinburgh Napier University School of Computing Merchiston Campus EdinburghEH10 5DT United Kingdom

Deep learning techniques are increasingly adept at distinguishing between human-written and AI-generated text. This study presents a deep learning-based text classification system to discern the source of text - whether it's generated by AI or authored by a human. We compiled a comprehensive and diverse dataset incorporating existing datasets, real-time tweets, and synthetic data generated by cutting-edge AI models such as Google Bard and OpenAI's ChatGPT. Seven deep learning techniques including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and Bidirectional RNN (BiRNN) and four text embedding techniques namely One-hot, Word2Vec, TF-IDF, and BERT, are employed to perform 28 experiments (4∗7). Our findings exhibit outstanding performance across all models. BERT-based models notably outshine the others, with BERT-CNN achieving the highest accuracy of 98.87%, an MCC of 0.9742, and an F1 measure of 0.9916. Following closely, Bert-BiLSTM attained an accuracy, MCC, and F1 score of 98.77%, 0.9720, and 0.9909, respectively. This paper demonstrates the effectiveness of deep learning approaches in distinguishing between human and AI-generated text, with potential applications in automated content moderation, detection of AI-generated spam, and safeguarding the authenticity of user-generated content. © 2020 IEEE.

关键词： Convolutional neural networks

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Analysis of Different Methods for Forecasting Transport Carbon Dioxide Emissions in the Philippines

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Transportation Research Procedia 2025年 82卷 3768-3789页

作者： Aaron Michael Salang Virgilio Ma. Ramos Arse John Salison John Justine Villar Intelligent Transportation Systems Laboratory National Center for Transportation Studies University of the Philippines Diliman Quezon City Philippines Department of Logistics and Information Engineering Tokyo University of Marine Science and Technology Tokyo Japan Scientific Computing Laboratory Department of Computer Science University of the Philippines Diliman Quezon City Philippines

Emission forecasts can be an important way of creating awareness among the public and decision-makers on solving environmental problems. The main goal of this study is to forecast and compare the transport CO2 emissions of the Philippines using four different forecasting models. We use the models of Holt-Winters Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA), Vector Autoregressive (VAR), and the Artificial Neural Network (ANN). The performance of the different forecasting methods was compared using the coefficient of determination (R2) and the root mean squared error (RMSE) values. Several economic variables from 1990 to 2019 and the transport Carbon Dioxide (CO2) emissions in the Philippines were utilized in this study. The result show that all four methods exhibit goodness of fit and accuracy results according to the statistical measures. In comparison, the multivariate methods (ANN & VAR) performed better than univariate methods (ARIMA & Holt-Winters).

关键词： Transport Emissions Forecasting Time-series modeling ARIMA ANN VAR Holt-Winters

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