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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Nu-Ri Park Min-Joong Kim Jongsu Lee Minah Seo Yong-Sang Ryu Myung-Ki Kim KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA School of Biomedical Engineering Korea University Seoul Republic of Korea Interdisciplinary Program in Precision Public Health Korea University Seoul Republic of Korea

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen (<3%... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Electric potential Accuracy Hydrogen Lasers and electrooptics Optical sensors Electro-optical waveguides

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Towards Gradient Equalization and Feature Diversification for Long-Tailed Multi-Label Image Recognition

SSRN

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SSRN 2023年

作者： Chen, Zhaomin Cui, Quan Zhang, Xiaoqin Deng, Ruoxi Xia, Chaoqun Lu, Shijian The National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China The College of Computer Science Artificial Intelligence Wenzhou University Wenzhou325035 China The Graduate School of Information Production and Systems Waseda University Fukuoka1698050 Japan School of Computer Science and Engineering Nanyang Technological University 639798 Singapore

Multi-label image recognition with convolutional neural networks has achieved remarkable progress in the past few years. However, most existing multi-label image recognition methods suffer from the long-tailed data distribution problem, \ie, head categories occupy most training samples, while tailed classes have few samples. This work firstly studies the influence of long-tailed data distribution on existing multi-label image recognition methods. Based on this, two crucial issues of the existing methods are identified: 1) severe gradient imbalance between head and tailed categories, even though re-balancing strategies are adopted;2) the lack of diversity of tail category training samples. To tackle the first issue, this paper proposes a group sampling strategy to create group-wise balanced data distribution. Meanwhile, a dynamic gradient balancing loss is proposed to equalize the gradient for all categories. To tackle the second issue, this paper proposes a diversity enhancement module to fuse the information across all categories, preventing the network from overfitting tail classes. Furthermore, it also balances the gradient, promoting the discriminability of learned classifiers. Our method significantly outperforms the baseline method and achieves competitive performance with state-of-the-art methods on VOC-LT and COCO-LT datasets. Extensive ablation studies are conducted to verify the effectiveness of the essential proposals. © 2023, The Authors. All rights reserved.

关键词： Image recognition

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Finding Top-k Longest Palindromes in Substrings

arXiv

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arXiv 2022年

作者： Mitani, Kazuki Mieno, Takuya Seto, Kazuhisa Horiyama, Takashi Graduate School of Information Science and Technology Hokkaido University Sapporo Japan Department of Computer and Network Engineering University of Electro-Communications Chofu Japan Faculty of Information Science and Technology Hokkaido University Sapporo Japan

Palindromes are strings that read the same forward and backward. Problems of computing palindromic structures in strings have been studied for many years with a motivation of their application to biology. The longest palindrome problem is one of the most important and classical problems regarding palindromic structures, that is, to compute the longest palindrome appearing in a string T of length n. The problem can be solved in O(n) time by the famous algorithm of Manacher [Journal of the ACM, 1975]. This paper generalizes the longest palindrome problem to the problem of finding top-k longest palindromes in an arbitrary substring, including the input string T itself. The internal top-k longest palindrome query is, given a substring T[i..j] of T and a positive integer k as a query, to compute the top-k longest palindromes appearing in T[i..j]. This paper proposes a linear-size data structure that can answer internal top-k longest palindromes query in optimal O(k) time. Also, given the input string T, our data structure can be constructed in O(nlog n) time. For k = 1, the construction time is reduced to O(n). © 2022, CC BY.

关键词： Data structures

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A Conflict Degree-Guided Quantum Group Consensus Method Based on Graph Convolutional Networks with Enhanced Sentiment Analysis for Consumer Electronics Products

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IEEE Transactions on Consumer Electronics 2024年

作者： Zhang, Chao Liang, Xinyu Sangaiah, Arun Kumar Li, Deyu Shanxi University School of Computer and Information Technology Key Laboratory of Computational Intelligence Chinese Information Processing Ministry of Education Taiyuan030006 China National Yunlin University of Science and Technology International Graduate Institute of Artificial Intelligence Douliou64002 Taiwan Sunway University School of Engineering and Technology Petaling Jaya Selangor 47500 Malaysia Chandigarh University University Centre for Research and Development Punjab Mohali140413 India

Artificial intelligence-based human-computer interaction (AI-HCI) and consumer electronics (CE) are closely linked, making AI-HCI crucial in e-commerce by providing intelligent and personalized product recommendations. In this context, consumers often select from multiple websites and products when purchasing CE products. This makes CE product selections conflicting and random to reach a consensus, often modeled as a quantum group consensus problem. Therefore, this paper aims to enhance consumer selections via intelligent decision support using online reviews. First, an ensemble technique, multi-objective salp swarm optimization bi-directional long short-term memory (MOSSO-BiLSTM) with gate recurrent unit (GRU), is employed for sentiment analysis (SA). Second, graph convolutional networks (GCN) is used to calculate weights for key indicators. Meanwhile, according to hybrid distances, an improved fuzzy C-means three-way (FCMTW) clustering analysis is explored. Third, a conflict degree-guided quantum two-stage consensus reaching process (CRP) is designed. It includes identification rules determined by quantum frameworks and modification rules determined by regret theory (RT). Fourth, multi-objective optimization by ratio analysis plus the full MULTIplicative form (MULTIMOORA) and RT are combined for stable optimal product selections. Finally, a case study of selecting solar emergency lights verifies the viability of the proposed methodology. © 1975-2011 IEEE.

关键词： Quantum electronics

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Passivity-Based Cooperative Dynamic Control for Human-Rigid Body Networks

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and Systems 2021年第11期141卷 1162-1174页

作者： Murao, Toshiyuki Koshio, Yusuke Kawai, Hiroyuki Yamauchi, Junya Hatanaka, Takeshi Fujita, Masayuki College of Engineering Kanazawa Institute of Technology 7-1 Ohgigaoka Nonoichi-shi Ishikawa921-8501 Japan Graduate Program in Mechanical Engineering Kanazawa Institute of Technology 7-1 Ohgigaoka Nonoichi-shi Ishikawa921-8501 Japan Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan School of Engineering Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8550 Japan

This paper presents passivity-based cooperative dynamic control for human-rigid body networks. In the human-rigid body network, a human operator sends a command signal to and receives visual information from only a part of the robotic group. In this setting, the proposed dynamic control scheme achieves not only pose synchronization of all mobile robots but also convergence to the reference pose that the human operator specifies. First, we derive robot dynamics of an omni-directional mobile robot through the Newton-Euler equation. Second, we design a control input to the robots with dynamics and show the passivity property of the robot system. Next, we discuss convergence analysis of the closed-loop system through the Lyapunov stability theorem and the LaSalle's invariance principle. The main advantage of the proposed method is to guarantee convergence of not only the position but also the orientation to the reference ones in the human-rigid body network. Finally, we provide simulation results and experimental results using four ground vehicles and a tablet PC to illustrate the performance of the proposed control method. © 2021 The Institute of Electrical Engineers of Japan.

关键词： Mobile robots

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A Stable Intelligent Model-Based Framework Using Graph Convolutional Networks and MULTIMOORA for Clinical Decision Support Systems

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IEEE Transactions on Consumer Electronics 2024年

作者： Zhang, Chao Li, Xiaochuan Bai, Wenhui Sangaiah, Arun Kumar Li, Deyu Shanxi University School of Computer and Information Technology Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Taiyuan030006 China National Yunlin University of Science and Technology International Graduate Institute of Artificial Intelligence Douliou64002 Taiwan Sunway University School of Engineering and Technology Selangor Petaling Jaya47500 Malaysia Chandigarh University University Centre for Research and Development Punjab Mohali140413 India

Artificial intelligence (AI) is rapidly advancing in consumer electronics, particularly in clinical decision support systems (CDSSs). AI-driven devices enhance the effectiveness of healthcare-related electronics via predictive health analytics, reshaping medical data collection, analysis, and decision-making. Nevertheless, due to the abundance of imprecise, hesitant, and fuzzy information in healthcare data stemming from consumer electronics, AI may misinterpret the data, resulting in erroneous and unstable medical decisions. Therefore, this paper endeavors to establish a stable intelligent medical decision model, employing two essential tools: graph convolutional networks (GCNs) and MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form). First, a granular computing (GrC) framework based on GCNs and MULTIMOORA is established. In this framework, GCNs are initially employed to process multi-modal data within the hesitant fuzzy linguistic (HFL) background. Second, a comprehensive HFL information system (IS) is constructed. Third, three types of multi-granularity (MG) HFL methods are developed via MULTIMOORA. To better address the bounded rationality of decision-makers (DMs), regret theory (RT) is utilized to consolidate the multi-modal data. Finally, the efficacy and practically of the proposed decision model are assessed via a case study on medication selections. © 1975-2011 IEEE.

关键词： Electronic health record

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch 22

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Obe, Ruth Kaufmann, Brandt Baird, Kaelen Kadel, Sam Soltani, Yasmin Cham, Mostafa Gobbert, Matthias K. Barajas, Carlos A. Jiang, Zhuoran Sharma, Vijay R. Ren, Lei Peterson, Stephen W. Poif, Jerimy C. U. of Houston-Clear Lake Dept. of Computer Science and of Software Engineering United States U. of San Francisco Dept. of Mathematics and Statistics United States Skidmore College Dept. of Computer Science and of Mathematics United States Mount Holyoke College Dept. of Computer Science and of Psychology United States U. of Houston Dept. of Biomedical Engineering United States U. of Maryland Dept. of Information Systems Baltimore County United States U. of Maryland Dept. of Mathematics and Statistics Baltimore County United States Medical Physics Graduate Program Duke University United States U. of Maryland School of Medicine Dept. of Radiation Oncology United States U. of Maryland School of Medicine Department of Radiation Oncology United States U. of Cape Town Dept. of Physics South Africa H3D Inc. United States

ISBN: (纸本)9798350345346

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy. © 2023 IEEE.

关键词： Clas-sification Compton camera Distributed Data Parallelism Proton beam therapy PyTorch Recurrent neural network

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Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes

arXiv

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arXiv 2023年

作者： Xu, Jian Du, Shian Yang, Junmei Ma, Qianli Zeng, Delu School of Mathematics South China University of Technology in Guangdong Province China Shenzhen International Graduate School Tsinghua University Shenzhen China School of Electronic and Information Engineering South China University of Technology Guangdong Province China School of Computer Science and Engineering South China University of Technology Guangdong Province China

Deep Gaussian Process (DGP) models offer a powerful nonparametric approach for Bayesian inference, but exact inference is typically intractable, motivating the use of various approximations. However, existing approaches, such as mean-field Gaussian assumptions, limit the expressiveness and efficacy of DGP models, while stochastic approximation can be computationally expensive. To tackle these challenges, we introduce Neural Operator Variational Inference (NOVI) for Deep Gaussian Processes. NOVI uses a neural generator to obtain a sampler and minimizes the Regularized Stein Discrepancy in L2 space between the generated distribution and true posterior. We solve the minimax problem using Monte Carlo estimation and subsampling stochastic optimization techniques. We demonstrate that the bias introduced by our method can be controlled by multiplying the Fisher divergence with a constant, which leads to robust error control and ensures the stability and precision of the algorithm. Our experiments on datasets ranging from hundreds to tens of thousands demonstrate the effectiveness and the faster convergence rate of the proposed method. We achieve a classification accuracy of 93.56 on the CIFAR10 dataset, outperforming SOTA Gaussian process methods. Furthermore, our method guarantees theoretically controlled prediction error for DGP models and demonstrates remarkable performance on various datasets. We are optimistic that NOVI has the potential to enhance the performance of deep Bayesian nonparametric models and could have significant implications for various practical applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Gaussian distribution

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Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection

arXiv

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arXiv 2024年

作者： Li, Jiaming Zhang, Jiacheng Li, Jichang Li, Ge Liu, Si Lin, Liang Li, Guanbin School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computer Science The University of Hong Kong Hong Kong SECE Shenzhen Graduate School Peking University Shenzhen China Institute of Artificial Intelligence Beihang University China GuangDong Province Key Laboratory of Information Security Technology China Research Institute Sun Yat-sen University Shenzhen China

Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowledge distillation to transfer insightful knowledge from pre-trained large-scale vision-language models to the task of object detection, significantly generalizing the powerful capabilities of the detector to identify more unknown object categories. However, these methods face significant challenges in background interpretation and model overfitting and thus often result in the loss of crucial background knowledge, giving rise to sub-optimal inference performance of the detector. To mitigate these issues, we present a novel OVD framework termed LBP to propose learning background prompts to harness explored implicit background knowledge, thus enhancing the detection performance w.r.t. base and novel categories. Specifically, we devise three modules: Background Category-specific Prompt, Background Object Discovery, and Inference Probability Rectification, to empower the detector to discover, represent, and leverage implicit object knowledge explored from background proposals. Evaluation on two benchmark datasets, OV-COCO and OV-LVIS, demonstrates the superiority of our proposed method over existing state-of-the-art approaches in handling the OVD tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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AI-Based Feature Extraction and Cosine Similarity for Automation of Student Learning Assessment

AI-Based Feature Extraction and Cosine Similarity for Automa...

引用

information Technology (ACIT)

作者： Bambang Dwi Wijanarko Bachtiar Rosilah Binti Hassan Dina Fitria Murad Ridho Bramulya Ihsan Yaya Heryadi Engkos Achmad Kuncoro Eka Julianti Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Master of English Education Graduate School Universitas Terbuka Jakarta Indonesia Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Manajemen Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Information System Department Universitas Terbuka Jakarta Indonesia

This research is ongoing research into the student learning process which aims to develop artificial intelligence-based technology to calculate essay exam scores automatically, based on the textual proximity of students' answers to the answer key. This research uses an approach based on natural language processing (NLP) and text analysis. Feature extraction was carried out using the BERT model and similarity comparison using the Cosine distance method. Model evaluation was carried out using sensitivity and specificity tests. The model evaluation results show a sensitivity of 98.21%, which means this assessment model is able to detect positive cases well.

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