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arXiv 2023年

作者： Yong, Suk Yee Ong, Cheng Soon Australia Astrophysics and Space Technologies Research Centre Macquarie University SydneyNSW2109 Australia School of Mathematical and Physical Sciences Macquarie University SydneyNSW2109 Australia Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia Machine Learning and Artificial Intelligence Future Science Platform CSIRO Australia Data61 CSIRO CanberraACT2601 Australia School of Computing The Australian National University CanberraACT2601 Australia

Precise measurements of the black hole mass are essential to gain insight on the black hole and host galaxy co-evolution. A direct measure of the black hole mass is often restricted to nearest galaxies and instead, an indirect method using the single-epoch virial black hole mass estimation is used for objects at high redshifts. However, this method is subjected to biases and uncertainties as it is reliant on the scaling relation from a small sample of local active galactic nuclei. In this study, we propose the application of conformalised quantile regression (CQR) to quantify the uncertainties of the black hole predictions in a machine learning setting. We compare CQR with various prediction interval techniques and demonstrated that CQR can provide a more useful prediction interval indicator. In contrast to baseline approaches for prediction interval estimation, we show that the CQR method provides prediction intervals that adjust to the black hole mass and its related properties. That is it yields a tighter constraint on the prediction interval (hence more certain) for a larger black hole mass, and accordingly, bright and broad spectral line width source. Using a combination of neural network model and CQR framework, the recovered virial black hole mass predictions and uncertainties are comparable to those measured from the Sloan Digital Sky Survey. The code is publicly available at https://***/yongsukyee/uncertain_blackholemass. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Mixed transformer U-Net for medical image segmentation

arXiv

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arXiv 2021年

作者： Wang, Hongyi Xie, Shiao Lin, Lanfen Iwamoto, Yutaro Han, Xian-Hua Chen, Yen-Wei Tong, Ruofeng College of Computer Science and Technology Zhejiang University China College of Information Science and Engineering Ritsumeikan University Japan Artificial Intelligence Research Center Yamaguchi University Japan Research Center for Healthcare Data Science Zhejiang Lab China

Though U-Net has achieved tremendous success in medical image segmentation tasks, it lacks the ability to explicitly model long-range dependencies. Therefore, Vision Transformers have emerged as alternative segmentation structures recently, for their innate ability of capturing long-range correlations through Self-Attention (SA). However, Transformers usually rely on large-scale pre-training and have high computational complexity. Furthermore, SA can only model self-affinities within a single sample, ignoring the potential correlations of the overall dataset. To address these problems, we propose a novel Transformer module named Mixed Transformer Module (MTM) for simultaneous inter- and intra- affinities learning. MTM first calculates self-affinities efficiently through our well-designed Local-Global Gaussian-Weighted Self-Attention (LGG-SA). Then, it mines inter-connections between data samples through External Attention (EA). By using MTM, we construct a U-shaped model named Mixed Transformer U-Net (MT-UNet) for accurate medical image segmentation. We test our method on two different public datasets, and the experimental results show that the proposed method achieves better performance over other state-of-the-art methods. The code is available at: https://***/Dootmaan/MT-UNet. Copyright © 2021, The Authors. All rights reserved.

关键词： Image segmentation

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Deep learning detection of anomalous patterns from bus trajectories for traffic insight analysis

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Knowledge-Based Systems 2021年 217卷 106833-106833页

作者： Zhang, Xiaocai Zheng, Yi Zhao, Zhixun Liu, Yuansheng Blumenstein, Michael Li, Jinyan Advanced Analytics Institute Faculty of Engineering and IT University of Technology Sydney UltimoNSW 2007 Australia National Key Laboratory of Science and Technology on Blind Signal Processing ChengduSichuan610041 China College of Information Science and Engineering Hunan University ChangshaHunan410082 China Australian Artificial Intelligence Institute & Faculty of Engineering and IT University of Technology Sydney UltimoNSW 2007 Australia

Existing data-driven methods for traffic anomaly detection are modeled on taxi trajectory datasets. The concern is that the data may contain much inaccuracy about the actual traffic situations, because taxi drivers often choose optimal routes to evade from the congestions caused by traffic anomalies. We use bus trajectory data in this work. Bus trajectories can capture real traffic conditions in the road networks without drivers’ preference, which are more objective and appropriate for accurately detecting anomalous patterns for a broad range of insight analyses on traffics. We proposed a deep learning-based feature visualization method to map 3-dimensional features into a red–green–blue (RGB) color space. A color trajectory (CT) is then derived by encoding a trajectory with the RGB colors. With the spatial and temporal properties extracted from the CT, spatio-temporal outliers are detected by a novel offline detection method. We then conduct GIS map fusion to obtain insights for better understanding the traffic anomaly locations, and more importantly the influences on the road affected by the corresponding anomalies. Extended from the offline detection, an online detection method is developed for real-time detection of anomalous patterns. Our proposed methods were tested on 3 real-world bus trajectory datasets to demonstrate the performance of high accuracies, high detection rates and relatively low false alarm rates. © 2021

关键词： Trajectories

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Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual data

Computing Open

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Computing Open 2024年第2期computing-open卷

作者： S. R. Mani Sekhar D. M. Mushtaq Ahmed G. M. Siddesh Department of Information Science and Engineering M. S. Ramaiah Institute of Technology Bangalore Karnataka India Department of Artificial Intelligence and Data Science M. S. Ramaiah Institute of Technology Bangalore Karnataka India

Smart cities leverage advanced technologies to enhance urban living through the real-time collection, processing, and analysis of contextual information. The potential to improve residents’ outdoor experiences in these cities increases dramatically as smart technologies are integrated into urban environments, making them more interconnected. It helps to explore the pivotal role of real-time data in optimizing various aspects of city management, focusing on key domains such as traffic, public transportation, emergency response, waste management, and environmental monitoring. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. This model addresses the dynamic and context-aware nature of Smart Cities by proposing a novel framework for real-time contextual information prediction and personalized outdoor activity suggestions for users. Leveraging the vast amount of data generated by Smart City infrastructure, this study integrates advanced data analysis techniques with deep learning models to enhance the urban living experience. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. The framework begins by collecting and process

关键词： Deep learning artificial intelligence smart city outdoor activities air pollution temperature

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RETRACTED ARTICLE: Improved Deep Learning Method for Real-Time Ship Detection and Localization From SAR Image

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Journal of the Indian Society of Remote Sensing 2023年第8期52卷 1855-1855页

作者： Raj J, Anil Idicula, Sumam Mary Paul, Binu Department of Computer Science Cochin University of Science and Technology Kochi India Department of Artificial Intelligence & Data Science Muthoot Institute of Technology and Science Varikoli Kochi India Division of Electronics Engineering School of Engineering Cochin University of Science and Technology Kochi India

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MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results 17th

MIPI 2022 Challenge on Under-Display Camera Image Restorati...

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17th European Conference on Computer Vision, ECCV 2022

作者： Feng, Ruicheng Li, Chongyi Zhou, Shangchen Sun, Wenxiu Zhu, Qingpeng Jiang, Jun Yang, Qingyu Loy, Chen Change Gu, Jinwei Zhu, Yurui Wang, Xi Fu, Xueyang Hu, Xiaowei Hu, Jinfan Liu, Xina Chen, Xiangyu Dong, Chao Zhang, Dafeng Huang, Feiyu Liu, Shizhuo Wang, Xiaobing Jin, Zhezhu Jiang, Xuhao Shao, Guangqi Wang, Xiaotao Lei, Lei Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Huang, Tao Fang, Zhenxuan Huang, Mengluan Xu, Junwei Zhang, Yong Yang, Yuechi Shu, Qidi Yang, Zhiwen Li, Shaocong Yao, Mingde Xu, Ruikang Guan, Yuanshen Huang, Jie Xiong, Zhiwei Zhu, Hangyan Liu, Ming Liu, Shaohui Zuo, Wangmeng Jia, Zhuang Song, Binbin Song, Ziqi Mao, Guiting Hou, Ben Liu, Zhimou Ke, Yi Ouyang, Dengpei Han, Dekui Zhang, Jinghao Zhu, Qi Zheng, Naishan Zhao, Feng Jin, Wu Conde, Marcos Nathan, Sabari Timofte, Radu Xu, Tianyi Xu, Jun Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Jiang, Biao Ding, Yuhan Li, WanZhang Feng, Xiaoyue Chen, Sijing Zhong, Tianheng Lu, Jiyang Chen, Hongming Fan, Zhentao Chen, Xiang Nanyang Technological University Singapore Singapore SenseBrain San Jose United States Shanghai AI Laboratory Shanghai China SenseTime Research and Tetras.AI Beijing China University of Science and Technology of China Hefei China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Samsung Research China Beijing China Xiaomi Beijing China Hefei University of Technology Hefei China School of Artificial Intelligence Xidian University Xi’an China School of Remote Sensing and Information Engineering Wuhan University Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Harbin Institute of Technology Harbin China University of Macau China Changsha Research Institute of Mining and Metallurgy Changsha China Tianjin University Tianjin China Computer Vision Lab University of Wurzburg Würzburg Germany Couger Inc. Tokyo Japan School of Statistics and Data Science Nankai University Tianjin China Founding Minds Software Thiruvananthapuram India Department of Electronics and Communication SRM University AP Amaravati India Fudan University Shanghai China Shenyang Aerospace University Shenyang China Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783031250712

Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://***/mipi-challenge/MIPI2022. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image reconstruction

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CFPChain: An Optimization of Research Funding Selection System Employing A Consortium Blockchain Based Approach

CFPChain: An Optimization of Research Funding Selection Syst...

引用

International Conference on Computer, Control, Informatics and Its Applications (IC3INA)

作者： Taufik Iqbal Ramdhani Akbari Indra Basuki Heru Susanto Raditya Panji Umbara Samsul Arifin Ninon Nurul Faiza Research Center for Artificial Intelligence and Cyber Security National Research and Innovation Agency Bandung Indonesia Faculty Computer Science Institut Teknologi Tangerang Selatan Indonesia Department of Informatics Faculty of Science & Technology Universitas Al Azhar Indonesia Jakarta Indonesia Department of Informatics Universitas Bunda Mulia Tangerang Selatan Indonesia Research Center for Geological Disaster National Research and Innovation Agency Tangerang Selatan Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Data and Information Center National Research and Innovation Agency Jakarta Indonesia

This paper addresses the challenges in the current centralized research funding selection system and proposes a solution using permissioned blockchain technology. The existing system suffers from drawbacks such as concealed data modification and lack of interoperability. The Blockchain approach, based on the principles of Bitcoin technology, offers high security, auditability, traceability, and integrity. By utilizing interconnected blocks and cryptography, the proposed system ensures the authenticity and consistency of data over time. The chosen blockchain platform, Hyperledger Fabric, provides crash-fault-tolerance consensus and private communication options for enhanced security and efficiency. The objective of this study is to develop a consortium blockchain-based research funding platform that enables collaboration among researchers from public agencies, universities, and companies, with funding provided by financial organizations. The proposed architecture model ensures tamper-proof data, accountability, and interoperability, benefiting researchers, reviewers, auditors, funders, and companies.

关键词：

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Secuyolo: Secure Object Detection Framework With Darknet-53 and CBAM in Edge Devices

引用

Concurrency and Computation: Practice and Experience 2025年第15-17期37卷

作者： P. Lakshmiramana A. Vadivel Computer Science & Engineering GITAM School of Technology (Research Scholar) & Madanapalle Institute of Technology & Science (Assistant Professor) GITAM (Deemed to Be University) Dodballapura Karnataka India Madanapalle Institute of Technology & Science Angallu India Artificial Intelligence and Data Science GITAM School of Technology GITAM (Deemed to Be University) Dodballapura Karnataka India

Object detection in edge computing has emerged as a pivotal solution for processing sensor data locally, thereby minimizing latency and bandwidth consumption. However, this introduces significant privacy concerns, as edge servers may exploit sensitive sensor data. Traditional frameworks often overlook data security and privacy protection, presenting challenges in balancing privacy with computational efficiency in resource-constrained environments. To address these issues and improve performance, we propose SecuYOLO CBAMNet53, a novel framework combining Secure YOLO V3 with Darknet-53 and the CBAM. This model enhances feature extraction capabilities, ensuring privacy protection and accurate real-time object detection on edge devices. Darknet-53 serves as a deep feature extractor, while CBAM boosts discriminative power through channel-wise and spatial attention, improving detection accuracy and handling complex scenes. It integrates YOLOv3 with security measures like data encryption, secure deployment, and differential privacy, safeguarding sensitive information during inference. By classifying data as public or private for smart IoT applications, SecuYOLO CBAMNet53 optimizes performance and prevents privacy breaches. The proposed SecuYOLO, employing Darknet-53 with CBAM as its backbone, achieves outstanding performance in both an average recall of 67.62 and an average precision of 51.2. The model's dynamic recalibration of feature maps and robust security mechanisms ensure reliable object detection and privacy protection, making it suitable for real-world applications.

关键词： attention module data privacy deep learning edge computing object detection YOLOv3

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Diff-Font: Diffusion Model for Robust One-Shot Font Generation

arXiv

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arXiv 2022年

作者： He, Haibin Chen, Xinyuan Wang, Chaoyue Liu, Juhua Du, Bo Tao, Dacheng Yu, Qiao Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Wuhan University Wuhan China Shanghai AI Laboratory Shanghai202150 China JD Explore Academy *** China National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China School of Computer Science Faculty of Engineering The University of Sydney Australia Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Font generation presents a significant challenge due to the intricate details needed, especially for languages with complex ideograms and numerous characters, such as Chinese and Korean. Although various few-shot (or even one-shot) font generation methods have been introduced, most of them rely on GAN-based image-to-image translation frameworks that still face (i) unstable training issues, (ii) limited fidelity in replicating font styles, and (iii) imprecise generation of complex characters. To tackle these problems, we propose a unified one-shot font generation framework called Diff-Font, based on the diffusion model. In particular, we approach font generation as a conditional generation task, where the content of characters is managed through predefined embedding tokens and the desired font style is extracted from a one-shot reference image. For glyph-rich characters such as Chinese and Korean, we incorporate additional inputs for strokes or components as fine-grained conditions. Owing to the proposed diffusion training process, these three types of information can be effectively modeled, resulting in stable training. Simultaneously, the integrity of character structures can be learned and preserved. To the best of our knowledge, Diff-Font is the first work to utilize a diffusion model for font generation tasks. Comprehensive experiments demonstrate that Diff-Font outperforms prior font generation methods in both high-fidelity font style replication and the generation of intricate characters. Our method achieves state-of-the-art results in both qualitative and quantitative aspects. Copyright © 2022, The Authors. All rights reserved.

关键词： Diffusion

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Performance analysis of a novel hybrid deep learning approach in classification of quality-related English text

引用

Measurement: Sensors 2023年 28卷

作者： Orosoo, Myagmarsuren Govindasamy, Santhakumar Bayarsaikhan, Narmandakh Rajkumari, Yaisna Fatma, Gulnaz Manikandan, R. Kiran Bala, B. School of Humanities and Social Sciences Mongolian National University of Education Mongolia Department of Electronics and Communication Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Theory and Methodology of Physical Education Mongolian National University of Education India Dept of Humanities and Social Sciences NIT Hamirpur India Dept of English Language Center Jazan University Jazan Saudi Arabia Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India

Text classification technique is advancing rapidly alongside AI technology, showing signs of maturity. Moreover, there are always many unrestricted constraints that text classification must deal with in practical settings. English text is indeed a significant component of textual data and a significant source of data for persons seeking data from other countries. This study enhances the text classification method currently in use using text classification depending upon English quality. By using an illustration of English quality-related text classification systems, the concept as well as execution of text classification systems is demonstrated, concluding the study on text classification algorithms. The main task of this article is to categorize, and analyze huge volumes of information in English text using technique of integrating qualitative. Therefore, the fundamental components of superior English compositions are attained using Lion Optimization Algorithm (LOA). Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) is utilized to classify the obtained texts. If there is a lot of training input, the typical English text classification method can easily display flaws like ambiguous characteristic elements. In light of such issues, the research suggests a quality-related English text classification approach based on convolutional neural network in order to enhance the precision and adaptability of English text classification. © 2023

关键词： Long short-term memory

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