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GPTR: Gestalt-Perception Transformer for Diagram Object Detection

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arXiv 2022年

作者： Hu, Xin Zhang, Lingling Liu, Jun Fan, Jinfu You, Yang Wu, Yaqiang Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University China Department of Control Science and Engineering Tongji University Shanghai China Department of Computer Science National University of Singapore Singapore Lenovo Research Beijing China

Diagram object detection is the key basis of practical applications such as textbook question answering. Because the diagram mainly consists of simple lines and color blocks, its visual features are sparser than those of natural images. In addition, diagrams usually express diverse knowledge, in which there are many low-frequency object categories in diagrams. These lead to the fact that traditional data-driven detection model is not suitable for diagrams. In this work, we propose a gestalt-perception transformer model for diagram object detection, which is based on an encoder-decoder architecture. Gestalt perception contains a series of laws to explain human perception, that the human visual system tends to perceive patches in an image that are similar, close or connected without abrupt directional changes as a perceptual whole object. Inspired by these thoughts, we build a gestalt-perception graph in transformer encoder, which is composed of diagram patches as nodes and the relationships between patches as edges. This graph aims to group these patches into objects via laws of similarity, proximity, and smoothness implied in these edges, so that the meaningful objects can be effectively detected. The experimental results demonstrate that the proposed GPTR achieves the best results in the diagram object detection task. Our model also obtains comparable results over the competitors in natural image object detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretation

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arXiv 2024年

作者： Zhang, Pengfei Li, Chang Zhang, Yongjun Qin, Rongjun Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province College of Urban and Environmental Science Central China Normal University Wuhan China School of Remote Sensing and Information Engineering Wuhan University China Department of Civil Environmental and Geodetic Engineering Department of Electrical and Computer Engineering Translational Data Analytics Institute The Ohio State University ColumbusOH United States

Remotely sensed imagery interpretation (RSII), including semantic segmentation and change detection, faces the three major problems: (1) objective representation of spatial distribution patterns with coexistence of spatial stationary and non-stationary;(2) edge uncertainty problem caused by downsampling encoder and intrinsic edge noises (e.g., mixed pixel and edge occlusion etc.);and (3) false detection problem caused by geometric registration error in change detection. To solve the aforementioned problems, uncertainty-diffusion-model-based high-Frequency TransFormer network (UDHF2-Net) is the first to be proposed, whose superiorities are as follows: (1) a spatially-stationary-and-non-stationary high-frequency connection paradigm (SHCP) is proposed to enhance the interaction of spatially frequency-wise stationary and non-stationary features to yield high-fidelity edge extraction result. Inspired by HRFormer, SHCP proposes high-frequency-wise stream to replace high-resolution-wise stream in HRFormer through the whole encoder-decoder process with parallel frequency-wise high-to-low streams, so it improves the edge extraction accuracy by continuously remaining high-frequency information;(2) a mask-and-geo-knowledge-based uncertainty diffusion module (MUDM), which is a self-supervised learning strategy, is proposed to improve the edge accuracy of extraction and change detection by gradually removing the simulated spectrum noises based on geo-knowledge and the generated diffused spectrum noises;(3) a frequency-wise semi-pseudo-Siamese UDHF2-Net is the first to be proposed to balance accuracy and complexity for change detection. Besides the aforementioned spectrum noises in semantic segmentation, MUDM is also a self-supervised learning strategy to effectively reduce the edge false change detection from the generated imagery with geometric registration error. In semantic segmentation experiments, UDHF2-Net achieved the best mIoU values of 92.00%, 87.31% and 55.27% respecti

关键词： Semantic Segmentation

Revolutionizing Industry5.0: Harnessing Advanced Technologies for Human-Robot Collaboration in Post-Pandamic Societal Resilience

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SSRN

SSRN 2024年

作者： Alsamhi, Saeed Hamood Hawbani, Ammar Sahal, Radhya Srivastava, Sumit Kumar, Santosh Nashwan, Farhan Zhao, Liang Al-Qaness, Mohammed A.A. Guizani, Mohsen Curry, Edward Insight Centre for Data Analytics University of Galaway Ireland Faculty of Engineering IBB University Ibb70270 Yemen School of Computer Science Shenyang Aerospace University Shenyang110136 China Hudida University Hudida Ibb Yemen Department of Electronics and Communication Engineering FET MJP Rohilkhand University Uttar Pradesh Bareilly243006 India Department of CSE IIIT Naya Raipur Chhattisgarh India College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Abu Dhabi United Arab Emirates Insight Centre for Data Analytics University of Galway Ireland

Over the last year, unprecedented Coronavirus Disease 2019 (COVID-19) occurrences have challenged the operation of industrial systems, significantly impacting the society industry. To cope with the COVID-19 challenges, the industry needed to take immediate action to address them for sustainable business. Recently, Industry 5.0 has emerged to focus more on collaborative approaches, including human and robot collaboration. Industry 5.0 combines robotic power with precision and human creativity to work effectively toward a unique solution demanded by the new area. Advanced technologies can transform our world, such as Beyond Fifth Generation (B5G), robotics, Artificial Intelligence (AI), blockchain, Unmanned Aerial Vehicles (UAVs), and Digital Twins (DTs). The bridge between robots and humans leads to an intelligent society that uses advanced technologies in smart healthcare, logistics, and manufacturing. In this paper, we provide a conceptual framework of the applicability of advanced technologies (i.e., AI, DTs, blockchain) to enable human-robot collaboration for healthcare 5.0, such as efficiently controlling the COVID-19 transmission rate. We discuss human-robot collaboration in manufacturing 5.0, healthcare 5.0, and human-UAV for industry 5.0 as application cases. Furthermore, we envision the revolution of Industry 5.0 with advanced technologies to combat the COVID-19-like pandemic impacts. © 2024, The Authors. All rights reserved.

关键词： Blockchain

Machine Learning, Deep Learning and data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review

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arXiv 2023年

作者： Razavi, Moein Ziyadidegan, Samira Mahmoudzadeh, Ahmadreza Kazeminasab, Saber Baharlouei, Elaheh Janfaza, Vahid Jahromi, Reza Sasangohar, Farzan Department of Industrial and Systems Engineering Texas A&M University United States Department of Computer Science and Engineering Texas A&M University United States Harvard Medical School Harvard University United States Department of Computer Science University of Houston United States Engineering Academic and Student Affairs College of Engineering Texas A&M University United States Ford Motor Company Global Data Insight & Analytics

Background: Mental stress and its consequent mental disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there's potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies employed in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent mental disorders. Objective: This review aims to investigate the scope of Machine Learning (ML) methodologies employed in the detection, prediction, and analysis of mental stress and its consequent mental disorders (MDs). Methods: Utilizing a rigorous scoping review process with Preferred Reporting Items for Systematic Reviews and Meta-Analyses extention for Scoping Reviews (PRISMA-ScR) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types employed in the context of stress and stress-related MDs. Results and Discussion: Total of 98 peer-reviewed publication were examined for this review. The findings highlight that Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) models consistently exhibit superior accuracy and robustness among all machine learning algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms. Conclusion: The synthesis of this review identifies significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the i

关键词： Dimensionality reduction

Wireless Power Transfer Technologies: State-Of-The-Art and Future Prospects

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Wireless Power Transfer Technologies: State-Of-The-Art and F...

Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Vivek Justus S.Viveka M.Sumithra Purushothaman V V.Samuthira Pandi Desidi Narsimha Reddy Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Wireless Power Transfer (WPT) technologies represent a transformative approach to energy transmission, offering advantages over traditional wired systems such as convenience, flexibility, and reduced maintenance. This paper presents a comprehensive review of current advancements and future prospects in WPT. The review encompasses three primary technologies: electromagnetic induction, magnetic resonance, and microwave transmission, each offering unique benefits and challenges. Recent developments have significantly enhanced WPT efficiency, extended transmission distances, and enabled integration with emerging technologies like Internet of Things (IoT) devices and electric vehicle (EV) charging infrastructure. Looking forward, the paper identifies several key research directions and challenges. These include improving efficiency through advanced materials and metamaterials, standardization efforts to promote interoperability and safety, and exploration of new applications such as wireless charging in smart cities and space-based applications. The potential impact of WPT on reducing reliance on fossil fuels and enabling sustainable energy practices underscores its importance in future energy landscapes. By synthesizing current knowledge and highlighting future opportunities, this study aims to guide researchers, engineers, and policymakers in accelerating the development and adoption of WPT technologies, paving the way for a moreefficient and interconnected energy ecosystem.

关键词： Costs Reviews Smart cities Magnetic resonance Wireless power transfer Standardization Telecommunication computing Smart grids Internet of Things Tuning

Pulmonary Nodule Detection Based on Multiscale Feature Fusion

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Computational and Mathematical Methods in Medicine 2022年第1期2022卷 8903037页

作者： Zhao, Yue Wang, Zhongyang Liu, Xinyao Chen, Qi Li, Chuangang Zhao, Hongshuo Wang, Zhiqiong College of Medicine & Biological Information Engineering Northeastern University Shenyang110169 China School of Computer Science & Engineering Northeastern University Shenyang110169 China Key Laboratory of Big Data Management and Analytics Northeastern University Liaoning Province Shenyang110169 China

As cancer with the highest morbidity and mortality in the world, lung cancer is characterized by pulmonary nodules in the early stage. The detection of pulmonary nodules is an important method for the early detection of lung cancer, which can greatly improve the survival rate of lung cancer patients. However, the accuracy of conventional detection methods for lung nodules is low. With the development of medical imaging technology, deep learning plays an increasingly important role in medical image detection, and pulmonary nodules can be accurately detected by CT images. Based on the above, a pulmonary nodule detection method based on deep learning is proposed. In the candidate nodule detection stage, the multiscale features and Faster R-CNN, a general-purpose detection framework based on deep learning, were combined together to improve the detection of small-sized lung nodules. In the false-positive nodule filtration stage, a 3D convolutional neural network based on multiscale fusion is designed to reduce false-positive nodules. The experiment results show that the candidate nodule detection model based on Faster R-CNN integrating multiscale features has achieved a sensitivity of 98.6%, 10% higher than that of the other single-scale model, the proposed method achieved a sensitivity of 90.5% at the level of 4 false-positive nodules per scan, and the CPM score reached 0.829. The results are higher than methods in other works of literature. It can be seen that the detection method of pulmonary nodules based on multiscale fusion has a higher detection rate for small nodules and improves the classification performance of true and false-positive pulmonary nodules. This will help doctors when making a lung cancer diagnosis. © 2022 Yue Zhao et al.

关键词： Convolutional neural networks

A Hybrid Neural Network with Smart Skip Connections for High-Precision, Low-Latency EMG-Based Hand Gesture Recognition

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A Hybrid Neural Network with Smart Skip Connections for High...

IEEE International Conference on Multi Topic

作者： Hafsa Wazir Jawad Ahmad Muazzam A. Khan Sana Ullah Jan Fadia Ali Khan Muhammad Shahbaz Khan ICESCO Chair for Big Data Analytics and Edge Computing Quaid-i-Azam University Islamabad Pakistan Cybersecurity Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Department of Computer Science HITEC University Taxila Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

Electromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. EMG signals measure the electrical activity of muscles during different motions. EMG signals play a key role in gesture recognition studies, such as hand gesture recognition. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition. The proposed model comprises convolutional neural networks with smart skip connections in conjunction with a Gated Recurrent Unit (GRU). The proposed model is trained on the complete Ninapro DB1 dataset. The proposed model boasts an accuracy of 99.7% in classifying 53 classes in just 25 milliseconds. In addition to being fast, the proposed model is lightweight with just 3,946 KB in size. The fast inference time and lightweight architecture of the proposed model makes it suitable for resource constrained IoT devices. Moreover, the proposed model has also been evaluated for the reliability parameters, i.e., Cohen’s kappa coefficient, Matthew’s correlation coefficient, and confidence intervals. The close to ideal results of these parameters validate the models performance on unseen data.

关键词： Hands Performance evaluation Correlation coefficient Accuracy Neural networks Gesture recognition Electromyography data models Internet of Things Reliability

SciQAG: A Framework for Auto-Generated science Question Answering dataset with Fine-grained Evaluation

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arXiv 2024年

作者： Wan, Yuwei Liu, Yixuan Ajith, Aswathy Grazian, Clara Hoex, Bram Zhang, Wenjie Kit, Chunyu Xie, Tong Foster, Ian GreenDynamics Pty. Ltd KensingtonNSW Australia Department of Linguistics and Translation City University of Hong Kong Hong Kong Department of Computer Science University of Chicago ChicagoIL United States School of Mathematics and Statistics University of Sydney CamperdownNSW Australia DARE ARC Training Centre in Data Analytics for Resources and Environments Australia School of Photovoltaic and Renewable Energy Engineering University of New South Wales KensingtonNSW Australia School of Computer Science and Engineering University of New South Wales KensingtonNSW Australia

We introduce SciQAG, a novel framework for automatically generating high-quality science question-answer pairs from a large corpus of scientific literature based on large language models (LLMs). SciQAG consists of a QA generator and a QA evaluator, which work together to extract diverse and research-level questions and answers from scientific papers. Utilizing this framework, we construct a large-scale, high-quality, open-ended science QA dataset containing 188,042 QA pairs extracted from 22,743 scientific papers across 24 scientific domains. We also introduce SciQAG-24D, a new benchmark task designed to evaluate the science question-answering ability of LLMs. Extensive experiments demonstrate that finetuning LLMs on the SciQAG dataset significantly improves their performance on both open-ended question answering and scientific tasks. To foster research and collaboration, we make the datasets, models, and evaluation codes publicly available, contributing to the advancement of science question answering and developing more interpretable and reasoningcapable AI systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

A New Approach for Calculation of Quasi-Normal Modes and Topological Charges of Regular Black Holes

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arXiv 2024年

作者： Zhang, M.Y. Hosseinifar, F. Chen, H. Sathiyaraj, T. Hassanabadi, H. College of Computer and Information Engineering Guizhou University of Commerce Guiyang550014 China Department of Physics Faculty of Science University of Hradec Králové Rokitanského 62 Hradec Králové500 03 Czech Republic School of Physics and Electronic Science Zunyi Normal University Zunyi563006 China Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur56000 Malaysia

This study examines the properties of a special regular black hole. This analysis investigates the Hawking temperature, remnant radius and mass, as well as the effect of parameter ξ on thermodynamic quantities like entropy, heat capacity, and free energy. The emission rate, evaporation process, quasi-normal modes by calculating Rosen-Morse potential, and topological behavior of the black hole are also explored. © 2024, CC0.

关键词： Black holes