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2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023

作者： Choi, Jaeyeol Jeong, Jong-Beom Park, Junhyeong Ryu, Eun-Seok Department of Applied Artificial Intelligence Seoul Korea Republic of Department of Computer Science Education Seoul Korea Republic of Department of Immersive Media Engineering Seoul Korea Republic of

ISBN: (纸本)9798350359855

This paper introduces a new method for synthesizing six degree of freedom (6DoF) videos using neural radiance fields, which allows training from plain 2D images to render 3D scene at arbitrary viewports. Neural network model representing a previous timepoint is fine-Tuned to train models for subsequent timepoints. Additionally, instant neural graphics primitives (Instant-NGP) is applied for speed improvement. The proposed method achieved both improved objective quality for same number of training iterations and enhanced consistency between frames. Furthermore, it shows superiority over other methods for generating 6DoF videos, in terms of quality and time efficiency. © 2023 IEEE.

关键词： Virtual reality

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HyenaPixel: Global Image Context with Convolutions 27

HyenaPixel: Global Image Context with Convolutions

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27th European Conference on artificial intelligence, ECAI 2024

作者： Spravil, Julian Houben, Sebastian Behnke, Sven Fraunhofer IAIS Germany University of Applied Sciences Bonn-Rhein-Sieg Germany University of Bonn Computer Science Institute VI Center for Robotics Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (纸本)9781643685489

In computer vision, a larger effective receptive field (ERF) is associated with better performance. While attention natively supports global context, its quadratic complexity limits its applicability to tasks that benefit from high-resolution input. In this work, we extend Hyena, a convolution-based attention replacement, from causal sequences to bidirectional data and two-dimensional image space. We scale Hyena's convolution kernels beyond the feature map size, up to 191×191, to maximize ERF while maintaining sub-quadratic complexity in the number of pixels. We integrate our two-dimensional Hyena, HyenaPixel, and bidirectional Hyena into the MetaFormer framework. For image categorization, HyenaPixel and bidirectional Hyena achieve a competitive ImageNet-1k top-1 accuracy of 84.9% and 85.2%, respectively, with no additional training data, while outperforming other convolutional and large-kernel networks. Combining HyenaPixel with attention further improves accuracy. We attribute the success of bidirectional Hyena to learning the data-dependent geometric arrangement of pixels without a fixed neighborhood definition. Experimental results on downstream tasks suggest that HyenaPixel with large filters and a fixed neighborhood leads to better localization performance. © 2024 The Authors.

关键词： Complex networks

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Enhanced Support Vectors based Oversampling Method 25

Enhanced Support Vectors based Oversampling Method

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Sabbah, Thabit Ashraf, Mahmoud Abrar, Mohammad Dhofar University Collage of Arts and Applied Sciences P.O.Box:2509 Salalah211 Oman University of Jeddah College of Computer Science and Engineering Department of Computer Science and Artificial Intelligence Jeddah Saudi Arabia Arab Open University Faculty of Computer Studies Muscat122 Oman

ISBN: (纸本)9798331540012

Class distribution imbalance in the dataset is a critical factor in machine learning based classification performance. Many oversampling techniques were used to overcome the effect of this problem and retrieve the balance state for effective modelling. This paper presents an enhanced technique based on the Support Vectors for oversampling the minority class by generating synthetic data for better performance. The proposed method is tested on machine-generated datasets for binary classification problems. Experiments show an average improvement of approximately 15% in the classification recall and AUC performance measures on both datasets using the proposed method over the leading of the studied oversampling techniques. The proposed method can benefit the real-life machine learning classification problems where the positive class forms the minority of the training dataset like fraud-detection, medical diagnosis, and other problems, by expanding the positive class samples with more discriminative and evenly distributed related synthetic samples. © 2024 IEEE.

关键词： Adversarial machine learning

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Some Results on Ricci-Yamabe Soliton and Its Cosmological Models

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IAENG International Journal of applied Mathematics 2025年第4期55卷 979-983页

作者： Pandey, Pankaj Yadav, Santosh Kumar Sharma, Kamakshi Chaturvedi, Braj Bhushan Department of Mathematics School of Chemical Engineering and Physical Sciences Lovely Professional University Punjab Phagwara144411 India Department of Mathematics School of Computer Science & Artificial Intelligence SR University Telangana Warangal506371 India Department of Pure & Applied Mathematics Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495009 India

This article aims to investigate the characteristics of Ricci-Yamabe Soliton (briefly: (RYS)n). We study the cosmological models on (RYS)4 under Lorentzian para Sasakian (LPS)4 spacetime. Parallel Ricci tensor, Poisson structure and gradient property also reveal on (RYS)n. Example of (RYS)4 constructed to verify the results. © (2025), (International Association of Engineers). All rights reserved.

关键词： Solitons

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A Global Model-Agnostic XAI method for the Automatic Formation of an Abstract Argumentation Framework and its Objective Evaluation 1

A Global Model-Agnostic XAI method for the Automatic Formati...

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1st International Workshop on Argumentation for eXplainable AI, ArgXAI 2022

作者： Vilone, Giulia Longo, Luca The Artificial Intelligence and Cognitive Load research lab The applied Intelligence Research Center School of Computer Science Technological University Dublin Dublin Ireland

Explainable artificial intelligence (XAI) aims to train data-driven, machine learning (ML) models possessing both high predictive accuracy and a high degree of explainability for humans. Comprehending and explaining the inferences of a model can be seen as a defeasible reasoning process which is expected to be non-monotonic meaning that a conclusion, linked to a set of premises, can be withdrawn when new information becomes available. Computational argumentation, a paradigm within artificial intelligence (AI), focuses on modeling defeasible reasoning. This research study explored a new way for the automatic formation of an argument-based representation of the inference process of a data-driven ML model to enhance its explainability by employing principles and techniques from computational argumentation, including weighted attacks within its argumentation process. An experiment was conducted on five datasets to test, in an objective manner, if the explanations of the proposed XAI method are more comprehensible than decision trees, which are considered naturally transparent. Findings demonstrate that usually the argument-based method can represent the logic of the model with fewer rules than a decision tree, but further work is required to achieve the same performances in terms of other characteristics, such as fidelity to the model. © 2022 Copyright for this paper by its authors.

关键词： Decision trees

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Action Recognition in Real-World Ambient Assisted Living Environment

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Big Data Mining and Analytics 2025年第4期8卷 914-932页

作者： Zakka, Vincent Gbouna Dai, Zhuangzhuang Manso, Luis J. School of Computer Science and Digital Technologies Aston University BirminghamB4 7ET United Kingdom Aston University Applied Artificial Intelligence and Robotics Department BirminghamB4 7ET United Kingdom

The growing ageing population and their preference to maintain independence by living in their own homes require proactive strategies to ensure safety and support. Ambient Assisted Living (AAL) technologies have emerged to facilitate ageing in place by offering continuous monitoring and assistance within the home. Within AAL technologies, action recognition plays a crucial role in interpreting human activities and detecting incidents like falls, mobility decline, or unusual behaviours that may signal worsening health conditions. However, action recognition in practical AAL applications presents challenges, including occlusions, noisy data, and the need for real-time performance. While advancements have been made in accuracy, robustness to noise, and computation efficiency, achieving a balance among them all remains a challenge. To address this challenge, this paper introduces the Robust and Efficient Temporal Convolution network (RE-TCN), which comprises three main elements: Adaptive Temporal Weighting (ATW), Depthwise Separable Convolutions (DSC), and data augmentation techniques. These elements aim to enhance the model's accuracy, robustness against noise and occlusion, and computational efficiency within real-world AAL contexts. RE-TCN outperforms existing models in terms of accuracy, noise and occlusion robustness, and has been validated on four benchmark datasets: NTU RGB+D 60, Northwestern-UCLA, SHREC'17, and DHG-14/28. The code is publicly available at: https://***/Gbouna/RE-TCN. © 2018 Tsinghua University Press.

关键词： Assisted living

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Robust Real-Time PCB Defect Detection Using Improved DETR

Robust Real-Time PCB Defect Detection Using Improved DETR

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Wu, Qingtian Yang, Jinfeng Yang, Ou Zhang, Liming Guan, Yisheng Institute of Applied Artificial Intelligence Guangdong-Hong Kong-Macao Greater Bay Area School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen China University of Macau Faculty of Sciences and Technology Department of Computer and Information Science SAR Macau China School of Mechanical and Electrical Engineering Guangdong University of Technology Guangzhou Guangdong China

ISBN: (纸本)9781665481090

This paper presents a PCB defect detection system utilizing the Real-Time Detection Transformer (RT-DETR) model. Originally designed for general object detection, we adapt RT-DETR for the specific task of PCB defect detection, achieving both high accuracy and rapid inference. Building on this baseline, we introduce re-parameterization techniques to further enhance the model's performance with less parameters computational cost. Additionally, we employ data augmentation strategies to enrich the diversity of the dataset, thereby improving the model's robustness and generalization capability. Through these efforts, the developed PCB defect detection system based on the RT-DETR model meets the expected requirements in terms of high accuracy and real-time inference speed. The code and models will be made publicly available. © 2024 IEEE.

关键词： Object detection

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Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

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computers, Materials & Continua 2024年第3期78卷 3215-3245页

作者： Shuting Ge Jin Ren Yihua Shi Yujun Zhang Shunzhi Yang Jinfeng Yang School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic UniversityShenzhen518055China Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518055China Industrial Training Centre Shenzhen Polytechnic UniversityShenzhen518055China

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling pa

关键词： Speech-text multimodal automatic speech recognition semantic alignment air traffic control communications dual-tower architecture

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Unleashing the Power of High-Throughput Bright-field Imaging for Enhanced Mesenchymal Cell Separation: A Novel Supervised Clustering Approach in Vitro Augmentation of Healthy and Stressful Conditions 2

Unleashing the Power of High-Throughput Bright-field Imaging...

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Emerging Technologies for Cell and Tissue Characterization II 2023

作者： Goktas, Polat Carbajo, Ricardo Simon UCD School of Computer Science University College Dublin Country Dublin Belfield Ireland CeADAR Ireland’s Centre for Applied Artificial Intelligence Country Dublin Clonskeagh Ireland

ISBN: (数字)9781510664685

ISBN: (纸本)9781510664678

The production of high yields of viable cells, especially Mesenchymal stem cells (MSCs), is a crucial yet challenging aspect in the field of cell therapy (CT). While progress has been made, there is still a need for quick, non-destructive ways to check the quality of the cells being produced to enhance cell manufacturing process. In light of this, our study aims to develop an accurate, interpretable machine learning technique that relies solely on bright-field (BF) images for enhanced differentiation of MSCs under different serum-containing conditions. Our investigation centers around the expansion of human MSCs derived from bone marrow cultivated in two specific media types: serum-containing (SC) and low-serum containing (LSC) media. The prevalent method of chemical staining for cell component identification is often time-intensive, costly, and potentially harmful to cells. To address these issues, we captured BF images at a 20X magnification with a Perkin Elmer Operatta screening system. Utilizing mean Shapley Adaptive exPlanations (SHAP) values obtained from the application of the 2-D discrete Fourier transform (DFT) module to BF images, we developed a supervised clustering approach within a tree-based machine learning model. The results of our experimental trials revealed the Random Forest model's efficacy in correctly classifying MSCs under varying conditions with a weighted accuracy of 80.15%. A further application of the DFT module to BF images significantly increased this accuracy to 93.26%. By transforming the original dataset into SHAP values using Random Forest classifiers, our supervised clustering approach effectively differentiates MSCs using label-free images. This innovative framework significantly contributes to the understanding of MSC health, enhances CT manufacturing processes, and holds potential to improve the efficacy of cell therapies. © 2023 SPIE.

关键词： Discrete Fourier transforms

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Enhancing Compressive Strength Prediction in Fiber Reinforced Geopolymer Concrete Through Machine Learning Algorithms 6

Enhancing Compressive Strength Prediction in Fiber Reinforce...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Thakur, Gaurav Agarwal, Arun Kumar Singh, Ajay Yamsani, Nagendar Uttaranchal University Department of Civil Engineering Uttaranchal Institute of Technology Dehradun India School of Applied & Life Sciences Uttaranchal University Dehradun India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350381689

This research focuses on accurately predicting the compressive strength of Fiber Reinforced Geopolymer Concrete (FRGC) using machine learning techniques, considering the material's complexity and diverse factors influencing its strength. A machine learning model was developed to forecast the compressive strength of FRGC, leveraging data analysis and comparison of regression techniques. Linear Regression, Lasso Regression, and Ridge Regression were assessed for their predictive accuracy. Feature selection methods were employed to identify influential factors impacting compressive strength. The Random Forest Regressor emerged as the most effective model, exhibiting the lowest Root Mean Square Error (RMSE). The findings offer a valuable tool for engineers to optimize FRGC properties, enhancing its strength and sustainability for various applications. This study underscores the potential of machine learning in streamlining material design processes in civil engineering. © 2024 IEEE.

关键词： Geopolymer concrete

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