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2025 ieee symposium on computational intelligence in Natural Language processing and Social Media, CI-NLPSoMe 2025

ISBN: (纸本)9798331508418

The proceedings contain 8 papers. The topics discussed include: multi-view autoencoders for fake news detection;identifying school shooter threats through online texts;detecting cyberbullying in Thai memes: a multimodal approach using deep learning;optimizing Chinese-to-English translation using large language models;RIAND: robustness-improved and accelerated neural-deduplication;conceptual in-context learning and chain of concepts: solving complex conceptual problems using large language models;analyzing the cognitive impact of trauma from a metaphorical perspective: a case study on the attempted assassination of Donald Trump;and SUPERB-EP: evaluating encoder pooling techniques in self-supervised learning models for speech classification.

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2025 ieee symposium on computational intelligence in Natural Language processing and Social Media, CI-NLPSoMe Companion 2025

2025 IEEE Symposium on Computational Intelligence in Natural...

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2025 ieee symposium on computational intelligence in Natural Language processing and Social Media, CI-NLPSoMe Companion 2025

ISBN: (纸本)9798331519742

The proceedings contain 12 papers. The topics discussed include: reconstructing weighted social networks after a node deleted with substitute node selection;ConText Mining: complementing topic models with few-shot in-context learning to generate interpretable topics;assessing personalized AI mentoring with large language models in the computing field;filtering hallucinations and omissions in large language models through a cognitive architecture;logical reasoning with LLMs via few-shot prompting and fine-tuning: a case study on turtle soup puzzles;synthetic feature augmentation improves generalization performance of language models;advancing natural language to SQL: a comparative study of open source LLMs on benchmark datasets;and To NER or not to NER? a case study of low-resource deontic modalities in EU legislation.

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ON-BOARD MULTISPECTRAL image COMPRESSION WITH AN ARTIFICIAL intelligence BASED ALGORITHM

ON-BOARD MULTISPECTRAL IMAGE COMPRESSION WITH AN ARTIFICIAL ...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Guerrisi, Giorgia Bencivenni, Gianmarco Schiavon, Giovanni Del Frate, Fabio Tor Vergata Univ Rome DICII I-00133 Rome Italy GEO K Srl I-00133 Rome Italy

ISBN: (纸本)9798350360332;9798350360325

Remote Sensing (RS) is applied for a variety of purposes, thanks to the large availability of heterogeneous data. Furthermore, the growing number of CubeSat missions is encouraging increasingly advanced, flexible, and configurable RS missions, that also include the use of Artificial intelligence (AI) on-board. Indeed, specific hardware allows advanced processing on-board the satellites even if the computational capability is not the same as on the ground. In the context of on-board processing, the compression of acquired images is crucial because permits to save bandwidth for data transmission. We propose an AI-based lossy image compression algorithm for multispectral images that can be executed on-board a CubeSat. The algorithm is based on a Convolutional AutoEncoder (CAE) Neural Network (NN). In lossy compression part of the information stored in the original image is lost. Therefore, the results evaluation includes the assessment of the usability of the decompressed images for common applications.

关键词： on-board processing artificial intelligence image compression multispectral images cubesats

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Signal Detection in Three-Dimensional Confocal Microscopy images through Deep Learning 18

Signal Detection in Three-Dimensional Confocal Microscopy Im...

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18th ieee International symposium on Applied computational intelligence and Informatics (SACI)

作者： Paulik, Robert Kozlovszky, Miklos Molnar, Bela Obuda Univ 3DHISTECH Ltd Budapest Hungary Obuda Univ BioTech Res Ctr Budapest Hungary 3DHISTECH Ltd Image Anal Dept Budapest Hungary

ISBN: (纸本)9798350329537;9798350329520

As routine pathology moves into the digital age;the spread of high-efficiency and high-resolution tissue scanners opens up the possibility of routine analysis of three-dimensional samples containing fluorescent genetic signals. One of these cornerstones is confocal microscopy, with the help of which cell nuclei and their signals can be imaged in three dimensions. This article presents a novel deep learning-based algorithm for detecting signals within three-dimensional confocal microscopy images. By leveraging the power of convolutional neural networks, our approach significantly improves the accuracy and efficiency of signal detection compared to traditional image processing methods, especially in the case of thick sections. We demonstrate the algorithmic effectiveness through validation on various samples, highlighting its potential to advance research in biology and medicine.

关键词： confocal microscope 3D nuclei signal detection convolutional neural network deep learning U-Net

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Generating image Counterfactuals in Deep Learning Models Without the Aid of Generative Models

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ieee SIGNAL processing LETTERS 2025年 32卷 1495-1499页

作者： Xu, Ao Li, Zihao Zhang, Yukai Wu, Tieru Jilin Univ Sch Artificial Intelligence Changchun 130000 Peoples R China

With the rapid development of artificial intelligence, particularly the rise of deep learning, the importance of Explainable Artificial intelligence has become increasingly prominent. Among its key techniques, counterfactual explanation plays a crucial role in understanding the decision-making mechanisms of opaque models. However, the high dimensionality and complex feature patterns of image data pose significant challenges for the task of generating counterfactuals for images. Existing literature has proposed various algorithms based on different assumptions, many of which rely on the existence of appropriate generative models. Some of these assumptions, particularly the assumption regarding the existence of generative models, may be overly stringent. To address this issue, this letter introduces a novel assumption-free image counterfactual generation algorithm, DFO-S, based on Score Matching and gradient-free optimization techniques. The proposed method achieves high-quality counterfactual generation without relying on generative models. Through extensive empirical analysis, we demonstrate the significant superiority of our method in terms of performance.

关键词： Signal processing algorithms Artificial intelligence Closed box Deep learning Vectors Optimization Data models computational modeling Training Smoothing methods explainable artificial intelli- gence image counterfactual explanation

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Orthogonal Motion Control in image processing Applications 24

Orthogonal Motion Control in Image Processing Applications

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24th ieee International symposium on computational intelligence and Informatics, CINTI 2024

作者： Kovács, Bence László, Sajó-Bohus Beszédes, Bertalan Obuda University Alba Regia Faculty Székesfehérvár Hungary

ISBN: (纸本)9798350353433

The aim of my project is to offer an automation solution for examining solid-state passive detectors frequently used in particle physics. Since the detector surface often exceeds the field of view of microscopes, the implementation of a two-dimensional mobile platform is necessary. Additionally, processing the captured images provides information on the incident particle energy, direction and mass. Automation accelerates the examination process and reduces the human factor inherent in the analysis. © 2024 ieee.

关键词： examination acceleration human factor elimination image processing movable stage solid-state nuclear detectors

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CSPN: A Category-Specific processing Network for Low-Light image Enhancement

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ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第11期34卷 11929-11941页

作者： Wu, Hongjun Wang, Chenxi Tu, Luwei Patsch, Constantin Jin, Zhi Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518107 Guangdong Peoples R China Tech Univ Munich Munich Inst Robot & Machine Intelligence TUM Sch Computat Informat & Technol Chair Media Technol D-80333 Munich Germany Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518107 Guangdong Peoples R China Guangdong Prov Key Lab Fire Sci & Technol Guangzhou 510006 Peoples R China

images captured in low-light conditions usually suffer from degradation problems. Recently, numerous deep learning-based methods are proposed for low-light image enhancement. They either focus on performance improvement with negligence of computational complicity, or are extremely computationally efficient networks with poor performance. In this work, we intend to figure out a solution, which strikes a balance between computational cost and performance. Moreover, we observe that different regions of an image contain different amounts of information, where the region with less information is easier to restore than that with more information. Hence, we propose to crop a low-light image into patches and classify these patches into "simple", "medium" and "hard" categories based on their involved information. Then, we enhance different patch categories with different network complexities, therefore, a Category-specific processing Network (CSPN) is proposed to achieve the computational complexity and performance balance. The patch classification is implemented by the proposed Grey-Level Co-occurrence Matrix (GLCM) entropy-based algorithm, which measures the content complexity of an image by analyzing the statistics of the difference between pixels. As the frequency domain contains exclusive feature information that is beneficial for improving image quality, the wavelet transform is introduced during the enhancement. Extensive experimental results demonstrate the superiority of our proposed CSPN over other state-of-the-art methods in various datasets with the least amount of computational cost.

关键词： Low-light image enhancement category-specific processing GLCM entropy wavelet transform

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Leveraging Cloud Resources for Distributed Training of Residual CNNs in Aerial image Classification

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ieee ACCESS 2025年 13卷 31131-31139页

作者： Kumar, Shantanu Singh, Shruti Kumar Dewangan, Narendra Amazon Seattle WA 98109 USA Washington State Univ IREACH Seattle WA 98195 USA Univ Petr & Energy Studies Sch Comp Sci Dehra Dun 248007 Uttaranchal India

Aerial image classification is crucial across multiple sectors, including environmental monitoring, agriculture, and urban planning. However, processing large-scale aerial imagery efficiently poses challenges in model performance, computational efficiency, and scalability. This research introduces a novel convolutional neural network (CNN) architecture tailored for cactus identification from aerial photographs. The proposed cloud-based pipeline enhances training efficiency through scalable data storage, preprocessing, and distributed training across platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. This comparative analysis demonstrates the model's computational efficiency, shorter training durations, and cost-effectiveness. The model integrates residual connections and depthwise separable convolutions, achieving 96.7% accuracy on the aerial cactus identification dataset. The results highlight the model's high performance, cost-efficiency, and scalability, making it suitable for real-world aerial image classification tasks. Future work aims to further optimize the model using advanced techniques and extend its application to multi-class classification challenges.

关键词： Cloud computing computational modeling Training Artificial intelligence Data models image classification Computer architecture computational efficiency Accuracy Scalability Aerial image classification convolutional neural network (CNN) cloud computing distributed model training

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DepthLux: Employing Depthwise Separable Convolutions for Low-Light image Enhancement

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SENSORS 2025年第5期25卷 1530-1530页

作者： Balmez, Raul Brateanu, Alexandru Orhei, Ciprian Ancuti, Codruta O. Ancuti, Cosmin UNIV MANCHESTER Dept Comp Sci MANCHESTER M13 9PL England Polytech Univ Timisoara Fac Elect Telecommun & Informat Technol Timisoara 300223 Romania IEEE Int Symposium Elect & Telecommun ISETC Timisoara Romania

Low-light image enhancement is an important task in computer vision, often made challenging by the limitations of image sensors, such as noise, low contrast, and color distortion. These challenges are further exacerbated by the computational demands of processing spatial dependencies under such conditions. We present a novel transformer-based framework that enhances efficiency by utilizing depthwise separable convolutions instead of conventional approaches. Additionally, an original feed-forward network design reduces the computational overhead while maintaining high performance. Experimental results demonstrate that this method achieves competitive results, providing a practical and effective solution for enhancing images captured in low-light environments.

关键词： image sensor restoration low-light enhancement vision transformer

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Bayesian Deep Learning for image Reconstruction: From structured sparsity to uncertainty estimation

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ieee SIGNAL processing MAGAZINE 2023年第1期40卷 73-84页

作者： Dong, Weisheng Wu, Jinjian Li, Leida Shi, Guangming Li, Xin Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Nanyang Technol Univ Singapore Singapore Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Rapid Rich Object Search Lab Singapore Singapore Xidian Univ Sch Elect Engn Xian Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Univ Sci & Technol China Elect Engn & informat Sci Hefei Peoples R China West Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

Conventional wisdom in model-based computational imaging incorporates physics-based imaging models, noise characteristics, and image priors into a unified Bayesian framework. Rapid advances in deep learning have inspired a new generation of data-driven computational imaging systems with performances even better than those of their model-based counterparts. However, the design of learning-based algorithms for computational imaging often lacks transparency, making it difficult to optimize the entire imaging system in a complete manner.

关键词： Deep learning Uncertainty image coding computational modeling Imaging Signal processing algorithms Network architecture

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