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arXiv 2024年

作者： Jiang, Ming Huang, Tingting Guo, Biao Lu, Yao Zhang, Feng The School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Metaverse Application Engineering Center Guangxi Nanning530000 China Guangxi Institute of Digital Technology Guangxi Nanning530000 China School of Information Engineering Guangxi Polytechnic of Construction Guangxi Nanning530007 China

In recent years, Large language models (LLMs) have garnered significant attention due to their superior performance in complex reasoning tasks. However, recent studies may diminish their reasoning capabilities markedly when problem descriptions contain irrelevant information, even with the use of advanced prompting techniques. To further investigate this issue, a dataset of primary school mathematics problems containing irrelevant information, named GSMIR, was constructed. Testing prominent LLMs and prompting techniques on this dataset revealed that while LLMs can identify irrelevant information, they do not effectively mitigate the interference it causes once identified. A novel automatic construction method, ATF, which enhances the ability of LLMs to identify and self-mitigate the influence of irrelevant information, is proposed to address this shortcoming. This method operates in two steps: first, analysis of irrelevant information, followed by its filtering. The ATF method, as demonstrated by experimental results, significantly improves the reasoning performance of LLMs and prompting techniques, even in the presence of irrelevant information on the GSMIR dataset.1 Copyright © 2024, The Authors. All rights reserved.

关键词： Information filtering

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Gradient Alignment Improves Test-Time Adaptation for Medical Image Segmentation 39

Gradient Alignment Improves Test-Time Adaptation for Medical...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Ziyang Ye, Yiwen Pan, Yongsheng Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China Research & Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen China Ningbo Institute Northwestern Polytechnical University Ningbo China

ISBN: (纸本)157735897X

Although recent years have witnessed significant advancements in medical image segmentation, the pervasive issue of domain shift among medical images from diverse centres hinders the effective deployment of pre-trained models. Many Test-time Adaptation (TTA) methods have been proposed to address this issue by fine-tuning pre-trained models with test data during inference. These methods, however, often suffer from less-satisfactory optimization due to suboptimal optimization direction (dictated by the gradient) and fixed step-size (predicated on the learning rate). In this paper, we propose the Gradient alignment-based Test-time adaptation (GraTa) method to improve both the gradient direction and learning rate in the optimization procedure. Unlike conventional TTA methods, which primarily optimize the pseudo gradient derived from a self-supervised objective, our method incorporates an auxiliary gradient with the pseudo one to facilitate gradient alignment. Such gradient alignment enables the model to excavate the similarities between different gradients and correct the gradient direction to approximate the empirical gradient related to the current segmentation task. Additionally, we design a dynamic learning rate based on the cosine similarity between the pseudo and auxiliary gradients, thereby empowering the adaptive fine-tuning of pre-trained models on diverse test data. Extensive experiments establish the effectiveness of the proposed gradient alignment and dynamic learning rate and substantiate the superiority of our GraTa method over other state-of-the-art TTA methods on a benchmark medical image segmentation task. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Image segmentation

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Machine Learning-Based Cybersecurity Optimization in Internet of Things-Enabled Autonomous Vehicles

Machine Learning-Based Cybersecurity Optimization in Interne...

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Innovative engineering sciences and Technological Research (ICIESTR), International Conference on

作者： Usman Ibrahim Musa Aminu Ibrahim Musa Yakubu Ibrahim Galadima Ramadan Osman Kantunsung Sakshi Gupta Sakshi Dua School of Computer Application Lovely Professional University Punjab India Department of Information Technology ESGT Benin University Cotonou Benin Republic Department of Computer Science Al-Qalam University Katsina Nigeria University College of Engineering Osmania University Telangana India Amity Institute of Information Technology Amity University Noida Uttar Pradesh India

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

The swift proliferation of Internet of Things-Enabled Autonomous Vehicles, or IoT-AVs, caused serious apprehensions about potential security weaknesses. In response, we provide a unique solution that improves cybersecurity in IoT-AVs by applying machine learning (ML) approaches. Three critical areas are the focus of our research: intrusion detection optimization (IDOpt), network reliability and latency optimization (NetOpt), and user authentication optimization (AuthOpt). Through biometric approaches which include fingerprint recognition, we accomplish powerful user authentication with 99% accuracy by using convolutional neural networks (CNNs). For Network Reliability and Latency Optimization, the Random Forest Regressor algorithm minimizes downtime and latency issues, improving vehicle performance with 97% accuracy. Additionally, we use the K-Nearest Neighbors (KNN) technique in intrusion detection optimization to protect IoT-AVs against harmful assaults. We use this ML model and obtain a 99% accuracy rate, allowing us to quickly identify and stop intrusions. The entire security posture of IoT-AVs is strengthened greatly by this optimization. To validate the effectiveness of our proposed approach, we compare our results with existing models and algorithms. The outcomes demonstrate the superiority of our ML-based optimizations over the previous approaches. Our integrated methodology establishes new benchmarks for security enhancement in IoT-AVs.

关键词： Technological innovation Accuracy Intrusion detection Authentication Nearest neighbor methods Internet Reliability Optimization Autonomous vehicles Random forests

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More than Encoder: Introducing Transformer Decoder to Upsample

More than Encoder: Introducing Transformer Decoder to Upsamp...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Li, Yijiang Cai, Wentian Gao, Ying Li, Chengming Hu, Xiping South China University of Technology School of Computer Science and Engineering Guangzhou China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen China Beijing Institute of Technology School of Medical Technology Beijing China

ISBN: (纸本)9781665468190

Medical image segmentation methods downsample images for feature extraction and then upsample them to restore resolution for pixel-level predictions. In such schema, upsample technique is vital in restoring information for better performance. However, existing upsample techniques leverage little information from downsampling paths. The local and detailed feature from the shallower layer such as boundary and tissue texture is crucial in segmentation, especially medical image segmentation. To this end, we propose a novel upsample approach for medical image segmentation, Window Attention Upsample (WAU), which upsamples features conditioned on local and detailed features from downsampling path in local windows by introducing attention decoders of Transformer. WAU could serve as a general upsample method and be incorporated into any segmentation model that possesses lateral connections. We first propose the Attention Upsample which consists of Attention Decoder (AD) and bilinear upsample. AD leverages pixel-level attention to model longrange dependency and global information for a better upsample. Bilinear upsample is introduced as the residual connection to complement the upsampled features. Moreover, considering the extensive memory and computation cost of pixel-level attention, we further design a window attention scheme to restrict attention computation in local windows instead of the global range. We evaluate our method (WAU) on classic UNet structure with lateral connections and achieve state-of-the-art performance on Medical Segmentation Decathlon (MSD) Brain and Automatic Cardiac Diagnosis Challenge (ACDC) datasets. We also validate the effectiveness of our method on multiple classic architectures and achieve consistent improvement. © 2022 IEEE.

关键词： Diagnosis

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Goods Anomaly Detection Based on Multi-Branch Feature Extraction and Foreground Mining

Goods Anomaly Detection Based on Multi-Branch Feature Extrac...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Zhiqian Jia Wei Li Zuoyong Li Zhipeng Mu Lihui Lin Shenghua Teng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Xiamen Air Traffic Management Station of CAAC Xiamen China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Data Science MinjiangUniversity Fuzhou China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Wuyi University Fujian China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

In recent years, significant advancements have been made in the field of anomaly detection. Expanding anomaly detection tasks to new scenarios has become a development avenue. Anomaly detection for the outer packaging of supermarket goods is a challenging task due to the diversity of goods’ appearances, which results in some detection methods suffering from limited sensitivity to anomalies and inadequate target mining. To address these issues, we propose a novel unsupervised anomaly detection method based on multi-branch feature extraction and foreground cosine structure mining strategy. Firstly, we use anomaly synthesis technique to enhance the model’s perception of anomalies and construct more precise decision boundaries in the feature space. Secondly, a multi-branch feature extractor extracts and integrates multi-level and multi-scale features, providing rich texture and semantic information to improve the model’s adaptability in real-world scenarios. Finally, the foreground cosine loss focuses on target mining while supplementing local structural information, further enhancing the model’s ability to detect subtle anomalies. To validate the effectiveness of the proposed method, extensive experiments were conducted on the GoodsAD dataset, sampled from supermarket scenarios. Experimental results show that this method achieves significant performance in image-level detection and pixel-level localization.

关键词： Location awareness Adaptation models Sensitivity Semantics Education Packaging Feature extraction Information technology Anomaly detection Biomedical imaging

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Revolutionizing Plant Disease Management Through Image Processing Technology

Revolutionizing Plant Disease Management Through Image Proce...

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Computational Intelligence and Sustainable engineering Solutions (CISES), International Conference on

作者： Gaurav Kumar Ankit Kumar Manoj Singhal Kamred Udham Singh Lalan Kumar Teekam Singh Department of Computer Engineering & Applications GLA University Mathura UP India Department of Information Technology GL Bajaj Institute of Technology and Management India School of Computing Graphic Era Hill University Dehradun India Department of Master of Computer Application GL Bajaj Institute of Technology and Management India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

Agricultural productivity plays a vital role in the economy, and disease detection in plants is crucial for the agriculture sector as plant diseases are widespread. Failure to take appropriate care can lead to serious consequences for plant quality, quantity, or productivity. For instance, a disease called little leaf disease has been found in pine trees in the United States, which can severely affect plant health. Automated methods for detecting plant diseases are useful as they reduce the need for manual inspection in large crop fields, and they can identify disease symptoms as soon as they appear on plant leaves. This paper presents an algorithm for image segmentation for the automatic identification and classification of plant leaf diseases. It also discusses various disease classification methods that can be used for plant leaf disease detection. Genetic algorithms are used for image segmentation, which is a crucial aspect of disease detection in plant leaves. The paper focuses on identifying the Powdery Mildew disease in organic grape products, which can cause significant damage if not detected in time. The accurate identification and classification of diseases from leaf images are crucial for preventing significant losses in output and agricultural inputs. The proposed process can help improve the accuracy and efficiency of disease detection in plants and lead to better agricultural productivity.

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Pinching Antennas: Principles, applications and Challenges

arXiv

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arXiv 2025年

作者： Yang, Zheng Wang, Ning Sun, Yanshi Ding, Zhiguo Schober, Robert Karagiannidis, George K. Wong, Vincent W.S. Dobre, Octavia A. Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou350117 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Khalifa University Abu Dhabi United Arab Emirates University of Manchester ManchesterM1 9BB United Kingdom Germany Group Electrical & Computer Engineering Dept. Aristotle University of Thessaloniki Thessaloniki54 124 Greece Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Department of Electrical and Computer Engineering Memorial University St. John’sNL Canada

Flexible-antenna systems, such as fluid antennas and movable antennas, have been recognized as key enabling technologies for sixth-generation (6G) wireless networks, as they can intelligently reconfigure the effective channel gains of the users and hence significantly improve their data transmission capabilities. However, existing flexible-antenna systems have been designed to combat small-scale fading in non-line-of-sight (NLoS) conditions. As a result, they lack the ability to establish line-of-sight links, which are typically 100 times stronger than NLoS links. In addition, existing flexible-antenna systems have limited flexibility, where adding/removing an antenna is not straightforward. This article introduces an innovative flexible-antenna system called pinching antennas, which are realized by applying small dielectric particles to waveguides. We first describe the basics of pinching-antenna systems and their ability to provide strong LoS links by deploying pinching antennas close to the users as well as their capability to scale up/down the antenna system. We then focus on communication scenarios with different numbers of waveguides and pinching antennas, where innovative approaches to implement multiple-input multiple-output and non-orthogonal multiple access are discussed. In addition, promising 6G-related applications of pinching antennas, including integrated sensing and communication and next-generation multiple access, are presented. Finally, important directions for future research, such as waveguide deployment and channel estimation, are highlighted. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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PseudoSC: A Binary Approximation to Stochastic Computing within Latent Operation-Space for Ultra-Lightweight on-Edge DNNs 23

PseudoSC: A Binary Approximation to Stochastic Computing wit...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Jihe Wang Zhaoqing Wang Danghui Wang School of Computer Science Northwestern Polytechnical University Xi'an China Engineering Research Center of Embedded System Integration Ministry of Education Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350323481

Recently, stochastic computing (SC) is increasingly popular in constructing MAC for on-edge DNNs benefiting from its outstanding energy-efficiency, including its adequate precision and gatelevel operation. However, current SC-DNN systems always include a lot of costly SNGs/APCs for inevitably switches between binary and stochastic domains, which mortgages incongruous resources to pay the "bill" of the domain-switches and impedes highly-concurrent deployments. In this work, PseudoSC, a binary approximation to low-discrepancy SC, is proposed to totally remove the domain-switch for SNG/APC-free SC-DNNs. Its basic idea is to virtually re-arrange a couple of stochastic operands into a 2-D latent op-space, in which, original Monte Carlo sampling can be partitioned into three sub-ops, i.e., two fixed binary-ops and a fractal recursion. In theory, the recursion forms an isomorphic partition of the sampling repeated in smaller scales until the binary base-case achieved, as a result, a SC-op is well approximated only with binary-ops. Based on above theory, a multi-lane micro-architecture is designed to unroll the recursion within a few cycles and its advantages on hardware saving is verified under popular DNNs. The evaluation shows that the DNN-models with our schemes achieve 98.7% accuracy of the fixed-point implementations, which significantly outperform other SOTA methods. In addition, its reduced structure improves the power efficiency by 3.67 times on average.

关键词： stochastic computing

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Research on Ornamental Fish Recognition Base on Improved Shufflenet V2 Network

Research on Ornamental Fish Recognition Base on Improved Shu...

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Big Data & Artificial Intelligence & Software engineering (ICBASE), International Conference on

作者： Xingyun Zhang Hongming Chen Longqing Zhang Qian Chen Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan China School of Computer Science Guangdong University of Science and Technology Dongguan China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

With the advancement of computer vision technology, fish recognition plays a crucial role in various fields such as aquaculture processing and disseminating fish knowledge. In this study, we propose an enhanced and lightweight fish recognition algorithm to accomplish fish classification recognition with limited data samples. Based on the ShuffleNet V2 network, we incorporate Transfer Learning and the SE attention mechanism. Additionally, we replace the ReLu activation function with the AM-Softmax activation function. Comparative evaluations are conducted against Resnet50, MobileNet V2, MobileNet V3, and the original ShuffleNet V2 network model. Our experimental findings demonstrate that the proposed algorithm outperforms the aforementioned models, resulting in recognition accuracy improvements of 16.01%, 19.05%, 15.04%, and 14.44% respectively. The final recognition accuracy achieved is 97.20%, successfully attaining the desired outcome.

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HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models

HSR-Diff: Hyperspectral Image Super-Resolution via Condition...

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International Conference on computer Vision (ICCV)

作者： Chanyue Wu Dong Wang Yunpeng Bai Hanyu Mao Ying Li Qiang Shen National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing School of Computer Science Northwestern Polytechnical University China Department of Computer Science Faculty of Business and Physical Sciences Aberystwyth University U.K.

Despite the proven significance of hyperspectral images (HSIs) in performing various computer vision tasks, its potential is adversely affected by the low-resolution (LR) property in the spatial domain, resulting from multiple physical factors. Inspired by recent advancements in deep generative models, we propose an HSI Super-resolution (SR) approach with Conditional Diffusion Models (HSR-Diff) that merges a high-resolution (HR) multispectral image (MSI) with the corresponding LR-HSI. HSR-Diff generates an HR-HSI via repeated refinement, in which the HR-HSI is initialized with pure Gaussian noise and iteratively refined. At each iteration, the noise is removed with a Conditional Denoising Transformer (CDFormer) that is trained on denoising at different noise levels, conditioned on the hierarchical feature maps of HR-MSI and LR-HSI. In addition, a progressive learning strategy is employed to exploit the global information of full-resolution images. Systematic experiments have been conducted on four public datasets, demonstrating that HSR-Diff outperforms state-of-the-art methods.

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