Side scan sonar(SSS)is an important means to detect and locate seafloor *** underwater vehicles(AUVs)carrying SSS stay near the seafloor to obtain high-resolution images and provide the outline of the target for *** t...
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Side scan sonar(SSS)is an important means to detect and locate seafloor *** underwater vehicles(AUVs)carrying SSS stay near the seafloor to obtain high-resolution images and provide the outline of the target for *** target feature information of an SSS image is similar to the background information,and a small target has less pixel information;therefore,accu-rately identifying and locating small targets in SSS images is *** collect the SSS images of iron metal balls(with a diameter of 1m)and rocks to solve the problem of target ***,the dataset contains two types of targets,namely,‘ball’and‘rock’.With the aim to enable AUVs to accurately and automatically identify small underwater targets in SSS images,this study designs a multisize parallel convolution module embedded in state-of-the-art *** attention mechanism transformer and a convolutional block attention module are also introduced to compare their contributions to small target detection *** performance of the proposed method is further evaluated by taking the lightweight networks Mobilenet3 and Shufflenet2 as the backbone network of *** study focuses on the performance of convolutional neural networks for the detection of small targets in SSS images,while another comparison experiment is carried out using traditional HOG+SVM to highlight the neural network’s *** study aims to improve the detection accuracy while ensuring the model efficiency to meet the real-time working requirements of AUV target detection.
The China Meteorological Assimilation Driving Dataset for the SWAT model (CMADS) has gained widespread use for its accuracy. This study focuses on the Baihe River Basin in Nanyang, using the SWAT tool and CMADS datase...
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As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention...
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As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.
In recent years, artificial intelligence technology has been widely used in many fields, such as computer vision, natural language processing and autonomous driving. Machine learning algorithms, as the core technique ...
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In recent years, artificial intelligence technology has been widely used in many fields, such as computer vision, natural language processing and autonomous driving. Machine learning algorithms, as the core technique of AI, have significantly facilitated people's lives. However, underlying fairness issues in machine learning systems can pose risks to individual fairness and social security. Studying fairness definitions, sources of problems, and testing and debugging methods of fairness can help ensure the fairness of machine learning systems and promote the wide application of artificial intelligence technology in various fields. This paper introduces relevant definitions of machine learning fairness and analyzes the sources of fairness problems. Besides, it provides guidance on fairness testing and debugging methods and summarizes popular datasets. This paper also discusses the technical advancements in machine learning fairness and highlights future challenges in this area.
Fuzzy logic helps manage human-like reasoning in system control, mainly when traditional analysis does not work due to complex control processes. Despite its usefulness, fuzzy logic faces challenges in decision-making...
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For the energy-saving scheduling problem of microwave heating titanium strip pickling flow shop, firstly, the microwave heating titanium strip pickling process is described as Flow Shop Scheduling Problem (FSSP), and ...
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Unsupervised person re-identification aims to learn discriminative feature representations for person retrieval from unlabeled datasets. Clustering-based methods achieve state-of-the-art performance in this research d...
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A remarkable feature of extended objects (EOs), compared to the traditional point target, is that an EO normally produces more than one measurement, resulting in challenges for finding the associations among objects a...
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Chinese character skeletons are key representations which abstract structures of Chinese characters into skeletons. Extraction the skeleton from images of Chinese characters is a fundamental task in field of Chinese c...
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In addressing the detection challenge of small targets with low echo intensity and high noise intensity in underwater environments with active sonar and random reverberation, we propose a target detection method that ...
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