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4th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2022

作者： Pan, Jianghuai College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China Jiangsu Automation Research Institute Lianyungang222006 China

ISBN: (数字)9781665486583

ISBN: (纸本)9781665486583

Aiming at the failure of multi-station TDOA positioning caused by factors such as unreasonable sensor distribution and measurement noise, a new robust multi-station TDOA positioning method based on Modified Singular Value Decomposition (MSVD) is proposed. In the new algorithm, we use the condition number to evaluate the ill-conditioned degree of the observation equation, and then use the idea of truncated singular value decomposition to correct the singular value with higher ill-conditioned, so as to suppress the influence of measurement noise. The new algorithm effectively considers the resolution and variance of positioning parameter evaluation, and achieves effective suppression of random observation noise in the observation equation. The simulation results show that, compared with the least squares method, the new algorithm can adapt to various multi-station deployment situations, and the positioning results are more accurate and stable. © 2022 IEEE.

关键词： Singular value decomposition

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LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment

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computers, Materials & Continua 2023年第4期75卷 1-17页

作者： Xiaorui Zhang Rui Jiang Wei Sun Aiguo Song Xindong Wei Ruohan Meng Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Instrument Science and Engineering Southeast UniversityNanjing211189China School of Teacher Education Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Science Engineering Nanyang Technological UniversitySingapore

Robust watermarking requires finding invariant features under multiple attacks to ensure correct *** learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread *** existing methods use 3×3 small kernel convolution to extract image features and embed the ***,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the *** address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss *** uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel ***,the modification of the embedded watermarking on the cover image is extended to more *** the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight ***,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image *** experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.

关键词： Robust watermarking large kernel convolution adaptive loss weights high-frequency wavelet loss deep learning

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Dense Spatial-Temporal Graph Convolutional Network Based on Lightweight OpenPose for Detecting Falls

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computers, Materials & Continua 2023年第10期77卷 47-61页

作者： Xiaorui Zhang Qijian Xie Wei Sun Yongjun Ren Mithun Mukherjee School of Computer Science Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China

Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research ***,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection *** solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight *** OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the *** bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in *** spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection *** representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation *** results show that the proposed model is superior to the current fall detection *** accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively.

关键词： Fall detection lightweight OpenPose spatial-temporal graph convolutional network dense blocks

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Advancing XAI: new properties to broaden semantic-based explanations of black-box learning models 28th

Advancing XAI: new properties to broaden semantic-based expl...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Kosov, Pavel Kadhi, Nahla El Zanni-Merk, Cecilia Gardashova, Latafat Azerbaijan State Oil and Industry University Computer Engineering Department BakuAZ1010 Azerbaijan Normandie Université INSA Rouen LITIS Rouen76000 France French-Azerbaijani University Computer Science Research Department BakuAZ1000 Azerbaijan ASOIU Scientific Research Institute Geotechnological Problems of Oil Gas and Chemistry BakuAZ1010 Azerbaijan

For a long time, experts in different areas where Artificial Intelligence (AI) is widely applied have been requesting more clarity for the decisions made by AI. DARPA came up with a new framework for eXplainable AI (XAI) where the system exploits an explainable model to provide explanations through the explanation interface to users based on the level of their expertise. Later, ontologies were integrated in various ways, which paved the way for clearer explanations. Provided enough Expert Knowledge, ontologies can be a potent tool in XAI. Based on the ideas of Bellucci et al., their explainable system provides comprehensive explanations based on"visible" properties found in images by Machine Learning (ML) models and described via ontologies. However, we believe that any property, not only visible ones, can be used to explore the data. New"explanatory" properties are proposed to be used for explanations. Our system exploits ML models and more user-oriented Expert Knowledge using a wider range of properties for objects to build a more profound XAI. © 2024 The Authors.

关键词： Adversarial machine learning

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Tomo-NeRL: Tomographic neural representation learning for implicit CT image reconstruction

Tomo-NeRL: Tomographic neural representation learning for im...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tang, Xiaoqin Tan, Boheng Guo, Yuanhao Yu, Xianping Kendrick, Jake Reynolds, Mark Southwest University College of Computer and Information Science Chongqing China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Research Institute of Highway Ministry of Transport Beijing China The University of Western Australia School of Physics Mathematics and Computing Perth Australia Perth Australia

ISBN: (纸本)9798350386226

CT image reconstruction is a challenging inverse problem of estimating image intensities from sensor signals. While deep learning-based methods hold promise, they are typically classified into fully-supervised, self-supervised, and case-by-case approaches, each offering distinct advantages and limitations. Among these, case-by-case reconstruction techniques, exemplified by models like NeRP, provide the advantage of flexible data-sampling and image sizes, making them well-suited for the complex CT inverse problem. However, existing case-by-case models encounter difficulties in achieving precise reconstructions, particularly in sparse-view scenarios. To address this limitation, we propose tomo-NeRL, a novel implicit reconstruction model that incorporates tomographic information of individual pixels within the reconstruction network. By harnessing this tomo-graphic data for implicit inverse learning, tomo-NeRL enhances reconstruction quality while effectively mitigating artifacts. The core innovation of tomo-NeRL lies in defining and extracting tomographic insights, such as visual characteristics and spatial variations specific to tomographic imaging, from the sinogram data for individual pixels. Moreover, our work introduces an adaptive inversion framework that amalgamates tomographic information from diverse projection angles, constructing a latent feature space that enhances the reconstruction process. Extensive experiments unequivocally showcase tomo-NeRL's outstanding reconstruction performance, excelling in artifact suppression and detail preservation, especially in sparse-view scenarios. © 2024 IEEE.

关键词： Image reconstruction

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Enhancing Long-range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-based Sentiment Analysis 31

Enhancing Long-range Dependency with State Space Model and K...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Lawan, Adamu Pu, Juhua Yunusa, Haruna Umar, Aliyu Lawan, Muhammad School of Computer Science and Technology Beihang University Beijing China National Research Institute for Teaching Materials in Information Science and Tech. China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computing University of Portsmouth Portsmouth United Kingdom Department of Information and Communication Technology Federal University Gusau Nigeria

ISBN: (纸本)9798891761964

Aspect-based Sentiment Analysis (ABSA) evaluates sentiments toward specific aspects of entities within the text. However, attention mechanisms and neural network models struggle with syntactic constraints. The quadratic complexity of attention mechanisms also limits their adoption for capturing long-range dependencies between aspect and opinion words in ABSA. This complexity can lead to the misinterpretation of irrelevant contextual words, restricting their effectiveness to short-range dependencies. To address the above problem, we present a novel approach to enhance long-range dependencies between aspect and opinion words in ABSA (MambaForGCN). This approach incorporates syntax-based Graph Convolutional Network (SynGCN) and MambaFormer (Mamba-Transformer) modules to encode input with dependency relations and semantic information. The Multihead Attention (MHA) and Selective State Space model (Mamba) blocks in the MambaFormer module serve as channels to enhance the model with short and long-range dependencies between aspect and opinion words. We also introduce the Kolmogorov-Arnold Networks (KANs) gated fusion, an adaptive feature representation system that integrates SynGCN and MambaFormer and captures non-linear, complex dependencies. Experimental results on three benchmark datasets demonstrate MambaForGCN's effectiveness, outperforming state-of-the-art (SOTA) baseline models. © 2025 Association for Computational Linguistics.

关键词： Semantics

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An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval

arXiv

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arXiv 2025年

作者： Cao, Min Zeng, ZiYin Lu, YuXin Ye, Mang Yi, Dong Wang, Jinqiao School of Computer Science and Technology Soochow University Taiwan School of Computer Science Wuhan University China Institute of Automation Chinese of Academy China Wuhan AI Research China

Data plays a pivotal role in Text-Based Person Retrieval (TBPR) research. Mainstream research paradigm necessitates real-world person images with manual textual annotations for training models, posing privacy-sensitive and labor-intensive issues. Several pioneering efforts explore synthetic data for TBPR but still rely on real data, keeping the aforementioned issues and also resulting in diversity-deficient issue in synthetic datasets, thus impacting TBPR performance. Moreover, these works tend to explore synthetic data for TBPR through limited perspectives, leading to exploration-restricted issue. In this paper, we conduct an empirical study to explore the potential of synthetic data for TBPR, highlighting three key aspects. (1) We propose an inter-class image generation pipeline, in which an automatic prompt construction strategy is introduced to guide generative Artificial Intelligence (AI) models in generating various inter-class images without reliance on original data. (2) We develop an intra-class image augmentation pipeline, in which the generative AI models are applied to further edit the images for obtaining various intra-class images. (3) Building upon the proposed pipelines and an automatic text generation pipeline, we explore the effectiveness of synthetic data in diverse scenarios through extensive experiments. Additionally, we experimentally investigate various noise-robust learning strategies to mitigate the inherent noise in synthetic data. We will release the code, along with the synthetic large-scale dataset generated by our pipelines, which are expected to advance practical TBPR research. © 2025, CC BY.

关键词： Data assimilation

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Momentum-Imbued Langevin Dynamics (MILD) for Faster Sampling

Momentum-Imbued Langevin Dynamics (MILD) for Faster Sampling

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Nishanth Shetty Manikanta Bandla Nishit Neema Siddarth Asokan Chandra Sekhar Seelamantula Department of Electrical Engineering Indian Institute of Science Bangalore Department of Computer Science and Automation Indian Institute of Science Bangalore Microsoft Research Lab India (MSRI) Bangalore

Score-based generative models have emerged as the state-of-the-art in generative modeling. In this paper, we introduce a novel sampling scheme that can be combined with pre-trained score-based diffusion models to speed up sampling by a factor of two to five in terms of the number of function evaluations (NFEs) with a superior Fréchet Inception distance (FID), compared to Annealed Langevin dynamics in noise-conditional score network (NCSN) and improved noise-conditional score network (NCSN++). The proposed sampling algorithm is inspired by momentum-based accelerated gradient descent used in convex optimization techniques. We validate the sampling efficiency of the proposed algorithm in terms of FID on CIFAR-10 and CelebA datasets.

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research on Path Planning of Mobile Robot Based on Reinforcement Learning

Research on Path Planning of Mobile Robot Based on Reinforce...

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2022 Chinese automation Congress, CAC 2022

作者： Ji, Maoyuan Li, Jianxing Li, Shanqiang Liu, Zhenyu Song, Jiang Liu, Lisang Fujian Institute of Engineering Fujian Industrial Automation Technology Research and Development Center Fuzhou China Fujian Institute of Engineering Department of Computer Science and Mathematics Fuzhou China

ISBN: (纸本)9781665465335

With the continuous change of artificial intelligence technology, the study of path planning for mobile robots is no longer limited to traditional path algorithms. Reinforcement learning, as an artificial intelligence algorithm with excellent performance in the field of path planning, has also gradually become the object of research on path planning. In order to find a fast path from the starting point to the end point of a mobile robot in a complex environment, reinforcement learning is used to find a valid path by sensing the environment and continuously receiving rewarding feedback through a method of trial and error learning like humans. Therefore, this paper selects two algorithms to verify their effectiveness on the basis of building a two-dimensional grid map. The final experimental results show that both reinforcement learning algorithms can eventually avoid obstacles and plan a valid path in a complex environment through continuous learning iterations. Q-Learning also reduces the path distance by 10% and the number of convergence iterations by 82% compared to Sarsa. © 2022 IEEE.

关键词： Reinforcement learning

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An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

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science China Earth sciences 2023年第11期66卷 2563-2578页

作者： Yunqiang ZHU Kai SUN Shu WANG Chenghu ZHOU Feng LU Hairong LV Qinjun QIU Xinbing WANG Yanmin QI State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Department of Automation Tsinghua UniversityBeijing 100084China School of Computer Science China University of GeosciencesWuhan 430074China Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science University of Nottingham Ningbo ChinaNingbo 315100China

Geoscience knowledge graph(GKG)can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related *** a result,it has gained significant attention and become a frontier in *** knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales,granularities,and ***,establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of ***,existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and *** address this issue,this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience *** this basis,an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and *** the constraint of a unified spatiotemporal ontology,this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal *** model can efficiently represent geoscience knowledge,thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge *** can further enable the alignment,transformation,computation,and reasoning of spatiotemporal information through a spatiotemporal ontology.

关键词： Geoscience Knowledge graph Representation model Spatiotemporal features Spatiotemporal relationships

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