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Automated Audio data Augmentation Network Using Bi-Level Optimization for Sound Event Localization and Detection

ieee SIGNAL PROCESSING LETTERS 2024年 31卷 2770-2774页

作者： Zhang, Wenjie Yu, Peng Yin, Jun Jiang, Xiaoheng Xu, Mingliang Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Minist Educ Engn Res Ctr Intelligent Swarm Syst Zhengzhou 450001 Peoples R China Natl Supercomp Ctr Zhengzhou 450001 Peoples R China

In sound event localization and detection (SELD), traditional methods often treat localization and detection algorithms separately from data augmentation. During the model training process, the strategy for data augmentation is typically implemented in a non-learnable manner. Existing audio data augmentation strategies struggle to find optimal parameter solutions for data augmentation that can be effectively applied to SELD systems. To address this challenge, we introduce an innovative network-based strategy, termed the Automated Audio data Augmentation (AADA) network. This strategy employs bi-level optimization to synergistically integrate audio data augmentation techniques with SELD tasks. In the AADA network, the lower-level SELD task serves as a constraint for the higher-level data augmentation process. The audio data augmentation parameters are adaptively optimized by utilizing the transfer of intermediate feature information from the SELD tasks, thus obtaining optimal parameters for these tasks. Evaluation of our approach on the Sony-TAU Realistic Spatial Soundscapes 2023 dataset achieves a SELD score of 0.4801, significantly surpassing the performance metrics of all traditional data augmentation strategies for SELD.

关键词： data augmentation Optimization data models Feature extraction Location awareness Training Adaptation models visualization Time-frequency analysis Spatial databases Sound event localization and detection audio data augmentation strategies bi-level optimization

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***: Compressing Linear Set Diagrams

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第6期29卷 2875-2887页

作者： Wallinger, Markus Dobler, Alexander Noellenburg, Martin TU Wien Algorithms & Complex Grp A-1040 Vienna Austria

Linear diagrams are used to visualize set systems by depicting set memberships as horizontal line segments in a matrix, where each set is represented as a row and each element as a column. Each such line segment of a set is shown in a contiguous horizontal range of cells of the matrix indicating that the corresponding elements in the columns belong to the set. As each set occupies its own row in the matrix, the total height of the resulting visualization is as large as the number of sets in the instance. Such a linear diagram can be visually sparse and intersecting sets containing the same element might be represented by distant rows. To alleviate such undesirable effects, we present ***, a new approach that achieves a more space-efficient representation of linear diagrams. First, we minimize the total number of gaps in the horizontal segments by reordering columns, a criterion that has been shown to increase readability in linear diagrams. The main difference of *** to linear diagrams is that multiple non-intersecting sets can be positioned in the same row of the matrix. Furthermore, we present several different rendering variations for a matrix-based representation that utilize the proposed row compression. We implemented the different steps of our approach in a visualization pipeline using integer-linear programming, and suitable heuristics aiming at sufficiently fast computations in practice. We conducted both a quantitative evaluation and a small-scale user experiment to compare the effects of compressing linear diagrams.

关键词： Task analysis visualization data visualization Image color analysis Heuristic algorithms Taxonomy Sparse matrices Computational experiment linear diagrams set visualization user evaluation

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MSPredictor: A Multi-Scale Dynamic Graph Neural Network for Multivariate Time Series Prediction

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ieee TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Wan, Jiashan Xia, Na Li, Gongwen Li, Jingyang Wu, Jinhua Pan, Xulei Lian, Mengqi Hefei Univ Technol Coll Comp & Informat Sci Hefei 230601 Peoples R China Hefei Univ Technol Anhui Prov Key Lab Ind Safety & Emergency Technol Hefei 230601 Peoples R China Anhui Inst Informat Technol Wuhu 241000 Peoples R China

In the field of multivariate time series prediction, capturing the dynamic relationships and complex cyclical patterns between sequences is key to improving prediction accuracy. To address this challenge, our paper introduces MSPredictor, a multi-scale dynamic graph neural network model, which uses Fast Fourier Transform for multi-scale decoupling in the frequency domain and employs Kolmogorov-Arnold Networks for multi-scale fusion, effectively extracting significant cyclical patterns. By decomposing the original series across different scales, MSPredictor accurately models complex cyclical patterns. To enhance the model's transparency and interpretability, we introduced the ClarityLens explanatory strategy, which employs visualization techniques to make the prediction process more transparent. Specifically, it displays the adjacency matrices learned at different scales, intuitively showing the dynamic correlations between series. We also visualized the proportion of different periods in the prediction results and the specific forecasting performance at each time scale. Extensive testing on multiple real-world datasets has demonstrated that the MSPredictor significantly outperforms existing benchmarks, validating its practicality and high transparency.

关键词： Time series analysis Predictive models Adaptation models Graph neural networks Correlation Computational modeling Transformers Deep learning data models Accuracy Multivariate time series graph neural networks multi-scale analysis Kolmogorov-Arnold networks explainable AI

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A Deep Generative Model for Reordering Adjacency Matrices

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第7期29卷 3195-3208页

作者： Kwon, Oh-Hyun Kao, Chiun-How Chen, Chun-houh Ma, Kwan-Liu Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Tamkang Univ Dept Stat New Taipei City 251301 Taiwan Acad Sinica Inst Stat Sci Taipei 11529 Taiwan

Depending on the node ordering, an adjacency matrix can highlight distinct characteristics of a graph. Deriving a "proper" node ordering is thus a critical step in visualizing a graph as an adjacency matrix. Users often try multiple matrix reorderings using different methods until they find one that meets the analysis goal. However, this trial-and-error approach is laborious and disorganized, which is especially challenging for novices. This paper presents a technique that enables users to effortlessly find a matrix reordering they want. Specifically, we design a generative model that learns a latent space of diverse matrix reorderings of the given graph. We also construct an intuitive user interface from the learned latent space by creating a map of various matrix reorderings. We demonstrate our approach through quantitative and qualitative evaluations of the generated reorderings and learned latent spaces. The results show that our model is capable of learning a latent space of diverse matrix reorderings. Most existing research in this area generally focused on developing algorithms that can compute "better" matrix reorderings for particular circumstances. This paper introduces a fundamentally new approach to matrix visualization of a graph, where a machine learning model learns to generate diverse matrix reorderings of a graph.

关键词： Sorting data visualization Neural networks Computational modeling Training Computer architecture Stochastic processes Graph visualization matrix visualization machine learning deep generative model visualization interface

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Alternate Learning-Based SNR-Adaptive Sparse Semantic Visual Transmission

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ieee TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第2期24卷 1737-1752页

作者： Tong, Siyu Yu, Xiaoxue Li, Rongpeng Lu, Kun Zhao, Zhifeng Zhang, Honggang Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Huawei Technol Co Ltd Shenzhen 518129 Peoples R China Zhejiang Univ Zhejiang Lab Hangzhou 310027 Peoples R China City Univ Macau Fac Data Sci Macau Peoples R China

Semantic Communication (SemCom) demonstrates strong superiority over conventional bit-level accurate transmission, by only attempting to recover the essential semantic information of data. Nevertheless, most SemCom works train the whole system in an End-to-End (E2E) way, with the assumption of a differentiable channel which is rare in reality applications. In this paper, to tackle the non-differentiability of channels, we propose an alternate learning-based sparse SemCom system with an SNR-adaptive capability for visual transmission, named SparseSBC-SADM. Specially, SparseSBC-SADM leverages two separate Deep Neural Network (DNN)-based models at the transmitter (TX) and receiver (RX), respectively. It alternates between learning the encoding and decoding processes, rather than the joint optimization commonly found in existing literature, to solve the non-differentiability in the channel. In particular, a "self-critic" training scheme is leveraged for stable training. Moreover, the DNN-based TX generates a sparse set of bits in deduced "semantic bases", by further incorporating a binary quantization module by combining Compressive Sensing (CS) and DNN on the basis of minimal detrimental effect to the semantic accuracy. Furthermore, enlightened from the denoising steps in the Denoising Diffusion Model (DDM), a lightweight, SNR-Adaptive Denoising Module (SADM) is provisionally deployed at RX to improve data reconstruction with a gate mechanism to determine the activation under poor channel conditions. Extensive simulation results validate that SparseSBC-SADM shows efficient and effective transmission performance under various channel conditions, and outperforms typical SemCom solutions.

关键词： Semantics Noise reduction Image coding visualization Wireless communication Decoding Training Image reconstruction Image communication Communication systems Sparse semantic communication visual transmission non-differentiable channel alternate self-critic learning denoising diffusion model

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JM3D & JM3D-LLM: Elevating 3D Representation With Joint Multi-Modal Cues

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ieee TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第4期47卷 2475-2492页

作者： Ji, Jiayi Wang, Haowei Wu, Changli Ma, Yiwei Sun, Xiaoshuai Ji, Rongrong Xiamen Univ Key Lab Multimedia Trusted Percept & Efficient Com Minist Educ China Xiamen 361005 Peoples R China Natl Univ Singapore Singapore 119077 Singapore Tencent Youtu Lab Shanghai 200000 Peoples R China

The rising importance of 3D representation learning, pivotal in computer vision, autonomous driving, and robotics, is evident. However, a prevailing trend, which straightforwardly resorted to transferring 2D alignment strategies to the 3D domain, encounters three distinct challenges: (1) Information Degradation: This arises from the alignment of 3D data with mere single-view 2D images and generic texts, neglecting the need for multi-view images and detailed subcategory texts. (2) Insufficient Synergy: These strategies align 3D representations to image and text features individually, hampering the overall optimization for 3D models. (3) Underutilization: The fine-grained information inherent in the learned representations is often not fully exploited, indicating a potential loss in detail. To address these issues, we introduce JM3D, a comprehensive approach integrating point cloud, text, and image. Key contributions include the Structured Multimodal Organizer (SMO), enriching vision-language representation with multiple views and hierarchical text, and the Joint Multi-modal Alignment (JMA), combining language understanding with visual representation. Our advanced model, JM3D-LLM, marries 3D representation with large language models via efficient fine-tuning. Evaluations on ModelNet40 and ScanObjectNN establish JM3D's superiority. The superior performance of JM3D-LLM further underscores the effectiveness of our representation transfer approach.

关键词： Three-dimensional displays Solid modeling Point cloud compression visualization Representation learning Feature extraction Large language models data models Degradation Contrastive learning 3D representation learning joint multi-modal alignment large language model structured multimodal organizer

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Progress of Spatial Geographic Phenomenon visualization Methods Based on Multi-Source data

Progress of Spatial Geographic Phenomenon Visualization Meth...

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2023 International Conference on data science and Informatics, ICDSI 2023

作者： Song, Yuyan Ma, Xiaoyan Jiang, Yanlin Feng, Li Beijing University of Agriculture Beijing China

ISBN: (纸本)9798350341560

The generation of massive and complex data in the context of the big data era, the rapid development of 5G technology and mathematical and statistical methods have made the spatial visualization methods of multi-source data more and more abundant. Different methods are applicable to different fields and data forms;therefore, the analysis for research methods has become an important topic. 698 papers from CNKI and Web of science Core Collection from 2015-2021 are selected as samples. Citespace is used to analyze the papers to find out the research hotspots and the development history of the past seven years at home and abroad about the field of spatial visualization based on multi-source data, then concluding that domestic research areas are mostly focused on first-Tier cities. The foreign research areas mostly focus on climate change, precipitation and green space, and both domestic and foreign research methods are dominated by machine learning. Further, three research methods commonly used in spatial visualization are summarized and analyzed, especially the machine learning method, to provide more scientific and effective new ideas for spatial visualization research. © 2023 ieee.

关键词： Machine learning

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Smart Decision Making Using data science Technology with COVID-19 data

Smart Decision Making Using Data Science Technology with COV...

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2023 Congress in Computer science, Computer Engineering, and Applied Computing, CSCE 2023

作者： Zhang, Shelley Rathi, Ankush Patil, Shivraj Jagdish Computer and Information Science University of Massachusetts Dartmouth North Dartmouth United States

ISBN: (纸本)9798350327595

The rapid spread of the novel coronavirus COVID-19 since December 2019 has posed unprecedented challenges to healthcare systems and decision-makers in public health. The responses to the virus differed from country to country, which led to variable outcomes throughout. Understanding how the answer to the virus and resource availability affected the outcomes is important. In this paper, we present our work on using various data science technologies including correlation analysis, regression analysis, and exploratory data analysis to understand the relationship among different variables and factors. We also present a set of data visualization tools that consist of two types of geospatial visualization functions, standard bar chart, stacked bar chart, and line chart. These tools help users to better understand the data and to make smart decisions in policies related to response, prevention, and intervention of pandemic episodes. © 2023 ieee.

关键词： data Analytics data visualization Healthcare Decision Support

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Efficient High-Frequency Texture Recovery Diffusion Model for Remote Sensing Image Super-Resolution

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ieee TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Weng, Wu-Ding Zheng, Chao-Wei Su, Jian-Nan Chen, Guang-Yong Gan, Min Fuzhou Univ Coll Comp & Data Sci Fuzhou 350116 Peoples R China Minist Educ Fujian Key Lab Network Comp & Intelligent Informat Key Lab Intelligent Metro Univ Fujian Fuzhou 350108 Peoples R China Minist Educ Engn Res Ctr Big Data Intelligence Fuzhou 350108 Peoples R China Putina Univ New Engn Ind Coll Putian 351100 Fujian Peoples R China Putian Univ Putian Elect Informat Ind Technol Res Inst Putian 351100 Fujian Peoples R China Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China

Remote sensing super-resolution (SR), which aims to reconstruct high-resolution (HR) images with rich spatial details from low-resolution (LR) remote sensing images predominantly composed of low-frequency components, presents a challenging yet practical task. Existing diffusion model (DM)-based methods for remote sensing SR are inefficient, requiring extensive iterations and often failing to recover high-frequency details adequately due to a lack of targeted processing for high-frequency components. To mitigate these challenges, this article introduces an efficient DM for remote sensing image SR, termed image reconstruction representation-diffusion model for super-resolution (IRR-DiffSR). IRR-DiffSR employs a feature extraction encoder to extract the image reconstruction representation (IRR) from ground-truth (GT) images, which makes the reconstruction network focus more on recovering high-frequency textures. Unlike traditional DM-based methods that learn the direct mapping from LR to HR images, IRR-DiffSR employs a pre-trained encoder to guide the DM in extracting consistent IRR directly from LR images. This auxiliary information aids in the efficient and effective reconstruction of high-frequency textures. By serving as an implicit reconstruction prior, this enables the DM to achieve accurate estimations with fewer iterations, thus assisting IRR-DiffSR in recovering high-frequency information more efficiently and effectively. Extensive experiments on four remote sensing datasets demonstrate that IRR-DiffSR achieves state-of-the-art reconstruction results in both real and synthetic scenarios. Specifically, in real scenarios, IRR-DiffSR outperforms the next best method by 0.766 and 0.69 in the naturalness image quality evaluator (NIQE), while in synthetic scenarios, it achieves peak signal-to-noise ratio (PSNR) improvements of 1.07 and 0.51. These results highlight the effectiveness and efficiency of IRR-DiffSR in recovering high-frequency details. Our code and pre-

关键词： Image reconstruction Remote sensing Superresolution Diffusion models Feature extraction Training data mining Image restoration visualization Brain modeling Diffusion model (DM) image reconstruction image super-resolution (SR) reconstruction representation remote sensing

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EEG-Based Motor Imagery Classification With Tuned Heuristic Fusion Graph Convolutional Network for Rehabilitation Training

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ieee TRANSACTIONS ON AUTOMATION science AND ENGINEERING 2025年 22卷 14928-14939页

作者： Shi, Kecheng Huang, Rui Lyu, Jianzhi Li, Zhe Mu, Fengjun Peng, Zhinan Zou, Chaobin Cheng, Hong Zhang, Jianwei Ghosh, Bijoy Kumar Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Univ Hamburg Dept Informat D-20148 Hamburg Germany Texas Tech Univ Dept Math & Stat Lubbock TX 79409 USA

Motor imagery-based brain-computer interfaces (MI-BCIs) hold significant promise for rehabilitation training in individuals with neurological impairments such as stroke and spinal cord injury (SCI). Achieving precise and robust lower limb movement prediction for each patient is crucial. However, the variability in MI response frequencies and brain activation patterns among subjects presents a great challenge to the generalizability of MI-BCIs. This paper proposes a Tuned Heuristic Fusion Graph Convolutional Network (THFGCN) for limb movement prediction in rehabilitation scenarios. THFGCN innovatively designs a learnable EEG frequency band tuned module and a heuristic space topology module. These two modules allow for the intricate extraction of both frequency and spatial topological features, utilizing graph adjacency matrices that encapsulate channel correlations and spatial relationships, hence fostering individualized analysis and enhanced generalizability across subjects. Furthermore, a spatio-temporal convolution module paired with a feature map attention mechanism is proposed to extract the critical spatio-temporal features of electroencephalogram (EEG) data. Validation experiments on the PhysioNet and LLM-BCImotion datasets against six mainstream methods demonstrate that THFGCN outperforms state-of-the-art methods, achieving 88.41% and 82.82% accuracy in the within-subject case, and 65.93% and 60.56% accuracy in the cross-subject case, respectively. Detailed frequency band weight and T-distributed Stochastic Neighbor Embedding visualization validate the effectiveness of proposed modules. Furthermore, feature interpretability analysis proves the extracted features' profound MI task relevance, underlining THFGCN's exceptional interpretability.

关键词： Feature extraction Electroencephalography Accuracy data mining Motors Limbs Training Topology Correlation Graph convolutional networks Motor imagery brain computer interface tuned heuristic fusion graph convolutional network feature visualization feature interpretability

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