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International Conference on Data engineering

作者： Ziyi Ma Jianye Yang Xu Zhou Guoqing Xiao Jianhua Wang Liang Yang Kenli Li Xuemin Lin School of Artificial Intelligence Hebei University of Technology China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University China Cyberspace Institute of Advanced Technology Guangzhou University China Department of New Networks PengCheng Laboratory China College of Computer Science and Electronic Engineering Hunan University China Shenzhen Research Institute Hunan University China Antai College of Economics and Management Shanghai Jiao Tong University China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Continuous subgraph matching (CSM) is a critical task for analyzing dynamic graphs and has a wide range of applications, such as merchant fraud detection, cyber-attack hunting, and rumor detection. Although many efficient CSM algorithms have been recently proposed, they are mainly designed to process a single query. However, in some application scenarios, multi-query oriented continuous subgraph matching (MQCSM) may be of more interest. To our knowledge, the two existing solutions to MQCSM are outdated due to unsatisfactory performance. In this paper, we propose MQ-Match, an efficient approach to MQCSM. First, we design a compact yet effective index structure CCG, which maintains the local matching result of vertices in the data graph using a directed graph. The directed edges in CCG can be utilized as an effective pruning rule for the subsequent incremental matching algorithm when expanding a partial match. Then, we develop a computation sharing incremental matching algorithm. In specific, a set of matching trees is constructed based on the depth-first search trees of the query graphs. By utilizing CCG, we conduct subgraph matching for the matching tree to collect the incremental matches for the query graphs, where the common structures of query graphs are matched only once. Extensive experiments show that MQ-Match can achieve 3.1x-7071.4x speedup over the competitors, and consumes much less memory under the majority of the experiment settings.

关键词： Heuristic algorithms Memory management Directed graphs Data engineering Fraud Computational efficiency Indexes

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Scalable Multi-view Regression Clustering for Large-scale Data

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhao, Xiaowei Fan, Jie Chang, Xiaojun Nie, Feiping Zhang, Qiang Guo, Jun Xidian University State Key Laboratory of Electromechanical Integrated Manufacturing of High-Performance Electronic Equipment Shaanxi Xi’an710071 China Xidian University Center for Complex Systems School of Mechano-Electronic Engineering Shaanxi Xi’an710071 China Northwest University School of Information Science and Technology Shaanxi Xi’an710127 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China Mohamed bin Zayed University of Artificial Intelligence Department of Computer Vision MBZUAI Abu Dhabi United Arab Emirates Xi’an710072 China

In recent years, unsupervised linear regression has attracted attention for its ability to directly capture the mapping relationship between samples and targets. However, existing algorithms can only utilize limited information from a single view, which often leads to unsatisfactory results. To address this problem, we propose a regression clustering model based on multi-view information fusion, called Scalable Multi-view Regression Clustering. This model consists of two parts: intra-view information fusion and inter-view information fusion. In the first part, to capture the local correlations among samples, we propose constructing view-specific bipartite graphs. Unlike traditional single-view and multi-view clustering algorithms, we treat the weights of the bipartite graph as additional features of the samples, thereby directly incorporating the local manifold structure of the samples at the feature level. Furthermore, since the original features of the samples also contain valuable information, we perform unsupervised linear regression separately on the samples represented by the original features and those represented by the bipartite graph weights in each view. The results are then integrated in a weighted manner. In the second part, we propose adaptively weighting the clustering results from each view to capture complementary information across views, thereby enhancing clustering performance. This strategy not only avoids the bipartite graph alignment issue in multi-view clustering but also enables clustering with linear time complexity, making it effective for handling large-scale data. An iterative optimization algorithm is developed to update all variables alternately. Experiments conducted on benchmark datasets demonstrate the superiority of our proposed model. © 2025 IEEE. All rights reserved.

关键词： Multiple linear regression

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EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living

arXiv

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arXiv 2023年

作者： Karnam, Naveen Kumar Turlapaty, Anish Chand Dubey, Shiv Ram Gokaraju, Balakrishna The Biosignal Analysis Lab The Indian Institute of Information Technology A.P. Sri City India The Computer Vision and Biometrics Laboratory Indian Institute of Information Technology U.P. Allahabad Prayagraj211015 India Department of Computational Data Science An Engineering North Carolina A and T State University GreensboroNC United States

In this paper, we present electromyography analysis of human activity - database 1 (EMAHA-DB1), a novel dataset of multi-channel surface electromyography (sEMG) signals to evaluate the activities of daily living (ADL). The dataset is acquired from 25 able-bodied subjects while performing 22 activities categorised according to functional arm activity behavioral system (FAABOS) (3 - full hand gestures, 6 - open/close office draw, 8 - grasping and holding of small office objects, 2 - flexion and extension of finger movements, 2 - writing and 1 - rest). The sEMG data is measured by a set of five Noraxon Ultium wireless sEMG sensors with Ag/Agcl electrodes placed on a human hand. The dataset is analyzed for hand activity recognition classification performance. The classification is performed using four state-of-the-art machine learning classifiers, including Random Forest (RF), Fine K-Nearest Neighbour (KNN), Ensemble KNN (sKNN) and Support Vector Machine (SVM) with seven combinations of time domain and frequency domain feature sets. The state-of-the-art classification accuracy on five FAABOS categories is 83.21% by using the SVM classifier with the third order polynomial kernel using energy feature and auto regressive feature set ensemble. The classification accuracy on 22 class hand activities is 75.39% by the same SVM classifier with the log moments in frequency domain (LMF) feature, modified LMF, time domain statistical (TDS) feature, spectral band powers (SBP), channel cross correlation and local binary patterns (LBP) set ensemble. The analysis depicts the technical challenges addressed by the dataset. The developed dataset can be used as a benchmark for various classification methods as well as for sEMG signal analysis corresponding to ADL and for the development of prosthetics and other wearable robotics. Copyright © 2023, The Authors. All rights reserved.

关键词： Local binary pattern

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Line Drawing Guided Progressive Inpainting of Mural Damage

arXiv

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arXiv 2022年

作者： Li, Luxi Zou, Qin Zhang, Fan Yu, Hongkai Chen, Long Song, Chengfang Huang, Xianfeng Wang, Xiaoguang Li, Qingquan Department of Computer Science Technology United International College of Beijing Normal University HongKong Baptist University Zhuhai China Machine Vision and Robotics Laboratory School of Computer Science Wuhan University Wuhan China State Key Laboratory of Surveying Mapping and Remote Sensing Information Engineering Wuhan University Wuhan China Department of Electrical Engineering and Computer Science Cleveland State University OH United States Institute of Automation Chinese Academy of Sciences Beijing China Cultural Heritage Intelligent Computing Laboratory Wuhan University Wuhan China Guangming Laboratory Shenzhen University Shenzhen China

Mural image inpainting is far less explored compared to its natural image counterpart and remains largely unsolved. Most existing image-inpainting methods tend to take the target image as the only input and directly repair the damage to generate a visually plausible result. These methods obtain high performance in restoration or completion of some pre-defined objects, e.g., human face, fabric texture, and printed texts, etc., however, are not suitable for repairing murals with varying subjects and large damaged areas. Moreover, due to discrete colors in paints, mural inpainting may suffer from apparent color bias. To this end, in this paper, we propose a line drawing guided progressive mural inpainting method. It divides the inpainting process into two steps: structure reconstruction and color correction, implemented by a structure reconstruction network (SRN) and a color correction network (CCN), respectively. In structure reconstruction, SRN utilizes the line drawing as an assistant to achieve large-scale content authenticity and structural stability. In color correction, CCN operates a local color adjustment for missing pixels which reduces the negative effects of color bias and edge jumping. The proposed approach is evaluated against the current state-of-the-art image inpainting methods. Qualitative and quantitative results demonstrate the superiority of the proposed method in mural image inpainting. The codes and data are available at https://***/qinnzou/mural-image-inpainting . © 2022, CC BY-NC-ND.

关键词： Restoration

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Interpretable breast cancer diagnosis using histopathology and lesion mask as domain concepts conditional simulation ultrasonography

引用

Information Fusion 2025年 123卷

作者： Dai, Guowei Wang, Chaoyu Tang, Qingfeng Zhang, Yi Dai, Duwei Qiao, Lang Yan, Jiaojun Chen, Hu College of Computer Science Sichuan University Chengdu610065 China National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu610065 China Chengdu University of Traditional Chinese Medicine Chengdu611137 China Institute of Oncology Chengdu University of Traditional Chinese Medicine Chengdu610036 China Digital and Intelligent Health Research Center Anqing Normal University Anqing246133 China School of Cyber Science and Engineering and the Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Chengdu610065 China National-Local Joint Engineering Research Center of Biodiagnosis & Biotherapy the Second Affiliated Hospital of Xi'an Jiaotong University Xi'an710004 China Department of Ultrasound Sichuan Integrative Medicine Hospital Chengdu610041 China

Breast cancer diagnosis using ultrasound imaging presents challenges due to inherent limitations in image quality and the complex nature of lesion interpretation. We propose SgmaFuse, a novel interpretable multimodal framework that integrates histopathological concepts and lesion masks information, treated as domain concepts, with ultrasound imaging for accurate and explainable breast cancer diagnosis. At its core, SgmaFuse employs a Spatially Guided Multi-Level Alignment Mechanism (SGMLAM) that orchestrates global–local feature interactions across modalities. This is achieved through a sophisticated hierarchical strategy incorporating cross-modal fusion and attention-based feature correspondence at four distinct levels: global image-report alignment, local mask-guided attention report alignment, local image diagnostic report alignment, and concept-level diagnostic report alignment. Concurrently, a Histological Semantic Activation Vector Learning (HSAVL) module, leveraging kernel Support Vector Machines, learns discriminative semantic concepts directly from histopathological data, thereby bridging the gap between ultrasound imaging features and established pathological patterns via robust concept-level alignment. The framework ability to provide transparent, structured diagnostic explanations through interpretable visual attention maps and semantic concept contributions demonstrates its potential as a reliable clinical decision support tool, particularly in the challenging domain of breast ultrasound diagnosis. © 2025 Elsevier B.V.

关键词： Ultrasonography

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Classifying Cover Crop Residue from RGB Images: A Simple SVM versus a SVM Ensemble

Classifying Cover Crop Residue from RGB Images: A Simple SVM...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Parth C. Upadhyay Lokesh Karanam John A. Lory Guilherme N. DeSouza Vision-Guided and Intelligent Robotics (ViGIR) Laboratory Department of Electrical Engineering and Computer Science (EECS) Division of Plant Sciences University of Missouri Columbia MO

ISBN: (纸本)9781728190495

Plant-derived crop residue on soil surface provides many important advantages including preventing erosion and conserving soil moisture. In that sense, making accurate determination on the percent of crop residue cover using RGB images can be a fundamental tool in protecting the soil. In our research, we approach the determination of such percentages as a classification problem, and in this paper, we compare two of these approaches. Both approaches relied on support vector machines (SVM) as the classifier of choice, and the same set of features, which were selected in our previous studies on the same topic. In this paper we developed a SVM ensemble with a hierarchical structure and compared it against a single, multi-class SVM classifier. In the SVM ensemble framework, four two-class SVMs and one five-class SVM were combined in sequence to better separate adjacent levels of residue cover. The rationale of the ensemble was to allow each of the two-class SVMs to find the hyperplanes that maximize the margin between the corresponding two consecutive classes. Then, based on the distance of the samples to these hyperplanes, probabilistic estimates of the data-point belonging to the class were computed and added as extra inputs for the last SVM. In order to enhance the performance of the ensemble, other considerations such as the use of Grid Search method for optimizing the hyperparameters were employed in the tuning of the SVMs. Numerical experiments were conducted over a dataset of 4,400 images, which were collected from 88 locations in 40 row crop fields in five Missouri counties between mid-April and early July in 2018 and 2019. The images were collected using a camera mounted on a tripod, with a spatial resolution of 0.014 cm pixel- 1 GSD (Ground Sampling Distance). The experiments highlighted the better performance of the proposed hierarchical ensemble classifier, which achieved a cross-validation accuracy of 86.3 % vs an accuracy of 80.4 % for the single SVM, whil

关键词： Support vector machines Measurement Training Search methods Soil moisture Crops Probabilistic logic

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Acoustic echo suppression using speech uncertainty in modulation domain

TechRxiv

引用

TechRxiv 2021年

作者： Shifas, Muhammed P.V. Jayakumar, E.P. Sathidevi, P.S. The Speech Signal Processing Laboratory Department of Computer Science University of Crete Greece Department of Electronics and Communication Engineering National Institute of Technology Calicut Kerala Kozhikode India

Quality degradation of near-end speech in mobile communication or hands free devices is mainly due to acoustic echoes and background noises. The received far-end speech gets reflected from the obstacles present in the surroundings creating acoustic echo. All other disturbances from the near-end environment are considered as background noises. A novel acoustic echo suppression scheme using speech uncertainty in modulation domain (MD) is proposed in this paper. State of the art acoustic echo suppression systems are based on either time domain or frequency domain analysis. In recent times, the modulation domain analysis is popularly used in speech processing, as it captures the human perceptual properties. Modulation domain provides the temporal variation of the acoustic magnitude spectra which acts as an information bearing signal. In this paper, a new method is developed and implemented to model the echo path and estimate the echo in modulation domain. Echo cancellation is done effectively by manipulating the modulation spectrum and employing speech uncertainty. In this method, the microphone input is modelled as a binary hypothesis process and the gain function is modified accordingly. The proposed method shows better performance as compared to other competitive methods for acoustic echo suppression with no audible degradation in the near-end speech. © 2021, CC BY.

关键词： speech enhancement

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Tunable unidirectional surface plasmon-polaritons at the interface between gyrotropic and isotropic conductors 22

Tunable unidirectional surface plasmon-polaritons at the int...

引用

22nd International Conference on Electromagnetics in Advanced Applications, ICEAA 2021

作者： Liang, Y. Pakniyat, S. Xiang, Y. Chen, J. Shi, F. Hanson, G.W. Cen, C. West Virginia University Department of Physics and Astronomy MorgantownWV26506 United States University of Wisconsin-Milwaukee Department of Electrical Engineering MilwaukeeWI53211 United States University of Pittsburgh Peterson Institute of Nano Science and Engineering Department of Electrical and Computer Engineering PittsburghPA15261 United States Guangxi Key Lab for Relativistic Astrophysics Center on Nanoenergy Research School of Physical Science and Technology Guangxi University Nanning Guangxi530004 China Key Laboratory of Computer Vision and System of Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781665413862

The propagation of SPPs in solid state systems is usually reciprocal, which means that a forward-traveling SPP wave can be easily backscattered by disorders. Since such backscattering is detrimental for SPP-based information processing applications, the development of nonreciprocal plasmonic platforms that enforce unidirectional SPP propagations is of great importance. © 2021 IEEE.

关键词： Electromagnetic wave polarization

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Toward Lung Ultrasound Automation: Fully Autonomous Robotic Longitudinal and Transverse Scans Along Intercostal Spaces

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical Robotics and Bionics 2025年第2期7卷 768-781页

作者： Long Lei Yingbai Hu Zixing Jiang Juzheng Miao Xiao Luo Yu Zhang Qiong Wang Shujun Wang Zheng Li Pheng-Ann Heng Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Multi-Scale Medical Robotics Centre Ltd. The Chinese University of Hong Kong Hong Kong China Department of Surgery The Chinese University of Hong Kong Hong Kong China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Biomedical Engineering The Hong Kong Polytechnic University Hong Kong China Department of Surgery Chow Yuk Ho Technology Centre for Innovative Medicine Li Ka Shing Institute of Health Sciences and Multiscale Medical Robotics Centre Ltd. The Chinese University of Hong Kong Hong Kong China

Lung ultrasound scanning is essential for diagnosing lung diseases. The scan effectiveness critically depends on both longitudinal and transverse scans through intercostal spaces to reduce rib shadowing interference, as well as maintaining the probe perpendicular to pleura for pathological artifact generation. Achieving this level of scan quality often depends heavily on the experience of doctors. Robotic ultrasound scanning shows promise, but currently lacks a direct path planning method for intercostal scanning, and probe orientation does not consider imaging differences between lungs and solid organs. In this paper, we aim to fully automate two fundamental operations in lung ultrasound scanning: longitudinal and transverse scans. We propose pioneering path planning methods along intercostal spaces and innovative solutions for adaptive probe posture adjustment using real-time pleural line feedback, specifically addressing the unique characteristics of lung ultrasound scanning. This ensures the acquisition of high-quality, diagnostically meaningful ultrasound images. In addition, we develop a robotic lung ultrasound system to validate the proposed methods. Extensive experimental results on two volunteers and a chest phantom confirm the efficacy of our methods, and demonstrate the system’s feasibility in automated lung ultrasound examinations. Our work lays a solid foundation for automated robotic complete lung scanning.

关键词： Ultrasonic imaging Lungs Probes Robots Robot kinematics Medical robotics Ribs Medical services Surface morphology Computed tomography

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Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation

arXiv

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arXiv 2024年

作者： Liu, Kang Ma, Zhuoqi Kang, Xiaolu Zhong, Zhusi Jiao, Zhicheng Baird, Grayson Bai, Harrison Miao, Qiguang School of Computer Science and Technology Xidian University Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Vision Xi’an China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an China Warren Alpert Medical School Brown University Providence United States School of Electronic Engineering Xidian University Xi’an China Department of Radiology and Radiological Sciences Johns Hopkins University School of Medicine Baltimore United States

The automated generation of imaging reports proves invaluable in alleviating the workload of radiologists. A clinically applicable reports generation algorithm should demonstrate its effectiveness in producing reports that accurately describe radiology findings and attend to patient-specific indications. In this paper, we introduce a novel method, Structural Entities extraction and patient indications Incorporation (SEI) for chest X-ray report generation. Specifically, we employ a structural entities extraction (SEE) approach to eliminate presentation-style vocabulary in reports and improve the quality of factual entity sequences. This reduces the noise in the following cross-modal alignment module by aligning X-ray images with factual entity sequences in reports, thereby enhancing the precision of cross-modal alignment and further aiding the model in gradient-free retrieval of similar historical cases. Subsequently, we propose a cross-modal fusion network to integrate information from X-ray images, similar historical cases, and patient-specific indications. This process allows the text decoder to attend to discriminative features of X-ray images, assimilate historical diagnostic information from similar cases, and understand the examination intention of patients. This, in turn, assists in triggering the text decoder to produce high-quality reports. Experiments conducted on MIMIC-CXR validate the superiority of SEI over state-of-the-art approaches on both natural language generation and clinical efficacy metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

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