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

作者： Xin Li Feng Xu Feifei Tao Yao Tong Xin Lyu Jianyi Zhong André Kaup College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Chair of Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study.

关键词： Semantic segmentation Frequency-domain analysis Urban planning Semantics Speech enhancement Signal processing Transformers Sensors Remote sensing Standards

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Bioinformatic analysis of chromatin organization and biased expression of duplicated genes between two poplars with a common whole-genome duplication

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Horticulture Research 2021年第1期8卷 806-817页

作者： Le Zhang Jingtian Zhao Hao Bi Xiangyu Yang Zhiyang Zhang Yutao Su Zhenghao Li Lei Zhang Brian J.Sanderson Jianquan Liu Tao Ma College of Computer Science&Medical Big Data Center of Sichuan University&Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education&College of Life Sciences Sichuan University610065 ChengduChina Key Laboratory of Systems Biology Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of Sciences310024 HangzhouChina Department of Biology West Virginia UniversityMorgantownWV 26506USA State Key Laboratory of Grassland Agro-Ecosystem Institute of Innovation Ecology&College of Life SciencesLanzhou University730000 LanzhouChina

The nonrandom three-dimensional organization of chromatin plays an important role in the regulation of gene ***,it remains unclear whether this organization is conserved and whether it is involved in regulating gene expression during speciation after whole-genome duplication(WGD)in *** this study,high-resolution interaction maps were generated using high-throughput chromatin conformation capture(Hi-C)techniques for two poplar species,Populus euphratica and Populus alba ***,which diverged~14 Mya after a common *** examined the similarities and differences in the hierarchical chromatin organization between the two species,including A/B compartment regions and topologically associating domains(TADs),as well as in their DNA methylation and gene expression *** found that chromatin status was strongly associated with epigenetic modifications and gene transcriptional activity,yet the conservation of hierarchical chromatin organization across the two species was *** divergence of gene expression between WGD-derived paralogs was associated with the strength of chromatin interactions,and colocalized paralogs exhibited strong similarities in epigenetic modifications and expression ***,the spatial localization of duplicated genes is highly correlated with biased expression during the diploidization *** study provides new insights into the evolution of chromatin organization and transcriptional regulation during the speciation process of poplars after WGD.

关键词： expression chromatin analysis

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Secure and Privacy-preserving data-sharing Framework based on Blockchain technology for Al-Najaf/Iraq Oil Refinery 19

Secure and Privacy-preserving Data-sharing Framework based o...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Umran, Samir M. Lu, SongFeng Abduljabbar, Zaid Ameen Lu, Zhi Feng, Bingyan Zheng, Lu Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Iraqi Cement State Company Ministry of Industry and Minerals Baghdad10011 Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China University of Basrah College of Education for Pure Sciences Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co. Ltd Wuhan430074 China South-Central University for Nationalities College of Computer Science Wuhan430074 China

ISBN: (纸本)9798350346558

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network;provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality. © 2022 IEEE.

关键词： Digital storage

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Component Fault Diagnosability of Hierarchical Cubic Networks

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ACM Transactions on Design Automation of Electronic Systems 2021年第3期28卷 1-19页

作者： Huang, Yanze Wen, Kui Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology No. 3 Xueyuan Road Fujian Fuzhou China College of Computer and Cyber Security Fujian Normal University No. 8 Xuefu South Road Fujian Fuzhou350117 China Department of Computer Science and Information Engineering National Cheng Kung University No. 1 University Road Tainan70101 Taiwan

The fault diagnosability of a network indicates the self-diagnosis ability of the network, thus it is an important measure of robustness of the network. As a neoteric feature for measuring fault diagnosability, the r-component diagnosability ctr(G) of a network G imposes the restriction that the number of components is at least r in the remaining network of G by deleting faulty set X, which enhances the diagnosability of G. In this article, we establish the r-component diagnosability for n-dimensional hierarchical cubic network HCNn, and we show that, under both PMC model and MM∗model, the r-component diagnosability of HCNn is rn-½(r-1)r+1 for n≥ 2 and 1≤ r≤ n-1. Moreover, we introduce the concepts of 0-PMC subgraph and 0-MM∗subgraph of HCNn. Then, we make use of 0-PMC subgraph and 0-MM∗subgraph of HCNn to design two algorithms under PMC model and MM∗model, respectively, which are practical and efficient for component fault diagnosis of HCNn. Besides, we compare the r-component diagnosability of HCNn with the extra conditional diagnosability, diagnosability, good-neighbor diagnosability, pessimistic diagnosability, and conditional diagnosability, and we verify that the r-component diagnosability of HCNn is higher than the other types of diagnosability. © 2023 held by the owner/author(s). publication rights licensed to ACM.

关键词： Failure analysis

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Fast Algorithm for Parallel Solving Inversion of Large Scale Small Matrices Based on Gpu

SSRN

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SSRN 2022年

作者： Jin, Xuebin Chen, Yewang Fan, Wentao Zhang, Yong Du, Jixiang The College of Computer Science and Technology Huaqiao University Xiamen China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China College of Mechanical Engineering and Automation Huaqiao University Xiamen China

Inverting a matrix is time-consuming, and many works focus on accelerating inverting a single large matrix by GPU. However, the problem of inverting large-scale small matrices has little attention. In this paper, we propose a Revised In-Place Inversion algorithm for inverting large-scale small matrices, which runs on the CUDA platform and utilizes multicore GPUs. The proposed algorithm is well-suited for large-scale-small matrices (dimension $n \leq 32$), which can invert millions of small matrices within tens of milliseconds. In addition, we find there is an upper bound of input data size for each GPU device, the performance will degrade if the input data size is too large. Hence, we improve the proposed method by dividing matrices into batches according to this finding. Experimental results show that this strategy can effectively alleviate the performance degradation for inverting large scale small matrices, and maintain the high-performance execution of the algorithm. © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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Multi-Object Tracking Based on Anchor-Free Detection and Message Passing Association

Multi-Object Tracking Based on Anchor-Free Detection and Mes...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhou Fang Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781665409858

Due to the current multi-target tracking work, in the scene of dense and staggered targets, the tracked targets are prone to ID switch, which greatly affects the tracking effect. Aiming at this problem, this paper proposed a new multi-target tracking algorithm: using an improved anchor-free detection method and extracting features from multiple aspects, combined with the message passing graph neural network to participate in the data association process to further improve the exchange of feature information, so as to improve the algorithm's ability to associate target information data between frames and improve the tracking accuracy. After conducting quantitative analysis and comparison experiments on datasets such as MOT17 and MOT20, the experimental results show that the algorithm in this paper can effectively improve the problem of target ID switching and provide better tracking accuracy and performance.

关键词： Industrial electronics Target tracking Statistical analysis Message passing Switches Feature extraction Real-time systems

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Mccseg: Morphological Guided Causal Constraint Network for Medical Image Multi-Object Segmentation

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SSRN 2023年

作者： Gao, Yifan Wei, Lifang Li, Jun Chang, Xinyue Zhang, Yulong Chen, Riqing Yang, Changcai Wei, Yi Dong, Heng Digital Fujian Research Institute of Big Data for Agriculture and Forestry College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou350002 China Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210000 China Department of Obstetrics & Gynecology College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics Fujian Medical University Fuzhou350001 China School of Mathematics and Computer Science Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyi University Fujian354300 China

Medical image multi-object segmentation aims to accurately extract each object that is great significance for the medical image analysis. Although several methods based on deep leaning have been widely developed, it is still challenging due to the similarities of different objects, the significant variability of morphology, data heterogeneity and the unbalanced distribution of samples. To address the above issues, we propose a novel Morphological Guided Causal Constraint segmentation network (MCCSeg) for medical image multi-object segmentation. We build a hybrid CNN-Transformer encoder module to obtain local and global features. Specifically, a Causal Constraint Module (CCM) is proposed for feature decorrelation by sample reweighting, which utilizes weight allocation to train the weight of training samples. The Random Fourier Features (RFF) are used to solve nonlinear dependency problems and remove correlations between features for eliminating the impact of spurious correlations. The morphological guidance module (MG) is designed to extract the boundary as prior morphology information for enhancing feature representation, which are concatenated into the deep supervision module in decoder for further optimizing feature extraction. The experiments demonstrate that MCCSeg outperforms other state-of-the-art methods, obtaining up 3.76% and 5.41% improvements in DICE and HD95 scores on Synapse dataset, respectively. And the ablation experiments further verify the effectiveness of MCCSeg in improving generalization. The source code will be released at https://***/YfGAO/MCCSeg. © 2023, The Authors. All rights reserved.

关键词： Morphology

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Morphological Guided Causal Constraint Network for Medical Image Multi-Object Segmentation

Morphological Guided Causal Constraint Network for Medical I...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yifan Gao Jun Li Xinyue Chang Yulong Zhang Riqing Chen Changcai Yang Yi Wei Heng Dong Lifang Wei College of Computer and Information Science Fujian Agriculture and Forestry University Fujian China Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics Fujian Medical University Fujian China Digital Fujian Research Institute of Big Data for Agriculture and Forestry Fujian Agriculture and Forestry University Fujian China School of Mathematics and Computer Science Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyi University Fujian China

Multi-objective segmentation (MOS) in medical images is to simultaneously extract multiple regions of interest in the medical images. Due to the unbalanced distribution of samples and the similarity and significant differences between features in medical images, current methods still struggle to achieve satisfactory results. In this context, we propose a novel Morphological Guided Causal Constrain segmentation network (MCCSeg) for medical image multi-object segmentation. We introduced a Causal Constrain Module (CCM) for feature decorrelation by sample reweighting. The morphological guidance module (MG) is designed to extract the boundary features as the prior shape information for enhancing feature representation. Our experiments demonstrate that MCCSeg outperforms other state-of-the-art methods, obtaining up 3.76% and 5.41% improvements in DICE and HD95 scores on Synapse dataset, respectively.

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Unified graph augmentations for generalized contrastive learning on graphs 24

Unified graph augmentations for generalized contrastive lear...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jiaming Zhuo Yintong Lu Hui Ning Kun Fu Bingxin Niu Dongxiao He Chuan Wang Yuanfang Guo Zhen Wang Xiaochun Cao Liang Yang Hebei Province Key Laboratory of Big Data Calculation School of Artificial Intelligence Hebei University of Technology Tianjin China College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Technology Beijing JiaoTong University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Artificial Intelligence OPtics and ElectroNics (iOPEN) School of Cybersecurity Northwestern Polytechnical University Xi'an China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China

ISBN: (纸本)9798331314385

In real-world scenarios, networks (graphs) and their tasks possess unique characteristics, requiring the development of a versatile graph augmentation (GA) to meet the varied demands of network analysis. Unfortunately, most Graph Contrastive Learning (GCL) frameworks are hampered by the specificity, complexity, and incompleteness of their GA techniques. Firstly, GAs designed for specific scenarios may compromise the universality of models if mishandled. Secondly, the process of identifying and generating optimal augmentations generally involves substantial computational overhead. Thirdly, the effectiveness of the GCL, even the learnable ones, is constrained by the finite selection of GAs available. To overcome the above limitations, this paper introduces a novel unified GA module dubbed UGA after reinterpreting the mechanism of GAs in GCLs from a message-passing perspective. Theoretically, this module is capable of unifying any explicit GAs, including node, edge, attribute, and subgraph augmentations. Based on the proposed UGA, a novel generalized GCL framework dubbed Graph cOntrastive UnifieD Augmentations (GOUDA) is proposed. It seamlessly integrates widely adopted contrastive losses and an introduced independence loss to fulfill the common requirements of consistency and diversity of augmentation across diverse scenarios. Evaluations across various datasets and tasks demonstrate the generality and efficiency of the proposed GOUDA over existing state-of-the-art GCLs.

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Leveraging proficiency and preference for online Karaoke recommendation

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Frontiers of computer science 2020年第2期14卷 273-290页

作者： Ming HE Hao GUO Guangyi LV Le WU Yong GE Enhong CHEN Haiping MA Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China School of Computer and Information Hefei University of TechnologyHefei 230026China Eller College of Management The University of ArizonaArizona 85721-0108USA iFlyTek Research Hefei 230026China

Recently,many online Karaoke(KTV)platforms have been released,where music lovers sing songs on these *** the meantime,the system automatically evaluates user proficiency according to their singing *** approximate songs to users can initialize singers5 participation and improve users,loyalty to these ***,this is not an easy task due to the unique characteristics of these ***,since users may be not achieving high scores evaluated by the system on their favorite songs,how to balance user preferences with user proficiency on singing for song recommendation is still ***,the sparsity of the user-song interaction behavior may greatly impact the recommendation *** solve the above two challenges,in this paper,we propose an informationfused song recommendation model by considering the unique characteristics of the singing ***,we first devise a pseudo-rating matrix by combing users’singing behavior and the system evaluations,thus users'preferences and proficiency are *** we mitigate the data sparsity problem by fusing users*and songs'rich information in the matrix factorization process of the pseudo-rating ***,extensive experimental results on a real-world dataset show the effectiveness of our proposed model.

关键词： KTV matrix factorization recommendation system

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