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2021 International Conference on Networking, Communications and information technology, NetCIT 2021

作者： Shanshan, Fu Zhiqiang, Ruan Minjiang University Center of Modem Education Technology and Information Management Fujian350108 China Minjiang University College of Computer and Control Engineering Fujian350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fujian350108 China Digital Fujian IoT Laboratory of Intelligent Production Fujian350108 China

ISBN: (纸本)9781665400701

In the era of artificial intelligence with big data as the background, the statistical analysis method based on complex network has become a research hotspot and actively promoted the development of big data visualization. Complex network is a new discipline developed by cross research with other disciplines. Its network topology has obvious clustering characteristics, that is, complex network cluster structure. Together with small world and scale-free, it is called the three most important and basic attributes of complex network topology. The fundamental purpose of complex network clustering algorithm is to detect the actual network cluster structure in complex network, which has very important practical value for mining the cluster structure in network and understanding its characteristics, and is conducive to understanding the functions of network, such as predicting the development of complex network, analyzing the behavior of social network, discovering hot topics on network, etc. This paper combines the methods and ideas of complex network analysis, carries out the method design and research of organization analysis, and tries to solve the relationship and characteristics of inter departmental cooperation within the organization. © 2021 IEEE.

关键词： Complex networks

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Efficient Semantic Segmentation via Lightweight Multiple-information Interaction Network

arXiv

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arXiv 2024年

作者： Qiu, Yangyang Xu, Guoan Gao, Guangwei Guo, Zhenhua Yu, Yi Lin, Chia-Wen The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai200240 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia The Tianyijiaotong Technology Ltd. Suzhou215100 China The Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8511 Japan The Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan

Recently, the integration of the local modeling capabilities of Convolutional Neural Networks (CNNs) with the global dependency strengths of Transformers has created a sensation in the semantic segmentation community. However, substantial computational workloads and high hardware memory demands remain major obstacles to their further application in real-time scenarios. In this work, we propose a lightweight multiple-information interaction network for real-time semantic segmentation, called LMIINet, which effectively combines CNNs and Transformers while reducing redundant computations and memory footprint. It features Lightweight Feature Interaction Bottleneck (LFIB) modules comprising efficient convolutions that enhance context integration. Additionally, improvements are made to the Flatten Transformer by enhancing local and global feature interaction to capture detailed semantic information. The incorporation of a combination coefficient learning scheme in both LFIB and Transformer blocks facilitates improved feature interaction. Extensive experiments demonstrate that LMIINet excels in balancing accuracy and efficiency. With only 0.72M parameters and 11.74G FLOPs, LMIINet achieves 72.0% mIoU at 100 FPS on the Cityscapes test set and 69.94% mIoU at 160 FPS on the CamVid test dataset using a single RTX2080Ti GPU. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Investigation of Solar Noise in Underwater Wireless Optical Communication Systems

Investigation of Solar Noise in Underwater Wireless Optical ...

引用

Intelligent Communication and Networking (ICN), International Conference on

作者： Xujie Li Baochao Wei Tianzhang Ge Haotian Li Ning Xu College of Information Science and Engineering Hohai University Nanjing China Jiangsu Health Development Research Center Nanjing China NHC Contraceptives Adverse Reaction Surveillance Center Nanjing China Jiangsu Provincial Medical Key Laboratory of Fertility Protection and Health Technology Assessment Nanjing China College of Computer and Information Hohai University Nanjing China

Solar noise as an innegligible interference in underwater wireless optical communication (UWOC) systems is a significant concern. But there are no studies on the Channel Impulse Response (CIR) in the presence of solar noise, and there are no functions available for fitting the CIR curve in this scenario. To address this problem, this paper employs Monte Carlo method to simulate the channel impulse response (CIR) of underwater optical communication systems with solar noise. Additionally, we propose a method that utilizes the composite exponential function (CEF) to fit the CIR curve of the turbid water under sunlight. We verify that the introduction of sunlight aggravates waveform broadening, and our newly proposed model has a better accuarcy of numerical fitting in turbid *** results lay the foundation for subsequent theoretical analysis of system performance.

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Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning

arXiv

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arXiv 2025年

作者： He, Yuting Wang, Boyu Ge, Rongjun Chen, Yang Yang, Guanyu Li, Shuo Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Nanjing China School of Instrument Science and Engineering Southeast University Nanjing China Department of Computer Science Western University LondonONN6A 3K7 Canada Department of Biomedical Engineering Department of Computer and Data Science Case Western Reserve University ClevelandOH44106 United States

Dense contrastive representation learning (DCRL) has greatly improved the learning efficiency for image dense prediction tasks, showing its great potential to reduce the large costs of medical image collection and dense annotation. However, the properties of medical images make unreliable correspondence discovery, bringing an open problem of large-scale false positive and negative (FP&N) pairs in DCRL. In this paper, we propose GEoMetric vIsual deNse sImilarity (GEMINI) learning which embeds the homeomorphism prior to DCRL and enables a reliable correspondence discovery for effective dense contrast. We proposes a deformable homeomorphism learning (DHL) which models the homeomorphism of medical images and learns to estimate a deformable mapping to predict the pixels’ correspondence under the condition of topological preservation. It effectively reduces the searching space of pairing and drives an implicit and soft learning of negative pairs via gradient. We also proposes a geometric semantic similarity (GSS) which extracts semantic information in features to measure the alignment degree for the correspondence learning. It will promote the learning efficiency and performance of deformation, constructing positive pairs reliably. We implement two practical variants on two typical representation learning tasks in our experiments. Our promising results on seven datasets which outperform the existing methods show our great superiority. We will release our code at a companion website. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Neural Network-Based Prescription of Chinese Herbal Medicines 11

Neural Network-Based Prescription of Chinese Herbal Medicine...

引用

11th International Conference on information technology in Medicine and Education, ITME 2021

作者： Zhao, Wen Lu, Weikai Zhou, Chang'en Li, Zuoyong Fan, Haoyi Lin, Xuejuan Yang, Zhaoyang Li, Candong Fujian University of Traditional Chinese Medicine School of Traditional Chinese Medicine Fuzhou350122 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China Harbin University of Science and Technology School of Computer Science and Technology Harbin150080 China

ISBN: (纸本)9781665406796

Objective: To develop a neural network model that recommends traditional Chinese medicine (TCM) herbal prescriptions. Methods: We constructed a new dataset of diagnosis and treatment knowledge from the Treatise on Febrile Diseases. Based on TCM's logical principles of 'syndrome differentiation' and 'state recognition', a back-propagation neural network model is proposed that simulates clinical diagnosis and treatment. Results: The proposed model is a four-layer BP neural network. Experiments on the constructed dataset show that the proposed method achieved the best precision, recall, and F1-scores. Conclusion: The proposed method provides much more accurate herbal prescription recommendations than logistic regression. © 2021 IEEE.

关键词： Diagnosis

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Empirical Comparisons of Deep Learning Networks on Liver Segmentation

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computers, Materials & Continua 2020年第3期62卷 1233-1247页

作者： Yi Shen Victor S.Sheng Lei Wang Jie Duan Xuefeng Xi Dengyong Zhang Ziming Cui School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhouChina Department of Computer Science University of Central ArkansasConwayArkansasUSA Huan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangshaChina

Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver *** recent years,due to the great improvement of hard device,many deep learning based methods have been proposed for automatic liver *** them,there are the plain neural network headed by FCN and the residual neural network headed by Resnet,both of which have many *** have achieved certain achievements in medical image *** this paper,we firstly select five representative structures,i.e.,FCN,U-Net,Segnet,Resnet and Densenet,to investigate their performance on liver *** original Resnet and Densenet could not perform image segmentation directly,we make some adjustments for them to perform live *** experimental results show that Densenet performs the best on liver segmentation,followed by *** perform much better than Segnet,U-Net,and *** Segnet,U-Net,and FCN,U-Net performs the best,followed by *** performs the worst.

关键词： Liver segmentation deep learning FCN U-Net Segnet Resnet Densenet

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Escalator passenger posture anomaly detection based on improved SSD model

Escalator passenger posture anomaly detection based on impro...

引用

IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Teng Xu Jian Chen Zuoyong Li Yuanzheng Cai School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

With the widespread use of escalators in daily life, escalator-related injury factors are threatening public health. To secure escalator system, most of existing studies focus on post hoc statistics and trend to fix these deficiencies to reduce escalator-related potential risks. However, they are not designed for early-stage detection of impending escalator unsafe factors. To solve this problem, this paper proposes an improved Single Shot MultiBox Detector (SSD) algorithm for anomaly detection of passenger posture on escalators. Firstly, we replace the widely used VGG16 backbone of SSD network by simpler MobileNetV2 to accelerate model inference. Secondly, the distance intersection over Union (DIoU) loss function is introduced to further improve performance. Based on the characteristics of IoU and considering the shortcomings of GIoU, DIoU directly regresses the Euclidean distance between the center points of the two boxes to accelerate the convergence. The experimental results show that the improved SSD escalator passenger posture anomaly detection algorithm can efficiently identify the dangerous posture of escalator passengers and start relevant safety protection measures. According to inference of model, the escalator system can be controlled in practice to formulate the corresponding safety protection measures, and improve the safety performance of the escalator operation.

关键词： Euclidean distance Market research Inference algorithms Real-time systems Safety Public healthcare information technology

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CA-MLIF: Cross-Attention and Multimodal Low-Rank Interaction Fusion Framework for Tumor Prognostic Prediction 39

CA-MLIF: Cross-Attention and Multimodal Low-Rank Interaction...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： An, Yajun Chen, Jiale Lin, Huan Liu, Zhenbing Feng, Siyang Zhang, Hualong Lan, Rushi Liu, Zaiyi Pan, Xipeng School of Computer Science and Information Security Guilin University of Electronic Technology Guilin 541004 China School of Artificial Intelligence and Big Data Chongqing College of Finance and Economics Chongqing 402160 China Department of Radiology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin 541004 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou 510080 China

ISBN: (纸本)157735897X

Cancer is a leading cause of death worldwide due to its aggressive nature and complex variability. Accurate prognosis is therefore challenging but essential for guiding personalized treatment and follow-up. Previous research often relied on single data sources, missing the opportunity to combine various types of patient information for more comprehensive survival predictions. To address these challenges, we propose a two-stage fusion method named Cross-Attention and Multimodal Low-Rank Interaction Fusion Framework (CA-MLIF). In the first stage, we propose a CA mechanism for real-time feature updates and cross-modal mutual learning to capture rich semantic information. In the second stage, we design a novel multimodal low-rank interaction fusion method for survival prediction. Specifically, we present modal attention mechanism (MAM) for feature filtration, low-rank multimodal fusion (LMF) for model complexity reduction, and optimal weight concatenation (OWC) for maximizing feature integration. Extensive experiments on two public datasets TCGA-GBMLGG and TCGA-KIRC, as well as a multi-center in-house lung adenocarcinoma (LUAD) dataset validate the effectiveness of CA-MLIF, which demonstrate that our method outperforms existing approaches in survival prediction under both pathology-gene fusion and CT-pathology fusion scenarios. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Learning to rank

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Time-delay Robust Safety Control of Connected Vehicles under Stochastic DoS Attack

引用

IEEE Internet of Things Journal 2025年

作者： Song, Xiulan Liu, Xueyang Shan, Hangguan Lu, Rongxing Zhejiang University of Technology College of Information Engineering Hangzhou310023 China University of Birmingham School of Engineering BirminghamB15 2TT United Kingdom Zhejiang University College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou310027 China University of New Brunswick Faculty of Computer Science FrederictonE3B 5A3 Canada

Connected vehicles utilize vehicle-to-vehicle (V2V) wireless communication networks to gather vital information for platoon tracking control, not only enhancing road traffic efficiency but also preventing collisions. This paper introduces a robust safety platooning control method for connected vehicles, which addresses safety challenges in heterogeneous vehicle platoon systems, including time delays, dynamic uncertainties, and stochastic denial-of-service (DoS) attacks. Specifically, we present an uncertain longitudinal dynamic model for heterogeneous vehicles to account for variable driving conditions. Then, a robust safety platooning controller, based on linear matrix inequality (LMI) and Markov jump system theory, is designed. This controller considers communication delays to ensure vehicle-following stability under dynamic uncertainties and stochastic DoS attacks. Finally, the effectiveness of the proposed algorithm is validated through co-simulations using MatLab and CarSim, as well as actual vehicle experiments, demonstrating its capability to mitigate the impact of communication delays, dynamic uncertainties, and stochastic DoS attacks. © 2014 IEEE.

关键词： Markov processes

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Triangle-oriented Community Detection considering Node Features and Network Topology

arXiv

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arXiv 2022年

作者： Gao, Guangliang Liang, Weichao Yuan, Ming Qian, Hanwei Wang, Qun Cao, Jie Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Jiangsu Provincial Key Laboratory of E-Business Nanjing University of Finance and Economics Nanjing China

The joint use of node features and network topology to detect communities is called community detection in attributed networks. Most of the existing work along this line has been carried out through objective function optimization and has proposed numerous approaches. However, they tend to focus only on lower-order details, i.e., capture node features and network topology from node and edge views, and purely seek a higher degree of optimization to guarantee the quality of the found communities, which exacerbates unbalanced communities and free-rider effect. To further clarify and reveal the intrinsic nature of networks, we conduct triangle-oriented community detection considering node features and network topology. Specifically, we first introduce a triangle-based quality metric to preserve higher-order details of node features and network topology, and then formulate so-called two-level constraints to encode lower-order details of node features and network topology. Finally, we develop a local search framework based on optimizing our objective function consisting of the proposed quality metric and two-level constraints to achieve both non-overlapping and overlapping community detection in attributed networks. Extensive experiments demonstrate the effectiveness and efficiency of our framework and its potential in alleviating unbalanced communities and free-rider effect. Copyright © 2022, The Authors. All rights reserved.

关键词： Network topology

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