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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Lung cancer

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A Probabilistic Framework for Pruning Transformers Via a Finite Admixture of Keys 48

A Probabilistic Framework for Pruning Transformers Via a Fin...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Nguyen, Tan M. Nguyen, Tam Bui, Long Do, Hai Nguyen, Duy Khuong Le, Dung D. Tran-The, Hung Ho, Nhat Osher, Stan J. Baraniuk, Richard G. University of California Department of Mathematics Los Angeles United States Rice University Department of Electrical and Computer Engineering Houston United States Fpt Software Ai Center Ha Noi Viet Nam Vin University College of Engineering and Computer Science Viet Nam Deakin University Applied Artificial Intelligence Institute Geelong Australia The University of Texas Department of Statistics and Data Sciences Austin United States

ISBN: (纸本)9781728163277

Pairwise dot product-based self-attention is key to the success of transformers which achieve state-of-the-art performance across a variety of applications in language and vision, but are costly to compute. It has been shown that most attention scores and keys in transformers are redundant and can be removed without loss of accuracy. In this paper, we develop a novel probabilistic framework for pruning attention scores and keys in transformers. We first formulate an admixture model of attention keys whose input data to be clustered are attention queries. We show that attention scores in self-attention correspond to the posterior distribution of this model when attention keys admit a uniform prior distribution. We then relax this uniform prior constraint and let the model learn these priors from data, resulting in a new Finite Admixture of Keys (FiAK). The learned priors are used for pruning away redundant attention scores and keys in the baseline transformers, improving the diversity of attention patterns that the models capture. We corroborate the efficiency of transformers pruned with FiAK on the ImageNet object classification and WikiText-103 language modeling tasks. Our experiments demonstrate that transformers pruned with FiAK yield similar or better accuracy than the baseline dense transformers while being much more efficient in terms of memory and computational cost. © 2023 IEEE.

关键词： admixture models pruning Transformers

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Incentive Mechanism of Selfish Nodes Based on Energy Optimization and Game Theory

Incentive Mechanism of Selfish Nodes Based on Energy Optimiz...

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IEEE International Conference on Systems, Man and Cybernetics

作者： XiangJia Dong Yuan Cheng Winston K. G. Seah Feng Zhang Gang Xu Engineering Research Center of Ecological Big Data Ministry of Education College of Computer Science Inner Mongolia University Hohhot China School of Engineering & Computer Science Victoria University of Wellington Wellington New Zealand School of Computer and Information Technology Shanxi University Taiyuan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the proliferation of mobile intelligent terminals, opportunistic networks have attracted widespread attention as a complementary technology to multi-network convergence. Different from traditional wireless networks, message delivery in opportunistic networks does not rely on a fixed infrastructure, but rather storing messages in a cache and utilizing the movement and encounters of nodes to relay messages. However, in practical application scenarios, nodes have limited storage space and energy and will easily exhibit selfishness. An increase in the number of selfish nodes will drastically degrade the performance of the network. To solve the problem of significant network performance degradation when the number of selfish nodes is high, this paper proposes an Incentive mechanism of Selfish nodes based on Energy optimization and Game Theory (ISEGT). The mechanism abstracts the process of forwarding messages by nodes into a bargaining game process, and selectively forwards messages based on nodes' remaining energy and other circumstances. The experimental results show that the ISEGT mechanism can motivate selfish nodes to actively participate in message forwarding, which improves the success rate of message delivery and the survival rate of nodes, and optimizes the overall performance of the network.

关键词： Degradation Wireless networks Games Network security Delays Game theory Relays Optimization Cybernetics Energy storage

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Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuclei Instance Segmentation in Histology Images

Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuc...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuening Wu Yiqing Shen Qing Zhao Yanlan Kang Ruiqi Hu Wenqiang Zhang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Department of Computer Science Johns Hopkins University Baltimore MD USA Department of Applied Mathematics Hong Kong Polytechnic University Hong Kong Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology School of Computer Science Fudan University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurate nuclei segmentation is fundamental in histology image analysis, playing an essential role in cancer grading and diagnosis. However, this task remains challenging due to variations in staining protocols, heterogeneity among nuclei types, and the densely clustered nature of nuclei. While SAM exhibits zero-shot generalization capabilities in natural image segmentation, its performance degrades when applied to nuclei segmentation in histology images. Existing adaptations of SAM for medical imaging primarily focus on organ or lesion segmentation, which differs substantially from nuclei segmentation due to the unique characteristics of nuclei—specifically, their sparse distribution combined with dense clustering. To address these challenges, we propose Edge-SAN (Segment Any Nuclei with Edge Prompting), an interactive segmentation foundation model specifically designed for nuclei segmentation. Edge-SAN introduces a novel edge prompting method that enhances the delineation of nuclei boundaries, particularly among densely clustered nuclei, by leveraging edge information to improve segmentation accuracy. We evaluate Edge-SAN on 12 diverse datasets in both few-shot and zero-shot scenarios, demonstrating its effectiveness as a foundation model for nuclei segmentation, achieving 66.81% AJI and 73.13% DSC—improvements of 16.33% and 15.48% over SAM-Med2D, respectively. The code is available at https://***/deep-geo/Edge-SAN.

关键词： Instance segmentation Training Technological innovation Accuracy Histopathology Foundation models Image edge detection Biological system modeling Real-time systems Cancer

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NewsThumbnail: Automatic Generation of News Video Thumbnail

NewsThumbnail: Automatic Generation of News Video Thumbnail

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Li, Jinyu Lin, Shujin Zhou, Fan Wang, Ruomei School of Computer Science and Engineering National Engineering Research Center of Digital Life China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion China Sun Yat-sen University School of Communication and Design Guangzhou China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Reading news is an important way for people to obtain information. People can quickly sort out the context of events through a short news video. However, there are numerous news generated around the world every day. It's challenging to locate the interesting video. Thumbnails are often used as video covers and play an important role in displaying video content and driving views. Video owners can choose from individual images or elaborate thumbnails to upload to the site. But manually selecting from a large number of frames is time-consuming, and customizing thumbnails requires a high degree of expertise. Therefore, this paper proposes an automatic generation method of news video thumbnail, which can screen out semantically similar contents according to user query and combine them into a thumbnail. In order to facilitate the screening of graphic materials, we also propose a video content structuring method based on multiple cues, which can accurately segment the video into theme units. At the same time, we designed a visual system to display thumbnails and designed a user survey to investigate the performance of this method in news retrieval and understanding. Compared with peer methods, the thumbnails generated by our method can help users better understand the video content and locate the videos they are interested in. © 2022 IEEE.

关键词： Video recording

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ARBS-YOLOv8 for Annotation Box Detection in Colonoscopy CADe Systems

ARBS-YOLOv8 for Annotation Box Detection in Colonoscopy CADe...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Ruiyao Zhang Naohisa Yoshida Osamu Dohi Boyuan Peng Xinkai Liu Jie Huang Xin Zhu Graduate School of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan Department of Molecular Gastroenterology and Hepatology Graduate School of Medical Science Kyoto Prefectural University of Medicine Kyoto Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Tokyo Japan

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

Colonoscopy is the gold standard for colorectal lesion detection, with its accuracy directly influencing patient prog-nosis. The accurate detection of Annotation Boxes in computer-aided Detection (CADe) systems is important for improving their performance in colonoscopy procedures. This research introduces a novel algorithm that combines active learning with a modified YOLOv8 algorithm, named ARBS-YOLOv8 (Active learning with RepVGG Block and SIoU loss function base on YOLOv8), for the detection of Annotation Boxes generated by colonoscopy CADe systems. We compared standard YOLOv8, YOLOv6, YOLOX algorithms and their active learning variants using colonoscopy video data from 31 patients. The active learning method involves analyzing false positives generated by the initially trained model on an active learning dataset. These false detection samples are then reintroduced into the training set as negative examples. This active learning method led to substantial performance improvements across all algorithms, with particularly notable increase in precision. Notably, the application of active learning to YOLOv6 resulted in a significant increase in Precision by 23.28%, reaching 95.79%. Furthermore, when active learning was integrated with YOLOv8, it achieved an impressive F1 Score of 99.70% on the test set. Compared to the standard YOLOv8, ARBS-YOLOv8 demonstrated 0.88% increase in F1 Score while maintaining high Sensitivity. This improvement suggests that ARBS-YOLOv8 is more effective at extracting features from the data and increase the F1 score in detecting Annotation Boxes.

关键词： Training Accuracy Sensitivity Annotations Active learning Colonoscopy Feature extraction Lesions data mining Standards

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The Analysis of Short Duration Power Quality Disturbances Using Short Time Fourier Transform

The Analysis of Short Duration Power Quality Disturbances Us...

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Smart and Sustainable Developments in Electrical engineering (SSDEE), IEEE International Conference on

作者： Madgula Satyanrayana Venkataramana Veeramsetty Durgam Rajababu Department of Electrical and Electronics Engineering SR University Warangal Telangana India Center for AI and Deep Learning School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

The present power system have been charac-terized by high dynamics due to advances such as the accessorial loads, switching power supply equipment and enhancement of power system regulations. The power quality (PQ) analysis was earlier performed using Fourier Transform (FT) where parameters such as time-amplitude variations and frequency-amplitude variations were used. However, these parameters focus on the stationary power quality disturbances(PQDs) and cannot capture non-stationary or short duration PQ disturbances. Modern power systems need a broader viewpoint based on the amplitude, duration, and frequency of interruptions. In this paper, Short Time Fourier Transform(STFT) is proposed for identification of short duration PQDs which include voltage sags, swells, interruptions, transient oscillations and harmonic. In this paper, power quality disturbances will be assessed by Short-Time Fourier Transform which enhances PQ analysis under dynamic conditions.

关键词： Wavelet transforms Time-frequency analysis Fourier transforms Voltage fluctuations Power quality Power system dynamics Power system stability Transient analysis Signal resolution Oscillators

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X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics

X-Stream: A Flexible, Adaptive Video Transformer for Privacy...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Dou Feng Lin Wang Shutong Chen Lingching Tung Fangming Liu National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Paderborn University Germany National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Guangxi University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Peng Cheng Laboratory China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Video stream analytics (VSA) systems fuel many exciting applications that facilitate people’s lives, but also raise critical concerns about exposing too much individuals’ privacy. To alleviate these concerns, various frameworks have been presented to enhance the privacy of VSA systems. Yet, existing solutions suffer two limitations: (1) being scenario-customized, thus limiting the generality of adapting to multifarious scenarios, (2) requiring complex, imperative programming, and tedious process, thus largely reducing the usability of such systems. In this paper, we present X-Stream, a privacy-preserving video transformer that achieves flexibility and efficiency for a large variety of VSA tasks. X-Stream features three major novel designs: (1) a declarative query interface that provides a simple yet expressive interface for users to describe both their privacy protection and content exposure requirements, (2) an adaptation mechanism that dynamically selects the most suitable privacy-preserving techniques and their parameters based on the current video context, and (3) an efficient execution engine that incorporates optimizations for multi-task deduplication and inter-frame inference. We implement X-Stream and evaluate it with representative VSA tasks and public video datasets. The results show that X-Stream achieves significantly improved privacy protection quality and performance over the state-of-the-art, while being simple to use.

关键词： Privacy Transforms Streaming media Transformers Multitasking Real-time systems Task analysis

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The Impact of Medical Resources and Oral Antiviral Drugs on SARS-CoV-2 Mortality—Hong Kong SAR,China,2022

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China CDC weekly 2024年第21期6卷 469-477,I0001-I0005页

作者： Zhiqi Zeng Jingyi Liang Zhijie Lin Wenda Guan Wei He Qianying Li Lixi Liang Weiqi Pan Zige Liu Guibin Lu Xiaoyan Deng Eric HY Lau Chitin Hon Zifeng Yang Guangzhou key laboratory for clinical rapid diagnosis and early warning of infectious diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao SARChina Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovative EngineeringMacao University of Science and TechnologyMacao SARChina State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory Healththe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina School of Public Health The University of Hong KongHong Kong SARChina Laboratory of Data Discovery for Health Hong Kong SARChina Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina

Introduction:The Omicron variant of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)demonstrates increased transmissibility compared to earlier strains,contributing to a significant number of fatalities in Hong Kong Special Administrative Region(HKSAR),*** medical resources and medications are essential in mitigating these *** study evaluates the effects of supplementary resources from the Chinese mainland during the fifth wave of the pandemic in ***:Vector autoregression(VAR)was employed to analyze data from the Oxford coronavirus disease 2019(COVID-19)Government Response Tracker to assess the effectiveness of control measures during five waves of the pandemic in ***,a transmission dynamics model was created to investigate the influence of supplementary medical resources from the Chinese mainland and oral medications on ***:In the initial four waves,workplace closures,restrictions on public events,international travel bans,and shielding the elderly significantly influenced pandemic ***,during the fifth wave,these measures showed no notable *** comparing a situation without extra medical resources or COVID-19 oral medication,there was a 17.7%decrease in COVID-19 fatalities with mainland medical resources and an additional 10.2%reduction with oral ***,they contributed to a 26.6%decline in ***:With the rapid spread of the virus,regional reallocation of medical resources may reduce mortality even when the local healthcare system is overstretched.

关键词： mainland Impact travel

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A Few-shot Classification Algorithm Based on Two-stream Frequency-Domain Information Networks

A Few-shot Classification Algorithm Based on Two-stream Freq...

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International Conference on Systems and Informatics (ICSai)

作者： Siquan Hu Jun Jia Zhiguo Shi Jun Zhao School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Big Data Center Beijing China

In the domain of few-shot learning, where the scarcity of training data poses a significant challenge, this paper introduces an innovative approach. We present a few-shot classification algorithm that utilizes the Two-Stream Frequency Domain Information Network. This algorithm delves deep into the efficacy of training different frequency components of images in the context of few-shot classification. It integrates filtered and enhanced image frequency domain information as supplementary data to be adaptively fused with the original image data, enhancing and augmenting the available information. Experimental results establish that the few-shot classification model, employing the Two-Stream Frequency Domain Information Network, outperforms leading algorithms in the field of few-shot learning. This enhancement is evidenced by an accuracy improvement of 1.06% and 0.93% on the miniImageNet and tiredImageNet datasets, respectively, when compared to the Meta-Baseline, illustrating the efficacy of this specialized approach.

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