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arXiv 2024年

作者： Guo, Hao Ma, Zihan Zeng, Zhi Luo, Minnan Zeng, Weixin Tang, Jiuyang Zhao, Xiang Laboratory for Big Data and Decision Nation University of Defence Technology China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset AMG, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model MGCA to achieve multimodal fake news detection and attribution. Experimental results demonstrate that AMG is a challenging dataset, and its attribution setting opens up new avenues for future research. © 2024, CC BY-NC-ND.

关键词： Fake detection

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data-Driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th IEEE data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 IEEE.

关键词： Cost functions

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Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 1014-1030页

作者： Zaipeng Xie Cheng Ji Chentai Qiao WenZhan Song Zewen Li Yufeng Zhang Yujing Zhang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjingChina College of Computer and Information Hohai UniversityNanjingChina Center for Cyber‐Physical Systems University of GeorgiaAthensGeorgiaUSA Information Networking Institute Carnegie Mellon UniversityPittsburghPennsylvaniaUSA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPennsylvaniaUSA

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual ***,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent *** tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration *** rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment *** addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent *** was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty *** experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

关键词： artificial intelligence techniques decision making intelligent multi‐agent systems

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High Order Conditional Random Field Based Cervical Cancer Histopathological Image Classification

High Order Conditional Random Field Based Cervical Cancer Hi...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Zhou, Shangqi Li, Xintong Fu, Yanyu Ouyang, Xinyu Yang, Jinzhu Grzegorzek, Marcin Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland

ISBN: (纸本)9789819708543

Cervical cancer ranks as the fourth most frequently diagnosed cancer. This disease has an extended precancerous stage and can be completely cured and prevented through early detection. Currently, the analysis of histopathological images of cervical cancer relies on manual assessment by pathologists, a subjective and time-consuming process. Moreover, there is limited research on differentiating the severity of cervical cancer in histopathological images. To address these challenges, this study proposes a classification algorithm based on high-order conditional random field for cervical cancer histopathologic images. The algorithm effectively categorizes images into high differentiation, medium differentiation, and low differentiation stages. Three deep learning models, namely VGG-16, Inception-V3, and ResNet-50, are utilized for pre-classification at the image block level. For image-level classification, the Visual Transformer model is employed. Finally, the block-level and image-level classification components are integrated using the conditional random field model, resulting in the high-order conditional random field model. Following training and testing using the dataset, the model achieves an overall accuracy of 75.4%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computer aided diagnosis

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Multiple Machine Learning Fusion Based Analysis of Fat Composition in CT Images

Multiple Machine Learning Fusion Based Analysis of Fat Compo...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Fu, Yanyu Quanyu, E. Zhou, Shangqi Ouyang, Xinyu Yang, Jinzhu Grzegorzek, Marcin Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland

ISBN: (纸本)9789819708543

Intra-abdominal fat is a crucial factor in determining metabolic syndrome and insulin resistance. Therefore, high levels of intra-abdominal fat are always accompanied by a series of diseases such as diabetes, ischemia, hypertension, and dyslipidemia. Although methods such as the ultrasonic method and electrical impedance method have been widely used to calculate body fat tissue in recent years, these methods only provide qualitative analysis of the overall degree of obesity. However, for quantitative analysis of abdominal fat and adipose tissue distribution in the human body, the computed tomography (CT) fat segmentation method is necessary. This paper focuses on machine learning, image segmentation, and multi-feature fusion of mean clustering as its main technical directions. In terms of application, it aims to perform a quantitative analysis of the distribution and proportion of intra-abdominal fat in the abdominal cavity of the human body. The study concluded that the quantitative analysis of fat after the fusion method is more accurate, with a subcutaneous fat accuracy rate of 84.26% and a visceral fat accuracy rate of 70.20%. It can effectively, conveniently, and objectively analyze the degree of obesity in the human body. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Development and validation of a nomogram prediction model for overall survival in patients with rectal cancer

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World Journal of Gastrointestinal Oncology 2025年第2期17卷 93-104页

作者： Ling Liang Xiao-Sheng Li Ze-Jun Huang Zu-Hai Hu Qian-Jie Xu Yu-Liang Yuan Wei Zhang Hai-Ke Lei Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment Chongqing University Cancer HospitalChongqing 400030China Chongqing Cancer Multiomics Big Data Application Engineering Research Center Chongqing University Cancer HospitalChongqing 400030China Department of Health Statistics School of Public HealthChongqing Medical UniversityChongqing 400016China

BACKGROUND Rectal cancer is prevalent and associated with substantial morbidity and *** To develop a nomogram prediction model for overall survival(OS)in patients with rectal cancer by leveraging a comprehensive analysis of demographic,clinicopathological,haematological,and follow-up data to identify independent prognostic *** We conducted a prospective cohort study in China involving rectal cancer patients and applied Cox regression and least absolute shrinkage and selection operator regression to assess the significance of various variables as independent prognostic factors for *** identified factors were integrated into a nomogram model,which was evaluated for predictive accuracy via the C-index,area under the curve(AUC),calibration curve,and decision curve analysis(DCA).RESULTS Multivariate analysis revealed independent predictors of OS,including the Karnofsky performance status,age,sex,TNM stage,chemotherapy,surgery,targeted therapy,β2-microglobulin,lactate dehydrogenase,and the neutrophil-to-lymphocyte *** nomogram demonstrated a C-index of 0.80 for the training and validation cohorts,with AUC values indicating high predictive accuracy for 1-year,3-year,and 5-year *** calibration curves confirmed the model's excellent agreement with the observed survival rates,and DCA revealed the superior clinical utility of the nomogram over the TNM staging *** In this study,a novel prognostic model that accurately predicts the OS of rectal cancer patients was *** model exhibited excellent discriminatory and calibration capabilities,thus offering a reliable tool for health care professionals to estimate patient survival.

关键词： Rectal cancer Overall survival Nomogram Prognosis

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Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

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Computers, Materials & Continua 2023年第4期75卷 293-309页

作者： Mingshuai Sheng Jingbing Li Uzair Aslam Bhatti Jing Liu Mengxing Huang Yen-Wei Chen School of Information and Communication Engineering Hainan UniversityHaikou570228China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570228China School of Computer Science and Technology Hainan UniversityHaikou570228China Research Center for Healthcare Data Science Zhejiang LabHangzhou311121China Graduate School of Information Science and Engineering Ritsumeikan UniversityKyoto5258577Japan

Medical images are used as a diagnostic tool, so protecting theirconfidentiality has long been a topic of study. From this, we propose aResnet50-DCT-based zero watermarking algorithm for use with medicalimages. To begin, we use Resnet50, a pre-training network, to draw out thedeep features of medical images. Then the deep features are transformedby DCT transform and the perceptual hash function is used to generatethe feature vector. The original watermark is chaotic scrambled to get theencrypted watermark, and the watermark information is embedded into theoriginal medical image by XOR operation, and the logical key vector isobtained and saved at the same time. Similarly, the same feature extractionmethod is used to extract the deep features of the medical image to be testedand generate the feature vector. Later, the XOR operation is carried outbetween the feature vector and the logical key vector, and the encryptedwatermark is extracted and decrypted to get the restored watermark;thenormalized correlation coefficient (NC) of the original watermark and therestored watermark is calculated to determine the ownership and watermarkinformation of the medical image to be tested. After calculation, most ofthe NC values are greater than 0.50. The experimental results demonstratethe algorithm’s robustness, invisibility, and security, as well as its ability toaccurately extract watermark information. The algorithm also shows goodresistance to conventional attacks and geometric attacks.

关键词： Medical images deep residual network resnet50-DCT privacy protection robustness security

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Analyzing the Convergence of Federated Learning with Biased Client Participation 9th

Analyzing the Convergence of Federated Learning with Bias...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Tan, Lei Hu, Miao Zhou, Yipeng Wu, Di School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China School of Computing Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia

ISBN: (纸本)9783031466632

Federated Learning (FL) is a promising decentralized machine learning framework that enables a massive number of clients (e.g., smartphones) to collaboratively train a global model over the Internet without sacrificing their privacy. Though FL’s efficacy in non-convex problems is proven, its convergence amidst biased client participation lacks theoretical study. In this paper, we analyze the convergence of FedAvg on non-convex problems, which is the most renowned FL algorithm. We assume even data distribution but non-IID among clients, and elucidate the convergence rate of FedAvg in situations characterized by biased client participation. Our analysis reveals that biased client participation can significantly reduce the precision of the FL model. We validate this through trace-driven experiments, demonstrating that unbiased client participation results in 11% to 50% higher test accuracy compared to extremely biased client participation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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In-context decision transformer: reinforcement learning via hierarchical chain-of-thought 24

In-context decision transformer: reinforcement learning via ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Sili Huang Jifeng Hu Hechang Chen Lichao Sun Bo Yang School of Artificial Intelligence and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China School of Artificial Intelligence Jilin University China Lehigh University Bethlehem Pennsylvania Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China

In-context learning is a promising approach for offline reinforcement learning (RL) to handle online tasks, which can be achieved by providing task prompts. Recent works demonstrated that in-context RL could emerge with self-improvement in a trial-and-error manner when treating RL tasks as an across-episodic sequential prediction problem. Despite the self-improvement not requiring gradient updates, current works still suffer from high computational costs when the across-episodic sequence increases with task horizons. To this end, we propose an In-context Decision Transformer (IDT) to achieve self-improvement in a high-level trial-and-error manner. Specifically, IDT is inspired by the efficient hierarchical structure of human decision-making and thus reconstructs the sequence to consist of high-level decisions instead of low-level actions that interact with environments. As one high-level decision can guide multi-step low-level actions, IDT naturally avoids excessively long sequences and solves online tasks more efficiently. Experimental results show that IDT achieves state-of-the-art in long-horizon tasks over current in-context RL methods. In particular, the online evaluation time of our IDT is 36× times faster than baselines in the D4RL benchmark and 27× times faster in the Grid World benchmark.

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A dual-stream spatial-temporal detector for action recognition 4

A dual-stream spatial-temporal detector for action recogniti...

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4th International Conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Wei, Pingping Li, Jiale Liu, Peiran Li, Li Xu, Yifei Wang, Ling State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiao-Tong University Xi’an710049 China School of Software Xi’an Jiao-Tong University Shaanxi Xi’an710049 China Shaanxi Joint Key Laboratory for Artifact Intelligence Shaanxi 710054 China Xi’an Key Laboratory for Social Intelligence and Complex Data Processing Shaanxi 710054 China Alltuu Inc. Zhejiang Hangzhou310000 China

ISBN: (纸本)9781510682528

In response to the issue that existing human action recognition models can not make full use of complementary information from different modalities, this thesis proposes a multi-path attention module MA to form the MA-GCN model. modalities, this thesis proposes a dual-stream human action recognition model SRHAR that fuses skeleton data and RGB data. This model utilizes LAF proposed in this thesis to fuse skeleton features and RGB features. The introduction of skeleton modality enables the RGB modality to obtain the RGB features. The introduction of skeleton modality enables the RGB modality to obtain complementary information, resulting in more accurate prediction results. This algorithm focuses more on recognition accuracy and has a slower recognition speed, but achieves leading performance in terms of accuracy on public datasets. © 2024 SPIE.

关键词： Musculoskeletal system

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