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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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DDPG Based Dynamic Scheduling Optimization Mechanism in Real Switch 15

DDPG Based Dynamic Scheduling Optimization Mechanism in Real...

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15th International Conference on Communication software and Networks, ICCSN 2023

作者： Wang, Xue Wang, Xingwei Jia, Jie Lu, Xijia Huang, Min School of Computer Science and Engineering Northeastern University China Software College Northeastern University China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350329360

Nowadays, more and more traffic with different service requirements is transmitted simultaneously in the network, resulting in network congestion. In order to improve network Quality of Service (QoS), traffic will be classified and processed according to its requirements. The switch uses fair queuing and its extension schemes to schedule it to reduce the packet loss rate. However, statistical data such as packet arrival rate cannot be accurately obtained in the switches, and discrete reinforcement learning cannot model dynamically changing traffic, making existing dynamic bandwidth allocation solutions challenging to implement. Meanwhile, the same weight configuration will obviously cause the packet loss rate to fluctuate, so it is challenging to design an effective reward function. This paper proposes a dynamic scheduling optimization mechanism based on Deep Deterministic Policy Gradient (DDPG), called DDSO, in an actual switch to schedule flows with different service requirements. Firstly, the flow scheduling problem is modeled as a Markov Decision Process (MDP), and DDPG is used to find the optimal queue weight configuration. Queue weight adjustment is a continuous control problem, and DDPG can solve this problem very well. Secondly, we introduce two one-dimensional arrays to design a new reward function, to reduce the influence of packet loss rate fluctuation on reward determination. At last, this mechanism is evaluated through a hardware testbed in two scenarios. The results show that DDSO can effectively schedule the flows with different service requirements and alleviate network congestion. © 2023 IEEE.

关键词： Reinforcement learning

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Automotive Lightweight Design Modeling And Intelligent Optimization Learn Key Technologies 10

Automotive Lightweight Design Modeling And Intelligent Optim...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Xu, Gejing Liang, Wei Cai, Jiahong Xiao, Jiahong Chen, Xingyu Gong, Yinyan QuanZhou University Information Engineering Software College QuanZhou362000 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350312461

The automotive industry has always been seeking innovative solutions to improve car performance, safety, and cost savings. Lightweight design technology has become one of the solutions. This article summarizes the modeling and optimization methods of automotive lightweight design, as well as key technologies based on intelligent optimization learning. First, this article outlines the basic concepts of automotive lightweight design, as well as the needs and challenges of the industry for lightweight design. Then, the modeling methods of lightweight design are introduced in detail, including geometric modeling, topology optimization, structural optimization, and multidisciplinary optimization. At the same time, commonly used materials, manufacturing processes, and testing methods in lightweight design are introduced, as well as relevant design guidelines and standards. This article also introduces some algorithms and their applicable scenarios. Additionally, this article summarizes the application prospects and future development directions of key technologies for automotive lightweight design modeling and intelligent optimization learning. We emphasize the opportunities and challenges in this field and propose how to continue promoting the development of lightweight design technology and responding to increasingly complex market demands. This article provides a systematic review of key technologies for automotive lightweight design modeling and intelligent optimization learning, which helps researchers and practitioners to deepen their understanding of the technical development and application trends in this field. © 2023 IEEE.

关键词： Automotive industry

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DPCNN-based Models for Text Classification 10

DPCNN-based Models for Text Classification

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhang, MeiJiao Pang, Jiacheng Cai, Jiahong Huo, Yingzi Yang, Ce Xiong, Huixuan QuanZhou University College of Information Engineering Software QuanZhou362000 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350312461

In recent years, with the wide application of CNN in the field of deep learning, the related model of CNN, the Deep Pyramid Convolutional Neural Networks for Text Categorization (DPCNN) model, has emerged, and by the idea of deepening the depth of the network to obtain the best accuracy, DPCNN has made breakthroughs in related fields, especially in the field of text categorization, and its concrete applications in solving practical problems have achieved good results. This paper first introduces the text classification system, then introduces the mainstream model CNN for text classification, after that this paper focuses on the analysis of the DPCNN model, introduces its background and its principle analysis, and introduces the application of DPCNN in specific examples, and finally summarizes and outlooks on DPCNN, emphasizes its application advantages and builds suitable application scenarios. © 2023 IEEE.

关键词： Convolutional neural networks

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Efficient Relaxed Functional Dependency Discovery with Minimal Set Cover 40

Efficient Relaxed Functional Dependency Discovery with Minim...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ding, Xiaoou Liu, Yida Wang, Hongzhi Wang, Chen Song, Yichen Yang, Donghua Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China National Engineering Research Center for Big Data Software EIRI Tsinghua University China

ISBN: (纸本)9798350317152

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2m-l) to O(1.5m-1). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks. © 2024 IEEE.

关键词： Quality management

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Explainable AI: A Way to Achieve Trustworthy AI 10

Explainable AI: A Way to Achieve Trustworthy AI

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10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024

作者： Li, Yanlu Xiao, Yufeng Gong, Yinyan Zhang, Ronglin Huo, Yingzi Wu, Yanbing Hunan Key Laboratory for Service Computing Novel Software Technology Xiangtan411201 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350389524

AI is black-box and non-explainable, in other words, due to the complexity of the decision-making process of AI, people are unable to know why and how AI makes the decision. For these reasons, people will question and worry about AI's decision-making. Against this background, researchers have proposed the concept of Explainable Artificial Intelligence (XAI). The aim of this paper is to help readers be able to better understand XAI through extensive surveys. In this paper, we discuss the concept of XAI, the motivation of the research, the current status of the research, and the difficulties in the research process. At the same time, we also discuss the specific methods to realize AI explainability by dividing AI explainability methods into two ways: transparent design and ex post facto interpretation. In addition, we select the three fields of healthcare, finance, and autonomous driving, discuss the application of XAI in these three fields, and select some representative results as examples through surveys. Finally, we summarize this article and present our outlook on the future development of XAI. © 2024 IEEE.

关键词： Machine learning

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Analysis of the Spatio-Temporal Characteristics of Winter Surface Urban Heat Island:A Case Study in Beijing,China

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Journal of Earth science 2024年第5期35卷 1640-1653页

作者： Shanshan Lu Fujiang Liu Yunshuang Ye Jiayu Tang Peng Li Weihua Lin Yan Guo Ruqiang Ma Jun Wang School of Geography and Information Engineering China University of GeosciencesWuhan 430074China AutoNavi Software Co. Ltd.Beijing 100102China School of Computer Science China University of GeosciencesWuhan 430078China Yantai Geological Survey of Coastal Resources Center Yantai 264000China

This study reveals the temporal and spatial evolution characteristics of the winter nighttime urban heat island(UHI)effect in the case of Beijing,*** land surface temperature(LST)is retrieved by radiative transfer equation by using the remote sensing data from Landsat ETM+/OLI_TIRS from 2007 to 2017 for the winter nighttime period,and LST is then divided by the mean-standard deviation method into different levels of thermal landscapes.A combination of the migration calculation of gravity center and multi-directional profile analysis is used to study the directional differentiation characteristics of LST and the migratory characteristics of the gravity center of ***,the overall temporal and spatial evolution characteristics of winter nighttime surface urban heat island(SUHI)in Beijing are studied,and the possible reasons for the changes are *** show that Beijing's UHI effect first increased and subsequently decreased from 2007 to *** winter heat island in the urban area developed from low-density agglomeration to high-density agglomeration to lowdensity ***,the high-level thermal landscapes migrated to the southwest along with the city center of gravity,and the expansion rate is fastest in the southwest,which is directly linked to the changes in the urban construction ***,the overall spatial distribution of winter nighttime LST is high in the east and south and low in the west and north,and is influenced by topography,land cover,urbanization,anthropogenic heat,and other factors as well.

关键词： land surface temperature urban heat island spatio-temporal characteristics winter environmentgeology

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Audio2AB:Audio-driven collaborative generation of virtual character animation

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虚拟现实与智能硬件（中英文） 2024年第1期6卷 56-70页

作者： Lichao NIU Wenjun XIE Dong WANG Zhongrui CAO Xiaoping LIU School of Computer Science and Information Engineering Hefei University of TechnologyHeifei 230009China School of Software Hefei University of TechnologyHeifei 230009China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of TechnologyHefei 230601China

Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of ***,few methods exist for generating full-body animations,and the portability of virtual character gestures and facial animations has not received sufficient *** Therefore,we propose a deep-learning-based audio-to-animation-and-blendshape(Audio2AB)network that generates gesture animations and ARK it's 52 facial expression parameter blendshape weights based on audio,audio-corresponding text,emotion labels,and semantic relevance labels to generate parametric data for full-body *** parameterization method can be used to drive full-body animations of virtual characters and improve their *** the experiment,we first downsampled the gesture and facial data to achieve the same temporal resolution for the input,output,and facial *** Audio2AB network then encoded the audio,audio-corresponding text,emotion labels,and semantic relevance labels,and then fused the text,emotion labels,and semantic relevance labels into the audio to obtain better audio ***,we established links between the body,gestures,and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss *** By using audio,audio-corresponding text,and emotional and semantic relevance labels as input,the trained Audio2AB network could generate gesture animation data containing blendshape ***,different 3D virtual character animations could be created through *** The experimental results showed that the proposed method could generate significant gestures and facial animations.

关键词： Audio-driven Virtual character Full-body animation Audio2AB Blendshape GAN-GF

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Research on Facial Landmark Detection Algorithm Based on Improved Attention Mechanism

Research on Facial Landmark Detection Algorithm Based on Imp...

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2022 International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2022

作者： Cui, Xi-Shuo Shi, Tian-Wei Cui, Wen-Hua Tao, Ye School of Electronic and Information Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China

ISBN: (纸本)9781510662988

In recent years, facial landmark detection has assumed an important role in various fields. However, the current facial landmark detection algorithms are still lacking in recognition accuracy. In order to solve the above problem, this paper uses Ghost bottleneck to replace the original bottleneck on the basis of the original model of PFLD model, and adds and improves the CBAM attention mechanism. The improved PFLD model increases the ability of the model to extract facial landmark and improves the accuracy of the algorithm. The improved model has high accuracy and low parametric number and improves the accuracy of facial landmark detection. It also provides a new idea for facial landmark detection task. © 2023 SPIE. All rights reserved.

关键词： Face recognition

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A Security Model of Multihospital FHIR Database Authorization Based on Secret Sharing and Blockchain

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IEEE Internet of Things Journal 2024年第6期11卷 10325-10335页

作者： Yang, Ching-Nung Li, Peng Cheng, Hsiang-Han Kuo, Hsin-Chuan Lu, Ming-Chan Xiong, Lizhi National Dong Hwa University Department of Computer Science and Information Engineering Hualien97401 Taiwan North China Electric Power University Department of Mathematics and Physics Hebei Key Laboratory of Physics and Energy Technology Baoding071003 China Nanjing University of Information Science and Technology School of Computer and Software Nanjing210044 China

Secret sharing (SS) is a threshold technology that shares a secret value by generating and distributing n shares in the way that a set of any k shares can recover the secret. On the other hand, blockchain is a decentralized system establishing a secure data structure for storing data in the form of a hash chain. Combining SS and blockchain is definitely a promising paradigm in the data security field. Recently, the fast healthcare interoperability resources (FHIRs) standard is defined for medical data exchange across various medical institutions. In this article, we combine SS, ElGamal's encryption/decryption, and blockchain to propose a multihospital FHIR database authorization (MFDA). To achieve a secure MFDA, the group-based SS (GSS) and the notion using GSS on exponent (referred to as EGSS) are proposed. The secrets of GSS and EGSS have the discrete logarithm problem (DLP) relation, on which the secrets can be used as ElGamal's private key and public key, respectively. Also, their shares will have DLP relation. By uploading public shares of EGSS on blockchain and using private shares of GSS in a private network, we can recover ElGamal's public key from a deployed smart contract on blockchain and recover ElGamal's private key from a qualified set. Meantime, when recovering private key, the private shares of GSS can be authenticated by public shares on blockchain to check tampering and corrupted of private shares during the communication in a private network. Theorems, examples, and experiments demonstrate that the proposed MFDA has efficient and secure database authorization. © 2014 IEEE.

关键词： Blockchain

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