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arXiv 2022年

作者： Luo, Yang Chen, Zhineng Zhou, Shengtian Gao, Xieping Fudan University Shanghai China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University China

Self-supervised learning (SSL) has drawn increasing attention in histopathological image analysis in recent years. Compared to contrastive learning which is troubled with the false negative problem, i.e., semantically similar images are selected as negative samples, masked autoencoders (MAE) building SSL from a generative paradigm is probably a more appropriate pre-training. In this paper, we introduce MAE and verify the effect of visible patches for histopathological image understanding. Moreover, a novel SD-MAE model is proposed to enable a self-distillation augmented MAE. Besides the reconstruction loss on masked image patches, SD-MAE further imposes the self-distillation loss on visible patches to enhance the representational capacity of the encoder located shallow layer. We apply SD-MAE to histopathological image classification, cell segmentation and object detection. Experiments demonstrate that SD-MAE shows highly competitive performance when compared with other SSL methods in these tasks. © 2022, CC BY-NC-SA.

关键词： Distillation

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Learning to Learn Transferable Generative Attack for Person Re-Identification

arXiv

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arXiv 2024年

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan The College of Electrical and Information Engineering Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology Hunan Changsha China The College of Information Science and Engineering Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Changsha China

Deep learning-based person re-identification (re-id) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based cross-model&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 21.5% and 11.3% on mean mAP drop rate, respectively. The code of MTGA will be released after the paper is accepted. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Fixed-Parameter Tractability for Book Drawing with Bounded Number of Crossings per Edge 15th

Fixed-Parameter Tractability for Book Drawing with Bounded...

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15th International Conference on Algorithmic Aspects in Information and Management, AAIM 2021

作者： Liu, Yunlong Li, Yixuan Huang, Jingui College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China Hunan Xiangjiang Artificial Intelligence Academy Changsha China

ISBN: (纸本)9783030931759

The problem book drawing with a bounded number of crossings per edge asks, given a graph G= (V, E) and two integers k, b, whether there is a k-page book drawing of G such that the maximum number of crossings per edge is at most b. In this paper, we study this problem from a parameterized complexity viewpoint. Specifically, we investigate the problem parameterized by both the maximum number b of crossings per edge and the vertex cover number τ of G, and show that this parameterized problem admits a kernel of size (3 b+ 1 ) · 2O(τ) and admits a fixed-parameter tractable algorithm running in time O((3b+1)·2(3b+2)·2O(τ)+τ·|V| ). Together with our previous result for fixed-order book drawing with a bounded number of crossings per edge (COCOA 2020), our result provides a more complete answer to a question posed by Bhore et al. (J. Graph Alg. Appl. 2020). © 2021, Springer Nature Switzerland AG.

关键词： Parameterization

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BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neural Network for the Segmentation of Breast Ultrasound Images

BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neu...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zhang, Xiang Li, Xuanya Hu, Kai Gao, Xieping Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province Xiangnan University Chenzhou423000 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9781665401265

In this paper, we propose a novel convolutional neural network based on boundary-guided and region-aware (BGRA-Net) for breast tumor segmentation in ultrasound images. In particular, in the encoding stage, we propose a boundary-guided module (BGM) to guide the learning of boundary features in the decoding stage by explicitly strengthening the extracted boundary information. Meanwhile, in the decoding stage, we propose a region-aware module (RAM) to integrate different levels of detailed and semantic features to improve the comprehensive representation of tumor regional features. Besides, a scale-adaptive module (SAM) is further proposed to capture the characteristics of tumors with different sizes between the encoding and decoding stages. To evaluate the effectiveness of our BGRA-Net, we conduct extensive experiments on the UDIAT dataset and compare it with eight state-of-the-art methods. The experimental results show that our BGRA-Net outperforms the state-of-the-art methods and can achieve accurate segmentation of breast tumors with ambiguous boundaries. © 2021 IEEE.

关键词： Tumors

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Semi-Supervised Feature Selection Based on Fuzzy Related Family

SSRN

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SSRN 2023年

作者： Yang, Tian Guo, Zhijun Li, Yuan-Jiang Deng, Yanfang Qian, Yuhua Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Hunan Changsha410081 China Institute of Big Data Science and Industry Shanxi University Shanxi Taiyuan030006 China

Existing machine learning algorithms face the problems of label-missing and high dimensionality. Feature selection is an effective dimensionality reduction method that can improve the efficiency and accuracy of subsequent machine learning tasks by removing irrelevant and redundant features. Related family is an efficient and rough set-based feature selection method, but is difficult to be used in simisupervised learning tasks. However, due to the difficulty of obtaining all labeled data, partially labeled data has become an important object that machine learning models must deal with. Therefore, this paper proposes a semi-supervised fuzzy related family-based feature selection method for partially labeled data. This method first calculates the fuzzy label values of unlabeled samples through fuzzy similarity relationships to establish a new fuzzy covering system. Then, based on the fuzzy covering system, it constructs a fuzzy related family based on consistent fuzzy set. Finally, based on the constructed feature significance measurement, a semi-supervised feature selection algorithm based on fuzzy related family (SFRF) is established. Compared with existing semi-supervised feature selection algorithms, SFRF can significantly improve the efficiency of feature selection while maintaining classification accuracy, with the average reduction efficiency of nine datasets increased by up to 201 times. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance

arXiv

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arXiv 2024年

作者： Dong, Li Peng, Yubo Jiang, Feibo Wang, Kezhi Yang, Kun School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China School of Intelligent Software and Engineering Nanjing University Suzhou China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China Department of Computer Science Brunel University London United Kingdom

In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network (IESN) to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider (1) Data privacy and security. (2) SC model adaptation for heterogeneous devices. (3) Explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. Then, we present an Adaptive Client Training (ACT) strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC (ESC) mechanism is designed, which introduces a leakyReLU-based activation mapping to explain the relationship between the extracted semantics and monitoring data. Finally, simulation results demonstrate the effectiveness of XSFL. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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A Multi-level Approach to Learning Early Warning based on Cognitive Diagnosis and Learning Behaviors Analysis

A Multi-level Approach to Learning Early Warning based on Co...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Hua Ma Wen Zhao Zixu Jiang Peiji Huang Wensheng Tang Hongyu Zhang Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China College of Information Science and Engineering Hunan Normal University Changsha China Tencent Cloud Computing (Changsha) Co. Ltd School of Business Central South University Changsha China

Learning early warning is of great significance for coping with students' learning risks. The existing research fails in modeling the fluctuation of students' learning states and providing the multi-level early warning for students at different levels. To address them, a new approach of learning early warning is proposed to predict at-risk students in e-learning environment by combining cognitive diagnosis with learning behaviors analysis. In this approach, the students' learning process is modeled from four dimensions, i.e., learning quality, learning engagement, latent learning state, and historical learning performance. The convolutional neural network and long short-term memory network are used to explore the students' latent learning features. Then, the Adaboost algorithm is applied to predict students' learning performance. Based on the predicted performance, the evaluation rules are designed to provide multi-level learning early warning for students. Finally, the experiments demonstrate that the proposed method could predict at-risk students efficiently and accurately.

关键词：

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A Short Flows Fast Transmission Algorithm Based on MPTCP Congestion Control 6th

A Short Flows Fast Transmission Algorithm Based on MPTCP Con...

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6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Zhong, Hua Dong, PingPing Tang, WenSheng Yang, Bo Xie, JingYun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

ISBN: (纸本)9783030578831

With the development of cloud computing and big data technology, traffic in the network presents new features. Real-time online interactive applications are increasing, mainly including web services, instant messaging, online commerce, advertising systems. They have very stringent latency requirements. However, the data traffic generated by data backup, movie download, data mining require high bandwidth. When large and small flows are transmitted concurrently, a series of problems will arise. MPTCP treats long and short flows equally. In this way, packets of long flows will preempt bandwidth at the bottleneck link, fill the router’s whole queue buffer. This will cause the short flows transmission timeout. To solve these problems, we propose a novel algorithm named MPTCP-SPTA (MPTCP short flows fast transmission algorithm). The algorithm give high priority to short flows based on the congestion control algorithm, then introduce the Explicit Congestion Notification mechanism(ECN) in MPTCP. Through extensive network simulator (NS-3) simulations, we demonstrate that MPTCP-SPTA outperforms TCP and MPTCP in three typical network topology, which can significantly reduce the completion time of the small flow and effectively improving the total throughput of the network. © 2020, Springer Nature Switzerland AG.

关键词： Web services

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Parameterized Algorithms for Fixed-Order Book Drawing with Bounded Number of Crossings per Edge 14th

Parameterized Algorithms for Fixed-Order Book Drawing with B...

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14th International Conference on Combinatorial Optimization and Applications, COCOA 2020

作者： Liu, Yunlong Chen, Jie Huang, Jingui Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

ISBN: (纸本)9783030648428

Given a graph G= (V, E) and a fixed linear order of V, the problem fixed-order book drawing with bounded number of crossings per edge asks whether there is a k-page book drawing of G such that the maximum number of crossings per edge is upper-bounded by an integer b. This problem was posed by Bhore et al. (GD 2019;J. Graph Algorithms Appl. 2020) and thought to be interesting for further investigation. In this paper, we study the fixed-parameter tractable algorithms for this problem. More precisely, we show that this problem parameterized by both the bounded number b of crossings per edge and the vertex cover number τ of the graph admits an algorithm running in time (b+2)O(τ3)·|V|, and this problem parameterized by both the bounded number b of crossings per edge and the pathwidth κ of the vertex ordering admits an algorithm running in time (b+2)O(κ2)·|V|. Our results provide a specifical answer to Bhore et al.’s question. © 2020, Springer Nature Switzerland AG.

关键词： Parameter estimation

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RIESZ TYPE THEOREMS FOR κ-PLURIHARMONIC MAPPINGS, INVARIANT HARMONIC QUASIREGULAR MAPPINGS AND HARMONIC QUASIREGULAR MAPPINGS

arXiv

引用

arXiv 2023年

作者： Chen, Shaolin Huang, Manzi College of Mathematics and Statistics Hengyang Normal University Hunan Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application 421002 China Key Laboratory of High Performance Computing and Stochastic Information Processing College of Mathematics and Statistics Hunan Normal University Hunan Changsha410081 China

The main purpose of this paper is to develop some methods to improve and generalize the main results in a recent paper by Liu and Zhu (Adv. Math., 2023, i.e., [19]). The paper consists of two parts. In the first part, we discuss the Riesz type theorem in the setting of n-dimensional complex spaces for all n ≥ 1. In this part, we first introduce the family of κ-pluriharmonic mappings of the n-dimensional complex unit ball. Then we establish two Riesz type theorems for these mappings, which are the n-dimensional versions of Theorems 1.1 and 1.2 in [19], respectively. Furthermore, even when n = 1, our first result shows that the assumption of the real parts of the mappings not being negative (or being negative) in [19, Theorem 1.1] is redundant;and our second result illustrates that the assumption of "quasiconformality" on the mappings in [19, Theorem 1.2] can be replaced by the weaker one of "quasiregularity". In the second part, we investigate the Riesz type theorem in the setting of n-dimensional real spaces for all n ≥ 2. In this part, first, we prove a Riesz type theorem for invariant harmonic quasiregular mappings of the unit n-dimensional real ball. Our result indicates that (i) the range of the parameter p discussed in [19, Theorem 1.3] can be changed from (1, 2) to (1, ∞);(ii) the assumption of the first coordinate functions of the mappings being non-zero in [19, Theorem 1.3] is redundant. In this way, we complete the discussions carried out in [19, Theorems 1.3 and 1.4]. Second, we obtain a Riesz type theorem for harmonic K-quasiregular mappings of the unit n-dimensional real ball. Our result demonstrates that the range of the parameter p discussed in [15, Theorem 2.1] can be changed from (1, 2) to (1, ∞).MSC Codes 30H10, 30C62 Copyright © 2023, The Authors. All rights reserved.

关键词： Harmonic analysis

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