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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Niu, Yudong Li, Yuchen Karras, Panagiotis Wang, Yanhao Li, Zhao School of Computing and Information Systems Singapore Management University Singapore University of Copenhagen Department of Computer Science Copenhagen Denmark School of Data Science and Engineering East China Normal University Shanghai China Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350317152

What is the widest community in which a person exercises a strong impact? Although extensive attention has been devoted to searching communities containing given individuals, the problem of finding their unique communities of influence has barely been examined. In this paper, we study the novel problem of Characteristic cOmmunity Discovery (COD) in attributed graphs. Our goal is to identify the largest community, taking into account the query attribute, in which the query node has a significant impact. The key challenge of the COD problem is that it requires evaluating the influence of the query node over a large number of hierarchically structured communities. We first propose a novel compressed COD evaluation approach to accelerate the influence estimation by eliminating redundant computations for overlapping communities. Then, we further devise a local hierarchical reclustering method to alleviate the skewness of hierarchical communities generated by global clustering for a specific query attribute. Extensive experiments confirm the effectiveness and efficiency of our solutions to COD: they find characteristic communities better than existing community search methods by several quality measures and achieve up to 25 x speedups against well-crafted baselines. © 2024 IEEE.

关键词： attributed graph characteristic community community hierarchy influence

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Task Separation and Knowledge Sharing for Class Incremental Learning 5

Task Separation and Knowledge Sharing for Class Incremental ...

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5th International Conference on Intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Zhang, Jiali Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China School of Mathematics and Information Science Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China

ISBN: (纸本)9798350368284

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and Knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid Knowledge Distillation (HKD). In logical distillation, we combine Global Knowledge Distillation (GKD) and Task Knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory. © 2024 IEEE.

关键词： Adversarial machine learning

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Resource abstraction and data placement for distributed hybrid memory pool

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Frontiers of Computer science 2021年第3期15卷 47-57页

作者： Tingting CHEN Haikun LIU Xiaofei LIAO Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Gluster and Grid Computing LabSchool of Gomputing Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Emerging byte-addressable non-volatile memory(NVM)technologies offer higher density and lower cost than DRAM,at the expense of lower performance and limited write *** have been many studies on hybrid NVM/DRAM memory management in a single physical ***,it is still an open problem on how to manage hybrid memories efficiently in a distributed *** paper proposes Alloy,a memory resource abstraction and data placement strategy for an RDMA-enabled distributed hybrid memory pool(DHMP).Alloy provides simple APIs for applications to utilize DRAM or NVM resource in the DHMP,without being aware of the hardware details of the *** propose a hotness-aware data placement scheme,which combines hot data migration,data replication and write merging together to improve application performance and reduce the cost of *** evaluate Alloy with several micro-benchmark workloads and public benchmark *** results show that Alloy can significantly reduce the DRAM usage in the DHMP by up to 95%,while reducing the total memory access time by up to 57%compared with the state-of-the-art approaches.

关键词： load balance distributed hybrid memory clouds

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Classifier Guidance Enhances Diffusion-Based Adversarial Purification by Preserving Predictive Information 27

Classifier Guidance Enhances Diffusion-Based Adversarial Pur...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhang, Mingkun Li, Jianing Chen, Wei Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781643685489

Adversarial purification is one of the promising approaches to defend neural networks against adversarial attacks. Recently, methods utilizing diffusion probabilistic models have achieved great success for adversarial purification in image classification tasks. However, such methods fall into the dilemma of balancing the needs for noise removal and information preservation. This paper points out that existing adversarial purification methods based on diffusion models gradually lose sample information during the core denoising process, causing occasional label shift in subsequent classification tasks. As a remedy, we suggest to suppress such information loss by introducing guidance from the classifier confidence. Specifically, we propose Classifier-cOnfidence gUided Purification (COUP) algorithm, which purifies adversarial examples while keeping away from the classifier decision boundary. Experimental results show that COUP can achieve better adversarial robustness under strong attack methods. © 2024 The Authors.

关键词： Generative adversarial networks

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Mixed Prototype Correction for Causal Inference in Medical Image Classification 24

Mixed Prototype Correction for Causal Inference in Medical I...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Yajie Huang, Zhi-An Hong, Zhiliang Wu, Songsong Wu, Jibin Tan, Kay Chen Department of Computing The Hong Kong Polytechnic University Hong Kong Dongguan China Shengli Clinical Medical College Fujian Medical University Fujian China Ultrasound Department Fuzhou University Affiliated Provincial Hospital Fuzhou China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798400706868

The heterogeneity of medical images poses significant challenges to accurate disease diagnosis. To tackle this issue, the impact of such heterogeneity on the causal relationship between image features and diagnostic labels should be incorporated into model design, which however remains underexplored. In this paper, we propose a mixed prototype correction for causal inference (MPCCI) method, aimed at mitigating the impact of unseen confounding factors on the causal relationships between medical images and disease labels, so as to enhance the diagnostic accuracy of deep learning models. The MPCCI comprises a causal inference component based on front-door adjustment and an adaptive training strategy. The causal inference component employs a multi-view feature extraction (MVFE) module to establish mediators, and a mixed prototype correction (MPC) module to execute causal interventions. Moreover, the adaptive training strategy incorporates both information purity and maturity metrics to maintain stable model training. Experimental evaluations on four medical image datasets, encompassing CT and ultrasound modalities, demonstrate the superior diagnostic accuracy and reliability of the proposed MPCCI. The code will be available at https://***/Yajie-Zhang/MPCCI. © 2024 Owner/Author.

关键词： Deep learning

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Multi-modal Denoising Diffusion Pre-training for Whole-Slide Image Classification 24

Multi-modal Denoising Diffusion Pre-training for Whole-Slide...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Lou, Wei Li, Guanbin Wan, Xiang Li, Haofeng The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Big Data Computing Shenzhen China

ISBN: (纸本)9798400706868

Whole-slide image (WSI) classification methods play a crucial role in tumor diagnosis. Most of them use hematoxylin and eosin (H&E) stained images, while Immunohistochemistry (IHC) staining provides molecular markers and protein expression information that highlights cancer regions. However, obtaining IHC-stained images requires higher costs in practice. In this work, we propose a multi-modal denoising diffusion pre-training framework that harnesses the advantages of IHC staining to learn visual representations. The framework is trained with the H&E-to-IHC re-staining task and IHC-stained image reconstruction task, which helps capture the structural similarity and staining difference between two image modalities. The trained model can then provide IHC-guided features, by taking only H&E-stained images as inputs. Besides, we build a new class-constraint constrastive loss to achieve the semantic consistency between dual-modal features from our pre-training framework. To integrate with WSI classifiers based on multi-instance learning, we further propose a bag feature augmentation strategy to extend bags with the features extracted by our pre-trained model. Experimental results on three datasets show that our pre-training framework effectively improves WSI classification and surpasses the state-of-the-art pre-training approaches. Code and model are released via https://***/lhaof/MDDP. © 2024 ACM.

关键词： Image reconstruction

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Joint Communication and Synchronization Performance Optimization in Digital Twin Enabled Networks

Joint Communication and Synchronization Performance Optimiza...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yu, Hanzhi Liu, Yuchen Chen, Mingzhe University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States

ISBN: (纸本)9798350351255

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT) that is a virtual representation of the physical network. The considered network includes a physical network where a base station (BS) serves a set of users, and a DNT that evolves with the status of both DNT and the physical network. The BS must use its limited spectrum resources to serve the users, as well as transmit the physical network information to the cloud server for DNT synchronization. Since the DNT can predict the physical network status, the BS may not need to transmit physical network information to the server at each time slot thus saving spectrum resources to serve users. However, if the BS does not transmit physical information to the DNT over a long period of time, the DNT may not be able to represent the physical network accurately. To this end, the BS must determine whether to send physical network information to the server to update DNT and the spectrum resources used for physical network information transmission and serving users. We formulate this resources allocation problem as an optimization problem aiming to maximize the sum of data rates of all users, while minimizing the gap between the states of the physical network and the DNT. The formulated problem is challenging to solve by conventional optimization methods, since the BS may not be able to know the future status of the DNT. To solve this problem, we design a gate recurrent unit (GRU) and soft action-critic (SAC) based algorithm. The GRU enables the DNT to predict its future states by using historical state data, and updating the DNT when the BS does not transmit physical network information. The SAC based algorithm enables the BS to learn the relationship between the physical network information transmission and the future status estimation accuracy of the DNT thus determining whether to transmit physical network information to the cloud server, ensuring an accurac

关键词： Resource allocation

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Measuring Access Inequality in A Hybrid Physical-Virtual World: : A Case Study of Racial Disparity of Healthcare Access During CoVID-19 30

Measuring Access Inequality in A Hybrid Physical-Virtual Wor...

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30th International Conference on Geoinformatics, Geoinformatics 2023

作者： Wu, Meiliu Huang, Qunying Gao, Song University of Wisconsin-Madison Spatial Computing and Data Mining Lab Department of Geography MadisonWI United States University of Wisconsin-Madison Geospatial Data Science Lab Department of Geography MadisonWI United States

ISBN: (纸本)9798350344240

The disparity of resource access (e.g., food and healthcare) among different population groups essentially reflects social inequalities. Emerging information and communications technology (ICT) has facilitated the teleactivities that can replace or complement traditional physical visits, yet existing approaches for measuring access disparity still fail to consider virtual interactions. To this end, this study proposes a unified framework to measure access inequality in both physical and virtual spaces simultaneously, using the POIs' visit patterns from mobile phone data to capture spatial unevenness of accessibility among different groups (physical space), as well as the Household Pulse Survey data to reveal the group disparity of teleactivity access (virtual space). In particular, a novel Access Inequity Index is proposed based on the Information Theory Index and the Theil Index, to reveal the access inequality in physical and virtual spaces respectively. Next, to demonstrate the feasibility of the proposed framework, we examine racial groups and their disparity in physical-virtual healthcare access during the pandemic (April-July 2021) in U.S. 15 most populated metropolitans. Our results indicate: (1) race is a significant risk marker for underlying conditions that affect health, including telehealth access;(2) the usage of telehealth access aligns with the risk for COVID-19 infection, hospitalization, and death by race (e.g., the minority groups Black and Others are more vulnerable and in the higher demand of telehealth service);and (3) residential segregation impacts on the segregated pattern of physical healthcare access by race (e.g., the Black-dominant healthcare access zone highly matches the Black residential cluster in the south of Chicago), while such impacts may differ in different kinds of healthcare services (e.g., physicians, mental health practitioners, and dentists). Compared with traditional single-space approaches, the proposed hybrid-spaces appro

关键词： Information theory

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Remaining Useful Life Prediction of Degraded Systems Based on Multiple Performance Indicators Constructed by Multiple Features

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IEEE Sensors Journal 2025年第11期25卷 19613-19631页

作者： Wu, Bin Shi, Hui Zeng, Jianchao Zhang, Xiaohong Wang, Zhijian Wang, Zuolu Taiyuan University of Science and Technology School of Electronic Information Engineering Taiyuan030024 China North University of China institute for Big Data and Visual Computing Taiyuan030051 China University of Huddersfield Centre for Efficiency and Performance Engineering HuddersfieldHD1 3DH United Kingdom

The health status assessment of industrial equipment is critical to fault prediction and health management technologies. To fully reflect system degradation and accurately predict the remaining useful life (RUL) of a system, the construction of multiple performance indicators is important. Thus, a new state-space model and RUL prediction method is proposed herein based on multiple indicators, considering the three correlation relationships among features and degradation indicators. First, according to the extracted and selected feature signals, various relationships such as the correlations among multiple features, multiple features and indicators, and multiple indicators are analyzed. Then an intuitive multi-indicator state-space model is established considering the three correlations and the identified degradation patterns. On this basis, three failure modes of the system are defined according to the multi-indicator complex correlation conditions, and the corresponding probability density function of the system RUL is calculated. Finally, the PHM2012 Challenge and XJTU-SY datasets are used as examples for experimental verification. Compared with other prediction methods, the proposed method can intuitively reflect the correlation between features and indicators, to solve the problem caused by the hidden random correlation. © 2001-2012 IEEE.

关键词： Prediction models

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Q-Rung Orthopair Fuzzy Sets-Enhanced FMEA for COVID-19 Risk Assessment

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 81-92页

作者： Abdullah, Lazim Awang, Noor Azzah Qiyas, Muhammad Special Interest Group of Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus21030 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Department of Mathematics Riphah International University Failsabad Campus Pakistan

Failure Modes and Effects Analysis (FMEA) is a widely used tool for risk analysis, primarily to identify risk factors affecting system quality. Due to the limitations of the traditional FMEA model, several recent models incorporating advanced fuzzy set extensions have been developed to enhance the reliability of risk assessment outcomes. However, most of these models limit expert flexibility in expressing preferences and often overlook the impact of unequal expert weights and the stability of risk ranking results. This study introduces a new FMEA model based on Q-Rung Orthopair Fuzzy Sets (Q-ROFSs), termed Q-ROFSs-FMEA. Q-ROFSs, an extension of intuitionistic fuzzy sets, introduce a new linguistic term. The Q-ROFSs-FMEA model considers the unequal weights of experts, enabling a dynamic representation of expert preferences. These weights and the linguistic evaluation of risk factors are integrated through an aggregation operator, facilitating consensus among experts. The model is applied to a case study on COVID-19 risk factors, revealing that ‘older age’ (risk priority number 0.000012) is the highest risk factor, while ‘gender’ (risk priority number -0.0037) is the lowest. It is found that the ranking of risk factors determined by the Q-ROFSs-FMEA model is obtained as FM1 FM3 FM6 FM4 FM5 FM7 FM8 FM2 . Furthermore, a comparative analysis indicates consistent ranking results across different models, demonstrating the reliability of the proposed model. The case study and comparative analysis validate the effectiveness and applicability of the Q-ROFSsbased risk assessment model. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Failure modes

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