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41st International Conference on Machine Learning, ICML 2024

作者： Wen, Jie Deng, Shijie Wong, Waikeung Chao, Guoqing Huang, Chao Fei, Lunke Xu, Yong Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China School of Fashion and Textiles The Hong Kong Polytechnic University Hong Kong Laboratory for Artificial Intelligence in Design Hong Kong School of Computer Science and Technology Harbin Institute of Technology Weihai China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China School of Computer Science and Technology Guangdong University of Technology Guangzhou China

As a branch of clustering, multi-view clustering has received much attention in recent years. In practical applications, a common phenomenon is that partial views of some samples may be missing in the collected multi-view data, which poses a severe challenge to design the multi-view learning model and explore complementary and consistent information. Currently, most of the incomplete multi-view clustering methods only focus on exploring the information of available views while few works study the missing view recovery for incomplete multi-view learning. To this end, we propose an innovative diffusion-based missing view generation (DMVG) network. Moreover, for the scenarios with high missing rates, we further propose an incomplete multi-view data augmentation strategy to enhance the recovery quality for the missing views. Extensive experimental results show that the proposed DMVG can not only accurately predict missing views, but also further enhance the subsequent clustering performance in comparison with several state-of-the-art incomplete multi-view clustering methods. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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An Integrative Machine Learning Strategy for the Prognosis of Heart Disease 1

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Hossain, Md. Ashraful Nuruzzaman Nobel, S.M. Mohsin Kabir, Md. Mridha, M.F. Shin, Jungpil Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka1216 Bangladesh Superior Polytechnic School University of Girona Girona17071 Spain Department of Computer Science American International University-Bangladesh Dhaka1229 Bangladesh Department of Computer Science and Engineering University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9789819761036

ISBN: (纸本)9789819761029

The incidence of heart disease is concerning, and timely detection is essential for improving the health of patients. Machine learning has shown potential in assisting in the prognosis of heart conditions. This research evaluated the effectiveness of various machine learning algorithms, including Random Forest, XGBoost, LGBM, and CatBoost, in accurately predicting instances of heart disease. Furthermore, we suggested a hybrid model that integrates all four models, yielding a 97.50% accuracy rate, 0.98% precision value, 0.97% recall value, and 0.98% F1-score. This hybrid model surpasses both individual models and current methodologies. The study utilized the "Heart Failure Prediction Dataset" acquired from Kaggle, which consisted of 11 prevalent variables such as age, blood pressure, and cholesterol levels. These findings indicate that the hybrid model has promise as a powerful tool for predicting heart disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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A federated learning attack method based on edge collaboration via cloud

A federated learning attack method based on edge collaborati...

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作者： Yang, Jie Baker, Thar Gill, Sukhpal Singh Yang, Xiaochuan Han, Weifeng Li, Yuanzhang School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Computer Science Beijing Institute of Technology Beijing China School of Mechanical Manufacturing and Automation Engineer of China Railway Tunnel Bureau Group Co. Ltd Henan Zhengzhou China

Federated learning (FL) is widely used in edge-cloud collaborative training due to its distributed architecture and privacy-preserving properties without sharing local data. FLTrust, the most state-of-the-art FL defense method, is a federated learning defense system with trust guidance. However, we found that FLTrust is not very robust. Therefore, in the edge collaboration scenario, we mainly study the poisoning attack on the FLTrust defense system. Due to the aggregation rule, FLTrust, with trust guidance, the model updates of participants with a significant deviation from the root gradient direction will be eliminated, which makes the poisoning effect on the global model not obvious. To solve this problem, under the premise of not being deleted by the FLTrust aggregation rules, we construct malicious model updates that deviate from the trust gradient to the greatest extent to achieve model poisoning attacks. First, we utilize the rotation of high-dimensional vectors around axes to construct malicious vectors with fixed orientations. Second, the malicious vector is constructed by the gradient inversion method to achieve an efficient and fast attack. Finally, a method of optimizing random noise is used to construct a malicious vector with a fixed direction. Experimental results show that our attack method reduces the model accuracy by 20%, severely undermining the usability of the model. Attacks are also successful hundreds of times faster than the FLTrust adaptive attack method. © 2022 John Wiley & Sons Ltd.

关键词： Vectors

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Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation 38

Addressing Asynchronicity in Clinical Multimodal Fusion via ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yao, Wenfang Liu, Chen Yin, Kejing Cheung, William K. Qin, Jing School of Nursing The Hong Kong Polytechnic University Hong Kong Department of Computer Science Hong Kong Baptist University Hong Kong School of Software Engineering South China University of Technology China

Integrating multi-modal clinical data, such as electronic health records (EHR) and chest X-ray images (CXR), is particularly beneficial for clinical prediction tasks. However, in a temporal setting, multi-modal data are often inherently asynchronous. EHR can be continuously collected but CXR is generally taken with a much longer interval due to its high cost and radiation dose. When clinical prediction is needed, the last available CXR image might have been outdated, leading to suboptimal predictions. To address this challenge, we propose DDL-CXR, a method that dynamically generates an up-to-date latent representation of the individualized CXR images. Our approach leverages latent diffusion models for patient-specific generation strategically conditioned on a previous CXR image and EHR time series, providing information regarding anatomical structures and disease progressions, respectively. In this way, the interaction across modalities could be better captured by the latent CXR generation process, ultimately improving the prediction performance. Experiments using MIMIC datasets show that the proposed model could effectively address asynchronicity in multimodal fusion and consistently outperform existing methods. © 2024 Neural information processing systems foundation. All rights reserved.

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Towards Deploying Parallelized Service Function Chains Under Dynamic Resource Request in Multi-Access Edge Computing

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IEEE Transactions on Network and Service Management 2024年

作者： Zhang, Dongliang Wang, Lei Rezaeipanah, Amin Henan Open University School of Information Engineering and Artificial Intelligence Zhengzhou450046 China Henan Polytechnic University School of Computer Science and Technology Jiaozuo454003 China Persian Gulf University Department of Computer Engineering Bushehr Iran

Resource distribution policy and how to assemble the Service Function Chain (SFC) in Multi-access Edge Computing (MEC) networks to meet service quality standards poses an important challenge for Network Function Virtualization (NFV) technology. Increasing the number of Virtual Network Functions (VNFs) leads to high-latency SFC assembly, which can be countered by network function parallelization. However, existing studies parallelize VNF for resource allocation in MEC by assuming that the demanded resources do not change during SFC assembly. To address these issues, this paper develops a Latencyaware VNF Parallelization strategy under Resource demand Uncertainty (LVPRU) in MEC. We formulate LVPRU under the assumption of resource uncertainty in MEC via Quadratic Integer Programming (QIP) and show that the problem is *** parallelizes VNFs by discovering dependencies between them and assembles multiple sub-SFCs instead of the original SFC. We apply Asynchronous Advantage Actor-Critic (A3C) as a deep reinforcement learning algorithm to assemble sub-SFCs. We finally evaluate the performance of LVPRU through trace-driven simulations. The evaluation results of proposed strategy are promising in different scenarios compared to benchmark algorithms. © 2004-2012 IEEE.

关键词： Network function virtualization

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Deep Learning-based Techniques for Precise Vehicle Detection and Distance Estimation in Autonomous Systems 11

Deep Learning-based Techniques for Precise Vehicle Detection...

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11th IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2023

作者： Srinivasa, C. Suresha, R. Manohar, N. Dharun, G.K. Sheela, T. Jipeng, Tian Department of Computer Science School of Computing Mysuru Amrita Vishwa Vidyapeetham India Mysuru India Computer Science College Zhongyuan University of Technology Henan Province Zhengzhou China

ISBN: (纸本)9798350360059

Vehicle detection, recognition, and distance estimation play a crucial role in computer vision, particularly in applications such as intelligent traffic systems. However, existing methods need help in achieving accurate vehicle detection, recognition, and distance estimation, often resulting in low detection accuracy and limited distance estimation capability. This paper proposes an integrated approach combining Faster R-CNN architecture with scale-based distance estimation to address these issues. Through experimental evaluations, the results validate the effectiveness of the proposed method in achieving 93.5%, 95.6%, and high detection accuracy, recognition accuracy, and accurate distance estimation. The proposed framework shows the potential to enhance the performance and robustness of vehicle detection and distance estimation systems, offering promising advancements in this field. ©2023 IEEE.

关键词： Intelligent systems

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On the parameterized complexity of minimum/maximum degree vertex deletion on several special graphs

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Frontiers of computer science 2023年第4期17卷 97-107页

作者： Jia LI Wenjun LI Yongjie YANG Xueying YANG School of Information Engineering Hunan Industry PolytechnicChangsha 410036China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha 410015China Chair of Economic Theory Saarland UniversitySaarbrücken 66123Germany

In the minimum degree vertex deletion problem,we are given a graph,a distinguished vertex in the graph,and an integer κ,and the question is whether we can delete at most κ vertices from the graph so that the distinguished vertex has the unique minimum *** maximum degree vertex deletion problem is defined analogously but here we want the distinguished vertex to have the unique maximum *** is known that both problems areΨ-hard and fixed-parameter intractable with respect to some natural *** this paper,we study the(parameterized)complexity of these two problems restricted to split graphs,p-degenerate graphs,and planar *** study provides a comprehensive complexity landscape of the two problems restricted to these special graphs.

关键词： minimum degree maximum degree vertex deletion split graphs planar graphs parameterized complexity

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Dependence of nitrogen vacancy color centers on nitrogen concentration in synthetic diamond

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Chinese Physics B 2022年第4期31卷 534-537页

作者： Yong Li Xiaozhou Chen Maowu Ran Yanchao She Zhengguo Xiao Meihua Hu Ying Wang Jun An Department of Physics and Electrical Engineering Tongren UniversityTongren 554300China School of Mechanical Electronic and Information Engineering China University of Mining and TechnologyBeijing 100083China School of Materials Science and Engineering Henan Polytechnic UniversityJiaozuo 454003China

Crystallization of diamond with different nitrogen concentrations was carried out with a FeNiCo-C system at pressure of 6.5 *** the nitrogen concentration in diamond increased,the color of the synthesized diamond crystals changed from colorless to yellow and finally to atrovirens(a dark green).All the Raman peaks for the obtained crystals were located at about 1330 cm^(-1)and contained only the sp^(3)hybrid diamond *** on Fourier transform infrared results,the nitrogen concentration of the colorless diamond was<1 ppm and absorption peaks corresponding to nitrogen impurities were not ***,the C-center nitrogen concentration of the atrovirens diamond reached 1030 ppm and the value of A-center nitrogen was approximately 180 ppm with a characteristic absorption peak at 1282 cm^(-1).Furthermore,neither the NV^(0)nor the NV^(-)optical color center existed in diamond crystal with nitrogen impurities of less than 1 ppm by photoluminescence ***,Ni-related centers located at 695 nm and 793.6 nm were observed in colorless *** NE8 color center at 793.6 nm has more potential for application than the common NV ***^(0)and NV^(-)optical color centers coexist in diamond without any additives in the synthesis ***,only the NV^(-)color center was noticed in diamond with a higher nitrogen concentration,which maximized optimization of the NV^(-)/NV^(0)ratio in the diamond *** study has provided a new way to prepare diamond containing only NV^(-)optical color centers.

关键词： high pressure and high temperature diamond nitrogen-vacancy color centers

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Image-assisted Label Connective Completion for Vessel Segmentation with Insufficient Annotations

Image-assisted Label Connective Completion for Vessel Segmen...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaoqi Zheng Baoyao Yang Xiuwen Fang Wenfang Yao Mang Ye School of Computer Science and Technology Guangdong University of Technology School of Computer Science Wuhan University School of Nursing The Hong Kong Polytechnic University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Automatic and accurate vessel segmentation is crucial for disease diagnosis. Deep learning methods are widely used, but their promising results rely on accurately annotated data. Due to complex vessel morphology and low-contrast image, accurate vessel delineation poses a practical challenge, resulting in insufficient annotations, which is a prominent form of noisy labels. This paper proposes an Image-assisted Label Connective Completion method, which enhances label’s vessel information by images under the supervision of connectivity to address insufficient annotation issue. Specifically, we develop an Image-guided Vessel Enhancement module, which transmits structural information extracted from images based on label navigation to label space, promoting completion of missing annotated parts in original labels. In addition, a branch completion-connectivity loss is designed and introduced as an auxiliary supervision to prevent vessel branch disconnection during label completion. Experimental results on DRIVE, CHASE DB1 and DCA1 datasets demonstrate that our method outperforms existing noisy labels learning methods.

关键词： Learning systems Image segmentation Accuracy Annotations Navigation Morphology Signal processing Noise measurement Medical diagnosis Speech processing

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Towards Accurate and Efficient Power Load Forecasting: A Cooperative Game-Based Optimization 7

Towards Accurate and Efficient Power Load Forecasting: A Coo...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Xue, Yisheng Xue, Yixun School of Computer Science Xi'an Polytechnic University Xi'an China College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan China

ISBN: (纸本)9798350345094

Power load forecasting has been widely used in the electrical industry, but its forecasting accuracy and efficiency tuning is challenging. Recent studies have attempted to address this problem using machine learning (ML). Nevertheless, there are two drawbacks. First, the prediction accuracy is heavily dependent on model hyperparameters. Second, model training on a large volume of data takes a long time overhead. To address these problems, we propose a novel approach called CogBO based on Bayesian Optimisation (BO) with cooperative games for the selection of the search space for hyperparameter weight analysis and Bayesian Optimisation during model training. CogBO uses two strategies. The first one is named WGS, which collects hyperparameters prior to model training and pre-analyses their impact on prediction accuracy through a combinatorial game. The second is called BOK, which is based on a Bayesian optimisation algorithm to repartition the parameter space by combining the degree of influence of the weights of the hyperparameters in WGS. We use a real test dataset to set up baseline experiments comparing linear analysis, regression analysis, and combinatorial game analysis with and without Bayesian optimisation for evaluating CogBO. The results in our experimental setting show that CogBO improves the prediction accuracy by almost 13% by the combinatorial game weighting of hyperparameters, while at the same time reduces time overhead by almost 30% on average, © 2023 IEEE.

关键词： Regression analysis

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