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Towards Efficient data-Centric Robust Machine Learning with Noise-based Augmentation

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arXiv 2022年

作者： Liu, Xiaogeng Wang, Haoyu Zhang, Yechao Wu, Fangzhou Hu, Shengshan School Of Cyber Science And Engineering National Engineering Research Center For Big Data Technology And System Services Computing Technology And System Lab Hubei Engineering Research Center On Big Data Security Huazhong University Of Science And Technology Wuhan430074 China

The data-centric machine learning aims to find effective ways to build appropriate datasets which can improve the performance of AI models. In this paper, we mainly focus on designing an efficient data-centric scheme to improve robustness for models towards unforeseen malicious inputs in the black-box test settings. Specifically, we introduce a noisedbased data augmentation method which is composed of Gaussian Noise, Salt-and-Pepper noise, and the PGD adversarial perturbations. The proposed method is built on lightweight algorithms and proved highly effective based on comprehensive evaluations, showing good efficiency on computation cost and robustness enhancement. In addition, we share our insights about the data-centric robust machine learning gained from our experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

Collaborative Recommendation Model Based on Multi-modal Multi-view Attention Network: Movie and literature cases

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arXiv 2023年

作者： Hu, Zheng Cai, Shi-Min Wang, Jun Zhou, Tao Compleχ Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China Big Data Research Center University of Electronic Science and Technology of China Chengdu610054 China

The existing collaborative recommendation models that use multi-modal information emphasize the representation of users’ preferences but easily ignore the representation of users’ dislikes. Nevertheless, modelling users’ dislikes facilitates comprehensively characterizing user profiles. Thus, the representation of users’ dislikes should be integrated into the user modelling when we construct a collaborative recommendation model. In this paper, we propose a novel Collaborative Recommendation Model based on Multi-modal multi-view Attention Network (CRMMAN), in which the users are represented from both preference and dislike views. Specifically, the users’ historical interactions are divided into positive and negative interactions, used to model the user’s preference and dislike views, respectively. Furthermore, the semantic and structural information extracted from the scene is employed to enrich the item representation. We validate CRMMAN by designing contrast experiments based on two benchmark MovieLens-1M and Book-Crossing datasets. Movielens-1m has about a million ratings, and Book-Crossing has about 300,000 ratings. Compared with the state-of-the-art knowledge-graph-based and multi-modal recommendation methods, the AUC, NDCG@5 and NDCG@10 are improved by 2.08%, 2.20% and 2.26% on average of two datasets. We also conduct controlled experiments to explore the effects of multi-modal information and multi-view mechanism. The experimental results show that both of them enhance the model’s performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Recommender systems

Contractible Regularization for Federated Learning on Non-IID data

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Contractible Regularization for Federated Learning on Non-II...

IEEE International Conference on data Mining (ICDM)

作者： Zifan Chen Zhe Wu Xian Wu Li Zhang Jie Zhao Yangtian Yan Yefeng Zheng Center for Data Science Peking University Beijing China Tencent Jarvis Lab Tencent Beijing China NELab for Big Data Analysis and Applications Peking University Beijing China

In the medical domain, gathering all data and training a global supervised model is very difficult due to scattered data from different hospitals and security and privacy concerns. In recent years, several federated learning models have been proposed for training over isolated data. These models usually employ a client-server framework: 1) train local models on clients in parallel; 2) aggregate local models on the server to produce a global one. By iterating the above two steps, federated learning aims to approximate the performance of a model centrally trained on data. However, due to the non-IID data distribution issue, local models could deviate from the optimal model resulting in a biased aggregated global model. To address this problem, we propose a contractible regularization (ConTre) to act on the local model’s latent space. On each client, we first project the input data into a latent space and then pose regularization to avoid converging too fast to bad local optima. The proposed regularization can be easily integrated into existing federated learning frameworks without bringing in additional parameters. According to experimental results on multiple natural and medical image datasets, the proposed ConTre can significantly improve the performance of various federated learning frameworks. Our code is available at https://***/czifan/***.

关键词： Training Learning systems Federated learning Hospitals Moon data models Servers

Detector Collapse: Physical-World Backdooring Object Detection to Catastrophic Overload or Blindness in Autonomous Driving

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arXiv 2024年

作者： Zhang, Hangtao Hu, Shengshan Wang, Yichen Zhang, Leo Yu Zhou, Ziqi Wang, Xianlong Zhang, Yanjun Chen, Chao School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab China School of Information and Communication Technology Griffith University Australia University of Technology Sydney Australia RMIT University Australia

Object detection tasks, crucial in safety-critical systems like autonomous driving, focus on pinpointing object locations. These detectors are known to be susceptible to backdoor attacks. However, existing backdoor techniques have primarily been adapted from classification tasks, overlooking deeper vulnerabilities specific to object detection. This paper is dedicated to bridging this gap by introducing Detector Collapse (DC), a brand-new backdoor attack paradigm tailored for object detection. DC is designed to instantly incapacitate detectors (i.e., severely impairing detector's performance and culminating in a denial-of-service). To this end, we develop two innovative attack schemes: SPONGE for triggering widespread misidentifications and BLINDING for rendering objects invisible. Remarkably, we introduce a novel poisoning strategy exploiting natural objects, enabling DC to act as a practical backdoor in real-world environments. Our experiments on different detectors across several benchmarks show a significant improvement (∼10%-60% absolute and ∼2-7× relative) in attack efficacy over state-of-the-art attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Denial-of-service attack

Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

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Optimizing Ethanol Production in Escherichia Coli Using a Hy...

6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

GraphMetaP: Efficient MetaPath Generation for Dynamic Heterogeneous Graph Models

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GraphMetaP: Efficient MetaPath Generation for Dynamic Hetero...

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Haiheng He Dan Chen Long Zheng Yu Huang Haifeng Liu Chaoqiang Liu Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Clusters and Grid Computing Lab Huazhong University of Science and Technology China Zhejiang Lab Hangzhou China

Metapath-based heterogeneous graph models (MHGM) show excellent performance in learning semantic and structural information in heterogeneous graphs. Metapath matching is an essential processing step in MHGM to find all metapath instances, bringing significant overhead compared to the total model execution time. Even worse, in dynamic heterogeneous graphs, metapath instances require to be rematched while graph updated. In this paper, we observe that only a small fraction of metapath instances change and propose GraphMetaP, an efficient incremental metapath maintenance method in order to eliminate the matching overhead in dynamic heterogeneous graphs. GraphMetaP introduces a novel format for metapath instances to capture the dependencies among the metapath instances. The format incrementally maintains metapath instances based on the graph updates to avoide the rematching metapath overhead for the updated graph. Furthermore, GraphMetaP uses the fold way to simplify the format in order to recover all metapath instances faster. Experiments show that GraphMetaP enables efficient maintenance of metapath instances on dynamic heterogeneous graphs and outperforms 172.4X on average compared to the matching metapath method.

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ECLIPSE: Expunging Clean-label Indiscriminate Poisons via Sparse Diffusion Purification

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arXiv 2024年

作者： Wang, Xianlong Hu, Shengshan Zhang, Yechao Zhou, Ziqi Zhang, Leo Yu Xu, Peng Wan, Wei Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Information and Communication Technology Griffith University SouthportQLD4215 Australia

Clean-label indiscriminate poisoning attacks add invisible perturbations to correctly labeled training images, thus dramatically reducing the generalization capability of the victim models. Recently, defense mechanisms such as adversarial training, image transformation techniques, and image purification have been proposed. However, these schemes are either susceptible to adaptive attacks, built on unrealistic assumptions, or only effective against specific poison types, limiting their universal applicability. In this research, we propose a more universally effective, practical, and robust defense scheme called ECLIPSE. We first investigate the impact of Gaussian noise on the poisons and theoretically prove that any kind of poison will be largely assimilated when imposing sufficient random noise. In light of this, we assume the victim has access to an extremely limited number of clean images (a more practical scene) and subsequently enlarge this sparse set for training a denoising probabilistic model (a universal denoising tool). We then introduce Gaussian noise to absorb the poisons and apply the model for denoising, resulting in a roughly purified dataset. Finally, to address the trade-off of the inconsistency in the assimilation sensitivity of different poisons by Gaussian noise, we propose a lightweight corruption compensation module to effectively eliminate residual poisons, providing a more universal defense approach. Extensive experiments demonstrate that our defense approach outperforms 10 state-of-the-art defenses. We also propose an adaptive attack against ECLIPSE and verify the robustness of our defense scheme. Our code is available at https://***/CGCL-codes/ECLIPSE. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

DomainVoyager: Embracing The Unknown Domain by Prompting for Automatic Augmentation

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DomainVoyager: Embracing The Unknown Domain by Prompting for...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Chuang Liu Haogang Zhu Xiu Su State Key Laboratory of Software Development Environment Lab Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Zhongfa Aviation Institute Beihang University Hangzhou P.R.China Zhongguancun Laboratory Beijing China Big Data Institute Central South University Changsha China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

For medical image analysis, domain generalization (DG) faces significant challenges due to variances in data across medical imaging devices. Addressing this, we introduce Prompt Guided Domain Aligning Augmentation (PGDAA), a novel approach that harnesses Large Language Models (LLMs) to iteratively refine data augmentation sequences in DG for medical segmentations. Specifically, by harnessing the LLM’s advanced capabilities in interpreting and responding to specific prompts, PGDAA iteratively voyages the parameter searching space for data augmentation, identifying optimal augmentation sequences. In each searching round, the LLM leverages provided prior data augmentation methods, their parameters, and corresponding evaluation results to acquire a sufficient performance memory bank, thereby proposing more effective augmentation sequences to significantly narrowing inter-domain gaps. Notably, this integration of LLMs in DG represents a pioneering application in the field, which adds minor training parameters and can be easily combined with other DG benchmarks for further improvements. Comprehensive experiments reveal our method outperforms the baseline by 4.47% and 5.08% on Fundus and Prostate datasets, achieving 90.10% and 89.28% accuracy, respectively.

关键词： Training Image segmentation Image analysis Accuracy Large language models Benchmark testing data augmentation

FEDGT: FEDERATED NODE CLASSIFICATION WITH SCAlabLE GRAPH TRANSFORMER

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arXiv 2024年

作者： Zhang, Zaixi Hu, Qingyong Yu, Yang Gao, Weibo Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Hong Kong University of Science and Technology Hong Kong

Graphs are widely used to model relational data. As graphs are getting larger and larger in real-world scenarios, there is a trend to store and compute subgraphs in multiple local systems. For example, recently proposed subgraph federated learning methods train Graph Neural Networks (GNNs) distributively on local subgraphs and aggregate GNN parameters with a central server. However, existing methods have the following limitations: (1) The links between local subgraphs are missing in subgraph federated learning. This could severely damage the performance of GNNs that follow message-passing paradigms to update node/edge features. (2) Most existing methods overlook the subgraph heterogeneity issue, brought by subgraphs being from different parts of the whole graph. To address the aforementioned challenges, we propose a scalable Federated Graph Transformer (FedGT) in the paper. Firstly, we design a hybrid attention scheme to reduce the complexity of the Graph Transformer to linear while ensuring a global receptive field with theoretical bounds. Specifically, each node attends to the sampled local neighbors and a set of curated global nodes to learn both local and global information and be robust to missing links. The global nodes are dynamically updated during training with an online clustering algorithm to capture the data distribution of the corresponding local subgraph. Secondly, FedGT computes clients' similarity based on the aligned global nodes with optimal transport. The similarity is then used to perform weighted averaging for personalized aggregation, which well addresses the data heterogeneity problem. Finally, extensive experimental results on 6 datasets and 2 subgraph settings demonstrate the superiority of FedGT. © 2024, CC BY.

关键词： Graph neural networks