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Proceedings of the 41st International Conference on Machine Learning

作者： Xue Yu Muchen Li Yan Leng Renjie Liao Center for Applied Statistics School of Statistics Renmin University of China Beijing China and Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Department of Computer Science University of British Columbia Vancouver BC Canada McCombs School of Business The University of Texas at Austin Austin TX Vector Institute for AI and Canada CIFAR AI Chair and Department of Electrical and Computer Engineering University of British Columbia Vancouver BC Canada

In network games, individuals interact strategically within network environments to maximize their utilities. However, obtaining network structures is challenging. In this work, we propose an unsupervised learning model, called data-dependent gated-prior graph variational autoencoder (GPGVAE), that infers the underlying latent interaction type (strategic complement vs. substitute) among individuals and the latent network structure based on their observed actions. Specially, we propose a spectral graph neural network (GNN) based encoder to predict the interaction type and a data-dependent gated prior that models network structures conditioned on the interaction type. We further propose a Transformer based mixture of Bernoulli encoder of network structures and a GNN based decoder of game actions. We systematically study the Monte Carlo gradient estimation methods and effectively train our model in a stage-wise fashion. Extensive experiments across various synthetic and real-world network games demonstrate that our model achieves state-of-the-art performances in inferring network structures and well captures interaction types.

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

arXiv

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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

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PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

arXiv

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arXiv 2024年

作者： Zhou, Man Su, Shuao Wang, Qian Li, Qi Zhou, Yuting Ma, Xiaojing Li, Zhengxiong School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China Institute for Network Sciences and Cyberspace Tsinghua University China Department of Computer Science and Engineering University of Colorado Denver United States Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Fingerprint authentication has been extensively employed in contemporary identity verification systems owing to its rapidity and cost-effectiveness. Due to its widespread use, fingerprint leakage may cause sensitive information theft, enormous economic and personnel losses, and even a potential compromise of national security. As a fingerprint that can coincidentally match a specific proportion of the overall fingerprint population, MasterPrint rings the alarm bells for the security of fingerprint authentication. In this paper, we propose a new side-channel attack on the minutiae-based Automatic Fingerprint Identification System (AFIS), called PrintListener, which leverages users’ fingertip swiping actions on the screen to extract fingerprint pattern features (the first-level features) and synthesizes a stronger targeted PatternMasterPrint with potential second-level features. The attack scenario of PrintListener is extensive and covert. It only needs to record users’ fingertip friction sound and can be launched by leveraging a large number of social media platforms. Extensive experimental results in real-world scenarios show that Printlistener can significantly improve the attack potency of MasterPrint. © 2024, CC0.

关键词： Friction

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Tracking Objects and Activities with Attention for Temporal Sentence Grounding

Tracking Objects and Activities with Attention for Temporal ...

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

作者： Zeyu Xiong Daizong Liu Pan Zhou Jiahao Zhu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wangxuan Institute of Computer Technology Peking University

Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets’ behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed.

关键词： Solid modeling Target tracking Three-dimensional displays Grounding Benchmark testing Signal processing Feature extraction

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SMOTE Oversampling and Near Miss Undersampling Based Diabetes Diagnosis from Imbalanced dataset with Xai Visualization

SMOTE Oversampling and Near Miss Undersampling Based Diabete...

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IEEE Symposium on computers and Communications (ISCC)

作者： Nasim Mahmud Nayan Ashraful Islam Muhammad Usama Islam Eshtiak Ahmed Mohammad Mobarak Hossain Md Zahangir Alam Department of Computer Science and Engineering University of Information Technology and Sciences Bangladesh Center for Computational and Data Sciences Independent University Bangladesh Department of Computer Science and Engineering Independent University Bangladesh School of Computing and Informatics University of Louisiana at Lafayette USA Department of Computer Science and Engineering Daffodil International University Bangladesh

This study investigated the predictive ability of ten different machine learning (ML) models for diabetes using a dataset that was not evenly distributed. Additionally, the study evaluated the effectiveness of two oversampling and undersampling methods, namely the Synthetic Minority Oversampling Technique (SMOTE) and the Near-Miss algorithm. Explainable Artificial Intelligence (Xai) techniques were employed to enhance the interpretability of the model's predictions. The results indicate that the extreme gradient boosting (XGB) model combined with SMOTE oversampling technique exhibited the highest accuracy and an F1-score of 99% and 1.00 respectively. Furthermore, the utilization of Xai methods increased the dependability of the model's decision-making process, rendering it more appropriate for clinical use. These results imply that integrating Xai with ML and oversampling techniques can enhance the early detection and management of diabetes, leading to better diagnosis and intervention.

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From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models

From Mimicking to Integrating: Knowledge Integration for Pre...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Li, Lei Lin, Yankai Ren, Xuancheng Zhao, Guangxiang Li, Peng Zhou, Jie Sun, Xu MOE Key Lab of Computational Linguistics School of Computer Science Peking University China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Tsinghua University China Pattern Recognition Center WeChat AI Tencent Inc. China

Investigating better ways to reuse the released pre-trained language models (PLMs) can significantly reduce the computational cost and the potential environmental side-effects. This paper explores a novel PLM reuse paradigm, Knowledge Integration (KI). Without human annotations available, KI aims to merge the knowledge from different teacher-PLMs, each of which specializes in a different classification problem, into a versatile student model. To achieve this, we first derive the correlation between virtual golden supervision and teacher predictions. We then design a Model Uncertainty-aware Knowledge Integration (MUKI) framework to recover the golden supervision for the student. Specifically, MUKI adopts Monte-Carlo Dropout to estimate model uncertainty for the supervision integration. An instance-wise re-weighting mechanism based on the margin of uncertainty scores is further incorporated, to deal with the potential conflicting supervision from teachers. Experimental results demonstrate that MUKI achieves substantial improvements over baselines on benchmark datasets. Further analysis shows that MUKI can generalize well for merging teacher models with heterogeneous architectures, and even teachers major in cross-lingual datasets. © 2022 Association for Computational Linguistics.

关键词： Integration

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Scheduling data flow between data centers based on software defined network 3

Scheduling data flow between data centers based on software ...

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2021 3rd International Conference on Artificial Intelligence and computer science, aiCS 2021

作者： Hu, Siquan Huang, Zhao Shi, Zhiguo School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan China Beijing Big Data Center Beijing China

For intelligent control of network traffic, to solve the problem of low utilization of public network bandwidth, we put forward scheduling data flow between data center based on SDN technology. First, we connect the data center distributed in different locations by wide area network and exchange or share the research data of high performance. Then, the system can sense the utilization and performance of the network bandwidth resources between the data center. Based on the results of perception, we can dynamically adjust and optimize the path of data and maximize the utilization of network link. SDN is a new software based network structure. This paper uses SDN network architecture to schedule the traffic of data center, and constructs a two-layer network suitable for cloud computing and virtualization environment. It realizes the global, dynamic and intelligent data flow. © Journal of Physics: Conference Series 2021.

关键词： Wide area networks

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A software defined network scheme for intra datacenter network based on Fat-tree topology 3

A software defined network scheme for intra datacenter netwo...

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2021 3rd International Conference on Artificial Intelligence and computer science, aiCS 2021

作者： Hu, Siquan Wang, Xinyu Shi, Zhiguo School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan China Beijing Big Data Center Beijing China

Nowadays datacenters face a big challenge, because the development of cloud services requests a large amount of capital input, high maintenance skills and cost. The major disadvantages of current datacenter network include the high cost of equipment and maintenance, QoS, failure recovery time, network virtualization etc. In this paper, we proposed a software defined datacenter network. The programmable controller is responsible for the management of the traffic scheduling including topology discovery, routing and Quality of Service. The SDN is based on Fat-tree network architecture and max-min fairness. A Genetic Algorithm optimized Radial Basis Function neural network is used to compute the service weight for maximizing the utilization of network resource. An experiment of the network bearing different kinds of services based on Openflow is described. This well-designed fat-tree network has high efficiency in traffic distribution, and has integrated the traditional network architecture and new SDN technology. © Journal of Physics: Conference Series 2021.

关键词： Quality of service

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GradedDAG: An Asynchronous DAG-based BFT Consensus with Lower Latency

GradedDAG: An Asynchronous DAG-based BFT Consensus with Lowe...

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Reliable Distributed Systems

作者： Xiaohai Dai Zhaonan Zhang Jiang Xiao Jingtao Yue Xia Xie Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science and Technology Hainan University China

To enable parallel processing, the Directed Acyclic Graph (DAG) structure is introduced to the design of asyn-chronous Byzantine Fault Tolerant (BFT) consensus protocols, known as DAG-based BFT. Existing DAG-based BFT protocols operate in successive waves, with each wave containing three or four Reliable Broadcast (RBC) rounds to broadcast data, resulting in high latency due to the three communication steps required in each RBC. For instance, Tusk, a state-of-the-art DAG-based BFT protocol, has a good-case latency of 7 communication steps and an expected worst latency of 21 communication steps. To reduce latency, we propose GradedDAG, a new DAG-based BFT consensus protocol based on our adapted RBC called Graded RBC (GRBC) and the Consistent Broadcast (CBC), with each wave consisting of only one GRBC round and one CBC round. Through GRBC, a replica can deliver data with a grade of 1 or 2, and a non-faulty replica delivering the data with grade 2 can ensure that more than 2/3 of replicas have delivered the same data. Meanwhile, through CBC, data delivered by different non-faulty replicas must be identical. In each wave, a block in the GRBC round will be elected as the leader. If a leader block has been delivered with grade 2, it and all its ancestor blocks can be committed. GradedDAG offers a good-case latency of 4 communication steps and an expected worst latency of 7.5 communication steps, significantly lower than the state-of-the-art. Experimental results demonstrate GradedDAG's feasibility and efficiency.

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A Hybrid Model of Deep Learning Features and Clinical Features for Severe Cases Predication of COVID-19

A Hybrid Model of Deep Learning Features and Clinical Featur...

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Medical Imaging 2023: computer-aided Diagnosis

作者： Kitrungrotsakul, Titinunt Chen, Qingqing Wu, Huitao Srichola, Preeyanuch Hu, Hongjie Zhu, Wenchao Chen, Chao Xu, Fangyi Zhou, Yong Lin, Lanfen Tong, Ruofeng Li, Jingsong Chen, Yen-Wei Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China Intelligent Healthcare Team Baidu Inc. Beijing China Kasetsart Agricultural and Agro-Industrial Product Improvement Institute Kasetsart University Bangkok Thailand College of Computer Science and Technology Zhejiang University Hangzhou China Graduate School of Information Science and Engineering Ritsumeikan University Shiga Japan

ISBN: (纸本)9781510660359

COVID-19 has spread around the world since 2019. Approximately 6.5% of COVID-19 a risk of developing severe disease with high mortality rate. To reduce the mortality rate and provide appropriate treatment, this research established an integrated models with to predict the clinical outcome of COVID-19 patients with clinical, deep learning and radiomics features. To obtain the optimal feature combination for prediction, 9 clinical features combination was selected from all available clinical factors after using LASSO, 18 deep learning features from U-Net architecture, and 9 radiomics features from segmentation result. A total of 213 COVID-19 patients and 335 non-COVID-19 patients from 5 hospitals were enrolled and used as training and test sample in this research. The proposed model obtained an accuracy, precision, recall, specificity, F1-score and ROC curve of 0.971, 0.943, 0.937, 0.974, 0.941 and 0.979, respectively, which exceeds the related work using only clinical, deep learning or radiomics factors. © 2023 SPIE.

关键词： COVID-19

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