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arXiv 2022年

作者： Bai, Jiawang Wu, Baoyuan Li, Zhifeng Xia, Shu-Tao The Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518057 China The School of Data Science Chinese University of Hong Kong Shenzhen China The Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data 518100 China Tencent Data Platform Shenzhen518057 China

To explore the vulnerability of deep neural networks (DNNs), many attack paradigms have been well studied, such as the poisoning-based backdoor attack in the training stage and the adversarial attack in the inference stage. In this paper, we study a novel attack paradigm, which modifies model parameters in the deployment stage. Considering the effectiveness and stealthiness goals, we provide a general formulation to perform the bit-flip based weight attack, where the effectiveness term could be customized depending on the attacker's purpose. Furthermore, we present two cases of the general formulation with different malicious purposes, i.e., single sample attack (SSA) and triggered samples attack (TSA). To this end, we formulate this problem as a mixed integer programming (MIP) to jointly determine the state of the binary bits (0 or 1) in the memory and learn the sample modification. Utilizing the latest technique in integer programming, we equivalently reformulate this MIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method. Consequently, the flipped critical bits can be easily determined through optimization, rather than using a heuristic strategy. Extensive experiments demonstrate the superiority of SSA and TSA in attacking DNNs. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Snapshot boosting: a fast ensemble framework for deep neural networks

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science China(Information sciences) 2020年第1期63卷 77-88页

作者： Wentao ZHANG Jiawei JIANG Yingxia SHAO Bin CUI Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications Department of Computer Science Beijing Key Lab of Intelligent Telecommunications Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications Key Lab of High Confidence Software Technologies Department of Computer SciencePeking University

Boosting has been proven to be effective in improving the generalization of machine learning models in many fields. It is capable of getting high-diversity base learners and getting an accurate ensemble model by combining a sufficient number of weak learners. However, it is rarely used in deep learning due to the high training budget of the neural network. Another method named snapshot ensemble can significantly reduce the training budget, but it is hard to balance the tradeoff between training costs and diversity. Inspired by the ideas of snapshot ensemble and boosting, we propose a method named snapshot boosting. A series of operations are performed to get many base models with high diversity and accuracy, such as the use of the validation set, the boosting-based training framework, and the effective ensemble strategy. Last, we evaluate our method on the computer vision(CV) and the natural language processing(NLP) tasks, and the results show that snapshot boosting can get a more balanced trade-off between training expenses and ensemble accuracy than other well-known ensemble methods.

关键词： ensemble learning deep learning boosting neural network snapshot ensemble

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A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systems

arXiv

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arXiv 2024年

作者： Zhu, Lixi Huang, Xiaowen Sang, Jitao School of Computer Science and Technology Beijing Jiaotong University Beijing China School of Computer Science and Technology Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Beijing Jiaotong University Ministry of Education Beijing China

Conversational Recommender System (CRS) leverages real-time feedback from users to dynamically model their preferences, thereby enhancing the system’s ability to provide personalized recommendations and improving the overall user experience. CRS has demonstrated significant promise, prompting researchers to concentrate their efforts on developing user simulators that are both more realistic and trustworthy. The emergence of Large Language Models (LLMs) has marked the onset of a new epoch in computational capabilities, exhibiting human-level intelligence in various tasks. Research efforts have been made to utilize LLMs for building user simulators to evaluate the performance of CRS. Although these efforts showcase innovation, they are accompanied by certain limitations. In this work, we introduce a Controllable, Scalable, and Human-Involved (CSHI) simulator framework that manages the behavior of user simulators across various stages via a plugin manager. CSHI customizes the simulation of user behavior and interactions to provide a more lifelike and convincing user interaction experience. Through experiments and case studies in two conversational recommendation scenarios, we show that our framework can adapt to a variety of conversational recommendation settings and effectively simulate users’ personalized preferences. Consequently, our simulator is able to generate feedback that closely mirrors that of real users. This facilitates a reliable assessment of existing CRS studies and promotes the creation of high-quality conversational recommendation datasets. © 2024, CC BY-NC-ND.

关键词： Recommender systems

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Exploring the Privacy Protection Capabilities of Chinese Large Language Models

arXiv

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arXiv 2024年

作者： Yang, Yuqi Huang, Xiaowen Sang, Jitao School of Computer Science and Technology Beijing Jiaotong University Beijing China School of Computer Science and Technology Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Key Laboratory of Big Data Artificial Intelligence in Transportation Beijing Jiaotong University Ministry of Education Beijing China

Large language models (LLMs), renowned for their impressive capabilities in various tasks, have significantly advanced artificial intelligence. Yet, these advancements have raised growing concerns about privacy and security implications. To address these issues and explain the risks inherent in these models, we have devised a three-tiered progressive framework tailored for evaluating privacy in language systems. This framework consists of progressively complex and in-depth privacy test tasks at each tier. Our primary objective is to comprehensively evaluate the sensitivity of large language models to private information, examining how effectively they discern, manage, and safeguard sensitive data in diverse scenarios. This systematic evaluation helps us understand the degree to which these models comply with privacy protection guidelines and the effectiveness of their inherent safeguards against privacy breaches. Our observations indicate that existing Chinese large language models universally show privacy protection shortcomings. It seems that at the moment this widespread issue is unavoidable and may pose corresponding privacy risks in applications based on these models. © 2024, CC BY.

关键词： Sensitive data

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ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism

ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognos...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinlei Yu Ahmed Elazab Ruiquan Ge Hui Jin Xinchen Jiang Gangyong Jia Qing Wu Qinglei Shi Changmiao Wang School of Computer Science Hangzhou Dianzi University Hangzhou China School of Biomedical Engineering Shenzhen University Shenzhen China School of Management and Economics The Chinese University of Hong Kong Shenzhen Shenzhen China School of Medicine The Chinese University of Hong Kong Shenzhen Shenzhen China Medical Big Data Lab Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Intracerebral hemorrhage (ICH) is the most fatal subtype of stroke and is characterized by a high incidence of disability. Accurate segmentation of the ICH region and prognosis prediction are critically important for developing and refining treatment plans for post-ICH patients. However, existing approaches address these two tasks independently and predominantly focus on imaging data alone, thereby neglecting the intrinsic correlation between the tasks and modalities. This paper introduces a multi-task network, ICH-SCNet, designed for both ICH segmentation and prognosis classification. Specifically, we integrate a SAM-CLIP cross-modal interaction mechanism that combines medical text and segmentation auxiliary information with neuroimaging data to enhance cross-modal feature recognition. Additionally, we develop an effective feature fusion module and a multi-task loss function to improve performance further. Extensive experiments on an ICH dataset reveal that our approach surpasses other state-of-the-art methods. It excels in the overall performance of classification tasks and outperforms competing models in all segmentation task metrics.

关键词： Neuroimaging Measurement Image segmentation Correlation Text recognition Refining Multitasking Prognostics and health management Hemorrhaging Biomedical imaging

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Container lifecycle-aware scheduling for serverless computing

Container lifecycle-aware scheduling for serverless computin...

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作者： Wu, Song Tao, Zhiheng Fan, Hao Huang, Zhuo Zhang, Xinmin Jin, Hai Yu, Chen Cao, Chun 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 Wuhan China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Elastic scaling in response to changes on demand is a main benefit of serverless computing. When bursty workloads arrive, a serverless platform launches many new containers and initializes function environments (known as cold starts), which incurs significant startup latency. To reduce cold starts, platforms usually pause a container after it serves a request, and reuse this container for subsequent requests. However, this reuse strategy cannot efficiently reduce cold starts because the schedulers are agnostic of container lifecycle. For example, it may ignore soon available containers or evict soon needed containers. We propose a container lifecycle-aware scheduling strategy for serverless computing, CAS. The key idea is to control distribution of requests and determine creation or eviction of containers according to different lifecycle phases of containers. We implement a prototype of CAS on OpenWhisk. Our evaluation shows that CAS reduces 81% cold starts and therefore brings a 63% reduction at 95th percentile latency compared with native scheduling strategy in OpenWhisk when there is worker contention between workloads, and does not add significant performance overhead. © 2021 John Wiley & Sons Ltd.

关键词： Containers

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Research on Individual Recognition and Prediction of Cocaine Addiction Based on Convolutional Neural Networks 3

Research on Individual Recognition and Prediction of Cocaine...

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Xiao, Hongbo Xiao, Jianhua Zhang, Lebing Tang, Bo Cao, Zhuyou Luo, Tianwang School of Computer Science and Engineering Huaihua University Hunan Huaihua China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9798400716775

In response to the current difficulty in identifying and predicting cocaine addicted individuals based on brain MRI images, this paper designs a deep learning recognition and prediction model based on convolutional neural networks. 29 cocaine addicted individuals and 24 healthy controls were selected from brain MRI images. After per-forming a series of data preprocessing operations such as skull dissection and data augmentation on brain MRI images, a deep learning model based on convolutional neural networks is constructed to process the processed brain MRI images of cocaine addicted individuals and healthy controls to identify and predict cocaine addicted individuals. The experimental results show that the recognition and prediction accuracy of deep learning models based on convolutional neural networks is 89%. Compared with machine learning models such as SVM and support vector machines, it greatly improves the accuracy of model prediction and can accurately, quickly, and effectively identify and predict cocaine addicted individuals. © 2023 ACM.

关键词： Forecasting

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Attention Synchronous Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Prediction 3

Attention Synchronous Based Spatial-Temporal Graph Convoluti...

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Cao, Zhuyou Luo, Tianwang Xiao, Jianhua Xiao, Hongbo Qing, Xinyi School of Computer Science and Engineering Huaihua University Hunan China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Huai-hua Hunan418000 China

ISBN: (纸本)9798400716775

This study introduces AS-STGCN, a novel traffic flow prediction model that incorporates an attention mechanism and a synchronized spatio-temporal graph convolutional network. In the field of traffic prediction, deep learning models (e.g., STGCN) have outperformed traditional methods, especially in capturing temporal and spatial correlations. AS-STGCN introduces a spatio-temporal attention mechanism and simultaneous convolution, which improves the prediction accuracy and demonstrates a unique capability in capturing dynamic spatio-temporal patterns in urban traffic networks. This research is of great significance in advancing the field of traffic flow prediction and providing a more comprehensive and accurate method for predicting urban traffic dynamics. © 2023 ACM.

关键词： Convolution

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Robust Decoding of the Auditory Attention from EEG Recordings Through Graph Convolutional Networks

Robust Decoding of the Auditory Attention from EEG Recording...

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

作者： Siqi Cai Ran Zhang Haizhou Li Department of Electrical and Computer Engineering National University of Singapore Singapore Machine Listening Lab University of Bremen Germany South China University of Technology Guangzhou China Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) China

Auditory attention decoding (AAD) with electroencephalography (EEG) holds great promise in brain-computer interface (BCI). Despite much progress, it remains a research topic on how to effectively evaluate the performance of EEG-based AAD algorithms under an appropriate setting that reflects the use scenarios. It is desired that systems are evaluated under cross-subject and cross-trial settings. However, systems are often reported under same-subject, same-trial settings, where test data are not truly separated from the training data, thus potentially leading to model overfitting due to data leakage. In this paper, we study the robustness of graph convolutional network (GCN), a novel approach to learning the intricate spatial patterns in multi-channel EEG signals, by comparing GCN across cross-subject, cross-trial, and same-subject, same-trial settings. On two publicly available AAD datasets, it is found that GCN exhibits remarkable robustness, outperforming previous conventional convolutional neural network (CNN) solutions. We confirm the superiority of our GCN-based AAD model in terms of generalization and robustness.

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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