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arXiv 2023年

作者： Xu, Xiangrong Wang, Liang Xiao, Limin Liu, Lei Xie, Xilong Han, Meng Liu, Hao State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China

GPU shared L1 cache is a promising architecture while still suffering from high resource contentions. We present a GPU shared L1 cache architecture with an aggregated tag array that minimizes the L1 cache contentions and takes full advantage of inter-core locality. The key idea is to decouple and aggregate the tag arrays of multiple L1 caches so that the cache requests can be compared with all tag arrays in parallel to probe the replicated data in other caches. The GPU caches are only accessed by other GPU cores when replicated data exists, filtering out unnecessary cache accesses that cause high resource contentions. The experimental results show that GPU IPC can be improved by 12% on average for applications with a high inter-core locality. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphics processing unit

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ROTATION HAS TWO SIDES: EVALUATING DATA AUGMENTATION FOR DEEP ONE-CLASS CLASSIFICATION 12

ROTATION HAS TWO SIDES: EVALUATING DATA AUGMENTATION FOR DEE...

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12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Guodong Wang, Yunhong Bao, Xiuguo Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Natl. Comp. Net. Emer. Resp. Tech. Team/Coord. Ctr. of China Beijing China

One-class classification (OCC) involves predicting whether a new data is normal or anomalous based solely on the data from a single class during training. Various attempts have been made to learn suitable representations for OCC within a self-supervised framework. Notably, discriminative methods that use geometric visual transformations, such as rotation, to generate pseudo-anomaly samples have exhibited impressive detection performance. Although rotation is commonly viewed as a distribution-shifting transformation and is widely used in the literature, the cause of its effectiveness remains a mystery. In this study, we are the first to make a surprising observation: there exists a strong linear relationship (Pearson's Correlation, r > 0.9) between the accuracy of rotation prediction and the performance of OCC. This suggests that a classifier that effectively distinguishes different rotations is more likely to excel in OCC, and vice versa. The root cause of this phenomenon can be attributed to the transformation bias in the dataset, where representations learned from transformations already present in the dataset tend to be less effective, making it essential to accurately estimate the transformation distribution before utilizing pretext tasks involving these transformations for reliable self-supervised representation learning. To the end, we propose a novel two-stage method to estimate the transformation distribution within the dataset. In the first stage, we learn general representations through standard contrastive pre-training. In the second stage, we select potentially semantics-preserving samples from the entire augmented dataset, which includes all rotations, by employing density matching with the provided reference distribution. By sorting samples based on semantics-preserving versus shifting transformations, we achieve improved performance on OCC benchmarks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Rotation

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Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection

arXiv

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arXiv 2023年

作者： Zhang, Tianyuan Xiao, Yisong Zhang, Xiaoya Li, Hao Wang, Lu School of Computer Science and Engineering Beihang University Beijing China State Key Lab of Software Development Environment Beihang University Beijing China

Adversarial attacks in the physical world can harm the robustness of detection models. Evaluating the robustness of detection models in the physical world can be challenging due to the time-consuming and lab.r-intensive nature of many experiments. Thus, virtual simulation experiments can provide a solution to this challenge. However, there is no unified detection benchmark based on virtual simulation environment. To address this challenge, we proposed an instant-level data generation pipeline based on the CARLA simulator. Using this pipeline, we generated the DCI dataset and conducted extensive experiments on three detection models and three physical adversarial attacks. The dataset covers 7 continuous and 1 discrete scenes, with over 40 angles, 20 distances, and 20,000 positions. The results indicate that Yolo v6 had strongest resistance, with only a 6.59% average AP drop, and ASA was the most effective attack algorithm with a 14.51% average AP reduction, twice that of other algorithms. Static scenes had higher recognition AP, and results under different weather conditions were similar. Adversarial attack algorithm improvement may be approaching its ’limitation’. Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

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PPIRB:Achieving an privacy-preserving image retrieval scheme Based on Blockchain 5

PPIRB:Achieving an privacy-preserving image retrieval scheme...

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5th International Conference on Data science and Information Technology, DSIT 2022

作者： Yuan, Bo Peng, Jiahui Li, Chunpei Qiu, Wangjie School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Guangxi Key Lab of Multi-source Information Mining Security Guangxi Normal University GuiLin China State Key Laboratory of Software Development Environment Beihang University Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

With the proliferation of cloud services, the work of large-scale image retrieval is carried out on remote cloud become a trend in order to get rid of the storage burden and computation. However, traditional retrieval work based on centralized cloud services is considered to take risk in giving away users' privacy in many researches. We need a secure way for retrieving and storing images by decentralized approach. In this paper, we propose a decentralized scheme named PPIRB(An Privacy-Preserving Image Retrieval Scheme Base on Blockchain) for retrieving images based on blockchain. We used the data structure called amplifying locality sensitive hashing (ALSH) to improve search accuracy. Searchable symmetric en-cryption(SSE) and ALSH used to realize the encryption retrieval of image index on the blockchain. Our scheme also uses IPFS to replace the cloud server to realize distributed storage, and uses secret sharing scheme to realize the access control. We implemented and evaluated the scheme in Ethereum, and the results show that our scheme is efficient and accurate in finding similar images while preserving privacy. The overall overhead is reasonable. © 2022 IEEE.

关键词： Blockchain

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Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation

Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adapt...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Haoxiang Ma Ran Qin Modi Shi Boyang Gao Di Huang State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Geometry Robotics and the School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper focuses on the sim-to-real issue of RGB-D grasp detection and formulates it as a domain adaptation problem. In this case, we present a global-to-local method to address hybrid domain gaps in RGB and depth data and insufficient multi-modal feature alignment. First, a self-supervised rotation pre-training strategy is adopted to deliver robust initialization for RGB and depth networks. We then propose a global-to-local alignment pipeline with individual global domain classifiers for scene features of RGB and depth images as well as a local one specifically working for grasp features in the two modalities. In particular, we propose a grasp prototype adaptation module, which aims to facilitate fine-grained local feature alignment by dynamically updating and matching the grasp prototypes from the simulation and real-world scenarios throughout the training process. Due to such designs, the proposed method substantially reduces the domain shift and thus leads to consistent performance improvements. Extensive experiments are conducted on the GraspNet-Planar benchmark and physical environment, and superior results are achieved which demonstrate the effectiveness of our method. Code is availab.e at https://***/mahaoxiang822/GL-MSDA.

关键词： Training Codes Pipelines Prototypes Benchmark testing Robustness Robotics and automation

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Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation

arXiv

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arXiv 2024年

作者： Ma, Haoxiang Qin, Ran Shi, Modi Gao, Boyang Huang, Di The State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China The Geometry Robotics and the School of Computer Science and Technology Harbin Institute of Technology Harbin China

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Lifeline Consensus: Protect Small-scale Blockchain From Typical Attacks

Lifeline Consensus: Protect Small-scale Blockchain From Typi...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Kundu Chen Jie Luo State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Haidian District Beijing China

Blockchain is a distributed network system known for its anonymity, security, and decentralized nature. While blockchains with robust networks such as Bitcoin and Ethereum are proving immutable to attacks, many others are vulnerable to security threats. To address this issue, we proposed the Lifeline consensus, which aims to protect small-scale blockchains by delivering safety from a strong leading blockchain to a relatively vulnerable following blockchain using a new type of data called Coil. Coil carries the proof-of-work evidence created by miners from an arbitrary following blockchain, which the leading blockchain then gathers and generates immutable blocks that contained them. Miners from the following blockchain can use the coils in the leading blockchain for validation, thereby protecting small-scale blockchains from typical blockchain threats such as 51% attacks and selfish mining. We implemented our protocol and encountered adversaries in the local network, and analyzed its performance. The results showed that the Lifeline protocol is dependable with a Trusted Leader. These findings suggest that our protocol has the potential to improve the security of small-scale blockchains and enhance the overall performance of the blockchain network.

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Effective Reinforcement Learning Based on Structural Information Principles

arXiv

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arXiv 2024年

作者： Zeng, Xianghua Peng, Hao Su, Dingli Li, Angsheng State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China School of Cyber Science and Technology Beihang University Beijing100191 China

Although Reinforcement Learning (RL) algorithms acquire sequential behavioral patterns through interactions with the environment, their effectiveness in noisy and high-dimensional scenarios typically relies on specific structural priors. In this paper, we propose a novel and general Structural Information principles-based framework for effective Decision-Making, namely SIDM, approached from an information-theoretic perspective. This paper presents a specific unsupervised partitioning method that forms vertex communities in the state and action spaces based on their feature similarities. An aggregation function, which utilizes structural entropy as the vertex weight, is devised within each community to obtain its embedding, thereby facilitating hierarchical state and action abstractions. By extracting abstract elements from historical trajectories, a directed, weighted, homogeneous transition graph is constructed. The minimization of this graph’s high-dimensional entropy leads to the generation of an optimal encoding tree. An innovative two-layer skill-based learning mechanism is introduced to compute the common path entropy of each state transition as its identified probability, thereby obviating the requirement for expert knowledge. Moreover, SIDM can be flexibly incorporated into various single-agent and multi-agent RL algorithms, enhancing their performance. Finally, extensive evaluations on challenging benchmarks demonstrate that, compared with SOTA baselines, our framework significantly and consistently improves the policy’s quality, stability, and efficiency up to 32.70%, 88.26%, and 64.86%, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Entropy

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Personalized Federated Learning System Based on Permissioned Blockchain

Personalized Federated Learning System Based on Permissioned...

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2021 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2021

作者： Yuan, Bo Qiu, Wangjie Beihang University School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beijing100191 China Beihang University State Key Laboratory of Software Development Environment Beijing100191 China

ISBN: (纸本)9781665428101

Federated learning ensures the privacy of data generated by large-scale IoT devices. Existing federated learning frameworks, based on centralized model coordinators, still face serious security challenges such as single point of failure and lack of privacy. In this paper, we propose a personalized federated learning system based on permissioned blockchain, which is divided into four layers of architecture are IoT device layer, network layer, edge computing layer, blockchain layer, and application layer, using permissioned blockchain as a federated learning server. And a permission blockchain-based personalized federation learning algorithm is proposed, which can achieve privacy protection and resistance to poisoning attacks with high accuracy. The experimental results revealed that the system has high privacy protection and anti-poisoning attack capability and can be deployed in edge computing situations. © 2021 IEEE.

关键词： Internet of things

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Mixture of Attention Heads: Selecting Attention Heads Per Token

Mixture of Attention Heads: Selecting Attention Heads Per To...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Zhang, Xiaofeng Shen, Yikang Huang, Zeyu Zhou, Jie Rong, Wenge Xiong, Zhang State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University China Sino-French Engineer School Beihang University China Mila University of Montreal Canada Wechat AI Tencent China

Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing. However, the study of MoE components mostly focused on the feedforward layer in Transformer architecture. This paper proposes the Mixture of Attention Heads (MoA), a new architecture that combines multi-head attention with the MoE mechanism. MoA includes a set of attention heads that each has its own set of parameters. Given an input, a router dynamically selects a subset of k attention heads per token. This conditional computation schema allows MoA to achieve stronger performance than the standard multi-head attention layer. Furthermore, the sparsely gated MoA can easily scale up the number of attention heads and the number of parameters while preserving computational efficiency. In addition to the performance improvements, MoA also automatically differentiates heads' utilities, providing a new perspective to discuss the model's interpretability. We conducted experiments on several important tasks, including Machine Translation and Masked Language Modeling. Experiments have shown promising results on several tasks against strong baselines that involve large and very deep models. © 2022 Association for Computational Linguistics.

关键词： Modeling languages

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