检索结果-内蒙古大学图书馆

Artificial Intelligence for Edge Service Optimization in Internet of Vehicles:A Survey

学校读者我要写书评

暂无评论

Tsinghua science and Technology 2022年第2期27卷 270-287页

作者： Xiaolong Xu Haoyuan Li Weijie Xu Zhongjian Liu Liang Yao Fei Dai the School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and Technology and EngineeringNanjing 210044China the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China the School of Big Data and Intelligence Engineering Southwest Forestry UniversityKunming 650233China

The Internet of Vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time ***,collected information is transmitted to a centralized resourceintensive cloud platform for service *** Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular ***,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time *** Intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources *** extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite *** address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in ***,we establish the general condition and relative concepts about IoV,EC,and ***,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service ***,we discuss a number of open issues in optimizing edge services with AI.

关键词： edge service internet of vehicles artificial intel igence

CODESWIFT: Accelerating LLM Inference for Efficient Code Generation

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Zhao, Qianhui Zhang, Li Liu, Fang Lian, Xiaoli Meng, Qiaoyuanhe Jiao, Ziqian Zhou, Zetong Zhang, Borui Guo, Runlin Li, Jia School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Beihang University China Key Lab of High Confidence Software Technology Peking University China

Code generation is a latency-sensitive task that demands high timeliness, but the autoregressive decoding mechanism of Large Language Models (LLMs) leads to poor inference efficiency. Existing LLM inference acceleration methods mainly focus on standalone functions using only built-in components. Moreover, they treat code like natural language sequences, ignoring its unique syntax and semantic characteristics. As a result, the effectiveness of these approaches in code generation tasks remains limited and fails to align with real-world programming scenarios. To alleviate this issue, we propose CODESWIFT, a simple yet highly efficient inference acceleration approach specifically designed for code generation, without comprising the quality of the output. CODESWIFT constructs a multi-source datastore, providing access to both general and project-specific knowledge, facilitating the retrieval of high-quality draft sequences. Moreover, CODESWIFT reduces retrieval cost by controlling retrieval timing, and enhances efficiency through parallel retrieval and a context- and LLM preference-aware cache. Experimental results show that CODESWIFT can reach up to 2.53× and 2.54× speedup compared to autoregressive decoding in repository-level and standalone code generation tasks, respectively, outperforming state-of-the-art inference acceleration approaches by up to 88%. Our code and data are available at https://***/r/CodeSwift. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

ATA-Cache: Contention Mitigation for GPU Shared L1 Cache with Aggregated Tag Array

学校读者我要写书评

暂无评论

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

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

学校读者我要写书评

暂无评论

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

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

Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection

学校读者我要写书评

暂无评论

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 labor-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

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

学校读者我要写书评

暂无评论

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

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

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

学校读者我要写书评

暂无评论

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

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 available at https://***/mahaoxiang822/GL-MSDA.

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

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

学校读者我要写书评

暂无评论

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

关键词：

Lifeline Consensus: Protect Small-scale Blockchain From Typical Attacks

学校读者我要写书评

暂无评论

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

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.

关键词：