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IEEE International Conference on Mechatronics and Automation

作者： Haotian Li Shuxiang Guo Chunying Li Jipeng Long National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Aerospace Center Hospital School of Life Science and the Key Laboratory of Convergence Medical Engineering System and Healthcare Technology Ministry of Industry and Information Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

With the growing demand for autonomous underwater vehicles (AUVs) capable of precise navigation in intricate environments, achieving accurate depth control becomes pivotal. This paper introduced a pioneering study on depth control for an "Egg-shaped" underwater robot (EUR) utilizing the backstepping sliding mode control (BSMC) algorithm. By analyzing the kinematic and kinetic models of the EUR, a specialized control system was devised. This paper significantly advanced underwater robotics by presenting a robust and efficient solution for depth control, employing Lyapunov function in stability analysis. Simulation and experimental results validated the performance of the proposed algorithm in regulating the robot's depth across diverse operating conditions, surpassing traditional control methods in terms of stability and accuracy.

关键词： Autonomous underwater vehicles Backstepping Accuracy Mechatronics Navigation Stability analysis Robustness

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Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach

arXiv

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arXiv 2024年

作者： Wan, Yao Wan, Guanghua Zhang, Shijie Zhang, Hongyu Zhou, Pan Jin, Hai Sun, Lichao 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 Huazhong University of Science and Technology China University of Leigh United States

Recent years have witnessed significant progress in developing deep learning-based models for automated code completion. Examples of such models include CodeGPT and StarCoder. These models are typically trained from a large amount of source code collected from open-source communities such as GitHub. Although using source code in GitHub has been a common practice for training deep-learning-based models for code completion, it may induce some legal and ethical issues such as copyright infringement. In this paper, we investigate the legal and ethical issues of current neural code completion models by answering the following question: Is my code used to train your neural code completion model? To this end, we tailor a membership inference approach (termed CodeMI) that was originally crafted for classification tasks to a more challenging task of code completion. In particular, since the target code completion models perform as opaque black boxes, preventing access to their training data and parameters, we opt to train multiple shadow models to mimic their behavior. The acquired posteriors from these shadow models are subsequently employed to train a membership classifier. Subsequently, the membership classifier can be effectively employed to deduce the membership status of a given code sample based on the output of a target code completion model. We comprehensively evaluate the effectiveness of this adapted approach across a diverse array of neural code completion models, (i.e., LSTM-based, CodeGPT, CodeGen, and StarCoder). Experimental results reveal that the LSTM-based and CodeGPT models suffer the membership leakage issue, which can be easily detected by our proposed membership inference approach with an accuracy of 0.842, and 0.730, respectively. Interestingly, our experiments also show that the data membership of current large language models of code, e.g., CodeGen and StarCoder, is difficult to detect, leaving amper space for further improvement. Finally, we also t

关键词： Deep learning

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Evaluation of the Dynamic Performance of a Novel Egg-shaped Underwater Robot

Evaluation of the Dynamic Performance of a Novel Egg-shaped ...

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IEEE International Conference on Mechatronics and Automation

作者： Jipeng Long Shuxiang Guo Chunying Li National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Aerospace Center Hospital School of Life Science and the Key Laboratory of Convergence Medical Engineering System and Healthcare Technology Ministry of Industry and Information Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

With the increasing demand for ocean exploration and underwater operations, the development of new autonomous underwater vehicles (AUVs) to adapt to specific marine environments has become crucial. This paper designed an innovative Egg-shaped Underwater Robot (EUR) aiming to optimize its mobility, stability and operational efficiency in complex underwater environments. Compared to traditional simulation robots of various types, the structure possessed the stability of a ball-shaped robot and the characteristics of a streamlined robot. The EUR adopted a unique elliptic structure to reduce underwater drag, improve maneuverability and enhance load capacity. Then, the overall design of the EUR was presented with detailed descriptions of the mechanical structure and electrical system respectively. Next, various motion states of the EUR and experimental validation were simulated by Computational Fluid Dynamics (CFD), and the experimental results showed that the motion characteristics of the EUR are acceptable, and the design was worthy of further study.

关键词： Autonomous underwater vehicles Mechatronics Computational fluid dynamics Oceans Propulsion Stability analysis Vectors

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Layered Structure Aware Dependent Microservice Placement Toward Cost Efficient Edge Clouds

Layered Structure Aware Dependent Microservice Placement Tow...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Deze Zeng Hongmin Geng Lin Gu Zhexiong Li School of Computer Science China University of Geosciences Wuhan China 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

Although the containers are featured by light-weightness, it is still resource-consuming to pull and startup a large container image, especially in relatively resource-constrained edge cloud. Fortunately, Docker, as the most widely used container, provides a unique layered architecture that allows the same layer to be shared between microservices so as to lower the deployment cost. Meanwhile, it is highly desirable to deploy dependent microservices of an application together to lower the operation cost. Therefore, the balancing of microservice deployment cost and the operation cost should be considered comprehensively to achieve minimal overall cost of an on-demand application. In this paper, we first formulate this problem into a Quadratic Integer Programming form (QIP) and prove it as a NP-hard problem. We further propose a Randomized Rounding-based Microservice Deployment and Layer Pulling (RR-MDLP) algorithm with low computation complexity and guaranteed approximation ratio. Through extensive experiments, we verify the high efficiency of our algorithm by the fact that it significantly outperforms existing state-of-the-art microservice deployment strategies.

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Study on the Path Planning Based on A* Algorithm for Vascular Intervention Robots

Study on the Path Planning Based on A* Algorithm for Vascula...

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IEEE International Conference on Mechatronics and Automation

作者： Huiyin Xu Shuxiang Guo Chunying Li Sheng Cao National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Aerospace Center Hospital School of Life Science and the Key Laboratory of Convergence Medical Engineering System and Healthcare Technology Ministry of Industry and Information Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

In vascular interventional surgery, accurate path planning is essential to improve surgical success and reduce risk. This paper aimed to verify the effectiveness and practicability of the A* algorithm in the shortest path planning of vascular interventional surgical robot systems. Computed tomography angiography (CTA) vascular images were processed in advance to extract features and calibrate obstacles, simulating the path planning problem in vascular interventional surgery. The A* algorithm was then employed to search for the shortest path from the starting point to the endpoint. Based on the planned path, assistance could be provided to surgeons during operative procedures. The experimental results indicated that the A* algorithm effectively navigated obstacles and identified the shortest path. Moreover, through user-interactive design, this paper offered an intuitive operational experience, allowing users to interactively choose start and end points and display the algorithm’s search process and outcomes in real-time. This paper demonstrated the potential application of the A* algorithm within vascular structures and laid the groundwork for the future development of more efficient and intelligent systems for planning and navigating vascular interventional surgeries.

关键词： Medical robotics Mechatronics Navigation Computed tomography Surgery Feature extraction Path planning

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Research on Image Captioning Based on Vision-language Pre-trained Models

Research on Image Captioning Based on Vision-language Pre-tr...

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International Conference on big data and Information Analytics (bigDIA)

作者： Shiwei Zou Yuxiang Xie Jie Yan Yingmei Wei Xidao Luan Department of System Engineering National University of Defense Technology Hunan China Key Laboratory of Big Data Department of System Engineering National University of Defense Technology Hunan Changsha China Department of Computer Science Changsha University Hunan China

Image captioning is a vision-language task that targets at describing an image by generating a coherent sentence automatically. This technology allows computers to understand and describe images like humans, enabling further processing of images. It combines computer vision and natural language processing, which has broad applications. This paper investigates the use of vision-language pre-trained models for image captioning. In this article, an encoder-decoder hybrid model based on PVT and BERT is pre-trained on a large number of image-text pairs, possessing both understanding and generation capabilities. It consists of three components: 1) an image encoder and a text encoder to extract image and text features respectively; 2) a multi-modal encoder to align the two modalities; and 3) a text decoder to generate text descriptions. This paper focuses on enhancing the image encoder of the model, which is crucial for image captioning models as it extracts high-resolution image features. Reducing training time and computational costs can also improve the applicability of pre-trained models. Improvements are made to the original model by modifying the ViT model architecture of the image encoding module. A feature pyramid structure and a spatial reduction attention mechanism is introduced to extract high-resolution image features while reducing computational complexity and memory usage, thereby enhancing the universality of the model. The model adopts a pure Transformer architecture, discarding the convolutional structure, and achieves excellent performance.

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Superpage-Friendly Page Table Design for Hybrid Memory systems

Superpage-Friendly Page Table Design for Hybrid Memory Syste...

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2020国际计算机前沿大会

作者： Xiaoyuan Wang Haikun Liu Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Service Computing Technology and System Lab Huazhong University of Science and Technology Cluster and Grid Computing Lab Huazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology

Page migration has long been adopted in hybrid memory systems comprising dynamic random access memory(DRAM) and non-volatile memories(NVMs), to improve the system performance and energy ***, page migration introduces some side effects, such as more translation lookaside buffer(TLB) misses, breaking memory contiguity, and extra memory accesses due to page table updating. In this paper, we propose superpagefriendly page table called SuperPT to reduce the performance overhead of serving TLB misses. By leveraging a virtual hashed page table and a hybrid DRAM allocator, SuperPT performs address translations in a flexible and efficient way while still remaining the contiguity within the migrated pages.

关键词： Pagetable Hybridmemorysystem Pagemigration Multiplepagesizes Addresstranslation

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Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning

arXiv

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arXiv 2024年

作者： Du, Xiaohu Wen, Ming Zhu, Jiahao Xie, Zifan Ji, Bin Liu, Huijun Shi, Xuanhua Jin, Hai 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 College of Computer National University of Defense Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab HUST Wuhan430074 China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security HUST Wuhan430074 China JinYinHu Laboratory Wuhan430077 China Cluster and Grid Computing Lab HUST Wuhan430074 China

Code Pre-trained Models (CodePTMs) based vulnerability detection have achieved promising results over recent years. However, these models struggle to generalize as they typically learn superficial mapping from source code to labels instead of understanding the root causes of code vulnerabilities, resulting in poor performance in real-world scenarios beyond the training instances. To tackle this challenge, we introduce VulLLM, a novel framework that integrates multi-task learning with Large Language Models (LLMs) to effectively mine deep-seated vulnerability features. Specifically, we construct two auxiliary tasks beyond the vulnerability detection task. First, we utilize the vulnerability patches to construct a vulnerability localization task. Second, based on the vulnerability features extracted from patches, we leverage GPT-4 to construct a vulnerability interpretation task. VulLLM innovatively augments vulnerability classification by leveraging generative LLMs to understand complex vulnerability patterns, thus compelling the model to capture the root causes of vulnerabilities rather than overfitting to spurious features of a single task. The experiments conducted on six large datasets demonstrate that VulLLM surpasses seven state-of-the-art models in terms of effectiveness, generalization, and robustness. © 2024, CC BY.

关键词： Large datasets

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RAHP: A Redundancy-aware Accelerator for High-performance Hypergraph Neural Network

RAHP: A Redundancy-aware Accelerator for High-performance Hy...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Hui Yu Yu Zhang Ligang He Yingqi Zhao Xintao Li Ruida Xin Jin Zhao Xiaofei Liao Haikun Liu Bingsheng He Hai Jin National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Warwick United Kingdom National University of Singapore Singapore

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Hypergraph Neural Network (HyperGNN) has emerged as a potent methodology for dissecting intricate multilateral connections among various entities. Current software/hardware solutions leverage a sequential execution model that relies on hyperedge and vertex indices for conducting standard matrix operations for HyperGNN inference. Yet, they are impeded by the dual challenges of redundant computation and irregular memory access overheads. This is primarily due to the frequent and repetitive access and updating of a number of feature vectors corresponding to the same hyperedges and vertices. To address these challenges, we propose the first redundancy-aware accelerator, RAHP, which enables high performance execution of HyperGNN inference. Specifically, we present a redundancy-aware asynchronous execution approach into the accelerator design for HyperGNN to reduce redundant computations and off-chip memory accesses. To unveil opportunities for data reuse and unlock the parallelism that existing HyperGNN solutions fail to capture, it prioritizes vertices with the highest degree as roots, prefetching other vertices along the hypergraph structure to capture the common vertices among multiple hyperedges, and synchronizing the computations of hyperedges and vertices in real-time. By such means, this facilitates the concurrent processing of relevant hyperedge and vertex computations of the common vertices along the hypergraph topology, resulting in smaller redundant computations overhead. Furthermore, by efficiently caching intermediate results of the common vertices, it curtails memory traffic and off-chip communications. To fully harness the performance potential of our proposed approach in the accelerator, RAHP incorporates a topology-driven data loading mechanism to minimize off-chip memory accesses on the fly. It is also endowed with an adaptive data synchronization scheme to mitigate the effects of conflicting updates of both hyperedges and vertices. Moreover, RAHP emplo

关键词： Computational modeling Neural networks Memory management Loading Energy conservation Vectors Software Topology Synchronization Standards

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EC²Detect: Real-Time Online Video Object Detection in Edge-Cloud Collaborative IoT

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IEEE Internet of Things Journal 2022年第20期9卷 20382-20392页

作者： Siyan Guo Cong Zhao Guiqin Wang Jiaqing Yang Shusen Yang National Engineering Laboratory for Big Data Analytics and the School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China Department of Computing Imperial College London London U.K. National Engineering Laboratory for Big Data Analytics and the School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Industrial Artificial Intelligence Center Pazhou Laboratory Guangzhou China

Video object detection is a fundamental technology of intelligent video analytics for Internet of Things (IoT) applications. However, even with extraordinary detection accuracy, predominating solutions based on deep convolutional neural networks (DCNNs) cannot achieve real-time online object detection on video streams with a low end-to-end (E2E) response latency and therefore cannot be applied to proliferating latency-sensitive IoT applications like autonomous driving requiring large-scale intelligent video analytics. To address this issue, we present EC2Detect, an edge-cloud collaborative real-time online video object detection method. Specifically, we propose a tracking-assisted object detection architecture based on edge-cloud collaboration with keyframe selection, where the accurate but heavy object detection is conducted by the Cloud on sparse keyframes adaptively selected according to their semantic variation, and the lightweight object tracking is used to localize and identify objects in other frames at edge devices. Extensive experiments of our real-world prototype demonstrate that, EC2Detect significantly outperforms state-of-the-art methods in terms of processing speed (up to $4.77\times $ faster), E2E latency (up to $8.12 \times $ lower), and edge-cloud bandwidth occupation ( $17 \times $ lower) with an acceptable mAP, which can effectively support large-scale intelligent video analytics in practice. Source code of EC2Detect is available at https://***/ECCDetect/ECCDetect .

关键词： Object detection Image edge detection Streaming media Internet of Things Collaboration Visual analytics Feature extraction

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