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International Conference on Software Engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

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Study on the Depth Control for an "Egg-shaped" Underwater Robot Based on Backstepping Sliding Mode Algorithm

Study on the Depth Control for an "Egg-shaped" Underwater Ro...

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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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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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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.

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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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DarkFed: A Data-Free Backdoor Attack in Federated Learning

arXiv

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arXiv 2024年

作者： Li, Minghui Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Zhang, Leo Yu Wang, Yichen School of Software Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System 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 China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Federated learning (FL) has been demonstrated to be susceptible to backdoor attacks. However, existing academic studies on FL backdoor attacks rely on a high proportion of real clients with main task-related data, which is impractical. In the context of real-world industrial scenarios, even the simplest defense suffices to defend against the state-of-the-art attack, 3DFed. A practical FL backdoor attack remains in a nascent stage of development. To bridge this gap, we present DarkFed. Initially, we emulate a series of fake clients, thereby achieving the attacker proportion typical of academic research scenarios. Given that these emulated fake clients lack genuine training data, we further propose a data-free approach to backdoor FL. Specifically, we delve into the feasibility of injecting a backdoor using a shadow dataset. Our exploration reveals that impressive attack performance can be achieved, even when there is a substantial gap between the shadow dataset and the main task dataset. This holds true even when employing synthetic data devoid of any semantic information as the shadow dataset. Subsequently, we strategically construct a series of covert backdoor updates in an optimized manner, mimicking the properties of benign updates, to evade detection by defenses. A substantial body of empirical evidence validates the tangible effectiveness of DarkFed. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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An Automated Penetration Semantic Knowledge Mining Algorithm Based on Bayesian Inference

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Computers, Materials & Continua 2021年第3期66卷 2573-2585页

作者： Yichao Zang Tairan Hu Tianyang Zhou Wanjiang Deng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000China National Engineering Technology Research Center of the Digital Switching System Zhengzhou450000China NUS Business School National University of SingaporeSingapore119077Singapore

Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration *** rule mining,a data mining technique,has been studied and explored for a long ***,few studies have focused on knowledge discovery in the penetration testing *** experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent *** address this problem,a Bayesian inference based penetration semantic knowledge mining algorithm is ***,a directed bipartite graph model,a kind of Bayesian network,is constructed to formalize penetration testing ***,we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution ***,irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the *** experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently.

关键词： Penetration semantic knowledge automated penetration testing Bayesian inference cyber security

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LinNet: Linear Network for Efficient Point Cloud Representation Learning 38

LinNet: Linear Network for Efficient Point Cloud Representat...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Deng, Hao Jing, Kunlei Cheng, Shengmei Liu, Cheng Ru, Jiawei Bo, Jiang Wang, Lin State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University China Shaanxi Key Laboratory of Higher Education Institution of Generative Artificial Intelligence and Mixed Reality China School of Software Engineering Xi'an Jiaotong University China Department of Computing The Hong Kong Polytechnic University Hong Kong

Point-based methods have made significant progress, but improving their scalability in large-scale 3D scenes is still a challenging problem. In this paper, we delve into the point-based method and develop a simpler, faster, stronger variant model, dubbed as LinNet. In particular, we first propose the disassembled set abstraction (DSA) module, which is more effective than the previous version of set abstraction. It achieves more efficient local aggregation by leveraging spatial anisotropy and channel anisotropy separately. Additionally, by mapping 3D point clouds onto 1D space-filling curves, we enable parallelization of downsampling and neighborhood queries on GPUs with linear complexity. LinNet, as a purely point-based method, outperforms most previous methods in both indoor and outdoor scenes without any extra attention, and sparse convolution but merely relying on a simple MLP. It achieves the mIoU of 73.7%, 81.4%, and 69.1% on the S3DIS Area5, NuScenes, and SemanticKITTI validation benchmarks, respectively, while speeding up almost 10x times over PointNeXt. Our work further reveals both the efficacy and efficiency potential of the vanilla point-based models in large-scale representation learning. Our code will be available at https://***/DengH293/LinNet. © 2024 Neural information processing systems foundation. All rights reserved.

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SocialEdge: Socialized Learning-Based Request Scheduling for Edge-Cloud systems

SocialEdge: Socialized Learning-Based Request Scheduling for...

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International Conference on Distributed computing systems

作者： Ziwei Wang Yunfeng Zhao Chao Qiu Qiang He Xin Wang Xiaofei Wang Qinghua Hu College of Intelligence and Computing Tianjin University Tianjin China Guangdong Laboratory of Artificial Intelligence and DigitalEconomy (SZ) Shenzhen China School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

The ability for cloud data centres and edge data centres to collaborate unleashes the potential of the edge-cloud system. However, its sophistication causes unexpected issues in request scheduling, such as Insufficient intelligence, complicated hierarchy and limited cooperation. Traditional resource optimization methods for the edge-cloud system struggle to accommodate such intricate situations. In this paper, inspired by the behaviors and regulations in human society, we propose a socialized learning-based scheduling approach for the edge-cloud system, namely SocialEdge. In order to adapt to time-varying requests and dynamic service prevalence, we propose a learning-based approach, multi-agent advantage actor-critic (MAA2C) and graph convolutional networks-based MAA2C for two phases, respectively, which can improve individual intelligence to achieve optimal dispatch and orchestration decisions. Then, to make use of hierarchy correlation, we develop the socialized refining inter the layers, where inference results from one layer guide the training process of the other layer so that optimization-critical knowledge can be abstracted. Besides, we apply socialized cooperation to each layer, where federated averaging with MAA2C is implemented to ensure cooperation over private data for achieving optimal global solutions. Experimental evaluations on a proof-of-concept testbed along with two real traces demonstrate that baselines have 105% more delay than SocialEdge while being 76.6% less time efficiency and 21.6% less throughput.

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Identification Algorithm Framework and Structural model on Input Pattern of Metamorphic Relations 9

Identification Algorithm Framework and Structural model on I...

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9th International Conference on Dependable systems and Their Applications, DSA 2022

作者： Yan, Shiyu Yang, Xiaohua Lu, Zhongjian Li, Meng Gong, Helin Liu, Jie Computer School University of South China Hunan Hengyang China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China Cnnc Key Laboratory on High Trusted Computing Hunan Hengyang China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Metamorphic Relation (MR) plays a crucial role in Metamorphic Testing (MT), which can be regarded as the implication relation from the input pattern to the output pattern. Based on MR's properties, the identification of Metamorphic Relations (MRs) can be from its input pattern and output pattern, respectively. However, the finding of the effective input pattern is very first and important. Especially for complicated mathematical function includes more than two input variables, we systematically analyze the structural model of input pattern in MR, propose the concept of the class of basic input pattern, as well as prove that any one input pattern can be expressed as a combination of basic input patterns in scientific computing programs. Moreover, the structural model of input pattern in MR is established based on previous proof. Finally, we put forward an identification algorithm framework and analysis its complexity. The core contribution of our method is to decompose the input pattern identification problem in multi-dimensional space into a single one-dimensional. For this reason, the examples show that the complexity of the algorithm is reduced significantly. © 2022 IEEE.

关键词： Computational complexity

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