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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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Smart Contract Vulnerability Detection Based on Deep and Cross Network 3

Smart Contract Vulnerability Detection Based on Deep and Cro...

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3rd International conference on computer Vision, Image and Deep Learning and International conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Li, Nianfeng Liu, Yang Li, Lina Wang, Yuying Changchun University College of Computer Science and Technology Changchun China Changchun University College of Cyber Security Changchun China

ISBN: (纸本)9781665459112

Smart contract is the key core part of the blockchain's operation, which has the feature that it cannot be changed after deploying, resulting in the fact that the vulnerabilities associated with smart contracts cannot be fixed. Currently, deep learning has the best performance for detecting smart contract vulnerabilities. However, the common deep learning method treats the smart contract source code as a text sequence, ignoring the running logic and data flow of the program, and can not extract the constructed features. In this paper, a modular vulnerability detection model Link-DC is proposed. the model takes the contract graph constructed by smart contracts and the pattern features defined by expert knowledge as the original data input to capture richer feature information through stitching. Then, the deep and cross network is adopted to construct low-dimensional and sparse features into high-dimensional nonlinear features, thus improving the performance and efficiency of detection. Extensive experiments are conducted on smart contracts with vulnerabilities on the Ether and VNTChain platforms. The experiment results show that the high detection accuracy performance of the Link-DC model is obtained, where the accuracy of detection of re-entry vulnerability, timestamp-dependent vulnerability, and infinite loop vulnerability reaches 94.37%, 92.11%, and 85.29%, respectively. © 2022 IEEE.

关键词： Smart contract

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PatchBert: Continuous Stable Patch Identification for Linux Kernel via Pre-trained Model Fine-tuning 31

PatchBert: Continuous Stable Patch Identification for Linux ...

引用

31st IEEE International conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Liu, Rongkai Shi, Heyuan Zhang, Yongchao Wang, Runzhe Shen, Yuheng Chen, Yuao Luo, Jing Shi, Xiaohai Hu, Chao Jiang, Yu School of Computer Science and Engineering China School of Electronic Information Central South University Changsha China Alibaba Group Beijing China School of Software Tsinghua University Beijing China

ISBN: (纸本)9798350330663

Stable patch identification is crucial in merging patches into stable versions, which helps ensure the stability of the Linux kernel. Although many tools have been proposed to mitigate the manual effort of stable patch identification, challenges still arise because they neglect continuous stable patch tracking and advanced Natural Language Processing (NLP) pre-training techniques. In this paper, in collaboration with developers from the openAnolis Linux operating system distribution community, we present a stable patch identification model called PatchBERT. It utilizes BERT and CodeBERT to capture the semantic patch representation from the commit message and code changes in a patch. We then perform patch classification and output the probability that the patch should be merged into the stable versions. We perform experiments on the dataset used by the previous methods. The experimental results show the superior performance of PatchBERT over state-of-the-art baselines. Additionally, it is common practice to train the model using the latest Linux patches and implement it in a real-world industrial setting. In this exercise, we randomly select 10,000 patches for identification, accurately identifying 8,617 patches and incorrectly identifying 1,383 patches. This practical outcome further confirms the effectiveness and utility of PatchBERT in real-world scenarios. © 2024 IEEE.

关键词： Semantics

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A PPO-guided multi-population co-evolution algorithm for Distributed Flexible Assembly Flowshop Scheduling Problem 24

A PPO-guided multi-population co-evolution algorithm for Dis...

引用

2nd International conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Zhao, Fuqing Du, Yuqing Yin, Fumin School of Computer and Communication Lanzhou University of Technology Gansu China

ISBN: (纸本)9798400710681

A distributed flexible assembly flowshop scheduling problem(DFAFSP), which holds significant applications in modern large-scale manufacturing systems, is investigated in this paper. A PPO-guided multi-population co-evolution algorithm (PPO-MPCE) is introduced in response to the DFAFSP problem. Automatic algorithm design and a co-evolution among two populations are utilized to minimize the total flowtime objective in DFAFSP. A three-segment encoding method is devised for DFAFSP, and each segment implements a unique search strategy tailored to address the problem from a distinct physical perspective. Eight search states and a reward function are designed to evaluate the current population state for the PPO mechanism. Finally, the performance of random-MPCE, PPO-MPCE, and five existing algorithms are evaluated on 68 small-scale and 30 large-scale test instances. Simulation experiments in this paper indicate that the proposed algorithms outperform the latest algorithms in performance. © 2024 Copyright held by the owner/author(s).

关键词： Scheduling algorithms

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Selective sampling and temporal positional encoding for monocular video based 3D human pose and shape estimation 5

Selective sampling and temporal positional encoding for mono...

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5th International conference on Signal Processing and computer Science, SPCS 2024

作者： Hou, Yupeng Wen, Han Zeng, Guangping School of Computer & Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

This study introduces a novel framework for video-based 3D human pose and shape estimation, termed Selective sampling and Temporal Positional Encoding (STPE). Our method leverages selective sampling and advanced positional encoding to tackle the temporal complexities of video data and the high cost and scarcity of annotated datasets. Inspired by the Masked Autoencoder (MAE), our approach adopts a selective sampling strategy that efficiently captures the essential dynamics of human motion from partial views, significantly reducing reliance on continuous frames. The framework incorporates Rotary Position Embedding (RoPE), using rotational angles to simplify positional encoding. This innovation decreases model complexity and boosts learning effectiveness. We also introduce randomized index positions during training, introducing variability and enhancing generalization across various datasets and motion patterns. Our model, validated on standard datasets like 3DPW, MPI-INF-3DHP, and Human3.6M, shows enhanced performance in accurate and robust 3D pose and shape capture compared to existing methods. Our results demonstrate that strategic frame sampling and sophisticated positional encoding can significantly improve accuracy and robustness of video-based pose estimation systems. © 2025 SPIE.

关键词： Video recording

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accparser: A Standalone OpenACC Parser and Its Usage on Mapping OpenACC to OpenMP Directives 25th

accparser: A Standalone OpenACC Parser and Its Usage on Ma...

引用

25th International conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Yi, Xinyao Wang, Anjia Yan, Yonghong University of North Carolina at Charlotte CharlotteNC28262 United States Intel Corporation HillsboroOR97124 United States

ISBN: (纸本)9789819642069

Heterogeneous computing with accelerators has emerged as an effective approach to high-performance computing. Directive-based programming models such as OpenMP and OpenACC simplify parallel programming for GPU accelerators by enabling compilers to translate directive-annotated code into GPU-optimized code automatically. This paper presents accparser, a standalone and unified OpenACC parser built using ANTLR 4. Designed for both C/C++ and Fortran, accparser provides a complete grammar for OpenACC 3.3, making it a valuable tool for compiler developers who aim to implement OpenACC program transformations and code generation. It supports the syntax and semantic verification of OpenACC constructs and helps to interpret the OpenACC standard. Additionally, it can be leveraged as a tool to assist in creating compiler passes that convert OpenACC programs into OpenMP programs, enabling full utilization of OpenMP’s compiler optimizations and runtime support. The source code for accparser is available under the 2-Clause BSD License at https://***/passlab/accparser. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： program compilers

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Indoor Automated Fire Extinguisher System using computer Vision 8

Indoor Automated Fire Extinguisher System using Computer Vis...

引用

8th International conference for Man Machine Systems, ICoMMS 2023

作者： Rosle, Muhammad Hisyam Chia, Khoo Yan Nasir, Abdul Faculty of Electrical Engineering and Technology Universiti Malaysia Perlis Perlis Arau02600 Malaysia Universiti Malaysia Perlis Perlis Arau02600 Malaysia Faculty of Electrical and Electronic Engineering Technology Universiti Malaysia Pahang Pahang Pekan26600 Malaysia

This paper presents the development of an automated fire extinguisher robot by employing fire recognition using computer vision. This project aims to develop a robot that can search, detect and extinguish small flames for indoor purposes. The robot is developed by applying open-source image processing to recognize the presence of fire. A control program was built to control the movement of the robot's servo motor. The performance of the fire recognition was analysed in different light intensities and angles. The development of this project makes use of a camera and computer vision in place of various sensors, such as gas sensors, temperature sensors, and infrared sensors, to detect fires. Furthermore, a microprocessor is employed to operate the water pump and servo motor, which drive the nozzle to the position specified by the microcontroller. The results reveal that the proposed vision-based fire detection system has a high classification accuracy correspond to fire recognition. © Published under licence by IOP Publishing Ltd.

关键词： Robots

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Mimicking Associative Learning of Rats via a Neuromorphic Robot in Open Field Maze Using Spatial Cell Models

Mimicking Associative Learning of Rats via a Neuromorphic Ro...

引用

2024 International conference on Neuromorphic Systems, ICONS 2024

作者： Liu, Tianze Bakr Siddique, Md Abu An, Hongyu Michigan Technological University Department of Electrical and Computer Engineering HoughtonMI United States

ISBN: (纸本)9798350368659

Data-driven Artificial Intelligence (AI) approaches have exhibited remarkable prowess across various cognitive tasks using extensive training data. However, the reliance on large datasets and neural networks presents challenges such as high-power consumption and limited adaptability, particularly in SWaP-constrained applications like planetary exploration. To address these issues, we propose enhancing the autonomous capabilities of intelligent robots by emulating the associative learning observed in animals. Associative learning enables animals to adapt to their environment by memorizing concurrent events. By replicating this mechanism, neuromorphic robots can navigate dynamic environments autonomously, learning from interactions to optimize performance. This paper explores the emulation of associative learning in rodents using neuromorphic robots within open-field maze environments, leveraging insights from spatial cells such as place and grid cells. By integrating these models, we aim to enable online associative learning for spatial tasks in real-time scenarios, bridging the gap between biological spatial cognition and robotics for advancements in autonomous systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Weakly-Supervised Graph Classification with Even a Single Key Subgraph Per Class 24

Weakly-Supervised Graph Classification with Even a Single Ke...

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24th IEEE International conference on Data Mining, ICDM 2024

作者： Zhang, Lu Zhang, Chenbo Guan, Jihong Zhou, Shuigeng School of Computer Science Fudan University Shanghai Key Lab of Intelligent Information Processing China Tongji University Department of Computer Science & Technology China

ISBN: (纸本)9798331506681

Traditional graph classification requires large amounts of labeled data, which is expensive and time-consuming to acquire, especially in some special scenarios that domain knowledge is indispensable for labeling graphs. Observing that some key subgraphs can determine the properties of graphs (e.g. the toxicity of drug molecules depend on some toxic functional groups), in this paper we explore to classify graphs using unlabeled graphs plus a small number of key subgraphs for each class, which is called weakly-supervised graph classification. To this end, we develop the WeGraph method, where the graph classifier is trained with subgraph-based self-supervised learning and divergence- minimization based fine-tuning. Moreover, we design a key subgraph extraction algorithm to iteratively extract and update the key subgraphs, which makes the training process a closed loop. We conduct extensive experiments on different types of graph datasets to evaluate the effectiveness of WeGraph. Experimental results show that WeGraph can achieve high performance even when only one key subgraph is provided for each class. © 2024 IEEE.

关键词： Self-supervised learning

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FLAREFUZZ: Fuzzing Javascript Engines via Deep Learning 20

FLAREFUZZ: Fuzzing Javascript Engines via Deep Learning

引用

20th International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Yiwei, Liu Nanxin, Zhou Yujun, Li School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9798350318982

During the past few years, Fuzz testing has been a popular technique for finding bugs in software. It continuously inputs a large number of test cases into the target program and monitors the execution status to discover potential vulnerabilities. The efficiency of fuzzing tools heavily relies on the quality of test cases. However, Fuzz testing faces a challenge due to the specific input requirements of many software applications. The JavaScript engine is used to interpret and execute JavaScript files, and it requires that the input files should meet the syntax and semantic rules of JavaScript. Traditional fuzzing tools usually ignore the file format requirements and struggle to generate valid test cases, which makes it difficult to expose deep vulnerabilities in JavaScript engines. To address this challenge and enhance the quality of test cases, this paper proposes a novel fuzz testing approach leveraging deep learning techniques, named FlareFuzz. FlareFuzz utilizes a GRU network to learn syntax and semantic knowledge from AST and CFG of JavaScript programs. The structured representation of the code contains rich semantic information, which helps the deep learning model capture the latent semantic rules from the corpus. In addition, the performance of the GRU network makes it more suitable for fuzz testing since it is faster than LSTM and effectively captures long-distance dependencies in sequences. Thus, the introduction of GRU helps FlareFuzz achieve high efficiency. Evaluations indicate that FlareFuzz performs better than previous approaches in generating test cases. Furthermore, experiments conducted on mainstream JavaScript engines demonstrate that FlareFuzz also performs well in real scenarios. © 2023 IEEE.

关键词： Engines

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ViewPCGC: View-Guided Learned Point Cloud Geometry Compression 24

ViewPCGC: View-Guided Learned Point Cloud Geometry Compressi...

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32nd ACM International conference on Multimedia, MM 2024

作者： Zheng, Huiming Gao, Wei Yu, Zhuozhen Zhao, Tiesong Li, Ge School of Electronic and Computer Engineering Shenzhen Graduate School Peng Cheng Laboratory Peking University Shenzhen China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University Shenzhen China Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China

ISBN: (纸本)9798400706868

With the rise of immersive media applications such as digital museums, virtual reality, and interactive exhibitions, point clouds, as a three-dimensional data storage format, have gained increasingly widespread attention. The massive data volume of point clouds imposes extremely high requirements on transmission bandwidth in the above applications, gradually becoming a bottleneck for immersive media applications. Although existing learning-based point cloud compression methods have achieved specific successes in compression efficiency by mining the spatial redundancy of their local structural features, these methods often overlook the intrinsic connections between point cloud data and other modality data (such as image modality), thereby limiting further improvements in compression efficiency. To address the limitation, we innovatively propose a view-guided learned point cloud geometry compression scheme, namely ViewPCGC. We adopt a novel self-attention mechanism and cross-modality attention mechanism based on sparse convolution to align the modality features of the point cloud and the view image, removing view redundancy through Modality Redundancy Removal Module (MRRM). Simultaneously, side information of the view image is introduced into the Conditional Checkboard Entropy Model (CCEM), significantly enhancing the accuracy of the probability density function estimation for point cloud geometry. In addition, we design a View-Guided Quality Enhancement Module (VG-QEM) in the decoder, utilizing the contour information of the point cloud in the view image to supplement reconstruction details. The superior experimental performance demonstrates the effectiveness of our method. Compared to the state-of-the-art point cloud geometry compression methods, ViewPCGC exhibits an average performance gain exceeding 10% on D1-PSNR metric. © 2024 ACM.

关键词： Redundancy

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