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Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

Digital Communications and Networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

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Joint Biomedical Entity and Relation Extraction Based on Multi-Granularity Convolutional Tokens Pairs of Labeling

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computers, Materials & Continua 2024年第9期80卷 4325-4340页

作者： Zhaojie Sun Linlin Xing Longbo Zhang Hongzhen Cai Maozu Guo Department of Computer Science and Technology Shandong University of TechnologyZibo255000China Department of Agricultural Engineering and Food Science Shandong University of TechnologyZibo255000China Department of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China

Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this *** a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or ***,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification *** the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word ***,we use a biaffine predictor to assist in predicting the labels of word pairs for relation *** model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous ***,we evaluated our model on two publicly accessible *** experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal *** the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal *** model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.

关键词： Deep learning biomedical joint extraction triple classification multi-granularity 2D convolution

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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

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Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

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Text-guided diverse-expression diffusion model for molecule generation

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Chinese Physics B 2025年第5期34卷 106-113页

作者： Wenchao Weng Hanyu Jiang Xiangjie Kong Giovanni Pau College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310014China Hangzhou Dianzi University ITMO Joint Institute Hangzhou Dianzi UniversityHangzhou 310018China Faculty of Engineering and Architecture Kore University of EnnaItaly

The task of molecule generation guided by specific text descriptions has been proposed to generate molecules that match given text *** methods typically use simplified molecular input line entry system(SMILES)to represent molecules and rely on diffusion models or autoregressive structures for ***,the one-to-many mapping diversity when using SMILES to represent molecules causes existing methods to require complex model architectures and larger training datasets to improve performance,which affects the efficiency of model training and *** this paper,we propose a text-guided diverse-expression diffusion(TGDD)model for molecule *** combines both SMILES and self-referencing embedded strings(SELFIES)into a novel diverse-expression molecular representation,enabling precise molecule mapping based on natural *** leveraging this diverse-expression representation,TGDD simplifies the segmented diffusion generation process,achieving faster training and reduced memory consumption,while also exhibiting stronger alignment with natural *** outperforms both TGM-LDM and the autoregressive model MolT5-Base on most evaluation metrics.

关键词： molecule generation diffusion model AI for science

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An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

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Journal of Mountain science 2024年第8期21卷 2739-2753页

作者： CHEN Jun HU Wang ZHANG Yu QIU Hongzhi WANG Renchao Sichuan Artificial Intelligence Research Institute Yibin 644000China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Architecture and Civil Engineering Chengdu UniversityChengdu 610106China

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and ***,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same *** order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement *** MIC,a suitable input sequence length is selected for the LSTM *** investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different *** teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as china Meteorological Administration(CMA)and Reliable Prognosis(https://***)to improve the model’s expression capability,and the student model learns sequence information from other time *** attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention ***,the predicted displacement is obtained through a linear *** proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention *** achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PC

关键词： Landslide prediction MIC LSTM Attention mechanism Teacher Student model Prediction stability and interpretability

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Trajectory equation of a lump before and after collision with other waves for generalized Hirota–Satsuma–Ito equation

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Chinese Physics B 2023年第10期32卷 163-171页

作者：夏亚荣张开开姚若侠申亚丽 School of Information and Engineering Xi'an UniversityXi'an 710065China School of Science Xi'an University of Architecture and TechnologyXi'an 710055China School of Computer Science Shaanxi Normal UniversityXi'an 710062China Maths and Information Technology School Yuncheng UniversityYuncheng 044000China

Based on the Hirota bilinear and long wave limit methods,the hybrid solutions of m-lump with n-soliton and nbreather wave for generalized Hirota–Satsuma–Ito(GHSI)equation are ***,by approximating solutions of the GHSI equation along some parallel orbits at infinity,the trajectory equation of a lump wave before and after collisions with n-soliton and n-breather wave are studied,and the expressions of phase shift for lump wave before and after collisions are ***,it is revealed that collisions between the lump wave and other waves are elastic,the corresponding collision diagrams are used to further explain.

关键词： Hirota bilinear method long wave limit hybrid solutions trajectory equation

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Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation 38

Efficient Adaptation of Pre-trained Vision Transformer via H...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Dong, Wei Sun, Yuan Yang, Yiting Zhang, Xing Lin, Zhijun Yan, Qingsen Zhang, Haokui Wang, Peng Yang, Yang Shen, Hengtao College of Information and Control Engineering Xi'an University of Architecture and Technology China School of Computer Science Northwestern Polytechnical University China School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Tongji University China

A common strategy for Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViTs) involves adapting the model to downstream tasks by learning a low-rank adaptation matrix. This matrix is decomposed into a product of down-projection and up-projection matrices, with the bottleneck dimensionality being crucial for reducing the number of learnable parameters, as exemplified by prevalent methods like LoRA and Adapter. However, these low-rank strategies typically employ a fixed bottleneck dimensionality, which limits their flexibility in handling layer-wise variations. To address this limitation, we propose a novel PEFT approach inspired by Singular Value Decomposition (SVD) for representing the adaptation matrix. SVD decomposes a matrix into the product of a left unitary matrix, a diagonal matrix of scaling values, and a right unitary matrix. We utilize Householder transformations to construct orthogonal matrices that efficiently mimic the unitary matrices, requiring only a vector. The diagonal values are learned in a layer-wise manner, allowing them to flexibly capture the unique properties of each layer. This approach enables the generation of adaptation matrices with varying ranks across different layers, providing greater flexibility in adapting pre-trained models. Experiments on standard downstream vision tasks demonstrate that our method achieves promising fine-tuning performance. © 2024 Neural information processing systems foundation. All rights reserved.

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iNUMAlloc: Towards Intelligent Memory Allocation for AI Accelerators with NUMA 21

iNUMAlloc: Towards Intelligent Memory Allocation for AI Acce...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xu, Yuanchao Qian, Ruyi Wang, Yida Huo, Qirun Capital Normal University College of Information Engineering Beijing China Skl of Computer Architecture Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9798350329223

The amazing success of deep neural network benefits from the rise of big data. As deep learning models are becoming more scale than ever before, their requirements for memory bandwidth are growing at a tremendous pace. Some AI accelerators adopt non-uniform memory access (NUMA) architecture to mitigate this issue and hence complicate device memory allocation. Although extensive studies have been conducted on how to mitigate resource contention and reduce latency, almost all of them target on CPU-oriented NUMA systems but not on AI accelerators where memory allocation precedes task scheduling. The current memory allocator generally adopts an interleaved memory allocation strategy, which is very easy to implement but far from *** tackle this issue, this paper proposes iNUMAlloc, an intelligent memory allocator specialized for AI accelerators with NUMA architecture by combining program behavior and predictable hardware resources altogether. Preliminary evaluation shows that it can help to improve the accuracy and efficiency of memory allocation, thereby achieving stable execution time. © 2023 IEEE.

关键词： Deep neural networks

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EagerReuse: An Efficient Memory Reuse Approach for Complex Computational Graph 29

EagerReuse: An Efficient Memory Reuse Approach for Complex C...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qian, Ruyi Cao, Bojun Gao, Mengjuan Shi, Qinwen Wang, Yida Xu, Yuanchao Huo, Qirun Qiu, Keni Capital Normal University College of Information Engineering Beijing China Institute of Computing Technology Cas Skl of Computer Architecture Beijing China

ISBN: (纸本)9798350330717

Memory reuse is a promising approach for deep neural network (DNN) to reduce memory consumption because it does not introduce any additional runtime overhead. We observe that existing memory reuse algorithms consider only the effect of an individual data feature (either tensor size or tensor lifetime) on memory reuse and ignore the relative position relationship (RPR) among tensors. As computational graphs grow slightly more complex, the mining of memory reuse becomes insufficient. To address this issue, we propose a new memory reuse algorithm - EagerReuse, which can exploit more memory reuse opportunities by analyzing RPR among tensors and reusing them as quickly as possible. We evaluated the algorithms with inference models in TensorFlow Model Garden, and the results show that the EagerReuse outperforms the state-of-the-art algorithms in three out of seven cases. For more complex computational graphs, EagerReuse can achieve better memory usage with slightly higher but acceptable overhead. © 2023 IEEE.

关键词： computational graph memory optimization memory reuse memory usage

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Prediction of the Pore-Pressure Built-Up and Temperature of Fire-Loaded Concrete with Pix2Pix

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computers, Materials & Continua 2024年第5期79卷 2907-2922页

作者： Xueya Wang Yiming Zhang Qi Liu Huanran Wang Mechanics and Materials Science Research Center Ningbo UniversityNingbo315211China Jinyun Institute Zhejiang Sci-Tech UniversityLishui321400China School of Civil Engineering and Architecture Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China

Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcingsteel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concreteis closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve theresolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. Torapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adoptedin this work, which is celebrated for its capabilities in image generation. The open-source dataset used hereinfeatures RGB images we generated using a sophisticated coupled model, while the grayscale images encapsulate the15 principal variables influencing spalling. After conducting a series of tests with different layers configurations,activation functions and loss functions, the Pix2Pix model suitable for assessing the spalling risk of fire-loadedconcrete has been meticulously designed and trained. The applicability and reliability of the Pix2Pix model inconcrete parameter prediction are verified by comparing its outcomes with those derived fromthe strong couplingTHC model. Notably, for the practical engineering applications, our findings indicate that utilizing monochromeimages as the initial target for analysis yields more dependable results. This work not only offers valuable insightsfor civil engineers specializing in concrete structures but also establishes a robust methodological approach forresearchers seeking to create similar predictive models.

关键词： Fire loaded concrete spalling risk pore pressure generative adversarial network(GAN) Pix2Pix

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