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Intelligent Contract Timestamp Vulnerability Detection Based on Key Control Flow Graph 36

Intelligent Contract Timestamp Vulnerability Detection Based...

引用

36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Quan, Qing Yu Zeng, Cheng Wang, Yi Feng Wang, Jiao Ran School of Computer Science and Information Engineering Hubei University Wuhan China School of Artificial Intelligence Hubei University Wuhan China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Wuhan China

ISBN: (纸本)1891706594

The extensive application of smart contract technology in the blockchain domain has positioned it as a key component of the digital economy. However, as the application scope of smart contracts expands, security issues have become increasingly evident, resulting in substantial economic losses. Current vulnerability detection methods largely depend on analyzing complete smart contract source code, which struggles to filter out extensive redundant information when identifying individual vulnerabilities. This leads to an insufficient exploration of the characteristics of smart contract code, which in turn results in lower overall accuracy of detection methods. In this context, the study introduces a method that transforms smart contract source code into a control flow graph, filtering out the main basic blocks based on key information about timestamp vulnerabilities. Subsequently, individual operation audits are conducted on these basic blocks, combined with the graphical structure of the control flow graph, utilizing gated graph neural networks for detecting timestamp vulnerabilities in smart contracts. Comparative experiments were performed on two public datasets, and the results indicate that compared to state-of-the-art detection methods, the proposed approach has improved the accuracy and F1 score for detecting timestamp vulnerabilities in smart contracts by 1.65% and 6.51%, providing valuable application prospects for the security of smart contracts. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Graph neural networks

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Joint Extraction of Interaction Features and Attention Mechanism Fused with Entity-Relations 36

Joint Extraction of Interaction Features and Attention Mecha...

引用

36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Su, Lisha Yan, Rong College of Computer Science Inner Mongolia University Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China

ISBN: (纸本)1891706594

Joint extraction of entities and relations is an essential task in information extraction. Recently, tagging-based models have gained attention but with poor performance on overlapping triplets, which confronted the issues like cascading errors, information redundancy, and sensitivity to subject extraction. To solve the issue of overlapping triplets, we propose a model combined Gated Attention Unit (GAU) and Multi-head Attention Mechanism (MHA) in this paper. This model distinguishes features for entities and relations, and performs entity recognition for each head entity, different head entities are matched to the same or different tail entities. Then specific tail entity tagging is applied to determine all possible relations and corresponding tails. At the same time, in order to better solve the long-distance dependence and complex multiple relations between entities, GAU and MHA are used to enhance the semantic representation, effectively identify entities and relations, and enhance the learning ability and generalization ability of the model. Experiment results on the public datasets DuIE2.0 and CMeIE show that our model performs better than baselines. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Semantics

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Measuring Spatial Specificity of Multiple Sclerosis Lesion Segmentation using Dice Spectra 28th

Measuring Spatial Specificity of Multiple Sclerosis Lesion S...

引用

28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Popa, Maria Badea, Liviu Department of Computer Science Babes-Bolyai University Mihail Kogalniceanu 1 Cluj-Napoca400084 Romania Artificial Intelligence Laboratory National Institute for Research and Development in Informatics 8-10 Averescu Boulevard Bucharest011455 Romania

Multiple Sclerosis (MS), an autoimmune disorder impacting the central nervous system, is increasingly prevalent. In this context, automated tools for MS lesions segmentation could aid healthcare professionals accelerate treatment decisions and monitor disease progression. In particular, detecting small lesions may prove pivotal for early disease detection. However, current evaluation metrics such as the Dice score estimate an algorithm's overall performance without considering its potential variability in handling lesions of diverse sizes. To address this issue, we propose an original metric, which we call ‘Dice spectrum' that offers a more accurate estimation of the model's performance on lesions of varying sizes. This is particularly important for early disease detection, which involves small lesions, for which the model may behave poorly despite high values of the normal Dice score. On the other hand, the Dice spectrum will clearly show the specific model's performance on such small lesions. © 2024 The Authors.

关键词： Deep learning

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Context-Aware Clinical Diagnosis Prediction via Hierarchical Ontology Representation 1

Context-Aware Clinical Diagnosis Prediction via Hierarchical...

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1st IEEE International Conference on Medical artificial intelligence, MedAI 2023

作者： Zhu, Bolin Liu, Chen Wang, Jiaojiao Li, Xin School of Computer Science & Technology Beijing Institute of Technology Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China

ISBN: (纸本)9798350358780

In recent years, the extensive utilization of electronic medical records has led to the preservation of a large amount of historical patient information, culminating in the formation of Electronic Health Record (EHR) dataset. Through technologies such as deep learning and representation learning, EHR dataset has been employed for tasks in the medical field, such as disease prediction. However, the challenges of how to better learn the embeddings of medical codes and how to effectively model historical visit sequences remain significant issues in the field. Therefore, we introduce a novel disease prediction methodology grounded on hierarchical ontology representation and disease evolution. First, we incorporate medical ontology to obtain external prior knowledge and simultaneously utilize the co-occurrence relationships between diseases to construct a disease co-occurrence graph. By integrating information from both sources, we achieve the final embedding of the medical codes. Building on this foundation, we delve deeper into exploring the evolution and changes in diseases, specifying different sequence modeling methods, thereby acquiring the embeddings of the patients. The application of medical ontology is beneficial in mitigating data inadequacy, while the exploration of dynamic disease evolution enhances the effectiveness of predictive models. Experimental results on the MIMIC-III dataset indicate that the proposed method achieved superior performance in disease pre-diction tasks, thus validating the effectiveness of this approach. © 2023 IEEE.

关键词： Forecasting

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An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards 17th

An Empowerment-based Solution to Robotic Manipulation Tasks ...

引用

17th Robotics: science and Systems, RSS 2021

作者： Dai, Siyu Xu, Wei Hofmann, Andreas Williams, Brian Robotics, Horizon Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology

ISBN: (纸本)9780992374778

In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals. To address the issues reinforcement learning algorithms face when task rewards are sparse, this paper proposes an intrinsic motivation approach that can be easily integrated into any standard reinforcement learning algorithm and can allow robotic manipulators to learn useful manipulation skills with only sparse extrinsic rewards. Through integrating and balancing empowerment and curiosity, this approach shows superior performance compared to other state-of-the-art intrinsic exploration approaches during extensive empirical testing. Qualitative analysis also shows that when combined with diversitydriven intrinsic motivations, this approach can help manipulators learn a set of diverse skills which could potentially be applied to other more complicated manipulation tasks and accelerate their learning process. © 2021, MIT Press Journals, All rights reserved.

关键词： Reinforcement learning

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Air Quality Index Prediction Model Integrating Spatiotemporal Information 5

Air Quality Index Prediction Model Integrating Spatiotempora...

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5th International Academic Exchange Conference on science and Technology Innovation, IAECST 2023

作者： Yao, Lantao Liu, Lizhi Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan430205 China Wuhan Institute of Technology School of Computer Science & Engineering Artificial Intelligence Wuhan430205 China

ISBN: (纸本)9798350357738

Aiming at the problem that traditional air quality prediction research ignores spatial information, this paper proposes an air quality index prediction model (SGCN-GRU) that integrates spatiotemporal information based on gated convolution (Gated-Conv), graph convolution (GCN), and gated recurrent unit (GRU). The model first calculates the geodesic distance between each other based on the longitude and latitude coordinates of each monitoring station in the input data and establishes a graph structure. The reciprocal of the geodesic distance is used as the edge weight in the graph structure, and then the data is input into the two-layer Gated-Conv and a layer of Cheb Graph-Conv is used to obtain spatial information in a spatial graph convolution block composed of a 'sandwich' structure, and then input the obtained feature map into GRU to capture the long-term dependencies existing in it, and finally obtain it through the output layer final prediction result. Experimental results show that SGCN-GRU achieves an effect of 14.67% on MAPE in one-hour AQI prediction. Compared with other benchmark models, SGCN-GRU performs best in multi-hour predictions. © 2023 IEEE.

关键词： Forecasting

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PM2.5 concentration prediction based on CNN-GRU model fused with Luong attention 5

PM2.5 concentration prediction based on CNN-GRU model fused ...

引用

2023 5th International Conference on Information science, Electrical, and Automation Engineering, ISEAE 2023

作者： Wang, Zhen Liu, Lizhi Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China School of Computer Science and Engineering Artificial Intelligence Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9781510667440

In order to improve the accuracy of PM2.5 concentration prediction, a CNN-GRU deep learning model based on fusion of Luong Attention is proposed. Firstly, the correlation between various air pollutants and meteorological factors and PM2.5 concentration is comprehensively analyzed, and the high correlation data is formed into a feature set. Secondly, the feature set is input into CNN for feature dimensioning, and then the output results of each time step are extracted through GRU. Finally, by introducing the Luong attention mechanism, the attention scores of the hidden states at each position of the output sequence are calculated, and the context vector is weighted to highlight the input step information that plays a key role in the prediction of PM2.5 concentration. The results show that using the CNN-GRU model with attention mechanism to predict the PM2.5 concentration in the next 24 hours, compared with the machine model and other deep learning models, RMSE and MAE have a certain decline, and have a higher generalization ability. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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CITE: Compact Interactive TransformEr for Multilingual Image Captioning 23

CITE: Compact Interactive TransformEr for Multilingual Image...

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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Xu, Yueyuan Hu, Zhenzhen Zhou, Yuanen Hao, Shijie Hong, Richang Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781450398572

Current state-of-the-art image captioning models generate captions in a single language, requiring a combination of multiple language specific models to build a multilingual image captioning system. However, as the number of supported languages increases, it leads to the parameters of the multilingual image captioning system grow linearly. To tackle this issue, we propose a single Compact Interactive TransformEr (CITE) model, which can describe an image in multiple languages simultaneously, making the captioning system more compact. Specifically, based on the standard Transformer model, we share the encoder and decoder backbone parameters and replace the self-attention sub-layer in the decoder with the interactive attention sub-layer. In addition, we extend the traditional monolingual reinforcement learning mechanism to a multilingual version to promote better description generation. Due to the wide use of Chinese and English, we evaluate the performance of our CITE model by simultaneously generating English and Chinese captions. We expand the image captions of the whole MSCOCO dataset and release a COCO-EN-CN dataset. Extensive experiments on the COCO-EN-CN dataset show that our single CITE model with more parameter-efficient can maintain the competitive performance or even better than the monolingual captioning models. © 2023 ACM.

关键词： Reinforcement learning

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Modeling Language Tokens as Functionals of Semantic Fields 41

Modeling Language Tokens as Functionals of Semantic Fields

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41st International Conference on Machine Learning, ICML 2024

作者： Pei, Zhengqi Zhang, Anran Wang, Shuhui Huang, Qingming Institute of Computing Technology Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Peng Cheng Laboratory China School of Computer Science and Technology University of Chinese Academy of Sciences China

Recent advances in natural language processing have relied heavily on using Transformer-based language models. However, Transformers often require large parameter sizes and model depth. Existing Transformer-free approaches using state-space models demonstrate superiority over Transformers, yet they still lack a neuro-biologically connection to the human brain. This paper proposes LasF, representing Language tokens as Functionals of semantic fields, to simulate the neuronal behaviors for better language modeling. The LasF module is equivalent to a nonlinear approximator tailored for sequential data. By replacing the final layers of pre-trained language models with the LasF module, we obtain LasF-based models. Experiments conducted for standard reading comprehension and question-answering tasks demonstrate that the LasF-based models consistently improve accuracy with fewer parameters. Besides, we use CommonsenseQA's blind test set to evaluate a full-parameter tuned LasF-based model, which outperforms the prior best ensemble and single models by 0.4% and 3.1%, respectively. Furthermore, our LasF-only language model trained from scratch outperforms existing parameter-efficient language models on standard datasets such as WikiText103 and PennTreebank. Copyright 2024 by the author(s)

关键词： Modeling languages

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PLIP: Language-Image Pre-training for Person Representation Learning 38

PLIP: Language-Image Pre-training for Person Representation ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zuo, Jialong Hong, Jiahao Zhang, Feng Yu, Changqian Zhou, Hanyu Gao, Changxin Sang, Nong Wang, Jingdong National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Skywork AI United States Department of Computer Vision Baidu Inc. China

Language-image pre-training is an effective technique for learning powerful representations in general domains. However, when directly turning to person representation learning, these general pre-training methods suffer from unsatisfactory performance. The reason is that they neglect critical person-related characteristics, i.e., fine-grained attributes and identities. To address this issue, we propose a novel language-image pre-training framework for person representation learning, termed PLIP. Specifically, we elaborately design three pretext tasks: 1) Text-guided Image Colorization, aims to establish the correspondence between the person-related image regions and the fine-grained color-part textual phrases. 2) Image-guided Attributes Prediction, aims to mine fine-grained attribute information of the person body in the image;and 3) Identity-based Vision-Language Contrast, aims to correlate the cross-modal representations at the identity level rather than the instance level. Moreover, to implement our pre-train framework, we construct a large-scale person dataset with image-text pairs named SYNTH-PEDES by automatically generating textual annotations. We pre-train PLIP on SYNTH-PEDES and evaluate our models by spanning downstream person-centric tasks. PLIP not only significantly improves existing methods on all these tasks, but also shows great ability in the zero-shot and domain generalization settings. © 2024 Neural information processing systems foundation. All rights reserved.

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