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SSRN 2022年

作者： Chen, Da Feng, Lin Fan, Yuqi Shang, Siyuan Wei, Zhenchun The Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230009 China

It is imperative to assure the security of smart contracts via intelligent vulnerability detection tools before deploying smart contracts on blockchains. The existing deep learning-based approaches fail to effectively capture the rich syntax and semantic information embedded in smart contracts. In this paper, we detect smart contract vulnerabilities at the function level by constructing a novel semantic graph (SG) for each function and learning the SGs using a new graph convolutional network EA-RGCN. Our proposed method consists of three stages. In the first stage, we create the SG which characterizes rich data-data, instruction-instruction and instruction-data relationships in the function code. In the second stage, we propose EA-RGCN which contains three parts: node and edge representation via word2vec, content feature extraction with a residual GCN (RGCN) module, and semantic feature extraction using an edge attention (EA) module. Finally, we concatenate the code content features and the semantic features to obtain the global code feature and use a Multilayer Perceptron (MLP) to identify whether the function is vulnerable. We conduct experiments on the dataset constructed from real-world smart contracts. Experimental results demonstrate that the proposed semantic graph and the EA-RGCN model can achieve superior performance. © 2022, The Authors. All rights reserved.

关键词： Smart contract

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An information model for highway operational risk management based on the IFC-Brick schema

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International Journal of Transportation Science and Technology 2023年第3期12卷 878-890页

作者： Bencheng Zhu Fujin Hou Tao Feng Tao Li Cancan Song The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji UniversityShanghai 201804China Shandong Hi-Speed Construction Management Group Co. Ltd.Jinan 250014ShandongChina Urban and Data Science Lab Graduate School of Advanced Science and EngineeringHiroshima University7390046 Higashi-HiroshimaJapan Department of the Built Environment Eindhoven University of TechnologyEindhoven 5600 MBthe Netherlands School of Civil Engineering Shanghai Normal UniversityNo.100Haisi RoadShanghai 201418China

With the development of highways,new technologies should be continuously introduced to improve highway traffic *** twin(DT)has been an emerging field of research in recent *** develop a digital twin management system,a data model is *** the field of highway operational risk management(HORM),however,the development of data models is still in its *** by the concept of linked data,in this paper,we attempt to propose an information model for *** main achievements of this paper include data architecture,identification and classification code methods,data interaction method,and the developed *** on data needs analysis,the highway information model architecture for risk management is defined as five layers:basic highway products,traffic sensors and equipment,traffic rules,traffic flow,and ***,according to the concepts of semantic data,these five layers can be classified into three categories:highway product data,topology data,and sensor *** the Industry Foundation Classes(IFC)standard and Brick schema were first proposed and applied in the building domain,some of their entities and relationships can also be applied to *** this end,we defined some new classes,a specific ontology,and an integrated framework for ***,a case study was carried *** such information model to highways has broad *** changes the file-based exchange method to the data-based one,which can promote highway data exchange and *** proposed information model could be of great significance for HORM.

关键词： Highway Information Model Operational Risk Management IFC-Brick Digital Twin

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Deep Semantic-Enhanced Event Detection via Symmetric Graph Convolutional Network

Deep Semantic-Enhanced Event Detection via Symmetric Graph C...

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IEEE International Conference on Big knowledge (ICBK)

作者： Chenchen Sun Xingrui Zhuo Zhenya Lu Chenyang Bu Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Event detection, an important research topic of information extraction, aims to automatically identify and classify event instances from the text. Previous studies have introduced methods combining syntactic information and graph convolutional networks into the field of event detection and verified their effectiveness. However, such methods often ignore the high-order information on the syntactic tree with noisy words, which limits their classification quality. In this paper, we propose a deep symmetric graph convolutional network to organically integrate high-order and low-order syntactic information to strengthen the semantic features of sentences. Specifically, we design a skip connection with attention gating mechanism, which selects valuable low-order syntactic information under the supervision of high-order syntactic information to strengthen the aggregation of high-order and low-order syntactic information. Then, a graph perturbation mechanism is proposed to discard noisy nodes on the syntactic graph to reduce the noisy information in the high-order syntactic information. We conducted extensive experiments on the widely used ACE 2005 benchmark, and the experimental results demonstrate that our method significantly outperforms state-of-the-art methods. Then, a graph perturbation mechanism is proposed to discard noisy nodes on the syntactic graph to reduce the noisy information in the high-order syntactic information. We conducted extensive experiments on the widely used ACE 2005 benchmark, and the experimental results demonstrate that our method significantly outperforms state-of-the-art methods. We conducted extensive experiments on the widely used ACE 2005 benchmark, and the experimental results demonstrate that our method significantly outperforms state-of-the-art methods. Then, a graph perturbation mechanism is proposed to discard noisy nodes on the syntactic graph to reduce the noisy information in the high-order syntactic information.

关键词： knowledge engineering Event detection Perturbation methods Semantics Syntactics Benchmark testing Information retrieval

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Multi-label classification with Missing labels by Preserving Feature-label Space Consistency

Multi-Label classification with Missing Labels by Preserving...

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IEEE International Conference on Big knowledge (ICBK)

作者： Zan Zhang Depeng Zhang Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Multi-label classification deals with the problem where an instance is associated with multiple labels simultaneously. Most existing multi-label classification algorithms assume that the labels of the training data are complete. However, we can obtain only a partial label set of each instance in some real applications since labelling data is difficult or costly. Some existing works on multi-label classification with missing labels focus on exploiting label correlations to complete the original label space and simultaneously build a multi-label learning model using label specific features. However, these methods may be suboptimal since they do not preserve feature-label space consistency. In this paper, we propose a Space Consistency-based Multi-label classification algorithm named SCML to address this issue. First, label correlation in label space is learned to augment the incomplete original label matrix to a new supplementary label matrix, and the multi-label classifier is constructed simultaneously based on the new supplementary label matrix. Then, correlation information in feature space is learned based on the probabilistic neighborhood similarities to preserve feature-label space consistency. Moreover, the proposed algorithm has an effective mechanism for learning label-specific features to improve the multi-label classification with missing labels. Extensive experiments on twelve benchmark data sets validate the effectiveness of the proposed approach for improving the generalization performance of state-of-the-art algorithms of multi-label learning with missing labels.

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label Enhanced Event Detection with Collective knowledge and Heterogeneous Graph

Label Enhanced Event Detection with Collective Knowledge and...

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IEEE International Conference on Big knowledge (ICBK)

作者： Gongqing Wu Zhenya Lu Zhuochun Miao Xingrui Zhuo Zan Zhang Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Event Detection (ED) aims to recognize instances of specified types of event triggers in text. Existing graph neural network-based models have achieved promising progress to alleviate this problem by capturing different orders of syntactic information, but they are limited by two issues. First, the long-range syntactic information between words is not fully exploited. Second, they ignore the semantic information provided by dependency labels which provide linguistic knowledge that is useful to ED. As a result, we proposed a label-enhanced dense graph convolutional network that employs dense connectivity and Graph Transformer Networks (GTN) to learn a flexible selection of edge types and composite relations between the words. Each layer can make use of the collective knowledge of dense blocks in order to model syntactic dependencies over long distances through dense connectivity. The proposed model achieves state-of-the-art performance for ED on common datasets after extensive experiments are conducted to show its advantages.

关键词： knowledge engineering Event detection Text recognition Semantics Syntactics Linguistics Transformers

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On charactering of information propagation in online social networks

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Journal of Networks 2013年第1期8卷 124-131页

作者： Han, Xiaoting Niu, Li School of Economics and Management Beihang University Beijing China Key Laboratory of Ministry of Education for Data Engineering and Knowledge Engineering Renmin University of China Beijing China School of Information Resource Management Renmin University of China Beijing China

Recent years have witnessed the explosive growth of online social networks (OSNs), which provide a perfect platform for observing the information propagation. Based on the theory of complex network analysis, considering each node in OSNs as an agent, an information propagation model called IP-OSN has been proposed in this paper, and numerical simulation experiments have been extensively conducted, which show the mechanism of information propagation in OSNs. It can be seen from the experimental results that along with the information spreading scope will be larger while time passing. Moreover, from the network structure perspective, some interesting findings are got. First, the number of initial known nodes has a certain impact on the information propagation speed;however it doesn't affect the number of final known and unknown nodes. Then, the number of neighbors has a great impact on the information propagation process and results. Finally, percentage of different user type is the decisive factor for information propagation either in final node number or propagation speed. This paper attempts to complement the theoretical framework of information propagation and complex networks, and thus to further promote the practice of information control in OSNs. © 2013 ACADEMY PUBLISHER.

关键词： Social networking (online)

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Combining Global and Sequential Patterns for Multivariate Time Series Forecasting

Combining Global and Sequential Patterns for Multivariate Ti...

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IEEE International Conference on Big data

作者： Zhaoxi Li Jun He Hongyan Liu Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China School of Economics and Management Tsinghua g University Beijing China

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Multivariate time series forecasting is very important for many applications. Many studies have been conducted for accurate and interpretable prediction methods. However, existing methods either cannot take both times series and covariates into consideration, lacking of interpretability, or ignore global trends across multivariate time series. In this paper, we aim to solve these issues. To this end, we propose a new model named TEDGE for accurate and interpretable time series prediction. In this model, we extract global trends hidden across multivariate times series to improve prediction accuracy. Meanwhile, we utilize a deep recurrent model with attention mechanism to find long-and short-term sequential patterns hidden in individual time series with interpretability. We conduct experiments on several datasets to evaluate the proposed models performance. Results demonstrate the superior performance of our proposed model.

关键词： Conferences Time series analysis Predictive models Big data Market research data models Forecasting

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PromptGCN: Bridging Subgraph Gaps in Lightweight GCNs

arXiv

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arXiv 2024年

作者： Ji, Shengwei Tian, Yujie Liu, Fei Li, Xinlu Wu, Le The School of Artificial Intelligence and Big Data Hefei University Hefei China The School of Computer Science and Information Engineering Hefei University of Technology Hefei China The Key Laboratory of Knowledge Engineering with Big Data Ministry of Education China

Graph Convolutional Networks (GCNs) are widely used in graph-based applications, such as social networks and recommendation systems. Nevertheless, large-scale graphs or deep aggregation layers in full-batch GCNs consume significant GPU memory, causing out of memory (OOM) errors on mainstream GPUs (e.g., 29GB memory consumption on the Ogbn-products graph with 5 layers). The subgraph sampling methods reduce memory consumption to achieve lightweight GCNs by partitioning the graph into multiple subgraphs and sequentially training GCNs on each subgraph. However, these methods yield gaps among subgraphs, i.e., GCNs can only be trained based on subgraphs instead of global graph information, which reduces the accuracy of GCNs. In this paper, we propose PromptGCN, a novel prompt-based lightweight GCN model to bridge the gaps among subgraphs. First, the learnable prompt embeddings are designed to obtain global information. Then, the prompts are attached into each subgraph to transfer the global information among subgraphs. Extensive experimental results on seven large-scale graphs demonstrate that PromptGCN exhibits superior performance compared to baselines. Notably, PromptGCN improves the accuracy of subgraph sampling methods by up to 5.48% on the Flickr dataset. Overall, PromptGCN can be easily combined with any subgraph sampling method to obtain a lightweight GCN model with higher accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphics processing unit

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Formal Analysis of Trusted Computing: One Case Study

Formal Analysis of Trusted Computing: One Case Study

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WRI International Conference on Communications and Mobile Computing, CMC

作者： HongWei Zhou JinHui Yuan Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China School of Information Renmin University of China Beijing China Institute of Electronic Technology Zhengzhou Information Engineering University of China Zhengzhou China

LS 2 is the logic to reason about the property of trusted computing. However, it lacks the capability of modeling the isolation provided by virtualization which is often involved in previous trusted computing system. With the support of changed LS 2 , we model three types of isolation. Moreover, we formally analyze the integrity measurement property of TrustVisor proposed recently which provides the isolated execution environment for security-sensitive code.

关键词： TV Computational modeling Analytical models Virtual machining Time measurement Access control

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B-IRP: An Improving Integrity Report Protocol for Remote Attestation

B-IRP: An Improving Integrity Report Protocol for Remote Att...

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WRI International Conference on Communications and Mobile Computing, CMC

Trusted platform module (TPM) has little computation capability, and it is the performance bottleneck of remote attestation. In the scenario where the server is the attestation-busy entity which answers attestation requirement frequently, the massive delay is inevitable. Without the modification to TPM, we propose Batch Integrity Report Protocol (BIRP) to overcome the performance bottleneck. B-IRP bundles these requirements in interval as a batch requirement, and creates the response messages for these requirements with one expensive TPM operation. Discussion shows that B-IRP has improved the performance without loss of security.

关键词： Protocols Servers Security Software Software measurement Computer architecture Delay

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