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A Deletable and Modifiable Blockchain Scheme Based on Record Verification Trees and the Multisignature Mechanism

Computer Modeling in engineering & Sciences 2021年第7期128卷 223-245页

作者： Daojun Han Jinyu Chen Lei Zhang Yatian Shen Yihua Gao Xueheng Wang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng47500China Institute of Data and Knowledge Engineering Henan UniversityKaifeng47500China School of Computer and Information Engineering Henan UniversityKaifeng47500China Department of Network and Finance Agricultural Bank of China Limited Xuchang BranchXuchang461000China School of Software Pingdingshan UniversityPingdingshan467000China Department of Network and Finance Agricultural Bank of China Limited Nanyang BranchNanyang473000China

As one of the most valuable technologies,blockchains have received extensive attention from researchers and industry circles and are widely applied in various ***,data on a blockchain cannot be *** a result,it is impossible to clean invalid and sensitive data and correct erroneous ***,to a certain extent,hinders the application of blockchains in supply chains and Internet of *** address this problem,this study presents a deletable and modifiable blockchain scheme(DMBlockChain)based on record verification trees(RVTrees)and the multisignature scheme.(1)In this scheme,an RVTree structure is designed and added to the block *** RVTree can not only ensure that a record is true and valid but,owing to its unique binary structure,also verify whether modification and deletion requests are valid.(2)In DMBlockChain,the multisignature mechanism is also *** mechanism requires the stakeholders’signatures for each modification or deletion request and thus ensures that a record will not be modified arbitrarily.A user’s request is deemed valid only if it is dually verified by the RVTree and the multisignature *** analysis finds that DMBlockChain can provide a secure and valid means for modifying and deleting records in a block while ensuring the integrity of the block and that DMBlockChain can effectively save space in some scenarios that require frequent records modification.

关键词： Blockchain record verification trees multisignature DMBlockChain

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Smart Contract Vulnerability Detection Based on Semantic Graph and Residual Graph Convolutional Networks with Edge Attention

SSRN

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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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Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

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IEEE Transactions on Affective Computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

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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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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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Evaluation of China's Digital Economy: A Case Study using Entropy and TOPSIS 11

Evaluation of China's Digital Economy: A Case Study using En...

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11th International Conference on information Technology and Quantitative Management, ITQM 2024

作者： Liu, Junjie Wang, Boya Xue, Jiayu Qu, Yi Shi, Yong Sino-Danish College University of Chinese Academy of Sciences Beijing100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315100 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

Recently, digital economy in social science has become increasingly significant. A well-constructed evaluation structure could generally and promptly describe and reveal its overall social digitalization development. Building on existent researches, this paper improves the evaluation system, which comprises four first-level indicators and thirty-one second-level indicators. Notably, this paper innovatively incorporates higher education into digital innovation of the evaluation. The Entropy method is utilized to assign weights to various indicators in an objective way, with employing the TOPSIS approach to prioritize the development of digital economy in places based on the closeness to the ideal solution. By analysing related data related from 31 provinces in China between 2017 to 2021, this paper greatly finds: Firstly, the development of digital economy vary significantly across provinces, with most provinces at a relatively poor level, indicating considerable potential for overall growth;Secondly, Guangdong, Jiangsu, Zhejiang, and Shandong consistently ranked in the top four from 2017 to 2021, reflecting their specific development attributes and their emphasis on digital economy;Thirdly, significant disparities exist both between regions and among provinces. Consequently, this paper suggests establishing a comprehensive national digital economy development plan, formulating region-specific digital economy policies tailored to local conditions, and encouraging leading provinces to actively share experiences with other provinces. © 2024 The Authors.

关键词： Digital economy Evaluation Multi-source information TOPSIS

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SwimVG: Step-wise Multimodal Fusion and Adaption for Visual Grounding

arXiv

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arXiv 2025年

作者： Shi, Liangtao Liu, Ting Hu, Xiantao Hu, Yue Yin, Quanjun Hong, Richang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230009 China Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China School of systems engineering National University of Defense Technology Hunan Province Changsha410073 China Department of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210014 China

Visual grounding aims to ground an image region through natural language, which heavily relies on cross-modal alignment. Most existing methods transfer visual/linguistic knowledge separately by fully fine-tuning uni-modal pre-trained models, followed by a simple stack of visual-language transformers for multimodal fusion. However, these approaches not only limit adequate interaction between visual and linguistic contexts, but also incur significant computational costs. Therefore, to address these issues, we explore a step-wise multimodal fusion and adaption framework, namely SwimVG. Specifically, SwimVG proposes step-wise multimodal prompts (Swip) and cross-modal interactive adapters (CIA) for visual grounding, replacing the cumbersome transformer stacks for multimodal fusion. Swip can improve the alignment between the vision and language representations step by step, in a token-level fusion manner. In addition, weight-level CIA further promotes multimodal fusion by cross-modal interaction. Swip and CIA are both parameter-efficient paradigms, and they fuse the cross-modal features from shallow to deep layers gradually. Experimental results on four widely-used benchmarks demonstrate that SwimVG achieves remarkable abilities and considerable benefits in terms of efficiency. Our code is available at https://***/liuting20/SwimVG. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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Semi-supervised Short Text Classification Based On Dual-channel data Augmentation

Semi-supervised Short Text Classification Based On Dual-chan...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiajun Li Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei City Anhui Province China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei City Anhui Province China

In practical applications, high-quality labeled data is critical for short text classification. But in many cases, it is expensive and time-consuming to obtain labeled information. Semi-supervised short text classification is hence attracting more attention. However, due to the sparsity of short texts, the performance of existing short text classification models always needs to be improved. Therefore, in this paper, we propose a semi-supervised Short text classification method based on Dual-Channel data Augmentation called SDCA. More specifically, in order to solve the sparsity of short texts, this model first adopts the multi-stage word-level TCN (Temporal Convolutional Network)-based attention to enhanced semantic features and an one-dimensional convolution-based attention mechanism to augment the relevance of surrounding short texts. Secondly, the unlabeled data are augmented by word embedding weighted augmentation and word replacement augmentation, so that the model can make full use of the unlabeled short texts and further enhance the network training. Finally, extensive experiments conducted on four benchmark datasets demonstrate the effectiveness of the proposed model on semi-supervised short text classification.

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Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction

arXiv

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arXiv 2023年

作者： Wan, Qizhi Wan, Changxuan Xiao, Keli Xiong, Hui Liu, Dexi Liu, Xiping Jiangxi Key Laboratory of Data and Knowledge Engineering China School of Information Management Jiangxi University of Finance and Economics Jiangxi Nanchang China College of Business Stony Brook University Stony BrookNY United States Guangdong Guangzhou China

Document-level event extraction is a long-standing challenging information retrieval problem involving a sequence of sub-tasks: entity extraction, event type judgment, and event type-specific multi-event extraction. However, addressing the problem as multiple learning tasks leads to increased model complexity. Also, existing methods insufficiently utilize the correlation of entities crossing different events, resulting in limited event extraction performance. This paper introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role and a multi-channel argument role prediction module. The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships. By leveraging the multichannel prediction module, we transform entity and multi-event extraction into a single task of predicting token-event pairs, thereby reducing the overall parameter size and enhancing model efficiency. The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F1 score, highlighting its superior performance in event extraction. Furthermore, an ablation study confirms the significant value of the proposed data structure in improving event extraction tasks, further validating its importance in enhancing the overall performance of the framework. Copyright © 2023, The Authors. All rights reserved.

关键词： data structures

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Production quality early warning method for firearms components based on cloud edge collaboration 23

Production quality early warning method for firearms compone...

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Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application

作者： Jiawei Sun Jia Hao Xiaoning Zhang School of Mechanical Engineering Beijing Institute of Technology China School of Mechanical Engineering Beijing Institute of Technology China Yangtze Delta Region Academy Beijing Institute of Technology China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application?Ministry of Industry and Information Technology Beijing Institute of Technology China and Tianjin Key Laboratory of Space Environment Simulation Technology China

ISBN: (纸本)9798400709449

Digital transformation in manufacturing has become a trend with the continuous development of cloud computing and edge computing. The production process of firearms components is highly challenging and quality problems in the production process may directly affect the safety of people's lives and property. Thus, this paper proposes a production-quality early warning method for firearms components based on cloud-edge collaboration. Real-time early warning and rapid processing for abnormal conditions in the production process are realized based on cloud data analysis and edge intelligent prediction, thereby improving production efficiency and quality. The cloud edge collaboration framework is developed for cloud training of the long short-term memory models and real-time sample edge acquisition, enhancing the adaptability of quality early warning algorithms under specific conditions and the real-time quality of early warning for firearms components.

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