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3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Liu, Jingwen Zhang, Xuedong School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9781510663473

Aiming at the low matching accuracy of existing local stereo matching algorithms in weak texture areas, a local stereo matching algorithm based on multi-matching cost fusion and guided filtering cost aggregation with adaptive parameters is proposed. First, use the gradient direction to improve the gradient cost, and calculate the matching cost by combining the gradient cost with the Census transform and color cost. Secondly, the cost is aggregated by the guided filtering of adaptive parameters;Finally, the final disparity map is obtained through disparity calculation and multi-step disparity refinement. The improved algorithm is tested on 15 training sets on the Middlebury3 platform, and the average false matching rates of bad4.0 in all areas and non-occluded areas are 19.9% and 13.2%, respectively, which is improved compared with AD-Census and other algorithms. © 2023 SPIE.

关键词： Textures

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Abstract-level Deductive Reasoning for Pre-trained Language Models 30

Abstract-level Deductive Reasoning for Pre-trained Language ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wu, Xin Cai, Yi Leung, Ho-Fung School of Software Engineering South China University of Technology China Ministry of Education China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9782493814104

Pre-trained Language Models have been shown to be able to emulate deductive reasoning in natural language. However, PLMs are easily affected by irrelevant information (e.g., entity) in instance-level proofs when learning deductive reasoning. To address this limitation, we propose an Abstract-level Deductive Reasoner (ADR). ADR is trained to predict the abstract reasoning proof of each sample, which guides PLMs to learn general reasoning patterns rather than instance-level knowledge. Experimental results demonstrate that ADR significantly reduces the impact of PLMs learning instance-level knowledge (over 70%). © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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Money Laundering Detection on Ethereum: Applying Traditional Approaches to New Scene 29

Money Laundering Detection on Ethereum: Applying Traditional...

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

作者： Yu, Yunmei Wu, Jiajing Lin, Dan Fu, Qishuang Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Sun Yat-Sen University School of Software Engineering Guangzhou China

ISBN: (纸本)9798350330717

With the continuous evolution of blockchain technology, cryptocurrency platforms such as Ethereum have emerged as centers for digital asset transactions and smart contracts deployment. However, this nascent financial ecosystem also introduces potential money laundering risks. The traditional financial industry has accumulated significant antimoney laundering (AML) experience and technical means for monitoring and detecting money laundering activities. Yet, the adaptability of these established AML algorithms in the context of blockchain remains unclear. This paper aims to investigate the practical adaptability of traditional AML algorithms on Ethereum data through empirical experiments. We gather eight real-world money laundering case datasets collected from Ethereum and conduct experiments using three traditional AML algorithms on these datasets. We evaluate the performance of these algorithms from various angles, including precision, recall, and the distribution of detected accounts' labels in comparison to the original datasets. It turns out algorithms demonstrate distinct performance in diverse money laundering cases, indicating that the adaptability of traditional AML algorithms on Ethereum data presents certain adaptability and limitations. Holoscope's accuracy demonstrates the value of dense subgraph properties in Ethereum money laundering detection, and further research can be conducted based on this model framework combined with the money laundering characteristics of Ethereum. Our study provides valuable insights for strengthening AML mechanisms on blockchain platforms and offers guidance for further research on detecting money laundering accounts in blockchain environments. © 2023 IEEE.

关键词： Anti-money laundering Blockchain Security Ethereum Money Laundering Detection

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A Two-Stream Word-level Information Fusion BERT Model for Event Element Entity Recognition 12

A Two-Stream Word-level Information Fusion BERT Model for Ev...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Zhang, Yan Li, Kaiwen Zhang, Jinghui Zhou, Jingya School of Information Engineering Shanghai Donghai Vocational& Technical Colledge Shanghai China School of Software Engineering Southeast University Nanjing China School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9798331511074

This paper primarily explores methods for enhancing the performance of event element entity recognition tasks by enriching the positional information of vocabulary and focusing on document context. It proposes and implements an event element entity recognition algorithm based on vocabulary-enhanced dual-stream encoders named Two-Stream Word-level Information Fusion BERT, TSWLIF-BERT. A word-level information fusion layer that fully integrates the vocabulary and its positional information was designed and implemented.: (1) Designed and implemented a word-level information fusion layer that fully integrates both the vocabulary and their positional information, deeply integrating the vocabulary and positional information in the pre-trained model, thereby obtaining an enhanced vocabulary model (Word-level Information Fusion BERT, WLIF-BERT).(2) Based on the model WLIF-BERT, designed a dual-stream encoder that considers cross-sentence information with a global encoder and focuses on sentence-internal information with a local encoder, fully modeling the contextual information.(3) Used masked conditional random fields to solve the problem of illegal sequence label transitions in models based on conditional random fields. Experiments show that this method significantly improves performance in event element entity recognition tasks. © 2024 IEEE.

关键词： Information fusion

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YFLM: An Improved Levenberg-Marquardt Algorithm for Global Bundle Adjustment 41st

YFLM: An Improved Levenberg-Marquardt Algorithm for Global ...

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41st computer Graphics International Conference, CGI 2024

作者： Peng, Jiaxin Li, Tao Jiang, Qin Liu, Jie Wang, Ruibo Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Hunan Changsha410073 China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9783031820205

The conventional Levenberg-Marquardt (LM) algorithm is a state-of-the-art trust-region optimization method for solving bundle adjustment problems in the Structure-from-Motion community, which not only takes advantage of the fast convergence of the Gauss-Newton method, but also the stability of the gradient descent method when approaching optimal solutions. However, the damping ratio of LM is simply provided by trial-and-error, which causes slow convergence rate for large-scale problems. This paper proposes the Yamashita-Fukushima LM (YFLM) algorithm to reduce the time complexity for global bundle adjustment, where the damping factor is determined by Yamashita and Fukushima’s method. YFLM dynamically calculates a more reasonable and optimal damping ratio according to the newest reprojection error. The experimental results show that the YFLM algorithm outperforms the conventional LM algorithm for most public bundle adjustment datasets. Besides this, the convergence of the YFLM algorithm is also evaluated with different σ∈(0,2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damping

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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Human Gait Recognition Algorithm Based on MobileNetV1 with Attention Mechanism 3

Human Gait Recognition Algorithm Based on MobileNetV1 with A...

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3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Zhang, Jinsha Zhang, Xuedong School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9781510663473

For embedded modern equipment, the current gait recognition algorithm model is difficult to deploy on it due to a large amount of gait frame image data, slow network processing speed, complex structure and low computational efficiency. In this paper, a lightweight convolutional network model integrating the attention mechanism is proposed. The algorithm first performs morphological processing on the image, extracts the gait contour image, and calculates the gait energy image;Integrates the attention mechanism with MobileNetV1;The feature information of the image is effectively extracted, and the parameters of the network are reduced. A number of body method validation experiments are conducted in the CAISIAB gait database of the Chinese Academy of sciences, and the experimental results are significantly improved with other deep learning models. © 2023 SPIE.

关键词： Computational efficiency

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Enhancing Weakly Supervised Medical Segmentation via Heterogeneous Co-training with Box-Wise Augmentation and Pseudo-Label Filtering 6th

Enhancing Weakly Supervised Medical Segmentation via Hetero...

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6th IFIP TC 12 International Conference on Intelligence science, ICIS 2024

作者： Wang, You Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang State Key Laboratory of Novel Software Technology National Insititute of Health-care Data Science Nanjing University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Data and Computer Science Shandong Woman’s University Jinan China

ISBN: (纸本)9783031712524

In this paper, we introduce an innovative approach to weakly supervised medical image segmentation with box annotations. Different from the previous methods which simply utilize a single conventional network with the same augmentation techniques widely used in supervised segmentation, we aim to introduce diverse augmentations and heterogenous networks to leverage the box annotations for promising generalization ability. Specifically, to amplify the diversity between the contents within the box and its surroundings, we propose the interior and exterior box augmentation (IEBA) technique, in which distinct augmentation techniques are employed for regions inside and outside the bounding boxes. Also, for the purpose of selecting pseudo-labels of superior quality, we propose the pseudo-label filter module (PLFM) to eliminate unreliable pseudo-labels. Besides, as CNN demonstrates superior capabilities in acquiring local information, and ViT specializes in capturing global context, we facilitate a bidirectional learning process between CNN and ViT through quadruple cross consistency losses (QCCL). In inference, we only employ the superior model from the validation set to obtain parameter efficiency. Our approach is evaluated across four tasks on two public datasets, utilizing the 3D dice similarity coefficient as the evaluation metric. The experimental results show that the proposed method outperforms the state-of-the-art comparison methods. © IFIP International Federation for Information Processing 2025.

关键词： Image segmentation

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A Cost-Efficient Anonymous Authenticated and Key Agreement Scheme for V2I-Based Vehicular Ad-Hoc Networks

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12621-12630页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The rise of smart cities is directly connected to the increasing use of vehicles. The growing vehicle utilization has driven the emergence of Vehicular Ad-hoc Networks (VANETs), facilitating instant information exchange among vehicles. The system provides essential information regarding road conditions, traffic patterns, and more relevant data. VANETs encompass two fundamental categories of communication exchanges, namely Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2I technology facilitates the integration of cars and transportation infrastructure, enabling effective communication between vehicles and infrastructure. Nevertheless, the potential of V2I communication has various security concerns arising from prevalent security threats. Current authentication techniques encounter challenges regarding complexity, security, and privacy considerations. We designed a hash-based lightweight and anonymous authentication scheme to address the aforementioned restrictions and enhance the effectiveness of authentication in V2I architecture. This scheme effectively combines identity, password, and bio-metric to enhance resistance against impersonation, denial of service, and privileged insider attacks. The devised scheme distinguishes itself by a comparative analysis and security proofs, highlighting its superior capability in guaranteeing secure authentication in V2I communication. The comprehensive security analysis conducted formally and informally showcases the robustness of the proposed solution against several threats. The performance evaluation results show that our scheme demonstrates a decrease in the computational cost of 51.40% approximately and a reduction in communication overhead of around 22.57%. These results establish the efficiency and scalability of the proposed scheme as a viable solution for V2I architecture. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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Vulnerability Detection of Ethereum Smart Contract Based on SolBERT-BiGRU-Attention Hybrid Neural Model

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computer Modeling in engineering & sciences 2023年第10期137卷 903-922页

作者： Guangxia Xu Lei Liu Jingnan Dong Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Software Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China

In recent years,with the great success of pre-trained language models,the pre-trained BERT model has been gradually applied to the field of source code ***,the time cost of training a language model from zero is very high,and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at *** this paper,we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained languagemodel BERT and connected to a bidirectional gate recurrent *** downstream neural network adopts the bidirectional gate recurrent unit neural network model with a hierarchical attention mechanism to mine more semantic features contained in the source code of smart contracts by using their *** experiments show that our proposed hybrid neural network model SolBERT-BiGRU-Attention is fitted by a large number of data samples with smart contract vulnerabilities,and it is found that compared with the existing methods,the accuracy of our model can reach 93.85%,and the Micro-F1 Score is 94.02%.

关键词： Smart contract pre-trained language model deep learning recurrent neural network blockchain security

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