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作者： Zhou, Peichi Li, Chen Zhang, Jian Wang, Changbo Qin, Hong Liu, Long School of Software Engineering East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China Department of Computer Science StonyBrook University Stony BrookNY United States

Road network design, as an important part of landscape modeling, shows a great significance in automatic driving, video game development, and disaster simulation. To date, this task remains labor-intensive, tedious and time-consuming. Many improved techniques have been proposed during the last two decades. Nevertheless, most of the state-of-the-art methods still encounter problems of intuitiveness, usefulness and/or interactivity. As a rapid deviation from the conventional road design, this paper advocates an improved road modeling framework for automatic and interactive road production driven by geographical maps (including elevation, water, vegetation maps). Our method integrates the capability of flexible image generation models with powerful transformer architecture to afford a vectorized road network. We firstly construct a dataset that includes road graphs, density map and their corresponding geographical maps. Secondly, we develop a density map generation network based on image translation model with an attention mechanism to predict a road density map. The usage of density map facilitates faster convergence and better performance, which also serves as the input for road graph generation. Thirdly, we employ the transformer architecture to evolve density maps to road graphs. Our comprehensive experimental results have verified the efficiency, robustness and applicability of our newly-proposed framework for road design. © 2024 John Wiley & Sons Ltd.

关键词： Roads and streets

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A Survey on Metaverse: Applications, Crimes and Governance

A Survey on Metaverse: Applications, Crimes and Governance

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Lin, Kaixin Wu, Jiajing Lin, Dan Zheng, Zibin Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Sun Yat-Sen University School of Software Engineering Zhuhai China

ISBN: (纸本)9798350333336

With the rapid development of blockchain and the increasing computing power, the realization of the metaverse has become possible. However, there are not yet well-regulated and standardized industry standards for the metaverse, thus various financial crimes have been bred in many metaverse projects: scams, code exploits, wash trading, money laundering, illegal services, shops, etc. For this reason, it is urgent to categorize the crimes that exist in the metaverse ecosystem and summarize the corresponding governance. In this paper, we first describe the background and applications of the metaverse. Next, we present detailed case studies on different types of crimes in the metaverse, and summarize the metaverse's current regulatory measures, related governance. Finally, we suggest possible directions and recommendations for the development of relevant measures and governance. The overview of this paper is expected to help readers gain a deeper understanding of the financial threats in the metaverse. © 2023 IEEE.

关键词： Crime

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MaGIC: Multi-modality Guided Image Completion 12

MaGIC: Multi-modality Guided Image Completion

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12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Hao Yu, Yongsheng Luo, Tiejian Fan, Heng Zhang, Libo School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Software Chinese Academy of Sciences China Department of Computer Science University of Rochester United States Department of Computer Science and Engineering University of North Texas United States

Vanilla image completion approaches exhibit sensitivity to large missing regions, attributed to the limited availability of reference information for plausible generation. To mitigate this, existing methods incorporate the extra cue as a guidance for image completion. Despite improvements, these approaches are often restricted to employing a single modality (e.g., segmentation or sketch maps), which lacks scalability in leveraging multi-modality for more plausible completion. In this paper, we propose a novel, simple yet effective method for Multi-modal Guided Image Completion, dubbed MaGIC, which not only supports a wide range of single modality as the guidance (e.g., text, canny edge, sketch, segmentation, depth, and pose), but also adapts to arbitrarily customized combination of these modalities (i.e., arbitrary multi-modality) for image completion. For building MaGIC, we first introduce a modality-specific conditional U-Net (MCU-Net) that injects single-modal signals into a U-Net denoiser for single-modal guided image completion. Then, we devise a consistent modality blending (CMB) method to leverage modality signals encoded in multiple learned MCU-Nets through gradient guidance in latent space. Our CMB is training-free, thereby avoiding the cumbersome joint re-training of different modalities, which is the secret of MaGIC to achieve exceptional flexibility in accommodating new modalities for completion. Experiments show the superiority of MaGIC over state-of-the-art methods and its generalization to various completion tasks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Image segmentation

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Multi-cost fusion stereo matching algorithm based on guided filter aggregation 3

Multi-cost fusion stereo matching algorithm based on guided ...

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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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