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UI layers merger: merging UI layers via visual learning and boundary prior

Frontiers of Information technology & Electronic Engineering 2023年第3期24卷 373-387页

作者： Yunnong CHEN Yankun ZHEN Chuning SHI Jiazhi LI Liuqing CHEN Zejian LI Lingyun SUN Tingting ZHOU Yanfang CHANG School of Software Technology Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies Hangzhou 310027China Alibaba Group Hangzhou 311121China Zhejiang-Singapore Innovation and AI Joint Research Lab Hangzhou 310027China

With the fast-growing graphical user interface(GUI)development workload in the Internet industry,some work attempted to generate maintainable front-end code from GUI *** can be more suitable for using user interface(UI)design drafts that contain UI ***,fragmented layers inevitably appear in the UI design drafts,which greatly reduces the quality of the generated *** of the existing automated GUI techniques detects and merges the fragmented layers to improve the accessibility of generated *** this paper,we propose UI layers merger(UILM),a vision-based method that can automatically detect and merge fragmented layers into UI *** UILM contains the merging area detector(MAD)and a layer merging *** MAD incorporates the boundary prior knowledge to accurately detect the boundaries of UI ***,the layer merging algorithm can search for the associated layers within the components’boundaries and merge them into a *** present a dynamic data augmentation approach to boost the performance of *** also construct a large-scale UI dataset for training the MAD and testing the performance of *** results show that the proposed method outperforms the best baseline regarding merging area detection and achieves decent layer merging accuracy.A user study on a real application also confirms the effectiveness of our UILM.

关键词： User interface(UI)to code UI design lint UI layer merging Object detection

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A blockchain Based Fund Management System for Construction Projects - A Comprehensive Case Study in Xiong'an New Area China

A Blockchain Based Fund Management System for Construction P...

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International Conference on Software Defined Systems (SDS)

作者： Wenlue Song Hanyuan Wu Hongwei Meng Yifu Bian Cong Tang Jiaqi Xi Haogang Zhu School of Computer Science and Engineering Beihang University Beijing China Xiong'an Blockchain Lab Xiong'an Intelligent City Innovation Federation Xiong'an New Area China Temi Tech ltd Beijing Xiong'an Huoshu Tech ltd Hebei Beijing China Beijing University of Technology Beijing China

As large-scale construction projects become increasingly complex, the use and integration of advanced technologies are being emphasized more and more. However, the construction industry often lags behind most industries in the application of digital technologies. In recent years, a decentralized, peer-to-peer blockchain technology has attracted widespread attention from academia and industry. This paper provides a solution that combines blockchain technology with construction project fund management. The system involves participants such as the owner's unit, construction companies, government departments, banks, etc., adopting the technical architecture of the “Xiong'an blockchain Underlying System”. The core business and key logic processing are all implemented through smart contracts, ensuring the transparency and traceability of the fund payment process. The goal of ensuring investment quality, standardizing investment behavior, and strengthening cost control is achieved through blockchain technology. The application of this system in the management of Xiong'an construction projects has verified that blockchain technology plays a significant positive role in strengthening fund management, enhancing fund supervision, and ensuring fund safety in the construction process of engineering projects. It helps to eliminate the common problems of multi-party trust and transparent supervision in the industry and can further improve the investment benefits of government investment projects and improve the management system and operation mechanism of investment projects.

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GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts 39

GraphMoRE: Mitigating Topological Heterogeneity via Mixture ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Zihao Sun, Qingyun Yuan, Haonan Fu, Xingcheng Zhou, Min Gao, Yisen Li, Jianxin SKLCCSE School of Computer Science and Engineering Beihang University China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China Huawei Technologies Co. Ltd. China Institute of Artificial Intelligence Beihang University China

ISBN: (纸本)157735897X

Real-world graphs have inherently complex and diverse topological patterns, known as topological heterogeneity. Most existing works learn graph representation in a single constant curvature space that is insufficient to match the complex geometric shapes, resulting in low-quality embeddings with high distortion. This also constitutes a critical challenge for graph foundation models, which are expected to uniformly handle a wide variety of diverse graph data. Recent studies have indicated that product manifold gains the possibility to address topological heterogeneity. However, the product manifold is still homogeneous, which is inadequate and inflexible for representing the mixed heterogeneous topology. In this paper, we propose a novel Graph Mixture of Riemannian Experts (GraphMoRE) framework to effectively tackle topological heterogeneity by personalized fine-grained topology geometry pattern preservation. Specifically, to minimize the embedding distortion, we propose a topology-aware gating mechanism to select the optimal embedding space for each node. By fusing the outputs of diverse Riemannian experts with learned gating weights, we construct personalized mixed curvature spaces for nodes, effectively embedding the graph into a heterogeneous manifold with varying curvatures at different points. Furthermore, to fairly measure pairwise distances between different embedding spaces, we present a concise and effective alignment strategy. Extensive experiments on real-world and synthetic datasets demonstrate that our method achieves superior performance with lower distortion, highlighting its potential for modeling complex graphs with topological heterogeneity, and providing a novel architectural perspective for graph foundation models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Geometry

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Representing Noisy Image Without Denoising

arXiv

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arXiv 2023年

作者： Qi, Shuren Zhang, Yushu Wang, Chao Xiang, Tao Cao, Xiaochun Xiang, Yong The College of Information Technology Jiangxi University of Finance and Economics Nanchang330013 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China The College of Computer Science Chongqing University Chongqing400044 China The School of Cyber Science and Technology Shenzhen Campus of Sun Yat-Sen University Shenzhen510275 China The Deakin Blockchain Innovation Laboratory School of Information Technology Deakin University BurwoodVIC3125 Australia

A long-standing topic in artificial intelligence is the effective recognition of patterns from noisy images. In this regard, the recent data-driven paradigm considers 1) improving the representation robustness by adding noisy samples in training phase (i.e., data augmentation) or 2) pre-processing the noisy image by learning to solve the inverse problem (i.e., image denoising). However, such methods generally exhibit inefficient process and unstable result, limiting their practical applications. In this paper, we explore a non-learning paradigm that aims to derive robust representation directly from noisy images, without the denoising as pre-processing. Here, the noise-robust representation is designed as Fractional-order Moments in Radon space (FMR), with also beneficial properties of orthogonality and rotation invariance. Unlike earlier integer-order methods, our work is a more generic design taking such classical methods as special cases, and the introduced fractional-order parameter offers time-frequency analysis capability that is not available in classical methods. Formally, both implicit and explicit paths for constructing the FMR are discussed in detail. Extensive simulation experiments and robust visual applications are provided to demonstrate the uniqueness and usefulness of our FMR, especially for noise robustness, rotation invariance, and time-frequency discriminability. Copyright © 2023, The Authors. All rights reserved.

关键词： Radon

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TransAVS: End-to-End Audio-Visual Segmentation with Transformer

TransAVS: End-to-End Audio-Visual Segmentation with Transfor...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yuhang Ling Yuxi Li Zhenye Gan Jiangning Zhang Mingmin Chi Yabiao Wang School of Computer Science Shanghai Key Laboratory of Data Science Fudan University China Youtu Lab Tencent Zhejiang University Zhongshan Fudan Joint Innovation Center Zhongshan PoolNet Technology Co. Ltd China

Audio-Visual Segmentation (AVS) is a challenging task, which aims to segment sounding objects in video frames by exploring audio signals. Generally AVS faces two key challenges: (1) Audio signals inherently exhibit a high degree of information density, as sounds produced by multiple objects are entangled within the same audio stream; (2) Objects of the same category tend to produce similar audio signals, making it difficult to distinguish between them and thus leading to unclear segmentation results. Toward this end, we propose TransAVS, the first Transformer-based end-to-end framework for AVS task. Specifically, TransAVS disentangles the audio stream as audio queries, which will interact with images and decode into segmentation masks with full transformer architectures. This scheme not only promotes comprehensive audio-image communication but also explicitly excavates instance cues encapsulated in the scene. Meanwhile, to encourage these audio queries to capture distinctive sounding objects instead of degrading to be homogeneous, we devise two self-supervised loss functions at both query and mask levels, allowing the model to capture distinctive features within similar audio data and achieve more precise segmentation. Our experiments demonstrate that TransAVS achieves state-of-the-art results on the AVSBench dataset, highlighting its effectiveness in bridging the gap between audio and visual modalities.

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RLGNN: Robust Graph Neural Networks with Noisy label Learning

RLGNN: Robust Graph Neural Networks with Noisy Label Learnin...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Haodong Qian De Li Qiyu Li Jinyan Wang Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Graph Neural Networks (GNNs) have become a powerful tool for processing graph-structured data. However, real-world data often faces the problem of label noise and sparsity, which will greatly affect the application of graph neural networks in the real world. Hence, we introduce Robust Graph Neural Networks with Noisy label Learning (RLGNN) to tackle the challenges posed by label noise and sparsity in graph-structured data. Its key components include unsupervised contrastive learning for deriving robust node representations, a two-stage clean sample selection mechanism, and a semi-supervised loss function to harness information from unlabeled nodes. RLGNN ingeniously merges contrastive learning with the prediction space, refining the label selection process and boosting the model’s robustness and generalizability. Comprehensive experiments indicate that RLGNN significantly surpasses existing models in managing label noise in semi-supervised node classification scenarios.

关键词： Filters Noise Refining Contrastive learning Semisupervised learning Predictive models Graph neural networks Robustness Noise measurement Faces

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Compound Jamming Detection Technique Under High Similarity Background

Compound Jamming Detection Technique Under High Similarity B...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology (IAICT)

作者： Zhaobin Li Jiaxiang Zhang Quanhua Liu Sanyuan Zhao Department of Computer Science & Technology Radar Research Lab Beijing Institute of Technology Beijing China Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Institute of Technology Beijing China Radar Research Lab Key Laboratory of Electronic and Information Technology in Satellite Navigation (Beijing Institute of Technology) School of Information and Electronics Beijing Institute of Technology Ministry of Education Beijing Institute of Technology Chongqing Innovation Center Beijing & Chongqing China Department of Computer Science & Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350353464

ISBN: (纸本)9798350353471

To address the issue of easy misidentification due to the similarity between suppression jamming and background noise on the time-frequency graph in radar jamming detection, this paper proposes a signal amplitude detection method based on empirically preset thresholds to distinguish them. To address the problem of misidentifying dense false targets and target signals caused by their identical mathematical models and similar features in the time-frequency graph—both appearing as linear shapes with the same tilt angle—we distinguish them by utilizing the evenly spaced distribution pattern of the dense false targets. The simulation results demonstrate that in the compound scenario of suppression jamming and dense false targets jamming, the YOLOv8 model achieves an F1-score of 95.7% for jamming detection. By integrating the outlier rejection module proposed in this paper, this performance can be enhanced to 98.6%.

关键词： YOLO Time-frequency analysis Accuracy Target recognition Simulation Radar detection Radar

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SphereDiffusion: Spherical Geometry-Aware Distortion Resilient Diffusion Model

arXiv

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arXiv 2024年

作者： Wu, Tao Li, Xuewei Qi, Zhongang Hu, Di Wang, Xintao Shan, Ying Li, Xi College of Computer Science and Technology Zhejiang University China ARC Lab Tencent PCG Gaoling School of Artificial Intelligence Renmin University of China China Zhejiang – Singapore Innovation and AI Joint Research Lab Hangzhou China

Controllable spherical panoramic image generation holds substantial applicative potential across a variety of domains. However, it remains a challenging task due to the inherent spherical distortion and geometry characteristics, resulting in low-quality content generation. In this paper, we introduce a novel framework of SphereDiffusion to address these unique challenges, for better generating high-quality and precisely controllable spherical panoramic images. For the spherical distortion characteristic, we embed the semantics of the distorted object with text encoding, then explicitly construct the relationship with text-object correspondence to better use the pre-trained knowledge of the planar images. Meanwhile, we employ a deformable technique to mitigate the semantic deviation in latent space caused by spherical distortion. For the spherical geometry characteristic, in virtue of spherical rotation invariance, we improve the data diversity and optimization objectives in the training process, enabling the model to better learn the spherical geometry characteristic. Furthermore, we enhance the denoising process of the diffusion model, enabling it to effectively use the learned geometric characteristic to ensure the boundary continuity of the generated images. With these specific techniques, experiments on Structured3D dataset show that SphereDiffusion significantly improves the quality of controllable spherical image generation and relatively reduces around 35% FID on average. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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FedISMH: Federated Learning Via Inference Similarity for Model Heterogeneous

FedISMH: Federated Learning Via Inference Similarity for Mod...

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IEEE International Conference on Big Data

作者： Yongdong Li Li-e Wang Zhigang Sun Xianxian Li Hengtong Chang Jinke Xu Caiyi Lin School of Computer Science and Engineering Guangxi Normal University Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Federated Learning (FL) is a privacy-preserving machine learning paradigm, enabling decentralized devices to collaboratively train models without sharing local data. Traditional FL approaches, however, rely on averaging parameters across clients with homogeneous models, which limits their applicability in scenarios where clients require heterogeneous models. In this paper, we propose FedISMH, a novel approach to address model heterogeneity in FL. Instead of directly applying knowledge distillation, FedISMH clusters clients based on the structural similarities of client models, where clients’ structural features can be extracted through either labeled or unlabeled dataset This allows the proposed model to identify clients with similar model architectures while preserving privacy. Additionally, FedISMH introduces a dynamic mechanism to manage noise clients by aligning them with the most structurally similar clusters, ensuring that their inclusion promote the performance of the cluster. Experimental results on MNIST and SVHN demonstrate that FedISMH consistently outperforms state-of-the-art methods in both IID and Non-IID settings, offering improved accuracy, robustness, and flexibility in heterogeneous FL environments.

关键词： Privacy Data privacy Accuracy Federated learning Computational modeling Noise Feature extraction Data models Servers Protection

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FedGAC: Graph Federated Learning with Gradients Aggregation Calibration for Non-IID and Long-Tailed Data

FedGAC: Graph Federated Learning with Gradients Aggregation ...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Jie Li Jinyan Wang Rongbin Deng Dongqi Yan Qiyu Li Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Federated graph learning (FGL) combines the advantages of graph neural networks and federated learning, extracting valuable information from decentralized graph data while preserving data privacy. Existing FGL methods typically consider mitigating the effect of non-IID under the class balanced assumption. However, in many real-world scenarios, the data class distribution often exhibits a long-tailed distribution, which can lead to the model being severely biased towards the head classes, thus affecting the overall performance of the global model. To address the challenges posed by non-IID and long-tailed distribution data in FGL, this paper proposes a method called FedGAC to train a globally balanced model. In the local stage, FedGAC employs feature-structure decoupled graph attention network to train client models, introduces class-importance balanced strategy to mitigate class imbalance issues, and leverages knowledge relearning strategy to enhance model generalization. In the global stage, FedGAC employs an adaptive gradients aggregation strategy that dynamically adjusts balancing factors based on the degree of class imbalance in each client’s data, thereby optimizing the aggregation process. Additionally, it utilizes global class gradients to calibrate learnable features. Experimental results demonstrate that, compared to the state-of-the-art federated learning methods, FedGAC achieves superior performance on highly non-IID and long-tailed distribution datasets.

关键词： Knowledge engineering Adaptation models Distributed processing Heavily-tailed distribution Federated learning Distributed databases Data models Graph neural networks Servers Optimization

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