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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuyang Yu Bangzhen Liu Chenxi Zheng Xuemiao Xu Shengfeng He Huaidong Zhang South China University of Technology State Key Laboratory of Subtropical Building Science Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Ministry of Education Key Laboratory of Big Data and Intelligent Robot Singapore Management University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this paper, we delve into a novel aspect of learning novel diffusion conditions with datasets an order of magnitude smaller. The rationale behind our approach is the elimination of textual constraints during the few-shot learning process. To that end, we implement two optimization strategies. The first, prompt-free conditional learning, utilizes a prompt-free encoder derived from a pre-trained Stable Diffusion model. This strategy is designed to adapt new conditions to the diffusion process by minimizing the textual-visual cor-relation, thereby ensuring a more precise alignment between the generated content and the specified conditions. The second strategy entails condition-specific negative rectification, which addresses the inconsistencies typically brought about by Classifier-free guidance in few-shot training con-texts. Our extensive experiments across a variety of condition modalities demonstrate the effectiveness and efficiency of our framework, yielding results comparable to those obtained with datasets a thousand times larger. Our codes are available at https://***/Yuyan9Yu/BeyondTextConstraint.

关键词： Training Computer vision Adaptation models Codes Text to image Diffusion processes Diffusion models

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Indoor Navigation Mechanism based on 5G network for Large Parking Garage 26

Indoor Navigation Mechanism based on 5G network for Large Pa...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xu, Xiaolong Ding, Qun Lin, Licheng Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9798350331684

With the increase of car ownership in China, the problem of difficult parking in cities has become more and more serious. In large parking garage, finding an ideal parking space has become a daily problem for people;especially during the peak usage period of parking garage, a large number of vehicles will be driven into the parking garage, which will bring congestion, and at the same time, the demand of a large number of users in the area will lead to network congestion under 4G network, and users cannot access the network to check and find parking spaces. Therefore, this paper proposes a global optimal navigation mechanism with 5G network based on the above mentioned problems by analyzing the congestion situation during the peak usage of the parking garage. This mechanism uses an optimal parking space selection model and a global optimal scheduling model that can avoid congestion. Based on the model proposed in this paper, we construct a navigation planning system based on mobile, front-end and back-end. With the ultra-low latency, ultrahigh transmission efficiency and reliability of 5G network, the mobile port can select the optimal parking space according to the user's preference, and the back-end can show the parking space and congestion in the parking garage in real time according to the actual situation of the parking garage. Finally, the performance of the model and system proposed in this paper is verified through experiments. © 2023 IEEE.

关键词： 5G mobile communication systems

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BENCHMARKING AGENTIC WORKFLOW GENERATION

arXiv

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arXiv 2024年

作者： Qiao, Shuofei Fang, Runnan Qiu, Zhisong Wang, Xiaobin Zhang, Ningyu Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Zhejiang University China Alibaba Group China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Large Language Models (LLMs), with their exceptional ability to handle a wide range of tasks, have driven significant advancements in tackling reasoning and planning tasks, wherein decomposing complex problems into executable workflows is a crucial step in this process. Existing workflow evaluation frameworks either focus solely on holistic performance or suffer from limitations such as restricted scenario coverage, simplistic workflow structures, and lax evaluation standards. To this end, we introduce WORFBENCH, a unified workflow generation benchmark with multi-faceted scenarios and intricate graph workflow structures. Additionally, we present WORFEVAL, a systemic evaluation protocol utilizing subsequence and subgraph matching algorithms to accurately quantify the LLM agent’s workflow generation capabilities. Through comprehensive evaluations across different types of LLMs, we discover distinct gaps between the sequence planning capabilities and graph planning capabilities of LLM agents, with even GPT-4 exhibiting a gap of around 15%. We also train two open-source models and evaluate their generalization abilities on held-out tasks. Furthermore, we observe that the generated workflows can enhance downstream tasks, enabling them to achieve superior performance with less time during inference1 Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Improving Cross-Domain Named Entity Recognition from the Perspective of Representation 28th

Improving Cross-Domain Named Entity Recognition from the P...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Xu, Jingyun Cai, Yi School of Software Engineering South China University of Technology Guangzhou510650 China The Key Laboratory of Big Data and Intelligent Robots South China University of Technology and the Pazhou Lab Guangzhou510335 China

ISBN: (纸本)9783031306778

Recently, cross-domain named entity recognition (cross-domain NER), which can reduce the high data annotation costs faced by fully-supervised methods, has drawn attention. Most competitive approaches mainly rely on pre-trained language models like BERT to represent words. As such, the original chaotic representations may bring challenges (e.g., entity span detection errors and entity type misclassification) for them. Motivated by this, this proposal proposes to improve cross-domain NER by refining the original representations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Contrastive Learning Cross-domain Named Entity Recognition Representation

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Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training 39

Graph Coarsening via Supervised Granular-Ball for Scalable G...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xia, Shuyin Ma, Xinjun Liu, Zhiyuan Liu, Cheng Zhao, Sen Wang, Guoyin Key Laboratory of Big Data Intelligent Computing China Chongqing Key Laboratory of Computational Intelligence China Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education China National Center for Applied Mathematics in Chongqing Chongqing Normal University Chongqing 401331 China

ISBN: (纸本)157735897X

Graph Neural Networks (GNNs) have demonstrated significant achievements in processing graph data, yet scalability remains a substantial challenge. To address this, numerous graph coarsening methods have been developed. However, most existing coarsening methods are training-dependent, leading to lower efficiency, and they all require a predefined coarsening rate, lacking an adaptive approach. In this paper, we employ granular-ball computing to effectively compress graph data. We construct a coarsened graph network by iteratively splitting the graph into granular-balls based on a purity threshold and using these granular-balls as super vertices. This granulation process significantly reduces the size of the original graph, thereby greatly enhancing the training efficiency and scalability of GNNs. Additionally, our algorithm can adaptively perform splitting without requiring a predefined coarsening rate. Experimental results demonstrate that our method achieves accuracy comparable to training on the original graph. Noise injection experiments further indicate that our method exhibits robust performance. Moreover, our approach can reduce the graph size by up to 20 times without compromising test accuracy, substantially enhancing the scalability of GNNs. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Hypertensive Pulse Diagnosis Method Based on Hilbert-Huang Transform and Feature Fusion Dimensionality Reduction 5

Hypertensive Pulse Diagnosis Method Based on Hilbert-Huang T...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Fan, Lin Yao, Ruiling Wang, Zhongmin School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi 'An Key Laboratory of Big Data and Intelligent Computing China School of Computer Science and Technology Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

With the development of artificial intelligence, pulse diagnosis has been standardized and objectified. However, there is a lack of research on the extraction and dimensionality reduction of hypertensive pulse features. We propose two effective features for distinguishing pulses of disease samples and a fusion dimensionality reduction method that combines linear and nonlinear dimensionality reduction. The results show that the proposed features and dimensionality reduction method make the classification accuracy of hypertension pulse feature reach 94.23%, and the training time of the classifier is reduced by 47 seconds, which improves the performance in terms of both accuracy and time. © 2022 ACM.

关键词： Hilbert-Huang transform

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GLSNet: A Global Guided Local Feature Stepwise Aggregation Network for polyp segmentation

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Biomedical Signal Processing and Control 2024年 87卷

作者： Pan, Xiaoying Ma, Chenyang Mu, Yaya Bi, Manrong Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi‘an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi‘an710121 China School of Computer Science & Technology Xi'an University of Post & Telecommunications Xi‘an710121 China

Accurate polyp segmentation is of great significance for the prevention and diagnosis of early colon cancer. Transformer-based image segmentation models have been proposed for polyp segmentation with good results, however, these methods do not sufficiently consider the negative impact of background noise in different levels of features, resulting in the loss of local details. To alleviate this problem, we propose a GLSNet (A Global Guided Local Feature Stepwise Aggregation Network for polyp segmentation) network, including a spatial feature enhancement (SFE) module, a globally guided local feature enhancement (GLFE) module, and a feature stepwise aggregation (FSA) module. SFE can enhance the spatial feature representation of polyps to better capture the polyp information in the features. GLFE uses high-level features to filter noise in low-level features to capture polyp information hidden in shallow features;FSA fuses the semantic and positional information of polyps across scales to obtain the final segmentation results. Qualitative and quantitative experiments were conducted on 7 benchmark datasets, and the experimental results demonstrated that GLSNet outperforms other existing methods and has stronger generalization performance. In particular, we achieved a mean Dice of 92.9% on the large-scale Kvasir dataset, and mean Dice of 81.3% and 81.6% on CVC-ColonDB and ETIS, respectively, which are significantly higher than those of the competing methods. © 2023

关键词： Benchmarking

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Dynamical relaxation of a long-range Kitaev chain

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Physical Review B 2024年第9期109卷 094309-094309页

作者： Yu-Huan Huang Yin-Tao Zou Chengxiang Ding School of Microelectronics and Data Science Anhui University of Technology Maanshan 243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Maanshan 243002 China

We study the universal real-time relaxation behaviors of a long-range Kitaev chain following a quench, and the results are compared to a short-range quantum XY chain. Our research includes both noncritical and critical quenches. In the case of noncritical quench, i.e., neither the initial state nor the postquench Hamiltonian is at a critical point of the equilibrium phase transition, a quench to the commensurate phase or incommensurate phase gives a scaling of t−3/2 or t−1/2, respectively, which is the same as the counterpart of the short-range quantum XY model. However, for a quench to the boundary line between the commensurate and incommensurate phases, the scaling law t−μ may be different from the t−3/4 law of the counterpart of the short-range quantum XY model. More interestingly, the decaying exponent μ may depend on the choice of the parameters of the postquench Hamiltonian because of the different asymptotic behaviors of the energy spectrum. Furthermore, in certain cases, the scaling behavior may be outside the range of predictions made by the stationary phase approximation, because an inflection point emerges in the energy spectrum. For the critical quench, i.e., the initial state or the postquench Hamiltonian is at a critical point of equilibrium phase transition, the aforementioned scaling law t−μ may be changed because of the gap-closing property of the energy spectrum of the critical point.

关键词： Quantum quench Spin relaxation Exact solutions for many-body systems Integrable systems

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The identification for time-varying parameter and noise of tuberculosis with seasonal changes based on data-behavior-driven system

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The European Physical Journal Special Topics 2024年 1-17页

作者： Wei, Kexin Ma, Shaojuan School of Mathematics and Information Science North Minzu University Yinchuan China Ningxia Key Laboratory of Intelligent Information and Big Data Processing Yinchuan China

Time-varying and seasonal parameter inversion in the mathematical model of infectious diseases and uncertainty quantization based on actual data have great significance for real quantitative transmission process. In this study, the behavior-driven mathematical model of infectious diseases and the data-driven parameter identification method are combined to quantify the transmission law of tuberculosis (TB). To begin with, according to the characteristics of TB transmission, the TS-SID model with time-varying is established. Then, the improved identification algorithm is proposed to track the fluctuation of disease infection rate and mortality rate considering the seasonal influence. Meanwhile, focusing on the influence of noise on the spread of diseases, noise reduction and uncertain quantization are carried out on the data to identify the noise distribution. In addition, predict the denoised sequence and superimpose the noise distribution, which can improve the rationality of prediction. Finally, the numerical comparison shows that seasonal time-varying tracking is good for grasping and predicting the disease evolution.

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intelligent Logistics Service Quality Assurance Mechanism Based on Federated Collaborative Cache in 5G+ Edge Computing Environment 16th

Intelligent Logistics Service Quality Assurance Mechanism Ba...

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16th EAI International Conference on Wireless Internet, WiCON 2023

作者： Liu, Yiwen Fu, Jinrong Zhao, Zikai Yang, Yahui Peng, Ling Qu, Taiguo Feng, Tao School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China School of Foreign Languages Huaihua University Huaihua418000 China

ISBN: (纸本)9783031580529

Aiming at the problem of quality assurance of intelligent logistics service in 5G+ edge computing environment, this paper proposes a mechanism based on federated cooperative cache, which aims to utilize the computing and storage resources of edge nodes to realize rapid processing and sharing of logistics data and improve the efficiency and reliability of logistics services. This paper first analyzes the characteristics and challenges of intelligent logistics services under 5G+ edge computing environment, and then introduces the concept and principle of federated cooperative cache, as well as its application scenarios and advantages in intelligent logistics services. Then, this paper designs an intelligent logistics service quality assurance mechanism based on federated cooperative cache, including five modules such as data partitioning, data transmission, data fusion, data access and data update, and gives the corresponding algorithms and processes. Finally, this paper verifies the effectiveness and performance of the proposed mechanism through simulation experiments. Compared with the traditional centralized cache and distributed cache, the proposed mechanism can reduce the data transmission delay, improve the data hit rate and data consistency, so as to ensure the quality of intelligent logistics services. In the future, the federated collaborative cache mechanism can be further optimized to consider the needs of multiple scenarios. And explore the application potential of other areas to drive the continuous development and innovation of intelligent logistics services. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Edge computing

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