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UJN-Land: A Large-Scale High-Resolution Parcel of Land of Multi-temporal dataset with CNN Based Semantic Segmentation Analysis 7th

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UJN-Land: A Large-Scale High-Resolution Parcel of Land of Mu...

7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Shen, Yuan Wang, Yulin Yang, Shiyong Li, Yan Han, Shiyuan Liu, Zhen Xu, Tao School of Information Science and Engineering University of Jinan Jinan250022 China Highresolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Shandong Institutes of Industrial Technonlogy Jinan250102 China Land Spatial Data and Remote Sensing Technology Institute of Shandong Province Jinan250002 China

ISBN: (纸本)9789811672125

Semantic segmentation of land in remote sensing images plays an important role in urban management and rural planning, and can provide intelligent analysis for urban development. Convolutional neural network (CNN) based methods have achieved good performance in object detection in computer vision, which benefits from the popularity of large-scale datasets. However, most of segmentation of land dataset were constructed based on various satellite images and samples that lack the number and diversity of existing datasets. These problems have brought challenges to the improvement and application of CNN based land parcel segmentation methods. This paper presented a large-scale high-resolution land dataset based on CNN analysis. More than 200,000 images of 256*256 pixels were obtained from Gaofen satellite images with a spatial resolution of 0.8 m, in which four category samples were manually annotated. In the experiment, three CNN based semantic segmentation methods were used as experimental baseline methods. The experimental results showed that the availability of the UJN-Land dataset is beneficial to the planning and management of cultivated land. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

Disentangled cascaded Graph Convolution networks for Multi-Behavior Recommendation

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arXiv 2024年

作者： Cheng, Zhiyong Dong, Jianhua Liu, Fan Zhu, Lei Yang, Xun Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology No. 485 Danxia Road Anhui Hefei230009 China Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences No. 19 Keyuan Road Shandong Jinan250014 China School of Computing National University of Singapore 21 Lower Kent Ridge Road Singapore119077 Singapore School of Electronic and Information Engineering University of Tongji No. 4800 Caoan Road Shanghai201804 China School of Information Science and Technology University of Science and Technology of China No. 443 Huangshan Road Anhui Hefei230027 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Centery No. 485 Danxia Road Anhui Hefei230009 China

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled cascaded Graph Convolutional network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation on benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN’s ability to effectively leverage multi-behavioral data, leading to more accurate recommendations. Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

Zero Stability Well Predicts Performance of Convolutional Neural networks

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arXiv 2022年

作者： Chen, Liangming Jin, Long Shang, Mingsheng Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences China Chongqing School University of Chinese Academy of Sciences China School of Information Science and Engineering Lanzhou University China

The question of what kind of convolutional neural network (CNN) structure performs well is fascinating. In this work, we move toward the answer with one more step by connecting zero stability and model performance. Specifically, we found that if a discrete solver of an ordinary differential equation is zero stable, the CNN corresponding to that solver performs well. We first give the interpretation of zero stability in the context of deep learning and then investigate the performance of existing first- and second-order CNNs under different zero-stable circumstances. Based on the preliminary observation, we provide a higher-order discretization to construct CNNs and then propose a zero-stable network (ZeroSNet). To guarantee zero stability of the ZeroSNet, we first deduce a structure that meets consistency conditions and then give a zero stable region of a training-free parameter. By analyzing the roots of a characteristic equation, we theoretically obtain the optimal coefficients of feature maps. Empirically, we present our results from three aspects: We provide extensive empirical evidence of different depth on different datasets to show that the moduli of the characteristic equation's roots are the keys for the performance of CNNs that require historical features;Our experiments show that ZeroSNet outperforms existing CNNs which is based on high-order discretization;ZeroSNets show better robustness against noises on the input. The source code is available at https://***/LongJin-lab/ZeroSNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation

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arXiv 2020年

作者： Li, Jianing Lan, Yanyan Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

The goal of text generation models is to fit the underlying real probability distribution of text. For performance evaluation, quality and diversity metrics are usually applied. However, it is still not clear to what extend can the quality-diversity evaluation reflect the distribution-fitting goal. In this paper, we try to reveal such relation in a theoretical approach. We prove that under certain conditions, a linear combination of quality and diversity constitutes a divergence metric between the generated distribution and the real distribution. We also show that the commonly used BLEU/Self-BLEU metric pair fails to match any divergence metric, thus propose CR/NRR as a substitute for quality/diversity metric pair. Copyright © 2020, The Authors. All rights reserved.

关键词： Probability distributions

ICT at TREC 2019: Fair Ranking Track 28

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ICT at TREC 2019: Fair Ranking Track

28th Text REtrieval Conference, TREC 2019

作者： Wang, Meng Zhang, Haopeng Liang, Fuhuai Feng, Bin Zhao, Di CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

In this paper, we will introduce our work in the 2019 TREC fair ranking task. In temporal academic search, more and more people choose to pay attention to the fairness constraints of ranking. The purpose of this task is to provide fairness exposure to different groups of authors, where the definition of group is diverse, such as based on demographics, height, topics, etc. For this reason, the model we design should consider the fairness of the ranking results while ensuring the relevance of the ranking results. We will show how to use our model to achieve the relevance of query results, then adjust the overall ranking results according to the fairness of documents, and finally achieve the relevance and fairness of document ordering. This paper will introduce the framework and methods of the fair ranking system, as well as the experimental results. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

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UniEmoX: Cross-modal Semantic-Guided Large-Scale Pretraining for Universal Scene Emotion Perception

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arXiv 2024年

作者： Chen, Chuang Sun, Xiao Liu, Zhi School of Artificial Intelligence Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machines School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Department of Computer and Network Engineering The University of Electro-Communications Chofu-shi Tokyo182-8585 Japan

Visual emotion analysis holds significant research value in both computer vision and psychology. However, existing methods for visual emotion analysis suffer from limited generalizability due to the ambiguity of emotion perception and the diversity of data scenarios. To tackle this issue, we introduce UniEmoX, a cross-modal semantic-guided large-scale pretraining framework. Inspired by psychological research emphasizing the inseparability of the emotional exploration process from the interaction between individuals and their environment, UniEmoX integrates scene-centric and person-centric low-level image spatial structural information, aiming to derive more nuanced and discriminative emotional representations. By exploiting the similarity between paired and unpaired image-text samples, UniEmoX distills rich semantic knowledge from the CLIP model to enhance emotional embedding representations more effectively. To the best of our knowledge, this is the first large-scale pretraining framework that integrates psychological theories with contemporary contrastive learning and masked image modeling techniques for emotion analysis across diverse scenarios. Additionally, we develop a visual emotional dataset titled Emo8. Emo8 samples cover a range of domains, including cartoon, natural, realistic, science fiction and advertising cover styles, covering nearly all common emotional scenes. Comprehensive experiments conducted on six benchmark datasets across two downstream tasks validate the effectiveness of UniEmoX. The source code is available at https://***/chincharles/u-emo. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

GraphWGAN: Graph Representation Learning with Wasserstein Generative Adversarial networks

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GraphWGAN: Graph Representation Learning with Wasserstein Ge...

International Conference on Big data and Smart computing (BIGCOMP)

作者： Rong Yan Huawei Shen Cao Qi Keting Cen Li Wang College of Big Data Taiyuan University Of Technology Taiyuan China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computing Technology Beijing China

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Graph representation learning aims to represent vertices as low-dimensional and real-valued vectors to facilitate subsequent downstream tasks, i.e., node classification, link predictions. Recently, some novel graph representation learning frameworks, which try to approximate the underlying true connectivity distribution of the vertices, show their superiority. These methods characterize the distance between the true connectivity distribution and generated connectivity distribution by Kullback-Leibler or Jensen-Shannon divergence. However, since these divergences are not continuous with respect to the generator's parameters, such methods easily lead to unstable training and poor convergence. In contrast, Wasserstein distance is continuous and differentiable almost everywhere, which means it can produce more reliable gradient, allowing the training more stable and more convergent. In this paper, we utilize Wasserstein distance to characterize the distance between the underlying true connectivity distribution and generated distribution in graph representation learning. Experimental results show that the accuracy of our method exceeds existing baselines in tasks of both node classification and link prediction.

关键词： Hafnium Conferences Big data

Channel Drop Out:A Simple Way to Prevent CNN from Overfitting in Motor Imagery Based BCI

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Channel Drop Out:A Simple Way to Prevent CNN from Overfittin...

2021国际计算机前沿大会

作者： Jing Luo Yaojie Wang Rong Xu Guangming Liu Xiaofan Wang Yijing Gong Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and EngineeringXi'an University of Technology Shaanxi Province Institute of Water Resources and Electric Power Investigation and Design

With the development of deep learning,many motor imagery brain-computer interfaces based on convolutional neural networks(CNNs) show outstanding ***,the trial number of EEG in the training set is usually limited,and redundancy extensively exists in multiple channel ***,overfitting often appears in CNN based motor imagery recognition model and greatly affects the performances of *** this paper,channel drop out is proposed to address this problem by data augmentation and ensemble ***,one of all EEG channels will be dropped and replaced by the mean signal of all EEG *** this way,the trial number in the training set was enlarged by channel drop *** at the testing stage,all the EEG trials processed by channel drop out were fed to the CNN model and the average output probabilities of them were applied to determine the *** experiments were conducted on two popular CNN models applied in motor imagery BCI and BCI Competition IV datasets 2 a to verify the performances of the proposed channel drop out *** results show that average improvements provided by channel drop out in two-category or four-category motor imagery classification are 2.83%and 2.65% compared with the original CNN *** the channel drop out approach significantly improves the performances of motor imagery based BCI.

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Graph Reciprocal Neural networks by Abstracting Node as Attribute

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Graph Reciprocal Neural Networks by Abstracting Node as Attr...

IEEE International Conference on data Mining (ICDM)

作者： Liang Yang Jiayi Wang Dongxiao He Chuan Wang Xiaochun Cao Bingxin Niu Zhen Wang School of Artificial Intelligence Hebei University of Technology Tianjin China College of Intelligence and Computing Tianjin University Tianjin China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen China School of Artificial Intelligence OPtics and ElectroNics (iOPEN) School of Cybersecurity Northwestern Polytechnical University Xi’an China

Graph neural network (GNN) can be formulated as the multiplication of the topology-related matrix (adjacency or Laplacian matrix) and node attribute matrix, i.e., operation in node-wise. Unfortunately, this unified formula reveals two inherent drawbacks. Firstly, the topology and node attribute are not reciprocal but biased. From employment, the topology information is repeatedly employed, while the node attribute is only used once. From parameterization perspective, the node attribute is parameterized with highly expressive MLPs, while topology is not. Secondly, the graph topology can not be fully explored. Only the local pairwise relation is explored, but the mesoscopic community structure, which is one of the most prominent characteristics of networks, is ignored. To alleviate these issues, this paper proposes the Graph Reciprocal network (GRN) by treating node attribute and topology reciprocal. Firstly, it is illustrated that the node can be regarded and utilized as another kind of attribute. Secondly, a novel node representation scheme is proposed from the theory of Quadratic networks, with a theoretical guarantee of the fine-grained element-wise product of the representations of the topology and attribute. Extensive experiments demonstrate the superior performance and robustness of the proposed GRN.

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