摘要：
WaveMLP has demonstrated remarkable performance in various vision tasks, such as dense feature detection and semantic segmentation. However, WaveMLP, as a local model, imposes limitations on fully connected layers by only allowing connections between tokens within the same local window. This constraint makes the model neglect the relationship among tokens in different windows, leading to a local token fusion and a degraded modeling performance. Specially, it poses challenges when dealing with hyperspectral image (HSI) classification tasks that require capturing long-range dependencies. To address this issue, this letter proposes a new position-aware WaveMLP, dubbed PA-WaveMLP, which incorporates a global polar positional encoding module (PPEM) into WaveMLP. PPEM is a lightweight method to encode the spatial relationship between land objects in distance and direction by using the radius and angle. By PPEM, the proposed PA-WaveMLP enables tokens to include their own spatial position information to the fusion process, allowing for the capture of long-range dependencies, while maintaining the excellent modeling capabilities of WaveMLP. The experimental results on three publicly available HSI datasets validate the effectiveness and generalizability of this newly proposed PA-WaveMLP. In particular, PA-WaveMLP model achieved an overall accuracy (OA) of 99.16%, 99.71%, and 99.47% on Indian Pines (IP), Pavia University (PU), and Salinas (SA), respectively.
作者机构：
[Zhang, Miao] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Zhang, Miao; He, Tingting; Dong, Ming; Dong, M] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.;[Zhang, Miao; He, Tingting; Dong, Ming; Dong, M] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China.;[He, Tingting; Dong, Ming; Dong, M] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
通讯机构：
[He, TT; Dong, M ] C;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
摘要：
Commonsense question answering (CQA) requires understanding and reasoning over QA context and related commonsense knowledge, such as a structured Knowledge Graph (KG). Existing studies combine language models and graph neural networks to model inference. However, traditional knowledge graph are mostly concept-based, ignoring direct path evidence necessary for accurate reasoning. In this paper, we propose MRGNN (Meta-path Reasoning Graph Neural Network), a novel model that comprehensively captures sequential semantic information from concepts and paths. In MRGNN, meta-paths are introduced as direct inference evidence and an original graph neural network is adopted to aggregate features from both concepts and paths simultaneously. We conduct sufficient experiments on the CommonsenceQA and OpenBookQA datasets, showing the effectiveness of MRGNN. Also, we conduct further ablation experiments and explain the reasoning behavior through the case study.
摘要：
<jats:title>Abstract</jats:title><jats:p>Event detection plays an essential role in the task of event extraction. It aims at identifying event trigger words in a sentence and classifying event types. Generally, multiple event types are usually well‐organized with a hierarchical structure in real‐world scenarios, and hierarchical correlations between event types can be used to enhance event detection performance. However, such kind of hierarchical information has received insufficient attention which can lead to misclassification between multiple event types. In addition, the most existing methods perform event detection in Euclidean space, which cannot adequately represent hierarchical relationships. To address these issues, we propose a novel event detection network HyperED which embeds the event context and types in Poincaré ball of hyperbolic geometry to help learn hierarchical features between events. Specifically, for the event detection context, we first leverage the pre‐trained BERT or BiLSTM in Euclidean space to learn the semantic features of ED sentences. Meanwhile, to make full use of the dependency knowledge, a GNN‐based model is applied when encoding event types to learn the correlations between events. Then we use a simple neural‐based transformation to project the embeddings into the Poincaré ball to capture hierarchical features, and a distance score in hyperbolic space is computed for prediction. The experiments on MAVEN and ACE 2005 datasets indicate the effectiveness of the HyperED model and prove the natural advantages of hyperbolic spaces in expressing hierarchies in an intuitive way.</jats:p>
期刊：
IEEE Transactions on Geoscience and Remote Sensing,2023年61:1-11 ISSN：0196-2892
通讯作者：
Fu, LH
作者机构：
[Fu, Lihua; Chen, Xingrong; Xu, Yuejiao; Niu, Xiao] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.;[Zhang, Meng] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Fu, LH ] C;China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.
关键词：
Tensors;Three-dimensional displays;Matrix decomposition;Correlation;Singular value decomposition;Frequency-domain analysis;Spectral analysis;3-D seismic data reconstruction;fully connected tensor network (FCTN);Hankel tensor;low rank
摘要：
Rank-reduction approaches assume that seismic data in the frequency–space domain is of low-rank after a specific pretransformation. The presence of noise or missing traces will increase the rank; therefore, seismic data can be denoised and recovered via rank-reduction techniques. The iterative weighted projection onto convex sets (POCS) framework can be used for noise attenuation and data reconstruction simultaneously. Multichannel singular spectrum analysis (MSSA) is a classic 3-D seismic data reconstruction algorithm that rearranges the temporal frequency slices of the data with missing traces into a block Hankel matrix and then uses randomized singular value decomposition (RSVD) to interpolate slices. To further improve the efficiency and precision of 3-D seismic data reconstruction, we introduce the fully connected tensor network (FCTN) decomposition over the Hankel tensor of the frequency slices. We show that our novel rank-reduction method estimates fewer parameters than MSSA, yielding more accurate and robust results. Moreover, FCTN decomposes a fourth-order tensor into four factor contractions, which breaks the limitations that traditional tensor decomposition methods, such as CANDECOMP/PARAFAC (CP) and Tucker decomposition, cannot establish the connections between different factors and are less effective at characterizing relationships. The newly proposed approach does not require singular value decomposition (SVD), leading to an overall reduction in computational complexity. Synthetic and field examples are used to compare the performance of our method with MSSA, and our numerical results reveal the better performance of the proposed FCTN decomposition method for seismic data with large gaps or a high missing ratio.
期刊：
JOURNAL OF SUPERCOMPUTING,2023年79(12):13724-13743 ISSN：0920-8542
通讯作者：
Xiang Li
作者机构：
[Wu, Fei; Li, Xiang; Zhang, Maoyuan] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Hubei, Peoples R China.;[Wu, Fei; Li, Xiang; Zhang, Maoyuan] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Wu, Fei; Li, Xiang; Zhang, Maoyuan] Cent China Normal Univ, Natl Language Resources Monitor & Res Ctr Network, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Xiang Li] H;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China<&wdkj&>School of Computer, Central China Normal University, Wuhan, China<&wdkj&>National Language Resources Monitor and Research Center for Network Media, Central China Normal University, Wuhan, China
摘要：
Cross-domain sentiment analysis (CDSA) aims to overcome domain discrepancy to judge the sentiment polarity of the target domain lacking labeled data. Recent research has focused on using domain adaptation approaches to address such domain migration problems. Among them, adversarial learning performs domain distribution alignment via domain confusion to transfer domain-invariant knowledge. However, this method that transforms feature representations to be domain-invariant tends to align only the marginal distribution, and may inevitably distort the original feature representations containing discriminative knowledge, thus making the conditional distribution inconsistent. To alleviate this problem, we propose adversarial domain adaptation with model-oriented knowledge adaptation (Moka-ADA) for the CDSA task. We adopt the adversarial discriminative domain adaptation (ADDA) framework to learn domain-invariant knowledge for marginal distribution alignment, based on which knowledge adaptation is conducted between the source and target models for conditional distribution alignment. Specifically, we design a dual structure with similarity constraints on intermediate feature representations and final classification probabilities, so that the target model in training learns discriminative knowledge from the trained source model. Experimental results on a publicly available sentiment analysis dataset show that our method achieves new state-of-the-art performance.
期刊：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2023年20:1-5 ISSN：1545-598X
通讯作者：
Zhang, Meng(m.zhang@mail.ccnu.edu.cn)
作者机构：
[Tang, Ping; Zhang, Meng] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[Liu, Zhihui] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.;[Song, Rong] Cent China Normal Univ, Sch Marxism, Wuhan 430079, Peoples R China.
通讯机构：
[Zhang, M.] C;Central China Normal University, China
期刊：
JOURNAL OF SUPERCOMPUTING,2023年79(6):6290-6308 ISSN：0920-8542
通讯作者：
Lisha Liu
作者机构：
[Liu, Lisha; Zhang, Maoyuan] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Peoples R China.;[Mi, Jiaxin; Liu, Lisha; Zhang, Maoyuan; Yuan, Xianqi] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Yuan, Xianqi] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netw, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Lisha Liu] H;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, China<&wdkj&>School of Computer, Central China Normal University, Wuhan, Hubei, China
期刊：
Lecture Notes on Data Engineering and Communications Technologies,2021年88:1713-1721 ISSN：2367-4512
作者机构：
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Hubei, Wuhan, 430079, China;School of Computer, Central China Normal University, Hubei, Wuhan, 430079, China
作者机构：
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, 430079, China;School of Computer, Central China Normal University, Wuhan, Hubei, 430079, China;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei, 430079, China;National Language Resources Monitor and Research Center for Network Media, Central China Normal University, Wuhan, Hubei, 430079, China;[Zhang M.] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, 430079, China, National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei, 430079, China, National Language Resources Monitor and Research Center for Network Media, Central China Normal University, Wuhan, Hubei, 430079, China
会议名称：
5th China Conference on Knowledge Graph, and Semantic Computing, CCKS 2020
会议时间：
12 November 2020 through 15 November 2020
关键词：
Deep learning;Dialogue generation;External knowledge
作者机构：
[Fang, Wenqian; Fu, Lihua; Li, Zhiming] China Univ Geosci Wuhan, Sch Math & Phys, Wuhan 430074, Peoples R China.;[Zhang, Meng] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Li, Zhiming] C;China Univ Geosci Wuhan, Sch Math & Phys, Wuhan 430074, Peoples R China.
关键词：
interpolation;signal processing
摘要：
Seismic data interpolation is an effective way of recovering missing traces and obtaining enough information for subsequent processing. Unlike traditional methods, deep neural network (DNN)-based methods do not need to make assumptions because they can self-learn the relationship between sampled data and complete data using large training data sets and complete the interpolation with a small computational burden. However, current DNN-based approaches only focus on reducing the difference between the recovered and original data during training, which helps to improve the quality of the reconstructed seismic data as a whole, while ignoring the characteristics of the local structure. We have developed a novel seismic U-net interpolator (SUIT) algorithm based on the framework of the U-net DNN in combination with a texture loss, rather than only optimizing for reconstruction loss. Texture loss is proposed to ensure the accuracy of local structural information, which is calculated by a pretrained texture extraction neural network. Furthermore, we use a trade-off parameter to balance the reconstruction error and texture loss, and a practical technique for selecting the associated weighting parameter. The feasibility of our method is assessed via synthetic and field data examples. Numerical tests show that SUIT is robust in noisy environments and that the trained network can reconstruct irregularly or regularly sampled seismic data. Our proposed algorithm performed better than DNN-based approaches that only use reconstruction loss and the traditional low-rank matrix fitting method.
期刊：
Journal of Information Security and Applications,2021年58:102724 ISSN：2214-2126
通讯作者：
Hsu, Chingfang
作者机构：
[Zhang, Maoyuan; Hsu, Chingfang] Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.;[Harn, Lein] Univ Missouri Kansas City, Dept Comp Sci Elect Engn, Kansas City, MO 64110 USA.;[Xia, Zhe] Wuhan Univ Technol, Dept Comp Sci, Wuhan 430071, Peoples R China.;[Li, Quanrun] Wuhan Univ, Sch Cyber Sci & Engn, Wuhan 430079, Peoples R China.
通讯机构：
[Hsu, Chingfang] C;Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.
关键词：
Combinatorial mathematics;Cryptography;Access structure;Group communications;Interpolating polynomials;Potential ability;Secret sharing schemes;Secure group communications;Single networks;Threshold access structures;5G mobile communication systems
摘要：
Seismic data are often undersampled owing to physical or financial limitations. However, complete and regularly sampled data are becoming increasingly critical in seismic processing. In this paper, we present an efficient two-dimensional (2D) seismic data reconstruction method that works on texture-based patches. It performs completion on a patch tensor, which folds texture-based patches into a tensor. Reconstruction is performed by reducing the rank using tensor completion algorithms. This approach differs from past methods, which proceed by unfolding matrices into columns and then applying common matrix completion approaches to deal with 2D seismic data reconstruction. Here, we first re-arrange the seismic data matrix into a third-order patch tensor, by stacking texture-based patches that are divided from seismic data. Then, the seismic data reconstruction problem is formulated into a low-rank tensor completion problem. This formulation avoids destroying the spatial structure, and better extracts the underlying useful information. The proposed method is efficient and gives an improved performance compared with traditional approaches. The effectiveness of our patch tensor-based framework is validated using two classical tensor completion algorithms, low-rank tensor completion (LRTC), and the parallel matrix factorization algorithm (TMac), on both synthetic and field data experiments.
关键词：
heterogeneous networks;social networks;friend recommendation;co-author recommendation;random walk with restart
摘要：
It is of great importance to recommend collaborators for scholars in academic social networks, which can benefit more scientific research results. Facing the problem of data sparsity of co-author recommendation in academic social networks, a novel recommendation algorithm named HeteroRWR (Heterogeneous Random Walk with Restart) is proposed. Different from the basic Random Walk with Restart (RWR) model which only walks in homogeneous networks, HeteroRWR implements multiple random walks in a heterogeneous network which integrates a citation network and a co-authorship network to mine the k mostly valuable co-authors for target users. By introducing the citation network, HeteroRWR algorithm can find more suitable candidate authors when the co-authorship network is extremely sparse. Candidate recommenders will not only have high topic similarities with target users, but also have good community centralities. Analyses on the convergence and time efficiency of the proposed approach are presented. Extensive experiments have been conducted on DBLP and CiteSeerX datasets. Experimental results demonstrate that HeteroRWR outperforms state-of-the-art baseline methods in terms of precision and recall rate even in the case of incorporating an incomplete citation dataset.
