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arXiv 2022年

作者： Zheng, Wenjie Wang, Wenxue Zhao, Shu Qian, Fulan Nanjing University of Science and Technology China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

Knowledge graph embeddings (KGE) have been validated as powerful methods for inferring missing links in knowledge graphs (KGs) that they typically map entities into Euclidean space and treat relations as transformations of entities. Recently, some Euclidean KGE methods have been enhanced to model semantic hierarchies commonly found in KGs, improving the performance of link prediction. To embed hierarchical data, hyperbolic space has emerged as a promising alternative to traditional Euclidean space, offering high fidelity and lower memory consumption. Unlike Euclidean, hyperbolic space provides countless curvatures to choose from. However, it is difficult for existing hyperbolic KGE methods to obtain the optimal curvature settings manually, thereby limiting their ability to effectively model semantic hierarchies. To address this limitation, we propose a novel KGE model called Hyperbolic Hierarchical KGE (HypHKGE). This model introduces attention-based learnable curvatures for hyperbolic space, which helps preserve rich semantic hierarchies. Furthermore, to utilize the preserved hierarchies for inferring missing links, we define hyperbolic hierarchical transformations based on the theory of hyperbolic geometry, including both inter-level and intra-level modeling. Experiments demonstrate the effectiveness of the proposed HypHKGE model on the three benchmark datasets (WN18RR, FB15K-237, and YAGO3-10). The source code will be publicly released at https://***/wjzheng96/HypHKGE. Copyright © 2022, The Authors. All rights reserved.

关键词： Knowledge graph

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Low Coupling Sparse Array Based on Uniform Linear Array Fitting Method

Low Coupling Sparse Array Based on Uniform Linear Array Fitt...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Yichang Yao Yingsong Li Liping Li Nikola Zlatanov College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Innopolis Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In order to solve the problem that traditional sparse arrays only emphasize high degrees of freedom and ignore low coupling, uniform array fitting method can design arrays with low coupling. However, the existing array coupling based on uniform array fitting method still has room for decline, and the value of weight function is not the minimum, so the performance of array DOA estimation needs to be further optimized. Therefore, based on the uniform matrix fitting method, a uniform matrix fitting 6-base-layer array is designed. By extending the spacing of each subarray of the uniform subarray in the base layer, the number of small array element spacing pairs in the differential cooperative array is reduced, and the coupling between the elements of the whole sparse array is reduced as a whole. The performance of DOA estimation of the designed array is simulated and analyzed by using spatial smoothing multiple signal classification algorithm.

关键词： Couplings Mutual coupling Direction-of-arrival estimation Smoothing methods Simulation Fitting Estimation

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Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search

arXiv

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arXiv 2023年

作者： Yang, Shangshang Ma, Haiping Zhen, Cheng Tian, Ye Zhang, Limiao Jin, Yaochu Zhang, Xingyi The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei230039 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Anhui Hefei230601 China The Faculty of Technology Bielefeld Unversity Bielefeld33619 Germany

Cognitive diagnosis plays a vital role in modern intelligent education platforms to reveal students’ proficiency in knowledge concepts for subsequent adaptive tasks. However, due to the requirement of high model interpretability, existing manually designed cognitive diagnosis models hold too simple architectures to meet the demand of current intelligent education systems, where the bias of human design also limits the emergence of effective cognitive diagnosis models. In this paper, we propose to automatically design novel cognitive diagnosis models by evolutionary multiobjective neural architecture search (NAS). Specifically, we observe existing models can be represented by a general model handling three given types of inputs and thus first design an expressive search space for the NAS task in cognitive diagnosis. Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task’s search space by maximizing model performance and interpretability. In the MOGP design, each architecture is transformed into a tree architecture and encoded by a tree for easy optimization, and a tailored genetic operation based on four sub-genetic operations is devised to generate offspring effectively. Besides, an initialization strategy is also suggested to accelerate the convergence by evolving half of the population from existing models’ variants. Experiments on two real-world datasets demonstrate that the cognitive diagnosis models searched by the proposed approach exhibit significantly better performance than existing models and also hold as good interpretability as human-designed models. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Equivalence and Duality of Polycyclic Codes Associated with Trinomials over Finite Fields

arXiv

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arXiv 2022年

作者： Shi, Minjia Lu, Haodong Zhou, Shuang Xu, Jiarui Zhu, Yuhang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China

In this paper, several conjectures proposed in [2] are studied, involving the equivalence and duality of polycyclic codes associated with trinomials. According to the results, we give methods to construct isodual and self-dual polycyclic codes, and study the self-orthogonal and dual-containing polycyclic codes over F2MSC Codes 94B05, 05E30 © 2022, CC0.

关键词： Algebra

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Gray Images of Cyclic Codes over p2 and pp2

arXiv

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arXiv 2022年

作者： Wang, Xuan Shi, Minjia Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China

In the paper, we firstly study the algebraic structures of ppk-additive cyclic codes and give the generator polynomials and the minimal spanning set of these codes. Secondly, a necessary and sufficient condition for the pp2-additive cyclic code whose Gray image is linear (not necessarily cyclic) over p is given. Moreover, as for some special families of cyclic codes over p2 and pp2, the linearity of the Gray images is *** Codes 94B05 © 2022, CC0.

关键词： Additives

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Near-field Source DOA Estimation Algorithm Based on De-impact Function under Impulse Noise

Near-field Source DOA Estimation Algorithm Based on De-impac...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Xingchao Xu Baohe Pang Yingsong Li Liping Li Dingcheng Zou Nikola Zlatanov College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Innopolis Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In addressing the issue of localizing near-field source signals in the presence of impulse noise, this manuscript introduces a novel de-impact function designed for preprocessing received signals to alleviate the interference caused by impulse noise. Simulation experiments verify the effectiveness of the proposed de-impact function in effectively suppressing impulse noise. Furthermore, to augment estimation precision, a novel algorithm for near-field source localization is proposed. Building upon the framework of correlation entropy theory, a new correntropy covariance matrix is formulated to replace the original signal covariance matrix for signal processing. Through a succession of low-bias approximations between near-field and far-field scenarios, the algorithm converts the original 2-D search into 1-D operations. Additionally, the conventional spectral peak search is replaced by polynomial solving to further mitigate the computational complexity of the algorithm.

关键词： Location awareness Direction-of-arrival estimation Noise signal processing algorithms Estimation Interference Approximation algorithms

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Structure-constrain Source Free Domain Adaptation with Regularized Knowledge Distillation of Source Model

Structure-constrain Source Free Domain Adaptation with Regul...

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Data Driven Control and Learning Systems (DDCLS)

作者： Dan Yang Xin Peng Cheng Su Dayu Tan Chaoyang Chen Weimin Zhong Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui China School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Transfer learning aims to leverage the knowledge of the source domain to help the learning of unlab.led target domain models. However, all conventional transfer learning methods assume that samples from source and target domains are always freely accessible. However, this assumption is rarely plausible in real-world due to data privacy. To combat this challenge, we introduce Structure-constrain Source Free Domain Adaptation with regularized Knowledge Distillation of source model (SSFDA-KD). Our method is based on the observations that a deterministic classification result can be obtained when the target data distribution aligns with the source data distribution, and a well-trained model not only yields a deterministic classification for a sample but also has clear category boundaries, which implies samples that are close together belong to the same category. From those, we first generate the source model with lab.led source data, which is regarded as a well-trained model. Then, we identify target prototypes with lower self-entropy and assign the pseudo-lab.ls to other samples according to the neighborhood structure. Finally, we refine the target model by minimizing prediction loss, constraining internal structure via triplet loss, and reducing knowledge distillation loss. Experiments of fault diagnosis based on WWTPs are conducted to validate its effectiveness.

关键词： Fault diagnosis Learning systems Adaptation models Data privacy Transfer learning Prototypes Predictive models

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Correction to: Singular value decomposition compressed ghost imaging

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Applied Physics B 2022年第4期128卷 1-2页

作者： Zhang, Cheng Tang, Jun Zhou, Jiaxuan Wei, Sui Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Key Laboratory of Modern Imaging and Displaying Technology Anhui University Hefei China

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A Relay Buffer Prediction Method Based on LSTM in Terminal Cooperative Communication

A Relay Buffer Prediction Method Based on LSTM in Terminal C...

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International Conference on Communication Technology (ICCT)

作者： Jie Fei Hui Zhi Kui Tie Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electronic and Information Engineering Anhui University Hefei China Ceyear Technologies (AnHui) CO. LTD Bengbu China

In terminal cooperative communication, relay assisted communication with buffer has significant advantages in improving system throughput, signal-to-noise ratio, and reducing system outage probability. The existing relay methods with buffer are all fixed buffer method with a fixed size of the buffer and only considering relay wholehearted cooperation and forwarding, that is, all buffer of the relay assists communication. However, when the forwarding task is small and there is a large idle in forwarding buffer, the buffer area cannot be fully utilized. When the forwarding task is large and forwarding buffer is busy, packet loss may occur in the forwarding task. In order to improve the usage efficiency of relay buffer and reduce packet loss, this paper proposes a relay buffer prediction method based on the long short-term memory network to predict the amount of data that the relay needs to forward. According to the prediction, the size of the relay buffer is divided, so that the buffer can be dynamically adjusted on demand. Simulation results show that, compared with the fixed buffer method, the relay buffer prediction method has a better buffer usage efficiency and a lower packet loss rate.

关键词：

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual lab.ling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise lab.l Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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