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arXiv 2020年

作者： Fan, Haoyi Zhang, Fengbin Li, Zuoyong School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121

Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactions between network structure and node attribute. In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings. Specifically, Anoma-lyDAE consists of a structure autoencoder and an attribute autoencoder to learn both node embedding and attribute embedding jointly in latent space. Moreover, attention mechanism is employed in structure encoder to learn the importance between a node and its neighbors for an effective capturing of structure pattern, which is important to anomaly detection. Besides, by taking both the node embedding and attribute embedding as inputs of attribute decoder, the cross-modality interactions between network structure and node attribute are learned during the reconstruction of node attribute. Finally, anomalies can be detected by measuring the reconstruction errors of nodes from both the structure and attribute perspectives. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed *** Codes 68T30 Copyright © 2020, The Authors. All rights reserved.

关键词： Anomaly detection

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Anomalydae: Dual Autoencoder for Anomaly Detection on Attributed Networks

Anomalydae: Dual Autoencoder for Anomaly Detection on Attrib...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Haoyi Fan Fengbin Zhang Zuoyong Li School of Computer Science and Technology Harbin University of Science and Technology Harbin China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

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More observation leads to more clarity: Multi-view collaboration network for camouflaged object detection

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Neurocomputing 2025年 645卷

作者： Wang, Fangyan Jiao, Ge Yue, Guowen College of Computer Science and Technology Hengyang Normal University Hengyang Hunan421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang Hunan421002 China Hunan Engineering Research Center of Cyberspace Security Technology and Applications Hengyang Normal University Hengyang Hunan421002 China

Currently, Camouflaged Object Detection (COD) methods often rely on single-view feature perception, which struggles to fully capture camouflaged objects due to environmental interference such as background clutter, lighting variations, and viewpoint changes. To address this, we propose the multi-view collaboration network (MCNet), inspired by human visual strategies for complex scene analysis. MCNet incorporates multiple perspectives for enhanced feature extraction. The global perception module takes the original, far, and near views, using different large-kernel convolutions and multi-head attention mechanisms for global feature embedding. In parallel, the local perception module processes the tilted, projected, and color-jittered views, extracting fine-grained local features through multi-branch deep convolutions and dilated convolutions. To facilitate deep interaction between global and local features, we introduce the hybrid interactive module, which explores the correlation of multi-view feature information and adaptively fuses features. For feature decoding, the dynamic pyramid shrinkage module integrates dynamic gated convolutions with a pyramid shrinkage mechanism, progressively aggregating semantic features through a hierarchical shrinking strategy and group fusion strategy. Experimental results on popular COD benchmark datasets show that MCNet outperforms 18 state-of-the-art methods. © 2025

关键词： Object detection

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Principled design of translation, scale, and rotation invariant variation operators for metaheuristics

arXiv

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arXiv 2021年

作者： Tian, Ye Zhang, Xingyi He, Cheng Tan, Kay Chen Jin, Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Guangdong Provincial Key Laboratory of Braininspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Computer Science University of Surrey Guildford SurreyGU2 7XH United Kingdom

In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed, this paper aims to design new operators automatically, which are expected to be search space independent and thus exhibit robust performance on different problems. For this purpose, this work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. Then, we deduce the generic form of translation, scale, and rotation invariant operators. Afterwards, a principled approach is proposed for the automated design of operators, which searches for highperformance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-theart ones on a variety of problems with complex landscapes and up to 1000 decision variables. © 2021, CC BY.

关键词： Heuristic algorithms

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Neural Network-Based Prescription of Chinese Herbal Medicines

Neural Network-Based Prescription of Chinese Herbal Medicine...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Wen Zhao Weikai Lu Chang'en Zhou Zuoyong Li Haoyi Fan Xuejuan Lin Zhaoyang Yang Candong Li School of Traditional Chinese Medicine Fujian University of Traditional Chinese Medicine Fuzhou China School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China School of Computer Science and Technology Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781728194202

Objective: To develop a neural network model that recommends traditional Chinese medicine (TCM) herbal prescriptions. Methods: We constructed a new dataset of diagnosis and treatment knowledge from the Treatise on Febrile Diseases. Based on TCM's logical principles of “syndrome differentiation” and “state recognition”, a back-propagation neural network model is proposed that simulates clinical diagnosis and treatment. Results: The proposed model is a four-layer BP neural network. Experiments on the constructed dataset show that the proposed method achieved the best precision, recall, and F1-scores. Conclusion: The proposed method provides much more accurate herbal prescription recommendations than logistic regression.

关键词： Neural networks Education Clinical diagnosis information technology Logistics Diseases

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Self-Supervised Speech Denoising Using Only Noisy Audio Signals

arXiv

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arXiv 2021年

作者： Wu, Jiasong Li, Qingchun Yang, Guanyu Li, Lei Senhadji, Lotfi Shu, Huazhong LIST Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing210096 China RennesF-35042 France The 28th Research Institute of China Electronics Technology Group Corporation Nanjing210007 China Univ Rennes INSERM LTSI UMR 1099 RennesF-35042 France

In traditional speech denoising tasks, clean audio signals are often used as the training target, but absolutely clean signals are collected from expensive recording equipment or in studios with the strict environments. To overcome this drawback, we propose an end-to-end self-supervised speech denoising training scheme using only noisy audio signals, named Only-Noisy Training (ONT), without extra training conditions. The proposed ONT strategy constructs training pairs only from each single noisy audio, and it contains two modules: training audio pairs generated module and speech denoising module. The first module adopts a random audio sub-sampler on each noisy audio to generate training pairs. The sub-sampled pairs are then fed into a novel complex-valued speech denoising module. Experimental results show that the proposed method not only eliminates the high dependence on clean targets of traditional audio denoising tasks, but also achieves on-par or better performance than other training strategies. Availability-ONT is available at https://***/liqingchunnnn/Only-Noisy-Training. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Using quantum thinking to construct a musical appreciation system 2nd

Using quantum thinking to construct a musical appreciation s...

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2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Peng, Mei Gangyi, Ding Qiankun, Jin Fuquan, Zhang School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9783030045845

With the richness of material and cultural life, participating in music activities and enjoying music works has become an indispensable activity in people’s leisure time. The rich emotions in music have different performances according to individual differences. After the composers create the music, the performer’s deductive processing and then the emotion is sublimated by the viewers finally. This paper proposes a new emotional system for basic emotional joy, anger, sadness, joy, etc., and interprets the superposition state of emotional expression in detail. The paper believes that the generation of consciousness and the resonance of thinking are precisely the result of the quantum collapse of mutual entanglement. © Springer Nature Switzerland AG 2019.

关键词： Music

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Augmented reality-based pre-visualization for film making 2nd

Augmented reality-based pre-visualization for film making

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2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Xue, Tong Ding, Gangyi Zhang, Fuquan School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

ISBN: (纸本)9783030045845

With the development of the augmented reality technologies, wearing devices such as Microsoft Hololens and Google glass become highly popular recently. In this paper, we proposed a novel augmented reality application that helps the film directors to complete and facilitate their film rehearsal process. We simulated a design space to provide film directors with a high-fidelity visual-audio experience. The directors wearing the hololens are allowed to observe the synthesized virtual-reality space in which they can recognize the whole design space, prefabricated boxes, and modify the space by defined gestures. Feedbacks from directors indicate the proposed tool is effective and the preliminary experimental results agree with our observations. In general, our research offers the guidelines for novel film rehearsal tactics. © Springer Nature Switzerland AG 2019.

关键词： Visualization

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Heterogeneous two-Stream Network with Hierarchical Feature Prefusion for Multispectral Pan-Sharpening

Heterogeneous two-Stream Network with Hierarchical Feature P...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Dong Wang Yunpeng Bai Bendu Bai Chanyue Wu Ying Li National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728176055;9781728176062

Multispectral (MS) pan-sharpening aims at producing a high spatial resolution (HR) MS image by fusing a single-band HR panchromatic (PAN) image and a corresponding MS image with low spatial resolution. In this paper, we propose a heterogeneous two-stream network (HTSNet) with hierarchical feature prefusion for MS pan-sharpening. The HTSNet employs a heterogeneous group of spatial and spectral streams for spatial and spectral information extraction, respectively. The spatial stream utilizes a 2D CNN for spatial information extraction from the PAN images, and the spectral stream obtains spectral feature cubes from the MS images by a 3D CNN. At the same time, a prefusion module is introduced to prefuse the spatial details with spectral information and transfer information between different streams, which can enhance later processing. In the experiment, the Gaofen-2 satellite dataset is utilized to compare the proposed method with the state-of-the-art MS pan-sharpening methods. Experimental results demonstrate the superiority of our HTSNet in terms of visual effect and quantitative qualities.

关键词： Satellites Three-dimensional displays Fuses Streaming media Signal processing information retrieval Visual effects

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Link perspective based network embedding for link prediction 6

Link perspective based network embedding for link prediction

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6th International Conference on Robotics and Artificial Intelligence, ICRAI 2020

作者： Ni, Qixuan Ma, Lina Ye, Yaling Wang, Yuyao Bu, Zhan School of Information Engineering Nanjing University of Finance and Economics Nanjing China WinGin Business-Intelligence Academy Nanjing Co. Ltd Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Jiangsu Provincial Key Laboratory of E-Business Nanjing University of Finance and Economics Nanjing China

ISBN: (纸本)9781450388597

Link prediction is an important task attracting much attention in the field of complex network, and it can be applied to many real-world scenarios such as recommendation engines, protein-protein interactions prediction, and stock index prediction. Recently, network embedding, which learns low-dimension latent representations of vertexes, has attracted considerable research efforts. It provides a new feasible solution to boost the accuracy of link prediction by preserving the rich structure information of the network. Most existing methods for network embedding depend exclusively on the perspective of nodes, but seldom focus on the perspective of links. In practice, different perspectives of network can bring us different information. As our empirical analysis shows, random walking from node perspective and link perspective of network could bring us different node sampling results, which may mean different partitions of communities. Thus, we designed a new network embedding approach for link prediction which incorporates the perspectives of both nodes and links. The aim of our method is to supplement the structure information from the perspective of links by using line graph, which improve the validity of nodes representations for link prediction. We compare our method with other popular approaches on four real-world networks and the experiment results demonstrate that our method improves performance in three link tasks. © 2020 Association for Computing Machinery. All rights reserved.

关键词： Forecasting

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