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arXiv 2022年

作者： Liu, Yuanyuan Long, Chengjiang Zhang, Zhaoxuan Liu, Bokai Zhang, Qiang Yin, Baocai Yang, Xin The Department of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Meta Reality Labs BurlingameCA94010 United States The Key Lab of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China The Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology Beijing University of Technology Beijing100124 China

3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing works is still far from being perfect for fine-grained and multi-label situations. In this paper, we propose a framework fully exploring contextual information for the 3D SGG task, which attempts to satisfy the requirements of fine-grained entity class, multiple relation labels, and high accuracy simultaneously. Our proposed approach is composed of a Graph Feature Extraction module and a Graph Contextual Reasoning module, achieving appropriate information-redundancy feature extraction, structured organization, and hierarchical inferring. Our approach achieves superior or competitive performance over previous methods on the 3DSSG dataset, especially on the relationship prediction sub-task. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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Deep Sentiment Learning Network for Temporal-aware Recommendation Based on User Reviews

Deep Sentiment Learning Network for Temporal-aware Recommend...

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IEEE International Conference on Big Data Analysis (ICBDA)

作者： Xinxin Li Tianqi Shang Dezhong Peng Xiaoyu Shi School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Computer Science Sichuan University Chengdu China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

Recently, many websites encourage users to fill in reviews on items and services to improve the quality of personalized recommendation, because of the rich sentiment information hidden in user reviews. However, most existing recommendation methods based on reviews heavily emphasize extracting the rich sentiment information from reviews by using deep learning technologies as far as possible and always ignore the effects of temporal dynamical of user preferences. To address the above issues, we propose DSL- TR, a deep sentiment learning framework for the temporal-aware recommendation, using an intricate combination of bidirectional long short-term memory (BLSTM) and Convolution Neural Network (CNN). It consists of four layers: embedding layer, BLSTM layer, CNN layer and feature fusion layer. In the embedding layer, we explicitly combine the time information in the review embedding layer by regarding the review time as an independent factor. Then, we utilize the BLSTM and CNN networks to extract the long and short-terms of latent features of user and item in parallel. Finally, the factorization machine technique, as a rating predictor, is introduced on the last layer to estimate the rating based on the learned user and item latent features. We conduct extensive experiments on Amazon's four data sets. The results demonstrate that our proposed method outperforms several state-of-art methods consistently.

关键词： Deep learning Convolution Conferences Neural networks Big Data Feature extraction Data mining

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MedSteGAN: Medical Embedding-Optimized Steganography with Improved GAN

SSRN

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SSRN 2023年

作者： Peng, Yuxiang Fu, Chong Zheng, Yu Tian, Yunjia Cao, Guixing Chen, Junxin School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China Department of Information Engineering The Chinese University of Hong Kong Ma Liu Shui Shatin New Territories Hong Kong Institute of Telecommunication Satellite China Academy of Space Technology Beijing100094 China School of Software Dalian University of Technology Liaoning Dalian116621 China

Medical diagnosing systems contain sensitive information, especially the resulting certificate of diagnosis that involves the patient’s privacy, required to be protected carefully. An effective way is to secretly embed privacy-sensitive information into medical-generated images, eventually being viewed as arbitrary images of nobody/nothing in public. The current research line of medical steganography targets simple data-hiding methods, yet remains barren in optimal embedding and stronger robustness. To fill this gap, we propose an end-to-end steganographic framework for medical images. Our idea is to integrate medical-image adaptive steganography with a generative adversarial network (GAN), named MedSteGAN. To improve both robustness, we design an optimized U-shape gen- erator that extracts features more efficiently utilizing larger kernel convolutions and the ReLU6 function. Also, deconvolutions are replaced by upsampling and convolution layers to avoid the checkerboard effect. To enhance security and image quality, we employ visual quality loss and mean squared error (MSE) loss based on the multi-scale structural similarity index metric (MS- SSIM) and the high-pass filters, respectively. Extensive experiments demonstrate that MedSteGAN outperforms state-of-the-art steganographic schemes in terms of robustness and image quality. © 2023, The Authors. All rights reserved.

关键词： Image quality

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Learning from pseudo lesion: A self-supervised framework for covid-19 diagnosis

arXiv

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arXiv 2021年

作者： Li, Zhongliang Jin, Zhihao Li, Xuechen Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by Ground Glass Opacity (GGO), a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo lesions generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo COVID-19 images. The pairs of normal and pseudo COVID-19 images were then used to train an encoder-decoder architecture based U-Net for image restoration, which does not require any labelled data. The pretrained encoder was then fine-tuned using labelled data for COVID-19 diagnosis task. Two public COVID- 19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis and the accuracy of the proposed method outperformed the supervised model pretrained on large scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively. © 2021, CC BY-NC-SA.

关键词： Coronavirus

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SMedBERT: A knowledge-enhanced pre-trained language model with structured semantics for medical text mining

arXiv

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arXiv 2021年

作者： Zhang, Taolin Cai, Zerui Wang, Chengyu Qiu, Minghui Yang, Bite He, Xiaofeng School of Software Engineering East China Normal University Alibaba Group Shanghai Key Laboratory of Trsustworthy Computing School of Computer Science and Technology East China Normal University Dxy Shanghai Research Institute for Intelligent Autonomous Systems

Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding. For medical domains, the background knowledge sources are especially useful, due to the massive medical terms and their complicated relations are difficult to understand in text. In this work, we introduce SMedBERT, a medical PLM trained on large-scale medical corpora, incorporating deep structured semantics knowledge from neighbours of linked-entity. In SMedBERT, the mention-neighbour hybrid attention is proposed to learn heterogeneousentity information, which infuses the semantic representations of entity types into the homogeneous neighbouring entity structure. Apart from knowledge integration as external features, we propose to employ the neighbors of linked-entities in the knowledge graph as additional global contexts of text mentions, allowing themto communicate via shared neighbors, thus enrich their semantic representations. Experiments demonstrate that SMedBERT significantly outperforms strong baselines in various knowledge-intensive Chinese medical tasks. It also improves the performance of other tasks such as question answering, questionmatching and natural language inference. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

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Deep learning-based hyperspectral and multispectral fusion techniques: Review, optimization, and perspectives

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Information Fusion 2025年 124卷

作者： Sen Luo Yurong Qian Lu Bai Yingying Fan Yuanxu Wang WeiQuan Kong Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Xinjiang University Urumqi 830091 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Urumqi 830046 China School of Computer Science and Technology Xinjiang University Urumqi 830046 China

Hyperspectral and Multispectral Image Fusion (HMIF) aim to acquire high spatial and spectral resolution images, which overcomes the inherent limitations of a single imaging device and effectively extends the applicability of spectral images on various downstream tasks. In recent years, Deep Learning (DL)-based image fusion technology breaks through the many limitations of traditional methods, rapidly develops a variety of neural network architectures and improvement strategies, and gradually overcomes a series of practical difficulties. However, a comprehensive overview of these deep learning architectures and various improvement techniques is still lacking. In this paper, we present a comprehensive review of deep learning techniques on HMIF, organize the fusion paradigms according to different researchers’ understanding and hypotheses of the problem, and sort out the common optimization strategies of neural network models, such as attention mechanism, multi-feature learning, combined with the variational and unmixing models of deep learning. In addition, we also provide a detailed compendium of the practical challenges and corresponding solution strategies for the first time, which involves the spatial-temporal misregistration, generalization enhancement and high-magnification fusion problems. Finally, we sort out and categorize the commonly used evaluation metrics for fused datasets, and discuss the potential research directions in HMIF. Through these works, we hope to provide a quick and clear reference for aspiring researchers in this area.

关键词：

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Semi-supervised classification method of SAR images using spectral clustering in contourlet domain 4

Semi-supervised classification method of SAR images using sp...

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2019 4th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2019

作者： Jiang, Kaiwen Zhang, Degan Xu, Haixia Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education 300384 China Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin China School of Electronic and Information Engineering Tianjin Vocational Institute Tianjin300410 China

A new based on Semi-supervised classification theory for SAR images in contourlet domain is proposed, in this paper. Attempting to get better and faster performance, the PSO algorithm (Particle swarm optimization algorithm) and contourlet domain is proposed to instead of traditional k-means algorithm. PSO is used to find the global optimum by performing a global search in the whole solution space. And then, contourlet is applied in front of construct the similarity matrix to extract more effective eigenvalues. In section five, the proposed algorithm got better classification results than the traditional k-means algorithm which is proved by experimental results show that in terms of running time, classification accuracy and Kappa coefficient. © Published under licence by IOP Publishing Ltd.

关键词： Synthetic aperture radar

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Group-wise inhibition based feature regularization for robust classification

arXiv

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arXiv 2021年

作者： Liu, Haozhe Wu, Haoqian Xie, Weicheng Liu, Feng Shen, Linlin 1Computer Vision Institute College of Computer Science and Software Engineering 2SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society 3National Engineering Laboratory for Big Data System Computing Technology 4Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features when learning, leading to the lack of feature diversity for final judgment. In our method, we propose to dynamically suppress significant activation values of CNN by group-wise inhibition, but not fixedly or randomly handle them when training. The feature maps with different activation distribution are then processed separately to take the feature independence into account. CNN is finally guided to learn richer discriminative features hierarchically for robust classification according to the proposed regularization. Our method is comprehensively evaluated under multiple settings, including classification against corruptions, adversarial attacks and low data regime. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both robustness and generalization performances, when compared with the state-of-the-art methods. Code is available at https://***/LinusWu/TENET_Training. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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IGTP: A next point-of-interest recommendation method that integrates geospatial and temporal preferences

Research Square

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Research Square 2021年

作者： Jiao, Xu Xiao, Yingyuan Zheng, Wenguang Zhu, Ke School of General Education Tianjin Foreign Studies University Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

With the rapid development of location-based social networks(LBSNs), point-of-interest(POI) recommendation has become an important way to meet the personalized needs of users. The purpose of POI recommendation is to provide personalized POI recommendation services for users. However, general POI recommendations cannot meet the individual needs of users. This is mainly because the decision-making process for users to choose POIs is very complicated and will be affected by various user contexts such as time, location, etc. This paper proposes a next POI recommendation method that integrates geospatial and temporal preferences, called IGTP. Compared with general POI recommendation, IGTP can provide more personalized recommendations for users according to their context information. First, IGTP uses users’ preferences information to model users’ check-in histories to effectively overcome the challenge of extremely sparse check-in data. Secondly, IGTP takes into account the geographic distance and density factors that affect people’s choice of POIs, and limits POIs to be recommended to the potential activitive area centered on the current loca- tion of the target user. Finally, IGTP integrates geospatial and users’ temporal preferences information into a unified recommendation process. Compared with six advanced baseline methods, the experimental results demonstrate that IGTP achieves much better performance. © 2021, CC BY.

关键词： Location

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DVO + LCLMF: A web service recommendation mechanism with QoS privacy preservation

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Concurrency and Computation: Practice and Experience 2022年第18期34卷

作者： Li, Kui Ji, Yi-mu Liu, Shang-dong Wu, Fei Yao, Hai-chang He, Jing Liu, Qiang Liu, Yan-lan Shao, Si-si You, Shuai School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Nantong Bigdata Research Key Laboratory for Rehabilitation Medicine The Second People's Hospital of Nantong Nantong China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China Institute of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC Australia

QoS-aware based web service recommendation is one of the crucial solutions to help users find high-quality web services. To accurately predict the QoS values of candidate services, it is usually required to collect historical QoS data of users (QoS data for short). If these collected QoS data are improperly processed, QoS data privacy may be threatened. However, how to accurately predict the QoS values of candidate services while protecting QoS data privacy has not been well studied. In response to the situation, we propose a hybrid web service recommendation mechanism, which is divided into three parts. In the first part, the QoS data privacy preservation algorithm, which called DVO, is proposed based on keeping the cosine similarity of QoS data unchanged, that is, to realize the confusion of QoS data while ensuring the availability of QoS data remains unchanged. In the second part, a hybrid matrix factorization model based on location information and service features, which called LCLMF, is proposed to improve the accuracy of QoS values prediction. According to DVO and LCLMF, the DVO + LCLMF is designed in the third part, which can accurately predict QoS values while protecting QoS data privacy. The experimental results show that DVO + LCLMF can accurately predict the QoS values of candidate services on the basis of attaining QoS data privacy protection. © 2022 John Wiley & Sons, Ltd.

关键词： Data privacy

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