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A hybrid filtering-based network document recommendation system in cloud storage

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A hybrid filtering-based network document recommendation sys...

International Journal of Computational Science and Engineering

作者： Wu, Yuezhong Liu, Qin Li, Changyun Wang, Guojun School of Information Science and Engineering Central South University Changsha410083 China School of Computer Science Hunan University of Technology Zhuzhou412007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Intelligent Information Perception and Processing Technology Hunan Province Key Laboratory School of Computer Science Hunan University of Technology Zhuzhou412007 China School of Computer Science and Educational Software Guangzhou University Guangzhou510006 China

Since the key requirement of users is to efficiently obtain personalised services from mass network document resources, a hybrid filtering-based network document recommendation system is designed with the method of incorporating the content-based recommendation and collaborative filtering recommendation based on the powerful and extensible storage and computing power in cloud storage. The proposed system realises the main service module on Hadoop and Mahout platforms, and processes the documents containing the information of user interests by applying AHP-based attribute weighted fusion method. Based on the network interaction, the proposed system not only has advantages on the extensible storage space and high recommendation precision but also has an essential role in realising network resources sharing and personalised recommendation. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Storage as a service (STaaS) Collaborative filtering information retrieval systems Recommender systems Cloud storages Collaborative filtering recommendations Content based recommendation Document recommendation Network interaction Personalised recommendations Recommendation precision User interest model

Human reappearance detection based on on-line learning

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Human reappearance detection based on on-line learning

作者： Hu, Lei Wang, Yizhou Jiang, Shuqiang Huang, Qingming Gao, Wen Key Lab of Intelligent Information Processing Chinese Academy of Sciences Beijing 100080 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Graduate University Chinese Academy of Sciences Beijing 100049 China Institute of Digital Media Peking University Beijing 100871 China Key Lab of Machine Perception Peking University MOE China

ISBN: (纸本)9781424421756

Many video surveillance applications require detecting human reappearances in a scene monitored by a camera or over a network of cameras. This is the human reappearance detection (HRD) problem. Studying this problem is important for analyzing a surveillance scenario at semantic level. In this paper, we propose a novel online learning frameworkfor solving HRD problem. Both generative model and discriminative model are employed in this framework and a voting scheme is presented to fuse the decisions ofboth models for determining whether a just entered person is one of those who have shown up, i.e. whether a reappearance happens. Both models will be updated based on mistake-driven online learning strategy. Our experimental results show that the adopted online learning framework not only improves the reappearance detection accuracy but also achieves high robustness in various surveillance scenes. © 2008 IEEE.

关键词： Cameras

Cross-Modality Perturbation Synergy Attack for Person Re-identification 38

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Cross-Modality Perturbation Synergy Attack for Person Re-ide...

38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China The Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024 Neural information processing systems foundation. All rights reserved.

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Reverse iontophoresis generated by porous microneedles produces an electroosmotic flow for glucose determination

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Talanta 2024年 267卷 125156页

作者： He, Qi-yao Zhao, Jia-hao Du, Shi-ming Li, De-gui Luo, Zhi-wei You, Xue-qiu Liu, Jing School of Navigation Jimei University Xiamen361021 China School of Marine Information Engineering Jimei University Xiamen361021 China School of Mechanical and Automotive Engineering Xiamen University of Technology Xiamen361024 China Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing China

Blood glucose level is a key indicator of a patient's health, notably the symptoms associated with diabetes. Painless and continuously monitored interstitial glucose is highly preferable for diabetes management. Microneedle arrays can be used as an effective but minimally invasive technique to break the skin barrier. Simultaneous reverse iontophoresis enables non-invasive interstitial fluid extraction from the intercellular matrix ≤25 mm under the skin's surface. Here, we describe an ion-conductive porous microneedle (PMN)-based glucose sensing device combined with reverse ion electroosmosis, in which glucose extraction can be significantly enhanced. Four novel benefits of ionophoresis are concurrently brought about by the charged PMN: (1) decreased transdermal resistance with the low invasive penetration of highly resistant stratum corneum;(2) a reduction of transdermal resistance through interconnectivity;(3) the movement of larger molecules through linked micropores;and (4) the production of an electroosmotic flow (EOF). The EOF generated by PMN is effectively extracted from the skin interstitial fluid for glucose concentration measurement. And this will likely contribute to the long-term home management of chronic diseases. © 2023 Elsevier B.V.

关键词： Ions

Learning compact target-oriented feature representations for visual tracking

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arXiv 2019年

作者： Li, Chenglong Huang, Yan Wang, Liang Tang, Jin Lin, Liang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Institute of Physical Science and Information Technology Anhui University Center for Research on Intelligent Perception and Computing NLPR CASIA School of Data and Computer Science Sun Yat-Sen University

Many state-of-the-art trackers usually resort to the pre-trained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundant, noisy and less discriminative for some certain instances, and the tracking performance might thus be affected. To handle this problem, we propose a novel approach, which takes both advantages of good generalization of generative models and excellent discrimination of discriminative models, for visual tracking. In particular, we learn compact, discriminative and target-oriented feature representations using the Laplacian coding algorithm that exploits the dependence among the input local features in a discriminative correlation filter framework. The feature representations and the correlation filter are jointly learnt to enhance to each other via a fast solver which only has very slight computational burden on the tracking speed. Extensive experiments on three benchmark datasets demonstrate that this proposed framework clearly outperforms baseline trackers with a modest impact on the frame rate, and performs comparably against the state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Target tracking

Mining latent structures for multimedia recommendation

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arXiv 2021年

作者： Zhang, Jinghao Zhu, Yanqiao Liu, Qiang Wu, Shu Wang, Shuhui Wang, Liang Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Artificial Intelligence Research Chinese Academy of Sciences

Multimedia content is of predominance in the modern Web era. Investigating how users interact with multimodal items is a continuing concern within the rapid development of recommender systems. The majority of previous work focuses on modeling user-item interactions with multimodal features included as side information. However, this scheme is not well-designed for multimedia recommendation. Specifically, only collaborative item-item relationships are implicitly modeled through high-order item-user-item relations. Considering that items are associated with rich contents in multiple modalities, we argue that the latent semantic item-item structures underlying these multimodal contents could be beneficial for learning better item representations and further boosting recommendation. To this end, we propose a LATent sTructure mining method for multImodal reCommEndation, which we term LATTICE for brevity. To be specific, in the proposed LATTICE model, we devise a novel modality-aware structure learning layer, which learns item-item structures for each modality and aggregates multiple modalities to obtain latent item graphs. Based on the learned latent graphs, we perform graph convolutions to explicitly inject high-order item affinities into item representations. These enriched item representations can then be plugged into existing collaborative filtering methods to make more accurate recommendations. Extensive experiments on three real-world datasets demonstrate the superiority of our method over state-of-the-art multimedia recommendation methods and validate the efficacy of mining latent item-item relationships from multimodal features. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

Learning to Learn Transferable Generative Attack for Person Re-Identification

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arXiv 2024年

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan The College of Electrical and Information Engineering Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology Hunan Changsha China The College of Information Science and Engineering Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Changsha China

Deep learning-based person re-identification (re-id) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based cross-model&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 21.5% and 11.3% on mean mAP drop rate, respectively. The code of MTGA will be released after the paper is accepted. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

Orthogonal Dual Graph Regularized Nonnegative Matrix Factorization 11th

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Orthogonal Dual Graph Regularized Nonnegative Matrix Factori...

11th International Conference on Image and Graphics, ICIG 2021

作者： He, Jinrong Shi, Yanxin Bai, Zongwen Bao, Zeyu Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs Northwest A&F University Shaanxi Yangling712100 China College of Mathematics and Computer Science Yan‘an University Yan‘an716000 China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Northwest A&F University Shaanxi Yangling712100 China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yanan University Shaanxi Yan’an716000 China University of Science and Technology of China Anhui Hefei230026 China

ISBN: (纸本)9783030873578

Nonnegative matrix factorization (NMF) is a classical low-rank approximation method of data matrix, which decomposes a high-dimensional data matrix into two nonnegative low-rank matrices, namely basis matrix and coefficient matrix. In order to capture the local geometric structure of original dataset, manifold learning methods are incorporated into NMF framework. Motivated by recent progress in dual graph regularization, by considering the geometric structures of both the data manifold and the feature manifold, Orthogonal Dual-graph NMF (ODNMF) algorithms were proposed, which imposed orthogonality constraints on basis matrix or coefficient matrix. Since the projection directions were mutually orthogonal, the representation power of data samples was enhanced, thus ODNMF methods are more robust for data clustering. The extensive experimental results on UCI, text and face image data sets have demonstrated the superiority of the proposed methods. © 2021, Springer Nature Switzerland AG.

关键词： Matrix algebra

Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning

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arXiv 2022年

作者： Chen, Weiling Lin, Rongfu Liao, Honggang Zhao, Tiesong Gu, Ke Le Callet, Patrick Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Peng Cheng Laboratory Shenzhen518000 China The Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Équipe Image Perception et Interaction Laboratoire des Sciences du Numérique de Nantes Université de Nantes France

The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience;while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater Image Utility Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision. Copyright © 2022, The Authors. All rights reserved.

关键词： Multimedia systems