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International Conference on data engineering

作者： Yanchao Tan Hang Lv Zihao Zhou Wenzhong Guo Bo Xiong Weiming Liu Chaochao Chen Shiping Wang Carl Yang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Department of Computer Science Emory University Atlanta United States

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity- based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable.

关键词： Measurement Accuracy Taxonomy Semantics Predictive models Performance gain Linguistics

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CSLP: Collaborative Solution to Long-Tail Problem and Popularity Bias in Sequential Recommendation

CSLP: Collaborative Solution to Long-Tail Problem and Popula...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yan Huang Zhenyu Yang Wenyue Hu Baojie Xu Zhibo Zhang The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Sequential Recommender Systems (SRS), leveraging the temporal information from users' behaviors, have noticeably improved user experience against traditional systems. However, these behaviors often follow long-tail distribution, making the systems biased towards popular items (i.e., popularity bias). Moreover, popularity bias would amplify the neglect of long-tail recommendations, thereby sharpening the long-tail problem. Previous researches usually address these challenges independently, focusing on reducing the over-recommendation of popular items or enhancing the representation quality of tail items. Indeed, it is possible to incorporate their merits to achieve the best of both worlds. Thus, we propose a novel and unified framework, named Collaborative Solution to Long tailed problem and Popularity bias (CSLP), to tackle both the long-tail problem and popularity bias simultaneously. To achieve this, we first introduce a representation enhancement module featuring dual generators to enhance user and item representations, particularly for those in the tail. On the other hand, a debiasing module incorporating an Inverse Propensity Score (IPS) with a clipping strategy is introduced to further alleviate the popularity bias. Specifically, this clipping strategy demonstrates a clear decrease in the original IPS method's variance, effectively improving the recommendation for stability and accuracy. Experiments on three widely-used datasets show CSLP's effectiveness in solving both issues. CSLP surpasses all baselines (traditional, popularity bias, and long-tail problem) in overall performance, significantly enhancing recommendation accuracy for both tail users and items, and achieving a more balanced ratio of recommendations between popular and tail items. Code is available at https://***/Echohuangyan/CSLP.

关键词： Hands Heavily-tailed distribution Accuracy Collaboration Tail Linear programming Generators User experience IP networks Recommender systems

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Quality Assessment of Electrocardiogram Signals Using Contrastive Learning

Quality Assessment of Electrocardiogram Signals Using Contra...

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

作者： Meiping Zhong Zuoyong Li ChuanSheng Wang Jiayan Huang WanQi Zhao Haoyi Fan College of Computer and Big Data Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona Spain New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. Therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods.

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Multi-modal multi-scale deep learning for large-scale image annotation

arXiv

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arXiv 2017年

作者： Niu, Yulei Lu, Zhiwu Wen, Ji-Rong Xiang, Tao Chang, Shih-Fu Beijing Key Laboratory of Big Data Management and Analysis Methods School of Information Renmin University of China Beijing100872 China School of Electronic Engineering and Computer Science Queen Mary University of London Mile End Road LondonE1 4NS United Kingdom Department of Electrical Engineering Columbia University New YorkNY10027 United States

Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn a rich feature representation suitable for predicting a diverse set of visual concepts ranging from object, scene to abstract concept;2) how to annotate an image with the optimal number of class labels. To address the first issue, we propose a novel multi-scale deep model for extracting rich and discriminative features capable of representing a wide range of visual concepts. Specifically, a novel two-branch deep neural network architecture is proposed which comprises a very deep main network branch and a companion feature fusion network branch designed for fusing the multi-scale features computed from the main branch. The deep model is also made multi-modal by taking noisy user-provided tags as model input to complement the image input. For tackling the second issue, we introduce a label quantity prediction auxiliary task to the main label prediction task to explicitly estimate the optimal label number for a given image. Extensive experiments are carried out on two large-scale image annotation benchmark datasets and the results show that our method significantly outperforms the state-of-the-art. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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Beyond 10logm Array Gain: A Beamforming Method Under Non-Gaussian Noise and Multi-Sources

SSRN

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SSRN 2023年

作者： Su, Xiruo Tao, Liurong Ren, Haoran Wu, Bin Ye, Lingyun College of Biomedical Engineering & Instrument Science Zhejiang University Hangzhou310027 China Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province School of Earth Sciences Zhejiang University Hangzhou310058 China Zhejiang Provincial Key Laboratory for Network Multimedia Technology Hangzhou310027 China Hainan Institute Zhejiang University Sanya572025 China Explosion and Seismic Sensing Research Center Advanced Technology Institute Zhejiang University Hangzhou310027 China

Researchers have applied deep learning techniques in the direction of arrival domain in various real-world applications, such as traffic positioning and hydro-acoustic detection. However, the effectiveness of these algorithms is dependent on the similarity of training data with actual data, which is challenging to obtain, particularly in hydro-acoustic environments. This is due to the limited array gain (AG) and constantly changing ambient noise. Moreover, the presence of non-Gaussian noise in the background further complicates the identification process, especially lower the basic 10logM AG. Additionally, the majority of existing algorithms are only capable of detecting up to four sources. To tackle this issue, we propose a novel method that employs a diagonal beam spectrum (DBS) feature to suppress non-Gaussian noise and enhance array gain. By utilizing DBS, our method surpasses the theoretical 10logM array gain and achieves an impressive 25logM array gain, while effectively detecting signals as low as -38dB (M denotes the number of sensors).We also use SA-U-NET, an image edge detection network based on DBS, to detect unlimited sound sources and optimize angular positioning. Our proposed method achieves up to 95.67% correctness (4 times better than the best baseline) in experiments , demonstrating the potential of DBS and SA-U-NET. © 2023, The Authors. All rights reserved.

关键词： Beamforming

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A novel TomoSAR imaging method with few observations based on nested array

arXiv

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arXiv 2022年

作者： Jiang, Pengyu Zhang, Zhe Zhang, Bingchen Xu, Zhongqiu Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Suzhou Aerospace Information Research Institute Suzhou215000 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou21500 China

Synthetic aperture radar tomography (TomoSAR) baseline optimization technique is capable of reducing system complexity and improving the temporal coherence of data, which has become an important research in the field of TomoSAR. In this paper, we propose a nested TomoSAR technique, which introduces the nested array into TomoSAR as the baseline configuration. This technique obtains uniform and continuous difference co-array through nested array to increase the degrees of freedom (DoF) of the system and expands the virtual aperture along the elevation direction. In order to make full use of the difference co-array, covariance matrix of the echo needs to be obtained. Therefore, we propose a TomoSAR sparse reconstruction algorithm based on nested array, which uses adaptive covariance matrix estimation to improve the estimation performance in complex scenes. We demonstrate the effectiveness of the proposed method through simulated and real data experiments. Compared with traditional TomoSAR and coprime TomoSAR, the imaging results of our proposed method have a better anti-noise performance and retain more image information. Copyright © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

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Forest Connectivity Evaluation by Integrating a Habitat Suitability Index, a Morphological Spatial Pattern and Network Analysis under Season and Disturbance Variations

SSRN

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SSRN 2021年

作者： Tang, Tao Li, Jiping Sun, Hua Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area Changsha410004 China Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province Changsha410004 China Research Centre of Forest Remote Sensing and Information Engineering Central South University of Forestry& Technology Changsha410004 China Faculty of Forestry Central South University of Forestry and Technology Hunan Province Changsha410004 China

Improving forest connectivity is the most commonly cited habitat fragmentation and climate adaptation tactics in biodiversity conservation. Graph-based networks, which rely on proper and species-oriented definitions of nodes and edges, are powerful tools for analyzing landscape connectivity. However, the studies evaluated the forest connectivity itself, ignoring the role of forest connectivity as a part of the landscape. Meantime, few previous studies identified source patches (nodes) from the four: land cover, location, morphological pattern and patch area. Also, evaluating forest connectivity under seasonal and disturbance variations remains a major and unresolved challenge. From the perspective of landscape ecosystem, we propose a new combination of the habitat suitability index (HSI), morphological spatial pattern analysis (MSPA) and network analysis to analyze functional connectivity under seasonal and disturbance variations, providing considerable synergies and potentially related benefits for forest landscape planning. The combined approach was applied to the Hupingshan and Houhe National Nature Reserve and three bordering counties, which are considered the most suitable places to harbor small populations of the South China tiger (Panthera tigris amoyensis). The results showed the forest areas of the five connectivity importance levels from low to high were 324, 1,708, 1,353, 954, and 798 km2, accounting for 6.3%, 33.2%, 26.3%, 18.6% and 15.5% of the total forest area, respectively. We come to the conclusion the proposed integrated method using seasonal and disturbance variations produced less biased and more practical estimates of forest connectivity than those based on purely spatial or temporally static connectivity. © 2021, The Authors. All rights reserved.

关键词： Conservation

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Diffusion Model-based Metaverse Rendering in UAV-Enabled Edge Networks With Dual Connectivity

Diffusion Model-based Metaverse Rendering in UAV-Enabled Edg...

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IEEE Conference on Wireless Communications and Networking

作者： Guoquan Wu Jiangtian Nie Jianhang Tang Yuling Chen Yang Zhang Luchao Han Zehui Xiong School of Information Science and Engineering Yanshan University China School of Computer Science and Engineering Nanyang Technological University Singapore State Key Laboratory of Public Big Data Guizhou University China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China National Natural Science Foundation of China China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Metaverse is an immersive, seamless, interactive, comprehensive virtual world, as well as a replication, extension, and transcendence of the real world. Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is becoming a key technology for ubiquitous Metaverse services. To enhance network resource utilization, we introduce dual connectivity (DC) technologies in UAV-enabled MEC, which increases the time complexity associated with resource management. Considering the specific features of DC communication channels, we propose a UAV-assisted Metaverse rendering problem to enhance the Metaverse service experience and reduce the energy cost of edge devices. To solve the rendering problem with low complexity, we propose a diffusion model-based Metaverse rendering algorithm, where a novel diffusion model is used to generate integer rendering decisions with the aid of the gradient provided by the model-based Metaverse rendering problem. Moreover, with the given rendering decisions, the communication and computation resource allocation results are derived by the model-based optimization method. Finally, we conduct extensive simulation experiments based on real-world datasets. Comprehensive simulation results demonstrate that the diffusion model-based Metaverse rendering algorithm can reduce the Metaverse frame rendering time and improve user experience.

关键词： Costs Metaverse Computational modeling System performance Rendering (computer graphics) Autonomous aerial vehicles User experience

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Spatiotemporal knowledge Distillation for Efficient Estimation of Aerial Video Saliency

arXiv

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arXiv 2019年

作者： Li, Jia Fu, Kui Zhao, Shengwei Ge, Shiming State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China School of Cyber Security at University of Chinese Academy of Sciences Beijing100095 China

—The performance of video saliency estimation techniques has achieved significant advances along with the rapid development of Convolutional Neural Networks (CNNs). However, devices like cameras and drones may have limited computational capability and storage space so that the direct deployment of complex deep saliency models becomes infeasible. To address this problem, this paper proposes a dynamic saliency estimation approach for aerial videos via spatiotemporal knowledge distillation. In this approach, five components are involved, including two teachers, two students and the desired spatiotemporal model. The knowledge of spatial and temporal saliency is first separately transferred from the two complex and redundant teachers to their simple and compact students, and the input scenes are also degraded from high-resolution to low-resolution to remove the probable data redundancy so as to greatly speed up the feature extraction process. After that, the desired spatiotemporal model is further trained by distilling and encoding the spatial and temporal saliency knowledge of two students into a unified network. In this manner, the inter-model redundancy can be further removed for the effective estimation of dynamic saliency on aerial videos. Experimental results show that the proposed approach outperforms ten state-of-the-art models in estimating visual saliency on aerial videos, while its speed reaches up to 28,738 FPS on the GPU platform. Copyright © 2019, The Authors. All rights reserved.

关键词： Students

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Disentangling Interest and Conformity Representation to Mitigate Popularity Bias for Sequential Recommendation

Disentangling Interest and Conformity Representation to Miti...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wenyue Hu Zhenyu Yang Yan Huang Zhibo Zhang Baojie Xu Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The objective of sequential recommendation is to predict user preferences for items based on historical interaction sequences. This process often leads to a phenomenon known as popularity bias, where popular items are excessively recommended. Conformity, the tendency of users to follow popular items, is a significant factor contributing to this issue. Previous methods have not adequately disentangled conformity and interest, failing to accurately model users’ true intent. To address this, we propose a novel Disentangled Interest and Conformity Sequential Recommendation method (DICSRec) to mitigate the popularity bias. Specifically, we first design an Intent Encoding Module (IEM), which includes two independent encoders for conformity and interest to model their representations. To better disentangle these two factors, we design a disentangling task with proxy-based self-supervised learning and orthogonal regularization. Furthermore, to provide the Intent Encoding Module with more global information, we design a Global Conformity-aware Module (GCM), which supplies item popularity information and aids in enhancing user conformity representation. Lastly, recognizing the varying significance of user conformity and interest, we propose an adaptive Fusion Prediction Module (FPM) that adaptively aggregates user conformity and interest representations for final prediction. Experiments on four real-world datasets consistently demonstrate the superiority of our method over advanced sequential recommendation models. Code implementation is available at: https://***/lyra0611/DICSRec.

关键词： Adaptation models Codes Fuses Aggregates Neural networks Self-supervised learning Multitasking

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