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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ronghuan Zhang Jing Zhao Ming Li Qingzhi Zou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center 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 School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural Networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity.

关键词： Accuracy Computational modeling Memory management Neural networks Feature extraction Transformers Sensors data mining Computational complexity Remote sensing

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Contrastive Learning for Adapting Language Model to Sequential Recommendation

Contrastive Learning for Adapting Language Model to Sequenti...

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IEEE International Conference on data Mining (ICDM)

作者： Fei-Yao Liang Wu-Dong Xi Xing-Xing Xing Wei Wan Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Contribution during internship at NetEase Games UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

With the explosive growth of information, recommendation systems have emerged to alleviate the problem of information overload. In order to improve the performance of recommendation systems, many existing methods introduce Large Language Models to extract textual information from description text. However, Large Language Models are trained on large-scale generic textual data and may face a semantic gap for downstream recommendation tasks. To address the above issues, we propose Contrastive Learning for Adapting Language Model to Sequential Recommendation (CLA-Rec). In CLA-Rec, we first extract text embeddings from description text using Large Language Models and align the text embeddings learned by Large Language Models with the collaborative information through contrastive learning to obtain high-quality item representations. Through semantic alignment, we bridge the semantic gap between Large Language Models and the recommendation task. To map textual information and collaborative information into user representations, we utilize a Transformer model to learn user representations and capture user preferences by combining the semantically aligned item representations. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches on multiple evaluation metrics, illustrating the effectiveness of the CLA-Rec model in adapting Large Language Models to recommendation tasks.

关键词： Measurement Adaptation models Large language models Semantics Collaboration Contrastive learning Transformers data models data mining Recommender systems

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A Rational Delegating Computation Protocol Based on Smart Contract 14th

A Rational Delegating Computation Protocol Based on Smart Co...

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14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Ma, Juan Chen, Yuling Liu, Guoxu Zhu, Hongliang Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and Technology Shouguang China Engineering Lab for Cloud Security and Information Security Center of BUPT Beijing China

ISBN: (纸本)9783030898137

The delegating computation has become an irreversible trend, together comes the pressing need for fairness and efficiency issues. To solve this problem, we leverage game theory to propose a smart contract-based solution. Firstly, according to the behavioral preferences of the participants, we design an incentive contract to describe the motivation of the participants. Secondly, to satisfy the fairness of the rational delegating computation, we propose a delegating computation protocol based on the smart contract. Specifically, rational participants are to gain the maximum utility and reach the Nash equilibrium in the protocol. Besides, we design a reputation mechanism with a reputation certificate, which measures the reputation from multiple dimensions. And the reputation is used to assure the client’s trust in the computing party to improve the efficiency of the protocol. Finally, the analysis results show that the proposed protocol solves the complex traditional verification problem. Meanwhile, we prove the fairness and correctness of the protocol. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Smart contract

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Advances in magnetic particle imaging and perspectives on liver imaging

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iLIVER 2022年第4期1卷 237-244页

作者： Wei Li Xiaohua Jia Lin Yin Zhiyun Yang Hui Hui Jianlin Li Wenhui Huang Jie Tian Shuixing Zhang Department of Radiology The First Affiliated HospitalJinan UniversityNo.613Huangpu West RoadTianhe DistrictGuangzhou510627GuangdongChina CAS Key Laboratory of Molecular Imaging Institute of AutomationBeijing100190China Beijing Key Laboratory of Molecular Imaging Beijing100190China University of Chinese Academy of Sciences Beijing100080China School of Food Science and Pharmaceutical Engineering Nanjing Normal UniversityNanjing 210023China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing100083China Zhuhai Precision Medical Center Zhuhai People's HospitalAffiliated with Jinan UniversityZhuhai519000China

Magnetic particle imaging(MPI)is an emerging technique to visualize the spatial distribution of super-paramagnetic iron oxide with high temporal–spatial resolution,high sensitivity,unlimited image depth,and true quantitative *** is based on the nonlinear response of superparamagnetic iron oxide in an alter-nating magnetic field without tissue background *** is a promising imaging modality for various applica-tions,including vascular imaging,cell tracking,tumor imaging,and catheter *** applications of liver imaging could be improved or created with *** this review,we cover the principle and construction of MPI,we evaluate the features and advantages of MPI with relation to its own rationale and via comparison with other imaging modalities,and we review MPI liver imaging applications with a view toward assisting hepatic researchers in drawing inspiration.

关键词： Magnetic particle imaging Liver imaging SPIO

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A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges

arXiv

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arXiv 2024年

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Faculty of Applied Science School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada The Chinese University of Hong Kong Shenzhen518172 China

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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An Interactive Attention Mechanism Fusion Network for Aspect-Based Multimodal Sentiment Analysis

An Interactive Attention Mechanism Fusion Network for Aspect...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jinghong Wang Yuan Gao Haokang Li College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Hebei Normal University Shijiazhuang China Hebei Polytechnic Institute College of Artificial Intelligence and Big Data Shijiazhuang China

The goal of aspect-based multimodal sentiment analysis (ABMSA) is to classify the sentiment associated with aspect words in a given context. Most current ABMSA models focus only on general inter-modal information interactions without considering both intra-modal and inter-modal information interactions and ignoring image noise. To address these issues, this paper proposes an Interactive Attention Mechanism Fusion Network (IAMFN) model. The model first designs an image-text fusion module based on the attention, which applies the attention mechanism to a recurrent neural network to fuse text and images while filtering the noise in the images, and finally adds the fused information to the aspect information step by step to obtain the dynamic inter-modal representation. In addition, this paper proposes an aspect-text fusion module based on the attention, which learns the intra-modal contextual representation by calculating the weights of each aspect word in the context. Finally, this paper stitches the information obtained from the two modules and feeds it into the fully connected softmax layers to predict sentiment polarity. We have conducted extensive experiments on two benchmark datasets and the experimental results show that our model achieves state-of-the-art performance.

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Impacts of Radiance Quantization on Surface Mapping: Comparisons among the Landsat Sensors 28

Impacts of Radiance Quantization on Surface Mapping: Compari...

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28th International Conference on Geoinformatics, Geoinformatics 2021

作者： Chen, Feng Zhang, Ziqun Zhang, Wenhao Song, Yuejun College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen361024 China Jiangxi Academy of Water Science and Engineering Jiangxi Key Laboratory of Soil Erosion and Prevention Nanchang330029 China

ISBN: (纸本)9781665420747

Radiance quantization and dynamic ranges are considered the basic characteristics of a sensor in radiometry. However, their impacts on surface mapping have not been fully investigated and understood (e.g., for different Landsat sensors), while much attention has been paid to the impacts related to other aspects (e.g., spatial resolution, the wavelength of spectral channels, and several spectral channels). In this paper, a synthesized data collection of multispectral reflectance and radiance (with and without considering actual quantization) for the Landsat sensors from hyperspectral records was generated. Based on the synthesized data, the impacts of radiance quantization were discussed in terms of channel reflectance and spectral indices. As the preliminary findings show, the quantization effects on channel reflectance vary among channels, presenting different impacts on spectral indices correspondingly, which affect the measures of between-sensor difference. It also suggests that the effects of quantization may relate to land surface conditions (e.g., land use and land cover) in addition to the sensor characteristics. © 2021 IEEE.

关键词： Landsat

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Audio-Visual Target Speaker Extraction with Reverse Selective Auditory Attention

arXiv

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arXiv 2024年

作者： Tao, Ruijie Qian, Xinyuan Jiang, Yidi Li, Junjie Wang, Jiadong Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computer Science and Technology University of Science and Technology Beijing Beijing100083 China Chinese University of Hong Kong Shenzhen518172 China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore University of Bremen 28359 Germany

Audio-visual target speaker extraction (AV-TSE) aims to extract the specific person’s speech from the audio mixture given auxiliary visual cues. Previous methods usually search for the target voice through speech-lip synchronization. However, this strategy mainly focuses on the existence of target speech, while ignoring the variations of the noise characteristics, i.e., interference speaker and the background noise. That may result in extracting noisy signals from the incorrect sound source in challenging acoustic situations. To this end, we propose a novel selective auditory attention mechanism, which can suppress interference speakers and non-speech signals to avoid incorrect speaker extraction. By estimating and utilizing the undesired noisy signal through this mechanism, we design an AV-TSE framework named Subtraction-and-ExtrAction network (SEANet) to suppress the noisy signals. We conduct abundant experiments by re-implementing three popular AV-TSE methods as the baselines and involving nine metrics for evaluation. The experimental results show that our proposed SEANet achieves state-of-the-art results and performs well for all five datasets. The code can be found in: https://***/TaoRuijie/*** © 2024, CC BY.

关键词： Background noise

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Versatile hydrogel based on polyvinyl alcohol/chitosan/regenerated silk fibroin 5

Versatile hydrogel based on polyvinyl alcohol/chitosan/regen...

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2021 5th International Conference on Green Composite Materials and Nanotechnology, GCMN 2021

作者： Sun, Qinyue Fu, Jizhu Zhang, Heng Yang, Huihui Wang, Yumin Xiong, Yi Zeng, Wei Wang, Faniu Xu, Chao National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information Engineering Anhui University Anhui Hefei230601 China Science and Technology Institute Key Laboratory of Textile Fiber and Products Ministry of Education Laboratory for Electron Microscopy Wuhan Textile University Hubei Wuhan430073 China School of Physics and Technology Moe Key Laboratory of Artificial Micro- and Nano-Structures and Center for Electron Microscopy Wuhan University Hubei Wuhan430072 China

Versatile sensors have broad application prospects in human motion detection, health monitoring, wearable electronic devices and flexible electronic skin and other emerging fields. In this work, the enhanced hydrogel was prepared by freezing and thawing process with calcium ion crosslinking. This composite material shows excellent flexibility and elasticity, and after being cut in half, it can automatically heal well in a short time without external force. It shows great potential in flexible and wearable devices. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Hydrogels

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Efficient Low-Resolution Face Recognition via Bridge Distillation

arXiv

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arXiv 2024年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Zhang, Yu Li, Jia The Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China SenseTime Group Limited 100084 China The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The Peng Cheng Laboratory Shenzhen518055 China

Face recognition in the wild is now advancing towards light-weight models, fast inference speed and resolution-adapted capability. In this paper, we propose a bridge distillation approach to turn a complex face model pretrained on private high-resolution faces into a light-weight one for low-resolution face recognition. In our approach, such a cross-dataset resolution-adapted knowledge transfer problem is solved via two-step distillation. In the first step, we conduct cross-dataset distillation to transfer the prior knowledge from private high-resolution faces to public high-resolution faces and generate compact and discriminative features. In the second step, the resolution-adapted distillation is conducted to further transfer the prior knowledge to synthetic low-resolution faces via multi-task learning. By learning low-resolution face representations and mimicking the adapted high-resolution knowledge, a light-weight student model can be constructed with high efficiency and promising accuracy in recognizing low-resolution faces. Experimental results show that the student model performs impressively in recognizing low-resolution faces with only 0.21M parameters and 0.057MB memory. Meanwhile, its speed reaches up to 14,705, 934 and 763 faces per second on GPU, CPU and mobile phone, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi-task learning

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