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Intersection of machine learning and mobile crowdsourcing: a systematic topic-driven review

Personal and Ubiquitous Computing 2025年第1期29卷 77-101页

作者： Chen, Weisi Hussain, Walayat Al-Qudah, Islam Al-Naymat, Ghazi Zhang, Xu School of Software Engineering Xiamen University of Technology Fujian Xiamen361024 China Peter Faber Business School Australian Catholic University North SydneyNSW2060 Australia Higher Colleges of Technology Abu Dhabi United Arab Emirates College of Engineering and Information Technology Ajman University Ajman United Arab Emirates

During the past decade of the big data era, mobile crowdsourcing has emerged as a popular research area, leveraging the collective intelligence and engagement of a vast number of individuals using their mobile devices. Another actively evolving area is machine learning, which has recently been augmented by the mobile crowdsourcing approach, especially for data collection and labeling. However, what happens when these two prevailing concepts meet? What topics have been discussed in recent literature? This paper adopts a systematic methodology, leveraging Latent Dirichlet allocation topic modeling for topic discovery from recent publications, to provide a comprehensive and insightful review of the intersection of machine learning and mobile crowdsourcing. Moreover, the paper highlights the emerging federated learning technology that integrates elements from both concepts. Key research questions are answered by examining discovered topics. The paper thoroughly discusses state-of-the-art developments and trends in combining these two concepts and explains the role of one concept in the other. The paper also addresses remaining challenges and outlines a future research agenda, including the potential incorporation of large language models into mobile crowdsourcing systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Data Analytics

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Multi-Hop Transmission Method of 5G Millimeter Wave Relay Based on Network Coding

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China Communications 2022年第12期19卷 14-26页

作者： Yilin Wang Weisheng He Xuwei Fan Lianfen Huang Jie Yang Yuliang Tang School of Information Xiamen UniversityXiamen 361005China Department of Software Engineering Xiamen Institute of Software TechnologyXiamen 361005China

With the rapid development of the Internet technology,millimeter wave(mmWave)will be used as a supplement to 5G low frequency bands to meet the extremely high system capacity requirements of 5G in hot *** 5G mmWave communication can adapt to the needs of 5G network and carry a large amount of transmitted data,transmission stability has become one of the key technical issues of 5G network mmWave communication due to problems such as strong attenuation and poor penetration of *** order to improve the efficiency of the mmWave multi-hop transmission,we propose a 5G mmWave multi-hop transmission method based on network coding,which can adapt to the current wireless network environment,improve spectrum efficiency and increase network *** on MATLAB simulation experiments,it is verified that the proposed method can greatly improve the transmission efficiency and reduce the signal loss under the premise of ensuring the accurate signal transmission.

关键词： 5G network millimeter wave relay multihop communication network coding

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Multi-layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images

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Science China(Information Sciences) 2023年第4期66卷 124-125页

作者： Jiao SHI Tiancheng WU Hanwen YU A.K.QIN Gwanggil JEON Yu LEI School of Electronics and Information Northwestern Polytechnical University School of Resources and Environment University of Electronic Science and Technology of China Department of Computer Science and Software Engineering Swinburne University of Technology Department of Embedded Systems Engineering Incheon National University

With the increasing complexity of application scenarios, the fusion of different remote sensing data types has gradually become a trend, which can greatly improve the utilization of massive remote sensing *** the problem of change detection for heterogeneous remote images can be much more complicated than the traditional change detection for homologous remote sensing images,

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Multimodal contrastive learning with neuroimaging and cognitive tests for Alzheimer's disease diagnosis

Multimodal contrastive learning with neuroimaging and cognit...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Liang Zhang, Jian Xu, Bo Yang, Yi Zhang, Yangqianhui Ma, Ruixin Dalian University of Technology School of Software Technology Dalian China Beihang University School of Reliability and Systems Engineering Beijing China Zhejiang University School of Mechanical Engineering Hangzhou China

ISBN: (纸本)9798350386226

Alzheimer's disease (AD) is a neurological illness that causes cognitive impairment. Computer-aided diagnosis can help diagnose Alzheimer's disease early before clinical symptoms appear. Currently, many deep learning methods show good performance in AD diagnosis. Still, most of these methods are based on single/multimodal neuroimaging, leading to a one-sided approach to disease modelling. Combining neuroimaging, cognitive tests, and demographics can significantly improve model performance and reduce the negative impact of noise. This study proposes a multimodal model that introduces contrastive learning, extracting and fusing feature representations separately from cognitive tests and neuroimaging data. After that, contrastive learning based on similarity is employed for both modalities' features, assisting the network in learning cross-modal features. Moreover, the hybrid attention mechanism of the Transformer encoder is explored for feature fusion. Experimental results on 2082 cases from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset validate the effectiveness of our proposed multimodal model. © 2024 IEEE.

关键词： Contrastive Learning

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Integrating topology and biological information to predict essential proteins via Shannon entropy

Integrating topology and biological information to predict e...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Yan Wang, Zhong He, Zengyou Zhang, Hexin Qin, Jing Dalian University School of Software Engineering Dalian China Dalian University of Technology School of Software Technology Dalian China Dalian Municipal Service Center for Natural Resources Affairs Dalian China

ISBN: (纸本)9798350386226

Identifying essential proteins is vital for deciphering the intricacies of disease mechanisms and devising efficacious therapeutic strategies. Over the past several decades, a plethora of algorithms have been proposed, aimed at synthesizing topological and biological information to address the complex challenge of essential protein identification. Nevertheless, a critical examination of the current methodologies reveals certain limitations: (1) the aggregation of diverse features in various methods often requires parameter tuning to maintain balance, potentially introducing instability and increasing complexity in practical scenarios;(2) traditional methods commonly combine various features without in-depth mathematical or physical interpretation, possibly falling short of fully revealing the principles behind the observed phenomena. Hence, we propose a new algorithm for essential protein detection, which is named ITBSE. The basic idea behind this method is to reconstruct the PPI network by removing false positive edges and the subsequent allocation of a protein score. This scoring process integrates both the topological attributes of the reconstructed PPI network and biological data, utilizing the computational framework of Shannon entropy for a comprehensive assessment. To evaluate the effectiveness of our method, we conduct the experiments on real PPI networks and compare with 10 popular methods including DC, BC, CC, LID, PR, DMNC, LAC, NC, PeC and esPOS. The comparison results demonstrate that ITBSE is able to achieve better performance than those competing algorithms. © 2024 IEEE.

关键词： Network topology

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MILG:Realistic lip-sync video generation with audio-modulated image inpainting

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Visual Informatics 2024年第3期8卷 71-81页

作者： Han Bao Xuhong Zhang Qinying Wang Kangming Liang Zonghui Wang Shouling Ji Wenzhi Chen School of Software Technology Zhejiang UniversityHangzhouChina College of Computer Science Zhejiang UniversityHangzhouChina College of Engineering Zhejiang UniversityHangzhouChina

Existing lip synchronization(lip-sync)methods generate accurately synchronized mouths and faces in a generated ***,they still confront the problem of artifacts in regions of non-interest(RONI),e.g.,background and other parts of a face,which decreases the overall visual *** solve these problems,we innovatively introduce diverse image inpainting to lip-sync *** propose Modulated Inpainting Lip-sync GAN(MILG),an audio-constraint inpainting network to predict synchronous *** utilizes prior knowledge of RONI and audio sequences to predict lip shape instead of image generation,which can keep the RONI ***,we integrate modulated spatially probabilistic diversity normalization(MSPD Norm)in our inpainting network,which helps the network generate fine-grained diverse mouth movements guided by the continuous audio ***,to lower the training overhead,we modify the contrastive loss in lipsync to support small-batch-size and few-sample *** experiments demonstrate that our approach outperforms the existing state-of-the-art of image quality and authenticity while keeping lip-sync.

关键词： Lip-sync Image inpainting Face generation Modulated SPD normalization

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Outlier item detection in bundle recommendation via the attention mechanism

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High-Confidence Computing 2024年第3期4卷 51-57页

作者： Yuan Liang School of Information Engineering Suqian UniversitySuqian 223800China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Bundle recommendation offers users more holistic insights by recommending multiple compatible items at ***,the intricate correlations between items,varied user preferences,and the pronounced data sparsity in combinations present significant challenges for bundle recommendation ***,current bundle recommendation methods fail to identify mismatched items within a given set,a process termed as‘‘outlier item detection’’.These outlier items are those with the weakest correlations within a *** them can aid users in refining their item *** the correlation among items can predict the detection of such outliers,the adaptability of combinations might not be adequately responsive to shifts in individual items during the learning *** limitation can hinder the algorithm’s *** tackle these challenges,we introduce an encoder–decoder architecture tailored for outlier item *** encoder learns potential item correlations through a self-attention ***,the decoder garners efficient inference frameworks by directly assessing item *** have validated the efficacy and efficiency of our proposed algorithm using real-world datasets.

关键词： Bundle recommendation Outlier item detection Attention mechanism Decoder encoder

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Research on the Performance of a Mooring Platform Under the Collision of Icebergs

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China Ocean engineering 2025年第1期39卷 43-57页

作者： ZHANG Yan ZHANG Song-bao WANG Yi-tao ZHAO Wei-dong GUI Hong-bin School of Ocean Engineering Harbin Institute of TechnologyWeihaiWeihai 264209China China Ship Scientific Research Center(CSSRC) Wuxi 214082China College of Shipbuilding Engineering Harbin Engineering UniversityHarbin 150001China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China

In the process of developing oil and gas resources in the Arctic,the impact of icebergs can pose a considerable threat to the structural safety of semi-submersible mooring platforms in ice *** the basis of the arbitrary Lagrangian Eulerian(ALE)algorithm,a numerical model for the interaction between an iceberg and a semi-submersible mooring platform is built in this ***,a mooring system with a link element is designed and *** ice material model for the target iceberg is built and validated.A numerical model for the interaction between an iceberg and a semi-submersible mooring platform is then built.A parametric study(cable angle,tension angle and number of cables)is carried out to study the performance of the mooring *** collision process between the semi-submersible mooring platform and the iceberg in the polar marine environment can be predicted by the present numerical model,and then the optimal mooring arrangement scheme can be *** research results in this work can provide a reference for the design of mooring systems.

关键词： ALE algorithm semi-submersible platform iceberg collision mooring system

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Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study 31

Do Current Video LLMs Have Strong OCR Abilities? A Prelimina...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Fei, Yulin Gao, Yuhui Xian, Xingyuan Zhang, Xiaojin Wu, Tao Chen, Wei School of Software Engineering Huazhong University of Science and Technology China Information Hub Hongkong University of Science and Technology Guangzhou China

ISBN: (纸本)9798891761964

With the rise of multi-modal large language models, accurately extracting and understanding textual information from video content-referred to as video-based optical character recognition (Video OCR)-has become a crucial capability. This paper introduces a novel benchmark designed to evaluate the video OCR performance of multi-modal models in videos. Comprising 1,028 videos and 2,961 question-answer pairs, this benchmark proposes several key challenges through 6 distinct sub-tasks: (1) Recognition of text content itself and its basic visual attributes, (2) Semantic and Spatial Comprehension of OCR objects in videos (3) Dynamic Motion detection and Temporal Localization. We developed this benchmark using a semi-automated approach that integrates the OCR ability of image LLMs with manual refinement, balancing efficiency, cost, and data quality. Our resource aims to help advance research in video LLMs and underscores the need for improving OCR ability for video LLMs. The benchmark will be released on https://***/YuHuiGao/***. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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EPAAMap: Enhanced Parallel Attention Aggregation HD Semantic Map in Complex Traffic Scenarios 7

EPAAMap: Enhanced Parallel Attention Aggregation HD Semantic...

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7th International Conference on Data Science and Information technology, DSIT 2024

作者： Zhang, Jingtao Wang, Xiangxiang Yu, Yongbing Hu, Qinghua Xue, Lirui He, Qi Fan, Manping Ren, Liyong University of Electronic Science and Technology of China School of Information and Software Engineering Cheng Du China

ISBN: (纸本)9798350384093

This paper addresses the significant challenges posed by road safety due to rapid urbanization and increasing vehicular traffic. High-definition (HD) semantic maps are essential for improving decision-making and safety in autonomous vehicles and intelligent driver assistance systems, which require detailed road environment information. Current multi-camera perception systems face limitations in efficiently integrating data, resulting in inconsistent spatial understanding. To tackle these issues, this propose a novel multi-scale feature optimization mechanism EPPAMap, including the efficient depthwise separable feature network(EDSFN) to improve semantic understanding. Additionally, we introduce the parallel attention aggregation module (PAAM) to capture richer contextual information and enhance feature aggregation. Experimental results demonstrate superior adaptability and performance in complex road scenarios, providing a solid foundation for HD semantic map construction and real-time traffic perception. © 2024 IEEE.

关键词： Autonomous vehicles

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