检索结果-内蒙古大学图书馆

DMSDRec: Dynamic Structure-aware Graph Masked Autoencoder and Spatiotemporal Diffusion for Next-POI Recommendation

IEEE Transactions on Services computing 2025年

作者： Li, Yue Zeng, Jun Tang, Haoran Wen, Junhao Gao, Min Zhou, Wei Chongqing University School of big data and software engineering Chongqing40044 China Hong Kong Polytechnic University Department of Computing Hung Hom Kowloon Hong Kong

The rise of smart devices has accelerated location-based services, creating vast trajectory data for Point-of-Interest (POI) recommendations. However, user omissions or privacy concerns often result in incomplete trajectory data, compromising sequential patterns and spatiotemporal relationships. Existing solutions using graph structures, self-supervised learning (SSL), or spatiotemporal contexts still face two limitations: (1) graph-based and SSL methods produce suboptimal trajectory representations due to respective inherent constraints;(2) noise interference persists when modeling distorted spatiotemporal signals. To mitigate these issues, we propose a Dynamic Structure-aware Graph Masked Autoencoder and Spatiotemporal Diffusion for Next-POI Recommendation (DMSDRec). Specifically, we introduce a dynamic structure-aware improved graph masked autoencoder that adaptively and dynamically distills global transitional information for self-supervised augmentation. It naturally avoids the noise introduced by existing SSL methods' dependency on manual views augmentation. Meanwhile, the masked reconstruction task synergistically enhances trajectory representations by capturing deeper cross-sequence dependencies. Additionally, we propose an effective latent-space spatiotemporal diffusion denoising method. First, we employ graph structures to model spatiotemporal relationships, utilizing higher-order structural information to alleviate the linear spatiotemporal relationship deviations caused by incomplete trajectories. Building on this, we implement diffusion models in the latent space to systematically identify and remove noise from spatiotemporal representations. Through experimental results on two real-world datasets, we demonstrate the superiority of our proposed DMSDRec in terms of recommendation accuracy and robustness. © 2008-2012 IEEE.

关键词： Dynamic Structure-awareness Graph Masked Autoencoder Next POI Recommendation Spatiotemporal Diffusion

来源：评论

学校读者我要写书评

暂无评论

Optimized Reading for ADHD and Dyslexia Users In VR

Optimized Reading for ADHD and Dyslexia Users In VR

引用

Computer Science, Computer engineering, & Applied computing (CSCE), Congress in

作者： Kyler Hoang Leonidas Deligiannidis School of Engineering Wentworth Institute of Technology Boston MA USA School of Computing and Data Science Wentworth Institute of Technology Boston MA USA

To combat the rising population of ADHD and dyslexic patients, current advancements in Unity and Oculus Integration are investigated and employed to design a user-friendly reading experience. Unlike physical novels that lack visual aid, our prototype ensures a distraction-free and minimalistic VR environment. It utilizes *** speech recognition to aid ADHD and dyslexic patients in mechanical and comprehensive reading. Users can also change the background color and music to enhance their facilitated experience. This approach allows the user to constantly focus on the text without averting their gaze. We believe that further development of this prototype can create a powerful tool that redefines reading E-books.

关键词：

来源：评论

学校读者我要写书评

暂无评论

YA-DA: YAng-Based data Model for Fine-Grained IIoT Air Quality Monitoring

YA-DA: YAng-Based DAta Model for Fine-Grained IIoT Air Quali...

引用

2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Yigit, Yagmur Huseynov, Khayal Ahmadi, Hamed Canberk, Berk Istanbul Technical University Department of Computer Engineering Turkey University of York Department of Electronic Engineering United Kingdom Istanbul Technical University Department of Artificial Intelligence and Data Engineering Turkey Edinburgh Napier University School of Computing Engineering and the Build Environment United Kingdom

ISBN: (纸本)9781665459754

With the development of industrialization, air pollution is also steadily on the rise since both industrial and daily activities generate a massive amount of air pollution. Since decreasing air pollution is critical for citizens' health and well-being, air pollution monitoring is becoming an essential topic. Industrial Internet of Things (IIoT) research focuses on this crucial area. Several attempts already exist for air pollution monitoring. However, none of them are improving the performance of IoT data collection at the desired level. Inspired by the genuine Yet Another Next Generation (YANG) data model, we propose a YAng-based data model (YA-DA) to improve the performance of IIoT data collection. Moreover, by taking advantage of digital twin (DT) technology, we propose a DT-enabled fine-grained IIoT air quality monitoring system using YA-DA. As a result, DT synchronization becomes fine-grained. In turn, we improve the performance of IIoT data collection resulting in lower round-trip time (RTT), higher DT synchronization, and lower DT latency. © 2022 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Two-in-one Knowledge Distillation for Efficient Facial Forgery Detection

arXiv

引用

arXiv 2023年

作者： Zhou, Chuyang Huang, Jiajun Liu, Daochang Du, Chengbin Ma, Siqi Nepal, Surya Xu, Chang School of Computing Science University of Sydney Australia School of Engineering and Information Technology UNSW Canberra Australia CSIRO Data61 Australia

Facial forgery detection is a crucial but extremely challenging topic, with the fast development of forgery techniques making the synthetic artifact highly indistinguishable. Prior works show that by mining both spatial and frequency information the forgery detection performance of deep learning models can be vastly improved. However, leveraging multiple types of information usually requires more than one branch in the neural network, which makes the model heavy and cumbersome. Knowledge distillation, as an important technique for efficient modelling, could be a possible remedy. We find that existing knowledge distillation methods have difficulties distilling a dual-branch model into a single-branch model. More specifically, knowledge distillation on both the spatial and frequency branches has degraded performance than distillation only on the spatial branch. To handle such problem, we propose a novel two-in-one knowledge distillation framework which can smoothly merge the information from a large dual-branch network into a small single-branch network, with the help of different dedicated feature projectors and the gradient homogenization technique. Experimental analysis on two datasets, FaceForensics++ and Celeb-DF, shows that our proposed framework achieves superior performance for facial forgery detection with much fewer parameters. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

UICopilot: Automating UI Synthesis via Hierarchical Code Generation from Webpage Designs 25

UICopilot: Automating UI Synthesis via Hierarchical Code Gen...

引用

34th ACM Web Conference, WWW 2025

作者： Gui, Yi Wan, Yao Li, Zhen Zhang, Zhongyi Chen, Dongping Zhang, Hongyu Su, Yi Chen, Bohua Zhou, Xing Jiang, Wenbin Zhang, Xiangliang Huazhong University of Science and Technology Wuhan China Chongqing University Chongqing China Hubei University of Automotive Technology Shiyan China Rabbitpre AI Shenzhen China University of Notre Dame Notre Dame United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology China

ISBN: (纸本)9798400712746

Automating the synthesis of User Interfaces (UIs) plays a crucial role in enhancing productivity and accelerating the development lifecycle, reducing both development time and manual effort. Recently, the rapid development of Multimodal Large Language Models (MLLMs) has made it possible to generate front-end Hypertext Markup Language (HTML) code directly from webpage designs. However, real-world webpages encompass not only a diverse array of HTML tags but also complex stylesheets, resulting in significantly lengthy code. The lengthy code poses challenges for the performance and efficiency of MLLMs, especially in capturing the structural information of UI designs. To address these challenges, this paper proposes UICopilot, a novel approach to automating UI synthesis via hierarchical code generation from webpage designs. The core idea of UICopilot is to decompose the generation process into two stages: first, generating the coarse-grained HTML hierarchical structure, followed by the generation of fine-grained code. To validate the effectiveness of UICopilot, we conduct experiments on a real-world dataset, i.e., WebCode2M. Experimental results demonstrate that UICopilot significantly outperforms existing baselines in both automatic evaluation metrics and human evaluations. Specifically, statistical analysis reveals that the majority of human annotators prefer the webpages generated by UICopilot over those produced by GPT-4V. © 2025 Copyright held by the owner/author(s).

关键词： Web Design

来源：评论

学校读者我要写书评

暂无评论

Meta Heuristic Image Optimisation in LiDAR Sensor Based on Cloud IoT Network and Deep Learning Algorithms

引用

Remote Sensing in Earth Systems Sciences 2025年第2期8卷 566-575页

作者： Meenakshi, K. Manoj, Gunna Diwakar, Meenakshi Prabakaran, Manjula Sravan, Padala Babu, Gaddam Ravindra Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Cyber Security Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad India Department of IT Panimalar Engineering College Chennai India Dept of CSE (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh Angallu India Computer Science and Engineering NIIT with learning partner Hainan Normal University Haikou China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India

LiDAR technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. The aim of this research is to propose a novel technique in cloud IoT network-based LiDAR sensor imaging with image optimization using a metaheuristic deep learning model. Input has been collected LiDAR image using a cloud IoT module and processed for noise removal and normalization. Then this processed image is classified and optimized utilizing a quantile transfer graph convolutional model with binary firefly whale colony optimization. The experimental analysis is carried out in terms of detection accuracy, random precision, recall, RMSE, and AUC for various LiDAR sensor-based datasets. Comparing the results of the various approaches reveals that the suggested approach outperforms them all with a recall of 94%, a random precision of 95%, and a detection accuracy of 98%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cloud IoT network Convolutional model Image optimization LiDAR sensor Metaheuristic deep learning

来源：评论

学校读者我要写书评

暂无评论

A Model of Integrating Bert and BiGRU+ Attention Dual-channel Mechanism for Investor Sentiment Analysis of Stock Price Forecast 24

A Model of Integrating Bert and BiGRU+ Attention Dual-channe...

引用

24th IEEE/ACIS International Winter Conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed computing, SNPD 2022

作者： Ma, Huawei Ma, Jixin Liang, Shengbin Du, Wencai Institute of Ai and Blockchain Guangzhou University Guangzhou China School of Computing and Mathematical Sciences The University of Greenwich London United Kingdom Institute of Data Engineering and Sciences The University of Saint Joseph China

ISBN: (纸本)9798350310412

Investor sentiment and emotions have a strong impact on financial markets. In recent years there has been increasing interest in analyzing the sentiment of investors for stock price prediction using machine learning. Existing prediction models mostly depend on the analysis of trading data and company profit. few prediction theories have been built based on individual investors' sentiments. The fundamental reason is the difficulty to measure individual investors' sentiment. © 2022 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Extracting High-order Connectivity of EEG-based Dynamic Functional Connectivity Networks for Diagnosis of Major Depressive Disorder 24

Extracting High-order Connectivity of EEG-based Dynamic Func...

引用

4th Asia-Pacific Artificial Intelligence and Big data Forum, AIBDF 2024

作者： Zhao, Feng Fan, Wenxuan Han, Zhongwei Chen, Hongyu School of Computer Science and Technology Shandong Technology and Business University Shandong Yantai China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Shandong Yantai China Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China Information Engineering College Yantai Institute of Technology Shandong Yantai China Shandong Technology and Business University Shandong Yantai China

ISBN: (纸本)9798400710865

Functional connectivity networks (FCNs), particularly those utilizing the phase lag index (PLI) method, have been instrumental in elucidating the pathological features of Major Depressive Disorder (MDD) by assessing the synchrony and lag of electroencephalogram (EEG) signal waveforms. Currently, the more prevalent methods are traditional FCNs that quantify phase synchronization as scalar values and dynamic FCNs (D-FCNs) that employ sliding window approaches to capture the instantaneous variations in functional connectivity. However, these two methods merely capture the low-level functional connections between paired regions of interest (ROIs) from both the static and dynamic perspectives, and fail to reflect the high-level functional connections involving multiple ROIs. Moreover, the feature dimension of the dynamic FCN is excessively high, and there exists a phase mismatch issue between different samples, which renders it impossible to be directly applied to the classification task, and no better solution is available at present. To mitigate these issues, we proposed a comprehensive framework that includes segmenting EEG sequences based on PLI to construct dynamic FCNs;employing seventh-order central moments for feature extraction to form low-order FCNs (Lo-FCNs);and establishing high-order FCNs (Ho-FCNs) based on Pearson correlation theory derived from Lo-FCNs. We conducted classification experiments across four distinct EEG frequency bands using our proposed framework while analyzing relevant features through graph theory and brain topological mapping techniques. The experiments showed that the proposed framework has a high classification accuracy rate in the classification experiments of MDD and NC, and proved the importance of high-order features in classification experiments, indicating that they may serve as physiological markers for MDD diagnosis. © 2024 Copyright held by the owner/author(s).

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

A Transformer-Based Spatio-Temporal Graph Neural Network for Anomaly Detection on Dynamic Graphs 12th

A Transformer-Based Spatio-Temporal Graph Neural Network for...

引用

12th CCF Conference on Bigdata, Bigdata 2024

作者： Gao, Yuanjun Zhu, Quntao Shi, Xuanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819610235

Dynamic graphs have emerged as a pivotal data structure underpinning real-world network applications. Against this backdrop, detecting anomalies in dynamic graphs has become particularly important, serving as a foundation for multiple downstream tasks. However, existing research has been limited by only considering the simplistic spatial features of the target edge and being confined to local spatial structures. This confinement imposes significant limitations on model performance. Furthermore, current models have not adequately addressed the issue of missing node attributes, relying solely on one-hot encoding to represent nodes, which restricts the expressive capacity of spatial features. In response to these challenges, we propose an end-to-end transformer-based spatio-temporal graph neural network model called TransSTGNN for detecting anomalous edges on dynamic graphs. This model innovatively integrates the graph convolution network with the transformer model to extract multi-dimensional spatial features of the target edge. Simultaneously, TransSTGNN incorporates a novel node enhancement module that reinforces the representation capability of spatial features by encoding the spatial attributes of nodes, effectively tackling the problem of missing node attributes. Extensive experiments confirm that TransSTGNN surpasses the state-of-the-art methods in anomaly detection on six benchmark datasets and effectively resolves the challenges mentioned above. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Three-dimensional cooperative guidance strategy and guidance law for intercepting highly maneuvering target

引用

Chinese Journal of Aeronautics 2021年第5期34卷 485-495页

作者： Ziyan CHEN Jianglong YU Xiwang DONG Zhang REN School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data cmd Brain Computing Beihang UniversityBeijing 100191China

Cooperative interception of the target with strong maneuverability by multiple missiles with weak maneuverability in the three-dimensional nonlinear model is ***,the three-dimensional nonlinear model of cooperative guidance is *** three-dimensional reachable region is represented composed of lateral acceleration and longitudinal acceleration in the two-dimensional coordinate ***,the problem of the multiple missile’s reachable coverage area is transformed into the problem of cooperative coverage.A cooperative coverage strategy is proposed and an algorithm for quickly calculating the number of required missiles is ***,the guidance law based on the cooperative coverage strategy is proposed,and it is proved that cooperative interception of the target can be achieved under the acceleration ***,the relations among the number of missiles,the initial array position of terminal guidance and the coverage area of the target’s large maneuver are *** dynamic adjustment strategy of guidance parameters is proposed to reduce the guidance ***,simulation results show that multiple missiles with low maneuverability can achieve effective interception of target with strong maneuverability through the proposed cooperative strategy and cooperative guidance method.

关键词： Acceleration constraint Cooperative interception Coverage strategy Guidance law Highly maneuvering target

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：