检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Liu, Zijin Zhao, Xiang Song, You School of Computer Science and Engineering Beihang University Beijing100191 China School of Software Beihang University Beijing100191 China

Spatiotemporal data imputation plays a crucial role in various fields such as traffic flow monitoring, air quality assessment, and climate prediction. However, spatiotemporal data collected by sensors often suffer from temporal incompleteness, and the sparse and uneven distribution of sensors leads to missing data in the spatial dimension. Among existing methods, autoregressive approaches are prone to error accumulation, while simple conditional diffusion models fail to adequately capture the spatiotemporal relationships between observed and missing data. To address these issues, we propose a novel two-stage Refined Diffusion Probability Impuation (RDPI) framework based on an initial network and a conditional diffusion model. In the initial stage, deterministic imputation methods are used to generate preliminary estimates of the missing data. In the refinement stage, residuals are treated as the diffusion target, and observed values are innovatively incorporated into the forward process. This results in a conditional diffusion model better suited for spatiotemporal data imputation, bridging the gap between the preliminary estimates and the true values. Experiments on multiple datasets demonstrate that RDPI not only achieves state-of-the-art imputation accuracy but also significantly reduces sampling computational costs. The implementation code is available at https://***/liuzjin/RDPI. Copyright © 2024, The Authors. All rights reserved.

关键词： Air quality

来源：评论

学校读者我要写书评

暂无评论

SSRNet: A Two-branch Real-time Semantic Segmentation Network for Road Scenes 2

SSRNet: A Two-branch Real-time Semantic Segmentation Network...

引用

2021 2nd International Conference on Machine Learning and computer Application, ICMLCA 2021

作者： Shao, Huiwei Zhao, Ji School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

ISBN: (纸本)9783800757398

Semantic segmentation, one of the fundamental tasks in computer vision, requires classification for each pixel in an image, and thus semantic segmentation is time-consuming. With the rise of technologies such as autonomous driving, the real-time capability of semantic segmentation is becoming more and more important. In this paper, the authors propose a novel two-way real-time semantic segmentation network (SSRNet). We create an efficient feature extraction residual block using channel split and channel shuffle to balance operational efficiency with feature extraction and design a shared spatial path. To improve the segmentation performance of the model, we designed a feature fusion module with a skip connection. Evaluated on a single 1080Ti GPU, SSRNet can run at over 85 FPS and achieve 71.2% MIoU on the Cityscapes dataset. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Chinese grammatical error diagnosis based on sequence tagging methods 2

Chinese grammatical error diagnosis based on sequence taggin...

引用

2021 2nd International Conference on Internet of Things, Artificial Intelligence and Mechanical Automation, IoTAIMA 2021

作者： Wang, H. Zhang, Y.J. Sun, X.M. School of Computer Science and Software Engineering University of Science and Technology Liao Ning China

In view of the problems such as exploding gradient or vanishing gradient or inefficiency caused by parallel problems when traditional neural networks deal with long text grammar error correction. In this paper, Chinese grammatical error detection is proposed as a sequence tagging problem for named entity recognition, and a Chinese grammatical error detection model based on BERT BILSTM CRF is designed and implemented by using Bert model to construct word vectors. Meanwhile, three groups of comparative tests were designed: CRF, BILSTM CRF and BERT CRF. In the end, BERT BILSTM CRF model achieves the error detection accuracy of 66.67% and 52.94% in M (missing error) and W (ordering error), respectively. BERT CRF and CRF models have also achieved good results in S (selection error) and R (redundant words). The research shows that it is feasible to solve grammatical error detection as a sequence labeling problem. On the other hand, a Chinese grammatical error detection model based on BERT BILSTM CRF is proposed for the first time. © Published under licence by IOP Publishing Ltd.

关键词： Error correction

来源：评论

学校读者我要写书评

暂无评论

Rolling Bearing Intelligent Faults Diagnosis Based on Deep Convolutional Neural Network

Rolling Bearing Intelligent Faults Diagnosis Based on Deep C...

引用

2022 International Conference on Signal Processing, computer Networks, and Communications, SPCNC 2022

作者： Wang, Wenjie Lv, Rongxin Ding, Haonan Li, Tengfeng Li, Tingyu Wang, Xin School of Computer Science Shenyang Aerospace University Shenyang China School of Innovation Shenyang Aerospace University Shenyang China School of Electrical Engineering Henan University of Technology Henan Zhengzhou China School of Resource and Security Engineering Henan Institute of Engineering Henan Zhengzhou China School of software Zhongyuan University of Technology Henan Zhengzhou China School of Information Science and Technology Zhengzhou Normal University Henan Zhengzhou China

ISBN: (纸本)9781510664616

In the processing, manufacturing, and production of modern fields, rolling bearings, the most basic module of most mechanical equipment, have a key role that cannot be ignored. This paper proposes three fault diagnosis model architectures for rolling bearings based on the deep convolutional neural network. Three models were tested on the industry-common Case Western Reserve University Dataset (CWRU). The original vibration signal acquisition and processing module mainly uses the vibration signal window translation method to complete the segmentation of overlapping signals and uses the Inception network to efficiently complete one-dimensional signal preprocessing. Finally, we use t-distributed stochastic neighbor embedding (t-SNE) to reduce the dimension and visualize the learned fault data distribution. © 2023 SPIE.

关键词： Roller bearings

来源：评论

学校读者我要写书评

暂无评论

Multimodal Knowledge Graph-Guided Cross-Modal Graph Network for Image-Text Retrieval

Multimodal Knowledge Graph-Guided Cross-Modal Graph Network ...

引用

International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Juncheng Zheng Meiyu Liang Yang Yu Junping Du Zhe Xue Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

Image-text retrieval is a fundamental cross-modal task, which dedicates to align the representation space between image modality and text modality. Existing cross-interactive image-text retrieval methods generate image and sentence em-beddings independently, introduce interaction-based networks for cross-modal reasoning, and then retrieve them using matching metrics. However, existing approaches do not consider fully utilizing semantic relationships among multimodal knowledge to enhance cross-modal fine-grained implicit semantic reasoning capabilities. In this paper, we propose Multimodal Knowledge Graph-guided Cross-modal Graph Network (MKCGN) that exploits multimodal knowledge graphs to explore cross-modal relationships and enhance global representations. In MKCGN, images generate semantic and spatial graphs, which are used to represent visual graphs, and sentences generate textual graphs based on word semantic relations. The visual and textual graphs are used to implement inter-modal reasoning respectively. Then we obtain interest embeddings of image regions and text words based on entity embed dings in Multimodal Knowledge Graph (MKG), which approximates and aligns the representation space of regions and words to a certain extent, thus obtaining effective inter-modal interactions and learning fine-grained cross-modal communication through graph node contrast loss for inter-modal semantic reasoning. Finally, we mine the implicit semantics and potential relationships of images and texts through the MKG as a means of enhancing the global representations and use cross-modal contrast loss to narrow the space of coarse-grained cross-modal representations. Experiments on the MS-COCO and Flickr30K benchmark datasets show that our proposed MKCGN outperforms state-of-the-art image-text retrieval methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

OCD Diagnosis with Multiple Spatial Similarity-Aware Learning and Diffusion Structure-Aware Graph Convolutional Network

OCD Diagnosis with Multiple Spatial Similarity-Aware Learnin...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liang, Yu Zhao, Chen Wang, Tianfu Peng, Ziwen Yang, Peng Lei, Baiying Shenzhen University School of Computer Science and Software Shenzhen China Shenzhen University Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen China South China Normal University Center for the Study of Applied Psychology Guangzhou China

ISBN: (纸本)9798350386226

Obsessive-Compulsive Disorder (OCD) is a hereditary mental illness, and unaffected first-degree relative (UFDR) is also at high risk. This study constructed a framework based on traditional machine learning and deep learning methods to identify OCD and UFDR. Specifically, we propose Multiple Spatial Similarity-Aware Learning (MSSL) models to construct Brain Functional Connectivity Networks (BFCNs) and use Diffusion Structure-Aware Graph Convolutional Network (DSAGCN) for feature learning. Two regularization terms are added to the MSSL model, one is to limit the similarity of functional connectivity of adjacent brain regions, and the other is to constrain the similarity of signals in adjacent brain regions. In this strategy, redundant information can be removed while constructing the interrelationships of various brain regions, which can reflect the interrelationships between brain *** graph diffusion is introduced to the DSAGCN model, which can not only solve the edge noise problem caused by the graph structure constructed by the custom adjacency matrix but also enrich the node features by using the position information of the nodes on the reconstructed graph *** framework has been validated on our dataset collected from local hospitals, and the experimental results show that our proposed method outperforms the state-of-the-art. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

FEAT: Towards Fast Environment-Adaptive Task Offloading and Power Allocation in MEC 42

FEAT: Towards Fast Environment-Adaptive Task Offloading and ...

引用

42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Ren, Tao Hu, Zheyuan He, Hang Niu, Jianwei Liu, Xuefeng Institute of Software Chinese Academy of Sciences Laboratory for Internet Software Technologies Beijing China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Mobile edge computing (MEC) has been proposed to provide mobile devices with both satisfactory computing resources and latency. Key issues in MEC include task offloading and power allocation (TOPA), for which deep reinforcement learning (DRL) is becoming a popular methodology. However, most DRL-based TOPA approaches are typically developed in a certain environment, lacking the adaptability to unseen environments. Motivated by this, this paper proposes a Fast Environment-Adaptive TOPA (FEAT) approach that could adapt to unseen environments with little fine-tuning. Specifically, we first split MEC states into the internal state and environmental state. Then, based on these two types of states, we develop two main components of FEAT - a group of internal state-dependent TOPA meta-policies and an environmental state-embedded steerer. Meta-policies learn TOPA skills within the internal state space (allowing reusing meta-policies in different environments), while the steerer learns to choose appropriate meta-policies according to embedded environmental states. When encountering an unseen environment with the same internal state space, FEAT only needs to fine-tune the steerer using the newly embedded environmental state with few internal state explorations. Extensive experimental results on simulation and testbed demonstrate that FEAT outperforms the state-of-the-art by more than 16.4% in terms of fine-tuning speeds. © 2023 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Motivating Users to Attend to Privacy: A Theory-Driven Design Study

arXiv

引用

arXiv 2024年

作者： Shiri, Varun Xiong, Maggie Cheng, Jinghui Guo, Jin L.C. Department of Computer and Software Engineering Polytechnique Montreal MontrealQC Canada School of Computer Science McGill University MontrealQC Canada

In modern technology environments, raising users’ privacy awareness is crucial. Existing efforts largely focused on privacy policy presentation and failed to systematically address a radical challenge of user motivation for initiating privacy awareness. Leveraging the Protection Motivation Theory (PMT), we proposed design ideas and categories dedicated to motivating users to engage with privacy-related information. Using these design ideas, we created a conceptual prototype, enhancing the current App Store product page. Results from an online experiment and follow-up interviews showed that our design effectively motivated participants to attend to privacy issues, raising both the threat appraisal and coping appraisal, two main factors in PMT. Our work indicated that effective design should consider combining PMT components, calibrating information content, and integrating other design elements, such as visual cues and user familiarity. Overall, our study contributes valuable design considerations driven by the PMT to amplify the motivational aspect of privacy communication. Copyright © 2024, The Authors. All rights reserved.

关键词： Product design

来源：评论

学校读者我要写书评

暂无评论

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning

arXiv

引用

arXiv 2024年

作者： Gao, Xin Lin, Yang Li, Ruiqing Wang, Yasha Chu, Xu Ma, Xinyu Yu, Hailong Software Engineering Institution School of Computer Science Peking University Beijing China Key Lab of High Confidence Software Technologies Ministry of Education Beijing China Center on Frontiers of Computing Studies School of Computer Science Peking University China National Engineering Research Center of Software Engineering Peking University Beijing China

Data mining and knowledge discovery are essential aspects of extracting valuable insights from vast datasets. Neural topic models (NTMs) have emerged as a valuable unsupervised tool in this field. However, the predominant objective in NTMs, which aims to discover topics maximizing data likelihood, often lacks alignment with the central goals of data mining and knowledge discovery which is to reveal interpretable insights from large data repositories. Overemphasizing likelihood maximization without incorporating topic regularization can lead to an overly expansive latent space for topic modeling. In this paper, we present an innovative approach to NTMs that addresses this misalignment by introducing contrastive learning measures to assess topic interpretability. We propose a novel NTM framework, named ContraTopic, that integrates a differentiable regularizer capable of evaluating multiple facets of topic interpretability throughout the training process. Our regularizer adopts a unique topic-wise contrastive methodology, fostering both internal coherence within topics and clear external distinctions among them. Comprehensive experiments conducted on three diverse datasets demonstrate that our approach consistently produces topics with superior interpretability compared to state-of-the-art NTMs. © 2024, CC BY.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

PERIODICITY DECOUPLING FRAMEWORK FOR LONG-TERM SERIES FORECASTING 12

PERIODICITY DECOUPLING FRAMEWORK FOR LONG-TERM SERIES FORECA...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Dai, Tao Wu, Beiliang Liu, Peiyuan Li, Naiqi Bao, Jigang Jiang, Yong Xia, Shu-Tao College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China WeBank Institute of Financial Technology Shenzhen University China

Convolutional neural network (CNN)-based and Transformer-based methods have recently made significant strides in time series forecasting, which excel at modeling local temporal variations or capturing long-term dependencies. However, real-world time series usually contain intricate temporal patterns, thus making it challenging for existing methods that mainly focus on temporal variations modeling from the 1D time series directly. Based on the intrinsic periodicity of time series, we propose a novel Periodicity Decoupling Framework (PDF) to capture 2D temporal variations of decoupled series for long-term series forecasting. Our PDF mainly consists of three components: multi-periodic decoupling block (MDB), dual variations modeling block (DVMB), and variations aggregation block (VAB). Unlike the previous methods that model 1D temporal variations, our PDF mainly models 2D temporal variations, decoupled from 1D time series by MDB. After that, DVMB attempts to further capture short-term and long-term variations, followed by VAB to make final predictions. Extensive experimental results across seven real-world long-term time series datasets demonstrate the superiority of our method over other state-of-the-art methods, in terms of both forecasting performance and computational efficiency. Code is available at https://***/Hank0626/PDF. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Time series

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：