检索结果-内蒙古大学图书馆

What is Discussed about COVID-19:A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling

引用

computers, Materials & Continua 2020年第9期64卷 1453-1471页

作者： Hengyang Lu Yutong Lou Bin Jin Ming Xu School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing UniversityNanjing210023China

Starting from late 2019,the new coronavirus disease(COVID-19)has become a global *** the development of online social media,people prefer to express their opinions and discuss the latest news *** have witnessed the positive influence of online social media,which helped citizens and governments track the development of this pandemic in *** is necessary to apply artificial intelligence(AI)techniques to online social media and automatically discover and track public opinions posted *** this paper,we take Sina Weibo,the most widely used online social media in China,for analysis and *** collect multi-modal microblogs about COVID-19 from 2020/1/1 to 2020/3/31 with a web crawler,including texts and images posted by *** order to effectively discover what is being discussed about COVID-19 without human labeling,we propose a unified multi-modal framework,including an unsupervised short-text topic model to discover and track bursty topics,and a self-supervised model to learn image features so that we can retrieve related images about *** results have shown the effectiveness and superiority of the proposed models,and also have shown the considerable application prospects for analyzing and tracking public opinions about COVID-19.

关键词： COVID-19 public opinion microblog topic model self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

On the Generalization and Causal Explanation in Self-Supervised Learning

arXiv

引用

arXiv 2024年

作者： Qiang, Wenwen Song, Zeen Gu, Ziyin Li, Jiangmeng Zheng, Changwen Sun, Fuchun Xiong, Hui The National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China The National Key Laboratory of Space Integrated Information System Department of Computer Science and Technology Tsinghua University Beijing China The Hong Kong University of Science and Technology China

Self-supervised learning (SSL) methods learn from unlabeled data and achieve high generalization performance on downstream tasks. However, they may also suffer from overfitting to their training data and lose the ability to adapt to new tasks. To investigate this phenomenon, we conduct experiments on various SSL methods and datasets and make two observations: (1) Overfitting occurs abruptly in later layers and epochs, while generalizing features are learned in early layers for all epochs;(2) Coding rate reduction can be used as an indicator to measure the degree of overfitting in SSL models. Based on these observations, we propose Undoing Memorization Mechanism (UMM), a plug-and-play method that mitigates overfitting of the pre-trained feature extractor by aligning the feature distributions of the early and the last layers to maximize the coding rate reduction of the last layer output. The learning process of UMM is a bi-level optimization process. We provide a causal analysis of UMM to explain how UMM can help the pre-trained feature extractor overcome overfitting and recover generalization. We also demonstrate that UMM significantly improves the generalization performance of SSL methods on various downstream tasks. The source code is to be released at https://***/ZeenSong/UMM. © 2024, CC BY.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

MGEoT: A Multi-grained Ensemble Method for Time Series Classification 18th

MGEoT: A Multi-grained Ensemble Method for Time Series Class...

引用

18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Wang, Ziyi Zhou, Yujie Li, Chun Shang, Lin Xue, Bing State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science and Technology Nanjing University Nanjing210023 China Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9783030891879

Classification of time series has attracted substantial interest over past decades. Methods based on Dynamic Time Warping (DTW), Symbolic Aggregate approXimation (SAX) and Shapelets are widely used and have achieved success in various real-world scenarios. However most existing time series classification methods either focus on global variation (e.g. DTW, SAX) or local variation (e.g. Shapelets). In this paper, we propose a Multi-Grained Ensemble Method for time series classification (MEGoT), which can make use of the variation of multi-grained data at the same time. In MEGoT, unstable base learners (Neural Networks) are assigned different weights to combine the ensemble. Different learners represent the learning features of different subsequences in time series, which can discover the discriminative regions, providing interpretability for classification. The training process of MGEoT is simpler and apt to parallel implementation. In the experiments, we conduct empirical evaluations and comparisons with various existing methods on 25 benchmark datasets. The final results show that dividing samples into smaller granularity is able to improve the diversity of ensemble, and MGEoT is competitive in accuracy under the Nemenyi test. Furthermore, MGEoT can discover the discriminative regions in time series, which may be neglected in the global methods. © 2021, Springer Nature Switzerland AG.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

UltraEdit: Instruction-based Fine-Grained Image Editing at Scale

arXiv

引用

arXiv 2024年

作者： Zhao, Haozhe Ma, Xiaojian Chen, Liang Si, Shuzheng Wu, Rujie An, Kaikai Yu, Peiyu Zhang, Minjia Li, Qing Chang, Baobao National Key Laboratory for Multimedia Information Processing Peking University China State Key Laboratory of General Artificial Intelligence BIGAI China School of Software and Microelectronics Peking University China Department of Computer Science and Technology Tsinghua University China School of Computer Science Peking University China UCLA United States UIUC United States

This paper presents ULTRAEDIT, a large-scale (~4M editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to address the drawbacks in existing image editing datasets like InstructPix2Pix [10] and MagicBrush [71], and provide a systematic approach to producing massive and high-quality image editing samples. ULTRAEDIT offers several distinct advantages: 1) It features a broader range of editing instructions by leveraging the creativity of large language models (LLMs) alongside in-context editing examples from human raters;2) Its data sources are based on real images, including photographs and artworks, which provide greater diversity and reduced bias compared to datasets solely generated by text-to-image models;3) It also supports region-based editing, enhanced by high-quality, automatically produced region annotations. Our experiments show that canonical diffusion-based editing baselines trained on ULTRAEDIT set new records on MagicBrush and Emu-Edit benchmarks. Our analysis further confirms the crucial role of real image anchors and region-based editing data. The dataset, code, and models are available in ***/pkunlp-icler/UltraEdit. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Driven Simultaneous Layout Decomposition and Mask Optimization

Deep Learning-Driven Simultaneous Layout Decomposition and M...

引用

作者： Zhong, Wei Hu, Shuxiang Ma, Yuzhe Yang, Haoyu Ma, Xiuyuan Yu, Bei DUTRU International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116024 China

Combining multiple patterning lithography (MPL) and optical proximity correction (OPC) pushes the limit of 193-nm wavelength lithography to go further. Considering that layout decomposition may generate plenty of solutions with diverse printabilities, relying on conventional mask optimization (MO) process to select the best candidate for manufacturing is computationally expensive. Therefore, an accurate and efficient printability estimation is crucial and can significantly accelerate the layout decomposition and MO (LDMO) flow. In this article, we propose a convolutional neural network (CNN)-based prediction and integrate it into our new high-performance LDMO framework. The optimization process can be considerably improved as the decomposition quality has been inferred in the early phase. To facilitate the network training and ensure better estimation accuracy, we develop sampling strategies for both layout and decomposition. Moreover, we enhance the layout sampling approach by adopting autoencoder to distance evaluation that promises superior sampling results. The experimental results demonstrate the effectiveness and the efficiency of the proposed algorithms. © 1982-2012 IEEE.

关键词： Manufacture

来源：评论

学校读者我要写书评

暂无评论

Building Device Models Using LLMs for Requirements Simulation in Smart Homes

Building Device Models Using LLMs for Requirements Simulatio...

引用

IEEE International Requirements Engineering Conference Workshops (REW)

作者： Shi Chen Xiaohong Chen Zhi Jin Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Key Laboratory of High-Confidence Software Technologies Peking University Ministry of Education China Department of Computer Science and Technology Peking University Beijing China

ISBN: (数字)9798350395518

ISBN: (纸本)9798350395525

The implementation of model-based requirements simulations in smart homes is an effective strategy that allows users to comprehend the execution impact of their personalized needs before deployment. This necessitates the use of device models in advance. In this paper, we propose an automatic modeling method for smart home devices that utilizes a Large Language Model (LLM) to assist end users in modeling their devices. The method involves extracting information directly from device manuals using the extraction capabilities of the LLM, deducing the automata models using the reasoning capabilities of the LLM, and verifying the model quality through the properties of valid device models. Our evaluation has demonstrated that our method can generate high-quality device models. The resultant model can be utilized for smart home scenario simulations, assisting users in validating their requirements. Thus, this provides a cost-effective and scalable solution for offering device models to simulate user requirements in smart homes.

关键词： Large language models Conferences Automata Smart homes Manuals Information retrieval Cognition

来源：评论

学校读者我要写书评

暂无评论

Knowledge Base Question Answering: A Semantic Parsing Perspective

arXiv

引用

arXiv 2022年

作者： Gu, Yu Pahuja, Vardaan Cheng, Gong Su, Yu Department of Computer Science and Engineering The Ohio State University United States State Key Laboratory for Novel Software Technology Nanjing University China

Recent advances in deep learning have greatly propelled the research on semantic parsing. Improvement has since been made in many downstream tasks, including natural language interface to web APIs, text-to-SQL generation, among others. However, despite the close connection shared with these tasks, research on question answering over knowledge bases (KBQA) has comparatively been progressing slowly. We identify and attribute this to two unique challenges of KBQA, schema-level complexity and fact-level complexity. In this survey, we situate KBQA in the broader literature of semantic parsing and give a comprehensive account of how existing KBQA approaches attempt to address the unique challenges. Regardless of the unique challenges, we argue that we can still take much inspiration from the literature of semantic parsing, which has been overlooked by existing research on KBQA. Based on our discussion, we can better understand the bottleneck of current KBQA research and shed light on promising directions for KBQA to keep up with the literature of semantic parsing, particularly in the era of pre-trained language models. © 2022, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

MRM: Site Selection via Mutil-round Meanshift for Global Fairness

MRM: Site Selection via Mutil-round Meanshift for Global Fai...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yu-xin Ge Hong-tao Luo Chen-xuan Fang Zhi-wei Zhu Chong-jun Wang Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University Nanjing University Nanjing China

Elevator maintenance plays an important role in the safety and economic aspects of daily life, and the location of elevator maintenance sites is crucial in this scenario. A reasonable location can help reduce economic expenses and ensure the healthy operation of the system of maintenance sites. However, due to the lack of candidate location sets and unevenly distributed data layers, existing coverage siting methods have difficulties and challenges in solving this problem, and ignore the fairness of load among different sites. To address above problems, We propose a heuristic coverage siting method called MRM, which is based on multi-round MeanShift clustering with coverage radius variation. Experiments on a real elevator dataset with noise show that MRM not only achieves 99.21 % coverage with fewer sites but also achieves fairly good global fairness.

关键词： Economics Distributed databases Maintenance engineering Elevators Safety Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Weighted Density for The Win: Accurate Subspace Density Clustering

Weighted Density for The Win: Accurate Subspace Density Clus...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Maixuan Peng Yuyang Wu Yang Lu Mengke Li Yiqun Zhang Yiu-Ming Cheung School of Computer Science and Technology Guangdong University of Technology Guangzhou China Faculty of Science Wilfrid Laurier University Brantford Canada Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

k-clustering typically struggles with the detection of irregular-distributed clusters due to the natural bias, while density clustering usually cannot well-adapt to different datasets and clustering tasks as it is not an oriented optimization process. This paper, therefore, proposes to perform density clustering in dynamically learned subspaces. To exploit the irregular-distributed clusters obtained by density clustering for the subspace determination, we design a new strategy to appropriately evaluate the importance of attributes. It turns out that the proposed Weighted Density-based Subspace Clustering (WDSC) algorithm inherits the unbiased merits of density clustering, and also upgrades the unlearning density clustering to be learnable under the subspace learning paradigm of k-clustering. A comprehensive evaluation including significance tests, ablation studies, qualitative comparisons, etc., shows the superiority of WDSC.

关键词： Accuracy Heuristic algorithms Signal processing algorithms Clustering algorithms Signal processing Acoustics Speech processing Optimization

来源：评论

学校读者我要写书评

暂无评论

CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

引用

39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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：