检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Wang, Jingyao Qiang, Wenwen Zheng, Changwen Sun, Fuchun University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

Fine-grained emotion recognition (FER) plays a vital role in various fields, such as disease diagnosis, personalized recommendations, and multimedia mining. However, existing FER methods face three key challenges in real-world applications: (i) they rely on large amounts of continuously annotated data to ensure accuracy since emotions are complex and ambiguous in reality, which is costly and time-consuming;(ii) they cannot capture the temporal heterogeneity caused by changing emotion patterns, because they usually assume that the temporal correlation within sampling periods is the same;(iii) they do not consider the spatial heterogeneity of different FER scenarios, that is, the distribution of emotion information in different data may have bias or interference. To address these challenges, we propose a Spatio-Temporal Fuzzy-oriented Multi-modal Meta-learning framework (ST-F2M). Specifically, ST-F2M first divides the multimodal videos into multiple views, and each view corresponds to one modality of one emotion. Multiple randomly selected views for the same emotion form a meta-training task. Next, ST-F2M uses an integrated module with spatial and temporal convolutions to encode the data of each task, reflecting the spatial and temporal heterogeneity. Then it adds fuzzy semantic information to each task based on generalized fuzzy rules, which helps handle the complexity and ambiguity of emotions. Finally, ST-F2M learns emotion-related general meta-knowledge through meta-recurrent neural networks to achieve fast and robust fine-grained emotion recognition. Extensive experiments show that ST-F2M outperforms various state-of-the-art methods in terms of accuracy and model efficiency. In addition, we construct ablation studies and further analysis to explore why ST-F2M performs well. Copyright © 2024, The Authors. All rights reserved.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Deep learning for automatically predicting early haematoma expansion in Chinese patients

引用

Stroke & Vascular Neurology 2021年第4期6卷 610-614,I0067-I0072页

作者： Jia-wei Zhong Yu-jia Jin Zai-jun Song Bo Lin Xiao-hui Lu Fang Chen Lu-sha Tong Department of Neurology Zhejiang University School of Medicine Second Affiliated HospitalHangzhouChina College of Computer Science and Technology Zhejiang UniversityHangzhouChina State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University School of Mechanical EngineeringHangzhouChina Department of Computer Science and Engineering Nanjing University of Aeronautics and AstronauticsNanjingChina

Background and purpose Early haematoma expansion is determinative in predicting outcome of intracerebral haemorrhage(ICH)*** aims of this study are to develop a novel prediction model for haematoma expansion by applying deep learning model and validate its prediction *** Data of this study were obtained from a prospectively enrolled cohort of patients with primary supratentorial ICH from our *** developed a deep learning model to predict haematoma expansion and compared its performance with conventional non-contrast CT(NCCT)*** evaluate the predictability of this model,it was also compared with a logistic regression model based on haematoma volume or the BAT *** A total of 266 patients were finally included for analysis,and 74(27.8%)of them experienced early haematoma *** deep learning model exhibited highest C statistic as 0.80,compared with 0.64,0.65,0.51,0.58 and 0.55 for hypodensities,black hole sign,blend sign,fluid level and irregular shape,*** the C statistics for swirl sign(0.70;p=0.211)and heterogenous density(0.70;p=0.141)were not significantly higher than that of the deep learning ***,the predictive value for the deep learning model was significantly superior to that of the logistic model of haematoma volume(0.62;p=0.042)and the BAT score(0.65;p=0.042).Conclusions Compared with the conventional NCCT markers and BAT predictive model,the deep learning algorithm showed superiority for predicting early haematoma expansion in ICH patients.

关键词： patients expansion predicting

来源：评论

学校读者我要写书评

暂无评论

Safe Online Convex Optimization with Heavy-Tailed Observation Noises 39

Safe Online Convex Optimization with Heavy-Tailed Observatio...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Yunhao Xue, Bo Hao, Yunzhi Li, Ying Wan, Yuanyu School of Software Technology Zhejiang University Ningbo China Department of Computer Science City University of Hong Kong China Big Graph Center School of Computer and Computing Science Hangzhou City University Hangzhou China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Bangsheng Technology Co. Ltd. Hangzhou China Institute of Blockchain and Data Security Hangzhou China

ISBN: (纸本)157735897X

We investigate safe online convex optimization (SOCO), where each decision must satisfy a set of unknown linear constraints. Assuming that the unknown constraints can be observed with a sub-Gaussian noise for each chosen decision, previous studies have established a high-probability regret bound of O(T2/3). However, this assumption may not hold in many practical scenarios. To address this limitation, in this paper, we relax the assumption to allow any noise that admits finite (1+ϵ)-th moments for some ϵ ∈ (0, 1], and propose two algorithms that enjoy an O(Tcϵ) regret bound with high probability, where T is the time horizon and cϵ = (1+ϵ)/(1+2ϵ). The key idea of our two algorithms is to respectively utilize the median-of-means and truncation techniques to achieve accurate estimation under heavy-tailed noises. To the best of our knowledge, these are the first algorithms designed to handle SOCO with heavy-tailed observation noises. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

Improved Methods of Pointer Mixture Network for Code Completion

Improved Methods of Pointer Mixture Network for Code Complet...

引用

IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Cheng Wei Zhiqiu Huang Yaoshen Yu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu China Collaborative Innovation Center of Novel Software Technology and Industrialization Jiangsu China Key Laboratory of Safety-Critical Software Nanjing University of Aeronautics and Astronautics Jiangsu China

Code completion is an efficient software development technique in modern integrated development environments (IDEs), which can predict the most likely code token(s) based on the context of the code to be completed, so as to improve the work efficiency of developers. The Pointer Mixture Network proposed in recent years has achieved good results in code completion, the contribution of this paper is to improve the Pointer Mixture Network’s method. We used one-hot encoding in the data preprocessing phase, which makes the distance between the tokens of calculation more reasonable, and also has an effect on the expansion characteristics of the code. Besides, we add label smoothing to avoid the overfitting of neural language networks and improve the generalization ability of the model. In neural language networks, we apply the three-layer LSTM, so that the hidden layers of LSTM can fully learn the context information. In terms of the optimizer, we choose NAdam whose performance is better than Adam used in the Pointer Mixture Network, which greatly accelerates the training speed of the model. Experiments show that our work exceeds the results obtained in the Pointer Mixture Network, which is in code completion tasks in Python and JavaScript programming languages.

关键词： Training Codes Smoothing methods Neural networks software quality Predictive models Encoding

来源：评论

学校读者我要写书评

暂无评论

Incremental Multi-Label Learning with Active Queries

引用

Journal of computer science & technology 2020年第2期35卷 234-246页

作者： Sheng-Jun Huang Guo-Xiang Li Wen-Yu Huang Shao-Yuan Li College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Pattern Analysis and Machine Intelligence Ministry of Industry and Information Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing University Nanjing 210023China

In multi-label learning,it is rather expensive to label instances since they are simultaneously associated with multiple ***,active learning,which reduces the labeling cost by actively querying the labels of the most valuable data,becomes particularly important for multi-label learning.A good multi-label active learning algorithm usually consists of two crucial elements:a reasonable criterion to evaluate the gain of querying the label for an instance,and an effective classification model,based on whose prediction the criterion can be accurately *** this paper,we first introduce an effective multi-label classification model by combining label ranking with threshold learning,which is incrementally trained to avoid retraining from scratch after every *** on this model,we then propose to exploit both uncertainty and diversity in the instance space as well as the label space,and actively query the instance-label pairs which can improve the classification model *** experiments on 20 datasets demonstrate the superiority of the proposed approach to state-of-the-art methods.

关键词： active learning multi-label learning uncertainty diversity

来源：评论

学校读者我要写书评

暂无评论

Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor

arXiv

引用

arXiv 2022年

作者： Zhang, Lijun Jiang, Wei Yi, Jinfeng Yang, Tianbao National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China *** Beijing100101 China Department of Computer Science The University of Iowa Iowa CityIA52242 United States

In this paper, we investigate an online prediction strategy named as Discounted-Normal-Predictor (Kapralov and Panigrahy, 2010) for smoothed online convex optimization (SOCO), in which the learner needs to minimize not only the hitting cost but also the switching cost. In the setting of learning with expert advice, Daniely and Mansour (2019) demonstrate that Discounted-Normal-Predictor can be utilized to yield nearly optimal regret bounds over any interval, even in the presence of switching costs. Inspired by their results, we develop a simple algorithm for SOCO: Combining online gradient descent (OGD) with different step sizes sequentially by Discounted-Normal-Predictor. Despite its simplicity, we prove that it is able to minimize the adaptive regret with switching cost, i.e., attaining nearly optimal regret with switching cost on every interval. By exploiting the theoretical guarantee of OGD for dynamic regret, we further show that the proposed algorithm can minimize the dynamic regret with switching cost in every interval. Copyright © 2022, The Authors. All rights reserved.

关键词： Gradient methods

来源：评论

学校读者我要写书评

暂无评论

FRAVaR: A Fast Failure Recovery Framework for Inter-DC Network

FRAVaR: A Fast Failure Recovery Framework for Inter-DC Netwo...

引用

IEEE Conference on Wireless Communications and Networking

作者： Haoqiang Huang Yuchao ZhangB Ran Wang Qiao Xiang Wendong Wang Xirong Que Ke Xu Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology School of Informatics Xiamen University China Department of Computer Science and Technology Tsinghua University China

Along with the development of 5G and IoT technologies in recent years, Inter Data Center (Inter-DC) network is facing an explosive growth of geographically distributed user data, which needs to be duplicated among DCs in a real-time manner. Transmission-based applications require high availability that is going beyond 99.99%. However, with the expansion of Inter-DC network scale, link failures are also growing, which seriously affects data transmission efficiency, so fast link failure recovery is then urgently needed. Many previous works have been done to achieve fast failure recovery, but most of them ignore two key points, 1) the cost of deploying recovery strategies, and 2) the side-effect of re-transmission to network availability. These two factors make the existing failure recovery process too slow to be practical in real-time online industrial environments. To achieve realistic fast recovery from Inter-DC network failures, we propose a failure recovery framework FRAVaR, which achieves high network availability with very little deployment overhead. Particularly, FRAVaR reduces the deployment overhead by a novel incremental routing strategy to isolate link failures. In other words, it only needs to shuffle a tiny amount of traffic within a small failure isolation domain. On this base, FRAVaR further adopts a risk assessment theory named Value-at-Risk (VaR) to control flow re-transmission. We implement a prototype of FRAVaR and conduct a series of experiments on 4 real InterDC network topologies (ATT North America, IBM, GlobalCenter, AGIS). Experiment results show that FRAVaR outperforms state-of-the-art solutions on the recovery speed by 70.2%. 1

关键词： Data centers Reactive power Network topology Prototypes Throughput Routing Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach

Webly Supervised Fine-Grained Recognition: Benchmark Dataset...

引用

International Conference on computer Vision (ICCV)

作者： Zeren Sun Yazhou Yao Xiu-Shen Wei Yongshun Zhang Fumin Shen Jianxin Wu Jian Zhang Heng Tao Shen School of Computer Science and Engineering Nanjing University of Science and Technology State Key Laboratory for Novel Software Technology Nanjing University University of Electronic Science and Technology of China University of Technology Sydney

ISBN: (纸本)9781665428132

Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets. Especially for fine-grained recognition, which targets at distinguishing subordinate categories, it will significantly reduce the labeling costs by leveraging free web data. Despite its significant practical and research value, the webly supervised fine-grained recognition problem is not extensively studied in the computer vision community, largely due to the lack of high-quality datasets. To fill this gap, in this paper we construct two new benchmark webly supervised fine-grained datasets, termed WebFG-496 and WebiNat-5089, respectively. In concretely, WebFG-496 consists of three sub-datasets containing a total of 53,339 web training images with 200 species of birds (Web-bird), 100 types of aircrafts (Web-aircraft), and 196 models of cars (Web-car). For WebiNat-5089, it contains 5089 sub-categories and more than 1.1 million web training images, which is the largest webly supervised fine-grained dataset ever. As a minor contribution, we also propose a novel webly supervised method (termed "Peer-learning") for benchmarking these datasets. Comprehensive experimental results and analyses on two new benchmark datasets demonstrate that the proposed method achieves superior performance over the competing baseline models and states-of-the-art. Our benchmark datasets and the source codes of Peer-learning have been made available at https://***/NUST-Machine-Intelligence-laboratory/weblyFG-dataset.

关键词： Training Deep learning Knowledge engineering computer vision Image recognition Target recognition Neural networks

来源：评论

学校读者我要写书评

暂无评论

MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment

arXiv

引用

arXiv 2024年

作者： Zhou, Kanglei Wang, Liyuan Zhang, Xingxing Shum, Hubert P.H. Li, Frederick W.B. Li, Jianguo Liang, Xiaohui State Key Laboratory of VR Technology and Systems Beihang University China Department of Computer Science and Technology Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University China Department of Computer Science Durham University United Kingdom Children’s Hospital of Capital Institute of Pediatrics China Zhongguancun Laboratory China

Action Quality Assessment (AQA) evaluates diverse skills but models struggle with non-stationary data. We propose Continual AQA (CAQA) to refine models using sparse new data. Feature replay preserves memory without storing raw inputs. However, the misalignment between static old features and the dynamically changing feature manifold causes severe catastrophic forgetting. To address this novel problem, we propose Manifold-Aligned Graph Regularization (MAGR), which first aligns deviated old features to the current feature manifold, ensuring representation consistency. It then constructs a graph jointly arranging old and new features aligned with quality scores. Experiments show MAGR outperforms recent strong baselines with up to 6.56%, 5.66%, 15.64%, and 9.05% correlation gains on the MTL-AQA, FineDiving, UNLV-Dive, and JDM-MSA split datasets, respectively. This validates MAGR for continual assessment challenges arising from non-stationary skill variations. Code is available at https://***/ZhouKanglei/MAGR_CAQA. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Webly supervised fine-grained recognition: Benchmark datasets and an approach

arXiv

引用

arXiv 2021年

作者： Sun, Zeren Yao, Yazhou Wei, Xiu-Shen Zhang, Yongshun Shen, Fumin Wu, Jianxin Zhang, Jian Shen, Heng Tao School of Computer Science and Engineering Nanjing University of Science and Technology State Key Laboratory for Novel Software Technology Nanjing University University of Electronic Science and Technology of China University of Technology Sydney

关键词： Birds

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：