检索结果-内蒙古大学图书馆

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

作者： Haonan Miao Peizheng Zhao Yuqi Sun Fang Nan Xiaolong Zhang Yaqiang Wu Feng Tian School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students’ gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods.

关键词： Three-dimensional displays Estimation Focusing Signal processing Acoustics Speech processing

来源：评论

学校读者我要写书评

暂无评论

Review of graph construction and graph learning in stock price prediction

引用

Procedia Computer Science 2022年 214卷 771-778页

作者： Yunong Wang Yi Qu Zhensong Chen Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Management and Engineering Capital University of Economics and Business Beijing 100070 China

Precise prediction of stock prices leads to more profits and more effective risk prevention, which is of great significance to both investors and regulators. Recent years, various kinds of information not directly-relevant with stock prices have received more attention, such as texts, images or connections. These external information has the potential of reflecting or influencing fluctuations, and thus, given the utilization of advanced analyzing techniques, the forecasting performance of stock prices could be promoted substantially. For instance, graph neural network models have expanded into many other disciplines including stock price prediction, and exhibited impressive representation learning ability. However, in stock markets, well-defined graphs are rarely seen and how to formulate the graph structures needed remains a challenging problem. Towards this end, this article presents a comprehensive overview of graph construction and graph learning in stock price prediction, by reviewing the existing studies, summarizing its general paradigm, special cases and proposing possible prospects. Our research not only systematically reveals the feasible ways of constructing graphs in financial markets, but also provides insights for further implementations of graph learning models into stock prediction tasks.

关键词： Stock price prediction Graph construction Graph learning data structure

来源：评论

学校读者我要写书评

暂无评论

Detection to anomalous data using hypersphere method with mapping transformation

引用

Expert Systems with Applications 2025年 286卷

作者： Zheng, Jian Jian, Zhang Yu, Kui Wang, Shiyan School of Artificial Intelligence Chongqing Technology and Business University Chongqing400067 China College of Information Science and Technology and Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural University Hebei071001 China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China

This paper proposes an anomaly detection approach with mapping transformation aiming for the data without containing noise. Using the twin hyperspheres constructed by support vector data description to mine anomalies from the data within the angular region transformed by a mapping function, instead of using the constructed twin hyperspheres to directly mine anomalies from the raw data. To effectively perceive anomalous data, an upper bound and a lower bound of a scaling factor were derived. To accurately separate the anomalous class from the normal class, a lower bound and an upper bound of an amplification factor were derived. Finally, experimental results on ten UCI datasets imply that the proposed method defeats against the opponents in detection performance, also imply that the derived scaling factor relies on data dimension, rather than depending on data volume and anomalous ratio. Moreover, the proposed method does not exhibit exponential training time so that it is suitable for anomaly detection to the large-scale datasets. We demonstrate that the derived scaling factor can tighten the spatial distance between anomalous data, thus enabling a compact clustering to the detected anomalies. The derived amplification factor can enlarge the spatial distance between the anomalous class and the normal class, thus stimulating the perception ability of the kernels. We find that those anomaly detectors relying on the kernels can obtain more benefits through using the amplification factor to stimulate their kernels to perceive anomalous classes than through optimizing their structures in detection performance. © 2025 Elsevier Ltd

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

Recurring Drift Detection and Model Selection-Based Ensemble Classification for data Streams with Unlabeled data

引用

New Generation Computing 2021年第2期39卷 341-376页

作者： Li, Peipei Wu, Man He, Junhong Hu, Xuegang Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei 230009 China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230009 China CloudWalk Technology Co. Ltd. Guangzhou China

data stream classification is widely popular in the field of network monitoring, sensor network and electronic commerce, etc. However, in the real-world applications, recurring concept drifting and label missing in data streams seriously aggravate the difficulty on the classification solutions. And this challenge has received little attention from the research community. Motivated by this, we propose a new ensemble classification approach based on the recurring concept drifting detection and model selection for data streams with unlabeled data. First, we build an ensemble model based on the classifiers and clusters. To improve the classification accuracy, we use the ensemble model to predict each data chunk and partition clusters according to the distribution of predicted class labels. Second, we adopt a new concept drifting detection method based on the divergence of concept distributions between adjoining data chunks to distinguish recurring concept drifts. All historical new concepts will be maintained. Meanwhile, we introduce the time-stamp-based weights for base models in the ensemble model. In the selection of the base model, we consider the time-stamp-based weight and the divergence between concept distributions simultaneously. Finally, extensive experiments conducted on four benchmark data sets show that our approach can quickly adapt to data streams with recurring concept drifts, and improve the classification accuracy compared to several state-of-the-art classification algorithms for data streams with concept drifts and unlabeled data. © 2021, Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature.

关键词： data stream classification Ensemble learning Recurring concept drift Unlabeled data

来源：评论

学校读者我要写书评

暂无评论

LEGNN: A Label Ensemble Perspective for Training a Label-Noise-Resistant GNN with Reduced Complexity

arXiv

引用

arXiv 2024年

作者： Zhao, Rui Shi, Bin Liang, Zhiming Ruan, Jianfei Dong, Bo Lin, Lu School of Computer Science and Technology Xi'an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China School of Distance Education Xi'an Jiaotong University China College of Information Sciences and Technology Penn State University United States

Graph Neural Networks (GNNs) have been widely employed for semi-supervised node classification tasks on graphs. However, the performance of GNNs is significantly affected by label noise, that is, a small amount of incorrectly labeled nodes can substantially misguide model training. Mainstream solutions define node classification with label noise (NCLN) as a reliable labeling task, often introducing node similarity with quadratic computational complexity to more accurately assess label reliability. To this end, in this paper, we introduce the Label Ensemble Graph Neural Network (LEGNN), a lower complexity method for robust GNNs training against label noise. LEGNN reframes NCLN as a label ensemble task, gathering informative multiple labels instead of constructing a single reliable label, avoiding high-complexity computations for reliability assessment. Specifically, LEGNN conducts a two-step process: bootstrapping neighboring contexts and robust learning with gathered multiple labels. In the former step, we apply random neighbor masks for each node and gather the predicted labels as a high-probability label set. This mitigates the impact of inaccurately labeled neighbors and diversifies the label set. In the latter step, we utilize a partial label learning based strategy to aggregate the high-probability label information for model training. Additionally, we symmetrically gather a low-probability label set to counteract potential noise from the bootstrapped high-probability label set. Extensive experiments on six datasets demonstrate that LEGNN achieves outstanding performance while ensuring efficiency. Moreover, it exhibits good scalability on dataset with over one hundred thousand nodes and one million edges. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised Short Text Classification Based On Dual-channel data Augmentation

Semi-supervised Short Text Classification Based On Dual-chan...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Jiajun Li Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei City Anhui Province China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei City Anhui Province China

In practical applications, high-quality labeled data is critical for short text classification. But in many cases, it is expensive and time-consuming to obtain labeled information. Semi-supervised short text classification is hence attracting more attention. However, due to the sparsity of short texts, the performance of existing short text classification models always needs to be improved. Therefore, in this paper, we propose a semi-supervised Short text classification method based on Dual-Channel data Augmentation called SDCA. More specifically, in order to solve the sparsity of short texts, this model first adopts the multi-stage word-level TCN (Temporal Convolutional Network)-based attention to enhanced semantic features and an one-dimensional convolution-based attention mechanism to augment the relevance of surrounding short texts. Secondly, the unlabeled data are augmented by word embedding weighted augmentation and word replacement augmentation, so that the model can make full use of the unlabeled short texts and further enhance the network training. Finally, extensive experiments conducted on four benchmark datasets demonstrate the effectiveness of the proposed model on semi-supervised short text classification.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on Crowdsourcing Truth Inference Method Based on Graph Embedding 12

Research on Crowdsourcing Truth Inference Method Based on Gr...

引用

12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Zhou, Liangzhu Zhuo, Xingrui Wu, Gongqing Zhang, Zan Bao, Xianyu Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data of Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438582

Crowdsourcing is a cheap and popular method to solve problems that are difficult for computers to handle. Due to the differences in ability among workers on crowdsourcing platforms, existing research use aggregation strategies to deal with the labels of different workers to improve the utility of crowdsourcing data. However, most of these studies are based on probabilistic graphical models, which have problems such as difficulty in setting initial parameters. This paper proposes a novel crowdsourcing method Truth Inference based on Graph Embedding (TIGE) for single-choice questions, the method draws on the idea of graph autoencoder, constructs feature vectors for each crowdsourcing task, embeds the relationship between crowdsourcing tasks and workers in graphs, then uses graph neural networks to convert crowdsourcing problems into graph node prediction problems. The feature vectors are continuously optimized in the convolutional layer to obtain the final result. Compared with the six state-of-the-art algorithms on real-world datasets, our method has significant advantages in accuracy and F1-score. © 2021 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Chinese Idiom Paraphrasing

arXiv

引用

arXiv 2022年

作者： Qiang, Jipeng Li, Yang Zhang, Chaowei Li, Yun Yuan, Yunhao Zhu, Yi Wu, Xindong School of Information Engineering Yangzhou University Jiangsu Yangzhou China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Anhui Hefei China

Idioms, are a kind of idiomatic expression in Chinese, most of which consist of four Chinese characters. Due to the properties of non-compositionality and metaphorical meaning, Chinese Idioms are hard to be understood by children and non-native speakers. This study proposes a novel task, denoted as Chinese Idiom Paraphrasing (CIP). CIP aims to rephrase idioms-included sentences to non-idiomatic ones under the premise of preserving the original sentence’s meaning. Since the sentences without idioms are easier handled by Chinese NLP systems, CIP can be used to pre-process Chinese datasets, thereby facilitating and improving the performance of Chinese NLP tasks, e.g., machine translation system, Chinese idiom cloze, and Chinese idiom embeddings. In this study, CIP task is treated as a special paraphrase generation task. To circumvent difficulties in acquiring annotations, we first establish a large-scale CIP dataset based on human and machine collaboration, which consists of 115,530 sentence pairs. We further deploy three baselines and two novel CIP approaches to deal with CIP problems. The results show that the proposed methods have better performances than the baselines based on the established CIP dataset. © 2022, CC BY.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic Constrained Optimization

Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic C...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Chenyang Bu Yuxin Liu Lizhong Zhang Tao Zhu Wenjian Luo Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China School of Computer Science and Information Engineering University of South China Hengyang Hunan China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Guangdong China

Nature-inspired population-based stochastic search algorithms (SSA) have demonstrated effectiveness in solving many real-world dynamic optimization problems (DOPs), such as dynamic optimal power flow (DOPF) problems. The basic idea of solving DOPs using SSAs is to “track the moving optima”, rather than solving the changed problems from scratch. Its hidden assumption is that the problems are slightly changed in general, and it is expected that the search process can be accelerated by learning from past search experiences. However, the hidden assumption that the current search process can always benefit from previous search experiences might not be true for certain cases. For example, if the current problem is not similar to any of its previous problems, the historical solutions might not be helpful for the current search. To reduce such negative transfer, the following issues are worthy of study: how to choose which historical problems to learn from, and how to determine the degree of learning from historical problems. To solve the above problems, we propose a dynamic processing framework based on fuzzy rules from the perspective of incorporating human knowledge for optima tracking. The experimental results on the DOPF problems show that the SSA with the proposed optima tracking strategy outperforms other comparative algorithms. We open up the code and data of our algorithm 4 4 https://***/DMiC-Lab-HFUT/SMDE-Transfer.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation

arXiv

引用

arXiv 2022年

作者： Liu, Kang Xue, Feng He, Xiangnan Guo, Dan Hong, Richang The School of Computer Science and Information Engineering Key Laboratory of Knowledge Engineering with Big Data Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Anhui Hefei China The School of Software Key Laboratory of Knowledge Engineering with Big Data Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Anhui Hefei China The University of Science and Technology of China Anhui Hefei China

Graph Convolution Networks (GCNs), with their efficient ability to capture high-order connectivity in graphs, have been widely applied in recommender systems. Stacking multiple neighbor aggregation is the major operation in GCNs. It implicitly captures popularity features because the number of neighbor nodes reflects the popularity of a node. However, existing GCN-based methods ignore a universal problem: users’ sensitivity to item popularity is differentiated, but the neighbor aggregations in GCNs actually fix this sensitivity through Graph Laplacian Normalization, leading to suboptimal personalization. In this work, we propose to model multi-grained popularity features and jointly learn them together with high-order connectivity, to match the differentiation of user preferences exhibited in popularity features. Specifically, we develop a Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering model, short for JMP-GCF, which uses a popularity-aware embedding generation to construct multi-grained popularity features, and uses the idea of joint learning to capture the signals within and between different granularities of popularity features that are relevant for modeling user preferences. Additionally, we propose a multistage stacked training strategy to speed up model convergence. We conduct extensive experiments on three public datasets to show the state-of-the-art performance of JMP-GCF. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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：