检索结果-内蒙古大学图书馆

33rd International Joint Conference on Artificial intelligence, IJCAI 2024

作者： Wang, Rui Hu, Chen Chen, Ziheng Wu, Xiao-Jun Song, Xiaoning School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China Department of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (纸本)9781956792041

In the community of artificial intelligence, significant progress has been made in encoding sequential data using deep learning techniques. Nevertheless, how to effectively mine useful information from channel dimensions remains a major challenge, as these features have a submanifold structure. Linear subspace, the basic element of the Grassmannian manifold, has proven to be an effective manifold-valued feature descriptor in statistical representation. Besides, the Euclidean self-attention mechanism has shown great success in capturing long-range relationships of data. Inspired by these facts, we extend the self-attention mechanism to the Grassmannian manifold. Our framework can effectively characterize the spatiotemporal fluctuations of sequential data encoded in the Grassmannian manifold. Extensive experimental results on three benchmarking datasets (a drone recognition dataset and two EEG signal classification datasets) demonstrate the superiority of our method over the state-of-the-art. The code and supplementary material for this work can be found at https://***/ChenHu-ML/GDLNet. © 2024 International Joint Conferences on Artificial intelligence. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

NABP: Social Network Alignment Algorithm based on Pre-trained Language Models 3

NABP: Social Network Alignment Algorithm based on Pre-traine...

引用

3rd International Conference on Artificial intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Ding, Linjun Chen, Jing School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computing Intelligence Jiangnan University Jiangsu Wuxi214122 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

The existing social network matching algorithms have problems in processing text attribute information, as they cannot handle polysemy issues of word meanings well and cannot effectively extract deep semantic information from the text. To address the aforementioned issues, this paper introduces pretrained models to extract information from attribute text and proposes a social network alignment algorithm called NABP (Network Alignment based on Pre-trained Language Models). NABP first preprocesses the attribute text data to extract the text feature vectors;then, it uses the DeepWalk algorithm to perform graph representation learning on the two networks to extract their structural features. Next, the attribute feature vectors and structural feature vectors are concatenated to form the final feature vector. Then, a projection function is learned using supervised information to further improve the algorithm's performance. To verify the effectiveness of NABP algorithm, this study conducted experiments on two real social network datasets. The results show that the NABP algorithm performs better than existing algorithms in both the Accuracy and Precision@k metrics, demonstrating the effectiveness of the algorithm and pre-trained language models. © 2023 SPIE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop

引用

Complex System Modeling and Simulation 2023年第1期3卷 47-58页

作者： Zaihe Cheng Wei Shen Wei Fang Jerry Chun-Wei Lin School of Internet of Things Wuxi Institute of TechnologyWuxi 214121China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan UniversityWuxi 214122China Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergen 5020Norway

High-utility itemset mining(HUIM)can consider not only the profit factor but also the profitable factor,which is an essential task in data ***,most HUIM algorithms are mainly developed on a single machine,which is inefficient for big data since limited memory and processing capacities are available.A parallel efficient high-utility itemset mining(P-EFIM)algorithm is proposed based on the Hadoop platform to solve this problem in this *** P-EFIM,the transaction-weighted utilization values are calculated and ordered for the itemsets with the MapReduce *** the ordered itemsets are renumbered,and the low-utility itemsets are pruned to improve the dataset *** the Map phase,the P-EFIM algorithm divides the task into multiple independent *** uses the proposed S-style distribution strategy to distribute the subtasks evenly across all nodes to ensure ***,the P-EFIM uses the EFIM algorithm to mine each subtask dataset to enhance the performance in the Reduce *** are performed on eight datasets,and the results show that the runtime performance of P-EFIM is significantly higher than that of the PHUI-Growth,which is also HUIM algorithm based on the Hadoop framework.

关键词： pattern mining data mining Hadoop parallel high-utility itemset mining big data

来源：评论

学校读者我要写书评

暂无评论

Mutual Information-Guided GA for Bayesian Network Structure Learning (Extended Abstract)

Mutual Information-Guided GA for Bayesian Network Structure ...

引用

International Conference on Data engineering

作者： Kefei Yan Wei Fang Hengyang Lu Xin Zhang Jun Sun Xiaojun Wu Department of Computer Science and Technology Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Bayesian network structure learning (BNSL) from data is an NP-hard problem. Genetic algorithms are powerful for solving combinatorial optimization problems, but the lack of effective guidance results in slow convergence and low accuracy regarding BNSL. To address this problem, we propose a mutual information (MI) guided genetic algorithm (MIGA) for BNSL in this paper, which uses MI to effectively search BN structures. In the initialization phase of MIGA, the population is generated by adding additional constraints based on MI to reach a higher score without losing diversity. By employing normalized MI and defining the population support, the potential dominance in the population can be identified and then used to design a novel crossover operator in order to preserve the dominant genes with a higher probability. Moreover, with the guidance of MI for removing loops from the structures, infeasible solutions can be handled in a straightforward and practical way. The proposed MIGA is evaluated on eleven well-known benchmark datasets and compared with four GA-based methods and four other state-of-the-art BNSL algorithms. Experimental results show that MIGA outperforms the compared algorithms in convergence and learning accuracy.

关键词： Accuracy NP-hard problem Sociology Search problems Bayes methods Statistics Optimization

来源：评论

学校读者我要写书评

暂无评论

MSC-Fuse: An Unsupervised Multi-scale Convolutional Fusion Framework for Infrared and Visible Image 11th

MSC-Fuse: An Unsupervised Multi-scale Convolutional Fusion F...

引用

11th International Conference on Image and Graphics, ICIG 2021

作者： Chen, Guo-Yang Wu, Xiao-Jun Li, Hui Xu, Tian-Yang Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Jiangsu Wuxi214122 China

ISBN: (纸本)9783030873547

Lacking the labeled data, how to establish an unsupervised learning method is essential for the infrared and visible image fusion task. As such, this article introduces a novel unsupervised learning fusion framework. Our proposed framework consists of three components: encoder, fusion layer, and decoder, respectively. Firstly, an encoder is designed to extract salient features from multiple source images. With the multi-scale convolution modules, the encoder can produce more useful features. Then these features are fused at the fusion layer. Finally, the decoder reconstructs the fused features to generate the fused image. To achieve the unsupervised training of the network, a no-reference quality metric and a pixel-level function are utilized to calculate the loss function. Experimental results show that compared with other fusion methods, our proposed method can achieve better performance in both objective and subjective assessments. © 2021, Springer Nature Switzerland AG.

关键词： Image fusion

来源：评论

学校读者我要写书评

暂无评论

Where to Attack: A Dynamic Locator Model for Backdoor Attack in Text Classifications 29

Where to Attack: A Dynamic Locator Model for Backdoor Attack...

引用

29th International Conference on computational Linguistics, COLING 2022

作者： Lu, Heng-Yang Fan, Chenyou Yang, Jun Hu, Cong Fang, Wei Wu, Xiao-Jun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University China School of Artificial Intelligence South China Normal University China Marcpoint Co. Ltd. China

Nowadays, deep-learning based NLP models are usually trained with large-scale third-party data which can be easily injected with malicious backdoors. Thus, BackDoor Attack (BDA) study has become a trending research to help promote the robustness of an NLP system. Text-based BDA aims to train a poisoned model with both clean and poisoned texts to perform normally on clean inputs while being misled to predict those trigger-embedded texts as target labels set by attackers. Previous works usually choose fixed Positions-to-Poison (P2P) first, then add triggers upon those positions such as letter insertion or deletion. However, considering the positions of words with important semantics may vary in different contexts, fixed P2P models are severely limited in flexibility and performance. We study the text-based BDA from the perspective of automatically and dynamically selecting P2P from contexts. We design a novel Locator model which can predict P2P dynamically without human intervention. Based on the predicted P2P, four effective strategies are introduced to show the BDA performance. Experiments on two public datasets show both tinier test accuracy gap on clean data and higher attack success rate on poisoned ones. Human evaluation with volunteers also shows the P2P predicted by our model are important for classification. Source code is available at https://***/jncsnlp/LocatorModel. © 2022 Proceedings - International Conference on computational Linguistics, COLING. All rights reserved.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

DreamNet: A Deep Riemannian Manifold Network for SPD Matrix Learning 16th

DreamNet: A Deep Riemannian Manifold Network for SPD Matrix...

引用

16th Asian Conference on Computer Vision, ACCV 2022

作者： Wang, Rui Wu, Xiao-Jun Chen, Ziheng Xu, Tianyang Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9783031263507

The methods of symmetric positive definite (SPD) matrix learning have attracted considerable attention in many pattern recognition tasks, as they are eligible to capture and learn appropriate statistical features while respecting the Riemannian geometry of SPD manifold where the data reside on. Accompanied with the advanced deep learning techniques, several Riemannian networks (RiemNets) for SPD matrix nonlinear processing have recently been studied. However, it is pertinent to ask, whether greater accuracy gains can be realized by simply increasing the depth of RiemNets. The answer appears to be negative, as deeper RiemNets may be difficult to train. To explore a possible solution to this issue, we propose a new architecture for SPD matrix learning. Specifically, to enrich the deep representations, we build a stacked Riemannian autoencoder (SRAE) on the tail of the backbone network, i.e., SPDNet [23]. With this design, the associated reconstruction error term can prompt the embedding functions of both SRAE and of each RAE to approach an identity mapping, which helps to prevent the degradation of statistical information. Then, we implant several residual-like blocks using shortcut connections to augment the representational capacity of SRAE, and to simplify the training of a deeper network. The experimental evidence demonstrates that our DreamNet can achieve improved accuracy with increased depth. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Mining High Quality patterns Using Multi-Objective Evolutionary Algorithm (Extended Abstract)

Mining High Quality Patterns Using Multi-Objective Evolution...

引用

International Conference on Data engineering

作者： Wei Fang Qiang Zhang Jun Sun Xiaojun Wu International Joint Laboratory on Artificial Intelligence of Jiangsu Province Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

Most studies on pattern mining have considered only one pattern, such as frequent pattern or high-utility pattern, which is difficult to meet the increasingly diverse needs of users. In this paper, a novel multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed with two kinds of population initialization strategies and an auxiliary tool. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. The proposed algorithm outperforms the state-of-the-art in terms of efficiency, quality, and convergence speed.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Feature Space Renormalization for Semi-supervised Learning

arXiv

引用

arXiv 2023年

作者： Sun, Jun Mao, Zhongjie Li, Chao Zhou, Chao Wu, Xiao-Jun The School of Artificial Intelligence and Computer Science Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University 1800 Lihu Avenue Jiangsu Wuxi214122 China The Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University 1800 Lihu Avenue Jiangsu Wuxi214122 China

Semi-supervised learning (SSL) has been proven to be a powerful method for leveraging unlabelled data to alleviate models' dependence on large labelled datasets. The common framework among recent approaches is to train the model on a large amount of unlabelled data with consistency regularization to constrain the model predictions to be invariant to input perturbation. However, the existing SSL frameworks still have room for improvement in the consistency regularization method. Instead of regularizing category predictions in the label space as in existing frameworks, this paper proposes a feature space renormalization (FSR) mechanism for SSL. First, we propose a feature space renormalization mechanism to substitute for the commonly used consistency regularization mechanism to learn better discriminative features. To apply this mechanism, we start by building a basic model and an empirical model and then introduce our mechanism to renormalize the feature learning of the basic model with the guidance of the empirical model. Second, we combine the proposed mechanism with pseudo-labelling to obtain a novel effective SSL model named FreMatch. The experimental results show that our method can achieve better performance on a variety of standard SSL benchmark datasets, and the proposed feature space renormalization mechanism can also enhance the performance of other SSL approaches. Copyright © 2023, 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

LE2Fusion: A novel local edge enhancement module for infrared and visible image fusion

arXiv

引用

arXiv 2023年

作者： Xiao, Yongbiao Li, Hui Cheng, Chunyang Song, Xiaoning Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China

Infrared and visible image fusion task aims to generate a fused image which contains salient features and rich texture details from multi-source images. However, under complex illumination conditions, few algorithms pay attention to the edge information of local regions which is crucial for downstream tasks. To this end, we propose a fusion network based on the local edge enhancement, named LE2Fusion. Specifically, a local edge enhancement (LE2) module is proposed to improve the edge information under complex illumination conditions and preserve the essential features of image. For feature extraction, a multi-scale residual attention (MRA) module is applied to extract rich features. Then, with LE2, a set of enhancement weights are generated which are utilized in feature fusion strategy and used to guide the image reconstruction. To better preserve the local detail information and structure information, the pixel intensity loss function based on the local region is also presented. The experiments demonstrate that the proposed method exhibits better fusion performance than the state-of-the-art fusion methods on public datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Image fusion

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：