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International Conference on Natural Computation (ICNC)

作者： K. Lavangnananda P. Sawasdimongkol Data & Knowledge Engineering Lab. School of Information Technology King Mongkut's University of Technology Thonburi Bangkok Thailand

Detecting abnormalities in manufacturing process at an early stage is advantageous. This can be done by monitoring its Control Chart Patterns (CCPs). These patterns can be considered as time series data. Therefore, accurate classification of CCPs is vital in the process control. There have been several types of CCPs classifiers with various degrees of accuracy. This work is concerned with implementation of a CCPs classifier based on neural networks and symbolic representation as its preprocessing method. CCPs are generated by the commonly used Generalized Autoregressive Conditional Heteroskedasticity (GARH) Model. Several of both feed forward and recurrent networks are investigated for the classifier. The symbolic representation of time series known as Symbolic Aggregate Approximation (SAX) is selected as its application was satisfactory in numerous similar works. In feed forward network, the Multilayer Perceptron network yields the best performance while Time-lag network yields the best performance for recurrent network. The results of neural network classifier which utilizes SAX in preprocessing in this work are superior than previous works which used the same CCPs model.

关键词： Time series analysis Biological neural networks Control charts Process control Feeds Multilayer perceptrons

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A Symbolic Rule Integration Framework with Logic Transformer for Inductive Relation Prediction 24

A Symbolic Rule Integration Framework with Logic Transformer...

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33rd ACM Web Conference, WWW 2024

作者： Pan, Yudai Liu, Jun Zhao, Tianzhe Zhang, Lingling Lin, Yun Dong, Jin Song School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Shaanxi Xi'an China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi'an China Shanghai Jiao Tong University Shanghai China National University of Singapore Singapore Singapore

ISBN: (纸本)9798400701719

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm by KG embeddings has a limitation to predict the relation between unseen entities. This situation is called an inductive setting, which is more common in the real-world scenario. To handle this issue, implicit symbolic rules have shown great potential in capturing the inductive capability. However, it is still challenging to obtain precise representations of logic rules from KGs. The argument variability and predicate non-commutativity in symbolic rule integration make the modeling of component symbols difficult. To this end, we propose a novel inductive relation prediction model named SymRITa with a logic transformer integrating rules. SymRITa firstly extracts the subgraph, whose embeddings are captured by a graph network. Meanwhile, symbolic rule graphs in the subgraph can be generated. Then, the symbolic rules are modeled by a proposed logic transformer. Specifically, the input format based on the subgraph-based embeddings is to focus on the argument variability in symbolic rules. In addition, a conjunction attention mechanism in the logic transformer can resolve predicate non-commutativity in the symbolic rule integration process. Finally, the subgraph-based and symbol-based embeddings obtained from the previous steps are combined for the training regime, and prediction results as well as rules explaining the reasoning process are explicitly output. Extensive experiments on twelve inductive datasets show that SymRITa achieves outstanding effectiveness compared to state-of-the-art inductive baselines. Moreover, the logic rules with corresponding confidences provide an interpretable paradigm. © 2024 ACM.

关键词： Forecasting

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Research on Classification lab.l Denoising Algorithm Based on Granular Ball 7

Research on Classification Label Denoising Algorithm Based o...

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7th International Conference on Cloud Computing and Big data Analytics, ICCCBDA 2022

作者： Kong, Weiyu Wu, Yanmin Qi, Jinli Chen, Yanyi Chongqing Key Lab. of Comp. Sci. and Technology Chongqing University of Posts and Telecommunications ChongQing China ChongQing College of Electronic Engineering Department of Artificial Intelligence and Big Data ChongQing China Chongqing University of Posts and Telecommunications Department of Software Engineering ChongQing China

ISBN: (纸本)9781665487115

This paper presents a granular ball denoising method (GBD) which can effectively improve the accuracy and robustness of classification algorithm. GBD method first uses the self-Adaptive hypersphere to cover the data space, then eliminates the data not contained in the hypersphere and the noise data in the sphere, and finally uses only the hypersphere data for training, so as to reduce the sample data and improve the data quality. Experiments show that using the data obtained by this method for training can greatly improve the accuracy of the classification algorithm. In addition, because the hypersphere has good adaptive ability, GBD also has excellent robustness. The experiment shows that GBD sampling data training can still get good results after adding noise to the data set. Therefore, GBD is an efficient and robust denoising method. © 2022 IEEE.

关键词： Training Cloud computing data integrity Noise reduction Big data Robustness Classification algorithms

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Deep Representation of Hierarchical Semantic Attributes for Zero-shot Learning

Deep Representation of Hierarchical Semantic Attributes for ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhaocheng Zhang Gang Yang School of Information Renmin University of China Beijing China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

On account of a large scale of dataset need to be annotated to fit for specific tasks, Zero-Shot Learning(ZSL) has invoked so much attention and got significant progress in recent research due to the prevalence of deep neural networks. At present, ZSL is mainly solved through the utilization of auxiliary information, such as semantic attributes and text descriptions. And then, we can employ the mapping method to bridge the gap between visual and semantic space. However, due to the lack of effective use of auxiliary information, this problem has not been solved well. Inspired by previous work, we consider that visual space can be used as the embedding space to get a stronger ability to express the precise characteristics of semantic information. Meanwhile, we take into account that there are some noise attributes in the annotated information of public datasets that need to be processed. Based on these considerations, we propose an end-to-end method with convolutional architecture, instead of conventionally linear projection, to provide a deep representation for semantic information to solve ZSL. Semantic features would express more detailed and precise information after being feed into our method. Besides, we use word embedding to generate some superclasses for original classes and propose a new loss function for these superclasses to assist in training. Experiments show that our method can get decent improvements for ZSL and Generalized Zero-Shot Learning(GZSL) on several public datasets.

关键词： Semantics Visualization Training Testing Task analysis Neural networks Probabilistic logic

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Applications of Molten Pool Visual Sensing and Machine Learning in Welding Quality Monitoring 7

Applications of Molten Pool Visual Sensing and Machine Learn...

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7th International Conference on Machinery, Material Science and engineering Application, MMSE 2021

作者： Sun, Meiling Yang, Mingxuan Wang, Binrui Qian, Lijuan Hong, Yuxiang College of Mechanical and Electrical Engineering China Jiliang University Hangzhou310018 China Key Lab. of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou310018 China

On-line monitoring of welding quality is very important to the realization of intelligent welding technology and has become a research hotspot in the field of welding technology. This article reviews the research results and latest research progress of welding quality on-line monitoring based on molten pool visual sensing in recent years. First, it introduces the characterization of welding quality by the two-dimensional geometric features and three-dimensional topographic features of the molten pool in detail, and then analyzes the application of machine learning and feature engineering in the online prediction of welding status;we discuss the deep neural network and welding quality at the end Convergence of online detection technology. The work done in this paper reviews the progress of online monitoring technology for welding quality and provides a basis for the follow-up work. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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A Secure Collusion-Aware and Probability-Aware Range Query Processing in Tiered Sensor Networks

A Secure Collusion-Aware and Probability-Aware Range Query P...

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Reliable Distributed Systems

作者： Lei Dong Xuan Chen Jianxiang Zhu Hong Chen Ke Wang Cuiping Li Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Beijing China School of Information Renmin University of China Beijing China School of Computing Science Simon Fraser University Burnaby BC Canada Key Lab. of Data Eng. & Knowledge Eng. of Minist. of Educ. Beijing China

With high expansibility and efficient power usage, tiered wireless sensor networks are widely deployed in many fields as an important part of Internet of Things (IoTs). It is challenging to process range query while protecting sensitive data from adversaries. Moreover, most existing work focuses on privacy-preserving range query neglecting collusion attacks and probability attacks, which are more threatening to network security. In this paper, we first propose a secure range query protocol called secRQ, which not only protects data privacy, but also resists collusion attacks and probability attacks in tiered wireless sensor networks. Generalized inverse matrices and distance-based range query mechanism are used to guarantee security as well as high efficiency and accuracy. Besides, we propose the mutual verification scheme to verify the integrity of query results. Finally, both theoretical analysis and experimental results confirm the security, efficiency and accuracy of secRQ.

关键词： Wireless sensor networks Protocols data privacy Biomedical monitoring Query processing Monitoring Heart rate

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Synchronization of fractional-order chaotic system with application to communication

Synchronization of fractional-order chaotic system with appl...

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2011 3rd International Asia Conference on Informatics in Control, Automation and Robotics, CAR 2011

作者： Wen, Tan Fengling, Jiang Xianqun, Liu Jian Xun, Liu Feng, Wu Key Lab. of Knowledge Processing and Networked Manufacture Hunan University of Science and Technology Xiangtan Hunan 411201 China College of Information and Electrical Engineering Hunan University of Science and Technology of China Xiangtan 411201 China College of Mechanical and Electrical Engineering Hunan University of Science and Technology of China Xiangtan 411201 China

ISBN: (纸本)9783642258985

This paper studied the synchronization of fractional-order chaotic system based on the theory of fractional calculus, and the synchronization between the two identical fractional-order Chen systems is realized after 0.6s by using the active control, and the simulation results verify the effectiveness of the examined method. Furthermore, the control scheme can be used in secure communication via the technology of chaotic masking. Numerical simulations coincide with the theoretical analysis. © 2011 Springer-Verlag.

关键词： Synchronization

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Research on Individual Recognition and Prediction of Cocaine Addiction Based on Convolutional Neural Networks 3

Research on Individual Recognition and Prediction of Cocaine...

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Xiao, Hongbo Xiao, Jianhua Zhang, Lebing Tang, Bo Cao, Zhuyou Luo, Tianwang School of Computer Science and Engineering Huaihua University Hunan Huaihua China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9798400716775

In response to the current difficulty in identifying and predicting cocaine addicted individuals based on brain MRI images, this paper designs a deep learning recognition and prediction model based on convolutional neural networks. 29 cocaine addicted individuals and 24 healthy controls were selected from brain MRI images. After per-forming a series of data preprocessing operations such as skull dissection and data augmentation on brain MRI images, a deep learning model based on convolutional neural networks is constructed to process the processed brain MRI images of cocaine addicted individuals and healthy controls to identify and predict cocaine addicted individuals. The experimental results show that the recognition and prediction accuracy of deep learning models based on convolutional neural networks is 89%. Compared with machine learning models such as SVM and support vector machines, it greatly improves the accuracy of model prediction and can accurately, quickly, and effectively identify and predict cocaine addicted individuals. © 2023 ACM.

关键词： Forecasting

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COCO-CN for cross-lingual image tagging, captioning and retrieval

arXiv

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arXiv 2018年

作者： Li, Xirong Xu, Chaoxi Wang, Xiaoxu Lan, Weiyu Jia, Zhengxiong Yang, Gang Xu, Jieping Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AI & Media Computing Lab School of Information Renmin University of China Beijing100872 China

This paper contributes to cross-lingual image annotation and retrieval in terms of data and baseline methods. We propose COCO-CN, a novel dataset enriching MS-COCO with manually written Chinese sentences and tags. For more effective annotation acquisition, we develop a recommendation-assisted collective annotation system, automatically providing an annotator with several tags and sentences deemed to be relevant with respect to the pictorial content. Having 20,342 images annotated with 27,218 Chinese sentences and 70,993 tags, COCO-CN is currently the largest Chinese-English dataset that provides a unified and challenging platform for cross-lingual image tagging, captioning and retrieval. We develop conceptually simple yet effective methods per task for learning from cross-lingual resources. Extensive experiments on the three tasks justify the viability of the proposed dataset and methods. data and code are publicly availab.e at https://***/li-xirong/coco-cn. Copyright © 2018, The Authors. All rights reserved.

关键词： Image annotation

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Attention Synchronous Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Prediction 3

Attention Synchronous Based Spatial-Temporal Graph Convoluti...

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Cao, Zhuyou Luo, Tianwang Xiao, Jianhua Xiao, Hongbo Qing, Xinyi School of Computer Science and Engineering Huaihua University Hunan China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Huai-hua Hunan418000 China

ISBN: (纸本)9798400716775

This study introduces AS-STGCN, a novel traffic flow prediction model that incorporates an attention mechanism and a synchronized spatio-temporal graph convolutional network. In the field of traffic prediction, deep learning models (e.g., STGCN) have outperformed traditional methods, especially in capturing temporal and spatial correlations. AS-STGCN introduces a spatio-temporal attention mechanism and simultaneous convolution, which improves the prediction accuracy and demonstrates a unique capability in capturing dynamic spatio-temporal patterns in urban traffic networks. This research is of great significance in advancing the field of traffic flow prediction and providing a more comprehensive and accurate method for predicting urban traffic dynamics. © 2023 ACM.

关键词： Convolution

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