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research and application of Transformer based anomaly detection model: A literature review

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Transformer, as one of the most advanced neural network models in Natural Language processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire research on Transformer-based anomaly detection, this review offers a fresh perspective on the concept of anomaly detection. We explore the current challenges of anomaly detection and provide detailed insights into the operating principles of Transformer and its variants in anomaly detection tasks. Additionally, we delineate various application scenarios for Transformer-based anomaly detection models and discuss the datasets and evaluation metrics employed. Furthermore, this review highlights the key challenges in Transformer-based anomaly detection research and conducts a comprehensive analysis of future research trends in this domain. The review includes an extensive compilation of over 100 core references related to Transformer-based anomaly detection. To the best of our knowledge, this is the first comprehensive review that focuses on the research related to Transformer in the context of anomaly detection. We hope that this paper can provide detailed technical information to researchers interested in Transformer-based anomaly detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

The Medical Cyber-physical systems Activity at EIT: A Look under the Hood

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The Medical Cyber-physical Systems Activity at EIT: A Look u...

Annual IEEE Symposium on Computer-Based Medical systems

作者： Daniel Sonntag Sonja Zillner Samarjit Chakraborty Andras Lorincz Esko Strommer Luciano Serafini German Research Center for Artificial Intelligence (DFKI) Saarbruecken Germany Corporate Technology Siemens AG Munich Germany Real-Time Computer Systems Technical University of Munich Munich Germany Neural Information Processing Group Eotvos Lorand University (ELTE) Budapest Hungary VTT Technical Research Centre of Finland Finland Data & Knowledge Manage. at Fondazione Bruno Kessler (FBK) Trento Italy

In this paper, we describe how we combine active and passive user input modes in clinical environments for knowledge discovery and knowledge acquisition towards decision support in clinical environments. Active input modes include digital pens, smartphones, and automatic handwriting recognition for a direct digitalisation of patient data. Passive input modes include sensors of the clinical environment and or mobile smartphones. This combination for knowledge acquisition and decision support (while using machine learning techniques) has not yet been explored in clinical environments and is of specific interest because it combines previously unconnected information sources for individualised treatments. The innovative aspect is a holistic view on individual patients based on ontologies, terminologies, and textual patient records whereby individual active and passive real-time patient data can be taken into account for improving clinical decision support.

关键词： data models Sensor systems Servers Real-time systems data mining Ontologies

A web-based integrated education system for a seamless environment among teachers, students, and administrators

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systems and Computers in Japan 2006年第5期37卷 14-24页

作者： Hanakawa, Noriko Akazawa, Yoshiko Mori, Akira Maeda, Toshiyuki Inoue, Shunji Tsutsui, Shigeyoshi Faculty of Management Information Hannan University Matsubara 580-8502 Japan System Engineering Division Solutions and Services Educational Solutions Group Fujitsu Limited Tokyo 105-7123 Japan Research Center for Computer Science Hannan University Matsubara 580-8502 Japan Faculty of Management Information Hannan University Corporate Information Graduate School Information Processing Society of Japan IEEE System Engineering Division Solutions and Services Educational Solutions Group Research Center for Computer Science Faculty of Management Information Hannan University Institute of Electrical Engineers of Japan Japanese Society for Artificial Intelligence Japan Fuzzy Knowledge and Information Society Institute of Systems Control and Information Engineers

The authors created a Web-based integrated education support system that unified the data in the administrative system and the education system in a university. The purpose of the system was to make the administrative system and education system data that had previously been separated seamless, and to implement seamless access from inside and outside the university while maintaining security. Moreover, the authors emphasized the development of a portal site as a point of contact for the system. As a result of using software packages and existing technology for the other servers and databases, and making a policy of not changing the administrative system at all, the authors were able to create the system in a short period of 5 months. After the system was operational, preparation and review for class could be done at home, and evaluations of reports and final determinations of grades could be done from outside the university. This confirmed that the education system was effective. Finally, the authors looked at the trouble and performance problems that occurred involving local rules in the business department during operation, as well as countermeasures for them. © 2006 Wiley Periodicals, Inc.

关键词： Learning systems

Recurrent Neural Networks for Modelling Gross Primary Production

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Recurrent Neural Networks for Modelling Gross Primary Produc...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： David Montero Miguel D. Mahecha Francesco Martinuzzi César Aybar Anne Klosterhalfen Alexander Knohl Franziska Koebsch Jesús Anaya Sebastian Wieneke Remote Sensing Centre for Earth System Research (RSC4Earth) Leipzig University Institute of Earth System Science & Remote Sensing Leipzig University German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Department of Remote Sensing Helmholtz Centre for Environmental Research (UFZ) Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Leipzig University Image Processing Laboratory Universitat de València Bioclimatology University of Göttingen Grupo de Investigaciones y Mediciones Ambientales (GEMA) Universidad de Medellín

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Accurate quantification of Gross Primary Production (GPP) is crucial for understanding terrestrial carbon dynamics. It represents the largest atmosphere-to-land CO 2 flux, especially significant for forests. Eddy Covariance (EC) measurements are widely used for ecosystem-scale GPP quantification but are globally sparse. In areas lacking local EC measurements, remote sensing (RS) data are typically utilised to estimate GPP after statistically relating them to in-situ data. Deep learning offers novel perspectives, and the potential of recurrent neural network architectures for estimating daily GPP remains underexplored. This study presents a comparative analysis of three architectures: Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), and Long-Short Term Memory (LSTMs). Our findings reveal comparable performance across all models for full-year and growing season predictions. Notably, LSTMs outperform in predicting climate-induced GPP extremes. Furthermore, our analysis highlights the importance of incorporating radiation and RS inputs (optical, temperature, and radar) for accurate GPP predictions, particularly during climate extremes.

关键词： Radar remote sensing Recurrent neural networks Laser radar Atmospheric measurements Ultraviolet sources Biological system modeling Production

Effective node vaccination and containing strategies to halt SIR epidemic spreading in real-world face-to-face contact networks

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Effective node vaccination and containing strategies to halt...

2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022

作者： Nguyen, Ngoc-Kim-Khanh Nguyen, Thanh-Trung Nguyen, Tuan-Anh Sartori, Fabio Turchetto, Massimiliano Scotognella, Francesco Alfieri, Roberto Cassi, Davide Nguyen, Quang Bellingeri, Michele Sciences Van Lang University Faculty of Fundamental 69/68 Dang Thuy Tram Street Ward 13 Binh Thanh District Ho Chi Minh Viet Nam Research Group of Intelligent Data Processing System Ho Chi Minh City University of Foreign Languages - Information Technology Ho Chi Minh City Viet Nam Università di Parma Dipartimento di Scienze Matematiche Fisiche e Informatiche via G.P. Usberti 7/a Parma43124 Italy Politecnico di Milano Dipartimento di Fisica Piazza Leonardo da Vinci 32 Milano20133 Italy Institute of Fundamental and Applied Sciences Duy Tan University Ho Chi Minh700000 Viet Nam

ISBN: (纸本)9781665461665

We model the COVID-19 spreading by running SIR Monte-Carlo simulations in four real face-to-face contact networks. We evaluate the effectiveness of the 'facemask use' and 'vaccination policies' to curb epidemic spreading. We model the facemask use policy by assuming a lower individual infection probability β. We found that while this strategy can delay the disease spreading, it does not significantly reduce the total number of infected individuals (TI), as 80% of the total population still is infected at the end of the epidemic. We model vaccination by setting individual's infection probability β=0, which is equivalent to remove nodes/individuals from the network. The vaccination was found to be very effective. Even with a partial vaccination of 30% of the population nodes selected considering their centrality measure ranking, such as degree, betweenness, or PageRank, it was possible to reduce the TI of 14%. Finally, yet importantly, random partial vaccination is not effective at all, meaning that most of the unvaccinated population will be infected. © 2022 IEEE.

关键词： COVID-19

Improving Pre-movement Pattern Detection with Filter Bank Selection

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arXiv 2022年

作者： Jia, Hao Sun, Zhe Duan, Feng Zhang, Yu Caiafa, Cesar F. Solé-Casals, Jordi Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain Computational Engineering Applications Unit Head Office for Information Systems and Cybersecurity RIKEN Saitama Japan College of Artificial Intelligence Nankai University Tianjin China Department of Bioengineering Lehigh University BethlehemPA18015 United States Instituto Argentino de Radioastronomía CONICET CCT La Plata/CIC-PBA/UNLP V. Elisa Argentina

Pre-movement decoding plays an important role in movement detection and is able to detect movement onset with low-frequency electroencephalogram (EEG) signals before the limb moves. In related studies, pre-movement decoding with standard task-related component analysis (STRCA) has been demonstrated to be efficient for classification between movement state and resting state. However, the accuracies of STRCA differ among subbands in the frequency domain. Due to individual differences, the best subband differs among subjects and is difficult to be determined. This study aims to improve the performance of the STRCA method by a feature selection on multiple subbands and avoid the selection of best subbands. This study first compares three frequency range settings (M1: subbands with equally spaced bandwidths;M2: subbands whose high cut-off frequencies are twice the low cut-off frequencies;M3: subbands that start at some specific fixed frequencies and end at the frequencies in an arithmetic sequence.). Then, we develop a mutual information based technique to select the features in these subbands. A binary support vector machine classifier is used to classify the selected essential features. The results show that M3 is a better setting than the other two settings. With the filter banks in M3, the classification accuracy of the proposed FBTRCA achieves 0.8700±0.1022, which means a significantly improved performance compared to STRCA (0.8287±0.1101) as well as to the cross validation and testing method (0.8431±0.1078). Copyright © 2022, The Authors. All rights reserved.

关键词： Decoding

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

Enabling Temporal-Spectral Decoding in Pre-movement Detection

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arXiv 2022年

作者： Jia, Hao Caiafa, Cesar F. Duan, Feng Zhang, Yu Sun, Zhe Solé-Casals, Jordi The Data and Signal Processing Research Group University of Vic-Central University of Catalonia Catalonia Vic Spain The Instituto Argentino de Radioastronomía CONICET CCT La Plata/CIC-PBA/UNLP V. Elisa Argentina The College of Artificial Intelligence Nankai University Tianjin China The College of Artificial Intelligence Nankai University Tianjin China The Department of Bioengineering The Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States The Computational Engineering Applications Unit Head Office for Information Systems and Cybersecurity RIKEN Saitama Japan The Department of Psychiatry University of Cambridge United Kingdom

Non-invasive brain-computer interfaces help the subjects to control external devices by brain intentions. The multi-class classification of upper limb movements can provide external devices with more control commands. The onsets of the upper limb movements are located by the external limb trajectory to eliminate the delay and bias among the trials. However, the trajectories are not recorded due to the limitation of experiments. The delay cannot be avoided in the analysis of signals. The delay negatively influences the classification performance, which limits the further application of upper limb movements in the brain-computer interface. This work focuses on multi-channel brain signals analysis in the temporal-frequency approach. It proposes the two-stage-training temporal-spectral neural network (TTSNet) to decode patterns from brain signals. The TTSNet first divides the signals into various filter banks. In each filter bank, task-related component analysis is used to reduce the dimension and reject the noise of the brain. A convolutional neural network (CNN) is then used to optimize the temporal characteristic of signals and extract class-related features. Finally, these class-related features from all filter banks are fused by concatenation and classified by the fully connected layer of the CNN. The proposed method is evaluated in two public datasets. The results show that TTSNet has an improved accuracy of 0.7456±0.1205 compared to the EEGNet of 0.6506±0.1275 (p Copyright © 2022, The Authors. All rights reserved.

关键词： Decoding

DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics