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24th International Conference on Artificial Intelligence in Education (AIED)

作者： Xiao, Qian Pitt, Breanne Johnston, Keith Wade, Vincent Trinity Coll Dublin Coll Green Dublin DO2 PN40 2 Ireland

ISBN: (纸本)9783031362712;9783031362729

Personalised or intelligent tutoring systems are being rapidly adopted because they enable tailored learner choices in, for example, exercise materials, study time, and intensity (i.e., the number of chosen exercises) over extended periods of time. This, however, poses significant challenges for profiling the characteristics of learner behaviors, mostly due to the great diversity in each individual's learning path, the timing of exercise accomplishments, and varying degrees of engagement over time. To address this problem, this paper proposes an innovative approach that uses self-supervised deep learning to consolidate learner behaviors and performance into compact representations via irregular multivariate time series modeling. These representations can be used to highlight learners' multi-dimensional behavioral characteristics on a massive scale for selfdirected learners who can freely pick exercises and study at their own pace. With experiments on a large-scale real-world dataset, we empirically show that our approach can effectively reveal learner individuality as well as commonality in characteristics.

关键词： Learner profiling lstm autoencoder Self-supervised learning

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Hyperspectral image classification by integrating attention-based lstm and hybrid spectral networks

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2022年第9期43卷 3450-3469页

作者： AL-Kubaisi, Mohammed Ahmed Shafri, Helmi Zulhaidi Mohd Ismail, Mohd Hasmadi Yusof, Mohd Johari Mohd Bin Hashim, Shaiful Jahari Univ Putra Malaysia UPM Fac Engn Dept Civil Engn Serdang 43400 Selangor Malaysia Minist Environm Directorate Anbar Environm Anbar Iraq Univ Putra Malaysia UPM Fac Engn Geospatial Informat Sci Res Ctr GISRC Serdang Selangor Malaysia Univ Putra Malaysia Fac Forestry Dept Forest Prod Serdang Selangor Darul Malaysia Univ Putra Malaysia Fac Design & Architecture Serdang Selangor Malaysia Univ Putra Malaysia Dept Comp & Commun Syst Engn Serdang Selangor Malaysia

Though hyperspectral remote sensing images contain rich spatial and spectral information, they pose challenges in terms of feature extraction and mining. This paper describes the integration of a dimensionality reduction technique that employs spectral attention and Hybrid Spectral Networks (HybridSN) with spatial attention for hyperspectral image classification. The goal of this approach is to improve the ability to classify hyperspectral images by increasing the capabilities of spectral-spatial feature fusion. Experiments on three hyperspectral datasets (Indian Pines, University of Pavia, and Houston University) demonstrate that our method's overall accuracy is 99.66%, 99.97%, and 99.17% under 20% of the training samples, respectively, which is superior to several well-known approaches.

关键词： Convolutional neural network hyperspectral image classification lstm autoencoder spectral-spatial attention

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Anomaly Detection in Power Consumption: A Comprehensive Multi-technique Approach 6th

Anomaly Detection in Power Consumption: A Comprehensive Mult...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Hrimech, Amine Mahmud, Jiyan Salim Data Science and Engineering Department Faculty of Informatics Eotvos Lorand University Pázmány Péter. 1/C 1117 Budapest Hungary

ISBN: (纸本)9789819734412

In a world where energy efficiency and system reliability play a vital role, safeguarding these systems from various anomalies is of utmost significance. This task is demanding and requires a substantial amount of effort and domain knowledge. This study presents a robust approach for anomaly detection in power consumption time series data collected from the SCADA system of Amendis in Tetouan City, Morocco. Investigating both anomaly detection for unlabeled data and the evaluation of unsupervised anomaly detection in power consumption, the research explores the influences of clustering for anomaly detection rate, introduces an effective ensemble approach, and incorporates the excess-mass metric for unsupervised anomaly detection evaluation. The integrated approach involves K-means clustering, ensemble anomaly detection, and lstm autoencoder. Experiments highlight the effectiveness of the methodology through different evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The ensemble method surpasses individual anomaly detection models. The lstm autoencoder achieves balanced precision, recall, and high accuracy. These results emphasize the potential of the lstm autoencoder as an effective approach for detecting anomalies in power consumption time series data, contributing to enhanced energy management practices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection Ensemble methods Isolation forest K-means clustering Local outlier factor lstm autoencoder Unsupervised learning

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A Graph Embedding Technique for Weighted Graphs Based on lstm autoencoders

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2020年第6期16卷 1407-1423页

作者： Seo, Minji Lee, Ki Yong Sookmyung Womens Univ Dept Comp Sci Seoul South Korea

A graph is a data structure consisting of nodes and edges between these nodes. Graph embedding is to generate a low dimensional vector for a given graph that best represents the characteristics of the graph. Recently, there have been studies on graph embedding, especially using deep learning techniques. However, until now, most deep learning-based graph embedding techniques have focused on unweighted graphs. Therefore, in this paper, we propose a graph embedding technique for weighted graphs based on long short-term memory (lstm) autoencoders. Given weighted graphs, we traverse each graph to extract node-weight sequences from the graph. Each node-weight sequence represents a path in the graph consisting of nodes and the weights between these nodes. We then train an lstm autoencoder on the extracted node-weight sequences and encode each node-weight sequence into a fixed-length vector using the trained lstm autoencoder. Finally, for each graph, we collect the encoding vectors obtained from the graph and combine them to generate the final embedding vector for the graph. These embedding vectors can be used to classify weighted graphs or to search for similar weighted graphs. The experiments on synthetic and real datasets show that the proposed method is effective in measuring the similarity between weighted graphs.

关键词： Graph Embedding Graph Similarity lstm autoencoder Weighted Graph Embedding Weighted Graph

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Fuzzy Controller-empowered autoencoder Framework for anomaly detection in Cyber Physical Systems

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COMPUTERS & ELECTRICAL ENGINEERING 2023年第1期108卷

作者： Gupta, Koyel Datta Singhal, Kartik Sharma, Deepak Kumar Sharma, Nonita Malebary, Sharaf Maharaja Surajmal Inst Technol Dept Comp Sci & Engn New Delhi India Netaji Subhas Univ Technol Dept Mfg Proc & Automation New Delhi India Indira Gandhi Delhi Tech Univ Women Dept Informat Technol Delhi India King Abdulaziz Univ Fac Comp & Informat Technol Rabigh Dept Informat Technol Jeddah Saudi Arabia

In recent times, Cyber Physical Systems (CPSs) have been extensively deployed in vital infrastructures to provide crucial services to society. In the smart factory scenario, the Industrial Control Systems (ICSs) are CPSs that autonomously regulate the manufacturing process through sensors and actuators. Protection of these industrial control systems against cyber threats is essential to prevent any malfunction in the production procedure. In this article, a Fuzzy Controller-enabled autoencoder Framework (FCAF) is proposed. The proposed framework can detect anomalies that may arise due to cyberattacks in the smart factory environment. The results show evidence that the model can sense any rapid deviation from normal behaviour once the attack commences and also detects unforeseen anomalies. The model further reduces the bias arising from the machine learning algorithm by introducing a fuzzy controller. The highest specificity and sensitivity reported by the proposed technique are 93% and 49.9%, respectively.

关键词： Cyber Physical System Cyber threats Decision-making system Fuzzy logic Fuzzy controller Industrial control system lstm autoencoder

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Control Channel Isolation in SDN Virtualization: A Machine Learning Approach 23

Control Channel Isolation in SDN Virtualization: A Machine L...

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23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Yoo, Yeonho Yang, Gyeongsik Shin, Changyong Lee, Jeunghwan Yoo, Chuck Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350301199

Performance isolation is an essential property that network virtualization must provide for clouds. This study addresses the performance isolation of the control plane in virtualized software-defined networking (SDN), which we call control channel isolation. First, we report that the control channel isolation is seriously broken in the existing network hypervisor in that the end-to-end control latency grows by up to 15x as the number of virtual switches increases. This jeopardizes the key network operations, such as routing, in datacenters. To address this issue, we take a machine learning approach that learns from the past control traffic as time-series data. We propose a new network hypervisor, Meteor, that designs an lstm autoencoder to predict the control traffic per virtual switch. Our evaluation results show that Meteor improves the processing latency per control message by up to 12.7x. Furthermore, Meteor reduces the end-to-end control latency by up to 73.7%, which makes it comparable to the non-virtualized SDN.

关键词： SDN Network virtualization Control channel Isolation Machine learning lstm autoencoder

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DeepSEAS: Smartphone-based Early Ailment Sensing Using Coupled lstm autoencoders 8

DeepSEAS: Smartphone-based Early Ailment Sensing Using Coupl...

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8th IEEE International Conference on Big Data (Big Data)

作者： Murthy, Shreesha Narasimha Asani, Florina Srikanthan, Srinarayan Agu, Emmanuel Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9781728162515

Infectious diseases epidemics such as the current COVID-19 pandemic have an immense impact on all facets of life. Consequently, the current dearth of effective and timely public health surveillance methods, especially at the individual level, have been accentuated, prompting research into supplementary methods. Sensor-rich, ubiquitously owned smartphones can now gather large volumes of data that has been utilized for passive and continuous physical and mental health assessment. In this paper, we propose a Deep learning based Smartphone Early Ailment Sensing (DeepSEAS) framework that predicts a smart-phone user's future manifestation of influenza-like biological symptoms (e.g. coughing and sneezing) a day early while they are still asymptomatic. DeepSEAS works by analyzing a subject's historical one-day smartphone sensor and mobility data. First, we utilize the mean shift clustering algorithm to create clusters of users with similar social and behavioral traits such as their socialization levels, social media presence, eating and working out habits. Then, DeepSEAS employs an end-to-end trainable lstm autoencoder (lstm AE) coupled with a Feed Forward Neural network classifier, achieving a sensitivity of 78% in correctly identifying users who will manifest biological symptoms a day later. DeepSEAS facilitates up-to-date influenza surveillance at the individual level, which could transform the current healthcare system. Early detection can enable asymptomatic users to be alerted, notified and isolated, which could reduce disease transmission.

关键词： WLAN Proximity lstm autoencoder Mean-Shift Clustering Influenza

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Analysis and Prediction of Deforming 3D Shapes Using Oriented Bounding Boxes and lstm autoencoders 29th

Analysis and Prediction of Deforming 3D Shapes Using Oriente...

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29th International Conference on Artificial Neural Networks (ICANN)

作者： Hahner, Sara Iza-Teran, Rodrigo Garcke, Jochen Fraunhofer Ctr Machine Learning St Augustin Germany SCAI St Augustin Germany Univ Bonn Inst Numer Simulat Bonn Germany

ISBN: (纸本)9783030616090;9783030616083

For sequences of complex 3D shapes in time we present a general approach to detect patterns for their analysis and to predict the deformation by making use of structural components of the complex shape. We incorporate long short-term memory (lstm) layers into an autoencoder to create low dimensional representations that allow the detection of patterns in the data and additionally detect the temporal dynamics in the deformation behavior. This is achieved with two decoders, one for reconstruction and one for prediction of future time steps of the sequence. In a preprocessing step the components of the studied object are converted to oriented bounding boxes which capture the impact of plastic deformation and allow reducing the dimensionality of the data describing the structure. The architecture is tested on the results of 196 car crash simulations of a model with 133 different components, where material properties are varied. In the latent representation we can detect patterns in the plastic deformation for the different components. The predicted bounding boxes give an estimate of the final simulation result and their quality is improved in comparison to different baselines.

关键词： lstm autoencoder 3D shape deformation analysis CAE analysis 3D time series

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Detecting APS failures using lstm-AE and anomaly transformer enhanced with human expert analysis

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ENGINEERING FAILURE ANALYSIS 2024年 165卷

作者： Mumcuoglu, Mehmet E. Farea, Shawqi M. Unel, Mustafa Mise, Serdar Unsal, Simge Cevik, Enes Yilmaz, Metin Koprubasi, Kerem Sabanci Univ Fac Engn & Nat Sci TR-34956 Istanbul Turkiye Ford Otosan R&D Prod Dev Team TR-34885 Istanbul Turkiye

This study develops a novel semi-supervised approach for detecting Air Pressure System (APS) failures in Heavy-Duty Vehicles (HDVs) by exploiting two modern Machine Learning (ML) models: Long Short-Term Memory autoencoder (lstm-AE) and Transformer for Anomaly Detection (TranAD), and enhancing their performance with human expertise. To tackle the failure detection problem, a dataset comprising 30 days of operational time-series data from 110 healthy vehicles with no recorded APS issues and 30 vehicles that experienced APS failures requiring road assistance was acquired. Several preprocessing steps are proposed and three key features are extracted as APS health indicators. These features are then utilized both in human expert analysis (HEA) and training of ML models. When compared to HEA, both lstm-AE and TranAD models exhibit superior performance individually in APS failure detection, achieving F1 scores of 0.75 and 0.79 respectively, and the same accuracy of 91.4%. Further, the integration of HEA with those ML models enhances model effectiveness in all experimental results, especially in reducing false alarms that cause customer dissatisfaction. The TranAD model combined with human expert analysis achieved the best performance with an unprecedented 0.82 F1 score and 92.8% accuracy. In addition to presenting a new methodology for failure detection, this paper suggests a way for more efficient and reliable predictive maintenance practices for HDVs.

关键词： Heavy-duty vehicles Air pressure system Failure detection Predictive maintenance TranAD lstm autoencoder

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TimeTector: A Twin-Branch Approach for Unsupervised Anomaly Detection in Livestock Sensor Noisy Data (TT-TBAD)

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SENSORS 2024年第8期24卷 2453页

作者： Kakar, Junaid Khan Hussain, Shahid Kim, Sang Cheol Kim, Hyongsuk Jeonbuk Natl Univ Dept Elect & Informat Engn Jeonju 54896 South Korea Jeonbuk Natl Univ Core Res Inst Intelligent Robots Jeonju 54896 South Korea Natl Univ Ireland Maynooth NUIM Innovat Value Inst IVI Sch Business Maynooth W23 F2H6 Ireland Jeonbuk Natl Univ Dept Elect Engn Jeonju 54896 South Korea

Unsupervised anomaly detection in multivariate time series sensor data is a complex task with diverse applications in different domains such as livestock farming and agriculture (LF&A), the Internet of Things (IoT), and human activity recognition (HAR). Advanced machine learning techniques are necessary to detect multi-sensor time series data anomalies. The primary focus of this research is to develop state-of-the-art machine learning methods for detecting anomalies in multi-sensor data. Time series sensors frequently produce multi-sensor data with anomalies, which makes it difficult to establish standard patterns that can capture spatial and temporal correlations. Our innovative approach enables the accurate identification of normal, abnormal, and noisy patterns, thus minimizing the risk of misinterpreting models when dealing with mixed noisy data during training. This can potentially result in the model deriving incorrect conclusions. To address these challenges, we propose a novel approach called "TimeTector-Twin-Branch Shared lstm autoencoder" which incorporates several Multi-Head Attention mechanisms. Additionally, our system now incorporates the Twin-Branch method which facilitates the simultaneous execution of multiple tasks, such as data reconstruction and prediction error, allowing for efficient multi-task learning. We also compare our proposed model to several benchmark anomaly detection models using our dataset, and the results show less error (MSE, MAE, and RMSE) in reconstruction and higher accuracy scores (precision, recall, and F1) against the baseline models, demonstrating that our approach outperforms these existing models.

关键词： anomaly detection unsupervised learning time series sensor lstm autoencoder multi-head attention mechanism DAE reconstruction and prediction branch livestock

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