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International Conference on Electronic Engineering and Information Systems (EEISS)

作者： Hu, Cunlin Ye, Ye Li, Jian Wang, Hongliang Zhou, Tao Xie, Nenggang Anhui Univ Technol Coll Mech Engn Maanshan Peoples R China Maanshan Peoples Hosp Maanshan Peoples R China Anhui Univ Technol Coll Management Sci & Engn Maanshan Peoples R China

ISBN: (纸本)9798350351040;9798350351033

Hybrid neural networks are able to capture the time-dependency of electroencephalography (EEG) signals, and can therefore effectively perform pattern recognition of motor imagery EEG signals. However, a sufficiently large amount of training data is required to achieve optimal results, so it is necessary to use data augmentation methods to increase the amount of data. Therefore, we propose a data augmentation method based on the Attention Convolutional Variation autoencoder (ACVAE) and design a Convolutional :Neural Network-Long Short -Term Memory (CNN-LSTN1) network for pattern recognition. The BCI Competition IV dataset 2a is used for experimental validation. The results show that the ACVAE method produces higher quality data, with the highest recognition accuracy of 97.16% for a single subject in the four-classification task. Compared to other methods, we proposed method shows excellent performance.

关键词： Convolutional,NTeural Network Long Short-Term Memory EEG recognition variational autoencoder

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Automated process monitoring in injection molding via representation learning and setpoint regression 11

Automated process monitoring in injection molding via repres...

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11th IEEE Swiss Conference on Data Science (SDS)

作者： Yan, Peng Abdulkadir, Ahmed Aguzzi, Giulia Schatte, Gerrit A. Grewe, Benjamin F. Stadelmann, Thilo ZHAW Sch Engn Ctr Artificial Intelligence Winterthur Switzerland Kistler Instrumente AG Innovat Lab Winterthur Switzerland Swiss Fed Inst Technol ETH AI Ctr Zurich Switzerland ECLT European Ctr Living Technol Venice Italy

ISBN: (纸本)9798350309300;9798350309294

Online process monitoring is essential to detect failures and respond promptly in automated industrial processes such as injection molding. Traditional systems rely on experienced operators manually defining operational boundaries around a reference signal. We propose a data-driven representation that auto-tunes the sensitivity to a pre-set specificity threshold and automatically detects anomalies alongside interpretable indices that help identify root causes. Our automated system achieved an average AUC of 0.998 and detected 100 percent of the anomalies with the proposed dynamic calibration of the data-driven embedding method. The dynamic calibration, which accounted for drift, boosts the average specificity from 0.362 to 0.869. The outputs also indicate the direction and relative magnitude of characteristic deviations caused by machine parameters, including holding pressure, mold temperature, and injection speed. The AI-derived process boundaries are superior to manual annotation in tested real-world production environments.

关键词： anomaly detection time series variational autoencoder root-cause analysis explainable AI transfer learning

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Machine Learning-Driven Discovery of Quadruple-Negative Breast Cancer Subtypes from Gene Expression Data 20th

Machine Learning-Driven Discovery of Quadruple-Negative Brea...

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20th International Symposium on Bioinformatics Research and Applications (ISBRA)

作者： Sahoo, Bikram Jinna, Nikita Rida, Padmashree Pinnix, Zandra Zelikovsky, Alex Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA City Hope Comprehens Canc Ctr Duarte CA 91010 USA Novazoi Theranost Salt Lake City UT 84105 USA Univ N Carolina Dept Biol & Marine Biol Wilmington NC 28403 USA

ISBN: (纸本)9789819751273;9789819751280

Unraveling the intricacies of Quadruple-Negative Breast Cancer (QNBC), this study leverages advanced analytics on RNAseq gene expression data. Employing unsupervised clustering techniques, our robust methodology encompasses data preprocessing for interpretability, dimensionality reduction via variational autoencoders and Principal Component Analysis (PCA), and optimization of k-means clustering using internal validation indices. The analysis unveils two distinct QNBC subtypes, substantiated by high Silhouette (0.24) and Calinski-Harabasz (28.81) scores. Statistical profiling elucidates the genetic signatures characterizing these clusters, with Cluster 1 exhibiting genes like OR6P1 and TMEM247, while Cluster 2 displays distinct markers such as RNF17 and PRAC1. These data-driven patient stratifications hold promise for personalized assessments and targeted interventions, contingent upon clinical validation. This research highlights the synergy of machine learning and statistical analysis in charting a course toward more effective QNBC management strategies.

关键词： Quadruple-Negative Breast Cancer (QNBC) QNBC subtypes Unsupervised Clustering variational autoencoder

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DiVerFed: Distribution-Aware Vertical Federated Learning for Missing Information 1

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17th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Qiu, Pengyu Liu, Yongchao Zeng, Xintan Zhejiang Univ 866 Yuhangtang Rd Hangzhou 310058 Peoples R China Ant Grp Ant A Space 569 Xixi Rd Hangzhou Peoples R China

ISBN: (数字)9789819754984

ISBN: (纸本)9789819754977;9789819754984

Vertical Federated Learning (VFL) is a prevalent paradigm designed to facilitate collaboration between multiple entities possessing distinct feature sets yet sharing a common user base for model training. However, challenges emerge when generating predictions for users with partial data, such as the scenario where a new user registers with only one participating company and submits a limited number of features. The typical method of addressing missing data involves padding the absent values with zeros or mean figures, which can cause an out-of-distribution issue, thereby precipitating a notable deterioration in model accuracy. To address this issue, we introduce DiVerFed, a distribution-aware VFL framework for missing information. DiVerFed's primary objective is to maintain robust model performance even when confronted with incomplete user information. Within this framework, we treat the VFL's top model as an encoder responsible for capturing the underlying data distribution. To make this distribution effectively, we incorporate a reconstruction component. Furthermore, we simulate the scenario of missing information by integrating a Feature-wise Dropout module during the training phase. To enhance the framework's efficacy in classification tasks, we also incorporate label information within the loss function to leverage a class-aware distribution to bolster the model's accuracy. Our experimental analyses confirm that DiVerFed significantly outperforms conventional approaches in classification tasks when information from only one party is accessible.

关键词： Vertical Federated Learning Machine Learning Missing Information variational autoencoder Feature-wise Dropout

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Generative Neural Network Channel Modeling for Millimeter-Wave UAV Communication

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第11期21卷 9417-9431页

作者： Xia, William Rangan, Sundeep Mezzavilla, Marco Lozano, Angel Geraci, Giovanni Semkin, Vasilii Loianno, Giuseppe NYU Tandon Sch Engn Brooklyn NY 11201 USA Univ Pompeu Fabra Dept Informat & Commun Technol Barcelona 08002 Spain VTT Tech Res Ctr Finland Ltd Espoo 02044 Finland

The millimeter wave bands are being increasingly considered for wireless communication to unmanned aerial vehicles (UAVs). Critical to this undertaking are statistical channel models that describe the distribution of constituent parameters in scenarios of interest. This paper presents a general modeling methodology based on data-training a generative neural network. The proposed generative model has a two-stage structure that first predicts the link state (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoencoder (VAE) that generates the path losses, delays, and angles of arrival and departure for all the propagation paths. The methodology is demonstrated for 28GHz air-to-ground channels between UAVs and a cellular system in representative urban environments, with training datasets produced through ray tracing. The demonstration extends to both standard base stations (installed at street level and downtilted) as well as dedicated base stations (mounted on rooftops and uptilted). The proposed approach is able to capture complex statistical relations in the data and it significantly outperforms standard 3GPP models, even after refitting the parameters of those models to the data.

关键词： UAV drone mmWave communication 5G cellular network air to ground channel model ray tracing variational autoencoder generative neural network 3GPP

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POSTERIOR VARIANCE-PARAMETERISED GAUSSIAN DROPOUT: IMPROVING DISENTANGLED SEQUENTIAL autoencoderS FOR ZERO-SHOT VOICE CONVERSION 49

POSTERIOR VARIANCE-PARAMETERISED GAUSSIAN DROPOUT: IMPROVING...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Luo, Yin-Jyun Dixon, Simon Queen Mary Univ London Ctr Digital Mus London England

ISBN: (纸本)9798350344868;9798350344851

The class of disentangled sequential auto-encoders factorises speech into time-invariant (global) and time-variant (local) representations for speaker identity and linguistic content, respectively. Many of the existing models employ this assumption to tackle zero-shot voice conversion (VC), which converts speaker characteristics of any given utterance to any novel speakers while preserving the linguistic content. However, balancing capacity between the two representations is intricate, as the global representation tends to collapse due to its lower information capacity along the time axis than that of the local representation. We propose a simple and effective dropout technique that applies an information bottleneck to the local representation via multiplicative Gaussian noise, in order to encourage the usage of the global one. We endow existing zero-shot VC models with the proposed method and show significant improvements in speaker conversion in terms of speaker verification acceptance rate and comparable or better intelligibility measured in character error rate.

关键词： Disentanglement variational autoencoder posterior collapse zero-shot voice conversion Gaussian dropout

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Identifying compound weather drivers of forest biomass loss with generative deep learning

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ENVIRONMENTAL DATA SCIENCE 2024年 3卷 e4-e4页

作者： Anand, Mohit Bohn, Friedrich J. Camps-Valls, Gustau Fischer, Rico Huth, Andreas Sweet, Lily-Belle Zscheischler, Jakob UFZ Helmholtz Ctr Environm Res Dept Compound Environm Risks Leipzig Germany Tech Univ Dresden Dresden Germany UFZ Helmholtz Ctr Environm Res Dept Computat Hydrosyst Leipzig Germany Univ Valencia Image Proc Lab IPL Valencia Spain UFZ Helmholtz Ctr Environm Res Dept Ecol Modelling Leipzig Germany

Globally, forests are net carbon sinks that partly mitigates anthropogenic climate change. However, there is evidence of increasing weather-induced tree mortality, which needs to be better understood to improve forest management under future climate conditions. Disentangling drivers of tree mortality is challenging because of their interacting behavior over multiple temporal scales. In this study, we take a data-driven approach to the problem. We generate hourly temperate weather data using a stochastic weather generator to simulate 160,000 years of beech, pine, and spruce forest dynamics with a forest gap model. These data are used to train a generative deep learning model (a modified variational autoencoder) to learn representations of three-year-long monthly weather conditions (precipitation, temperature, and solar radiation) in an unsupervised way. We then associate these weather representations with years of high biomass loss in the forests and derive weather prototypes associated with such years. The identified prototype weather conditions are associated with 5 -22% higher median biomass loss compared to the median of all samples, depending on the forest type and the prototype. When prototype weather conditions co-occur, these numbers increase to 10 -25%. Our research illustrates how generative deep learning can discover compounding weather patterns associated with extreme impacts. Impact Statement Tree mortality is a complex phenomenon involving multiple processes at different temporal scales. Here, we rely on very long simulations of a forest model and develop a method based on generative deep learning to find the relationship between complex weather patterns and tree mortality. The generative nature of the method allows for the generation of new realistic weather conditions outside of the provided samples, which are associated with high biomass loss in a forest. Furthermore, the method can be applied to different weather-driven impacts, adding to the growin

关键词： compound events extreme events forest mortality generative deep learning variational autoencoder

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Detecting Unknown Threats in Smart Contracts With Domain Adaptation 2

Detecting Unknown Threats in Smart Contracts With Domain Ada...

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2nd IEEE International Conference on Software Services Engineering (SSE)

作者： Ye, Weiwei Yang, Liu Duan, Li Li, Chao Ni, Wei Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing 100044 Peoples R China Civil Aviat Univ China Informat Secur Evaluat Ctr Civil Aviat Tianjin 300300 Peoples R China CSIRO Data 61 Sydney NSW 2122 Australia

ISBN: (纸本)9798350368529;9798350368512

Unknown smart contract vulnerabilities objectively exist in addition to common vulnerabilities, and their potential risks cannot be ignored. Therefore, it is crucial to enhance the model's ability to detect these unknown threats. Detection models are typically trained for specific types of vulnerability data. As a result, their effectiveness in detecting new vulnerabilities can be unsatisfactory. When a vulnerability is not defined in the model, it is considered an unknown vulnerability. Simulating attack scenarios or manual auditing is typically required for unknown threat detection. However, this results in a limited number of unknown threat samples available in the smart contract dataset, which greatly hinders the effectiveness of unknown threat detection due to dataset imbalance. In this paper, in order to improve the detection ability of unknown vulnerabilities of smart contracts, we propose a new method of unknown threat detection, which firstly expands the unknown threat samples by equalizing the data features of opcodes and source codes through a variational autoencoder. Then, a domain-adaptive training model DSN is used to learn the private and public features of the source and target domains of smart contracts, respectively. Transferring features and adversarial learning between the source and target domains is achieved through domain classification and reconfiguration tasks. The experiments demonstrate that this method can significantly enhance the ability to identify unknown threats in smart contracts.

关键词： Blockchain Smart Contracts Vulnerability Detection variational autoencoder Domain Adaptation Unknown Threats

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GAS PATH FAULT DIAGNOSIS METHOD UNDER SMALL SAMPLES WITH INTER-CLASS IMBALANCE 69

GAS PATH FAULT DIAGNOSIS METHOD UNDER SMALL SAMPLES WITH INT...

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69th ASME Turbomachinery Technical Conference and Exposition (ASME Turbo Expo) (GT)

作者： Wang, Kun He, Ai Liu, Yuan Xiao, Tingyu Chen, Zhixiong Hu, Zhongzhi Tsinghua Univ Inst Aero Engine Beijing Peoples R China AECC Hunan Aviat Powerplant Res Inst Zhuzhou Peoples R China

ISBN: (纸本)9780791887929

Current data-driven methods for gas path fault diagnosis in aero-engines often require extensive fault sample sets, which are challenging and expensive to obtain in practice. Additionally, even with limited samples, these methods face issues of interclass imbalance, including imbalances in normal and fault data quantities, imbalances in the quantities of different fault classes, and imbalances in fault severity. Additionally, current methods for simulating gas path faults solely focus on the degradation of gas path component efficiency, neglecting the non-linear alterations in component characteristics caused by faults and the interconnected effects between components. Therefore, we propose to use a transfer learning-based variational autoencoder (TL-VAE) approach to generate fault samples and optimize the accuracy of gas path fault diagnosis. First, we train the VAE using normal engine operation samples. Then, by incorporating transfer learning, we retrain the VAE using a small number of fault samples by fine-tuning certain weights of VAE. This allows us to combine the operational state features from the source domain with the fault features from the target domain, fitting the distribution of the measured parameters of the faulty engine. This enables the TL-VAE to function as a generator of fault samples. Subsequent fault diagnosis strategies rely on mature classification methods and the generated samples, including Softmax and SVM classifiers. We validate the effectiveness and superiority of the proposed method through simulation. The experimental results demonstrate that the proposed method significantly improves fault diagnosis results with limited samples. Especially within the coverage of the generated samples, fault diagnosis accuracy (FDA) of the Softmax and SVM classifiers significantly improved from 73.3% and 66.5%, respectively, to a perfect 100% after employing the proposed approach. The FDA of Softmax and SVM classifiers with the proposed method is impr

关键词： Data-driven gas path fault diagnosis Small samples Inter-class imbalance Transfer learning variational autoencoder

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Context-Aware Trajectory Anomaly Detection 1

Context-Aware Trajectory Anomaly Detection

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1st International Workshop on Geospatial Anomaly Detection

作者： Hu, Haoji Kim, Jina Zhou, Jinwei Kirsanova, Sofia Lee, JangHyeon Chiang, Yao-Yi Univ Minnesota Twin Cities Minneapolis MN 55455 USA

ISBN: (纸本)9798400711442

Trajectory anomaly detection is crucial for effective decision-making in urban and human mobility management. Existing methods of trajectory anomaly detection generally focus on training a trajectory generative model and evaluating the likelihood of reconstructing a given trajectory. However, previous work often lacks important contextual information on the trajectory, such as the agent's information (e.g., agent ID) or geographic information (e.g., Points of Interest (POI)), which could provide additional information on accurately capturing anomalous behaviors. To fill this gap, we propose a context-aware anomaly detection approach that models contextual information related to trajectories. The proposed method is based on a trajectory reconstruction framework guided by contextual factors such as agent ID and contextual POI embedding. The injection of contextual information aims to improve the performance of anomaly detection. We conducted experiments in two cities and demonstrated that the proposed approach significantly outperformed existing methods by effectively modeling contextual information. Overall, this paper paves a newdirection for advancing trajectory anomaly detection.

关键词： trajectory anomaly detection variational autoencoder contextual information

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