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Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds
[article]
2019
arXiv
pre-print
Preserved Fulltext
The AE is trained to minimize the sample mean of the anomaly score of normal sounds in a mini-batch. ...
To decrease anomaly scores for both frequent- and rare-normal sounds, we propose batch uniformization, a training method for unsupervised-ADS for minimizing a weighted average of the anomaly score on each ...
In this paper, we propose batch uniformization, which is a training method for deep neural network (DNN)-based unsupervised-ADS. ...
arXiv:1907.08338v1
fatcat:fj5ospaflbcdtplre2zll3hkvy
Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures
2020
Symmetry
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In our future research, we intend to investigate the effectiveness of deep representations, extracted using ISA-95:2005 Level 2-3 traffic comprising of SCADA systems, for anomaly detection in critical ...
Results showed that deep representations are a promising feature in engineering replacement to develop anomaly detection models for IT infrastructure security. ...
Acknowledgments: We are thankful for the help and guidance provided by P.D.D. Dominic and Yasir Saleem; without their advice, it would have been impossible to achieve goals of this research. ...
doi:10.3390/sym12111882
fatcat:gs2oqpz525hifldtegp3ecp5oe
CNN-MoE based framework for classification of respiratory anomalies and lung disease detection
2021
IEEE journal of biomedical and health informatics
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This aims to classify anomalies in respiratory cycles and detect diseases, from respiratory sound recordings. ...
Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, confirm three main contributions towards respiratory- sound analysis. ...
One for Task 1 anomaly cycle classification, and the other for Task 2 respiratory disease detection; both summarised in Table VII. ...
doi:10.1109/jbhi.2021.3064237
pmid:33684048
fatcat:47qnag274vfgrj3mwy3e4ybm4i
CNN-MoE based framework for classification of respiratory anomalies and lung disease detection
[article]
2020
arXiv
pre-print
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This aims to classify anomalies in respiratory cycles and detect disease, from respiratory sound recordings. ...
Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, confirm three main contributions towards respiratory-sound analysis. ...
One for Task 1 anomaly cycle classification, and the other for Task 2 respiratory disease detection, both summarised in Table VII . ...
arXiv:2004.04072v2
fatcat:o3zqojnjvrfqhmx2obyu7776ci
Video Abnormal Event Detection by Learning to Complete Visual Cloze Tests
[article]
2021
arXiv
pre-print
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Although deep neural networks (DNNs) enable great progress in video abnormal event detection (VAD), existing solutions typically suffer from two issues: (1) The localization of video events cannot be both ...
For each marked region, a normalized patch sequence is extracted from current and adjacent frames and stacked into a STC. ...
For frame scoring, the maximum of all events' scores on a frame is viewed as the frame score. ...
arXiv:2108.02356v2
fatcat:7sl2musf7vecrdtdhwcqb2nsjy
SLA^2P: Self-supervised Anomaly Detection with Adversarial Perturbation
[article]
2021
arXiv
pre-print
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In this work, we propose a novel and powerful framework, dubbed as SLA^2P, for unsupervised anomaly detection. ...
Anomaly detection is a fundamental yet challenging problem in machine learning due to the lack of label information. ...
Thanks to the marvelous representation ability of DNNs, many reconstruction-based methods using DNNs are developed for UAD in recent years. ...
arXiv:2111.12896v1
fatcat:x3pbxey3hbgotneeux3vde5emq
Robust Deep Learning Frameworks for Acoustic Scene and Respiratory Sound Classification
[article]
2021
arXiv
pre-print
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This thesis focuses on dealing with the task of acoustic scene classification (ASC), and then applied the techniques developed for ASC to a real-life application of detecting respiratory disease. ...
Building upon the proposed ASC framework, the ICBHI tasks were tackled with a deep learning framework, and the resulting system shown to be capable at detecting respiratory anomaly cycles and diseases. ...
For instance, quite environments such as in park or in home are very challenging to detect if only based on static sound scene information. ...
arXiv:2107.09268v1
fatcat:u62axiqr3bb5rn2qim3xcvjb6y
A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data
2020
Applied Sciences
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of the contextual data to extract features for anomaly detection. ...
In this paper, a new hybrid approach based on deep learning is proposed to solve the anomaly detection problem in multivariate spatio-temporal dataset. ...
Finally, we would like to conduct research on more efficient data imputation techniques to improve the anomaly detection process for real life datasets. ...
doi:10.3390/app10155191
fatcat:ohgvishfing2fnsneqw6owdzjy
Deep Learning Approaches for Predictive Masquerade Detection
2018
Security and Communication Networks
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In this paper, we present a comprehensive empirical study in the area of anomaly-based masquerade detection using three deep learning models, namely, Deep Neural Networks (DNN), Long Short-Term Memory ...
In a static approach, DNN and LSTM-RNN models are used along with a Particle Swarm Optimization-based algorithm for their hyperparameters selection. ...
Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper. ...
doi:10.1155/2018/9327215
fatcat:xokoxyih4bcopdkxxc4wk7xa3i
MARVEL - D3.1: Multimodal and privacy-aware audio-visual intelligence – initial version
2022
Zenodo
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These include methods for Sound Event De- tection, Sound Event Localisation and Detection, Automated Audio Captioning, Visual Anomaly Detection, Visual Crowd Counting, Audio-Visual Crowd Counting, as well ...
as methodologies for improving the training and efficiency of AI models under supervised, unsupervised, and cross-modal contrastive learning settings. ...
The authors thank the APPLE consortium for the help in the project. ...
doi:10.5281/zenodo.6821317
fatcat:eia7rkk5lfbg7khs3qcat5qd3m
Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey
2020
ACM Computing Surveys
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Based on the specification and the available computing resources, the ML models are developed to meet the specified requirements while optimizing the training processes in terms of the cost of time and ...
Some of these achievements are based on the combination of DL and RL, i.e., Deep Reinforcement Learning. ...
RF is one of the most popular classifiers due to its great generalization capability. For example, it has been used for intrusion detection [56, 229] and anomaly detection. ...
doi:10.1145/3398020
fatcat:zzgfcjxjxbhnhf53dmlo63rs3i
Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey
[article]
2020
arXiv
pre-print
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This paper provides a comprehensive and systematic survey on the development lifecycle of ML-based IoT application. ...
Hence, orchestrating ML pipelines that encompasses model training and implication involved in holistic development lifecycle of an IoT application often leads to complex system integration. ...
RF is one of the most popular classifiers due to its great generalization capability. For example, it has been used for intrusion detection [56, 229] and anomaly detection. ...
arXiv:1910.05433v5
fatcat:ffvjipmylve6feuzdbav2syxfu
Automatic Assessment of Speech Intelligibility for Individuals With Aphasia
2016
IEEE/ACM Transactions on Audio Speech and Language Processing
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Unfortunately, the lack of feedback for verbal expression in existing programs hinders the applicability and effectiveness of this form of treatment. ...
We present our method for eliciting reliable ground-truth labels for speech intelligibility based on the perceptual judgment of nonexpert human evaluators. ...
ACKNOWLEDGMENT We would like to thank Patrick Shin, Yoolim Jung, Kelly Karpus, Carly Swiftney, Benjamin Fine, Tasneem Tweel, Lucie Farrugia, Rebecca Rosen, Lily Chen, Meng Du, and the UMAP staff for their ...
doi:10.1109/taslp.2016.2598428
fatcat:mllmqirdyjbcxh2lnfhol73aia
Review: Deep Learning in Electron Microscopy
[article]
2020
arXiv
pre-print
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For context, we review popular applications of deep learning in electron microscopy. ...
Finally, we discuss future directions of deep learning in electron microscopy. ...
In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a creative commons 4.0 73 license. ...
arXiv:2009.08328v4
fatcat:umocfp5dgvfqzck4ontlflh5ca
A Comparison of Machine Learning Techniques for the Quality Classification of Molded Products
2022
Information
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the viability of the use of ML-based techniques for this purpose. ...
In the manufacturing sector, these technological advancements are making Industry 4.0 a reality, with data-driven methodologies based on machine learning (ML) that are capable of extracting knowledge from ...
Acknowledgments: The authors thanks "iGuzzini Illuminazione" for granting the access to the data used in this research.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/info13060272
fatcat:75zzp3vry5h2rcu75foj32mbmu
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