| 1. |
- Abdelnour, Jerome, et al.
(author)
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NAAQA: A Neural Architecture for Acoustic Question Answering
- 2022
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In: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : Institute of Electrical and Electronics Engineers (IEEE). - 0162-8828 .- 1939-3539 .- 2160-9292. ; , s. 1-12
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Journal article (peer-reviewed)
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| 2. |
- Adiban, Mohammad, et al.
(author)
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A step-by-step training method for multi generator GANs with application to anomaly detection and cybersecurity
- 2023
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In: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 537, s. 296-308
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Journal article (peer-reviewed)abstract
- Cyber attacks and anomaly detection are problems where the data is often highly unbalanced towards normal observations. Furthermore, the anomalies observed in real applications may be significantly different from the ones contained in the training data. It is, therefore, desirable to study methods that are able to detect anomalies only based on the distribution of the normal data. To address this problem, we propose a novel objective function for generative adversarial networks (GANs), referred to as STEPGAN. STEP-GAN simulates the distribution of possible anomalies by learning a modified version of the distribution of the task-specific normal data. It leverages multiple generators in a step-by-step interaction with a discriminator in order to capture different modes in the data distribution. The discriminator is optimized to distinguish not only between normal data and anomalies but also between the different generators, thus encouraging each generator to model a different mode in the distribution. This reduces the well-known mode collapse problem in GAN models considerably. We tested our method in the areas of power systems and network traffic control systems (NTCSs) using two publicly available highly imbalanced datasets, ICS (Industrial Control System) security dataset and UNSW-NB15, respectively. In both application domains, STEP-GAN outperforms the state-of-the-art systems as well as the two baseline systems we implemented as a comparison. In order to assess the generality of our model, additional experiments were carried out on seven real-world numerical datasets for anomaly detection in a variety of domains. In all datasets, the number of normal samples is significantly more than that of abnormal samples. Experimental results show that STEP-GAN outperforms several semi-supervised methods while being competitive with supervised methods.
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| 3. |
- Adiban, Mohammad, et al.
(author)
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Hierarchical Residual Learning Based Vector Quantized Variational Autoencorder for Image Reconstruction and Generation
- 2022
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In: The 33<sup>rd</sup> British Machine Vision Conference Proceedings.
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Conference paper (peer-reviewed)abstract
- We propose a multi-layer variational autoencoder method, we call HR-VQVAE, thatlearns hierarchical discrete representations of the data. By utilizing a novel objectivefunction, each layer in HR-VQVAE learns a discrete representation of the residual fromprevious layers through a vector quantized encoder. Furthermore, the representations ateach layer are hierarchically linked to those at previous layers. We evaluate our methodon the tasks of image reconstruction and generation. Experimental results demonstratethat the discrete representations learned by HR-VQVAE enable the decoder to reconstructhigh-quality images with less distortion than the baseline methods, namely VQVAE andVQVAE-2. HR-VQVAE can also generate high-quality and diverse images that outperform state-of-the-art generative models, providing further verification of the efficiency ofthe learned representations. The hierarchical nature of HR-VQVAE i) reduces the decoding search time, making the method particularly suitable for high-load tasks and ii) allowsto increase the codebook size without incurring the codebook collapse problem.
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| 4. |
- Adiban, M., et al.
(author)
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Step-gan : A one-class anomaly detection model with applications to power system security
- 2021
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In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2605-2609
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Conference paper (peer-reviewed)abstract
- Smart grid systems (SGSs), and in particular power systems, play a vital role in today's urban life. The security of these grids is now threatened by adversaries that use false data injection (FDI) to produce a breach of availability, integrity, or confidential principles of the system. We propose a novel structure for the multigenerator generative adversarial network (GAN) to address the challenges of detecting adversarial attacks. We modify the GAN objective function and the training procedure for the malicious anomaly detection task. The model only requires normal operation data to be trained, making it cheaper to deploy and robust against unseen attacks. Moreover, the model operates on the raw input data, eliminating the need for feature extraction. We show that the model reduces the well-known mode collapse problem of GAN-based systems, it has low computational complexity and considerably outperforms the baseline system (OCAN) with about 55% in terms of accuracy on a freely available cyber attack dataset.
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| 5. |
- Ásgrímsson, David Steinar, et al.
(author)
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Bayesian Deep Learning for Vibration-Based Bridge Damage Detection
- 2022
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In: Structural Integrity. - Cham : Springer Nature. ; , s. 27-43
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Book chapter (peer-reviewed)abstract
- A machine learning approach to damage detection is presented for a bridge structural health monitoring (SHM) system. The method is validated on the renowned Z24 bridge benchmark dataset where a sensor instrumented, three-span bridge was monitored for almost a year before being deliberately damaged in a realistic and controlled way. Several damage cases were successfully detected, making this a viable approach in a data-based bridge SHM system. The method addresses directly a critical issue in most data-based SHM systems, which is that the collected training data will not contain all natural weather events and load conditions. A SHM system that is trained on such limited data must be able to handle uncertainty in its predictions to prevent false damage detections. A Bayesian autoencoder neural network is trained to reconstruct raw sensor data sequences, with uncertainty bounds in prediction. The uncertainty-adjusted reconstruction error of an unseen sequence is compared to a healthy-state error distribution, and the sequence is accepted or rejected based on the fidelity of the reconstruction. If the proportion of rejected sequences goes over a predetermined threshold, the bridge is determined to be in a damaged state. This is a fully operational, machine learning-based bridge damage detection system that is learned directly from raw sensor data.
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| 6. |
- Cao, Xinwei, et al.
(author)
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An Analysis of Goodness of Pronunciation for Child Speech
- 2023
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In: Interspeech 2023. - : International Speech Communication Association. ; , s. 4613-4617
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Conference paper (peer-reviewed)abstract
- In this paper, we study the use of goodness of pronunciation (GOP) on child speech. We first compare the distributions of GOP scores on several open datasets representing various dimensions of speech variability. We show that the GOP distribution over CMU Kids, corresponding to young age, has larger spread than those on datasets representing other dimensions, i.e., accent, dialect, spontaneity and environmental conditions. We hypothesize that the increased variability of pronunciation in young age may impair the use of traditional mispronunciation detection methods for children. To support this hypothesis, we perform simulated mispronunciation experiments both for children and adults using different variants of the GOP algorithm. We also compare the results to real-case mispronunciations for native children showing that GOP is less effective for child speech than for adult speech.
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| 7. |
- Getman, Yaroslav, et al.
(author)
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Developing an AI-Assisted Low-Resource Spoken Language Learning App for Children
- 2023
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In: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 11, s. 86025-86037
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Journal article (peer-reviewed)abstract
- Computer-assisted Language Learning (CALL) is a rapidly developing area accelerated by advancements in the field of AI. A well-designed and reliable CALL system allows students to practice language skills, like pronunciation, any time outside of the classroom. Furthermore, gamification via mobile applications has shown encouraging results on learning outcomes and motivates young users to practice more and perceive language learning as a positive experience. In this work, we adapt the latest speech recognition technology to be a part of an online pronunciation training system for small children. As part of our gamified mobile application, our models will assess the pronunciation quality of young Swedish children diagnosed with Speech Sound Disorder, and participating in speech therapy. Additionally, the models provide feedback to young non-native children learning to pronounce Swedish and Finnish words. Our experiments revealed that these new models fit into an online game as they function as speech recognizers and pronunciation evaluators simultaneously. To make our systems more trustworthy and explainable, we investigated whether the combination of modern input attribution algorithms and time-aligned transcripts can explain the decisions made by the models, give us insights into how the models work and provide a tool to develop more reliable solutions.
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| 8. |
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| 9. |
- Rugayan, Janine, et al.
(author)
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Perceptual and Task-Oriented Assessment of a Semantic Metric for ASR Evaluation
- 2023
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In: Interspeech 2023. - : International Speech Communication Association. ; , s. 2158-2162
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Conference paper (peer-reviewed)abstract
- Automatic speech recognition (ASR) systems have become a vital part of our everyday lives through their many applications. However, as much as we have developed in this regard, our most common evaluation method for ASR systems still remains to be word error rate (WER). WER does not give information on the severity of errors, which strongly impacts practical performance. As such, we examine a semantic-based metric called Aligned Semantic Distance (ASD) against WER and demonstrate its advantage over WER in two facets. First, we conduct a survey asking participants to score reference text and ASR transcription pairs. We perform a correlation analysis and show that ASD is more correlated to the human evaluation scores compared to WER. We also explore the feasibility of predicting human perception using ASD. Second, we demonstrate that ASD is more effective than WER as an indicator of performance on downstream NLP tasks such as named entity recognition and sentiment classification.
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| 10. |
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