| 1. |
- Abdelnour, Jerome, et al.
(författare)
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NAAQA: A Neural Architecture for Acoustic Question Answering
- 2022
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Ingår i: 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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Tidskriftsartikel (refereegranskat)
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| 2. |
- Adiban, Mohammad, et al.
(författare)
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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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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 537, s. 296-308
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Hierarchical Residual Learning Based Vector Quantized Variational Autoencorder for Image Reconstruction and Generation
- 2022
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Ingår i: The 33<sup>rd</sup> British Machine Vision Conference Proceedings.
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Konferensbidrag (refereegranskat)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.
(författare)
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Step-gan : A one-class anomaly detection model with applications to power system security
- 2021
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Ingår i: 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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Konferensbidrag (refereegranskat)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. |
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| 6. |
- Agelfors, Eva, et al.
(författare)
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Synthetic visual speech driven from auditory speech
- 1999
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Ingår i: Proceedings of Audio-Visual Speech Processing (AVSP'99)).
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Konferensbidrag (refereegranskat)abstract
- We have developed two different methods for using auditory, telephone speech to drive the movements of a synthetic face. In the first method, Hidden Markov Models (HMMs) were trained on a phonetically transcribed telephone speech database. The output of the HMMs was then fed into a rulebased visual speech synthesizer as a string of phonemes together with time labels. In the second method, Artificial Neural Networks (ANNs) were trained on the same database to map acoustic parameters directly to facial control parameters. These target parameter trajectories were generated by using phoneme strings from a database as input to the visual speech synthesis The two methods were evaluated through audiovisual intelligibility tests with ten hearing impaired persons, and compared to “ideal” articulations (where no recognition was involved), a natural face, and to the intelligibility of the audio alone. It was found that the HMM method performs considerably better than the audio alone condition (54% and 34% keywords correct respectively), but not as well as the “ideal” articulating artificial face (64%). The intelligibility for the ANN method was 34% keywords correct.
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| 7. |
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| 8. |
- Agelfors, Eva, et al.
(författare)
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User evaluation of the SYNFACE talking head telephone
- 2006
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Ingår i: Computers Helping People With Special Needs, Proceedings. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 3540360204 ; , s. 579-586
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Konferensbidrag (refereegranskat)abstract
- The talking-head telephone, Synface, is a lip-reading support for people with hearing-impairment. It has been tested by 49 users with varying degrees of hearing-impaired in UK and Sweden in lab and home environments. Synface was found to give support to the users, especially in perceiving numbers and addresses and an enjoyable way to communicate. A majority deemed Synface to be a useful product.
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| 9. |
- Al Moubayed, Samer, et al.
(författare)
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Studies on Using the SynFace Talking Head for the Hearing Impaired
- 2009
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Ingår i: Proceedings of Fonetik'09. - Stockholm : Stockholm University. - 9789163348921 ; , s. 140-143
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- SynFace is a lip-synchronized talking agent which is optimized as a visual reading support for the hearing impaired. In this paper wepresent the large scale hearing impaired user studies carried out for three languages in the Hearing at Home project. The user tests focuson measuring the gain in Speech Reception Threshold in Noise and the effort scaling when using SynFace by hearing impaired people, where groups of hearing impaired subjects with different impairment levels from mild to severe and cochlear implants are tested. Preliminaryanalysis of the results does not show significant gain in SRT or in effort scaling. But looking at large cross-subject variability in both tests, it isclear that many subjects benefit from SynFace especially with speech with stereo babble.
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| 10. |
- Al Moubayed, Samer, et al.
(författare)
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SynFace Phone Recognizer for Swedish Wideband and Narrowband Speech
- 2008
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Ingår i: Proceedings of The second Swedish Language Technology Conference (SLTC). - Stockholm, Sweden.. ; , s. 3-6
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we present new results and comparisons of the real-time lips synchronized talking head SynFace on different Swedish databases and bandwidth. The work involves training SynFace on narrow-band telephone speech from the Swedish SpeechDat, and on the narrow-band and wide-band Speecon corpus. Auditory perceptual tests are getting established for SynFace as an audio visual hearing support for the hearing-impaired. Preliminary results show high recognition accuracy compared to other languages.
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