Proceedings of the Eighth Australian and New Zealand Intelligent Information Systems Conference

These are the full proceedings of the conference.

Neural networks for time-varying data

This paper reviews some basic issues and methods involved in using neural networks to respond in a desired fashion to a temporally-varying environment. Some popular network models and training methods are introduced. A speech recognition example is then used to illustrate the central difficulty of temporal data processing: learning to notice and remember relevant contextual information. Feedforward network methods are applicable to cases where this problem is not severe. The application of these methods are explained and applications are discussed in the areas of pure mathematics, chemical and physical systems, and economic systems. A more powerful but less practical algorithm for temporal problems, the moving targets algorithm, is sketched and discussed. For completeness, a few remarks are made on reinforcement learning.

Attention deficits in dyslexia:Evidence for an automatisation deficit?

Both attentional difficulties and rapid processing deficits have recently been linked with dyslexia. We report two studies comparing the performance of dyslexic and control teenagers on attentional tasks. The two studies were based on two different conceptions of attention. Study 1 employed a design that allowed three key components of attention - focusing, switching, and sustaining - to be investigated separately. One hypothesis under investigation was that rapid processing problems - in particular impaired ability to switch attention rapidly - might be associated with dyslexia. However, although dyslexic participants were significantly less accurate than their controls in a condition where they had to switch attention between two target types, the nature of the deficit suggested that the problem was not in switching attention per se. Thus, in Study 2, we explored an alternative interpretation of the Study 1 results in terms of the classic capacity-limited models of "central" attention. We contrasted two hypotheses: (1) that dyslexic teenagers have reduced cognitive resources versus (2) that they suffer from a general impairment in the ability to automatise basic skills. To investigate the automaticity of the shape recognition component of the task a similar attention paradigm to that used in Study 1 was employed, but using degraded, as well as intact, stimuli. It was found that stimulus degradation led to relatively less impairment for dyslexic than for matched control groups. The results support the hypothesis that dyslexic people suffer from a general impairment in the ability to automatise skills - in this case the skill of automatic shape recognition.

Perception of concurrent sentences with harmonic or frequency-shifted voiced excitation:performance of human listeners and of computational models based on autocorrelation

Keyword identification in one of two simultaneous sentences is improved when the sentences differ in F0, particularly when they are almost continuously voiced. Sentences of this kind were recorded, monotonised using PSOLA, and re-synthesised to give a range of harmonic ?F0s (0, 1, 3, and 10 semitones). They were additionally re-synthesised by LPC with the LPC residual frequency shifted by 25% of F0, to give excitation with inharmonic but regularly spaced components. Perceptual identification of frequency-shifted sentences showed a similar large improvement with nominal ?F0 as seen for harmonic sentences, although overall performance was about 10% poorer. We compared performance with that of two autocorrelation-based computational models comprising four stages: (i) peripheral frequency selectivity and half-wave rectification; (ii) within-channel periodicity extraction; (iii) identification of the two major peaks in the summary autocorrelation function (SACF); (iv) a template-based approach to speech recognition using dynamic time warping. One model sampled the correlogram at the target-F0 period and performed spectral matching; the other deselected channels dominated by the interferer and performed matching on the short-lag portion of the residual SACF. Both models reproduced the monotonic increase observed in human performance with increasing ?F0 for the harmonic stimuli, but not for the frequency-shifted stimuli. A revised version of the spectral-matching model, which groups patterns of periodicity that lie on a curve in the frequency-delay plane, showed a closer match to the perceptual data for frequency-shifted sentences. The results extend the range of phenomena originally attributed to harmonic processing to grouping by common spectral pattern.

Verbal Dialogue versus Written Dialogue

Modern technology has moved on and completely changed the way that people can use the telephone or mobile to dialogue with information held on computers. Well developed “written speech analysis” does not work with “verbal speech”. The main purpose of our article is, firstly, to highlights the problems and, secondly, to shows the possible ways to solve these problems.

Detection and Classification of Whale Acoustic Signals

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.

In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.

In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.

Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.

We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.

Development of an active orthosis prototype for lower limbs

ARAUJO, Márcio V. ; ALSINA, Pablo J. ; MEDEIROS, Adelardo A. D. ; PEREIRA, Jonathan P.P. ; DOMINGOS, Elber C. ; ARAÚJO, Fábio M.U. ; SILVA, Jáder S. . Development of an Active Orthosis Prototype for Lower Limbs. In: INTERNATIONAL CONGRESS OF MECHANICAL ENGINEERING, 20., 2009, Gramado, RS. Proceedings… Gramado, RS: [s. n.], 2009

Development of an active orthosis prototype for lower limbs

ARAUJO, Márcio V. ; ALSINA, Pablo J. ; MEDEIROS, Adelardo A. D. ; PEREIRA, Jonathan P.P. ; DOMINGOS, Elber C. ; ARAÚJO, Fábio M.U. ; SILVA, Jáder S. . Development of an Active Orthosis Prototype for Lower Limbs. In: INTERNATIONAL CONGRESS OF MECHANICAL ENGINEERING, 20., 2009, Gramado, RS. Proceedings… Gramado, RS: [s. n.], 2009

Neuromorphic model for sound source segregation

While humans can easily segregate and track a speaker's voice in a loud noisy environment, most modern speech recognition systems still perform poorly in loud background noise. The computational principles behind auditory source segregation in humans is not yet fully understood. In this dissertation, we develop a computational model for source segregation inspired by auditory processing in the brain. To support the key principles behind the computational model, we conduct a series of electro-encephalography experiments using both simple tone-based stimuli and more natural speech stimulus. Most source segregation algorithms utilize some form of prior information about the target speaker or use more than one simultaneous recording of the noisy speech mixtures. Other methods develop models on the noise characteristics. Source segregation of simultaneous speech mixtures with a single microphone recording and no knowledge of the target speaker is still a challenge. Using the principle of temporal coherence, we develop a novel computational model that exploits the difference in the temporal evolution of features that belong to different sources to perform unsupervised monaural source segregation. While using no prior information about the target speaker, this method can gracefully incorporate knowledge about the target speaker to further enhance the segregation.Through a series of EEG experiments we collect neurological evidence to support the principle behind the model. Aside from its unusual structure and computational innovations, the proposed model provides testable hypotheses of the physiological mechanisms of the remarkable perceptual ability of humans to segregate acoustic sources, and of its psychophysical manifestations in navigating complex sensory environments. Results from EEG experiments provide further insights into the assumptions behind the model and provide motivation for future single unit studies that can provide more direct evidence for the principle of temporal coherence.

Fabrication and Pseudo-Analog Characteristics of Ta2O5 -Based ReRAM Cell

Memristori on yksi elektroniikan peruskomponenteista vastuksen, kondensaattorin ja kelan lisäksi. Se on passiivinen komponentti, jonka teorian kehitti Leon Chua vuonna 1971. Kesti kuitenkin yli kolmekymmentä vuotta ennen kuin teoria pystyttiin yhdistämään kokeellisiin tuloksiin. Vuonna 2008 Hewlett Packard julkaisi artikkelin, jossa he väittivät valmistaneensa ensimmäisen toimivan memristorin. Memristori eli muistivastus on resistiivinen komponentti, jonka vastusarvoa pystytään muuttamaan. Nimens mukaisesti memristori kykenee myös säilyttämään vastusarvonsa ilman jatkuvaa virtaa ja jännitettä. Tyypillisesti memristorilla on vähintään kaksi vastusarvoa, joista kumpikin pystytään valitsemaan syöttämällä komponentille jännitettä tai virtaa. Tämän vuoksi memristoreita kutsutaankin usein resistiivisiksi kytkimiksi. Resistiivisiä kytkimiä tutkitaan nykyään paljon erityisesti niiden mahdollistaman muistiteknologian takia. Resistiivisistä kytkimistä rakennettua muistia kutsutaan ReRAM-muistiksi (lyhenne sanoista resistive random access memory). ReRAM-muisti on Flash-muistin tapaan haihtumaton muisti, jota voidaan sähköisesti ohjelmoida tai tyhjentää. Flash-muistia käytetään tällä hetkellä esimerkiksi muistitikuissa. ReRAM-muisti mahdollistaa kuitenkin nopeamman ja vähävirtaiseman toiminnan Flashiin verrattuna, joten se on tulevaisuudessa varteenotettava kilpailija markkinoilla. ReRAM-muisti mahdollistaa myös useammin bitin tallentamisen yhteen muistisoluun binäärisen (”0” tai ”1”) toiminnan sijaan. Tyypillisesti ReRAM-muistisolulla on kaksi rajoittavaa vastusarvoa, mutta näiden kahden tilan välille pystytään mahdollisesti ohjelmoimaan useampia tiloja. Muistisoluja voidaan kutsua analogisiksi, jos tilojen määrää ei ole rajoitettu. Analogisilla muistisoluilla olisi mahdollista rakentaa tehokkaasti esimerkiksi neuroverkkoja. Neuroverkoilla pyritään mallintamaan aivojen toimintaa ja suorittamaan tehtäviä, jotka ovat tyypillisesti vaikeita perinteisille tietokoneohjelmille. Neuroverkkoja käytetään esimerkiksi puheentunnistuksessa tai tekoälytoteutuksissa. Tässä diplomityössä tarkastellaan Ta2O5 -perustuvan ReRAM-muistisolun analogista toimintaa pitäen mielessä soveltuvuus neuroverkkoihin. ReRAM-muistisolun valmistus ja mittaustulokset käydään läpi. Muistisolun toiminta on harvoin täysin analogista, koska kahden rajoittavan vastusarvon välillä on usein rajattu määrä tiloja. Tämän vuoksi toimintaa kutsutaan pseudoanalogiseksi. Mittaustulokset osoittavat, että yksittäinen ReRAM-muistisolu kykenee binääriseen toimintaan hyvin. Joiltain osin yksittäinen solu kykenee tallentamaan useampia tiloja, mutta vastusarvoissa on peräkkäisten ohjelmointisyklien välillä suurta vaihtelevuutta, joka hankaloittaa tulkintaa. Valmistettu ReRAM-muistisolu ei sellaisenaan kykene toimimaan pseudoanalogisena muistina, vaan se vaati rinnalleen virtaa rajoittavan komponentin. Myös valmistusprosessin kehittäminen vähentäisi yksittäisen solun toiminnassa esiintyvää varianssia, jolloin sen toiminta muistuttaisi enemmän pseudoanalogista muistia.

Una codificación prefija para un idioma artificial

This work focuses in the formal and technical analysis of some aspects of a constructed language. As a first part of the work, a possible coding for the language will be studied, emphasizing the pre x coding, for which an extension of the Hu man algorithm from binary to n-ary will be implemented. Because of that in the language we can't know a priori the frequency of use of the words, a study will be done and several strategies will be proposed for an open words system, analyzing previously the existing number of words in current natural languages. As a possible upgrade of the coding, we'll take also a look to the synchronization loss problem, as well as to its solution: the self-synchronization, a t-codes study with the number of possible words for the language, as well as other alternatives. Finally, and from a less formal approach, several applications for the language have been developed: A voice synthesizer, a speech recognition system and a system font for the use of the language in text processors. For each of these applications, the process used for its construction, as well as the problems encountered and still to solve in each will be detailed.

Equivalent Hearing Loss in Children

Early intervention is the key to spoken language for hearing impaired children. A severe hearing loss diagnosis in young children raises the urgent question on the optimal type of hearing aid device. As there is no recent data on comparing selection criteria for a specific hearing aid device, the goal of the Hearing Evaluation of Auditory Rehabilitation Devices (hEARd) project (Coninx & Vermeulen, 2012) evolved to collect and analyze interlingually comparable normative data on the speech perception performances of children with hearing aids and children with cochlear implants (CI). METHOD: In various institutions for hearing rehabilitation in Belgium, Germany and the Netherlands the Adaptive Auditory Speech Test AAST was used in the hEARd project, to determine speech perception abilities in kindergarten and school aged hearing impaired children. Results in the speech audiometric procedures were matched to the unaided hearing loss values of children using hearing aids and compared to results of children using CI. 277 data sets of hearing impaired children were analyzed. Results of children using hearing aids were summarized in groups as to their unaided hearing loss values. The grouping was related to the World Health Organization’s (WHO) grading of hearing impairment from mild (25–40 dB HL) to moderate (41–60 dB HL), severe (61-80 dB HL) and profound hearing impairment (80 dB HL and higher). RESULTS: AAST speech recognition results in quiet showed a significantly better performance for the CI group in comparison to the group of profoundly impaired hearing aid users as well as the group of severely impaired hearing aid users. However the CI users’ performances in speech perception in noise did not vary from the hearing aid users’ performances. Within the collected data analyses showed that children with a CI show an equivalent performance on speech perception in quiet as children using hearing aids with a “moderate” hearing impairment.

Implementazione di un sistema di traduzione automatica: un caso di studio per la traduzione italiano-cinese

Il lavoro di tesi presentato è nato da una collaborazione con il Politecnico di Macao, i referenti sono: Prof. Rita Tse, Prof. Marcus Im e Prof. Su-Kit Tang. L'obiettivo consiste nella creazione di un modello di traduzione automatica italiano-cinese e nell'osservarne il comportamento, al fine di determinare se sia o meno possibile l'impresa. Il trattato approfondisce l'argomento noto come Neural Language Processing (NLP), rientrando dunque nell'ambito delle traduzioni automatiche. Sono servizi che, attraverso l'ausilio dell'intelligenza artificiale sono in grado di elaborare il linguaggio naturale, per poi interpretarlo e tradurlo. NLP è una branca dell'informatica che unisce: computer science, intelligenza artificiale e studio di lingue. Dal punto di vista della ricerca, le più grandi sfide in questo ambito coinvolgono: il riconoscimento vocale (speech-recognition), comprensione del testo (natural-language understanding) e infine la generazione automatica di testo (natural-language generation). Lo stato dell'arte attuale è stato definito dall'articolo "Attention is all you need" \cite{vaswani2017attention}, presentato nel 2017 a partire da una collaborazione di ricercatori della Cornell University.\\ I modelli di traduzione automatica più noti ed utilizzati al momento sono i Neural Machine Translators (NMT), ovvero modelli che attraverso le reti neurali artificiali profonde, sono in grado effettuare traduzioni o predizioni. La qualità delle traduzioni è particolarmente buona, tanto da arrivare quasi a raggiungere la qualità di una traduzione umana. Il lavoro infatti si concentrerà largamente sullo studio e utilizzo di NMT, allo scopo di proporre un modello funzionale e che sia in grado di performare al meglio nelle traduzioni da italiano a cinese e viceversa.

Semantic priming in Alzheimer's dementia: Evidence for dissociation of automatic and attentional processes

The nature of the semantic memory deficit in dementia of the Alzheimer's type (DAT) was investigated in a semantic priming task which was designed to assess both automatic and attention-induced priming effects. Ten DAT patients and 10 age-matched control subjects completed a word naming semantic priming task in which both relatedness proportion (RP) and stimulus-onset asynchrony (SOA) were varied. A clear dissociation between automatic and attentional priming effects in both groups was demonstrated; however, the DAT subjects pattern of priming deviated significantly from that of the normal controls. The DAT patients failed to produce any priming under conditions which encouraged automatic semantic processing and produced facilitation only when the RP was high. In addition, the DAT group produced hyperpriming, with significantly larger facilitation effects than the control group. These results suggest an impairment of automatic spreading activation in DAT and have implications for theories of semantic memory impairment in DAT as well as models of normal priming. (C) 2001 Academic Press.