Extracting Knowledge from Stream Behavioural Patterns

The increasing number of small, cheap devices full of sensing capabilities lead to an untapped source of information that can be explored to improve and optimize several systems. Yet, as this number grows it becomes increasingly difficult to manage and organize all this new information. The lack of a standard context representation scheme is one of the main difficulties in this research area (Antunes et al., 2016b). With this in mind we propose a stream characterization model which aims to provide the foundations of a new stream similarity metric. Complementing previous work on context organization, we aim to provide an automatic organizational model without enforcing specific representations

Extracting semantic entities and events from sports tweets

Large volumes of user-generated content on practically every major issue and event are being created on the microblogging site Twitter. This content can be combined and processed to detect events, entities and popular moods to feed various knowledge-intensive practical applications. On the downside, these content items are very noisy and highly informal, making it difficult to extract sense out of the stream. In this paper, we exploit various approaches to detect the named entities and significant micro-events from users’ tweets during a live sports event. Here we describe how combining linguistic features with background knowledge and the use of Twitter-specific features can achieve high, precise detection results (f-measure = 87%) in different datasets. A study was conducted on tweets from cricket matches in the ICC World Cup in order to augment the event-related non-textual media with collective intelligence

Modelling patterns in continuous streams of data

The untapped source of information, extracted from the increasing number of sensors, can be explored to improve and optimize several systems. Yet, hand in hand with this growth goes the increasing difficulty to manage and organize all this new information. The lack of a standard context representation scheme is one of the main struggles in this research area, conventional methods for extracting knowledge from data rely on a standard representation or a priori relation. Which may not be feasible for IoT and M2M scenarios, with this in mind we propose a stream characterization model which aims to provide the foundations for a novel stream similarity metric. Complementing previous work on context organization, we aim to provide an automatic stream organizational model without enforcing specific representations. In this paper we extend our work on stream characterization and devise a novel similarity metho

Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis

Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 201

What can developmental disorders tell us about the neurocomputational constraints that shape development? the case of Williams syndrome

The uneven cognitive phenotype in the adult outcome of Williams syndrome has led some researchers to make strong claims about the modularity of the brain and the purported genetically determined, innate specification of cognitive modules. Such arguments have particularly been marshaled with respect to language. We challenge this direct generalization from adult phenotypic outcomes to genetic specification and consider instead how genetic disorders provide clues to the constraints on plasticity that shape the outcome of development. We specifically examine behavioral studies, brain imaging, and computational modeling of language in Williams syndrome but contend that our theoretical arguments apply equally to other cognitive domains and other developmental disorders. While acknowledging that selective deficits in normal adult patients might justify claims about cognitive modularity, we question whether similar, seemingly selective deficits found in genetic disorders can be used to argue that such cognitive modules are prespecified in infant brains. Cognitive modules are, in our view, the outcome of development, not its starting point. We note that most work on genetic disorders ignores one vital factor, the actual process of ontogenetic development, and argue that it is vital to view genetic disorders as proceeding under different neurocomputational constraints, not as demonstrations of static modularity

Attentional capture by meaning: A multi-level modelling study

We present a computational study of attentional capture by meaning, based on Barnard et al's key-distractor attentional blink task. We highlight a sequence of models, from an abstract black-box to a structurally detailed white-box model. Each of these models reproduces the major findings from the key-distractor blink task. We argue that such multi-level modelling gives greater confidence in the theoretical position encapsulated by these models

Language learning in infancy

Although immensely complex, speech is also a very efficient means of communication between humans. Understanding how we acquire the skills necessary for perceiving and producing speech remains an intriguing goal for research. However, while learning is likely to begin as soon as we start hearing speech, the tools for studying the language acquisition strategies in the earliest stages of development remain scarce. One prospective strategy is statistical learning. In order to investigate its role in language development, we designed a new research method. The method was tested in adults using magnetoencephalography (MEG) as a measure of cortical activity. Neonatal brain activity was measured with electroencephalography (EEG). Additionally, we developed a method for assessing the integration of seen and heard syllables in the developing brain as well as a method for assessing the role of visual speech when learning phoneme categories. The MEG study showed that adults learn statistical properties of speech during passive listening of syllables. The amplitude of the N400m component of the event-related magnetic fields (ERFs) reflected the location of syllables within pseudowords. The amplitude was also enhanced for syllables in a statistically unexpected position. The results suggest a role for the N400m component in statistical learning studies in adults. Using the same research design with sleeping newborn infants, the auditory event-related potentials (ERPs) measured with EEG reflected the location of syllables within pseudowords. The results were successfully replicated in another group of infants. The results show that even newborn infants have a powerful mechanism for automatic extraction of statistical characteristics from speech. We also found that 5-month-old infants integrate some auditory and visual syllables into a fused percept, whereas other syllable combinations are not fully integrated. Auditory syllables were paired with visual syllables possessing a different phonetic identity, and the ERPs for these artificial syllable combinations were compared with the ERPs for normal syllables. For congruent auditory-visual syllable combinations, the ERPs did not differ from those for normal syllables. However, for incongruent auditory-visual syllable combinations, we observed a mismatch response in the ERPs. The results show an early ability to perceive speech cross-modally. Finally, we exposed two groups of 6-month-old infants to artificially created auditory syllables located between two stereotypical English syllables in the formant space. The auditory syllables followed, equally for both groups, a unimodal statistical distribution, suggestive of a single phoneme category. The visual syllables combined with the auditory syllables, however, were different for the two groups, one group receiving visual stimuli suggestive of two separate phoneme categories, the other receiving visual stimuli suggestive of only one phoneme category. After a short exposure, we observed different learning outcomes for the two groups of infants. The results thus show that visual speech can influence learning of phoneme categories. Altogether, the results demonstrate that complex language learning skills exist from birth. They also suggest a role for the visual component of speech in the learning of phoneme categories.Puhe on monimuotoinen signaali, joka välittää ihmistenvälistä kommunikaatiota erityisen tehokkaasti. On osin vielä hämärän peitossa, miten opimme puhumaan ja havaitsemaan puhetta syntymän jälkeen. Oppiminen alkanee heti, kun alamme kuulla puhetta. Puheen sisältämät lukuisat tilastolliset säännönmukaisuudet saattavat auttaa oppimista. Niiden hyödyntämistä kutsutaan tilastolliseksi oppimiseksi. Tilastollista oppimista ajatellaan kielenoppimisessa voitavan käyttää jatkuvan puheen jakamiseksi erillisiksi sanoiksi. Tässä tutkimuksessa kehitettiin uusi aivomittauksiin perustuva tutkimusmenetelmä tilastollisen kielenoppimisen mittaamiseksi. Menetelmää testattiin aikuisilla mittaamalla aivokuoriaktivaatiota magnetoenkefalografiaa käyttäen. Tulokset osoittivat, että aikuiset oppivat tilastollisia ominaisuuksia tavuvirrasta silloinkin, kun he eivät kiinnitä siihen tietoisesti huomiota. Tilastollista oppimista pystyttiin mittaamaan aivojen magneettisissa herätevasteissa näkyvän N400m-vasteen yhteydessä. Samaa koeasetelmaa käytettiin mittamaan tilastollisen kielenoppimisen kykyjä vastasyntyneiltä käyttäen elektroenkefalografiaa aivotoiminnan mittarina. Tulokset osoittivat, että vastasyntyneet oppivat tilastollisia riippuvuuksia tavujen välillä unen aikana kuulemastaan tavuvirrasta. Tutkimustulos toistui myös toisella ryhmällä vastasyntyneitä. Jo vastasyntyneillä on siis hyvä puheen tilastollisten ominaisuuksien oppimiskyky. Tehokkaassa puheenhavaitsemisessa olennaisena pidetään kykyä yhdistää eli integroida kuultu puhe ja nähty artikulaatio yhdeksi havainnoksi. Ajatellaan, että aikuisilla aivot käsittelevät integroidun puheen tehokkaammin kuin erilliset kuulo- ja näköhavainnot. Kehitimme koeasetelman kuullun ja nähdyn puheen integroinnin mittaamiseksi viiden kuukauden ikäisillä vauvoilla. Tulokset osoittivat, että tässä kehitysvaiheessa vauvat muodostavat yhdistyneen havainnon tietyistä kuulluista ja nähdyistä tavuista, kun taas toisia tavuyhdistelmiä ei integroida. Koska integrointi ei onnistu tilanteessa, jossa muodostuva havainto ei ole äidinkielen sääntöjen mukainen, tulokset myös viittaavat siihen, että viiden kuukauden iässä vauvat ovat jo omaksuneet tietoa äidinkielen tyypillisistä rakenteista. Lopuksi arvioitiin myös sitä, millainen rooli nähdyllä puheella saattaisi olla kielenoppimisessa. Kaksi ryhmää kuuden kuukauden ikäisiä vauvoja osallistui kokeeseen. Vauvoille näytettiin ruudulta puhetta, jossa videoon tavallisesta artikulaatiosta oli liitetty keinotekoisesti muokattuja tavuääniä. Osalle vauvoista nähty puhe sisälsi vihjeen kahdesta erilaisesta tavuryhmästä, kun taas toiselle ryhmälle nähty puhe pysyi koko ajan samanlaisena. Kuultu puhe oli kaikille vauvoille täysin samanlaista. Lyhyen katselu- ja kuunteluajan jälkeen vauvojen havaintoa kyseisistä tavuäänistä testattiin. Eri ryhmillä havaittiin erilaiset oppimistulokset, joka viittaa siihen, että nähdyillä artikulaatioilla oli vaikutusta oppimiseen. Nähty puhe siis voi vaikuttaa puheäänten oppimiseen tässä kehitysvaiheessa. Kokonaisuudessaan tässä väitöskirjassa esitetyt tulokset korostavat varhaisimpien vaiheiden merkitystä kielenoppimisessa. Pystymme oppimaan puheen ominaisuuksia monipuolisesti heti syntymästä lähtien. Tulokset myös viittaavat siihen, että nähdyllä puheella saattaa olla tärkeä rooli puheäänien oppimisessa ensimmäisen vuoden aikana