40 research outputs found

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Human-Machine Communication: Complete Volume. Volume 6

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    his is the complete volume of HMC Volume 6

    Social attention in young children with autism spectrum disorder: Investigating cross-contextual gaze behaviours, and their relationship to autism severity, cognitive skills and social functioning

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    Social communication and interaction challenges are characteristic of autism spectrum disorder (ASD). Social attention has emerged to be an important behavioural phenotype in ASD, with accumulating evidence suggesting associations with social functioning and developmental outcomes. However, research gaps remain concerning the nature of social attention, the variability demonstrated across different experimental tasks and social contexts, and the ecologically validity of research methods. This thesis aimed to address these substantive and methodological issues by examining social attention patterns in a young cohort of autistic children, and their age-matched neurotypical peers, across three experimental contexts: 1) a traditional, eye-tracking task with static stimuli, 2) a novel, dynamic eye-tracking task incorporating shared book reading (SBR), and 3) an evaluation of the association in social attention across the two eye-tracking tasks and a play-based social interaction task. In Chapter 2, the influence of circumscribed interests (CI) on social attention patterns was investigated. The results of this study suggested there to be a reduced role for CIs and atypical attention patterns in both social and non-social domains. In Chapter 3, a novel SBR task was developed as a dynamic, ecologically relevant eye-tracking task designed to assess social and joint attention behaviours. Results indicated reduced social and joint attention behaviours, in conjunction with increased attention to non-salient background objects in autistic children. Associations between reduced social attention and poorer social functioning and cognitive skills were also evident in this cohort. In Chapter 4, the social attention patterns of the autistic cohort as measured by the two previous eye tracking tasks were correlated with these patterns in a live, play-based social interaction task between a researcher and the autistic child. Cross-contextual associations in social attention between the social interaction and dynamic tasks, and the dynamic and static tasks were observed. In contrast, there was no significant association in social attention patterns between the social interaction and static tasks. These outcomes contribute new insights into the social attention behaviours of autistic children, and evidence in favor of examining these behaviours in ecologically relevant contexts. They also contribute to evidence associating social attention with autism symptomatology and cognitive functioning. Ultimately, the outcomes of this research may improve our understanding of the needs of autistic children across social, cognitive and adaptive functioning domains

    Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

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    The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

    Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

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    The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

    Affective Computing

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    This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing

    Preface

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    Improving chemistry learning outcomes for vocational students using ARIAS learning model

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    Abstract: Improving chemistry learning outcomes for vocational students using ARIAS learning model. Objectives: This classroom action research was carried out to investigate students’ chemistry learning outcomes at vocational level through ARIAS learning model. Methods: The study was conducted in two cycles, each cycle consisting of two meetings. Findings: The mean score before the treatment (T0) was 66.89 with learning mastery percentage of 40.54%, then the mean score increased to 76.51 after teacher applied ARIAS learning model in cycle I (T1) with learning mastery percentage of 62.16%. The improvement of the mean score of students’ cognitive learning outcomes also occurred in the last cycle. The mean score of student cognitive test result was 84.09 with learning mastery percentage of 89.19%. Students also gave positive responses of learning process. Conclusion: It indicated that the ARIAS learning model was proven to be able to effectively improve student learning outcomes, especially in Chemistry subjects at the vocational level.Keywords: Classroom action research, ARIAS learning model, chemistry learning outcomes.Abstrak: Meningkatkan hasil belajar kimia siswa kejuruan melalui model pembelajaran ARIAS. Tujuan: Tujuan: Menginvestigasi hasil belajar peserta didik pada bidang kejuruan dengan menerapkan model pembelajaran ARIAS. Metode: Penelitian ini dilakukan dengan dua siklus, masing-masing siklus terdiri dari 2 pertemuan. Temuan: Rata-rata hasil belajar sebelum diberikan perlakukan (T0) adalah sebesar 66,89 dengan persentase ketuntasan belajar sebesar 40,54%, kemudian rata-rata skor hasil belajar meningkat menjadi 76,51 setelah guru menerapkan model pembelajaran ARIAS pada siklus pertama. Peningkatan rata-rata skor hasil belajar kognitif siswa juga terjadi pada siklus terakhir. Skor rata-rata hasil tes kognitif siswa adalah 84.09 dengan persentase ketuntasan belajar sebesar 89,19%. Siswa juga memberikan respon positif terhadap proses pembelajaran. Kesimpulan: Model pembelajaran ARIAS terbukti dapat secara efektif meningkatkan hasil belajar siswa, terutama dalam mata pelajaran Kimia di tingkat kejuruan.Kata kunci: Penelitian tindakan kelas, model pembelajaran ARIAS, hasil belajar kimia.DOI: http://dx.doi.org/10.23960/jpp.v8.i2.20181

    Speech verification for computer assisted pronunciation training

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    Computer assisted pronunciation training (CAPT) is an approach that uses computer technology and computer-based resources in teaching and learning pronunciation. It is also part of computer assisted language learning (CALL) technology that has been widely applied to online learning platforms in the past years. This thesis deals with one of the central tasks in CAPT, i.e. speech veri- fication. The goal is to provide a framework that identifies pronunciation errors in speech data of second language (L2) learners and generates feedback with information and instruction for error correction. Furthermore, the framework is supposed to support the adaptation to new L1-L2 language pairs with minimal adjustment and modification. The central result is a novel approach to L2 speech verification, which combines both modern language technologies and linguistic expertise. For pronunciation verification, we select a set of L2 speech data, create alias phonemes from the errors annotated by linguists, then train an acoustic model with mixed L2 and gold standard data and perform HTK phoneme recognition to identify the error phonemes. For prosody verification, FD-PSOLA and Dynamic time warping are both applied to verify the differences in duration, pitch and stress. Feedback is generated for both verifications. Our feedback is presented to learners not only visually as with other existing CAPT systems, but also perceptually by synthesizing the learner’s own audio, e.g. for prosody verification, the gold standard prosody is transplanted onto the learner’s own voice. The framework is self-adaptable under semi-supervision, and requires only a certain amount of mixed gold standard and annotated L2 speech data for boot- strapping. Verified speech data is validated by linguists, annotated in case of wrong verification, and used in the next iteration of training. Mary Annotation Tool (MAT) is developed as an open-source component of MARYTTS for both annotating and validating. To deal with uncertain pauses and interruptions in L2 speech, the silence model in HTK is also adapted, and used in all components of the framework where forced alignment is required. Various evaluations are conducted that help us obtain insights into the applicability and potential of our CAPT system. The pronunciation verification shows high accuracy in both precision and recall, and encourages us to acquire more error-annotated L2 speech data to enhance the trained acoustic model. To test the effect of feedback, a progressive evaluation is carried out and it shows that our perceptual feedback helps learners realize their errors, which they could not otherwise observe from visual feedback and textual instructions. In order to im- prove the user interface, a questionnaire is also designed to collect the learners’ experiences and suggestions.Computer Assisted Pronunciation Training (CAPT) ist ein Ansatz, der mittels Computer und computergestützten Ressourcen das Erlernen der korrekten Aussprache im Fremdsprachenunterricht erleichtert. Dieser Ansatz ist ein Teil der Computer Assisted Language Learning (CALL) Technologie, die seit mehreren Jahren auf Online-Lernplattformen häufig zum Einsatz kommt. Diese Arbeit ist der Sprachverifikation gewidmet, einer der zentralen Aufgaben innerhalb des CAPT. Das Ziel ist, ein Framework zur Identifikation von Aussprachefehlern zu entwickeln fürMenschen, die eine Fremdsprache (L2-Sprache) erlernen. Dabei soll Feedback mit fehlerspezifischen Informationen und Anweisungen für eine richtige Aussprache erzeugt werden. Darüber hinaus soll das Rahmenwerk die Anpassung an neue Sprachenpaare (L1-L2) mit minimalen Adaptationen und Modifikationen unterstützen. Das zentrale Ergebnis ist ein neuartiger Ansatz für die L2-Sprachprüfung, der sowohl auf modernen Sprachtechnologien als auch auf corpuslinguistischen Ansätzen beruht. Für die Ausspracheüberprüfung erstellen wir Alias-Phoneme aus Fehlern, die von Linguisten annotiert wurden. Dann trainieren wir ein akustisches Modell mit gemischten L2- und Goldstandarddaten und führen eine HTK-Phonemerkennung3 aus, um die Fehlerphoneme zu identifizieren. Für die Prosodieüberprüfung werden sowohl FD-PSOLA4 und Dynamic Time Warping angewendet, um die Unterschiede in der Dauer, Tonhöhe und Betonung zwischen dem Gesprochenen und dem Goldstandard zu verifizieren. Feedbacks werden für beide Überprüfungen generiert und den Lernenden nicht nur visuell präsentiert, so wie in anderen vorhandenen CAPT-Systemen, sondern auch perzeptuell vorgestellt. So wird unter anderem für die Prosodieverifikation die Goldstandardprosodie auf die eigene Stimme des Lernenden übergetragen. Zur Anpassung des Frameworks an weitere L1-L2 Sprachdaten muss das System über Maschinelles Lernen trainiert werden. Da es sich um ein semi-überwachtes Lernverfahren handelt, sind nur eine gewisseMenge an gemischten Goldstandardund annotierten L2-Sprachdaten für das Bootstrapping erforderlich. Verifizierte Sprachdaten werden von Linguisten validiert, im Falle einer falschen Verifizierung nochmals annotiert, und bei der nächsten Iteration des Trainings verwendet. Für die Annotation und Validierung wurde das Mary Annotation Tool (MAT) als Open-Source-Komponente von MARYTTS entwickelt. Um mit unsicheren Pausen und Unterbrechungen in der L2-Sprache umzugehen, wurde auch das sogenannte Stillmodell in HTK angepasst und in allen Komponenten des Rahmenwerks verwendet, in denen Forced Alignment erforderlich ist. Unterschiedliche Evaluierungen wurden durchgeführt, um Erkenntnisse über die Anwendungspotenziale und die Beschränkungen des Systems zu gewinnen. Die Ausspracheüberprüfung zeigt eine hohe Genauigkeit sowohl bei der Präzision als auch beim Recall. Dadurch war es möglich weitere fehlerbehaftete L2-Sprachdaten zu verwenden, um somit das trainierte akustische Modell zu verbessern. Um die Wirkung des Feedbacks zu testen, wird eine progressive Auswertung durchgeführt. Das Ergebnis zeigt, dass perzeptive Feedbacks dabei helfen, dass die Lernenden sogar Fehler erkennen, die sie nicht aus visuellen Feedbacks und Textanweisungen beobachten können. Zudem wurden mittels Fragebogen die Erfahrungen und Anregungen der Benutzeroberfläche der Lernenden gesammelt, um das System künftig zu verbessern. 3 Hidden Markov Toolkit 4 Pitch Synchronous Overlap and Ad
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