Computational Understanding, Generation and Evaluation of Creative Expressions

Computational creativity has received a good amount of research interest in generating creative artefacts programmatically. At the same time, research has been conducted in computational aesthetics, which essentially tries to analyse creativity exhibited in art. This thesis aims to unite these two distinct lines of research in the context of natural language generation by building, from models for interpretation and generation, a cohesive whole that can assess its own generations. I present a novel method for interpreting one of the most difficult rhetoric devices in the figurative use of language: metaphors. The method does not rely on hand-annotated data and it is purely data-driven. It obtains the state of the art results and is comparable to the interpretations given by humans. We show how a metaphor interpretation model can be used in generating metaphors and metaphorical expressions. Furthermore, as a creative natural language generation task, we demonstrate assigning creative names to colours using an algorithmic approach that leverages a knowledge base of stereotypical associations for colours. Colour names produced by the approach were favoured by human judges to names given by humans 70% of the time. A genetic algorithm-based method is elaborated for slogan generation. The use of a genetic algorithm makes it possible to model the generation of text while optimising multiple fitness functions, as part of the evolutionary process, to assess the aesthetic quality of the output. Our evaluation indicates that having multiple balanced aesthetics outperforms a single maximised aesthetic. From an interplay of neural networks and the traditional AI approach of genetic algorithms, we present a symbiotic framework. This is called the master-apprentice framework. This makes it possible for the system to produce more diverse output as the neural network can learn from both the genetic algorithm and real people. The master-apprentice framework emphasises a strong theoretical foundation for the creative problem one seeks to solve. From this theoretical foundation, a reasoned evaluation method can be derived. This thesis presents two different evaluation practices based on two different theories on computational creativity. This research is conducted in two distinct practical tasks: pun generation in English and poetry generation in Finnish.Laskennallista luovuutta on tutkittu paljon puhtaan tuottamisen näkökulmasta ja saman aikaan tutkimusta on tehty laskennallisen estetiikan saralla. Väitöskirjani yhdistää näitä kahta eri koulukuntaa, sillä kehittämäni laskennallisesti luovat järjestelmät käyttävät tuottamisessa apuna estetiikkaa; järjestelmät siis tulkitsevat teoksiaan samaan aikaan, kun ne niitä tuottavat. Käsittelen väitöskirjassani metaforien automaattista tulkintaa, värien nimien tuottamista, sloganien tuottamista sekä suomenkielisen runouden tuottamista. Metodeina käytän perinteistä koneoppimisalgoritmia, eli niin kutsuttua geneettistä algoritmia, sekä neuroverkkoja. Niiden yhdistelmää nimitän mestari ja oppipoika -malliksi, jossa geneettinen algoritmi opettaa neuroverkkoja

Demographic-Aware Natural Language Processing

The underlying traits of our demographic group affect and shape our thoughts, and therefore surface in the way we express ourselves and employ language in our day-to-day life. Understanding and analyzing language use in people from different demographic backgrounds help uncover their demographic particularities. Conversely, leveraging these differences could lead to the development of better language representations, thus enabling further demographic-focused refinements in natural language processing (NLP) tasks. In this thesis, I employ methods rooted in computational linguistics to better understand various demographic groups through their language use. The thesis makes two main contributions. First, it provides empirical evidence that words are indeed used differently by different demographic groups in naturally occurring text. Through experiments conducted on large datasets which display usage scenarios for hundreds of frequent words, I show that automatic classification methods can be effective in distinguishing between word usages of different demographic groups. I compare the encoding ability of the utilized features by conducting feature analyses, and shed light on how various attributes contribute to highlighting the differences. Second, the thesis explores whether demographic differences in word usage by different groups can inform the development of more refined approaches to NLP tasks. Specifically, I start by investigating the task of word association prediction. The thesis shows that going beyond the traditional ``one-size-fits-all'' approach, demographic-aware models achieve better performances in predicting word associations for different demographic groups than generic ones. Next, I investigate the impact of demographic information on part-of-speech tagging and syntactic parsing, and the experiments reveal numerous part-of-speech tags and syntactic relations, whose predictions benefit from the prevalence of a specific group in the training data. Finally, I explore demographic-specific humor generation, and develop a humor generation framework to fill-in the blanks to generate funny stories, while taking into account people's demographic backgrounds.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155164/1/gaparna_1.pd

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

Designing Embodied Interactive Software Agents for E-Learning: Principles, Components, and Roles

Embodied interactive software agents are complex autonomous, adaptive, and social software systems with a digital embodiment that enables them to act on and react to other entities (users, objects, and other agents) in their environment through bodily actions, which include the use of verbal and non-verbal communicative behaviors in face-to-face interactions with the user. These agents have been developed for various roles in different application domains, in which they perform tasks that have been assigned to them by their developers or delegated to them by their users or by other agents. In computer-assisted learning, embodied interactive pedagogical software agents have the general task to promote human learning by working with students (and other agents) in computer-based learning environments, among them e-learning platforms based on Internet technologies, such as the Virtual Linguistics Campus (www.linguistics-online.com). In these environments, pedagogical agents provide contextualized, qualified, personalized, and timely assistance, cooperation, instruction, motivation, and services for both individual learners and groups of learners. This thesis develops a comprehensive, multidisciplinary, and user-oriented view of the design of embodied interactive pedagogical software agents, which integrates theoretical and practical insights from various academic and other fields. The research intends to contribute to the scientific understanding of issues, methods, theories, and technologies that are involved in the design, implementation, and evaluation of embodied interactive software agents for different roles in e-learning and other areas. For developers, the thesis provides sixteen basic principles (Added Value, Perceptible Qualities, Balanced Design, Coherence, Consistency, Completeness, Comprehensibility, Individuality, Variability, Communicative Ability, Modularity, Teamwork, Participatory Design, Role Awareness, Cultural Awareness, and Relationship Building) plus a large number of specific guidelines for the design of embodied interactive software agents and their components. Furthermore, it offers critical reviews of theories, concepts, approaches, and technologies from different areas and disciplines that are relevant to agent design. Finally, it discusses three pedagogical agent roles (virtual native speaker, coach, and peer) in the scenario of the linguistic fieldwork classes on the Virtual Linguistics Campus and presents detailed considerations for the design of an agent for one of these roles (the virtual native speaker)

Quantitative Multimodal Mapping Of Seizure Networks In Drug-Resistant Epilepsy

Over 15 million people worldwide suffer from localization-related drug-resistant epilepsy. These patients are candidates for targeted surgical therapies such as surgical resection, laser thermal ablation, and neurostimulation. While seizure localization is needed prior to surgical intervention, this process is challenging, invasive, and often inconclusive. In this work, I aim to exploit the power of multimodal high-resolution imaging and intracranial electroencephalography (iEEG) data to map seizure networks in drug-resistant epilepsy patients, with a focus on minimizing invasiveness. Given compelling evidence that epilepsy is a disease of distorted brain networks as opposed to well-defined focal lesions, I employ a graph-theoretical approach to map structural and functional brain networks and identify putative targets for removal. The first section focuses on mesial temporal lobe epilepsy (TLE), the most common type of localization-related epilepsy. Using high-resolution structural and functional 7T MRI, I demonstrate that noninvasive neuroimaging-based network properties within the medial temporal lobe can serve as useful biomarkers for TLE cases in which conventional imaging and volumetric analysis are insufficient. The second section expands to all forms of localization-related epilepsy. Using iEEG recordings, I provide a framework for the utility of interictal network synchrony in identifying candidate resection zones, with the goal of reducing the need for prolonged invasive implants. In the third section, I generate a pipeline for integrated analysis of iEEG and MRI networks, paving the way for future large-scale studies that can effectively harness synergy between different modalities. This multimodal approach has the potential to provide fundamental insights into the pathology of an epileptic brain, robustly identify areas of seizure onset and spread, and ultimately inform clinical decision making

Visual Impairment and Blindness

Blindness and vision impairment affect at least 2.2 billion people worldwide with most individuals having a preventable vision impairment. The majority of people with vision impairment are older than 50 years, however, vision loss can affect people of all ages. Reduced eyesight can have major and long-lasting effects on all aspects of life, including daily personal activities, interacting with the community, school and work opportunities, and the ability to access public services. This book provides an overview of the effects of blindness and visual impairment in the context of the most common causes of blindness in older adults as well as children, including retinal disorders, cataracts, glaucoma, and macular or corneal degeneration

Neural Correlates of Early-Stage Visual Processing Differences in Developmental Dyslexia

Reading requires the successful recruitment and coordination of brain networks to translate visual symbols into phonemes, which are then sequenced to match speech sounds and matched onto semantic representations. Although phonemic awareness is understood to be a core deficit associated with reading disability, neuroimaging has demonstrated an association between poor reading and disruption to various interrelated areas in the brain. This includes one of the major visual pathways, the magnocellular pathway, which contributes to the dorsal pathway in the brain and the processing of motion. For at least two decades, researchers have observed differences in motion processing, supported by the magnocellular pathway, between individuals with and without dyslexia (Eden et al., 1996; Gori et al., 2016; Livingstone et al., 1991; Wilmer, 2004). Further, psychometric studies report an association between reading ability and dorsal stream sensitivity in adults and in children before and after learning to read (Boets et al., 2011; Kevan & Pammer, 2009). Studies of the development of the major visual pathways have suggested that the magnocellular pathway follows a protracted course of development, which raises the possibility that it is vulnerable to pathological change during development and also has the potential for greater plasticity (Armstrong et al., 2002; Stevens & Neville, 2006). To explore the potential differences in early-stage visual processing, this dissertation study investigated whether neurophysiological measures, as indexed by event-related potentials (ERP), may differ between adults with and without dyslexia to stimuli tailored to evoke a response from each of two major visual pathways: magnocellular and parvocellular. The P1 component was elicited in response to motion stimuli designed to probe magnocellular pathways, and the N1 component was elicited in response to color stimuli designed for parvocellular processing. Group comparisons revealed statistically significant group differences in P1 amplitude for the motion/magnocellular condition, but no differences were found for N1 ERP measures for the parvocellular/color condition. Moderate to strong correlations between P1 measures in response to the magnocellular/motion condition were observed in relation to specific behavioral assessments: nonverbal reasoning and memory, orthographic choice, the word identification subtest from the Woodcock Reading Mastery Test (3rd edition: WRMT-III, Woodcock, 2011), and the sight word efficiency subtest from the Test of Word Reading Efficiency (2nd edition: TOWRE-2, Wagner, Torgesen, & Rashotte, 2011). These results are indicative of an early-stage visual processing disruption in individuals with dyslexia observable at the level of the brain. Due to the compounding impact of even small disruptions of sensory and cognitive processing on learning, refining our knowledge of the underlying neural mechanisms of reading may permit earlier identification and potentially more focused interventions that could yield better outcomes for struggling readers. Additionally, the association of those differences with measures of word decoding will inform further research into the underlying neural mechanisms that may contribute to dyslexia and skilled reading