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Whiting, Jeffrey S. "Cognitive and Behavioral Model Ensembles for Autonomous Virtual Characters." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1873.pdf.
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Dinerstein, Jonathan J. "Improving and Extending Behavioral Animation Through Machine Learning." BYU ScholarsArchive, 2005. https://scholarsarchive.byu.edu/etd/310.
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Behavioral animation has become popular for creating virtual characters that are autonomous agents and thus self-animating. This is useful for lessening the workload of human animators, populating virtual environments with interactive agents, etc. Unfortunately, current behavioral animation techniques suffer from three key problems: (1) deliberative behavioral models (i.e., cognitive models) are slow to execute; (2) interactive virtual characters cannot adapt online due to interaction with a human user; (3) programming of behavioral models is a difficult and time-intensive process. This dissertation presents a collection of papers that seek to overcome each of these problems. Specifically, these issues are alleviated through novel machine learning schemes. Problem 1 is addressed by using fast regression techniques to quickly approximate a cognitive model. Problem 2 is addressed by a novel multi-level technique composed of custom machine learning methods to gather salient knowledge with which to guide decision making. Finally, Problem 3 is addressed through programming-by-demonstration, allowing a non technical user to quickly and intuitively specify agent behavior.
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Kobeissi, Meriana. "A conversational AI Framework for Cognitive Process Analysis." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS025.
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Les processus métier (BP) sont les piliers fondamentaux des organisations, englobant toute une gamme d'activités structurées visant à atteindre des objectifs organisationnels distincts. Ces processus, caractérisés par une multitude de tâches, d'interactions et de flux de travail, offrent une méthodologie structurée pour superviser les opérations cruciales dans divers secteurs. Une découverte essentielle pour les organisations a été la reconnaissance de la valeur profonde inhérente aux données produites pendant ces processus. L'analyse des processus, une discipline spécialisée, explore ces journaux de données, facilitant une compréhension plus profonde et l'amélioration des BP. Cette analyse peut être catégorisée en deux perspectives : le niveau d'instance, qui se concentre sur les exécutions individuelles de processus, et le niveau de processus, qui examine le processus global.Cependant, l'application de l'analyse des processus pose des défis aux utilisateurs, impliquant la nécessité d'accéder aux données, de naviguer dans les API de bas niveau et d'utiliser des méthodes dépendantes d'outils. L'application dans le monde réel rencontre souvent des complexités et des obstacles centrés sur l'utilisateur.Plus précisément, l'analyse de niveau d'instance exige des utilisateurs qu'ils accèdent aux données d'exécution de processus stockées, une tâche qui peut être complexe pour les professionnels de l'entreprise en raison de l'exigence de maîtriser des langages de requête complexes tels que SQL et CYPHER. En revanche, l'analyse de niveau de processus des données de processus implique l'utilisation de méthodes et d'algorithmes qui exploitent les données d'exécution de processus extraites des systèmes d'information. Ces méthodologies sont regroupées sous le terme de techniques d'exploration de processus. L'application de l'exploration de processus confronte les analystes à la tâche complexe de sélection de méthodes, qui consiste à trier des descriptions de méthodes non structurées. De plus, l'application des méthodes d'exploration de processus dépend d'outils spécifiques et nécessite un certain niveau d'expertise technique.Pour relever ces défis, cette thèse présente des solutions basées sur l'IA, mettant l'accent sur l'intégration de capacités cognitives dans l'analyse des processus pour faciliter les tâches d'analyse tant au niveau de l'instance qu'au niveau du processus pour tous les utilisateurs. Les objectifs principaux sont doubles : premièrement, améliorer l'accessibilité des données d'exécution de processus en créant une interface capable de construire automatiquement la requête de base correspondante à partir du langage naturel. Ceci est complété par la proposition d'une technique de stockage adaptée et d'un langage de requête autour desquels l'interface doit être conçue. À cet égard, nous introduisons un méta-modèle graphique basé sur le graphe de propriétés étiquetées (LPG) pour le stockage efficace des données. Deuxièmement, pour rationaliser la découverte et l'accessibilité des techniques d'exploration de processus, nous présentons une architecture orientée services.Pour valider notre méta-modèle graphique, nous avons utilisé deux ensembles de données de processus accessibles au public disponibles à la fois au format CSV et OCEL. Ces ensembles de données ont été essentiels pour évaluer les performances de notre pipeline de requêtes en langage naturel. Nous avons recueilli des requêtes en langage naturel auprès d'utilisateurs externes et en avons généré d'autres à l'aide d'outils de paraphrase. Notre cadre orienté services a été évalué à l'aide de requêtes en langage naturel spécialement conçues pour les descriptions de services d'exploration de processus. De plus, nous avons mené une étude de cas avec des participants externes pour évaluer l'expérience utilisateur et recueillir des commentaires. Nous fournissons publiquement les résultats de l'évaluation pour garantir la reproductibilité dans le domaine étudié<br>Business processes (BP) are the foundational pillars of organizations, encapsulating a range of structured activities aimed at fulfilling distinct organizational objectives. These processes, characterized by a plethora of tasks, interactions, and workflows, offer a structured methodology for overseeing crucial operations across diverse sectors. A pivotal insight for organizations has been the discernment of the profound value inherent in the data produced during these processes. Process analysis, a specialized discipline, ventures into these data logs, facilitating a deeper comprehension and enhancement of BPs. This analysis can be categorized into two perspectives: instance-level, which focuses on individual process executions, and process-level, which examines the overarching process.However, applying process analysis in practice poses challenges for users, involving the need to access data, navigate low-level APIs, and employ tool-dependent methods. Real-world application often encounters complexities and user-centric obstacles.Specifically, instance-level analysis demands users to access stored process execution data, a task that can be intricate for business professionals due to the requirement of mastering complex query languages like SQL and CYPHER. Conversely, process-level analysis of process data involves the utilization of methods and algorithms that harness process execution data extracted from information systems. These methodologies collectively fall under the umbrella of process mining techniques. The application of process mining confronts analysts with the intricate task of method selection, which involves sifting through unstructured method descriptions. Additionally, the application of process mining methods depends on specific tools and necessitates a certain level of technical expertise.To address these challenges, this thesis introduces AI-driven solutions, with a focus on integrating cognitive capabilities into process analysis to facilitate analysis tasks at both the instance level and the process level for all users. The primary objectives are twofold: Firstly, to enhance the accessibility of process execution data by creating an interface capable of automatically constructing the corresponding database query from natural language. This is complemented by proposing a suitable storage technique and query language that the interface should be designed around. In this regard, we introduce a graph metamodel based on Labeled Property Graph (LPG) for efficient data storage. Secondly, to streamline the discovery and accessibility of process mining techniques, we present a service-oriented architecture. This architecture comprises three core components: an LPG meta-model detailing process mining methods, a service-oriented REST API design tailored for these methods, and a component adept at matching user requirements expressed in natural language with appropriate services.For the validation of our graph metamodel, we utilized two publicly accessible process datasets available in both CSV and OCEL formats. These datasets were instrumental in evaluating the performance of our NL querying pipeline. We gathered NL queries from external users and produced additional ones through paraphrasing tools. Our service-oriented framework underwent an assessment using NL queries specifically designed for process mining service descriptions. Additionally, we carried out a use case study with external participants to evaluate user experience and to gather feedback. We publically provide the evaluation results to ensure reproducibility in the studied area
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Johnson, Barbara Denise. "Modeling Cognitive Authority Relationships." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc955042/.
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Information-seeking behavior is a mixture of activities and attitudes, oftentimes motivated by an individual's need to make a decision. One underlying element of this mixture is cognitive authority - which sources (e.g., individuals, institutions, texts, etc.) can be trusted to fulfil the information needs? In order to gain insight into the dynamics of cognitive authority selection behavior which is an information seeking behavior, this study explored primary source text data (316 text records) that reflected selection in the mundaneness of life (advice column submissions and responses). Linguistic analysis was performed on the data using the Linguistic Inquiry Word Count (LIWC2015) software package. Pearson correlation and 1-sample T tests revealed the same 45 statistically significant relationships (SSRs) in the word usage behavior of all subgroups. As a result of the study, the gap in research formed from the lack of quantitative models of cognitive authority relationships was addressed via the development of the Wordprint Classification System which was used to generate a cognitive authority relationship model in the form of a cognitive authority intra-segment wordprint. The findings and implications of this study may provide a contribution to the body of work in the area of information literacy and information seeker behavior by revealing factors that information scientists can address to help meet information seekers' needs. Additionally, the Wordprint Classification System may be used in such disciplines as psychology, marketing, and forensic linguistics to create to create models of various relationships or individuals through the use of written or spoken word usage patterns.
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Taheri, Sojasi Yousef. "Modeling automated legal and ethical compliance for trustworthy AI." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS225.
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Les avancées en intelligence artificielle ont conduit à des enjeux juridiques et éthiques significatifs liés à la vie privée, aux biais, à la responsabilité, etc. Ces dernières années, de nombreuses réglementations ont été mises en place pour limiter ou atténuer les risques associés à l'IA. Le respect de ces réglementations est nécessaire pour la fiabilité des systèmes d'IA et pour garantir une utilisation responsable. De plus, des systèmes d'IA fiables doivent également être éthiques, en assurant une conformité avec les normes éthiques. La conformité aux lois applicables et l'adhésion aux principes éthiques sont essentielles pour la plupart des applications de l'IA. Nous étudions ce problème du point de vue des agents d'IA. En d'autres termes, comment un agent peut-il garantir que ses actions respectent les normes juridiques et éthiques. Nous nous intéressons aux approches basées sur le raisonnement logique pour intégrer la conformité juridique et éthiques dans le processus de planification de l'agent. Le domaine spécifique dans lequel nous poursuivons notre objectif est le traitement des données personnelles, c'est-à-dire, les actions de l'agent impliquent l'utilisation et le traitement des données personnelles. Une réglementation applicable dans ce domaine est le Règlement Général sur la Protection des Données (RGPD). De plus, le traitement des données personnelles peut entraîner certains risques éthiques en matière de vie privée ou de biais.Nous abordons cette question à travers une série de contributions présentées dans cette thèse. Nous commençons par la question de la conformité au RGPD. Nous adoptons le Calcul des Événements avec la Programmation par Ensembles de Réponses (ASP) pour modéliser les actions des agents et l'utiliser pour planifier et vérifier la conformité au RGPD. Un langage de policy est utilisé pour représenter les obligations et exigences du RGPD. Ensuite, nous examinons la question de la conformité éthique. Un modèle d'utilité ordinale pluraliste est proposé, permettant d'évaluer les actions en fonction des valeurs morales. Ce modèle est basé sur plusieurs critères et utilise des systèmes de vote pour agréger les évaluations sur une échelle ordinale. Nous intégrons ensuite ce modèle d'utilité et le cadre de conformité juridique dans un planificateur de Réseau de Tâches Hiérarchiques (HTN). Dans cette contribution, les normes juridiques sont considérées comme des contraintes "hard" et les normes éthiques comme des contraintes "soft". Enfin, en dernière étape, nous explorons davantage les combinaisons possibles de la conformité juridique et éthique avec l'agent de planification et proposons un cadre unifié. Ce cadre capture l'interaction et les conflits entre les normes juridiques et éthiques et est testé dans un cas d'utilisation avec des systèmes d'IA gérant la livraison d'articles médicaux<br>The advancements in artificial intelligence have led to significant legal and ethical issues related to privacy, bias, accountability, etc. In recent years, many regulations have been put in place to limit or mitigate the risks associated with AI. Compliance with these regulations are necessary for the reliability of AI systems and to ensure that they are being used responsibly. In addition, reliable AI systems should also be ethical, ensuring alignment with ethical norms. Compliance with applicable laws and adherence to ethical principles are essential for most AI applications. We investigate this problem from the point of view of AI agents. In other words, how an agent can ensure the compliance of its actions with legal and ethical norms. We are interested in approaches based on logical reasoning to integrate legal and ethical compliance in the agent's planning process. The specific domain in which we pursue our objective is the processing of personal data. i.e., the agent's actions involve the use and processing of personal data. A regulation that applies in such a domain is the General Data Protection Regulations (GDPR). In addition, processing of personal data may entail certain ethical risks with respect to privacy or bias.We address this issue through a series of contributions presented in this thesis. We start with the issue of GDPR compliance. We adopt Event Calculus with Answer Set Programming(ASP) to model agents' actions and use it for planning and checking the compliance with GDPR. A policy language is used to represent the GDPR obligations and requirements. Then we investigate the issue of ethical compliance. A pluralistic ordinal utility model is proposed that allows one to evaluate actions based on moral values. This model is based on multiple criteria and uses voting systems to aggregate evaluations on an ordinal scale. We then integrate this utility model and the legal compliance framework in a Hierarchical Task Network(HTN) planner. In this contribution, legal norms are considered hard constraints and ethical norm as soft constraint. Finally, as a last step, we further explore the possible combinations of legal and ethical compliance with the planning agent and propose a unified framework. This framework captures the interaction and conflicts between legal and ethical norms and is tested in a use case with AI systems managing the delivery of health care items
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Musgrave, John. "Cognitive Malice Representation and Identification." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1565348664149804.
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Bahers, Quentin. "AI Planning-Based Service Modeling for the Internet of Things." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-246212.
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It is estimated that by 2020, more than 50 billion devices will be interconnected, to form what is called the Internet of Things. Those devices range from consumer electronics to utility meters, including vehicles. Provided with sensory capabilities, those objects will be able to transmit valuable information about their environment, not only to humans, but even more importantly to other machines, which should ultimately be able to interpret and take decisions based on the information received. This “smartness” implies gifting those devices with a certain degree of automation. This Master’s Thesis investigates how recent advances in artificial intelligence planning can be helpful in building such systems. In particular, an artificial intelligence planner able to generate workflows for most of IoT-related use cases has been connected to an IoT platform. A performance study of a state-of-the planner, Fast Downward, on one of the most challenging IoT application, Smart Garbage Collection (which is similar to the Traveling Salesman Problem) has also been carried out. Eventually, different pre-processing and clustering techniques are suggested to tackle the latest AI planners’ inefficiency on quickly finding plans for the most difficult tasks.
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MARRONE, FRANCESCO. "Memristor-based hardware accelerators: from device modeling to AI applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972305.
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Lobaccaro, Luigi <1992>. "Ai confini del senso. La schizofrenia tra semiotica, psicopatologia e scienze cognitive." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10237/1/Ai%20confini%20del%20senso_Luigi%20Lobaccaro.pdf.
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La schizofrenia rappresenta uno dei più grandi enigmi per l’impresa conoscitiva umana: non si conosce la sua eziologia, né le sue basi biologiche e cerebrali. Non è neanche chiaro cosa accada nell’esperienza di chi ne soffre, che sembra vivere in un mondo altro. La scarsa conoscenza dell’esperienza schizofrenica e la distanza tra questa e il senso comune hanno portato molti studiosi a inquadrare questo disturbo come illogico, irrazionale, insensato. Il presente lavoro tenta di confutare tale impostazione, mostrando come il mondo di senso dello schizofrenico si altera, non si disgrega; si trasforma, non si annulla.
Il campo di studi all’interno del quale si colloca la ricerca è la semiotica, disciplina che studia i sistemi e i processi di significazione e i modi attraverso cui l’essere umano dà senso al mondo. L’intera indagine è inserita in un quadro interdisciplinare in costante dialogo con la psicopatologia fenomenologica e le scienze cognitive contemporanee, e si sviluppa a partire da numerosi testi autobiografici di pazienti schizofrenici, report psichiatrici, articoli di giornale, film e romanzi sul tema.
L’ipotesi su cui si muove il lavoro è che sia possibile comprendere la schizofrenia come un problema costitutivamente semiotico, il cui nucleo è da rintracciarsi in una radicale metamorfosi delle modalità di produrre e interpretare il significato. La scommessa sottesa è che la semiotica possa contribuire in modo sostanziale alla comprensione delle modalità attraverso cui la nostra cultura concettualizza la schizofrenia e dei modi in cui gli schizofrenici danno senso al mondo.
Il lavoro indaga, quindi, i legami tra schizofrenia e cultura, la storia del concetto nosografico, e le alterazioni dei processi di significazione nei casi di eloquio disorganizzato, nei racconti autobiografici e nei deliri, cercando anche di fornire strumenti utili alla pratica clinica.<br>Schizophrenia represents one of the greatest enigmas for human knowledge: its aetiology is unknown, as its biological and neurological basis. It is even unclear what happens in the sufferers' experience, who seem to live in another world. The lack of knowledge of the schizophrenic experience and the distance between this experience and common sense have led many scholars to define this disorder as illogical, irrational, and senseless. On the contrary, the present work shows how the schizophrenic world of meaning alters, does not disintegrate; it changes, does not dissolve.
The investigation is situated in an interdisciplinary framework where Semiotics, the discipline that accounts for systems and processes of signification, dialogues with phenomenological psychopathology and contemporary cognitive sciences. The inquiry is developed by taking into account several autobiographical texts of schizophrenic patients, psychiatric reports, newspaper articles, movies and novels on this subject.
The leading hypothesis is that it is possible to understand schizophrenia as a constitutively semiotic problem, represented by a radical metamorphosis of meaning production and interpretation. So the bet we made is that Semiotics can contribute to understanding how our culture conceptualises schizophrenia, and the ways in which people with schizophrenia make sense of the world.
Therefore, the thesis investigates the links between schizophrenia and culture, the history of the nosographic concept, and the alterations of the signification processes in disorganised speech, autobiographical narratives, and delusions, providing valuable tools for clinical practice.
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Bican, Can. "An Evaluation Of Cognitive Modeling Tools." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608425/index.pdf.
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This thesis evaluates several aspects of the cognitive modeling tools, using a questionnaire as the survey method. We try to assess the the suitability for cognitive modeling task of the cognitive modeling tools, from the perspective of international community of cognitive modeling tool users. Part of this assessment is done with respect to general usability of software and the rest is specialized for the cognitive modeling issues. Frequency and correlation analyses reveal that there is a significant relationship between suitability as a software product and suitability as a cognitive modeling tool. Specifically, there are correlations between the features of the tool involving flexibility, presentation of input and output and the process of design, implementation and evaluation of a cognitive modeling tool, while these processes are negatively related to adversely effecting features of the tool, such as having to do extra tasks that are not related to the actual task. Our study confirms that a cognitive modeling tool can also be evaluated from the perspective of a general purpose software product, and also gives clues about directions for improvement to tool developers.
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Carbonaro, Michael David. "Computational cognitive modeling of concept attainment." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22960.pdf.
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Termini, Pietro Savio. "Insect brain modeling for cognitive robotics." Doctoral thesis, Università di Catania, 2013. http://hdl.handle.net/10761/1304.
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During his history, man has always tried to imitate nature, to better know and understand himself.
Thanks to the improvement of electronic technologies, the instruments with which he has followed this target have been improved, with results more and more realistic. Trying to artificially reproduce what nature has slowly realized and time tested, humans find amazing problems and solutions, that give a contribution to their life quality. Robots are the way humans try to reproduce animals, and study their interaction with the environment. Robots help to better know animals and animals help to create efficient robots. Analyzing animal brains leads to new control systems that could allow robots to be able to orient themselves, to take decisions, to survive alone, to complete dangerous missions, to be completely autonomous. Robot implementation could also lead to predictions, for basic science, deriving from emergent properties of models.
The goal of this work is to design and simulate a simplified model of the brain of the Drosophila melanogaster.
Fly has a small brain that shows a wealth of complex behaviors. Genetic techniques allow to remove parts of the Drosophila brain and the analysis of mutants behavior can lead to hypotheses about the functions of every single brain part.
The bio-inspired models have been implemented in robotic structures and in robot simulators, in order to make comparisons between the real fly and the modeled one. This PhD research has been oriented toward two main lines: the first one has been directed to the insect brain modeling;
the second line has been oriented to the simulation and the implementation of the model in robotic structures and then to the analysis of the behavior of the robots in order to obtain comparative results with real flies as well as predictions about the behavior of the real flies under particular circumstances.
This thesis is divided in seven chapters. The first four chapters describe an original robotic architecture inspired by
the fruit fly \emph{Drosophila melanogaster}. In the last three chapters, instead,
one peculiar part of the insect brain, the Mushroom Bodies, is analyzed and modeled.
This structure presents a lot of interesting capabilities and it deserves attention and investigation.
Biological details will be given step by step, in order to emphasize the link between
biology and models
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Maccarini, Francesco. "Modeling orchestration for computer assisted analysis and human-AI co-creativity." Electronic Thesis or Diss., Université de Lille (2022-....), 2024. http://www.theses.fr/2024ULILB047.
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L'orchestration est l'art de composer pour un ensemble d'instruments, impliquant le mélange et le contraste des timbres instrumentaux pour créer un son orchestral cohérent. Elle exige à la fois une planification artistique de haut niveau des idées musicales et un contrôle précis et minutieux des détails techniques. Chaque partie doit pouvoir être jouée sur l'instrument qui lui est propre et s'accorder avec le reste de l'ensemble.Cette thèse vise à formaliser et à élargir les connaissances sur l'orchestration en la décrivant sous l'angle des mathématiques et de l'informatique, tout en proposant des modèles et des outils pour l'analyse informatique de la musique orchestrale et l'orchestration co-créative homme-machine.L'un des points clés de cette thèse est le concept de *texture orchestrale*, qui fait référence aux rôles, fonctions et combinaisons des parties instrumentales au sein d'une composition. Nous présentons trois modèles abstraits d'orchestration, nous publions un corpus multimodal de partitions orchestrales annotées et nous proposons un cadre pour l'orchestration assistée par ordinateur, qui a été appliqué avec succès dans un projet d'orchestration co-créative<br>Orchestration is the art of composing for an ensemble of instruments, involving the blending and contrasting of instrumental timbres to create a cohesive orchestral sound. It requires both high-level artistic planning of musical ideas and precise, fine-grained control over technical details. Each part must be playable on its specific instrument and fit well with the rest of the ensemble.This thesis aims at formalizing and expanding the knowledge of orchestration by describing it through the lens of mathematics and computer science, while proposing models and tools for the computational analysis of orchestral music and human-machine co-creative orchestration.A key focus of this thesis is the concept of *orchestral texture*, which refers to the roles, functions, and combinations of the instrumental parts within a composition. We introduce three abstract models of orchestration, release a multi-modal corpus of annotated orchestral scores, and propose a framework for computer-assisted orchestration, which has been successfully applied in a co-creative orchestration project
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Atasoy, Guzide. "Using Cognitive Maps For Modeling Project Success." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608545/index.pdf.
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In order to evaluate a project as successful or not, initially, the questions of &ldquo<br>what are
the factors affecting the success&rdquo<br>and &ldquo<br>according to whom and which criteria should
the success be measured&rdquo<br>should be answered. Both the factors and their influences
vary depending on a project&rsquo<br>s specific characteristics, different environmental factors
affecting it, and different parties involved. These factors are not independent of each
other and the interrelationship between them should be investigated as a whole in
order to model the project success. Moreover, parties involved in a project usually
have different objectives and the performance indicators used to measure project
success differ according to company priorities, preferences and attitudes. Thus, there
exists a need to develop a project success model that contains the interrelationships
between factors such as risks, decisions, and strategies, project success criteria,
objectives and the relations of the factors with the objectives. A cognitive map (CM) is a strong visual tool to reflect the beliefs and knowledge of
people about a situation or domain, identifying the causes, effects and the relations
between them. This qualitative technique being enhanced by quantifiable properties
makes it appropriate to be utilized to model the project success.
As a result, the objective of this study is to demonstrate the application of CMs as a
powerful tool for modeling project success. It is hypothesized that CMs can be
effectively used to model the factors affecting success of a construction project, to
reflect the interrelations between project success factors, to demonstrate the different
objectives of parties involved in a project and show how the project success can be
defined differently, by different parties. This technique is applied to a real
construction project realized in Turkey. CMs of two consortium contractors and
client organization involved in the project are constructed and the differences
between the perceptions of three parties are revealed by content and structural
analyses. Finally, the benefits and shortcomings of using CMs for modeling project
success are discussed by referring to case study findings.
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Cornel, Reuben Francis. "Coglaborate - An Environment For Collaborative Cognitive Modeling." NCSU, 2009. http://www.lib.ncsu.edu/theses/available/etd-11052009-142649/.
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Cognitive scientists who build computational models of their work, as exemplified by the ACT-R and Soar research communities, have limited means of sharing knowledge: annual conferences and workshops, summer schools, and model code distributed via Web sites. The consequence is that results obtained by different groups are scattered across the Internet, making it difficult for researchers to obtain a comprehensive view of cognitive modeling research. The goal of my project is to develop a collaborative modeling environment for cognitive scientists in which they can develop and share models. The current system supports collaboration by providing a structured representation for ACT-R cognitive models using frames. The rationale for providing a structured representation for cognitive models is two-fold: it not only provides a mechanism for sharing models (i.e. via consistent APIs); it also enables the application of analytical techniques to cognitive models. As a proof of concept for the approach, a medium-scale modeling application has been developed, integrating an extension of ACT-R developed elsewhere, to solve synonym crossword puzzles.
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KRAYANI, ALI. "Learning Self-Awareness Models for Physical Layer Security in Cognitive and AI-enabled Radios." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1074612.
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Cognitive Radio (CR) is a paradigm shift in wireless communications to resolve the spectrum scarcity issue with the ability to self-organize, self-plan and self-regulate. On the other hand, wireless devices that can learn from their environment can also be taught things by malicious elements of their environment, and hence, malicious attacks are a great concern in the CR, especially for physical layer security. This thesis introduces a data-driven Self-Awareness (SA) module in CR that can support the system to establish secure networks against various attacks from malicious users. Such users can manipulate the radio spectrum to make the CR learn wrong behaviours and take mistaken actions. The SA module consists of several functionalities that allow the radio to learn a hierarchical representation of the environment and grow its long-term memory incrementally. Therefore, this novel SA module is a way forward towards realizing the original vision of CR (i.e. Mitola's Radio) and AI-enabled radios. This thesis starts with a basic SA module implemented in two applications, namely the CR-based IoT and CR-based mmWave. The two applications differ in the data dimensionality (high and low) and the PHY-layer level at which the SA module is implemented. Choosing an appropriate learning algorithm for each application is crucial to achieving good performance. To this purpose, several generative models such as Generative Adversarial Networks, Variational AutoEncoders and Dynamic Bayesian Networks, and unsupervised machine learning algorithms such as Self Organizing Maps Growing Neural Gas with different configurations are proposed, and their performances are analysed. In addition, we studied the integration of CR and UAVs from the physical layer security perspective. It is shown how the acquired knowledge from previous experience within the Bayesian Filtering facilitates the radio spectrum perception and allows the UAV to detect any jamming attacks immediately. Moreover, exploiting the generalized errors during abnormal situations permits characterizing and identifying the jammer at multiple levels and learning a dynamic model that embeds its dynamic behaviour. Besides, a proactive consequence can be drawn after estimating the jammer's signal to act efficiently by mitigating its effects on the received stimuli or by designing an efficient resource allocation for anti-jamming using Active Inference. Experimental results show that introducing the novel SA functionalities provides the high accuracy of characterizing, detecting, classifying and predicting the jammer's activities and outperforms conventional detection methods such as Energy detectors and advanced classification methods such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Stacked Autoencoder (SAE). It also verifies that the proposed approach achieves a higher degree of explainability than deep learning techniques and verifies the capability to learn an efficient strategy to avoid future attacks with higher convergence speed compared to conventional Frequency Hopping and Q-learning.
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Gestlöf, Rikard, and Johannes Sörman. "Contact-free Cognitive Load Classification based on Psycho-Physiological Parameters." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44695.
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Cognitive load (CL) is a concept that describes the relationship between the cognitive demands from a task and the environment the task is taking place in, which influences the user’s cognitive resources. High cognitive load leads to higher chance of a mistake while a user is performing a task. CL has great impact on driving performance, although the effect of CL is task dependent. It has been proven that CL selectively impairs non-automized aspects of driving performance while automized driving tasks are unaffected. The most common way of measuring CL is electroencephalography (EEG), which might be a problem in some situations since its contact-based and must be connected to the head of the test subject. Contact-based ways of measuring different physiological parameters can be a problem since they might affect the results of the research. Since the wirings sometimes might be loose and that the test subject moves etc. However, the biggest concern with contact-based systems is that they are hard to involve practically. The reason for this is simply that a user cannot relax, and that the sensors attached to the test subjects can affect them to not provide normal results. The goal of the research is to test the performance of data gathered with a contact-free camera-based system compared to a contact-based shimmer GSR+ system in detecting cognitive load. Both data collection approaches will extract the heart rate (HR) and interbeat interval (IBI) while test subjects perform different tasks during a controlled experiment. Based on the gathered IBI, 13 different heart rate variability (HRV) features will be extracted to determine different levels of cognitive load. In order to determine which system that is better at measuring different levels of CL, three major stress level phases were used in a controlled experiment. These three stress level phases were the reference point for low CL where test subjects sat normal (S0), normal CL where the test subjects performed easy puzzles and drove normally in a video game (S1) and high CL where the test subjects completed hard puzzles and drove on the hardest course of a video game while answering math questions (S2). To classify the extracted HRV features from the data into the three different levels of CL two different machine learning (ML) algorithms, support vector machine (SVM) and k-nearest-neighbor (KNN) were implemented. Both binary and multiclass feature matrixes were created with all combinations between the different stress levels of the collected data. In order to get the best classification accuracy with the ML algorithms, different optimizations such as kernelfunctions were chosen for the different feature matrixes. The results of this research proved that the ML algorithms achieved a higher classification accuracy for the data collected with the contact-free system than the shimmer sense system. The highest mean classification accuracy was 81% on binary classification for S0-S2 collected by the camera while driving using Fine KNN. The highest F1 score was 88%, which was achieved with medium gaussian SVM for the class combination S0-(S1/S2) feature matrix recorded with the camera system. It was concluded that the data gathered with the contact-free camera system achieved a higher accuracy than the contact-based system. Also, that KNN achieved the higher accuracy overall, than SVM for the data. This research proves that a contact-free camera-based system can detect cognitive better than a contact-based shimmer sense GSR+ system with a high classification accuracy.
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Wheeler, Michael. "The philosophy of situated activity." Thesis, University of Sussex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363345.
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POCHET, AXELLE DANY JULIETTE. "MODELING OF GEOBODIES: AI FOR SEISMIC FAULT DETECTION AND ALL-QUADRILATERAL MESH GENERATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35861@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>A exploração segura de reservatórios de petróleo necessita uma boa modelagem numérica dos objetos geológicos da sub superfície, que inclui entre outras etapas: interpretação sísmica e geração de malha. Esta tese apresenta um estudo nessas duas áreas. O primeiro estudo é uma contribuição para interpretação de dados sísmicos, que se baseia na detecção automática de falhas sísmicas usando redes neurais profundas. Em particular, usamos Redes Neurais Convolucionais (RNCs) diretamente sobre mapas de amplitude sísmica, com a particularidade de usar dados sintéticos para treinar a rede com o objetivo final de classificar dados reais. Num segundo estudo, propomos um novo algoritmo para geração de malhas bidimensionais de quadrilaterais para estudos geomecânicos, baseado numa abordagem inovadora do método de quadtree: definimos novos padrões de subdivisão para adaptar a malha de maneira eficiente a qualquer geometria de entrada. As malhas obtidas podem ser usadas para simulações com o Método de Elementos Finitos (MEF).<br>Safe oil exploration requires good numerical modeling of the subsurface geobodies, which includes among other steps: seismic interpretation and mesh generation. This thesis presents a study in these two areas. The first study is a contribution to data interpretation, examining the possibilities of automatic seismic fault detection using deep learning methods. In particular, we use Convolutional Neural Networks (CNNs) on seismic amplitude maps, with the particularity to use synthetic data for training with the goal to classify real data. In the second study, we propose a new two-dimensional all-quadrilateral meshing algorithm for geomechanical domains, based on an innovative quadtree approach: we define new subdivision patterns to efficiently adapt the mesh to any input geometry. The resulting mesh is suited for Finite Element Method (FEM) simulations.
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Zhang, Yueqian. "Resource Clogging Attacks in Mobile Crowd-Sensing: AI-based Modeling, Detection and Mitigation." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40082.
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Mobile Crowdsensing (MCS) has emerged as a ubiquitous solution for data collection from embedded sensors of the smart devices to improve the sensing capacity and reduce the sensing costs in large regions. Due to the ubiquitous nature of MCS, smart devices require cyber protection against adversaries that are becoming smarter with the objective of clogging the resources and spreading misinformation in such a non-dedicated sensing environment. In an MCS setting, one of the various adversary types has the primary goal of keeping participant devices occupied by submitting fake/illegitimate sensing tasks so as to clog the participant resources such as the battery, sensing, storage, and computing. With this in mind, this thesis proposes a systematical study of fake task injection in MCS, including modeling, detection, and mitigation of such resource clogging attacks.
We introduce modeling of fake task attacks in MCS intending to clog the server and drain battery energy from mobile devices. We creatively grant mobility to the tasks for more extensive coverage of potential participants and propose two take movement patterns, namely Zone-free Movement (ZFM) model and Zone-limited Movement (ZLM) model. Based on the attack model and task movement patterns, we design task features and create structured simulation settings that can be modified to adapt different research scenarios and research purposes.
Since the development of a secure sensing campaign highly depends on the existence of a realistic adversarial model. With this in mind, we apply the self-organizing feature map (SOFM) to maximize the number of impacted participants and recruits according to the user movement pattern of these cities. Our simulation results verify the magnified effect of SOFM-based fake task injection comparing with randomly selected attack regions in terms of more affected recruits and participants, and increased energy consumption in the recruited devices due to the illegitimate task submission.
For the sake of a secure MCS platform, we introduce Machine Learning (ML) methods into the MCS server to detect and eliminate the fake tasks, making sure the tasks arrived at the user side are legitimate tasks. In our work, two machine learning algorithms, Random Forest and Gradient Boosting are adopted to train the system to predict the legitimacy of a task, and Gradient Boosting is proven to be a more promising algorithm. We have validated the feasibility of ML in differentiating the legitimacy of tasks in terms of precision, recall, and F1 score. By comparing the energy-consuming, effected recruits, and impacted candidates with and without ML, we convince the efficiency of applying ML to mitigate the effect of fake task injection.
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Reynolds, Hayley J. Davison (Hayley Jaye Davison). "Modeling the air traffic controller's cognitive projection process." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35589.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.<br>Includes bibliographical references (p. 117-123).<br>Cognitive projection enables the operator of a supervisory control system, such as air traffic control, to use predicted future behavior of the system to make decisions about if and how to control the system. New procedures and technologies being implemented in the air traffic control system innately affect the information used for projection and the type of projection required from the controller. Because cognitive projection is not well-understood, launching these projection-impacting technologies and procedures could result in the reluctance of the air traffic controllers to accept these advancements or limit the system performance. A Projection Process Model and a Projection Error Concept were proposed to describe the controller's projection process and the contextual system influences on the projection process. The two primary influences on the projection process were information/display system and task-based projection requirements. A mismatch between the information/display system states and the task-based projection requirements was described through a cognitive transform concept. The projection process itself is composed of the state mental model and the time into the future over which the projection is made.<br>(cont.) Hypotheses based on the assumptions of the Projection Process Model and Projection Error Concept were probed through an experiment using an ATC task paradigm. Results were consistent with the proposed models. They suggested that the controllers were able to incorporate higher-level dynamics into the state mental models used for projection and that the quality of the state mental model used was marginally influenced by the error tolerance required in the task. The application of the Projection Process Model and Projection Error Concept was then illustrated through the analysis of the impact on projection from two ATC domain examples of technology and procedure implementation. The Constant Descent Approach Procedure in the TRACON impacted the intent, projection timespan, and abstractions used in the mental model of the controllers. The Oceanic ATC surveillance, communication and workstation improvements resulted in an impact on the states to be projected, intent, projection timespan, and human/automation projection responsibility. Suggestions for improved transition for the projection process were then provided based on the analysis.<br>by Hayley J. Davison Reynolds.<br>Ph.D.
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Jin, Lifeng. "Computational Modeling of Syntax Acquisition with Cognitive Constraints." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1594934826359118.
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Schultheis, Holger. "Computational cognitive modeling of control in spatial cognition." Lengerich Berlin Bremen Miami, Fla. Riga Viernheim Wien Zagreb Pabst Science Publ, 2009. http://d-nb.info/998029661/04.
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Stegner, Wayne. "Context-Aware Malware Detection Using Topic Modeling." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin162766765703398.
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Kurup, Unmesh. "Design and use of a bimodal cognitive architecture for diagrammatic reasoning and cognitive modeling." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1198526352.
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Watt, Stuart Neil Kennaway. "Seeing things as people : anthropomorphism and common-sense psychology." Thesis, Open University, 1997. http://oro.open.ac.uk/57722/.
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This thesis is about common-sense psychology and its role in cognitive science. Put simply, the argument is that common-sense psychology is important because it offers clues to some complex problems in cognitive science, and because common-sense psychology has significant effects on our intuitions, both in science and on an everyday level. The thesis develops a theory of anthropomorphism in common-sense psychology. Anthropomorphism, the natural human tendency to ascribe human characteristics (and especially human mental characteristics) to things that aren't human, is an important theme in the thesis. Anthropomorphism reveals an endemic anthropocentricity that deeply influences our thinking about other minds. The thesis then constructs a descriptive model of anthropomorphism in common-sense psychology, and uses it to analyse two studies of the ascription of mental states. The first, Baron- Cohen et al. 's (1985) false belief test, shows how cognitive modelling can be used to compare different theories of common-sense psychology. The second study, Searle's (1980) `Chinese Room', shows 'that this same model can reproduce the patterns of scientific intuitions taken to systems which pass the Turing test (Turing, 1950), suggesting that it is best seen as a common-sense test for a mind, not a scientific one. Finally, the thesis argues that scientific theories involving the ascription of mentality through a model or a metaphor are partly dependent on each individual scientist's common-sense psychology. To conclude, this thesis develops an interdisciplinary study of common-sense psychology and shows that its effects are more wide ranging than is commonly thought. This means that it affects science more than might be expected, but that careful study can help us to become mindful of these effects. Within this new framework, a proper understanding of common-sense psychology could lay important new foundations for the future of cognitive science.
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TOMA, ANDREA. "PHY-layer Security in Cognitive Radio Networks through Learning Deep Generative Models: an AI-based approach." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1003576.
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Recently, Cognitive Radio (CR) has been intended as an intelligent radio endowed with cognition which can be developed by implementing Artificial Intelligence (AI) techniques. Specifically, data-driven Self-Awareness (SA) functionalities, such as detection of spectrum abnormalities, can be effectively implemented as shown by the proposed research. One important application is PHY-layer security since it is essential to establish secure wireless communications against external jamming attacks.
In this framework, signals are non-stationary and features from such kind of dynamic spectrum, with multiple high sampling rate signals, are then extracted through the Stockwell Transform (ST) with dual-resolution which has been proposed and validated in this work as part of spectrum sensing techniques.
Afterwards, analysis of the state-of-the-art about learning dynamic models from observed features describes theoretical aspects of Machine Learning (ML). In particular, following the recent advances of ML, learning deep generative models with several layers of non-linear processing has been selected as AI method for the proposed spectrum abnormality detection in CR for a brain-inspired, data-driven SA.
In the proposed approach, the features extracted from the ST representation of the wideband spectrum are organized in a high-dimensional generalized state vector and, then, a generative model is learned and employed to detect any deviation from normal situations in the analysed spectrum (abnormal signals or behaviours). Specifically, conditional GAN (C-GAN), auxiliary classifier GAN (AC-GAN), and deep VAE have been considered as deep generative models.
A dataset of a dynamic spectrum with multi-OFDM signals has been generated by using the National Instruments mm-Wave Transceiver which operates at 28 GHz (central carrier frequency) with 800 MHz frequency range. Training of the deep generative model is performed on the generalized state vector representing the mmWave spectrum with normality pattern without any malicious activity. Testing is based on new and independent data samples corresponding to abnormality pattern where the moving signal follows a different behaviour which has not been observed during training.
An abnormality indicator is measured and used for the binary classification (normality hypothesis otherwise abnormality hypothesis), while the performance of the generative models is evaluated and compared through ROC curves and accuracy metrics.
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Akter, Lutfa. "Modeling, forecasting and resource allocation in cognitive radio networks." Diss., Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/3892.
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Berman, Ohad. "Modeling cognitive and tactical aspects in hunter-killer missions." Thesis, Monterey, Calif. : Naval Postgraduate School, 2006. http://bosun.nps.edu/uhtbin/hyperion.exe/06Dec%5FBerman.pdf.
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Thesis (M.S. in Operations Research)--Naval Postgraduate School, December 2006.<br>Thesis Advisor(s): Moshe Kress, Kyle Lin. "December 2006." Includes bibliographical references (p. 87-89). Also available in print.
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Urgen, Burcu Aysen. "A Philosophical Analysis Of Computational Modeling In Cognitive Science." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608832/index.pdf.
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This study analyses the methodology of computational cognitive modeling as one of the ways of conducting research in cognitive science. The aim of the study is to provide an understanding of the place of computational cognitive models in understanding human cognition. Considering the vast number of computational cognitive models which have been just given to account for some cognitive phenomenon by solely simulating some experimental study and fitting to empirical data, a practice-oriented approach is adopted in this study to understand the work of the modeler, and accordingly to discover the potential of computational cognitive models, apart from their being simulation tools. In pursuit of this aim, a framework with a practice-oriented approach from the philosophy of science literature, which is Morgan &<br>Morrison (1999)&rsquo<br>s account, is employed on a case study. The framework emphasizes four key elements to understand the place of models in science, which are the construction of models, the function of models, the representation they provide, and the ways we learn from models. The case study
Q-Soar (Simon, Newell &<br>Klahr, 1991), is a model built with Soar cognitive architecture (Laird, Newell &<br>Rosenbloom, 1987) which is representative of a class of computational cognitive models. Discussions are included for how to make generalizations for computational cognitive models out of this class, i.e. for models that are built with other modeling paradigms.
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Kilic, Ozkan. "Cognitive Aspects Of Conceptual Modeling Diagrams: An Experimental Study." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12608849/index.pdf.
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This thesis is about diagrammatic reasoning and error-finding in conceptual modeling diagrams. Specifically, the differences of the cognitive strategies and behaviors of notation-familiar participants versus domain-familiar participants working on conceptual modeling diagrams are inspected. The domain-familiar participants are experienced in the topic being represented, but they do not have any formal training in software development representations. On the other hand, the notation-familiar participants are educated in software representations, but unfamiliar with the topic represented. The main experiment and the follow-up experiment also aim to study how some properties of diagrams affect the error-finding behaviors. The participant groups&rsquo<br>performances in the main experiment are investigated and compared by the analysis of verbal protocol data and eye movement data. The combination of the two different methods enhances detailed analyses. In the follow-up experiment, only eye movement data is involved to evaluate how some properties of diagrams affect problem-solving. By means of both experiments, it is concluded that diagrammatic complexity has a negative effect on reasoning whereas the degree of causal chaining improves diagrammatic reasoning. In the main experiment, some differences in the diagrammatic reasoning processes between the groups are observed, too. The notation-familiar participants are observed to be more successful in error-finding although they are unfamiliar with the topic. This study underlines the interaction of cognitive science and software engineering by integrating eye movement data, verbal protocol analysis and performance data into the cognitive inspection of software engineering notations.
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Trapani, Catherine Schuler. "Multilevel modeling of cognitive ability in highly functioning adults." Thesis, Fordham University, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3564971.
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<p> The goal of this research was to study differences in cognitive performance on verbal and quantitative measures among subjects of different ages. Data was gathered on subjects ranging in age from 16 to 80 years of age from birth-cohorts from 1927 to 1990. In addition to year of birth, personal characteristics of gender, race/ethnicity and undergraduate area of study were obtained. Multilevel models were built that predict cognitive performance as a function of age, cohort and other non-independent personal characteristics . Verbal performance rises as the age of the test-taker rises; quantitative performance declines as the age of the test-taker rises. After controlling for the race/ethnicity and gender of the test-taker, there are both age and cohort effects for verbal and quantitative models. On the verbal measure, the cohort effect favors those test-takers born at an earlier time. There is an interaction between age and cohort on the quantitative measure. This data is secondary analysis and the records are from those test-takers who choose to take a consequential assessment. When the multilevel models are produced independently for those test-takers ages 20-39 and those ages 40-64, different results are seen between the two age groups. There is little difference in performance for 20-39 year olds on the verbal measure other than a positive effect for age at time of test. For the test-taker aged 40-64, there is a positive effect due to age, a positive cohort effect and a negative interaction effect between age and science study. Comparing the 20-39 year olds with the 40-64 year olds on the quantitative measure, the decline in performance for the older group is one-fourth the rate of decline in the younger group. For the quantitative measure, after controlling for age, there is a positive cohort effect for both age groups. </p>
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Rao, Rashmi Jayathirtha. "Modeling learning behaviour and cognitive bias from web logs." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492560600002105.
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Johnson, Joseph G. "A computational modeling account of robust preference reversal phenomena." [Bloomington, Ind.] : Indiana University, 2004. http://wwwlib.umi.com/dissertations/fullcit/3162242.
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Thesis (Ph.D.)--Indiana University, Dept. of Psychology, 2004.<br>Title from PDF t.p. (viewed Dec. 1, 2008). Source: Dissertation Abstracts International, Volume: 66-01, Section: B, page: 0586. Chair: Jerome R. Busemeyer.
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Perdue, Nicholas. "Cognitive Agents and Pedestrian-Oriented Redevelopment." Thesis, University of Oregon, 2016. http://hdl.handle.net/1794/20675.
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Walking is one of the most commonplace forms of human expressions, yet the forms, motivations, and practices of walking vary greatly and are often at odds with dominant discourses in urban and transportation planning. As interest in pedestrian-oriented studies continues to grow, there is danger that dominant discourses will continue to reinforce the framing of pedestrians and the practices of walking as slower moving versions of the private automobile and ignore deeply embedded emotional, personal, and cognitive aspects. As such, understandings of pedestrian transportation and human agency during walking must be explored in increasingly human-centered terms in order to understand how changes to the material environment actually impact people and daily practices. The purpose of this dissertation is to give considerably more attention to the human elements of walking by creating a set of new theoretical and practical frameworks for deeper representations of the pedestrian in the urban space and within a larger transportation system. The three articles presented in this dissertation outline an alternative, human-centered representation of the pedestrian, providing theoretical, methodological, and practical solutions to conceptualize how soft variables such as emotion, motivation, and especially cognition influence the practices of walking.<br>10000-01-01
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Eklund, Joakim, and Fred Isaksson. "Identifying & Evaluating SystemComponents for Cognitive Trustin AI-Automated Service Encounters : Trusting a Study- & Vocational Chatbot." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-386044.
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The intensifying idea that AI soon will be a part of our everyday life allows for dreams about the complex relationship we one day could have with non-biological social intelligence. However, establishing societal and individual acceptance of AI-powered autonomy in disciplines built upon to the reliance to human competence raises a number of pressing challenges. One of them being, what system components will engender respectively counteract cognitive trust in socially oriented AI-automated processes? This masters thesis tackles the seemingly ambiguous concept of trust in automation by identifying and evaluating system components that affect trust in a confined and contextualised setting. Practically, we design, construct and test an AI-powered chatbot, Ava, that contains socially oriented questions and feedback about study- and vocational guidance. Through a comparative study of different system versions, including both quantitative and qualitative data, we contribute to the framework for identifying and evaluating human trust in AI-Automated service encounters. We show how targeted alterations to design choices constituting the system components transparency, unbiasses and system performance, identified to affect trust, has consequences on the perception of the cognitive trust concepts integrity, benevolence and ability. Our results display a way of conduct for practitioners looking to prioritise and develop trustworthy autonomy. More specifically, we account for how cognitive trust is decreased when system opacity is increased. Moreover, we display even more concerning effects on trust due to micking contextual bias in the conversation agent.
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Stuhlmüller, Andreas. "Modeling cognition with probabilistic programs : representations and algorithms." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100860.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 167-176).<br>This thesis develops probabilistic programming as a productive metaphor for understanding cognition, both with respect to mental representations and the manipulation of such representations. In the first half of the thesis, I demonstrate the representational power of probabilistic programs in the domains of concept learning and social reasoning. I provide examples of richly structured concepts, defined in terms of systems of relations, subparts, and recursive embeddings, that are naturally expressed as programs and show initial experimental evidence that they match human generalization patterns. I then proceed to models of reasoning about reasoning, a domain where the expressive power of probabilistic programs is necessary to formalize our intuitive domain understanding due to the fact that, unlike previous formalisms, probabilistic programs allow conditioning to be represented in a model, not just applied to a model. I illustrate this insight with programs that model nested reasoning in game theory, artificial intelligence, and linguistics. In the second half, I develop three inference algorithms with the dual intent of showing how to efficiently compute the marginal distributions defined by probabilistic programs, and providing building blocks for process-level accounts of human cognition. First, I describe a Dynamic Programming algorithm for computing the marginal distribution of discrete probabilistic programs by compiling to systems of equations and show that it can make inference in models of "reasoning about reasoning" tractable by merging and reusing subcomputations. Second, I introduce the setting of amortized inference and show how learning inverse models lets us leverage samples generated by other inference algorithms to compile probabilistic models into fast recognition functions. Third, I develop a generic approach to coarse-to-fine inference in probabilistic programs and provide evidence that it can speed up inference in models with large state spaces that have appropriate hierarchical structure. Finally, I substantiate the claim that probabilistic programming is a productive metaphor by outlining new research questions that have been opened up by this line of investigation.<br>by Andreas Stuhlmüller.<br>Ph. D.
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Ding, Xiuhua. "MODELING DEMENTIA RISK, COGNITIVE CHANGE, PREDICTIVE RULES IN LONGITUDINAL STUDIES." UKnowledge, 2016. http://uknowledge.uky.edu/epb_etds/9.
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Dementia is increasing recognized as a major problem to public health worldwide. Prevention and treatment strategies are in critical need. Nowadays, research for dementia usually featured as complex longitudinal studies, which provide extensive information and also propose challenge to statistical methodology. The purpose of this dissertation research was to apply statistical methodology in the field of dementia to strengthen the understanding of dementia from three perspectives: 1) Application of statistical methodology to investigate the association between potential risk factors and incident dementia. 2) Application of statistical methodology to analyze changes over time, or trajectory, in cognitive tests and symptoms. 3) Application of statistical learning methods to predict development of dementia in the future.
Prevention of Alzheimer’s disease with Vitamin E and Selenium (PREADViSE) (7547 subjects included) and Alzheimer’s disease Neuroimaging Initiative (ADNI) (591 participants included) were used in this dissertation. The first study, “Self-reported sleep apnea and dementia risk: Findings from the PREADViSE Alzheimer’s disease prevention trial ”, shows that self-reported baseline history of sleep apnea was borderline significantly associated with risk of dementia after adjustment for confounding. Stratified analysis by APOE ε4 carrier status showed that baseline history of sleep apnea was associated with significantly increased risk of dementia in APOE ε4 non-carriers. The second study, “comparison of trajectories of episodic memory for over 10 years between baseline normal and MCI ADNI subjects,” shows that estimated 30% normal subjects at baseline assigned to group 3 and 6 stay stable for over 9 years, and normal subjects at baseline assigned to Group 1 (18.18%) and Group 5 (16.67%) were more likely to develop into dementia. In contrast to groups identified for normal subjects, all trajectory groups for MCI subjects at baseline showed the tendency to decline. The third study, “comparison between neural network and logistic regression in PREADViSE trial,” demonstrates that neural network has slightly better predictive performance than logistic regression, and also it can reveal complex relationships among covariates. In third study, the effect of years of education on response variable depends on years of age, status of APOE ɛ4 allele and memory change.
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Babaei, Alireza. "Statistical interference modeling and coexistence strategies in cognitive wireless networks." Fairfax, VA : George Mason University, 2009. http://hdl.handle.net/1920/4596.
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Thesis (Ph.D.)--George Mason University, 2009.<br>Vita: p. 83. Thesis director: Bijan Jabbari. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical and Computer Engineering. Title from PDF t.p. (viewed Oct. 12, 2009). Includes bibliographical references (p. 79-82). Also issued in print.
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Nguyen, Quoc Tim H. "Modeling completion at a community college." Thesis, California State University, Long Beach, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=1523327.
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<p> The purpose of the current study was to assess a model of college completion at a 2-year community college based on Tinto's Theory of Student Drop Out and current factors known to impact college completion. A freshman cohort (<i>n</i> = 2,846) that attended a large-urban community college was assessed. Logistic regression analysis found student age and math proficiency when entering college were significant factors in the model. The older the student was when first enrolling, the lower their likelihood of completing college. The more remediation a student needed in math skills, then the less likely she or he was in completing college. Placement into developmental (remedial) English writing courses did not seem to suppress completion, and was a non-significant finding in the model. Reading proficiency and participation in a student success course (first-year seminar) were not significant factors in the model, though estimated coefficients aligned with research literature.</p>
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Benson, Carol Trinko Jones Graham A. "Assessing students' thinking in modeling probability contexts." Normal, Ill. Illinois State University, 2000. http://wwwlib.umi.com/cr/ilstu/fullcit?p9986725.
Full textAbstract:
Thesis (Ph. D.)--Illinois State University, 2000.<br>Title from title page screen, viewed May 11, 2006. Dissertation Committee: Graham A. Jones (chair), Kenneth N. Berk, Patricia Klass, Cynthia W. Langrall, Edward S. Mooney. Includes bibliographical references (leaves 115-124) and abstract. Also available in print.
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Swan, Bret R. "The Effects of Business Process Management Cognitive Resources and User Cognitive Differences on Outcomes of User Comprehension." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/26969.
Full textAbstract:
There is a growing need to study factors that affect user comprehension of Business Process Management (BPM) information portrayed by graphical process models (GPMs). For example, deployment of BPM Systems, unique types of enterprise-level information systems, has dramatically increased in recent years. This increase is primarily because BPM Systems give a variety of managers across an enterprise the ability to directly design, configure, enact, monitor, diagnose, and control business processes that other types of enterprise systems do not. This is possible because BPM Systems uniquely rely on GPMs derived from formal graph theory. Besides controlling the business processes, these GPMs, such as metagraphs and Unified Modeling Language (UML) diagrams, portray business process information (BPI) and prompt BPM managers to apply their training and expertise to deal with BPM situations. As a result, GPMs are the primary information artifacts for decision-making and communication among different, often geographically dispersed stakeholders.
Therefore, user comprehension of these unique GPMs is critical to the efficient and effective development, deployment, and utilization of BPM Systems. User comprehension outcomes are jointly affected by the (1) BPM cognitive resources available to each manager (including the type of GPM, BPI, and user educational training and experience), and (2) cognitive differences between individual BPM managers (such as their mental workload, cognitive styles and cognitive abilities). Although research has studied GPMs in various contexts, there is apparently no empirical research investigating GPM user comprehension in the context of BPM Systems. This research makes an important contribution by addressing this gap in the literature.
Statement of the Objective
The purpose of this research is to empirically study how BPM cognitive resources and cognitive differences between individuals affect outcomes of GPM user comprehension. This research centered on the following objectives:
A. Investigate whether more positive user comprehension outcomes are produced by novice users if a single GPM technique is used to portray different types of BPI (e.g., as with metagraphs) or if different GPM techniques are used to portray different types of BPI (e.g., as with UML diagrams).
B. Investigate whether one type of BPI is more easily comprehended and interpreted by novice users irrespective of the type of GPM or the type of educational training of the user.
C. Investigate whether users with a specific type of user educational training can more easily comprehend and interpret BPM information irrespective of the type of GPM or the type of BPI.
D. Evaluate influences of individual cognitive differences (i.e., mental workload, cognitive styles, and cognitive abilities) on outcomes of user comprehension.
In order to accomplish these objectives, this study: (a) defined a theoretical framework conceptualizing user comprehension outcomes in terms of the interaction between cognitive resources external to the user and individual differences affecting how users cognitively process BPI, (b) empirically tested an operational research model of GPM user comprehension that is based on the theoretical framework, and (c) interpreted the experimental results in the context of related literatures.
Description of Research Methods
This study empirically tested relationships between several variables representing BPM cognitive resources and individual cognitive differences hypothesized as influencing the outcomes of user comprehension. A laboratory experiment, involving 87 upper-level undergraduate students from two universities, analyzed relationships between participant comprehension of two types of GPMs (i.e., metagraphs and UML diagrams) used to portray three types of BPI (i.e., task-centric, resource-centric, and information-centric BPI) by novice GPM users possessing different educational training (i.e., industrial engineering, business management, and computer science training). Dependent variables included assessments of task accuracy, task timeliness, subjective mental workload, and self-efficacy. Covariate effects were also analyzed for two types of participant cognitive abilities (i.e., general cognitive ability (GCA) and attentional abilities) and two types of participant cognitive styles (extroversion-introversion and sensing-intuitive). Multivariate analysis techniques were used to analyze and interpret the data.
Discussion of Results
The type of GPM and participantsâ GCA produced significant effects on the dependent variables in this study. For example, metagraph users produced significantly more desirable results than UML users across all dependent variables, contrary to what was hypothesized. However, if only the BPM cognitive resources (i.e., GPM Type, BPM Type, and the Type of Participant Education) were studied in relation to user comprehension outcomes, spurious conclusions would have been reached. When individual cognitive differences were included in the research model and analyses, results showed participants with higher GCA produced significantly more positive user comprehension outcomes compared to participants with lower GCAs. Also, many of the impacts of differences in the types of BPI and the types of UET were moderated by the differences in participantsâ GCA and attentional abilities. In addition, the relationship between subjective mental workload and task performance (i.e., accuracy and timeliness) suggest a possible GPM cognitive â profileâ for user comprehension tasks in a BPM Systems context. These results have important implications for future research and practice in several bodies of knowledge, including GPM user comprehension in management systems engineering, BPM modeling, BPM Systems, HCI, and cognitive ergonomics literature.<br>Ph. D.
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Dwivedi, Y. K., L. Hughes, Elvira Ismagilova, et al. "Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and Policy." Elsevier, 2019. http://hdl.handle.net/10454/17208.
Full textAbstract:
Yes<br>As far back as the industrial revolution, significant development in technical innovation has succeeded
in transforming numerous manual tasks and processes that had been in existence for decades where
humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same
transformative potential for the augmentation and potential replacement of human tasks and activities
within a wide range of industrial, intellectual and social applications. The pace of change for this new
AI technological age is staggering, with new breakthroughs in algorithmic machine learning and
autonomous decision-making, engendering new opportunities for continued innovation. The impact of
AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply
chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings
together the collective insight from a number of leading expert contributors to highlight the significant
opportunities, realistic assessment of impact, challenges and potential research agenda posed by the
rapid emergence of AI within a number of domains: business and management, government, public
sector, and science and technology. This research offers significant and timely insight to AI technology
and its impact on the future of industry and society in general, whilst recognising the societal and
industrial influence on pace and direction of AI development.
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REPETTO, MARCO. "Black-box supervised learning and empirical assessment: new perspectives in credit risk modeling." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/402366.
Full textAbstract:
I recenti algoritmi di apprendimento automatico ad alte prestazioni sono convincenti ma opachi, quindi spesso è difficile capire come arrivano alle loro previsioni, dando origine a problemi di interpretabilità.
Questi problemi sono particolarmente rilevanti nell'apprendimento supervisionato, dove questi modelli "black-box" non sono facilmente comprensibili per le parti interessate.
Un numero crescente di lavori si concentra sul rendere più interpretabili i modelli di apprendimento automatico, in particolare quelli di apprendimento profondo.
Gli approcci attualmente proposti si basano su un'interpretazione post-hoc, utilizzando metodi come la mappatura della salienza e le dipendenze parziali.
Nonostante i progressi compiuti, l'interpretabilità è ancora un'area di ricerca attiva e non esiste una soluzione definitiva. Inoltre, nei processi decisionali ad alto rischio, l'interpretabilità post-hoc può essere subottimale.
Un esempio è il campo della modellazione del rischio di credito aziendale.
In questi campi, i modelli di classificazione discriminano tra buoni e cattivi mutuatari.
Di conseguenza, gli istituti di credito possono utilizzare questi modelli per negare le richieste di prestito.
Il rifiuto di un prestito può essere particolarmente dannoso quando il mutuatario non può appellarsi o avere una spiegazione e una motivazione della decisione.
In questi casi, quindi, è fondamentale capire perché questi modelli producono un determinato risultato e orientare il processo di apprendimento verso previsioni basate sui fondamentali.
Questa tesi si concentra sul concetto di Interpretable Machine Learning, con particolare attenzione al contesto della modellazione del rischio di credito.
In particolare, la tesi ruota attorno a tre argomenti: l'interpretabilità agnostica del modello, l'interpretazione post-hoc nel rischio di credito e l'apprendimento guidato dall'interpretabilità.
Più specificamente, il primo capitolo è un'introduzione guidata alle tecniche model-agnostic che caratterizzano l'attuale panorama del Machine Learning e alle loro implementazioni.
Il secondo capitolo si concentra su un'analisi empirica del rischio di credito delle piccole e medie imprese italiane.
Propone una pipeline analitica in cui l'interpretabilità post-hoc gioca un ruolo cruciale nel trovare le basi rilevanti che portano un'impresa al fallimento.
Il terzo e ultimo articolo propone una nuova metodologia di iniezione di conoscenza multicriteriale.
La metodologia si basa sulla doppia retropropagazione e può migliorare le prestazioni del modello, soprattutto in caso di scarsità di dati.
Il vantaggio essenziale di questa metodologia è che permette al decisore di imporre le sue conoscenze pregresse all'inizio del processo di apprendimento, facendo previsioni che si allineano con i fondamentali.<br>Recent highly performant Machine Learning algorithms are compelling but opaque, so it is often hard to understand how they arrive at their predictions giving rise to interpretability issues.
Such issues are particularly relevant in supervised learning, where such black-box models are not easily understandable by the stakeholders involved.
A growing body of work focuses on making Machine Learning, particularly Deep Learning models, more interpretable.
The currently proposed approaches rely on post-hoc interpretation, using methods such as saliency mapping and partial dependencies.
Despite the advances that have been made, interpretability is still an active area of research, and there is no silver bullet solution. Moreover, in high-stakes decision-making, post-hoc interpretability may be sub-optimal.
An example is the field of enterprise credit risk modeling.
In such fields, classification models discriminate between good and bad borrowers.
As a result, lenders can use these models to deny loan requests.
Loan denial can be especially harmful when the borrower cannot appeal or have the decision explained and grounded by fundamentals.
Therefore in such cases, it is crucial to understand why these models produce a given output and steer the learning process toward predictions based on fundamentals.
This dissertation focuses on the concept of Interpretable Machine Learning, with particular attention to the context of credit risk modeling.
In particular, the dissertation revolves around three topics: model agnostic interpretability, post-hoc interpretation in credit risk, and interpretability-driven learning.
More specifically, the first chapter is a guided introduction to the model-agnostic techniques shaping today’s landscape of Machine Learning and their implementations.
The second chapter focuses on an empirical analysis of the credit risk of Italian Small and Medium Enterprises.
It proposes an analytical pipeline in which post-hoc interpretability plays a crucial role in finding the relevant underpinnings that drive a firm into bankruptcy.
The third and last paper proposes a novel multicriteria knowledge injection methodology.
The methodology is based on double backpropagation and can improve model performance, especially in the case of scarce data.
The essential advantage of such methodology is that it allows the decision maker to impose his previous knowledge at the beginning of the learning process, making predictions that align with the fundamentals.
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Hsiao, Janet Hui-wen. "Hemispheric processing in reading Chinese characters : statistical, experimental, and cognitive modeling." Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/2562.
Full textAbstract:
In Chinese orthography, phonetic compounds comprise about 80% of the most frequent characters. They contain separate phonological and semantic elements, referred to as phonetic and semantic radicals respectively. A dominant type exists in which the se-mantic radical appears on the left and the phonetic radical on the right (SP characters); an opposite, minority structure also exists in which the semantic radical appears on the right and the phonetic radical on the left (PS characters). Through statistical analyses, connectionist modelling, behavioural experiments, and neuroimaging studies, this dis-sertation demonstrates that the distinct structures of these two types of characters allow us crucial insights into the relationship between brain structure and reading processes. The statistical analyses of a Chinese lexical database show that, because of the different information profiles of SP and PS characters and the imbalanced distribution between them in the lexicon, the overall information is skewed to the right. This information skew provides important opportunities to examine the interaction between foveal split-ting and the information structure of the characters. The foveal splitting hypothesis as-sumes a vertical meridian split in the foveal representation and the consequent contra-lateral projection to the two cerebral hemispheres; it has been shown to have important implications for visual word recognition. The square shape and the condensed structure of Chinese characters make them a severe test case for the split fovea claim. Through a lateralized cueing examination and a TMS study of the semantic radical combinability effect with foveally presented characters in character semantic judgements, a flexible division of labour between the hemispheres in character recognition is demonstrated, with each hemisphere responding optimally to the information in the contralateral visual hemifield. The interaction between stimulation site and radical combinability in the TMS study also provides further support for the split fovea claim, suggesting functional foveal splitting as a universal processing constraint in reading. Even if foveal splitting is true, it is still unclear about how far the effects of foveal split-ting can extend from the retina into the process of character recognition. We show that, in naming isolated, foveally presented SP and PS characters, adult male and female readers process them differently, with opposite patterns of ease and difficulty: males responded significantly faster to SP than PS characters; females showed a non-significant tendency in the opposite direction. This result is also supported by a corre-sponding ERP study showing larger N350 amplitude elicited by PS character than SP characters in the male brain, and an opposite pattern in the female brain. The split fovea claim suggests that the two halves of a centrally fixated character are initially processed in different hemispheres. The male brain typically relies more on the left hemisphere for phonological processing compared with the female brain, causing this gender difference to emerge. This interaction is also predicted by an implemented computational model, contrasting a split cognitive architecture, in which the mapping between orthography to phonology is mediated by two partially encapsulated, interconnected processing do-mains, and a non-split cognitive architecture, in which the mapping is mediated by a single, undifferentiated processing domain. Thus, the effects of foveal splitting in read-ing extend far enough to interact with the gender of the reader in a naturalistic reading task. In short, this dissertation demonstrates that foveal splitting is a universal language proc-essing phenomenon, precise enough to project the two radicals of a centrally-fixated Chinese character to different hemispheres to allow a flexible division of labour be-tween the two hemispheres to emerge, and its effects in reading extend far enough into word recognition to interact with the gender of the reader in a naturalistic reading task. The results can also be extrapolated to Chinese word and sentence processing as well as to other languages. This dissertation thus has contributed to a better understanding of the relationship between brain structure and language processes.
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Smith, William A. "Teaching Object-Oriented Programming with Modeling Tools: Effects on Cognitive Load." NSUWorks, 2002. http://nsuworks.nova.edu/gscis_etd/848.
Full textAbstract:
In order to assist learners with object-oriented programming and design principles, many types of visual modeling tools have been developed to demonstrate otherwise abstract concepts. Although businesses and educational institutions alike have embraced the most mature of these tools, the Unified Modeling Language (UML) notation, it has not been demonstrated that such graphical aids can equally facilitate learning and achievement by all individuals regardless of their prior experience with procedural programming languages. Other visual modeling aids that similarly integrate textual explanations with graphical models have been demonstrated in other studies to increase the cognitive load of learners and to negatively impact the effectiveness of the instruction when used with learners who possess high prior experience in a related area. The goal of this research was to determine the appropriateness of the use of UML modeling tools with students who possess high prior experience in a conceptually different area of programming and design. Specifically, this research examined the effects that teaching object-oriented programming and design with visual UML object modeling tools had on the cognitive load and achievement of experienced procedural programmer’s enrolled in a C++ programming class. Data for this study were collected from four sections of the Introduction to C++ classes at Tulsa Community College. Data were collected on the participants during the experimental semester by using a student programming self-efficacy survey and a series of four object-oriented programming and design achievement tests. On each test, students were asked to subjectively evaluate their cognitive load for the material covered by each question.
The data were analyzed for any significant interactions among the student's prior experience with procedural languages, their exposure to UML, their cognitive load, and achievement. It was expected that the results would show a significant negative relationship between the student's prior experience and their level of achievement when exposed to UML content in the classroom. However, due to multiple violations of tile assumptions of the statistical tests outlined in this study, the results of this study could only be descriptively analyzed. The descriptive analysis revealed only small differences in the levels of achievement and cognitive load among the study's population.
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D'Alessandro, Marco. "Cognitive Modeling of high-level cognition through Discrete State Dynamic processes." Doctoral thesis, Università degli studi di Trento, 2002. http://hdl.handle.net/11572/290039.
Full textAbstract:
Modeling complex cognitive phenomena is a challenging task, especially when it is required to account for the functioning of a cognitive system interacting with an uncertain and changing environment. Psychometrics offers a heterogeneous corpus of computational tools to infer latent cognitive constructs from the observation of behavioral outcomes. However, there is not an explicit consensus regarding the optimal way to properly take into account the intrinsic dynamic properties of the environment, as well as the dynamic nature of cognitive states. In the present dissertation, we explore the potentials of relying on discrete state dynamic models to formally account for the unfolding of cognitive sub-processes in changing task environments. In particular, we propose Probabilistic Graphical Models (PGMs) as an ideal and unifying mathematical language to represent cognitive dynamics as structured graphs codifying (causal) relationships between cognitive sub-components which unfolds in discrete time. We propose several works demonstrating the advantage and the representational power of such a modeling framework, by providing dynamic models of cognition specified according to different levels of abstraction.
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Gao, Cunhao. "Some Modeling and Optimization Problems in Cognitive Radio Ad Hoc Networks." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/35020.
Full textAbstract:
Since its inception, cognitive radio (CR) has quickly been accepted as the enabling radio technology for next-generation wireless communications. A CR promises unprecedented flexibility in radio functionalities via programmability at the lowest layer, which was once done in hardware. Due to its spectrum sensing, learning, and adaptation capabilities, CR is able to address the heart of the problem associated with spectrum scarcity (via dynamic spectrum access (DSA)) and interoperability (via channel switching). It is envisioned that CR will be employed as a general radio platform upon which numerous wireless applications can be implemented.
For both theoretical and practical purposes, it is important for network researchers to model a cognitive radio ad hoc network (CRN) and optimize its performance. Such efforts are important not only for theoretical understanding, but also in that such results can be used as benchmarks for the design of distributed algorithms and protocols. However, due to some unique characteristics associated with CRNs, existing analytical techniques may not be applied directly. As a result, new theoretical results, along with new mathematical techniques, need to be developed. In this thesis, we focus on modeling and optimization of CRNs. In particular, we will study multicast communications in CRN and MIMO-empowered CRN, which we describe as follows.
An important service that must be supported by CRNs is multicast. Although there are a lot of research on multicast in ad hoc networks, those results cannot be applied to a CRN, because of the complexity associated with a CR node (e.g., multiple available frequency bands, difference in available bands from neighboring nodes). In addition, a single-layer approach (e.g., multicast routing) is overly simplistic when resource optimization (i.e., minimizing network resource) is the main objective. For this purpose, a cross-layer approach is usually necessary, which should include joint consideration of multiple lower layers, in addition to network layer. However, such a joint formulation is usually highly complex and difficult. In this thesis, we aim to develop some novel algorithms that provide near-optimal solutions. Our goal is to minimize the required network-wide resource to support a set of multicast sessions, with a certain bit rate for each multicast session. The unique characteristics associated with CR and distinguish this problem from existing multicast research for ad hoc networks. In this work, we formulate this problem via a cross-layer approach with joint consideration of scheduling and routing. Although the problem formulation is in the form of mixed integer linear program (MILP), we are successful in developing a polynomial time algorithm that offers highly competitive solution. The main ideas of the algorithm include identification of key integer variables, fixing these variables via a series of relaxed linear program (LP), and tying up such integer fixing with a bottom-up tree construction. By comparing with a lower bound, we find that the proposed algorithm can provide a solution that is very close to the optimum.
In parallel to the development of CR for DSA, multiple-input multiple-output (MIMO) has widely been accepted and now implemented in commercial wireless products to increase capacity. The goal of MIMO and how it operates are largely independent and orthogonal to CR. Instead of exploiting idle channels for wireless communications, MIMO attempts to increase capacity within the same channel via space-time processing. Assuming that CR and MIMO will ultimately marry each other and offer the ultimate flexibility in DSA and spectrum efficiency, we would like to inquire the potential capacity gain in this marriage. In particular, we are interested in how such marriage will affect the capacity of a user communication session in a multi-hop CRN. We explore MIMO-empowered CR network, which we call CRN<sup>MIMO</sup>, to achieve ultimate flexibility in DSA and spectrum efficiency. Given that CR and MIMO handle interference at different levels (across channels vs. within a channel), we are interested in how joint optimization of both will maximize user capacity in a multi-hop network. To answer this question, we develop a tractable mathematical model for CRN<sup>MIMO</sup>, which captures the essence of channel assignment (for CR) and degree-of-freedom (DoF) allocation (for MIMO). Based on this mathematical model, we use numerical results to show how channel assignment in CRN and DoF allocation in MIMO can be jointly optimized to maximize capacity. More important, for a CRN<sup>MIMO</sup> with A<sub>MIMO</sub> antennas at each node, we show that joint optimization of CR and MIMO offers more than A<sub>MIMO</sub>-fold capacity increase than a CRN with only a single antenna at each node.<br>Master of Science
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Schalley, Andrea C. "Cognitive modeling and verbal semantics : a representational framework based on UML /." Berlin : Mouton de Gruyter, 2004. http://catalogue.bnf.fr/ark:/12148/cb392279387.
Full text
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D'Alessandro, Marco. "Cognitive Modeling of high-level cognition through Discrete State Dynamic processes." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/290039.
Full textAbstract:
Modeling complex cognitive phenomena is a challenging task, especially when it is required to account for the functioning of a cognitive system interacting with an uncertain and changing environment. Psychometrics offers a heterogeneous corpus of computational tools to infer latent cognitive constructs from the observation of behavioral outcomes. However, there is not an explicit consensus regarding the optimal way to properly take into account the intrinsic dynamic properties of the environment, as well as the dynamic nature of cognitive states. In the present dissertation, we explore the potentials of relying on discrete state dynamic models to formally account for the unfolding of cognitive sub-processes in changing task environments. In particular, we propose Probabilistic Graphical Models (PGMs) as an ideal and unifying mathematical language to represent cognitive dynamics as structured graphs codifying (causal) relationships between cognitive sub-components which unfolds in discrete time. We propose several works demonstrating the advantage and the representational power of such a modeling framework, by providing dynamic models of cognition specified according to different levels of abstraction.