Metacognition is an umbrella term that encompasses many related constructs about the knowledge and regulation of one's own thinking processes. Metacognitive knowledge about memory and attention has been found to correlate with intelligence levels and has been viewed as one component of giftedness. In this paper, definitions of both metacognition and giftedness are explained and situated in context so that the relationship between the two may be explored further. I also describe traditional and nontraditional methods of identifying children as gifted. While previous studies of metacognitive differences between gifted and nongifted children have been based on students traditionally identified as gifted, my study employed a non-traditional identification method, the DISCOVER assessment. In the study, I examine metacognitive knowledge about three elements: memory, attention, and decision making, in gifted and nongifted second-graders through an interview. The two main purposes of the study were to explore metacognitive knowledge about decision making, which had not been studied previously, and to see if varying the method of identification for giftedness would affect the metacognitive advantage for gifted children found in prior studies. No significant differences were found among the types of metacognitive knowledge studied. Statistically significant differences were found between the scores of gifted and nongifted children, with gifted children demonstrating greater ability to articulate their metacognitive knowledge. A qualitative discussion of students' responses illustrates areas in which the two groups of children differed in their understanding of their own thinking.

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abstract: Acoustic emission (AE) signals have been widely employed for tracking material properties and structural characteristics. In this study, the aim is to analyze the AE signals gathered during a scanning probe lithography process to classify the known microstructure types and discover unknown surface microstructures/anomalies. To achieve this, a Hidden Markov Model is developed to consider the temporal dependency of the high-resolution AE data. Furthermore, the posterior classification probability and the negative likelihood score for microstructure classification and discovery are computed. Subsequently, a diagnostic procedure to identify the dominant AE frequencies that were used to track the microstructural characteristics is presented. In addition, machine learning methods such as KNN, Naive Bayes, and Logistic Regression classifiers are applied. Finally, the proposed approach applied to identify the surface microstructures of additively manufactured Ti-6Al-4V and show that it not only achieved a high classification accuracy (e.g., more than 90\%) but also correctly identified the microstructural anomalies that may be subjected to further investigation to discover new material phases/properties. / Dissertation/Thesis / Masters Thesis Statistics 2020

Talvez uma das maiores capacidades do ser humano seja a sua habilidade de aprender a partir de observações e transmitir o que aprendeu para outros humanos. Durante séculos, a humanidade vem tentado compreender o mundo em que vive e, a partir desse novo conhecimento adquirido, melhorar o mundo em que vive. O desenvolvimento da tecnologia colocou a descoberta de conhecimento em um momento ímpar na história da humanidade. Com os progressos da Ciência da Computação, e, em particular, da Inteligência Artificial - IA - e Aprendizado de Máquina -AM, hoje em dia é possível, a partir de métodos de inferência indutiva e utilizando um conjunto de exemplos, descobrir algum tipo de conhecimento implícito nesses exemplos. Entretanto, por ser uma área de pesquisa relativamente nova, e por envolver um processo tanto iterativo quanto interativo, atualmente existem poucas ferramentas que suportam eficientemente a descoberta de conhecimento a partir dos dados. Essa falta de ferramentas se agrava ainda mais no que se refere ao seu uso por pesquisadores em Aprendizado de Máquina e Aquisição de Conhecimento. Esses fatores, além do fato que algumas pesquisas em nosso Laboratório de Inteligência Computacional - LABIC - têm alguns componentes em comum, motivaram a elaboração do projeto Discover, que consiste em uma estratégia de trabalho em conjunto, envolvendo um conjunto de ferramentas que se integram e interajam, e que supram as necessidades de pesquisa dos integrantes do nosso laboratório. O Discover também pode ser utilizado como um campo de prova para desenvolver novas ferramentas e testar novas idéias. Como o Discover tem como principal finalidade o seu uso e extensão por pesquisadores, uma questão principal é que a arquitetura do projeto seja flexível o suficiente para permitir que novas pesquisas sejam englobadas e, simultaneamente, deve impor determinados padrões que permitam a integração eficiente de seus componentes. Neste trabalho, é proposto um framework de integração de componentes que tem como principal objetivo possibilitar a criação de um sistema computacional a partir das ferramentas desenvolvidas para serem utilizadas no projeto Discover. Esse framework compreende um mecanismo de adaptação de interface que cria uma camada (interface horizontal) sobre essas ferramentas, um poderoso mecanismo de metadados, que é utilizado para descrever tanto os componentes que implementam as funcionalidades do sistema quanto as configurações de experimentos criadas pelos usuário, que serão executadas pelo framework, e um ambiente de execução para essas configurações de experimentos. / One of human greatest capability is the ability to learn from observed instances of the world and to transmit what have been learnt to others. For thousands of years, we have tried to understand the world, and used the acquired knowledge to improve it. Nowadays, due to the progress in digital data acquisition and storage technology as well as significant progress in the field of Artificial Intelligence - AI, particularly Machine Learning - ML, it is possible to use inductive inference in huge databases in order to find, or discover, new knowledge from these data. The discipline concerned with this task has become known as Knowledge Discovery from Databases - KDD. However, this relatively new research area offers few tools that can efficiently be used to acquire knowledge from data. With these in mind, a group of researchers at the Computational Intelligence Laboratory - LABIC - is working on a system, called Discover, in order to help our research activities in KDD and ML. The aim of the system is to integrate ML algorithms mostly used by the community with the data and knowledge processing tools developed as the results of our work. The system can also be used as a workbench for new tools and ideas. As the main concern of the Discover is related to its use and extension by researches, an important question is related to the flexibility of its architecture. Furthermore, the Discover architecture should allow new tools be easily incorporated. Also, it should impose strong patterns to guarantee efficient component integration. In this work, we propose a component integration framework that aims the development of an integrated computational environment using the tools already implemented in the Discover project. The proposed component integration framework has been developed keeping in mind its future integration with new tools. This framework offers an interface adapter mechanism that creates a layer (horizontal interface) over these tools, a powerful metadata mechanism, which is used to describe both components implementing systems' functionalities and experiment configurations created by the user, and an environment that enables these experiment execution.

Talvez uma das maiores capacidades do ser humano seja a sua habilidade de aprender a partir de observações e transmitir o que aprendeu para outros humanos. Durante séculos, a humanidade vem tentado compreender o mundo em que vive e, a partir desse novo conhecimento adquirido, melhorar o mundo em que vive. O desenvolvimento da tecnologia colocou a descoberta de conhecimento em um momento ímpar na história da humanidade. Com os progressos da Ciência da Computação, e, em particular, da Inteligência Artificial - IA - e Aprendizado de Máquina -AM, hoje em dia é possível, a partir de métodos de inferência indutiva e utilizando um conjunto de exemplos, descobrir algum tipo de conhecimento implícito nesses exemplos. Entretanto, por ser uma área de pesquisa relativamente nova, e por envolver um processo tanto iterativo quanto interativo, atualmente existem poucas ferramentas que suportam eficientemente a descoberta de conhecimento a partir dos dados. Essa falta de ferramentas se agrava ainda mais no que se refere ao seu uso por pesquisadores em Aprendizado de Máquina e Aquisição de Conhecimento. Esses fatores, além do fato que algumas pesquisas em nosso Laboratório de Inteligência Computacional - LABIC - têm alguns componentes em comum, motivaram a elaboração do projeto Discover, que consiste em uma estratégia de trabalho em conjunto, envolvendo um conjunto de ferramentas que se integram e interajam, e que supram as necessidades de pesquisa dos integrantes do nosso laboratório. O Discover também pode ser utilizado como um campo de prova para desenvolver novas ferramentas e testar novas idéias. Como o Discover tem como principal finalidade o seu uso e extensão por pesquisadores, uma questão principal é que a arquitetura do projeto seja flexível o suficiente para permitir que novas pesquisas sejam englobadas e, simultaneamente, deve impor determinados padrões que permitam a integração eficiente de seus componentes. Neste trabalho, é proposto um framework de integração de componentes que tem como principal objetivo possibilitar a criação de um sistema computacional a partir das ferramentas desenvolvidas para serem utilizadas no projeto Discover. Esse framework compreende um mecanismo de adaptação de interface que cria uma camada (interface horizontal) sobre essas ferramentas, um poderoso mecanismo de metadados, que é utilizado para descrever tanto os componentes que implementam as funcionalidades do sistema quanto as configurações de experimentos criadas pelos usuário, que serão executadas pelo framework, e um ambiente de execução para essas configurações de experimentos. / One of human greatest capability is the ability to learn from observed instances of the world and to transmit what have been learnt to others. For thousands of years, we have tried to understand the world, and used the acquired knowledge to improve it. Nowadays, due to the progress in digital data acquisition and storage technology as well as significant progress in the field of Artificial Intelligence - AI, particularly Machine Learning - ML, it is possible to use inductive inference in huge databases in order to find, or discover, new knowledge from these data. The discipline concerned with this task has become known as Knowledge Discovery from Databases - KDD. However, this relatively new research area offers few tools that can efficiently be used to acquire knowledge from data. With these in mind, a group of researchers at the Computational Intelligence Laboratory - LABIC - is working on a system, called Discover, in order to help our research activities in KDD and ML. The aim of the system is to integrate ML algorithms mostly used by the community with the data and knowledge processing tools developed as the results of our work. The system can also be used as a workbench for new tools and ideas. As the main concern of the Discover is related to its use and extension by researches, an important question is related to the flexibility of its architecture. Furthermore, the Discover architecture should allow new tools be easily incorporated. Also, it should impose strong patterns to guarantee efficient component integration. In this work, we propose a component integration framework that aims the development of an integrated computational environment using the tools already implemented in the Discover project. The proposed component integration framework has been developed keeping in mind its future integration with new tools. This framework offers an interface adapter mechanism that creates a layer (horizontal interface) over these tools, a powerful metadata mechanism, which is used to describe both components implementing systems' functionalities and experiment configurations created by the user, and an environment that enables these experiment execution.

The purpose of this study was to revise and revalidate the scoring procedure of the DISCOVER Mathematics Assessment to allow evaluators to better measure creative problem solving ability in mathematics, identify gifted students, and evaluate the programs developed for fostering creative problem solving. The data for this study consisted of 233 students selected from five different grade levels. I conducted descriptive statistics and regression analyses to compare the relationships of both the original and revised versions of the scoring system with general creativity. I found that range increased from the original to the revised version of the scoring system for mathematical problem solving performance in semi-open-ended problems, however it decreased for overall performance and performance in open-ended problems. Variance, on the other hand, increased for both overall problem solving performance and performance in semi-open-ended problems, and decreased for performance in open-ended problems from the original to the revised version of the scoring system. Furthermore, in the revised model all three variables of the creative mathematical problem solving performance (overall performance, performance in semi-open-ended problems, and performance in open-ended problems) explained more variance in general creativity than the original version. Statistically, the differences between the original and the revised versions were significant for all three variables, except for creative mathematical problem solving performance in open-ended problems. Across grade levels, I found that for the group Lower Grade Levels (grade levels 1 and 2), the explained variance in general creativity increased from the original to the revised version for both overall performance and performance in semi-open-ended problems. However, it decreased for performance in open-ended problems. On the other hand for the group Higher Grade Levels (grade levels 3, 4, and 5) the explained variance in general creativity increased for all three variables from the original to the revised version. Statistically, the only significant difference between the original and the revised versions was for overall problem solving performance in Higher Grade Levels. I concluded that the revised version of the scoring system was more effective when predicting variance in general creativity for overall problem solving performance, and performance in semi-open-ended problems. Also, it predicted more variance in general creativity for the group Higher Grade Levels than the group Lower Grade Levels. Therefore, I suggested that quality should be considered as well as fluency, flexibility, and originality when scoring assessments for creative problem solving ability in mathematics.

Actinomycins are a class of compounds consisting of phenoxazinone-like core attached to two peptidolactone rings, denoted as α and β. A unique component of a few families—actinomycins G, Y, and Z—is a chlorinated β-ring threonine residue. Families G and Y also contained an actinomycin that possess a β-ring heterocycle (actinomycins G5 and Y5, respectively); prior to this work, no β-ring heterocycle-containing actinomycins were reported for the Z family. Unlike other actinomycin derivatives, Y5’s cytotoxicity was abolished while still maintaining some antibacterial potency. We constructed a model compound to probe the physical properties of the actinomycin core to test conditions under which heterocycle formation would occur. We also analyzed the gene clusters of these actinomycin producers for gene candidates to from this structural motif. We found the the actinomycin core aniline to have pKa values of 2.976 and 8.429 and a significant shift in UV absorption between 300-310nm when the group becomes charged. We also found cyclization conditions and no obvious gene candidates to form the β-ring heterocycle based on our gene cluster analysis. We hypothesize that the familial diversity of the actinomycin G, Y and Z familes is due to the reactivity of the phenoxazinone-like core.

Musikstreamingtjänster som Spotify erbjuder en omfattande mängd musik online jämfört med innan digitaliseringen. Trots detta förändrade musiklandskap, kvarstår fortfarande önskan att samla, byta ut och dela musik. Däremot har musikstreamingtjänster medfört ett ökat behov av att navigera användarna genom de omfattande låtarkiven. Spotifys spellista Discover Weekly syftar till att tillhandahålla användarna med 30 nya låtar varje måndag. Låtarna är personanpassade utifrån användarnas smakprofiler, som är kalibrerade av rekommendationsalgoritmer. Musikstreamingtjänsten konstaterar att de vill erbjuda en explorativ användarupplevelse med hjälp av rekommendationsalgoritmerna. I denna studie valde vi att utvärdera hur användarna själva upplever de personanpassade låtarna på Discover Weekly. Tidigare studier om Spotify, musikupptäckter i en digital era, rekommendationsalgoritmer, och effekterna av algoritmer ansågs vara relevanta för att utforska hur låtarna på Discover Weekly upplevs vara. Frågeställningen undersöktes genom inledande intervjuer, en veckas lyssningsperiod av Discover Weekly samt uppföljande intervjuer med en testgrupp bestående av studenter från KTH. Resultatet från intervjuerna visade att låtarna på spellistan upplevdes vara ensidiga där användarna gillade låtarna som stack ut från mängden i stil av tempo eller genre. Majoriteten av testpersonerna uppskattar ändå rekommendationsalgoritmen. Flera testpersoner förklarade att de önskade mer explorativa alternativ på spellistan för att stödja det givna löftet om utforskande av nya musikupptäckter vilket namnet, Discover Weekly, indikerar på. Deltagarna ansåg dock fortfarande att icke-datordrivna rekommendationer var att föredra och mer pålitliga. Vårt resultat kan därmed jämföras med tidigare studiers teorier där algoritmer / Music streaming services like Spotify provide a staggering amount of music online compared to before digitalization. Even though the music landscape is changing, the desire to collect, exchange, and share music remains. However, music streaming services have contributed to an increased desire to navigate users through the extensive song archives. Spotify's Discover Weekly playlist aims to provide users with 30 new songs every Monday. The songs are personalized based on the users' taste profiles, which are calibrated by recommendation algorithms. The music streaming service states that they want to offer an exploratory user experience using the recommendation algorithms. In this study, we evaluate how users themselves experience the personalized songs on Discover Weekly. Previous related research regarding Spotify, music discovery in a digital era, recommendation algorithms, and algorithmic effects were considered relevant when exploring how the songs on Discover Weekly are experienced to be. The thesis was investigated through introductory interviews, one week listening of Discover Weekly, and follow-up interviews with a test group consisting of students from KTH Royal Institute of Technology. The results from the interviews showed that the songs on the playlist were perceived as indistinguishable, where the users liked the songs that stood out from the rest in tempo or genre. The majority of the participants still appreciate the recommendation algorithm. Several participants explained that they wished for more exploratory options on the playlist to support the promise of exploring new music discoveries as the name Discover Weekly indicates. However, the participants still considered user recommendations as more reliable than algorithmically driven recommendations. Our findings support previous researchers’ insights where these algorithms can affect the diversification of consumption on Spotify.

The Real Engagement in Active Problem Solving (REAPS) model was developed in 2004 by C. June Maker and colleagues as an intervention for gifted students to develop creative problem solving ability through the use of real-world problems. The primary purpose of this study was to examine the effects of the REAPS model on developing students' general creativity and creative problem solving in science with two durations as independent variables. The long duration of the REAPS model implementation lasted five academic quarters or approximately 10 months; the short duration lasted two quarters or approximately four months. The dependent variables were students' general creativity and creative problem solving in science. The second purpose of the study was to explore which aspects of creative problem solving (i.e., generating ideas, generating different types of ideas, generating original ideas, adding details to ideas, generating ideas with social impact, finding problems, generating and elaborating on solutions, and classifying elements) were most affected by the long duration of the intervention. The REAPS model in conjunction with Amabile's (1983; 1996) model of creative performance provided the theoretical framework for this study. The study was conducted using data from the Project of Differentiation for Diverse Learners in Regular Classrooms (i.e., the Australian Project) in which one public elementary school in the eastern region of Australia cooperated with the DISCOVER research team at the University of Arizona. All students in the school from first to sixth grade participated in the study. The total sample was 360 students, of which 115 were exposed to a long duration and 245 to a short duration of the REAPS model. The principal investigators used a quasi-experimental research design in which all students in the school received the treatment for different durations. Students in both groups completed pre- and posttests using the Test of Creative Thinking-Drawing Production (TCT-DP) and the Test of Creative Problem Solving in Science (TCPS-S).A one-way analysis of covariance (ANCOVA) was conducted to control for differences between the two groups on pretest results. Statistically significant differences were not found between posttest scores on the TCT-DP for the two durations of REAPS model implementation. However, statistically significant differences were found between posttest scores on the TCPS-S. These findings are consistent with Amabile's (1983; 1996) model of creative performance, particularly her explanation that domain-specific creativity requires knowledge such as specific content and technical skills that must be learned prior to being applied creatively. The findings are also consistent with literature in which researchers have found that longer interventions typically result in expected positive growth in domain-specific creativity, while both longer and shorter interventions have been found effective in improving domain-general creativity. Change scores were also calculated between pre- and posttest scores on the 8 aspects of creativity (Maker, Jo, Alfaiz, & Alhusaini, 2015a), and a binary logistic regression was conducted to assess which were the most affected by the long duration of the intervention. The regression model was statistically significant, with aspects of generating ideas, adding details to ideas, and finding problems being the most affected by the long duration of the intervention. Based on these findings, the researcher believes that the REAPS model is a useful intervention to develop students' creativity. Future researchers should implement the model for longer durations if they are interested in developing students' domain-specific creative problem solving ability.

D.Litt. et Phil. (Psychology) / Career planning is a developmental process that can be facilitated by career development programmes. One of the recent developments in these programmes was the introduction of computerized career counselling which enables the user to explore career activities independently. An experimental investigation in which the DISCOVER computerized system was used, was conducted at two universities in an attempt to determine whether DISCOVER could be a useful aid for career counselling in South Africa. First-year university students (N=106) were randomly assigned to one of four groups according to the Solomon Four Group Experimental Design. Subjects completed a biographical questionnaire, the Career Maturity Scale (CMS), Senior Aptitude Test (SAT), New South African Group Test (NSAGT), Sixteen Personality Factor Questionnaire (16PF), PHSF Relations Questionnaire (PHSF), Survey of Study Habits and Attitudes (SSHA), Self-Directed Search (SDS) and the 19 Field Interest Inventory (19FII). Evaluation questionnaires concerning the use of DISCOVER were ·also completed by students as well as counsellors. The main hypothesis, namely that there would be a statistically significant difference between the mean posttest scores on the CMS of the experimental and the control groups, was tested according to the integrated statistical procedure suggested by Spector (1981). The results showed that there is. a statistically significant increase in the career maturity of students after they had used the DISCOVER system, compared with students from the control group (F, 12,15; p <0,0007). After posttest adjustment for pretest differences, the effect of the DISCOVER programme was still statistically significant. It could be predicted with 95 , certainty that a person who used the DISCOVER programme would show an increase of between 0,68 and 2,21 points in his total score on the Career Maturity Scale (CMS). There was no statistically significant interaction effect between the DISCOVER intervention and the pretest. Variables such as aptitude, personality and study habits, correlated statistically significant (p <0,05) with the increase in career maturity after the use of the DISCOVER system.