A Data Driven Machine Learning Approach to Prediction of Stacking Fault Energy in Austenitic Steels

The Material Genome Initiative (MGI) calls for establishing frameworks and adopting methodologies to accelerate materials discovery and deployment. The Integrated Computational Materials Engineering (ICME) approach and Materials Informatics leveraging materials data are two very important pillars to the initiative. This research is a data driven materials informatics approach to enable an ICME project on steel alloy design. For the alloy design problem there was a need to predict Stacking Fault Energy (SFE) for any untested alloy composition. SFE is a crucial parameter in determining different deformation regimes in austenitic steels. The SFE itself is dependent on the chemical composition and temperature in steels. There has been considerable study on determination of SFE in steels by experimental and computational methods. While the experimental methods investigate an alloy to find SFE, computational models have been constructed to predict SFE for a given composition and temperature. However, it is shown in this thesis that there are large inconsistencies in experimental data, as well as unavailability of robust computational models to predict SFE in truly multicomponent steel alloys. In this work, a data-driven machine learning approach to mine the literature of SFE in steels with the final aim of predicting deformation regimes for potentially unknown and untested alloy compositions has been demonstrated. Algorithms at the fore-front of Machine Learning have been used to visualize the SFE data and then construct classifiers to predict SFE regime in steels. This machine-learning modeling approach can help accelerate alloy discovery of austenitic steels by linking composition to desired mechanical behavior

Speech technologies for the audiovisual and multimedia interaction environments

361 p

FINE-GRAINED EMOTION DETECTION IN MICROBLOG TEXT

Automatic emotion detection in text is concerned with using natural language processing techniques to recognize emotions expressed in written discourse. Endowing computers with the ability to recognize emotions in a particular kind of text, microblogs, has important applications in sentiment analysis and affective computing. In order to build computational models that can recognize the emotions represented in tweets we need to identify a set of suitable emotion categories. Prior work has mainly focused on building computational models for only a small set of six basic emotions (happiness, sadness, fear, anger, disgust, and surprise). This thesis describes a taxonomy of 28 emotion categories, an expansion of these six basic emotions, developed inductively from data. This set of 28 emotion categories represents a set of fine-grained emotion categories that are representative of the range of emotions expressed in tweets, microblog posts on Twitter. The ability of humans to recognize these fine-grained emotion categories is characterized using inter-annotator reliability measures based on annotations provided by expert and novice annotators. A set of 15,553 human-annotated tweets form a gold standard corpus, EmoTweet-28. For each emotion category, we have extracted a set of linguistic cues (i.e., punctuation marks, emoticons, emojis, abbreviated forms, interjections, lemmas, hashtags and collocations) that can serve as salient indicators for that emotion category. We evaluated the performance of automatic classification techniques on the set of 28 emotion categories through a series of experiments using several classifier and feature combinations. Our results shows that it is feasible to extend machine learning classification to fine-grained emotion detection in tweets (i.e., as many as 28 emotion categories) with results that are comparable to state-of-the-art classifiers that detect six to eight basic emotions in text. Classifiers using features extracted from the linguistic cues associated with each category equal or better the performance of conventional corpus-based and lexicon-based features for fine-grained emotion classification. This thesis makes an important theoretical contribution in the development of a taxonomy of emotion in text. In addition, this research also makes several practical contributions, particularly in the creation of language resources (i.e., corpus and lexicon) and machine learning models for fine-grained emotion detection in text

State and Local Fiscal Behavior and Federal Grant Policy

macroeconomics, federal grant policy, state fiscal, local fiscal

Artifact Development for the Prediction of Stress Levels on Higher Education Students using Machine Learning

Stress is an adaptative reaction of an organism, human or not, to the demands of fitting in an environment (Kav Vedhara, 1996). When stress originates in an educational context, it is common to refer to it as a student and their mechanisms to adapt and cope with the academic demand. All humans experience stress during their lifetime, but when this overwhelmed feeling is prolonged can affect human behaviour and the ability to deal with physical and emotional pressure, having, as a result, a different range of problems. It is important for higher-level educations institutions, such as colleges and universities, to be aware and have a deep knowledge of the levels of academic stress in their students

An implementation and improvement of the ToPs predicting algorithm

In this work we implemented and we improved the ToPs predictive algorithm, first described in February 2018, by J. Yoon, W. R. Zame, and M. van der Schaa

Text Mining Methods for Analyzing Online Health Information and Communication

The Internet provides an alternative way to share health information. Specifically, social network systems such as Twitter, Facebook, Reddit, and disease specific online support forums are increasingly being used to share information on health related topics. This could be in the form of personal health information disclosure to seek suggestions or answering other patients\u27 questions based on their history. This social media uptake gives a new angle to improve the current health communication landscape with consumer generated content from social platforms. With these online modes of communication, health providers can offer more immediate support to the people seeking advice. Non-profit organizations and federal agencies can also diffuse preventative information in such networks for better outcomes. Researchers in health communication can mine user generated content on social networks to understand themes and derive insights into patient experiences that may be impractical to glean through traditional surveys. The main difficulty in mining social health data is in separating the signal from the noise. Social data is characterized by informal nature of content, typos, emoticons, tonal variations (e.g. sarcasm), and ambiguities arising from polysemous words, all of which make it difficult in building automated systems for deriving insights from such sources. In this dissertation, we present four efforts to mine health related insights from user generated social data. In the first effort, we build a model to identify marketing tweets on electronic cigarettes (e-cigs) and assess different topics in marketing and non-marketing messages on e-cigs on Twitter. In our next effort, we build ensemble models to classify messages on a mental health forum for triaging posts whose authors need immediate attention from trained moderators to prevent self-harm. The third effort deals with models from our participation in a shared task on identifying tweets that discuss adverse drug reactions and those that mention medication intake. In the final task, we build a classifier that identifies whether a particular tweet about the popular Juul e-cig indicates the tweeter actually using the product. Our methods range from linear classifiers (e.g., logistic regression), classical nonlinear models (e.g., nearest neighbors), recent deep neural networks (e.g., convolutional neural networks), and ensembles of all these models in using different supervised training regimens (e.g., co-training). The focus is more on task specific system building than on building specific individual models. Overall, we demonstrate that it is possible to glean insights from social data on health related topics through natural language processing and machine learning with use-cases from substance use and mental health