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Unstructured data such as texts, strings, images, audios, videos are everywhere due to the social interaction on the Internet and the high-throughput technology in sciences, e.g., chemistry and biology. However, for traditional machine learning algorithms, classifying a text document is far more difficult than classifying a data entry in a spreadsheet. We have to convert the unstructured data into some numeric vectors which can then be understood by machine learning algorithms. For example, a sentence is first converted to a vector of word counts, and then fed into a classification algorithm such as logistic regression and support vector machine. The creation of such numerical vectors is very challenging and difficult. Recent progress in deep learning provides us a new way to jointly learn features and train classifiers for unstructured data. For example, recurrent neural networks proved successful at learning from a sequence of word indices; convolutional neural networks are effective to learn from videos, which are sequences of pixel matrices. Our research focuses on developing novel deep learning approaches for text and graph data. Breakthroughs using deep learning have been made during the last few years for many core tasks in natural language processing, such as machine translation, POS tagging, named entity recognition, etc. However, when it comes to informal and noisy text data, such as tweets, HTMLs, OCR, there are two major issues with modern deep learning technologies. First, deep learning requires large amount of labeled data to train an effective model; second, neural network architectures that work with natural language are not proper with informal text. In this thesis, we address the two important issues and develop new deep learning approaches in four supervised and unsupervised tasks with noisy text. We first present a deep feature engineering approach for informative tweets discovery during the emerging disasters. We propose to use unlabeled microblogs to cluster words into a limited number of clusters and use the word clusters as features for tweets discovery. Our results indicate that when the number of labeled tweets is 100 or less, the proposed approach is superior to the standard classification based on the bag or words feature representation. We then introduce a human-in-the-loop (HIL) framework for entity identification from noisy web text. Our work explores ways to combine the expressive power of REs, ability of deep learning to learn from large data into a new integrated framework for entity identification from web data. The evaluation on several entity identification problems shows that the proposed framework achieves very high accuracy while requiring only a modest human involvement. We further extend the framework of entity identification to an iterative HIL framework that addresses the entity recognition problem. We particularly investigate how human invest their time when a user is allowed to choose between regex construction and manual labeling. Finally, we address a fundamental problem in the text mining domain, i.e, embedding of rare and out-of-vocabulary (OOV) words, by refining word embedding models and character embedding models in an iterative way. We illustrate the simplicity but effectiveness of our method when applying it to online professional profiles allowing noisy user input. Graph neural networks have been shown great success in the domain of drug design and material sciences, where organic molecules and crystal structures of materials are represented as attributed graphs. A deep learning architecture that is capable of learning from graph nodes and graph edges is crucial for property estimation of molecules. In this dissertation, We propose a simple graph representation for molecules and three neural network architectures that is able to directly learn predictive functions from graphs. We discover that, it is true graph networks are superior than feature-driven algorithms for formation energy prediction. However, the superiority can not be reproduced on band gap prediction. We also discovered that our proposed simple shallow neural networks perform comparably with the state-of-the-art deep neural networks. / Computer and Information Science

Employee performance evaluations have been subject to a lot of criticism and organizations are now leveraging artificial intelligence (AI) to enhance and maximize the efficiency and accuracy of these performance evaluations. Although organizations assume that AI-driven performance evaluation systems will enhance traditional performance evaluation systems, a growing body of research documents the phenomenon of algorithm aversion, the human tendency to discount algorithm/computer generated advice more heavily than human advice. Using an employee performance evaluation setting, I conduct an experiment to examine how managers will resolve differences between two contradictory judgments, their own judgment and an AI's judgment. I find that in addition to algorithm aversion and an individual's attitude towards technology, the performance evaluation measures (objective or subjective), and more importantly, the consequence of the decision on the employee strongly influenced the manager's reliance on AI. Specifically, managers resolved conflict between AI and their own decision by relying on decisions that were in the employee's favor. The study contributes to existing research on the adoption of AI and management accounting research.

Accurate image reconstruction is fundamental to medical imaging diagnostics, involving the transformation of data from the sensor domain to the image domain by solving an inverse problem. In Magnetic Resonance Imaging (MRI), measurements are acquired in the k-space spatial frequency domain, and the inverse Fourier Transform is applied to reconstruct the image for diagnosis. However, exact solutions to the inverse problem using analytical models are often not possible. Partial measurements are often acquired to decrease scanning time, resulting in ill-posed inverse problems that necessitate a series of signal processing steps for optimal reconstructions. Supervised deep learning approaches have been explored for solving such inverse problems, including image reconstruction. While deep learning can tackle these challenges in a single reconstruction step, training deployable models can be challenging due to encountering unseen data distributions that deviate from the training data in real-world scenarios. In this dissertation, we first investigate the impact of complex input data design, data augmentations, adversarial noise, and hallucinations on reconstruction accuracy and robustness of deep learning-based image reconstruction methods. We illustrate how the complex input data design and architectural modifications can notably enhance performance accuracy. We showcase the emergence of artifacts when training lacks proper data augmentations such as multiple field-of-views in the dataset. Additionally, we study the effectiveness of deep learning when exposed to Gaussian versus engineered adversarial noise, proposing a technique to adapt the numerical properties of the training dataset for resilience against adversarial noise. Finally, we investigate the occurrence of hallucinations on undersampled out-of-distribution (OOD) data reconstructions and propose a method for quantifying and mitigating them through domain adaptation techniques. Due to encountering OOD data in real-world settings, it is essential to assess whether a given input falls within the training data distribution, in-distribution (ID), particularly when reconstructing medical images for diagnostic purposes. We propose a single model variance method based on the local Lipschitz metric to distinguish OOD images from ID. Our method achieves an impressive area under the curve of 99.94% for True Positive Rate versus False Positive Rate. Empirically, we demonstrate a very strong relationship between the local Lipschitz value and mean absolute error (MAE), supported by a high Spearman's rank correlation coefficient of 0.8475. Through selective prediction, we demonstrate a method to determine the local Lipschitz threshold for uncertainty as it relates to optimal model performance. Our study was validated using the AUTOMAP architecture for sensor-to-image domain MRI reconstruction. We compare our proposed approach with baseline methods of Monte-Carlo dropout and deep ensembles as well as the state-of-the-art Mean Variance Estimation (MVE) network approach. Furthermore, we showcase the versatility of our approach to other architectures and learned functions, including the UNET architecture for MRI denoising and Computed Tomography (CT) sparse-to-full view reconstruction applications. Lastly, we expand the field of deep learning to solve inverse problems to Nitrogen-vacancy (NV) center diamond magnetometry, a quantum sensing technique that measures the magnetic field produced by circuits using the NV center optical defect. We designed a MAGnetic Inverse Calculation UNET (MAGIC-UNET) to reconstruct current density images using magnetic fields as input by solving the inverse Biot-Savart law and compared it to the analytical Fourier Method. We find that the deep learning solution using the MAGIC-UNET has greater accuracy on simulated and NV-diamond magnetometry experimental data compared to the analytical Fourier Method. It also significantly reduces the magnetometer collection time due to requiring fewer signal averages. These results expand the application scope of NV-diamond magnetometry to weak current sources and the use of DL to solve inverse problems to the quantum sensor domain.

This research is about how important emotional intelligence and cross cultural inteligence are to entrepreneurship, and also how global leadership will affect entrepreneauship in subsidiary.

This study was conducted to consider the overlap of intelligence and memory as measured by the Wechsler Intelligence Scale for Children-Revised (WISC-R) and the Wide Range Assessment of Memory and Learning (WRAML). Archival data were analyzed with canonical correlation to determine the overlap of the two instruments. Results indicated that the WRAML seems to yield similar information as that obtained through administration of the WISC-R. This overlap appears due to the short-term memory components of Digit Span and Arithmetic. Therefore, the assumption that the WRAML provides unique information concerning short-term memory seems questionable. Implications concerning the possible clinical utility of the WRAML are discussed. / Department of Educational Psychology

Thesis (Ph. D.) University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 8, 2007) Includes bibliographical references.

This study firstly focuses on the response to the post-Cold War era with the shift of the focus of intelligence to terrorism, proliferation of weapons of mass destruction, and transnational organised crime. Intelligence cooperation in respect of international crimes, including mercenary crimes, piracy and war crimes, crimes against humanity and genocide is analysed, as well as peacekeeping intelligence. Secondly the focus is on intelligence cooperation in response to the events of 11 September 2001 in the United States of America, and intelligence failures in respect of weapons of mass destruction in Iraq. Intelligence cooperation on the national level is analysed with reference to the United Kingdom and the United States of America; on regional level, with reference to the African Union, the European Union and South East Asia; and on international level with reference to INTERPOL and the United Nations. International and regional obligations in respect of intelligence cooperation are described and analysed and both the drivers of intelligence cooperation and the challenges to intelligence cooperation are analysed. Best practices are identified and proposals made to improve intelligence cooperation on the mentioned levels, in combating international crimes, including a high degree of cooperation between crime intelligence and positive intelligence. / Thesis (DPhil)--University of Pretoria, 2010. / Political Sciences / unrestricted

Motivated by the lack of models that can bring a general preparation formula for developing competitive intelligence in Trade Show, the authors researched in a theoretical database to develop a model that can bring a general vision for a company that wants to start to organize the personnel to gather competitive intelligence in trade show events.          Furthermore, was discovered that not many firms do actually realize the opportunities they have to acquire competitive intelligence in trade shows, that way the model was developed to facilitate the process.         The discovering of the research pointed Socialization as well as relationship building and strengthening were the main channels to acquire information. In addition, Trade Shows create a rich environment where most of the stakeholders are present and also willing to share knowledge and information, creating an even more favorable place to develop this kind of intelligence.         The model presented takes in consideration the company’s stakeholder, and how to allocate personnel in booths and in extensive research around the area, it also classifies the visitors and attendees in different groups, facilitating the organizing process to understand and explore more easily and efficiently the Trade Show. The models also suggest approaches to each group in order to don’t invest resources in an ineffective way.

Thesis (M.A. in National Security Affairs and Civil-Military Relations)--Naval Postgraduate School, December 2002. / Thesis advisor(s): Tom Bruneau, Harold Trinkunas. Includes bibliographical references (p. 89-93). Also available online.