Dissertations / Theses on the topic '170203 Knowledge Representation and Machine Learning'
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Leitner, Jürgen. "From vision to actions: Towards adaptive and autonomous humanoid robots." Thesis, Università della Svizzera Italiana, 2014. https://eprints.qut.edu.au/90178/2/2014INFO020.pdf.
Full textAlirezaie, Marjan. "Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.
Full textTuovinen, L. (Lauri). "From machine learning to learning with machines:remodeling the knowledge discovery process." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526205243.
Full textTiivistelmä Tiedonlouhintateknologialla etsitään automoidusti tietoa suurista määristä digitaalista dataa. Vakiintunut prosessimalli kuvaa tiedonlouhintaprosessia lineaarisesti ja teknologiakeskeisesti sarjana muunnoksia, jotka jalostavat raakadataa yhä abstraktimpiin ja tiivistetympiin esitysmuotoihin. Todellisissa tiedonlouhintaprosesseissa on kuitenkin aina osa-alueita, joita tällainen malli ei kata riittävän hyvin. Erityisesti on huomattava, että eräät prosessin tärkeimmistä toimijoista ovat ihmisiä, eivät teknologiaa, ja että heidän toimintansa prosessissa on luonteeltaan vuorovaikutteista eikä sarjallista. Tässä väitöskirjassa ehdotetaan vakiintuneen mallin täydentämistä siten, että tämä tiedonlouhintaprosessin laiminlyöty ulottuvuus otetaan huomioon. Ehdotettu prosessimalli koostuu kolmesta osamallista, jotka ovat tietomalli, työnkulkumalli ja arkkitehtuurimalli. Kukin osamalli tarkastelee tiedonlouhintaprosessia eri näkökulmasta: tietomallin näkökulma käsittää tiedon eri olomuodot sekä muunnokset olomuotojen välillä, työnkulkumalli kuvaa prosessin toimijat sekä niiden väliset vuorovaikutukset, ja arkkitehtuurimalli ohjaa prosessin suorittamista tukevien ohjelmistojen suunnittelua. Väitöskirjassa määritellään aluksi kullekin osamallille joukko vaatimuksia, minkä jälkeen esitetään vaatimusten täyttämiseksi suunniteltu ratkaisu. Lopuksi palataan tarkastelemaan vaatimuksia ja osoitetaan, kuinka ne on otettu ratkaisussa huomioon. Väitöskirjan pääasiallinen kontribuutio on se, että se avaa tiedonlouhintaprosessiin valtavirran käsityksiä laajemman tarkastelukulman. Väitöskirjan sisältämä täydennetty prosessimalli hyödyntää vakiintunutta mallia, mutta laajentaa sitä kokoamalla tiedonhallinnan ja tietämyksen esittämisen, tiedon louhinnan työnkulun sekä ohjelmistoarkkitehtuurin osatekijöiksi yhdistettyyn malliin. Lisäksi malli kattaa aiheita, joita tavallisesti ei oteta huomioon tai joiden ei katsota kuuluvan osaksi tiedonlouhintaprosessia; tällaisia ovat esimerkiksi tiedon louhintaan liittyvät filosofiset kysymykset. Väitöskirjassa käsitellään myös kahta ohjelmistokehystä ja neljää tapaustutkimuksena esiteltävää sovellusta, jotka edustavat teknisiä ratkaisuja eräisiin yksittäisiin tiedonlouhintaprosessin osaongelmiin. Kehykset ja sovellukset toteuttavat ja havainnollistavat useita ehdotetun prosessimallin merkittävimpiä ominaisuuksia
Duminy, Willem H. "A learning framework for zero-knowledge game playing agents." Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-10172007-153836.
Full textOramas, Martín Sergio. "Knowledge extraction and representation learning for music recommendation and classification." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/457709.
Full textEn esta tesis, abordamos los problemas de clasificar y recomendar música en grandes colecciones, centrándonos en el enriquecimiento semántico de descripciones (biografías, reseñas, metadatos), y en el aprovechamiento de datos multimodales (textos, audios e imágenes). Primero nos centramos en enlazar textos con bases de conocimiento y en su construcción automatizada. Luego mostramos cómo el modelado de información semántica puede impactar en estudios musicológicos, y contribuye a superar a métodos basados en texto, tanto en similitud como en clasificación y recomendación de música. A continuación, investigamos el aprendizaje de nuevas representaciones de datos a partir de contenidos multimodales utilizando redes neuronales, y lo aplicamos a los problemas de recomendar música nueva y clasificar géneros musicales con múltiples etiquetas, mostrando que el enriquecimiento semántico y la combinación de representaciones aprendidas produce mejores resultados.
Choi, Jin-Woo. "Action Recognition with Knowledge Transfer." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/101780.
Full textDoctor of Philosophy
Recent progress on deep learning has shown remarkable action recognition performance. The remarkable performance is often achieved by transferring the knowledge learned from existing large-scale data to the small-scale data specific to applications. However, existing action recog- nition models do not always work well on new tasks and datasets because of the following two problems. i) Current action recognition datasets have a spurious correlation between action types and background scene types. The models trained on these datasets are biased towards the scene instead of focusing on the actual action. This scene bias leads to poor performance on the new datasets and tasks. ii) Directly testing the model trained on the source data on the target data leads to poor performance as the source, and target distributions are different. Fine-tuning the model on the target data can mitigate this issue. However, manual labeling small-scale target videos is labor-intensive. In this dissertation, I propose solutions to these two problems. To tackle the first problem, I propose to learn scene-invariant action representations to mitigate background scene- biased human action recognition models for the first problem. Specifically, the proposed method learns representations that cannot predict the scene types and the correct actions when there is no evidence. I validate the proposed method's effectiveness by transferring the pre-trained model to multiple action understanding tasks. The results show consistent improvement over the baselines for every task and dataset. To handle the second problem, I formulate human action recognition as an unsupervised learning problem on the target data. In this setting, we have many labeled videos as source data and unlabeled videos as target data. We can use already existing labeled video datasets as source data in this setting. The task is to align the source and target feature distributions so that the learned model can generalize well on the target data. I propose 1) aligning the more important temporal part of each video and 2) encouraging the model to focus on action, not the background scene. The proposed method is simple and intuitive while achieving state-of-the-art performance without training on a lot of labeled target videos. I relax the unsupervised target data setting to a sparsely labeled target data setting. Here, we have many labeled videos as source data and sparsely labeled videos as target data. The setting is practical as sometimes we can afford a little bit of cost for labeling target data. I propose multiple video data augmentation methods to inject color, spatial, temporal, and scene invariances to the action recognition model in this setting. The resulting method shows favorable performance on the public benchmarks.
Sudre, Gustavo. "Characterizing the Spatiotemporal Neural Representation of Concrete Nouns Across Paradigms." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/315.
Full textDuminy, Willem Harklaas. "A learning framework for zero-knowledge game playing agents." Diss., University of Pretoria, 2007. http://hdl.handle.net/2263/28767.
Full textDissertation (MSc)--University of Pretoria, 2007.
Computer Science
MSc
Unrestricted
Jones, Joshua K. "Empirically-based self-diagnosis and repair of domain knowledge." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33931.
Full textBulgarov, Florin Adrian. "Toward Supporting Fine-Grained, Structured, Meaningful and Engaging Feedback in Educational Applications." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404562/.
Full textPanesar, Kulvinder. "Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutions." Universitat Politécnica de Valéncia, 2020. http://hdl.handle.net/10454/18121.
Full textThis paper aims to demystify the hype and attention on chatbots and its association with conversational artificial intelligence. Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions. However, what is under the hood, and how far and to what extent can chatbots/conversational artificial intelligence solutions work – is our question. Natural language is the most easily understood knowledge representation for people, but certainly not the best for computers because of its inherent ambiguous, complex and dynamic nature. We will critique the knowledge representation of heavy statistical chatbot solutions against linguistics alternatives. In order to react intelligently to the user, natural language solutions must critically consider other factors such as context, memory, intelligent understanding, previous experience, and personalized knowledge of the user. We will delve into the spectrum of conversational interfaces and focus on a strong artificial intelligence concept. This is explored via a text based conversational software agents with a deep strategic role to hold a conversation and enable the mechanisms need to plan, and to decide what to do next, and manage the dialogue to achieve a goal. To demonstrate this, a deep linguistically aware and knowledge aware text based conversational agent (LING-CSA) presents a proof-of-concept of a non-statistical conversational AI solution.
Capellier, Édouard. "Application of machine learning techniques for evidential 3D perception, in the context of autonomous driving." Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2534.
Full textThe perception task is paramount for self-driving vehicles. Being able to extract accurate and significant information from sensor inputs is mandatory, so as to ensure a safe operation. The recent progresses of machine-learning techniques revolutionize the way perception modules, for autonomous driving, are being developed and evaluated, while allowing to vastly overpass previous state-of-the-art results in practically all the perception-related tasks. Therefore, efficient and accurate ways to model the knowledge that is used by a self-driving vehicle is mandatory. Indeed, self-awareness, and appropriate modeling of the doubts, are desirable properties for such system. In this work, we assumed that the evidence theory was an efficient way to finely model the information extracted from deep neural networks. Based on those intuitions, we developed three perception modules that rely on machine learning, and the evidence theory. Those modules were tested on real-life data. First, we proposed an asynchronous evidential occupancy grid mapping algorithm, that fused semantic segmentation results obtained from RGB images, and LIDAR scans. Its asynchronous nature makes it particularly efficient to handle sensor failures. The semantic information is used to define decay rates at the cell level, and handle potentially moving object. Then, we proposed an evidential classifier of LIDAR objects. This system is trained to distinguish between vehicles and vulnerable road users, that are detected via a clustering algorithm. The classifier can be reinterpreted as performing a fusion of simple evidential mass functions. Moreover, a simple statistical filtering scheme can be used to filter outputs of the classifier that are incoherent with regards to the training set, so as to allow the classifier to work in open world, and reject other types of objects. Finally, we investigated the possibility to perform road detection in LIDAR scans, from deep neural networks. We proposed two architectures that are inspired by recent state-of-the-art LIDAR processing systems. A training dataset was acquired and labeled in a semi-automatic fashion from road maps. A set of fused neural networks reaches satisfactory results, which allowed us to use them in an evidential road mapping and object detection algorithm, that manages to run at 10 Hz
Verbancsics, Phillip. "Effective task transfer through indirect encoding." Doctoral diss., University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4716.
Full textID: 030646258; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (Ph.D.)--University of Central Florida, 2011.; Includes bibliographical references (p. 144-152).
Ph.D.
Doctorate
Computer Science
Engineering and Computer Science
Computer Science
Lacroix, Timothée. "Décompositions tensorielles pour la complétion de bases de connaissance." Thesis, Paris Est, 2020. http://www.theses.fr/2020PESC1002.
Full textIn this thesis, we focus on the problem of link prediction in binary tensors of order three and four containing positive observations only. Tensors of this type appear in web recommender systems, in bio-informatics for the completion of protein interaction databases, or more generally for the completion of knowledge bases. We benchmark our completion methods on knowledge bases which represent a variety of relationnal data and scales.Our approach is parallel to that of matrix completion. We optimize a non-convex regularised empirical risk objective over low-rank tensors. Our method is empirically validated on several databases, performing better than the state of the art.These performances however can only be reached for ranks that would not scale to full modern knowledge bases such as Wikidata. We focus on the Tucker decomposition which is more expressive than the Canonical decomposition but also harder to optimize. By fixing the adaptive algorithm Adagrad, we obtain a method to efficiently optimize Tucker decompositions with a fixed random core tensor. With these method, we obtain improved performances on several benchmarks for limited parameters per entities.Finally, we study the case of temporal knowledge bases, in which the predicates are only valid over certain time intervals. We propose a low-rank formulation and regularizer adapted to the temporal structure of the problem and obtain better performances than the state of the art
Wu, Xiaobing. "Knowledge representation and learning for semistructured data." Phd thesis, 2006. http://hdl.handle.net/1885/151253.
Full textAlShahrani, Mona. "Knowledge Graph Representation Learning: Approaches and Applications in Biomedicine." Diss., 2019. http://hdl.handle.net/10754/660002.
Full text(8314707), Debasmit Das. "On Transfer Learning Techniques for Machine Learning." Thesis, 2020.
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Recent progress in machine learning has been mainly due to the availability of large amounts of annotated data used for training complex models with deep architectures. Annotating this training data becomes burdensome and creates a major bottleneck in maintaining machine-learning databases. Moreover, these trained models fail to generalize to new categories or new varieties of the same categories. This is because new categories or new varieties have data distribution different from the training data distribution. To tackle these problems, this thesis proposes to develop a family of transfer-learning techniques that can deal with different training (source) and testing (target) distributions with the assumption that the availability of annotated data is limited in the testing domain. This is done by using the auxiliary data-abundant source domain from which useful knowledge is transferred that can be applied to data-scarce target domain. This transferable knowledge serves as a prior that biases target-domain predictions and prevents the target-domain model from overfitting. Specifically, we explore structural priors that encode relational knowledge between different data entities, which provides more informative bias than traditional priors. The choice of the structural prior depends on the information availability and the similarity between the two domains. Depending on the domain similarity and the information availability, we divide the transfer learning problem into four major categories and propose different structural priors to solve each of these sub-problems.
This thesis first focuses on the unsupervised-domain-adaptation problem, where we propose to minimize domain discrepancy by transforming labeled source-domain data to be close to unlabeled target-domain data. For this problem, the categories remain the same across the two domains and hence we assume that the structural relationship between the source-domain samples is carried over to the target domain. Thus, graph or hyper-graph is constructed as the structural prior from both domains and a graph/hyper-graph matching formulation is used to transform samples in the source domain to be closer to samples in the target domain. An efficient optimization scheme is then proposed to tackle the time and memory inefficiencies associated with the matching problem. The few-shot learning problem is studied next, where we propose to transfer knowledge from source-domain categories containing abundantly labeled data to novel categories in the target domain that contains only few labeled data. The knowledge transfer biases the novel category predictions and prevents the model from overfitting. The knowledge is encoded using a neural-network-based prior that transforms a data sample to its corresponding class prototype. This neural network is trained from the source-domain data and applied to the target-domain data, where it transforms the few-shot samples to the novel-class prototypes for better recognition performance. The few-shot learning problem is then extended to the situation, where we do not have access to the source-domain data but only have access to the source-domain class prototypes. In this limited information setting, parametric neural-network-based priors would overfit to the source-class prototypes and hence we seek a non-parametric-based prior using manifolds. A piecewise linear manifold is used as a structural prior to fit the source-domain-class prototypes. This structure is extended to the target domain, where the novel-class prototypes are found by projecting the few-shot samples onto the manifold. Finally, the zero-shot learning problem is addressed, which is an extreme case of the few-shot learning problem where we do not have any labeled data in the target domain. However, we have high-level information for both the source and target domain categories in the form of semantic descriptors. We learn the relation between the sample space and the semantic space, using a regularized neural network so that classification of the novel categories can be carried out in a common representation space. This same neural network is then used in the target domain to relate the two spaces. In case we want to generate data for the novel categories in the target domain, we can use a constrained generative adversarial network instead of a traditional neural network. Thus, we use structural priors like graphs, neural networks and manifolds to relate various data entities like samples, prototypes and semantics for these different transfer learning sub-problems. We explore additional post-processing steps like pseudo-labeling, domain adaptation and calibration and enforce algorithmic and architectural constraints to further improve recognition performance. Experimental results on standard transfer learning image recognition datasets produced competitive results with respect to previous work. Further experimentation and analyses of these methods provided better understanding of machine learning as well.
(11197908), Yicheng Cheng. "Machine Learning in the Open World." Thesis, 2021.
Find full text(9089423), Daniel Mas Montserrat. "Machine Learning-Based Multimedia Analytics." Thesis, 2020.
Find full textMachine learning is widely used to extract meaningful information from video, images, audio, text, and other multimedia data. Through a hierarchical structure, modern neural networks coupled with backpropagation learn to extract information from large amounts of data and to perform specific tasks such as classification or regression. In this thesis, we explore various approaches to multimedia analytics with neural networks. We present several image synthesis and rendering techniques to generate new images for training neural networks. Furthermore, we present multiple neural network architectures and systems for commercial logo detection, 3D pose estimation and tracking, deepfakes detection, and manipulation detection in satellite images.
Alachram, Halima. "Knowledge Integration and Representation for Biomedical Analysis." Doctoral thesis, 2021. http://hdl.handle.net/21.11130/00-1735-0000-0005-158D-5.
Full text(7486115), Gagandeep Singh Khanuja. "A STUDY OF REAL TIME SEARCH IN FLOOD SCENES FROM UAV VIDEOS USING DEEP LEARNING TECHNIQUES." Thesis, 2019.
Find full text(8617635), Rehana Mahfuz. "Defending Against Adversarial Attacks Using Denoising Autoencoders." Thesis, 2020.
Find full text(11167824), Saurabh Devulapalli. "A Machine Learning Approach for Uniform Intrusion Detection." Thesis, 2021.
Find full text(10725357), Siddharth Divi. "UNIFYING DISTILLATION WITH PERSONALIZATION IN FEDERATED LEARNING." Thesis, 2021.
Find full text(9179561), Hogun Park. "Neural Representation Learning for Semi-Supervised Node Classification and Explainability." Thesis, 2020.
Find full text(8811842), Lukasz Burzawa. "Acceleration of PDE-based biological simulation through the development of neural network metamodels." Thesis, 2020.
Find full text(11170170), Zhi Huang. "Integrative Analysis of Multimodal Biomedical Data with Machine Learning." Thesis, 2021.
Find full text(10157291), Yi-Yu Lai. "Relational Representation Learning Incorporating Textual Communication for Social Networks." Thesis, 2021.
Find full text(7013450), Enas Ahmad Alikhashashneh. "USING MACHINE LEARNING TECHNIQUES TO IMPROVE STATIC CODE ANALYSIS TOOLS USEFULNESS." Thesis, 2019.
Find full textThis dissertation proposes an approach to reduce the cost of manual inspections for as large a number of false positive warnings that are being reported by Static Code Analysis (SCA) tools as much as possible using Machine Learning (ML) techniques. The proposed approach neither assume to use the particular SCA tools nor depends on the specific programming language used to write the target source code or the application. To reduce the number of false positive warnings we first evaluated a number of SCA tools in terms of software engineering metrics using a highlighted synthetic source code named the Juliet test suite. From this evaluation, we concluded that the SCA tools report plenty of false positive warnings that need a manual inspection. Then we generated a number of datasets from the source code that forced the SCA tool to generate either true positive, false positive, or false negative warnings. The datasets, then, were used to train four of ML classifiers in order to classify the collected warnings from the synthetic source code. From the experimental results of the ML classifiers, we observed that the classifier that built using the Random Forests
(RF) technique outperformed the rest of the classifiers. Lastly, using this classifier and an instance-based transfer learning technique, we ranked a number of warnings that were aggregated from various open-source software projects. The experimental results show that the proposed approach to reduce the cost of the manual inspection of the false positive warnings outperformed the random ranking algorithm and was highly correlated with the ranked list that the optimal ranking algorithm generated.
(11181642), Deboleena Roy. "Exploring Methods for Efficient Learning in Neural Networks." Thesis, 2021.
Find full text(11196552), Kevin Segundo Bello Medina. "STRUCTURED PREDICTION: STATISTICAL AND COMPUTATIONAL GUARANTEES IN LEARNING AND INFERENCE." Thesis, 2021.
Find full text(9740444), Amirreza Salamat. "Heterogeneous Graph Based Neural Network for Social Recommendations with Balanced Random Walk Initialization." Thesis, 2021.
Find full textMokhtari, Vahid. "Gathering and conceptualizing plan-based robot activity experiences for long-term competence enhancement." Doctoral thesis, 2018. http://hdl.handle.net/10773/28534.
Full textAprendizagem de robôs é uma direção de pesquisa proeminente em robótica inteligente. Em robótica, é necessário lidar com a questão da integração de várias tecnologias, como percepção, planeamento, atuação e aprendizagem. Na aprendizagem de robôs, o objetivo a longo prazo é desenvolver robôs que aprendem a executar tarefas e melhoram continuamente os seus conhecimentos e habilidades através da observação e exploração do ambiente e interação com os utilizadores. A investigação tem-se centrado na aprendizagem de comportamentos básicos, ao passo que a aprendizagem de representações de atividades de alto nível, que se decompõem em sequências de ações, e de classes de actividades, não tem sido suficientemente abordada. A aprendizagem ao nível da tarefa é fundamental para aumentar a autonomia e a flexibilidade dos robôs. O conhecimento de alto nível permite tornar o software dos robôs menos dependente da plataforma e facilita a troca de conhecimento entre robôs diferentes. O objetivo desta tese é contribuir para o desenvolvimento de capacidades cognitivas para robôs, incluindo aquisição supervisionada de experiência através da interação humano-robô, aprendizagem de tarefas de alto nível com base nas experiências acumuladas e planeamento de tarefas usando o conhecimento adquirido. Propõe-se uma abordagem que integra diversas funcionalidades cognitivas para aprendizagem e reprodução de aspetos de alto nível detetados nas experiências acumuladas. Em particular, nós propomos e formalizamos a noção de Domínio de Planeamento Baseado na Experiência (Experience-Based Planning Domain, or EBPD) para aprendizagem e planeamento num âmbito temporal alargado. Uma interface para interação humano-robô é usada para fornecer ao robô instruções passo-a-passo sobre como realizar tarefas. Propõe-se uma abordagem para extrair experiências de atividades baseadas em planos, incluindo as percepções relevantes e as ações executadas pelo robô. Uma metodologia de conceitualização é apresentada para a aquisição de conhecimento de tarefa na forma de schemata a partir de experiências. São utilizadas diferentes técnicas, incluindo generalização dedutiva, diferentes formas de abstracção e extração de características. A metodologia inclui detecção de ciclos, inferência de âmbito de aplicação e inferência de objetivos. A resolução de problemas em EBPDs é alcançada usando um sistema de planeamento com duas camadas, uma para planeamento abstrato, aplicando um schema aprendido, e outra para planeamento detalhado. A arquitetura e os métodos de aprendizagem e planeamento são aplicados e avaliados em vários cenários reais e simulados. Finalmente, os métodos de aprendizagem desenvolvidos são comparados e as condições onde cada um deles tem melhor aplicabilidade são discutidos.
Programa Doutoral em Informática
(5929691), Asish Ghoshal. "Efficient Algorithms for Learning Combinatorial Structures from Limited Data." Thesis, 2019.
Find full text(11197824), Kiirthanaa Gangadharan. "Deep Transferable Intelligence for Wearable Big Data Pattern Detection." Thesis, 2021.
Find full text(8119418), Hafiz Muhammad Junaid Khan. "A MACHINE LEARNING BASED WEB SERVICE FOR MALICIOUS URL DETECTION IN A BROWSER." Thesis, 2019.
Find full text(9156518), Natalia S. Sanchez Tamayo. "Learning Multi-step Dual-arm Tasks From Demonstrations." Thesis, 2020.
Find full textIn this thesis, a framework for multi-step surgical task automation is designed and implemented by leveraging deep imitation learning. The framework optimizes Recurrent Neural Networks (RNNs) for the execution of the whole surgical tasks while considering tooltip translations, rotations as well as gripper actions. The network architecture proposed implicitly optimizes for the interaction between two robotic arms as opposed to modeling each arm independently. The networks were trained directly from the human demonstrations and do not require to create task specific hand-crafted models or to manually segment the demonstrations.
The proposed framework was implemented and evaluated in simulation for two relevant surgical tasks, the peg transfer task and the surgical debridement. The tasks were tested under random initial conditions to challenge the robustness of the networks to generalize to variable settings. The performance of the framework was assessed using task and subtask success as well as a set of quantitative metrics. Experimental evaluation showed favorable results for automating surgical tasks under variable conditions for the surgical debridement, which obtained a task success rate comparable to the human task success. For the peg transfer task, the framework displayed moderate overall task success. Quantitative metrics indicate that the robot generated trajectories possess similar or better motion economy that the human demonstrations.
(7042994), Jackson B. Bennett. "Attitude and Adoption: Understanding Climate Change Through Predictive Modeling." Thesis, 2019.
Find full text(7460849), Aldo Fabrizio Porco. "USING MODULAR ARCHITECTURES TO PREDICT CHANGE OF BELIEFS IN ONLINE DEBATES." Thesis, 2019.
Find full textResearchers studying persuasion have mostly focused on modeling arguments to understand how people’s beliefs can change. However, in order to convince an audience the speakers usually adapt their speech. This can be seen often in political campaigns when ideas are phrased - framed - in different ways according to the geo-graphical region the candidate is in. This practice suggests that, in order to change people’s beliefs, it is important to take into account their previous perspectives and topics of interest.
In this work we propose ChangeMyStance, a novel task to predict if a user would change their mind after being exposed to opposing views on a particular subject. This setting takes into account users’ beliefs before a debate, thus modeling their preconceived notions about the topic. Moreover, we explore a new approach to solve the problem, where the task is decomposed into ”simpler” problems. Breaking the main objective into several tasks allows to build expert modules that combined produce better results. This strategy significantly outperforms a BERT end-to-end model over the same inputs.
(9189470), Abhinand Ayyaswamy. "Computational Modeling of Hypersonic Turbulent Boundary Layers By Using Machine Learning." Thesis, 2020.
Find full text(6634508), Amruthavarshini Talikoti. "ESTIMATING PHENYLALANINE OF COMMERCIAL FOODS : A COMPARISON BETWEEN A MATHEMATICAL APPROACH AND A MACHINE LEARNING APPROACH." Thesis, 2019.
Find full textPhenylketonuria (PKU) is an inherited metabolic disorder affecting 1 in every 10,000 to 15,000 newborns in the United States every year. Caused by a genetic mutation, PKU results in an excessive build up of the amino acid Phenylalanine (Phe) in the body leading to symptoms including but not limited to intellectual disability, hyperactivity, psychiatric disorders and seizures. Most PKU patients must follow a strict diet limited in Phe. The aim of this research study is to formulate, implement and compare techniques for Phe estimation in commercial foods using the information on the food label (Nutritional Fact Label and ordered ingredient list). Ideally, the techniques should be both accurate and amenable to a user friendly implementation as a Phe calculator that would aid PKU patients monitor their dietary Phe intake.
The first approach to solve the above problem is a mathematical one that comprises three steps. The three steps were separately proposed as methods by Jieun Kim in her dissertation. It was assumed that the third method, which is more computationally expensive, was the most accurate one. However, by performing the three methods subsequently in three different steps and combining the results, we actually obtained better results than by merely using the third method.
The first step makes use of the protein content in the foods and Phe:protein multipliers. The second step enumerates all the ingredients in the food and uses the minimum and maximum Phe:protein multipliers of the ingredients along with the protein content. The third step lists the ingredients in decreasing order of their weights, which gives rise to inequality constraints. These constraints hold assuming that there is no loss in the preparation process. The inequality constraints are optimized numerically in two phases. The first involves nutrient content estimation by approximating the ingredient amounts. The second phase is a refinement of the above estimates using the Simplex algorithm. The final Phe range is obtained by performing an interval intersection of the results of the three steps. We implemented all three steps as web applications. Our proposed three-step method yields a high accuracy of Phe estimation (error <= +/- 13.04mg Phe per serving for 90% of foods).
The above mathematical procedure is contrasted against a machine learning approach that uses the data in an existing database as training data to infer the Phe in any given food. Specifically, we use the K-Nearest Neighbors (K-NN) classification method using a feature vector containing the (rounded) nutrient data. In other words, the Phe content of the test food is a weighted average of the Phe values of the neighbors closest to it using the nutrient values as attributes. A four-fold cross validation is carried out to determine the hyper-parameters and the training is performed using the United States Department of Agriculture (USDA) food nutrient database. Our tests indicate that this approach is not very accurate for general foods (error <= +/- 50mg Phe per 100g in about 38% of the foods tested). However, for low-protein foods which are typically consumed by PKU patients, the accuracy increases significantly (error <= +/- 50mg Phe per 100g in over 77% foods).
The machine learning approach is more user-friendly than the mathematical approach. It is convenient, fast and easy to use as it takes into account just the nutrient information. In contrast, the mathematical method additionally takes as input a detailed ingredient list, which is cumbersome to be located in a food database and entered as input. However, the Mathematical method has the added advantage of providing error bounds for the Phe estimate. It is also more accurate than the ML method. This may be due to the fact that for the ML method, the nutrition facts alone are not sufficient to estimate Phe and that additional information like the ingredients list is required.
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Find full text(5931056), Kyle Haas. "Transfer Learning for Medication Adherence Prediction from Social Forums Self-Reported Data." Thesis, 2019.
Find full textMedication non-adherence and non-compliance left unaddressed can compound into severe medical problems for patients. Identifying patients that are likely to become non-adherent can help reduce these problems. Despite these benefits, monitoring adherence at scale is cost-prohibitive. Social forums offer an easily accessible, affordable, and timely alternative to the traditional methods based on claims data. This study investigates the potential of medication adherence prediction based on social forum data for diabetes and fibromyalgia therapies by using transfer learning from the Medical Expenditure Panel Survey (MEPS).
Predictive adherence models are developed by using both survey and social forums data and different random forest (RF) techniques. The first of these implementations uses binned inputs from k-means clustering. The second technique is based on ternary trees instead of the widely used binary decision trees. These techniques are able to handle missing data, a prevalent characteristic of social forums data.
The results of this study show that transfer learning between survey models and social forum models is possible. Using MEPS survey data and the techniques listed above to derive RF models, less than 5% difference in accuracy was observed between the MEPS test dataset and the social forum test dataset. Along with these RF techniques, another RF implementation with imputed means for the missing values was developed and shown to predict adherence for social forum patients with an accuracy >70%.
This thesis shows that a model trained with verified survey data can be used to complement traditional medical adherence models by predicting adherence from unverified, self-reported data in a dynamic and timely manner. Furthermore, this model provides a method for discovering objective insights from subjective social reports. Additional investigation is needed to improve the prediction accuracy of the proposed model and to assess biases that may be inherent to self-reported adherence measures in social health networks.
(6636128), Nidhi Nandkishor Sakhala. "Generation of cyber attack data using generative techniques." Thesis, 2019.
Find full textThe presence of attacks in day-to-day traffic flow in connected networks is considerably less compared to genuine traffic flow. Yet, the consequences of these attacks are disastrous. It is very important to identify if the network is being attacked and block these attempts to protect the network system. Failure to block these attacks can lead to loss of confidential information and reputation and can also lead to financial loss. One of the strategies to identify these attacks is to use machine learning algorithms that learn to identify attacks by looking at previous examples. But since the number of attacks is small, it is difficult to train these machine learning algorithms. This study aims to use generative techniques to create new attack samples that can be used to train the machine learning based intrusion detection systems to identify more attacks. Two metrics are used to verify that the training has improved and a binary classifier is used to perform a two-sample test for verifying the generated attacks.
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Find full text(8086769), Andrew Paul Hoblitzell. "Deep Learning Based User Models for Interactive Optimization of Watershed Designs." Thesis, 2019.
Find full textThis dissertation combines stakeholder and analytical intelligence for consensus decision-making via an interactive optimization process. This dissertation outlines techniques for developing user models of subjective criteria of human stakeholders for an environmental decision support system called WRESTORE. The dissertation compares several user modeling techniques and develops methods for incorporating such user models selectively for interactive optimization, combining multiple objective and subjective criteria.
This dissertation describes additional functionality for our watershed planning system, called WRESTORE (Watershed REstoration Using Spatio-Temporal Optimization of REsources) (http://wrestore.iupui.edu). Techniques for performing the interactive optimization process in the presence of limited data are described. This work adds a user modeling component that develops a computational model of a stakeholder’s preferences and then integrates the user model component into the decision support system.
Our system is one of many decision support systems and is dependent upon stake- holder interaction. The user modeling component within the system utilizes deep learning, which can be challenging with limited data. Our work integrates user models with limited data with application-specific techniques to address some of these challenges. The dissertation describes steps for implementing accurate virtual stakeholder models based on limited training data.
Another method for dealing with limited data, based upon computing training data uncertainty, is also presented in this dissertation. Results presented show more stable convergence in fewer iterations when using an uncertainty-based incremental sampling method than when using stability based sampling or random sampling. The technique is described in additional detail.
The dissertation also discusses non-stationary reinforcement-based feature selection for the interactive optimization component of our system. The presented results indicate that the proposed feature selection approach can effectively mitigate against superfluous and adversarial dimensions which if left untreated can lead to degradation in both computational performance and interactive optimization performance against analytically determined environmental fitness functions.
The contribution of this dissertation lays the foundation for developing a framework for multi-stakeholder consensus decision-making in the presence of limited data.
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