ISAR: Ein Autorensystem für Interaktive Tische

Developing augmented reality systems involves several challenges, that prevent end users and experts from non-technical domains, such as education, to experiment with this technology. In this research we introduce ISAR, an authoring system for augmented reality tabletops targeting users from non-technical domains. ISAR allows non-technical users to create their own interactive tabletop applications and experiment with the use of this technology in domains such as educations, industrial training, and medical rehabilitation.Die Entwicklung von Augmented-Reality-Systemen ist mit mehreren Herausforderungen verbunden, die Endbenutzer und Experten aus nicht-technischen Bereichen, wie z.B. dem Bildungswesen, daran hindern, mit dieser Technologie zu experimentieren. In dieser Forschung stellen wir ISAR vor, ein Autorensystem für Augmented-Reality-Tabletops, das sich an Benutzer aus nicht-technischen Bereichen richtet. ISAR ermöglicht es nicht-technischen Anwendern, ihre eigenen interaktiven Tabletop-Anwendungen zu erstellen und mit dem Einsatz dieser Technologie in Bereichen wie Bildung, industrieller Ausbildung und medizinischer Rehabilitation zu experimentieren

Gradient boosting in automatic machine learning: feature selection and hyperparameter optimization

Das Ziel des automatischen maschinellen Lernens (AutoML) ist es, alle Aspekte der Modellwahl in prädiktiver Modellierung zu automatisieren. Diese Arbeit beschäftigt sich mit Gradienten Boosting im Kontext von AutoML mit einem Fokus auf Gradient Tree Boosting und komponentenweisem Boosting. Beide Techniken haben eine gemeinsame Methodik, aber ihre Zielsetzung ist unterschiedlich. Während Gradient Tree Boosting im maschinellen Lernen als leistungsfähiger Vorhersagealgorithmus weit verbreitet ist, wurde komponentenweises Boosting im Rahmen der Modellierung hochdimensionaler Daten entwickelt. Erweiterungen des komponentenweisen Boostings auf multidimensionale Vorhersagefunktionen werden in dieser Arbeit ebenfalls untersucht. Die Herausforderung der Hyperparameteroptimierung wird mit Fokus auf Bayesianische Optimierung und effiziente Stopping-Strategien diskutiert. Ein groß angelegter Benchmark über Hyperparameter verschiedener Lernalgorithmen, zeigt den kritischen Einfluss von Hyperparameter Konfigurationen auf die Qualität der Modelle. Diese Daten können als Grundlage für neue AutoML- und Meta-Lernansätze verwendet werden. Darüber hinaus werden fortgeschrittene Strategien zur Variablenselektion zusammengefasst und eine neue Methode auf Basis von permutierten Variablen vorgestellt. Schließlich wird ein AutoML-Ansatz vorgeschlagen, der auf den Ergebnissen und Best Practices für die Variablenselektion und Hyperparameteroptimierung basiert. Ziel ist es AutoML zu vereinfachen und zu stabilisieren sowie eine hohe Vorhersagegenauigkeit zu gewährleisten. Dieser Ansatz wird mit AutoML-Methoden, die wesentlich komplexere Suchräume und Ensembling Techniken besitzen, verglichen. Vier Softwarepakete für die statistische Programmiersprache R sind Teil dieser Arbeit, die neu entwickelt oder erweitert wurden: mlrMBO: Ein generisches Paket für die Bayesianische Optimierung; autoxgboost: Ein AutoML System, das sich vollständig auf Gradient Tree Boosting fokusiert; compboost: Ein modulares, in C++ geschriebenes Framework für komponentenweises Boosting; gamboostLSS: Ein Framework für komponentenweises Boosting additiver Modelle für Location, Scale und Shape.The goal of automatic machine learning (AutoML) is to automate all aspects of model selection in (supervised) predictive modeling. This thesis deals with gradient boosting techniques in the context of AutoML with a focus on gradient tree boosting and component-wise gradient boosting. Both techniques have a common methodology, but their goal is quite different. While gradient tree boosting is widely used in machine learning as a powerful prediction algorithm, component-wise gradient boosting strength is in feature selection and modeling of high-dimensional data. Extensions of component-wise gradient boosting to multidimensional prediction functions are considered as well. Focusing on Bayesian optimization and efficient early stopping strategies the challenge of hyperparameter optimization for these algorithms is discussed. Difficulty in the optimization of these algorithms is shown by a large scale random search on hyperparameters for machine learning algorithms, that can build the foundation of new AutoML and metalearning approaches. Furthermore, advanced feature selection strategies are summarized and a new method based on shadow features is introduced. Finally, an AutoML approach based on the results and best practices for feature selection and hyperparameter optimization is proposed, with the goal of simplifying and stabilizing AutoML while maintaining high prediction accuracy. This is compared to AutoML approaches using much more complex search spaces and ensembling techniques. Four software packages for the statistical programming language R have been newly developed or extended as a part of this thesis: mlrMBO: A general framework for Bayesian optimization; autoxgboost: An automatic machine learning framework that heavily utilizes gradient tree boosting; compboost: A modular framework for component-wise boosting written in C++; gamboostLSS: A framework for component-wise boosting for generalized additive models for location scale and shape

Sixth NASA Glenn Research Center Propulsion Control and Diagnostics (PCD) Workshop

The Intelligent Control and Autonomy Branch at NASA Glenn Research Center hosted the Sixth Propulsion Control and Diagnostics Workshop on August 22-24, 2017. The objectives of this workshop were to disseminate information about research being performed in support of NASA Aeronautics programs; get feedback from peers on the research; and identify opportunities for collaboration. There were presentations and posters by NASA researchers, Department of Defense representatives, and engine manufacturers on aspects of turbine engine modeling, control, and diagnostics

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Marine biodiversity assessments using aquatic internet of things

While Ubiquitous Computing remains vastly applied in urban environments, it is still scarce in oceanic environments. Current equipment used for biodiversity assessments remain at a high cost, being still inaccessible to wider audiences. More accessible IoT (Internet of Things) solutions need to be implemented to tackle these issues and provide alternatives to facilitate data collection in-the-wild. While the ocean remains a very harsh environment to apply such devices, it is still providing the opportunity to further explore the biodiversity, being that current marine taxa is estimated to be 200k, while this number can be actually in millions. The main goal of this thesis is to provide an apparatus and architecture for aerial marine biodiversity assessments, based on low-cost MCUs (Microcontroller unit) and microcomputers. In addition, the apparatus will provide a proof of concept for collecting and classifying the collected media. The thesis will also explore and contribute to the latest IoT and machine learning techniques (e.g. Python, TensorFlow) when applied to ocean settings. The final product of the thesis will enhance the state of the art in technologies applied to marine biology assessments.A computação ubíqua é imensamente utilizada em ambientes urbanos, mas ainda é escassa em ambientes oceânicos. Os equipamentos atuais utilizados para o estudo de biodiversidade são de custo alto, e geralmente inacessíveis para o público geral. Uma solução IoT mais acessível necessita de ser implementada para combater estes problemas e fornecer alternativas para facilitar a recolha de dados na natureza. Embora o oceano seja um ambiente severo para aplicar estes dispositivos, este fornece mais oportunidades para explorar a biodiversidade, sendo que a taxa de marinha atual é estimada ser 200 mil, mas este número pode estar nos milhões. O objetivo principal desta tese é fornecer um aparelho e uma arquitetura para estudos aéreos de biodiversidade marinha, baseado em microcontroladores low-cost e microcomputadores. Adi cionalmente, este aparelho irá fornecer uma prova de conceito para coletar e classificar a mídia coletada. A tese irá também explorar e contribuir para as técnicas mais recentes de machine learn ing (e.g. Python, TensorFlow) quando aplicadas num cenário de oceano. O produto final desta tese vai elevar o estado da arte em tecnologias aplicadas a estudos de biologia marinha

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments

OSEM : occupant-specific energy monitoring.

Electricity has become prevalent in modern day lives. Almost all the comforts people enjoy today, like home heating and cooling, indoor and outdoor lighting, computers, home and office appliances, depend on electricity. Moreover, the demand for electricity is increasing across the globe. The increasing demand for electricity and the increased awareness about carbon footprints have raised interest in the implementation of energy efficiency measures. A feasible remedy to conserve energy is to provide energy consumption feedback. This approach has suggested the possibility of considerable reduction in the energy consumption, which is in the range of 3.8% to 12%. Currently, research is on-going to monitor energy consumption of individual appliances. However, various approaches studied so far are limited to group-level feedback. The limitation of this approach is that the occupant of a house/building is unaware of his/her energy consumption pattern and has no information regarding how his/her energy-related behavior is affecting the overall energy consumption of a house/building. Energy consumption of a house/building largely depends on the energy-related behavior of individual occupants. Therefore, research in the area of individualized energy-usage feedback is essential. The OSEM (Occupant-Specific Energy Monitoring) system presented in this work is capable of monitoring individualized energy usage. OSEM system uses the electromagnetic field (EMF) radiated by appliances as a signature for appliance identification. An EMF sensor was designed and fabricated to collect the EMF radiated by appliances. OSEM uses proximity sensing to confirm the energy-related activity. Once confirmed, this activity is attributed to the occupant who initiated it. Bluetooth Low Energy technology was used for proximity sensing. This OSEM system would provide a detailed energy consumption report of individual occupants, which would help the occupants understand their energy consumption patterns and in turn encourage them to undertake energy conservation measures

Матеріали 4-го семінару молодих вчених з комп'ютерних наук та програмної інженерії (CS&SE@SW 2021), віртуальний захід, м. Кривий Ріг, Україна, 18 грудня 2021 р.

Матеріали 4-го семінару молодих вчених з комп'ютерних наук та програмної інженерії (CS&SE@SW 2021), віртуальний захід, м. Кривий Ріг, Україна, 18 грудня 2021 р.Proceedings of the 4th Workshop for Young Scientists in Computer Science & Software Engineering (CS&SE@SW 2021), Virtual Event, Kryvyi Rih, Ukraine, December 18, 2021

Proceedings of the 1st International Conference on Live Coding

Open Access peer reviewed papers on live coding published at the 1st International Conference on Live Coding (ICLC) in Leeds