FEAT-REP : representing features in CAD/CAM

When CAD/CAM experts view a workpiece, they perceive it in terms of their own expertise. These terms, called features, which are build upon a syntax (geometry) and a semantic (e.g. skeletal plans in manufacturing or functional relations in design), provide an abstraction mechanism to facilitate the creation, manufacturing and analysis of workpieces. Our goal is to enable experts to represent their own feature-language via a feature-grammar in the computer to build feature-based systems e.g. CAPP systems. The application of formal language terminology to the feature definitions facilitates the use of well-known formal language methods in conjunction with our flexible knowledge representation formalism FEAT-REP which will be presented in this paper

FEAT-REP : representing features in CAD/CAM

When CAD/CAM experts view a workpiece, they perceive it in terms of their own expertise. These terms, called features, which are build upon a syntax (geometry) and a semantic (e.g. skeletal plans in manufacturing or functional relations in design), provide an abstraction mechanism to facilitate the creation, manufacturing and analysis of workpieces. Our goal is to enable experts to represent their own feature-language via a feature-grammar in the computer to build feature-based systems e.g. CAPP systems. The application of formal language terminology to the feature definitions facilitates the use of well-known formal language methods in conjunction with our flexible knowledge representation formalism FEAT-REP which will be presented in this paper

Adaptation in Deep Learning Models: Algorithms and Applications

Artificial intelligence has been successful to match or even surpass human abilities e.g., recognizing images, playing games, and understanding languages. At the current state, powerful machine learning models learn from data under a stationary environment while humans are capable of learning in dynamic, changing, and sequential conditions. In pursuing the idea of open-ended learning for machine intelligence, we contribute to provide algorithms and analyses for generally capable models via adaptation. In this thesis, the model adaptation problem is defined as the impediment of intelligent machines to learn to modify their behaviors for new purposes or new uses. The ultimate goal is to develop machine intelligence that has the ability to adapt itself by not only following at our behest but also understanding the environment. Our works populate in the area of deep neural networks and transfer learning. Throughout our works, developing adaptive models is divided into four major problems: (1) few-shot learning, (2) fast model adaptation, (3) continual learning, and (4) architecture search. In few-shot learning, a model is expected to change its behavior when facing a new context or an unseen task with limited data. Another important problem within few-shot learning is to adapt quickly from a few data. In the problem of continual learning, the model needs to adapt sequentially depending on the given task. In architecture search, we look for a high-performing configuration for connecting among nodes in a model. To approach the problem of few-shot learning, we opt to use the strategy in transfer learning with a pretrained Convolutional Neural Network (CNN) for novel tasks with limited-data annotations. Inspired by the success of subspace methods for visual recognition, we develop a classifier using subspaces to improve the generalization capability to novel concepts. We also investigate few-shot learning in multi-label classification, and propose a multi-label propagation technique by constructing a graph from the representations of support samples. In pursuing fast model adaptation, we use the idea of preconditioners in optimization. Specifically, the problem revolves in \textit{meta-learning}, where the agent needs to learn a family of tasks and adapt quickly to a new task. Our algorithm uses a non-linear function to generate the preconditioner for modulating the gradient when updating the model. Our experiments show that the model converges more quickly than other types of preconditioners in the same problem. In the problem of continual learning, the model needs to sequentially learn and adapt the network parameters for new tasks without forgetting the previously learned tasks. To this end, we investigate the knowledge distillation approach, where the old model guides the current model to find the balance between the current task and the prior tasks. Our approach models the smoothness between two tasks using the geodesic flow, and the objective is to maximize similarity of the projected responses along the geodesic flow. In neural architecture search, the optimal architecture depends on the task objectives. We observe that searching for an optimal architecture is not trivial while the data annotations is noisy. The study investigates the impact of label noise in obtaining the best performance when optimizing a neural architecture, while also reducing the performance deterioration because of overfitting to noisy labels. We use the mutual information bottleneck to design a noise injection module that can alleviate the impact of learning under label noise. In summary, our works in this thesis address some major problems in model adaptation e.g., few-shot learning, meta-learning, continual learning, and neural architecture search. The solutions are expected to contribute to the arsenal of model adaptation algorithms and the analyses shed light on the essential aspects in adaptation strategies

Improving the performance of gas sensor systems with advanced data evaluation, operation, and calibration methods

In order to facilitate the widespread use of gas sensors, some challenges must still be overcome. Many of those are related to the reliable quantification of ultra-low concentrations of specific compounds in a background of other gases. This thesis focuses on three important items in the measurement chain: sensor material and operating modes, evaluation of the resulting data, and test gas generation for efficient sensor calibration. New operating modes and materials for gas-sensitive field-effect transistors have been investigated. Tungsten trioxide as gate oxide can improve the selectivity to hazardous volatile organic compounds like naphthalene even in a strong and variable ethanol background. The influence of gate bias and ultraviolet light has been studied with respect to the transport of oxygen anions on the sensor surface and was used to improve classification and quantification of different gases. DAV3E, an internationally recognized MATLAB-based toolbox for the evaluation of cyclic sensor data, has been developed and published as opensource. It provides a user-friendly graphical interface and specially tailored algorithms from multivariate statistics. The laboratory tests conducted during this project have been extended with an interlaboratory study and a field test, both yielding valuable insights for future, more complex sensor calibration. A novel, efficient calibration approach has been proposed and evaluated with ten different gas sensor systems.Vor der weitverbreiteten Nutzung von Gassensoren stehen noch einige Herausforderungen, insbesondere die zuverlässige Messung ultrakleiner Konzentrationen bestimmter Substanzen vor einem Hintergrund anderer Gase. Diese Arbeit konzentriert sich auf drei wichtige Glieder der erforderlichen Messkette: Material und Betriebsweise von Sensoren, Auswertung der anfallenden Daten sowie Generierung von Testgasen zur effizienten Kalibrierung. Neue Betriebsmodi und Materialien für gassensitive Feldeffekttransistoren wurden getestet. Wolframtrioxid kann als Gateoxid die Selektivität für flüchtige organische Verbindungen wie Naphthalin in einem variierenden Ethanolhintergrund verbessern. Der Einfluss von Gate-Bias und ultravioletter Strahlung auf die Bewegung von Sauerstoffionen auf der Oberfläche wurde untersucht und genutzt, um die Klassifizierung und Quantifizierung von Gasen zu verbessern. Eine international anerkannte MATLAB-Toolbox zur Auswertung zyklischer Sensordaten, DAV3E, wurde entwickelt und als open source veröffentlicht. Sie stellt eine nutzerfreundliche Oberfläche und speziell angepasste Algorithmen der multivariaten Statistik zur Verfügung. Die Laborexperimente wurden ergänzt durch vergleichende Messungen in zwei unabhängigen Laboren und einen Feldtest, womit wertvolle Erkenntnisse für die künftig notwendige, komplexe Kalibrierung von Sensoren gewonnen wurden. Ein neuartiger, effizienter Kalibrieransatz wurde vorgestellt und mit zehn unterschiedlichen Sensorsystemen evaluiert

An overview of computer vision

An overview of computer vision is provided. Image understanding and scene analysis are emphasized, and pertinent aspects of pattern recognition are treated. The basic approach to computer vision systems, the techniques utilized, applications, the current existing systems and state-of-the-art issues and research requirements, who is doing it and who is funding it, and future trends and expectations are reviewed

Logic and Automata

Mathematical logic and automata theory are two scientific disciplines with a fundamentally close relationship. The authors of Logic and Automata take the occasion of the sixtieth birthday of Wolfgang Thomas to present a tour d'horizon of automata theory and logic. The twenty papers in this volume cover many different facets of logic and automata theory, emphasizing the connections to other disciplines such as games, algorithms, and semigroup theory, as well as discussing current challenges in the field

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Topic driven testing

Modern interactive applications offer so many interaction opportunities that automated exploration and testing becomes practically impossible without some domain specific guidance towards relevant functionality. In this dissertation, we present a novel fundamental graphical user interface testing method called topic-driven testing. We mine the semantic meaning of interactive elements, guide testing, and identify core functionality of applications. The semantic interpretation is close to human understanding and allows us to learn specifications and transfer knowledge across multiple applications independent of the underlying device, platform, programming language, or technology stack—to the best of our knowledge a unique feature of our technique. Our tool ATTABOY is able to take an existing Web application test suite say from Amazon, execute it on ebay, and thus guide testing to relevant core functionality. Tested on different application domains such as eCommerce, news pages, mail clients, it can trans- fer on average sixty percent of the tested application behavior to new apps—without any human intervention. On top of that, topic-driven testing can go with even more vague instructions of how-to descriptions or use-case descriptions. Given an instruction, say “add item to shopping cart”, it tests the specified behavior in an application–both in a browser as well as in mobile apps. It thus improves state-of-the-art UI testing frame- works, creates change resilient UI tests, and lays the foundation for learning, transfer- ring, and enforcing common application behavior. The prototype is up to five times faster than existing random testing frameworks and tests functions that are hard to cover by non-trained approaches.Moderne interaktive Anwendungen bieten so viele Interaktionsmöglichkeiten, dass eine vollständige automatische Exploration und das Testen aller Szenarien praktisch unmöglich ist. Stattdessen muss die Testprozedur auf relevante Kernfunktionalität ausgerichtet werden. Diese Arbeit stellt ein neues fundamentales Testprinzip genannt thematisches Testen vor, das beliebige Anwendungen u ̈ber die graphische Oberfläche testet. Wir untersuchen die semantische Bedeutung von interagierbaren Elementen um die Kernfunktionenen von Anwendungen zu identifizieren und entsprechende Tests zu erzeugen. Statt typischen starren Testinstruktionen orientiert sich diese Art von Tests an menschlichen Anwendungsfällen in natürlicher Sprache. Dies erlaubt es, Software Spezifikationen zu erlernen und Wissen von einer Anwendung auf andere zu übertragen unabhängig von der Anwendungsart, der Programmiersprache, dem Testgerät oder der -Plattform. Nach unserem Kenntnisstand ist unser Ansatz der Erste dieser Art. Wir präsentieren ATTABOY, ein Programm, das eine existierende Testsammlung für eine Webanwendung (z.B. für Amazon) nimmt und in einer beliebigen anderen Anwendung (sagen wir ebay) ausführt. Dadurch werden Tests für Kernfunktionen generiert. Bei der ersten Ausführung auf Anwendungen aus den Domänen Online Shopping, Nachrichtenseiten und eMail, erzeugt der Prototyp sechzig Prozent der Tests automatisch. Ohne zusätzlichen manuellen Aufwand. Darüber hinaus interpretiert themen- getriebenes Testen auch vage Anweisungen beispielsweise von How-to Anleitungen oder Anwendungsbeschreibungen. Eine Anweisung wie "Fügen Sie das Produkt in den Warenkorb hinzu" testet das entsprechende Verhalten in der Anwendung. Sowohl im Browser, als auch in einer mobilen Anwendung. Die erzeugten Tests sind robuster und effektiver als vergleichbar erzeugte Tests. Der Prototyp testet die Zielfunktionalität fünf mal schneller und testet dabei Funktionen die durch nicht spezialisierte Ansätze kaum zu erreichen sind