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The way solutions are represented, or encoded, is usually the result of domain knowledge and experience. In this work, we combine MAP-Elites with Variational Autoencoders to learn a Data-Driven Encoding (DDE) that captures the essence of the highest-performing solutions while still able to encode a wide array of solutions. Our approach learns this data-driven encoding during optimization by balancing between exploiting the DDE to generalize the knowledge contained in the current archive of elites and exploring new representations that are not yet captured by the DDE. Learning representation during optimization allows the algorithm to solve high-dimensional problems, and provides a low-dimensional representation which can be then be re-used. We evaluate the DDE approach by evolving solutions for inverse kinematics of a planar arm (200 joint angles) and for gaits of a 6-legged robot in action space (a sequence of 60 positions for each of the 12 joints). We show that the DDE approach not only accelerates and improves optimization, but produces a powerful encoding that captures a bias for high performance while expressing a variety of solutions.
A novel approach to produce 2D designs by adapting the HyperNEAT algorithm to evolve non-uniform rational basis splines (NURBS) is presented. This representation is proposed as an alternative to previous pixel-based approaches primarily motivated by aesthetic interests, and not designed for optimization tasks. This spline representation outperforms previous pixel-based approaches on target matching tasks, performing well even in matching irregular target shapes. In addition to improved evolvability in the face of a well defined fitness metric, a NURBS representation has the added virtues of being continuous rather than discrete, as well as being intuitive and easily modified by graphic and industrial designers.
Optimization plays an essential role in industrial design, but is not limited to minimization of a simple function, such as cost or strength. These tools are also used in conceptual phases, to better understand what is possible. To support this exploration we focus on Quality Diversity (QD) algorithms, which produce sets of varied, high performing solutions. These techniques often require the evaluation of millions of solutions -- making them impractical in design cases. In this thesis we propose methods to radically improve the data-efficiency of QD with machine learning, enabling its application to design. In our first contribution, we develop a method of modeling the performance of evolved neural networks used for control and design. The structures of these networks grow and change, making them difficult to model -- but with a new method we are able to estimate their performance based on their heredity, improving data-efficiency by several times. In our second contribution we combine model-based optimization with MAP-Elites, a QD algorithm. A model of performance is created from known designs, and MAP-Elites creates a new set of designs using this approximation. A subset of these designs are the evaluated to improve the model, and the process repeats. We show that this approach improves the efficiency of MAP-Elites by orders of magnitude. Our third contribution integrates generative models into MAP-Elites to learn domain specific encodings. A variational autoencoder is trained on the solutions produced by MAP-Elites, capturing the common “recipe” for high performance. This learned encoding can then be reused by other algorithms for rapid optimization, including MAP-Elites. Throughout this thesis, though the focus of our vision is design, we examine applications in other fields, such as robotics. These advances are not exclusive to design, but serve as foundational work on the integration of QD and machine learning.
Comparative Evaluation of Pretrained Transfer Learning Models on Automatic Short Answer Grading
(2020)
Automatic Short Answer Grading (ASAG) is the process of grading the student answers by computational approaches given a question and the desired answer. Previous works implemented the methods of concept mapping, facet mapping, and some used the conventional word embeddings for extracting semantic features. They extracted multiple features manually to train on the corresponding datasets. We use pretrained embeddings of the transfer learning models, ELMo, BERT, GPT, and GPT-2 to assess their efficiency on this task. We train with a single feature, cosine similarity, extracted from the embeddings of these models. We compare the RMSE scores and correlation measurements of the four models with previous works on Mohler dataset. Our work demonstrates that ELMo outperformed the other three models. We also, briefly describe the four transfer learning models and conclude with the possible causes of poor results of transfer learning models.
IT-gestütztes IT-Controlling
(2013)
In Zeiten knapper Budgets kommt der Auswahl der »richtigen« IT-Projekte eine steigende Bedeutung zu. Die Auswahl erfolgt häufig auf der Grundlage nutzenorientierter Kriterien, insbesondere der ROI-Kennzahl. Für viele IT-Security- Projekte ist die Ermittlung eines (positiven) ROI (Return on Investment) aber nicht möglich. Dennoch ist sicherzustellen, dass ausreichende Budgetmittel für IT-Sicherheitsmaßnahmen zur Verfügung stehen. Der Beitrag typisiert unterschiedliche Formen von IT-Security-Projekten und versucht anhand von mehreren Praxisbeispielen aufzuzeigen, in welchen Fällen ein positiver ROI darstellbar ist.
Geschäftsprozessmanagement
(2005)
Masterkurs IT-Controlling
(2005)
Das Leitbildcontrolling-Konzept für die IT - IT-Controlling: Vom Konzept zur Umsetzung (Zielformulierung, Zielsteuerung, Zielerfüllung) - Einsatz strategischer IT-Controlling-Werkzeuge - Operative Werkzeuge - IT-Kostenrechnung - IT-bezogene Deckungsbeitragsrechnung - Prozesskostenrechnung für das IT-Controlling
Masterkurs IT-Controlling
(2006)
Seit Erscheinen der 1. Auflage 2004 ist dieses Werk das erste Buch, das mit einem geschlossenen IT-Controllingkonzept aufwartet. Hiervon profitiert sowohl die Lehre wie auch die Gestaltung der Unternehmenswirklichkeit. Das Werk gilt inzwischen als das Standard-Werk schlechthin und wird in Hochschulen und Seminaren als praxisgerechte Grundlage nachdrücklich empfohlen. Nachvollziehbar für die Praxis wird die Darstellung durch geeignete Beispiele aus bedeutenden Unternehmen. Die 3. Auflage wurde um die Themenbereiche "Target Costing" und "Wertermittlung der IT" nochmals erweitert.
Masterkurs IT-Controlling
(2010)
Masterkurs IT-Controlling
(2014)
Grundkurs IT-Controlling
(2004)
IT-Sicherheit
(2017)
Andreas Gadatsch und Markus Mangiapane erläutern zentrale Aspekte der Digitalisierung und der IT-Sicherheit, ohne die digitale Geschäftsmodelle und -prozesse nicht realisierbar sind. Die Autoren möchten den Leser für aktuelle Trends im Informationsmanagement und deren Auswirkungen auf IT-Sicherheit sensibilisieren. Wenn man von jedem Punkt der Welt aus einen Prozess nutzen kann, so kann man ihm auch jederzeit von jedem Ort aus schaden, ihn stoppen, verändern oder Daten manipulieren. IT-Sicherheit ist daher die Grundlage zur Realisierung digitaler Prozesse. (Verlagsangaben)