Dieser Konferenzband ist das Ergebnis von SPRING 2022, der Summerschool Python remote für Ienieur*innen, einer von der Technischen Unviersität Dresden organisierten Online-Summerschool, welche in der Woche vom 07.06. bis 10.06.2022 stattfand. Das erste Kapitel befasst sich mit den Rahmenbedingungen, Vorüberlegungen und der organisatorischen Umsetzung der Summerschool mit dem Ziel, die Erfahrungen der Organisator*innen mit interessierten Nachahmenden zu teilen. Das zweite Kapitel fasst die Inhalte und Übungsaufgaben der theoretischen Einheiten der Summerschool zusammen, aufgeteilt in die Inhalte für Anfänger*innen und die Inhalte für Fortgeschrittene. Im letzten Kapitel finden sich die Beiträge des Symposiums, einer Abendveranstaltung im Rahmen der Summerschool, bei der die fortgeschrittenen Teilnehmenden ihre Erfahrungen zum Einsatz von Python aus ihrem Ingenieur*innenalltag teilen konnten.:1 Organisatorisches 5 Abstract 5 1.1 Organisation einer Online-Summerschool Python für Ingenieur*innen 6 1.1.1 Motivation 6 1.1.2 Team und Teilnehmende 7 1.1.3 Ablauf 9 1.1.4 Evaluation und Ausblick 13 2 Workshops 19 2.1 Python für Ingenieur*innen - Programmiereinstieg 20 2.2 Python für Ingenieur*innen - Fortgeschrittene 27 2.2.1 Numpy-Übungen 27 2.2.2 Einschub: Effizienter Umgang mit Dokumentationen und anderen Online-Ressourcen 30 2.2.3 Matplotlib-Übungen 31 2.2.4 Pandas-Übungen 32 3 Beiträge zum Symposium 35 3.1 Automatisierte Messdatenauswertung am Motorprüfstand in Python 36 3.2 Opto-Mechatronic Screening Module for 3D Tumour Model Eng. 38 3.3 Python in der Regelungs- und Automatisierungstechnik 40 3.4 Leben mit Python 45 / This conference volume is the result of SPRING 2022, the summer school Python remote for engineers, an online summer school organized by the Technische Unviersität Dresden, which took place in the week from 07.06. to 10.06.2022. The first chapter deals with the framework conditions, preliminary considerations and the organizational implementation of the summer school with the aim of sharing the experiences of the organizers with interested imitators. The second chapter summarizes the content and exercises of the theoretical units of the summer school, divided into content for beginners and content for advanced participants. The last chapter contains the contributions from the symposium, an evening event held as part of the summer school, where the advanced participants were able to share their experiences of using Python in their everyday engineering work.:1 Organisatorisches 5 Abstract 5 1.1 Organisation einer Online-Summerschool Python für Ingenieur*innen 6 1.1.1 Motivation 6 1.1.2 Team und Teilnehmende 7 1.1.3 Ablauf 9 1.1.4 Evaluation und Ausblick 13 2 Workshops 19 2.1 Python für Ingenieur*innen - Programmiereinstieg 20 2.2 Python für Ingenieur*innen - Fortgeschrittene 27 2.2.1 Numpy-Übungen 27 2.2.2 Einschub: Effizienter Umgang mit Dokumentationen und anderen Online-Ressourcen 30 2.2.3 Matplotlib-Übungen 31 2.2.4 Pandas-Übungen 32 3 Beiträge zum Symposium 35 3.1 Automatisierte Messdatenauswertung am Motorprüfstand in Python 36 3.2 Opto-Mechatronic Screening Module for 3D Tumour Model Eng. 38 3.3 Python in der Regelungs- und Automatisierungstechnik 40 3.4 Leben mit Python 45

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In diesem Beitrag wird über die Ergebnisse eines laufenden Digital Humanities-Projekt zur Sentiment Analysis in literarischen Texten berichtet und die Implikation von diesem diskutiert. In dem Projekt werden verschiedene Methoden der Sentiment Analysis auf Texte historischer Dramen des 18. Jahrhunderts von G. E. Lessing implementiert und gegeneinander evaluiert. Zur Evaluation wurde ein von Menschen bezüglich des Sentiments annotiertes Testkorpus erstellt. Basierend auf den ersten Erfahrungen des Projekts diskutieren wir über Probleme und Herausforderungen, die sich aus der Perspektive der Informatik zur Sentiment Analysis historischer Dramen ergaben. Es wird deutlich, dass bestehende Standardlösungen der Sentiment Analysis für dieses spezifische Szenario nicht ohne Weiteres anwendbar sind. Vielmehr ist die Informatik gefordert, die bestehenden Methoden anzupassen, weiterzuentwickeln und sich mit besonderen Eigenheiten der Textform historischer literarischer Texte auseinanderzusetzen.

Dieser Beitrag beschreibt informatische Herausforderungen im Kontext eines Digital Humanities-Projekts zur Erschließung und Analyse historischer Wetteraufzeichnungen im Zeitraum 1774 - 1827. Bei der Erschließung der handschriftlichen Aufzeichnungen, die Besonderheiten wie numerische Messwerte in Tabellenstruktur und überlagernde Notizen enthalten, soll langfristig ein entsprechend trainierter OCR-Ansatz (optical character recognition) zum Einsatz kommen. Für die Erstellung entsprechender Trainingsdaten sowie für die manuelle Korrektur der automatisch erkannten Daten ergeben sich zunächst softwareergonomische Herausforderungen aus Perspektive der Medieninformatik. Der Fokus dieses Beitrags liegt daher auf der Erstellung von Tools unter Berücksichtigung von Prinzipien des usability engineering und des user-centered design (UCD) für geisteswissenschaftliche Forschungsvorhaben.

We present results from a project on sentiment analysis of drama texts, more concretely the plays of Gotthold Ephraim Lessing. We conducted an annotation study to create a gold standard for a systematic evaluation. The gold standard consists of 200 speeches of Lessing’s plays and was manually annotated with sentiment information by five annotators. We use the gold stand-ard data to evaluate the performance of different German sentiment lexicons and processing configurations like lemmatization, the extension of lexicons with historical linguistic variants, and stop words elimination, to explore the influence of these parameters and to find best prac-tices for our domain of application. The best performing configuration accomplishes an accu-racy of 70%. We discuss the problems and challenges for sentiment analysis in this area and describe our next steps toward further research

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Der zunehmenden Verwendung computergestützter Methoden in der geisteswissenschaftlichen Forschung im Sinne der „Digital Humanities“ steht häufig eine vernachlässigte Usability der digitalen Tools gegenüber. So trägt der mangelnde Einsatz etablierter Praktiken des Usability Engineering entscheidend dazu bei, dass Erst- und Gelegenheitsnutzer_Innen davon abgehalten werden, digitale Ressourcen und komplexe Softwarewerkzeuge in der eigenen Forschung zu nutzen. Dies bestätigen die in diesem Beitrag präsentierten Ergebnisse einer Usability- Studie, die exemplarisch anhand der weitverbreiteten OCR-Software Transkribus durchgeführt wurde. Die Evaluationsergebnisse offenbaren teils erhebliche Usability-Probleme der Software, die durch einen nutzerzentrierten Entwicklungsprozess verhindert werden hätten können. Die Studie zeigt, dass iterative Usability-Tests ein unverzichtbares Werkzeug darstellen, um ein Tool mit hoher Benutzerfreundlichkeit zu erstellen und dessen Verwendung damit auch einer breiten Nutzerschaft aus geistes- und kulturwissenschaftlichen Domänen zu ermöglichen.

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Today, cyber-physical systems (CPSs) are ubiquitous. Whether it is robotics, electric vehicles, the smart home, autonomous driving, or smart prosthetics, CPSs shape our day-to-day lives. Yet, designing and programming CPSs becomes evermore challenging as the overall complexity of systems increases. CPSs need to interface (potentially distributed) computation with concurrent processes in the physical world while fulfilling strict safety requirements. Modern and popular frameworks for designing CPS applications, such as ROS and AUTOSAR, address the complexity challenges by emphasizing scalability and reactivity. This, however, comes at the cost of compromising determinism and the time predictability of applications, which ultimately compromises safety. This thesis argues that this compromise is not a necessity and demonstrates that scalability can be achieved while ensuring a predictable execution. At the core of this thesis is the novel reactor model of computation (MoC) that promises to provide timed semantics, reactivity, scalability, and determinism. A comprehensive study of related models indicates that there is indeed no other MoC that provides similar properties. The main contribution of this thesis is the introduction of a complete set of tools that make the reactor model accessible for CPS design and a demonstration of their ability to facilitate the development of scalable deterministic software. After introducing the reactor model, we discuss its key principles and utility through an adaptation of reactors in the DEAR framework. This framework integrates reactors with a popular runtime for adaptive automotive applications developed by AUTOSAR. An existing AUTOSAR demonstrator application serves as a case study that exposes the problem of nondeterminism in modern CPS frameworks. We show that the reactor model and its implementation in the DEAR framework are applicable for achieving determinism in industrial use cases. Building on the reactor model, we introduce the polyglot coordination language Lingua Franca (LF), which enables the definition of reactor programs independent of a concrete target programming language. Based on the DEAR framework, we develop a full-fledged C++ reactor runtime and a code generation backend for LF. Various use cases studied throughout the thesis illustrate the general applicability of reactors and LF to CPS design, and a comprehensive performance evaluation using an optimized version of the C++ reactor runtime demonstrates the scalability of LF programs. We also discuss some limitations of the current scheduling mechanisms and show how they can be overcome by partitioning programs. Finally, we consider design space exploration (DSE) techniques to further improve the scalability of LF programs and manage hardware complexity by automating the process of allocating hardware resources to specific components in the program. This thesis contributes the Mocasin framework, which resembles a modular platform for prototyping and researching DSE flows. While a concrete integration with LF remains for future work, Mocasin provides a foundation for exploring DSE in Lingua Franca.

Nowadays, implementing a complex system, which executes various applications with different levels of assurance, is a growing trend in modern embedded real-time systems to meet cost, timing, and power consumption requirements. Medical devices, automotive, and avionics industries are the most common safety-critical applications, exploiting these systems known as Mixed-Criticality (MC) systems. MC applications are real-time, and to ensure the correctness of these applications, it is essential to meet strict timing requirements as well as functional specifications. The correct design of such MC systems requires a thorough understanding of the system's functions and their importance to the system. A failure/deadline miss in functions with various criticality levels has a different impact on the system, from no effect to catastrophic consequences. Failure in the execution of tasks with higher criticality levels (HC tasks) may lead to system failure and cause irreparable damage to the system, while although Low-Criticality (LC) tasks assist the system in carrying out its mission successfully, their failure has less impact on the system's functionality and does not harm the system itself to fail. In order to guarantee the MC system safety, tasks are analyzed with different assumptions to obtain different Worst-Case Execution Times (WCETs) corresponding to the multiple criticality levels and the operation mode of the system. If the execution time of at least one HC task exceeds its low WCET, the system switches from low-criticality mode (LO mode) to high-criticality mode (HI mode). Then, all HC tasks continue executing by considering the high WCET to guarantee the system's safety. In this HI mode, all or some LC tasks are dropped/degraded in favor of HC tasks to ensure HC tasks' correct execution. Determining an appropriate low WCET for each HC task is crucial in designing efficient MC systems and ensuring QoS maximization. However, in the case where the low WCETs are set correctly, it is not recommended to drop/degrade the LC tasks in the HI mode due to its negative impact on the other functions or on the entire system in accomplishing its mission correctly. Therefore, how to analyze the task dropping in the HI mode is a significant challenge in designing efficient MC systems that must be considered to guarantee the successful execution of all HC tasks to prevent catastrophic damages while improving the QoS. Due to the continuous rise in computational demand for MC tasks in safety-critical applications, like controlling autonomous driving, the designers are motivated to deploy MC applications on multi-core platforms. Although the parallel execution feature of multi-core platforms helps to improve QoS and ensures the real-timeliness, high power consumption and temperature of cores may make the system more susceptible to failures and instability, which is not desirable in MC applications. Therefore, improving the QoS while managing the power consumption and guaranteeing real-time constraints is the critical issue in designing such MC systems in multi-core platforms. This thesis addresses the challenges associated with efficient MC system design. We first focus on application analysis by determining the appropriate WCET by proposing a novel approach to provide a reasonable trade-off between the number of scheduled LC tasks at design-time and the probability of mode switching at run-time to improve the system utilization and QoS. The approach presents an analytic-based scheme to obtain low WCETs based on the Chebyshev theorem at design-time. We also show the relationship between the low WCETs and mode switching probability, and formulate and solve the problem for improving resource utilization and reducing the mode switching probability. Further, we analyze the LC task dropping in the HI mode to improve QoS. We first propose a heuristic in which a new metric is defined that determines the number of allowable drops in the HI mode. Then, the task schedulability analysis is developed based on the new metric. Since the occurrence of the worst-case scenario at run-time is a rare event, a learning-based drop-aware task scheduling mechanism is then proposed, which carefully monitors the alterations in the behavior of MC systems at run-time to exploit the dynamic slacks for improving the QoS. Another critical design challenge is how to improve QoS using the parallel feature of multi-core platforms while managing the power consumption and temperature of these platforms. We develop a tree of possible task mapping and scheduling at design-time to cover all possible scenarios of task overrunning and reduce the LC task drop rate in the HI mode while managing the power and temperature in each scenario of task scheduling. Since the dynamic slack is generated due to the early execution of tasks at run-time, we propose an online approach to reduce the power consumption and maximum temperature by using low-power techniques like DVFS and task re-mapping, while preserving the QoS. Specifically, our approach examines multiple tasks ahead to determine the most appropriate task for the slack assignment that has the most significant effect on power consumption and temperature. However, changing the frequency and selecting a proper task for slack assignment and a suitable core for task re-mapping at run-time can be time-consuming and may cause deadline violation. Therefore, we analyze and optimize the run-time scheduler.:1. Introduction 1.1. Mixed-Criticality Application Design 1.2. Mixed-Criticality Hardware Design 1.3. Certain Challenges and Questions 1.4. Thesis Key Contributions 1.4.1. Application Analysis and Modeling 1.4.2. Multi-Core Mixed-Criticality System Design 1.5. Thesis Overview 2. Preliminaries and Literature Reviews 2.1. Preliminaries 2.1.1. Mixed-Criticality Systems 2.1.2. Fault-Tolerance, Fault Model and Safety Requirements 2.1.3. Hardware Architectural Modeling 2.1.4. Low-Power Techniques and Power Consumption Model 2.2. Related Works 2.2.1. Mixed-Criticality Task Scheduling Mechanisms 2.2.2. QoS Improvement Methods in Mixed-Criticality Systems 2.2.3. QoS-Aware Power and Thermal Management in Multi-Core Mixed-Criticality Systems 2.3. Conclusion 3. Bounding Time in Mixed-Criticality Systems 3.1. BOT-MICS: A Design-Time WCET Adjustment Approach 3.1.1. Motivational Example 3.1.2. BOT-MICS in Detail 3.1.3. Evaluation 3.2. A Run-Time WCET Adjustment Approach 3.2.1. Motivational Example 3.2.2. ADAPTIVE in Detail 3.2.3. Evaluation 3.3. Conclusion 4. Safety- and Task-Drop-Aware Mixed-Criticality Task Scheduling 4.1. Problem Objectives and Motivational Example 4.2. FANTOM in detail 4.2.1. Safety Quantification 4.2.2. MC Tasks Utilization Bounds Definition 4.2.3. Scheduling Analysis 4.2.4. System Upper Bound Utilization 4.2.5. A General Design Time Scheduling Algorithm 4.3. Evaluation 4.3.1. Evaluation with Real-Life Benchmarks 4.3.2. Evaluation with Synthetic Task Sets 4.4. Conclusion 5. Learning-Based Drop-Aware Mixed-Criticality Task Scheduling 5.1. Motivational Example and Problem Statement 5.2. Proposed Method in Detail 5.2.1. An Overview of the Design-Time Approach 5.2.2. Run-Time Approach: Employment of SOLID 5.2.3. LIQUID Approach 5.3. Evaluation 5.3.1. Evaluation with Real-Life Benchmarks 5.3.2. Evaluation with Synthetic Task Sets 5.3.3. Investigating the Timing and Memory Overheads of ML Technique 5.4. Conclusion 6. Fault-Tolerance and Power-Aware Multi-Core Mixed-Criticality System Design 6.1. Problem Objectives and Motivational Example 6.2. Design Methodology 6.3. Tree Generation and Fault-Tolerant Scheduling and Mapping 6.3.1. Making Scheduling Tree 6.3.2. Mapping and Scheduling 6.3.3. Time Complexity Analysis 6.3.4. Memory Space Analysis 6.4. Evaluation 6.4.1. Experimental Setup 6.4.2. Analyzing the Tree Construction Time 6.4.3. Analyzing the Run-Time Timing Overhead 6.4.4. Peak Power Management and Thermal Distribution for Real-Life and Synthetic Applications 6.4.5. Analyzing the QoS of LC Tasks 6.4.6. Analyzing the Peak Power Consumption and Maximum Temperature 6.4.7. Effect of Varying Different Parameters on Acceptance Ratio 6.4.8. Investigating Different Approaches at Run-Time 6.5. Conclusion 7. QoS- and Power-Aware Run-Time Scheduler for Multi-Core Mixed-Criticality Systems 7.1. Research Questions, Objectives and Motivational Example 7.2. Design-Time Approach 7.3. Run-Time Mixed-Criticality Scheduler 7.3.1. Selecting the Appropriate Task to Assign Slack 7.3.2. Re-Mapping Technique 7.3.3. Run-Time Management Algorithm 7.3.4. DVFS governor in Clustered Multi-Core Platforms 7.4. Run-Time Scheduler Algorithm Optimization 7.5. Evaluation 7.5.1. Experimental Setup 7.5.2. Analyzing the Relevance Between a Core Temperature and Energy Consumption 7.5.3. The Effect of Varying Parameters of Cost Functions 7.5.4. The Optimum Number of Tasks to Look-Ahead and the Effect of Task Re-mapping 7.5.5. The Analysis of Scheduler Timings Overhead on Different Real Platforms 7.5.6. The Latency of Changing Frequency in Real Platform 7.5.7. The Effect of Latency on System Schedulability 7.5.8. The Analysis of the Proposed Method on Peak Power, Energy and Maximum Temperature Improvement 7.5.9. The Analysis of the Proposed Method on Peak power, Energy and Maximum Temperature Improvement in a Multi-Core Platform Based on the ODROID-XU3 Architecture 7.5.10. Evaluation of Running Real MC Task Graph Model (Unmanned Air Vehicle) on Real Platform 7.6. Conclusion 8. Conclusion and Future Work 8.1. Conclusions 8.2. Future Work