EU-IoT and OntoCommons Workshop Ontological interoperability, standardization recommendations discussion - C. Sofia, Rute; Giuffrudita, Rita (EU-IoT and OntoCommons Workshop Ontological interoperability, standardization recommendations discussion, 2022)

Organizational Capabilities for AI Implementation—Coping with Inscrutability and Data Dependency in AI - Weber, Michael; Engert, Martin; Schaffer, Norman; Weking, Jörg; Krcmar, Helmut (Information Systems Frontiers, 2022)
Artifcial Intelligence (AI) implementation incorporates challenges that are unique to the context of AI, such as dealing with probabilistic outputs. To address these challenges, recent research suggests that organizations should develop specifc capabilities for AI implementation. Currently, we lack a thorough understanding of how certain capabilities facilitate AI implementation. It remains unclear how they help organizations to cope with AI’s unique characteristics. To address this research gap, we employ a qualitative research approach and conduct 25 explorative interviews with experts on AI implementation. We derive four organizational capabilities for AI implementation: AI Project Planning and Co-Development help to cope with the inscrutability in AI, which complicates the planning of AI projects and communication between diferent stakeholders. Data Management and AI Model Lifecycle Management help to cope with the data dependency in AI, which challenges organizations to provide the proper data foundation and continuously adjust AI systems as the data evolves. We contribute to our understanding of the sociotechnical implications of AI’s characteristics and further develop the concept of organizational capabilities as an important success factor for AI implementation. For practice, we provide actionable recommendations to develop organizational capabilities for AI implementation.

Challenges in Multi-View Model Consistency Management for Systems Engineering - Bergemann, Sebastian (Modellierung 2022 Satellite Events, 2022)
A way to handle the complexity of cyber-physical systems is model-based systems engineering (MBSE) with multiple viewpoints. These viewpoints satisfy different concerns, but they likely have information dependencies and overlaps among each other. Inconsistencies can be introduced whenever there are changes in only some of the views without consistent synchronization in other dependent views. In this paper, we motivate why consistency management is important in multi-view MBSE and define requirements for it. By analyzing the State of the Art, we identify limitations in (multi-view) consistency management approaches, especially for inconsistency detection. Besides general performance issues, we notice primarily that most approaches are limited to or at least tested on only very specific views and tools with homogeneous models and few specific predefined consistency rules. Furthermore, in most approaches we cannot find solutions regarding subsequent updates of consistency rules by the user, allowance of tolerating inconsistency, and handling confidentiality. These literature gaps pose open research challenges for making multi-view consistency management more applicable in the industry.
Keywords: Model-based Systems Engineering; MBSE; SpesML; Consistency

Early Assessment of System-Level Safety Mechanisms through Co-Simulation-based Fault Injection - Munaro, Tiziano; Muntean, Irina (2022 IEEE Intelligent Vehicles Symposium (IV), 2022)
Depending on the autonomy level, safety assessment leads to different functional safety requirements for advanced driver-assistance systems and autonomous driving functions. To provide the necessary guarantees, technical safety requirements are derived that support the safety case by means of appropriate system architectures. These build on safety mechanisms: Technical solutions responsible for maintaining the intended functionality (fail-operational) or transition to a safe state in the presence of hardware and software faults (fail-safe). As the choice and implementation of such safety mechanisms are critical decisions with a high impact on the overall architecture, their early validation is crucial for an efficient engineering process. However, analytical safety analysis techniques applied to date support only coarse time models and do not provide explicit guidance for considering systemic real-time properties of closed-loop systems. Therefore, we propose a simulation-based fault injection framework to identify problematic emerging temporal behaviors such as instability. In contrast to existing solutions, we leverage the Functional Mock-up Interface (FMI) standard for black-box co-simulation to overcome intellectual property concerns in distributed automotive supply chains and to account for heterogeneous tool landscapes. By considering the allocation of software units to processing elements as well as the communication infrastructure, our contribution allows for the injection and propagation of faults affecting a vehicle's software and its electrical/electronic (E/E) architecture, which is crucial for the assessment of safety mechanisms. Experimental results obtained by applying the approach to an industry-oriented use case indicate its validity and low overhead.
Keywords: Model-based Systems Engineering; MbSE; Co-Simulation; Safety Architecture Patterns; ISO 26262

Localization in Unprecedentedly Crowded Airspace for UAVs and SUAVs - Khelifi, Manel (IEEE Access, 2022)
The unprecedented proliferation of Unmanned Aerial Vehicles (UAVs), and Swarm Unmanned Aerial Vehicles (SUAVs) have garnered considerable attention from industry and academia owing to their extensive landscape of applications from disaster relief towards smart agriculture. However, flying several UAVs at once poses many challenges to safely and efficiently localizing and monitoring them. Further, they need to maintain their formation distance to avoid collision between team members and any environmental obstacles. Besides, SUAVs are mainly equipped with an on-board Global Positioning System (GPS) receivers to obtain their positions, but they are not accurate enough and suffer from several vulnerabilities that restrict their applications. Thus, in GPS-denied situations, the acquisition of the positions of UAVs can be assisted by alternative technologies and solutions. This paper is one of the foremost in-depth works that present the topic of localization of SUAVs from various perspectives including current research challenges on positioning systems, telecommunication, and path planning, along with future opportunities on automated delivery services such as medicine, remote inspection of industrial sites, and precision agriculture.

ML-based data classification and data aggregation on the edge - Karabulut, Erkan; C. Sofia, Rute; Ott, Jörg (MSc Thesis, 2022)
Keywords: ML; IIoT; semantic technologies; matchmaking; data aggregation; Edge

Providentia - A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation - Krämmer, Annkathrin; Schöller, Christoph; Gulati, Dhiraj; Lakshminarasimhan, Venkatnarayanan; Kurz, Franz; Rosenbaum, Dominik; Lenz, Claus; Knoll, Alois (Journal of Field Robotics, 2022)
The environmental perception of an autonomous vehicle is limited by its physical sensor ranges and algorithmic performance, as well as by occlusions that degrade its understanding of an ongoing traffic situation. This not only poses a significant threat to safety and limits driving speeds, but it can also lead to inconvenient maneuvers. Intelligent Infrastructure Systems can help to alleviate these problems. An Intelligent Infrastructure System can fill in the gaps in a vehicle's perception and extend its field of view by providing additional detailed information about its surroundings, in the form of a digital model of the current traffic situation, i.e. a digital twin. However, detailed descriptions of such systems and working prototypes demonstrating their feasibility are scarce. In this paper, we propose a hardware and software architecture that enables such a reliable Intelligent Infrastructure System to be built. We have implemented this system in the real world and demonstrate its ability to create an accurate digital twin of an extended highway stretch, thus enhancing an autonomous vehicle's perception beyond the limits of its on-board sensors. Furthermore, we evaluate the accuracy and reliability of the digital twin by using aerial images and earth observation methods for generating ground truth data.

Risk-Based Safety Envelopes for Autonomous Vehicles Under Perception Uncertainty - Bernhard, Julian; Hart, Patrick; Sahu, Amit; Schöller, Christoph; Guzman Cancimance, Michell (Intelligent Vehicles Symposium (IV), 2022)
Ensuring the safety of autonomous vehicles, given the uncertainty in sensing other road users, is an open problem. Moreover, separate safety specifications for perception and planning components raise how to assess the overall system safety. This work provides a probabilistic approach to calculate safety envelopes under perception uncertainty. The probabilistic envelope definition is based on a risk threshold. It limits the cumulative probability that the actual safety envelope in a fully observable environment is larger than an applied envelope and is solved using iterative worst-case analysis of envelopes. Our approach extends non-probabilistic envelopes - in this work, the Responsibility-Sensitive Safety (RSS) - to handle uncertainties. To evaluate our probabilistic envelope approach, we compare it in a simulated highway merging scenario against several baseline safety architectures. Our evaluation shows that our model allows adjusting safety and performance based on a chosen risk level and the amount of perception uncertainty. We conclude with an outline of how to formally argue safety under perception uncertainty using our formulation of envelope violation risk.

Architecture Synthesis for Optimized and Flexible Production - Terzimehić, Tarik (2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), 2022)
The fourth industrial revolution (Industry 4.0) anticipates frequent synthesis and optimization of different architectural design decisions (ADDs) – such as deployment of software components to hardware components, service composition, production planning, and topology (plant layout) synthesis. The frequent manual search for valid and optimal architectural designs is a time- and cognition-consuming task for an engineer. This asks for automating the process of deriving different ADDs. Although automating different ADDs is intensely investigated in other domains, the current research works 1) require higher engineering effort for specifying architecture optimization problems; 2) conduct (only) sequential ADDs, leading to lower solution quality (i.e., sub-optimal production); 3) neglect reconfigurability and reliability of architectures, and, thereby, offer no solution for production downtime; 4) neglect event-based execution semantics while considering timing-related issues. Therefore, I propose a Satisfiability Modulo Theories (SMT)-based framework for joint synthesis and optimization of multi-dimensional ADDs using industrial automation domain models (e.g., plant topology, product recipes, stations capabilities, etc.). This research should bring the following benefits for the practitioners and researchers: 1) reduction of engineering effort for conducting different ADDs; 2) improvement of different quality attributes (e.g., production performance, reconfigurability, reliability, etc.); 3) guideline/support for a practitioner in choosing ADDs workflow to improve given quality attributes.
Keywords: architecture synthesis; optimization; design space exploration; Industry 4.0; deployment; service composition; model-based systems engineering; MbSE

Continuous Software Engineering in the Wild - Klotins, Eriks; Gorschek, Tony (Proceedings of the 14th International Software Quality Days, 2022)

Spiking Neural Units ermöglichen effiziente ereignisgesteuerte Kameras - von Arnim, Axel; Pantazi, Angeliki (IBM blog, 2022)
Ob Quantum Computing, Künstliche Intelligenz oder Deep Learning – die Computertechnologie entwickelt sich mit rasender Geschwindigkeit. Von der Natur kann sie sich dabei immer noch einiges abschauen. Nervensysteme, darin eingebettete neuronale Netze und die Übertragung von Impulsen über Nervenzellen sind beispielsweise ein Wunder an Dynamik und Energieeffizienz. Mit Hilfe von sogenannten „gepulsten neuronalen Netzen“, englisch Spiking Neural Networks, lassen sich diese Eigenschaften auf Computeranwendungen übertragen. Welche Vorteile sich daraus ergeben, zeigt ein aktuelles Forschungsprojekt mit sogenannten ereignisbasierten Kameras, bei dem die von IBM entwickelten Spiking Neural Units zum Einsatz kamen. Diese zwei Technologien, inspiriert von der Biologie, sind Bausteine des stark herauskommenden Trends in der KI: Neuromorphic Computing, oder, anders ausgedrückt, Computing nach biologischen neuronalen Prozessen. Diese werden in diesem Post erklärt.
Keywords: neuromorphic; IBM; SNU; optical flow; famous; drone

Using a Semantic Knowledge Base to Improve the Management of Security Reports in Industrial DevOps Projects - Voggenreiter, Markus; Schöpp, Ulrich (44nd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP ’22), 2022)
Integrating security activities into the software development life-cycle to detect security flaws is essential for any project. These activities produce reports that must be managed and looped back to project stakeholders like developers to enable security improvements. This so-called Feedback Loop is a crucial part of any project and is required by various industrial security standards and models. However, the operation of this loop presents a variety of challenges. These challenges range from ensuring that feedback data is of sufficient quality over providing different stakeholders with the information they need to the enormous effort to manage the reports. In this paper, we propose a novel approach for treating findings from security activity reports as belief in a Knowledge Base (KB). By utilizing continuous logical inferences, we derive information necessary for practitioners and address existing challenges in the industry. This approach is currently evaluated in industrial DevOps projects, using data from continuous security testing.

A Proposal for Time-Aware Scheduling in Wireless Industrial IoT Environments - Schneider, Ben; C. Sofia, Rute (inProc. IEEE/IFIP NOMS 2022, 2022)
Keywords: IIoT; TSN; wireless; Wi-Fi6

Building Trust by Supporting Situation Awareness: Exploring Pilots' Design Requirements for Decision Support Tools - Storath, Cara; Zhang, Zelun Tony; Liu, Yuanting; Hußmann, Heinrich (CHI TRAIT '22: Workshop on Trust and Reliance in Human-AI Teams at CHI 2022, 2022)
Supporting pilots with a decision support tool (DST) during high-workload scenarios is a promising and potentially very helpful application for AI in aviation. Nevertheless, design requirements and opportunities for trustworthy DSTs within the aviation domain have not been explored much in the scientific literature. To address this gap, we explore the decision-making process of pilots with respect to user requirements for the use case of diversions. We do so via two prototypes, each representing a role the AI could have in a DST: A) Unobtrusively hinting at data points the pilot should be aware of. B) Actively suggesting and ranking diversion options based on criteria the pilot has previously defined. Our work-in-progress feedback study reveals four preliminary main findings: 1) Pilots demand guaranteed trustworthiness of such a system and refuse trust calibration in the moment of emergency. 2) We may need to look beyond trust calibration for isolated decision points and rather design for the process leading to the decision. 3) An unobtrusive, augmenting AI seems to be preferred over an AI proposing and ranking diversion options at decision time. 4) Shifting the design goal toward supporting situation awareness rather than the decision itself may be a promising approach to increase trust and reliance.
Keywords: human-AI interaction; decision support tools; decision support systems; human-AI teaming; aviation

A Model-based System Engineering Plugin for Safety Architecture Pattern Synthesis - Dantas, Yuri Gil; Munaro, Tiziano; Cârlan, Carmen; Nigam, Vivek; Barner, Simon; Fan, Shiqing; Pretschner, Alexander; Schöpp, Ulrich; Tverdyshev, Sergey (Proceeding of the 10th International Conference on Model-Driven Engineering and Software Development (MODELSWARD), 2022)
Safety architecture patterns are abstract representations to address faults in the system architecture. In the current state of practice, the decision of which safety architecture pattern to deploy and where in the system architecture is carried out manually by a safety expert. This decision may be time consuming or even lead to human errors. This paper presents Safety Pattern Synthesis, a tool for automating the recommendation of safety architecture patterns during the design of safety-critical systems: 1) Safety Pattern Synthesis recommends patterns to address faults in the system architecture (possibly resulting in more than one architectural solution), 2) the user selects the system architecture with patterns based on, e.g., the criteria provided by Safety Pattern Synthesis, and 3) Safety Pattern Synthesis provides certain requirements that shall be considered in the overall safety engineering process. The proposed tool has been implemented as a plugin in the model-based system engineering tool called AutoFOCUS3. Safety Pattern Synthesis is implemented in Java while using a logic-programming engine as a backend to reason about the safety of the system architecture. This paper provides implementation details about Safety Pattern Synthesis and its applicability in an industrial case study taken from the automotive domain.
Keywords: Model-based Systems Engineering; MbSE; Safety Architecture Patterns; Automation; Tooling; DSE

Causality in Requirements Artifacts: Prevalence, Detection, and Impact - Frattini, Julian; Fischbach, Jannik; Mendez, Daniel; Unterkalmsteiner, Michael; Vogelsang, Andreas; Wnuk, Krzysztof (Requirements Engineering, 2022)
Background: Causal relations in natural language (NL) requirements convey strong, semantic information. Automatically extracting such causal information enables multiple use cases, such as test case generation, but it also requires to reliably detect causal relations in the first place. Currently, this is still a cumbersome task as causality in NL requirements is still barely understood and, thus, barely detectable. Objective: In our empirically informed research, we aim at better understanding the notion of causality and supporting the automatic extraction of causal relations in NL requirements. Method: In a first case study, we investigate 14.983 sentences from 53 requirements documents to understand the extent and form in which causality occurs. Second, we present and evaluate a tool-supported approach, called CiRA, for causality detection. We conclude with a second case study where we demonstrate the applicability of our tool and investigate the impact of causality on NL requirements. Results: The first case study shows that causality constitutes around 28% of all NL requirements sentences. We then demonstrate that our detection tool achieves a macro-F1 score of 82% on real-world data and that it outperforms related approaches with an average gain of 11.06% in macro-Recall and 11.43% in macro-Precision. Finally, our second case study corroborates the positive correlations of causality with features of NL requirements. Conclusion: The results strengthen our confidence in the eligibility of causal relations for downstream reuse, while our tool and publicly available data constitute a first step in the ongoing endeavors of utilizing causality in RE and beyond.

Design Thinking for Software Engineering: Creating Human-oriented Software-intensive Products and Services - Hehn, Jennifer; Mendez, Daniel; Brenner, Walter; Broy, Manfred (Springer, 2022)
This book explores the possibility of integrating design thinking into today’s technical contexts. Despite the popularity of design thinking in research and practice, this area is still too often treated in isolation without a clear, consistent connection to the world of software development. The book presents design thinking approaches and experiences that can facilitate the development of software-intensive products and services. It argues that design thinking and related software engineering practices, including requirements engineering and user-centric design (UX) approaches, are not mutually exclusive. Rather, they provide complementary methods and tools for designing software-intensive systems with a human-centric approach. Bringing together prominent experts and practitioners to share their insights, approaches and experiences, the book sheds new light on the specific interpretations and meanings of design thinking in various fields such as engineering, management, and information technology. As such, it provides a framework for professionals to demonstrate the potential of design thinking for software development, while offering academic researchers a roadmap for further research.

Design-time Specification of Dynamic Modular Safety Cases in Support of Run-Time Safety Assessment - Mirzaei, Elham; Cârlan, Carmen; Thomas, Carsten; Gallina, Barbara (Proceedings of the Thirtieth Safety-Critical Systems Symposium, 2022)
Open Adaptive Complex Systems – such as road vehicle platoons or fleets of cooperative robots – may use dynamic reconfiguration to adapt to system or environment changes. One approach enabling this feature is Service-oriented Reconfiguration, where new configurations are created by composing the available services in an unconstrained manner. Due to the high number of possible service compositions, not all configurations can be pre-assured at design-time. Despite recent progress, there is no satisfactory approach for specifying safety cases in support of their re-evaluation at run-time, after system reconfiguration. To this end, in previous work, we introduced Dynamic Modular Safety Cases (DMSC). A DMSC is a modular safety case, which can be dynamically re-constructed and re-assessed given service reconfiguration. In continuation of the previous work, in this paper we provide guidelines for specifying safety cases at design-time, whose modular structure mirrors the system service decomposition, to enable their re-construction and re-evaluation at run-time in the event of a system reconfiguration. Aiming to support the specification of DMSC, we extend FASTEN, an engineering tool for the design and verification of safety-critical systems. We exemplify the specification of DMSCs in FASTEN for an illustrative example from the smart factory domain.
Keywords: Model-based Systems Engineering; MbSE

Exploring Architectural Design Decisions in Industry 4.0: A Literature Review and Taxonomy - Terzimehić, Tarik; Dorofeev, Kirill; Voss, Sebastian (Software Engineering 2022 - Fachtagung des GI-Fachbereichs Softwaretechnik, 2022)
Paper accepted in the Foundation Track of ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS), 2021. Architectural design decisions, such as service deployment and composition, plant layout synthesis, or production planning, are an indispensable and overarching part of an industrial manufacturing system design. In the fourth industrial revolution (Industry 4.0), frequent production changes trigger their synthesis, and preferably optimization. Yet, knowledge on architecture synthesis and optimization has been scattered around other domains, such as generic software engineering. We take a step towards synthesizing current knowledge on architectural design decisions in Industry 4.0. We developed a taxonomy describing architectural models, design decisions, and optimization possibilities. The developed taxonomy serves as a guideline for comparing different possibilities (e.g., application of different optimization algorithms) and selecting appropriate ones for a given context. Furthermore, we reviewed and mapped 30 relevant research works to the taxonomy, identifying research trends and gaps. We discuss interesting, and yet uncovered topics that emerged from our review.
Keywords: architecture synthesis; optimization; taxonomy; design space exploration; MbSE; model-based systems engineering; Industry 4.0

The human side of Software Engineering Teams: an Investigation of Contemporary Challenges - Hoffmann, Marco; Mendez, Daniel; Fagerholm, Fabian; Luckhardt, Anton (Transactions on Software Engineering, 2022)
There have been recent calls for research on the human side of software engineering and its impact on various factors such as productivity, developer happiness and project success. An analysis of which challenges in software engineering teams are most frequent is still missing. We aim to provide a starting point for a theory about relevant human challenges and their causes in software engineering. We establish a reusable set of challenges and start out by investigating the effect of team virtualization. Virtual teams often use digital communication and consist of members with different nationalities. We designed a survey instrument and asked respondents to assess the frequency and criticality of a set of challenges, separated in context "within teams" as well as "between teams and clients", compiled from previous empiric work, blog posts and pilot survey feedback. For the team challenges, we asked if mitigation measures were already in place. Respondents were also asked to provide information about their team setup. The survey also measured Schwartz human values. Finally, respondents were asked if there were additional challenges at their workplace. We report on the results obtained from 192 respondents. We present a set of challenges that takes the survey feedback into account and introduce two categories of challenges; "interpersonal" and "intrapersonal". We found no evidence for links between human values and challenges. We found some significant links between the number of distinct nationalities in a team and certain challenges, with less frequent and critical challenges occurring if 2-3 different nationalities were present compared to a team having members of just one nationality or more than three. A higher degree of virtualization seems to increase the frequency of some human challenges.

Towards “Government as a Platform”: An analysis framework for public sector infrastructure - Kuhn, Peter; Dallner, Simon; Buchinger, Matthias; Balta, Dian (Wirtschaftsinformatik 2022 Proceedings, 2022)
“Government as a Platform” (GaaP) is a promising approach to the digital transformation of the public sector. The approach sees Government as an open platform on which people inside and outside the government can innovate and co-create better public services. On a technical level, this is enabled by public sector infrastructure that also follows the approach. However, it remains unclear how exactly GaaP can be applied to public sector infrastructure in practice. In order to tackle this challenge, we develop a framework for the analysis of public infrastructure regarding its platform character. We apply the framework to a current public infrastructure project in Germany to demonstrate its applicability and infer possible future improvements. We contribute to literature by integrating GaaP literature with ideas and concepts from general IS platform literature and contribute to practice by providing a tool that supports the application of GaaP.

Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach - Baracaldo, Nathalie; Anwar, Ali; Purcell, Mark; Rawat, Ambrish; Sinn, Mathieu; Altakrouri, Bashar; Balta, Dian; Sellami, Mahdi; Kuhn, Peter; Buchinger, Matthias (arXiv, 2022)
Federated Learning (FL) is a novel paradigm for the shared training of models based on decentralized and private data. With respect to ethical guidelines, FL is promising regarding privacy, but needs to excel vis-à-vis transparency and trustworthiness. In particular, FL has to address the accountability of the parties involved and their adherence to rules, law and principles. We introduce AF^2 Framework, where we instrument FL with accountability by fusing verifiable claims with tamper-evident facts, into reproducible arguments. We build on AI FactSheets for instilling transparency and trustworthiness into the AI lifecycle and expand it to incorporate dynamic and nested facts, as well as complex model compositions in FL. Based on our approach, an auditor can validate, reproduce and certify a FL process. This can be directly applied in practice to address the challenges of AI engineering and ethics.
Keywords: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); FOS: Computer and information sciences; FOS: Computer and information sciences

An Evaluation of a Semantic Thing To Service Matching Approach in Industrial IoT Environments - Bnouhanna, Nisrine; Karabulut, Erkan; C. Sofia, Rute; Seder, E.; Insolvibile, G. (inProc. 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2022, pp. 433-438, doi: 10.1109/PerComWorkshops53856.2022.9767519. Pisa, Italy., 2022)
Industrial Internet of Things Platforms enable the use of available data to improve production and business processes. However, the data exchange and provisioning between the data sources and platform services remain a challenge as such platforms are usually vendor specific, proprietary, and associated with specific IoT hardware. Therefore, we propose a detailed description of an open-source software-based solution,Thing to Service Matching (TSMatch) which performs fine-grained semantic matching between available IoT data and services. Moreover,the paper presents the actual implementation of the proposed solution in 2 different Aerospace production environments, and a performance evaluation in a testbed environment.
Keywords: semantic matchmaking; IoT; IIoT

Barriers of applying Government as a Platform in Practice: Evidence from Germany - Kuhn, Peter; Buchinger, Matthias; Balta, Dian; Matthes, Florian (Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS 2022), 2022)
Government as a Platform (GaaP) is a promising approach to the digital transformation of the public sector. GaaP aims at the development of efficient and user-friendly services by exploiting platform principles such as openness, modularization and co-creation. Hence, GaaP claims to deliver a new level of stakeholder participation in the production of public services. However, the success of GaaP is arguably bound to the context of a country. To address the potential impact of a country’s context, the goal of this paper is to identify barriers and measures to overcome them in the application of GaaP in the federal context of Germany. We conduct a literature review and investigate a use case of a German digital government agency by means of documents, expert interviews and workshops. The agency applies GaaP to its architecture management of the federal IT infrastructure. We find five barriers and three measures to overcome. We conclude by discussing implications for theory and practice.

Dimensions of Accountability in Inter-organizational Business Processes - Buchinger, Matthias; Kuhn, Peter; Balta, Dian (Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS 2022), 2022)
Inter-organizational business processes are the basis of a globalized, highly dynamic, and digitalized world, en-abling faster and cost-effective transactions. At the same time, they raise business vulnerabilities. A partic-ular vulnerability is linked to the substantiation of trust between actors in dynamic business relationships, as trust affects interdependencies and complexity. An ap-proach to address this vulnerability is the introduction of accountability mechanisms. Extant research suggests that accountability enables revealing causality and a transparent allocation of responsibilities for each pro-cess step. Thereby, corresponding actors can judge upon misbehavior and verify trust claims. Unfortu-nately, a thorough understanding of accountability and its dimensions accountability in the context of IBP is still missing. To address this gap, we develop a framework with dimensions of accountability. We demonstrate the resulting framework in an industrial supply chain case and derive implications for theory and practice.

(Proceedings) Software Quality Days: Software Quality. What‘s The Next Big Thing in Software Engineering and Quality? - Mendez, Daniel; Wimmer, Manuel; Winkler, Dietmar; Biffl, Stefan; Bergsmann, Johannes (Software Quality Days: Software Quality. What‘s The Next Big Thing in Software Engineering and Quality?, 2022)

A Live Extensible Ontology of Quality Factors for Textual Requirements - Frattini, Julian; Montgomery, Lloyd; Fischbach, Jannik; Unterkalmsteiner, Michael; Mendez, Daniel; Fucci, Davide (Proc. of the International Requirements Engineering Conference, 2022)

Assets in Software Engineering: What are they after all? - Zabardast, Ehsan; Frattini, Julian; Gonzalez-Huerta, Javier; Mendez, Daniel; Gorschek, Tony; Wnuk, Krzysztof (Journal of Systems and Software, 2022)
During the development and maintenance of software-intensive products or services, we depend on various artefacts. Some of those artefacts, we deem central to the feasibility of a project and the product's final quality. Typically, these central artefacts are referred to as assets. However, despite their central role in the software development process, little thought is yet invested into what eventually characterises as an asset, often resulting in many terms and underlying concepts being mixed and used inconsistently. A precise terminology of assets and related concepts, such as asset degradation, are crucial for setting up a new generation of cost-effective software engineering practices. In this position paper, we critically reflect upon the notion of assets in software engineering. As a starting point, we define the terminology and concepts of assets and extend the reasoning behind them. We explore assets' characteristics and discuss what asset degradation is as well as its various types and the implications that asset degradation might bring for the planning, realisation, and evolution of software-intensive products and services over time. We aspire to contribute to a more standardised definition of assets in software engineering and foster research endeavours and their practical dissemination in a common, more unified direction.

Automatic Creation of Acceptance Tests by Extracting Conditionals from Requirements: NLP Approach and Case Study - Mendez, Daniel; Fischbach, Jannik; Frattini, Julian; Vogelsang, Andreas; Unterkalmsteiner, Michael; Yousefi, Parisa; Restrepo Henao, Pablo; Juricic, Tedi; Wiecher, Carsten; Radduenz, Jeannette (Journal of Systems and Software, 2022)
Acceptance testing is crucial to determine whether a system fulfills end-user requirements. However, the creation of acceptance tests is a laborious task entailing two major challenges: (1) practitioners need to determine the right set of test cases that fully covers a requirement, and (2) they need to create test cases manually due to insufficient tool support. Existing approaches for automatically deriving test cases require semi-formal or even formal notations of requirements, though unrestricted natural language is prevalent in practice. In this paper, we present our tool-supported approach CiRA (Conditionals in Requirements Artifacts) capable of creating the minimal set of required test cases from conditional statements in informal requirements. We demonstrate the feasibility of CiRA in a case study with three industry partners. In our study, out of 578 manually created test cases, 71.8 % can be generated automatically. Additionally, CiRA discovered 80 relevant test cases that were missed in manual test case design. CiRA is publicly available at this http URL.

Calibrating Agent-Based Models of Innovation Diffusion with Gradients - Kotthoff, Florian; Hamacher, Thomas (Journal of Artificial Societies and Social Simulation, 2022)
Consumer behavior and the decision to adopt an innovation are governed by various motives, which models find difficult to represent. A promising way to introduce the required complexity into modeling approaches is to simulate all consumers individually within an agent-based model (ABM). However, ABMs are complex and introduce new challenges. Especially the calibration of empirical ABMs was identified as a key difficulty in many works. In this work, a general ABM for simulating the Diffusion of Innovations is described. The ABM is differentiable and can employ gradient-based calibration methods, enabling the simultaneous calibration of large numbers of free parameters in large-scale models. The ABM and calibration method are tested by fitting a simulation with 25 free parameters to the large data set of privately owned photovoltaic systems in Germany, where the model achieves a coefficient of determination of R2 ≃ 0.7.

Feature Sets in Just-in-Time Defect Prediction: An Empirical Evaluation - Bludau, Peter; Pretschner, Alexander (Proceedings of the 18th International Conference on Predictive Models and Data Analytics in Software Engineering, 2022)
Just-in-time defect prediction assigns a defect risk to each new change to a software repository in order to prioritize review and testing efforts. Over the last decades different approaches were proposed in literature to craft more accurate prediction models. However, defect prediction is still not widely used in industry, due to predictions with varying performance. In this study, we evaluate existing features on six open-source projects and propose two new features sets, not yet discussed in literature. By combining all feature sets, we improve MCC by on average 21%, leading to the best performing models when compared to state-of-the-art approaches. We also evaluate effort-awareness and find that on average 14% more defects can be identified, inspecting 20% of changed lines.
Keywords: machine learning; JIT defect prediction; empirical evaluation

Laplace Approximation for Real-time Uncertainty Estimation in Object Detection - Gui, Ming; Qiu, Tianming; Bauer, Fridolin; Shen, Hao (25th IEEE International Conference on Intelligent Transportation Systems (ITSC), 2022)

Only Time Will Tell: Modelling Information Diffusion in Code Review with Time-Varying Hypergraphs - Dorner, Michael; Smite, Darja; Mendez, Daniel; Wnuk, Krzysztof; Czerwonka, Jacek (Proc. of the 16th International Symposium on Empirical Software Engineering and Measurement, 2022)

PR-SZZ: How pull requests can support the tracing of defects in software repositories - Bludau, Peter; Pretschner, Alexander (2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 2022)
The SZZ algorithm represents a standard way to identify bug fixing commits as well as inducing counterparts. It forms the basis for data sets used in numerous empirical studies. Since its creation, multiple extensions have been proposed to enhance its performance. For historical reasons, related work relies on commit messages to map bug tickets to possibly related code with no additional data used to trace inducing commits from these fixes. Therefore, we present an updated version of SZZ utilizing pull requests, which are widely adopted today. We evaluate our approach in comparison to existing SZZ variants by conducting experiments and analyzing the usage of pull requests, inner commits, and merge strategies. We base our results on 6 open-source projects with more than 50k commits and 35k pull requests. With respect to bug fixing commits, on average 18% of bug tickets can be additionally mapped to a fixing commit, resulting in an overall F-score of 0.75, an improvement of 40 percentage points. By selecting an inducing commit, we manage to reduce the false-positives and increase precision by on average 16 percentage points in comparison to existing approaches.

SViT: Hybrid Vision Transformer Models with Scattering Transform - Qiu, Tianming; Gui, Ming; Yan, Cheng; Zhao, Ziqing; Shen, Hao (32nd IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2022)

Towards Cost-beneﬁt Evaluation for Continuous Software Engineering Activities - Klotins, Eriks; Gorschek, Tony (Empirical Software Engineering, 2022)

Understanding the Implementation of Technical Measures in the Process of Data Privacy Compliance - Klymenko, Oleksandra; Meisenbacher, Stephan; Kosenkov, Oleksandr; Elahidoost, Parisa; Mendez, Daniel; Matthes, Florian (Proc. of the 16th International Symposium on Empirical Software Engineering and Measurement, 2022)

Work-From-Home is Here to Stay: Call for Flexibility in Post-Pandemic Work Policies - Smite, Darja; Mendez, Daniel; Gonzalez-Huerta, Javier; Brede Moe, Nils; Hildrum, Jarle (Journal of Systems and Software, 2022)
In early 2020, the Covid-19 pandemic forced employees in tech companies worldwide to abruptly transition from working in offices to working from their homes. During two years of predominantly working from home, employees and managers alike formed expectations about what post-pandemic working life should look like. Many companies are currently experimenting with new work policies that balance both employee- and manager expectations to where, when and how work should be done in the future. In this article, we gather experiences from 17 companies and their sites, covering 12 countries. We share the results of corporate surveys of employee preferences for working from home and analyse new work policies. Our results are threefold. First, through the new work policies all companies are formally giving more flexibility to the employees with regards to working time and work location. Second, there is a great variation in how much flexibility the companies are willing to yield to the employees. The variation is related both to industry type, size of the companies, and company culture. Third, we document a change in the psychological contract between employees and managers, where the option of working from home is converted from an exclusive perk that managers could choose to give to the few, to a core privilege that all employees feel they are entitled to. Finally, there are indications that as the companies learn and solicit feedback regarding the efficiency of the chosen strategies, we might see further developments and changes of the work policies with respect to how much flexibility to work whenever and from anywhere they grant. Through these findings, the paper contributes to a growing literature about the new trends emerging from the pandemic in tech companies and spells out practical implications onwards.

CONASENSE 2021 Symposium Report - C. Sofia, Rute; Prasad, Ramjee (2021)

Laplace Approximation with Diagonalized Hessian for Over-parameterized Neural Networks - Gui, Ming; Zhao, Ziqing; Qiu, Tianming; Shen, Hao (NeurIPS 2021, 2021)
Bayesian Neural Networks (BNNs) provide valid uncertainty estimation on their feedforward outputs. However, it can become computationally prohibitive to apply them to modern large-scale neural networks. In this work, we combine the Laplace approximation with linearized inference for a real-time and robust uncertainty evaluation. Specifically, we study the effectiveness and computational necessity of a diagonal Hessian approximation in the Laplace approximation on over-parameterized networks. The proposed approach is investigated on object detection tasks in an autonomous driving scenario and demonstrates faster inference speed and convincing results.

ML-based Data Classification and Data Aggregation on the Edge - Karabulut, Erkan; Bnouhanna, Nisrine; C. Sofia, Rute (CoNEXT-SW '21: Proceedings of the CoNEXT Student Wokshop, 2021)
This study focuses on sensor classification using machine learning algorithms, to improve data aggregation on the Edge. This aspect is particularly important in large-scale Internet of Things environments, where data aggregation derived from sensors from different vendors often requires human intervention. The proposed research is focused on relying on machine learning to classify sensors based on their semantic descriptions.

Requirements for Reliable Wireless Industrial Services (draft-ietf-raw-industrial-requirements-00) - C. Sofia, Rute; Kovatsch, Matthias; M. Mendes, Paulo (IETF RAW WG draft, 2021)
ndling reliable wireless services within the context of industrial environments. The goal of the draft is to bring awareness to communication requirements of current and future wireless industrial services; how can they co-exist with wired infrastructures; key drivers for reliable wireless integration; relevant communication requirements to take into consideration; current and future challenges derived from the use of wireless.
Keywords: IIoT; wireless; industrial applications

Low-Redundancy Realization of Safety-Critical Automotive Functions - Barner, Simon; Matthes, Stefan; Dormann, Holger; Schingale, Angelika; Kaulfersch, Eberhard; Eichhorst, Michael; Scheiter, Lutz; Schmidt, Holger; Gebert, Jürgen (MikroSystemTechnik (MST) Kongress, 2021)
We propose a low-redundancy architecture for safety-critical automotive functions that is motivated by the discrepancy of fail-operational requirements of autonomous driving (AD) applications, and the market’s cost pressure. We base on low-overhead monitoring structures for thermomechanical fatigue that generate data to predict the remaining useful life-time (RUL) for individual elements. It is analysed in a cloud backend by means of a machine-learning model trained with data from accelerated aging tests and finite elements (FE) simulations. We employ model-based engineering to automatically synthesize a reconfiguration strategy that maximizes the remaining system utility by relocating software components from processing elements with impending failures. For evaluation, we consider the reconfiguration of a critical software component, and the integration of the low-redundancy monitoring concept into an electric power steering (EPS).
Keywords: fail-operational architecture; predictive maintenance; system reconfiguration

A vision on Smart, Decentralised Edge Computing Research Directions - C. Sofia, Rute; Soldatos, John (EU-IoT white paper, 2021)
Organisations across a variety of industries rely today on the Internet of Things (IoT) to, on the one hand, increase operational efficiency and, on the other hand, to improve the overall decision making processes and increase the value-add of their products/business. From a computer science perspective, IoT is a system of interconnected cyber-physical devices, software, data, and people. The aim is to allow data to be accessible anywhere and anytime, so IoT relies on Cloud computing to provide this support from a storage perspective. The data accessibility and computational power that the Cloud provides has led to an increase in the generated data, which had, as consequence, an increase in energy consumption, among other aspects. Cloud-based services are highly relevant in case an organisation aims at extending storage capacity, or to allow for remote work operations. These are aspects that IoT systems require, but in addition to data storage, the key aspects in IoT are data processing and analysis. The data processing and analysis are therefore provided via the Cloud, in a centralised way. IoT, being based on the Internet, requires support for service and data space decentralisation, to further evolve. Predictions point out to having 75% of enterprise data supported by Edge computing in 20251. Relevant to this evolution is to discuss the different perspectives of Edge computing and Edge-Cloud; and to have a perspective on the most recent developments in Europe concerning Next Generation IoT and Edge computing. This white paper is therefore a tool expected to assist in such cross-exploration, via the clarification of Edge computing notions, a brief introduction to NGIoT flagship projects, their current standardisation and pre-standardisation efforts. For this purpose, this white paper provides the following contributions.: • To provide an overview on different Edge computing concepts, contributing to a better understanding of concepts such as “far Edge”, or “near Edge”. • To explain the EU-IoT Edge computing functionality in terms of the 4 identified EU-IoT scope areas: Human/IoT interfaces; far Edge; near Edge; Infrastructure; Data Spaces. • To discuss existing Edge research directions in active NGIoT European projects that are under the umbrella of EU-IoT. • To provide an initial perspective on current Edge standardisation efforts being taken by the projects under the umbrella of EU-IoT.
Keywords: Edge computing; IIoT

Exploring Architectural Design Decisions in Industry 4.0: A Literature Review and Taxonomy - Terzimehić, Tarik; Dorofeev, Kirill; Voss, Sebastian (ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS), 2021)
Architectural design decisions, such as service deployment and composition, plant layout synthesis, or production planning, are an indispensable and overarching part of an industrial manufacturing system design. In the fourth industrial revolution (Industry 4.0), frequent production changes trigger their synthesis, and preferably optimization. Yet, knowledge on architecture synthesis and optimization has been scattered around other domains, such as generic software engineering. We take a step towards synthesizing current knowledge on architectural design decisions in Industry 4.0. We developed a taxonomy describing architectural models, design decisions, and optimization possibilities. The developed taxonomy serves as a guideline for comparing different possibilities (e.g., application of different optimization algorithms) and selecting appropriate ones for a given context. Furthermore, we reviewed and mapped 30 relevant research works to the taxonomy, identifying research trends and gaps. We discuss interesting, and yet uncovered topics that emerged from our review.
Keywords: architecture synthesis; optimization; taxonomy; design-space exploration; DSE; model-based systems engineering; MbSE; Industry 4.0

The Internet-of-Things Open Source Ecosystem in 2021 - Soldatos, John; C. Sofia, Rute; Calisti, Monique; Rublova, Dariya (EU-IoT White Paper Series, 2021)
For over a decade the Open Source Software (OSS) community has played a significant role in the development, deployment, and standardisation of the Internet of Things (IoT). This is evidenced by the increasing number of OSS projects that are developing essential components for the majority of IoT deployments worldwide, as well as by the number of innovative IoT projects that over the years have been developed and released as open source. Recently, the European Commission (EC) has released a policy brief on Open Source Software, which outlines and acknowledges the importance of OSS for ensuring more rapid innovation in several domains, including the IoT and Artificial Intelligence (AI). This whitepaper aims at providing an overview of the IoT OSS projects landscape including: • A presentation of IoT projects of prominent OSS ecosystems (e.g., Apache, Linux Foundation, Eclipse Foundation), including information on the role of European organisations and European OSS communities within these contexts. • A review and analysis of more than 120 OSS projects covering different IoT themes, including OSS IoT hardware, IoT gateways, IoT device middleware, Edge Computing Middleware, IoT Cloud Platforms, and IoT development environments. The projects are analysed in terms of their different characteristics such as their OSS license, their community size, their acceptance by the OSS community, the language(s) they are written in, as well as the IoT standards that they support or promote. • A presentation of recent open-source developments carried out in EU projects, including the projects of the H2020-ICT56-2020 call that are closely collaborating with the EU-IoT project. Furthermore, the present whitepaper aims at introducing the EU-IoT IoT OSS projects catalogue, which is developed by the H2020 EU-IoT project and is already publicly accessible through the NGIoT website1. The catalogue aims at providing a searchable directory of IoT OSS projects. At the time of writing, it comprises a critical mass of over 100 projects, including most of the projects that are analysed in the scope of this whitepaper. The catalogue is extensible in terms of new projects, as EU-IoT enables the IoT and OSS communities to submit new OSS project entries. Overall, the EU-IoT Open Source project catalogue will facilitate the IoT community to access information about the rich set of available IoT projects through a single entry point. This will alleviate the fragmentation of information about OSS projects, which is currently faced by organizations that wish to use OSS components in their projects
Keywords: IIoT; OSS; Edge

Accountable Federated Machine Learning in Government: Engineering and Management Insights - Balta, Dian; Sellami, Mahdi; Kuhn, Peter; Schöpp, Ulrich; Buchinger, Matthias; Baracaldo, Nathalie; Anwar, Ali; Ludwig, Heiko; Sinn, Mathieu; Purcell, Mark; Altakrouri, Bashar (Electronic Participation - 13th IFIP WG 8.5 International Conference, ePart 2021, 2021)
Machine learning offers promising capabilities to improve administrative procedures. At the same time, adequate training of models using traditional learning techniques requires the collection and storage of enough training data in a central place. Unfortunately, due to legislative and jurisdictional constraints, data in a central place is scarce and training a model becomes unfeasible. Against this backdrop, federated machine learning, a technique to collaboratively train models without transferring data to a centralized location, has been recently proposed. With each government entity keeping their data private, new applications that previously were impossible now can be a reality. In this paper, we demonstrate that accountability for the federated machine learning process becomes paramount to fully overcoming legislative and jurisdictional constraints. In particular, it ensures that all government entities' data are adequately included in the model and that evidence on fairness and reproducibility is curated towards trustworthiness. We also present an analysis framework suitable for governmental scenarios and illustrate its exemplary application for online citizen participation scenarios. We discuss our findings in terms of engineering and management implications: feasibility evaluation, general architecture, involved actors as well as verifiable claims for trustworthy machine learning.

Concept for Modeling and Usage of Functionally Described Capabilities and Skills - Motsch, William; Dorofeev, Kirill; Gerber, Kathrin; Knoch, Sönke; David, Alexander; Ruskowski, Martin (IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2021)

FloMo: Tractable Motion Prediction with Normalizing Flows - Schöller, Christoph; Knoll, Alois (International Conference on Intelligent Robots and Systems (IROS), 2021)
The future motion of traffic participants is inherently uncertain. To plan safely, therefore, an autonomous agent must take into account multiple possible outcomes and prioritize them. Recently, this problem has been addressed with generative neural networks. However, most generative models either do not learn the true underlying trajectory distribution reliably, or do not allow likelihoods to be associated with predictions. In our work, we model motion prediction directly as a density estimation problem with a normalizing flow between a noise sample and the future motion distribution. Our model, named FloMo, allows likelihoods to be computed in a single network pass and can be trained directly with maximum likelihood estimation. Furthermore, we propose a method to stabilize training flows on trajectory datasets and a new data augmentation transformation that improves the performance and generalization of our model. Our method achieves state-of-the-art performance on three popular prediction datasets, with a significant gap to most competing models.

Generation of Inter-PLC Communication in Distributed Control Systems Using IEC 61499 - Sieffert, David; Dorofeev, Kirill (IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2021)

Generation of the Orchestrator Code for Skill-Based Automation Systems - Dorofeev, Kirill; Bergemann, Sebastian; Terzimehić, Tarik; Vogel, Julian; Thies, Michael; Zoitl, Alois (IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2021)
Engineering distributed control systems is a complex task, where the complexity and, thus, costs of the software development are growing rapidly. To keep the software complexity at an acceptable level and save time for development and subsequent maintenance, new solutions are sorely needed. In this paper, we investigate the advantages of using skills - universal control device interfaces - for generating complex logic in distributed control environments. By enriching a skill interface with the information about the dynamic behavior of a component under control, we compose the functionalities of individual control devices. The logic, required for such composition, is automatically derived from the interface description and the production plan, identifying the required skill sequence. The evaluation, executed using two industrial demonstrators, shows that the approach successfully handles different behavioral models and is capable of generating fault-tolerant orchestrators, including error handling scenarios. Overall, for developing a complex logic in a distributed skill-based automation system, the proposed approach shows significant savings in development time allowing to automate the skill composition task.
Keywords: Industry 4.0; skills; service-oriented architecture; IEC 61499; service composition; orchestration; PLC code generation; BaSys; Model-based Systems Engineering; MbSE

How Templated Requirements Specifications Inhibit Creativity in Software Engineering - Mohanani, Rahul; Ralph, Paul; Turhan, Burak; Mandic, Vladimir (Transactions on Software Engineering, 2021)
Desiderata is a general term for stakeholder needs, desires or preferences. Recent experiments demonstrate that presenting desiderata as templated requirements specifications leads to less creative solutions. However, these experiments do not establish how the presentation of desiderata affects design creativity. This study, therefore, aims to explore the cognitive mechanisms by which presenting desiderata as templated requirements specifications reduces creativity during software design. Forty-two software designers, organized into 21 pairs, participated in a dialog-based protocol study. Their interactions were transcribed and the transcripts were analyzed in two ways: (1) using inductive process coding and (2) using an a-priori coding scheme focusing on fixation and critical thinking. Process coding shows that participants exhibited seven categories of behavior: making design moves, uncritically accepting, rejecting, grouping, questioning, assuming and considering quality criteria. Closed coding shows that participants tend to accept given requirements and priority levels while rejecting newer, more innovative design ideas. Overall, the results suggest that designers fixate on desiderata presented as templated requirements specifications, hindering critical thinking. More precisely, requirements fixation mediates the negative relationship between specification formality and creativity.

How to Redesign Government Processes for Proactive Public Services? - Kuhn, Peter; Buchinger, Matthias; Balta, Dian (Electronic Government, 2021)
Proactive government is a promising approach to user-friendly public services. By acting proactively instead of reacting, governments reduce the interaction effort of a public service for the user and, in doing so, increase its user-friendliness. However, implementing such proactive services in practice is challenging and requires the redesign of the according processes. For example, the data that currently is provided by the user, now has to be collected by someone else. The goal of this paper is to identify reoccurring challenges in the redesign of processes for proactive public services and to develop strategies to overcome them. We analyse business processes from nine public services and conduct ten expert interviews with practitioners. We synthesis the reoccurring challenges into three implementation dimensions and present three implementation strategies. The implementation dimensions inform the feasibility of proactive services in practice and the strategies can be used by practitioners to streamline their efforts to provide user-friendly public services.

PCTMA-Net: Point Cloud Transformer with Morphing Atlas-based Point Generation Network for Dense Point Cloud Completion - Lin, Jianjie; Rickert, Markus; Perzylo, Alexander; Knoll, Alois (Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021)
Inferring a complete 3D geometry given an incomplete point cloud is essential in many vision and robotics applications. Previous work mainly relies on a global feature extracted by a Multi-layer Perceptron (MLP) for predicting the shape geometry. This suffers from a loss of structural details, as its point generator fails to capture the detailed topology and structure of point clouds using only the global features. The irregular nature of point clouds makes this task more challenging. This paper presents a novel method for shape completion to address this problem. The Transformer structure is currently a standard approach for natural language processing tasks and its inherent nature of permutation invariance makes it well suited for learning point clouds. Furthermore, the Transformer's attention mechanism can effectively capture the local context within a point cloud and efficiently exploit its incomplete local structure details. A morphing-atlas-based point generation network further fully utilizes the extracted point Transformer feature to predict the missing region using charts defined on the shape. Shape completion is achieved via the concatenation of all predicting charts on the surface. Extensive experiments on the Completion3D and KITTI data sets demonstrate that the proposed PCTMA-Net outperforms the state-of-the-art shape completion approaches and has a 10% relative improvement over the next best-performing method.
Keywords: robotics; kbse

Pilot Attitudes Toward AI in the Cockpit: Implications for Design - Zhang, Zelun Tony; Liu, Yuanting; Hußmann, Heinrich (2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS), 2021)
As the aviation industry is actively working on adopting AI for air traffic, stakeholders agree on the need for a human-centered approach. However, automation design is often driven by user-centered intentions, while the development is actually technology-centered. This can be attributed to a discrepancy between the system designers’ perspective and complexities in real-world use. The same can be currently observed with AI applications where most design efforts focus on the interface between humans and AI, while the overall system design is built on preconceived assumptions. To understand potential usability issues of AI-driven cockpit assistant systems from the users’ perspective, we conducted interviews with four experienced pilots. While our participants did discuss interface issues, they were much more concerned about how autonomous systems could be a burden if the operational complexity exceeds their capabilities. Besides commonly addressed human-AI interface issues, our results thus point to the need for more consideration of operational complexities on a system-design level.
Keywords: interviews; thematic analysis; intelligent cockpit assistant systems; human-AI interaction; imperfect AI

PLC Integration into Industry 4.0 Middleware: Function Block Library for the Interaction with REST and OPC UA Asset Administration Shells - Gampig, Jonas; Terzimehić, Tarik; Dorofeev, Kirill (IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2021)
An asset administration shell (AAS), as a key concept of Industry 4.0 (I4.0), provides a machine-accessible interface to any kind of asset. To enable interoperability and smooth integration of the devices into the I4.0 middleware, an application implementing the device's functionality should be able to interact with different AASs. In this work, we investigate the integration of the Programmable Logic Controller (PLC) runtime systems into the I4.0 middleware. For doing this, we specify the function blocks (FBs) for connecting the PLCs with AASs and other I4.0 components, such as registry and discovery server. We analyze the requirements of such FBs while focusing on REST/HTTP- and OPC UA-based AASs, and provide interface specification for IEC 61499- and IEC 61131-3-based FBs. Furthermore, we implemented an FB library that enables communication with an AAS from the respective control applications. Those FBs allow accessing properties and invoking operations of remote AASs, as well as hosting AASs submodels. Common functionalities, such as registering the runtime system at the registry component, or finding AASs, are also supported. The results obtained in this paper will ease interaction with the complex AAS structure from the low-level devices.
Keywords: Industry 4.0; model-based systems engineering; MbSE; IEC 61499; IEC 61131-3; asset administration shell; digital twin; PLC; OPC UA; REST

Towards Service Deployment and Composition in Industry 4.0 - Terzimehić, Tarik; Dorofeev, Kirill; Bergemann, Sebastian; Zoitl, Alois; Voss, Sebastian (IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2021)
The fourth industrial revolution (Industry 4.0) anticipates unplanned changes of production processes. Production changes may trigger synthesis, and preferably optimization, of architecture-level decisions, such as service deployment and composition. Performing such architecture-level decisions manually is difficult due to the ever-rising complexity of Industry 4.0 systems. In order to (semi-)automate the architecture synthesis and optimization, we propose an approach of service deployment and composition by using existing domain models. Our contribution is threefold: (1) We suggest a workflow with domain models for architecture synthesis in the industrial automation. (2) We display the formalization on a part of the workflow and provide an initial prototype of service deployment synthesis using a satisfiability modulo theories (SMT) solver. (3) We envision a way of service composition and code generation, and prototypically implemented it as conclusion of the suggested workflow. We demonstrate the practical use of the proposed approach in the Industry 4.0 scenario, i.e., flexible production of new products.
Keywords: Industry 4.0; IEC 61499; deployment synthesis; service composition; code generation; model-based systems engineering; MbSE; design-space exploration; DSE

Type-based Enforcement of Infinitary Trace Properties for Java - Erbatur, Serdar; Schöpp, Ulrich; Xu, Chuangjie (PPDP 2021: 23rd International Symposium on Principles and Practice of Declarative Programming, 2021)
A common approach to improve software quality is to use programming guidelines to avoid common kinds of errors. In this paper, we consider the problem of enforcing guidelines for Featherweight Java (FJ). We formalize guidelines as sets of finite or infinite execution traces and develop a region-based type and effect system for FJ that can enforce such guidelines. We build on the work by Erbatur, Hofmann and Zălinescu, who presented a type system for verifying the finite event traces of terminating FJ programs. We refine this type system, separating region typing from FJ typing, and use ideas of Hofmann and Chen to extend it to capture also infinite traces produced by non-terminating programs. Our type and effect system can express properties of both finite and infinite traces and can compute information about the possible infinite traces of FJ programs. Specifically, the set of infinite traces of a method is constructed as the greatest fixed point of the operator which calculates the possible traces of method bodies. Our type inference algorithm is realized by working with the finitary abstraction of the system based on Büchi automata.

A Spiking Central Pattern Generator for the control of a simulated lamprey robot running on SpiNNaker and Loihi neuromorphic boards - Angelidis, Emmanouil; Buchholz, Emanuel; Arreguit O'Neil, Jonathan Patrick; Rougé, Alexis; Stewart, Terrence; von Arnim, Axel; Knoll, Alois; Ijspeert, Auke (Neuromorphic Computing and Engineering, 2021)
Central Pattern Generators (CPGs) models have been long used to investigate both the neural mechanisms that underlie animal locomotion as well as a tool for robotic research. In this work we propose a spiking CPG neural network and its implementation on neuromorphic hardware as a means to control a simulated lamprey model. To construct our CPG model, we employ the naturally emerging dynamical systems that arise through the use of recurrent neural populations in the Neural Engineering Framework (NEF). We define the mathematical formulation behind our model, which consists of a system of coupled abstract oscillators modulated by high-level signals, capable of producing a variety of output gaits. We show that with this mathematical formulation of the Central Pattern Generator model, the model can be turned into a Spiking Neural Network (SNN) that can be easily simulated with Nengo, an SNN simulator. The spiking CPG model is then used to produce the swimming gaits of a simulated lamprey robot model in various scenarios. We show that by modifying the input to the network, which can be provided by sensory information, the robot can be controlled dynamically in direction and pace. The proposed methodology can be generalized to other types of CPGs suitable for both engineering applications and scientific research. We test our system on two neuromorphic platforms, SpiNNaker and Loihi. Finally, we show that this category of spiking algorithms shows a promising potential to exploit the theoretical advantages of neuromorphic hardware in terms of energy efficiency and computational speed.
Keywords: neurorobotics; neuromorphic; neuromorphic computing; HBP; NRP; virtual; robotics Neuromorphic Computing; HBP; Human Brain Project; Neurorobotics; Neurorobotics Platform; Neuroscience; Artificial Intelligence; KI; Spiking Neural Networks

How Business Model Innovation fosters Organizational Resilience during COVID-19 - Schaffer, Norman; Garoz Pérez, Patricia; Weking, Jörg (Americas Conference on Information Systems (AMCIS) 2021, 2021, 2021)
The COVID-19 pandemic imposes various challenges on societies as well as on organizations, especially in the medical sector. Organizational resilience is a central ability to strive through these challenges. Business model innovation can be a tool to build organizational resilience. Yet, it is unclear how business model innovation fosters organizational resilience. Therefore, we conduct a longitudinal case study on Laboratory Inc., which adapts to the situation, innovates its business model to allow testing for the virus from home, and transmits results digitally. Our results show how organizational resilience is built by business model innovation. The business model innovations performed are not temporary, but lead to a new status of the organization, preparing it for future crises. At the same time, we demonstrate how digital innovations help to overcome crises and support socio-economic value. Our findings contribute to research on organizational resilience as well as on business models under external threats.

Safety Case Maintenance: A Systematic Literature Review - Cârlan, Carmen; Gallina, Barbara; Soima, Liana (Proceedings of the 40th International Conference on Computer Safety, Reliability and Security (SAFECOMP), 2021)
Safety standards from different domains recommend the execution of a process for keeping the system safety case up to date, whenever the system undergoes a change, however, without providing any more specific guidelines on how to do this. Even if several (semi)automated safety case maintenance approaches have been proposed in the literature, currently, in the industry, the execution of this process is still manual, being error prone and expensive. To this end, we present in this paper the results of what is, to the best of our knowledge, the first Systematic Literature Review (SLR) conducted with the goal to provide a holistic overview of state-of-the-art safety case maintenance approaches. For each identified approach, we analyze its strengths and weaknesses. We observe that existing approaches are pessimistic, identifying a larger number of safety case elements as impacted by a change than the number of the actually impacted elements. Also, there is limited quantitative impact assessment. Further, existing approaches only address a few system change scenarios when providing guidelines for updating the safety case.
Keywords: Safety case maintenance; Systematic literature review; Model-based Systems Engineering; MbSE

A generic type system for featherweight Java - Schöpp, Ulrich; Xu, Chuangjie (FTfJP 2021: Proceedings of the 23rd ACM International Workshop on Formal Techniques for Java-like Programs, 2021)
We introduce a generic type system for Featherweight Java (FJ) that is parametrized with a monad-like structure, and prove a uniform soundness theorem. Its instances include some region type systems studied by Martin Hofmann et al. as well as a new one that performs more precise analysis of trace-based properties. Their soundness is guaranteed by the uniform theorem. We only need to verify some natural conditions. Instead of refining the FJ type system as in the previous work, our region type system is separate from the FJ type system, making it simpler and also easier to move to larger fragments of Java. Moreover, the uniform framework helps to avoid redundant work on the meta-theory when extending the system to cover other language features such as exception handling.

Empowering Workers in a Mixed Skills Concept for Collaborative Robot Systems - Huchler, Norbert; Kessler, Ingmar; Lay, Florian; Perzylo, Alexander; Seidler, Michael; Steinmetz, Franz (Workshop on Accessibility of Robot Programming and Work of the Future, Robotics: Science and Systems (RSS), 2021)
One aspect of digital transformation in manufacturing is the trend toward mass customization, which requires a more flexible production paradigm. Human-robot collaboration and knowledge-based engineering are approaches that meet these requirements. In our work, we combine them in our mixed skills concept that incorporates the strengths of human workers and robots. We assume that while the workplaces of many workers may change, they will continue to play a vital role due to their experience and flexibility. They can perform various types of tasks that are still beyond the capabilities of robots. Yet, their responsibilities may shift towards decision makers and problem solvers for robots. Our approach to facilitate such collaboration is to apply insights from social science regarding empowerment in the work context to determine design goals and potential solutions for collaborative robot systems. The technical implementation is based on semantic descriptions of relevant aspects of automation using OWL ontologies and intuitive user interfaces.
Keywords: kbse; empress

Forward Reasoning Decision Support: Toward a More Complete View of the Human-AI Interaction Design Space - Zhang, Zelun Tony; Liu, Yuanting; Hußmann, Heinrich (CHItaly '21: 14th Biannual Conference of the Italian SIGCHI Chapter, 2021)
Decision support systems based on AI are usually designed to generate complete outputs entirely automatically and to explain those to users. However, explanations, no matter how well designed, might not adequately address the output uncertainty of such systems in many applications. This is especially the case when the human-out-of-the-loop problem persists, which is a fundamental human limitation. There is no reason to limit decision support systems to such backward reasoning designs, though. We argue how more interactive forward reasoning designs where users are actively involved in the task can be effective in managing output uncertainty. We therefore call for a more complete view of the design space for decision support systems that includes both backward and forward reasoning designs. We argue that such a more complete view is necessary to overcome the barriers that hinder AI deployment especially in high-stakes applications.

How to Manage Output Uncertainty: Targeting the Actual End User Problem in Interactions with AI - Zhang, Zelun Tony; Hußmann, Heinrich (Joint Proceedings of the ACM IUI 2021 Workshops, 2021)
Given the opaqueness and complexity of modern AI algorithms, there is currently a strong focus on developing transparent and explainable AI, especially in high-stakes domains. We claim that opaqueness and complexity are not the core issues for end users when interacting with AI. Instead, we propose that the output uncertainty inherent to AI systems is the actual problem, with opaqueness and complexity as contributing factors. Transparency and explainability should therefore not be the end goals, as such a focus tends to place the human into a passive supervisory role in what is in reality an algorithm-centered system design. To enable effective management of output uncertainty, we believe it is necessary to focus on truly human-centered AI designs that keep the human in an active role of control. We discuss the conceptual implications of such a shift in focus and give examples from literature to illustrate the more holistic, interactive designs that we envision.

Supercap-Python: An Open-source Python based Super capacitor Modelling Package - Pampana, Venkatesh; Lavin, Daniel; Duchon, Markus; Srivastava, Ankit (ICGET 2021, 2021)
Supercapacitors have attained high power density and exceptional durability with the recent advancement in terms of their materials and chemistries. The potential scientific and industrial applications of supercapacitors are being explored continually. This instigates the need for model-based analysis and synthesis tools, which can describe dynamic phenomena, support multiphysics problems, and allow for immediate use in design and advanced control analysis. For these aspects, modelling of supercapacitors would be beneficial. However, there are no open-source simulation tools on supercapacitors available for the scientific community to work with. This paper presents the development of an open-source supercapacitor modelling package in python language. The proposed package is evaluated by comparing the results with a standard MATLAB/Simulink supercapacitor model. The simulation results have shown that both models yielded similar envelope.

Toward Embodied Intelligence: Smart Things on the Rise - Broering, Arne; Niedermeier, Christoph; Olaru, Ioana; Schöpp, Ulrich; Telschig, Kilian; Villnow, Michael (IEEE Computer, 2021)
Embodied intelligence will unleash the potential of artificial intelligence by providing it with a body and impact every area of life. The success of the adoption of the embodied intelligence market depends on solving the technical, legal, economic, and social issues it brings with it.
Keywords: economics; law; artificial intelligence; market research; social factors

CyberGSN: A Semi-formal Language for Specifying Safety Cases - Beyene, Tewodros; Cârlan, Carmen (Proceedings of the 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 2021)
The use of safety cases to explicitly present safety considerations and decisions is a common practice in the safety-critical domain. A safety case can be used to scrutinize the safety assessment approach used by practitioners internally, or as an input for the certification process for an external certifying authority. However, safety cases are still created manually to explicate the followed safety assessment and assurance measures. In addition, although safety cases may be created in a modular way by multiple entities, and it may be critical for each entity to digitally sign its part of the assurance for accountability, the common notations are not expressive enough to include the notion of entity. Especially in cyber-security applications, the notion of entity is very critical. In this paper, we propose a formal logic based language called CyberGSN, with an explicit notion of entity, that can be used for specifying safety cases and safety case patterns, enabling the automated creation and maintenance of safety cases.
Keywords: Safety Case; Pattern; Entity; Decentralization; Model-based Systems Engineering; MbSE

Dynamic Texture Recognition via Nuclear Distances on Kernelized Scattering Histogram Spaces - Sagel, Alexander; Wörmann, Julian; Shen, Hao (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021)

FROM SPECIALIZATION TO PLATFORMIZATION: BUSINESS MODEL EVOLUTION IN THE CASE OF SERVICENOW - Schaffer, Norman; Ritzenhoff, Matthias; Engert, Martin; Krcmar, Helmut (Twenty-Ninth European Conference on Information Systems (ECIS 2021), 2021, 2021)
Currently, platform-based business models are most successful regarding revenue growth and market shares. However, the stepwise evolution of organizations’ business models towards multi-sided platforms is not fully understood. Therefore, we conduct a longitudinal case study on the IT organization ServiceNow. Based on publicly available data, we build on research on business model evolution, platform emergence, and platform ecosystems to analyze the evolution of ServiceNow’s business model between 2004-2020. We derive four distinct mechanisms comprising continuous value proposition extension and enablement of value co-creation. These are enabled by opening towards partners and iteratively addressing new customer segments. We contribute to research on business model evolution with insights on the evolution towards platform business models. Besides, we complement the perspective of platform emergence by a nuanced business model view, bridging these two literature streams. Practitioners benefit from the mechanisms to guide their business model evolution towards a multi-sided platform business model.

Neural Network and Correlation based Earth-Fault Localization utilizing a Digital Twin of a Medium-Voltage Grid - Wörmann, Julian; Urban, Melanie; Grubinger, David; Silva, Nuno; Schwefel, Hans-Peter (e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems, 2021)

Sensitivity Analysis of Earth Fault Localization based on Voltage Signatures in Medium Voltage Grids - Wörmann, Julian; Tafehi, Ehsan; Duchon, Markus; Silva, Nuno; Schwefel, Hans-Peter (ICC 2021 - IEEE International Conference on Communications, 2021)

A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA - Maniglia da Silva, Daniel; I. Carvalho, Liliana; Soares, José; C. Sofia, Rute (MDPI Appl. Sci. 2021, 11(11), 4879; Highly Cited Paper Award, 2021)
oT data exchange is supported today by different communication protocols and different protocolar frameworks, each of which with its own advantages and disadvantages, and often co-existing in a way that is mandated by vendor policies. Although different protocols are relevant in different domains, there is not a protocol that provides better performance (jitter, latency, energy consumption) across different scenarios. The focus of this work is two-fold. First, to provide a comparison of the different available solutions in terms of protocolar features such as type of transport, type of communication pattern support, security aspects, including Named-data networking as relevant example of an Information-centric networking architecture. Secondly, the work focuses on evaluating three of the most popular protocols used both in Consumer as well as in Industrial IoT environments: MQTT, CoAP, and OPC UA. The experimentation has been carried out first on a local testbed for MQTT, COAP and OPC UA. Then, larger experiments have been carried out for MQTT and CoAP, based on the large-scale FIT-IoT testbed. Results show that CoAP is the protocol that achieves across all scenarios lowest time-to-completion, while OPC UA, albeit exhibiting less variability, resulted in higher time-to-completion in comparison to CoAP or MQTT.
Keywords: IIoT; networking protocols; networking architectures; performance evaluation

Holonic architectures for IoT-empowered energy management in districts - Mayer, Jan; Pampana, Venkatesh; Bernard, Michael; Bytschkow, Denis; Stohl, Thomas; Gupta, Pragya Kirti; Duchon, Markus (WF-IOT IEEE Conference, 2021)
For the future energy system, the transition from centralized fossil-based plants to a sustainable energy system means the emergence of a huge number of distributed renewable energy sources.Weather-dependent and volatile power generation as well as an increasingly spiky electric demand, arising from ongoing electrification of other sectors, require predictive and flexible control strategies already on the district level. With an increasing number of IoT-enabled field devices, multiple energy vectors thereby lead to a complex interplay, where micro and macro objectives may sometimes differ or even oppose. In this work we propose holonic whole-part architectures for multienergy management systems. Building up a district’s energy system from self-sufficient sub-units, enables the preservation of local control schemes and reduces complexity for energy management entities or energy markets on hierarchically higher levels. Additionally, resilience and topological flexibility can be enhanced through dynamic reconfiguration of the holons. Two field examples for cross-sector realizations of IoT-based multienergy management in districts are presented, which demonstrate the implementation of the concept once at the district level and once at the device level.

FASTEN: An Extensible Platform to Experiment with Rigorous Modeling of Safety-Critical Systems - Ratiu, Daniel; Nordmann, Arne; Munk, Peter; Cârlan, Carmen; Voelter, Markus (Domain-Specific Languages in Practice, 2021)
The increasing complexity of safety-critical systems and the shorter time-to-market requires a high degree of automation during all development phases from requirements specification to design, implementation, verification, and safety assurance. To make this feasible, we need to describe different system aspects using appropriate models that are semantically rich and, whenever possible, formally defined such that they are verifiable by automated methods. At the same time, they must be easy to understand by practitioners and must allow them to capture the domain concepts with minimal encoding bias. In this chapter, we describe FASTEN, an open-source research environment for model-based specification and design of safety-critical systems using domain-specific languages. FASTEN enables the experimentation with modeling abstractions at different levels of rigor and their integration in today’s development processes. We present an overview of the currently available domain-specific languages (DSLs) used to formally specify requirements, system designs, and assurance arguments. These DSLs have been developed and used in technology transfer projects by researchers from different organizations—Siemens, Bosch, fortiss, and itemis. Last but not least, we discuss lessons learned from implementing the languages and interacting with practitioners and discuss the language engineering features of MPS that enabled our approach and its open challenges.
Keywords: Model-based Systems Engineering; MbSE

Smart Self-Adaptive Cyber-Physical Systems: How can Exploration and Learning Improve Performance in a Partially Observable Multi-Agent Context? - Petrovska, Ana; Neuss, Malte; Bergemann, Sebastian; Büchner, Martin; Shohab, Ansab (ADAPTIVE 2021: The Thirteenth International Conference on Adaptive and Self-Adaptive Systems and Applications, 2021)
Cyber-physical systems (CPSs) are software-intensive systems that are embedded in the physical world to monitor, control and coordinate a variety of processes in both the physical and the digital world. As a result, they often operate in complex, dynamic, and unanticipated environments with various potential sources of run-time changes and uncertainties, that could potentially lead the CPSs to faults, and even to complete system failures. To cope with these changes, the systems should have the capabilities to self-adapt in order to continue meeting their functional specifications. In this paper, we investigate how creating self-adaptive CPSs which are able to collaborate and learn in a dynamic, partially observable, multi-agent context, can not only preserve but also improve the performance, despite all the changes introduced to the system at run-time. We evaluate the proposed methodology on an in-house developed, multi-agent system from the robotics domain.
Keywords: self-adaptive systems; cyber-physical systems; collaboration; learning; partial observability; Model-based Systems Engineering; MbSE

IoT Thing To Service Semantic Matching - Bnouhanna, Nisrine; C. Sofia, Rute; Pretschner, Alexander (inProc IEEE Percom 2021 (Best PhD paper award), 2021)
We propose an automated IoT data source (Thing) to service semantic matching mechanism that selects an optimal set of available IoT data sources in order to satisfy service requirements and needs. The main motivation is to provide a better use of data captured via IoT data sources. To achieve this goal, we propose a three-fold approach: (i) modelling service requirements based on existing service descriptions; (ii) automating the selection of an optimal set of Things that best meet the service requirements; (iii) selecting data processing services that provide additional service requirements.
Keywords: IIoT; semantic technologies; matchmaking

Ontology-Based Decision Support System for the Nitrogen Fertilization of Winter Wheat - Kessler, Ingmar; Perzylo, Alexander; Rickert, Markus (Metadata and Semantics Research (MTSR 2020), 2021)
Digital technologies are already used in several aspects of agriculture. However, decision-making in crop production is still often a manual process that relies on various heterogeneous data sources. Small-scale farmers and their local consultants are particularly burdened by increasingly complex requirements. Regional circumstances and regulations play an essential role and need to be considered. This paper presents an ontology-based decision support system for the nitrogen fertilization of winter wheat in Bavaria, Germany. Semantic Web and Linked Data technologies were employed to both reuse and model new common semantic structures for interrelated knowledge. Many relevant general and regional data sources from multiple domains were not yet available in RDF. Hence, we used several tools to transform relevant data into corresponding OWL ontologies and combined them in a central knowledge base. The GUI application of the decision support system queries it to parameterize requests to external web services and to show relevant information in an integrated view. It further uses SPARQL queries to automatically generate recommendations for farmers and their consultants.
Keywords: robotics; farmexpert

AI-Blueprint for Deep Neural Networks - Wozniak, Ernest; Putzer, Henrik J.; Cârlan, Carmen (Proceedings of the Workshop on Artificial Intelligence Safety (SafeAI '21), 2021)
Development of trustworthy (e.g., safety and/or security critical) hardware/software-based systems needs to rely on well-defined process models. However, engineering trustworthy systems implemented with artificial intelligence (AI) is still poorly discussed. This is, to large extend, due to the standpoint in which AI is a technique applied within software engineering. This work follows a different viewpoint in which AI represents a 3rd kind technology (next to software and hardware), with close connections to software. Consequently, the contribution of this paper is the presentation of a process model, tailored to AI engineering. Its objective is to support the development of trustworthy systems, for which parts of their safety and/or security critical functionality are implemented with AI. As such, it considers methods and metrics at different AI development phases that shall be used to achieve higher confidence in the satisfaction of trustworthiness prop- erties of a developed system.
Keywords: Safety Case; Model-based Systems Engineering; MbSE

RMC Factory: A New Approach for Avionics Software Reuse - Dieudonne, Laurent; Bayha, Andreas; Müller, Benedikt (3rd Workshop on Avionics Systems and Software Engineering (AvioSE21, SE 2021 Satellite Events), 2021)
In contrast to many other industries, the reuse-rate of software in the avionics domain is very low. This situation originates mainly from the incomplete specifications for reuse in the avionics standards. In particular, these ones address almost exclusively the reuse of certified software previously developed for a specific project and consider only big monolithic executable components like complete applications or operating system modules. This paper describes a new approach enabling an efficient reuse of components from small up to big scale, specifically developed for reuse and conceived to fulfill the fundamentals DO-178C requirements while maximizing the reuse of certification artefacts. This presented approach combines methods, tool infrastructure and process extensions and we denote it as Reusable Modular Component (RMC) Factory. We also resent an implementation of the tool infrastructure and discuss our lessons learned from this mplementation and the first experiments.
Keywords: Model-based Systems Engineering; MbSE; Avionics; component; reuse; RMC; software factory; certification; process; tool chain

A Generic Plug & Produce System Composed of Semantic OPC UA Skills - Profanter, Stefan; Perzylo, Alexander; Rickert, Markus; Knoll, Alois (IEEE Open Journal of the Industrial Electronics Society, 2021)
Typical industrial workcells are composed of a plenitude of devices from various manufacturers, which rely on their own specific control interfaces. To reduce setup and reconfiguration times, a hardware-agnostic Plug & Produce system is required. In this paper, we present a system architecture that uses generic and semantically augmented OPC UA skills for robots, tools, and other system components. Standardized skill interfaces and parameters facilitate flexible component interchange and automatic parametrization with a focus on reusability of skills across different platforms and domains. The hierarchical composition of such skills allows for additional abstraction through the grouping of functionalities. Through the extension of OPC UA discovery services, available skills are dynamically detected whenever a manufacturing system's component is updated. The introduced Plug & Produce system is evaluated in multiple industrial workcells composed of robots, tool changer, electric parallel gripper, and vacuum gripper - all controlled via the proposed OPC UA skill interface. The evaluation of our system architecture demonstrates the applicability of the Plug & Produce concept in the domain of robot-based industrial assembly. Although it is necessary to adapt existing hardware to comply with the semantic skill concept, the initial one-time effort yields reoccurring efficiency gains during system reconfiguration. In particular, small lot production benefits from reduced changeover times.
Keywords: robotics; iriss

Handling Concurrency in Embedded Software System from Architectural Point of View - Jahić, Jasmin; Diewald, Alexander (HiPEAC conference, 2021)
Keywords: Model-based Systems Engineering; MbSE

Towards Interoperability of Data Platforms for Smart Cities - Buchinger, Matthias; Kuhn, Peter; Balta, Dian (Handbook of Smart Cities, 2021)

Towards Interoperability of Smart City Data Platforms - Buchinger, Matthias; Kuhn, Peter; Kalogeropoulos, Anastasios; Balta, Dian (Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS 2021), 2021, 2021)
Keywords: odim; diprolea

(Proceedings) Software Quality Days: Software Quality. Future Perspectives on Software Engineering Quality - Winkler, Dietmar; Biffl, Stefan; Mendez, Daniel; Wimmer, Manuel; Bergsmann, Johannes (Software Quality Days: Software Quality. Future Perspectives on Software Engineering Quality, 2021)

A Study about the Knowledge and Use of Requirements Engineering Standards in Industry - Glinz, Martin; Franch, Xavier; Mendez, Daniel; Seyff, Norbert (Transactions on Software Engineering, 2021)

An ICT System To Assist Earth Fault Localization - Santander, Juan; Duchon, Markus (DSOGRI 2021, 2021)

An ICT System to Assist Earth Fault Localization - Chaves, Juan Felipe; Schäler, Christine; Drenjanac, Domagoj; Duchon, Markus; Fotso, Serge (European Dependable Computing Conference, 2021)

Automatic Detection of Causality in Requirement Artifacts: the CiRA Approach - Fischbach, Jannik; Frattini, Julian; Spaans, Arjen; Kummeth, Maximilian; Vogelsang, Andreas; Mendez, Daniel; Unterkalmsteiner, Michael (Proc. 27th International Working Conference on Requirement Engineering: Foundation for Software Quality, 2021)

Enterprise-Driven Open Source Software: A Case Study on Security Automation - Angermeir, Florian; Voggenreiter, Markus; Moyón, Fabiola; Mendez, Daniel (Proc. 43rd International Conference on Software Engineering, 2021)

Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks - Fischbach, Jannik; Springer, Tobias; Frattini, Julian; Femmer, Henning; Vogelsang, Andreas; Mendez, Daniel (Proc. 8th International Workshop on Artificial Intelligence and Requirements Engineering, 2021)

How Do Practitioners Interpret Conditionals in Requirements? - Fischbach, Jannik; Frattini, Julian; Mendez, Daniel; Unterkalmsteiner, Michael; Femmer, Henning; Vogelsang, Andreas (Proc. of the 22nd International Conference on Product-Focused Software Process Improvement, 2021)

Is Secure Coding Education in the Industry Needed? An Investigation Through a Large Scale Survey - Espinha Gasiba, Tiago; Lechner, Ulrike; Pinto-Albuquerque, Maria; Mendez, Daniel (Proc. of the 43rd International Conference on Software Engineering, 2021)

On Understanding the Relation of Knowledge and Confidence to Requirements Quality - Dehghani, Razieh; Wnuk, Krzysztof; Mendez, Daniel; Gorschek, Tony; Ramsin, Raman (Proc. 27th International Working Conference on Requirement Engineering: Foundation for Software Quality, 2021)

Sensitivity Analysis of Earth Fault Localization based on Voltage Signatures in Medium Voltage Grids - Wörmann, Julian; Tafehi, Ehsan; Duchon, Markus; Silva, Nuno; Schwefel, Hans-Peter (ICC 2021-IEEE International Conference on Communications, 2021)

Solving Problems or Enabling Problem-Solving? From Purity in Empirical Software Engineering to Effective Co-Production - Gorschek, Tony; Mendez, Daniel (Proc. 13th International Software Quality Days (Invited Keynote Paper), 2021)

The Digitized Ecosystem of Tourism in Europe: Current Trends and Implications - Schaffer, Norman; Engert, Martin; Sommer, Guido; Shokoui, Jasmin; Krcmar, Helmut (Information and Communication Technologies in Tourism 2021, 2021)

Towards cross-commodity energy-sharing communities–A review of the market, regulatory, and technical situation - Paiho, Satu; Kiljander, Jussi; Sarala, Roope; Saarikivi, Olli; Bajpai, Arpit; Duchon, Markus; Pahl, Marc-Oliver; Wüstrich, Lars; Lübben, Christian; Kirdan, Erkin; Schindler, Josef; Numminen, Jussi; et al. (Renewable and Sustainable Energy Reviews, 2021)

Understanding Peer Review of Software Engineering Papers - Mendez, Daniel; Carver, Jeffrey C.; Torchiano, Marco; Ernst, Neil A. (Empirical Software Engineering Journal, 2021)
Peer review is a key activity intended to preserve the quality and integrity of scientific publications. However, in practice it is far from perfect. We aim at understanding how reviewers, including those who have won awards for reviewing, perform their reviews of software engineering papers to identify both what makes a good reviewing approach and what makes a good paper. We first conducted a series of in-person interviews with well-respected reviewers in the software engineering field. Then, we used the results of those interviews to develop a questionnaire used in an online survey and sent out to reviewers from well-respected venues covering a number of software engineering disciplines, some of whom had won awards for their reviewing efforts. We analyzed the responses from the interviews and from 175 reviewers who completed the online survey (including both reviewers who had won awards and those who had not). We report on several descriptive results, including: 45% of award-winners are reviewing 20+ conference papers a year, while 28% of non-award winners conduct that many. 88% of reviewers are taking more than two hours on journal reviews. We also report on qualitative results. To write a good review, the important criteria were it should be factual and helpful, ranked above others such as being detailed or kind. The most important features of papers that result in positive reviews are clear and supported validation, an interesting problem, and novelty. Conversely, negative reviews tend to result from papers that have a mismatch between the method and the claims and from those with overly grandiose claims. The main recommendation for authors is to make the contribution of the work very clear in their paper. In addition, reviewers viewed data availability and its consistency as being important.

Unsupervised Learning of Joint Embeddings for Node Representation and Community Detection - Khan, Rayyan Ahmad; Anwaar, Muhammad Umer; Kaddah, Omran; Han, Zhiwei; Kleinsteuber, Martin (the Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference (2021), 2021)
In this paper, we study how to simultaneously learn two highly correlated tasks of graph analysis, i.e., community detection and node representation learning. We propose an efficient generative model called VECoDeR for jointly learning Variational Embeddings for Community Detection and node Representation. VECoDeR assumes that every node can be a member of one or more communities. The node embeddings are learned in such a way that connected nodes are not only "closer" to each other but also share similar community assignments. A joint learning framework leverages community-aware node embeddings for better community detection. We demonstrate on several graph datasets that VECoDeR effectively out-performs many competitive baselines on all three tasks i.e. node classification, overlapping community detection and non-overlapping community detection. We also show that VECoDeR is computationally efficient and has quite robust performance with varying hyperparameters.

Vision for an Artefact-based Approach to Regulatory Requirements Engineering - Kosenkov, Oleksandr; Unterkalmsteiner, Michael; Mendez, Daniel; Fucci, Davide (Proc. 15th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2021)

A Formal Security Assessment Framework for Cooperative Adaptive Cruise Control - Dantas, Yuri Gil; Nigam, Vivek; Talcott, Carolyn (IEEE Vehicular Networking Conference (VNC), 2020)
For increased safety and fuel-efficiency, vehicle platoons use Cooperative Adaptive Cruise Control (CACC) where vehicles adapt their state, incl. speed and position, based on information exchanged between vehicles. Intruders, however, may carry out attacks against CACC platoons by exploiting the communication channels used to cause harm, e.g., a vehicle crash. Therefore, during design-phase, engineers should provide evidence supporting platoon security. This paper proposes a formal framework for the security verification of CACC platoons to provide such evidence based on precise mathematical models. Our vehicle platoon models support the specification of both cyber, e.g., communication protocols, and physical, e.g., speeds, position, vehicle behaviors. Moreover, we propose intruder models that are parametric on his capabilities of manipulating communication channels, i.e., message injection and blocking. Our model is implemented enabling the automated formal verification involving both platoon and intruder models. We validate our machinery with a number of attacks taken from the literature and novel attacks discovered by using our formal machinery.
Keywords: attacks; formal verification; platoon; security; AutoFOCUS3; AF3

Architectures for Flexible Collaborative Systems - Böhm, Birthe; Cârlan, Carmen; Sohr, Annelie; Unverdorben, Stephan; Vollmar, Jan (Model-Based Engineering of Collaborative Embedded Systems, 2020)
Collaborative systems are characterized by their interaction with other systems in collaborative system groups in order to reach a common goal. These systems interact based on fixed rules and have the ability to change structurally, if necessary. Changes in the collaboration are usually triggered from outside and are time-discrete with a rather wide time scale. The architectures of these systems and system groups must support flexibility and adaptability at runtime while also ensuring specific qualities, although these changes and their consequences cannot be fully foreseen in all combinations at design time. In order to enable knowledge preservation and reuse for the design of system architectures for flexible collaborative systems and system groups, we present a method for designing reference architectures for systems and system groups. For this approach, we present an example of a reference architecture for an operator assistance system. To adequately consider safety requirements during the design, we further introduce a method which adapts safety argumentation for flexible collaborative systems to changes in their specification or operating context.
Keywords: Model-based systems engineering; MbSE

Linear Differential Games for Cooperative Behavior Planning of Autonomous Vehicles Using Mixed-Integer Programming - Kessler, Tobias; Esterle, Klemens; Knoll, Alois (Proceedings of 59th IEEE Conference on Decision and Control (CDC), 2020)
Cooperatively planning for multiple agents has been proposed as a promising method for strategic and motion planning for automated vehicles. By taking into account the intent of every agent, the ego agent can incorporate future interactions with human-driven vehicles into its planning. The problem is often formulated as a multi-agent game and solved using iterative algorithms operating on a discretized action or state space. Even if converging to a Nash equilibrium, the result will often be only sub-optimal. In this paper, we define a linear differential game for a set of interacting agents and solve it to optimality using mixed-integer programming. A disjunctive formulation of the orientation allows us to formulate linear constraints to prevent agent-to-agent collision while preserving the non-holonomic motion properties of the vehicle model. Soft constraints account for prediction errors. We then define a joint cost function, where a cooperation factor can adapt between altruistic, cooperative, and egoistic behavior. We study the influence of the cooperation factor to solve scenarios, where interaction between the agents is necessary to solve them successfully. The approach is then evaluated in a racing scenario, where we show the applicability of the formulation in a closed-loop receding horizon replanning fashion. By accounting for inaccuracies in the cooperative assumption and the actual behavior, we can indeed successfully plan an optimal control strategy interacting closely with other agents.

A Review on Scaling Mobile Sensing Platforms for Human Recognition: Challenges and Recommendations for Future Research - I. Carvalho, Liliana; C. Sofia, Rute (MDPI IoT, November 2020. https://doi.org/10.3390/iot1020025, 2020)
Mobile sensing has been gaining ground due to the increasing capabilities of mobile and personal devices that are carried around by citizens, giving access to a large variety of data and services based on the way humans interact. Mobile sensing brings several advantages in terms of the richness of available data, particularly for human activity recognition. Nevertheless, the infrastructure required to support large-scale mobile sensing requires an interoperable design, which is still hard to achieve today. This review paper contributes to raising awareness of challenges faced today by mobile sensing platforms that perform learning and behavior inference with respect to human routines: how current solutions perform activity recognition, which classification models they consider, and which types of behavior inferences can be seamlessly provided. The paper provides a set of guidelines that contribute to a better functional design of mobile sensing infrastructures, keeping scalability as well as interoperability in mind. https://www.mdpi.com/2624-831X/1/2/25
Keywords: IIoT; sensing; Edge; context-awareness

Enhancing State-of-the-art Safety Case Patterns to Support Change Impact Analysis - Cârlan, Carmen; Gallina, Barbara (Proceedings of the 30th European Safety and Reliability Conference (ESREL-2020), 2020)
The new generation of safety-critical systems will be interconnected, having other systems as collaborating partners for achieving common goals (e.g., interconnected cyber-physical systems such as connected cars, or collaborative embedded systems such as an advanced driver assistance system connected with different sensors). Frequent new business goals of such systems are to enable new collaborations with new types of technical systems, thus changing their operating context, which triggers the need for agile development in automotive. In safety-critical domains, a change in the operating context triggers the need for impact analysis on the artefacts generated during the safety lifecycle. Impact analyses are time and resource consuming, hindering agile development. Hence, the need for automation. Safety cases comprise safety arguments explicitly specifying the traces among the artefacts generated during the safety lifecycle. Our longer term goal is to support the automated identification of the artefacts affected by changes in the system's operating context, while proposing an automated change impact analysis executed on the system's safety case. To ensure completeness of the results of such analysis, in this work, we enhance state-of-the-art safety case patterns by referencing all artefacts generated during the safety lifecycle. Further, we enable the explicit specification of the properties of the operating context for which we foresee certain changes. We evaluate our patterns by using them for the construction of the safety case of a simplified airbag system.
Keywords: Model-based systems engineering; MbSE

Harmonic frequency estimation with MEMO-ESPRIT Technique using MATLAB and Python - Srivastava, Ankit; Tiwari, AN; Pampana, Venkatesh; Singh, SN (IEEE, 2020)
This paper presents the use of Modified Exact Model Order (MEMO) estimation technique for detecting number of modes present in a mixture of sinusoid and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique) i.e. harmonic/Interharmonics frequency components present in a signal. With increase in number of semiconductor power electronic devices, a large increase in the harmonics/interharmonics injections has witnessed in the system. Accurate estimation of these harmonic/interharmonics components are very important for their mitigation as these can create a lot of problems. This paper shows the signals with harmonic as well as interharmonic components which can be generated from arcing loads, variable frequency drives, cycloconverters etc. and their accurate estimation using MEMO-ESPRIT method. The algorithm was modelled and analyzed in MATLAB and Python platforms. The results show that the harmonics/interharmonics could be detected accurately using this method.

6D Pose Estimation for Flexible Production with Small Lot Sizes based on CAD Models using Gaussian Process Implicit Surfaces - Lin, Jianjie; Rickert, Markus; Knoll, Alois (Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020)
We propose a surface-to-surface (S2S) point registration algorithm by exploiting the Gaussian Process Implicit Surfaces for partially overlapping 3D surfaces to estimate the 6D pose transformation. Unlike traditional approaches, that separate the corresponding search and update steps in the inner loop, we formulate the point registration as a nonlinear non-constraints optimization problem which does not explicitly use any corresponding points between two point sets. According to the implicit function theorem, we form one point set as a Gaussian Process Implicit Surfaces utilizing the signed distance function, which implicitly creates three manifolds. Points on the same manifold share the same function value, indicated as {1, 0, -1}. The problem is thus converted into finding a rigid transformation that minimizes the inherent function value. This can be solved by using a Gauss-Newton (GN) or Levenberg-Marquardt (LM) solver. In the case of a partially overlapping 3D surface, the Fast Point Feature Histogram (FPFH) algorithm is applied to both point sets and a Principal Component Analysis (PCA) is performed on the result. Based on this, the initial transformation can then be computed. We conduct experiments on multiple point sets to evaluate the effectiveness of our proposed approach against existing state-of-the-art methods.
Keywords: robotics

A Discussion on Context-awareness to Better Support the IoT Cloud/Edge Continuum - Maniglia da Silva, Daniel; C. Sofia, Rute (IEEE Access, volume 8. DOI: 10.1109/ACCESS.2020.3032388, 2020)
This paper debates on notions of context-awareness as a relevant asset of networking and computing architectures for an Internet of Things (IoT), in particular in regards to a smoother support of the networking operation between Cloud and Edge. Specifically, the paper debates on notions of context-awareness and goes over different types of context-awareness indicators that are being applied to Edge selection algorithms, covering the approaches currently used, the role of the algorithms applied, their scope, and contemplated performance metrics. Lastly, the paper provides guidelines for future research in the context of Cloud-Edge and the application of context-awareness to assist in a higher degree of automation of the network and, as consequence, a better support of the Cloud to Edge continuum.
Keywords: Context-awareness; Internet of Things; Edge Computing; IIoT

BARK: Open Behavior Benchmarking in Multi-Agent Environments - Bernhard, Julian; Esterle, Klemens; Hart, Patrick; Kessler, Tobias (Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020)
Predicting and planning interactive behaviors in complex traffic situations presents a challenging task. Especially in scenarios involving multiple traffic participants that interact densely, autonomous vehicles still struggle to interpret situations and to eventually achieve their own mission goal. As driving tests are costly and challenging scenarios are hard to find and reproduce, simulation is widely used to develop, test, and benchmark behavior models. However, most simulations rely on datasets and simplistic behavior models for traffic participants and do not cover the full variety of real-world, interactive human behaviors. In this work, we introduce BARK, an open-source behavior benchmarking environment designed to mitigate the shortcomings stated above. In BARK, behavior models are (re-)used for planning, prediction, and simulation. A range of models is currently available, such as Monte-Carlo Tree Search and Reinforcement Learning-based behavior models. We use a public dataset and sampling-based scenario generation to show the inter-exchangeability of behavior models in BARK. We evaluate how well the models used cope with interactions and how robust they are towards exchanging behavior models. Our evaluation shows that BARK provides a suitable framework for a systematic development of behavior models.

Checkable Safety Cases: Enabling Automated Consistency Checks between Safety Work Products - Cârlan, Carmen; Petrișor, Daniel; Gallina, Barbara; Schoenhaar, Hannes (Proceedings of the 10th IEEE International Workshop on Software Certification (WoSoCer) co-located with ISSRE, 2020)
Keywords: Model-based systems engineering; MbSE

Formalizing Traffic Rules for Machine Interpretability - Esterle, Klemens; Gressenbuch, Luis; Knoll, Alois (Proceedings of the 3rd IEEE Connected and Automated Vehicles Symposium (CAVS), 2020)
Autonomous vehicles need to be designed to abide by the same rules that humans follow. This is challenging, because traffic rules are fuzzy and not well defined, making them incomprehensible to machines. Satisfaction cannot be incorporated in a planning component without proper formalization, nor can it be monitored and verified during simulation or testing. However, no research work has provided a consistent set of machine-interpretable traffic rules for a given operational driving domain. In this paper, we propose a methodology for the legal study and formalization of traffic rules in a formal language. We use Linear Temporal Logic as a formal specification language to describe temporal behaviors, capable of capturing a wide range of traffic rules. We contribute a formalized set of traffic rules for dual carriageways and evaluate the effectiveness of our formalized rules on a public dataset.

Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments - Hart, Patrick; Knoll, Alois (In Proceedings of the 31st IEEE Intelligent Vehicles Symposium (IV), 2020)
Most reinforcement learning approaches used in behavior generation utilize vectorial information as input. However, this requires the network to have a pre-defined input-size – in semantic environments this means assuming the maximum number of vehicles. Additionally, this vectorial representation is not invariant to the order and number of vehicles. To mitigate the above-stated disadvantages, we propose combining graph neural networks with actor-critic reinforcement learning. As graph neural networks apply the same network to every vehicle and aggregate incoming edge information, they are invariant to the number and order of vehicles. This makes them ideal candidates to be used as networks in semantic environments – environments consisting of objects lists. Graph neural networks exhibit some other advantages that make them favorable to be used in semantic environments. The relational information is explicitly given and does not have to be inferred. Moreover, graph neural networks propagate information through the network and can gather higher-degree information. We demonstrate our approach using a highway lane-change scenario and compare the performance of graph neural networks to conventional ones. We show that graph neural networks are capable of handling scenarios with a varying number and order of vehicles during training and application.

Optimal Behavior Planning for Autonomous Driving: A Generic Mixed-Integer Formulation - Esterle, Klemens; Kessler, Tobias; Knoll, Alois (Proceedings of the 31st IEEE Intelligent Vehicles Symposium (IV), 2020)
Mixed-Integer Quadratic Programming (MIQP) has been identified as a suitable approach for finding an optimal solution to the behavior planning problem with low runtimes. Logical constraints and continuous equations are optimized alongside. However, it has only been formulated for a straight road, omitting common situations such as taking turns at intersections. This has prevented the model from being used in reality so far. Based on a triple integrator model formulation, we compute the orientation of the vehicle and model it in a disjunctive manner. That allows us to formulate linear constraints to account for the non-holonomy and collision avoidance. These constraints are approximations, for which we introduce the theory. We show the applicability in two benchmark scenarios and prove the feasibility by solving the same models using nonlinear optimization. This new model will allow researchers to leverage the benefits of MIQP, such as logical constraints, or global optimality.

FASTEN.Safe: A Model-Driven Engineering Tool to Experiment with Checkable Assurance Cases - Cârlan, Carmen; Ratiu, Daniel (Proceedings of the International Conference on Computer Safety, Reliability, and Security (SAFECOMP), 2020)
The Goal Structuring Notation (GSN) is popular among safety engineers for modeling assurance cases. GSN elements are specified using plain natural language text, this giving safety engineers great flexibility to express their arguments. However, pure textual arguments introduce ambiguities and prevent automation. Currently, assurance cases are verified by manual reviews, which are error prone, time consuming, and not adequate for today’s systems complexity and agile development methodologies. In this paper we present our research tool FASTEN.Safe, which extends GSN with a set of higher-level modeling language constructs capturing recurring argumentation patterns and integrating formal system models. This allows automatically checking 1) the intrinsic consistency of assurance models, 2) the consistency of arguments with system models and 3) the verification of safety claims themselves by using external verification tools. FASTEN.Safe is open source and allows experimenting with language abstractions to bridge the world of GSN-based arguments that are common among safety engineers and the world of formal methods that enable automation. Last but not least, we report on the preliminary experience gained with FASTEN.Safe.
Keywords: Model-based systems engineering; MbSE

A Safety Case Pattern for Systems with Machine Learning Components - Wozniak, Ernest; Cârlan, Carmen; Acar-Celik, Esra; Putzer, Henrik J. (Proceedings of the International Conference on Computer Safety, Reliability, and Security (SAFECOMP), 2020)
Several standards from the domain of safety critical systems, in order to support the argumentation of the safety assurance of a system under development, recommend the construction of a safety case. This activity is guided by the objectives to be met, recommended or required by the standards along the safety lifecycle. Ongoing attempts to use Machine Learning (ML) for safety critical functionality revealed certain deficits. For instance, the widely recognized standard for functional safety of automotive systems, ISO 26262, which can be used as a basis to construct a safety case, does not reason about ML. To this end, the goal of this work is to provide a pattern for arguing about the correct implementation of safety requirements in system components based on ML. The pattern is integrated within an overall encompassing approach for safety case generation for automotive systems and its applicability is showcased on a pedestrian avoidance system.
Keywords: Model-based systems engineering; MbSE

An Ontology-based Metamodel for Capability Descriptions - Weser, Michael; Bock, Jürgen; Schmitt, Siwara; Perzylo, Alexander; Evers, Kathrin (Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2020)
This paper presents an approach to describe abilities of manufacturing resources by a formal description of capabilities using Semantic Web technologies. A hierarchical ontology architecture is proposed to represent, publish, and extend knowledge on capabilities for different application domains and use cases. Furthermore, the paper describes patterns of how the underlying formal logic can be used in taxonomy modeling and the inference of implicit capability facts. The usability and performance of the approach was validated by formalizing capability knowledge of related work and evaluated in benchmarking a prototypical implemented tool for managing and querying catalogs of resources and their capabilities. The proposed concept is intended to be used as a foundation for a future multi-layered feasibility checking, which evaluates the compatibility of resources and their offered skills with the requirements of manufacturing tasks at symbolic and subsymbolic levels. Extended evaluations might be based on parameters, analytics, simulation, and other means.
Keywords: robotics; basys 4.0

Firefighter Virtual Reality Simulation for Personalized Stress Detection - Klingner, Sören; Han, Zhiwei; Liu, Yuanting; Fang, Fang; Altakrouri, Bashar; Michel, Bruno; Weiss, Jonas R. M.; Sridhar, Arvind; Chau, Sophie Mai (the 43rd German Conference on AI, 2020)
Classifying stress in firefighters poses challenges, such as accurate personalized labeling, unobtrusive recording, and training of adequate models. Acquisition of labeled data and verification in cage mazes or during hot trainings is time consuming. Virtual Reality (VR) and Internet of Things (IoT) wearables provide new opportunities to create better stressors for firefighter missions through an immersive simulation. In this demo, we present a VR-based setup that enables to simulate firefighter missions to trigger and more easily record specific stress levels. The goal is to create labeled datasets for personalized multilevel stress detection models that include multiple biosignals, such as heart rate variability from electrocardiographic RR intervals. The multi-level stress setups can be configured, consisting of different levels of mental stressors. The demo shows how we established the recording of a baseline and virtual missions with varying challenge levels to create a personalized stress calibration.

Generation of Co-simulation Algorithms Subject to Simulator Contracts - Gomes, Cláudio; Thule, Casper; Lúcio, Levi; Vangheluwe, Hans; Larsen, Peter Gorm (Software Engineering and Formal Method (SEFM 2019), 2020)
Correct co-simulation results require a careful consideration of how the interacting simulators are implemented. In version 2.0 of the FMI Standard, input handling implementation is left implicit, which leads to the situation where a simulator can be interacted with in a manner that its implementation does not expect, yielding incorrect results. In this paper, we build on prior work to make information about each simulator implementation explicit, in order to derive correct interactions with it. The formalization we use is specific to two kinds of contracts, but could serve as a basis to a general approach to black box co-simulation. The algorithm we propose generates a co-simulation execution plan in linear time. It has been successfully applied to an industrial case study, and the results are available online.
Keywords: Co-simulation; Prolog; Contract-based code generation; Constraint solving; Model-based Systems Engineering; MbSE

Information-centric Routing for Opportunistic Wireless Networks - draft-mendes-icnrg-dabber-05 - M. Mendes, Paulo; C. Sofia, Rute; Tsaoussidis, Vassilis; Borrego, Carlos (IETF Network Working Group draft, experimental, 2020)
This draft describes the Data reAchaBility BasEd Routing (DABBER) protocol. DABBER aims to provide a name-based routing solution to support the operation of Information-centric Networking frameworks in opportunistic wireless networks. By "opportunistic wireless networks" it is meant multi-hop wireless networks where finding an end-to-end path between any pair of nodes at any moment in time may be a challenge. The goal is to assist in better defining opportunities for the transmission of Interest packets in a store-carry-and-forward manner, based on a proactive approach. The document describes how to integrate DABBER in a networking node that implements some ICN approach, such as Named-Data Networking (NDN) or Content Centric Networking (CCN), along with the specification of the proactive approach based on the dissemination of name-prefix information.
Keywords: ICN; NDN; routing; IIoT

Less Manual Work for Safety Engineers: Towards an Automated Safety Reasoning with Safety Patterns (Application Paper) - Dantas, Yuri Gil; Kondeva, Antoaneta; Nigam, Vivek (36th International Conference on Logic Programming (ICLP), 2020)
The development of safety-critical systems requires the control of hazards that can potentially cause harm. To this end, safety engineers rely during the development phase on architectural solutions, called safety patterns, such as safety monitors, voters, and watchdogs. The goal of these patterns is to control (identified) faults that can trigger hazards. Safety patterns can control such faults by e.g., increasing the redundancy of the system. Currently, the reasoning of which pattern to use at which part of the target system to control which hazard is documented mostly in textual form or by means of models, such as GSN-models, with limited support for automation. This paper proposes the use of logic programming engines for the auto- mated reasoning about system safety. We propose a domain-specific language for embedded system safety and specify as disjunctive logic programs reasoning principles used by safety engineers to deploy safety patterns, e.g., when to use safety monitors, or watchdogs. Our machinery enables two types of automated safety reasoning: (1) identification of which hazards can be controlled and which ones cannot be controlled by the existing safety patterns; and (2) automated recommendation of which patterns could be used at which place of the system to control potential hazards. Finally, we apply our machinery to two examples taken from the automotive domain: an adaptive cruise control system and a battery management system.

Service Quality Through Government Proactivity: The Concept of Non-interaction - Kuhn, Peter; Balta, Dian (Electronic Government, 2020)
In the course of digitalization, governments increasingly aim at improving service quality for their users. To support this aim, government proactivity is considered a suitable approach: reduction of interactions or even non-interaction between users and government. While government proactivity can be crucial for improving the service quality perceived by its users, there is a lacking of understanding how exactly non-interaction interplays with service quality. Understanding this interplay is important, given that for some services non-interaction might also decrease service quality: for instance, non-interactive student loans spare the user the application but at the same time cause automatic debt. We introduce the purpose of an interaction as a lens to help understand this interplay. The lens utilizes the fact that the purpose of an interaction has to be fulfilled also in a proactive, non-interactive version of the service. We operationalize the lens by proposing two groups of interaction purposes and integrating them with service quality dimensions in an analysis framework. The framework can be used to analyze individual services towards their service quality in a proactive, non-interactive version of the service. Our work contributes to theory by developing a set of interaction purposes to study service interactions and the qualitative interplay of non-interaction and service quality on a service level. Based on an exemplary application of the service free school transport, we demonstrate how government service designers can adapt the framework into a tool for the evaluation and design of individual services.

The Joy of Collaborating with Highly Automated Vehicles - Wiegand, Gesa; Holländer, Kai; Rupp, Katharina; Hußmann, Heinrich (AutomotiveUI '20: 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2020)
Fully autonomous driving leaves drivers with little opportunity to intervene in the driving decision. Giving drivers more control can enhance their driving experience. We develop two collaborative interface concepts to increase the user experience of drivers in autonomous vehicles. Our aim is to increase the joy of driving and to give drivers competence and autonomy even when driving autonomously. In a driving simulator study (N = 24) we investigate how vehicles and drivers can collaborate to decide on driving actions together. We compare autonomous driving (AD), the option to take back driving control (TBC) and two collaborative driving interface concepts by evaluating usability, user experience, workload, psychological needs, performance criteria and interview statements. The collaborative interfaces significantly increase autonomy and competence compared to AD. Joy is highly represented in the qualitative data during TBC and collaboration. Collaboration proves to be good for situations in which quick decisions are called for.
Keywords: Automated Vehicles; Human-Vehicle Interaction

Toward a Knowledge-Based Data Backbone for Seamless Digital Engineering in Smart Factories - Perzylo, Alexander; Kessler, Ingmar; Profanter, Stefan; Rickert, Markus (Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2020)
Digital transformation efforts in manufacturing companies bear the potential to reduce product costs and increase the flexibility of production systems. The semantic integration of data and information along the value chain enables the automated interpretation of interrelations between its different aspects such as product design, production process and manufacturing resources. These interrelations can be used to automatically generate semantic process descriptions and execute corresponding robot motions. An initial one-time effort to model the required knowledge of a particular application domain can make the manufacturing of high-variant products in small batches or even lot size one production more efficient. This paper introduces a knowledge-based digital engineering concept to automate engineering and production activities without human involvement. The concept was integrated and evaluated in a physical robot workcell where automotive fuse boxes are autonomously fitted with different fuse configurations.
Keywords: robotics; data backbone

Towards Automating Safety and Security Co-Analysis with Patterns - Dantas, Yuri Gil; Kondeva, Antoaneta; Nigam, Vivek (39th International Conference on Computer Safety, Reliability and Security (SafeComp), 2020)

Partner Programs and Complementor Assessment in Platform Ecosystems: A Multiple-Case Study - Engert, Martin; Hein, Andreas; Krcmar, Helmut (Twenty-Sixth Americas Conference on Information Systems: AMCIS 2020. A Virtual Conference, 2020)

Requirements and Design Principles for Business Model Tools - Schaffer, Norman; Stähler, Olivia; Weking, Jörg (Americas Conference on Information Systems (AMCIS 2020), 2020)

Requirements-based Code Model Checking - Schöpp, Ulrich; Schweiger, Andreas; Reich, Marina; Chuprina, Tatiana; Lúcio, Levi; Brüning, Hartmut (IEEE Workshop on Formal Requirements (FORMREQ2020), 2020)
Building the system right is the objective of quality assurance methods. Though testing is the most prominent and widely-adopted means, it cannot prove the absence of software's defects. Therefore, static measures such as formal proofs can complement dynamic methods. However, these techniques require the formal statement of requirements, which is still a challenge in industry development. This paper suggests a way of formalizing requirements in controlled natural language in a way that applies directly to C program code. By mapping natural language terms to conditional breakpoints, requirements can be translated to formal language expressed in observer automata. The creation of a mapping between natural language terms and code is supported by natural language processing methods. Finally, the observer automata are model checked against the code. In our approach we demonstrate the described steps using a set of realistically shaped requirements, which are common in the avionics domain. We implemented a simple tool hiding the abstract and mathematical details, which performs the proofs automatically. The paper is presented as an approach towards the seamless verification of code against requirements typically found in the avionics domain.

A Tool to Model and Simulate Dynamic Business Models - Schaffer, Norman; Engert, Martin; Leontjevs, Girts; Krcmar, Helmut (33rd Bled eConference, 2020)

An Analysis of Business Model Component Interrelations - Schaffer, Norman; Drieschner, Clemens; Krcmar, Helmut (Twenty-Third Pacific Asia Conference on Information Systems (PACIS 2020), 2020)

Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience - Allegra Mascaro, Anna Letizia; Falotico, Egidio; Petkoski, Spase; Pasquini, Maria; Vannucci, Lorenzo; Tort-Colet, Nuria; Conti, Emilia; Resta, Francesco; Spalletti, Cristina; Tata Ramalingasetty, Shravan; von Arnim, Axel; Formento, Emanuele; et al. (Frontiers in Systems Neuroscience, 2020)
Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.
Keywords: Neuromorphic Computing; HBP; Human Brain Project; Neurorobotics; Neurorobotics Platform; Neuroscience; Artificial Intelligence; KI; Spiking Neural Networks neurorobotics; neuromorphic; neuromorphic computing; HBP; NRP; virtual; robotics

Hardware architecture exploration: automatic exploration of distributed automotive hardware architectures - Eder, Johannes; Voss, Sebastian; Bayha, Andreas; Ipatiov, Alexandru; Khalil, Maged (Software and Systems Modeling, 2020)
As the engineering of distributed embedded systems is getting more and more complex, due to increasingly sophisticated functionalities demanding more and more powerful hardware, model-based development of software-intensive embedded systems has become a de facto standard in recent years. Among other advantages, it enables design space exploration methods allowing for frontloading techniques which support a system architect already at early stages of development. In this paper, we want to present an approach which is capable of automatically generating automotive E/E architectures (electric/electronic architecture; in-car network of processing units and buses). Based on the concept of viewpoints, we will introduce dedicated technical meta-models, a language to formally describe a hardware architecture exploration problem and an automatic exploration approach using satisfiability modulo theories. We will furthermore introduce a dedicated methodology and show how an exploration integrates into a system development process. In the end, we will evaluate our approach by applying it to an industrial use case provided by Continental.
Keywords: AutoFOCUS3; design-space exploration; DSE; architecture synthesis; deployment synthesis; HW/SW co-design; model-based systems engineering; MbSE; case study

Global Mobility Management for Next Generation Networks - C. Sofia, Rute (Global Mobility Management for Next Generation Networks, 2020)
This book provides a fresh look at the most recent evolution of mobility management paradigms for next generation networks. It covers the basic principles of mobility management, its underlying technologies and environments, and the reasons for specific architectural design choices. The book serves to de-construct the way that mobility management has been developed up until recently. To that end, it goes beyond more traditional, centralized approaches to the topic, and offers insights into practical ways of distributing mobility management in a way that is more suited for operators, and end-users. It explains such key concepts as mobility management and estimation, showing how and why these aspects should be integrated into future mobility management solutions. The text is of interest to students, computer network engineers, and researchers involved in innovation and standardization activities focused on the evolution of mobility management aspects.
Keywords: Network architectures and protocols; Mobility management; Mobility modelling; IIoT

Leveraging Context-awareness to Better Support the IoT Cloud-Edge Continuum - I. Carvalho, Liliana; Maniglia da Silva, Daniel; C. Sofia, Rute (InProc. of The Fifth International Conference on Fog and Mobile Edge Computing (FMEC 2020). Paris, France, 2020)
Novel Internet of Things (IoT) requirements derived from a broader interconnection of heterogeneous devices have pushed the horizons of Cloud computing and are giving rise to a wider decentralisation of applications and data centers. An answer to the underlying network concerns, such as the need to lower the resulting latency due to heavy computation needs, or safety aspects, gave rise to Edge/Fog computing, where IoT functionality can be also supported closer to data sources. While it is today feasible to perform some IoT functionality on the Edge, the orchestration of operations between Edge and Cloud requires an automated support, where context-awareness plays a key role in assisting the network in deciding when and where to store data and to perform computation. This work is focused on the application of context-awareness to support a smoother operation of the Edge to Cloud operation, aiming at lowering latency, in particular when real-time or close-to-real-time data exchange is present.
Keywords: IIoT; context-awareness; wireless; sensing

Native OPC UA Handling and IEC 61499 PLC Integration within the Arrowhead Framework - Cabral, Jose; Dorofeev, Kirill; Varga, Pal (3RD IEEE International Conference on Industrial Cyber-Physical Systems, 2020)
The Arrowhead framework enables interoperability and integrability for new and legacy systems by applying SOA principles at the IIoT domain. The core systems of Arrowhead provide service registration, discovery, security and other services for application systems within the System of Systems domain under their authority. While the application systems can communicate with each other using any protocol that fits them, the core Arrowhead services have been available as RESTful HTTP implementation only. This practically meant that those application systems without built-in HTTP protocol support, must have used translators to connect to the Arrowhead services. The current paper describes a pragmatic enhancement for OPC UA endpoints, so they are now able to utilize Arrowhead core services natively. Moreover, the paper provides a solution for IEC 61499 PLC integration, and a practical use case description as proof of concept.

A Smart Energy Management System for cross-sectoral coupling and water-energy nexus - Pampana, Venkatesh; Gupta, Pragya Kirti; Duchon, Markus (SMARTGREENS 2020, 2020)
Cross-sectoral coupling is one of the newly emerging research topics that refers to the idea of interconnecting and integrating the energy consuming sectors like buildings (heating and cooling), transport, water supply systems and other energy intensive process with the power-producing sector. The cross-sectoral integration of the water-energy nexus and the sustainability issues surrounding the availability of clean water and energy has drawn the attention to the problem from all around the globe. Smart decision-making and control systems can improve the efficiency of the overall operation of both water and energy systems. At a technological level, there have been attempts to optimize coupling points between the electricity and water systems to increase efficiency of both. Most of the optimization and smart decision-making systems focus on energy system and consider heterogeneous infrastructure in the form of energy consumption devices. In the scope of water-energy nexus, energy efficient decisions would have implications on water infrastructure. Tools and platforms for water-energy nexus are required, such that planning and executing the decisions and their implications on both energy and water infrastructure can be seen. Most of the existing controllers are specifically designed to efficiently serve either energy or water systems. In this paper, we propose a software architecture for the platform that is capable of monitoring, controlling, decision making and analysing the effect of decisions for water and energy nexus.

Dynamic Variational Autoencoders for Visual Process Modeling - Sagel, Alexander; Shen, Hao (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020)

Skill-Based Engineering in Industrial Automation Domain: Skills Modeling and Orchestration - Dorofeev, Kirill (42nd International Conference on Software Engineering (ICSE) Companion, 2020)
Software engineering in the industrial automation domain requires generic methods to keep the development complexity at an acceptable level. However, nowadays various PLC vendors use different dialects of the standardized programming languages in their tools, which hinders the re-usability and interoperability across the platforms. The service-oriented approaches can serve to overcome interoperability issues. In distributed control systems, the functionality of an automation component can be offered to the other parties that constitute a production system via a standardized interface, easing the orchestration of the whole system. This paper proposes such a generic interface that hides away the low-level implementation details of a particular functionality and provides a common semantic model for the execution. Further, we show how using such an interface can help to support and automate the overall engineering process, combining the functionality of different components to fulfill a production task. The reference implementation of the proposed concept was used in an industrial demonstrator, which shows the benefits in the system flexibility due to components interoperability and re-usability compared to the traditional control approaches.

Sub-Dip: Optimization On A Subspace With Deep Image Prior Regularization And Application To Superresolution - Sagel, Alexander; Roumy, Aline; Guillemot, Christine (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020)

Daten vernetzen, unabhängig beraten - Weckesser, Fabian; Peisl, Sebastian; Beck, Michael; Hartmann, Anja; Röhrl, Gerhard; Kessler, Ingmar; Perzylo, Alexander; Rickert, Markus (Bayerisches Landwirtschaftliches Wochenblatt, 2020)
Mit einem Forschungsprojekt steigt das Landeskuratorium für pflanzliche Erzeugung in die digitale Beratung ein. FarmExpert 4.0 soll eine Fülle an Daten in einer Softwarearchitektur zum Nutzen der Landwirte vernetzen. Landwirte und Berater müssen bei pflanzenbaulichen Fragestellungen oft von Hand Fachwissen in Datenquellen nach-schlagen und miteinander kombinieren. Hinzu kommt, dass die gesetzlichen Rahmenbedingungen komplexer werden. Ein aktuelles Beispiel dafür ist die Düngeverordnung (DüV). Sonder- und Ausnahmeregelungen machen die Entscheidungsfindung für den Landwirt nicht einfacher. Der Betriebsleiter wünscht sich Unterstützung beim Management seines Betriebes und bei der Erfüllung seiner Dokumentationspflichten. Es wäre viel gewonnen, wenn es hierfür eine neutrale Softwarearchitektur gäbe, die zu den Fragen des Landwirts die wichtigsten Informationen auf all seinen mobilen Endgeräten bereitstellt.
Keywords: robotics; farmexpert

Semantical Analysis of Contextual Types - Pientka, Brigitte; Schöpp, Ulrich (Foundations of Software Science and Computation Structures - 23rd International Conference, FOSSACS 2020, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020, Dublin, Ireland, April 25-30, 2020, 2020)
We describe a category-theoretic semantics for a simply typed variant of Cocon, a contextual modal type theory where the box modality mediates between the weak function space that is used to represent higher-order abstract syntax (HOAS) trees and the strong function space that describes (recursive) computations about them. What makes Cocon different from standard type theories is the presence of first-class contexts and contextual objects to describe syntax trees that are closed with respect to a given context of assumptions. Following M. Hofmann’s work, we use a presheaf model to characterise HOAS trees. Surprisingly, this model already provides the necessary structure to also model Cocon. In particular, we can capture the contextual objects of Cocon using a comonad ♭ that restricts presheaves to their closed elements. This gives a simple semantic characterisation of the invariants of contextual types (e.g. substitution invariance) and identifies Cocon as a type-theoretic syntax of presheaf models. We express our category-theoretic constructions by using a modal internal type theory that is implemented in Agda-Flat.

Business Strategies for Data Monetization: Deriving Insights from Practice - Baecker, Julius; Engert, Martin; Pfaff, Matthias; Krcmar, Helmut (15th International Conference on Wirtschaftsinformatik, 2020)

Distributed Ledger Technology in the Banking Sector: A Method for the Evaluation of Use Cases - Buchinger, Matthias; Balta, Dian; Krcmar, Helmut (Wirtschaftsinformatik 2020 Proceedings (WI2020), 2020)

Forwarding in Opportunistic Information-CentricNetworks: an Optimal Stopping Approach - Borrego, Carlos; Amadeo, Marica; Molinaro, Antonella; M. Mendes, Paulo; C. Sofia, Rute; Magaia, Naercio; Borrel, Joan (IEEE Communications Magazine, Volume 58, Issue 5, pp 56-61. 10.1109/MCOM.001.1900774., 2020)
Keywords: IIoT; wireless; routing; opportunistic networking; ICN

Was sind Herausforderungen proaktiver Verwaltungsleistungen in Deutschland? - Kuhn, Peter; Balta, Dian; Krcmar, Helmut (Wirtschaftsinformatik 2020 Proceedings, 2020)

Center for Code Excellence - Kroß, Johannes; Bludau, Peter; Pretschner, Alexander (Software Engineering 2020, 2020)
Das Center for Code Excellence (CCE) richtet sich sowohl an Softwareentwickler als auch Projekt- und Unternehmensmanager. Wir haben das Ziel, kleine und mittelständische Unternehmen in die Lage zu versetzen, herausragende, nachhaltige und zukunftsweisende Software zu entwickeln. Dieser Beitrag stellt die Motivation und Ansätze des CCE kurz vor sowie die drei Schwerpunkte - Bewertung der Softwaretechnik, Trendanalysen und Erforschung von Lehrmethoden.

Mobile Pervasive Augmented Reality Systems - MPARS The Role of User Preferences in the Perceived Quality of Experience in Outdoor Applications - Pascoal, Rui; de Almeida, Ana; C. Sofia, Rute (ACM Transactions on Internet Technology, 2020)
After presenting a brief evolution of augmented reality applications from mobile systems (MARS) into mobile pervasive augmented reality systems (MPARS), this work addresses the reasons and aspects required to boost acceptance and use of mobile pervasive augmented reality systems for outdoor applications. The need to develop context-aware close-to-real-time feedback mechanisms that take into consideration a continuous measurement of Quality of Experience is also discussed. For this purpose, we delve into how can user preferences be integrated in context-aware feedback systems and propose a theoretical model for measuring Quality of Experience in MPARS. The model is derived from an analysis of previous technology adoption models and incorporates the knowledge of user preferences extracted from data gathered from a public questionnaire deployment. The how and why can such a model be integrated into future MPARS solutions is also addressed, concerning in particular the systems applicability in the context of outdoor activities.
Keywords: IIoT; AR; ML; sensing; tactile Internet

Vorausschauende Wahrnehmung für sicheres automatisiertes Fahren: Validierung intelligenter Infrastruktursysteme am Beispiel von Providentia - Krämmer, Annkathrin; Schöller, Christoph; Kurz, Franz; Rosenbaum, Dominik; Knoll, Alois (Internationales Verkehrswesen, 2020)
Intelligente Infrastruktursysteme können den Wahrnehmungshorizont von automatisier-ten Fahrzeugen stark erweitern und dadurch sicheres, vorausschauendes Fahren ermög-lichen. Dafür muss klar sein, wie genau das von ihnen erstellte Abbild der aktuellen Verkehrssituation ist. Aufgrund der fehlenden Grundwahrheit der Fahrzeugpositionen gestaltet sich eine Validierung jedoch schwierig, es bedarf neuer Ideen. In diesem Artikel wird am Beispiel des Providentia-Systems ein Konzept präsentiert, wie intelligente Infrastruktursysteme mittels Luftbildauswertung validiert werden können.

What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction - Schöller, Christoph; Aravantinos, Vincent; Lay, Florian; Knoll, Alois (Robotics and Automation Letters (RA-L), 2020)
Pedestrian motion prediction is a fundamental task for autonomous robots and vehicles to operate safely. In recent years many complex approaches based on neural networks have been proposed to address this problem. In this work we show that - surprisingly - a simple Constant Velocity Model can outperform even state-of-the-art neural models. This indicates that either neural networks are not able to make use of the additional information they are provided with, or that this information is not as relevant as commonly believed. Therefore, we analyze how neural networks process their input and how it impacts their predictions. Our analysis reveals pitfalls in training neural networks for pedestrian motion prediction and clarifies false assumptions about the problem itself. In particular, neural networks implicitly learn environmental priors that negatively impact their generalization capability, the motion history of pedestrians is irrelevant and interactions are too complex to predict. Our work shows how neural networks for pedestrian motion prediction can be thoroughly evaluated and our results indicate which research directions for neural motion prediction are promising in future.

(Proceedings) Software Quality Days: Software Quality. Quality Intelligence in Software and Systems Engineering - Winkler, Dietmar; Biffl, Stefan; Mendez, Daniel (Software Quality Days: Software Quality. Quality Intelligence in Software and Systems Engineering, 2020)

A Light-weight Tool for the Self-Assessment of Security Compliance in Software Development — An Industry Case - Moyón, Fabiola; Bayr, Christoph; Mendez, Daniel; Dännert, Sebastian; Beckers, Kristian (Proc. 46th International Conference on Current Trends in Theory and Practice of Computer Science, 2020)

An Efficient Approach for Reviewing Security-Related Aspects in Agile Requirements Specifications of Web Applications - Mendez, Daniel; Villamizar, Hugo; Kalinowski, Marcos; Garcia, Alessandro (Requirements Engineering Journal, 2020)

Automatic Extraction of Cause-Effect-Relations from Requirements Artifacts - Frattini, Julian; Junker, Maximilian; Unterkalmsteiner, Michael; Mendez, Daniel (Proc. International Conference on Automated Software Engineering, 2020)

Awareness of Secure Coding Guidelines in the Industry – A first data analysis - Espinha Gasiba, Tiago; Lechner, Ulrike; Pinto-Albuquerque, Maria; Mendez, Daniel (Proc. 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 2020)

Beep Beep: Building Trust with Sound - Schmidmaier, Matthias; Runge, Dominik; Hußmann, Heinrich (the ACM Conference on Human Factors in Computing System, 2020)
Audio is one modality that besides content transmission offers non-verbal cues that influence emotional perception. This allows to increase trust for example in privacy-sensitive systems like digital assistants. In this work we focus on basic audio feedback and explore how parameters like melody, pitch or tempo influence the creation of trust. We refer to related research in trust perception of voice, and evaluate if the derived concepts can be universally applied to simple sound patterns. Our study (n=39) shows significant effects for melody and mode, while tendencies were found for pitch and individual user preferences. We consider our findings to serve as basis for research towards the design of unobtrusive and trustworthy user experiences.

Center for Code Excellence Trends – Eine Plattform für Trends in Softwaretechnologien - Kroß, Johannes; Bludau, Peter; Pretschner, Alexander (Informatik Spektrum, 2020)
Neue Softwaretechnologien entstehen täglich. Da deren Vielfalt wächst und deren Lebensdauer aber abnimmt, müssen Unternehmen und Fachleute Trends erkennen und Technologien bewerten können, um fundierte Designentscheidungen für nachhaltige Softwaresysteme zu treffen. Die Wahrnehmung neuer Trends außerhalb der eigenen Fachkompetenz kann jedoch schwierig sein und die Entscheidung für bestimmte Technologien kann durch persönliche Vorurteile beeinflusst werden. In diesem Beitrag stellen wir unsere Plattform vor, um Trends und Beziehungen von Softwaretechnologien zu visualisieren. Basierend auf öffentlichen Stellenanzeigen, Open-Source-Projekten und Online-Diskussionen erkennen wir (i) automatisch neue Technologien, Kompetenzen und Techniken, (ii) lernen Beziehungen zwischen ihnen und (iii) bewerten ihre Trendentwicklung. Wir demonstrieren die Ergebnisse unseres Ansatzes, indem wir exemplarische Vergleiche und Zusammenhänge aufzeigen. Unsere Plattform soll Organisationen und Einzelpersonen eine unabhängige Bewertung und Informationsquelle zur Entscheidungsunterstützung bieten.

Challenges in Survey Research - Wagner, Stefan; Mendez, Daniel; Graziotin, Daniel; Kalinowski, Marcos; Felderer, Michael (M. Felderer, G.-H. Travassos (eds.) Contemporary Empirical Methods in Software Engineering, 2020)

Compliance Requirements in Large-Scale Software Development: An Industrial Case Study - Usman, Muhammad; Felderer, Michael; Unterkalmsteiner, Michael; Klotins, Eriks; Mendez, Daniel; Alegroth, Emil (Proc. 21st International Conference on Product-Focused Software Process Improvement, 2020)

Data-driven Risk Management for Requirements Engineering: An Automated Approach based on Bayesian Networks - Wiesweg, Florian; Vogelsang, Andreas; Mendez, Daniel (Proc. 28th International Requirements Engineering Conference, 2020)

Describing Capabilities of Industrie 4.0 Components - Bayha, Andreas; Bock, Jürgen; Boss, Birgit; Diedrich, Christian; Malakuti, Somayeh (German Electrical and Electronics Manufacturers Association, Frankfurt am Main, Germany, 2020)
Keywords: Model-based Systems Engineering; MbSE

Digitale Lösungen für KMU: Was können und sollen kleine und mittlere Unternehmen (KMU) digital anbieten? Das Forschungsprojekt BayernCloud hat dazu den ländlichen Tourismus unter die Lupe genommen. - Schaffer, Norman; Kuhn, Peter; Balta, Dian; Krcmar, Helmut (LandInForm - Magazin für ländliche Räume, 2020)

Draw with Me: Human-in-the-Loop for Image Restoration - Weber, Thomas; Han, Zhiwei; Matthes, Stefan; Hußmann, Heinrich; Liu, Yuanting (the 43rd German Conference on AI, 2020)
The purpose of image restoration is to recover the original state of damaged images. To overcome the disadvantages of the traditional, manual image restoration process, like the high time consumption and required domain knowledge, automatic inpainting methods have been developed. These methods, however, can have limitations for complex images and may require a lot of input data. To mitigate those, we present "interactive Deep Image Prior", a combination of manual and automated, Deep-Image-Prior-based restoration in the form of an interactive process with the human in the loop. In this process a human can iteratively embed knowledge to provide guidance and control for the automated inpainting process. For this purpose, we extended Deep Image Prior with a user interface which we subsequently analyzed in a user study. Our key question is whether the interactivity increases the restoration quality subjectively and objectively. Secondarily, we were also interested in how such a collaborative system is perceived by users. Our evaluation shows that, even with very little human guidance, our interactive approach has a restoration performance on par or superior to other methods. Meanwhile, very positive results of our user study suggest that learning systems with the human-in-the-loop positively contribute to user satisfaction. We therefore conclude that an interactive, cooperative approach is a viable option for image restoration and potentially other ML tasks where human knowledge can be a correcting or guiding influence.
Keywords: Interactive Machine Learning; Image Restoration; Image Prior

Draw with me: human-in-the-loop for image restoration - Weber, Thomas; Han, Zhiwei; Matthes, Stefan; Hußmann, Heinrich; Liu, Yuanting (the 25th International Conference on Intelligent User Interfaces, 2020)
The purpose of image restoration is to recover the original state of damaged images. To overcome the disadvantages of the traditional, manual image restoration process, like the high time consumption and required domain knowledge, automatic inpainting methods have been developed. These methods, however, can have limitations for complex images and may require a lot of input data. To mitigate those, we present "interactive Deep Image Prior", a combination of manual and automated, Deep-Image-Prior-based restoration in the form of an interactive process with the human in the loop. In this process a human can iteratively embed knowledge to provide guidance and control for the automated inpainting process. For this purpose, we extended Deep Image Prior with a user interface which we subsequently analyzed in a user study. Our key question is whether the interactivity increases the restoration quality subjectively and objectively. Secondarily, we were also interested in how such a collaborative system is perceived by users. Our evaluation shows that, even with very little human guidance, our interactive approach has a restoration performance on par or superior to other methods. Meanwhile, very positive results of our user study suggest that learning systems with the human-in-the-loop positively contribute to user satisfaction. We therefore conclude that an interactive, cooperative approach is a viable option for image restoration and potentially other ML tasks where human knowledge can be a correcting or guiding influence.
Keywords: Interactive Machine Learning; Image Restoration; Image Prior

How do Practitioners Perceive the Relevance of Requirements Engineering Research? - Oriol, Marc; Franch, Xavier; Mendez, Daniel; Vogelsang, Andreas; Heldal, Rogardt; Knauss, Eric; Travassos, Guilherme H.; Carver, Jeffrey C.; Zimmermann, Thomas (Transactions on Software Engineering, 2020)

How to integrate Security Compliance Requirements with Agile Software Engineering at Scale - Moyón, Fabiola; Mendez, Daniel; Beckers, Kristian; Klepper, Sebastian (Proc. 21st International Conference on Product-Focused Software Process Improvement, 2020)

Integration of Security Standards in DevOps Pipelines: An Industry Case Study - Moyón, Fabiola; Soares, Rafael; Pinto-Albuquerque, Maria; Mendez, Daniel; Beckers, Kristian (Proc. 21st International Conference on Product-Focused Software Process Improvement, 2020)

Metapath- and Entity-aware Graph Neural Network for Recommendation - Han, Zhiwei; Anwaar, Muhammad Umer; Arumugaswamy, Shyam; Weber, Thomas; Qiu, Tianming; Shen, Hao; Liu, Yuanting; Kleinsteuber, Martin (Arxiv, 2020)
In graph neural networks (GNNs), message passing iteratively aggregates nodes' information from their direct neighbors while neglecting the sequential nature of multi-hop node connections. Such sequential node connections e.g., metapaths, capture critical insights for downstream tasks. Concretely, in recommender systems (RSs), disregarding these insights leads to inadequate distillation of collaborative signals. In this paper, we employ collaborative subgraphs (CSGs) and metapaths to form metapath-aware subgraphs, which explicitly capture sequential semantics in graph structures. We propose meta-path and entity awared graph neural network, which trains multilayer GNNs to perform metapath-aware information aggregation on such subgraphs. This aggregated information from different metapaths is then fused using attention mechanism. Finally, PEAGNN gives us the representations for node and subgraph, which can be used to train MLP for predicting score for target user-item pairs. To leverage the local structure of CSGs, we present entity-awareness that acts as a contrastive regularizer on node embedding. Moreover, PEAGNN can be combined with prominent layers such as GAT, GCN and GraphSage. Our empirical evaluation shows that our proposed technique outperforms competitive baselines on several datasets for recommendation tasks. Further analysis demonstrates that PEAGNN also learns meaningful metapath combinations from a given set of metapaths.

Open Science in Software Engineering - Mendez, Daniel; Graziotin, Daniel; Seibold, Heidi; Wagner, Stefan (M. Felderer, G.-H. Travassos (eds.) Contemporary Empirical Methods in Software Engineering, 2020)

Personalized Stress Detection with Self-supervised Learned Features - Matthes, Stefan; Han, Zhiwei; Qiu, Tianming; Michel, Bruno; Klingner, Sören; Shen, Hao; Liu, Yuanting; Altakrouri, Bashar (Arxiv, 2020)
Automated stress detection using physiological sensors is challenging due to inaccurate labeling and individual bias in the sensor data. Previous methods consider stress detection as a supervised classification task, where bad labeling leads to a large performance drop. Furthermore, the poor generalizability to unseen subjects reveals the importance of personalizing stress detection for both interand intra-individual sensor data variability. Towards this end we present a label-free feature extractor and an efficient personalization method with the ”human in the loop” approach. First, we capture the intra-individual variability and encode it in self-supervised learned features, which are usually well separable and independent of noisy stress labels. Next, personalization is achieved by assigning labels to critical reference points via very few interactions between subject and wearable device. The promising results of the conducted experiments show the effectiveness and efficiency of our proposed method.
Keywords: stress detection; self-supervised learning

Security Compliance in Agile Software Development: A Systematic Mapping Study - Moyón, Fabiola; Almeida, Pamela; Riofrio, Daniel; Mendez, Daniel; Kalinowski, Marcos (Proc. 45th Euromicro SEAA, 2020)

Standardization of Forms in Governments: A Meta-Model for a Reference Form Modeling Language - Scholta, Hendrik; Balta, Dian; Räckers, Michael; Becker, Jörg; Krcmar, Helmut (Business & Information Systems Engineering, forthcoming, 2020)

Towards Artefact-based Requirements Engineering for Data-Centric Systems - Chuprina, Tatiana; Mendez, Daniel; Wnuk, Krzysztof (Proc. 2nd International Workshop on Requirements Engineering for Artificial Intelligence, 2020)

Using Process Models to understand Security Standards - Moyón, Fabiola; Mendez, Daniel; Beckers, Kristian; Klepper, Sebastian (Proc. SOFSEM 2021: Theory and Practice of Computer Science, 2020)

Views on Quality Requirements in Academia and Practice: Commonalities, Differences, and Context-Dependent Grey Areas - Vogelsang, Andreas; Eckhardt, Jonas; Mendez, Daniel; Berger, Moritz (Views on Quality Requirements in Academia and Practice: Commonalities, Differences, and Context-Dependent Grey Areas, 2020)

What Makes Agile Test Artifacts Useful? An Activity-Based Quality Model from a Practitioners’ Perspective - Fischbach, Jannik; Femmer, Henning; Mendez, Daniel; Fucci, Davide; Vogelsang, Andreas (Proc. International Symposium on Empirical Software Engineering and Measurement, 2020)

Capability-based semantic interoperability of manufacturing resources: A BaSys 4.0 perspective - Perzylo, Alexander; Vogel, Julian; Lúcio, Levi; Weser, Michael; Malakuti, Somayeh; Venet, Pierre; Aravantinos, Vincent; Deppe, Torben (IFAC-PapersOnLine, 2019)
In distributed manufacturing systems, the level of interoperability of hardware and software components depends on the quality and flexibility of their information models. Syntactic descriptions of input and output parameters, e.g., using interface description languages (IDL), are not suffcient when it comes to evaluating whether a manufacturing resource provides the capabilities that are required for performing a particular process step on a product. The semantics of capabilities needs to be explicitly modelled and must be provided together with manufacturing resources. In this paper, we introduce concepts developed by the German BaSys 4.0 initiative dealing with semantically describing manufacturing skills, orchestrating higher-level skills from basic skills, and using them in a cognitive manufacturing framework.
IFAC Conference on Manufacturing Modeling, Management, and Control (MIM)
Keywords: robotics; basys 4.0; model-based systems engineering; MbSE

An Analysis of Fog Computing Data Placement Algorithms - Maniglia da Silva, Daniel; Asaamoning, Godwin; Orrillo, Hector; C. Sofia, Rute; M. Mendes, Paulo (Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (EFIOT workshop) 2019, 2019)
This work evaluates three Fog Computing data placement algo- rithms via experiments carried out with the iFogSim simulator. The paper describes the three algorithms (Cloud-only, Mapping, Edge-ward) in the context of an Internet of Things scenario, which has been based on an e-Health system with variations in applications and network topology. Results achieved show that edge placement strategies are beneficial to assist cloud computing in lowering latency and cloud energy expenditure.
Keywords: IIoT; Edge; Fog

DYNAMIC BUSINESS MODELS: A COMPREHENSIVE CLASSIFICATION OF LITERATURE - Schaffer, Norman; Pfaff, Matthias; Krcmar, Helmut (MCIS 2019 Proceedings. Naples, Italy, 2019)

Lane-Merging Using Policy-based Reinforcement Learning and Post-Optimization - Hart, Patrick; Knoll, Alois; Rychly, Leonard (2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019)
Many current behavior generation methods struggle to handle real-world traffic situations as they do not scale well with complexity. However, behaviors can be learned off-line using data-driven approaches. Especially, reinforcement learning is promising as it implicitly learns how to behave utilizing collected experiences. In this work, we combine policy-based reinforcement learning with local optimization to foster and synthesize the best of the two methodologies. The policy-based reinforcement learning algorithm provides an initial solution and guiding reference for the post-optimization. Therefore, the optimizer only has to compute a single homotopy class, e.g. drive behind or in front of the other vehicle. By storing the state-history during reinforcement learning, it can be used for constraint checking and the optimizer can account for interactions. The post-optimization additionally acts as a safety-layer and the novel method, thus, can be applied in safety-critical applications. We evaluate the proposed method using lane-change scenarios with a varying number of vehicles.

nn-dependability-kit: Engineering Neural Networks for Safety-Critical Autonomous Driving Systems - Cheng, Chih-Hong; Huang, Chung-Hao; Nührenberg, Georg (Proceedings of the IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2019)

Semantic Mates: Intuitive Geometric Constraints for Efficient Assembly Specifications - Wildgrube, Fabian; Perzylo, Alexander; Rickert, Markus; Knoll, Alois (Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019)
In this paper, we enhance our knowledge-based and constraint-based approach of robot programming with the concept of Semantic Mates. They describe intended mechanical connections between parts of an assembly. This allows deriving appropriate assembly poses from the type of connection and the geometric properties of the involved parts. The paper presents an ontology-based representation of Semantic Mates that is used to augment object models with additional information regarding their potential use in an assembly. Such semantically annotated object models can be used in our instruction framework to program a robot to perform assembly tasks through simple drag-and-drop operations in a graphical user interface. We conducted a user study with 21 participants in order to evaluate the efficiency and usability of the Semantic Mates concept based on a use-case from the domain of mechanical assembly. Across different experience levels in robotics, the participants achieved a significantly faster workflow and improved perceived usability compared to the manual specification of constraint-based assembly operations.
Keywords: robotics; smecobot

eGovernment MONITOR 2019 - Scheiber, Patricia; Müller, Lena-Sophie; Krcmar, Helmut; Exel, Stefanie; Motzet, Kerstin; Bastin, Melanie (Initiative D21 e. V. und fortiss GmbH, 2019)
Nutzung und Akzeptanz digitaler Verwaltungsangebote – Deutschland, Österreich und Schweiz im Vergleich

Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks - Brunner, Thomas; Diehl, Frederik; Truong Le, Michael; Knoll, Alois (Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2019)
We consider adversarial examples for image classification in the black-box decision-based setting. Here, an attacker cannot access confidence scores, but only the final label. Most attacks for this scenario are either unreliable or inefficient. Focusing on the latter, we show that a specific class of attacks, Boundary Attacks, can be reinterpreted as a biased sampling framework that gains efficiency from domain knowledge. We identify three such biases, image frequency, regional masks and surrogate gradients, and evaluate their performance against an ImageNet classifier. We show that the combination of these biases outperforms the state of the art by a wide margin. We also showcase an efficient way to attack the Google Cloud Vision API, where we craft convincing perturbations with just a few hundred queries. Finally, the methods we propose have also been found to work very well against strong defenses: Our targeted attack won second place in the NeurIPS 2018 Adversarial Vision Challenge.

Mobility Management via Inference of Roaming Behavior - Aponte, Omar; C. Sofia, Rute (WiMob2019 (STWiMob'19), 2019)
Abstract—This paper contributes to the topic of mobility management anticipation via the exploitation of preferred net- work ranking utility functions. The work proposes, implements, and evaluates a set of ranking functions based on Quality of Service and Quality of Experience indicators. Such indicators are obtained via wireless overhearing, or via active network probing. The functions also take into consideration recommendations from neighboring nodes. The implementation of the functions has been integrated into open-source software. The proposed implementation is evaluated via experiments carried out on a wireless testbed in terms of handover time to completion, total handovers successfully achieved, and rejected handovers. Results obtained show that overhearing is relevant to estimate mobility, as it brings in more sensitivity and lowers the cost of active probing.
Keywords: Mobility management; mobility anticipation; wireless networks; IIoT

Targetless Rotational Auto-Calibration of Radar and Camera for Intelligent Transportation Systems - Schöller, Christoph; Schnettler, Maximilian; Krämmer, Annkathrin; Hinz, Gereon Michael; Bakovic, Maida; Güzet, Müge; Knoll, Alois (Intelligent Transportation Systems Conference (ITSC), 2019)
Most intelligent transportation systems use a combination of radar sensors and cameras for robust vehicle perception. The calibration of these heterogeneous sensor types in an automatic fashion during system operation is challenging due to differing physical measurement principles and the high sparsity of traffic radars. We propose - to the best of our knowledge - the first data-driven method for automatic rotational radar-camera calibration without dedicated calibration targets. Our approach is based on a coarse and a fine convolutional neural network. We employ a boosting-inspired training algorithm, where we train the fine network on the residual error of the coarse network. Due to the unavailability of public datasets combining radar and camera measurements, we recorded our own real-world data. We demonstrate that our method is able to reach precise and robust sensor registration and show its generalization capabilities to different sensor alignments and perspectives.

TSNSCHED: Automated Schedule Generation for Time Sensitive Networking - Cassimiro T. dos Santos, Aellison; Schneider, Ben; Nigam, Vivek (Formal Methods in Computer-Aided Design 2019, 2019)
Time Sensitive Networking (TSN) is a set of standards enabling high performance deterministic communication using different scheduling mechanisms. Due to the size of industrial networks, configuring TSN networks is challenging to be done manually. We present TSNsched, a tool for automatic generation of schedules for TSN. TSNsched takes as input the logical topology of a network, expressed as flows, and outputs schedules for TSN switches using an SMT-solver. The generated schedule guarantees the desired network performance (specified in terms of latency and jitter), if such schedules exist. TSNsched can synthesize IEEE 802.1Qbv schedules and supports unicast and multicast flows, such as, in Publish/Subscribe networks. TSNsched can be run as a standalone tool and also allows rapid prototyping with the available JAVA API. We evaluate TSNsched on a number of realistic-size network topologies. TSNsched can generate high performance schedules, with average latency less than 1000us, and average jitter less than 20us, for TSN networks, with up to 73 subscribers and up to 10 multicast flows.

OPC UA NodeSet Ontologies as a Pillar of Representing Semantic Digital Twins of Manufacturing Resources - Perzylo, Alexander; Profanter, Stefan; Rickert, Markus; Knoll, Alois (Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2019)
The effectiveness of cognitive manufacturing systems in agile production environments heavily depend on the automatic assessment of various levels of interoperability between manufacturing resources. For taking informed decisions, a semantically rich representation of all resources in a workcell or production line is required. OPC UA provides means for communication and information exchange in such distributed settings. This paper proposes a semantic representation of a resource's properties, in which we use OWL ontologies to encode the information models that can be found in OPC UA NodeSet specifications. We further combine these models with an OWL-based description of the resource's geometry and – if applicable – its kinematic model. This leads to a comprehensive semantic representation of hardware and software features of a manufacturing resource, which we call semantic digital twin. Among other things, it reduces costs through virtual prototyping and enables the automatic deployment of manufacturing tasks in production lines. As a result, small-batch assemblies become financially viable. In order to minimize the effort of creating OWL-based UA NodeSet descriptions, we provide a software tool for the automatic transformation of XML-based NodeSet specifications that adhere to the OPC Foundation's NodeSet2 XML schema.
Keywords: robotics; data backbone; basys 4.0

A Hardware-Agnostic OPC UA Skill Model for Robot Manipulators and Tools - Profanter, Stefan; Breitkreuz, Ari; Rickert, Markus; Knoll, Alois (Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2019)
The current trend to lot-size-one production requires reduced integration effort and easy reuse of available devices inside the production line. These devices have to offer a uniform interface to fulfill these requirements. This paper presents a hardware-agnostic skill model using the semantic modeling capabilities of OPC UA. The model provides a standardized interface to hardware or software functionality while offering an intuitive way of grouping multiple skills to a higher hierarchical abstraction. Our skill model is based on OPC UA Programs and modeled as an open source NodeSet. We hereby focus on the reusability of the skills for many different domains. The model is evaluated by controlling three different industrial robots and their tools through the same skill interface. The evaluation shows that our generic OPC UA skill model can be used as a standardized control interface for device and software components in industrial manufacturing. With our solution new components can easily be exchanged without changing the interface. This is not only true for industrial robots, but for any device which provides a controllable functionality.
Keywords: robotics; data backbone

Agile Operational Behavior for the Control-Level Devices in Plug&Produce Production Environments - Dorofeev, Kirill; Profanter, Stefan; Cabral, Jose; Ferreira, Pedro; Zoitl, Alois (Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2019)
The ongoing manufacturing systems transformation from mass production towards mass customization requires more flexible engineering solutions than the existing ones. The recently proposed control architectures target, among other plug-andproduce features, a reduction of configuration times. This is relevant for building a new production line as well as for faster reconfiguration when adding new hardware and product variants to an existing manufacturing line. This paper identifies operational requirements for such reconfiguration scenarios and proposes a way to implement them using the concept of a device adapter. The device adapter contains a device description and constantly updates it following the reconfiguration changes happening in a manufacturing system. This allows not only to detect the changes in the hardware, which appear in the production system, using the device discovery mechanisms but also automatically adapt the software. Preliminary tests have been performed on a demonstrator that shows both virtual and physical executions combined in a single system. The proposed solution supports automatic hardware and software reconfiguration on-the-fly without a need to stop and restart the whole production system.
Keywords: industry 4.0

An Approach for an Automated Adaption of KPI Ontologies by Reusing Systems Engineering Data - Walzel, Hendrik; Vathoopan, Milan; Zoitl, Alois; Knoll, Alois (2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2019)
Technological progress leads to an increased utilization of data analysis and Business Intelligence that support manufacturing management decisions. Many promising solutions utilize semantic technologies. However, the deployment and maintenance of semantic technologies especially in reconfigurable manufacturing environments require a lot of manual effort. Concepts to embed them in an automated environment, as required by Reconfigurable Manufacturing Systems, are limited. In this paper, we present an approach to reuse systems engineering data to guide an automated process that updates a production data knowledge base. Thereby, an ontology that integrates distributed operational data to compute Key Performance Indicators such as the Overall Equipment Effectiveness index can be updated during the manufacturing reconfiguration process. This reduces the effort to handle the required changes of semantic data integration systems and enables a cost-effective adaption of the Business Intelligence for Reconfigurable Manufacturing Systems.
Keywords: Semantics; Modeling; Knowledge based systems; Ontologies; Manufacturing systems; reconfigurable manufacturing systems; semantic data integration; KPI; OEE; systems engineering

Blackbox AI: What is in the Box? - Balta, Dian; Kuhn, Peter; Sellami, Mahdi; Kalogeropoulos, Anastasios; Krcmar, Helmut (Proceedings of ongoing research, practitioners, posters, workshops, and projects of the international conference egov-cedem-epart, 2019)
We present insights from a chatbot prototype for online citizen participation and discuss particular benefits and caveats of artificial intelligence (AI) application in the government domain. We present an argument that AI represents a blackbox not only in terms the reasoning process itself, but also in terms of applying different building blocks “out-of-the-box”. Our research shows that customizing an AI application involves a hardly manageable combination of buildings blocks: various machine learning techniques, data sources and user interaction designs. Since those building blocks might not fit the requirements of a particular use case, the required effort to configure the expected behaviour should not be underestimated.

Cross-Factory Information Exchange for Cloud-Based Monitoring of Collaborative Manufacturing Networks - Bnouhanna, Nisrine; Neugschwandtner, Georg (Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2019)
Cloud-based platforms and ecosystems are widely considered as a means of supporting collaborative manufacturing networks (CMN) among companies and, in particular, manufacturing SMEs. Due to the spatial separation of manufacturing activities among the CMN partners, there is a need for monitoring and coordinating the collaborative manufacturing processes. However, little research investigates inter-enterprise information exchange systems required for such monitoring and coordination. This paper proposes an approach for cross-factory information exchange considering connectivity and interoperability between various factory data sources and cloud-based collaborative production monitoring services. The paper also introduces a cross-factory information exchange mechanism, meta-models, and architecture to facilitate the ad-hoc, target specific data exchange and addresses SMEs’ adoption barriers, namely trust, security, data control, and integration efforts. A proof of concept of the proposed approach is presented.

Evaluating Skill-Based Control Architecture for Flexible Automation Systems - Dorofeev, Kirill; Wenger, Monika (Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2019)
For the manufacturing of customized products, systems require easier and better adaptivity of the production plant especially in terms of the control software. In recent research projects, a skill-based architecture has been introduced and presented within an industrial demonstrator proposing the required adaptivity. Within this work, the skill-based architecture is compared to a more traditional component-based hierarchical approach to identify strength and weakness of this approach, which then enables further improvements of the skill-based architecture. The flexibility of control software can not directly be measured. Therefore execution time, modularity, readability, and reusability considering the impact of component exchanges are used as evaluation criteria. Modularity and reusability are measured by the number of needed Function Blocks (FBs) and that can also be used for other systems respectively. To evaluate readability three derived metrics from the basic Halstead Metric are used. The comparison shows that the skill-based architecture is more complex but much more flexible than the more traditional component-based hierarchical approach.
Keywords: industrie 4.0

Function Blocks for the Interaction with the Asset Administration Shell - Terzimehić, Tarik; Bayha, Andreas; Dorofeev, Kirill (Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2019)
An asset administration shell (AAS), as a key concept of the Industry 4.0, provides a machine accessible interface to any kind of asset. An application implementing the devices functionality should be able to interact with different AASs. In this work, we specify the function blocks (FBs) for accessing properties and invoking operations of AASs. We analyzed the nature and requirements of such FBs while focusing on REST/HTTP- and OPC UA-based AASs and provided IEC 61499-based implementation. The expected outcome of this paper will ease an interaction with the complex AAS structure from the low-level devices.
Keywords: Industry 4.0; IEC 61499; IEC 61131-3; asset administration shell; digital twin; PLC; OPC UA; REST; model-based systems engineering; MbSE

Generating Requirements out of Thin Air: Towards Automated Feature Identification for New Apps - Iqbal, Tahira; Seyff, Norbert; Mendez, Daniel (Proc. of the 3rd International Workshop on Crowd-Based Requirements Engineering, 2019)
App store mining has proven to be a promising technique for requirements elicitation as companies can gain valuable knowledge to maintain and evolve existing apps. However, despite first advancements in using mining techniques for requirements elicitation, little is yet known how to distill requirements for new apps based on existing (similar) solutions and how exactly practitioners would benefit from such a technique. In the proposed work, we focus on exploring information (e.g. app store data) provided by the crowd about existing solutions to identify key features of applications in a particular domain. We argue that these discovered features and other related influential aspects (e.g. ratings) can help practitioners(e.g. software developer) to identify potential key features for new applications. To support this argument, we first conducted an interview study with practitioners to understand the extent to which such an approach would find champions in practice. In this paper, we present the first results of our ongoing research in the context of a larger road-map. Our interview study confirms that practitioners see the need for our envisioned approach. Furthermore, we present an early conceptual solution to discuss the feasibility of our approach. However, this manuscript is also intended to foster discussions on the extent to which machine learning can and should be applied to elicit automated requirements on crowd generated data on different forums and to identify further collaborations in this endeavor.

How to Streamline AI Application in Government? A Case Study on Citizen Participation in Germany - Balta, Dian; Kuhn, Peter; Sellami, Mahdi; Kulus, Daniel; Lieven, Claudius; Krcmar, Helmut (Electronic Government, 2019)
Artificial intelligence (AI) technologies are on the rise in almost every aspect of society, business and government. Especially in government, it is of interest how the application of AI can be streamlined: at least, in a controlled environment, in order to be able to evaluate potential (positive and negative) impact. Unfortunately, reuse in development of AI applications and their evaluation results lack interoperability and transferability. One potential remedy to this challenge would be to apply standardized artefacts: not only on a technical level, but also on an organization or semantic level. This paper presents findings from a qualitative explorative case study on online citizen participation in Germany that reveal insights on the current standardization level of AI applications. In order to provide an in-depth analysis, the research involves evaluation of two particular AI approaches to natural language processing. Our findings suggest that standardization artefacts for streamlining AI application exist predominantly on a technical level and are still limited.
Keywords: Natural language processing; Standardization; Government

In Search for Consensus - Balta, Dian; Kalogeropoulos, Anastasios; Kuhn, Peter; Krcmar, Helmut (Proceedings of ongoing research, practitioners, posters, workshops, and projects of the international conference egov-cedem-epart, 2019)
We present our research-in-progress on designing Blockchain/ Distributed Ledger Technologies-based IT-architectures in government. Our approach consists of three steps: need analysis, feasibility study, and architecture design based on exchangeable building blocks. We argue that DLT should be defined as a trust layer atop of existing IT-infrastructure instead of replacing existing information systems. To support our claim, we provide an exemplary application.

Insights from Natural Language Processing - Balta, Dian; Sellami, Mahdi; Kuhn, Peter; Krcmar, Helmut (Proceedings of ongoing research, practitioners, posters, workshops, and projects of the international conference egov-cedem-epart, 2019)
We present an exemplary text categorization pipeline for online citizen participation and aim at discussing our ongoing research in terms of insights from natural language processing (NLP) application. For each of the steps in the categorization pipeline, we share our experience in terms of challenges and potential measures to address these challenges.

Plug and Produce for Industry 4.0 using Software-defined Networking and OPC UA - Madiwalar, Basavaraj; Schneider, Ben; Profanter, Stefan (Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), 2019)
The manufacturers are in quest for flexible and agile production facilities capable of accommodating changes to product specification. The need for flexible production facilities is stemming from the desire for customized products and fluctuating market trends. Industry 4.0 impels for adaptable manufacturing plants by employing intelligent devices and advanced communication technologies. The complexity of the configuration process determines the adaptability of production facilities to accommodate changes to the production process. We propose a systematic integration process and multi-level production system using Software-defined Networking (SDN) and OPC Unified Architecture (OPC UA) to reduce the configuration complexity to a Plug and Produce level. OPC UA, as a service-oriented middleware, provides the tool-set for semantic modeling and automatic device discovery. However, due to the multicast nature of the OPC UA discovery mechanism, the existing approaches require intelligence at the device level to select the desired device to connect to. In contrast, our proposed solution shifts the intelligence to a centralized SDN controller to route multicast traffic to facilitate device discovery. The combination of an SDN controller and OPC UA discovery enables the integration of new devices by adding more intelligence to the device discovery.
Keywords: industrie 4.0; opc ua; software-defined networking; automatic discovery

Semantics of Co-simulation Algorithms with Simulator Contracts - Gomes, Cláudio; Lúcio, Levi; Vangheluwe, Hans (2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), 2019)
The rapid adoption of co-simulation techniques allows for holistic complex system development. However, ensuring trustworthy results when combining simulators requires a careful consideration of their implementation and capabilities. Especially in black box integration, these are frequently left implicit. In this paper, we explore a way to account for simulator capabilities, by formalizing the execution of a co-simulation that respects such contracts. This formalization is specific to two kinds of contracts, but could serve as a basis to a general approach to black box co-simulation. An example application of the semantics to generate master algorithms is presented.
Keywords: co-simulation; prolog; contract-based-design; constraint-solving; Model-based Systems Engineering; MbSE