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Faced with the triad of time-cost-quality, the realization of production tasks under economic conditions is not trivial. Since the number of Artificial-Intelligence-(AI)-based applications in business processes is increasing more and more nowadays, the efficient design of AI cases for production processes as well as their target-oriented improvement is essential, so that production outcomes satisfy high quality criteria and economic requirements. Both challenge production management and data scientists, aiming to assign ideal manifestations of artificial neural networks (ANNs) to a certain task. Faced with new attempts of ANN-based production process improvements [8], this paper continues research about the optimal creation, provision and utilization of ANNs. Moreover, it presents a mechanism for AI case-based reasoning for ANNs. Experiments clarify continuously improving ANN knowledge bases by this mechanism empirically. Its proof-of-concept is demonstrated by the example of four production simulation scenarios, which cover the most relevant use cases and will be the basis for examining AI cases on a quantitative level.
Virtual reality can have advantages for education and learning. However, it must be adequately designed so that the learner benefits from the technological possibilities. Understanding the underlying effects of the virtual learning environment and the learner’s prior experience with virtual reality or prior knowledge of the content is necessary to design a proper virtual learning environment. This article presents a pre-study testing the design of a virtual learning environment for engineering vocational training courses. In the pre-study, 12 employees of two companies joined the training course in one of the two degrees of immersion (desktop VR and VR HMD). Quantitative results on learning success, cognitive load, usability, and motivation and qualitative learning process data were presented. The qualitative data assessment shows that overall, the employees were satisfied with the learning environment regardless of the level of immersion and that the participants asked for more guidance and structure accompanying the learning process. Further research is needed to test for solid group differences.
This chapter reviews the interplay of agglomeration and pollution as well as the effect of energy policies on pollution in an urban context. It starts by describing the effect of agglomeration on pollution. While this effect is theoretically ambiguous, empirical research tends to find that larger cities are more polluted, but per capita emissions fall with city size. The chapter discusses the implications for optimal city size. Conversely, urban pollution tends to discourage agglomeration if larger cities are more exposed to pollution. The chapter then considers various energy policies and their effect on urban pollution. Specifically, it looks at the effects of energy and transport policies as well as urban policies such as zoning.
Green recovery
(2023)
This chapter reviews how the European Union has fared in enabling a green recovery in the aftermath of the Covid-19 crisis, drawing comparisons to developments after the financial crisis. The chapter focuses on the European Commission and its evolving role in promoting decarbonisation efforts in its Member States, paying particular attention to its role in financing investments in low-carbon assets. It considers both the direct effects of green stimulus policies on decarbonisation in the EU and how these actions have shaped the capacities of the Commission as an actor in the field of climate and energy policy. The analysis reveals a significant expansion of the Commission’s role compared to the period following the financial crisis. EU-level measures have provided incentives for Member States to direct large volumes of financing towards investments in climate-friendly assets. Nevertheless, the ultimate impact will largely be shaped by implementation at the national level.
SNS Democracy Council 2023
(2023)
Transboundary problems such as climate change, military conflicts, trade barriers, and refugee flows require increased collaboration across borders. This is to a large extent possible using existing international organizations. In such a case, however, they need to be considerably strengthened – while current trends take us in the opposite direction, according to the researchers in the SNS Democracy Council 2023.
Conclusion
(2023)
Based on the previous findings in this book, Chapter 18 by Heike Krieger and Andrea Liese discusses the general dynamics of change or metamorphosis in the international legal order. They discern a mixed picture of an international order between metamorphosis—that is, a more fundamental transformation—of international law, norm change, turbulences, and robustness. They explain drivers of change and highlight factors such as national interests during the war on terror, changing long-term foreign policy beliefs, and the rise in populism and autocracy, before discussing the most common strategies the actors involved use. Other relevant factors include changes in the political environment, such as shocks and power shifts or the ambiguous role of fragmentation. Moreover, they identify factors that make legal norms robust, including the vital role of norm defenders and legal and institutional structures as stabilizing elements. Krieger and Liese conclude by cautioning that if the attacks on the international order continue at the current frequency and magnitude, a metamorphosis of international law will likely be unstoppable.
International law is constantly navigating the tension between preserving the status quo and adapting to new exigencies. But when and how do such adaptation processes give way to a more profound transformation, if not a crisis of international law? To address the question of how attacks on the international legal order are changing the value orientation of international law, this book brings together scholars of international law and international relations. By combining theoretical and methodological analyses with individual case studies, this book offers readers conceptualizations and tools to systematically examine value change and explore the drivers and mechanisms of these processes. These case studies scrutinize value change in the foundational norms of the post-1945 order and in norms representing the rise of the international legal order post-1990. They cover diverse issues: the prohibition of torture, the protection of women’s rights, the prohibition of the use of force, the non-proliferation of nuclear weapons, sustainability norms, and accountability for core international crimes. The challenges to each norm, the reactions by norm defenders, and the fate of each norm are also studied. Combined, the analyses show that while a few norms have remained surprisingly robust, several are changing, either in substance or in legal or social validity. The book concludes by integrating the conceptual and empirical insights from this interdisciplinary exchange to assess and explain the ambiguous nature of value change in international law beyond the extremes of mere progress or decline.
This chapter addresses the role of evaluation of and in public administration. We focus on two analytical key dimensions: a) the provider of the evaluation and b) the subject of the evaluation. Four major types of evaluation are distinguished: (1) external institutional evaluation, (2) internal institutional evaluation, (3) external evaluation of administrative action/results, (4) internal evaluation of administrative action/results. Type 1 and 2 refer to evaluation of administrative structures and processes as the subject of administrative reform. Type 3 and 4 represent different versions of evaluation in public administration, because the subject is administrative action and its outputs. The chapter highlights salient approaches and organizational settings of evaluation and provides insights into the institutionalization of an evaluation function in public administration. Finally, the chapter draws lessons regarding strengths and potentials but also remaining weaknesses and challenges of evaluation of and in public administration.
Fighting false information
(2023)
The digital transformation poses challenges for public sector organizations (PSOs) such as the dissemination of false information in social media which can cause uncertainty among citizens and decrease trust in the public sector. Some PSOs already successfully deploy conversational agents (CAs) to communicate with citizens and support digital service delivery. In this paper, we used design science research (DSR) to examine how CAs could be designed to assist PSOs in fighting false information online. We conducted a workshop with the municipality of Kristiansand, Norway to define objectives that a CA would have to meet for addressing the identified false information challenges. A prototypical CA was developed and evaluated in two iterations with the municipality and students from Norway. This research-in-progress paper presents findings and next steps of the DSR process. This research contributes to advancing the digital transformation of the public sector in combating false information problems.
This technical report presents the results of student projects which were prepared during the lecture “Operating Systems II” offered by the “Operating Systems and Middleware” group at HPI in the Summer term of 2020. The lecture covered ad- vanced aspects of operating system implementation and architecture on topics such as Virtualization, File Systems and Input/Output Systems. In addition to attending the lecture, the participating students were encouraged to gather practical experience by completing a project on a closely related topic over the course of the semester. The results of 10 selected exceptional projects are covered in this report.
The students have completed hands-on projects on the topics of Operating System Design Concepts and Implementation, Hardware/Software Co-Design, Reverse Engineering, Quantum Computing, Static Source-Code Analysis, Operating Systems History, Application Binary Formats and more. It should be recognized that over the course of the semester all of these projects have achieved outstanding results which went far beyond the scope and the expec- tations of the lecture, and we would like to thank all participating students for their commitment and their effort in completing their respective projects, as well as their work on compiling this report.
In the last two centuries BC, with the Republic limping towards its end, the cultivated ruling elite began to lose its moral and political authority.1 Its members not only held themselves responsible for the so-called crisis of tradition, but at the same time also conveyed the impression of a loss of memory, as if all Romans were suffering from some kind of amnesia or identity crisis.2 In particular, institutional figures such as pontiffs and augurs, who had preserved Rome’s memory throughout its history, were accused of neglecting their duties and, by extension, of allowing ancient practices and values to slowly disappear.3 Accordingly, Cicero and Varro, both perfect representatives of this elite, employed recurrent terms such as neglect (neglegentia/neglegere), involuntary abandon (amittere), oblivion (oblivio), vanishing of institutions (evanescere), and ignorance (ignoratio/ignorare) to describe this critical loss of information; they depicted the citizenry of Rome (civitas) as disoriented and estranged, incapable of sharing any common knowledge or values.
Digital technology offers significant political, economic, and societal opportunities. At the same time, the notion of digital sovereignty has become a leitmotif in German discourse: the state’s capacity to assume its responsibilities and safeguard society’s – and individuals’ – ability to shape the digital transformation in a self-determined way. The education sector is exemplary for the challenge faced by Germany, and indeed Europe, of harnessing the benefits of digital technology while navigating concerns around sovereignty. It encompasses education as a core public good, a rapidly growing field of business, and growing pools of highly sensitive personal data. The report describes pathways to mitigating the tension between digitalization and sovereignty at three different levels – state, economy, and individual – through the lens of concrete technical projects in the education sector: the HPI Schul-Cloud (state sovereignty), the MERLOT data spaces (economic sovereignty), and the openHPI platform (individual sovereignty).
How do social changes, new technologies or new management trends affect communication work? A team of researchers at Leipzig University and the University of Potsdam (Germany) observed new developments in related disciplines. As a result, the five most important trends for corporate communications are identified annually and published in the Communications Trend Radar. Thus, Communications managers can identify challenges and opportunities at an early stage, take a position, address issues and make decisions. For 2023, the Communications Trend Radar identifies five key trends for corporate communications: State Revival, Scarcity Management, Unimagination, Parallel Worlds, Augemented Workflows.
During the outbreak of the COVID-19 pandemic, many people shared their symptoms across Online Social Networks (OSNs) like Twitter, hoping for others’ advice or moral support. Prior studies have shown that those who disclose health-related information across OSNs often tend to regret it and delete their publications afterwards. Hence, deleted posts containing sensitive data can be seen as manifestations of online regrets. In this work, we present an analysis of deleted content on Twitter during the outbreak of the COVID-19 pandemic. For this, we collected more than 3.67 million tweets describing COVID-19 symptoms (e.g., fever, cough, and fatigue) posted between January and April 2020. We observed that around 24% of the tweets containing personal pronouns were deleted either by their authors or by the platform after one year.
As a practical application of the resulting dataset, we explored its suitability for the automatic classification of regrettable content on Twitter.
In this chapter, we conduct bibliometric performance analyses and a co-citation analysis on all articles relating to family firms indexed in Scopus and Web of Science and all articles published in the Family Business Review, Journal of Family Business Management, and the Journal of Family Business Strategy. Based on the literature sample of 4,056 articles published between 1960 and 2020 by 3,600 authors in 783 journals and their 175,163 references, we identify the most productive and most cited journals, the most cited authors, and the 25 most cited articles. Our science mapping reveals the agency theory, definitions, entrepreneurship, internationalization, ownership, resources, socioemotional wealth, and succession as the predominant research themes in family firm research. Whereas entrepreneurship explicitly appears in one of the clusters, innovation does not yet. Based on our findings, we propose a research framework and point to several research gaps to be addressed by future research.
Despite the merits of public and social media in private and professional spaces, citizens and professionals are increasingly exposed to cyberabuse, such as cyberbullying and hate speech. Thus, Law Enforcement Agencies (LEA) are deployed in many countries and organisations to enhance the preventive and reactive capabilities against cyberabuse. However, their tasks are getting more complex by the increasing amount and varying quality of information disseminated into public channels. Adopting the perspectives of Crisis Informatics and safety-critical Human-Computer Interaction (HCI) and based on both a narrative literature review and group discussions, this paper first outlines the research agenda of the CYLENCE project, which seeks to design strategies and tools for cross-media reporting, detection, and treatment of cyberbullying and hatespeech in investigative and law enforcement agencies. Second, it identifies and elaborates seven research challenges with regard to the monitoring, analysis and communication of cyberabuse in LEAs, which serve as a starting point for in-depth research within the project.
The authors propose that while tacit knowledge is a valuable resource for developing new business models, its externalization presents several challenges. One major challenge is that individuals often don’t recognize their tacit knowledge resources, while another is the reluctance to share one’s knowledge with others. Addressing these challenges, the authors present an application-oriented serious game-based haptic modeling approach for externalize tacit knowledge, which can be used to develop the first versions of business models based on tacit knowledge. Both conceptual and practical design fundamentals are presented based on elaborated theoretical approaches, which were developed with the help of a design science approach. The development of the research process is presented step by step, whereby we focused on the high accessibility of the presented research. Practitioners are presented with guidelines for implementing their serious game projects. Scientists benefit from starting points for their research topics of externalization, internalization, and socialization of tacit knowledge, development of business models, and serious games or gamification. The paper concludes with open research desiderata and questions from the presented research process.
Business processes are regularly modified either to capture requirements from the organization’s environment or due to internal optimization and restructuring. Implementing the changes into the individual work routines is aided by change management tools. These tools aim at the acceptance of the process by and empowerment of the process executor. They cover a wide range of general factors and seldom accurately address the changes in task execution and sequence. Furthermore, change is only framed as a learning activity, while most obstacles to change arise from the inability to unlearn or forget behavioural patterns one is acquainted with. Therefore, this paper aims to develop and demonstrate a notation to capture changes in business processes and identify elements that are likely to present obstacles during change. It connects existing research from changes in work routines and psychological insights from unlearning and intentional forgetting to the BPM domain. The results contribute to more transparency in business process models regarding knowledge changes. They provide better means to understand the dynamics and barriers of change processes.
Decubitus is one of the most relevant diseases in nursing and the most expensive to treat. It is caused by sustained pressure on tissue, so it particularly affects bed-bound patients. This work lays a foundation for pressure mattress-based decubitus prophylaxis by implementing a solution to the single-frame 2D Human Pose Estimation problem.
For this, methods of Deep Learning are employed. Two approaches are examined, a coarse-to-fine Convolutional Neural Network for direct regression of joint coordinates and a U-Net for the derivation of probability distribution heatmaps.
We conclude that training our models on a combined dataset of the publicly available Bodies at Rest and SLP data yields the best results. Furthermore, various preprocessing techniques are investigated, and a hyperparameter optimization is performed to discover an improved model architecture.
Another finding indicates that the heatmap-based approach outperforms direct regression.
This model achieves a mean per-joint position error of 9.11 cm for the Bodies at Rest data and 7.43 cm for the SLP data.
We find that it generalizes well on data from mattresses other than those seen during training but has difficulties detecting the arms correctly.
Additionally, we give a brief overview of the medical data annotation tool annoto we developed in the bachelor project and furthermore conclude that the Scrum framework and agile practices enhanced our development workflow.
RailChain
(2023)
The RailChain project designed, implemented, and experimentally evaluated a juridical recorder that is based on a distributed consensus protocol. That juridical blockchain recorder has been realized as distributed ledger on board the advanced TrainLab (ICE-TD 605 017) of Deutsche Bahn.
For the project, a consortium consisting of DB Systel, Siemens, Siemens Mobility, the Hasso Plattner Institute for Digital Engineering, Technische Universität Braunschweig, TÜV Rheinland InterTraffic, and Spherity has been formed. These partners not only concentrated competencies in railway operation, computer science, regulation, and approval, but also combined experiences from industry, research from academia, and enthusiasm from startups.
Distributed ledger technologies (DLTs) define distributed databases and express a digital protocol for transactions between business partners without the need for a trusted intermediary. The implementation of a blockchain with real-time requirements for the local network of a railway system (e.g., interlocking or train) allows to log data in the distributed system verifiably in real-time. For this, railway-specific assumptions can be leveraged to make modifications to standard blockchains protocols.
EULYNX and OCORA (Open CCS On-board Reference Architecture) are parts of a future European reference architecture for control command and signalling (CCS, Reference CCS Architecture – RCA). Both architectural concepts outline heterogeneous IT systems with components from multiple manufacturers. Such systems introduce novel challenges for the approved and safety-relevant CCS of railways which were considered neither for road-side nor for on-board systems so far. Logging implementations, such as the common juridical recorder on vehicles, can no longer be realized as a central component of a single manufacturer. All centralized approaches are in question.
The research project RailChain is funded by the mFUND program and gives practical evidence that distributed consensus protocols are a proper means to immutably (for legal purposes) store state information of many system components from multiple manufacturers. The results of RailChain have been published, prototypically implemented, and experimentally evaluated in large-scale field tests on the advanced TrainLab. At the same time, the project showed how RailChain can be integrated into the road-side and on-board architecture given by OCORA and EULYNX.
Logged data can now be analysed sooner and also their trustworthiness is being increased. This enables, e.g., auditable predictive maintenance, because it is ensured that data is authentic and unmodified at any point in time.
Like conventional software projects, projects in model-driven software engineering require adequate management of multiple versions of development artifacts, importantly allowing living with temporary inconsistencies. In the case of model-driven software engineering, employed versioning approaches also have to handle situations where different artifacts, that is, different models, are linked via automatic model transformations.
In this report, we propose a technique for jointly handling the transformation of multiple versions of a source model into corresponding versions of a target model, which enables the use of a more compact representation that may afford improved execution time of both the transformation and further analysis operations. Our approach is based on the well-known formalism of triple graph grammars and a previously introduced encoding of model version histories called multi-version models. In addition to showing the correctness of our approach with respect to the standard semantics of triple graph grammars, we conduct an empirical evaluation that demonstrates the potential benefit regarding execution time performance.
Modular and incremental global model management with extended generalized discrimination networks
(2023)
Complex projects developed under the model-driven engineering paradigm nowadays often involve several interrelated models, which are automatically processed via a multitude of model operations. Modular and incremental construction and execution of such networks of models and model operations are required to accommodate efficient development with potentially large-scale models. The underlying problem is also called Global Model Management.
In this report, we propose an approach to modular and incremental Global Model Management via an extension to the existing technique of Generalized Discrimination Networks (GDNs). In addition to further generalizing the notion of query operations employed in GDNs, we adapt the previously query-only mechanism to operations with side effects to integrate model transformation and model synchronization. We provide incremental algorithms for the execution of the resulting extended Generalized Discrimination Networks (eGDNs), as well as a prototypical implementation for a number of example eGDN operations.
Based on this prototypical implementation, we experiment with an application scenario from the software development domain to empirically evaluate our approach with respect to scalability and conceptually demonstrate its applicability in a typical scenario. Initial results confirm that the presented approach can indeed be employed to realize efficient Global Model Management in the considered scenario.