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On null quadrature domains
(2006)
Over the last few decades, a network of misogynist blogs, websites, wikis, and forums has developed, where users share their bigoted, sexist, and toxic views of society in general and masculinity and femininity in particular. This chapter outlines conceptual framework of hegemonic and hybrid masculinity. It provides a brief overview of the historical development of the manosphere and its various configurations and present our analysis of the masculinities performed by the five groups of the manosphere. The concept of hegemonic masculinity was articulated by Connell and colleagues in the 1980s as “the pattern of practice that allowed men’s dominance over women to continue.” Prior to the advent of the manosphere, an online iteration of male supremacist mobilizations, both Men’s Rights Activists and Pick-up artists developed as offline movements in the 1970s. MRAs perceive their respective societies as inherently stacked against men. This chapter analyses the masculinities of the manosphere and how they “repudiat[e] and reif[y]” hegemonic masculinity and male supremacism.
Network approach to supply chain management : terms, scope of issues and lines of development
(2009)
Music and Artistic Education
(2017)
Multiculturalism and beyond : perspektives and new publicatons on the culture debate in the USA
(2003)
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.
In the last decade, there has been an increasing interest in compensating thermally induced errors to improve the manufacturing accuracy of modular tool systems. These modular tool systems are interfaces between spindle and workpiece and consist of several complicatedly formed parts. Their thermal behavior is dominated by nonlinearities, delay and hysteresis effects even in tools with simpler geometry and it is difficult to describe it theoretically. Due to the dominant nonlinear nature of this behavior the so far used linear regression between the temperatures and the displacements is insufficient. Therefore, in this study we test the hypothesis whether we can reliably predict such thermal displacements via nonlinear temperature-displacement regression functions. These functions are estimated firstly from learning measurements using the alternating conditional expectation (ACE) algorithm and then tested on independent data sets. First, we analyze data that were generated by a finite element spindle model. We find that our approach is a powerful tool to describe the relation between temperatures and displacements for simulated data. Next, we analyze the temperature-displacement relationship in a silent real experimental setup, where the tool system is thermally forced. Again, the ACE-algorithm is powerful to estimate the deformation with high precision. The corresponding errors obtained by using the nonlinear regression approach are 10-fold lower in comparison to multiple linear regression analysis. Finally, we investigate the thermal behavior of a modular tool system in a working milling machine and get again promising results. The thermally induced errors can be estimated with 1-2${mu m}$ accuracy using this nonlinear regression analysis. Therefore, this approach seems to be very useful for the development of new modular tool systems.
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.
Modeling and Formal Analysis of Meta-Ecosystems with Dynamic Structure using Graph Transformation
(2022)
The dynamics of ecosystems is of crucial importance. Various model-based approaches exist to understand and analyze their internal effects. In this paper, we model the space structure dynamics and ecological dynamics of meta-ecosystems using the formal technique of Graph Transformation (short GT). We build GT models to describe how a meta-ecosystem (modeled as a graph) can evolve over time (modeled by GT rules) and to analyze these GT models with respect to qualitative properties such as the existence of structural stabilities. As a case study, we build three GT models describing the space structure dynamics and ecological dynamics of three different savanna meta-ecosystems. The first GT model considers a savanna meta-ecosystem that is limited in space to two ecosystem patches, whereas the other two GT models consider two savanna meta-ecosystems that are unlimited in the number of ecosystem patches and only differ in one GT rule describing how the space structure of the meta-ecosystem grows. In the first two GT models, the space structure dynamics and ecological dynamics of the meta-ecosystem shows two main structural stabilities: the first one based on grassland-savanna-woodland transitions and the second one based on grassland-desert transitions. The transition between these two structural stabilities is driven by high-intensity fires affecting the tree components. In the third GT model, the GT rule for savanna regeneration induces desertification and therefore a collapse of the meta-ecosystem. We believe that GT models provide a complementary avenue to that of existing approaches to rigorously study ecological phenomena.
Missing out on life
(2020)
Mobile devices have become an integral part of everyday life due to
their portability. As literature shows, technology use is not only beneficial but also has dark sides, such as addiction. Parents face the need to balance perceived benefits and risks of children’s exposure to mobile technologies. However, no study has uncovered what kind of benefits and concerns parents consider when implementing technology-related rules. We built on qualitative responses of 300
parents of children aged two to thirteen to explore concerns about, and perceived benefits of children’s smartphone and tablet usage, as well as the rules parents have developed regarding technology use. Findings point to concerns regarding children’s development, as well as benefits for both children and parents, and ultimately to new insights about mobile technology mediation. These results provide practical guidance for parents, physicians and mobile industry
stakeholders, trying to ensure that children are acting responsibly with mobile technology.
The metaverse is envisioned as a virtual shared space facilitated by emerging technologies such as virtual reality (VR), augmented reality (AR), the Internet of Things (IoT), 5G, artificial intelligence (AI), big data, spatial computing, and digital twins (Allam et al., 2022; Dwivedi et al., 2022; Ravenscraft, 2022; Wiles, 2022). While still a nascent concept, the metaverse has the potential to “transform the physical world, as well as transport or extend physical activities to a virtual world” (Wiles, 2022). Big data technologies will also be essential in managing the enormous amounts of data created in the metaverse (Sun et al., 2022). Metaverse technologies can offer the public sector a host of benefits, such as simplified information exchange, stronger communication with citizens, better access to public services, or benefiting from a new virtual economy. Implementations are underway in several cities around the world (Geraghty et al., 2022). In this paper, we analyze metaverse opportunities for the public sector and explore their application in the context of Germany’s Federal Employment Agency. Based on an analysis of academic literature and practical examples, we create a capability map for potential metaverse business capabilities for different areas of the public sector (broadly defined). These include education (virtual training and simulation, digital campuses that offer not just online instruction but a holistic university campus experience, etc.), tourism (virtual travel to remote locations and museums, virtual festival participation, etc.), health (employee training – as for emergency situations, virtual simulations for patient treatment – for example, for depression or anxiety, etc.), military (virtual training to experience operational scenarios without being exposed to a real-world threats, practice strategic decision-making, or gain technical knowledge for operating and repairing equipment, etc.), administrative services (document processing, virtual consultations for citizens, etc.), judiciary (AI decision-making aids, virtual proceedings, etc.), public safety (virtual training for procedural issues, special operations, or unusual situations, etc.), emergency management (training for natural disasters, etc.), and city planning (visualization of future development projects and interactive feedback, traffic management, attraction gamification, etc.), among others. We further identify several metaverse application areas for Germany's Federal Employment Agency. These applications can help it realize the goals of the German government for digital transformation that enables faster, more effective, and innovative government services. They include training of employees, training of customers, and career coaching for customers. These applications can be implemented using interactive learning games with AI agents, virtual representations of the organizational spaces, and avatars interacting with each other in these spaces. Metaverse applications will both use big data (to design the virtual environments) and generate big data (from virtual interactions). Issues related to data availability, quality, storage, processing (and related computing power requirements), interoperability, sharing, privacy and security will need to be addressed in these emerging metaverse applications (Sun et al., 2022). Special attention is needed to understand the potential for power inequities (wealth inequity, algorithmic bias, digital exclusion) due to technologies such as VR (Egliston & Carter, 2021), harmful surveillance practices (Bibri & Allam, 2022), and undesirable user behavior or negative psychological impacts (Dwivedi et al., 2022). The results of this exploratory study can inform public sector organizations of emerging metaverse opportunities and enable them to develop plans for action as more of the metaverse technologies become a reality. While the metaverse body of research is still small and research agendas are only now starting to emerge (Dwivedi et al., 2022), this study offers a building block for future development and analysis of metaverse applications.
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.
As part of the digitization, the role of artificial systems as new actors in knowledge-intensive processes requires to recognize them as a new form of knowledge bearers side by side with traditional knowledge bearers, such as individuals, groups, organizations. By now, artificial intelligence (AI) methods were used in knowledge management (KM) for knowledge discovery, for the reinterpreting of information, and recent works focus on the studying of different AI technologies implementation for knowledge management, like big data, ontology-based methods and intelligent agents [1]. However, a lack of holistic management approach is present, that considers artificial systems as knowledge bearers. The paper therefore designs a new kind of KM approach, that integrates the technical level of knowledge and manifests as Neuronal KM (NKM). Superimposing traditional KM approaches with the NKM, the Symbiotic Knowledge Management (SKM) is conceptualized furthermore, so that human as well as artificial kinds of knowledge bearers can be managed as symbiosis. First use cases demonstrate the new KM, NKM and SKM approaches in a proof-of-concept and exemplify their differences.
This chapter analyses managerial reforms at the subnational level of government from a comparative perspective and outlines possible routes for future comparative research. It examines reforms of the external relationships between local governments and private service providers, which were aimed at transforming the organizational macro-setting of local service provision, the task portfolio and functional profile of local governments. The chapter then moves to scrutinizing internal managerial reforms concerned with the modernization of organization and processes and the improvement of management capacities inside local administrations meant to strengthen performance, output- and consumer-orientation in local service delivery. The country sample includes the United Kingdom (England), Sweden, and Germany that represent three distinct types of administrative culture and local government in Europe.
Looking for participation
(2022)
A stronger learner orientation through participatory learning increases learning motivation and results. But what does participatory learning mean? Where do learning factories and fabrication laboratories (FabLabs) stand in this context, and how can didactic implementation be improved in this respect? Using a newly developed analytical framework, which contains elements of the stage model of participation and general media didactics, we compare a FabLab and a learning factory example concerning the degree of participation. From this, we derive guidelines for designing participative teaching and learning processes in learning factories. We explain how FabLabs can be an inspiration for the didactic design of learning factories.
We conduct a laboratory experiment to study how locus of control operates through people's preferences and beliefs to influence their decisions. Using the principal-agent setting of the delegation game, we test four key channels that conceptually link locus of control to decision-making: (i) preference for agency; (ii) optimism and (iii) confidence regarding the return to effort; and (iv) illusion of control. Knowing the return and cost of stated effort, principals either retain or delegate the right to make an investment decision that generates payoffs for themselves and their agents. Extending the game to the context in which the return to stated effort is unknown allows us to explicitly study the relationship between locus of control and beliefs about the return to effort. We find that internal locus of control is linked to the preference for agency, an effect that is driven by women. We find no evidence that locus of control influences optimism and confidence about the return to stated effort, or that it operates through an illusion of control.
Living in a world of plenty?
(2020)
Inequality in the distribution of economic wealth within populations has been rising steadily over the past century, having reached unprecedented highs in many Western societies. However, this development is not reflected in people’s perceptions of wealth inequality, as the public tends to underestimate it. Research suggests that inequality estimates are derived from personal reference groups, which, as we propose, are expanded by social network site (SNS) use. As content on SNSs frequently revolves around events of consumption, signaling enhanced overall population wealth, this study tests the hypothesis that SNS use distorts inequality perceptions downward, i.e., increases the perception of societal equality. Responses of 534 survey participants in the United States confirm that SNS use negatively predicts perceived inequality. The relationship is stronger the more SNS users perceive the content they encounter online as real, supporting the assumption that observing other people’s behavior online lowers estimates of nationwide wealth inequality. These findings provide novel insights on inequality misperceptions by suggesting individuals’ SNS use as a new predictor of perceived wealth inequality.
Lists as embedded structures and the prosody of list construction as an interactional resource
(2003)
With larger artificial neural networks (ANN) and deeper neural architectures, common methods for training ANN, such as backpropagation, are key to learning success. Their role becomes particularly important when interpreting and controlling structures that evolve through machine learning. This work aims to extend previous research on backpropagation-based methods by presenting a modified, full-gradient version of the backpropagation learning algorithm that preserves (or rather crystallizes) selected neural weights while leaving other weights adaptable (or rather fluid). In a design-science-oriented manner, a prototype of a feedforward ANN is demonstrated and refined using the new learning method. The results show that the so-called crystallizing backpropagation increases the control possibilities of neural structures and interpretation chances, while learning can be carried out as usual. Since neural hierarchies are established because of the algorithm, ANN compartments start to function in terms of cognitive levels. This study shows the importance of dealing with ANN in hierarchies through backpropagation and brings in learning methods as novel ways of interacting with ANN. Practitioners will benefit from this interactive process because they can restrict neural learning to specific architectural components of ANN and can focus further development on specific areas of higher cognitive levels without the risk of destroying valuable ANN structures.
Learning from failure
(2022)
Regression testing is a widespread practice in today's software industry to ensure software product quality. Developers derive a set of test cases, and execute them frequently to ensure that their change did not adversely affect existing functionality. As the software product and its test suite grow, the time to feedback during regression test sessions increases, and impedes programmer productivity: developers wait longer for tests to complete, and delays in fault detection render fault removal increasingly difficult.
Test case prioritization addresses the problem of long feedback loops by reordering test cases, such that test cases of high failure probability run first, and test case failures become actionable early in the testing process. We ask, given test execution schedules reconstructed from publicly available data, to which extent can their fault detection efficiency improved, and which technique yields the most efficient test schedules with respect to APFD?
To this end, we recover regression 6200 test sessions from the build log files of Travis CI, a popular continuous integration service, and gather 62000 accompanying changelists. We evaluate the efficiency of current test schedules, and examine the prioritization results of state-of-the-art lightweight, history-based heuristics. We propose and evaluate a novel set of prioritization algorithms, which connect software changes and test failures in a matrix-like data structure.
Our studies indicate that the optimization potential is substantial, because the existing test plans score only 30% APFD. The predictive power of past test failures proves to be outstanding: simple heuristics, such as repeating tests with failures in recent sessions, result in efficiency scores of 95% APFD. The best-performing matrix-based heuristic achieves a similar score of 92.5% APFD. In contrast to prior approaches, we argue that matrix-based techniques are useful beyond the scope of effective prioritization, and enable a number of use cases involving software maintenance.
We validate our findings from continuous integration processes by extending a continuous testing tool within development environments with means of test prioritization, and pose further research questions. We think that our findings are suited to propel adoption of (continuous) testing practices, and that programmers' toolboxes should contain test prioritization as an existential productivity tool.
Law of raw data
(2021)
Law of Raw Data gives an overview of the legal situation across major countries and how such data is contractually handled in practice in the respective countries. In recent years, digital technologies have transformed business and society, impacting all sectors of the economy and a wide variety of areas of life. Digitization is leading to rapidly growing volumes of data with great economic potential. Data, in its raw or unstructured form, has become an important and valuable economic asset, and protection of raw data has become a crucial subject for the intellectual property community. As legislators struggle to develop a settled legal regime in this complex area, this invaluable handbook will offer a careful and dedicated analysis of the legal instruments and remedies, both existing and potential, that provide such protection across a wide variety of national legal systems.
What’s in this book:
Produced under the auspices of the International Association for the Protection of International Property (AIPPI), more than forty active specialists of the association from twenty-three countries worldwide contribute national chapters on the relevant law in their respective jurisdictions. The contributions thoroughly explain how each country approaches such crucial matters as the following:
if there is any intellectual property right available to protect raw data; the nature of such intellectual property rights that exist in unstructured data; contracts on data and which legal boundaries stand in the way of contract drafting; liability for data products or services; and questions of international private law and cross-border portability.
Each country’s rules concerning specific forms of data – such as data embedded in household appliances and consumer goods, criminal offence data, data relating to human genetics, tax and bank secrecy, medical records, and clinical trial data – are described, drawing on legislation, regulation, and case law.
How this will help you:
A matchless legal resource on one of the most important raw materials of the twenty-first century, this book provides corporate counsel, practitioners and policymakers working in the field of intellectual property rights, and concerned academics with both a broad-based global overview on emerging legal strategies in the protection of unstructured data and the latest information on existing legislation and regulation in the area.
Crochet is a popular handcraft all over the world. While other techniques such as knitting or weaving have received technical support over the years through machines, crochet is still a purely manual craft. Not just the act of crochet itself is manual but also the process of creating instructions for new crochet patterns, which is barely supported by domain specific digital solutions. This leads to unstructured and often also ambiguous and erroneous pattern instructions. In this report, we propose a concept to digitally represent crochet patterns. This format incorporates crochet techniques which allows domain specific support for crochet pattern designers during the pattern creation and instruction writing process. As contributions, we present a thorough domain analysis, the concept of a graph structure used as domain specific language to specify crochet patterns and a prototype of a projectional editor using the graph as representation format of patterns and a diagramming system to visualize them in 2D and 3D. By analyzing the domain, we learned about crochet techniques and pain points of designers in their pattern creation workflow. These insights are the basis on which we defined the pattern representation. In order to evaluate our concept, we built a prototype by which the feasibility of the concept is shown and we tested the software with professional crochet designers who approved of the concept.
We welcome you to the 54th Hawaii International Conference on System Sciences (HICSS-54) conference. This is the fifth year for the Organizational Learning Minitrack which has had the usual growing pains: two years ago, we added the topic of Unlearning and joined with the Intentional Forgetting Minitrack - as these topics are all organizationally-based knowledge management issues. We proudly bring you the latest research focused on the methods to develop and maintain organizational learning within the Knowledge Innovation and Entrepreneurial Systems Track. The ability to update, change and use current knowledge effectively, especially in light of the ongoing knowledge explosion, can be costly for any organization. Organizations that consider themselves “learning” or “knowledge-based” organizations must develop a competent workforce using KM strategies. Success in organizations involves developing a variety of human factors for changing competencies. With technological change, modification and revisions, many skills require updating for a competitive advantage in the marketplace. The focus on new techniques and insights into how individuals and organizations use their knowledge is our focus for the improvement of organizational
learning in this Minitrack.
It’s personal
(2021)
The new technologies of the Fourth Industrial Revolution (4IR) are disrupting traditional models of work and learning. While the impact of digitalization on education was already a point of serious deliberation, the COVID-19 pandemic has expedited ongoing transitions. With 90% of the world’s student population having been impacted by national lockdowns—online learning has gone from being a luxury to a necessity, in a context where around 3.6 billion people are offline. As the impacts of the 4IR unfold alongside the current crisis, it is not enough for future policy pathways to prioritize educational attainment in the traditional sense; it is essential to reimagine education itself as well as its delivery entirely. Future policy narratives will need to evaluate the very process of learning and identify the ways in which technology can help reduce existing disparities and enhance digital access, literacy and fluency in a scalable manner. In this context, this chapter analyses the status quo of online learning in India and Germany. Drawing on the experiences of these two economies with distinct trajectories of digitalization, the chapter explores how new technologies intersect with traditional education and local sociocultural conditions. Further, the limitations and opportunities presented by dominant ed-tech models is critically analyzed against the ongoing COVID-19 pandemic.