Method support for enterprise architecture management capabilities

"What can our EA organization do and/or what should it be capable of?". In order to answer this questions, a capability-based method is developed, which assists in the identification, structuring and management of capabilities. The approach is embedded in a process comprising four building blocks providing appropriated procedures, concepts and supporting tools evolved from theory and practical use cases. The guide represents a flexible method for capability newcomers and experienced audiences to optimize enterprises’ economic impacts of EAM supporting the alignment of business and IT.„Was muss unser UAM leisten können?“ Als Grundlage für die Beantwortung dieser Frage sollen Konzepte aus dem Fähigkeitenmanagement genutzt werden. Im Rahmen dieser Arbeit wird eine fähigkeitenbasierte Methode entwickelt, welche Unternehmen bei der Identifikation, Strukturierung und Verwaltung von UAM-Fähigkeiten unterstützt. Der Ansatz ist in einen Prozess eingegliedert, welcher vier Hauptbestandteile beinhaltet und die für die Durchführung notwendigen Vorgehen, Konzepte und Hilfsmittel beschreibt, welche wiederrum in Kooperationen mit der Praxis getestet wurden

Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information

Advances in machine learning technologies have led to increasingly powerful models in particular in the context of big data. Yet, many application scenarios demand for robustly interpretable models rather than optimum model accuracy; as an example, this is the case if potential biomarkers or causal factors should be discovered based on a set of given measurements. In this contribution, we focus on feature selection paradigms, which enable us to uncover relevant factors of a given regularity based on a sparse model. We focus on the important specific setting of linear ordinal regression, i.e.\ data have to be ranked into one of a finite number of ordered categories by a linear projection. Unlike previous work, we consider the case that features are potentially redundant, such that no unique minimum set of relevant features exists. We aim for an identification of all strongly and all weakly relevant features as well as their type of relevance (strong or weak); we achieve this goal by determining feature relevance bounds, which correspond to the minimum and maximum feature relevance, respectively, if searched over all equivalent models. In addition, we discuss how this setting enables us to substitute some of the features, e.g.\ due to their semantics, and how to extend the framework of feature relevance intervals to the setting of privileged information, i.e.\ potentially relevant information is available for training purposes only, but cannot be used for the prediction itself.Comment: Preprint accepted at Neurocomputin

Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information

Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, Hammer B. Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing. 2020.Advances in machine learning technologies have led to increasingly powerful models in particular in the context of big data. Yet, many application scenarios demand for robustly interpretable models rather than optimum model accuracy; as an example, this is the case if potential biomarkers or causal factors should be discovered based on a set of given measurements. In this contribution, we focus on feature selection paradigms, which enable us to uncover relevant factors of a given regularity based on a sparse model. We focus on the important specific setting of linear ordinal regression, i.e. data have to be ranked into one of a finite number of ordered categories by a linear projection. Unlike previous work, we consider the case that features are potentially redundant, such that no unique minimum set of relevant features exists. We aim for an identification of all strongly and all weakly relevant features as well as their type of relevance (strong or weak); we achieve this goal by determining feature relevance bounds, which correspond to the minimum and maximum feature relevance, respectively, if searched over all equivalent models. In addition, we discuss how this setting enables us to substitute some of the features, e.g. due to their semantics, and how to extend the framework of feature relevance intervals to the setting of privileged information, i.e. potentially relevant information is available for training purposes only, but cannot be used for the prediction itself

The Relative information content of complementary and supplementary narrative commentary in UK interim reports.

The main objective of the research is to investigate the relative information content of complementary and supplementary narrative commentaries in UK interim reports. The study also examines the relative importance of complementary and supplementary narrative attributes. The subsidiary objective of the study is to investigate incremental information content of complementary and supplementary narratives. The study used 309 interim reports of 103 companies for the years 2005 to 2007. The returns used were daily market adjusted cumulative abnormal returns ±5 days around the announcement of interim reports. The disclosure index method was used to capture complementary and supplementary information using disclosure variety (number of information items) and disclosure depth set of attributes (good news, amounts and comparison of current with past performance, reasons for performance and forward-looking). The control variables included financial performance measures of dividend yield, earnings per share and total assets. Event studies based multiple regression models were used to measure information content. The findings in respect of the main objective indicate that supplementary narratives had higher but insignificant infonnation content than complementary narratives for the model based on disclosure variety. However, when disclosure depth is used, complementary narratives have higher and significant relative information content than supplementary narratives. The results also show that complementary good news, complementary amounts and comparisons of current with past performance and complementary reasons for performance were associated with returns unlike their respective counterparts in supplementary narratives. Both complementary and supplementary forward-looking attributes were not associated with returns. The results of the subsidiary objective suggest that the disclosure varit)ty model combining complementary and supplementary narratives when compared with the disclosure variety model having supplementary narratives does not have a significant difference. All other incremental information content comparisons based on either disclosure variety or disclosure depth had significant differences. This study has a number of research and policy implications, especially after the 2007 subprime financial crisis

Finding headspace in green workplaces : the restorative value of science park open space

Person-environment relationships in five urban-fringe science parks in central Scotland were investigated through the application of a mixed method case study design. The study sought to explore the impact of greenspace at these knowledge-sector workplaces on employee wellbeing, with particular focus on restorative effects of viewing and spending time in green environments. The thesis also aims to develop understanding of how workers at these sites engage with, and relate to, the outdoor environment at their workplace. Both quantitative and qualitative data were collected; the former through an online questionnaire (n=366), and the latter through in-depth semi-structured walking interviews (n=16) conducted on and around the sites. This research is the first to provide evidence of wellbeing benefits of greenspace in the context of UK workplaces. Its focus on the landscape of science parks is of particular relevance given the prominence of this development model in planning policy to promote regional economic growth, as well as the central role of employee functioning in the productivity of innovative knowledge-sector businesses. The insights gained through the research point to a number of conclusions for the planning and design of future business sites at the urban fringe. The research also makes an original contribution to the international research on restorative environments in its exploration of how different types and designs of open space impact on the wellbeing of workers and, in particular, how individual factors influence responses to elements of open space design and management in the workplace context

Making of a Voiceless Youth: Corruption in Bosnia and Herzegovina's Higher Education

This research has analyzed a set of structural elements, procedures, and behaviors within Bosnia and Herzegovina's (thereafter, "Bosnia" or "B&H") higher education that have jointly created an encouraging space for the increasing and self-serving utilization of higher education by the country's post-war elite. Of the particular interest is this elite's impact on the forms of educational corruption, which have shifted away from standard bribing processes and moved toward more complex favor reciprocation networks. This process has ensured that today's corruption is perceived as a norm in Bosnia's higher education. Its prevalence has disrupted existing social mobility mechanisms and created a duality in the social mobility process so that the unprivileged still work hard to obtain their degrees while those with social connections are reliant on Turner's (1960) sponsorship model. The analysis goes beyond dissecting corruption's impact on modes of social mobility by redefining Hirschman's (1970) notions of voice, exit, and loyalty within higher education and expanding his theoretical framework to adequately capture and understand the unique set of coping mechanisms that has emerged within Bosnia's corrupt higher education. I reinterpret the voice mechanism that Hirschman sees as a political tool capable of bringing about change as, ironically, severely diminished in its power when observed within a corrupt environment. I further reformulate the notion of exit and contextualize it within the corrupt Bosnian educational system by differentiating amongst various types of exit. In the process, the study finds that Bosnian students often remain in the same educational institution despite the high level of perceived corruption. By ignoring their immediate surroundings and rather than departing physically as Hirschman would expect, students choose to exit mentally from the corrupt operational framework in which they continue to function physically. Lastly, with hard-work and morality marginalized, the question remains open on when the youth will push the educational system in Bosnia toward a tipping point, regain their voice, and transform from an indolent mass to an active reformer. Projects requiring greater transparency of the exam and grading procedures, enhancing external support, and providing spaces for disclosure and adequate management of incidences of corruption, when and if detected, would constitute a meaningful starting point that would help incentivize change. In the absence of concern with the current level of educational corruption, however, the dominance of the incompetent elites will only continue to dilute the effectiveness of the aid being poured into the EU's broader nation-building agenda for post-war Bosnia and Herzegovina

Abundant information resources online, equalized development? Using the internet for learning and the mitigation of unequal occupational mobilities

This study explores the possibility that using the internet for learning mitigates the inequality of occupational mobility between rural migrant workers, a disadvantaged group in cities, and their advantaged counterparts, urban resident workers, in urban China. To investigate the mitigation of unequal occupational mobilities, this study examines, a) the extent to which using the internet for learning offers greater labour market benefits for the disadvantaged – rural migrant workers, and b) the extent to which rural migrant and urban resident workers have equal use of the internet for learning. This study uses quantitative and qualitative data in a complementary manner, with the quantitative analysis (data from China Family Panel Studies) being used to offer more rigorous results of comparison and the qualitative findings (data from 24 additional semi-structured interviews) being used to enrich explanations to interpret the observed comparative results. The results show a ‘negative selection’ phenomenon in using the internet for learning. That is, while rural migrant workers seem to be able to get more labour market benefits from learning online, they are actually less likely to use the internet for learning in the first place. As such, the results do not show that using the internet for learning mitigates the inequality of occupational mobility between the two groups. Structural inequalities cause rural migrant workers more excluded from using the internet for learning in the first place. The stronger ‘learning-mobility' relationship for rural migrant workers merely reflects their deprivation of skill- and non-skill-related resources for occupational attainment in the urban labour market. The ‘negative selection’ phenomenon in using the internet for learning demonstrates the way that pre-existing structural inequalities are constantly being reproduced with new manifestations in an ever-changing world

The relative information content of complementary and supplementary narrative commentary in UK interim reports

The main objective of the research is to investigate the relative information content of complementary and supplementary narrative commentaries in UK interim reports. The study also examines the relative importance of complementary and supplementary narrative attributes. The subsidiary objective of the study is to investigate incremental information content of complementary and supplementary narratives. The study used 309 interim reports of 103 companies for the years 2005 to 2007. The returns used were daily market adjusted cumulative abnormal returns ±5 days around the announcement of interim reports. The disclosure index method was used to capture complementary and supplementary information using disclosure variety (number of information items) and disclosure depth set of attributes (good news, amounts and comparison of current with past performance, reasons for performance and forward-looking). The control variables included financial performance measures of dividend yield, earnings per share and total assets. Event studies based multiple regression models were used to measure information content. The findings in respect of the main objective indicate that supplementary narratives had higher but insignificant infonnation content than complementary narratives for the model based on disclosure variety. However, when disclosure depth is used, complementary narratives have higher and significant relative information content than supplementary narratives. The results also show that complementary good news, complementary amounts and comparisons of current with past performance and complementary reasons for performance were associated with returns unlike their respective counterparts in supplementary narratives. Both complementary and supplementary forward-looking attributes were not associated with returns. The results of the subsidiary objective suggest that the disclosure varit)ty model combining complementary and supplementary narratives when compared with the disclosure variety model having supplementary narratives does not have a significant difference. All other incremental information content comparisons based on either disclosure variety or disclosure depth had significant differences. This study has a number of research and policy implications, especially after the 2007 subprime financial crisis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

xxAI - Beyond Explainable AI

This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.https://digitalcommons.unomaha.edu/isqafacbooks/1000/thumbnail.jp

xxAI - Beyond Explainable AI

This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science