Government Transparency: Six Strategies for More Open and Participatory Government

Offers strategies for realizing Knight's 2009 call for e-government and openness using Web 2.0 and 3.0 technologies, including public-private partnerships to develop applications, flexible procurement procedures, and better community broadband access

Automated Generation of User Guidance by Combining Computation and Deduction

Herewith, a fairly old concept is published for the first time and named "Lucas Interpretation". This has been implemented in a prototype, which has been proved useful in educational practice and has gained academic relevance with an emerging generation of educational mathematics assistants (EMA) based on Computer Theorem Proving (CTP). Automated Theorem Proving (ATP), i.e. deduction, is the most reliable technology used to check user input. However ATP is inherently weak in automatically generating solutions for arbitrary problems in applied mathematics. This weakness is crucial for EMAs: when ATP checks user input as incorrect and the learner gets stuck then the system should be able to suggest possible next steps. The key idea of Lucas Interpretation is to compute the steps of a calculation following a program written in a novel CTP-based programming language, i.e. computation provides the next steps. User guidance is generated by combining deduction and computation: the latter is performed by a specific language interpreter, which works like a debugger and hands over control to the learner at breakpoints, i.e. tactics generating the steps of calculation. The interpreter also builds up logical contexts providing ATP with the data required for checking user input, thus combining computation and deduction. The paper describes the concepts underlying Lucas Interpretation so that open questions can adequately be addressed, and prerequisites for further work are provided.Comment: In Proceedings THedu'11, arXiv:1202.453

The Importance of Transparency and Willingness to Share Personal Information

This study investigates the extent to which individuals are willing to share their sensitive personal information with companies. The study examines whether skepticism can influence willingness to share information. Additionally, it seeks to determine whether transparency can moderate the relationship between skepticism and willingness to share and whether 1) companies perceived motives, 2) individual’s prior privacy violations, 3) individuals’ propensity to take risks, and 4) individuals self-efficacy act as antecedents of skepticism. Partial Least Squares (PLS) regression is used to examine the relationships between all the factors. The findings indicate that skepticism does have a negative impact on willingness to share personal information and that transparency can reduce skepticis

Architecture and Performance of the Mether Network Shared Memory

Mether is a Network Shared Memory (NSM). It allows applications on autonomous computers connected by a network to share a segment of memory. NSMs offer the attraction of a simple abstraction for shared state, i.e., shared memory. NSMs have a potential performance problem in the cost of remote references, which is typically solved by grouping memory into larger units such as pages, and caching pages. While Mether employs grouping and caching to reduce the average memory reference delay, it also removes the need for many remote references (page faults) by providing a facility with relaxed consistency requirements. Applications ported from a multiprocessor supercomputer with shared memory to a 16-workstation Mether configuration showed a cost/performance advantage of over 300 in favor of the Mether system. While Mether is currently implemented for Sun-3 and Sun-4 systems connected via Ethernet, other characteristics (such as a choice of page sizes and a semaphore-like access mode useful for process synchronization) should suit it to a wide variety of networks. A reimplementation for an alternate configuration employing packet-switched networks is in progress

Designing Data Spaces

This open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and applications in various industries. To this end, the book is structured in four parts: Part I “Foundations and Contexts” provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II “Data Space Technologies” subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various “Use Cases and Data Ecosystems” from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several “Solutions and Applications”, eg including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more. Overall, the book provides professionals in industry with an encompassing overview of the technological and economic aspects of data spaces, based on the International Data Spaces and Gaia-X initiatives. It presents implementations and business cases and gives an outlook to future developments. In doing so, it aims at proliferating the vision of a social data market economy based on data spaces which embrace trust and data sovereignty

Legal Compatibility as a Characteristic of Sociotechnical Systems

Legal compatibility as a characteristic of sociotechnical systems aims at the greatest possible compliance with higher-order legal goals for minimizing social risks of technical systems and extends legality, which refers to the prevention of lawlessness. The paper analyzes the criteria for legal compatibility by reviewing specifications of legally compatible systems and shows goals and resulting requirements to foster legal compatibility. These comprise the following areas: avoiding personal reference in data, ensuring information security, enabling freedom of decision, increasing transparency, ensuring traceability, and increasing usability, whereby traceability and the avoidance of personal reference pursue conflicting goals. The presentation of the goals including their dependencies, relationships, and conflicts in form of standardized requirements explains legal compatibility and summarizes the requirements necessary for the development of legally compatible Systems

Industry 4.0 and the future of manufacturing. Theoretical base and empirical analyses

A new industrial revolution \u2013 also called \u201cIndustry 4.0\u201d \u2013 is unfolding fueled by the introduction of broadly interconnected digital technologies, including the Internet of Things, cloud computing, artificial intelligence and additive manufacturing. Many industries are witnessing the entrance of new players integrating new technologies into disruptive business models; incumbents are also urged to rethink how they operate against trends that are expected to further accelerate in the current pandemic situation. The overarching aim of the research presented in this doctoral dissertation is to investigate to what extent Industry 4.0 represents a fundamental challenge to existing paradigms and requires researchers to modify their theoretical frameworks to approach emerging issues. With this in mind, each chapter can be seen as a step forward in journey whereby some core issues come progressively into focus. The starting point is a conceptual work analyzing the phenomenon \u2013 \u201cIndustry 4.0\u201d and similar labels \u2013 and its underlying technological and non-technological components. As a second step \u2013 under the assumption of Industry 4.0 having paradigmatic properties comparable to previous industrial revolutions \u2013 potential new configurations of manufacturing value chains are investigated. Through a future-oriented expert study, eight scenarios are conceived identifying critical drivers to value chain configurations. Finally, one of these critical drivers \u2013 data sharing in inter-organizational relationships \uac\u2013 is investigated through the development of a multiple case study analysis in the automotive sector. The contribution of this dissertation to the academic debate is at least twofold. On the one hand, the research highlights the cornerstones of the phenomenon to make sense of its overarching features and building elements. This contributes to lay solid theoretical foundations needed to advance the understanding in the field. On the other hand, my empirical investigations suggest that several barriers counterbalance the technological drivers for change, posing significant questions as for when and how the future of manufacturing will materialize. Overall, an approach focused on understanding how technologies influence the assumptions behind the current reasoning might lead at a synthesis between \u201cold\u201d and \u201cnew\u201d elements in the Industry 4.0 phenomenon

Competitive advantage during industry 4.0: the case for South African manufacturing SMEs

A research report submitted to the Faculty of Engineering and the Built Environment, Uni- versity of the Witwatersrand, Johannesburg, in partial fullfilment of the requirements for the degree of Master of Science in Engineering. Johannesburg, May 2018With the expected disruption of industry 4.0 and the current challenges that SMEs face in South Africa, there is an increasing threat that SMEs will lose any competitive advantage they currently have. This exploratory study investigates how South African manufacturing SMEs can remain competitive during the fourth industrial revolution. Data, in the form of current literature, was analysed using thematic content analysis. From the analysis process, 8 emergent themes were used to organise the results of the study. Notable findings towards generating competitive advantage included: The location of SMEs within clusters, collaboration with disruption leaders, the sharing of outcomes across the value chain, the shift of business models towards a service and software orientation, the use of data driven insights to find and capture high margin markets and the increased effectiveness of labour through technology use. The study also found that the use of the IoT and cloud computing can significantly reduce infrastructure requirements and promote a competitive advantage.MT 201