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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.
The COVID-19 virus has hit Germany as unexpectedly as other European countries. For a few weeks, Germans thought that COVID-19 was an issue for Asian states and not for their country. Although Germany continues to be affected by the coronavirus, the situation is nowhere as dire as it was in Britain, Italy or Spain. The race to lift restrictions in Germany began in May, and by early June, the country may be back to normal. Germany, with its enormous financial resources and a well-equipped medical sector, appears to be better placed than other economies to weather the storm.
Germany as a leading power
(2020)
The ellipticity of operators on a manifold with edge is defined as the bijectivity of the components of a principal symbolic hierarchy sigma = (sigma(psi), sigma(boolean AND)), where the second component takes values in operators on the infinite model cone of the local wedges. In the general understanding of edge problems there are two basic aspects: Quantisation of edge-degenerate operators in weighted Sobolev spaces, and verifying the ellipticity of the principal edge symbol sigma(boolean AND) which includes the (in general not explicity known) number of additional conditions of trace and potential type on the edge. We focus here on these questions and give explicit answers for a wide class of elliptic operators that are connected with the ellipticity of edge boundary value problems and reductions to the boundary. In particular, we study the edge quantisation and ellipticity for Dirichlet-Neumann operators with respect to interfaces of some codimension on a boundary. We show analogues of the Agranovich-Dynin formula for edge boundary value problems.
Defining britishhness from the margins : Peter Weir's gallipoli and hugh hudson's chariots of fire
(2003)
While school supervision structures in the German Länder were extensively reformed during the last decades, systematic analyses of these reforms are missing. This chapter contributes to this research gap by providing an overview of the implemented reforms of school supervision structures in the German Länder. The effects of these reforms are analysed in order to answer the question of whether a convergence of school supervision systems is a result of these reforms. In a first step, a distinction is made to identify system-changing reforms. Although a decrease of the number or a concentration on one school supervision system is not a result of the analysis, it is argued that there is a convergence of school supervision structures, as a clear trend against school supervision systems with lower school supervisory boards can be observed.
During reading, our eyes perform complicated sequences of fixations on words. Stochastic models of eye movement control suggest that this seemingly erratic behaviour can be attributed to noise in the oculomotor system and random fluctuations in lexical processing. Here, we present a qualitative analysis of a recently published dynamical model [Engbert et al., 2002] and propose that deterministic nonlinear control accounts for much of the observed complexity of eye movement patterns during reading. Based on a symbolic coding technique we analyze robust statistical features of simulated fixation sequences
This study evaluates the challenges, institutional impacts and responses of German local authorities to the COVID-19 pandemic from a political science point of view. The main research question is how they have contributed to combat the COVID-19 pandemic and to what extent the strengths and weaknesses of the German model of municipal autonomy have influenced their policy. It analyses the adaptation strategies of German local authorities and assesses the effectiveness of their actions up to now. Their implementation is then evaluated in five selected issues, e.g. adjustment organization and staff, challenges for local finances, local politics and citizen’s participation. This analysis is reflecting the scientific debate in Germany since the beginning of 2020, based on the available analyses of political science, law, economics, sociology and geography until end of March 2021.
Today, InfiniBand is an evolving high speed interconnect technology to build high performance computing clusters, that achieve top 10 rankings in the current top 500 of the worldwide fastest supercomputers. Network interfaces (called host channel adapters) provide transport layer services over connections and datagrams in reliable or unreliable manner. Additionally, InfiniBand supports remote direct memory access (RDMA) primitives that allow for one- sided communication. Using server load balancing together with a high performance cluster makes it possible to build a fast, scalable, and reliable service infrastructure. We have designed and implemented a scalable load balancer for InfiniBand clusters called SLIBNet. Our investigations show that the InfiniBand architecture offers features which perfectly support load balancing. We want to thank the Megware Computer GmbH for providing us an InfiniBand switch to realize a server load balancing testbed.
After some seventy years of intensive debates, there is an increasingly strong consensus within the academic and practitioner communities that development is both an objective and a process towards improving the quality of people's lives in various societal dimensions – economic, social, environmental, cultural and political – and about how subjectively satisfied they are with it. Since 2015, the seventeen Sustainable Development Goals (SDGs) of the United Nations (UN) reflect such consensus. The sections behind this argument are based on a review of (i) three key theoretical contributions to development and different phases of development thinking; (ii) global and regional governance arrangements and institutions for development cooperation; (iii) upcoming challenges to development policy and practice stemming from a series of new global challenges; and, (iv) development policy as a long and steady, increasingly global and participatory learning process.
We and AI
(2021)
Influence of hormone replacement therapy on proteomic pattern in serum of postmenopausal women
(2004)
Objectives: Proteomics approaches to cardiovascular biology and disease hold the promise of identifying specific proteins and peptides or modification thereof to assist in the identification of novel biomarkers. Method: By using surface-enhanced laser desorption and ionization time of flight mass spectroscopy (SELDI-TOF-MS) serum peptide and protein patterns were detected enabling to discriminate between postmenopausal women with and without hormone replacement therapy (HRT). Results: Serum of 13 HRT and 27 control subjects was analyzed and 42 peptides and proteins could be tentatively identified based on their molecular weight and binding characteristics on the chip surface. By using decision tree-based Biomarker Patterns (TM) Software classification and regression analysis a discriminatory function was developed allowing to distinguish between HRT women and controls correctly and, thus, yielding a sensitivity of 100% and a specificity of 100%. The results show that peptide and protein patterns have the potential to deliver novel biomarkers as well as pinpointing targets for improved treatment. The biomarkers obtained represent a promising tool to discriminate between HRT users and non-users. Conclusion: According to a tentative identification of the markers by their molecular weight and binding characteristics, most of them appear to be part of the inflammation induced acute-phase response. (c) 2004 Elsevier Ireland Ltd. All rights reserved
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.
Symplectic geometry, wigner-weyl-moyal calculus, and quantum mechanics, in phase space ; Part 1
(2006)
Symplectic geometry, wigner-weyl-moyal calculus, and quantum mechanics, in phase space ; Part 2
(2006)
Symplectic geometry, wigner-weyl-moyal calculus, and quantum mechanics, in phase space ; Part 3
(2006)
Nils-Hendrik Grohmann beschäftigt sich mit dem noch andauernden Stärkungsprozess der UN-Menschenrechtsvertragsorgane. Er analysiert, welche rechtlichen Befugnisse die Ausschüsse haben, ob sie von sich aus Vorschläge einbringen können und inwieweit sie ihre Verfahrensweisen bisher aufeinander abgestimmt haben. Ein weiterer Schwerpunkt liegt auf der Zusammenarbeit zwischen den verschiedenen Ausschüssen und der Frage, welche Rolle das Treffen der Vorsitzenden bei der Stärkung spielen kann.
This contribution presents an approach for requirement oriented team building in industrial processes like product development. This will be based on the knowledge modelling and description language (KMDL(R)) that enables the modelling and analysis of knowledge intensive business processes. First the basic elements of the modelling technique are described, presenting the concept and the description language. Furthermore it is shown how the KMDL(R) process models can be used as a basis for the team building component. Therefore, an algorithm was developed that is able to propose a team composition for a specific task by analyzing the knowledge and skills of the employees, which will be contrasted to the process requirements. This can be used as guidance for team building decisions.
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.
Manufacturing companies still have relatively few points of contact with the circular economy. Especially, extending life time of whole products or parts via remanufacturing is an promising approach to reduce waste. However, necessary cost-efficient assessment of the condition of the individual parts is challenging and assessment procedures are technically complex (e.g., scanning and testing procedures). Furthermore, these assessment procedures are usually only available after the disassembly process has been completed. This is where conceptualization, data acquisition and simulation of remanufacturing processes can help. One major constraining aspect of remanufacturing is reducing logistic efforts, since these also have negative external effects on the environment. Thus regionalization is an additional but in the end consequential challenge for remanufacturing. This article aims to fill a gap by providing an regional remanufacturing approach, in particular the design of local remanufacturing chains. Thereby, further focus lies on modeling and simulating alternative courses of action, including feasibility study and eco-nomic assessment.
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.
The business problem of having inefficient processes, imprecise process analyses and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS) and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.
Context-aware, intelligent musical instruments for improving knowledge-intensive business processes
(2022)
With shorter song publication cycles in music industries and a reduced number of physical contact opportunities because of disruptions that may be an obstacle for musicians to cooperate, collaborative time consumption is a highly relevant target factor providing a chance for feedback in contemporary music production processes. This work aims to extend prior research on knowledge transfer velocity by augmenting traditional designs of musical instruments with (I) Digital Twins, (II) Internet of Things and (III) Cyber-Physical System capabilities and consider a new type of musical instrument as a tool to improve knowledge transfers at knowledge-intensive forms of business processes. In a design-science-oriented way, a prototype of a sensitive guitar is constructed as information and cyber-physical system. Findings show that this intelligent SensGuitar increases feedback opportunities. This study establishes the importance of conversion-specific music production processes and novel forms of interactions at guitar playing as drivers of high knowledge transfer velocities in teams and among individuals.
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.
A growing number of business processes can be characterized as knowledge-intensive. The ability to speed up the transfer of knowledge between any kind of knowledge carriers in business processes with AR techniques can lead to a huge competitive advantage, for instance in manufacturing. This includes the transfer of person-bound knowledge as well as externalized knowledge of physical and virtual objects. The contribution builds on a time-dependent knowledge transfer model and conceptualizes an adaptable, AR-based application. Having the intention to accelerate the speed of knowledge transfers between a manufacturer and an information system, empirical results of an experimentation show the validity of this approach. For the first time, it will be possible to discover how to improve the transfer among knowledge carriers of an organization with knowledge-driven information systems (KDIS). Within an experiment setting, the paper shows how to improve the quantitative effects regarding the quality and amount of time needed for an example manufacturing process realization by an adaptable KDIS.
Accelerating knowledge
(2019)
As knowledge-intensive processes are often carried out in teams and demand for knowledge transfers among various knowledge carriers, any optimization in regard to the acceleration of knowledge transfers obtains a great economic potential. Exemplified with product development projects, knowledge transfers focus on knowledge acquired in former situations and product generations. An adjustment in the manifestation of knowledge transfers in its concrete situation, here called intervention, therefore can directly be connected to the adequate speed optimization of knowledge-intensive process steps. This contribution presents the specification of seven concrete interventions following an intervention template. Further, it describes the design and results of a workshop with experts as a descriptive study. The workshop was used to assess the practical relevance of interventions designed as well as the identification of practical success factors and barriers of their implementation.
Since more and more production tasks are enabled by Industry 4.0 techniques, the number of knowledge-intensive production tasks increases as trivial tasks can be automated and only non-trivial tasks demand human-machine interactions. With this, challenges regarding the competence of production workers, the complexity of tasks and stickiness of required knowledge occur [1]. Furthermore, workers experience time pressure which can lead to a decrease in output quality. Cyber-Physical Systems (CPS) have the potential to assist workers in knowledge-intensive work grounded on quantitative insights about knowledge transfer activities [2]. By providing contextual and situational awareness as well as complex classification and selection algorithms, CPS are able to ease knowledge transfer in a way that production time and quality is improved significantly. CPS have only been used for direct production and process optimization, knowledge transfers have only been regarded in assistance systems with little contextual awareness. Embedding production and knowledge transfer optimization thus show potential for further improvements. This contribution outlines the requirements and a framework to design these systems. It accounts for the relevant factors.
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.