A practical guide to computer simulations

Here practical aspects of conducting research via computer simulations are discussed. The following issues are addressed: software engineering, object-oriented software development, programming style, macros, make files, scripts, libraries, random numbers, testing, debugging, data plotting, curve fitting, finite-size scaling, information retrieval, and preparing presentations. Because of the limited space, usually only short introductions to the specific areas are given and references to more extensive literature are cited. All examples of code are in C/C++.Comment: 69 pages, with permission of Wiley-VCH, see http://www.wiley-vch.de (some screenshots with poor quality due to arXiv size restrictions) A comprehensively extended version will appear in spring 2009 as book at Word-Scientific, see http://www.worldscibooks.com/physics/6988.htm

Affordance-Experimentation-Actualization Theory in Artificial Intelligence Research – A Predictive Maintenance Story

Artificial intelligence currently counts among the most prominent digital technologies and promises to generate significant business value in the future. Despite a growing body of knowledge, research could further benefit from incorporating technological features, human actors, and organizational goals into the examination of artificial intelligence-enabled systems. This integrative perspective is crucial for effective implementation. Our study intends to fill this gap by introducing affordance-experimentation-actualization theory to artificial intelligence research. In doing so, we conduct a case study on the implementation of predictive maintenance using affordance-experimentation-actualization theory as our theoretical lens. From our study, we find further evidence for the existence of the experimentation phase during which organizations make new technologies ready for effective use. We propose extending the experimentation phase with the activity of ‘conceptual exploration’ in order to make affordance-experimentation-actualization theory applicable to a broader range of technologies and the domain of AI-enabled systems in particular

The Evolution of an Architectural Paradigm - Using Blockchain to Build a Cross-Organizational Enterprise Service Bus

Cross-organizational collaboration and the exchange of process data are indispensable for many processes in federally organized governments. Conventional IT solutions, such as cross-organizational workflow management systems, address these requirements through centralized process management and architectures. However, such centralization is difficult and often undesirable in federal contexts. One alternative solution that emphasizes decentralized process management and a decentralized architecture is the blockchain solution of Germany’s Federal Office for Migration and Refugees. Here, we investigate the architecture of this solution and examine how it addresses the requirements of federal contexts. We find that the solution’s architecture resembles an improvement and cross-organizational adaption of an old architectural paradigm, the enterprise service bus

Artificial Intelligence as a Call for Retail Banking: Applying Digital Options Thinking to Artificial Intelligence Adoption

Technology-driven challenges, both existing and emerging, require banks to invest in IT capabilities, especially in artificial intelligence (AI). Digital options theory presents a valuable guide rail for these investments. However, the nature of AI as a moving frontier of computing requires certain extensions to established digital option thinking. Based on interviews with 23 experts in the retail banking industry, we highlight the importance of thinking broadly when laying the foundation for AI options and being mindful of the dynamic effects of contextual factors. Drawing from digital options theory and the Technology-Organization-Environment framework as dual lens, our study adds a structured approach to consciously balance resources and AI-related capability investments with a broader consideration of the banking industry’s complex environment. In this way, our study complements recent research on the interplay between incumbents’ resources and digital opportunities

Efficient Maximum Likelihood Estimation for Pedigree Data with the Sum-Product Algorithm

In this paper, we analyze data sets consisting of pedigrees where the response is the age at onset of colorectal cancer (CRC). The occurrence of familial clusters of CRC suggests the existence of a latent, inheritable risk factor. We aimed to compute the probability of a family possessing this risk factor, as well as the hazard rate increase for these risk factor carriers. Due to the inheritability of this risk factor, the estimation necessitates a costly marginalization of the likelihood. We therefore developed an EM algorithm by applying factor graphs and the sum-product algorithm in the E-step, reducing the computational complexity from exponential to linear in the number of family members. Our algorithm is as precise as a direct likelihood maximization in a simulation study and a real family study on CRC risk. For 250 simulated families of size 19 and 21, the runtime of our algorithm is faster by a factor of 4 and 29, respectively. On the largest family (23 members) in the real data, our algorithm is 6 times faster. We introduce a flexible and runtime-efficient tool for statistical inference in biomedical event data that opens the door for advanced analyses of pedigree data

The Social Construction of Self-Sovereign Identity: An Extended Model of Interpretive Flexibility

User-centric identity management systems are gaining momentum as concerns about Big Tech and Big Government rise. Many of these systems are framed as offering Self-Sovereign Identity (SSI). Yet, competing appropriation and the social embedding of SSI have resulted in diverging interpretations. These vague and value-laden interpretations can damage the public discourse and risk misrepresenting values and affordances that technology offers to users. To unpack the various social and technical understandings of SSI, we adopt an ‘interpretive flexibility’ lens. Based on a qualitative inductive interview study, we find that SSI’s interpretation is strongly mediated by surrounding institutional properties. Our study helps to better navigate these different perceptions and highlights the need for a multidimensional framework that can improve the understanding of complex socio-technical systems for digital government practitioners, researchers, and policy-makers

A Study of Deep Learning for Network Traffic Data Forecasting

We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic engineering, e.g., intelligent routing, which can optimize network performance, especially in large networks. In a nutshell, we wish to predict, in advance, the bit rate for a transmission, based on low-dimensional connection metadata ("flows") that is available whenever a communication is initiated. Our study has several genuinely new points: First, it is performed on a large dataset (~50 million flows), which requires a new training scheme that operates on successive blocks of data since the whole dataset is too large for in-memory processing. Additionally, we are the first to propose and perform a more fine-grained prediction that distinguishes between low, medium and high bit rates instead of just "mice" and "elephant" flows. Lastly, we apply state-of-the-art visualization and clustering techniques to flow data and show that visualizations are insightful despite the heterogeneous and non-metric nature of the data. We developed a processing pipeline to handle the highly non-trivial acquisition process and allow for proper data preprocessing to be able to apply DNNs to network traffic data. We conduct DNN hyper-parameter optimization as well as feature selection experiments, which clearly show that fine-grained network traffic forecasting is feasible, and that domain-dependent data enrichment and augmentation strategies can improve results. An outlook about the fundamental challenges presented by network traffic analysis (high data throughput, unbalanced and dynamic classes, changing statistics, outlier detection) concludes the article.Comment: 16 pages, 12 figures, 28th International Conference on Artificial Neural Networks (ICANN 2019