The AIQ Meta-Testbed: Pragmatically Bridging Academic AI Testing and Industrial Q Needs

AI solutions seem to appear in any and all application domains. As AI becomes more pervasive, the importance of quality assurance increases. Unfortunately, there is no consensus on what artificial intelligence means and interpretations range from simple statistical analysis to sentient humanoid robots. On top of that, quality is a notoriously hard concept to pinpoint. What does this mean for AI quality? In this paper, we share our working definition and a pragmatic approach to address the corresponding quality assurance with a focus on testing. Finally, we present our ongoing work on establishing the AIQ Meta-Testbed.Comment: Accepted for publication in the Proc. of the Software Quality Days 2021, Vienna, Austri

Digitalisierung: Perspektiven für Arbeitsmodelle der Zukunft in Wirtschaft und Verwaltung

Die Digitalisierung ist derzeit einer der wichtigsten Wandlungstreiber in Wirtschaft und Gesellschaft. Die aus den neuen technischen Möglichkeiten resultierenden Veränderungen wirken sich auch auf das Verhalten von Kunden und Mitarbeitenden aus. In Bezug auf die Zusammenarbeit in Unternehmen und Organisationen sind bestehende Arbeitsmodelle für administrative Arbeit anzupassen und neue Formen digitaler Kollaboration einzuführen. Hiermit verbunden sind Änderungen der Arbeitszeitmodelle, der Organisation der Mitarbeitenden, der Art der Zusammenarbeit und Führung sowie der Gestaltung von Büroräumlichkeiten. In einem vergleichenden Case-Study-Ansatz von zwei Schweizer Großunternehmen und einer großen Verwaltungseinheit des Bundes werden die existierenden Arbeitsmodelle für Büroarbeit analysiert und auf Entwicklungsperspektiven hin untersucht. Ein derzeit vorhandener Mix aus traditionellen und agilen Arbeitsmodellen wird sich in Zukunft im digitalen Kontext voraussichtlich unternehmensspezifisch stärker in Richtung einer höheren Agilität weiterentwickeln

Big GABA II: Water-referenced edited MR spectroscopy at 25 research sites

Accurate and reliable quantification of brain metabolites measured in vivo using 1H magnetic resonance spectroscopy (MRS) is a topic of continued interest. Aside from differences in the basic approach to quantification, the quantification of metabolite data acquired at different sites and on different platforms poses an additional methodological challenge. In this study, spectrally edited γ-aminobutyric acid (GABA) MRS data were analyzed and GABA levels were quantified relative to an internal tissue water reference. Data from 284 volunteers scanned across 25 research sites were collected using GABA+ (GABA + co-edited macromolecules (MM)) and MM-suppressed GABA editing. The unsuppressed water signal from the volume of interest was acquired for concentration referencing. Whole-brain T1-weighted structural images were acquired and segmented to determine gray matter, white matter and cerebrospinal fluid voxel tissue fractions. Water-referenced GABA measurements were fully corrected for tissue-dependent signal relaxation and water visibility effects. The cohort-wide coefficient of variation was 17% for the GABA + data and 29% for the MM-suppressed GABA data. The mean within-site coefficient of variation was 10% for the GABA + data and 19% for the MM-suppressed GABA data. Vendor differences contributed 53% to the total variance in the GABA + data, while the remaining variance was attributed to site- (11%) and participant-level (36%) effects. For the MM-suppressed data, 54% of the variance was attributed to site differences, while the remaining 46% was attributed to participant differences. Results from an exploratory analysis suggested that the vendor differences were related to the unsuppressed water signal acquisition. Discounting the observed vendor-specific effects, water-referenced GABA measurements exhibit similar levels of variance to creatine-referenced GABA measurements. It is concluded that quantification using internal tissue water referencing is a viable and reliable method for the quantification of in vivo GABA levels

The Digital Business Architect – Towards Method Support for Digital Innovation and Transformation

Part 2: Short PapersInternational audienceDigitalization is currently the most important driver of economic growth. Inspired by increasing digital networking and smart automation possibilities, omnipresent access technologies and dynamic customer requirements, modern enterprises work more and more on additional and new digital business models. The aim is to exploit potentials for new and especially digital business models much faster and to cope with the resulting challenges. This requires a technical integration of different disciplines, new qualification profiles and new methodical approaches. The paper proposes the aggregation of selected approaches from the areas of digital business model development, capability management and enterprise architecture management as a methodical basis for the training profile of a “Digital Business Architect” and “Digital Innovation and Transformation Process (DITP)”

Goal-Oriented Approach to Enable New Business Models for SME Using Smart Products

Part 2: Collaborative Environments and New Product DevelopmentInternational audienceThe manufacturing industry has to exploit trends like “Industrie 4.0” and digitization not only to design production more efficiently, but also to create and develop new and innovative business models [1, p. 2]. New business models ensure that even SMEs are able to open up new markets and canvass new customers [2, p. 82ff.]. This means that in order to stay competitive, SMEs must transform their existing business models [3, p. 2ff.]. The creation of new business models require smart products [4, p. 1, 5, p. 235, 6, p. 13, 7, p. 2, 8, p. 322, 9, p. 7]. The required data base for new business models cannot be provided by SMEs alone, whereas smart products are able to provide a foundation, given the creation of smart data and smart services they enable [5, p. 235]. These services then expand functions and functionality of smart products and define new business models [10, 6f.]. However, the development of smart products by small and medium-sized enterprises is still lined with obstacles [11, p. 640]. Regarding the product development process the inclusion of smart products means that new and SME-unknown domains diffuse during the process [12, p. 2]. Although there are many models regarding this process there appears to be a substantial lack of taking into account the competencies enabled by the implementation of digital technologies. Hence, several SME-supporting approaches fail to address the two major challenges these enterprises are faced with [13, p. 8]. This paper generally describes valid objectives containing relevant stakeholders and their allocation to the phases of the product life cycle. Within each objective the potential benefit for customers and producers is analyzed. The model given in this paper helps SMEs in defining the initiation of a product development project more precisely and hence also eases project scoping and targeting for the smartification of an already existing product