Fakultät Informatik und Mathematik
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One of the most popular training algorithms for deep neural networks is the Adaptive Moment Estimation (Adam) introduced by Kingma and Ba. Despite its success in many applications there is no satisfactory convergence analysis: only local convergence can be shown for batch mode under some restrictions on the hyperparameters, counterexamples exist for incremental mode. Recent results show that for simple quadratic objective functions limit cycles of period 2 exist in batch mode, but only for atypical hyperparameters, and only for the algorithm without bias correction. We extend the convergence analysis to all choices of the hyperparameters for quadratic functions. This finally answers the question of convergence for Adam in batch mode to the negative. We analyze the stability of these limit cycles and relate our analysis to other results where approximate convergence was shown, but under the additional assumption of bounded gradients which does not apply to quadratic functions. The investigation heavily relies on the use of computer algebra due to the complexity of the equations.
Shadow IT and Business-managed IT describe the autonomous deployment/procurement or management of Information Technology (IT) instances, i.e., software, hardware, or IT services, by business entities. For Shadow IT, this happens covertly, i.e., without alignment with the IT organization; for Business-managed IT this happens overtly, i.e., in alignment with the IT organization or in a split responsibility model. We conduct a systematic literature review and structure the identified research themes in a framework of causing factors, outcomes, and governance. As causing factors, we identify enablers, motivators, and missing barriers. Outcomes can be benefits as well as risks/shortcomings of Shadow IT and Business-managed IT. Concerning governance, we distinguish two subcategories: general governance for Shadow IT and Business-managed IT and instance governance for overt Business-managed IT. Thus, a specific set of governance approaches exists for Business-managed IT that cannot be applied to Shadow IT due to its covert nature. Hence, we extend the existing conceptual understanding and allocate research themes to Shadow IT, Business-managed IT, or both concepts and particularly distinguish the governance of the two concepts. Besides, we find that governance themes have been the primary research focus since 2016, whereas older publications (until 2015) focused on causing factors.
Purpose
This paper aims to investigate the main tasks, necessary skills, and the implementation of the offshore coordinator’s role to facilitate knowledge transfer in information systems (IS) offshoring.
Design/methodology/approach
This empirical exploratory study uses the classical Delphi method that includes one qualitative and two quantitative rounds to collect data on IS experts’ perceptions to seek a consensus among them.
Findings
The participants agreed, with strong consensus, for a set of 16 tasks and 15 skills. The tasks focused primarily on relationship management and facilitating knowledge transfer on different levels. The set of skills consists of approximately 25 per cent “hard” skills, e.g. professional language skills and project management skills, and approximately 75 per cent “soft” skills, e.g. interpersonal and communication skills and the ability to deal with conflict. Two factors mainly influence implementing the offshore coordinator role: project size and the number of projects to be supported simultaneously.
Practical implications
The findings provide indications of how to define and fulfill this crucial role in practice to facilitate the knowledge transfer process in a positive way.
Originality/value
Similarities in previous research findings are aggregated to examine the intermediate role in detail from a consolidated perspective. This results in the first comprehensive set of critical tasks and skills assigned to the competency dimensions of the universal competency framework, demonstrating which and how many competency dimensions are critical.
Incomplete conceptualization of the information technology outsourcing (ITO) literature represents a challenge for navigating extant research and engaging into purposeful academic discourse. We extend the analysis of empirical findings on determinants of ITO decisions, outcomes, and governance. We identify increasing levels of research maturity, analyze effects of 38 new independent variables, highlight contradictory findings, and observe increasing interest in emerging topics such as innovation through ITO and multisourcing.
To prepare their IT landscape for future business challenges, companies are changing their IT sourcing arrangements by using selective sourcing approaches as well as multi-sourcing with more but smaller sourcing contracts. Companies therefore have to reconsider and re-evaluate their IT sourcing setup more frequently. Collecting data from 251 global experts, we empirically tested the effect of service quality, relationship quality, and switching costs on IT sourcing decisions using partial least squares (PLS) analysis. Drawing on previously conducted expert interviews, our model extends previous studies and introduces a decision maker’s sourcing preferences as a not yet examined moderator on IT sourcing decisions. This allows us to investigate the influence of the decision maker’s beliefs on the decision process. We were able to confirm the negative effect of switching costs on a decision in favor of backsourcing, however we could not find significant support for the remaining hypotheses. We further discuss potential reasons for our findings and suggest future research opportunities based on our contribution.
Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma
(2019)
Computer-aided diagnosis using deep learning (CAD-DL) may be an instrument to improve endoscopic assessment of Barrett’s oesophagus
(BE) and early oesophageal adenocarcinoma (EAC). Based on still images from two databases, the diagnosis of EAC by CAD-DL reached sensitivities/specificities of 97%/88% (Augsburg data) and 92%/100% (Medical Image Computing and Computer-Assisted Intervention [MICCAI]
data) for white light (WL) images and 94%/80% for narrow band images (NBI) (Augsburg data), respectively. Tumour margins delineated by
experts into images were detected satisfactorily with a Dice coefficient (D) of 0.72. This could be a first step towards CAD-DL for BE assessment. If developed further, it could become a useful
adjunctive tool for patient management.