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Aufsicht und Rechtsdurchsetzung bei unzulässigem Einsatz von Cookies & Co. unter Geltung des TTDSG
(2022)
Purpose
In the determination of the measurement uncertainty, the GUM procedure requires the building of a measurement model that establishes a functional relationship between the measurand and all influencing quantities. Since the effort of modelling as well as quantifying the measurement uncertainties depend on the number of influencing quantities considered, the aim of this study is to determine relevant influencing quantities and to remove irrelevant ones from the dataset.
Design/methodology/approach
In this work, it was investigated whether the effort of modelling for the determination of measurement uncertainty can be reduced by the use of feature selection (FS) methods. For this purpose, 9 different FS methods were tested on 16 artificial test datasets, whose properties (number of data points, number of features, complexity, features with low influence and redundant features) were varied via a design of experiments.
Findings
Based on a success metric, the stability, universality and complexity of the method, two FS methods could be identified that reliably identify relevant and irrelevant influencing quantities for a measurement model.
Originality/value
For the first time, FS methods were applied to datasets with properties of classical measurement processes. The simulation-based results serve as a basis for further research in the field of FS for measurement models. The identified algorithms will be applied to real measurement processes in the future.
Digital start-ups are perceived as an engine for innovation and job promotor. While success factors for non-IT start-ups have already been extensively researched, this study sheds light on digital entrepreneurs, whose business model relies primarily on services based on digital technologies. Applying the Grounded Theory method, we identify relevant environmental success factors for digital entrepreneurs. The study’s research contribution is threefold. First, we provide 16 relevant and less relevant environmental success factors, which enables a comparison with prior identified factors. We found out that several prior environmental success factors, such as accessibility to transportation or the availability of land and facilities are less relevant for a digital entrepreneur. Second, we derive and discuss hypotheses for the influence of these factors on digital start-up success. Third, we present a theoretical model that lays the foundation for explaining the environmental influence on digital
entrepreneurship success.
Im Handel mit Kraftfahrzeugen gehören Aspekte des gutgläubigen Erwerbs zu den beinahe alltäglichen Standardproblemen. Der BGH fügt in seiner Entscheidung v. 23.9.2022–VZR148/21, MDR 2022, 1541 diesem im Detail breit gefächerten Themenfeld einen weiteren Mosaikstein hinzu: Der Erwerber erhielt das verkaufte Kfz ohne Übergabe einer Zulassungsbescheinigung Teil II, behauptet aber, diese Bescheinigung sei dem vom ihm eingeschalteten Vermittler bei Erwerb (als Fälschung) vorgelegt worden. Tatsächlich befand sich das Original durchgängig beim wahren Eigentümer, der nunmehr Herausgabe des Fahrzeugs verlangt. Der BGH schützt in dieser Gestaltung im Ergebnis den Erwerber. Die Entscheidung ist in mehrfacher Hinsicht bemerkenswert.
Inhaltsverzeichnis - Bachelorarbeiten
1. Zukunft der Rente – Kann eine Erwerbstätigenversicherung das
demografische Problem nachhaltig lösen?
- Marcel Sept | Seite 4-73
2. Die versammlungsgebundenen Aktionärsrechte in der neuen
virtuellen Hauptversammlung – eine Bewertung aus Unternehmer- und Aktionärssicht
- Christina Zillinger | Seite 74-130
3. Praxisleitfaden für den Arbeitgeber zum gesetzlichen
Sonderkündigungsschutz – Was muss der Arbeitgeber tun, um durch
Gesetz besonders geschützten Arbeitnehmern kündigen zu können?
- Johanna Wiechert | Seite 131-195
4. Die Steuervergünstigung bei Umstrukturierungen im Konzern nach
§ 6a GrEStG im Lichte der aktuellen Rechtsprechung
- Marius Thelen | Seite 196-265
AI-based systems are nearing ubiquity not only in everyday low-stakes activities but also in medical procedures. To protect patients and physicians alike, explainability requirements have been proposed for the operation of AI-based decision support systems (AI-DSS), which adds hurdles to the productive use of AI in clinical contexts. This raises two questions: Who decides these requirements? And how should access to AI-DSS be provided to communities that reject these standards (particularly when such communities are expert-scarce)? This chapter investigates a dilemma that emerges from the implementation of global AI governance. While rejecting global AI governance limits the ability to help communities in need, global AI governance risks undermining and subjecting health-insecure communities to the force of the neo-colonial world order. For this, this chapter first surveys the current landscape of AI governance and introduces the approach of relational egalitarianism as key to (global health) justice. To discuss the two horns of the referred dilemma, the core power imbalances faced by health-insecure collectives (HICs) are examined. The chapter argues that only strong demands of a dual strategy towards health-secure collectives can both remedy the immediate needs of HICs and enable them to become healthcare independent.
Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance.
However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP.
The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.
Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.