Contributions in Merlin

In an environment of accelerating technological change and increasing digitalization, firms need to adopt new technologies faster than ever before to stay competitive. This paper examines whether updates of education curricula help to bring new technologies faster into firms’ workplaces. We study technology changes and curriculum updates from an early wave of digitalization (i.e., computer-numerically controlled machinery, computer-aided design, and desktop publishing software). We take a text-as-data approach and tap into two novel data sources to measure change in educational content and the use of technology at the workplace: first, vocational education curricula and, second, firms’ job advertisements. To examine the causal effects of adding new technology skills to curricula on the diffusion of these technologies in firms’ workplaces (measured by job advertisements), we use an event study design. Our results show that curriculum updates substantially shorten the time it takes for new technologies to arrive in firms’ workplaces, especially for mainstream firms.

To better understand the physiological basis of obesity in women, we investigated whether obesity or menstrual cycle phase affects laboratory test-meal size or meal-stimulated plasma cholecystokinin (CCK) concentration. Women with healthy weight (body mass index [BMI] of 18.5-24.9 kg/m2, N = 16) or obesity (BMI 30-39.9 kg/m2, N = 20) were tested once in the late-follicular or peri-ovulatory phase (LF/PO) and once in the mid-luteal phase (ML). Meals of ham sandwiches were offered and blood was sampled. Menstrual cycle phases were verified with participants’ reports of menses and measurements of progesterone and luteinizing hormone (LH) concentrations. Women with obesity ate significantly larger meals than women with healthy weight, (mean, 711 [95% CI, 402-1013] kJ, P = 0.001, during the LF/PO and 426 [105-734] kJ, P = 0.027, larger during the ML). Women with healthy weight ate smaller meals during LF/PO than ML (decrease, 510 [192-821 kJ], P = 0.008), but women with obesity did not (decrease, 226 [−87-542] kJ, P = 0.15). CCK concentrations 18 to 30 minutes after meal onset were lower in women with obesity than in women with healthy weight during LF/PO (3.6 [3.1-4.1] vs 6.1 [4.5-7.7] pmol/L; P = 0.004), but not during ML, with a significant interaction effect (1.8 [1.2-2.4] pmol/L, P = 0.048). Women with obesity consumed larger meals than women with healthy weight but displayed reduced meal-stimulated plasma CCK concentrations. These data are consistent with the hypothesis that a defect in CCK secretion compromises satiation in obese women and contributes to the development or maintenance of obesity.

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Code review consists of manual inspection, discussion, and judgment of source code by developers other than the code's author. Due to discussions around competing ideas and group decision-making processes, interpersonal conflicts during code reviews are expected. This study systematically investigates how developers perceive code review conflicts and addresses interpersonal conflicts during code reviews as a theoretical construct. Through the thematic analysis of interviews conducted with 22 developers, we confirm that conflicts during code reviews are commonplace, anticipated and seen as normal by developers. Even though conflicts do happen and carry a negative impact for the review, conflicts-if resolved constructively-can also create value and bring improvement. Moreover, the analysis provided insights on how strongly conflicts during code review and its context (i.e., code, developer, team, organization) are intertwined. Finally, there are aspects specific to code review conflicts that call for the research and application of customized conflict resolution and management techniques, some of which are discussed in this paper. Data and material: https://doi.org/10.5281/zenodo.5848794

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During the last 10 years of Video Browser Showdown (VBS), there were many different approaches tested for known-item search and ad-hoc search tasks. Undoubtedly, teams incorporating state-of-the-art models from the machine learning domain had an advantage over teams focusing just on interactive interfaces. On the other hand, VBS results indicate that effective means of interaction with a search system is still necessary to accomplish challenging search tasks. In this tutorial, we summarize successful deep models tested at the Video Browser Showdown as well as interfaces designed on top of corresponding distance/similarity spaces. Our broad experience with competition organization and evaluation will be presented as well, focusing on promising findings and also challenging problems from the most recent iterations of the Video Browser Showdown.

Evaluation campaigns for interactive multimedia retrieval, such as the Video Browser Shodown (VBS) or the Lifelog Search Challenge (LSC), so far imposed constraints on both simultaneity and locality of all participants, requiring them to solve the same tasks in the same place, at the same time and under the same conditions. These constraints are in contrast to other evaluation campaigns that do not focus on interactivity, where participants can process the tasks in any place at any time. The recent travel restrictions necessitated the relaxation of the locality constraint of interactive campaigns, enabling participants to take place from an arbitrary location. Born out of necessity, this relaxation turned out to be a boon since it greatly simplified the evaluation process and enabled organisation of ad-hoc evaluations outside of the large campaigns. However, it also introduced an additional complication in cases where participants were spread over several time zones. In this paper, we introduce an evaluation scheme for interactive retrieval evaluation that relaxes both the simultaneity and locality constraints, enabling participation from any place at any time within a predefined time frame. This scheme, as implemented in the Distributed Retrieval Evaluation Server (DRES), enables novel ways of conducting interactive retrieval evaluation and bridged the gap between interactive campaigns and non-interactive ones.

This study investigates the impact of a tertiary education expansion on regional firm development, as measured by average profits per firm. We exploit the quasi-random establishment of universities of applied sciences (UASs) – bachelor’s degree-granting three-year colleges teaching and conducting applied research – to construct treatment and control groups and to apply both a difference-in-differences model and an event study design. We find that after the establishment of new UASs in Switzerland, average profits per firm in the treated municipalities increase by 19.6% more than in the control group. This increase corresponds roughly to an additional annual growth in average profits per firm in the treatment group of 0.7%. The effects start shortly after the establishment of UASs but also persist over a period of up to 10 years.

Isolation forest is a popular anomaly detector due to its model-free structure and validity in detecting outliers across different types of datasets. This thesis presents solutions for two main issues in terms of isolation-based outlier detection algorithms. First, there is an adjustment to the isolation-based outlier detection model to improve the detection accuracy of Isolation Forest (iForest) and Extended Isolation Forest (EIF). The EIF is an extension of iForest, which addresses the block artifacts issue of iForest. Motivated by the failures of outlier detection on some real-world benchmark datasets by EIF, an adjusted EIF regarding the problem arising from the randomness of split hyperplane is presented. The outlier detection accuracy and precision of the adjusted EIF show that it is capable of enhancing the performance of both iForest and EIF. However, the drawback of the adjustment is that it is not time efficient. Second, this thesis proposes methods to generate a credible image presentation with outliers scattered in the relative areas based on isolation-based detection for multi-variate datasets. Inspired by the fact that neither iForest nor EIF can detect local outliers for each class in multi-class datasets, and few related works have been done in this direction, several class-wise detectors based on EIF and adjusted EIF are proposed in this thesis. By comparing the graphs, one of the methods achieves the best performance in providing insights for identifying potential outliers in clustering datasets.

Simulation-based testing helps in the improvement of cyber-physical systems (CPS) such as self-driving cars (SDC) because it increases the efficiency, diversity, and relevance of tests from a human perspective. The importance of human feedback in validating test cases cannot be overstated. Despite this, testing SDCs in simulated environments does not take human factors into account. Previous research demonstrates how to optimize the test case through selection, improve classification and accuracy when test cases result in a fault, and improve testing cost-effectiveness. However, test validity, relevance, and safety perception from a hu- man point of view were not addressed. In this thesis, we investigate the variety of possible scenarios (static and dynamic obstacles) and examine how humans perceive safety and the level of realism of the SDC test case with various factors such as interaction with the car and different views (i.e., the VR view, the outside view, and the driver’s view). We propose an approach called SDC-Alabaster (SDC humAn-in-the Loop simulAtion-BASed Testing sElf- driving caRs) that uses a virtual reality (VR) headset to illustrate SDC test scenarios, create the sensation of being in SDCs and to enable users to experiment with the experience. Our results show the perception of realism and safety without obstacles is higher than with ob- stacles, and CARLA was more realistic and safer than the BeamNG simulator with a p-value > 0.01e-16, The distribution is 85%( ˆA12). Our results also show interactions with vehicles make humans safer compared to those without interactions with a p-value > 0.001, and the distribution is 36%( ˆA12), and users’ perceptions of safety and realism vary with and without VR headsets, and the failure cases that are most important to test are also regarded as less re- alistic by participants’. In addition, we discovered factors such as using an advanced AI agent for traffic cars, using voice feedback in VR, and integrating participants’ driving will help test scenarios be more realistic, and the perception of participants’ safety can be improved in simulation-based testing of SDCs.