Welcome to Merlin

Various fuel treatment practices involve removing all or some of the vegetation (fuel) from a landscape to reduce the potential for fires and their severity. Fuel treatments form the first line of defense against large-scale wildfires. In this study, we formulate and solve a bilevel integer programming model, where the fuel treatment planner (modeled as the leader) determines appropriate locations and types of treatments to minimize expected losses from wildfires. The follower (i.e., the lower-level decision-maker) corresponds to nature, which is adversarial to the leader and designs a wildfire attack (i.e., locations and time periods, where and when, respectively, wildfires occur) to disrupt the leader’s objective function, e.g., the total expected area burnt. Both levels in the model involve integrality restrictions for decision variables; hence, we explore the model’s difficulty from the computational complexity perspective. Then, we design specialized solution methods for general and some special cases. We perform experiments with semi-synthetic and real-life instances to illustrate the performance of our approaches. We also explore numerically the fundamental differences in the structural properties of solutions arising from bilevel model and its single-level counterpart. These disparities encompass factors like the types of treatments applied and the choice of treated areas. Additionally, we conduct various types of sensitivity analysis on the performance of the obtained policies and illustrate the value of the bilevel solutions.

We investigate the impact of dividend policy on earnings quality and opportunistic earnings management for individual banks across 45 developed and developing countries between 1996 and 2019. Our estimates show that high dividend payments reduce earnings management, hence mitigate agency problems. This mitigation is especially prevalent among well-capitalised and non-listed banks. Greater investor protection and government regulation appear to strengthen the negative association between dividend policy and earnings management. Our results hold robustly across many different specifications.

Despite the growing interest in explainable AI in the RecSys community, the evaluation of explanations is still an open research topic. Typically, explanations are evaluated using offline metrics, with a case study, or through a user study. In my research, I will have a closer look at the evaluation of the effects of explanations on users. I investigate two possible factors that can impact the effects reported in recent publications, namely the explanation design and content as well as the users themselves. I further address the problem of determining promising explanations for an application scenario from a seemingly endless pool of options. Lastly, I propose a user study to close some of the research gaps established in the surveys and investigate how recommender systems explanations impact the understanding of users with different backgrounds.

Ransomware has remained one of the most notorious threats in the cybersecurity field, for which Moving Target Defense (MTD) has been proposed as a novel defense paradigm. Although various approaches leverage MTD, few of them rely on the operating system and, specifically, the file system, thereby making them dependent on other computing devices, rendering defense against certain threats unrealistic. File-based approaches are less studied here while showing limitations in resource usage and defense effectiveness. Furthermore, existing ransomware defenses merely restore data or detect attacks without preventing them. Thus, this paper introduces the MTFS file system and the design and implementation of three novel MTD techniques – one delaying attackers, one trapping recursive directory traversal, and another one hiding file types. The effectiveness of the techniques is shown in three experiments. First, it is demonstrated that the techniques can delay and mitigate ransomware on real IoT devices. Secondly, in a broader scope, the solution was confronted with 13 ransomware samples, highlighting that it can save 97% of the files. Regarding overhead, the defense system consumes only a small amount of resources, highlighting the feasibility of proactive defense.

In einem jährlich auf www.swissauditmonitor.ch veröffentlichten Report werden die Revisionshonorare, die zusätzlichen Honorare, die Mandatsdauer der Revisionsgesellschaften und die verwendeten Rechnungslegungsstandards von Unternehmen des Swiss Market Index (SMI) und des Swiss Performance Index (SPI) erhoben und ausgewertet. Der 8. Ausgabe des Swiss Audit Monitor liegt das Geschäftsjahr 2023 zugrunde. Dieser Beitrag fasst die wichtigsten Erkenntnisse zusammen.

We study the market’s reaction to the disclosure of EU-wide stress test results across four periods (2014, 2016, 2018, and 2021). Our novel approach contributes to the literature by studying how stress test disclosures influence both the mean and variance (first and second moments) of bank stock performance, extending beyond previous studies focused mainly on the first moment of equity returns. Using one-factor market and structural Engle–Siriwardane type GARCH models, we find that the publication of stress tests provides new information to the markets: Banks with weaker performance in the tests experience, on average, a reduction in stock returns and an increase in volatility, while the reverse holds for banks performing well. Our findings confirm the important role of stress tests in enhancing transparency and market discipline, thereby supporting investors in assessing the resilience of the banking sector more effectively, particularly during periods of higher uncertainty.

Purpose Our study explores the effects of immigration on the employment of native middle-skilled workers, focusing on how this effect varies with the specificity of their occupational skill bundles. Design/methodology/approach Exploiting the 2002 opening of the Swiss labor market to EU workers and using register data on the location and occupation of these workers, our findings provide novel results on the labor market effects of immigration. Findings We show that the inflow of EU workers led to an increase in the employment of native middle-skilled workers with highly specific occupational skills. This finding could be attributed to immigrant workers reducing existing skill gaps, enhancing the quality of job-worker matches, and alleviating firms' capacity restrictions. This allowed firms to create new jobs, thereby providing increased employment options for middle-skilled workers with highly specialized skills. Originality/value Previous literature has predominantly highlighted the disadvantages of specific occupational skills compared to general skills in the context of labor market shocks. However, our findings reveal that workers with specific occupational skills can benefit from an immigration-driven labour market shock. These results suggest that policy conclusions regarding the role of specific occupational skills should be more nuanced.

Background Modern approaches such as patient-centered care ask health care providers (eg, nurses, physicians, and dietitians) to activate and include patients to participate in their health care. Mobile health (mHealth) is integral in this endeavor to be more patient centric. However, structural and regulatory barriers have hindered its adoption. Existing mHealth apps often fail to activate and engage patients sufficiently. Moreover, such systems seldom integrate well with health care providers’ workflow. Objective This study investigated how patient-provider communication behaviors change when introducing patient-generated data into patient-provider communication. Methods We adopted the design science approach to design PatientHub, an integrated digital health system that engages patients and providers in patient-centered care for weight management. PatientHub was developed in 4 iterations and was evaluated in a 3-week field study with 27 patients and 6 physicians. We analyzed 54 video recordings of PatientHub-supported consultations and interviews with patients and physicians. Results PatientHub introduces patient-generated data into patient-provider communication. We observed 3 emerging behaviors when introducing patient-generated data into consultations. We named these behaviors emotion labeling, expectation decelerating, and decision ping-pong. Our findings show how these behaviors enhance patient-provider communication and facilitate patient-centered care. Introducing patient-generated data leads to behaviors that make consultations more personal, actionable, trustworthy, and equal. Conclusions The results of this study indicate that patient-generated data facilitate patient-centered care by activating and engaging patients and providers. We propose 3 design principles for patient-centered communication. Patient-centered communication informs the design of future mHealth systems and offers insights into the inner workings of mHealth-supported patient-provider communication in chronic care.

Threat modeling has been successfully applied to model technical threats within information systems. However, a lack of methods focusing on non-technical assets and their representation can be observed in theory and practice. Following the voices of industry practitioners, this paper explored how to model insider threats based on business process models. Hence, this study developed a novel insider threat knowledge base and a threat modeling application that leverages Business Process Modeling and Notation (BPMN). Finally, to understand how well the theoretic knowledge and its prototype translate into practice, the study conducted a real-world case study of an IT provider's business process and an experimental deployment for a real voting process. The results indicate that even without annotation, BPMN diagrams can be leveraged to automatically identify insider threats in an organization.

Inaccurate beliefs about social norms can reduce useful social interactions and adversely affect an individual’s ability to deal with negative shocks. We implement a randomized controlled trial with low-income workers in urban India who lack access to formal financial and healthcare support. We find that the majority of individuals underestimate their community’s willingness to engage in dialogue around financial and mental health concerns. Belief correction leads to a large increase in the demand for network-based assistance. We show that the effects are driven by a reduction in the perceived costs of violating social norms arising due to concerns around reputation and insensitivity. We structurally estimate a network diffusion model and predict that our belief correction intervention will not lead to a shift in equilibrium engagement. Consistent with this, 2 years later, we find that the average beliefs of those exposed to the intervention are significantly more optimistic but still lower than the information delivered in the experiment. We compute the strength of counterfactual interventions needed to generate a sustained effect and find that belief correction can be used to generate both the demand and funding for such policies.