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In supply chain management of port operations querying and visualizing network data often requires complex joins, nested queries and predefined reporting templates, which can hinder exploratory analysis and decision-making. When relational databases become too rigid and cumbersome for transactional processing, we envision an alternative approach using graph data models, provided by transforming entities (e.g. containers, vessels, terminals) into nodes and their relationships (e.g. arrival, loading, handling) into edges. To investigate the potential benefits of transforming relational supply chain data into a graph-based model, we designed and implemented a structured approach that integrated data processing, transformation and performance analysis across both relational and graph databases.
Sustainability communication as an increasingly circumscribable research field grounds in communication, management and marketing concepts. This chapter focuses on communication of sustainability, sustainable consumption and green claiming and discusses the effects of non-transparent sustainability communication and greenwashing. It specifically presents a study on how the suspicion of greenwashing in green advertising affects attitudes towards the ad and the brand, as well as the purchase intention. The affect transfer hypothesis is used as a model to understand advertising effects in this context. Additionally, this study delves into potential influencing factors on suspicion of greenwashing, such as the type of green advertising and the “lifestyle of health and sustainability” (LOHAS). The research question is addressed through a quantitative experimental online survey. The results demonstrate that the suspicion of greenwashing has a negative effect on attitude towards the ad and on attitude towards the brand, as well as on purchase intention. The chapter therefore offers new insights into one of the critical aspects of strategic sustainable communication and lays the groundwork for future research on “washing” (green, pink, rainbow, etc.) and the need for re-framing sustainability in business-consumer discourses.
Phishing is an increasing threat to the security of end-users, networks, and organizations. Phishing simulations via email are a widespread tool used to measure user awareness, especially in workplace settings. However, current studies focusing on large-scale analysis of phishing simulations often have issues: The phishing simulations were conducted using a small sample size (mostly one or two organizations), or while many emails are sent, the analysis focuses only on specific companies. This study analyzes phishing simulations conducted over three years at 36 organizations with over 68 000 delivered emails. We compare different dimensions of the organizations where these simulations were conducted, such as the economic sector and departments. Furthermore, we evaluate various dimensions of phishing simulation campaigns, such as detection difficulty and the scenario under which the simulation occurs. Our findings indicate significant disparities in the results, such as the industry sector in which the company operates. Moreover, we find substantial differences between the success rates of varying scenarios used for phishing emails.
Hybrid broadcast broadband television (HbbTV) is an evolving technology that connects linear TV with modern HTML5 applications, delivering extras like games, videos, and online shopping. However, its bidirectional transmission functionality raises privacy concerns, as it introduces new tracking methods for TV channels. While previous studies focused on security issues or user awareness of HbbTV privacy challenges, a detailed examination of the tracking and transparency mechanisms of the HbbTV ecosystem is still missing. This study fills this gap by extensively analyzing these features within the European HbbTV ecosystem, and in particular within German-language TV channels. We monitored more than 350 TV channels for over 400 hours, evaluating 1) prevalent HbbTV tracking methods, 2) consent notice prevalence and user interactions, and 3) privacy policy disclosures. Our findings indicate that the HbbTV tracking system operates independently of the Web, consent notices exploit system constraints to influence users, and privacy policies often do not align with actual data practices.
KI-Lösungen und -Systemen wird im wirtschaftlichen sowie gesellschaftlichen Kontext zunehmend Bedeutung beigemessen. Doch aufgrund damit verbundener Implikationen darf die Diskussion hinsichtlich der Verantwortung beispielsweise mit Blick auf die Haftung für die Verursachung von Schäden nicht ausbleiben. Die Frage ist jedoch: Wer trägt die Verantwortung wofür? Insbesondere unter dem Aspekt, dass sich Verantwortung nicht umfassend reglementieren lässt. Zur Beantwortung und entsprechender Handlungsweise bedarf es hier eines gemeinsamen Spielverständnisses aller Beteiligten – denn nur so lässt sich gewährleisten, dass KI verantwortungsvoll eingesetzt werden kann, auch mit Blick auf die Zukunft. Im Beitrag wird hierfür ein Lösungsansatz vorgestellt.
Unter dem Titel „WE! Vom Labor in den Mittelstand: Westfälische Erfinderinnen. Analyse der Potenziale und Sichtbarmachung innovativer Frauen in regionalen Innovationsökosystemen“ (kurz WE!) widmete sich ein interdisziplinäres Team aus Forschenden an der Westfälischen Hochschule von 2021-2024 der Erforschung und Sichtbarmachung innovativer Frauen: Forscherinnen, Gründerinnen, Spezialistinnen in der Wirtschaft und Initiatorinnen in sozialen Bereichen aus dem Münsterland und dem Ruhrgebiet. Damit lag dem Projekt WE! ein breiter Innovationsbegriff zugrunde, der Produkt- und Verfahrensinnovationen sowie auch Dienstleistungs- und soziale Innovationen berücksichtigte.
Function detection is a well-known problem in binary analysis. While prior work has focused on Linux/ELF, Windows/PE binaries have only partially been considered. This paper introduces FuncPEval, a dataset for Windows x86 and x64 PE files, featuring Chromium and the Conti ransomware, along with ground truth data for 1,092,820 function starts. Utilizing FuncPEval, we evaluate five heuristics-based (Ghidra, IDA, Nucleus, rev.ng, SMDA) and three machine-learning-based (DeepDi, RNN, XDA) function start detection tools. Among these, IDA achieves the highest F1-score (98.44%) for Chromium x64, while DeepDi closely follows (97%) but stands out as the fastest. Towards explainability, we examine the impact of padding between functions on the detection results, finding all tested tools, except rev.ng, are susceptible to randomized padding. The randomized padding significantly diminishes the effectiveness of the RNN, XDA, and Nucleus. Among the learning-based tools, DeepDi exhibits the least sensitivity, while Nucleus is the most adversely affected among the non-learning-based tools.
Mixed Reality (MR) is a technology with strong potential for advancing research in Human-Robot Interaction (HRI) for space exploration. Apart from the efficiency and high flexibility MR can offer, we argue that its benefits for HRI research in space contexts lies particularly in its ability to aid human-in-the-loop development, offer realistic hybrid simulations, and foster broader participation in HRI research in the space exploration context. However, we believe that this is only plausible if MR-based simulations can yield comparable results to fully physical approaches in human-centred studies. In this position paper, we highlight several arguments in favour of MR as a tool for space HRI research, while emphasising the importance of the open question regarding its scientific validity. We believe MR could become a central tool for preparing for future human-robotic space exploration missions and significantly diversify research in this domain.
ABSTRACT
Trust is important for collaboration. In hybrid teams of humans and robots, trust enables smooth collaboration and reduces risks. Just as collaboration between humans and robots differs from interpersonal collaboration, so does the nature of trust in human-robot interaction (HRI). Therefore, further investigations on trust formation and dissolution in HRI, factors affecting it, and means for keeping trust on an appropriate level are needed. However, our knowledge of interpersonal trust and trust in autonomous agents cannot be transferred directly to HRI. In this paper, we present a study with 32 participants on trust formation and dissolution as well as forecasting to influence trust in an industry robot. Results show differences in dynamics and factors of trust formation and dissolution. Additionally, we find that the effect of forecasting on trust depends on task success. These findings support the design of trustful human-robot interaction and corresponding robotic team members.

