@article{WienrichCarolusMarkusetal.2023, author = {Wienrich, Carolin and Carolus, Astrid and Markus, Andr{\´e} and Augustin, Yannik and Pfister, Jan and Hotho, Andreas}, title = {Long-term effects of perceived friendship with intelligent voice assistants on usage behavior, user experience, and social perceptions}, series = {Computers}, volume = {12}, journal = {Computers}, number = {4}, issn = {2073-431X}, doi = {10.3390/computers12040077}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313552}, year = {2023}, abstract = {Social patterns and roles can develop when users talk to intelligent voice assistants (IVAs) daily. The current study investigates whether users assign different roles to devices and how this affects their usage behavior, user experience, and social perceptions. Since social roles take time to establish, we equipped 106 participants with Alexa or Google assistants and some smart home devices and observed their interactions for nine months. We analyzed diverse subjective (questionnaire) and objective data (interaction data). By combining social science and data science analyses, we identified two distinct clusters—users who assigned a friendship role to IVAs over time and users who did not. Interestingly, these clusters exhibited significant differences in their usage behavior, user experience, and social perceptions of the devices. For example, participants who assigned a role to IVAs attributed more friendship to them used them more frequently, reported more enjoyment during interactions, and perceived more empathy for IVAs. In addition, these users had distinct personal requirements, for example, they reported more loneliness. This study provides valuable insights into the role-specific effects and consequences of voice assistants. Recent developments in conversational language models such as ChatGPT suggest that the findings of this study could make an important contribution to the design of dialogic human-AI interactions.}, language = {en} } @article{MuellerLeppichGeissetal.2023, author = {M{\"u}ller, Konstantin and Leppich, Robert and Geiß, Christian and Borst, Vanessa and Pelizari, Patrick Aravena and Kounev, Samuel and Taubenb{\"o}ck, Hannes}, title = {Deep neural network regression for normalized digital surface model generation with Sentinel-2 imagery}, series = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, volume = {16}, journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, issn = {1939-1404}, doi = {10.1109/JSTARS.2023.3297710}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349424}, pages = {8508-8519}, year = {2023}, abstract = {In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time consuming and costly. Only a few approaches exist to create high-resolution nDSMs for extensive areas. This article explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixelwise regression, our U-Net base models can achieve a mean height error of approximately 2 m. Moreover, through our enhancements to the model architecture, we reduce the model error by more than 7\%.}, language = {en} } @article{LimanMayFetteetal.2023, author = {Liman, Leon and May, Bernd and Fette, Georg and Krebs, Jonathan and Puppe, Frank}, title = {Using a clinical data warehouse to calculate and present key metrics for the radiology department: implementation and performance evaluation}, series = {JMIR Medical Informatics}, volume = {11}, journal = {JMIR Medical Informatics}, issn = {2291-9694}, doi = {10.2196/41808}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349411}, year = {2023}, abstract = {Background: Due to the importance of radiologic examinations, such as X-rays or computed tomography scans, for many clinical diagnoses, the optimal use of the radiology department is 1 of the primary goals of many hospitals. Objective: This study aims to calculate the key metrics of this use by creating a radiology data warehouse solution, where data from radiology information systems (RISs) can be imported and then queried using a query language as well as a graphical user interface (GUI). Methods: Using a simple configuration file, the developed system allowed for the processing of radiology data exported from any kind of RIS into a Microsoft Excel, comma-separated value (CSV), or JavaScript Object Notation (JSON) file. These data were then imported into a clinical data warehouse. Additional values based on the radiology data were calculated during this import process by implementing 1 of several provided interfaces. Afterward, the query language and GUI of the data warehouse were used to configure and calculate reports on these data. For the most common types of requested reports, a web interface was created to view their numbers as graphics. Results: The tool was successfully tested with the data of 4 different German hospitals from 2018 to 2021, with a total of 1,436,111 examinations. The user feedback was good, since all their queries could be answered if the available data were sufficient. The initial processing of the radiology data for using them with the clinical data warehouse took (depending on the amount of data provided by each hospital) between 7 minutes and 1 hour 11 minutes. Calculating 3 reports of different complexities on the data of each hospital was possible in 1-3 seconds for reports with up to 200 individual calculations and in up to 1.5 minutes for reports with up to 8200 individual calculations. Conclusions: A system was developed with the main advantage of being generic concerning the export of different RISs as well as concerning the configuration of queries for various reports. The queries could be configured easily using the GUI of the data warehouse, and their results could be exported into the standard formats Excel and CSV for further processing.}, language = {en} } @article{SeufertPoigneeSeufertetal.2023, author = {Seufert, Anika and Poign{\´e}e, Fabian and Seufert, Michael and Hoßfeld, Tobias}, title = {Share and multiply: modeling communication and generated traffic in private WhatsApp groups}, series = {IEEE Access}, volume = {11}, journal = {IEEE Access}, doi = {10.1109/ACCESS.2023.3254913}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349430}, pages = {25401-25414}, year = {2023}, abstract = {Group-based communication is a highly popular communication paradigm, which is especially prominent in mobile instant messaging (MIM) applications, such as WhatsApp. Chat groups in MIM applications facilitate the sharing of various types of messages (e.g., text, voice, image, video) among a large number of participants. As each message has to be transmitted to every other member of the group, which multiplies the traffic, this has a massive impact on the underlying communication networks. However, most chat groups are private and network operators cannot obtain deep insights into MIM communication via network measurements due to end-to-end encryption. Thus, the generation of traffic is not well understood, given that it depends on sizes of communication groups, speed of communication, and exchanged message types. In this work, we provide a huge data set of 5,956 private WhatsApp chat histories, which contains over 76 million messages from more than 117,000 users. We describe and model the properties of chat groups and users, and the communication within these chat groups, which gives unprecedented insights into private MIM communication. In addition, we conduct exemplary measurements for the most popular message types, which empower the provided models to estimate the traffic over time in a chat group.}, language = {en} } @article{HossfeldHeegaardKellerer2023, author = {Hossfeld, Tobias and Heegaard, Poul E. and Kellerer, Wolfgang}, title = {Comparing the scalability of communication networks and systems}, series = {IEEE Access}, volume = {11}, journal = {IEEE Access}, doi = {10.1109/ACCESS.2023.3314201}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349403}, pages = {101474-101497}, year = {2023}, abstract = {Scalability is often mentioned in literature, but a stringent definition is missing. In particular, there is no general scalability assessment which clearly indicates whether a system scales or not or whether a system scales better than another. The key contribution of this article is the definition of a scalability index (SI) which quantifies if a system scales in comparison to another system, a hypothetical system, e.g., linear system, or the theoretically optimal system. The suggested SI generalizes different metrics from literature, which are specialized cases of our SI. The primary target of our scalability framework is, however, benchmarking of two systems, which does not require any reference system. The SI is demonstrated and evaluated for different use cases, that are (1) the performance of an IoT load balancer depending on the system load, (2) the availability of a communication system depending on the size and structure of the network, (3) scalability comparison of different location selection mechanisms in fog computing with respect to delays and energy consumption; (4) comparison of time-sensitive networking (TSN) mechanisms in terms of efficiency and utilization. Finally, we discuss how to use and how not to use the SI and give recommendations and guidelines in practice. To the best of our knowledge, this is the first work which provides a general SI for the comparison and benchmarking of systems, which is the primary target of our scalability analysis.}, language = {en} } @article{GreubelAndresHennecke2023, author = {Greubel, Andr{\´e} and Andres, Daniela and Hennecke, Martin}, title = {Analyzing reporting on ransomware incidents: a case study}, series = {Social Sciences}, volume = {12}, journal = {Social Sciences}, number = {5}, issn = {2076-0760}, doi = {10.3390/socsci12050265}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313746}, year = {2023}, abstract = {Knowledge about ransomware is important for protecting sensitive data and for participating in public debates about suitable regulation regarding its security. However, as of now, this topic has received little to no attention in most school curricula. As such, it is desirable to analyze what citizens can learn about this topic outside of formal education, e.g., from news articles. This analysis is both relevant to analyzing the public discourse about ransomware, as well as to identify what aspects of this topic should be included in the limited time available for this topic in formal education. Thus, this paper was motivated both by educational and media research. The central goal is to explore how the media reports on this topic and, additionally, to identify potential misconceptions that could stem from this reporting. To do so, we conducted an exploratory case study into the reporting of 109 media articles regarding a high-impact ransomware event: the shutdown of the Colonial Pipeline (located in the east of the USA). We analyzed how the articles introduced central terminology, what details were provided, what details were not, and what (mis-)conceptions readers might receive from them. Our results show that an introduction of the terminology and technical concepts of security is insufficient for a complete understanding of the incident. Most importantly, the articles may lead to four misconceptions about ransomware that are likely to lead to misleading conclusions about the responsibility for the incident and possible political and technical options to prevent such attacks in the future.}, language = {en} }