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Gamification is increasingly successful in the field of education and health. However, beyond call-centers and applications in human resources, its utilization within companies remains limited. In this paper, we examine the acceptance of gamification in a large company (with over 17,000 employees) across three generations, namely X, Y, and Z. Furthermore, we investigate which gamification elements are suited for business contexts, such as the dissemination of company principles and facts, or the organization of work tasks. To this end, we conducted focus group discussions, developed the prototype of a gamified company app, and performed a large-scale evaluation with 367 company employees. The results reveal statistically significant intergenerational disparities in the acceptance of gamification: younger employees, especially those belonging to Generation Z, enjoy gamification more than older employees and are most likely to engage with a gamified app in the workplace. The results further show a nuanced range of preferences regarding gamification elements: avatars are popular among all generations, badges are predominantly appreciated by Generations Z and Y, while leaderboards are solely liked by Generation Z. Drawing upon these insights, we provide recommendations for future gamification projects within business contexts. We hope that the results of our study regarding the preferences of the gamification elements and understanding generational differences in acceptance and usage of gamification will help to create more engaging and effective apps, especially within the corporate landscape.
A report from the World Economic Forum (2019) stated loneliness as the third societal stressor in the world, mainly in western countries. Moreover, research shows that loneliness tends to be experienced more severely by young adults than other age groups (Rokach, 2000), which is the case of university students who face profound periods of loneliness when attending university in a new place (Diehl et al., 2018). Digital technology, especially mental health apps (MHapps), have been viewed as promising solutions to address this distress in universities, however, little evidence on this topic reveals uncertainty around how these resources impact individual well-being. Therefore, this research proposed to investigate how the gamified social mobile app Noneliness reduced loneliness rates and other associated mental health issues of students from a German university. As little work has focused on digital apps targeting loneliness, this project also proposed to describe and discuss the app’s design and development processes. A multimethod approach was adopted: literature review on high-efficacy MHapps design, gamification for mental health and loneliness interventions; User Experience Design and Human-centered Computing. Evaluations occurred according to the app’s development iterations, which assessed four versions (from prototype to Beta) through quantitative and qualitative studies with university students. The main results obtained regarding the design aspects were: users' preference for minimalistic interfaces; importance in maintaining privacy and establishing trust among users; students' willingness to use an online support space for emotional and educational support. Most used features were those related to group discussions, private chats and university social events. Preferred gamification elements were those that provided positive reinforcement to motivate social interactions (e.g. Points, Levels and Achievements). Results of a pilot randomized controlled trial with university students (N = 12), showed no statistically significant interactions in reducing loneliness among experimental group members (n = 7, x² = 3.500, p-value = 0.477, Cramer’s V = 0.27) who made continued use of the app for six weeks. On the other hand, the app showed effects of moderate magnitude on loneliness reduction in this group. The app also demonstrated relatively strong magnitude effects on other associated variables, such as depression and stress in the experimental group. In addition to motivating the conduct of further studies with larger samples, the findings point to a potential app effectiveness not only to reduce loneliness, but also other variables that may be associated with the distress.
It is generally agreed that the development and deployment of an important amount of IoT devices throughout the world has revolutionized our lives in a way that we can rely on these devices to complete certain tasks that may have not been possible just years ago which also brought a new level of convenience and value to our lives.
This technology is allowing us in a smart home environment to remotely control doors, windows, and fridges, purchase online, stream music easily with the use of voice assistants such as Amazon Echo Alexa, also close a garage door from anywhere in the world to cite some examples as this technology has added value to several domains ranging from household environments, cites, industries by exchanging and transferring data between these devices and customers. Many of these devices’ sensors, collect and share information in real-time which enables us to make important business decisions.
However, these devices pose some risks and also some security and privacy challenges that need to be addressed to reach their full potential or be considered to be secure. That is why, comprehensive risk analysis techniques are essential to enhance the security posture of IoT devices as they can help evaluate the robustness and reliability towards potential susceptibility to risks, and vulnerabilities that IoT devices in a smart home setting might possess.
This approach relies on the basis of ISO/IEC 27005 methodology and risk matrix method to highlight the level of risks, impact, and likelihood that an IoT device in smart home settings can have, map the related vulnerability, threats and risks and propose the necessary mitigation strategies or countermeasures that can be taken to secure a device and therefore satisfying some security principles. Around 30 risks were identified on Amazon Echo and the related IoT system using the methodology. A detailed list of countermeasures is proposed as a result of the risk analysis. These results, in turn, can be used to elevate the security posture of the device.
In pandemic times, the possibilities for conventional sports activities are severely limited; many sports facilities are closed or can only be used with restrictions. To counteract this lack of health activities and social exchange, people are increasingly adopting new digital sports solutions—a behavior change that had already started with the trend towards fitness apps and activity trackers. Existing research suggests that digital solutions increase the motivation to move and stay active. This work further investigates the potentials of digital sports incorporating the dimensions gender and preference for team sports versus individual sports. The study focuses on potential users, who were mostly younger professionals and academics. The results show that the SARS-CoV-19 pandemic had a significant negative impact on sports activity, particularly on persons preferring team sports. To compensate, most participants use more digital sports than before, and there is a positive correlation between the time spent physically active during the pandemic and the increase in motivation through digital sports. Nevertheless, there is still considerable skepticism regarding the potential of digital sports solutions to increase the motivation to do sports, increase performance, or raise a sense of team spirit when done in groups.
The progress in machine learning has led to advanced deep neural networks. These networks are widely used in computer vision tasks and safety-critical applications. The automotive industry, in particular, has experienced a significant transformation with the integration of deep learning techniques and neural networks. This integration contributes to the realization of autonomous driving systems. Object detection is a crucial element in autonomous driving. It contributes to vehicular safety and operational efficiency. This technology allows vehicles to perceive and identify their surroundings. It detects objects like pedestrians, vehicles, road signs, and obstacles. Object detection has evolved from being a conceptual necessity to an integral part of advanced driver assistance systems (ADAS) and the foundation of autonomous driving technologies. These advancements enable vehicles to make real-time decisions based on their understanding of the environment, improving safety and driving experiences. However, the increasing reliance on deep neural networks for object detection and autonomous driving has brought attention to potential vulnerabilities within these systems. Recent research has highlighted the susceptibility of these systems to adversarial attacks. Adversarial attacks are well-designed inputs that exploit weaknesses in the deep learning models underlying object detection. Successful attacks can cause misclassifications and critical errors, posing a significant threat to the functionality and safety of autonomous vehicles. With the rapid development of object detection systems, the vulnerability to adversarial attacks has become a major concern. These attacks manipulate inputs to deceive the target system, significantly compromising the reliability and safety of autonomous vehicles. In this study, we focus on analyzing adversarial attacks on state-of-the-art object detection models. We create adversarial examples to test the models’ robustness. We also check if the attacks work on a different object detection model meant for similar tasks. Additionally, we extensively evaluate recent defense mechanisms to see how effective they are in protecting deep neural networks (DNNs) from adversarial attacks and provide a comprehensive overview of the most commonly used defense strategies against adversarial attacks, highlighting how they can be implemented practically in real-world situations.
Soziale Roboter, die mit uns kommunizieren und menschliche Verhaltensmuster imitieren, sind ein wichtiges Zukunftsthema. Während viele Arbeiten ihr Design und ihre Akzeptanz erforschen, gibt es bislang nur wenige Untersuchungen zu ihrer Marktfähigkeit. Der Schwerpunkt dieser Arbeit liegt auf dem Einsatz sozialer Roboter in den Bereichen Gesundheit und Pflege, wo die zukünftige Integration sozialer Roboter ein enormes Potenzial hat. Eine Studie mit 197 Personen aus Italien und Deutschland untersucht gewünschte Funktionalitäten und Kaufpräferenzen und berücksichtigt hierbei kulturelle Unterschiede. Dabei bestätigte sich die Wichtigkeit mehrerer Dimensionen des ALMERE-Modells (z. B. wahrgenommene Freude, Nützlichkeit und Vertrauenswürdigkeit). Die Akzeptanz korreliert stark mit der Investitionsbereitschaft. Viele ältere Personen betrachten soziale Roboter als „assistierende technische Geräte“ und erwarten, dass diese von Versicherungen und der öffentlichen Hand bezuschusst werden. Um ihren zukünftigen Einsatz zu erleichtern, sollten soziale Roboter in die Datenbanken medizinischer Hilfsmittel integriert werden.
Ansätze für den Einfluss der Social-Media-Kanäle TikTok und Instagram auf die Kaufentscheidung
(2023)
Social-Media-Nutzer und Nutzerinnen sind online und lernen dadurch neue Produkte kennen. Gerade auf Social-Media-Plattformen wie Instagram und TikTok werden die Nutzer und Nutzerinnen durch verschiedene Mittel häufig auf Produkte aufmerksam gemacht und zum Kaufen motiviert.
Das Ziel der vorliegenden Arbeit ist es, herauszufinden welche Aspekte auf den Plattformen Instagram und TikTok die Nutzenden dazu animiert bei einem Drogerieunternehmen einzukaufen. Dazu wird die folgende Forschungsfrage gestellt: Wie kann ein Drogerieunternehmen die Kaufentscheidung auf Instagram und TikTok beeinflussen?
Aus den resultierenden Ergebnissen einer qualitative Studie mittels 8 Interviews werden Handlungsempfehlungen für die Unternehmen der Drogeriebranche abgeleitet. Die Ergebnisse der Forschung sind Ansätze, welche Drogerien auf den Social-Media-Plattformen nutzen können, um die Kaufentscheidung ihrer Follower und Followerinnen positiv zu beeinflussen.
Diese Thesis beschäftigt sich mit den Techniken von Code Injection und API Hooking, die von Malware verwendet werden, um sich in laufende Prozesse einzuschleusen und deren Verhalten zu manipulieren. Darüber hinaus erklärt sie die Grundlagen der Betriebssystemarchitektur, der DLLs, der Win32 API und der PE-Dateien, die für das Verständnis dieser Techniken notwendig sind. Die Thesis stellt verschiedene Methoden von Code Injection und API Hooking vor, wie z.B. DLL Injection, PE Injection, Process Hollowing, Inline Hooking und IAT Hooking, und zeigt anhand von Codebeispielen, wie sie funktionieren. Des Weiteren wird auch beschrieben, wie man Code Injection und API Hooking mithilfe verschiedene Tools und Techniken wie VADs, Speicherforensik und maschinelles Lernen erkennen und verhindern kann. Die Thesis diskutiert außerdem mögliche Gegenmaßnahmen, die das Betriebssystem oder die Anwendungen anwenden können, um sich vor Code Injection und API Hooking zu schützen, wie z.B. ASLR, DEP, ACG, IAF und andere. Zuletzt wird mit einer Zusammenfassung und einem Ausblick auf die zukünftigen Herausforderungen und Möglichkeiten in diesem Bereich abgeschlossen.
Das Thema dieser Masterthesis lautet „Camera Stream Solution – Marktübersicht, Lösungsansätze, Prototyp“. Mit dieser Arbeit wird eine Videostreaming-Lösung für die Herrenknecht-Plattform CONNECTED realisiert. Dabei geht es um die Bildschirmaufnahme von Navigations- und Steuerungsbildschirmen auf Tunnelbohrmaschinen und die Übertragung dieser Aufnahmen in die Cloud. Letztlich wird ermöglicht die Aufnahmen in nahezu Echtzeit als Videostream in einem Videoplayer wiederzugeben.
Zu Beginn werden die Grundlagen zur Datenübertragung im Internet sowie zum Streaming erläutert. Im Anschluss wird eine Marktübersicht verschiedener Streaming-Komponenten gegeben sowie einige Lösungsansätze vorgestellt und anhand ausgewählter Kriterien verglichen. Im nächsten Schritt wird die Implementierung eines Prototyps behandelt. Dieser nutzt unter anderem ffmpeg für die Bildschirmaufnahme und die Kodierung sowie die Streaming-Protokolle RTMP (Real Time Messaging Protocol) und HLS (HTTP Live Streaming). Zur Realisierung der Architektur gehört auch die Entwicklung einer REST-API und eines REST-Clients in C#.
Mit dem Projekt wird eine „echte“ Streaming-Lösung für die Kundenplattform CONNECTED entwickelt, die einen Videostream mit 24 Bildern pro Sekunde bietet, um die bisherige Darstellung von Screenshots auf der Plattform zu ersetzen.