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Abstract
In this paper, we shed light on shared hosting services’ security and trust implications and measure their attack surfaces. To do so, we analyzed 30 shared hosters and found that all of them might leak relevant information, which could be abused unnoticed. An adversary could use this attack surface to covertly extract data from various third parties registered with a shared hoster. Furthermore, we found that most hosters suffer from vulnerabilities that can be used by an internal attacker (i.e., someone using the service) to compromise other hosted services or the entire system.
Abstract
For years, researchers have been analyzing mobile Android apps to investigate diverse properties such as software engineering practices, business models, security, privacy, or usability, as well as differences between marketplaces. While similar studies on iOS have been limited, recent work has started to analyze and compare Android apps with those for iOS. To obtain the most representative analysis results across platforms, the ideal approach is to compare their characteristics and behavior for the same set of apps, e. g., to study a set of apps for iOS and their respective counterparts for Android. Previous work has only attempted to identify and evaluate such cross-platform apps to a limited degree, mostly comparing sets of apps independently drawn from app stores, manually matching small sets of apps, or relying on brittle matches based on app and developer names. This results in (1) comparing apps whose behavior and properties significantly differ, (2) limited scalability, and (3) the risk of matching only a small fraction of apps.
In this work, we propose a novel approach to create an extensive dataset of cross-platform apps for the iOS and Android ecosystems. We describe an analysis pipeline for discovering, retrieving, and matching apps from the Apple App Store and Google Play Store that we used to create a set of 3,322 cross-platform apps out of 10,000 popular apps for iOS and Android, respectively. We evaluate existing and new approaches for cross-platform app matching against a set of reference pairs that we obtained from Google's data migration service. We identify a combination of seven features from app store metadata and the apps themselves to match iOS and Android apps with high confidence (95.82 %). Compared to previous attempts that identified 14 % of apps as cross-platform, we are able to match 34 % of apps in our dataset. To foster future research in the cross-platform analysis of mobile apps, we make our pipeline available to the community.
Abstract
This paper challenges the conventional assumption in cybersecurity that users act as rational actors. Despite numerous technical solutions, awareness campaigns, and organizational strategies aimed at bolstering cybersecurity, these often overlook the prevalence of non-rational user behavior. Our study, involving a survey of 208 participants, empirically demonstrates this aspect. We found that a significant portion of users (55.3%) would accept a substantial risk (35%) to click on a potentially malicious link or attachment. This propensity increases to 61% when users are led to believe there is a 65% chance of facing no adverse consequences. To address this irrationality, we explored the efficacy of nudging mechanisms within email systems. Our qualitative user study revealed that incorporating a simple colored nudge in the email intably enhance the ability of users to discern malicious emails, improving decision-making accuracy by an average of 10%.
Abstract
Filter lists are used by various users, tools, and researchers to identify tracking technologies on the Web. These lists are created and maintained by dedicated communities. Aside from popular blocking lists (e.g., EasyList), the communities create region-specific blocklists that account for trackers and ads that are only common in these regions. The lists aim to keep the size of a general blocklist minimal while protecting users against region-specific trackers.
In this paper, we perform a large-scale Web measurement study
to understand how different region-specific filter lists (e.g., a blocklist specifically designed for French users) protect users when visiting websites. We define three privacy scenarios to understand when and how users benefit from these regional lists and what effect they have in practice. The results show that although the lists differ significantly, the number of rules they contain is unrelated to the number of blocked requests. We find that the lists’ overall efficacy varies notably. Filter lists also do not meet the expectation that they increase user protection in the regions for which they were designed. Finally, we show that the majority of the rules on the lists were not used in our experiment and that only a fraction of the rules would provide comparable protection for users.
Experiencing relational devaluation at work through social stressors has been linked to various detrimental outcomes. In the current study, we investigate the role of hardiness and mindfulness as personal resources which help employees to effectively cope with such stressors and thereby prevent burnout.
We focus on trait mindfulness as the innate capacity of paying and maintaining attention to present-moment experiences with an open and nonjudgmental attitude. It has been shown to promote concentration and well-being and to facilitate decision making; it is often seen as an important resource for overcoming challenges in everyday work life.
Hardiness also constitutes a personality profile of dispositional resilience that describes how people deal with stressful events and includes the core aspects of challenge (conviction that challenges offer opportunities), engagement (actively tackling tasks and challenges) and a sense of control (conviction of influence over one's own life circumstances). People with high hardiness show better health and higher job satisfaction and performance.
We build our hypothesis according to the extended version of the Job Demands–Resources model, which states that personal resources protect employees from burnout, because they shape employees’ perceptions of and reactions towards their work environment. In a similar vein, stress theory suggests that personal resources mitigate burnout through lower stress appraisals, greater use of adaptive coping, and flexibility in matching coping to appraisals.
We measured social stressors at work with the scale developed by Frese et al and further asked participants to work on the Maslach Burnout Inventory, the Mindful Attention and Awareness Scale and the short version of the Revised Norwegian Dispositional Resilience (Hardiness) Scale. Our cross-sectional study was based on a sample of N = 174 employees from a broad range of organizations and job types.
Statistical Analyses revealed significant negative correlations of both personal resources with reported symptoms of burnout and the perception of social stressors as well. However, in line with prior research, they indeed did not attenuate the relationship between social stressors and emotional exhaustion at work. Theoretical and practical implications as well as limitations and avenues for future research are discussed.
The precision of yield calculation of modern design and simulation software for photovoltaic systems strongly rely, beside the accuracy of the specified module and inverter data, on the quality of the weather data. Since data from weather stations is not available for most locations world-wide this data is calculated by using modern interpolation methods. Beside this, simulation software typically uses historical weather data. In this work the mismatch of yield simulation results based on proprietary data, meaning interpolated or also called synthetical data, and data coming from a weather station in proximity to the installation is evaluated. The simulated data sets are compared to measurement data as obtained by the inverter output and hence give a profound understanding how interpolated data may influence the simulation results. The outcome shows that the quality of the yield simulation, if compared to the measurement data, is increased by a factor of up to four if on-site weather data is used as input for the simulation. The largest source of deviation is irradiation, which varies up to 10% if synthetical and measured irradiation on-site is compared. The second largest sources for simulation mismatches are power calculation and module temperature correction.
When organizing intercultural and interdisciplinary Project-Based Learning (PBL) activities across Higher Education Institutions (HEIs), the organizational and resource implications, along with the associated financial challenges, soon become crucial. Even promising approaches may not take off as a simple ‘return on investment’ view and funding decision may not fully address their various impacts on students, staff, institutions and society.
This paper explores the experiences within a distributed interdisciplinary project-based learning program run from 2020 to 2023 and involving more than 150 students from four continents learning 21st century skills by collaborating over one semester to address real-world problems faced by clients in partner countries. While the primary goal of this distributed interdisciplinary and intercultural project-based learning program was to offer students a truly Global Intercultural Project Experience (GIPE), this paper explores its broader impact. We found that the program significantly influenced both the academic and administrative staff at all partner universities. Furthermore, we examine the program's effect on the participating institutions themselves over the four-year period. Our conclusion is that the invaluable benefits of such interdisciplinary project-based learning extend well beyond financial metrics. They include enhanced student learning experiences, strengthened cooperation and mutual learning between academics and administrative staff, improved institutional reputation, and positive societal impact.
Thus, we worked hard to convince both our university management and the world's largest funding organisation for the international exchange of students and researchers to grant financial support for another 3-year period in 2025 to 2027 during which the GIPE concept will be further developed and a permanent organizational structure shall be established based on an extended network of partner institutions and sponsors around the world.
Unleashing Personalized Education Using Large Language Models in Online Collaborative Settings
(2024)
The Artificial Intelligence community has long pursued personalized education. Over the past decades, efforts have ranged from automated advisors to Intelligent Tutoring Systems, all aimed at tailoring learning experiences to students' individual needs and interests. Unfortunately, many of these endeavors remained largely theoretical or proposed solutions challenging to implement in real-world scenarios. However, we are now in the era of Large Language Models (LLMs) like ChatGPT, Mistral, or Claude, which exhibit promising capabilities with significant potential to impact personalized education. For instance, ChatGPT 4 can assist students in using the Socratic method in their learning process. Despite the immense possibilities these technologies offer, limited significant results are showcasing the impact of LLMs in educational settings. Therefore, this paper aims to present tools and strategies based on LLMs to address personalized education within online collaborative learning settings. To do so, we propose RAGs (Retrieval-Augmented Generation) agents that could be added to online collaborative learning platforms: a) the Oracle agent, capable of answering questions related to topics and materials uploaded to the platform.; b) the Summary agent, which can summarize and present content based on students' profiles.; c) the Socratic agent, guiding students in learning topics through close interaction.; d) the Forum agent, analyzing students' forum posts to identify challenging topics and suggest ways to overcome difficulties or foster peer collaboration.; e) the Assessment agent, presenting personalized challenges based on students' needs. f) the Proactive agent, analyzing student activity and suggesting learning paths as needed. Importantly, each RAG agent can leverage historical student data to personalize the learning experience effectively. To assess the effectiveness of this personalized approach, we plan to evaluate the use of RAGs in online collaborative learning platforms compared to previous online learning courses conducted in previous years.
This paper discusses the experiences of a distributed interdisciplinary project-based learning program for students across continents. For the years 2020 until 2023, we received seed-funding for four annual projects to engage students from Germany (Europe), Namibia (Africa), Indonesia (Asia), and Peru (Latin-America) to collaborate over one semester on interdisciplinary projects contributing to the solution of some real-life client’s problems in the partner countries. During this period, more than 150 students embarked on these projects with 116 of them being selected for a scholarship for an international mobility. With the guidance and support by academics from all partner universities, the students success-fully completed each project expressing deep appreciation for the learning opportunities while over¬coming challenges of working across widespread time zones, different cultures, changing requirements, and various technical difficulties.
While the primary aim of this distributed interdisciplinary and intercultural project-based learning program was to provide students with a truly Global Intercultural Project Experience (GIPE), in this paper we investigate on its impact in a broader sense as it was observed that this program also had a significant impact on both academic and administrative staff at all partner universities. Finally, we also reveal the impact of this four-year-program on the participating institutions themselves and conclude that the invaluable returns of such interdisciplinary project-based learning extend far beyond financial metrics. It encompasses enhanced student learning experiences, strengthened cooperation and mutual learning between academics and administrative staff, as well as institutional reputation, and societal impact.
This Paper explores how emergent technologies such as 6G and tactile Internet can potentially enhance cognitive, personal informatics (CPI) in participatory healthcare, promoting patient-centered healthcare models through high-speed, reliable communication networks. It highlights the transition to improved patient engagement and better health outcomes facilitated by these technologies, underscoring the importance of ultra-reliable, low-latency communications (URLLC) and realizing the tactile Internet’s potential in healthcare. This innovation could dramatically transform telemedicine and mobile health (mHealth) by enabling remote healthcare delivery while providing a better understanding of the inner workings of the patient. While generating many advantages, these developments have disadvantages and risks. Therefore, this study addresses the critical security and privacy concerns related to the digital transformation of healthcare. Our work focuses on the challenges of managing and understanding cognitive data within the CPI and the potential threats from analyzing such data. It proposed a comprehensive analysis of potential vulnerabilities and cyber threats, emphasizing the need for robust security frameworks designed with resilience in mind to protect sensitive cognitive data. We present scenarios for reward and punishment systems and their impacts on users. In conclusion, we outline a vision for the future of secure, resilient, and patient-centric digital healthcare systems that leverage 6G and the tactile Internet to enhance the CPI. We offer policy recommendations and strategic directions for stakeholders to create a secure, empowering environment for patients to manage their cognitive health information.