Online social networks (OSNs) have become an integral part of social interaction and communication between people. Reasons include the ubiquity of OSNs that is offered through mobile devices and the possibility to bridge spatial and temporal communication boundaries. However, several researchers have raised privacy concerns due to the large amount of user data shared on OSNs. Yet, despite the large body of research addressing OSN privacy issues, little differentiation of data types on social network sites is made and a generally accepted classification and terminology for such data is missing. The lack of a terminology impedes comparability of related work and discussions among researchers, especially in the case of privacy implications of different data types. To overcome these shortcomings, this paper develops a well-founded terminology based on a thorough literature analysis and a conceptualization of typical OSN user activities. The terminology is organized hierarchically resulting in a taxonomy of data types. The paper furthermore discusses and develops a metric to assess the privacy relevance of different data types. Finally, the taxonomy is applied to the five major OSNs to evaluate its generalizability.
Reputation systems have been extensively explored in various disciplines and application areas. A problem in this context is that the computation engines applied by most reputation systems available are designed from scratch and rarely consider well established concepts and achievements made by others. Thus, approved models and promising approaches may get lost in the shuffle. In this work, we aim to foster reuse in respect of trust and reputation systems by providing a hierarchical component taxonomy of computation engines which serves as a natural framework for the design of new reputation systems. In order to assist the design process we, furthermore, provide a component repository that contains design knowledge on both a conceptual and an implementation level. To evaluate our approach we conduct a descriptive scenario-based analysis which shows that it has an obvious utility from a practical point of view. Matching the identified components and the properties of trust introduced in literature, we finally show which properties of trust are widely covered by common models and which aspects have only rarely been considered so far.
Abstract-Transaction context is an important aspect that should be taken into account for reputation-based trust assessment, because referrals are bound to the situation-specific context in which they were created. The non-consideration of transaction context may cause several threats such as the value imbalance problem. Exploiting this weakness, a seller can build high reputation by selling cheap products while cheating on the expensive ones. In the recent years, multiple approaches have been introduced that address this challenge. All of them chose metrics leading to numerical reputation values. These values, however, are non-transparent and quite hard to understand for the end-user. In this work, in contrast, we combine reputation assessment and visual analytics to provide an interactive visualization of multivariate reputation data. We thereby allow the user to analyze the data sets and draw conclusions by himself. In this way, we enhance transparency, involve the user in the evaluation process and as a consequence increase the users' trust in the reputation system.
This paper presents an approach for evaluating exoskeleton support concepts through biomechanical analyses on a musculoskeletal human model. By simplifying the support forces of an exoskeleton as external forces, different support concepts can be biomechanically evaluated for the respective use case without concrete design specifications of the exoskeleton. This enables an estimation of the resulting relief and strain on the human body in the early stages of exoskeleton development. To present the approach, the use case of working at and above head height with a power tool is chosen.
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