Trustworthiness in neural networks is crucial for their deployment in critical applications, where reliability, confidence, and uncertainty play a pivotal role in decision-making. Traditional performance metrics such as accuracy and precision fail to capture these aspects, particularly in cases where models exhibit overconfidence. To address these limitations, this paper introduces a novel framework for quantifying the trustworthiness of neural networks by incorporating subjective logic into the evaluation of Expected Calibration Error (ECE). This method provides a comprehensive measure of trust, distrust, and uncertainty by clustering predicted probabilities and fusing opinions using appropriate fusion operators. We demonstrate the effectiveness of this approach through experiments on the MNIST and CIFAR-10 datasets, where post-calibration results indicate improved trustworthiness. The proposed framework offers a more interpretable and nuanced assessment of AI models, with potential applications in sensitive domains such as healthcare and autonomous systems.

Subjective Logic (SL) enriches probabilistic logic by incorporating uncertainty and subjective belief ownership, enabling the expression of uncertainty about subjective beliefs. Unlike traditional probabilistic logics, SL 1) accommodates situations where different agents express beliefs about the same proposition, integrating the subjective nature and ownership of beliefs; and 2) addresses existing limitations in Dempster-Shafer Theory of evidence (DST), particularly in modelling trust transitivity. In modern computer systems, trust assessment extends beyond direct relationships to complex networks, necessitating the consideration of referral and direct trust relationships. This paper introduces a novel trust discount operator for referral edges in complex networks, addressing challenges in discounting trust across two and multiple referral edges. Through our empirical analysis, we demonstrate the effectiveness of the proposed operator and establish a relationship between path length and trustworthiness.

The purpose of multi-source fusion is to combine information from more than two evidence sources, or subjective opinions from multiple actors. For subjective logic, a number of different fusion operators have been proposed, each matching a fusion scenario with different assumptions. However, not all of these operators are associative, and therefore multi-source fusion is not well-defined for these settings. In this paper, we address this challenge, and define multi-source fusion for weighted belief fusion (WBF) and consensus & compromise fusion (CCF). For WBF, we show the definition to be equivalent to the intuitive formulation under the bijective mapping between subjective logic and Dirichlet evidence PDFs. For CCF, since there is no independent generalization, we show that the resulting multi-source fusion produces valid opinions, and explain why our generalization is sound. For completeness, we also provide corrections to previous results for averaging and cumulative belief fusion (ABF and CBF), as well as belief constraint fusion (BCF), which is an extension of Dempster's rule. With our generalizations of fusion operators, fusing information from multiple sources is now well-defined for all different fusion types defined in subjective logic. This enables wider applicability of subjective logic in applications where multiple actors interact.

Most techniques for personal data synchronization only allow one-to-one or star network topologies. Enkel DS presented in this paper extends OMA SyncML DS, an open industry standard for optimistic data synchronization. Enkel DS allows many-to-many topologies including cycles enabling a human user to synchronize each personal device with another.

Recently many prominent web sites face so called Distributed Denial of Service Attacks (DDoS). While former security threats could be faced by a tight security policy and active measures like using rewalls, vendor patches etc. these DDoS are new in suchway that there is no completely satisfying protection yet. In this paper we categorize different forms of attacks and give an overview over the most common DDoS tools. Furthermore we present a solution based on Class Based Routing mechanisms in the Linux kernel that will prevent the most severe impacts of DDoS on clusters of web servers with a prepended load balancing server. The goal is to keep the web servers under attack responding to the normal client requests. Some performance tests and a comparison to other approaches conclude our paper.