This article introduces foundational components for decentralized B2B data sharing based on the solid protocol, emphasizing data sovereignty, security, and interoperability. These components are: (1) Authorization app (AuthApp) – facilitating granular control and compliance in access granting and revocation processes; (2) rights delegation proxy (RDP) – supporting controlled delegation of rights, enabling natural persons to act on behalf of organizations while ensuring privacy and traceability; (3) data provisioning proxy (DPP) – allowing seamless and secure data provisioning across organizations while masking the identity of upstream data sources to protect business interests. The components enable the creation of end-to-end, standards-based, flexible data value chains. We validate their applicability through a real-world financial services use case involving loan processing, which illustrates data sharing and protection challenges in B2B ecosystems.

Over the past few decades, computational methods have been developed to estimate perceptual audio quality. These methods, also referred to as objective quality measures, are usually developed and intended for a specific application domain. Because of their convenience, they are often used outside their original intended domain, even if it is unclear whether they provide reliable quality estimates in this case. This work studies the correlation of well-known state-of-the-art objective measures with human perceptual scores in two different domains: audio coding and source separation. The following objective measures are considered: fwSNRseg, dLLR, PESQ, PEAQ, POLQA, PEMO-Q, ViSQOLAudio, (SI-)BSSEval, PEASS, LKR-PI, 2f-model, and HAAQI. Additionally, a novel measure (SI-SA2f) is presented, based on the 2f-model and a BSSEval-based signal decomposition. We use perceptual scores from 7 listening tests about audio coding and 7 listening tests about source separation as ground-truth data for the correlation analysis. The results show that one method (2f-model) performs significantly better than the others on both domains and indicate that the dataset for training the method and a robust underlying auditory model are crucial factors towards a universal, domain-independent objective measure.

In this work we tackle the problem of real-time alignment of active shape models to new object instances at video frame rate. To achieve this we use edge orientation information as the basic image feature. Unlike in the original active shape framework we incorporate the image features directly into the model vector. We also introduce a new update rule for a model point in a local surrounding. We demonstrate the effectiveness of the proposed approach with a face segmentation task. There we are able to fit a new model face on average within 20 msec on 500 MHz Pentium II PC if the initial model position and size does not deviate too much from the true position. The latter is assured by a face detection step which is carried out before the active shape alignment.