Active domain adaptation (ADA) aims to select a small set of target samples for annotation and use them for training to maximally boost the adaptation performance. However, most existing ADA methods only rely on the original output of the model, without considering the relationship between the source and target domain features, which may lead to selecting uninformative samples. In this paper, we propose an effective ADA framework: Prototype-Driven Active Domain Adaptation with density consideration (PDADA). It selects the most valuable target samples in the presence of domain shift through two criteria: Density-Conscious Domainness (DCD) and Prototype-Driven Informativeness (PDI). Furthermore, considering the class imbalance and cluster looseness issues in sample selection and domain adaptation, we develop a Class Balanced Expansion (CBE) algorithm and the Adversarial Active Domain Adaptation via Protecting Structured Information (AADA-PSI). Extensive experiments demonstrate that under the cooperation of the above components, PDADA outperforms previous methods on several challenging benchmarks and can be generalized to multi-source active domain adaptation setting.

Recently, instance contrastive learning achieves good results in unsupervised domain adaptation. It reduces the distances between positive samples and the anchor, increases the distances between negative samples and the anchor, and learns discriminative feature representations for target samples. However, most recent methods for identifying positive and negative samples are based on whether the pseudo-labels of samples and the pseudo-label of the anchor correspond to the same class. Due to the lack of target labels, many uncertain data are mistakenly labeled during the training process, and many low training potential data are also utilized. To address these problems, we propose Low Category Uncertainty and High Training Potential Instance Learning for Unsupervised Domain Adaptation (LUHP). We first propose a weight to measure the category uncertainty of the target sample. We can effectively filter the samples near the decision boundary through category uncertainty thresholds which are calculated by weights. Then we propose a new loss to focus on samples with high training potential. Finally, for anchors with low category uncertainty, we propose a sample reuse strategy to make the model more robust. We demonstrate the effectiveness of LUHP by showing the results of four datasets widely used in unsupervised domain adaptation.

Self-sovereign identity (SSI) systems are novel blockchain-based solutions that are said to shift the control of data records from organizations to individuals. Contrary to conventional blockchains, such as Bitcoin or Ethereum, many SSI systems do not capture on ledger the exchange of transactional data between individuals. By not capturing the exchange of transaction data such SSI systems have the advantage of complying with privacy regulations such as the EU’s General Data Protection Regulations, but, at the same time, have the disadvantage of not capturing evidence that an exchange has happened. Such evidence, however, may be needed for audit and accountability purposes. To achieve these objectives and to preserve privacy, we leverage archival principles to introduce a novel concept of a proof registry, which we define as a set of technical components, data structures, and process flows, that assures that authoritative records offering evidence of transactions is captured, stored, and accessible. This solution solves the compliance and accountability problem while preserving the self-sovereignty and privacy of involved parties.