This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming (ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions' prior probability, confidence (uncertainty), and the models' expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets.

Recent research has shown that integrating domain knowledge into deep learning architectures is effective -- it helps reduce the amount of required data, improves the accuracy of the models' decisions, and improves the interpretability of models. However, the research community is missing a convened benchmark for systematically evaluating knowledge integration methods. In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. In all cases, we model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints. We report the results of these models using a new set of extended evaluation criteria in addition to the task performances for a more in-depth analysis. This effort provides a framework for a more comprehensive and systematic comparison of constraint integration techniques and for identifying related research challenges. It will facilitate further research for alleviating some problems of state-of-the-art neural models.

After two decades of in-RDBMS machine learning research and implementations, database systems have not made a compelling case for data scientists to move their workflows there. A transition phase is currently under way, where the database community with all the experience of the past is looking for crucial features, such as data versioning and data governance, that would make DBMSes attractive to data scientists, and where the definition of in-RDBMS machine learning becomes less rigid with the adoption of data lakes and the interoperability with systems like TensorFlow and open formats like ONNX. Overall, we are very happy with the content of the 1st DBAI, as this included insightful presentations and a constructive panel discussion. I'd like to sincerely thank my fellow organizers (Nikolaos Vasilogou, Parisa Kordjamshidi, Maximilian Schleich, Kirk Pruhs and Zenna Tavares), the PC members, the speakers and panelists, the sponsors, the volunteers and last but not least the authors and attendees for contributing each in his/her own way in making DBAI'21 a successful workshop. I really hope we will have the opportunity to organize another DBAI soon.

Overall, we are very happy with the content of the 1st DBAI, as this included insightful presentations and a constructive panel discussion. I'd like to sincerely thank my fellow organizers (Nikolaos Vasilogou, Parisa Kordjamshidi, Maximilian Schleich, Kirk Pruhs and Zenna Tavares), the PC members, the speakers and panelists, the sponsors, the volunteers and last but not least the authors and attendees for contributing each in his/her own way in making DBAI'21 a successful workshop. I really hope we will have the opportunity to organize another DBAI soon.

Saul is a modeling language implemented as a domain specific language (DSL) in Scala. The flexibility in designing above components helps rapid development of intelligent AI systems with one or more learned functions that interact with each other. Saul offers a convenient, declarative syntax for classifier and constraint definition directly in terms of the objects in the programmer's application. With Saul, the details of feature extraction, learning, model evaluation, and inference are all abstracted away from the programmer, leaving him to reason more directly about his application. The project contains three modules.