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This Lutra submodule provides a common framework that other submodules in Lutra use and extend. It includes an implementation of OTTR's data model with classes for templates, instances, types, etc.; a system for handling results and error messages; core interfaces and abstract classes for streamed pipelines for parsing and writing templates and instances; as well as implementation of the TemplateStore, a container for templates, with algorithms for checking correctness of templates and expanding and checking correctness of instances. Lutra is the reference implementation of the OTTR framework. For more information about OTTR, see http://ottr.xyz.

Maximum Likelihood Rule Ensembles (MLRules) is a new rule induction algorithm for solving classification problems via probability estimation. The ensemble is built using boosting, by greedily minimizing the negative loglikelihood which results in estimating the class conditional probability distribution. The main advantage of decision rules is their simplicity and comprehensibility: they are logical statements of the form "if condition then decision", which is probably the easiest form of model to interpret. On the other hand, by exploiting a powerful statistical technique to induce the rules, the final ensemble has very high prediction accuracy. Fork of the original code located at: http://www.cs.put.poznan.pl/wkotlowski/software-mlrules.html

CITA-Cloud is an open-source blockchain development framework. It is released under the Apache 2.0 license and provides an architecture and development tools for customization. Its aim is to overcome the scalability and programmability challenges of blockchain technology at the underlying level. Leveraging cloud-native capabilities, it allows flexible replacement of blockchain components. Research institutions can easily integrate and transform their research results. Ultimately, the goal is to achieve flexibility, openness, and interoperability at the underlying level of blockchain for different scenarios.

Azure Video Analyzer provides a platform to build intelligent video applications that span the edge and the cloud. The platform offers the capability to capture, record, and analyze live videos along with publishing the results, video, and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open-source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline. Use the client library for Video Analyzer Edge to simplify interactions with the Microsoft Azure IoT SDKs (https://github.com/azure/azure-iot-sdks) and programmatically construct pipeline topologies and live pipelines.

Virgil Security is presenting an implementation of the [Double Ratchet](https://signal.org/docs/specifications/doubleratchet/) algorithm, which is used by parties to exchange encrypted messages based on a shared secret key. The implementation includes: - **Virgil Perfect Forward Secrecy (PFS) service** – a standalone web-service that is dedicated to managing one-time keys and long-time keys that are based on their Identity Public Keys (public keys that are contained in user cards published on Virgil Cards service); - **Ratchet SDK** – interacts with PFS service for publishing and managing one-time keys and long-time keys and interacts with Virgil Cards service for retrieving user's indentity cards which the OTK and LTK are based on. The parties derive new keys for every Double Ratchet message so that previous private keys cannot be calculated from new ones. The parties that participate in the communication also send Diffie-Hellman public values attached to their messages. The results of Diffie-Hellman calculations are mixed into the derived keys so that the new private keys cannot be calculated from the previous ones.

Virgil Security is presenting an implementation of the [Double Ratchet](https://signal.org/docs/specifications/doubleratchet/) algorithm, which is used by parties to exchange encrypted messages based on a shared secret key. The implementation includes: - **Virgil Perfect Forward Secrecy (PFS) service** – a standalone web-service that is dedicated to managing one-time keys and long-time keys that are based on their Identity Public Keys (public keys that are contained in user cards published on Virgil Cards service); - **Ratchet SDK** – interacts with PFS service for publishing and managing one-time keys and long-time keys and interacts with Virgil Cards service for retrieving user's indentity cards which the OTK and LTK are based on. The parties derive new keys for every Double Ratchet message so that previous private keys cannot be calculated from new ones. The parties that participate in the communication also send Diffie-Hellman public values attached to their messages. The results of Diffie-Hellman calculations are mixed into the derived keys so that the new private keys cannot be calculated from the previous ones.

ModelCC is a model-based parser generator (a.k.a. compiler compiler) that decouples language specification from language processing, avoiding some of the problems caused by grammar-driven parser generators. ModelCC receives a conceptual model as input, along with constraints that annotate it. It is then able to create a parser for the desired textual language and the generated parser fully automates the instantiation of the language conceptual model. ModelCC also includes a built-in reference resolution mechanism that results in abstract syntax graphs, rather than mere abstract syntax trees.