Federated learning enables training a global machine learning model from data distributed across multiple sites, without having to move the data. This is particularly relevant in healthcare applications, where data is rife with personal, highly-sensitive information, and data analysis methods must provably comply with regulatory guidelines. Although federated learning prevents sharing raw data, it is still possible to launch privacy attacks on the model parameters that are exposed during the training process, or on the generated machine learning model. In this paper, we propose the first syntactic approach for offering privacy in the context of federated learning. Unlike the state-of-the-art differential privacy-based frameworks, our approach aims to maximize utility or model performance, while supporting a defensible level of privacy, as demanded by GDPR and HIPAA. We perform a comprehensive empirical evaluation on two important problems in the healthcare domain, using real-world electronic health data of 1 million patients. The results demonstrate the effectiveness of our approach in achieving high model performance, while offering the desired level of privacy. Through comparative studies, we also show that, for varying datasets, experimental setups, and privacy budgets, our approach offers higher model performance than differential privacy-based techniques in federated learning.

This paper discusses the implementation and evaluation of automatically generated cloze questions in the style of the definitions found in Collins COBUILD English language learner’s dictionary. The definitions and the cloze questions are used in an automated reading tutor to help second and third grade students learn new vocabulary. A parser provides syntactic phrase structure trees for the definitions. With these parse trees as input, a pattern matching program uses a set of syntactic patterns to extract the phrases that make up the cloze question answers and distracters.

We are interested in constructing a computer agent whose behaviour will be intelligent enough to perform cooperative tasks involving other agents like itself. The construction of such agents has been a major goal of artificial intelligence research. One of the key tasks such an agent must perform is to form plans to carry out its intentions in a complex world in which other planning agents also exist. To construct such agents, it will be necessary to address a number of issues that concern the interaction of knowledge, actions, and planning. Briefly stated, an agent at planning time must take into account what his future states of knowledge will be if he is to form plans that he can execute; and if he must incorporate the plans of other agents into his own, then he must also be able to reason about the knowledge and plans of other agents in an appropriate way.