The supervised learning methods applying evolutionary algorithms to generate knowledge model are extremely costly in time and space. Fundamentally, this high computational cost is fundamentally due to the evaluation process that needs to go through the whole datasets to assess their goodness of the genetic individuals. Often, this process carries out some redundant operations which can be avoid...

We enhance a temporal knowledge base population system to improve the quality of distantly supervised training data and identify a minimal feature set for classification. The approach uses multi-class logistic regression to eliminate individual features based on the strength of their association with a temporal label followed by semi-supervised relabeling using a subset of human annotations and...

Classical propositional logic is an appealing option for modelling argumentation but the computational viability of generating an argument is an issue. Here we propose ameliorating this problem by harnessing the notion of a connection graph to reduce the search space when seeking all the arguments for a claim from a knowledgebase. For a set of clauses, a connection graph is a graph where each n...

In this work, we revisit the notion of Decentralized Traceable Attribute-Based Signatures (DTABS) introduced by El Kaafarani et al. (CT-RSA 2014) and improve the state-of-the-art in three dimensions: Firstly, we provide a new stronger security model which circumvents some shortcomings in existing models. Our model minimizes the trust placed in attribute authorities and hence provides, among oth...

My research focuses on the intersection of computer systems, algorithmic game theory, and economic mechanism design. My interdisciplinary work has introduced computer architects to a rich body of knowledge in economics and game theory. My dissertation marks the beginning of a paradigm shift in computer architecture toward more robust, game-theoretic analysis of shared systems. As a result, my w...

Based on the notion of accumulators, we propose a new cryptographic scheme called universal accumulators. This scheme enables one to commit to a set of values using a short accumulator and to efficiently compute a membership witness of any value that has been accumulated. Unlike traditional accumulators, this scheme also enables one to efficiently compute a nonmembership witness of any value th...

We study the query evaluation problem in probabilistic databases in the presence of probabilistic existential rules. Our focus is on the Datalog± family of languages for which we define the probabilistic counterpart using a flexible and compact encoding of probabilities. This formalism can be viewed as a generalization of probabilistic databases, as it allows to generate new facts from the give...

The paper shows industrial practicians how current case-based reasoning technology can be used in engineering domains. It analyses knowledge needed to support case retrieval. It starts with descriptions of retrieval algorithms for several levels of expressiveness of the case representation language, and it discusses where the algorithmic complexity results from. To illustrate problems of curren...

Relation extraction methods are essential for creating robust text mining tools to help researchers find useful knowledge in the vast published literature. Easy-touse and generalizable methods are needed to encourage an ecosystem in which researchers can easily use shared resources and build upon each others’ methods. We present the Kindred Python package1 for relation extraction. It builds upo...