Service-oriented coordination platform for technology-enhanced learning

It is currently difficult to coordinate learning processes, not only because multiple stakeholders are involved (such as students, teachers, administrative staff, technical staff), but also because these processes are driven by sophisticated rules (such as rules on how to provide learning material, rules on how to assess students’ progress, rules on how to share educational responsibilities). This is one of the reasons for the slow progress in technology-enhanced learning. Consequently, there is a clear demand for technological facilitation of the coordination of learning processes. In this work, we suggest some solution directions that are based on SOA (Service-Oriented Architecture). In particular, we propose a coordination service pattern consistent with SOA and based on requirements that follow from an analysis of both learning processes and potentially useful support technologies. We present the service pattern considering both functional and non-functional issues, and we address policy enforcement as well. Finally, we complement our proposed architecture-level solution directions with an example. The example illustrates our ideas and is also used to identify: (i) a short list of educational IT services; (ii) related non-functional concerns; they will be considered in future work

Data Extraction via Semantic Regular Expression Synthesis

Many data extraction tasks of practical relevance require not only syntactic pattern matching but also semantic reasoning about the content of the underlying text. While regular expressions are very well suited for tasks that require only syntactic pattern matching, they fall short for data extraction tasks that involve both a syntactic and semantic component. To address this issue, we introduce semantic regexes, a generalization of regular expressions that facilitates combined syntactic and semantic reasoning about textual data. We also propose a novel learning algorithm that can synthesize semantic regexes from a small number of positive and negative examples. Our proposed learning algorithm uses a combination of neural sketch generation and compositional type-directed synthesis for fast and effective generalization from a small number of examples. We have implemented these ideas in a new tool called Smore and evaluated it on representative data extraction tasks involving several textual datasets. Our evaluation shows that semantic regexes can better support complex data extraction tasks than standard regular expressions and that our learning algorithm significantly outperforms existing tools, including state-of-the-art neural networks and program synthesis tools

Proceedings from the Synthetic LBD International Seminar

On May 9, 2017, we hosted a seminar to discuss the conditions necessary to im- plement the SynLBD approach with interested parties, with the goal of providing a straightforward toolkit to implement the same procedure on other data. The proceed- ings summarize the discussions during the workshop

Synthesizing Conjunctive Queries for Code Search

This paper presents Squid, a new conjunctive query synthesis algorithm for searching code with target patterns. Given positive and negative examples along with a natural language description, Squid analyzes the relations derived from the examples by a Datalog-based program analyzer and synthesizes a conjunctive query expressing the search intent. The synthesized query can be further used to search for desired grammatical constructs in the editor. To achieve high efficiency, we prune the huge search space by removing unnecessary relations and enumerating query candidates via refinement. We also introduce two quantitative metrics for query prioritization to select the queries from multiple candidates, yielding desired queries for code search. We have evaluated Squid on over thirty code search tasks. It is shown that Squid successfully synthesizes the conjunctive queries for all the tasks, taking only 2.56 seconds on average