A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD

Secondary predication in Russian

The paper makes two contributions to semantic typology of secondary predicates. It provides an explanation of the fact that Russian has no resultative secondary predicates, relating this explanation to the interpretation of secondary predicates in English. And it relates depictive secondary predicates in Russian, which usually occur in the instrumental case, to other uses of the instrumental case in Russian, establishing here, too, a difference to English concerning the scope of the secondary predication phenomenon

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

An architecture and execution environment for component integration rules

The Integration Rules (IRules) project at Arizona State University (http://www.eas.asu.edu/~irules) is developing a declarative event-based approach to component integration. Integration rules are based on the concept of active database rules, providing an active approach for specifying event- driven activity in a distributed environment. The IRules project consists of a knowledge model that specifies the IRules Definition Language and an execution model that supports integration rule execution. This research focuses on the execution model and the architectural design parts of the IRules project. The main objective of this research is to develop a distributed execution environment for using integration rules in the integration of black-box components. In particular, this research will investigate the design of an architecture that supports the IRules semantic framework, the development of an execution model for rule and transaction processing, and the design of a rule processing algorithm for coordinating the execution of integration rules. This research will combine the distributed computing framework of Jini, the asynchronous event notification mechanism of the Java Message Service (JMS), and the distributed blocking access functionality of JavaSpaces to support active rule processing in a distributed environment. The limitations of the underlying Enterprise JavaBeans (EJB) component model pose transaction processing challenges for the integration process. This research will develop a suitable transaction model and processing logic to overcome the limitations of the underlying EJB component model. Furthermore, the architectural design will allow an easy extension of the system to accommodate other component models. This research is expected to contribute to nested rule and transaction processing for active rules that have not been previously addressed in distributed rule processing environments. The development of the IRules execution environment will also contribute to the use of distributed rule- based techniques for eventdriven component integration

Using the VO to Study the Time Domain

Just as the astronomical "Time Domain" is a catch-phrase for a diverse group of different science objectives involving time-varying phenomena in all astrophysical regimes from the solar system to cosmological scales, so the "Virtual Observatory" is a complex set of community-wide activities from archives to astroinformatics. This workshop touched on some aspects of adapting and developing those semantic and network technologies in order to address transient and time-domain research challenges. It discussed the VOEvent format for representing alerts and reports on celestial transient events, the SkyAlert and ATELstream facilities for distributing these alerts, and the IVOA time-series protocol and time-series tools provided by the VAO. Those tools and infrastructure are available today to address the real-world needs of astronomers.Comment: Contribution to the proceedings of IAU Symposium 285, "New Horizons in Time Domain Astronomy": http://www.physics.ox.ac.uk/IAUS285/, 6 page

G-Asks: An Intelligent Automatic Question Generation System for Academic Writing Support

Many electronic feedback systems have been proposed for writing support. However, most of these systems only aim at supporting writing to communicate instead of writing to learn, as in the case of literature review writing. Trigger questions are potentially forms of support for writing to learn, but current automatic question generation approaches focus on factual question generation for reading comprehension or vocabulary assessment. This article presents a novel Automatic Question Generation (AQG) system, called G-Asks, which generates specific trigger questions as a form of support for students' learning through writing. We conducted a large-scale case study, including 24 human supervisors and 33 research students, in an Engineering Research Method course at The University of Sydney and compared questions generated by G-Asks with human generated question. The results indicate that G-Asks can generate questions as useful as human supervisors (`useful' is one of five question quality measures) while significantly outperforming Human Peer and Generic Questions in most quality measures after filtering out questions with grammatical and semantic errors. Furthermore, we identified the most frequent question types, derived from the human supervisors' questions and discussed how the human supervisors generate such questions from the source text