Effects of corpus-based instruction on phraseology in learner English

This study analyses the effects of data-driven learning (DDL) on the phraseology used by 223 English students at an Italian university. The students studied the genre of opinion survey reports through paper-based and hands-on exploration of a reference corpus. They then wrote their own report and a learner corpus of these texts was compiled. A contrastive interlanguage analysis approach (Granger, 2002) was adopted to compare the phraseology of key items in the learner corpus with that found in the reference corpus. Comparison is also made with a learner corpus of reports produced by a previous cohort of students who had not used the reference corpus. Students who had done DDL tasks used a wider range of genre-appropriate phraseology and produced a lower number of stock phrases than those who had not. The study also finds evidence that students use more phrases encountered in paper-based concordancing tasks than in hands-on tasks.Unlike in previous DDL studies, observations of the learning of a specific text-type through DDL in the present study are based on the comparison with both a control learner corpus and an expert corpus.The study also considers the use of DDL with a large class size

DAS: a data management system for instrument tests and operations

The Data Access System (DAS) is a metadata and data management software system, providing a reusable solution for the storage of data acquired both from telescopes and auxiliary data sources during the instrument development phases and operations. It is part of the Customizable Instrument WorkStation system (CIWS-FW), a framework for the storage, processing and quick-look at the data acquired from scientific instruments. The DAS provides a data access layer mainly targeted to software applications: quick-look displays, pre-processing pipelines and scientific workflows. It is logically organized in three main components: an intuitive and compact Data Definition Language (DAS DDL) in XML format, aimed for user-defined data types; an Application Programming Interface (DAS API), automatically adding classes and methods supporting the DDL data types, and providing an object-oriented query language; a data management component, which maps the metadata of the DDL data types in a relational Data Base Management System (DBMS), and stores the data in a shared (network) file system. With the DAS DDL, developers define the data model for a particular project, specifying for each data type the metadata attributes, the data format and layout (if applicable), and named references to related or aggregated data types. Together with the DDL user-defined data types, the DAS API acts as the only interface to store, query and retrieve the metadata and data in the DAS system, providing both an abstract interface and a data model specific one in C, C++ and Python. The mapping of metadata in the back-end database is automatic and supports several relational DBMSs, including MySQL, Oracle and PostgreSQL.Comment: Accepted for pubblication on ADASS Conference Serie

Hadoop Performance Analysis Model with Deep Data Locality

Background: Hadoop has become the base framework on the big data system via the simple concept that moving computation is cheaper than moving data. Hadoop increases a data locality in the Hadoop Distributed File System (HDFS) to improve the performance of the system. The network traffic among nodes in the big data system is reduced by increasing a data-local on the machine. Traditional research increased the data-local on one of the MapReduce stages to increase the Hadoop performance. However, there is currently no mathematical performance model for the data locality on the Hadoop. Methods: This study made the Hadoop performance analysis model with data locality for analyzing the entire process of MapReduce. In this paper, the data locality concept on the map stage and shuffle stage was explained. Also, this research showed how to apply the Hadoop performance analysis model to increase the performance of the Hadoop system by making the deep data locality. Results: This research proved the deep data locality for increasing performance of Hadoop via three tests, such as, a simulation base test, a cloud test and a physical test. According to the test, the authors improved the Hadoop system by over 34% by using the deep data locality. Conclusions: The deep data locality improved the Hadoop performance by reducing the data movement in HDFS

A Survey of Languages for Specifying Dynamics: A Knowledge Engineering Perspective

A number of formal specification languages for knowledge-based systems has been developed. Characteristics for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects. They have to provide the means to specify a complex and large amount of knowledge and they have to provide the means to specify the dynamic reasoning behavior of a knowledge-based system. We focus on the second aspect. For this purpose, we survey existing approaches for specifying dynamic behavior in related areas of research. In fact, we have taken approaches for the specification of information systems (Language for Conceptual Modeling and TROLL), approaches for the specification of database updates and logic programming (Transaction Logic and Dynamic Database Logic) and the generic specification framework of abstract state machine

A methodology for the evaluation of program cost and schedule risk for the SEASAT program

An interactive computerized project management software package (RISKNET) is designed to analyze the effect of the risk involved in each specific activity on the results of the total SEASAT-A program. Both the time and the cost of each distinct activity can be modeled with an uncertainty interval so as to provide the project manager with not only the expected time and cost for the completion of the total program, but also with the expected range of costs corresponding to any desired level of significance. The nature of the SEASAT-A program is described. The capabilities of RISKNET and the implementation plan of a RISKNET analysis for the development of SEASAT-A are presented

Data modelling for emergency response

Emergency response is one of the most demanding phases in disaster management. The fire brigade, paramedics, police and municipality are the organisations involved in the first response to the incident. They coordinate their work based on welldefined policies and procedures, but they also need the most complete and up-todate information about the incident, which would allow a reliable decision-making.\ud There is a variety of systems answering the needs of different emergency responders, but they have many drawbacks: the systems are developed for a specific sector; it is difficult to exchange information between systems; the systems offer too much or little information, etc. Several systems have been developed to share information during emergencies but usually they maintain the nformation that is coming from field operations in an unstructured way.\ud This report presents a data model for organisation of dynamic data (operational and situational data) for emergency response. The model is developed within the RGI-239 project ‘Geographical Data Infrastructure for Disaster Management’ (GDI4DM)

Design of an intelligent information system for in-flight emergency assistance

The present research has as its goal the development of AI tools to help flight crews cope with in-flight malfunctions. The relevant tasks in such situations include diagnosis, prognosis, and recovery plan generation. Investigation of the information requirements of these tasks has shown that the determination of paths figures largely: what components or systems are connected to what others, how are they connected, whether connections satisfying certain criteria exist, and a number of related queries. The formulation of such queries frequently requires capabilities of the second-order predicate calculus. An information system is described that features second-order logic capabilities, and is oriented toward efficient formulation and execution of such queries

Flexible Multi-layer Sparse Approximations of Matrices and Applications

The computational cost of many signal processing and machine learning techniques is often dominated by the cost of applying certain linear operators to high-dimensional vectors. This paper introduces an algorithm aimed at reducing the complexity of applying linear operators in high dimension by approximately factorizing the corresponding matrix into few sparse factors. The approach relies on recent advances in non-convex optimization. It is first explained and analyzed in details and then demonstrated experimentally on various problems including dictionary learning for image denoising, and the approximation of large matrices arising in inverse problems