A Shared Task on Bandit Learning for Machine Translation

We introduce and describe the results of a novel shared task on bandit learning for machine translation. The task was organized jointly by Amazon and Heidelberg University for the first time at the Second Conference on Machine Translation (WMT 2017). The goal of the task is to encourage research on learning machine translation from weak user feedback instead of human references or post-edits. On each of a sequence of rounds, a machine translation system is required to propose a translation for an input, and receives a real-valued estimate of the quality of the proposed translation for learning. This paper describes the shared task's learning and evaluation setup, using services hosted on Amazon Web Services (AWS), the data and evaluation metrics, and the results of various machine translation architectures and learning protocols.Comment: Conference on Machine Translation (WMT) 201

The Design and Implementation of Manufacturing Infrastructures

Merged with duplicate record 10026.1/822 on 12.04.2017 by CS (TIS)This work addresses the problem of difficulties in the implementation of Computer Aided Production Management systems, and presents a methodology for their implementation which significantly improves on current practice. The methodology provides a structured approach which leads the company through a series of strategic business decisions which establish the context within which the solution must operate. The work focusses attention upon the design of the whole system with respect to the relationship between computerised functions and the infrastructure of human elements which facilitate and govern the system's operation. A model of the tasks involved in production management is presented. The model is used to structure decisions relating to the design of the infrastructure. The research work described proceeded in two distinct stages. In the first stage the author participated with other researchers who jointly developed the framework of the process methodology for CAPM implementation. This stage provides the context for the development of the task model approach to the design of the system, which represents the author's individual contribution (see section 1.8). The task model can be used as a tool to identify the options available for the way each task within production management may be executed, giving the user a basis for the design of a particular system while not advocating any particular solution. By the use of this approach the user is encouraged to consider the options available and to adopt an integrated approach which looks at all areas of production management, not only those for which there is a pressing problem or a tempting solution. This work results in a contribution to the development of the process methodology, the development of a tool in the CAPM task model and a review of the factors involved in the design of a system including both human and computerised elements

Design requirements for SRB production control system. Volume 5: Appendices

A questionnaire to be used to screen potential candidate production control software packages is presented

Microcomputers and Local Government: A Handbook -- Participants\u27 Manual

This handbook has its origins in a conversation between the author and Dr. Theodore Maher in the spring of 1981. The conversation centered on the role that we believed soon would be played by microcomputers in local governments and the concomitant need for training and technical assistance in the acquisition and use of microcomputers

Introduction to IntelliSIM 1.0

IntelliSIM is a prototype for a new generation of knowledge-based simulation tool that has been developed by the Systems Simulation Laboratory at Arizona State University. This tool is a computer environment that allows non-simulation trained modelers to predict the performance of a manufacturing system for which the necessary data is available. The system provides predictive data on such items as throughput time, queue levels, equipment utilization, reactions to machine failures, etc. With IntelliSIM, the benefits of discrete-event simulation can be exploited without requiring the high level of expertise necessary to successfully conduct a sound simulation study. The approach offered with IntelliSIM is one which will offer substantial savings over currently available simulation tools. This document is Version 1 (1992) of the user manual for the IntelliSIM software

Introduction to IntelliSIM 1.0

IntelliSIM is a prototype for a new generation of knowledge-based simulation tool that has been developed by the Systems Simulation Laboratory at Arizona State University. This tool is a computer environment that allows non-simulation trained modelers to predict the performance of a manufacturing system for which the necessary data is available. The system provides predictive data on such items as throughput time, queue levels, equipment utilization, reactions to machine failures, etc. With IntelliSIM, the benefits of discrete-event simulation can be exploited without requiring the high level of expertise necessary to successfully conduct a sound simulation study. The approach offered with IntelliSIM is one which will offer substantial savings over currently available simulation tools. This document is Version 1 (1992) of the user manual for the IntelliSIM software

Shuttle spacelab simulation using a Lear jet aircraft: Mission no. 3 (ASSESS program)

The third ASSESS mission using a Lear Jet aircraft conducted to continue the study of scientific experiment operations in a simulated Spacelab environment. Prior to the mission, research planning and equipment preparation were observed and documented. A flight readiness review for the experiment was conducted. Nine of the ten scheduled flights were completed during simulation mission and all major science objectives were accomplished. The equipment was well qualified for flight and gave little trouble; telescope malfunctions occurred early in the mission and were corrected. Both real-time and post-observation data evaluation were used to assess research progress and to plan subsequent flight observations for maximum effectiveness