Subjective Sampling Approaches to Resource Estimation

This paper suggests deficiencies in present sampling approaches to regional resource estimation, and ways in which these deficiencies might be remedied. General approaches to resource estimation are discussed, as are requirements which well conceived approaches should satisfy. Using presently available theory, a comprehensive sampling approach to estimation can be formulated. The results of such an analysis are directly incorporable into decisions concerning exploration strategy optimization. However, further computational and experimental work are required before this approach is operational

Comments on Decision Objectives and Attributes for the Nuclear Siting Study

The object of this paper is to summarize discussions at IIASA on attributes or indices for siting decision making. While I have attempted to include differing views on most attributes, I make no pretense of this being an unbiased review. In addressing decisions of any type and public policy decisions in particular, the choices which one makes of goals, attributes, and normative models fairly well determines a priori what the conclusions will be. It is here that decisions are actually made. Therefore it is absolutely necessary that we be judicious in our selections. In some sense, all that follows these choices is a technical follow through, although this somewhat overstates the point. The present paper may be summarized as follows. First, a short discussion of goals and attributes is presented; then a set of attributes is listed according to inferred objectives; and finally, each objective and attribute is reviewed and recommendations are made

Spatial Process Modeling for Air Pollution Standards: A Problem Statement

Spatial process models have application to problems in several disciplines. The problem presented here treats monitoring and control of air pollution, but the methodological base seems similar to several other problems, and the hope in outlining this problem is to perhaps generate interest in others working on similar problems, or towards work on this problem itself

Stochastic Tools for Network Intrusion Detection

With the rapid development of Internet and the sharp increase of network crime, network security has become very important and received a lot of attention. We model security issues as stochastic systems. This allows us to find weaknesses in existing security systems and propose new solutions. Exploring the vulnerabilities of existing security tools can prevent cyber-attacks from taking advantages of the system weaknesses. We propose a hybrid network security scheme including intrusion detection systems (IDSs) and honeypots scattered throughout the network. This combines the advantages of two security technologies. A honeypot is an activity-based network security system, which could be the logical supplement of the passive detection policies used by IDSs. This integration forces us to balance security performance versus cost by scheduling device activities for the proposed system. By formulating the scheduling problem as a decentralized partially observable Markov decision process (DEC-POMDP), decisions are made in a distributed manner at each device without requiring centralized control. The partially observable Markov decision process (POMDP) is a useful choice for controlling stochastic systems. As a combination of two Markov models, POMDPs combine the strength of hidden Markov Model (HMM) (capturing dynamics that depend on unobserved states) and that of Markov decision process (MDP) (taking the decision aspect into account). Decision making under uncertainty is used in many parts of business and science.We use here for security tools.We adopt a high-quality approximation solution for finite-space POMDPs with the average cost criterion, and their extension to DEC-POMDPs. We show how this tool could be used to design a network security framework.Comment: Accepted by International Symposium on Sensor Networks, Systems and Security (2017

Standards for Facility Siting: Uncertain Utility in Decision-Making

One approach to regulating private siting decisions is by setting standards on the impacts of large constructed facilities. Theoretical structures of preferences for and among preferences, however, lead to implications which are sometimes overlooked in standard setting. Further, a central issue is that the objective function describing societal preferences is uncertain. Analytically including objective function uncertainty in standard setting allows information from several sources to be quantitatively coalesced, allows allocation decisions for investment in preference assessment to be quantitatively analyzed, and leads to speculations on the handling of temporal changes in preference

A Very low bit-rate speech recognition system

When using extracted speech feature coefficients for speech synthesis, quantization is considered a lossy compression scheme. The data being compressed cannot be recovered or reconstructed exactly. However, in a speech recognition system for command and control purposes, a certain amount of quantization can be allowed, with comparable results. In some cases, quantization even serves to close the gaps between the coefficients of the incoming speech signal and those of the templates. Since the coefficients are not being used to reconstruct the signal, a very coarse quantization can be used, enabling a very low bit-rate transmission with very good recognition results. To reduce the bandwidth further, a binary coding procedure, such as Huffman or Arithmetic Coding, can be applied to the quantized coefficients. Upon receipt of the transmission, the quantized coefficients are decoded and used to perform speech recognition. The sets of coefficients are compared to the templates for each of the commands in the vocabulary. Speech, however, is dynamic in nature and a dynamic recognition procedure is needed to allow for different vocal inflections and durations. A procedure called Dynamic Time Warping is used to warp the time axis of the templates to more closely fit the information coming in. By combining all these techniques, a very accurate, very low bit-rate recognizer has been developed and is discussed in this paper

Probability Theory in Geological Exploration

The purpose of this review is to summarize briefly certain applications of probability theory and statistics to geological exploration and inference, and in particular to problems of economic geology. The hope is that this summary will be of use in planning the IIASA conference on resource estimation scheduled for May, 1975, and will provide a reference within which to review certain contributions to that conference

Extensions of K.J. Roy's and R.E. Roadifer's Subjective Approach to Oil Resource Estimation

Messrs. Roy and Roadifer, in separate papers, have proposed similar methodologies for making regional resource estimates. The idea underlying their approach is quite interesting, and may have extensions incorporable in larger schemes of estimation. Roy's and Roadifer's approach adopts a simple "structural" model of deposit size, and uses the subjective feelings of geologists to generate probability density functions (pdf's) of the model parameters. A pdf of regional resources is approximated by using Monte Carlo simulation

Sampling for Group Utility

A sampling theory approach is developed for estimating group utility functions for inclusion in decision-analytic approaches to public plan evaluation. This approach is based. on Bayesian sampling theory and leads to estimates of group utility accounting for sampling and measurement error. The results of the estimation may be directly incorporated in decision analysis. The strength of this approach is that it leads to more rigorously based estimates of interest group utility functions than commonly used. surrogates, and can be analytically balanced with other forms of preference information such as market data

Study Abroad in Teacher Education

Introduction to Special Themed Issue: Study Abroad in Teacher Educatio