The primary goal of this effort is to establish an approach for determining the trustworthiness of a datum element produced from one or more sources. This discussion develops the notion of Trustworthiness as a measurement of corroboration derived by assessing the level of conformity to existing beliefs. A technique is proposed for combining trustworthiness of sources and specific datum elements.

The effectiveness of automated system management is dependent on the domain-specific information that is encoded within the management framework. Existing approaches for defining the domain knowledge are categorized into white-box and black-box approaches, each of which has limitations. White-box approaches define detailed formulas for system behavior, and are limited by excessive complexity and brittleness of the information. On the other hand, black-box techniques gather domain knowledge by monitoring the system; they are error-prone and require an infeasible number of iterations to converge in real-world systems. Monitormining is a gray-box approach for creating domain knowledge in automated system management; it combines simple designer-defined specifications with the information gathered using machine learning. The designer specifications enumerate input parameters for the system behavior functions, while regression techniques (such as neural networks, support vector machines) are used to derive the mathematical function that relates these parameters. These functions are constantly refined at run-time, by periodically invoking regression on the newly monitored data. Monitormining has the advantage of reduced complexity of the designer specifications, better accuracy of regression functions due to a reduced parameter set, and self-evolving with the changes in the system. Our initial experimental results of applying monitormining are quite promising.

The authors present a methodology for designing experiments to characterize detection algorithms. The usual method is to vary parameters of the input images or parameters of the algorithms and then construct operating curves that relate the probability of misdetection and false alarm for each parameter setting. Such an analysis does not integrate the performance of the numerous operating curves. A methodology is outlined for summarizing many operating curves into a few performance curves. This methodology is adapted from the human psychophysics literature and is general to any detection algorithm. The central concept is to measure the effect of variables in terms of the equivalent effect of a critical signal variable. The methodology is demonstrated by comparing the performance of two line detection algorithms.>

This paper describes a new device, the Intel®8087 Numeric Data Processor, with unprecedented speed, accuracy and capability. Its modified stack architecture and instruction set are explained illustrative examples are included. The 8087, which conforms to the proposed IEEE Floating-Point Standard, is a coprocessor in the Intel®8086 family. It supports seven data types: three REAL, three INTEGER and one packed BCD format, and performs all necessary numeric operations from addition to logarithmic and trigonometric functions.