Blogs represent an important new arena for knowledge discovery in open source intelligence gathering. Bloggers are a
vast network of human (and sometimes non-human) information sources monitoring important local and global events,
and other blogs, for items of interest upon which they comment. Increasingly, issues erupt from the blog world and into
the real world. In order to monitor blogging about important events, we must develop models and metrics that represent
blogs correctly. The structure of blogs requires new techniques for evaluating such metrics as the relevance, specificity,
credibility and timeliness of blog entries. Techniques that have been developed for standard information retrieval
purposes (e.g. Google's PageRank) are suboptimal when applied to blogs because of their high degree of exophoricity,
quotation, brevity, and rapidity of update. In this paper, we offer new metrics related for blog entry relevance,
specificity, timeliness and credibility that we are implementing in a blog search and analysis tool for international blogs.
This tools utilizes new blog-specific metrics and techniques for extracting the necessary information from blog entries
automatically, using some shallow natural language processing techniques supported by background knowledge
captured in domain-specific ontologies.
KEYWORDS: Forensic science, Sensors, Information fusion, Pattern recognition, Data processing, Defense and security, Computing systems, Systems modeling, Situational awareness sensors, Detection and tracking algorithms
In future battle spaces, multiple disparate sensors and unmanned vehicles will be in simultaneous use and form ad hoc networks whose services collectively reason on the situation. These networks may come under attack by malignant devices sending false information. The network services must evaluate incoming information to determine if the information is relevant and trustworthy. Information Forensics services can accomplish this evaluation by interrogating the source. The competency of an interrogator can be quantified by the level of their questions. This paper will discuss the different levels of abstraction in learning and how they relate to networks that support active querying.
KEYWORDS: Receivers, Global Positioning System, Electronics, Interfaces, Sensors, Digital signal processing, Fourier transforms, Signal processing, Error analysis, Video
This paper presents issues related to effects generated in avionic electronics by terrestrial neutron environments and methods for mitigating the effects through part selection, circuit design and system architecture design. The paper includes an explanation of the System Hardening Upset Recovery (SHUR) technology macro cell library and demonstrates how the available functions can be applied to implement robust system operation in the presence of neutron-induced component upsets and package latchup. Recent data on electronic parts and reactor tests performed on components is presented to demonstrate the susceptibility of electronics and components to terrestrial neutrons.
In gauging the generalization capability of a classifier, a good evaluation technique should adhere to certain principles. For instance, the technique should evaluate a selected classifier, not simply an architecture. Secondly, a solution should be assessable at the classifier’s design and, further, throughout its application. Additionally, the technique should be insensitive to data presentation and cover a significant portion of the classifier’s domain. Such principles call for methods beyond supervised learning and statistical training techniques such as cross validation.
For this paper, we shall discuss the evaluation of a generalization in application. For illustration, we will present a method for the multilayer perceptron (MLP) that may be drawn from the unlabeled data collected in the operational use of a given classifier. These conclusions support self-supervised learning and computational
methods that isolate unstable, nonrepresentational regions in the classifier.
The training of good generalizations must mitigate both memorization and arrogance. Memorization is characterized as being too timid in associating new observations with previous experience. Contrarily, arrogance is being too bold. In classification problems, memorization is traditionally assessed via error matrices and iterative error-based techniques such as cross validation. These techniques, however, do nothing to assess arrogance in classification. To identify arrogant classifications, we propose a confusion-based figure of merit which we shall call the ordered veracity-experience response curve, or OVER curve. To produce the OVER curve, one must employ expert classifiers. An expert is a special classifier - a relational computation with not only a mechanism for decision making but also a quantifiable skill level. In this paper, we define the elements of both the expert classifier and OVER curve and, then, demonstrate their utility using the multilayer perceptron.
A challenge for intelligent computing is translating the skills of innovation into mathematical theory and persistent learning algorithms. Computational intelligence differs from artificial intelligence in that artificial intelligence reasons over symbols while computational intelligence reasons over sub-symbolic data and information. Natural symbos arise from shared human experiences. The creative quality of human interaction suggests symbol generation involves a collection of cooperative agents capable of representing relative experience, negotiating innovation, and---finally---building consensus. As hybrids of sub-symbolic and symbolic reasoning become the norm, it is necessary to formalize the design and evaluation of artificial symbols. In this paper, we delineate the difference between sub-symbolic patterns and symbolic experience. Further, we propose fundamental theory supporting the autonomous construction of artificial symbols which---we assert---is the ultimate culmination of an intelligent computation. We apply this theory to model selection among neural networks.
Given a classifier trained on two-class data one wishes to determine how well the classifier will perform on new, unseen data. To do this task one typically uses the data to estimate a distribution of the data, generate new data from this distribution, and then test the data. Also, hold-out methods are used including cross-validation. We propose a new method that uses computational geometry techniques that produces a partial ordering on subsets in feature space and measures how well the classifier will perform on these subsets. There are some conditions on the classifier that must be satisfied in order that this measure, in fact, exists. We give the details for these conditions as well as the results concerning this special collection of classifiers. We derive the measure that quantifies the generalization capability for the special collection classifier.
An intelligent agent---defined as an autonomous, adaptive, cooperative computer program---must credibly represent its expertise in negotiations with peer agents. Given an agent-based classifier, the determination of where in the domain the classifier is an expert must be explicitly stated. Likewise, where the classifier is confused should also be represented. Currently, an error measures provides an estimate of the relative size of the expertise and confusion sets, but error does not offer a distinct opinion on an untruthed feature vector's membership---i.e., whether its classification is based on specific information, conjecture or chance. We propose the theory for estimating the complete membership of a classifier's expertise sets and confusion sets. From these sets, we construct a 4-value classifier that hypothesizes for each new feature vector whether its classification can be made confidently or not. Examples are given that demonstrate the utility of this theory using multilayer perceptrons.
Given a finite collection of classifiers trained on two-class data one wishes to fuse the classifiers to form a new classifier with improved performance. Typically, the fusion is done at the output level using logical ANDs and ORs. The proposed fusion is based on the location of the feature vector with respect to the expertise sets and confusion sets of the classifiers. Given a feature vector x, if any one of the classifiers is an expert on x then the fusion rule should be an OR. If the classifiers are confused at x then the fusion rule should be defined is such a way to reflect this confusion or uncertainty. We give this fusion rule that is based upon the confusion sets as well as the expertise sets. We believe that this fusion rule will produce classifiers that perform better than classifiers that resulted from other fusion rules.
Given a classifier, presently we use a confusion matrix to quantify how much the classifier deviates from truth based upon training data. Shortcomings to this limited application of the confusion matrix are that (1) it does not communicate data trends in feature space, for example where errors congregate, and (2) the truth mapping is largely unknown except for a small, potentially biased sample set. In practice, one does not have truth but has to rely on an expert's opinion. We propose the mathematical theory of confusion comparing and contrasting the opinions of two experts (i.e., two classifiers). This theory has advantages over traditional confusion matrices in that it provides a capability for expressing classification confidence over ALL of feature space, not just at sampled truth. This theory quantifies different types of confusion between classifiers and yields a region of feature space where confusion occurs. An example using Artificial Neural Networks will be given.
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