Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several realworld databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.

A significant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. We present a novel algorithm, Trepan, for extracting comprehensible, symbolic representations from trained neural networks. Our algorithm uses queries to induce a decision tree that approximates the concept represented by a given network. Our experiments demonstrate that Trepan is able to produce decision trees that maintain a high level of fidelity to their respective networks while being comprehensible and accurate. Unlike previous work in this area, our algorithm is general in its applicability and scales well to large networks and problems with high-dimensional input spaces.

To Mom, Dad, and Susan, for their support and encouragement.

A new receiver-based playout scheduling scheme is proposed to improve the trade-off between buffering delay and late loss for real-time voice communication over IP networks. The scheme estimates the network delay from past statistics and adaptively adjusts the playout time of the voice packets. In contrast to previous work, the adjustment is not only performed between talkspurts, but also within talkspurts in a highly dynamic way. Proper reconstruction of continuous playout speech is achieved by scaling individual voice packets using a time-file modification technique based on the WSOLA algorithm. Results of subjective listening tests show that this operation does not impair audio quality, since the adaptation process requires infrequent scaling of the voice packets and low playout jitter is perceptually tolerable. The same timescale modification technique is also used to conceal packet loss at very low delay,i.e.,one packet time. Simulation results based on Internet measurements show that the trade-off between buffering delay and late loss can be improved significantly. The overall audio quality is investigated based on subjective listening tests,showing typical gains of 1 on a 5point MOS scale.

Abstract — We consider a wireless sensor network con-sisting of a set of sensors deployed randomly. A point in the monitored area is covered if it is within the sensing range of a sensor. In some applications, when the network is sufficiently dense, area coverage can be approximated by guaranteeing point coverage. In this case, all the points of wireless devices could be used to represent the whole area, and the working sensors are supposed to cover all the sensors. Many applications related to security and reliability require guaranteed k-coverage of the area at all times. In this paper, we formalize the k-(Connected) Coverage Set (k-CCS/k-CS) problems, develop a linear programming algorithm, and design two non-global solu-tions for them. Some theoretical analysis is also provided followed by simulation results. Index Terms — Coverage problem, linear programming, localized algorithms, reliability, wireless sensor networks.

Recent trends in information management involve the periodic transcription of data onto secondary devices in a networked environment, and the proper scheduling of these transcriptions is critical for efficient data management. To assist in the scheduling process, we are interested in modeling data obsolescence, that is, the reduction of consistency over time between a relation and its replica. The modeling is based on techniques from the field of stochastic processes, and provides several stochastic models for content evolution in the base relations of a database, taking referential integrity constraints into account. These models are general enough to accommodate most of the common scenarios in databases, including batch insertions and life spans both with and without memory. As an initial "proof of concept" of the applicability of our approach, we validate the insertion portion of our model framework via experiments with real data feeds. We also discuss a set of transcription protocols which make use of the proposed stochastic model.

This paper presents a new, powerful statistical testing of symmetric ciphers and hash functions which allowed us to detect biases in both of these systems where previously known tests failed. We first give a complete characterization of the Algebraic Normal Form (ANF) of random Boolean functions by means of the M obius transform. Then we built a new testing based on the comparison between the structure of the different Boolean functions Algebraic Normal Forms characterizing symmetric ciphers and hash functions and those of purely random Boolean functions. Detailed testing results on several cryptosystems are presented. As a main result we show that AES, DES Snow and Lili-128 fail all or part of the tests and thus present strong biases.

As the logical next step after sequencing the mouse genome, biologists have developed laboratory methods for rapidly determining where each of the 30K genes in the mouse genome is synthesizing protein. Applying these methods to the mouse brain, biologists are currently generating large numbers of 2D cross-sectional images that record the expression pattern for each gene in the mouse genome. In this paper, we describe the structure of a geometric database for the mouse brain that allows biologists to organize and search this gene expression data. The

A significant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. We have developed an algorithm, called Trepan, for extracting comprehensible, symbolic representations from trained neural networks. Given a trained network, Trepan produces a decision tree that approximates the concept represented by the network. In this article, we discuss the application of Trepan to a neural network trained on a noisy time series task: predicting the Dollar-Mark exchange rate. We present experiments that show that Trepan is able to extract a decision tree from this network that equals the network in terms of predictive accuracy, yet provides a comprehensible concept representation. Moreover, our experiments indicate that decision trees induced directly from the training data using conventional algorithms do not match the accuracy nor the comprehensibility of the tree extracted by Trepan. 1 Introduction Neural networks are perhaps the most wi...

The mixture model likelihood function is invariant with respect to permutation of the components of the mixture. If functions of interest are permutation sensitive, as in classification applications, then interpretation of the likelihood function requires valid inequality constraints and a very large sample may be required to resolve ambiguities. If functions of interest are permutation invariant, as in prediction applications, then there are no such problems of interpretation. Contrary to assessments in some recent publications, simple and widely used Markov chain Monte Carlo (MCMC) algorithms with data augmentation reliably recover the entire posterior distribution. 1 1