Most methods of classification either ignore feature analysis or do it in a separate phase, offline prior to the main classification task. This paper proposes a neuro-fuzzy scheme for designing a classifier along with feature selection. It is a four-layered feed-forward network for realizing a fuzzy rule-based classifier. The network is trained by error backpropagation in three phases. In the first phase, the network learns the important features and the classification rules. In the subsequent phases, the network is pruned to an "optimal" architecture that represents an "optimal" set of rules. Pruning is found to drastically reduce the size of the network without degrading the performance. The pruned network is further tuned to improve performance. The rules learned by the network can be easily read from the network. The system is tested on both synthetic and real data sets and found to perform quite well.
This work builds on earlier work by Rogaway at Asiacrypt 2004 on tweakable block cipher (TBC) and modes of operations. Our first contribution is to generalize Rogaway's TBC construction by working over a ring R R R and by the use of a masking sequence of functions. The ring R R R can be instantiated as either GF(2 n ) or as 2 . Further, over GF(2 n ), efficient instantiations of the masking sequence of functions can be done using either a binary linear feedback shift register (LFSR); a powering construction; a cellular automata map; or by using a word-oriented LFSR. Rogaway's TBC construction was built from the powering construction over GF(2 n ). Our second contribution is to use the general TBC construction to instantiate constructions of various modes of operations including authenticated encryption (AE) and message authentication code (MAC). In particular, this gives rise to a family of efficient one-pass AE modes of operation. Out of these, the mode of operation obtained by the use of word-oriented LFSR promises to provide a masking method which is more efficient than the one used in the well known AE protocol called OCB1. Index Terms-Authenticated encryption with associated data, message authentication code, modes of operations, tweakable block cipher (TBC).
Abstract-Suppose for a given classification or function approximation (FA) problem data are collected using sensors. From the output of the th sensor, features are extracted, thereby generating = =1 features, so for the task we have as input data along with their corresponding outputs or class labels . Here, we propose two connectionist schemes that can simultaneously select the useful sensors and learn the relation between and . One scheme is based on the radial basis function (RBF) network and the other uses the multilayered perceptron (MLP) network. Both schemes are shown to possess the universal approximation property. Simulations show that the methods can detect the bad/derogatory groups of features online and can eliminate the effect of these bad features while doing the FA or classification task.
Abstract. We present PEP, which is a new construction of a tweakable strong pseudo-random permutation. PEP uses a hash-encrypt-hash approach which has been recently used in the construction of HCTR. This approach is different from the encrypt-mask-encrypt approach of constructions such as CMC, EME and EME * . The general hash-encrypthash approach was earlier used by Naor-Reingold to provide a generic construction technique for an SPRP (but not a tweakable SPRP). PEP can be seen as the development of the Naor-Reingold approach into a fully specified mode of operation with a concrete security reduction for a tweakable strong pseudo-random permutation. HCTR is also based on the Naor-Reingold approach but its security bound is weaker than PEP. Compared to previous known constructions, PEP is the only known construction of tweakable SPRP which uses a single key, is efficiently parallelizable and can handle an arbitrary number of blocks.
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