Stefan C. Kremer

New directions in fuzzy automata

Automata are the prime example of general computational systems over discrete spaces. The incorporation of fuzzy logic into automata theory resulted in fuzzy auotomata which can handle continuous spaces. Moreover, they are able to model uncertainty which is inherent in many applications. Deterministic Finite-state Automata (DFA) have been the architecture, most used in many applications, but, the increasing interest in using fuzzy logic for many new areas necessitates that the formalism of fuzzy automata be more developed and better established to fulfill implementational requirements in a well-defined manner. This need is due to the fact that despite the long history of fuzzy automata and lots of research being done on that, there are still some issues which have not been well-established and issues which need some kind of revision. In particular, we focus on membership assignment, output mapping, multi-membership resolution, and the concept of acceptance for fuzzy automata. We develop a new general definition for fuzzy automata, and based on that, develop well-defined and application-driven methodologies to establish a better ground for fuzzy automata and pave the way for forthcoming applications.

On the computational power of Elman-style recurrent networks

Recently, Elman (1991) has proposed a simple recurrent network which is able to identify and classify temporal patterns. Despite the fact that Elman networks have been used extensively in many different fields, their theoretical capabilities have not been completely defined. Research in the 1960s showed that for every finite state machine there exists a recurrent artificial neural network which approximates it to an arbitrary degree of precision. This paper extends that result to architectures meeting the constraints of Elman networks, thus proving that their computational power is as great as that of finite state machines.

Clustering unlabeled data with SOMs improves classification of labeled real-world data

We show the use of a self organizing map to cluster unlabeled data and to infer possible labelings from the clusters. Our inferred labels are presented to a multilayer perceptron along with labeled data, performance is improved over using only the labeled data. Results are presented for a number of popular real-world benchmark problems from domains other than text. This shows one way in which unlabeled data can be used to enhance supervised learning in a general-purpose neural network.

A taxonomy for spatiotemporal connectionist networks revisited: the unsupervised case

Spatiotemporal connectionist networks (STCNs) comprise an important class of neural models that can deal with patterns distributed in both time and space. In this article, we widen the application domain of the taxonomy for supervised STCNs recently proposed by Kremer (2001) to the unsupervised case. This is possible through a reinterpretation of the state vector as a vector of latent (hidden) variables, as proposed by Meinicke (2000). The goal of this generalized taxonomy is then to provide a nonlinear generative framework for describing unsupervised spatiotemporal networks, making it easier to compare and contrast their representational and operational characteristics. Computational properties, representational issues, and learning are also discussed, and a number of references to the relevant source publications are provided. It is argued that the proposed approach is simple and more powerful than the previous attempts from a descriptive and predictive viewpoint. We also discuss the relation of this taxonomy with automata theory and state-space modeling and suggest directions for further work.

Inducing grammars from sparse data sets: a survey of algorithms and results

This paper provides a comprehensive survey of the field of grammar induction applied to randomly generated languages using sparse example sets.

Spatiotemporal Connectionist Networks: A Taxonomy and Review

This article reviews connectionist network architectures and training algorithms that are capable of dealing with patterns distributed across both space and timespatiotemporal patterns. It provides common mathematical, algorithmic, and illustrative frameworks for describing spatiotemporal networks, making it easier to compare and contrast their representational and operational characteristics. Computational power, representational issues, and learning are discussed. In additional references to the relevant source publications are provided. This article can serve as a guide to prospective users of spatiotemporal networks by providing an overview of the operational and representational alternatives available.

A survey of QoS/QoE mechanisms in heterogeneous wireless networks

Heterogeneous Wireless Networks (HWNs) are an important step in making connectivity ubiquitous and pervasive. Leveraging the increasing variety of connectivity options available to devices solves many problems such as capacity, spectrum efficiency, coverage and reliability. Anytime decisions are made for selection, handover, scheduling or routing many performance metrics along with energy efficiency and cost for access must be considered. The increased number of choices in an HWN makes the problem more difficult than traditional homogeneous networks since each Radio Access Technology (RAT) has unique characteristics. For instance, Bluetooth networks have low range and speed but are cheap compared to 4G networks. These types of observations can be factored into decision making in HWNs. Quality of Service and Experience should be considered so that the best possible configuration of connectivity, price and user application is made. All of this should occur autonomously. This paper provides a survey of recent works in HWNs with these ideas in mind. Existing approaches are categorized by function. Limitations and strengths of solutions are highlighted and comparisons between approaches are made to provide a starting point for further research in the area.

Comments on "Constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution"

Giles et al. (1995) have proven that Fahlmans recurrent cascade correlation (RCC) architecture is not capable of realizing finite state automata that have state-cycles of length more than two under a constant input signal. This paper extends the conclusions of Giles et al. by showing that there exists a corollary to their original proof which identifies a large second class of automata, that is also unrepresentable by RCC.

Distinguishing ecological from evolutionary approaches to transposable elements

Considerable variation exists not only in the kinds of transposable elements (TEs) occurring within the genomes of different species, but also in their abundance and distribution. Noting a similarity to the assortment of organisms among ecosystems, some researchers have called for an ecological approach to the study of transposon dynamics. However, there are several ways to adopt such an approach, and it is sometimes unclear what an ecological perspective will add to the existing co‐evolutionary framework for explaining transposon‐host interactions. This review aims to clarify the conceptual foundations of transposon ecology in order to evaluate its explanatory prospects. We begin by identifying three unanswered questions regarding the abundance and distribution of TEs that potentially call for an ecological explanation. We then offer an operational distinction between evolutionary and ecological approaches to these questions. By determining the amount of variance in transposon abundance and distribution that is explained by ecological and evolutionary factors, respectively, it is possible empirically to assess the prospects for each of these explanatory frameworks. To illustrate how this methodology applies to a concrete example, we analyzed whole‐genome data for one set of distantly related mammals and another more closely related group of arthropods. Our expectation was that ecological factors are most informative for explaining differences among individual TE lineages, rather than TE families, and for explaining their distribution among closely related as opposed to distantly related host genomes. We found that, in these data sets, ecological factors do in fact explain most of the variation in TE abundance and distribution among TE lineages across less distantly related host organisms. Evolutionary factors were not significant at these levels. However, the explanatory roles of evolution and ecology become inverted at the level of TE families or among more distantly related genomes. Not only does this example demonstrate the utility of our distinction between ecological and evolutionary perspectives, it further suggests an appropriate explanatory domain for the burgeoning discipline of transposon ecology. The fact that ecological processes appear to be impacting TE lineages over relatively short time scales further raises the possibility that transposons might serve as useful model systems for testing more general hypotheses in ecology.

Network intrusion detection system based on recursive feature addition and bigram technique

Abstract Network and Internet security is a critical universal issue. The increased rate of cyber terrorism has put national security under risk. In addition, Internet attacks have caused severe damages to different sectors (i.e., individuals, economy, enterprises, organizations and governments). Network Intrusion Detection Systems (NIDS) are one of the solutions against these attacks. However, NIDS always need to improve their performance in terms of increasing the accuracy and decreasing false alarms. Integrating feature selection with intrusion detection has shown to be a successful approach since feature selection can help in selecting the most informative features from the entire set of features. Usually, for the stealthy and low profile attacks (zero – day attacks), there are few neatly concealed packets distributed over a long period of time to mislead firewalls and NIDS. Besides, there are many features extracted from those packets, which may make some machine learning-based feature selection methods to suffer from overfitting especially when the data have large numbers of features and relatively small numbers of examples. In this paper, we are proposing a NIDS based on a feature selection method called Recursive Feature Addition (RFA) and bigram technique. The system has been designed, implemented and tested. We tested the model on the ISCX 2012 data set, which is one of the most well-known and recent data sets for intrusion detection purposes. Furthermore, we are proposing a bigram technique to encode payload string features into a useful representation that can be used in feature selection. In addition, we propose a new evaluation metric called (combined) that combines accuracy, detection rate and false alarm rate in a way that helps in comparing different systems and selecting the best among them. The designed feature selection-based system has shown a noticeable improvement on the performance using different metrics.