TOWARD A THEORY OF THE DEEP STRUCTURE OF INFORMATION SYSTEMS

The deep structure of an information system comprises those properties that manifest the meaning of the real-world system that the information system is intended to model. In this paper we describe three models that we have developed of information systems decl}.structure properties. The first, the representational model, proposes a set of constructs that enable the ontological completeness of an information systems grammar to be evaluated. The second, the state-tracking model, proposes four requirements that information systems must satisfy if they are to faithfully track the real-world system they are intended to model. The third, the good-decomposition model, proposes a set of necessary conditions that an information system must meet if it is to be well decomposed. The three models facilitate the evaluation of grammars used to analyze, design, and implement information systems and specific scripts that represent implemented information systems

Deep learning systems as complex networks

Thanks to the availability of large scale digital datasets and massive amounts of computational power, deep learning algorithms can learn representations of data by exploiting multiple levels of abstraction. These machine learning methods have greatly improved the state-of-the-art in many challenging cognitive tasks, such as visual object recognition, speech processing, natural language understanding and automatic translation. In particular, one class of deep learning models, known as deep belief networks, can discover intricate statistical structure in large data sets in a completely unsupervised fashion, by learning a generative model of the data using Hebbian-like learning mechanisms. Although these self-organizing systems can be conveniently formalized within the framework of statistical mechanics, their internal functioning remains opaque, because their emergent dynamics cannot be solved analytically. In this article we propose to study deep belief networks using techniques commonly employed in the study of complex networks, in order to gain some insights into the structural and functional properties of the computational graph resulting from the learning process.Comment: 20 pages, 9 figure

Industrial Relations System Transformation

This paper analyzes the concept of “transformation” that many allege has occurred recently in a wide variety of national industrial relations systems. After a summary of the debate, with particular reference to the contentious case of Germany, the authors attempt to develop a definition of industrial relations system transformation on the basis of biological analogies and, in particular, the “punctuated equilibrium” theory. They examine the cases of the United States, Sweden, South Africa, and New Zealand, and conclude that the application of the biological frameworks raises a set of fundamental questions that must be addressed in order for the debate over the existence of industrial relations transformation to move forward

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of dimensionality of input space due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using seven datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four popular continual learning algorithms and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System

Space exploration: The interstellar goal and Titan demonstration

Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed