Multi-enterprise processes (MEPs) are workflows consisting of a set of activities that are implem... more Multi-enterprise processes (MEPs) are workflows consisting of a set of activities that are implemented by different enterprises. Tightly coupled Virtual Enterprises (VEs) typically agree on abstract MEPs (reference MEPs), to which each enterprise contributes single-enterprise processes (SEPs) that implement and refine the activities in the reference MEP. On the other end of the spectrum, loosely coupled VEs use service-based MEPs that fuse together heterogeneous services implemented and provided by different enterprises. Existing process models usually couple activities with their implementation. Therefore, they cannot effectively support such MEPs. In this paper, we introduce a Polymorphic Process Model (PPM) that supports both reference process- and service-based MEPs. To accomplish this, PPM decouples activity interface from activity implementation, and provides process polymorphism to support their mapping. In particular, PPM determines activity types from the activity interfaces, permits activity interface subtyping, and provides for the mapping of MEP activity types to concrete implementations via interface matching. We illustrate that these key PPM capabilities permit the late binding and use of multiple activity implementations within a MEP without modifying the MEP at run time or enumerating the alternative implementation at specification time.
Artificial neural networks can be employed to solve a wide spectrum of problems in optimization, ... more Artificial neural networks can be employed to solve a wide spectrum of problems in optimization, parallel computing, matrix algebra and signal processing. Taking a computational approach, this book explains how ANNs provide solutions in real time, and allow the ...
This is the first part of the axiomatics of the Mizar system. It includes the axioms of the Tarsk... more This is the first part of the axiomatics of the Mizar system. It includes the axioms of the Tarski Grothendieck set theory. They are: the axiom stating that everything is a set, the extensionality axiom, the definitional axiom of the singleton, the definitional axiom of the pair, the definitional axiom of the union of a family of sets, the definitional axiom of the boolean (the power set) of a set, the regularity axiom, the definitional axiom of the ordered pair, the Tarski's axiom A introduced in [1] (see also ), and the Fraenkel scheme. Also, the definition of equinumerosity is introduced.
Worldwide, there has been a rapid growth in interest in rough set theory and its applications in ... more Worldwide, there has been a rapid growth in interest in rough set theory and its applications in recent years. Evidence of this can be found in the increasing number of high-quality articles on rough sets and related topics that have been published in a variety of international journals, symposia, workshops, and international conferences in recent years. In addition, many international workshops and conferences have included special sessions on the theory and applications of rough sets in their programs. Rough set theory has led to many interesting applications and extensions. It seems that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, mereology, knowledge discovery, decision analysis, and expert systems. In the article, we present the basic concepts of rough set theory and point out some rough setbased research directions and applications.
... Neither approach uses term information or implements any information retrieval service exploi... more ... Neither approach uses term information or implements any information retrieval service exploiting hypertext clustering. ... work for multiple coexisting cluster hierarchies in a dis-... the abstraction of information spaces into manageable data sets in order to provide scalable ser-vices. ...
Learning algorithms and underlying basic mathematical ideas are presented for the problem of adap... more Learning algorithms and underlying basic mathematical ideas are presented for the problem of adaptive blind signal processing, especially instantaneous blind separation and multichannel blind deconvolution/equalization of independent source signals. We discuss recent developments of adaptive learning algorithms based on the natural gradient approach and their properties concerning convergence, stability, and efficiency. Several promising schemas are proposed and reviewed in the paper. Emphasis is given to neural networks or adaptive filtering models and associated online adaptive nonlinear learning algorithms. Computer simulations illustrate the performances of the developed algorithms. Some results presented in this paper are new and are being published for the first time.
Multi-enterprise processes (MEPs) are workflows consisting of a set of activities that are implem... more Multi-enterprise processes (MEPs) are workflows consisting of a set of activities that are implemented by different enterprises. Tightly coupled Virtual Enterprises (VEs) typically agree on abstract MEPs (reference MEPs), to which each enterprise contributes single-enterprise processes (SEPs) that implement and refine the activities in the reference MEP. On the other end of the spectrum, loosely coupled VEs use service-based MEPs that fuse together heterogeneous services implemented and provided by different enterprises. Existing process models usually couple activities with their implementation. Therefore, they cannot effectively support such MEPs. In this paper, we introduce a Polymorphic Process Model (PPM) that supports both reference process- and service-based MEPs. To accomplish this, PPM decouples activity interface from activity implementation, and provides process polymorphism to support their mapping. In particular, PPM determines activity types from the activity interfaces, permits activity interface subtyping, and provides for the mapping of MEP activity types to concrete implementations via interface matching. We illustrate that these key PPM capabilities permit the late binding and use of multiple activity implementations within a MEP without modifying the MEP at run time or enumerating the alternative implementation at specification time.
Artificial neural networks can be employed to solve a wide spectrum of problems in optimization, ... more Artificial neural networks can be employed to solve a wide spectrum of problems in optimization, parallel computing, matrix algebra and signal processing. Taking a computational approach, this book explains how ANNs provide solutions in real time, and allow the ...
This is the first part of the axiomatics of the Mizar system. It includes the axioms of the Tarsk... more This is the first part of the axiomatics of the Mizar system. It includes the axioms of the Tarski Grothendieck set theory. They are: the axiom stating that everything is a set, the extensionality axiom, the definitional axiom of the singleton, the definitional axiom of the pair, the definitional axiom of the union of a family of sets, the definitional axiom of the boolean (the power set) of a set, the regularity axiom, the definitional axiom of the ordered pair, the Tarski's axiom A introduced in [1] (see also ), and the Fraenkel scheme. Also, the definition of equinumerosity is introduced.
Worldwide, there has been a rapid growth in interest in rough set theory and its applications in ... more Worldwide, there has been a rapid growth in interest in rough set theory and its applications in recent years. Evidence of this can be found in the increasing number of high-quality articles on rough sets and related topics that have been published in a variety of international journals, symposia, workshops, and international conferences in recent years. In addition, many international workshops and conferences have included special sessions on the theory and applications of rough sets in their programs. Rough set theory has led to many interesting applications and extensions. It seems that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, mereology, knowledge discovery, decision analysis, and expert systems. In the article, we present the basic concepts of rough set theory and point out some rough setbased research directions and applications.
... Neither approach uses term information or implements any information retrieval service exploi... more ... Neither approach uses term information or implements any information retrieval service exploiting hypertext clustering. ... work for multiple coexisting cluster hierarchies in a dis-... the abstraction of information spaces into manageable data sets in order to provide scalable ser-vices. ...
Learning algorithms and underlying basic mathematical ideas are presented for the problem of adap... more Learning algorithms and underlying basic mathematical ideas are presented for the problem of adaptive blind signal processing, especially instantaneous blind separation and multichannel blind deconvolution/equalization of independent source signals. We discuss recent developments of adaptive learning algorithms based on the natural gradient approach and their properties concerning convergence, stability, and efficiency. Several promising schemas are proposed and reviewed in the paper. Emphasis is given to neural networks or adaptive filtering models and associated online adaptive nonlinear learning algorithms. Computer simulations illustrate the performances of the developed algorithms. Some results presented in this paper are new and are being published for the first time.
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Papers by Andrzej S.