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Web Intelligence and Intelligent Agent Technology Workshops (WI-IAT)

作者： Andreas L. Symeonidis Vivia Nikolaidou Pericles A. Mitkas Intelligent Systems and Software Engineering Laboratory Informatics and Telematics Institute CERTH Thessaloniki Greece Electrical and Computer Engineering Department Aristotle University of Thessaloniki Thessaloniki Greece

Supply chain management (SCM) environments are often dynamic markets providing a plethora of information, either complete or incomplete. It is, therefore, evident that such environments demand intelligent solutions, which can perceive variations and act in order to achieve maximum revenue. To do so, they must also provide some sophisticated mechanism for exploiting the full potential of the environments they inhabit. Advancing on the way autonomous solutions usually deal with the SCM process, we have built a robust and highly-adaptable mechanism for efficiently dealing with all SCM facets, while at the same time incorporating a module that exploits data mining technology in order to forecast the price of the winning bid in a given order and, thus, adjust its bidding strategy. The paper presents our agent, Mertacor, and focuses on the forecasting mechanism it incorporates, aiming to optimal agent efficiency

关键词： Data mining Supply chain management Intelligent agent Delta modulation Supply chains Robustness Manufacturing Assembly Data engineering Intelligent systems

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Dynamic In-Network Classification for Service Function Chaining ready SDN networks

Dynamic In-Network Classification for Service Function Chain...

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International Conference on the Network of the Future (NOF)

作者： Marco Polverini Jaime Galán-Jiménez Francesco G. Lavacca Antonio Cianfrani Vincenzo Eramo DIET Department University of Rome "Sapienza" Rome Italy Department of Computer Systems and Telematics Engineering University of Extremadura Spain Fondazione Ugo Bordoni Italy

ISBN: (数字)9781728144450

ISBN: (纸本)9781728144467

Service Function Chaining (SFC) paradigm consists in steering traffic flows through an ordered set of Service Functions (SFs) so that to realize complex end to end services. SFC architecture introduces all the logical functions that need to be developed in order to provide the required service. The SFC overlay infrastructure can be built on top of many different underlay network technologies. The high flexibility and centrally controlled feature of Software Defined Networking (SDN), make SDN networks to be a perfect underlay to build the SFC architecture. Due to Ternary Content Address Memory (TCAM) limited size, SDN switches have a limitation in the number of flow rules that can be hosted. This constraint is particularly penalizing in case of the SFC classifier function, since it requires to manage a high number of different flows. The limitation imposed by the TCAM size on the SFC classifier can be a bottleneck for the number of SFC requests that the SDN-based SFC architecture can handle. In this paper we define the Dynamic Chain Request Classification Offloading (D-CRCO) problem, as the one of maximizing the number of accepted SFC requests, having the possibility of: i) implement the SFC classifier also in a node that is internal to the SDN-based SFC domain, and ii) install classification rules in a reactive fashion. Furthermore, we propose the Dynamic Nearest Node (DNN) heuristic to solve the D-CRCO problem. Performance evaluation shows that by using DNN heuristic it is possible to triple the number of accepted requests, with respect to existing solutions.

关键词： computer architecture IP networks Bandwidth Performance evaluation Routing Servers Software

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GeneCity: A Multi Agent Simulation Environment for Hereditary Diseases

GeneCity: A Multi Agent Simulation Environment for Hereditar...

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ACS/IEEE International Conference on computer systems and Applications

作者： D.G. Eliades A.L. Symeonidis P.A. Mitkas Electrical and Computer Engineering Department Aristotle University of Thessaloniki Thessaloniki Greece Intelligent Systems and Software Engineering Laboratory Informatics and Telematics Institute CERTH Thessaloniki Greece

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Data Mining-Driven Analysis and Decomposition in Agent Supply Chain Management Networks

Data Mining-Driven Analysis and Decomposition in Agent Suppl...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Kyriakos C. Chatzidimitriou Andreas L. Symeonidis Pericles A. Mitkas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Intelligent Systems and Software Engineering Laboratory Informatics and Telematics Institute CERTH Thessaloniki Greece

In complex and dynamic environments where interdependencies cannot monotonously determine causality, data mining techniques may be employed in order to analyze the problem, extract key features and identify pivotal factors. Typical cases of such complexity and dynamicity are supply chain networks, where a number of involved stakeholders struggle towards their own benefit. These stakeholders may be agents with varying degrees of autonomy and intelligence, in a constant effort to establish beneficiary contracts and maximize own revenue. In this paper, we illustrate the benefits of data mining analysis on a well-established agent supply chain management network. We apply data mining techniques, both at a macro and micro level, analyze the results and discuss them in the context of agent performance improvement.

关键词： Data analysis Supply chain management Delta modulation Intelligent agent Data mining Algorithm design and analysis Supply chains Intelligent networks Contracts Context modeling

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2010年 6264 LNCS卷 VI页

作者： Katsikas, Sokratis Lopez, Javier Soriano, Miguel University of Piraeus Digital Systems Piraeus 18534 Greece University of Malaga Computer Science Department 29071 Malaga Spain Technical University of Catalonia Department of Telematics Engineering 08034 Barcelona Spain

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Adaptive importance sampling simulation of queueing networks

Adaptive importance sampling simulation of queueing networks

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Simulation Winter Conference

作者： P.-T. De Boer V.F. Nicola R.Y. Rubinstein Telematics Systems and Services Department of Computer Science University of Twente Enschede Netherlands William Davidson Faculty of Industrial Engineering and Management Haifa Israel

In this paper, a method is presented for the efficient estimation of rare-event (overflow) probabilities in Jackson queueing networks using importance sampling. The method differs in two ways from methods discussed in most earlier literature: the change of measure is state-dependent, i.e., it is a function of the content of the buffers, and the change of measure is determined using a cross-entropy-based adaptive procedure. This method yields asymptotically efficient estimation of overflow probabilities of queueing models for which it has been shown that methods using a state-independent change of measure are not asymptotically efficient. Numerical results demonstrating the effectiveness of the method are presented as well.

关键词： Monte Carlo methods telematics Computational modeling computer science Industrial engineering computer network management engineering management State estimation Yield estimation Application software

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An optimization model for the management of security risks in banking companies

An optimization model for the management of security risks i...

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IEEE International Conference on e-Commerce Technology (CEC)

作者： Ulrich Faisst O. Prokein Department of Information Systems & Financial Engineering Business Schooli University of Augsburg Germany Institute of Computer Science and Social Studies Department of Telematics University of Freiburg Germany

The increasing importance of information and communication technologies (ICT), new regulatory obligations (e.g. Basel II) and growing external risks (e.g. hacker attacks) put security risks in the management focus of banking companies. The management has to decide whether to accept expected losses or to invest in technical security mechanisms in order to decrease the frequency of events or to invest in insurance policies in order to lower the severity of events. This paper contributes to the development of an optimization model that aims to determine the optimal amount to be invested in technical security mechanisms and insurance policies. Furthermore the model considers budget and risk limits as constraints and is supposed to help practitioners in controlling security risks.

关键词： Risk management Banking Information security Investments Insurance Data security Communication system security Protection Companies Costs

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A Semi-Automated Approach to Process Discovery from Multiple Natural Language Documents

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Procedia computer Science 2024年 239卷 1662-1669页

作者： Nicolas Mauro de Moreira Bohnenberger Encarna Sosa-Sánchez Lucineia Heloisa Thom Institute of Informatics – Federal University of Rio Grande do Sul (UFRGS) Porto Alegre 91501-970 Brazil Department of Computer Systems and Telematics Engineering – University of Extremadura Cáceres 10003 Spain

The discovery of process information in natural language documents is hindered by the innate ambiguity of natural language. This issue is even more challenging when process information is spread throughout several documents, each with the possibility of different formats, writers, and original purposes, that must be read and interpreted individually by a business analyst before their information can be uncovered. This work presents a semi-automated approach to process discovery from multiple documents simultaneously, making use of semantic similarity measures and natural language processing techniques to identify, gather, and extract process information from these documents. An experiment is conducted with this approach to demonstrate its use and results.

关键词： Business Process Management Business Process Discovery Semantic Similarity

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An Algorithm to Compute Any Simple K-Gon of a Maximum-Area or Perimeter Inscribed in a Region of Interest

SSRN

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SSRN 2022年

作者： Molano, R. Ávila, M.M. Sancho, J.C. Rodríguez, P.G. Caro, A. Department of Mathematics Universidad de Extremadura Cáceres10003 Spain Department of Computer and Telematics Systems Engineering Universidad de Extremadura Cáceres10003 Spain

Computational and mathematical models are research subjects for solving engineering, computer science and computer vision problems. Image preprocessing usually needs to efficiently compute polygons related to some previously delimited region of interest. Most of the solved problems are limited to the search for some type of polygon with n sides (triangles, rectangles, squares...) with maximum area, maximum perimeter, or similar. This paper presents a generic algorithm that computes the polygon of any number of sides (any simple k -gon) inscribed in a region of interest (in any closed contour without restrictions). The polygon obtained fulfils the requirements specified by the user: maximum area or perimeter or minimum area or perimeter. No previous work has been proposed to obtain any k -gon inscribed in any unconstrained contour. The algorithms and mathematical models are presented and explained, and the source code is available in a GitHub repository for research purposes. © 2022, The Authors. All rights reserved.

关键词： computer programming

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Specialized neural network and fuzzy logic algorithm in comparison for word frequency prediction in document filtering

Specialized neural network and fuzzy logic algorithm in comp...

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1998 IEEE International Conference on Fuzzy systems, FUZZY 1998

作者： Baranyi, Péter Aradi, Petra Kóczy, László T. Gedeon, Tamás D. Dept. of Automation T. U. of Budapest Hungary Department of Systems and Control Engineering T.U. of Budapest Hungary Dept. of Telecommunication and Telematics T. U. of Budapest Hungary School of Computer Science and Eng. U. of New South Wales Australia

ISBN: (纸本)078034863X

For very large document collections or high volume streams of documents, finding relevant documents is a major information filtering problem. A major aid to information retrieval systems produces a word frequency measure estimated from some important parts of the document using neural network approaches. In this paper a fuzzy logic technique and as its simplified case a neural network algorithm will be proposed for this task. The comparison of these two and an alternative neural network algorithm will also be discussed. © 1998 IEEE.

关键词： Information retrieval systems

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