الصفحة 2
الصفحة 2
Certification and security in inter-organizational E-services ; IFIP 18th World Computer Congress, August 22-27, 2004, Toulouse, France
This collection of papers offers real-life application experiences, research results and methodological proposals of direct interest to systems experts and users in governmental, industrial and academic communities. This book also documents several important developments. The uptake of distributed computational infrastructure oriented to service provision, like Web-Services and Grid, is making C&S even more important. E-services based on legacy systems managed by autonomous and independent organizations, a common situation in the public administration sector, increase overall complexity. The increased presence and use of e-service IT-infrastructures depends on the critical ability required for all security issues, from the basic (availability, authentication, integrity, confidentiality) to the more complex (e.g. authorization, non-repudiation).
Bio-inspired credit risk analysis : Computational intelligence with support vector machines
Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties.
Argumentation in multi-agent systems ; Third International Workshop, ArgMAS 2006, Hakodate, Japan, May 8, 2006, revised selected and invited papers
Argumentation provides tools for designing, implementing and analyzing sophisticated forms of interaction among rational agents. It has made a solid contribution to the practice of multiagent dialogues. Application domains include: legal disputes, business negotiation, labor disputes, team formation, scientific inquiry, deliberative democracy, ontology reconciliation, risk analysis, scheduling, and logistics.
Applied Civil Engineering Risk Analysis
Povides readers with the tools needed to determine the probability of failure, and when multiplied by the consequences of failure, illustrates how to assess the risk of civil engineering problems. Presenting methods for quantifying uncertainty that exists in engineering analysis and design, with an emphasis on fostering more accurate analysis and design.
Anti-fragile ICT Systems
Introduces a novel approach to the design and operation of large ICT systems. It views the technical solutions and their stakeholders as complex adaptive systems and argues that traditional risk analyses cannot predict all future incidents with major impacts. To avoid unacceptable events, it is necessary to establish and operate anti-fragile ICT systems that limit the impact of all incidents, and which learn from small-impact incidents how to function increasingly well in changing environments. The book applies four design principles and one operational principle to achieve anti-fragility for different classes of incidents. It discusses how systems can achieve high availability, prevent malware epidemics, and detect anomalies. Analyses of Netflix’s media streaming solution, Norwegian telecom infrastructures, e-government platforms, and Numenta’s anomaly detection software show that cloud computing is essential to achieving anti-fragility for classes of events with negative impacts.
An Introduction to continuous-time stochastic processes : Theory, models, and applications to finance, biology, and medicine
This book is introduction to the theory of continuous-time stochastic processes. A balance of theory and applications, the work features concrete examples of modeling real-world problems from biology, medicine, industrial applications, finance, and insurance using stochastic methods. No previous knowledge of stochastic processes is required. Key topics covered include: * Interacting particles and agent-based models: from polymers to ants * Population dynamics: from birth and death processes to epidemics * Financial market models: the non-arbitrage principle * Contingent claim valuation models: the risk-neutral valuation theory * Risk analysis in insurance
Agent-oriented software engineering VII ; 7th International Workshop, AOSE 2006, Hakodate, Japan, May 8, 2006, Revised and Invited Papers
Software architectures that contain many dynamically interacting components, each with their own thread of control, and engaging in complex coordination protocols, are difficult to correctly and efficiently engineer. Agent-oriented modelling techniques are important for supporting the design and development of such applications.The book is organized in topical sections on modelling and design of agent systems, modelling open agent systems, formal reasoning about designs, as well as testing, debugging and evolvability.
Advanced Techniques in Knowledge Discovery and Data Mining
This explosion is a result of the growing use of electronic media. But what is data mining (DM)? A Web search using the Google search engine retrieves many (really many) definitions of data mining. We include here a few interesting ones. One of the simpler definitions is: “As the term suggests, data mining is the analysis of data to establish relationships and identify patterns” [1]. It focuses on identifying relations in data. Our next example is more elaborate: An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis .
