Rules Of Rss
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HIGH COURTS ON R S S
Author: Sahitya Sindhu Prakashana
language: en
Publisher: Rashtrotthana sahitya
Release Date:
HIGH COURTS ON R. S. S. Collection Of judgements of various High Courts Of Bharat. Author: Sahitya Sindhu Prakashana
Rules and Rule Markup Languages for the Semantic Web
The Semantic Web is a worldwide endeavor to advance the Web by enriching its content with semantic metainformation that can be processed by inferen- enabled Web applications. Taxonomies and rules, along with their automated reasoning techniques, are the main components of Semantic Web ontologies. Rule systems are considered to be a major area in the further development of the Semantic Web. On one hand, rules can specify declarative knowledge in ontology languages, expressing constraints or transformations, either in conju- tionwith,orasanalternativeto,descriptionlogics.Ontheotherhand,rulescan specify behavioral knowledge, enforcing policies or reacting to events/changes. Finally, rule markup languages such as RuleML allow us to publish rules on the Web, to process rules in general XML environments as well as special rule engines, to exchange rules between di?erent applications and tools via XSLT translators, as well as to embed rules into other XML content and vice versa. This workshop was dedicated to all aspects of rules and rule markup l- guages for the Semantic Web. RuleML 2004 was the third in a series of wo- shops that was initiated with the International Semantic Web Conference. The previous workshops were held on Sardinia, Italy (2002), and on Sanibel Island, USA (2003). Thisyearwehad25submissions,ofwhich11wereacceptedasregularpapers and another ?ve as short papers describing tools. Wearegratefultoourtwoinvitedspeakers,MikeDeanfromBBNandChr- tine Golbreich from the University of Rennes. Our thanks also go to all subm- ters and reviewers without whom the workshop and these proceedings could not have succeeded.
Safety Assurance under Uncertainties
Safety assurance of software systems has never been as imminent a problem as it is today. Practitioners and researchers who work on the problem face a challenge unique to modern software systems: uncertainties. For one, the cyber-physical nature of modern software systems as exemplified by automated driving systems mandates environmental uncertainties to be addressed and the resulting hazards to be mitigated. Besides, the abundance of statistical machine-learning components massive numerical computing units for statistical reasoning such as deep neural networks make systems hard to explain, understand, analyze, or verify. The book is the first to provide a comprehensive overview of such united and interdisciplinary efforts. Driven by automated driving systems as a leading example, the book describes diverse techniques to specify, model, test, analyze, and verify modern software systems. Coming out of a collaboration between industry and basic academic research, the book covers both practical analysis techniques (readily applicable to existing systems) and more long-range design techniques (that call for new designs but bring a greater degree of assurance). The book provides high-level intuitions and use-cases of each technique, rather than technical details, with plenty of pointers for interested readers.