الصفحة 1
الصفحة 1
Ontology Matching
Euzenat and Shvaiko’s book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities.
Obstructions in Security-Aware Business Processes : Analysis, Detection, and Handling
This book explores the dilemma-like stalemate between security and regulatory compliance in business processes on the one hand and business continuity and governance on the other. The growing number of regulations, e.g., on information security, data protection, or privacy, implemented in increasingly digitized businesses can have an obstructive effect on the automated execution of business processes. Such security-related obstructions can particularly occur when an access control-based implementation of regulations blocks the execution of business processes. By handling obstructions, security in business processes is supposed to be improved. For this, the book presents a framework that allows the comprehensive analysis, detection, and handling of obstructions in a security-sensitive way. Thereby, methods based on common organizational security policies, process models, and logs are proposed. The Petri net-based modeling and related semantic and language-based research, as well as the analysis of event data and machine learning methods finally lead to the development of algorithms and experiments that can detect and resolve obstructions and are reproducible with the provided software.
Object-Oriented Programming and Java
Object-Oriented Programming and Java presents two important topics in contemporary software development: object-oriented programming and Java. This book takes a different teaching approach from most available literature, it begins with the description of real-world object interaction scenarios and explains how they can be translated, represented and executed using object-oriented programming paradigm.
Nonlinear Analyses and Algorithms for Speech Processing ; International Conference on Non-Linear Speech Processing, NOLISP 2005, Barcelona, Spain, April 19-22, 2005, Revised Selected Papers
We present in this volume the collection of ?nally accepted papers of NOLISP 2005 conference. It has been the third event in a series of events related to N- linear speech processing, in the framework of the European COST action 277 “Nonlinear speech processing”. Many speci?cs of the speech signal are not well addressed by conv- tional models currently used in the ?eld of speech processing. The purpose of NOLISP is to present and discuss novel ideas, work and results related to alternative techniques for speech processing, which depart from mainstream approaches. With this intention in mind, we provide an open forum for discussion. Alt- nate approaches are appreciated, although the results achieved at present may not clearly surpass results based on state-of-the-art methods. The call for papers was launched at the beginning of 2005, addressing the following domains: 1. Non-Linear Approximation and Estimation 2. Non-Linear Oscillators and Predictors 3. Higher-Order Statistics 4. Independent Component Analysis 5. Nearest Neighbors 6. Neural Networks 7. Decision Trees 8. Non-Parametric Models 9. Dynamics of Non-Linear Systems 10. Fractal Methods 11. Chaos Modeling 12. Non-Linear Di?erential Equations 13. Others All the main ?elds of speech processing are targeted by the workshop, namely: 1. Speech Coding:Thebit rateavailablefor speechsignalsmustbe strictly l- ited in order to accommodate the constraints of the channel resource.
Next generation information technologies and systems ; 6th International Conference, NGITS 2006, Kebbutz Sehfayim, Israel, July 4-6, 2006, Proceedings
The selected papers may be classified roughly in ten broad areas: ? Information systems development ? Distributed systems ? Semi-structured data ? Data mining and agent-oriented computing ? User-oriented design ? Frameworks, models and taxonomies ? Simulation and incremental computing ? Information integration ? Security and privacy ? Next-generation applications This event is the culmination of efforts by many talented and dedicated individuals.
New insights in machine learning and deep neural networks
Gatheres ten exemplary papers, each delineating advancements within the spheres of machine learning and deep neural networks. Commencing with a thorough exploration by Figueira and Vaz, readers are introduced to the nuances of synthetic data generation and evaluation, followed closely by Silva and Pedroso's systematic approach to leveraging deep reinforcement learning within the intricate realm of delivery logistics. Kamran et al. contribute an astute methodology for camouflage object segmentation, whereas Pinheiro and collaborators offer a crafted semi-supervised strategy for predicting EGFR mutations via CT images. Subsequent contributions, such as Lee and Yoo's framework for portrait emotion recognition and Balakrishnan et al.'s analytical exploration of transformer models for Twitter disaster detection, further exemplify the depth of research contained herein.
New Directions in Human Information Behavior
The book presents chapters by an interdisciplinary range of scholars who show new directions that often challenge the established views and paradigms of information behavior studies. Beginning with an evolutionary framework, the book examines information behaviors over various epochs of human existence from the Palaeolithic Era and within pre-literate societies, to contemporary behaviors by 21st century humans. Drawing upon social and psychological science theories the book presents a more integrated and holistic approach to the understanding of information behaviors that include multitasking and non-linear longitudinal processes, individuals’ information ground, information practices and information sharing, digital behaviors and human information organizing behaviors. The final chapter of the book integrates these new approaches and presents an overview of the key trends, theories and models for further research.
Neuroscribe = نيوروسكرايب
Neuroscribe is a cutting-edge deep learning framework designed to address the complexities and inefficiencies encountered in existing frameworks like PyTorch and TensorFlow. Aimed at streamlining model development and enhancing performance across diverse hardware environments, NeuroScribe offers a lightweight and flexible solution. The framework features a robust tensor library, an auto-differentiation engine, a comprehensive neural network module, and advanced optimization algorithms. With built-in visualization tools and a user-friendly interface, NeuroScribe simplifies both beginner and advanced workflows. Its cross-platform compatibility, supported by CUDA and Metal Performance Shaders (MPS), ensures optimal performance, and in some scenarios, NeuroScribe demonstrates superior speed compared to leading frameworks. Additionally, NeuroScribe introduces unique libraries and features not found in other frameworks, further enhancing its versatility and appeal. The modular architecture and automatic system detection further enhance its adaptability, making NeuroScribe a versatile and powerful tool for deep learning practitioners.
Network-Centric Service-Oriented Enterprise
Network-Centric Service-Oriented Enterprise (NSCOE) is seen as heralding the next generation of mainstream Enterprise-business information collaboration solution that can enforce information and decision superiority in the decentralized, loosely-coupled, and highly interoperable service environments. Network-Centric Service Oriented Enterprise establishes a system-of-systems (SoS) view of information technologies, offering a synergistic combination of data and information-processing capacity upon an innovative networked-management framework.
Network-Centric Collaboration and Supporting Frameworks ; IFIP TC 5 WG 5.5, Seventh IFIP Working Conference on Virtual Enterprises, 25-27 September 2006, Helsinki, Finland
This book contains a number of selected articles from PRO-VE’06, the seventh working conference on virtual enterprises held in Helsinki, Finland, which was sponsored by the International Federation for Information Processing (IFIP) and the Society of Collaborative Networks (SOCOLNET). Being recognized as the most focused scientific and technical conference on Collaborative Networks, PRO-VE continues offering the opportunity for presentation and discussion of both the latest research developments as well as the practical application case studies. Following the vision of IFIP and SOCOLNET, the PRO-VE conference offers a forum for collaboration and knowledge exchange among experts from different regions of the world.
Network Classification For Traffic Management : Anomaly detection, feature selection, clustering and classification
Investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. With the massive increase of data and traffic on the Internet within the 5G, IoT and smart cities frameworks, current network classification and analysis techniques are falling short. Novel approaches using machine learning algorithms are needed to cope with and manage real-world network traffic, including supervised, semi-supervised, and unsupervised classification techniques. Accurate and effective classification of network traffic will lead to better quality of service and more secure and manageable networks. Investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. The authors explore novel methods for enhancing network statistics at the transport layer, helping to identify optimal feature selection through a global optimization approach and providing automatic labelling for raw traffic through a SemTra framework to maintain provable privacy on information disclosure properties.
Multiple Classifier Systems ; 2nd International Workshop, MCS 2001 Cambridge, UK, July 2-4, 2001 Proceedings
Driven by the requirements of a large number of practical and commercially - portant applications, the last decade has witnessed considerable advances in p- tern recognition. Better understanding of the design issues and new paradigms, such as the Support Vector Machine, have contributed to the development of - proved methods of pattern classi cation. However, while any performance gains are welcome, and often extremely signi cant from the practical point of view, it is increasingly more challenging to reach the point of perfection as de ned by the theoretical optimality of decision making in a given decision framework. The asymptoticity of gains that can be made for a single classi er is a re?- tion of the fact that any particular design, regardless of how good it is, simply provides just one estimate of the optimal decision rule.
Multi-Agent Programming : Languages, Platforms and Applications
Part I describes four approaches that are based on computational logic or process algebra--Jason, 3APL, IMPACT, and CLAIM/SyMPA. These programming languages have formal semantics and use heavy machinery based on formal methods, but also provide working platforms for the development of multi-agent systems. Part II presents agent languages and platforms that extend or are based on Java--JADE, Jadex, and JACKTM. Although these have no formal semantics, the languages are well documented and the platforms provide a variety of tools that have been extensively used in practice. Part III provides two significant industry specific applications--The DEFACTO System for coordinating human-agent teams for the future of disaster response, and the ARTIMIS rational dialogue agent technology. The book also features seven appendices, summarising each of the agent programming languages, hence facilitating comparison of the approaches. In particular, Appendix A describes the criteria used for comparing the agent languages and platforms.
Mule 2 : A Developers Guide
Mule 2: A Developer's Guide introduces the Mule 2.0 integration platform for developers of enterprise integration applications who wish to leverage Mule as a lightweight messaging framework that contains a distributable object broker for managing communication between applications. The book is based on insight, knowledge, and experience resulting from working with Mule. The text provides support, consulting, and training to developers implementing Mule in a broad range of scenarios ranging from small projects through to large corporations developing major deployments.
MooTools Essentials : The Official MooTools Reference for JavaScript™ and Ajax Development
Mootools is a light, modular JavaScript framework that makes adding Ajax, animations, and interactive elements to your site a breeze. But it's more than fancy effects and shortcuts; Mootools enhances the JavaScript language and makes writing clean, object–oriented code almost pleasant. Unlocking the power of Mootools, and therefore JavaScript, isn't that hard, but knowing where to start can be.
Modular Programming Languages ; 7th Joint Modular Languages Conference, JMLC 2006, Oxford, UK, September 13-15, 2006, Proceedings
On behalf of the Steering Committee we are pleased to present the proceedings of the 2006 Joint Modular Languages Conference (JMLC), organized by Oxford Brookes University, Oxford, UK and held at Jesus College, Oxford. The mission of JMLC is to explore the concepts of well-structured programming languages and software and those of teaching good design and programming style. JMLC 2006 was the seventh in a series of successful conferences with themes including the construction of large and distributed software systems, and software en- neering aspects in new and dynamic application areas.
Modular Algorithms in Symbolic Summation and Symbolic Integration
Brings together two streams in computer algebra: symbolic integration and summation on the one hand, and fast algorithmics on the other hand. In many algorithmically oriented areas of computer science, the analysis of al gorithms placed into the lime light by DonKnuth’stalkat the 1970ICM –provides a crystal-clear criterion for success. The researcher who designs an algorithm that is faster (asymptotically, in the worst case) than any previous method receives instant gratification : her result will be recognized as valuable. Al as, the downside is that such results come along quite infrequently, despite our best efforts. An alternative evaluation method is to run a new algorithm on examples; this has its obvious problems, but is sometimes the best we can do. George Collins, one of the fathers of computer algebra and a great experimenter,wrote in 1969: “I think this demonstrates again that a simple analysis is often more revealing than a ream of empirical data (although both are important). ” Within computer algebra, some areas have traditionally followed the former methodology, notably some parts of polynomial algebra and linear algebra. Other areas, such as polynomial system solving, have not yet been amenable to this - proach. The usual “input size” parameters of computer science seem inadequate, and although some natural “geometric” parameters have been identified (solution dimension, regularity), not all (potential) major progress can be expressed in this framework. Symbolic integration and summation have been in a similar state.
Modern IoT onboarding platforms for advanced applications: a practitioner’s guide to KIS.ME
There is no doubt that digitalization solutions from Industry 4.0 and the Internet of Things (IoT) can be perceived as excellent candidate strategies capable of handling the above-stated issues concerning measurements and transparency. However, IoT tools themselves can provide appropriate data only, while their efficient integration and application are possible using a dedicated onboarding platform only. To settle this issue, the book undertakes the problem of modern IoT onboarding platforms for the advanced applications pertaining to manufacturing and logistics. In particular, instead of deliberating about a possible hypothetic platforms, an existing and efficient one is employed, which is called KIS.ME. KIS.ME (Keep It Simple. Manage Everything) is a complete IoT solution for a simple integration in manufacturing and logistics.
Modern Deep Learning Design and Application Development : Versatile Tools to Solve Deep Learning Problems
Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking. You will: Improve the performance of deep learning models by using pre-trained models, extracting rich features, and automating optimization. Compress deep learning models while maintaining performance. Reframe a wide variety of difficult problems and design effective deep learning solutions to solve them. Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches.
Modelling and Reasoning with Vague Concepts
This volume outlines a formal representation framework for modelling and reasoning with vague concepts in Artificial Intelligence. The new calculus has many applications, especially in automated reasoning, learning, data analysis and information fusion. This book gives a rigorous introduction to label semantics theory, illustrated with many examples, and suggests clear operational interpretations of the proposed measures. It also provides a detailed description of how the theory can be applied in data analysis and information fusion based on a range of benchmark problems.
