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Deep Learning-Based Face Analytics
Provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field.
Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics : Techniques and Applications
Examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever.
Deep Learning with PyTorch Lightning : Build and train high-performance artificial intelligence and self-supervised models using Python
You’ll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide. In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning.
Deep learning methods for converting speech to text = تقنيات التعلم العميق في تحويل الصوت إلى نص
Aims to design and develop a system capable of extracting audio content from films and audio recordings and converting it into text using deep learning techniques. This is done by analyzing audio patterns, extracting sounds and words from the video, and then converting them into written text. Deep learning, a branch of artificial intelligence, is used to accomplish this task. The study also includes comparing different deep learning techniques to determine their effectiveness in this context.
Deep learning for computational problems in hardware security : Modeling attacks on strong physically unclonable function circuits
Discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security.
Deep learning architecture and application
As one of the fastest-growing topics in machine learning, deep learning algorithms have achieved unprecedented success in recent years. Novel paradigms (such as contrastive learning and few-shot learning) in deep learning and rising neural network architectures (e.g., transformer and masked autoencoder) are dramatically changing the field of data-driven algorithms. More importantly, deep learning models are redefining the next generation of industrial applications spanning image recognition, speech processing, language translation, healthcare, and other sciences. For example, recent advances in deep representation learning are allowing us to learn about protein 3D structures, which sheds new light on fundamental medicine and biology along with potentially bringing in billions of dollars (e.g., in the pharmaceutical market).
Deep learning approaches to cloud security
Covering one of the most important subjects to our society today, cloud security, this editorial team delves into solutions taken from evolving deep learning approaches, solutions allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts.
Deep learning approach for text summarization
Machine learning and deep learning, as we know, have started ruling over almost every field in the computing industry and so, has revolutionized the process of text summarization too. Automatic text summarization is an advancing realm of the natural language processing research in which concise textual summaries are generated from lengthy input documents. Extensive research has been carried out on how automatic summarization can be prosecuted through various extractive and abstractive techniques. In this paper, we address all the approaches to text summarization and present the modus operandi of an Architecture called Encoder Decoder, under the machine learning approach.
Decrypted Secrets : Methods and Maxims of Cryptology
Cryptology, for millennia a "secret science", is rapidly gaining in practical importance for the protection of communication channels, databases, and software. Beside its role in computerized information systems (public key systems), more and more applications within computer systems and networks are appearing, which also extend to access rights and source file protection. The first part of this book treats secret codes and their uses - cryptography. The second part deals with the process of covertly decrypting a secret code - cryptanaly-sis - where in particular advice on assessing methods is given. The book presupposes only elementary mathematical knowledge.
Declarative agent languages and technologiesV ; 5th International Workshop, DALT 2007, Honolulu, HI, USA, May 14, 2007, Revised Selected and Invited Papers
This book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Declarative Agent Languages and Technologies, DALT 2007, held in Honolulu, USA, in 2007.
Declarative agent languages and technologies III ; 3rd International Workshop, DALT 2005, Utrecht, The Netherlands, July 25, 2005, Selected and Revised Papers
The workshop on Declarative Agent Languages and Technologies is a we- established venue for researchers interested in sharing their experiences in the areas of declarative and formal aspects of agents and multi-agent systems, and in engineering and technology. Today it is still a challenge to develop techno- gies that can satisfy the requirements of complex agent systems. The design and development of multi-agent systems still calls for models and technologies that ensure predictability, enable feature discovery, allow for the veri?cation of properties, and guarantee ?exibility. Declarative approaches are potentially a valuable means for satisfying the needs of multi-agent system developers and for specifying multi-agent systems.
Datatype-Generic Programming ; International Spring School, SSDGP 2006, Nottingham, UK, April 24-27, 2006, Revised Lectures
A leitmotif in the evolution of programming paradigms has been the level and extent of parametrisation that is facilitated — the so-called genericity of the paradigm. The sorts of parameters that can be envisaged in a programming language range from simple values, like integers and fioating-point numbers, through structured values, types and classes, to kinds (the type of types and/or classes).Datatype-generic programming is about parametrising programsby the structure of the data that they manipulate. To appreciate the importance of data type genericity,one need look no further than the internet. The internet is a massive repository of structured data, but the structure is rarely exploited. For example, compression of data can be much more efiective if its structure is known, but most compression algorithms regard the input data as simply a string of bits, and take no account of its internal organisation. Datatype-generic programming is about exploiting the structure of data when it is relevant and ignoring it when it is not. Programming languages most c- monly used at the present time do not provide efiective mechanisms for do- menting and implementing datatype genericity.
Dataset Studio
Data is the new oil, which means that AI engineers can face difficulties in locating suitable datasets. Dataset Studio is a comprehensive platform designed to support AI engineers in the creation and optimization of datasets. This project offers a diverse range of services that encompass data collection, data augmentation, and data classification. As a result, this software empowers engineers by automatically generating structured data through the utilization of advanced tools and AI techniques. By automating the laborious tasks of manual data collection and extraction, Dataset Studio effectively streamlines the workflow for AI engineers, enabling them to save valuable time and focus on the more intricate aspects of dataset development and refinement.
Data-Driven Fault Detection and Reasoning for Industrial Monitoring
Assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring.
Databases, Information Systems, and Peer-to-Peer Computing ; International Workshops, DBISP2P 2005/2006, Trondheim, Norway, August 28-29, 2006, Revised Selected Papers
The P2P paradigm lends itself to constructing large-scale, complex, adaptive, autonomous and heterogeneous database and information systems, endowed with clearly specified and difierential capabilities to negotiate, bargain, coordinate and self-organize the information exchanges in large-scale networks. This vision will have a radical impact on the structure of complex organizations (business, sci- tific or otherwise) and on the emergence and the formation of social communities, and on how the information is organized and processed. The P2P information paradigm naturally encompasses static and wireless connectivity and static and mobile architectures. Wireless connectivity combined with the increasingly small and powerful mobile devices and sensors poses new challenges as well as opp- tunities to the database community. Information becomes ubiquitous, highly distributed and accessible anywhere and at any time over highly dynamic, - stable networks with very severe constraints on the information management and processing capabilities.
Databases, information systems, and peer-to-peer computing ; 2nd international workshop, DBISP2P 2004, Toronto, Canada, August 29-30, 2004, revised selected papers
Peer-to-peer (P2P) paradigm lends itself to constructing large-scale complex, adaptive, - tonomous and heterogeneous database and information systems, endowed with clearly speci?ed and di?erential capabilities to negotiate, bargain, coordinate, and self-organize the information exchanges in large-scale networks. This vision will have a radical impact on the structure of complex organizations (business, scienti?c, or otherwise) and on the emergence and the formation of social c- munities, and on how the information is organized and processed. The P2P information paradigm naturally encompasses static and wireless connectivity, and static and mobile architectures. Wireless connectivity c- bined with the increasingly small and powerful mobile devices and sensors pose new challenges to as well as opportunities for the database community. Inf- mation becomes ubiquitous, highly distributed and accessible anywhere and at any time over highly dynamic, unstable networks with very severe constraints on the information management and processing capabilities.
Databases in networked information systems; 4th international workshop, DNIS 2005, Aizu-Wakamatsu, Japan, March 28-30, 2005, Proceedings
This book constitutes the refereed proceedings of the 4th International Workshop on Databases in Networked Information Systems, DNIS 2005, held in Aizu-Wakamatsu, Japan in March 2005. The 17 revised full papers presented together with 8 invited papers were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on information interchange and management systems, Web data management systems, networked information systems applications, and networked information systems implementations.
Databases in Networked Information Systems ; 5th International Workshop, DNIS 2007, Aizu-Wakamatsu, Japan, October 17-19, 2007, Proceedings
This book Is focusing on data semantics and infrastructure for information management and interchange, the papers are organized in topical sections on geospatial decision-making, Web data management systems, infrastructure of networked information systems, and Web query and web mining systems.
Databases and information systems ; 14th International Baltic Conference, DB&IS 2020, Tallinn, Estonia, June 16–19, 2020, Proceedings
This book constitutes the refereed proceedings of the 14th International Baltic Conference on Databases and Information Systems, DB&IS 2020, held in Tallinn, Estonia, in June 2020.* The 22 revised papers presented were carefully reviewed and selected from 52 submissions. The papers are centered around topics like architectures and quality of information systems, artificial intelligence in information systems, data and knowledge engineering, enterprise and information systems engineering, security of information systems.
Database Systems for Advanced Applications ; Vol. 3882 ; 11th International Conference, DASFAA 2006, Singapore, April 12-15, 2006, Proceedings
This book constitutes the refereed proceedings of the 11th International Conference on Database Systems for Advanced Applications, DASFAA 2006, held in Singapore in April 2006.
