الصفحة 87
الصفحة 87
Designing Ubiquitous Information Environments : Socio-Technical Issues and Challenges; IFIP TC8 WG 8.2 International Working Conference, August 1-3, 2005, Cleveland, Ohio, U.S.A.
The book brings in diverse perspectives on ubiquitous information environments, from computer-supported collaborative work, institutional perspective, diffusion of innovation, management, sociology, individual cognition, and software engineering. It also covers a variety of technologies that make up ubiquitous information environments including RFID, wireless grid, GPS, mobile phones, and wireless local area network. The papers cover many contexts of ubiquitous computing including personal use, library, automobile, healthcare, police, professional knowledge work, remote diagnostics of machines, and marketing, attesting to the wide range of potential of ubiquitous information environments.
Designing machine learning systems : An iterative process for production-ready applications
Machine learning systems are both complex and unique. Each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. The book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems
Designing a world-class architecture firm : The people, stories, and strategies behind HOK
Offers exclusive insights into the revolutionary strategies behind one of the top ten largest architecture firms in the world. Written with dashes of memoir by the former CEO, Patrick MacLeamy, this book offers practicing architecture professionals in small to mid-sized firms and other design professionals such as interior designers and urban planners with detailed guidance for reinvigorating company culture, establishing financial metrics, attracting and retaining talent, diversifying services and firm expansion. This book is flavored with dozens of quirky stories from MacLeamy's time at the helm of HOK, and while it is not a design book - MacLeamy offers insights into many of HOK's most iconic projects, including: Smithsonian National Air and Space Museum in Washington, D.C., Oriole Park at Camden Yards in Baltimore, MD, and more
Designing a human future with machines
What is human flourishing in an age of machine intelligence, when many claim that the world's most complex problems can be reduced to narrow technical questions? Does more computing make us more intelligent, or simply more computationally powerful? We need not always resist reduction; our ability to simplify helps us interpret complicated situations. The trick is to know when and how to do so. Against Reduction offers a collection of provocative and illuminating essays that consider different ways of recognizing and addressing the reduction in our approach to artificial intelligence, and ultimately to ourselves.
Design computing and cognition 06 ; 1st ed. ; Proceedings of the 2nd International conference on design computing and cognition
This is the second volume of the new conference series Design Computing and Cognition (DCC) that takes over from and subsumes the successful series Artificial Intelligence in Design (AID) published by Kluwer since 1992. The AID volumes have become standard reference texts for the field. It is expected that the DCC volumes will perform the same role.
Design and Analysis of Learning Classifier Systems : A Probabilistic Approach
This book provides a comprehensive introduction to the design and analysis of Learning Classifier Systems (LCS) from the perspective of machine learning. LCS are a family of methods for handling unsupervised learning, supervised learning and sequential decision tasks by decomposing larger problem spaces into easy-to-handle subproblems.
Deoxynucleoside Analogs in Cancer Therapy
Emerging as an important new volume in the renowned Cancer Drug Discovery and Development™ series, Deoxynucleoside Analogs in Cancer Therapy expertly summarizes the current status of development and application of deoxynucleoside analogs. Authoritative up-to-date reviews are presented by scientists well known in their specific areas and all contributions include valuable sound advice on structure and topics. Organized into several sections, the first part covers general aspects of drug uptake and metabolism and explains how novel technology has enabled a rapid expansion of this field. The second part is concerned with a number of specific drugs including cytarabine, gemcitabine, troxacitabine, clofarabine and Ara-G. The final section covers pharmacokinetics, prodrugs, and specific applications such as radiosensitization, gene therapy, and the use of deoxynucleoside analogs as tracers
Dentofacial Esthetics : From Macro to Micro
Dives deep into dentofacial esthetics and teaches you how to evaluate each patient who walks through your door from the macro to the micro, focusing first on the big picture and then working your way to the minute details in order to treatment plan for the best possible outcome. The author's mantra is that "If you don't see it, you won't treat it," so his goal is to educate dentists and orthodontists about what they should be seeing in order to yield maximally esthetic outcomes, taking into consideration concepts like esthetic balance and smile projection.
Dental fear and anxiety in pediatric patients: Practical strategies to help children cope
A comprehensive guide to pediatric dental fear/anxiety (DFA) and phobia that will provide practitioners with a full understanding of the etiology, prevalence, assessment, and management of these conditions. The coping styles of children when under stress are explored, with discussion of their relevance to the assessment visit and treatment allocation. Practical treatment techniques are comprehensively covered, from non-pharmacological behavioral strategies relevant for children with no or mild DFA to those approaches more appropriate for children with severe DFA/phobia. The importance of the use of language and communication skills to build rapport and allay anxiety is explored. Relaxation and hypnosis techniques are described, with guidance on how to introduce these to patients and their parents/carers. Techniques that help children cope when receiving injections are detailed, including systematic needle desensitization
Dental Care for Children with Special Needs : A Clinical Guide
This concise manual offers best practice guidance on dental treatment of pediatric patients with special health care needs (CSHCN). Readers will find up-to-date information on case-based treatment planning, alternative caries management strategies, the use of behavioral and pharmacological interventions to facilitate delivery of quality treatment, and a team approach to care.
Dental care and oral health during the COVID-19 pandemic
Disseminates relevant knowledge of the recent SARS-Cov-2 pandemic with a focus on related applications in the fields of medicine and dentistry. Different types of manuscripts concerning these topics will be considered, including clinical studies, trials, systematic reviews, prospective studies, and proposals of new protocols or scientific evidence regarding dental clinics and SARS-Cov-2 infections.
Delivery of drugs ; Vol.2 : Expectations and realities of multifunctional drug delivery systems
Examines the formulation of micro-nanosized drug delivery systems and recaps opportunities for using physical methods to improve efficacy via mechano-, electroporation. The book highlights innovative delivery methods like PIPAC, including discussions on the regulatory aspects of complex injectables. Written by a diverse range of international researchers from industry and academia, the chapters examine specific aspects of characterization and manufacturing for pharmaceutical applications as well as regulatory and policy aspects.This book connects formulation scientists, regulatory experts, engineers, clinical experts and regulatory stakeholders.
Defects of Secretion in Cystic Fibrosis
This book brings together physicians, physiologists, and other scientists involved in basic research, from molecular biology to drug design and introduces novel investigative and therapeutic aspects of secretion disorders relevant in cystic fibrosis and related diseases. This book will be of interest to Molecular biologists, physiologists, scientists working in pharmaceutical research and drug developement, physicians and researchers in Cystic fibrosis and related diseases.
Deepfake detection
The rise of large language models (LLMs) and the increasing sophistication of deepfake images have made detecting synthetic content a pressing challenge. Several approaches have been proposed to tackle this problem, including statistical analysis, and machine learning algorithms. In this project, A novel zero-shot approach is proposed that utilizes the power of LLMs to detect fake text. The pre-trained LLM is fine-tuned to enhance its ability to differentiate real and fake text. The approach uses the LLM to detect text by analyzing the log probabilities of the text. For detecting fake images, computer vision algorithms and neural networks are used to analyze facial features. The facial region is cropped and preprocessed and the neural network identifies patterns indicative of synthetic content.
Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety
Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
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 to See : Towards New Foundations of Computer Vision
Topics and features: Presents a curiosity-driven approach, posing questions to stimulate readers to design novel computational models of vision Offers a rethinking of computer vision, arguing for an approach based on vision in nature, versus regarding visual signals as collections of images Provides an interdisciplinary commentary, aiming to unify computer vision, machine learning, human vision, and computational neuroscience Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions.
Deep learning pipeline : Building a deep learning model with TensorFlow
Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets.
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.
