Legal Challenges of a Transformation from a Product-Centric to a Service-Centric Business Model and Procedures
Recent Advances in Neuromorphic Computing
Artificial Intelligence (AI) is a transformative technology that reshapes our daily lives. Machine Learning (ML), the engine of such a revolution, empowers computers to learn from data, driving innovation in areas such as medicine, robotics, and smart cities through edge applications. These applications bring AI processing closer to the data source, enabling real-time insights and decisions. This evolution is fueled by advancements in hardware and architecture: (1) neuromorphic computing promises unparalleled efficiency; (2) in-memory computing eliminates data access bottlenecks, while emerging memory materials offer denser, faster, and more energy-efficient storage. Looking ahead, AI promises even more profound changes. For instance, explainable AI will make decision-making more transparent, and truly autonomous systems will adapt to unforeseen circumstances. Last but not least, the convergence of AI with quantum computing could unlock entirely new possibilities. This journey showcases a deep understanding of both the theoretical foundations and practical applications of AI. It also demands careful consideration of ethical implications and a commitment to responsible development, ensuring that AI benefits all of humanity.
Why Machines Learn
A rich, narrative explanation of the mathematics that has brought us machine learning and the ongoing explosion of artificial intelligence Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumor is cancerous, or deciding if someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics--the study of genomes, extrasolar planets, even the intricacies of quantum systems. And all this before large language models such as ChatGPT came on the scene. We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning, while suggesting intriguing links between artificial and natural intelligence. Might the same math underpin them both? As Ananthaswamy resonantly concludes, to make safe and effective use of artificial intelligence, we need to understand its profound capabilities and limitations, the clues to which lie in the math that makes machine learning possible. In a brand-new afterword exclusively in the paperback edition, Ananthaswamy dives into the Transformer architecture that makes large language models like ChatGPT possible and points to groundbreaking future directions enabled by the technology.
Image Processing and Computer Vision Masterclass with Python
Image processing and computer vision technologies, combined with the rapid advancements in generative AI, have become foundational to many modern applications. As visual data continues to grow exponentially, the ability to analyze, interpret, and generate images using advanced algorithms and AI is more critical than ever for driving innovation across industries.This book provides a thorough exploration of advanced techniques and practical implementations in the field of computer vision. This book offers a problem-oriented approach that bridges traditional image processing with modern machine learning and generative AI methods. ​​This new edition significantly expands into specialized domains with medical imaging applications using professional libraries like pydicom, ITK, and nnUNet for clinical diagnosis, including COVID-19 detection and brain tumor segmentation, plus remote sensing analysis with satellite processing.By the end of this book, readers will have developed strong practical skills in both classical and cutting-edge image processing and computer vision techniques, empowered to confidently design, implement, and adapt solutions across a wide range of real-world applications. They will emerge with a deep understanding of theory, hands-on coding experience, and the ability to leverage AI and generative models to push the boundaries of visual computing. WHAT YOU WILL LEARN● Restore and enhance images using classical and deep learning methods.● Segment images with advanced clustering and neural network techniques.● Extract and match features for image alignment and recognition.● Build and train image classifiers with ML and AI.● Learn advanced restoration and inpainting techniques using cutting-edge deep learning models. ● Explore specialized domain expertise in medical imaging applications using professional libraries. WHO THIS BOOK IS FORThis book is ideal for undergraduate and graduate students, researchers, and professionals in computer vision, image processing, and AI. It also serves computer vision engineers, image analysts, data scientists, software engineers, and industry practitioners seeking practical, hands-on expertise using Python.
Automated Deduction - Cade 30
This open access book constitutes the proceedings of the 30th International Conference on Automated Deduction, CADE 30, which took place in Stuttgart, Germany, during July 2025. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience. The 33 full papers and 4 short papers included in these proceedings were carefully reviewed and selected from 87 submissions. They were organized in topical sections on SMT; rewriting; formalizations in Isabelle/HOL; calculi; machine learning for automated deduction; model checking and quantifier elimination; saturation; equational reasoning; non-classical logics; and SAT.
Evolving Compact Decision Rule Sets
With the increased proliferation of computing equipment, there has been a corresponding explosion in the number and size of databases. Although a great deal of time and e ort is spent building and maintaining these databases, it is nonetheless rare that this valuable resource is exploited to its fullest. The principle reason for this paradox isthat many organizations lack the insight and/or expertise to e ectively translate this information into usable knowledge. While data mining technology holds the promise of automatically extracting useful patterns (such as decision rules) from data, this potential has yet to be realized. One of the major technical impediments is that the current generation of data mining tools produce decision rule sets that are very accurate, but extremely complex and difficult to interpret. As a result, there is a clear need for methods that yield decision rule sets that are both accurate and compact. The development of the Genetic Rule and Classi er Construction Environment (GRaCCE) is proposed as an alternative to existing decision rule induction (DRI) algorithms. GRaCCE is a multi-phase algorithm which harnesses the power of evolutionary search to mine classi cation rules from data. These rules are based on piece-wise linear estimates of the Bayes decision boundary within a winnowed subset of the data.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Coalition Formation Under Uncertainty
Many multiagent systems require allocation of agents to tasks in order to ensure successful task execution. Most systems that perform this allocation assume that the quantity of agents needed for a task is known beforehand. Coalition formation approaches relax this assumption, allowing multiple agents to be dynamically assigned. Unfortunately, many current approaches to coalition formation lack provisions for uncertainty. This prevents application of coalition formation techniques to complex domains, such as real-world robotic systems and agent domains where full state knowledge is not available. Those that do handle uncertainty have no ability to handle dynamic addition or removal of agents from the collective and they constrain the environment to limit the sources of uncertainty.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Feature Extraction Using Principal and Independent Component Analysis aor Hyperspectral Imagery
Hyperspectral imagery (HSI) analysis is frequently employed by the Department of Defense for the purpose of classifying objects within an image as a form of target detection. In this research a robust Two-Phase Filtering Independent Component Analysis (ICA) Target Detection Method is proposed and validated. This new method resolves two main challenges encountered when implementing target detection methods using ICA, a high order statistics feature extraction (FE) method. The first challenge is the high computational demand imposed by the large volume of data associated with HSI during the FE process. To alleviate the effort required for ICA data processing, principal component analysis (PCA), a classical second order statistics method, is used for data reduction. Furthermore, the performance of using PCA under classification is compared against recently developed supervised FE techniques. The second challenge arises during the feature selection (FS) phase after the statistically independent components have been extracted. Current ICA target FS techniques have shown to be either unreliable or require significant user-intervention. A reliable FS process is essential in automating the target detection process. This proposed method uses ICA to extract independent features from the retained principal components, and is followed by an unsupervised target FS with a two-phase filtering process using kurtosis and mean silhouette values. This method achieved promising results when tested against a wide range of benchmark images.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Realtime Color Stereovision Processing
Recent developments in aviation have made micro air vehicles (MAVs) a reality. These featherweight palm-sized radio-controlled flying saucers embody the future of air-to-ground combat. No one has ever successfully implemented an autonomous control system for MAVs. Because MAVs are physically small with limited energy supplies, video signals offer superiority over radar for navigational applications. This research takes a step forward in realtime machine vision processing. It investigates techniques for implementing a realtime stereovision processing system using two miniature color cameras. The effects of poor-quality optics are overcome by a robust algorithm, which operates in realtime and achieves frame rates up to 10 fps in ideal conditions. The vision system implements innovative work in the following five areas of vision processing: fast image registration preprocessing, object detection, feature correspondence, distortion-compensated ranging, and multiscale nominal frequency-based object recognition. Results indicate that the system can provide adequate obstacle avoidance feedback for autonomous vehicle control. However, typical relative position errors are about 10%--to high for surveillance applications. The range of operation is also limited to between 6 - 30m. The root of this limitation is imprecise feature correspondence: with perfect feature correspondence the range would extend to between 0.5 - 30m. Stereo camera separation limits the near range, while optical resolution limits the far range. Image frame sizes are 160x120 pixels. Increasing this size will improve far range characteristics but will also decrease frame rate.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Evolving Compact Decision Rule Sets
With the increased proliferation of computing equipment, there has been a corresponding explosion in the number and size of databases. Although a great deal of time and e ort is spent building and maintaining these databases, it is nonetheless rare that this valuable resource is exploited to its fullest. The principle reason for this paradox isthat many organizations lack the insight and/or expertise to e ectively translate this information into usable knowledge. While data mining technology holds the promise of automatically extracting useful patterns (such as decision rules) from data, this potential has yet to be realized. One of the major technical impediments is that the current generation of data mining tools produce decision rule sets that are very accurate, but extremely complex and difficult to interpret. As a result, there is a clear need for methods that yield decision rule sets that are both accurate and compact. The development of the Genetic Rule and Classi er Construction Environment (GRaCCE) is proposed as an alternative to existing decision rule induction (DRI) algorithms. GRaCCE is a multi-phase algorithm which harnesses the power of evolutionary search to mine classi cation rules from data. These rules are based on piece-wise linear estimates of the Bayes decision boundary within a winnowed subset of the data.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Robot Localizationusing Visualimage Mapping
One critical step in providing the Air Force the capability to explore unknown environments is for an autonomous agent to determine its location. The calculation of the robot's pose is an optimization problem making use of the robot's internal navigation sensors and data fusion of range sensor readings in calculating the most likely pose. This data fusion process requires the simultaneous generation of a map which the autonomous vehicle can then use for obstacle avoidance, communication with other agents in the same environment, and target location. Our solution entails mounting a Class 1 laser to an ERS-7 AIBO. The laser projects a horizontal line on obstacles in the AIBO camera's field of view. Range readings are determined by capturing and processing multiple image frames, resolving the laser line to the horizon, and extracting distance information to each obstacle. This range data is then used in conjunction with mapping and localization software to accurately navigate the AIBO.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Multi-Robot Fastslam for Large Domains
For a robot to build a map of its surrounding area it must have accurate position information within the area and to obtain the accurate position information within the area, the robot need to have an accurate map of the area. This circular problem is the Simultaneous Localization and Mapping (SLAM) problem. An efficient algorithm to solve it is FastSLAM, which is based on the Rao-Blackwellized particle filter. FastSLAM solves the SLAM problem for single-robot mapping using particles to represent the posterior of the robot pose and the map. Each particle of the filter possesses its own global map which is likely to be a grid map. The memory space required for these entire maps pose a serious limitation to the algorithm's capability when the problem space is large. In addition this problem will only get worse if the algorithm is adapted to a multirobot mapping. This thesis presents an alternate mapping algorithm that extends this single-robot FastSLAM algorithm to a multi-robot mapping algorithm that uses Absolute Space Representations to represent the world.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning
This research merges the hierarchical reinforcement learning (HRL) domain and the ant colony optimization (ACO) domain. The merger produces a HRL ACO algorithm capable of generating solutions for both domains. This research also provides two specific implementations of the new algorithm: the first a modification to Dietterich's MAXQ-Q HRL algorithm, the second a hierarchical ACO algorithm. These implementations generate faster results, with little to no significant change in the quality of solutions for the tested problem domains. The application of ACO to the MAXQ-Q algorithm replaces the reinforcement learning, Q-learning and SARSA, with the modified ant colony optimization method, Ant-Q. This algorithm, MAXQ-AntQ, converges to solutions not significantly different from MAXQ-Q in 88% of the time. This research then transfers HRL techniques to the ACO domain and traveling salesman problem (TSP). To apply HRL to ACO, a hierarchy must be created for the TSP. A data clustering algorithm creates these subtasks, with an ACO algorithm to solve the individual and complete problems. This research tests two clustering algorithms, k-means and G-means.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Artificial Media
A groundbreaking exploration of the evolving relationship between the fields of artificial intelligence and creativity studies, Artificial Media charts the course of a transformative path toward hybrid methodologies involving computing and human-centric approaches. Scholars and practitioners from leading research centers in South America, Asia and Europe delve into theoretical and philosophical frameworks, practical deployments and data-based critical analyses of artificial-media initiatives that reconfigure authorship and collaboration. Co-creation, collective memory, and situated-knowledge practices are featured in multiple hands-on examples of technological design, music, visual-arts, journalistic and educational projects that address the ethical and social implications of generative techniques. Through an interdisciplinary lens, this collection, projects a nuanced panorama of both the remarkable results and the complex challenges of emerging artificial-media methods, offering practical insights for anyone seeking to engage with the future of creativity in the age of autonomous machines.
Artificial Intelligence Security and Safety
This book proposes the architecture of artificial intelligence (AI) security and safety, discusses the topics about AI for security, AI security and AI safety, and makes an in-depth study on the ethical code of AI security and safety. Meanwhile, this book makes a detailed analysis of "artificial intelligence actant" (AIA) concept and its possible security problems, proposes the solutions for the AIA safely hoop, and provides the assessment and detection methods for AIA. Finally, this book discusses the AI cutting-edge technologies, as well as the future development trend of AI security and safety. This book is suitable for researchers, practitioners, regulators and enthusiasts in the field of AI, cyberspace security, etc.
Pattern Recognition and Image Analysis
The two volume set LNCS 15937 + 15938 constitutes the proceedings of the 12th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2025, which took place in Coimbra, Portugal, during June 30-July 3, 2025. The 67 full papers included in the proceedings were carefully reviewed and selected from 115 submissions. They were organized in topical sections as follows: Part I: Computer vision; faces, body, fingerprints and biometrics; machine and deep learning; explainability, bias and fairness in DL; Part II: Natural language processing; biomedical applications; and other applications.
Tactile Robotics
Tactile Robotics structures and unifies the information processing of tactile data--not only for extracting object property but also for controller computation. This book systematically introduces tactile sensors, perception, and control, providing readers with no prior background with a better sense and knowledge of robotics and machine learning and helping users understand the concept of tactile robots and their various applications for use in real-world scenarios.
Convergence of Artificial Intelligence, Machine Learning, and the Internet of Things in Industry 4.0 Applications
Information Technology and Systems
This book comprises papers written in English and accepted for presentation and discussion at the 2025 International Conference on Information Technology & Systems (ICITS'25), held at Instituto Polit矇cnico Nacional (IPN), Mexico City, Mexico, from January 22 to 24, 2025. ICITS'25 serves as a global forum for researchers and practitioners to present and discuss recent findings, innovations, current trends, professional experiences, and challenges in modern information technology and systems research, along with their technological developments and applications. The main topics covered include: Information and Knowledge Management; Organizational Models and Information Systems; Software and Systems Modeling; Software Systems, Architectures, Applications, and Tools; Multimedia Systems and Applications; Computer Networks, Mobility, and Pervasive Systems; Intelligent and Decision Support Systems; Big Data Analytics and Applications; Human-Computer Interaction; Ethics, Computers, and Security; Health Informatics; Information Technologies in Education; Media, Applied Technology, and Communication. The primary audience for this book includes postgraduate students and researchers in the field of Information Systems and Technologies. The secondary audience consists of undergraduate students and professionals working in related domains.
Computer Vision and Image Processing
The Six-volume proceedings set CCIS 2473 and 2478 constitutes the refereed proceedings of the 9th International Conference on Computer Vision and Image Processing, CVIP 2024, held in Chennai, India, during December 19-21, 2024. The 178 full papers presented were carefully reviewed and selected from 647 submissions.The papers focus on various important and emerging topics in image processing, computer vision applications, deep learning, and machine learning techniques in the domain.
Digital Twins
This book centres on the topic of digital twins for superior healthcare decision support, as access is enabled to large volumes of multi-dimensional data such as patient's electronic medical records, medical scans, and data.
Intelligent Business Analytics
The book provides comprehensive and in-depth exploration of how soft computing techniques can be applied in the domain of business analytics. The book shows these techniques can be leveraged to extract valuable insights from vast and complex datasets to enable businesses to make data-driven decisions for improve competitive advantage.
The Definitive Game Narrative Guide
The Definitive Game Narrative Guide is the ultimate start and end point for storytelling in video games. Whether you're an aspiring writer or a seasoned game developer, this book offers an in-depth, comprehensive look at the entire narrative process.
Designing Virtual Worlds Volume I
Designing Virtual Worlds stands as the most comprehensive examination of virtual-world design ever written. This seminal work is a tour de force, remarkable for its intellectual breadth, encompassing the literary, economic, sociological, psychological, physical, technological, and ethical foundations of virtual worlds.
Leveraging Artificial Intelligence in Cloud, Edge, Fog and Mobile Computing
In an era defined by rapid technological advancements, the convergence of artificial intelligence (AI) with cloud, edge, fog, and mobile computing is transforming the landscape of computing and data processing. These emerging technologies are not only enhancing computational capabilities but also paving the way for innovative applications across diverse industries, from healthcare and finance to transportation and entertainment.Leveraging Artificial Intelligence in Cloud, Edge, Fog and Mobile Computing explores the symbiotic relationship between AI and these computing paradigms. As AI continues to evolve, its integration with cloud, edge, fog, and mobile computing platforms is unlocking new potentials and driving efficiencies and enabling real-time, intelligent decision-making processes.The book begins with an in-depth examination of the foundational principles of cloud, edge, fog, and mobile computing, followed by a detailed analysis of how AI technologies are being embedded within these frameworks. It then delves into the unique advantages and challenges of each paradigm, highlighting their roles in facilitating seamless, decentralized data processing and enhancing user experiences.The book is structured to provide a comprehensive understanding of the current state and future directions of AI in these computing environments.The book is intended to serve as a resource and inspiration for those seeking to explore the vast potential of AI in the realms of cloud, edge, fog, and mobile computing. Its goal is to spark new ideas, foster innovation, and contribute to the ongoing dialogue on the future of intelligent computing.
Pattern Recognition and Image Analysis
The two volume set LNCS 15937 + 15938 constitutes the proceedings of the 12th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2025, which took place in Coimbra, Portugal, during June 30-July 3, 2025. The 67 full papers included in the proceedings were carefully reviewed and selected from 115 submissions. They were organized in topical sections as follows: Part I: Computer vision; faces, body, fingerprints and biometrics; machine and deep learning; explainability, bias and fairness in DL; Part II: Natural language processing; biomedical applications; and other applications.
Intelligent and Fuzzy Systems
Artificial Intelligence in Human-Centric, Resilient & Sustainable Industries This book focuses on benefiting artificial intelligent tools in our business and social life under emerging conditions. Human-centric, resilient, and sustainable industries are built on ideals like human-centricity, ecological advantages, or social benefits. The mission of human-centric artificial intelligence is to improve people's lives by offering solutions that boost productivity, accessibility to resources, security, well-being, and general quality of life. The latest intelligent methods and techniques on human-centric, resilient, and sustainable industries are introduced by theory and applications. This book covers the chapters of world-wide known experts on machine learning, medical image processing, process intelligence, process mining, and others. The intended readers are intelligent systems researchers, lecturers, M.Sc. and Ph.D. students trying to develop approaches giving human needs, values, and viewpoints top priority through artificial intelligent systems.
Advanced Smart Information and Communication Technology and Systems
Evaluating the Performance of Multiple Classifier System
Receiver Operating Characteristic (ROC) curve, which graphs the trade-off between the conditional probabilities of an MCS in which Boolean rules are used to combine individual decisions. The method required performance data similar to the data available in the ROC curves for the entire system. A consequence of this result is that one can save time and money by effectively evaluating the performance of an MCS without performing experiments.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
The Generalizability of Private Sector Research on Software Project Management in Two USAF Organizations
Project managers typically set three success criteria for their projects: meet specifications, be on time, and be on budget. However, software projects frequently fail to meet these criteria. Software engineers, acquisition officers, and project managers have all studied this issue and made recommendations for achieving success. But most of this research in peer reviewed journals has focused on the private sector. Researchers have also identified software acquisitions as one of the major differences between the private sector and public sector MIS. This indicates that the elements for a successful software project in the public sector may be different from the private sector. Private sector project success depends on many elements. Three of them are user interaction with the project's development, critical success factors, and how the project manager prioritizes the traditional success criteria. High user interaction causes high customer satisfaction, even when the traditional success criteria are not completely met. Critical success factors are those factors a project manager must properly handle to avoid failure. And priorities influence which success criteria the project manager will most likely succeed in meeting.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Social Networking Website Users and Privacy Concerns
Social networking websites are the fastest growing entity on the Internet. Users of social networking websites post personal information and pictures on these websites. Privacy and social networking websites has been previously studied, however, since those studies were conducted the rules for those websites have changed dramatically. A mixed methods approach was used in this study to examine what privacy concerns users of social networking websites have, whether it's regarding information on their accounts or the pictures they have posted. This study also considered if there were common personality traits present in people with those concerns. A comparison of user preferences between MySpace and Facebook was also conducted. Quantitative data in the form of survey information was used in addition to qualitative data gathered from semistructured interviews. This study supports that Social Desirability Bias was correlated with a user being selective of what pictures were displayed on social networking website accounts. Few users expressed a preference for one social networking website over the other. Over half of the participants did express concern for their privacy on social networking website accounts, but there were no personality factors that showed to be predictive of that concern.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
They Tried to Warn Us
They warned us. Not in slogans. Not in press releases. Not in Twitter threads or TED Talks.They warned us in books, essays, interviews, and thought experiments. They warned us with metaphors and manifestos, parables and polemics. And for the most part, we ignored them.This book is about those voices. They came from across centuries and continents: poets, scientists, philosophers, engineers, journalists, prophets. Some were household names. Others were dismissed, marginalized, or forgotten. What united them wasn't ideology or discipline, but vision. Each of them looked beyond the surface of their time - and saw the future rushing toward us. And then they tried to stop it.The PremiseThey Tried to Warn Us began as a podcast (which you can access at: https: //open.spotify.com/show/3IlsQV7KXuaseN1d3n7Hix). A thought experiment. What if we could bring back the thinkers who saw what was coming - and ask them what they make of our world today?
Taking the High Ground
Cloud computing offers tremendous opportunities for private industry, governments, and even individuals to access massive amounts of computational resources on-demand at very low cost. Recent advancements in bandwidth availability, virtualization technologies, distributed programming paradigms, security services and general public awareness havecontributed to this new business model for employing information technology (IT) resources. IT managers face tough decisions as they attempt to balance the pros and cons of integrating commercial cloud computing into their existing IT architectures. On one hand, cloud computing provides on-demand scalability, reduces capital and operational expenses, decreases barriers to entry, and enables organizations to refocus on core competencies rather than on IT expertise.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Using Upper Layer Weights to Efficiently Construct and Train Feedforward Neural Networks Executing Backpropagation
Feed-forward neural networks executing back propagation are a common tool for regression and pattern recognition problems. These types of neural networks can adjust themselves to data without any prior knowledge of the input data. Feed-forward neural networks with a hidden layer can approximate any function with arbitrary accuracy. In this research, the upper layer weights of the neural network structure are used to determine an effective middle layer structure and when to terminate training. By combining these two techniques with signal-to-noise ratio feature selection, a process is created to construct an efficient neural network structure. The results of this research show that for data sets tested thus far, these methods yield efficient neural network structure in minimal training time. Data sets used include an XOR data set, Fisher's Iris problem, a financial industry data set, among others.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Improved Multispectral Skin Detection and Its Application to Search Space Reduction for Dismount Detection Based on Histograms of Oriented Gradients
Due to the general shift from conventional warfare to terrorism and urban warfare by enemies of the United States in the late 20th Century, locating and tracking individuals of interest have become critically important. Dismount detection and tracking are vital to provide security and intelligence in both combat and homeland defense scenarios including base defense, combat search and rescue (CSAR), and border patrol. This thesis focuses on exploiting recent advances in skin detection research to reliably detect dismounts in a scene. To this end, a signal-plus-noise model is developed to map modeled skin spectra to the imaging response of an arbitrary sensor, enabling an in-depth exploration of multispectral features as they are encountered in the real world for improved skin detection. Knowledge of skin locations within an image is exploited to cue a robust dismount detection algorithm, significantly improving dismount detection performance and efficiencyThis work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Knowledge Base Support for Design and Synthesis of Multi-Agent Systems
Agent Tool is an AFIT-produced, AFOSR-sponsored multi-agent system (MAS) development tool intended for production of MASs that meet military requirements. This research focuses on enabling MAS design and synthesis tools like agent Tool to store, retrieve, and filter persistent, reusable, and reliable agent domain knowledge. This "enabling" is vital if such tools are expected to produce consistent, maintainable, and verifiable agent applications on short timetables. Enabling requires: 1) modeling the agent knowledge domain, 2) designing and employing a persistent knowledge base, and 3) bridging that domain model to the knowledgebase with an extensible domain interchange grammar. The achieved interchange grammar, called Multi-Agent Markup Language (MAML), is presented and shown to be capable of representing MAS design knowledge in a concise and easily parsed form that is readily stored and retrieved in the knowledge base. The selected knowledge base, called the Agent Random-Access Meta-Structure (ARAMS), is shown to support MAML and operate in a distributed environment that permits sharing of agent development knowledge between various tools and tool instances. Tests of MAML and ARAMS with agent Tool are summarized, and related future work suggested.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Explicit Building Block Multiobjective Evolutionary Computation
This dissertation presents principles, techniques, and performance of evolutionary computation optimization methods. Evolutionary computation concepts examined are algorithm convergence, population diversity and sizing, genotype and phenotype partitioning, archiving, BB concepts, parallel evolutionary algorithm (EA) models, robustness, visualization of evolutionary process, and performance in terms of e ectiveness and e ciency. Additional contributions include the extension of explicit BB de nitions to clarify the meanings for good single and multiobjective BBs and a new visualization technique is developed for viewing genotype, phenotype, and the evolutionary process in nding Pareto front vectors. The culmination of this research is explicit BB state-of-the-art MOEA technology based on the MOEA design, BB classi er type assessment, solution evolution visualization, and insight into MOEA test metric validation and usage as applied to the following: test suite, deception, bioinformatics, unmanned vehicle -ight pattern, and digital symbol set design MOPs.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Multi-Objective Mission Route Planning Using Particle Swarm Optimization
The Mission Routing Problem (MRP) is the selection of a vehicle path starting at a point, going through enemy terrain defended by radar sites to get to the target(s) and returning to a safe destination (usually the starting point). The MRP is a three-dimensional, multi-objective path search with constraints such as fuel expenditure, time limits, multi-targets, and radar sites with different levels of risks. It can severely task all the resources (people, hardware, software) of the system trying to compute the possible routes. The nature of the problem can cause operational planning systems to take longer to generate a solution than the time available. Since time is critical in MRP, it is important that a solution is reached within a relatively short time. It is not worth generating the solution if it takes days to calculate since the information may become invalid during that time. Particle Swarm Optimization (PSO) is an Evolutionary Algorithm (EA) technique that tries to find optimal solutions to complex problems using particles that interact with each other. Both Particle Swarm Optimization (PSO) and the Ant System (AS) have been shown to provide good solutions to Traveling Salesman Problem (TSP). PSO_AS is a synthesis of PSO and Ant System (AS). PSO_AS is a new approach for solving the MRP, and it produces good solutions. This thesis presents a new algorithm (PSO_AS) that functions to find the optimal solution by exploring the MRP search space stochastically.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Evaluating the Performance of Multiple Classifier System
Receiver Operating Characteristic (ROC) curve, which graphs the trade-off between the conditional probabilities of an MCS in which Boolean rules are used to combine individual decisions. The method required performance data similar to the data available in the ROC curves for the entire system. A consequence of this result is that one can save time and money by effectively evaluating the performance of an MCS without performing experiments.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Explicit Building Block Multiobjective Evolutionary Computation
This dissertation presents principles, techniques, and performance of evolutionary computation optimization methods. Evolutionary computation concepts examined are algorithm convergence, population diversity and sizing, genotype and phenotype partitioning, archiving, BB concepts, parallel evolutionary algorithm (EA) models, robustness, visualization of evolutionary process, and performance in terms of e ectiveness and e ciency. Additional contributions include the extension of explicit BB de nitions to clarify the meanings for good single and multiobjective BBs and a new visualization technique is developed for viewing genotype, phenotype, and the evolutionary process in nding Pareto front vectors. The culmination of this research is explicit BB state-of-the-art MOEA technology based on the MOEA design, BB classi er type assessment, solution evolution visualization, and insight into MOEA test metric validation and usage as applied to the following: test suite, deception, bioinformatics, unmanned vehicle -ight pattern, and digital symbol set design MOPs.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Multi-Objective Mission Route Planning Using Particle Swarm Optimization
The Mission Routing Problem (MRP) is the selection of a vehicle path starting at a point, going through enemy terrain defended by radar sites to get to the target(s) and returning to a safe destination (usually the starting point). The MRP is a three-dimensional, multi-objective path search with constraints such as fuel expenditure, time limits, multi-targets, and radar sites with different levels of risks. It can severely task all the resources (people, hardware, software) of the system trying to compute the possible routes. The nature of the problem can cause operational planning systems to take longer to generate a solution than the time available. Since time is critical in MRP, it is important that a solution is reached within a relatively short time. It is not worth generating the solution if it takes days to calculate since the information may become invalid during that time. Particle Swarm Optimization (PSO) is an Evolutionary Algorithm (EA) technique that tries to find optimal solutions to complex problems using particles that interact with each other. Both Particle Swarm Optimization (PSO) and the Ant System (AS) have been shown to provide good solutions to Traveling Salesman Problem (TSP). PSO_AS is a synthesis of PSO and Ant System (AS). PSO_AS is a new approach for solving the MRP, and it produces good solutions. This thesis presents a new algorithm (PSO_AS) that functions to find the optimal solution by exploring the MRP search space stochastically.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
The CURE
In The CURE, visionary entrepreneur Gwen Swan unveils a bold new paradigm at the intersection of blockchain, artificial intelligence, and multi-omics healthcare. Chronic and complex diseases-cancer, cardiovascular disorders, genetic and rare conditions, and neurodegenerative illnesses-have long evaded one-size-fits-all therapies. Rising costs, inequitable access, and reactive treatment models leave millions at risk. Swan's groundbreaking thesis is simple but revolutionary: leverage a purpose-built cryptocurrency, CURE, to fund and incentivize truly preventive, precision-tailored miRNA-based treatments, transforming how we pay for, develop, and deliver medicine.CURE is more than a digital token. It is the financial backbone of the CureLogic AI ecosystem, enabling patients, providers, insurers, and institutions to pre-purchase bespoke, microRNA-signature drug plans at a 50% discount-before disease even takes hold. Each token transaction triggers advanced AI diagnostics that analyze a patient's unique genomics, proteomics, blood chemistry, and environmental factors. Within this data framework, CureLogic's proprietary algorithms sift through 55 million miRNA variants and a 25 million-compound drug library to generate individualized treatment protocols down to precise molecular dosages. The result: therapies tailored to each person's biology, powered by transparent, auditable blockchain payments.As both a compelling manifesto and a practical playbook, The CURE charts a path toward a future where preventive precision medicine becomes the norm, not the exception. By uniting cutting-edge AI diagnostics, molecular therapeutics, and blockchain-based incentives, Gwen Swan lays the foundation for eliminating the world's deadliest diseases-and for extending healthy, vibrant lives worldwide. Whether you are a clinician, biotech innovator, insurer, or patient advocate, The CURE offers the vision and the tools to participate in this healthcare revolution.
Feature Extraction Using Principal and Independent Component Analysis aor Hyperspectral Imagery
Hyperspectral imagery (HSI) analysis is frequently employed by the Department of Defense for the purpose of classifying objects within an image as a form of target detection. In this research a robust Two-Phase Filtering Independent Component Analysis (ICA) Target Detection Method is proposed and validated. This new method resolves two main challenges encountered when implementing target detection methods using ICA, a high order statistics feature extraction (FE) method. The first challenge is the high computational demand imposed by the large volume of data associated with HSI during the FE process. To alleviate the effort required for ICA data processing, principal component analysis (PCA), a classical second order statistics method, is used for data reduction. Furthermore, the performance of using PCA under classification is compared against recently developed supervised FE techniques. The second challenge arises during the feature selection (FS) phase after the statistically independent components have been extracted. Current ICA target FS techniques have shown to be either unreliable or require significant user-intervention. A reliable FS process is essential in automating the target detection process. This proposed method uses ICA to extract independent features from the retained principal components, and is followed by an unsupervised target FS with a two-phase filtering process using kurtosis and mean silhouette values. This method achieved promising results when tested against a wide range of benchmark images.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Kernelized Locality Sensitive Hashing for Fast Image Landmark Association
As the concept of war has evolved, navigation in urban environments where GPS may be degraded is increasingly becoming more important. Two existing solutions are vision-aided navigation and vision-based Simultaneous Localization and Mapping (SLAM). The problem, however, is that vision-based navigation techniques can require excessive amounts of memory and increased computational complexity resulting in a decrease in speed. This research focuses on techniques to improve such issues by speeding up and optimizing the data association process in vision-based SLAM. Specifically, this work studies the current methods that algorithms use to associate a current robot pose to that of one previously seen and introduce another method to the image mapping arena for comparison. The current method, kd-trees, is ecient in lower dimensions, but does not narrow the search space enough in higher dimensional datasets. In this research, Kernelized Locality-Sensitive Hashing (KLSH) is implemented to conduct the aforementioned pose associations. Results on KLSH shows that fewer image comparisons are required for location identification than that of other methods. This work can then be extended into a vision-SLAM implementation to subsequently produce a map.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Digital Strategy and Governance in Transformative Technologies
Digital Strategy and Governance in Transformative Technologies offers a comprehensive exploration of how emerging technologies are reshaping business operations, governance structures, and societal interactions.
Digital Strategy and Governance in Transformative Technologies
Digital Strategy and Governance in Transformative Technologies offers a comprehensive exploration of how emerging technologies are reshaping business operations, governance structures, and societal interactions.
Futureshock
This book provides an accessible introduction to leading edge topics today, from AI ethics and cybersecurity, through to augmented realities, virtual interfaces, and much more. This collection of writings by experts in their respective fields, invites the reader to explore new worlds that race towards us.
