Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, Blue Sky, and Wideaied
This three-volume set CCIS 2590-2592 constitutes poster papers and late breaking results, workshops and tutorials, practitioners, industry and policy track, doctoral consortium, blue sky and wideAIED papers presented at the 26th International Conference on Artificial Intelligence in Education, AIED 2025, held in Palermo, Italy, during July 22-26, 2025. The 72 full papers and 73 short papers (72 of them presented as posters) presented in this book were carefully reviewed and selected from 296 submissions. They are organized in topical sections as follows: Part I: BlueSky; Practitioners, Industry and Policy; WideAIED; Doctoral Consortium. Part II: Late Breaking Results; Part III: Late Breaking Results; Workshops and Tutorials.
Advanced Intelligent Computing Technology and Applications
This 20-volume set LNCS 15842-15861 constitutes - in conjunction with the 4-volume set LNAI 15862-15865 and the 4-volume set LNBI 15866-15869 - the refereed proceedings of the 21st International Conference on Intelligent Computing, ICIC 2025, held in Ningbo, China, during July 26-29, 2025. The total of 1206 regular papers were carefully reviewed and selected from 4032 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications".
Advanced Intelligent Computing Technology and Applications
This 20-volume set LNCS 15842-15861 constitutes - in conjunction with the 4-volume set LNAI 15862-15865 and the 4-volume set LNBI 15866-15869 - the refereed proceedings of the 21st International Conference on Intelligent Computing, ICIC 2025, held in Ningbo, China, during July 26-29, 2025. The total of 1206 regular papers were carefully reviewed and selected from 4032 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications".
Advanced Intelligent Computing Technology and Applications
The 20-volume set LNCS 15842-15861, together with the 4-volume set LNAI 15862-15865 and the 4-volume set LNBI 15866-15869, constitutes the refereed proceedings of the 21st International Conference on Intelligent Computing, ICIC 2025, held in Ningbo, China, during July 26-29, 2025. The 1206 papers presented in these proceedings books were carefully reviewed and selected from 4032 submissions. They deal with emerging and challenging topics in artificial intelligence, machine learning, pattern recognition, bioinformatics, and computational biology.
Ai-Native Software Delivery
AI coding assistants are helping teams create software faster than ever. But to turn that speed into real innovation, organizations must go beyond writing code and deliver software quickly, securely, and reliably.While AI-assisted coding is now mainstream, what happens after the code is written is still catching up. AI-Native Software Delivery is your practical guide to applying AI across the entire delivery lifecycle, from commit to production and beyond.Written for software engineers, DevOps leaders, and tech executives, this book explores how leading teams are using AI to streamline CI/CD, manage cloud costs, strengthen security, and eliminate operational toil. The book also uncovers the risks of brittle automation and shows you how to avoid building systems that don't scale.You'll learn how to: Integrate AI across delivery workflows to accelerate time to valueAvoid common pitfalls of fragmented automation strategiesAdopt DevSecOps principles that scale with your teamApply real-world practices in AIOps, chaos engineering, and SREFuture-proof your delivery with intelligent pipelines and feedback loopsWhether you're evolving a legacy delivery process or designing a new platform, this guide will help you lead your organization into the AI-native future of software delivery.
Implementing and Administering Cisco Solutions 200-301 CCNA Exam Guide - Second Edition
Get exam-ready for the CCNA 200-301 v1.1 certification exam with Cisco experts Glen D. Singh and Neil Anderson using practical labs and focused strategies.Includes mock exams, flashcards, exam tips, and a free eBook PDF with your purchase.Key Features: - Complete coverage of all CCNA 200-301 v1.1 exam objectives aligned with Cisco's official blueprint- Build foundational skills in switching, routing, IP services, security, wireless, and automation- Configure networks with through 30+ hands-on labs using Cisco Packet Tracer scenarios- Test your exam readiness with 2 mocks, 170+ review questions, and detailed explanationsBook Description: Kickstart your networking career with confidence by acing the CCNA exam on your first try. The Cisco Certified Network Associate (CCNA) certification opens doors to high-demand roles in networking and security. This fully updated second edition makes exam success achievable, even if you're just starting out. Aligned with the latest Cisco blueprint, this CCNA 200-301 exam guide combines real-world examples, step-by-step labs, and clear explanations to help you master all six exam domains.You'll build a solid foundation in switching, routing, IP addressing, network services, wireless technologies, security, and automation. Along the way, you'll sharpen your skills with hands-on configuration tasks, visual diagrams, and simulation exercises using Cisco Packet Tracer.Each chapter includes review questions that reflect actual exam difficulty, helping you stay on track and gauge your readiness. You'll also get access to online extras: over 170 practice questions, two full-length mock exams, interactive flashcards, exam tips from Cisco experts, and more than 30 practice labs.From exam strategies to high-demand skills, this guide offers everything you need to get certified, hired, or grow in your network engineering and security administration roles.What You Will Learn: - Understand how switching, routing, and IP addressing work in network environments- Create VLANs and configure static and dynamic routing using Cisco CLI commands- Set up IP services including DHCP, NAT, DNS, and NTP across network devices- Apply wireless settings, security features, and access control to secure networks- Use Cisco Packet Tracer to build, test, and troubleshoot network configurations- Solve realistic practice questions that mirror the actual CCNA 200-301 v1.1 exam formatWho this book is for: This exam guide is for IT professionals looking to advance their network engineering and security administration careers. If you're aiming to earn your Cisco CCNA certification and launch a career as a network security professional, this book is the perfect resource. While no prior knowledge of Cisco technologies is required, a basic understanding of industry-standard networking fundamentals will help you easily grasp the topics covered.Table of Contents- Introduction to Networking- Getting Started with Cisco IOS Devices- Network Architectures and Physical Infrastructure- IPv4 and IPv6 Addresses- Practical Subnetting- Wireless Architectures and Virtualization- Implementing VLANs and Interswitch Connectivity- EtherChannels and Layer 2 Discovery Protocols- Understanding and Configuring Spanning Tree- Interpreting Routing Components- Understanding Static and Dynamic Routing- Network Address Translation- Network Services and IP Operations- Exploring Network Security- Device Access Controls and VPNs- Implementing Access Controls Lists (ACLs)- Implementing Layer 2 and Wireless Security- Network Automation and Programmability Techniques
Building Business-Ready Generative AI Systems
Supercharge your business with context-aware AI controllers, adaptive agents, multimodal reasoning functionality, neuroscientific memory systems, and flexible handler mechanisms that integrate the emerging generative AI models.Get with your book: PDF copy, AI Assistant, and Next-Gen Reader free.Key Features: - Build an adaptive, context-aware AI controller with advanced memory strategies- Enhance GenAISys with multi-domain, multimodal reasoning capabilities and Chain of Thought (CoT)- Seamlessly integrate cutting-edge OpenAI and DeepSeek models as you see fitBook Description: Standalone LLMs no longer deliver sufficient business value on their own. This guide moves beyond basic chatbots, showing you how to build agentic, ChatGPT-grade systems capable of sophisticated semantic and sentiment analysis, powered by context engineering.You'll design AI controller architectures with multi-user memory retention to dynamically adapt your system to diverse user and system inputs. You'll architect a Retrieval-Augmented Generation system with Pinecone to combine instruction-driven scenarios. Through context engineering, you'll minimize token usage, maximize response quality, and create systems that reason across complex tasks with precision. You'll enhance your system's intelligence with multimodal capabilities-image generation, voice interactions, and machine-driven reasoning-leveraging Chain-of-Thought and context chaining to address cross-domain automation challenges. You'll also integrate OpenAI's suite and DeepSeek-R1 without disrupting your existing GenAISys ecosystem.With context engineering as the backbone, every step becomes a deliberate act of shaping model behavior. Your GenAISys will apply neuroscience-inspired insights to marketing strategies, predict human mobility, integrate smoothly into human workflows, and connect to live external data, all wrapped in a polished, investor-ready interface.What You Will Learn: - Implement an AI controller with a conversation AI agent and orchestrator at its core- Build contextual awareness with short-term, long-term, and cross-session memory- Design cross-domain automation with multimodal reasoning, image generation, and voice features- Expand a CoT agent by integrating consumer-memory understanding- Integrate cutting-edge models of your choice without disrupting your existing GenAISys- Connect to real-time external data while blocking security breachesWho this book is for: This book is for AI and Machine Learning Engineers seeking to enhance their understanding of Generative AI and its enterprise applications. It will particularly benefit those interested in building AI agents, creating advanced orchestration systems, and leveraging AI for automation in marketing, production, and logistics. Software architects and enterprise developers looking to build scalable AI-driven systems will also find immense value in this guide. No prior superintelligence experience is necessary, but familiarity with AI concepts is recommended.Table of Contents- Defining a Business-Ready Generative AI System- Building the Generative AI Controller- Integrating Dynamic RAG into the GenAISys- Building the AI Controller Orchestration Interface- Adding Multimodal, Multifunctional Reasoning with Chain of Thought- Reasoning E-Marketing AI Agents- Enhancing the GenAISys with DeepSeek- GenAISys for Trajectory Simulation and Prediction- Upgrading the GenAISys with Data Security and Moderation for Customer Service- Presenting Your Business-Ready Generative AI System
5000 Years Of Science
What is science, really-and where is it taking us?In this sweeping journey across five millennia, 5000 Years of Science traces the rise of human understanding, from ancient Mesopotamian star charts to cutting-edge theories of artificial intelligence and quantum consciousness. This is not just a history of inventions and discoveries-it's a bold reinterpretation of science as the evolving expression of our deepest survival instincts, cognitive tools, and existential questions.Explore how early civilisations laid the mathematical and astronomical foundations of science. Witness the intellectual explosions of the Scientific Renaissance and Enlightenment. Follow the dramatic revolutions of chemistry, physics, biology, and computing. And then step into the future-with explorations of AI, neuroscience, quantum biology, and the search for a unified theory of consciousness.A Journey Through Human Discovery - and a Radical New Idea That May Change Everything We Know About the MindWhat if consciousness isn't a mystery... but an evolved tool for survival?In this bold and sweeping book, 5000 Years of Science traces the story of human discovery-from the fires of early civilisation to the quantum codes of artificial intelligence. With every chapter, the reader journeys through astronomy, biology, physics, and psychology-culminating in a groundbreaking theory that redefines what it means to be conscious.Introducing Affective Survival Theory (AST)-a new scientific framework that argues feeling is not a byproduct of thought, but the engine of survival. Pain, pleasure, fear, hunger, love: these are not abstract states, but evolution's way of tagging experience with meaning. From the reflex of a single cell to the awe of cosmic wonder, AST reveals how consciousness evolved not to think first-but to feel and adapt.���� Inside This Book: The 5000-year timeline of science, from ancient astronomy to AIThe rise of artificial intelligence and the limits of machine mindsWhy consciousness evolved through emotionally charged survivalHow AST compares to other leading theories (IIT, GWT, Orch OR, etc.)A reframe of the hard problem of consciousness into the hard advantage of feelingThe emotional roots of memory, imagination, reasoning, and transcendenceWhether you're a science lover, philosopher, futurist, or simply curious about the nature of the mind, 5000 Years of Science will challenge your assumptions, ignite your curiosity, and change how you see yourself-and consciousness itself.This is more than a book. It's the next chapter in the human story.
Computer Aided Verification
This open access 4-volume set constitutes the proceedings of the 37th International Conference on Computer Aided Verification, CAV 2025, held in Zagreb, Croatia, in July 23-25, 2025. The 51 regular papers presented together 24 tool papers, 4 casestudy papers in these proceedings were carefully reviewed and selected from 305 submissions. The accepted papers cover a wide spectrum of topics, from theoretical results to applications of formal methods. These papers apply or extend formal methods to a wide range of domains such as concurrency, machine learning and neural networks, quantum systems, as well as hybrid and stochastic systems.
Computer Aided Verification
This open access 4-volume set constitutes the proceedings of the 37th International Conference on Computer Aided Verification, CAV 2025, held in Zagreb, Croatia, in July 23-25, 2025. The 51 regular papers presented together 24 tool papers, 4 casestudy papers in these proceedings were carefully reviewed and selected from 305 submissions. The accepted papers cover a wide spectrum of topics, from theoretical results to applications of formal methods. These papers apply or extend formal methods to a wide range of domains such as concurrency, machine learning and neural networks, quantum systems, as well as hybrid and stochastic systems.
Computer Aided Verification
This open access 4-volume set constitutes the proceedings of the 37th International Conference on Computer Aided Verification, CAV 2025, held in Zagreb, Croatia, in July 23-25, 2025. The 51 regular papers presented together 24 tool papers, 4 casestudy papers in these proceedings were carefully reviewed and selected from 305 submissions. The accepted papers cover a wide spectrum of topics, from theoretical results to applications of formal methods. These papers apply or extend formal methods to a wide range of domains such as concurrency, machine learning and neural networks, quantum systems, as well as hybrid and stochastic systems.
A Gentle Introduction to Data, Learning, and Model Order Reduction
Advanced Intelligent Computing Technology and Applications
This 20-volume set LNCS 15842-15861 constitutes - in conjunction with the 4-volume set LNAI 15862-15865 and the 4-volume set LNBI 15866-15869 - the refereed proceedings of the 21st International Conference on Intelligent Computing, ICIC 2025, held in Ningbo, China, during July 26-29, 2025. The total of 1206 regular papers were carefully reviewed and selected from 4032 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications".
Paradata
To make sense of data and use it effectively, it is essential to know where it comes from and how it has been processed and used. This is the domain of paradata, an emerging interdisciplinary field with wide applications. As digital data rapidly accumulates in repositories worldwide, this comprehensive introductory book, the first of its kind, shows how to make that data accessible and reusable. In addition to covering basic concepts of paradata, the book supports practice with coverage of methods for generating, documenting, identifying and managing paradata, including formal metadata, narrative descriptions and qualitative and quantitative backtracking. The book also develops a unifying reference model to help readers contextualise the role of paradata within a wider system of knowledge, practices and processes, and provides a vision for the future of the field. This guide to general principles and practice is ideal for researchers, students and data managers. This title is also available as open access on Cambridge Core.
Trustworthy Multimodal Intelligent Systems for Independent Living
This book is an essential guide for anyone interested in how artificial intelligence can enhance the quality of life for individuals who wish to maintain autonomy in their own homes. The author begins by introducing the reader to AI applications in independent living environments, such as smart assisted homes and AI-driven personalization, and thoughtfully explores the ethical challenges involved. With a strong focus on the intersection of technology and human needs, the book provides a detailed roadmap for building intelligent systems that promote safety, independence, and dignity, especially for elderly or vulnerable populations. The author offers both foundational knowledge and critical discussions around the opportunities and limitations of AI when applied to daily life scenarios. A major strength of the book lies in its thorough examination of multimodal systems. Readers are introduced to a rich array of sensor technologies including wearable devices, environmental sensors, vision-based systems, and sound-based inputs. These components are described not only in terms of their individual functionalities but also in how they interact and fuse data to support complex inference tasks. The text walks the reader through system architectures--centralized and distributed--while emphasizing data fusion, synchronization, and real-time versus batch processing. Through practical examples such as fall detection alerts and activity recognition, the book highlights the engineering challenges and solutions involved in building robust, responsive, and user-accepted assistive systems. Ethical deployment, user engagement, long-term maintenance, and family involvement are all addressed in ways that reflect real-world concerns and user diversity. The book also tackles some of the most pressing topics in AI today: data privacy, explainability, and trust. With an entire section dedicated to synthetic data, it explains how artificial data can be used to train effective models while safeguarding user privacy.
Spoken Language Processing
This book tackles the complexities of spontaneous human dialogues, exploring the challenges of unscripted conversations with their interruptions, overlaps, and disfluencies for conversational AI. It provides a comprehensive exploration of the differences between spontaneous and prescriptive languages, examining the impact of these differences on machine learning models. The author examines the technical aspects of processing spontaneous speech, including automatic speech recognition and transcript engineering, discussing the design and development of AI systems capable of handling the nuances of spontaneous dialogues. Written for researchers in natural language processing and students interested in AI and machine learning applications for spontaneous human communication, this book serves as a guide for understanding the latest advancements in the field and developing more robust and effective Conversational AI systems.
Artificial Life and Evolutionary Computation
This book constitutes revised selected papers from the 18th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2024, which took place in Namur, Belgium, during September 11-13, 2024. The 23 full papers included in this book were carefully reviewed and selected from 40 submissions.The workshop brings together computer scientists, mathematicians, biologists, psychologists, and cognitive scientists to discuss issues related to the original of life, evolution and adaption, collective and social behaviours and other topics related to the development of technological solutions inspired by biological principles.
Neural Information Processing
The sixteen-volume set, CCIS 2282-2297, constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024.The 472 regular papers presented in this proceedings set were carefully reviewed and selected from 1301 submissions. These papers primarily focus on the following areas: Theory and algorithms; Cognitive neurosciences; Human-centered computing; and Applications.
Advanced Intelligent Computing Technology and Applications
The 12-volume set CCIS 2564-2575, together with the 28-volume set LNCS/LNAI/LNBI 15842-15869, constitutes the refereed proceedings of the 21st International Conference on Intelligent Computing, ICIC 2025, held in Ningbo, China, during July 26-29, 2025. The 523 papers presented in these proceedings books were carefully reviewed and selected from 4032 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications".
Neural Information Processing
The sixteen-volume set, CCIS 2282-2297, constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024.The 472 regular papers presented in this proceedings set were carefully reviewed and selected from 1301 submissions. These papers primarily focus on the following areas: Theory and algorithms; Cognitive neurosciences; Human-centered computing; and Applications.
Advanced Intelligent Computing Technology and Applications
This 20-volume set LNCS 15842-15861 constitutes - in conjunction with the 4-volume set LNAI 15862-15865 and the 4-volume set LNBI 15866-15869 - the refereed proceedings of the 21st International Conference on Intelligent Computing, ICIC 2025, held in Ningbo, China, during July 26-29, 2025. The total of 1206 regular papers were carefully reviewed and selected from 4032 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications".
Computer Aided Verification
This open access 4-volume set constitutes the proceedings of the 37th International Conference on Computer Aided Verification, CAV 2025, held in Zagreb, Croatia, in July 23-25, 2025. The 51 regular papers presented together 24 tool papers, 4 casestudy papers in these proceedings were carefully reviewed and selected from 305 submissions. The accepted papers cover a wide spectrum of topics, from theoretical results to applications of formal methods. These papers apply or extend formal methods to a wide range of domains such as concurrency, machine learning and neural networks, quantum systems, as well as hybrid and stochastic systems.
A Beginner's Guide to Generative AI
This book is the essential guide for anyone curious about AI's creative power. In the rapidly evolving landscape of artificial intelligence, generative AI stands out as one of the most transformative technologies of our time. Designed for beginners and requiring no prior knowledge of AI, this book breaks down the fundamentals of generative AI, from text and image generation to the workings of models like ChatGPT and Google Bard. The authors provide step-by-step coverage of the essential concepts and techniques that power generative AI. From the basics of how machines learn to generate text and images, to the intricate workings of models like Transformers, ChatGPT, and Google Bard, readers will gain a solid foundation in AI's most cutting-edge tools. Rather than focusing on a single method, the authors introduce a spectrum of generative modeling techniques, including diffusion models, variational autoencoders, and transformers. This comprehensive exposure ensures readers will be well-prepared to understand and adapt to the rapidly evolving AI landscape. In addition, real-world applications of generative AI across various industries are explored including healthcare innovations, business analytics, and legal technology, and the authors provide practical insights and examples that show how generative AI is revolutionizing these fields.
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.
A Hierarchical Technique for Mechanical Theorem Proving and its Application to Programming Language Design
"A Hierarchical Technique for Mechanical Theorem Proving and its Application to Programming Language Design" explores innovative methods in automated theorem proving, a crucial area within artificial intelligence and computer science. This work delves into a hierarchical approach aimed at enhancing the efficiency and effectiveness of mechanical theorem proving systems. The research focuses on applying these techniques to the design and development of programming languages, suggesting potential advancements in language construction and validation.Authored by Norman Rubin, this study from 1975 provides valuable insights into the intersection of logic, computation, and programming. It remains relevant for researchers and practitioners interested in the historical development of AI and the theoretical foundations of programming language design.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.
Climbing the Tower of Babel
Machine translation of language isn't rocket science. It's even more difficult. Despite revolutionary leaps in hardware, software, and linguistics throughout the 20th century, technology has delivered only rudimentary solutions for a handful of the world's 6800 languages. By 2035, however, advances in computers and artificial intelligence, the convergence of information-based devices, and the maturity of linguistics and programming tools will dissolve barriers to language and bring game-changing transparency to this greatest human challenge. Perfect machine translation will permit instantaneous availability of all languages, fusion with video and imagery data-streams, and unprecedented multi-domain awareness. In short, machine translation will guarantee communication and understanding. Accordingly, the Air Force and the Department of Defense must plan now to integrate this spectacular capability to further a gamut of national security interests.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 Intelligence
Artificial intelligence (AI) is transforming multiple aspects of human life, raising fundamental questions: Is AI truly intelligent? Can it surpass human capabilities? What are its risks and opportunities? In this book, I aim to clarify what AI really is, debunking myths while offering a rigorous, balanced perspective on its impact. Rather than chasing the latest AI trends, I will focus on the core principles that define it, tracing its evolution from early pioneers like Alan Turing to today's advanced systems. AI remains in the realm of narrow intelligence, excelling at specific tasks but far from replicating human cognition. Yet, its ability to process vast data, predict behaviors, and generate creative content is reshaping industries, from healthcare to finance. At the heart of AI's progress is machine learning, particularly neural networks, which rely more on data-driven training than traditional scientific theory. However, this innovation comes with challenges: environmental costs, job market disruptions, ethical dilemmas, and the black box problem--AI's decision-making opacity, which raises concerns about trust and accountability. AI also plays a growing role in global power dynamics, influencing governance, security, and even democracy. Nations leading AI development gain strategic advantages, but without careful regulation, AI could fuel inequality, surveillance, and manipulation. Despite the fears AI evokes, it is neither an existential threat nor a magical solution. My goal is not to celebrate or demonize it but to provide a critical framework for understanding this technological revolution. By fostering awareness, we can shape AI's integration into society in a way that aligns with human values and scientific progress.
Power, Performance, and Perception (P3)
Currently, there are two distinct approaches to assist information technology managers in the successful implementation of office automation software. The first approach resides within the field of usability engineering, while the second approach is derived from the discipline of management information systems (MIS). The usability engineering approach has focused the question, "can users use the system?" while the MIS approach has answered the question, "will users use the system?" However, neither approach has successfully produced conclusive evidence that explains what characteristics facilitate system use as well as influence user acceptance of the system. This study reports on the validity of a new model, entitled the Power, Performance, Perception (P 3 ) model, that links the constructs of usability engineering to user acceptance. For this study, speech recognition software (SRS), selected as the target technology due to its novelty and practical application to office automation software, was used in an experimental setting to validate the P 3 model. As a secondary focus, this research also examined the viability of employing SRS in an Air Force office setting. The results of this study failed to validate the P 3 model. However, an alternate model for predicting user acceptance, the Usability-Acceptance Model, did emerge from the research which showed that the usability metric of user satisfaction can explain 53% of the variance of user intention to use a new technology. Additionally, the results of this study indicate that while users in a typical Air Force office environment would utilize SRS for text processing, the issue of increased productivity bears further examination.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 Feature Extraction, Feature Selection, and Identification Techniques That Create a Fast Unsupervised Hyperspectral Target Detection Algorithm
This research extends the emerging field of hyperspectral image (HSI) target detectors that assume a global linear mixture model (LMM) of HSI and employ independent component analysis (ICA) to unmix HSI images. Via new techniques to fully automate feature extraction, feature selection, and target pixel identification, an autonomous global anomaly detector, AutoGAD, has been developed for potential employment in an operational environment for real-time processing of HSI targets. For dimensionality reduction (initial feature extraction prior to ICA), a geometric solution that effectively approximates the number of distinct spectral signals is presented.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.
AI In Finance
AI in Finance: The Future of Money, Markets, and Investment Starts HereThe financial world is evolving-and artificial intelligence (AI) is at the center of this transformation. From robo-advisors and algorithmic trading to fraud detection and credit scoring, AI in Finance is your essential guide to understanding how intelligent technologies are reshaping every corner of the financial sector.Whether you're an investor, finance professional, student, or entrepreneur, this book provides the knowledge you need to navigate and thrive in an AI-powered economy. Complex concepts are explained in clear, practical terms, so you can take full advantage of this once-in-a-generation revolution in finance.���� What You'll Learn Inside: ���� AI in Banking and Financial Institutions Learn how leading banks and credit unions are using AI to streamline operations, enhance customer service, automate loan approvals, and reduce risk. From natural language chatbots to robotic process automation (RPA), discover how AI is saving billions annually in financial services.���� Predictive Analytics and Forecasting Dive into the power of machine learning models that analyze financial data to predict market trends, customer behavior, and business outcomes. Understand how hedge funds, fintech startups, and asset managers use predictive tools to gain a competitive edge.���� Robo-Advisors and Wealth Management Explore how digital financial advisors provide personalized investment advice using real-time data. Compare platforms like Betterment, Wealthfront, and Schwab Intelligent Portfolios-and learn how AI creates diversified portfolios based on your risk profile.���� Algorithmic Trading and Quantitative Finance Discover how high-frequency trading firms use AI and deep learning to execute trades in milliseconds. Understand the impact of data-driven strategies on global markets-and how even retail investors can harness simplified tools to compete.����️ Fraud Detection and Risk Management Find out how AI is being used to detect financial fraud in real time. From transaction pattern recognition to anomaly detection, AI is improving security across payment systems, insurance claims, and identity verification.���� Credit Scoring and Underwriting Learn how AI models go beyond traditional FICO scores to assess creditworthiness using alternative data like mobile activity, income patterns, and even social media behavior. See how fintech lenders are disrupting old systems.���� AI in Personal Finance & Budgeting Apps Discover how tools like Mint, Cleo, and YNAB use AI to provide users with automated insights, savings plans, and financial coaching. Understand how smart notifications and behavior tracking improve money habits.���� Fintech Startups and Innovation Explore the new wave of fintech companies harnessing AI to solve complex financial problems-like blockchain compliance, micro-investing, decentralized finance (DeFi), and peer-to-peer lending.���� AI Ethics, Bias, and Regulation in Finance Address the concerns around transparency, algorithmic bias, and responsible AI. Learn what financial institutions must consider to maintain ethical standards and comply with emerging regulations.
Multiagent Systems Engineering
This thesis defines a methodology for the creation of multiagent systems, the Multiagent Systems Engineering (MaSE) methodology. The methodology is a key issue in the development of any complex system and there is currently no standard or widely used methodology in the realm of multiagent systems. MaSE to covers the entire software lifecycle, starting from an initial prose specification, and creating a set of formal design documents in a graphical style based on a formal syntax. The final product of MaSE is a diagram describing the deployment of a system of intelligent agents that communicate through structured conversations. MaSE was created with the intention of being supported an automated design tool. The tool built to support MaSE, agent Tool, is a multiagent system development tool for designing and synthesizing complex multiagent systems.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.
You've Got This, Human
YOU'VE GOT THIS, HUMANA No-Nonsense Guide to Mastering AI, Keeping Your Job Safe, and Thriving in the Digital AgeA practical guide for anyone worried about how AI will change their job and what to do about it.If you've ever felt anxious about AI replacing your role, overwhelmed by tech jargon, or unsure how to stay relevant in a rapidly changing world, this book is for you.You've Got This, Human delivers a clear message: you don't need to become a tech expert to survive the AI revolution.What you need is the right mindset, the right tools, and a simple, human-centered strategy. This book gives you exactly that.Inside, you'll learn how to: Understand what AI actually is without the hype or fearSpot which parts of your job are at risk, enhanced, or uniquely humanUse the AI Impact Assessment and Human-AI Skills Matrix to map your future with clarityBuild the human skills AI can't replace and leverage them powerfullyUse AI tools, prompts, and apps to get more done in less timeStay sane, sharp, and centered in a world that's moving fastYou don't need to outrun AI. You just need to learn how to run with it.Backed by real-life stories, interactive tools, and a refreshingly direct tone, You've Got This, Human is the playbook every modern professional needs. It works no matter your industry, age, or level of tech experience.This isn't a book about AI.It's a book about you - your power, your adaptability, and your future.
Applying Image Matching to Video Analysis
Dealing with the volume of multimedia collected on a daily basis for intelligence gathering and digital forensics investigations requires significant manual analysis. A component of this problem is that a video may be reanalyzed that has already been analyzed. Identifying duplicate video sequences is difficult due to differences in videos of varying quality and size. This research uses a kd-tree structure to increase image matching speed. Keypoints are generated and added to a kd-tree of a large dimensionality (128 dimensions). All of the keypoints for the set of images are used to construct a global kd-tree, which allows nearest neighbor searches and speeds up image matching. The kd-tree performed matching of a 125 image set 1.6 times faster than Scale Invariant Feature Transform (SIFT). Images were matched in the same time as Speeded Up Robust Features (SURF). For a 298 image set, the kd-tree with RANSAC performed 5.5 times faster compared to SIFT and 2.42 times faster than SURF. Without RANSAC the kd-tree performed 6.4 times faster than SIFT and 2.8 times faster than SURF. The order images are compared to the same images of different qualities, did not produce significantly more matches when a higher quality image is compared to a lower quality one or vice versa. Size comparisons varied much more than the quality comparisons, suggesting size has a greater influence on matching than quality.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.
A Monocular Vision Based Approach to Flocking
Flocking is seen in nature as a means for self protection, more efficient foraging, and other search behaviors. Although much research has been done regarding the application of this principle to autonomous vehicles, the majority of the research has relied on GPS information, broadcast communication, an omniscient central controller, or some other form of "global" knowledge. This approach, while effective, has serious drawbacks, especially regarding stealth, reliability, and biological grounding. This research effort uses three Pioneer P2-AT8 robots to achieve flocking behavior without the use of global knowledge. The sensory inputs are limited to two cameras, offset such that the area of stereo vision is minimal, thus making stereo image analysis techniques effectively impossible, but allowing a much larger effective field of vision. The flocking algorithm analyzes these images and updates each robot's velocity vector according to the relative position, heading, and speed of its nearest neighbor. The result of this velocity update is an eventual stabilization of speed and heading, resulting in a coherent, stable flock, demonstrated in both software simulation and in hardware.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.
A Tabu Search Metaheuristic for the Air Refueling Tanker Assignment Problem
In a joint effort between Air Mobility Command (AMC) and the Air Force Institute of Technology, we present a Tanker Assignment Problem (TAI`) Tool capable of providing tanker mission plans for deployment scenarios. Due to the complex nature of extracting a mission plan from the Combined Mating and Ranging Planning System (CMARPS), AMC requires a tool to provide similar results in a simpler and less time consuming manner. The tool developed allows AMC to input several receiver groups consisting of various aircraft types and numbers. Each receiver group contains a point of origin and destination, with the option of providing one waypoint along the path. In addition, each group has a ready to load date (RLD) and required delivery date (RDD). The user is also able to specify the locations of military tanker aircraft. The main goal of this tool is to assign the tankers to the different refueling points of the receiver groups so that all receiver groups arrive before their RDD. Secondary goals include the reuse of tankers and limiting the total flight distance for all tanker aircraft. The TAP Tool uses the heuristic technique tabu search to determine an assignment of tankers to receiver groups during a deployment.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.
AI Business Startup Guide
Unlock the Future of Business: How to Start a Business with AIIn an era where technology is reshaping every industry, artificial intelligence (AI) is becoming an essential tool for entrepreneurs looking to gain a competitive edge. How to Start a Business with AI is your ultimate guide to harnessing the power of AI to launch and grow a successful business in the digital age. Whether you're an aspiring entrepreneur or an experienced business owner, this book provides practical strategies and valuable insights into leveraging AI for every aspect of your business-from customer service to marketing, sales, and beyond.Written by Eric LeBouthillier, a seasoned entrepreneur and AI expert, this book explores how AI can not only improve efficiency but also revolutionize how you approach business operations. With AI rapidly transforming industries like healthcare, retail, finance, and marketing, understanding how to integrate these advanced technologies is more crucial than ever.What You'll Learn: How to Leverage AI to Start a BusinessLearn how AI can enhance your decision-making and streamline your business startup processes. Eric walks you through the essential steps to integrate AI technologies, even if you have no prior experience in tech.Understand the practical applications of AI, such as automating repetitive tasks, improving customer interactions, and creating efficient workflows.Identifying the Best AI Tools for Your BusinessFrom chatbots to data analysis tools, this book covers the best AI solutions available for entrepreneurs. Learn how to select the right tools that align with your business goals, ensuring that AI supports and enhances your business operations.Eric provides a comprehensive guide to popular AI tools for specific business functions, such as sales automation, customer support, and marketing.Building an AI-Powered Business ModelDiscover how to integrate AI into your business model to improve both customer satisfaction and profitability. Whether you're launching an AI-powered product or simply incorporating AI into your existing operations, Eric provides actionable steps to create an AI-driven strategy.Learn the importance of data and how AI can help you collect, analyze, and utilize data to make smarter business decisions.AI in Marketing and SalesFind out how AI can revolutionize your marketing efforts. Learn about AI-driven personalization, automated email campaigns, and predictive analytics that can help you target the right customers and increase conversion rates.Explore how AI tools can optimize your sales funnel, identify high-quality leads, and personalize sales pitches, leading to faster and more efficient sales cycles.
AI In Cybersecurity
AI in Cybersecurity: The Ultimate Guide for Modern Threat Defense in the Digital Age In an era where data is currency and cyberattacks are more sophisticated than ever, businesses and individuals alike must rethink their approach to digital security. AI in Cybersecurity is the definitive resource that explores how artificial intelligence is transforming cyber defense systems, offering proactive, intelligent, and scalable protection in an evolving threat landscape.This comprehensive guide dives deep into the intersection of artificial intelligence and cybersecurity. Whether you're a small business owner, IT professional, executive, or tech enthusiast, this book delivers actionable insights, up-to-date frameworks, and practical use cases that show how AI is being deployed to detect threats, prevent breaches, and predict vulnerabilities before they become catastrophic.���� What You'll Learn Inside This Book: How machine learning and deep learning algorithms are used for real-time threat detection.The role of AI in identifying phishing attacks, malware, ransomware, and insider threats.Case studies from organizations using AI to secure data, networks, and cloud infrastructure.Key differences between rule-based systems and AI-driven defense.The impact of natural language processing (NLP) on analyzing threat intelligence and dark web chatter.Practical tools and platforms that leverage AI for cybersecurity automation and risk scoring.Ethical implications of AI-powered surveillance and privacy enforcement.How AI can help SMBs, enterprises, and government agencies scale their cybersecurity efforts.Unlike traditional cybersecurity books that focus only on manual defenses or complex technical concepts, AI in Cybersecurity is crafted to be accessible, insightful, and practical. Readers gain a strategic overview of how AI is enhancing digital resilience while also receiving real-world tactics that can be implemented immediately.���� Who This Book Is For: Small and Medium Businesses (SMBs) looking to protect their digital assets without large IT budgets.Cybersecurity professionals who want to upskill and stay ahead of AI-driven threat vectors.CISOs, CTOs, and IT managers responsible for digital risk management and compliance.Tech students and educators exploring the future of secure AI-driven infrastructure.Entrepreneurs and startups aiming to embed cybersecurity into their AI products from day one.���� Why It Matters Now More Than Ever: Cybercrime is projected to cost the global economy over $10 trillion annually by 2025. As threats evolve faster than human teams can respond, AI offers a revolutionary leap forward-delivering speed.
Ai-Driven Marketing
AI-Driven Marketing: The Future of Smarter, Faster, More Profitable Campaigns Is HereDiscover how artificial intelligence is revolutionizing marketing-and how you can stay ahead of the curve.AI-Driven Marketing is the ultimate guide for marketers, entrepreneurs, freelancers, and business owners who want to harness the power of artificial intelligence to drive sales, boost productivity, and create personalized customer experiences at scale.This comprehensive book covers the most important areas of modern marketing-content creation, advertising, data analysis, SEO, social media, email automation, and customer segmentation-through the lens of AI and machine learning.Whether you're a solo creator or part of a marketing team, this book gives you practical, step-by-step strategies to integrate AI tools into your workflow with ease. Learn how to work smarter, not harder, and get better results with less effort.You'll also find insights on integrating AI with CRM platforms, streamlining workflows between marketing and sales, and using AI to build predictive customer journeys that anticipate buyer behavior. This guide goes beyond theory-it delivers the tools and know-how you need to build marketing systems that scale.What You'll Learn: ✅ AI-Powered Content Creation Discover how to use tools like ChatGPT, Jasper, and Copy.ai to generate high-converting ad copy, blog posts, and product descriptions in minutes.✅ Customer Segmentation & Personalization Use machine learning algorithms to tailor campaigns to your audience and dramatically increase engagement and ROI.✅ Automated Email & Social Media Marketing Master platforms that schedule and optimize outreach using AI, helping you nurture leads automatically and boost conversions.✅ Predictive Analytics & Data-Driven Decisions Learn how to analyze customer behavior using AI to forecast trends and make smarter marketing decisions.✅ AI for Ad Targeting & Budget Optimization Stop wasting ad spend. Use AI tools to pinpoint your ideal audience and test campaigns at scale for maximum ROI.✅ SEO Automation & Keyword Research Tap into AI platforms that streamline SEO research, analyze competitors, and optimize content for search rankings-fast.✅ Visual Content with AI Design Tools Create eye-catching graphics, social posts, and branding elements with no design experience-using AI-powered tools like Canva AI and Midjourney.✅ Ethical AI Use & Human Oversight Learn the importance of combining AI power with human creativity and ethical marketing practices.✅ Real-World Case Studies Dive into detailed case studies showing how real businesses are increasing revenue, cutting costs, and growing their audiences through AI.This book is written for non-technical readers-no coding, no jargon-just powerful, clear guidance. You'll walk away with a toolbox of AI resources, expert tips, and real-world examples you can apply immediately to grow your business and enhance your career.Whether you're just starting or looking to upgrade your marketing game, AI-Driven Marketing provides the blueprint for scaling your brand in the age of automation-and staying ahead of your competition.
Climbing the Tower of Babel
Machine translation of language isn't rocket science. It's even more difficult. Despite revolutionary leaps in hardware, software, and linguistics throughout the 20th century, technology has delivered only rudimentary solutions for a handful of the world's 6800 languages. By 2035, however, advances in computers and artificial intelligence, the convergence of information-based devices, and the maturity of linguistics and programming tools will dissolve barriers to language and bring game-changing transparency to this greatest human challenge. Perfect machine translation will permit instantaneous availability of all languages, fusion with video and imagery data-streams, and unprecedented multi-domain awareness. In short, machine translation will guarantee communication and understanding. Accordingly, the Air Force and the Department of Defense must plan now to integrate this spectacular capability to further a gamut of national security interests.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.
Applying Image Matching to Video Analysis
Dealing with the volume of multimedia collected on a daily basis for intelligence gathering and digital forensics investigations requires significant manual analysis. A component of this problem is that a video may be reanalyzed that has already been analyzed. Identifying duplicate video sequences is difficult due to differences in videos of varying quality and size. This research uses a kd-tree structure to increase image matching speed. Keypoints are generated and added to a kd-tree of a large dimensionality (128 dimensions). All of the keypoints for the set of images are used to construct a global kd-tree, which allows nearest neighbor searches and speeds up image matching. The kd-tree performed matching of a 125 image set 1.6 times faster than Scale Invariant Feature Transform (SIFT). Images were matched in the same time as Speeded Up Robust Features (SURF). For a 298 image set, the kd-tree with RANSAC performed 5.5 times faster compared to SIFT and 2.42 times faster than SURF. Without RANSAC the kd-tree performed 6.4 times faster than SIFT and 2.8 times faster than SURF. The order images are compared to the same images of different qualities, did not produce significantly more matches when a higher quality image is compared to a lower quality one or vice versa. Size comparisons varied much more than the quality comparisons, suggesting size has a greater influence on matching than quality.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.
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.
Improved Feature Extraction, Feature Selection, and Identification Techniques That Create a Fast Unsupervised Hyperspectral Target Detection Algorithm
This research extends the emerging field of hyperspectral image (HSI) target detectors that assume a global linear mixture model (LMM) of HSI and employ independent component analysis (ICA) to unmix HSI images. Via new techniques to fully automate feature extraction, feature selection, and target pixel identification, an autonomous global anomaly detector, AutoGAD, has been developed for potential employment in an operational environment for real-time processing of HSI targets. For dimensionality reduction (initial feature extraction prior to ICA), a geometric solution that effectively approximates the number of distinct spectral signals is presented.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.
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.
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.
Power, Performance, and Perception (P3)
Currently, there are two distinct approaches to assist information technology managers in the successful implementation of office automation software. The first approach resides within the field of usability engineering, while the second approach is derived from the discipline of management information systems (MIS). The usability engineering approach has focused the question, "can users use the system?" while the MIS approach has answered the question, "will users use the system?" However, neither approach has successfully produced conclusive evidence that explains what characteristics facilitate system use as well as influence user acceptance of the system. This study reports on the validity of a new model, entitled the Power, Performance, Perception (P 3 ) model, that links the constructs of usability engineering to user acceptance. For this study, speech recognition software (SRS), selected as the target technology due to its novelty and practical application to office automation software, was used in an experimental setting to validate the P 3 model. As a secondary focus, this research also examined the viability of employing SRS in an Air Force office setting. The results of this study failed to validate the P 3 model. However, an alternate model for predicting user acceptance, the Usability-Acceptance Model, did emerge from the research which showed that the usability metric of user satisfaction can explain 53% of the variance of user intention to use a new technology. Additionally, the results of this study indicate that while users in a typical Air Force office environment would utilize SRS for text processing, the issue of increased productivity bears further examination.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.
