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".
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".
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.
The Deep Learning Revolution
How deep learning--from Google Translate to driverless cars to personal cognitive assistants--is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
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.
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.
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 are presented, together with 24 tool papers and 4 case-study papers, 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.
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.
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.
Computer Vision and Image Processing
The Six-volume proceedings set LNCS 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.
Attacks on Artificial Intelligence
Cyber attacks, both on national infrastructures and private companies, have ramped up exponentially in recent years. AI tools and algorithms can help detect and fend off cyber threats, but they can also be used by hackers in their cyber attacks. Defensive measures to address cyber attacks are not sufficient for artificial intelligence-based architectures, which present a new range of challenges. This new volume takes these concerns into consideration and examines recent developments and issues in attacks that target AI based systems and cyber infrastructures while also presenting research on using AI technologies to prevent attacks.
Biometrics
Companies are embracing biometrics as an additional layer of security and convenience for their customers, providing an enhanced user experience and reducing the risk of fraud. This book provides an overview of biometrics including its theoretical foundations and practical applications and offers insights into its impact on society.
International Conference on Advanced Intelligent Systems for Sustainable Development (Ai2sd 2024)
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.
AI-Based Data Analytics
This book covers various topics related to marketing and business analytics. It explores how organizations can increase their profits by making better decisions in a timely manner through the use of data analytics. This book is meant for students, practitioners, industry professionals, researchers, and academics working in the field of commerce and marketing, big data analytics, and organizational decision-making. Highlights of the book include: The role of Explainable AI in improving customer experiences in e-commerce Sentiment analysis of social media Data analytics in business intelligence Federated learning for business intelligence AI-based planning of business management An AI-based business model innovation in new technologies An analysis of social media marketing and online impulse buying behaviour AI-Based Data Analytics: Applications for Business Management has two primary focuses. The first is on analytics for decision-making and covers big data analytics for market intelligence, data analytics and consumer behavior, and the role of big data analytics in organizational decision-making. The book's second focus is on digital marketing and includes the prediction of marketing by consumer analytics, web analytics for digital marketing, smart retailing, and leveraging web analytics for optimizing digital marketing strategies.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Artificial Intelligence in Education
This six-volume set LNAI 15877-15882 constitutes the refereed proceedings of the 26th International Conference on Artificial Intelligence in Education, AIED 2025, held in Palermo, Italy, during July 22-26, 2025. The 130 full papers and 129 short papers presented in this book were carefully reviewed and selected from 711 submissions. The conference program comprises seven thematic tracks: Track 1: AIED Architectures and Tools Track 2: Machine Learning and Generative AI: Emphasising datadrivenTrack 3: Learning, Teaching, and Pedagogy Track 4: Human-Centred Design and Design-Based Research Track 5: Teaching AI Track 6: Ethics, Equity, and AIED in Society Track 7: Theoretical Aspects of AIED and AI-Based Modelling forEducation
Artificial Intelligence in Education
This six-volume set LNAI 15877-15882 constitutes the refereed proceedings of the 26th International Conference on Artificial Intelligence in Education, AIED 2025, held in Palermo, Italy, during July 22-26, 2025. The 130 full papers and 129 short papers presented in this book were carefully reviewed and selected from 711 submissions. The conference program comprises seven thematic tracks: Track 1: AIED Architectures and Tools Track 2: Machine Learning and Generative AI: Emphasising datadrivenTrack 3: Learning, Teaching, and Pedagogy Track 4: Human-Centred Design and Design-Based Research Track 5: Teaching AI Track 6: Ethics, Equity, and AIED in Society Track 7: Theoretical Aspects of AIED and AI-Based Modelling forEducation
