Information Technology

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https://oerrepository.ntt.edu.vn/handle/298300331/491

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Đang hiển thị 1 - 20 trong tổng số 55

A Tale of Two Systems
(Oregon State University, 2017) Reitsma, René; Krueger, Kevin
This is the story of a web-based information system rebuild. The system in question is www.teachengineering.org, a digital library of K-12 engineering curriculum that was built from the ground up with established technology and which for 13 years enjoyed lasting support from its growing user community and its sponsors. These 13 years, however, cover the period during which smart phones and tablets became commonplace, during which the Internet of Things started replacing the Semantic Web, during which NoSQL databases made their way out of the research labs and into everyday development shops, during which we collectively started moving IT functions and services into ‘the cloud,’ and during which computing performance doubled a few times, yet again. During this same period, TeachEngineering’s user base grew from a few hundred to more than 3 million users annually, its collection size quadrupled, it went through several user interface renewals, and significant functionality was added while having an exemplary service record, and it enjoyed continued financial support from its sponsors. In this monograph we provide a side-by-side of this rebuild. We lay out the choices made in the old architecture —we refer to it as TE 1.0— and compare and contrast them with the choices made for TE 2.0. We explain why both the 1.0 and 2.0 choices were made and discuss the advantages and disadvantages associated with them.
Advanced Image Processing and Computer Vision
(MDPI, 2025) Tomassini, Selene; Dewan, M. Ali Akber
Advanced image processing (AIP) and computer vision (CV) remain dynamic and rapidly evolving research areas that drive innovation across a wide range of real-world applications. AI has become a fundamental enabler of AIP and CV, enhancing their capabilities and expanding their impact in multiple domains. While current AI-based algorithms have achieved impressive results through data-driven approaches, many challenges persist, particularly when dealing with limited or low-quality data. These limitations highlight the need for novel methods that are capable of improving accuracy, robustness, and adaptability in complex environments. This Reprint brings together original research articles, comprehensive reviews, and in-depth technical studies focused on the development and application of innovative AI-based algorithms for AIP and CV. It emphasizes emerging approaches that address data scarcity, optimize performance in critical settings, and align with human-centric principles, including ethical and fair AI. Although special attention is given to medical and healthcare applications, the Reprint also reflects the broader relevance of these technologies across agriculture, environmental monitoring, and other applied fields.
Advanced Numerical Methods in Applied Sciences
(MDPI, 2019) Brugnano, Luigi; Iavernaro, Felice
The use of scientific computing tools is currently customary for solving problems at several complexity levels in Applied Sciences. The great need for reliable software in the scientific community conveys a continuous stimulus to develop new and better performing numerical methods that are able to grasp the particular features of the problem at hand. This has been the case for many different settings of numerical analysis, and this Special Issue aims at covering some important developments in various areas of application.
Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis
(MDPI, 2025) García-Méndez, Silvia; Costa-Montenegro, Enrique; Arriba-Pérez, Francisco De
This Special Issue examines the intersection of NLP, semantic networks, and sentiment analysis, exploring how these technologies can contribute to more intelligent systems. The contributions gathered address, among other topics, the detection of irony and sarcasm, the generation of semantic explanations, the automatic construction of semantic graphs, the expansion of linguistic resources for underrepresented languages, and the use of large language models. Ultimately, this Special Issue aims not only to reflect the state of the art in the areas above but also to stimulate critical reflection on the methodological challenges, ethical implications, and interdisciplinary opportunities that arise at the intersection of computational linguistics and artificial intelligence.
Agency in Teaching and Learning with Digital Technology: Opportunities and Challenges
(MDPI, 2025) Engeness, Irina; Gamlem, Siv M.
This Reprint explores how agency is conceptualized, enacted, and supported in digital teaching and learning environments. Bringing together empirical and theoretical contributions, it examines both student and teacher agency as essential for navigating technology-rich educational contexts shaped by artificial intelligence and digital transformation. The studies span diverse settings—from early childhood to higher and professional education—and reveal how digital tools can enhance autonomy, engagement, and reflective practice when used thoughtfully. Emphasis is placed on pedagogical design, professional development, and institutional support as conditions for fostering agency. Collectively, the Reprint advances theoretical and practical understanding of how digital learning can empower learners and educators to act critically and creatively within evolving educational ecologies.
Agile Processes in Software Engineering and Extreme Programming
(SpringerLink, 2022) Baumeister, Hubert; Lichter, Horst; Riebisch, Mattias
This book is open access under a CC BY license. The volume constitutes the proceedings of the 18th International Conference on Agile Software Development, XP 2017, held in Cologne, Germany, in May 2017. The 14 full and 6 short papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections named: improving agile processes; agile in organization; and safety critical software. In addition, the volume contains 3 doctoral symposium papers (from 4 papers submitted).
An Introduction to Python Jupyter Notebooks for College Math Teachers
(2024) Isihara, Paul; Wagner, Claire; Jantsch, Peter; VanDrunen, Thomas
This book is an introduction to the use of Python Jupyter Notebooks (JNBs) for college math teachers and their students. The book is an evolving work in progress, but we hope this 1st Edition may already be quite useful to a college math teacher who is interested to use Jupyter Notebooks in their courses. Each section of the book has been written in the form of a Jupyter Notebook and can be downloaded using the download button at the top menu bar. In some cases, additional data files or image files may need to be downloaded as well. Each chapter contains a JNB lab with solutions. Experienced teachers can modify these labs and create new labs tailored to their courses. The chapters were written by different authors/authorship teams, and as such, vary in style. Each chapter essentially can be read independently of the others as long as one has the pre-requisite mathematical knowledge. Some effort has been made to standardize the chapter formats, and the index may help the reader find specific topics of interest. There is virtually no limit to what can be done with JNBs, and we hope this work serves as a useful starting point for teachers and students to enrich and enliven the study of mathematics using this tool. The JNBs used to create this book contain special markdown code which are needed to produce features such as indexing, referencing, and highlight boxes in the Jupyter Book. Some editing of a downloaded JNB may may make it more suitable for classroom use.
An Open Guide to Data Structures and Algorithms
(PALNI, 2023) Bible, Paul W.; Moser, Lucas; Scarlato, Mia M.
This textbook serves as a gentle introduction for undergraduates to theoretical concepts in data structures and algorithms in computer science while providing coverage of practical implementation (coding) issues. The field of computer science (CS) supports a multitude of essential technologies in science, engineering, and communication as a social medium. The varied and interconnected nature of computer technology permeates countless career paths making CS a popular and growing major program. Mastery of the science behind computer science relies on an understanding of the theory of algorithms and data structures. These concepts underlie the fundamental tradeoffs that dictate performance in terms of speed, memory usage, and programming complexity that separate novice programmers from professional practitioners.
Artificial Intelligence and Librarianship - 3rd Edition
(SoftOption, 2024) Frické, Martin
Courses on Artificial Intelligence (AI) and Librarianship in ALA-accredited Masters of Library and Information (MLIS) degrees are rare. We have all been surprised by ChatGPT and similar Large Language Models. Generative AI is an important new area for librarianship. It is also developing so rapidly that no one can really keep up. Those trying to produce AI courses for the MLIS degree need all the help they can get. This book is a gesture of support. It consists of about 100,000 words on the topic, with a 4-500 item bibliography. The third edition has changes and additions. These include: - A new Chapter 6 on evaluation and the future - New materials in Chapter 5 on current large language and multimodal models - Scattered revisions, corrections, and updates.
Big Data and Artificial Intelligence: Volume I
(MDPI, 2025) Lytras, Miltiadis D.; Serban, Andreea Claudia
The Topic “Big Data and Artificial Intelligence” explores the transformative impact of AI and Big Data across diverse domains, highlighting their role as catalysts for innovation, decision-making, and business model evolution. This reprint presents cutting-edge research, covering the full spectrum of AI and Big Data applications, from theoretical foundations to real-world implementations. Key topics include enabling technologies such as machine learning, neural networks, natural language processing, AI agents, analytics, and distributed computing. Methodological advancements, strategic frameworks, and models are examined, addressing areas such as sustainable development, industry innovation, ethical considerations, and social impact. Additionally, the collection showcases best practices, R&D project outcomes, and industry–government collaborations, offering valuable insights into the latest developments in AI-driven decision-making and data ecosystems. Edited by Prof. Miltiadis D. Lytras and Prof. Andreea Claudia Serban, this reprint compiles high-quality contributions from leading researchers and practitioners, providing a comprehensive resource for academics, industry experts, and policymakers interested in the evolving landscape of artificial intelligence and Big Data. The participating journals include Big Data and Cognitive Computing, Future Internet, Information, Remote Sensing, and Sustainability.
Big Data and Artificial Intelligence: Volume II
(MDPI, 2025) Lytras, Miltiadis D.; Serban, Andreea Claudia
The Topic “Big Data and Artificial Intelligence” explores the transformative impact of AI and Big Data across diverse domains, highlighting their role as catalysts for innovation, decision-making, and business model evolution. This reprint presents cutting-edge research, covering the full spectrum of AI and Big Data applications, from theoretical foundations to real-world implementations. Key topics include enabling technologies such as machine learning, neural networks, natural language processing, AI agents, analytics, and distributed computing. Methodological advancements, strategic frameworks, and models are examined, addressing areas such as sustainable development, industry innovation, ethical considerations, and social impact. Additionally, the collection showcases best practices, R&D project outcomes, and industry–government collaborations, offering valuable insights into the latest developments in AI-driven decision-making and data ecosystems. Edited by Prof. Miltiadis D. Lytras and Prof. Andreea Claudia Serban, this reprint compiles high-quality contributions from leading researchers and practitioners, providing a comprehensive resource for academics, industry experts, and policymakers interested in the evolving landscape of artificial intelligence and Big Data. The participating journals include Big Data and Cognitive Computing, Future Internet, Information, Remote Sensing, and Sustainability.
Big Data and Artificial Intelligence: Volume III
(MDPI, 2025) Lytras, Miltiadis D.; Serban, Andreea Claudia
The Topic “Big Data and Artificial Intelligence” explores the transformative impact of AI and Big Data across diverse domains, highlighting their role as catalysts for innovation, decision-making, and business model evolution. This reprint presents cutting-edge research, covering the full spectrum of AI and Big Data applications, from theoretical foundations to real-world implementations. Key topics include enabling technologies such as machine learning, neural networks, natural language processing, AI agents, analytics, and distributed computing. Methodological advancements, strategic frameworks, and models are examined, addressing areas such as sustainable development, industry innovation, ethical considerations, and social impact. Additionally, the collection showcases best practices, R&D project outcomes, and industry–government collaborations, offering valuable insights into the latest developments in AI-driven decision-making and data ecosystems. Edited by Prof. Miltiadis D. Lytras and Prof. Andreea Claudia Serban, this reprint compiles high-quality contributions from leading researchers and practitioners, providing a comprehensive resource for academics, industry experts, and policymakers interested in the evolving landscape of artificial intelligence and Big Data. The participating journals include Big Data and Cognitive Computing, Future Internet, Information, Remote Sensing, and Sustainability.
Building User Interfaces for Modern Web Applications: React Programming
(2024) Yang, Cheer-Sun
In this eTextbook, the prerequisite concepts about HTML/CSS, JavaScript, and Bootstrap/React-Bootstrap are introduced first, followed by the main React language features. Finally, the Software Engineering Principles are introduced from the design, development, to debugging and maintenance. The main objectives are threefold: (1) provide concepts about JavaScript Programming, (2) introduce the concepts of modularity, functional programming, and (3) teach the concept of reusable User Interface (UI) as the front-end of modern model-view-controller (MVC) web applications. Although learning other technologies in the React ecosystem is imminent, it is the hope that this book paves the groundwork for the future learning and growing in the field of modern UI development.
Database Design - 2nd Edition
(BCcampus, 2014) Watt, Adrienne
This second edition of Database Design book covers the concepts used in database systems and the database design process. Topics include: The history of databases Characteristics and benefits of databases Data models Data modelling Classification of database management systems Integrity rules and constraints Functional dependencies Normalization Database development process New to this edition are more examples, highlighted and defined key terms, both throughout and at the end of each chapter, and end-of-chapter review exercises. Two new chapters have been added on SQL, along with appendices that include a data model example, sample ERD exercises and SQL lab with solutions.
Discrete Mathematics: An Open Introduction
(2016) Levin, Oscar
The text began as a set of lecture notes for the discrete mathematics course at the University of Northern Colorado. This course serves both as an introduction to topics in discrete math and as the "introduction to proofs" course for math majors. The course is usually taught with a large amount of student inquiry, and this text is written to help facilitate this. Four main topics are covered: counting, sequences, logic, and graph theory. Along the way, proofs are introduced, including proofs by contradiction, proofs by induction, and combinatorial proofs. An introductory chapter covering mathematical statements, sets, and functions helps students gain familiarity with the language of mathematics, and two additional topics (generating functions and number theory) are also included. While the book began as a set of lecture notes, it now contains a number of features that should support its use as a primary textbook: 473 exercises, including 275 with solutions and another 109 with hints. Exercises range from easy to quite involved, with many problems suitable for homework. Investigate! activities throughout the text to support active, inquiry based learning. A full index and list of symbols. Consistent and helpful page layout and formatting (i.e., examples are easy to identify, important definitions and theorems in boxes, etc.).
Dynamic data processing: Recursive least-squares
(TU Delft Open, 2024) Teunissen, Peter J.G.
This book is a follow-up on Adjustment theory. It extends the theory to the case of time-varying parameters with an emphasis on their recursive determination. Least-squares estimation will be the leading principle used. A least-squares solution is said to be recursive when the method of computation enables sequential, rather than batch, processing of the measurement data. The recursive equations enable the updating of parameter estimates for new observations without the need to store all past observations. Methods of recursive least-squares estimation are therefore particularly useful for applications in which the time-varying parameters need to be instantly determined. Important examples of such applications can be found in the fields of real-time kinematic positioning, navigation and guidance, or multivariate time series analysis. The goal of this book is therefore to convey the necessary knowledge to be able to process sequentially collected measurements for the purpose of estimating time-varying parameters. When determining time-varying parameters from sequentially collected measurement data, one can discriminate between three types of estimation problems: filtering, prediction and smoothing. Filtering aims at the determination of current parameter values, while smoothing and prediction aim at the determination of respectively past and future parameter values. The emphasis in this book will be on recursive least-squares filtering. The theory is worked out for the important case of linear(ized) models. The measurement-update and time-update equations of recursive least-squares are discussed in detail. Models with sequentially collected data, but time-invariant parameters are treated first. In this case only the measurement-update equations apply. State-space models for dynamic systems are discussed so as to include time-varying parameters. This includes their linearization and the construction of the state transition matrix. Elements from the theory of random functions are used to describe the propagation laws for linear dynamic systems. The theory is illustrated by means of many worked out examples. They are drawn from applications such as kinematic positioning, satellite orbit determination and inertial navigation.
Emergent Quantum Mechanics: David Bohm Centennial Perspectives
(MDPI, 2025) Walleczek, Jan; Grössing, Gerhard; Pylkkänen, Paavo; Hiley, Basil
Emergent quantum mechanics explores the possibility of an ontology for quantum mechanics. The resurgence of interest in "deeper-level" theories for quantum phenomena challenges the standard, textbook interpretation. The book presents expert views that critically evaluate the significance—for 21st century physics—of ontological quantum mechanics, an approach that David Bohm helped pioneer. The possibility of a deterministic quantum theory was first introduced with the original de Broglie-Bohm theory, which has also been developed as Bohmian mechanics. The wide range of perspectives that were contributed to this book on the occasion of David Bohm’s centennial celebration provide ample evidence for the physical consistency of ontological quantum mechanics. The book addresses deeper-level questions such as the following: Is reality intrinsically random or fundamentally interconnected? Is the universe local or nonlocal? Might a radically new conception of reality include a form of quantum causality or quantum ontology? What is the role of the experimenter agent? As the book demonstrates, the advancement of ‘quantum ontology’—as a scientific concept—marks a clear break with classical reality. The search for quantum reality entails unconventional causal structures and non-classical ontology, which can be fully consistent with the known record of quantum observations in the laboratory.
Evidence-based Software Engineering
(Knowledge Software, 2020) Jones, Derek M.
This book discusses what is currently known about software engineering, based on an analysis of all the publicly available data. This aim is not as ambitious as it sounds, because there is not a great deal of data publicly available. The intent is to provide material that is useful to professional developers working in industry; until recently researchers in software engineering have been more interested in vanity work, promoted by ego and bluster. The material is organized in two parts, the first covering software engineering and the second the statistics likely to be needed for the analysis of software engineering data.
Foundations of Software Science and Computation Structures
(SpringerLink, 2018) Baier, Christel; Lago, Ugo Dal
This book constitutes the proceedings of the 21st International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2018, which took place in Thessaloniki, Greece, in April 2018, held as part of the European Joint Conference on Theory and Practice of Software, ETAPS 2018. The 31 papers presented in this volume were carefully reviewed and selected from 103 submissions. The papers are organized in topical sections named: semantics; linearity; concurrency; lambda-calculi and types; category theory and quantum control; quantitative models; logics and equational theories; and graphs and automata.
Foundations of Trusted Autonomy
(SpringerLink, 2018) Abbass, Hussein A.; Scholz, Jason; Reid, Darryn J.
This book is open access under a CC BY 4.0 license. This book establishes the foundations needed to realize the ultimate goals for artificial intelligence, such as autonomy and trustworthiness. Aimed at scientists, researchers, technologists, practitioners, and students, it brings together contributions offering the basics, the challenges and the state-of-the-art on trusted autonomous systems in a single volume. The book is structured in three parts, with chapters written by eminent researchers and outstanding practitioners and users in the field. The first part covers foundational artificial intelligence technologies, while the second part covers philosophical, practical and technological perspectives on trust. Lastly, the third part presents advanced topics necessary to create future trusted autonomous systems. The book augments theory with real-world applications including cybersecurity, defence and space.

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