Towards Autonomous Robotic Systems
This LNAI 16045 volume constitutes the proceedings of the 26th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2025, held in York, UK, duing August 20-22, 2025. The 32 full papers and 4 short papers, together with 4 invited papers presented in these volumes were carefully reviewed and selected from 74 submissions. The papers were categorised into seven groups: Human-Robot Interaction and Teleoperation, Sensing and Perception, Locomotion and Control, Manipulation and Dexterous Interaction, Software Engineering for Robotics, Underwater Robotics and Autonomy, Aerial Robotics and Path Planning, and Challenging Environments.
Computing Power Drives the Future
Computing power has long grown at an exponential rate. That rapid advance has allowed digital systems to do more each year. Computer power crossed a major threshold when it made "Artificial Intelligence" driven by deep neural networks economically feasible. And huge investments are being made in ever-larger computer centers to support AI. This book challenges the assumptions behind those huge investments and explains why the generative AI that is making all the news is over-rated.Computer power crossing a threshold allowed deep neural nets to be practical, and they have indeed been used effectively for many limited applications. Meisel discusses the "next big thing" that exponential growth in computer processing speed will allow. The book provides a realistic description of what we can expect as computer power grows ever more quickly than most past innovations, with major impacts on society, the economy, and competition between countries.
Artificial Intelligence for Fault Detection and Diagnosis
Fault detection and diagnosis (FDD) is an important task in manufacturing and mechatronic systems for reducing costs and improving productivity. Traditionally, the states of machines and their faults are manually checked, a process which is time-consuming and expensive. Therefore, it is desirable to develop intelligent systems to achieve automatic FDD. Artificial intelligence as a concept covers a wide range of algorithms that mimic the human mind, thinking and acting like humans to solve important tasks in different fields. In recent years, many AI algorithms have been applied to FDD, including data processing, feature analysis, and classification. Typical methods include deep neural networks, long short-term memory, convolutional neural networks, random forest, and evolutionary computation. However, the potential of AI has not been comprehensively investigated in FDD. This remains a challenging task due to many factors, such as changeable equipment working states, incomplete information, a lack of sufficient training data, complex relationships between faults and symptoms, imbalanced data, and the requirement of having domain knowledge. This reprint is a collection of research regarding AI techniques applied to various FDD tasks.
Fuzzy Sets and Fuzzy Systems
Over time, the concepts of cost, time, delivery, space, quality, durability, and price began to gain more significance in the emerging trends of information technology when it comes to solving managerial decision-making problems in supply chain models, transportation issues, inventory control issues, and related areas. In uncertain situations, competition is becoming more difficult by the day. For instance, a number of diverse aspects, such as the cost of manufacturing and one's degree of income, frequently influence consumer demand. In these situations, the demand is either still unmet or remarkably difficult to meet in the real-world market. Due to their numeric membership functions, fuzzy sets cannot always represent this uncertainty explicitly. However, it has been discovered that type two, random fuzzy, bifuzzy, and fuzzy random sets are more suited to account for intrinsic uncertainty. These various fuzzy systems are able to manage greater degrees of ambiguity in increasingly difficult real-world issues. However, it is crucial to employ optimization techniques in order to obtain the ideal design as diverse fuzzy systems grow more complicated to conceive. This Reprint highlights novel modeling techniques and fuzzy optimization strategies applied to real-world problems, including sustainable green supply chain systems, relief logistics under uncertainty, and cloud-based software prediction, alongside theoretical developments in fuzzy systems.
Algorithms for Games AI
Games have long been excellent benchmarks for AI algorithms for two reasons. Initially, games are developed to assess and challenge human intelligence, and the variety of games can provide a rich context for evaluating different cognitive and decision-making abilities. Secondly, addressing complex real-world challenges often requires repeated trial and error, which can be very costly. Games offer a low-cost or even zero-cost platform for validating various algorithms and solutions by simulating or emulating real-world scenarios. Algorithms initially developed for gaming are subsequently applied to various real-world problems, generating social benefits across all aspects of life. This Special Issue, entitled "Algorithms for Game AI", explores new and innovative approaches for addressing challenges in game AI. These approaches range from traditional algorithms like planning and searching to modern algorithms such as deep reinforcement learning. The papers in this Special Issue address both the theoretical and practical challenges of the application of these algorithms. This reprint presents eleven papers covering a wide range of game AI topics, including the quantification of non-transitivity in chess, the expressiveness of level generators in Super Mario Bros, Mahjong as a new game AI benchmark, new MARL algorithms to reduce Q-value bias, surveys of various AI algorithms in cyber defense, energy areas and games, the application of MCTS in Amazons, the application of deep reinforcement learning in autonomous vehicle driving, and the application of transformers in both offline RL and imitation learning.
Security and Privacy of Cyber-Physical Systems
This book examines vulnerability threats and attack detection and mitigation, including the associated legal requirements, regulatory frameworks, and policies for enabling the security and privacy of cyber-physical systems. It provides researchers, academics, and practitioners with new insights into the real-world scenarios of deploying, applying, and managing security and privacy frameworks in modern cyber-physical systems.It addresses critical security and privacy concerns, including theoretical analysis, novel system architecture design and implementation, vulnerability discovery, analysis, mitigation, emerging application scenarios, experimental frameworks, and social and ethical dilemmas affecting all parties in cyber-physical systems. The book is an ideal reference for practitioners and researchers in cyber-physical systems, security and privacy, the Internet of Things, advanced cryptography, cyber defensive walls, industrial systems, and cyber threats. It is also a suitable textbook for graduate and senior undergraduate courses in these subjects.
Artificial Intelligence and Machine Learning for Smart Community
Collaborative Computing: Networking, Applications and Worksharing
The three-volume set LNICST 624, 625, 626 constitutes the refereed proceedings of the 20th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2024, held in Wuzhen, China, during November 14-17, 2024. The 62 full papers were carefully reviewed and selected from 173 submissions. They are categorized under the topical sections as follows: Edge computing & Task scheduling Deep Learning and application Blockchain applications Security and Privacy Protection Representation learning & Collaborative working Graph neural networks & Recommendation systems Federated Learning and application
Collaborative Computing: Networking, Applications and Worksharing
The three-volume set LNICST 624, 625, 626 constitutes the refereed proceedings of the 20th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2024, held in Wuzhen, China, during November 14-17, 2024. The 62 full papers were carefully reviewed and selected from 173 submissions. They are categorized under the topical sections as follows: Edge computing & Task scheduling Deep Learning and application Blockchain applications Security and Privacy Protection Representation learning & Collaborative working Graph neural networks & Recommendation systems Federated Learning and application
Collaborative Computing: Networking, Applications and Worksharing
The three-volume set LNICST 624, 625, 626 constitutes the refereed proceedings of the 20th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2024, held in Wuzhen, China, during November 14-17, 2024. The 62 full papers were carefully reviewed and selected from 173 submissions. They are categorized under the topical sections as follows: Edge computing & Task scheduling Deep Learning and application Blockchain applications Security and Privacy Protection Representation learning & Collaborative working Graph neural networks & Recommendation systems Federated Learning and application
Mathematical Modelling and Numerical Analysis in Electrical Engineering, 2nd Edition
Application of Machine Learning and Optimization Methods in Engineering Mathematics
New Trends in Mathematical Modeling, Analysis and Optimization for Engineering and Mechanics
Identification of Command and Control Information Requirements for the Cyberspace Domain
Project-Based Learning in Integrated STEM Education
Project-Based Learning (PBL) is a powerful pedagogical approach that fosters deep, interdisciplinary learning by engaging students in meaningful, real-world problems. As STEM education continues to gain prominence in K-16 curricula, the integration of PBL becomes increasingly vital for preparing students to think critically, collaborate across disciplines, and apply their knowledge in authentic contexts. This reprint of the Special Issue of Education Sciences invites scholarly contributions that explore the implementation and impact of project-based learning across K-16 educational settings. The articles in this reprint include original research, theoretical analyses, and descriptions of promising practices that explore how PBL supports four key themes: interdisciplinary STEM learning, civic engagement, equity and identity, and pedagogical innovations and frameworks.
Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling, 2nd Edition
The present Special Issue of the MDPI journal Mathematics, titled "Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling, 2nd Edition", contains a total of eight articles that cover a wide range of topics related to the theory, models, and applications of project planning, project scheduling, and operation research problems in logistics. These topics include, among others, scheduling problems, research allocation problems in project planning, routing and warehousing problems in logistics, and risk aggregation problems in risk assessments.It is hoped that the reprint will be interesting and useful for those working in the area of project planning, scheduling, routing, and other operation research and risk management problems in logistics. It is designed with people with mathematical backgrounds in mind, who are open to become familiar with recent advances of operation research and optimization problems. Each article reflects on novel challenges, such as pandemic situations and flexible, fast-changing environments.
AI in Medical Imaging and Image Processing
This compilation emphasizes the transformative role of artificial intelligence (AI) and machine learning (ML) in the healthcare field, illustrating their potential to refine diagnostics, treatment protocols, and patient management. The studies confront critical healthcare challenges, presenting solutions that improve precision, efficiency, and accessibility. Featured are works on enhancing diagnostics, employing models like convolutional neural networks (CNNs) and transformers for the early and accurate identification of various conditions, such as different cancers, stroke, intracranial hemorrhage, and acute aortic syndrome. The collection also delves into AI applications in surgical planning and intraoperative guidance, with research analyzing preoperative imaging predictors and AI tools for detecting surgical wound infections. New AI methodologies for addressing rare and complex diagnoses, such as early-stage osteosarcoma detection, bone mineral density screening in cystic fibrosis, and biomarker identification in leukemia, are included. Additionally, the compilation addresses the practical aspects of AI integration, such as interrater variability, reproducibility, and the necessity for standardized benchmarks. This collection serves as a valuable resource for healthcare professionals, researchers, and technologists aiming to comprehend and utilize AI's potential in medicine.
Cyber-Ransom-Proof Home Network
Why you need this bookRansomware gangs no longer target just corporations. A hijacked baby cam, a bricked thermostat, or a demand for Bitcoin to turn your lights back on can hit any household. Veteran penetration tester Jasper Lowe shows you how to out-engineer criminals with consumer hardware and open-source software - no computer-science degree required.What you'll masterISP-independent defence - cache DNS, mirror firmware, and keep Home Assistant running when the internet-or the attacker-pulls the plug.DIY hardware firewall - flash inexpensive single-board routers with OpenWRT, write tight iptables rules, and add automatic IP-block-list feeds."Guest-IoT-Work" network slicing - VLANs, MAC whitelists, and WPA3 keys that quarantine every smart bulb without throttling your Zoom calls.Ransomware kill-switch backups - air-gapped snapshots, immutable ZFS datasets, and a Raspberry Pi that boots an emergency media server.Offline voice & automation - swap cloud skill servers for open-source alternatives so Alexa-style convenience survives an outage.Rapid incident response - three-command triage to freeze traffic, trace the breach, and re-flash compromised gadgets in under an hour.Real-world walkthroughs include: blocking Mirai clones on a TP-Link router, converting an old laptop into a Security Onion sensor, and building a Faraday-cage charging locker for guests' devices.About the authorJasper Lowe has broken into Fortune-500 networks (legally) for fifteen years, teaches blue-team tactics at DEF CON's Blue Village, and runs the popular "Home Lab Ops" YouTube channel.
Introduction to Deep Learning Business Applications for Developers
Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles. An Introduction to Deep Learning Business Applications for Developers covers some common DL algorithms such as content-based recommendation algorithms and natural language processing. You'll explore examples, such as video prediction with fully convolutional neural networks (FCNN) and residual neural networks (ResNets). You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. There will also be mention of the powerful set of algorithms called Generative Adversarial Neural networks (GANs) that can be applied for image colorization, image completion, and style transfer.After reading this book you will have an overview of the exciting field of deep neural networks and an understanding of most of the major applications of deep learning. The book contains some coding examples, tricks, and insights on how to train deep learning models using the Keras framework.What You Will LearnFind out about deep learning and why it is so powerfulWork with the major algorithms available to train deep learning modelsSee the major breakthroughs in terms of applications of deep learning Run simple examples with a selection of deep learning libraries Discover the areas of impact of deep learning in businessWho This Book Is For Data scientists, entrepreneurs, and business developers.
Graph Algorithms
This is a Reprint of one of the first Special Issues of Algorithms ever published. It focuses on the theoretical and practical performance of algorithms for solving computational problems involving graphs. Several highly cited articles are included, which investigate topics such as the maximum clique problem, the Dubins traveling salesman problem, and computing the eccentricity distribution of a graph.
Computing and Combinatorics
This two-volume set, LNCS 15983 and 15984, constitutes the referred proceedings of the 31st International Computing and Combinatorics Conference, COCOON 2025, held in Chengdu, China, during August 15-17, 2025. The 54 full papers were carefully reviewed and selected from 191 submissions. COCOON 2025 provided an excellent venue for researchers working in the topical sections as follows: Part I: Approximation Algorithms, Combinatorial Optimization, Computational Complexity, Computational Geometry, Economics and Computation.Part II: Graph Algorithms and Graph Theory, Learning and Data-Related Theory, Parameterized Algorithms, String Algorithms and Discrete Structures.
Computing and Combinatorics
This two-volume set, LNCS 15983 and 15984, constitutes the referred proceedings of the 31st International Computing and Combinatorics Conference, COCOON 2025, held in Chengdu, China, during August 15-17, 2025. The 54 full papers were carefully reviewed and selected from 191 submissions. COCOON 2025 provided an excellent venue for researchers working in the topical sections as follows: Part I: Approximation Algorithms, Combinatorial Optimization, Computational Complexity, Computational Geometry, Economics and Computation.Part II: Graph Algorithms and Graph Theory, Learning and Data-Related Theory, Parameterized Algorithms, String Algorithms and Discrete Structures.
Symbolic Mathematics with Python
This book provides a hands-on approach to computer symbolic computation using the elementary commands of Python. Symbolic computer mathematics is the study of algorithms that can be implemented as computer programs which provide exact results rather than numerical approximations. The author begins by discussing Python essentials an number theory. Then, the book covers the simplification and evaluation of expressions involving multivariate rational functions, the exact solutions of systems of linear equations, and applications of polynomial algebra. Programs in symbolic differentiation and indefinite integration programs are also developed.
