Algorithms and data structures Books

Book SynopsisThis book is the first one that provides a solid bridge between algorithmic information theory and statistical mechanics. Algorithmic information theory (AIT) is a theory of program size and recently is also known as algorithmic randomness. AIT provides a framework for characterizing the notion of randomness for an individual object and for studying it closely and comprehensively. In this book, a statistical mechanical interpretation of AIT is introduced while explaining the basic notions and results of AIT to the reader who has an acquaintance with an elementary theory of computation.A simplification of the setting of AIT is the noiseless source coding in information theory. First, in the book, a statistical mechanical interpretation of the noiseless source coding scheme is introduced. It can be seen that the notions in statistical mechanics such as entropy, temperature, and thermal equilibrium are translated into the context of noiseless source coding in a natural manner. Then, the framework of AIT is introduced. On this basis, the introduction of a statistical mechanical interpretation of AIT is begun. Namely, the notion of thermodynamic quantities, such as free energy, energy, and entropy, is introduced into AIT. In the interpretation, the temperature is shown to be equal to the partial randomness of the values of all these thermodynamic quantities, where the notion of partial randomness is a stronger representation of the compression rate measured by means of program-size complexity. Additionally, it is demonstrated that this situation holds for the temperature itself as a thermodynamic quantity. That is, for each of all the thermodynamic quantities above, the computability of its value at temperature T gives a sufficient condition for T to be a fixed point on partial randomness.In this groundbreaking book, the current status of the interpretation from both mathematical and physical points of view is reported. For example, a total statistical mechanical interpretation of AIT that actualizes a perfect correspondence to normal statistical mechanics can be developed by identifying a microcanonical ensemble in the framework of AIT. As a result, the statistical mechanical meaning of the thermodynamic quantities of AIT is clarified. In the book, the close relationship of the interpretation to Landauer's principle is pointed out.Table of ContentsStatistical Mechanical Interpretation of Noiseless Source Coding.- Algorithmic Information Theory.- Partial Randomness.- Temperature Equals to Partial Randomness.- Fixed Point Theorems on Partial Randomness.- Statistical Mechanical Meaning of the Thermodynamic Quantities of AIT.- The Partial Randomness of Recursively Enumerable Reals.- Computation-Theoretic Clarification of the Phase Transition at Temperature T=1.- Other Related Results and Future Development.

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Book SynopsisThis book presents best selected research papers presented at the International Conference on Computer Networks, Big Data and IoT (ICCBI 2020), organized by Vaigai College Engineering, Madurai, Tamil Nadu, India, during 15–16 December 2020. The book covers original papers on computer networks, network protocols and wireless networks, data communication technologies and network security. The book is a valuable resource and reference for researchers, instructors, students, scientists, engineers, managers and industry practitioners in those important areas.Table of ContentsMaximizing Network Lifetime in WSN using Ant Colony Algorithm.- Deep Ensemble Approach for Question Answer System.- Information Sharing Over Social Media Analysis Using Centrality Measure.- Indoor Mobile Robot Navigation using Deep Convolutional Neural Network.- Etaheuristic Enabled Shortest Path Selection for IoT based Wireless Sensor Network.- Generation of Random Binary Sequence using Adaptive Row-Column Approach and Synthetic Colour Image.- A Study of Mobile Adhoc Network and its Performance Optimizaiton Algorithm.- Sentimental Analysis on Twitter Data of Political Domain.- Big Social Media Analytics: Applications and Challenges.- Intelligent Computing Application for Cloud Enhancing Health Care Services.- Corona Virus Detection and Classification using X-Rays and CT Scans with Machine Learning Techniques.- Security Issues and Solutions in E-Health and Telemedicine.- Accident Alert System with False Alarm Switch.- A Deep Learning Approach to Detect Lumpy Skin Disease in Cows.- Algorithmic Trading using Machine Learning and Neural Network.- Analysis on Intrusion Detection System using Machine Learning Techniques.- Content Related Feature Analysis for Fake Online Consumer Review Detection.- Approaches in Assistive Technology: A Survey on Existing Assistive Wearable Technology for The Visually-Impaired.- Filter Bank Multicarrier Systems using Gaussian Pulse based Filter Design for 5G Technologies.- Data Streaming Architecture for Visualizing Cryptocurrency Temporal Data.- Integration of IoT and SDN to Mitigate DDoS with RYU Controller.- A Framework for monitoring Patient’s Vital Signs with Internet-of-Things and Blockchain Technology.- Network Intrusion Detection using Cross Bagging based Stacking Model.- Performance Study of Free Space Optical System under Varied Atmospheric Conditions.- Review on Energy Efficient Routing Protocols in WSN.Comparative Analysis of Traffic and Congestion in Software Defined Networks.- Automatic Vehicle Service Monitoring and Tracking System using IoT and Machine Learning.

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Book SynopsisThis two-volume book provides an insight into the 10th International Conference on Soft Computing for Problem Solving (SocProS 2020). This international conference is a joint technical collaboration of Soft Computing Research Society and Indian Institute of Technology Indore. The book presents the latest achievements and innovations in the interdisciplinary areas of soft computing. It brings together the researchers, engineers and practitioners to discuss thought-provoking developments and challenges, in order to select potential future directions. It covers original research papers in the areas including but not limited to algorithms (artificial immune system, artificial neural network, genetic algorithm, genetic programming and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). The book will be beneficial for young as well as experienced researchers dealing across complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task.Table of ContentsA deep semi-supervised approach for multi-label land-cover classification under scarcity of labelled images.- Role of individual samples in Modified Possibilistic c-Means classifier for handling heterogeneity within mustard crop.- Specially Structured Flow Shop Scheduling Models with processing times as Trapezoidal Fuzzy Numbers to optimize Waiting time of Jobs.- Potential Fishing Zone Characterization in the Indian Ocean by Machine Learning Approach.- A novel method to optimize interval length for intuitionistic fuzzy time series.- Low Altitude Unmanned Aerial Vehicle For Real Time Green House Plant Disease Monitoring Using Convolutional Neural Network.

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Book SynopsisThis book aims to address emerging challenges in the field of agriculture and natural resource management using the principles and applications of data science (DS). The book is organized in three sections, and it has fourteen chapters dealing with specialized areas. The chapters are written by experts sharing their experiences very lucidly through case studies, suitable illustrations and tables. The contents have been designed to fulfil the needs of geospatial, data science, agricultural, natural resources and environmental sciences of traditional universities, agricultural universities, technological universities, research institutes and academic colleges worldwide. It will help the planners, policymakers and extension scientists in planning and sustainable management of agriculture and natural resources. The authors believe that with its uniqueness the book is one of the important efforts in the contemporary cyber-physical systems.Table of ContentsData Science: Principles and Concepts in Data Analysis and Modelling.- Data Science: Tools, Techniques and Potential Applications in Earth Observation Studies.- Data Science in Agriculture and Natural Resource Management: An Overview.- Applications of Reinforcement Learning and Recurrent Neural Network Based Deep Learning Frameworks in Agriculture.- Precision Farming Using Emerging Technologies.- An Architecture for Quality Centric Crop Production.- Integrating UAV and Field Sensor Data for Better Decision Making in Broadacre Cropping Systems.- Object Based Crop Classification for Precision Farming.- Disruptive Innovations in Precision Agriculture - Towards BD Analytics for Better GeoFarmatics.- A Paradigm-shift in Global Cropland Maps and Products for Food and Water Security in the Twenty-first Century: Petabyte Scale Satellite Big-data Analytics, Machine Learning, and Cloud Computing.- Big Data Analytics for Climate Resilient Supply Chains: Opportunities and Way Forward.- Mapping Croplands Using Machine Learning Algorithms and Spectral Matching Techniques.- Applications of Computer Vision in Precision Agriculture.- Innovative Geoportal Platforms for Sustainable Management of Natural Resources.

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Book SynopsisDeep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning.This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.Table of ContentsChapter 1. Representation Learning.- Chapter 2. Graph Representation Learning.- Chapter 3. Graph Neural Networks.- Chapter 4. Graph Neural Networks for Node Classification.- Chapter 5. The Expressive Power of Graph Neural Networks.- Chapter 6. Graph Neural Networks: Scalability.- Chapter 7. Interpretability in Graph Neural Networks.- Chapter 8. "Graph Neural Networks: Adversarial Robustness".- Chapter 9. Graph Neural Networks: Graph Classification.- Chapter 10. Graph Neural Networks: Link Prediction.- Chapter 11. Graph Neural Networks: Graph Generation.- Chapter 12. Graph Neural Networks: Graph Transformation.- Chapter 13. Graph Neural Networks: Graph Matching.- Chapter 14. "Graph Neural Networks: Graph Structure Learning". Chapter 15. Dynamic Graph Neural Networks.- Chapter 16. Heterogeneous Graph Neural Networks.- Chapter 17. Graph Neural Network: AutoML.- Chapter 18. Graph Neural Networks: Self-supervised Learning.- Chapter 19. Graph Neural Network in Modern Recommender Systems.- Chapter 20. Graph Neural Network in Computer Vision.- Chapter 21. Graph Neural Networks in Natural Language Processing.- Chapter 22. Graph Neural Networks in Program Analysis.- Chapter 23. Graph Neural Networks in Software Mining.- Chapter 24. "GNN-based Biomedical Knowledge Graph Mining in Drug Development".- Chapter 25. "Graph Neural Networks in Predicting Protein Function and Interactions".- Chapter 26. Graph Neural Networks in Anomaly Detection.- Chapter 27. Graph Neural Networks in Urban Intelligence.

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Book SynopsisThis book focuses on research aspects of ensemble approaches of machine learning techniques that can be applied to address the big data problems. In this book, various advancements of machine learning algorithms to extract data-driven decisions from big data in diverse domains such as the banking sector, healthcare, social media, and video surveillance are presented in several chapters. Each of them has separate functionalities, which can be leveraged to solve a specific set of big data applications. This book is a potential resource for various advances in the field of machine learning and data science to solve big data problems with many objectives. It has been observed from the literature that several works have been focused on the advancement of machine learning in various fields like biomedical, stock prediction, sentiment analysis, etc. However, limited discussions have been carried out on application of advanced machine learning techniques in solving big data problems. Table of ContentsDeep Learning for Supervised Learning.- Deep Learning for Unsupervised Learning.- Support Vector Machine for Regression.- Support Vector Machine for Classification.- Decision Tree for Regression.- Higher Order Neural Networks.- Competitive Learning.- Semi-supervised Learning.- Multi-objective Optimization Techniques.- Techniques for Feature Selection/Extraction.- Techniques for Task Relevant Big Data Analysis.- Techniques for Post Processing Task in Big Data Analysis.- Customer Relationship Management.

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Book SynopsisThis book addresses several knowledge discovery problems on multi-sourced data where the theories, techniques, and methods in data cleaning, data mining, and natural language processing are synthetically used. This book mainly focuses on three data models: the multi-sourced isomorphic data, the multi-sourced heterogeneous data, and the text data. On the basis of three data models, this book studies the knowledge discovery problems including truth discovery and fact discovery on multi-sourced data from four important properties: relevance, inconsistency, sparseness, and heterogeneity, which is useful for specialists as well as graduate students. Data, even describing the same object or event, can come from a variety of sources such as crowd workers and social media users. However, noisy pieces of data or information are unavoidable. Facing the daunting scale of data, it is unrealistic to expect humans to “label” or tell which data source is more reliable. Hence, it is crucial to identify trustworthy information from multiple noisy information sources, referring to the task of knowledge discovery. At present, the knowledge discovery research for multi-sourced data mainly faces two challenges. On the structural level, it is essential to consider the different characteristics of data composition and application scenarios and define the knowledge discovery problem on different occasions. On the algorithm level, the knowledge discovery task needs to consider different levels of information conflicts and design efficient algorithms to mine more valuable information using multiple clues. Existing knowledge discovery methods have defects on both the structural level and the algorithm level, making the knowledge discovery problem far from totally solved.Table of ContentsChapter 1 Introduction 1.1 Knowledge Discovery 1.2 Main Challenges 1.3 Book Overview Chapter 2 Functional-dependency-based truth discovery for isomorphic data 2.1 Handling independent constraints 2.2 Handling inter-related constraints 2.3 Inter-source data aggregation 2.4 Update source weights Chapter 3 Denial-constraint-based truth discovery for isomorphic data Describe the truth discovery strategies for isomorphic data based on denial constraints 4.1 Denial constraint transformation 4.2 Optimized solution 4.3 Scalable strategies Chapter 4 Pattern discovery for heterogeneous data 4.1 Problem definition for multi-source heterogeneous data 4.2 Optimization framework 4.3 PatternFinder algorithm 4.4 The optimized grouping strategy Chapter 5 Deep fact discovery for text data 5.1 Fact extraction via mining patterns 5.2 The CNN-LSTM architecture 5.3 The fact encoder and pattern embedding 5.4 Training and inference

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Book SynopsisThis textbook explains the fundamentals of Markov Chain Monte Carlo (MCMC) without assuming advanced knowledge of mathematics and programming. MCMC is a powerful technique that can be used to integrate complicated functions or to handle complicated probability distributions. MCMC is frequently used in diverse fields where statistical methods are important – e.g. Bayesian statistics, quantum physics, machine learning, computer science, computational biology, and mathematical economics. This book aims to equip readers with a sound understanding of MCMC and enable them to write simulation codes by themselves. The content consists of six chapters. Following Chap. 2, which introduces readers to the Monte Carlo algorithm and highlights the advantages of MCMC, Chap. 3 presents the general aspects of MCMC. Chap. 4 illustrates the essence of MCMC through the simple example of the Metropolis algorithm. In turn, Chap. 5 explains the HMC algorithm, Gibbs sampling algorithm and Metropolis-Hastings algorithm, discussing their pros, cons and pitfalls. Lastly, Chap. 6 presents several applications of MCMC. Including a wealth of examples and exercises with solutions, as well as sample codes and further math topics in the Appendix, this book offers a valuable asset for students and beginners in various fields. Table of ContentsChapter 1: Introduction.- Chapter 2: What is the Monte Carlo method?.- Chapter 3: General Aspects of Markov Chain Monte Carlo.- Chapter 4: Metropolis Algorithm.- Chapter 5: Other Useful Algorithms.- Chapter 6: Applications of Markov Chain Monte Carlo.

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Book SynopsisThis book features high-quality, peer-reviewed research papers presented at the Fifth International Conference on Smart Technologies in Data Science and Communication (SMARTDSC 2022), held Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India, on 16 – 17 June 2022. It includes innovative and novel contributions in the areas of data analytics, communication and soft computing. Table of ContentsA Graph based model for Discovering Host-based hook Attacks.- E-Health Care Patient Information Retrieval and Monitoring System Using SVM.- Number Plate Recognition using Optical Character Recognition (OCA) and Connected Component Analysis (CCA).- Cartoonify an Image with Open cv using Python.- Web Design as an Important Factor in the Success of a Website.- Earlier Selection of Routes for Data Transfer in both Wired and Wireless Networks.

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Book SynopsisThis book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning). Readers may be particularly interested in the sections on consensus protocols, optimization skills, accelerated mechanisms, event-triggered strategies, variance-reduction communication techniques, etc., in connection with distributed optimization in various networked systems. This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike.Table of ContentsChapter 1. Distributed Nesterov-Like Accelerated Algorithms in Networked Systems with Directed Communications.- Chapter 2. Distributed Stochastic Projected Gradient Algorithms for Composite Constrained Optimization in Networked Systems.- Chapter 3. Distributed Proximal Stochastic Gradient Algorithms for Coupled Composite Optimization in Networked Systems.- Chapter 4. Distributed Subgradient Algorithms Based on Event-Triggered Strategy in Networked Systems.- Chapter 5. Distributed Accelerated Stochastic Algorithms Based on Event-Triggered Strategy in Networked Systems.- Chapter 6. Event-Triggered Based Distributed Optimal Economic Dispatch in Smart Grids.- Chapter 7. Fast Distributed Optimal Economic Dispatch in Dynamic Smart Grids with Directed Communications.- Chapter 8. Accelerated Distributed Optimal Economic Dispatch in Smart Grids with Directed Communications.- Chapter 9. Privacy Preserving Distributed Online Learning with Time-Varying and Directed Communications.

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Book SynopsisMatching problems with preferences are all around us: they arise when agents seek to be allocated to one another on the basis of ranked preferences over potential outcomes. Efficient algorithms are needed for producing matchings that optimise the satisfaction of the agents according to their preference lists.In recent years there has been a sharp increase in the study of algorithmic aspects of matching problems with preferences, partly reflecting the growing number of applications of these problems worldwide. The importance of the research area was recognised in 2012 through the award of the Nobel Prize in Economic Sciences to Alvin Roth and Lloyd Shapley.This book describes the most important results in this area, providing a timely update to The Stable Marriage Problem: Structure and Algorithms (D Gusfield and R W Irving, MIT Press, 1989) in connection with stable matching problems, whilst also broadening the scope to include matching problems with preferences under a range of alternative optimality criteria.Table of ContentsPreliminary Definitions, Results and Motivation; Stable Matching Problems: The Stable Marriage Problem: An Update; SM and HR with Indifference; The Stable Roommates Problem; Further Stable Matching Problems; Other Optimal Matching Problems: Pareto Optimal Matchings; Popular Matchings; Profile-Based Optimal Matchings.

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Book SynopsisThe 7-volume set LNCS 14487-14493 constitutes the proceedings of the 23rd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2023, which took place in Tianjin, China, during October, 2023. The 145 full papers included in this book were carefully reviewed and selected from 439 submissions.

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Book SynopsisThe 7-volume set LNCS 14487-14493 constitutes the proceedings of the 23rd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2023, which took place in Tianjin, China, during October, 2023. The 145 full papers included in this book were carefully reviewed and selected from 439 submissions.

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Book SynopsisThis open access book discusses the theory and methods of hypergraph computation. Many underlying relationships among data can be represented using graphs, for example in the areas including computer vision, molecular chemistry, molecular biology, etc. In the last decade, methods like graph-based learning and neural network methods have been developed to process such data, they are particularly suitable for handling relational learning tasks. In many real-world problems, however, relationships among the objects of our interest are more complex than pair-wise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected valuable for learning tasks. Hypergraph, as a generation of graph, has shown superior performance on modelling complex correlations compared with graph. Recent years have witnessed a great popularity of researches on hypergraph-related AI methods, which have been used in computer vision, social media analysis, etc. We summarize these attempts as a new computing paradigm, called hypergraph computation, which is to formulate the high-order correlations underneath the data using hypergraph, and then conduct semantic computing on the hypergraph for different applications. The content of this book consists of hypergraph computation paradigms, hypergraph modelling, hypergraph structure evolution, hypergraph neural networks, and applications of hypergraph computation in different fields. We further summarize recent achievements and future directions on hypergraph computation in this book.Table of Contents

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Book SynopsisThis open access book discusses the theory and methods of hypergraph computation. Many underlying relationships among data can be represented using graphs, for example in the areas including computer vision, molecular chemistry, molecular biology, etc. In the last decade, methods like graph-based learning and neural network methods have been developed to process such data, they are particularly suitable for handling relational learning tasks. In many real-world problems, however, relationships among the objects of our interest are more complex than pair-wise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected valuable for learning tasks. Hypergraph, as a generation of graph, has shown superior performance on modelling complex correlations compared with graph. Recent years have witnessed a great popularity of researches on hypergraph-related AI methods, which have been used in computer vision, social media analysis, etc. We summarize these attempts as a new computing paradigm, called hypergraph computation, which is to formulate the high-order correlations underneath the data using hypergraph, and then conduct semantic computing on the hypergraph for different applications. The content of this book consists of hypergraph computation paradigms, hypergraph modelling, hypergraph structure evolution, hypergraph neural networks, and applications of hypergraph computation in different fields. We further summarize recent achievements and future directions on hypergraph computation in this book.Table of Contents

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Book SynopsisThis volume LNCS 14252 constitutes the refereed proceedings of 25th International Conference on Information and Communications Security, ICICS 2023, held in Tianjin, China, during November 18–20, 2023. The 38 full papers presented together with 6 short papers were carefully reviewed and selected from 181 submissions. The conference focuses on: Symmetric-Key Cryptography; Public-Key Cryptography; Applied Cryptography; Authentication and Authorization; Privacy and Anonymity; Security and Privacy of AI; Blockchain and Cryptocurrencies; and System and Network Security. Table of Contents​Symmetric-Key Cryptography.- SAT-aided Differential Cryptanalysis of Lightweight Block Ciphers Midori, MANTIS and QARMA.- Improved Related-Key Rectangle Attack against the Full AES-192.- Block Ciphers Classification Based on Randomness Test Statistic Value via LightGBM.- Cryptanalysis of Two White-Box Implementations of the CLEFIA Block Cipher.- PAE: Towards More Efficient and BBB-secure AE From a Single Public Permutation.- Public-Key Cryptography.- A Polynomial-time Attack on G2SIDH.- Improvements of Homomorphic Secure Evaluation of Inverse Square Root.- Oblivious Transfer from Rerandomizable PKE.- Forward Secure Lattice-based Ring Signature Scheme in the Standard Model.- Applied Cryptography.- Secure Multi-Party Computation with Legally-Enforceable Fairness.- On-demand Allocation of Cryptographic Computing Resource with Load Prediction.- Private Message Franking with After Opening Privacy.- Semi-Honest 2-Party Faithful Truncation from Two-Bit Extraction.- Outsourcing Verifiable Distributed Oblivious Polynomial Evaluation from Threshold Cryptography.- Authentication and Authorization.- PiXi: Password Inspiration by Exploring Information.- Security Analysis of Alignment-Robust Cancelable Biometric Scheme for Iris Verification.- A Certificateless Conditional Anonymous Authentication Scheme for Satellite Internet of Things.- BLAC: A Blockchain-based Lightweight Access Control Scheme in Vehicular Social Networks.- Privacy and Anonymity.- Link Prediction-Based Multi-Identity Recognition of Darknet Vendors.- CryptoMask: Privacy-preserving Face Recognition.- Efficient Private Multiset ID Protocols.- Zoomer: A Website Fingerprinting Attack against Tor Hidden Services.- An Enhanced Privacy-preserving Hierarchical Federated Learning Framework for IoV.- Security and Privacy of AI.- Revisiting the Deep Learning-based Eavesdropping Attacks via Facial Dynamics from VR Motion Sensors.- Multi-scale Features Destructive Universal Adversarial Perturbations.- Pixel-Wise Reconstruction of Private Data in Split Federated Learning.- Neural Network Backdoor Attacks Fully Controlled by Composite Natural Utterance Fragments.- Black-Box Fairness Testing with Shadow Models.- Graph Unlearning using Knowledge Distillation.- AFLOW: Developing Adversarial Examples under Extremely Noise-limited Settings.- Learning to Detect Deepfakes via Adaptive Attention and Constrained Difference.- A Novel Deep Ensemble Framework for Online Signature Verification Using Temporal and Spatial Representation.- Blockchain and Cryptocurrencies.- SCOPE: A Cross-Chain Supervision Scheme for Consortium Blockchains.- Subsidy Bridge: Rewarding Cross-blockchain Relayers with Subsidy.- Towards Efficient and Privacy-Preserving Anomaly Detection of Blockchain-based Cryptocurrency Transactions.- Blockchain based Publicly Auditable Multi-Party Computation with Cheater Detection.- BDTS: Blockchain-based Data Trading System.- Illegal Accounts Detection on Ethereum using Heterogeneous Graph Transformer Networks.- System and Network security.- DRoT: A Decentralised Root of Trust for Trusted Networks.- Finding Missing Security Operation Bugs via Program Slicing and Differential Check.- TimeClave: Oblivious In-enclave Time series Processing System.- Efficient and Appropriate Key Generation Scheme in Different IoT Scenarios.- A Fake News Detection Method Based on A Multimodal Cooperative Attention Network.

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Book SynopsisData science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. This third edition delves into the latest advancements in AI, particularly focusing on large language models (LLMs), with clear distinctions made between AI and traditional data science, including AI's ability to emulate human decision-making. Author Stylianos Kampakis introduces you to the critical aspect of ethics in AI, an area of growing importance and scrutiny. The narrative examines the ethical considerations intrinsic to the development and deployment of AI technologies, including bias, fairness, transparency, and accountability. You'll be provided with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated edition also includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists. Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization.The Decision Maker's Handbook to Data Sciencebridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide. What You Will LearnIntegrate AI with other innovative technologies Explore anticipated ethical, regulatory, and technical landscapes that will shape the future of AI and data scienceDiscover how to hire and manage data scientistsBuild the right environment in order to make your organization data-drivenWho This Book Is ForStartup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science.

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Book SynopsisUnderstand and implement data structures and bridge the gap between theory and application. This book covers a wide range of data structures, from basic arrays and linked lists to advanced trees and graphs, providing readers with in-depth insights into their implementation and optimization in C++. You'll explore crucial topics to optimize performance and enhance their careers in software development. In today's environment of growing complexity and problem scale, a profound grasp of C++ data structures, including efficient data handling and storage, is more relevant than ever. This book introduces fundamental principles of data structures and design, progressing to essential concepts for high-performance application. Finally, you'll explore the application of data structures in real-world scenarios, including case studies and use in machine learning and big data. This practical, step-by-step approach, featuring numerous code examples, performance analysis and best practices, is written with a wide range of C++ programmers in mind. So, if you're looking to solve complex data structure problems using C++, this book is your complete guide. What You Will LearnWrite robust and efficient C++ code. Apply data structures in real-world scenarios. Transition from basic to advanced data structuresUnderstand best practices and performance analysis. Design a flexible and efficient data structure library. Who This Book is For Software developers and engineers seeking to deepen their knowledge of data structures and enhanced coding efficiency, and ideal for those with a foundational understanding of C++ syntax. Secondary audiences include entry-level programmers seeking deeper dive into data structures, enhancing their skills, and preparing them for more advanced programming tasks. Finally, computer science students or programmers aiming to transition to C++ may find value in this book.

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