Data mining Books

Book SynopsisThis book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens.Table of ContentsData Analytics - Data and Relations - Data Preprocessing - Data Visualization - Correlation - Regression - Forecasting - Classification - Clustering.

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Book SynopsisFür Leser, die bereits die Grundlagen der Wissensverarbeitung und Computernetzwerke beherrschen, gibt das Buch einen Überblick über innovative Verfahren, die die automatisierte Suche, Recherche, Klassifikation und Verwaltung von Texten im Kontext dezentraler Systeme und vor allem im WWW erlauben. Besondere Aufmerksamkeit wird dabei auf eine personalisierte Verarbeitung gerichtet, die auch zeitliche Aspekte, wie z. B. das digitale Vergessen, einbeziehen. An vielen Stellen werden auf interessante und neuartige Art und Weise Analogien aus anderen Wissensgebieten, so z. B. zur Verarbeitung von Informationen und zum Lernen im menschlichen Gehirn sowie der Natur schlechthin genutzt.Table of ContentsWissensverarbeitung im menschlichen Gehirn - Lernen - Netzwerke für die Textanalyse - Digitale Updates und digitales Vergessen - Exploration von Netzwerkstrukturen - Konzepte des Text Minings in dezentralen Systemen - Informationsmanagement im Web

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Book SynopsisThis book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications.

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Book SynopsisThis book presents a comprehensive and systematic introduction to transforming process-oriented data into information about the underlying business process, which is essential for all kinds of decision-making. To that end, the authors develop step-by-step models and analytical tools for obtaining high-quality data structured in such a way that complex analytical tools can be applied. The main emphasis is on process mining and data mining techniques and the combination of these methods for process-oriented data. After a general introduction to the business intelligence (BI) process and its constituent tasks in chapter 1, chapter 2 discusses different approaches to modeling in BI applications. Chapter 3 is an overview and provides details of data provisioning, including a section on big data. Chapter 4 tackles data description, visualization, and reporting. Chapter 5 introduces data mining techniques for cross-sectional data. Different techniques for the analysis of temporal data are then detailed in Chapter 6. Subsequently, chapter 7 explains techniques for the analysis of process data, followed by the introduction of analysis techniques for multiple BI perspectives in chapter 8. The book closes with a summary and discussion in chapter 9. Throughout the book, (mostly open source) tools are recommended, described and applied; a more detailed survey on tools can be found in the appendix, and a detailed code for the solutions together with instructions on how to install the software used can be found on the accompanying website. Also, all concepts presented are illustrated and selected examples and exercises are provided.The book is suitable for graduate students in computer science, and the dedicated website with examples and solutions makes the book ideal as a textbook for a first course in business intelligence in computer science or business information systems. Additionally, practitioners and industrial developers who are interested in the concepts behind business intelligence will benefit from the clear explanations and many examples.Trade Review“The usage of examples and case studies enable real life application and brings asophisticated text to life. … the book is a comprehensive and thoroughly well thought out introduction to the subject of business intelligence and the reader will not be left wanting as the clear examples are numerous. … Readers interested in the value of data and the concepts behind business intelligence will find the book and its accompanying website highly informative.” (Georgette Banham, bcs, The Chartered Institute for IT, bcs.org, August, 2016)“This book focuses primarily on the data mining, data warehousing, data analytics, data visualization, data presentation, and process analysis dimensions of BI in detail. … One of the noteworthy strengths of this book is the inclusion of comprehensive lists with very recent and relevant references for BI at the end of each chapter. This should make the book very useful for academic research on the topic.” (Satya Prakash Saraswat, Computing Reviews, February, 2016)Table of Contents1 Introduction.- 2 Modeling in Business Intelligence.- 3 Data Provisioning.- 4 Data Description and Visualization.- 5 Data Mining for Cross-Sectional Data.- 6 Data Mining for Temporal Data.- 7 Process Analysis.- 8 Analysis of Multiple Business Perspectives.- 9 Summary.- A Survey on Business Intelligence Tools.

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Book SynopsisThe LNCS journal Transactions on Large-scale Data and Knowledge-centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 54th issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains three fully revised and extended papers and two additional extended keynotes selected from the 38th conference on Data Management - Principles, Technologies and Applications, BDA 2022. The topics cover a wide range of timely data management research topics on temporal graph management, tensor-based data mining, time-series prediction, healthcare analytics over knowledge graphs, and explanation of database query answers.Table of ContentsClock-G: Temporal graph management system.- TSPredIT: Integrated tuning of data preprocessing and time series prediction models.- A guide to the Tucker tensor decomposition for data mining: exploratory analysis, clustering and classification.- Challenges for Healthcare Data Analytics over Knowledge Graphs.- From Database Repairs to Causality in Databases and Beyond.

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Book SynopsisDeep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.Table of Contents

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Book SynopsisWith the proliferation of GPS devices in daily life, trajectory data that records where and when people move is now readily available on a large scale. As one of the most typical representatives, it has now become widely recognized that taxi trajectory data provides rich opportunities to enable promising smart urban services. Yet, a considerable gap still exists between the raw data available, and the extraction of actionable intelligence. This gap poses fundamental challenges on how we can achieve such intelligence. These challenges include inaccuracy issues, large data volumes to process, and sparse GPS data, to name but a few. Moreover, the movements of taxis and the leaving trajectory data are the result of a complex interplay between several parties, including drivers, passengers, travellers, urban planners, etc. In this book, we present our latest findings on mining taxi GPS trajectory data to enable a number of smart urban services, and to bring us one step closer to the vision of smart mobility. Firstly, we focus on some fundamental issues in trajectory data mining and analytics, including data map-matching, data compression, and data protection. Secondly, driven by the real needs and the most common concerns of each party involved, we formulate each problem mathematically and propose novel data mining or machine learning methods to solve it. Extensive evaluations with real-world datasets are also provided, to demonstrate the effectiveness and efficiency of using trajectory data. Unlike other books, which deal with people and goods transportation separately, this book also extends smart urban services to goods transportation by introducing the idea of crowdshipping, i.e., recruiting taxis to make package deliveries on the basis of real-time information. Since people and goods are two essential components of smart cities, we feel this extension is bot logical and essential. Lastly, we discuss the most important scientific problems and open issues in mining GPS trajectory data.Table of Contents1. Trajectory data map-matching 1.1 Introduction 1.2 Definitions and problem formulation 1.3 SD-Matching algorithm 1.4 Evaluations 1.5 Conclusions and discussions 2. Trajectory data compression 2.1 Introduction 2.2 Basic concepts and system overview 2.3 HCC algorithm 2.4 System implementation 2.5 Evaluations 2.6 Conclusions 3. Trajectory data protection 3.1 Introduction 3.2 Preliminary 3.3 Trajectory protection mechanism 3.4 Performance evaluations 3.5 Conclusions Part II: Enabling Smart Urban Services: Travellers 4. TripPlanner: Personalized trip planning leveraging heterogeneous trajectory data 4.1 Introduction 4.2 TripPlanner System 4.3 Dynamic network modelling 4.4 The two-phase approach 4.5 System evaluations 4.6 Conclusions and future work 5. ScenicPlanner: Recommending the most beautiful driving routes 5.1 Introduction 5.2 Preliminary 5.3 The two-phase approach 5.4 Experimental evaluations 5.5 Conclusion and future work Part III: Enabling Smart Urban Services: Drivers 6. GreenPlanner: Planning fuel-efficient driving routes 6.1 Introduction 6.2 Basic concepts and problem formulation 6.3 Personal fuel consumption model building 6.4 Fuel-efficient driving route planning 6.5 Evaluations 6.6 Conclusions and future work 7. Hunting or waiting: Earning more by understanding taxi service strategies 7.1 Introduction 7.2 Empirical study 7.3 Taxi strategy formulation 7.4 Understanding taxi service strategies 7.5 Conclusions Part IV: Enabling Smart Urban Services: Passengers 8. iBOAT: Real-time detection of anomalous taxi trajectories from GPS traces 8.1 Introduction 8.2 Preliminaries and problem definition 8.3 Isolation-based online anomalous trajectory detection 8.4 Empirical evaluations 8.5 Fraud behaviour analysis 8.6 Conclusions and future work 9. Real-Time imputing trip purpose leveraging heterogeneous trajectory data 9.1 Introduction 9.2 Basic concepts and problem statement 9.3 Imputing trip purposes 9.4 Enabling real-time response 9.5 Evaluations 9.6 Conclusions and future work Part V: Enabling Smart Urban Services: Urban Planners 10. GPS environment friendliness estimation with trajectory data 10.1 Introduction 10.2 Basic concepts 10.3 Methodology 10.4 Experiments 10.5 Limitations and future work 10.6 Conclusions 11. B-Planner: Planning night bus routes using taxi trajectory data 11.1 Introduction 11.2 Candidate bus stop identification 11.3 Bus route selection 11.4 Experimental evaluations 11.5 Conclusions and future work 12. VizTripPurpose: Understanding city-wide passengers’ travel behaviours 12.1 Introduction 12.2 System overview 12.3 Trip2Vec model 12.4 User interfaces 12.5 Case studies 12.6 Conclusions and future work Part VI: Enabling Smart Urban Services: Beyond People Transportation 13. CrowdDeliver: Arriving as soon as possible 13.1 Introduction 13.2 Basic concepts, assumptions and problem statement 13.3 Overview of CrowdDeliver 13.4 Two-phase approach 13.5 Evaluations 13.6 Conclusions and future work 14. CrowdExpress: Arriving by the user-specified deadline 14.1 Introduction 14.2 Preliminary, problem statement and system overview 14.3 Offline package transport network building 14.4 Online taxi scheduling and package routing 14.5 Experimental evaluations 14.6 Conclusions and future work Part VII: Open Issues and Conclusions 15. Open Issues 16. Conclusions

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Book SynopsisThis book presents the data privacy protection which has been extensively applied in our current era of big data. However, research into big data privacy is still in its infancy. Given the fact that existing protection methods can result in low data utility and unbalanced trade-offs, personalized privacy protection has become a rapidly expanding research topic.In this book, the authors explore emerging threats and existing privacy protection methods, and discuss in detail both the advantages and disadvantages of personalized privacy protection. Traditional methods, such as differential privacy and cryptography, are discussed using a comparative and intersectional approach, and are contrasted with emerging methods like federated learning and generative adversarial nets.The advances discussed cover various applications, e.g. cyber-physical systems, social networks, and location-based services. Given its scope, the book is of interest to scientists, policy-makers, researchers, and postgraduates alike.Table of Contents· Chapter 1: Introductiono Privacy research landscape o Personalized privacy overview o Contribution of this book o Remainder of the book · Chapter 2: Current Methods of Privacy Protection o Cryptography based methods o Differential privacy methods o Anonymity-based methods o Clustering-base methods o Machine learning and AI methods · Chapter 3: Privacy Attacks o Attack classification o Rationale of the attacks o The comparison of attacks · Chapter 4: Personalize Privacy Defense o Personalized privacy in cyber-physical systems o Personalized privacy in social networks o Personalized privacy in smart city o Personalized privacy in location-based services o Personalized privacy on the rise · Chapter 5: Future Directions o Trade-off optimization o Decentralized privacy protection o Privacy-preserving federated learning o Federated generative adversarial nets · Chapter6: Summary and Outlook

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Book SynopsisThis two volume set (CCIS 1451 and 1452) constitutes the refereed proceedings of the 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021 held in Taiyuan, China, in September 2021.The 81 papers presented in these two volumes were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on big data management and applications; social media and recommendation systems; infrastructure for data science; basic theory and techniques for data science; machine learning for data science; multimedia data management and analysis; ​social media and recommendation systems; data security and privacy; applications of data science; education research, methods and materials for data science and engineering; research demo.Table of ContentsBig Data Management and Applications.- Social Media and Recommendation Systems.- Infrastructure for Data Science.- Basic Theory and Techniques for Data Science.- Machine Learning for Data Science.- Multimedia Data Management and Analysis.

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Book SynopsisThis two volume set (CCIS 1451 and 1452) constitutes the refereed proceedings of the 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021 held in Taiyuan, China, in September 2021.The 81 papers presented in these two volumes were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on big data management and applications; social media and recommendation systems; infrastructure for data science; basic theory and techniques for data science; machine learning for data science; multimedia data management and analysis; ​social media and recommendation systems; data security and privacy; applications of data science; education research, methods and materials for data science and engineering; research demo.Table of ContentsSocial Media and Recommendation Systems.- Data Security and Privacy.- Applications of Data Science.- Education research, methods and materials for data science and engineering.- Research demo.

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Book SynopsisThe development of information technology has made it possible to collect large amounts of spatial data on a daily basis. It is of enormous significance when it comes to discovering implicit, non-trivial and potentially valuable information from this spatial data. Spatial co-location patterns reveal the distribution rules of spatial features, which can be valuable for application users. This book provides commercial software developers with proven and effective algorithms for detecting and filtering these implicit patterns, and includes easily implemented pseudocode for all the algorithms. Furthermore, it offers a basis for further research in this promising field.Preference-based co-location pattern mining refers to mining constrained or condensed co-location patterns instead of mining all prevalent co-location patterns. Based on the authors’ recent research, the book highlights techniques for solving a range of problems in this context, including maximal co-location pattern mining, closed co-location pattern mining, top-k co-location pattern mining, non-redundant co-location pattern mining, dominant co-location pattern mining, high utility co-location pattern mining, user-preferred co-location pattern mining, and similarity measures between spatial co-location patterns.Presenting a systematic, mathematical study of preference-based spatial co-location pattern mining, this book can be used both as a textbook for those new to the topic and as a reference resource for experienced professionals.Table of Contents

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Book SynopsisThis book provides a comprehensive overview of network behavior analysis that mines Internet traffic data in order to extract, model, and make sense of behavioral patterns in Internet “objects” such as end hosts, smartphones, Internet of things, and applications. The objective of this book is to fill the book publication gap in network behavior analysis, which has recently become an increasingly important component of comprehensive network security solutions for data center networks, backbone networks, enterprise networks, and edge networks.The book presents fundamental principles and best practices for measuring, extracting, modeling and analyzing network behavior for end hosts and applications on the basis of Internet traffic data. In addition, it explains the concept and key elements (e.g., what, who, where, when, and why) of communication patterns and network behavior of end hosts and network applications, drawing on data mining, machine learning, information theory, probabilistic graphical and structural modeling to do so. The book also discusses the benefits of network behavior analysis for applications in cybersecurity monitoring, Internet traffic profiling, anomaly traffic detection, and emerging application detections.The book will be of particular interest to researchers and practitioners in the fields of Internet measurement, traffic analysis, and cybersecurity, since it provides a spectrum of innovative techniques for summarizing behavior models, structural models, and graphic models of Internet traffic, and explains how to leverage the results for a broad range of real-world applications in network management, security operations, and cyber-intelligent analysis. After finishing this book, readers will 1) have learned the principles and practices of measuring, modeling, and analyzing network behavior on the basis of massive Internet traffic data; 2) be able to make sense of network behavior for a spectrum of applications ranging from cybersecurity and network monitoring to emerging application detection; and 3) understand how to explore network behavior analysis to complement traditional perimeter-based firewall and intrusion detection systems in order to detect unusual traffic patterns or zero-day security threats using data mining and machine learning techniques. To ideally benefit from this book, readers should have a basic grasp of TCP/IP protocols, data packets, network flows, and Internet applications.Table of ContentsChapter 1: Introduction.- Chapter 2: Background of Network Behavior Modeling and Analysis.- Chapter 3: Behavior Modeling of Network Traffic.- Chapter 4: Structural Modeling of Network Traffic.- Chapter 5: Graphic Modeling of Network Traffic.- Chapter 6: Real-Time Network Behavior Analysis.- Chapter 7: Applications.- Chapter 8: Research Frontiers of Network Behavior Analysis.

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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 SynopsisThe book features original papers from International Conference on Computational Methods and Data Engineering (ICCMDE 2021), organized by School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India, during November 25–26, 2021. The book covers innovative and cutting-edge work of researchers, developers, and practitioners from academia and industry working in the area of advanced computing.Table of ContentsChapter 1. A Graph Based Extractive Assamese Text Summarization.- Chapter 2. Internet of Things (IoT) for Secure Data and M2M Communications.- Chapter 3. Deveelopment of Walking Assistants for Visually Challenged Person.- Chapter 4. A PERFORMANCE STUDY OF PREDICTION MODELS FOR DIABETES PREDICTION USING MACHINE LEARNING.- Chapter 5. Orthopantomogram (OPG) Image Analysis using Bounding Box Algorithm.- Chapter 6. Design and Analysis of an Improved Artificial Neural Network Controller for the Energy Efficiency Enhancement of Wind Power Plant.- Chapter 7. Detection of Renal Calculi using Convolutional Neural Networks.- Chapter 8. Question Answering and Text Generation using BERT and GPT-2 Model.- Chapter 9. Improved Lenet Model for Flower Classification Using GPU Computing.- Chapter 10. Distributed computing over the Next Generation Mobile Communication Network (NGMCN) is an emerging technology.- Chapter 11. The ELF Tribe: Redefining Primary Education in a Post-COVID Era.- Chapter 12. An Extreme Machine Learning Mdel for Evaluating Landslide Hazard Zonation in Nilgiris District, Causative Factors and Risk Assessment using Earth Observation Techniques.- Chapter 13. Analysis of Cross-Site Scripting Vulnerabilities in Various Day-to-Day Web Applications.- Chapter 14. Detecting Cyberbullying with text classification using 1DCNN and Glove Embeddings.- Chapter 15. A Bayesian Network Based Software Requirement Complexity Prediction Model.

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Book SynopsisThis book presents high-quality research papers presented at 2nd International Conference on Smart Data Intelligence (ICSMDI 2022) organized by Kongunadu College of Engineering and Technology at Trichy, Tamil Nadu, India, during April 2022. This book brings out the new advances and research results in the fields of algorithmic design, data analysis, and implementation on various real-time applications. It discusses many emerging related fields like big data, data science, artificial intelligence, machine learning, and deep learning which have deployed a paradigm shift in various data-driven approaches that tends to evolve new data-driven research opportunities in various influential domains like social networks, healthcare, information, and communication applications.Table of ContentsDetection and Analysis of Sentiments on Twitter Using Machine Learning Algorithm.- Malware Attack Detection on IoT Devices Using Machine Learning.- BeSafe: IoT Based Safety Band.- Stock Market Analysis and forecasting with Statistical and Deep Learning Methods.- The paradigm shift in Higher Education from traditional learning to digitalisation.- Designing a socially intelligent system by cognitive modeling of human-environment interaction.- Web-Based recycle Waste Management for Ecommerce.

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Book SynopsisThis book constitutes the refereed proceedings of the 21st International Symposium on Knowledge and Systems Sciences, KSS 2022, held in Beijing, China, in June 2022.The 14 revised full papers and 3 short paper presented were carefully reviewed and selected from 51 submissions. The papers are organized in topical secions on ​data mining and machine learning; model-based systems engineering; complex systems modeling and knowledge technologies.Table of ContentsData Mining and Machine Learning.- Model-based Systems Engineering.- Complex Systems Modeling and Knowledge Technologies.

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Book SynopsisThis book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2022), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from 26 to 27 March 2022. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.Table of ContentsCancer Prognosis by Using Machine Learning and Data Science: A systematic review.- Understanding Agriculture Scenario of Punjab: A Qualitative Research of Crop parameter in relation to fertilizer usage.- Face Detection Based Border Security System Using Haar-Cascade and LBPH Algorithm.- Proposed Experimental Design of a Portable COVID-19 Screening Device Using Cough Audio Samples.- Big Data Framework for Analytics Business Intelligence.- Technological Impacts of AI on Hospitality and Tourism Industry.- Improvement of Real-Time Kinematic Positioning Using Kalman Filter-Based Singular Spectrum Analysis during Geomagnetic Storm for Thailand sector.

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Book SynopsisThis book constitutes the refereed proceedings of the 20th Australasian Conference on Data Mining, AusDM 2022, held in Western Sydney, Australia, during December 12–15, 2022. The 17 full papers included in this book were carefully reviewed and selected from 44 submissions. They were organized in topical sections as ​research track and application track.Table of Contents​Research Track.- Measuring Content Preservation in Textual Style Transfer.- A Temperature-Modified Dynamic Embedded Topic Model.- Measuring Difficulty of Learning using Ensembles Methods.- Graph Embeddings for Non-IID Data Feature Representation Learning.- Enhancing Understandability of Omics Data with SHAP, Embedding Projections and Interactive Visualisations.- WinDrift: Early Detction of Concept Drift through the use of Corresponding and Hierarchical Time Windows.- Investigation of Explainability Techniques for Multimodal Transformers.- Effective Imbalance Learning Utilizing Informative Data.- Interpretable Decision Tree via Human-In-The-Loop-Learning.- Application Track.- A Comparative Look at the Resilience of Discriminative and Generative Classifiers to Missing Data in Longitudinal Datasets.- Hierarchical Topic Model Inference by Community Discovery on Word Co-Occurrence Networks.- UMLS-Based Question-Answering Approach for Automatic Initial Frailty Assessment.- Natural Language Query For Technical Knowledge Graph Navigation.- Decomposition of Service Level Encoding for Anomaly Detcion.- Improving Ads-Profitability Using Traffic Fingerprints.- Attractiveness Analysis for Health Claims on Food Packages.- SchedmaDB: A Dataset for Structures in Relational Data.

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Book SynopsisLoTraNet: Locality-guided Transformer Network for Image Manipulation Localization.- Progressive EMD-based Trajectory Prediction: A Multistage Approach for Enhanced Human Trajectory Forecasting.- Dual-Level Contrastive Learning Framework.- DLAFormer: A Novel Approach to Image Super-Resolution with Comprehensive Attention Mechanisms.- Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semantics.- CoMISI: Multimodal Speaker Identification in Diverse Audio-Visual Conditions through Cross-Modal Interaction.- Multi-scale Spatial Feature Aggregation For Effcient Super Resolution.- SCANet: Split Coordinate Attention Network for Building Footprint Extraction.- XFusion: Cross-Attention Transformer for Multi-Focus Image Fusion.- Guided DiffusionDet: Guided Diffusion Model for Object Detection with Resample Mechanism.- Mutual Information-based Mixed Precision Quantization.- MLLM-Driven Semantic Enhancement and Alignment for Text-Based Person Search.- TFCM: Tuning-Free Facial Concept-Erasure in Text-to-Image Models through Attention and Sample Modulation.- Selecting the Best Sequential Transfer Path for Medical Image Segmentation with Limited Labeled Data.- Knowledge Distillation with Differentiable Optimal Transport on Graph Neural Networks.- Test-Time Intensity Consistency Adaptation for Shadow Detection.- Learning from Noisy Labels for Long-tailed Data via Optimal Transport.- LCRPS: Large-Capacity Residual Plane Steganography Based on Multiple Adversarial Networks.- Aesthetics-Guided Multi-scale Feature Fusion for Style Transfer.- BEVRoad: A Cross-Modal and Temporary-Recurrent 3D Object Detector for Infrastructure Perception.- Dilated Pyramid Attention in Hierarchical Vision Transformer for Texture Recognition.- Attention-based Domain Adaptive YOLO For Cross-domain Object Detection.- In-WSOD: Integrality Weakly Supervised Object Detection with Classification and Localization Consistency.- GLEGNet: Infrared and Visible Image Fusion Via Global-Local Feature Extraction and Edge-Gradient Preservation.- Mending of Spatio-Temporal Dependencies in Block Adjacency Matrix.- CaDT-Net: A Cascaded Deformable Transformer Network for Multiclass Breast Cancer Histopathological Image Classification.- DIFA: Deformable Implicit Feature Alignment for Roadside Cooperative Perception.- Transferring Teacher’s Invariance to Student Through Data Augmentation Optimization.- AARR-Net: An Attention Assistance Feature Fusion and Model Recursive Recovery Network for Category-level 6D Object Pose Estimation.- BRS-YOLO: A Balanced Optical Remote Sensing Object Detection Method.- HDKI: A Hierarchical Deep Koopman Framework for Spatio-Temporal Prediction with Image Observations.

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Book SynopsisThis two-volume set (CCIS 1879 and 1880) constitutes the refereed proceedings of the 9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023 held in Harbin, China, during September 22–24, 2023. The 52 full papers and 14 short papers presented in these two volumes were carefully reviewed and selected from 244 submissions. The papers are organized in the following topical sections:Part I: Applications of Data Science, Big Data Management and Applications, Big Data Mining and Knowledge Management, Data Visualization, Data-driven Security, Infrastructure for Data Science, Machine Learning for Data Science and Multimedia Data Management and Analysis.Part II: Data-driven Healthcare, Data-driven Smart City/Planet, Social Media and Recommendation Systems and Education using big data, intelligent computing or data mining, etc.Table of Contents​Applications of Data Science.- Construction of Software Design and Programming Practice Course in Information and Communication Engineering.- A Self-Attention-Based Stock Prediction Method Using Long Short-Term Memory Network Architecture.- CAD-based Research on the Design of a Standard Unit Cabinet for Custom Furniture of the Cabinet Type.- An Improved War Strategy Optimization Algorithm for Big Data Analytics.- Research on Path Planning of Mobile Robots Based on Dyna-RQ.- Small Target Helmet Wearing Detection Algorithm based on Improved YOLO V5.- Research on Dance Evaluation Technology based on Human Posture Recognition.- Multiple-channel Weight-based CNN Fault Diagnosis Method.- Big Data Management and Applications.- Design and Implementation of Key-Value Database for Ship Virtual Test Platform Based on Distributed System.- Big Data Mining and Knowledge Management.- Research on Multi-modal Time Series Data Prediction Method Based on Dualstage Attention Mechanism.- Prediction of Time Series Data with Low Latitude Features.- Lightweight and Efficient Attention-based Superresolution Generative Adversarial Networks.- The Multisource Time Series Data Granularity Conversion Method.- Outlier Detection Model Based on Autoencoder and Data Augmentation for High-Dimensional Sparse Data.- Dimension Reduction Based on Sampling.- Complex Time Series Analysis Based on Conditional Random Fields.- Feature Extraction of Time Series Data Based on CNNCBAM.- Optimization of a Network Topology Generation Algorithm based on Spatial Information Network.- Data Visualization.- MBTIviz: A Visualization System for Research on Psycho-demographics and Personality.- Data-driven Security.- Distributed Implementation of SM4 Block Cipher Algorithm based on SPDZ Secure Multi-party Computation Protocol.- DP-ASSGD: Differential Privacy Protection Based on Stochastic Gradient Descent Optimization.- Study on Tourism Workers ’ Intercultural Communication Competence.- A Novel Federated Learning with Bidirectional Adaptive Differential Privacy.- Chaos-Based Construction of LWEs in Lattice-Based Cryptosystems.- Security Compressed Sensing Image Encryption Algorithm Based on Elliptic Curve.- Infrastructure for Data Science.- Two-dimensional Code Transmission System Based on Side Channel Feedback.- An Updatable and Revocable Decentralized Identity Management Scheme based on Blockchain.- Cloud-Edge Intelligent Collaborative Computing Model based on Transfer Learning in IoT.- Design and Validation of a Hardware-in-the-loop based Automated Driving Simulation Test Platform.- Machine Learning for Data Science.- Improving Transferability Reversible Adversarial Examples based on Flipping Transformation.- Rolling Iterative Prediction for Correlated Multivariate Time Series.- Multimedia Data Management and Analysis.- Video Popularity Prediction Based on Knowledge Graph and LSTM Network.- Design and Implementation of Speech Generation and Demonstration Research Based on Deep Learning.- Testing and Improvement of OCR Recognition Technology in Export-OrientedChinese Dictionary APP.

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Book SynopsisThe six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields. Table of ContentsText to Image Generation with Conformer-GAN.- MGFNet: A Multi-Granularity Feature Fusion and Mining Network for Visible-Infrared Person Re-Identification.- Isomorphic Dual-Branch Network for Non-homogeneous Image Dehazing and Super-Resolution.- Hi-Stega : A Hierarchical Linguistic Steganography Framework Combining Retrieval and Generation.- Effi-Seg: Rethinking EfficientNet Architecture for Real-time Semantic Segmentation.- Quantum Autoencoder Frameworks for Network Anomaly Detection.- Spatially-Aware Human-Object Interaction Detection with Cross-Modal Enhancement.- Intelligent trajectory tracking control of unmanned parafoil system based on SAC optimized LADRC.- CATS: Connection-aware and Interaction-based Text Steganalysis in Social Networks.- Syntax Tree Constrained Graph Network for Visual Question Answering.- CKR-Calibrator: Convolution Kernel Robustness Evaluation and Calibration.- SGLP-Net: Sparse Graph Label Propagation Network for Weakly-Supervised Temporal Action Localization.- VFIQ: A Novel Model of ViT-FSIMc Hybrid Siamese Network for Image Quality Assessment.- Spiking Reinforcement Learning for Weakly-supervised Anomaly Detection.- Resource-aware DNN Partitioning for Privacy-sensitive Edge-Cloud Systems.- A frequency reconfigurable multi-mode printed antenna.- Multi-view Contrastive learning for Knowledge-aware Recommendation.- PYGC: a PinYin Language Model Guided Correction Model for Chinese Spell Checking.- Empirical Analysis of Multi-label Classification on GitterCom using BERT.- A lightweight safety helmet detection network based on bidirectional connection module and Polarized Self-Attention.- Direct Inter-Intra View Association for Light Field Super-Resolution.- Responsive CPG-Based Locomotion Control for Quadruped Robots.- Vessel Behavior Anomaly Detection using Graph Attention Network.- TASFormer: Task-aware Image Segmentation Transformer.- Unsupervised Joint-Semantics Autoencoder Hashing for Multimedia Retrieval.- TKGR-RHETNE：A New Temporal Knowledge Graph Reasoning Model via Jointly Modeling Relevant Historical Event and Temporal Neighborhood Event Context.- High-Resolution Self-Attention with Fair Loss for Point Cloud Segmentation.- Transformer-based Video Deinterlacing Method.- SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack.- CSEC: A Chinese Semantic Error Correction Dataset for Written Correction.- Contrastive Kernel Subspace Clustering.- UATR: An Uncertainty Aware Two-stage Refinement Model for Targeted Sentiment Analysis.- AttIN: Paying More Attention to Neighborhood Information for Entity Typing in Knowledge Graphs.- Text-based Person Re-ID by Saliency Mask and Dynamic Label Smoothing.- Robust Multi-view Spectral Clustering with Auto-encoder for Preserving Information.- Learnable Color Image Zero-Watermarking Based on Feature Comparison.- P-IoU: Accurate Motion Prediction based Data Association for Multi-Object Tracking.- WCA-VFnet:a dedicated complex forest smoke fire detector.- Label Selection Algorithm Based on Ant Colony Optimization and Reinforcement Learning for Multi-label Classification.- Reversible Data Hiding Based on Adaptive Embedding with Local Complexity.- Generalized Category Discovery with Clustering Assignment Consistency.- CInvISP: Conditional Invertible Image Signal Processing Pipeline.- Ignored Details in Eyes: Exposing GAN-generated Faces by Sclera.- A Developer Recommendation Method Based on Disentangled.- Graph Convolutional Network.- Novel Method for Radar Echo Target Detection.

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Book Synopsis

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