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• Minimum Diameter $k$-Steiner Forest.- Factors Impacting the Label Denoising of Neural Relation Extraction.- Makespan Minimization on Unrelated Parallel Machines with a Few Bags.- Channel Assignment with r-Dynamic Coloring.- Profit Maximization Problem with Coupons in Social Networks.- A Bicriteria Approximation Algorithm for Minimum Submodular Cost Partial Multi-Cover Problem.- A Novel Approach to Verifying Context Free Properties of Programs.- Determination of Dual Distances for a Kind of Perfect Mixed Codes.- Approximation and Competitive Algorithms for Single-Minded Selling Problem.- An Empirical Analysis of Feasibility Checking Algorithms for UTVPI Constraints.- Quality-aware Online Task Assignment Using Latent Topic Model.- Calibration Scheduling with Time Slot Cost.- The k-power domination problem in weighted trees.- General Rumor Blocking: An Efficient Random Algorithm with Martingale Approach.- A Robust Power Optimization Algorithm to Balance Base Stations' Load in LTE-A Network.- Faster Compression of Patterns to Rectangle Rule Lists.- Algorithm Designs for Dynamic Ridesharing System.- New LP Relaxations for Minimum Cycle/Path/Tree Cover Problems.- Computation of Kullback-Leibler Divergence between Labeled Stochastic Systems with Non-Identical State Spaces.- Order preserving barrier coverage with weighted sensors on a line.- Achieving Location Truthfulness in Rebalancing Supply-Demand Distribution for Bike Sharing.- Approximation algorithms and a hardness result for the three-machine proportionate mixed-shop problem.- A New Algorithm Design Technique for Hard Problems, Building on Methods of Complexity Theory.- Community-based Acceptance Probability Maximization for Target Users on Social Networks.- Knowledge Graph Embedding Based on Subgraph-aware Proximity.
• (source: Nielsen Book Data)

This volume constitutes the proceedings of the 12th International Conference on Algorithmic Aspects in Information and Management, AAIM 2018, held in Dallas, Texas, USA, in December 2018. The 25 full papers presented were carefully reviewed and selected. The papers deal with current trends of research on algorithms, data structures, operation research, combinatorial optimization and their applications.
(source: Nielsen Book Data)

• Intro; Preface; Organization; Contents; On the Trustworthy Fulfillment of Commitments; 1 Motivation; 2 Computational Models of Commitment; 3 Problem Formulation; 4 Commitment Semantics; 4.1 Relationship to Other Commitment Semantics; 4.2 Semantics-Respecting Algorithms; 4.3 Semantics with Other Kinds of Uncertainty; 5 Implications for Non-Decision-Theoretic Agents; 6 Conclusions; References; Evaluating the Efficiency of Robust Team Formation Algorithms; 1 Introduction; 2 Problem Definition; 3 Related Work; 4 Approximations of the TORTF Problem; 4.1 Greedy Algorithms; 4.2 Genetic Algorithm

• 4.3 Linear Programming Approach5 Results and Discussion; 5.1 Datasets; 5.2 Results; 6 Conclusions; References; Social Welfare in One-Sided Matching Mechanisms; 1 Introduction; 1.1 Our Results; 1.2 Discussion and Related Work; 2 Preliminaries; 3 Price of Anarchy Guarantees; 4 Lower Bounds; 5 General Solution Concepts; 6 Extensions; 6.1 Price of Stability; 6.2 Unit-Range Representation; 7 Conclusion and Future Work; References; Using Multiagent Negotiation to Model Water Resources Systems Operations; 1 Introduction; 2 Related Work; 3 The Case Study; 4 The Negotiation Protocols

• 4.1 Point-Based Protocol4.2 Set-Based Protocol; 5 Simulations; 6 Conclusions; References; To Big Wing, or Not to Big Wing, Now an Answer; 1 Introduction; 1.1 The Battle of Britain; 1.2 The Lanchester Model; 1.3 Agent Based Models; 2 Model Design; 2.1 RAF Forces; 2.2 German Forces; 2.3 Model Functionality; 3 Experiments; 4 Results; 5 Conclusion; References; How Testable Are BDI Agents? An Analysis of Branch Coverage; 1 Introduction; 2 Belief-Desire-Intention (BDI) Agents; 3 All-Edge Coverage Analysis; 3.1 Removing Failure Handling; 3.2 Simplifying for Uniform Programs

• 4 All-Edges vs. All-Paths5 BDI vs. Procedural; 6 Conclusion; References; Dynamics of Fairness in Groups of Autonomous Learning Agents; 1 Introduction; 2 Multiplayer Ultimatum Game; 2.1 Sub-game Perfect Equilibrium; 3 Learning Model; 4 Results; 5 Discussion and Conclusion; References; Using Stackelberg Games to Model Electric Power Grid Investments in Renewable Energy Settings; 1 Introduction; 2 Related Work; 3 Curtailment Rules; 3.1 Effects of Curtailment Strategies on Renewable Capacity Utilisation
• An Illustration; 4 Renewable Investment in Single Locations

• 4.1 Individual Generator Incentives4.2 Total Generation Capacity; 5 Transmission Investment in Multiple Locations; 5.1 Implementation in Areas with High Curtailment; 5.2 Transmission Investment as a Stackelberg Game; 6 Network Upgrade Case Study; 7 Conclusions and Future Work; References; Multi-scale Simulation for Crowd Management: A Case Study in an Urban Scenario; 1 Introduction; 2 Related Works; 3 A Multi-scale Model for the Simulation of Urban Scenarios; 3.1 The Discrete Microscopic Model; 3.2 The Mesoscopic Model; 3.3 Strategic Model; 4 Analysis of an Urban Scenario

This book features a selection of best papers from 11 workshops held at the International Conference on Autonomous Agents and Multiagent Systems, in Singapore in May 2016. The 11 full papers were carefully reviewed and selected for inclusion in this volume. They cover specific topics, both theoretical and applied, in the general area of autonomous agents and multiagent systems.

• A Language for Trust Modelling (TRUST workshop)
• Abstraction Methods for Solving Graph-Based Security Games (SECMAS workshop)
• Can I Do That? Discovering Domain Axioms Using Declarative Programming and Relational Reinforcement Learning (ARMS workshop)
• Simultaneous Optimization And Sampling Of Agent Trajectories Over A Network (OPTMAS workshop)
• POMDPs for Assisting Homeless Shelters
• Computational and Deployment Challenges (IDEAS workshop)
• Summarizing simulation results using causally-relevant states (MABS workshop)
• Augmenting Agent Computational Environments with Quantitative Reasoning Modules and Customisable Bridge Rules (EMAS workshop)
• Using Awareness to Promote Richer, More Human-Like Behaviors in Artificial Agents (ALA workshop)
• Using GDL to Represent Domain Knowledge for Automated Negotiations (ACAN workshop)
• Simulating Urban Growth with Raster and Vector models: A case study for the city of Can Tho, Vietnam (ABMUS workshop)
• Gamification of Multi-Agent Systems Theory Classes (COIN/CARE workshop)
• Analysis of Market Trend Regimes for March 2011 USDJPY Exchange Rate Tick Data (WEIN workshop).

This book constitutes the most visionary papers of the AAMAS 2016 Workshops, held in Singapore, Singapore, in May 2016. The 12 revised full papers presented were carefully reviewed and selected from the 12 workshops. They cover specific topics, both theoretical and applied, in the general area of autonomous agents and multiagent systems.

• Urban Systems Modelling.- Towards an Agent-Based Simulation of Housing in Urban Beirut.- Simulating Urban Growth with Raster and Vector models: A case study for the city of Can Tho, Vietnam.- Integrating Behavior and Microsimulation Models.- Agent-Based Modelling for Urban Planning Current Limitations and Future Trends.- Traffic Simulation in Urban Modelling.- Software Architecture for a Transparent and Versatile Traffic Simulation.- A Generic Software Framework for Carsharing Modelling based on a Large-Scale Multi-Agent Traffic Simulation Platform.- Mapping bicycling patterns with an agent-based model, census and crowdsourced data.- Transportation in Agent-Based Urban Modelling.- Applications.- Simulation-aided Crowd Management: a Multi-scale Model for an Urban Case Study.- A National Heat Demand Model for Germany.- How Smart is the Smart City? Assessing the Impact of ICT on Cities.
• (source: Nielsen Book Data)

This book constitutes revised, selected, and invited papers from the First International Workshop on Agent Based Modelling of Urban Systems, ABMUS 2016, held in conjunction with AAMAS 2016 in Singapore in May 2016. The 11 papers presented in this volume were carefully reviewed and selected from 20 submissions. They were organized in topical sections named: urban systems modeling; traffic simulation in urban modeling; and applications. .
(source: Nielsen Book Data)

• Invited Papers.- Information extraction and Integration.- Data Mining and Knowledge Discovery.- Indexing, Query Processing and Optimization.- Data Quality and Data Cleansing.- Distributed Data Platforms, Including Cloud Data Systems, Key-Value Stores, and Big Data Systems.- Streaming Data Analysis.- Web, XML and Semi-Structured Databases.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 22nd European Conference on Advances in Databases and Information Systems, ADBIS 2018, held in Budapest, Hungary, in September 2018.The 17 regular papers presented together with two invited papers were carefully selected and reviewed from numerous submissions. The papers are organized in topical sections such as information extraction and integration; data mining and knowledge discovery; indexing, query processing and optimization; data quality and data cleansing; distributed data platforms, including cloud data systems, key-value stores, and big data systems; and streaming data analysis; web, XML and semi-structured databases.
(source: Nielsen Book Data)

• Keynote Extended Abstracts.- Blockchains and Databases: Opportunities and Challenges for the Permissioned and the Permissionless.- Processing Temporal and Time Series Data: Present State and Future Challenges.- Integrating (Very) Heterogeneous Data Sources: a Structured and an Unstructured Perspective.- Data Access and Database Performance.- Upper Bound on the Size of FP-tree.- An Efficient Index for Reachability Queries in Public Transport Networks.- Context-Free Path Querying by Kronecker Product.- Pattern Sampling in Distributed Databases.-Can We Probabilistically Generate Uniformly Distributed Relation Instances Efficiently?.- Machine Learning.- Towards Proximity Graph Auto-Configuration: an Approach Based on Meta-Learning.- Fake News Detection Based on Subjective Opinions.- Improving on Coalitional Prediction Explanation.- Data Processing.- JSON Functionally.- Semantic Web.- Template-Based Multi-Solution for Data-to-Text Generation (on RDF).- Distributed Tree-Pattern Matching in Big Data Analytics Systems.- Data Analytics.- Iterations and Propensity Score Matching in MonetDB.- The Tell-Tale Cube.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 24th European Conference on Advances in Databases and Information Systems, ADBIS 2020, held in Lyon, France, in August 2020.* The 13 full papers presented were carefully reviewed and selected from 82 submissions. The papers cover a wide range of topics from different areas of research in database and information systems technologies and their advanced applications from theoretical foundations to optimizing index structures. They are organized in the following topical sections: keynote extended abstracts; data access and database performance; machine learning; semantic web; data processing; and data analytics. * The conference was held virtually due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Data Access and Database Performance.- Machine Learning.- Data Processing.- Semantic Web.- Data Analytics.
• (source: Nielsen Book Data)

This book constitutes thoroughly refereed short papers of the 24th European Conference on Advances in Databases and Information Systems, ADBIS 2020, held in August 2020. ADBIS 2020 was to be held in Lyon, France, however due to COVID-19 pandemic the conference was held in online format. The 18 presented short research papers were carefully reviewed and selected from 69 submissions. The papers are organized in the following sections: data access and database performance; machine learning; data processing; semantic web; data analytics.
(source: Nielsen Book Data)

This book constitutes the proceedings of the 12th International Conference on Advanced Data Mining and Applications, ADMA 2016, held in Gold Coast, Australia, in December 2016. The 70 papers presented in this volume were carefully reviewed and selected from 105 submissions. The selected papers covered a wide variety of important topics in the area of data mining, including parallel and distributed data mining algorithms, mining on data streams, graph mining, spatial data mining, multimedia data mining, Web mining, the Internet of Things, health informatics, and biomedical data mining.
(source: Nielsen Book Data)

• Database and Distributed Machine Learning.- Querying and Mining Strings Made Easy Distributed Training Large-Scale Deep Architectures.- Fault Detection and Localization in Distributed Systems using Recurrent Convolutional Neural Networks.- Discovering Group Skylines with Constraints by Early Candidate Pruning.- Comparing MapReduce-Based k-NN Similarity Joins On Hadoop For High-dimensional Data.- A Higher-Fidelity Frugal Quantile Estimator.- Recommender System.- Fair Recommendations Through Diversity Promotion.- A Hierarchical Bayesian Factorization Model for Implicit and Explicit Feedback Data.- Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities.- Group Recommender Model Based on Preference Interaction.- Identification of Grey Sheep Users By Histogram Inter
• section In Recommender Systems.- Social Network and Social Media.- A Feature-based Approach for the Redefined Link Prediction Problem in Signed Networks.- From Mutual Friends to Overlapping Community Detection: A Non-negative Matrix Factorization Approach.- Calling for Response: Automatically Distinguishing Situation-aware Tweets During Crises.- Efficient Revenue Maximization for Viral Marketing in Social Networks.- Generating Life Course Trajectory Sequences with Recurrent Neural Networks and Application to Early Detection on Social Disadvantage.- FRISK: A Multilingual Approach to Find twitteR InterestS via wiKipedia.- A Solution to Tweet-Based User Identification across Online Social Networks.- Machine Learning.- Supervised Feature Selection Algorithm Based on Low-Rank and Manifold Learning.- Mixed Membership Sparse Gaussian Conditional Random Fields.- Effects of Dynamic Subspacing in Random Forest.- Diversity and Locality in Multi-Component, Multi-Layer Predictive Systems: A Mutual Information Based Approach.- Hybrid Subspace Mixture Models For Prediction and Anomaly Detection in High Dimensions.- Classification and Clustering Methods.- StruClus: Scalable Structural Graph Set Clustering with Representative Sampling.- Employing Hierarchical Clustering and Reinforcement Learning for Attribute-based Zero-Shot Classification.- Environmental Sound Recognition using Masked Conditional Neural Networks.- Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure.- A Framework for Clustering and Dynamic Maintenance of XML Documents.- Language-independent Twitter Classification using Character-based Convolutional Networks.- Behavior Modeling and User Profiling.- Modeling Check-in Behavior with Geographical Neighborhood Influence of Venues.- An empirical study on collective online behaviors of extremist supporters. -Your Moves, Your Device: Establishing Behavior Profiles using Tensors.- An Approach for Identifying Author Profiles of Blogs.- Generating Topics of Interests for Research Communities.- An Evolutionary Approach for Learning Conditional Preference Network from Inconsistent Examples.- Bioinformatic and Medical Data Analysis.- Predicting Clinical Outcomes of Alzheimer's Disease from Complex Brain Networks.- Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties.- Multiclass Lung Cancer Diagnosis by Gene Expression Programming and Microarray Datasets.- Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers.- Spatio-temporal Data.- People-Centric Mobile Crowdsensing Platform for Urban Design.- Long-Term User Location Prediction Using Deep Learning and Periodic Pattern Mining.- An Intelligent Weighted Fuzzy Time Series Model Based on A Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting.- Mobile Robot Scheduling with Multiple Trips and Time Windows.- Natural Language Processing and Text Mining.- Feature Analysis for Duplicate Detection in Programming QA Communities.- A Joint Human/Machine Process for Coding Events and Conflict Drivers.- Quality Prediction of Newly Proposed Questions in CQA by Leveraging Weakly Supervised Learning.- Improving Chinese Sentiment Analysis via Segmentation-based Representation Using Parallel CNN.- Entity Recognition by Distant Supervision with Soft List Constraint.- Structured Sentiment Analysis.- Data Mining Applications.- Improving Real-Time Bidding Using a Constrained Markov Decision Process.- PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network.- Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments.- Color-sketch simulator: a guide for color-based visual known-item search.- Applications.- Making Use of External Company Data to Improve the Classification of Bank Transactions.- Mining Load Profile Patterns for Australian Electricity Consumers.- STA: a Spatio-temporal Thematic Analytics Framework for Urban Ground Sensing.- Privacy and Utility Preservation for Location Data Using Stay Region Analysis.- Location-aware Human Activity Recognition.- Demos.- SWYSWYK: a new Sharing Paradigm for the Personal Cloud.- Tools and Infrastructure for Supporting Enterprise Knowledge Graphs.- An Interactive Web-based Toolset for Knowledge Discovery from Short Text Log Data.- Carbon: Forecasting Civil Unrest Events by Monitoring News and Social Media.- A system for Querying and Analyzing Urban Regions.- Detect tracking behavior among trajectory data.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, held in Singapore in November 2017. The 20 full and 38 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers were organized in topical sections named: database and distributed machine learning; recommender system; social network and social media; machine learning; classification and clustering methods; behavior modeling and user profiling; bioinformatics and medical data analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos. .
(source: Nielsen Book Data)

• Learning Agents' Relations in Interactive Multiagent Dynamic Influence Diagrams.- Agent-Based Customer Profile Learning in 3G Recommender Systems: Ontology-Driven Multi-source Cross-Domain Case.- Modeling Temporal Propagation Dynamics in Multiplex Networks.- Mining Movement Patterns from Video Data to Inform Multi-agent Based Simulation.- Accessory-Based Multi-agent Simulating Platform on the Web.- Performance Evaluation of Agents and Multi-agent Systems Using Formal Specifications in Z Notation.- Reputation in Communities of Agent-Based Web Services Through Data Mining.- Data Mining Process Optimization in Computational Multi-agent Systems.- Diversifying the Storytelling Using Bayesian Networks.- A Coupled Similarity Kernel for Pairwise Support Vector Machine.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 10th International Workshop on Agents and Data Mining Interactions, ADMI 2014, held in Paris, France, in May 2014. The 11 papers presented were carefully reviewed and selected for inclusion in this volume.
(source: Nielsen Book Data)

• Analytical Solution for "EinStein wurfelt nicht!" with One Stone.- Toward Solving EinStein wurfelt nicht!.- Analysis of Fred Horn's Gloop Puzzle.- Set Matching: An Enhancement of the Hales-Jewett Pairing Strategy Playing Hanabi Near-Optimally Optimal Play of the Farkle Dice GameDeep df-pn and its Efficient Implementations.- Improved Policy Networks for Computer Go.- Exploring Positional Linear Go.- Influence of Search Depth on Position Evaluation.- Evaluating Chess-like Games Using Generated Natural Language Descriptions.- Machine Learning in the Game of Breakthrough.- A Curling Agent Based on the Monte-Carlo Tree Search Considering the Similarity of the Best Action among Similar States.- Exploration Bonuses Based on Upper Confidence Bounds for Sparse Developing a 2048 Player with Backward Temporal Coherence Learning and Restart.- A Little Bit of Frustration Can Go a Long Way.- Automated Adaptation and Assessment in Serious Games: A Portable Tool for Supporting Learning.- An Analysis of Majority Voting in Homogeneous Groups for Checkers: Understanding Group Performance through Unbalance.- Yasol: An Open Source Solver for Quantified Mixed Integer Programs.
• (source: Nielsen Book Data)

• Analytical Solution for EinStein w⦥lt nicht!" with One Stone
• Toward Solving EinStein w⦥lt nicht!
• Analysis of Fred Horn's Gloop Puzzle
• Set Matching: An Enhancement of the Hales-Jewett Pairing Strategy Playing Hanabi Near-Optimally Optimal Play of the Farkle Dice GameDeep df-pn and its Efficient Implementations
• Improved Policy Networks for Computer Go
• Exploring Positional Linear Go
• Influence of Search Depth on Position Evaluation
• Evaluating Chess-like Games Using Generated Natural Language Descriptions
• Machine Learning in the Game of Breakthrough
• A Curling Agent Based on the Monte-Carlo Tree Search Considering the Similarity of the Best Action among Similar States
• Exploration Bonuses Based on Upper Confidence Bounds for Sparse Developing a 2048 Player with Backward Temporal Coherence Learning and Restart
• A Little Bit of Frustration Can Go a Long Way
• Automated Adaptation and Assessment in Serious Games: A Portable Tool for Supporting Learning
• An Analysis of Majority Voting in Homogeneous Groups for Checkers: Understanding Group Performance through Unbalance
• Yasol: An Open Source Solver for Quantified Mixed Integer Programs.

This book constitutes the refereed conference proceedings of the 15th International Conference, ACG 2017, held in Leiden, The Netherlands, in July 2017.The 19 revised full papers were selected from 23 submissions and cover a wide range of computer games. They are grouped in four classes according to the order of publication: games and puzzles, go and chess, machine learning and MCTS, and gaming.
(source: Nielsen Book Data)

• An Introduction to Recommender Systems.- Neighborhood-Based Collaborative Filtering.- Model-Based Collaborative Filtering.- Content-Based Recommender Systems.- Knowledge-Based Recommender Systems.- Ensemble-Based and Hybrid Recommender Systems.- Evaluating Recommender Systems.- Context-Sensitive Recommender Systems.- Time- and Location-Sensitive Recommender Systems.- Structural Recommendations in Networks.- Social and Trust-Centric Recommender Systems.- Attack-Resistant Recommender Systems.- Advanced Topics in Recommender Systems.
• (source: Nielsen Book Data)

This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: - Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. - Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. - Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.
(source: Nielsen Book Data)

• Invited Talks.- Peer-to-Peer Data Mining, Privacy Issues, and Games.- Ontos Solutions for Semantic Web: Text Mining, Navigation and Analytics.- Robust Agent Communities.- WI Based Multi-aspect Data Analysis in a Brain Informatics Portal.- Agent and Data Mining.- Agent-Mining Interaction: An Emerging Area.- Evaluating Knowledge Intensive Multi-agent Systems.- Towards an Ant System for Autonomous Agents.- Semantic Modelling in Agent-Based Software Development.- Combination Methodologies of Multi-agent Hyper Surface Classifiers: Design and Implementation Issues.- Security in a Mobile Agent Based DDM Infrastructure.- Automatic Extraction of Business Rules to Improve Quality in Planning and Consolidation in Transport Logistics Based on Multi-agent Clustering.- Intelligent Agents for Real Time Data Mining in Telecommunications Networks.- Architecture of Typical Sensor Agent for Learning and Classification Network.- Self-organizing Multi-agent Systems for Data Mining.- Role-Based Decision Mining for Multiagent Emergency Response Management.- Agent Competition and Data Mining.- Virtual Markets: Q-Learning Sellers with Simple State Representation.- Fusion of Dependence Networks in Multi-agent Systems - Application to Support Net-Enabled Littoral Surveillance.- Multi-agent Framework for Simulation of Adaptive Cooperative Defense Against Internet Attacks.- On Competing Agents Consistent with Expert Knowledge.- On-Line Agent Teamwork Training Using Immunological Network Model.- Text Mining, Semantic Web, and Agents.- Combination of Rough Sets and Genetic Algorithms for Text Classification.- Multi-agent Meta-search Engine Based on Domain Ontology.- Efficient Search Technique for Agent-Based P2P Information Retrieval.- Classification of Web Documents Using Concept Extraction from Ontologies.- Emotional Cognitive Agents with Adaptive Ontologies.- Viral Knowledge Acquisition Through Social Networks.- Chinese Weblog Pages Classification Based on Folksonomy and Support Vector Machines.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Second International Workshop on Autonomous Intelligent Systems: Agents and Data Mining, AIS-ADM 2007, held in St. Petersburg, Russia in June 2007. The 17 revised full papers and six revised short papers presented together with four invited lectures cover agent and data mining, agent competition and data mining, as well as text mining, semantic Web, and agents.
(source: Nielsen Book Data)

• Machine generated contents note: Invited Papers
• A Probabilistic Rating System for Team Competitions with Individual Contributions / Sergey Nikolenko
• Sequential Hierarchical Image Recognition Based on the Pyramid Histograms of Oriented Gradients with Small Samples / Natalya S. Belova
• Discerning Depression Propensity Among Participants of Suicide and Depression-Related Groups of Vk.com / Maxim Kharchenko
• Tutorial
• Normalization of Non-standard Words with Finite State Transducers for Russian Speech Synthesis / Artem Lukanin
• Analysis of Images and Videos
• Transform Coding Method for Hyperspectral Data: Influence of Block Characteristics to Compression Quality / Ruslan Yuzkiv
• Frechet Filters for Color and Hyperspectral Images Filtering / Tat'yana Fedorova
• Fast Global Image Denoising Algorithm on the Basis of Nonstationary Gamma-Normal Statistical Model / Olga Krasotkina.

• Note continued: Theoretical Approach to Developing Efficient Algorithms of Fingerprint Enhancement / Maxim Pasynkov
• Remote Sensing Data Verification Using Model-Oriented Descriptors / Vladislav Myasnikov
• New Bi-, Tri-, and Fourlateral Filters for Color and Hyperspectral Images Filtering / Ivan Artemov
• Frequency Analysis of Gradient Descent Method and Accuracy of Iterative Image Restoration / Vladislav Kuznetsov
• Shape Matching Based on Skeletonization and Alignment of Primitive Chains / Oleg Seredin
• Color Image Restoration with Fuzzy Gaussian Mixture Model Driven Nonlocal Filter / Radhakrishnan Delhibabu
• A Phase Unwrapping Algorithm for Interferometric Phase Images / Andrey Sosnovsky
• Robust Image Watermarking on Triangle Grid of Feature Points / Victor Fedoseev
• Pattern Recognition and Machine Learning
• Traffic Flow Forecasting Algorithm Based on Combination of Adaptive Elementary Predictors / Vladislav Myasnikov.

• Note continued: Analysis of the Adaptive Nature of Collaborative Filtering Techniques in Dynamic Environment / Sheikh Muhammad Sarwar
• A Texture Fuzzy Classifier Based on the Training Set Clustering by a Self-Organizing Neural Network / Dmitry Lykom
• Learning Representations in Directed Networks / Sergey O. Bartunov
• Distorted High-Dimensional Binary Patterns Search by Scalar Neural Network Tree / Magomed Malsagov
• Hybrid Classification Approach to Decision Support for Endoscopy in Gastrointestinal Tract / Olga A. Buntseva
• User Similarity Computation for Collaborative Filtering Using Dynamic Implicit Trust / Mahamudul Hasan
• Similarity Aggregation for Collaborative Filtering / Dmitry I. Ignatov
• Distributed Coordinate Descent for L1-regularized Logistic Regression / Alexander Genkin
• Social Network Analysis
• Building Profiles of Blog Users Based on Comment Graph Analysis: The Habrahabr.ru Case / Rostislav Yavorskiy.

• Note continued: Formation and Evolution Mechanisms in Online Network of Students: The Vkontakte Case / Maria Yudkevich
• Large-Scale Parallel Matching of Social Network Profiles / Sergei Obiedkov
• Identification of Autopoietic Communication Patterns in Social and Economic Networks / Olga M. Zvereva
• Text Mining and Natural Language Processing
• A Heuristic Strategy for Extracting Terms from Scientific Texts / Natalia E. Efremova
• Text Analysis with Enhanced Annotated Suffix Trees: Algorithms and Implementation / Mikhail Dubov
• Morphological Analyzer and Generator for Russian and Ukrainian Languages / Mikhail Korobov
• Semantic Role Labeling for Russian Language Based on Russian FrameBank / Ilya Kuznetsov
• Supervised Approach to Finding Most Frequent Senses in Russian / Ilia Chetviorkin
• FrameBank: A Database of Russian Lexical Constructions / Egor Kashkin
• TagBag: Annotating a Foreign Language Lexical Resource with Pictures ... / Dmitry Ustalov.

• Note continued: BigARTM: Open Source Library for Regularized Multimodal Topic Modeling of Large Collections / Marina Dudarenko
• Industry Talk
• ATM Service Cost Optimization Using Predictive Encashment Strategy / Alois Knoll
• Industry Papers
• Comparison of Deep Learning Libraries on the Problem of Handwritten Digit Classification / Pavel Druzhkov
• Methods of Localization of Some Anthropometric Features of Face / Svetlana Volkova
• Ontological Representation of Networks for IDS in Cyber-Physical Systems / Vasily A. Sartakov
• Determination of the Relative Position of Space Vehicles by Detection and Tracking of Natural Visual Features with the Existing TV-Cameras / Filipp Gundelakh
• Implementation of Agile Concepts in Recommender Systems for Data Processing and Analyses / Nataly Zhukova.

This book constitutes the proceedings of the Fourth International Conference on Analysis of Images, Social Networks and Texts, AIST 2015, held in Yekaterinburg, Russia, in April 2015. The 24 full and 8 short papers were carefully reviewed and selected from 140 submissions. The papers are organized in topical sections on analysis of images and videos; pattern recognition and machine learning; social network analysis; text mining and natural language processing.
(source: Nielsen Book Data)

• Natural language processing
• General topics of data analysis
• Analysis of images and video
• Optimization problems on graphs and network structures
• Analysis of dynamic behavior through event data
• Social network analysis.

This book constitutes the proceedings of the 6th International Conference on Analysis of Images, Social Networks and Texts, AIST 2017, held in Moscow, Russia, in July 2017. The 29 full papers and 8 short papers were carefully reviewed and selected from 127 submissions. The papers are organized in topical sections on natural language processing; general topics of data analysis; analysis of images and video; optimization problems on graphs and network structures; analysis of dynamic behavior through event data; social network analysis.

• Machine generated contents note: pt. 1 State of the Art
• ch. 1 Introduction
• 1.1. Organization of the book
• 1.2. Utterance corpus
• 1.3. Datasets from the UCI repository
• 1.3.1. Wine dataset (wine)
• 1.3.2. Wisconsin breast cancer dataset (breast)
• 1.3.3. Handwritten digits dataset (Pendig)
• 1.3.4. Pima Indians diabetes (diabetes)
• 1.3.5. Iris dataset (Iris)
• 1.4. Microarray dataset
• 1.5. Simulated datasets
• 1.5.1. Mixtures of Gaussians
• 1.5.2. Spatial datasets with non-homogeneous inter-cluster distance
• ch. 2 State of the Art in Clustering and Semi-Supervised Techniques
• 2.1. Introduction
• 2.2. Unsupervised machine learning (clustering)
• 2.3. A brief history of cluster analysis
• 2.4. Cluster algorithms
• 2.4.1. Hierarchical algorithms
• 2.4.1.1. Agglomerative clustering
• 2.4.1.2. Divisive algorithms
• 2.4.2. Model-based clustering
• 2.4.2.1. The expectation maximization (EM) algorithm
• 2.4.3. Partitional competitive models.

• 2.4.3.1. K-means
• 2.4.3.2. Neural gas
• 2.4.3.3. Partitioning around Medoids (PAM)
• 2.4.3.4. Self-organizing maps
• 2.4.4. Density-based clustering
• 2.4.4.1. Direct density reachability
• 2.4.4.2. Density reachability
• 2.4.4.3. Density connection
• 2.4.4.4. Border points
• 2.4.4.5. Noise points
• 2.4.4.6. DBSCAN algorithm
• 2.4.5. Graph-based clustering
• 2.4.5.1. Pole-based overlapping clustering
• 2.4.6. Affectation stage
• 2.4.6.1. Advantages and drawbacks
• 2.5. Applications of cluster analysis
• 2.5.1. Image segmentation
• 2.5.2. Molecular biology
• 2.5.2.1. Biological considerations
• 2.5.3. Information retrieval and document clustering
• 2.5.3.1. Document pre-processing
• 2.5.3.2. Boolean model representation
• 2.5.3.3. Vector space model
• 2.5.3.4. Term weighting
• 2.5.3.5. Probabilistic models
• 2.5.4. Clustering documents in information retrieval
• 2.5.4.1. Clustering of presented results
• 2.5.4.2. Post-retrieval document browsing (Scatter-Gather)
• 2.6. Evaluation methods.

• 2.7. Internal cluster evaluation
• 2.7.1. Entropy
• 2.7.2. Purity
• 2.7.3. Normalized mutual information
• 2.8. External cluster validation
• 2.8.1. Hartigan
• 2.8.2. Davies Bouldin index
• 2.8.3. Krzanowski and Lai index
• 2.8.4. Silhouette
• 2.8.5. Gap statistic
• 2.9. Semi-supervised learning
• 2.9.1. Self training
• 2.9.2. Co-training
• 2.9.3. Generative models
• 2.10. Summary
• pt. 2 Approaches to Semi-Supervised Classification
• ch. 3 Semi-Supervised Classification Using Prior Word Clustering
• 3.1. Introduction
• 3.2. Dataset
• 3.3. Utterance classification scheme
• 3.3.1. Pre-processing
• 3.3.1.1. Utterance vector representation
• 3.3.2. Utterance classification
• 3.4. Semi-supervised approach based on term clustering
• 3.4.1. Term clustering
• 3.4.2. Semantic term dissimilarity
• 3.4.2.1. Term vector of lexical co-occurrences
• 3.4.2.2. Metric of dissimilarity
• 3.4.3. Term vector truncation
• 3.4.4. Term clustering
• 3.4.5. Feature extraction and utterance feature vector.

• 3.4.6. Evaluation
• 3.5. Disambiguation
• 3.5.1. Evaluation
• 3.6. Summary
• ch. 4 Semi-Supervised Classification Using Pattern Clustering
• 4.1. Introduction
• 4.2. New semi-supervised algorithm using the cluster and label strategy
• 4.2.1. Block diagram
• 4.2.1.1. Dataset
• 4.2.1.2. Clustering
• 4.2.1.3. Optimum cluster labeling
• 4.2.1.4. Classification
• 4.3. Optimum cluster labeling
• 4.3.1. Problem definition
• 4.3.2. The Hungarian algorithm
• 4.3.2.1. Weighted complete bipartite graph
• 4.3.2.2. Matching, perfect matching and maximum weight matching
• 4.3.2.3. Objective of Hungarian method
• 4.3.2.4. Complexity considerations
• 4.3.3. Genetic algorithms
• 4.3.3.1. Reproduction operators
• 4.3.3.2. Forming the next generation
• 4.3.3.3. GAs applied to optimum cluster labeling
• 4.3.3.4. Comparison of methods
• 4.4. Supervised classification block
• 4.4.1. Support vector machines
• 4.4.1.1. The kernel trick for nonlinearly separable classes
• 4.4.1.2. Multi-class classification
• 4.4.2. Example.

• 4.5. Datasets
• 4.5.1. Mixtures of Gaussians
• 4.5.2. Datasets from the UCI repository
• 4.5.2.1. Iris dataset (Iris)
• 4.5.2.2. Wine dataset (wine)
• 4.5.2.3. Wisconsin breast cancer dataset (breast)
• 4.5.2.4. Handwritten digits dataset (Pendig)
• 4.5.2.5. Pima Indians diabetes (diabetes)
• 4.5.3. Utterance dataset
• 4.6. An analysis of the bounds for the cluster and label approaches
• 4.7. Extension through cluster pruning
• 4.7.1. Determination of silhouette thresholds
• 4.7.2. Evaluation of the cluster pruning approach
• 4.8. Simulations and results
• 4.9. Summary
• pt. 3 Contributions to Unsupervised Classification -- Algorithms to Detect the Optimal Number of Clusters
• ch. 5 Detection of the Number of Clusters through Non-Parametric Clustering Algorithms
• 5.1. Introduction
• 5.2. New hierarchical pole-based clustering algorithm
• 5.2.1. Pole-based clustering basis module
• 5.2.2. Hierarchical pole-based clustering
• 5.3. Evaluation
• 5.3.1. Cluster evaluation metrics
• 5.4. Datasets.

• 5.4.1. Results
• 5.4.2. Complexity considerations for large databases
• 5.5. Summary
• ch. 6 Detecting the Number of Clusters through Cluster Validation
• 6.1. Introduction
• 6.2. Cluster validation methods
• 6.2.1. Dunn index
• 6.2.2. Hartigan
• 6.2.3. Davies Bouldin index
• 6.2.4. Krzanowski and Lai index
• 6.2.5. Silhouette
• 6.2.6. Hubert's & gamma;
• 6.2.7. Gap statistic
• 6.3. Combination approach based on quantiles
• 6.4. Datasets
• 6.4.1. Mixtures of Gaussians
• 6.4.2. Cancer DNA-microarray dataset
• 6.4.3. Iris dataset
• 6.5. Results
• 6.5.1. Validation results of the five Gaussian dataset
• 6.5.2. Validation results of the mixture of seven Gaussians
• 6.5.3. Validation results of the NCI60 dataset
• 6.5.4. Validation results of the Iris dataset
• 6.5.5. Discussion
• 6.6. Application of speech utterances
• 6.7. Summary.

"This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described"-- Provided by publisher.

• Machine generated contents note: pt. 1 State of the Art
• ch. 1 Introduction
• 1.1. Organization of the book
• 1.2. Utterance corpus
• 1.3. Datasets from the UCI repository
• 1.3.1. Wine dataset (wine)
• 1.3.2. Wisconsin breast cancer dataset (breast)
• 1.3.3. Handwritten digits dataset (Pendig)
• 1.3.4. Pima Indians diabetes (diabetes)
• 1.3.5. Iris dataset (Iris)
• 1.4. Microarray dataset
• 1.5. Simulated datasets
• 1.5.1. Mixtures of Gaussians
• 1.5.2. Spatial datasets with non-homogeneous inter-cluster distance
• ch. 2 State of the Art in Clustering and Semi-Supervised Techniques
• 2.1. Introduction
• 2.2. Unsupervised machine learning (clustering)
• 2.3. A brief history of cluster analysis
• 2.4. Cluster algorithms
• 2.4.1. Hierarchical algorithms
• 2.4.1.1. Agglomerative clustering
• 2.4.1.2. Divisive algorithms
• 2.4.2. Model-based clustering
• 2.4.2.1. The expectation maximization (EM) algorithm
• 2.4.3. Partitional competitive models.

• 2.4.3.1. K-means
• 2.4.3.2. Neural gas
• 2.4.3.3. Partitioning around Medoids (PAM)
• 2.4.3.4. Self-organizing maps
• 2.4.4. Density-based clustering
• 2.4.4.1. Direct density reachability
• 2.4.4.2. Density reachability
• 2.4.4.3. Density connection
• 2.4.4.4. Border points
• 2.4.4.5. Noise points
• 2.4.4.6. DBSCAN algorithm
• 2.4.5. Graph-based clustering
• 2.4.5.1. Pole-based overlapping clustering
• 2.4.6. Affectation stage
• 2.4.6.1. Advantages and drawbacks
• 2.5. Applications of cluster analysis
• 2.5.1. Image segmentation
• 2.5.2. Molecular biology
• 2.5.2.1. Biological considerations
• 2.5.3. Information retrieval and document clustering
• 2.5.3.1. Document pre-processing
• 2.5.3.2. Boolean model representation
• 2.5.3.3. Vector space model
• 2.5.3.4. Term weighting
• 2.5.3.5. Probabilistic models
• 2.5.4. Clustering documents in information retrieval
• 2.5.4.1. Clustering of presented results
• 2.5.4.2. Post-retrieval document browsing (Scatter-Gather)
• 2.6. Evaluation methods.

• 2.7. Internal cluster evaluation
• 2.7.1. Entropy
• 2.7.2. Purity
• 2.7.3. Normalized mutual information
• 2.8. External cluster validation
• 2.8.1. Hartigan
• 2.8.2. Davies Bouldin index
• 2.8.3. Krzanowski and Lai index
• 2.8.4. Silhouette
• 2.8.5. Gap statistic
• 2.9. Semi-supervised learning
• 2.9.1. Self training
• 2.9.2. Co-training
• 2.9.3. Generative models
• 2.10. Summary
• pt. 2 Approaches to Semi-Supervised Classification
• ch. 3 Semi-Supervised Classification Using Prior Word Clustering
• 3.1. Introduction
• 3.2. Dataset
• 3.3. Utterance classification scheme
• 3.3.1. Pre-processing
• 3.3.1.1. Utterance vector representation
• 3.3.2. Utterance classification
• 3.4. Semi-supervised approach based on term clustering
• 3.4.1. Term clustering
• 3.4.2. Semantic term dissimilarity
• 3.4.2.1. Term vector of lexical co-occurrences
• 3.4.2.2. Metric of dissimilarity
• 3.4.3. Term vector truncation
• 3.4.4. Term clustering
• 3.4.5. Feature extraction and utterance feature vector.

• 3.4.6. Evaluation
• 3.5. Disambiguation
• 3.5.1. Evaluation
• 3.6. Summary
• ch. 4 Semi-Supervised Classification Using Pattern Clustering
• 4.1. Introduction
• 4.2. New semi-supervised algorithm using the cluster and label strategy
• 4.2.1. Block diagram
• 4.2.1.1. Dataset
• 4.2.1.2. Clustering
• 4.2.1.3. Optimum cluster labeling
• 4.2.1.4. Classification
• 4.3. Optimum cluster labeling
• 4.3.1. Problem definition
• 4.3.2. The Hungarian algorithm
• 4.3.2.1. Weighted complete bipartite graph
• 4.3.2.2. Matching, perfect matching and maximum weight matching
• 4.3.2.3. Objective of Hungarian method
• 4.3.2.4. Complexity considerations
• 4.3.3. Genetic algorithms
• 4.3.3.1. Reproduction operators
• 4.3.3.2. Forming the next generation
• 4.3.3.3. GAs applied to optimum cluster labeling
• 4.3.3.4. Comparison of methods
• 4.4. Supervised classification block
• 4.4.1. Support vector machines
• 4.4.1.1. The kernel trick for nonlinearly separable classes
• 4.4.1.2. Multi-class classification
• 4.4.2. Example.

• 4.5. Datasets
• 4.5.1. Mixtures of Gaussians
• 4.5.2. Datasets from the UCI repository
• 4.5.2.1. Iris dataset (Iris)
• 4.5.2.2. Wine dataset (wine)
• 4.5.2.3. Wisconsin breast cancer dataset (breast)
• 4.5.2.4. Handwritten digits dataset (Pendig)
• 4.5.2.5. Pima Indians diabetes (diabetes)
• 4.5.3. Utterance dataset
• 4.6. An analysis of the bounds for the cluster and label approaches
• 4.7. Extension through cluster pruning
• 4.7.1. Determination of silhouette thresholds
• 4.7.2. Evaluation of the cluster pruning approach
• 4.8. Simulations and results
• 4.9. Summary
• pt. 3 Contributions to Unsupervised Classification -- Algorithms to Detect the Optimal Number of Clusters
• ch. 5 Detection of the Number of Clusters through Non-Parametric Clustering Algorithms
• 5.1. Introduction
• 5.2. New hierarchical pole-based clustering algorithm
• 5.2.1. Pole-based clustering basis module
• 5.2.2. Hierarchical pole-based clustering
• 5.3. Evaluation
• 5.3.1. Cluster evaluation metrics
• 5.4. Datasets.

• 5.4.1. Results
• 5.4.2. Complexity considerations for large databases
• 5.5. Summary
• ch. 6 Detecting the Number of Clusters through Cluster Validation
• 6.1. Introduction
• 6.2. Cluster validation methods
• 6.2.1. Dunn index
• 6.2.2. Hartigan
• 6.2.3. Davies Bouldin index
• 6.2.4. Krzanowski and Lai index
• 6.2.5. Silhouette
• 6.2.6. Hubert's & gamma;
• 6.2.7. Gap statistic
• 6.3. Combination approach based on quantiles
• 6.4. Datasets
• 6.4.1. Mixtures of Gaussians
• 6.4.2. Cancer DNA-microarray dataset
• 6.4.3. Iris dataset
• 6.5. Results
• 6.5.1. Validation results of the five Gaussian dataset
• 6.5.2. Validation results of the mixture of seven Gaussians
• 6.5.3. Validation results of the NCI60 dataset
• 6.5.4. Validation results of the Iris dataset
• 6.5.5. Discussion
• 6.6. Application of speech utterances
• 6.7. Summary.

"This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described"-- Provided by publisher

• Error bounds, sample compression schemes
• Statistical learning, theory, evolvability
• Exact and interactive learning
• Complexity of teaching models
• Inductive inference
• Online learning
• Bandits and reinforcement learning
• Clustering.

This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari, Italy, in October 2016, co-located with the 19th International Conference on Discovery Science, DS 2016. The 24 regular papers presented in this volume were carefully reviewed and selected from 45 submissions. In addition the book contains 5 abstracts of invited talks. The papers are organized in topical sections named: error bounds, sample compression schemes; statistical learning, theory, evolvability; exact and interactive learning; complexity of teaching models; inductive inference; online learning; bandits and reinforcement learning; and clustering.

• Intro; Preface; Organization; Contents; An Ecological View of Smart Home Technologies; Abstract; 1 Introduction; 2 Domotics as Home Automation; 3 The Ecology of the Smart Home; 3.1 A Smart Home Is not a HaaS; 3.2 Traditional Home Services; 3.3 The Smart Home as an Inside-Out Autonomous Robot; 4 Intelligent Services for the Smart Home; 4.1 Tool Services; 4.2 Housekeeping Services; 4.3 Advisor Services; 4.4 Media Services; 4.5 Categories of Service Are Based on Interaction; 5 Qualities and Show Stoppers for Smart Home Services; 5.1 Controllability; 5.2 Reliability and Maintainability

• 5.3 Usability5.4 Durability; 5.5 Security, Privacy and Trustworthiness; 6 Concluding Remarks; References; Modeling and Assessing Young Children Abilities and Development in Ambient Intelligence; Abstract; 1 Introduction; 2 Related Work; 2.1 Modelling User Abilities and Performance in Ambient Intelligence; 2.2 Software Assessment Tools; 3 Background; 3.1 Play and Its Contribution to Child's Development; 3.2 Knowledge Models and Assessment Tools; 4 The BEAN Framework; 4.1 Bean Model: A Knowledge-Based Data Model; 4.2 Reasoning Mechanism; 4.3 Reporting Facilities; 5 A Case Study: The Tower Game

• 6 Conclusions and Future WorkAcknowledgments; References; Augmented Home Inventories; Abstract; 1 Introduction; 2 Home Inventories: A Brief History; 3 New Household Items; 4 Emerging Home Entities and Societies; 5 Challenging the Home Inventory; 6 Conclusions; References; Ambient Intelligence from Senior Citizens' Perspectives: Understanding Privacy Concerns, Technology Acceptance, and Expectations; 1 Introduction; 2 Related Work; 3 Methods; 3.1 Sample; 3.2 Questionnaire Design; 4 Results; 4.1 Importance of Ambient Intelligence Features; 4.2 Acceptable System Limitations

• 4.3 Fears Associated with the Use of Ambient Intelligence Technologies4.4 Detailed Feature Comparison; 4.5 Comparison of Four Ambient Intelligence System Types; 5 Discussion and Summary; 5.1 Limitations; 5.2 Main Findings; 6 Summary; References; Person Identification by Analyzing Door Accelerations in Time and Frequency Domain; Abstract; 1 Introduction; 2 Background; 2.1 Physics and Acceleration Signal Description; 2.2 Related Work; 3 Time Domain Identification; 3.1 Feature-Based Identification; 3.2 Signal-Based Identification; 4 Frequency Domain Identification

• 4.1 Feature-Based Identification4.2 Signal-Based Identification; 5 Experiments; 5.1 Time Domain; 5.2 Frequency Domain; 5.3 Combining the Time and Frequency Domain Methods; 6 Conclusions; Acknowledgements; References; Design Factors for Flexible Capacitive Sensors in Ambient Intelligence; 1 Introduction; 2 Related Work; 3 Evaluating Flexible Capacitive Sensors; 4 Electrode Material Evaluation; 4.1 Measurement Setup; 4.2 Electrode Materials; 5 Results; 5.1 Self Capacitance Measurements; 5.2 Mutual Capacitance Measurements; 6 Design Factors; 6.1 On Materials; 6.2 On Size; 6.3 On Modes

This book constitutes the refereed proceedings of the 12th European Conference on Ambient Intelligence, AmI 2015, held in Athens, Greece, in November 2015. The 21 revised full papers presented together with 5 short papers were carefully reviewed and selected from 48 submissions. Over the past 20 years, the vision of Ambient Intelligence has gradually materialized into a plethora of technologies and devices, which are being introduced into almost every aspect of everyday life, thus affecting our abilities, activities, behavior and in the end, shaping a new way of thinking.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 32nd Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2018, held in Bergamo, Italy, in July 2018. The 16 full papers and 5 short papers presented were carefully reviewed and selected from 50 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections on administration, access control policies, privacy-preserving access and computation, integrity and user interaction, security analysis and private evaluation, fixing vulnerabilities, and networked systems.
(source: Nielsen Book Data)