%{search_type} search results

• 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)

• Preferences.- Manipulation.- Learning and other issues.- Utility and decision theory.- Agumentation.- Bribery and control.- Social choice.- Allocation and other problems.- Doctoral consortium.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed conference proceedings of the 4th International Conference on Algorithmic Decision Theory , ADT 2015, held in September 2015 in Lexington, USA. The 32 full papers presented were carefully selected from 76 submissions. The papers are organized in topical sections such as preferences; manipulation, learning and other issues; utility and decision theory; argumentation; bribery and control; social choice; allocation and other problems; doctoral consortium.
(source: Nielsen Book Data)

• Preferences and multi-criteria decision aiding.- Decision making and voting.- Game theory and decision theory.- Allocation and matching. .
• (source: Nielsen Book Data)

This book constitutes the conference proceedings of the 5th International Conference on Algorithmic Decision Theory , ADT 2017, held in Luxembourg, in October 2017.The 22 full papers presented together with 6 short papers, 4 keynote abstracts, and 6 Doctoral Consortium papers, were carefully selected from 45 submissions. The papers are organized in topical sections on preferences and multi-criteria decision aiding; decision making and voting; game theory and decision theory; and allocation and matching. .
(source: Nielsen Book Data)

• Monte-Carlo Tree Search and its enhancements
• Theoretical aspects and complexity.-Analysis of game characteristics
• Search algorithms
• Machine learning.

This book constitutes the thoroughly refereed post-conference proceedings of the 14th International Conference on Advances in Computer Games, ACG 2015, held in Leiden, The Netherlands, in July 2015. The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers cover a wide range of topics such as Monte-Carlo Tree Search and its enhancements; theoretical aspects and complexity; analysis of game characteristics; search algorithms; and machine learning.

• 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)

• Analysis of the impact of permissions on the vulnerability of mobile applications
• On the Relevance of Using Multi-layered Security in the Opportunistic Internet-of-Things
• Analysis of Software Vulnerabilities Using Machine Learning Techniques
• Africas Multilateral Legal Framework on Personal Data Security: What Prospects for the Digital Environment?
• A LoRaWAN coverage testBed and a multi-optional communication architecture for smart city feasibility in developing countries
• Effective Management of Delays at Road Intersections using Smart Traffic Light System
• Binary Search Based PSO for Master Node Enumeration and Placement in a Smart Water Metering Network
• State of Internet Measurement in Africa
• A Survey
• I2PA, U-prove, and Idemix: An Evaluation of Memory Usage and Computing Time Efficiency in an IoT Context
• A Hybrid Network Model embracing NB-IoT and D2D Communications : Stochastic Geometry Analysis
• Data Management and IT Applications
• Laws and Regulations on Big Data Management: The Case of South Africa
• Big Data Processing Using Hadoop and Spark: The Case of Meteorology Data
• Mobile health applications future trends and challenges
• AmonAI: a students academic performances prediction system
• Factors Influencing the Adoption of M-Government: Perspectives from a Namibian Marginalised Community
• Recent Approaches to Drift Effects in Credit Rating Models.

This book constitutes the thoroughly refereed proceedings of the 11th International Conference on e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2019, held in Porto-Novo, Benin, in December 2019. The 19 full papers were carefully selected from 46 submissions. The accepted papers provide a wide range of research topics including targeted infrastructures, Internet of Things (IoT), wireless and mobile networks, intelligent transportation systems (ITS), software and network security, cloud and virtualization, data analytics, and machine learning.

• Preface.- 1 Linear Algebra and Optimization: An Introduction.- 2 Linear Transformations and Linear Systems.- 3 Eigenvectors and Diagonalizable Matrices.- 4 Optimization Basics: A Machine Learning View.- 5 Advanced Optimization Solutions.- 6 Constrained Optimization and Duality.- 7 Singular Value Decomposition.- 8 Matrix Factorization.- 9 The Linear Algebra of Similarity.- 10 The Linear Algebra of Graphs.- 11 Optimization in Computational Graphs.- Index.
• (source: Nielsen Book Data)

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution's manual. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The "parent problem" of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.
(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)

• Architectures.- Mathematical foundations.- Algorithms.- Safety.- Understanding.- Human cognition.- Philosophy.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 10th International Conference on Artificial General Intelligence, AGI 2017, held in Melbourne, VIC, Australia, in August 2017. The 24 regular papers presented in this book together with 1 short paper were carefully reviewed and selected from 35 submissions. They cover topics such as architectures; mathematical foundations; algorithms; safety; understanding; human cognition; and philosophy.
(source: Nielsen Book Data)

• Hybrid Strategies Towards Safe "Self-Aware" Super-intelligent Systems.- Request Confirmation Networks in MicroPsi 2.- Task Analysis for Teaching Cumulative Learners.- Associative Memory: A Spiking Neural Network Robotic Implementation.- A Comprehensive Ethical Framework for AI Entities: Foundations.- Partial Operator Induction with Beta Distributions.- Solving Tree Problems with Category Theory.- Goal-directed Procedure Learning.- Can Machines Design? An Artificial General Intelligence Approach.- Resource-constrained Social Evidence Based Cognitive Model for Empathy-driven Artificial Intelligence.- Unsupervised Language Learning in OpenCog.- Functionalist Emotion Model in NARS.- Towards a Sociological Conception of Artificial Intelligence.- Efficient Concept Formation in Large State Spaces.- DSO Cognitive Architecture: Implementation and Validation of the Global Workspace Enhancement.- The Foundations of Deep Learning with a Path Towards General Intelligence.- Zeta Distribution and Transfer Learning Problem.- Vision System for AGI: Problems and Directions.- Semantic Image Retrieval by Uniting Deep Neural Networks and Cognitive Architectures.- The Temporal Singularity: Time-accelerated Simulated Civilizations and Their Implications.- A Computational Theory for Life-Long Learning of Semantics.- Cumulative Learning with Causal-Relational Models.- Transforming Kantian Aesthetic Principles into Qualitative Hermeneutics for Contemplative AGI Agents.- Towards General Evaluation of Intelligent Systems: Using Semantic Analysis to Improve Environments in the AIQ Test.- Perception from an AGI Perspective.- A Phenomenologically Justifiable Simulation of Mental Modeling.- A Time-critical Simulation of Language Comprehension.- How Failure Facilitates Success.- Adaptive Compressed Search.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 11th International Conference on Artificial General Intelligence, AGI 2018, held in Prague, Czech Republic, in August 2018. The 19 regular papers and 10 poster papers presented in this book were carefully reviewed and selected from 52 submissions. The conference encourage interdisciplinary research based on different understandings of intelligence, and exploring different approaches. As the AI field becomes increasingly commercialized and well accepted, maintaining and emphasizing a coherent focus on the AGI goals at the heart of the field remains more critical than ever.
(source: Nielsen Book Data)

• AGI and the Knight-Darwin Law: why idealized AGI reproduction requires collaboration.- Error-Correction for AI Safety.- Artificial Creativity Augmentation.- The hierarchical memory based on compartmental spiking neuron model.- The Dynamics of Growing Symbols: A Ludics Approach to Language Design by Autonomous Agents.- Approach for development of engineering tools based on knowledge graphs and context separation.- Towards Dynamic Process Composition in the DSO Cognitive Architecture.- SAGE: Task-Environment Platform for Evaluating a Broad Range of AI Learners.- Post-Turing Methodology: Breaking the Wall on the Way to Artificial General Intelligence.- Self-explaining AI as an alternative to interpretable AI.- AGI needs the Humanities.- A report of a recent book: "AI and Human Thought and Emotion".- Cognitive Machinery and Behaviours.- Combinatorial Decision Dags: A Natural Computational Model for General Intelligence.- What Kind of Programming Language Best Suits Integrative AGI?.- Guiding Symbolic Natural Language Grammar Induction via Transformer-Based Sequence Probabilities.- Embedding Vector Differences Can Be Aligned With Uncertain Intensional Logic Differences.- Delta Schema Network in Model-based Reinforcement Learning.- Information Digital Twin{Enabling Agents to Anticipate Changes in their Tasks.- 'OpenNARS for Applications': Architecture and Control.- Towards AGI Agent Safety by Iteratively Improving the Utility Function.- Learning to Model Another Agent's Beliefs: A Preliminary Approach.- An Attentional Control Mechanism for Reasoning and Learning.- Hyperdimensional Representations in Semiotic Approach to AGI.- The Conditions of Artificial General Intelligence: Logic, Autonomy, Resilience, Integrity, Morality, Emotion, Embodiment, and Embeddedness.- Position paper: The use of engineering approach in creation of artificial general intelligence.- How do you test the strength of AI?.- Omega: An Architecture for AI Unification.- Analyzing Elementary School Olympiad Math Tasks as a Benchmark for AGI.- The meaning of things as a concept in a strong AI architecture.- Toward a General Believable Model of Human-Analogous Intelligent Socially Emotional Behavior.- Autonomous Cumulative Transfer Learning.- New Brain Simulator II Open-Source Software.- Experience-specific AGI Paradigms.- Psychological portrait of a virtual agent in the Teleport game paradigm.- Logical probabilistic biologically inspired cognitive architecture.- An Architecture for Real-time Reasoning and Learning.- A Model for Artificial General Intelligence.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th International Conference on Artificial General Intelligence, AGI 2020, held in St. Petersburg, Russia, in September 2020.The 30 full papers and 8 short papers presented in this book were carefully reviewed and selected from 60 submissions. The papers cover topics such as AGI architectures, artificial creativity and AI safety, transfer learning, AI unification and benchmarks for AGI.
(source: Nielsen Book Data)

• Self modification of policy and utility function in rational agents
• Avoiding wireheading with value reinforcement learning
• Death and suicide in universal Artificial Intelligence
• Ultimate Intelligence: Physical complexity and limits of inductive systems
• Open ended intelligence
• The AGI containment problem
• Imitation learning as cause-effect reasoning
• Some theorems in incremental compression
• Rethinking sigma's graphical architecture: An extension to neural networks.

This book constitutes the refereed proceedings of the 9th International Conference on Artificial General Intelligence, AGI 2016, held in New York City, NY, USA, in July 2016 as part of HLAI 2016, the Joint Multi-Conference on Human-Level Artificial Intelligence 2016. The 24 full papers, 2 short papers, and 10 poster papers presented were carefully reviewed and selected from 67 submissions. AGI research differs from the ordinary AI research by stressing on the versatility and wholeness of intelligence, and by carrying out the engineering practice according to an outline of a system comparable to the human mind inSelf a certain sense.

• Title page; Preface; Contents; Introduction: Aspects of Artificial General Intelligence; A Collection of Definitions of Intelligence; Four Contemporary AGI Designs: A Comparative Treatment; A Foundational Architecture for Artificial General Intelligence; A Working Hypothesis for General Intelligence; From NARS to a Thinking Machine; Adaptive Algorithmic Hybrids for Human-Level Artificial Intelligence; Cognitive Map Dimensions of the Human Value System Extracted from Natural Language; Program Evolution for General Intelligence.

Examines the creation of software programs displaying broad, deep, human-style general intelligence. This work features papers presented at the 2006 AGIRI (Artificial General Intelligence Research Institute) workshop, which illustrates that it is a fit and proper subject for serious science and engineering exploration.

• A Two-Stage Bi-LSTM Model for Chinese Company Name Recognition.- Multi-modal Multi-scale Speech Expression Evaluation in Computer-Assisted Language Learning.- From Global to Local: Local Popularity Prediction Using Context-embedded LSTM Recurrent Network.- Matching Low-Quality photo to DSLR-Quality with Deep Convolutional Networks.- Learning Frame-Level Recurrent Neural Networks Representations for Query-by-Example Spoken Term Detection on Mobile Devices.- Plant Identification based on Image Set Analysis.- Economic Index Forecasting via Multi-Scale Recursive Dynamic Factor Analysis.- Sub-Goal Oriented A* Search.- Towards Efficient Mobile Augmented Reality in Indoor Environments.- MAD-API: Detection, Correction and Explanation of API Misuses in Distributed Android Applications.- Relaxed Event-triggered Control of Networked Control Systems under Denial of Service Attacks.- Sentiment Analysis Based on Hybrid Bi-Attention mechanism in Mobile Application.- Automotive Diagnostics as a Service: An Artificially Intelligent Mobile Application for Tire Condition Assessment.- AICDS: An infant crying detection system based on lightweight convolutional neural network.- Exploring Trends of Lung Cancer Research Based on Word Representation.- Effective Facial Obstructions Removal With Enhanced Cycle-Consistent Generative Adversarial Networks.- Applied Analysis of Social Network Data in Personal Credit Evaluation.- Deep Neural Network Based Frame Reconstruction For Optimized Video Coding.- Detection and tracking of moving objects system for indoor mobile robots with a low-cost laser scanner.- Using IT/IS Applications to Empower Physically Challenged Individuals to Enjoy a High Quality of Life.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the International Conference on Artificial Intelligence and Mobile Services, AIMS 2018, held as part of SCF 2018, in Seattle, WA, USA, in June 2018. The 20 papers presented in this volume were carefully reviewed and selected from numerous submissions. The papers cover different aspects of mobile services from business management to computing systems, algorithms and applications. They promote technological technological innovations in research and development of mobile services, including, but not limited to, wireless and sensor networks, mobile and wearable computing, mobile enterprise and eCommerce, ubiquitous collaborative and social services, machine-to-machine and Internet-of-things, clouds, cyber-physical integration, and big data analytics for mobility-enabled services.
(source: Nielsen Book Data)

• Research Track.- Infant Sound Classification on Multi-stage CNNs with Hybrid Features and Prior Knowledge.- Building Vector Representations for Candidates and Projects in a CV Recommender System.- Candidate Classification and Skill Recommendation in a CV Recommender System.- A Novel Method to Estimate Students' Knowledge Assessment.- Answer Selection Based on Mixed Embedding and Composite Features.- A Neural Framework for Chinese Medical Named Entity Recognition.- An Annotated Chinese Corpus for Rumor Veracity Detection.- Attention-based Asymmetric Fusion Network for Saliency Prediction in 3D Images.- Review Spam Detection Based on Multidimensional.- Application Track.- Rehabilitation XAI to Predict Outcome with Optimal Therapies.- A Mobile Application using Deep Learning to Automatically Classify Adult-only Images.- Short Paper Track.- OSAF_e: One-Stage Anchor Free Object Detection Method Considering Effective Area.- Attention-based Interaction Trajectory Prediction.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of SCF 2020, during September 18-20, 2020. The conference was planned to take place in Honolulu, HI, USA and was changed to a virtual format due to the COVID-19 pandemic. The 11 full and 2 short papers presented were carefully reviewed and selected from 42 submissions. They cover topics in AI Modeling, AI Analysis, AI and Mobile Applications, AI Architecture, AI Management, AI Engineering, Mobile backend as a service (MBaaS), User experience of AI and mobile services.
(source: Nielsen Book Data)

• Adaptive Instruction Design and Authoring.- Interoperability and Standardization in Adaptive Instructional Systems.- Instructional Theories in Adaptive Instruction.- Learner Assessment and Modelling.- AI in Adaptive Instructional Systems, Conversational Tutors.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the First International Conference on Adaptive Instructional Systems, AIS 2019, held in July 2019 as part of HCI International 2019 in Orlando, FL, USA. HCII 2019 received a total of 5029 submissions, of which 1275 papers and 209 posters were accepted for publication after a careful reviewing process. The 50 papers presented in this volume are organized in topical sections named: Adaptive Instruction Design and Authoring, Interoperability and Standardization in Adaptive Instructional Systems, Instructional Theories in Adaptive Instruction, Learner Assessment and Modelling, AI in Adaptive Instructional Systems, Conversational Tutors.
(source: Nielsen Book Data)

• Designing and Developing Adaptive Instructional Systems.- Sensor-based Adaptive Instructional Systems in Live Simulation Training.- An Ambient & Pervasive Personalized Learning Ecosystem: "Smart Learning" in the Age of the Internet of Things.- Bridging Conceptual Models and Architectural Interchange for Adaptive Instructional Systems.- Dewey's Ethics of Moral Principles and Deliberation: Extending IEEE's Ethics Initiative for Adaptive Instructional Systems.- Realistic and Relevant Role-Players for Experiential Learning.- Learning Traces, Measurement and Assessment Templates for AIS Interoperability.- Supporting different Roles and Responsibilities in Developing and Using Context-based Adaptive Personalized Collaboration Environments Compliant to the Law.- Experiential Instruction of Metacognitive Strategies.- Falling Forward: Lessons Learned from Real-Life Implementation of Adaptive Learning Solutions.- Usability Dimensions of Simulated Trainers for Buried Explosives.- Toward Zero Authoring: Considering How to Maximize Courseware Quality and Affordability Simultaneously.- Agent-Based Methods in Support of Adaptive Instructional Decisions.- Representing Functional Relationships of Adaptive Instructional Systems in a Conceptual Model.- Knowledge-to-Information Translation Training (KITT): An Adaptive Approach to Explainable Artificial Intelligence.- User Rights and Adaptive A/IS - From Passive Interaction to Real Empowerment.- Supporting Metacognitive Learning Strategies through an Adaptive Application.- Towards Iteration by Design: An Interaction Design Concept for Safety Critical Systems.- Learner Modelling and Methods of Adaptation.- Bayesian Student Modelling in the AC&NL Tutor.- Nature at Your Service - Nature Inspired Representations Combined with Eye-gaze Features to Infer User Attention and Provide Contextualized Support.- Adapting Instruction by Measuring Engagement with Machine Learning in Virtual Reality Training.- Realizing the Promise of AI-Powered, Adaptive, Automated, Instant Feedback on Writing for Students in Grade 3-8 with an IEP.- Declarative Knowledge Extraction in the AC&NL Tutor.- On the Importance of Adaptive Operator Training in Human-Swarm Interaction.- The Mental Machine: Classifying Mental Workload State from Unobtrusive Heart Rate- measures Using Machine Learning.- Production Implementation of Recurrent Neural Networks in Adaptive Instructional Systems.- Pilot State Monitoring for Cursus Recommendation.- Experimental Evaluation of Heart-based Workload Measures as Related to Their Suitability for Real-time Applications.- EEG Covariance-based Estimation of Cooperative States in Teammates.- Adapting the Zone of Proximal Development to the Wicked Environments of Professional Practice.- An Adaptive Instructional System for the Retention of Complex Skills.- Learner Modeling in the Context of Caring Assessments.- Evaluating the Effectiveness of Adaptive Instructional Systems.- The Evolving Assessment Landscape and Adaptive Instructional Systems: Moving Beyond Good Intentions.- Contextual Barriers to Validity in Adaptive Instruction and Assessment.- Does Time Matter in Learning? A Computer Simulation of Carroll's Model of Learning.- Competency Development through Experiential Training: Mapping Scenarios with Assessments.- Does Gamification Work? Analyzing Effects of Game Features on Learning in an Adaptive Scenario-Based Trainer.- From "Knowing What" to "Knowing When": Exploring a Concept of Situation Awareness Synchrony for Evaluating SA Dynamics in Teams.- Exploring Video Engagement in an Intelligent Tutoring System.- Using a Non-Player Character to Improve Training Outcomes for Submarine Electronic Warfare Operators.- The Impact of Adaptive Activities in Acrobatiq Courseware: Investigating the Efficacy of Formative Adaptive Activities on Learning Estimates and Summative Assessment Scores.- A Mastery Approach to Flashcard-based Adaptive Training. .
• (source: Nielsen Book Data)

This volume constitutes the refereed proceedings of the Second International Conference on Adaptive Instructional Systems, AIS 2020, which was due to be held in July 2020 as part of HCI International 2020 in Copenhagen, Denmark. The conference was held virtually due to the COVID-19 pandemic. A total of 1439 papers and 238 posters have been accepted for publication in the HCII 2020 proceedings from a total of 6326 submissions. The 41 papers presented in this volume were organized in topical sections as follows: designing and developing adaptive instructional systems; learner modelling and methods of adaptation; evaluating the effectiveness of adaptive instructional systems. Chapter "Exploring Video Engagement in an Intelligent Tutoring System" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
(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)

• Artificial Intelligence, Theorem Proving and SAT Solving.- Symbolic and Numerical Computation.- Intelligent Documents and Collective Intelligence.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th International Conference on Artificial Intelligence and Symbolic Computation, AISC 2018, held in Suzhou, China, in September 2018. The 13 full papers presented together with 5 short and 2 invited papers were carefully reviewed and selected from 31 submissions. The AISC conference is an important forum when it comes to ensuring that ideas, theoretical insights, methods and results from traditional AI can be discussed and showcased, while fostering new links with other areas of AI such as probabilistic reasoning and deep learning. .
(source: Nielsen Book Data)