Advanced Computational Methods for Knowledge Engineering

Proceedings of the 5th International Conference on Computer Science, Applied Mathematics and Applications, ICCSAMA 2017
 
 
Springer (Verlag)
  • erschienen am 26. Juni 2017
  • |
  • XVI, 228 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
E-Book | PDF mit Wasserzeichen-DRM | Systemvoraussetzungen
978-3-319-61911-8 (ISBN)
 

These proceedings consist of 19 papers, which have been peer-reviewed by international program committee and selected for the 5th International Conference on Computer Science, Applied Mathematics and Applications (ICCSAMA 2017), which was held on June 30-July 1, 2017 in Berlin, Germany. The respective chapters discuss both theoretical and practical issues in connection with computational methods and optimization methods for knowledge engineering. The broad range of application areas discussed includes network computing, simulation, intelligent and adaptive e-learning, information retrieval, sentiment analysis, autonomous underwater vehicles, social media analysis, natural language processing, biomimetics in organizations, and cash management.

In addition to pure content, the book offers many inspiring ideas and suggests new research directions, making it a valuable resource for graduate students, Ph.D. students, and researchers in Computer Science and Applied Mathematics alike.

1st ed. 2018
  • Englisch
  • Cham
  • |
  • Schweiz
Springer International Publishing
  • 100
  • |
  • 92 s/w Abbildungen, 100 farbige Tabellen
  • |
  • 92 schwarz-weiße Abbildungen, 100 farbige Tabellen, Bibliographie
  • 17,75 MB
978-3-319-61911-8 (9783319619118)
10.1007/978-3-319-61911-8
weitere Ausgaben werden ermittelt
  • Intro
  • Preface
  • ICCSAMA 2017 Organization
  • General Chair
  • General Co-chair
  • Program Chairs
  • Organizing Committee
  • Steering Committee
  • Program Committee
  • Keynotes
  • Forty Years Experience on R&D and Education of Systems Sciences
  • How We Grasp "Causal Relation" in Historical Learning?
  • Contents
  • Stochastic DCA for Sparse Multiclass Logistic Regression
  • 1 Introduction
  • 2 Solution Method via Stochastic DCA
  • 2.1 Outline of DC Programming, DCA and Stochastic DCA
  • 2.2 Stochastic DCA for Solving the Sparse Multiclass Logistic Regression Problem
  • 3 Numerical Experiment
  • 3.1 Datasets
  • 3.2 Comparative Algorithms
  • 3.3 Experiment Setting
  • 3.4 Experimental Results on Synthetic Datasets
  • 3.5 Experimental Results on Real Datasets
  • 4 Conclusion
  • References
  • Reformulation of the Quadratic Multidimensional Knapsack Problem as Copositive/Completely Positive Prorams
  • 1 Introduction
  • 2 Preliminaries
  • 2.1 Copositive and Completely Positive Cones
  • 2.2 Copositive and Completely Positive Programs and Their Duals
  • 2.3 Quadratic Optimization Problems and Completely Positive Programs
  • 3 Construction of Equivalent Completely Positive and Copositive Optimization Problems
  • References
  • DC Programming and DCA for Enhancing Physical Layer Security in Amplify-and-Forward Relay Beamforming Networks Based on the SNR Approach
  • 1 Introduction
  • 1.1 Physical Layer Security in AF Beamforming Protocol
  • 1.2 DC Programming and DCA
  • 2 System Model and Received SNR Maximization Problem
  • 2.1 System Model
  • 2.2 Received SNR Maximization Problem
  • 3 Existing work
  • 4 DC Programming and DCA for Problem (9)
  • 5 Numerical Results
  • 6 Conclusion
  • References
  • A Cash-Flow-Based Optimization Model for Corporate Cash Management: A Monte-Carlo Simulation Approach
  • 1 Introduction
  • 1.1 Literature Review
  • 1.2 Contributions of this Paper
  • 2 Notation and Basic Definitions
  • 3 The Cash Management Model
  • 3.1 Objective Functions and Constraints
  • 3.2 Types of Risk in Cash Flows
  • 4 Cash Flow at Risk
  • 4.1 Description of the Cash Flow at Risk
  • 4.2 Determining the Overall Risk
  • 5 Numerical Experiments
  • 5.1 Test Data
  • 5.2 Results
  • 6 Conclusion
  • References
  • Demand Side Management: A Case for Disruptive Behaviour
  • 1 Introduction
  • 2 Relevant Work
  • 3 The Model
  • 3.1 The Agents
  • 3.2 The Market
  • 4 Experimental Set-Up
  • 5 Results and Analysis
  • 6 Conclusion and Further Work
  • A Model Flow Diagram
  • References
  • Enhancing Reduced Order Model Predictive Control for Autonomous Underwater Vehicle
  • 1 Fixed-Period Problems: The Sublinear Case
  • 2 Mathematical Modelling of Autonomous Underwater Vehicle
  • 2.1 Kinematics
  • 2.2 Kinetics
  • 3 Proposed Method
  • 3.1 Reduced Order Models
  • 3.2 MPC for Reduced Order Models
  • 3.3 Extended Kalman Filter (EKF)
  • 4 Simulation Results
  • 5 Conclusion
  • References
  • Comparison of Feedback Strategies for Supporting Programming Learning in Integrated Development Environments (IDEs)
  • Abstract
  • 1 Introduction
  • 2 Related Work
  • 3 Approach: Design and Implementation
  • 3.1 Feedback Based on Stack Trace Analysis
  • 3.2 Feedback Based on Comparisons to Sample Programs
  • 3.3 Feedback Summary Panel
  • 4 Evaluation
  • 4.1 Hypotheses
  • 4.2 Study Design
  • 4.3 Results
  • 5 Conclusion and Future Work
  • Acknowledgement
  • References
  • Using Online Synchronous Interschool Tournaments to Boost Student Engagement and Learning in Hands-On Physics Lessons
  • Abstract
  • 1 Introduction
  • 2 Methods
  • 3 Results
  • 4 Conclusions
  • Acknowledgments
  • References
  • Story-Based Multimedia Analysis Using Social Network Technique
  • 1 Introduction
  • 2 The Occurrences of Characters
  • 2.1 Character's Occurrences Model
  • 2.2 Representation of Characters' Appearance Aspects
  • 3 Story-Based Multimedia Analysis
  • 4 Experimental Results and Discussion
  • 4.1 Parameter Setting
  • 4.2 System Proposed and Evaluation Results
  • 5 Conclusion
  • References
  • Plot-Creation Support System for Writing Novels
  • Abstract
  • 1 Introduction
  • 2 Process of Writing Novels
  • 3 Plot-Construction Model
  • 4 Plot-Creation Support
  • 5 Prototype System
  • 6 Evaluation
  • 6.1 Experimental Setting
  • 6.2 Result
  • 7 Conclusion
  • Acknowledgements
  • References
  • An Improved Algorithm for Mining Top-k Association Rules
  • Abstract
  • 1 Introduction
  • 2 Basic Concepts
  • 3 Related Works
  • 3.1 Mining Frequent Itemsets
  • 3.2 Mining Top-K/Top-Rank-k Frequent Itemsets
  • 3.3 Mining Top-k Association Rules
  • 4 Proposed Algorithm
  • 5 Experimental Results
  • 5.1 Experimental Databases and Environments
  • 5.2 Experimental Results
  • 6 Conclusions and Future Works
  • Acknowledgments
  • References
  • A Deep Architecture for Sentiment Analysis of News Articles
  • Abstract
  • 1 Introduction
  • 2 Background
  • 2.1 Convolution Neural Networks (CNN)
  • 2.2 Word Embedding
  • 2.3 Long Short-Term Memory (LSTM)
  • 3 The Proposed Deep Architecture
  • 4 Experimental Results
  • 4.1 Implementation Details
  • 4.2 Experimental Results
  • 5 Conclusion
  • Acknowledgments
  • References
  • Sentiment Polarity Detection in Social Networks: An Approach for Asthma Disease Management
  • Abstract
  • 1 Introduction
  • 2 State of the Art
  • 3 Sentiment Polarity Identification
  • 3.1 Normalization Module
  • 3.2 Semantic Annotation Module
  • 3.3 Sentiment Polarity Identification Module
  • 4 Evaluation
  • 4.1 Procedure
  • 4.2 Results
  • 5 Conclusions and Future Work
  • Acknowledgements
  • References
  • An Overview of Information Discovery Using Latent Semantic Indexing
  • Abstract
  • 1 Introduction
  • 2 Latent Semantic Indexing
  • 3 Relevant Work
  • 4 Example Applications
  • 4.1 Query Expansion
  • 4.2 Topic Discovery via Clustering
  • 4.3 Novelty Detection
  • 4.4 Item-Item Relationship Discovery
  • 4.5 Term-Concept Relationship Discovery
  • 4.6 Discovery Incorporating Spatiotemporal Data
  • 5 Conclusion
  • References
  • Similarity Measures for Music Information Retrieval
  • Abstract
  • 1 Introduction
  • 2 Identity, Similarity and Melodic Analogy
  • 3 Information Theory
  • 4 Analysis of the Musical Message
  • 5 Obtained Results
  • 6 Discussion and Conclusions
  • References
  • An Early-Biologisation Process to Improve the Acceptance of Biomimetics in Organizations
  • Abstract
  • 1 Introduction
  • 2 Biomimetic Engineering Process Should Start with an Early-Biologisation Process
  • 3 Supporting the Search for Nature's Solution Strategies with a Technology-Biology-Dictionary
  • 3.1 The Basic Concept of a Technology-Biology Dictionary
  • 3.2 Automatic Generation of a Technology Thesaurus
  • 4 Evaluation
  • 5 Implications
  • 5.1 Implications for Further Research
  • 5.2 Implications for Practionners
  • References
  • Bidirectional Deep Learning of Context Representation for Joint Word Segmentation and POS Tagging
  • 1 Introduction
  • 2 Dynamic Neural Architecture
  • 2.1 Character-Level n-Gram Embedding
  • 2.2 Word Boundary Inference
  • 2.3 Word Embedding and Two-Level Backoff Models
  • 2.4 Tag Inference
  • 3 Structured Perceptron Algorithm
  • 3.1 Parameter Estimation
  • 3.2 Joint Inference of Word Segmentation and POS Tagging
  • 4 Experiments
  • 4.1 Settings
  • 4.2 Multilingual Results
  • 4.3 Effects of n-Grams and Backoff Models
  • 4.4 Effects of Word Embedding and Context Embedding
  • 4.5 Effects of Mini-Batch Sizes
  • 5 Conclusion
  • References
  • A Model for a Computing Cluster with Two Asynchronous Servers
  • 1 Introduction
  • 2 Asynchronous Working Vacations
  • 2.1 The Steady State Probabilities
  • 2.2 Performance Measures
  • 3 Numeric Results
  • References
  • A Review of Technologies for Conversational Systems
  • Abstract
  • 1 Introduction
  • 2 Methodology
  • 3 Results
  • 3.1 Chatbots
  • 3.2 Dialog Systems
  • 3.3 Evaluation Methods
  • 4 Discussion and Conclusions
  • Appendix: Table of Reviewed Conversational Systems
  • References
  • Author Index

Dateiformat: PDF
Kopierschutz: Adobe-DRM (Digital Rights Management)

Systemvoraussetzungen:

Computer (Windows; MacOS X; Linux): Installieren Sie bereits vor dem Download die kostenlose Software Adobe Digital Editions (siehe E-Book Hilfe).

Tablet/Smartphone (Android; iOS): Installieren Sie bereits vor dem Download die kostenlose App Adobe Digital Editions (siehe E-Book Hilfe).

E-Book-Reader: Bookeen, Kobo, Pocketbook, Sony, Tolino u.v.a.m. (nicht Kindle)

Das Dateiformat PDF zeigt auf jeder Hardware eine Buchseite stets identisch an. Daher ist eine PDF auch für ein komplexes Layout geeignet, wie es bei Lehr- und Fachbüchern verwendet wird (Bilder, Tabellen, Spalten, Fußnoten). Bei kleinen Displays von E-Readern oder Smartphones sind PDF leider eher nervig, weil zu viel Scrollen notwendig ist. Mit Adobe-DRM wird hier ein "harter" Kopierschutz verwendet. Wenn die notwendigen Voraussetzungen nicht vorliegen, können Sie das E-Book leider nicht öffnen. Daher müssen Sie bereits vor dem Download Ihre Lese-Hardware vorbereiten.

Bitte beachten Sie bei der Verwendung der Lese-Software Adobe Digital Editions: wir empfehlen Ihnen unbedingt nach Installation der Lese-Software diese mit Ihrer persönlichen Adobe-ID zu autorisieren!

Weitere Informationen finden Sie in unserer E-Book Hilfe.


Dateiformat: PDF
Kopierschutz: Wasserzeichen-DRM (Digital Rights Management)

Systemvoraussetzungen:

Computer (Windows; MacOS X; Linux): Verwenden Sie zum Lesen die kostenlose Software Adobe Reader, Adobe Digital Editions oder einen anderen PDF-Viewer Ihrer Wahl (siehe E-Book Hilfe).

Tablet/Smartphone (Android; iOS): Installieren Sie die kostenlose App Adobe Digital Editions oder eine andere Lese-App für E-Books (siehe E-Book Hilfe).

E-Book-Reader: Bookeen, Kobo, Pocketbook, Sony, Tolino u.v.a.m. (nur bedingt: Kindle)

Das Dateiformat PDF zeigt auf jeder Hardware eine Buchseite stets identisch an. Daher ist eine PDF auch für ein komplexes Layout geeignet, wie es bei Lehr- und Fachbüchern verwendet wird (Bilder, Tabellen, Spalten, Fußnoten). Bei kleinen Displays von E-Readern oder Smartphones sind PDF leider eher nervig, weil zu viel Scrollen notwendig ist. Mit Wasserzeichen-DRM wird hier ein "weicher" Kopierschutz verwendet. Daher ist technisch zwar alles möglich - sogar eine unzulässige Weitergabe. Aber an sichtbaren und unsichtbaren Stellen wird der Käufer des E-Books als Wasserzeichen hinterlegt, sodass im Falle eines Missbrauchs die Spur zurückverfolgt werden kann.

Weitere Informationen finden Sie in unserer E-Book Hilfe.


Download (sofort verfügbar)

160,49 €
inkl. 7% MwSt.
Download / Einzel-Lizenz
PDF mit Adobe-DRM
siehe Systemvoraussetzungen
E-Book bestellen

160,49 €
inkl. 7% MwSt.
Download / Einzel-Lizenz
PDF mit Wasserzeichen-DRM
siehe Systemvoraussetzungen
E-Book bestellen