SubjectsSubjects(version: 837)
Course, academic year 2018/2019
   Login via CAS
Challenge Response Framework - NSWI171
Title in English: Challenge Response Framework
Guaranteed by: Department of Software Engineering (32-KSI)
Faculty: Faculty of Mathematics and Physics
Actual: from 2018 to 2019
Semester: winter
E-Credits: 3
Hours per week, examination: winter s.:2/0 Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: English
Teaching methods: full-time
Additional information: http://www.ksi.mff.cuni.cz/~vojtas/vyuka/vyuka.html
Guarantor: prof. RNDr. Peter Vojtáš, DrSc.
Class: DS, softwarové systémy
Informatika Mgr. - volitelný
Matematika a informatika
Classification: Informatics > Informatics, Software Applications, Computer Graphics and Geometry, Database Systems, Didactics of Informatics, Discrete Mathematics, External Subjects, General Subjects, Computer and Formal Linguistics, Optimalization, Programming, Software Engineering, Theoretical Computer Science
Philosophy > Logic
Annotation
Last update: RNDr. Michal Kopecký, Ph.D. (17.04.2018)
Challenge-Response Framework (CRF) appears in different parts of mathematics and computer science as e.g. query-answer, client-server service request, requirements-solution, input-output, observation-prediction, huge unstructured web information-computer understandable web information, human search-recommendation and complexity search problems etc. We present an overview of both formal model of, and experimental experience with, CRF. This opens area for PhD and/or software projects. Students will be guided through relevant literature, projects, benchmarks and experiments.
Literature
Last update: RNDr. Michal Kopecký, Ph.D. (17.04.2018)
  • Abiteboul S., Hull R., Vianu V.: Foundations of Databases, Addison-Wesley 1995
  • E. Ries. The Lean Startup: How Today's Entrepreneurs Use … Crown Business 2011
  • D. Harel, D. Kozen, J. Tiuryn. Dynamic Logic. The MIT Press 2000
  • C. Manning, P. Raghavan, etal. An Introduction to Information Retrieval. Cambridge UP 2009
  • R. Fagin, A. Lotem, M. Naor. Optimal aggregation algorithms for middleware, J. Computer and System Sciences 66 (2003) 614-656
  • P. Vojtas. Generalized Galois-Tukey connections between explicit relations on classical objects of real analysis, Israel Math. Conf. Proc. 6 (1993) 619-643
  • A. Blass, Combinatorial Cardinal Characteristics of the Continuum, In Handbook of Set Theory, M. Foreman and A. Kanamori eds. Springer Netherlands 2010, 395-489
  • T. Furche, G. Gottlob et al. DIADEM: Thousands of Websites to a Single Database, Proc. VLDB Endowment, Vol. 7, No. 14
  • PhD thesis of A. Eckhardt, L. Peska, J. Dedek and I. Lasek and several master theses.
  • J. B. Peterson. 12 Rules for Life: An Antidote to Chaos. Random House Canada 2018
  • J. B. Peterson. Maps of Meaning: The Architecture of Belief. Routledge 1999
  • Kahneman, D., Thinking, fast and slow Farrar, Straus and Giroux 2013,
  • Kahneman, D., Well-being : the foundations of hedonic psychology, Russell Sage Found. 2003
  • D. Kahneman, P. Slovic, A. Tversky Eds. Judgment Under Uncertainty: Heuristics and Biases, Cambridge University Press; 1982
  • IBM's Role in Creating the Workforce of the Future (T-shape skills) http://www-05.ibm.com/de/ibm/engagement/university_relations/pdf/Beyond_IT_report_IBM_Workforce_of_the_Future.pdf

Syllabus
Last update: RNDr. Michal Kopecký, Ph.D. (17.04.2018)
  • Motivations for Challenge Response Framework (CRF)
  • Lean startup software engineering (SE) methodology and CRF
  • Data with ordering, threshold algorithm, optimality with(out) random/sequential access
  • GAP, Preferential Datalog (PD), deductive and modeling aspects of logic
  • Monotone preference model, fixpoint, approximate completeness of Preferential Datalog
  • NLP models, query languages semantics of web information
  • OWL, UML, modeling languages
  • Dynamic model of web Semantization, decidability of Dynamic Logic,
  • Inductive aspects of logic, IGAP, IPD, acceptable response business relevant metrics
  • Learning preferences from human rating and behavior, benchmarks, competitions
  • Learning annotation of web resources in social context
  • User intuitive tableau query, complexity of containment, theoretical CRF model
  • Unifying theoretical and SE aspects of CRF

 
Charles University | Information system of Charles University | http://www.cuni.cz/UKEN-329.html