People have difficulties using computers. Some have more difficulties than others. There is a need for guidance in how to evaluate and improve the accessibility of systems for users. Since different users have considerably different accessibility needs, accessibility is a very complex issue.
ISO 9241-171 defines accessibility as the "usability of a product, service, environment or facility by people with the widest range of capabilities." While this definition can help manufacturers make their products more accessible to more people, it does not ensure that a given product is accessible to a particular individual.
A reference model is presented to act as a theoretical foundation. This Universal Access Reference Model (UARM) focuses on the accessibility of the interaction between users and systems, and provides a mechanism to share knowledge and abilities between users and systems. The UARM also suggests the role assistive technologies (ATs) can play in this interaction. The Common Accessibility Profile (CAP), which is based on the UARM, can be used to describe accessibility.
The CAP is a framework for identifying the accessibility issues of individual users with particular systems configurations. It profiles the capabilities of systems and users to communicate. The CAP can also profile environmental interference to this communication and the use of ATs to transform communication abilities. The CAP model can be extended as further general or domain specific requirements are standardized.
The CAP provides a model that can be used to structure various specifications in a manner that, in the future, will allow computational combination and comparison of profiles.
Recognizing its potential impact, the CAP is now being standardized by the User Interface subcommittee the International Organization for Standardization and the International Electrotechnical Commission.
Two-way latent grouping model for user preference prediction
Eerika Savia, Kai Puolamäki, Janne Sinkkonen and Samuel Kaski
In: UAI 2005, 26-29 July 2005, Edinburgh, Scotland.
An interactive provides varying levels of interactivity, ranging from simple point-and-click interaction through sophisticated search techniques to the analysis, manipulation, and application of information in new and authentic contexts.
J. Hausmann, и S. Kent. SoftVis '03: Proceedings of the 2003 ACM symposium on Software visualization, стр. 169--178. New York, NY, USA, ACM Press, (2003)
J. Hausmann, и S. Kent. SoftVis '03: Proceedings of the 2003 ACM symposium on Software visualization, стр. 169--178. New York, NY, USA, ACM Press, (2003)
I. Cadez, D. Heckerman, C. Meek, P. Smyth, и S. White. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, стр. 280--284. ACM, (2000)
L. Wu, M. Li, Z. Li, W. Ma, и N. Yu. MIR '07: Proceedings of the international workshop on Workshop on multimedia information retrieval, стр. 115--124. New York, NY, USA, ACM, (2007)
A. Shaikh, R. Clarisó, U. Wiil, и N. Memon. Proceedings of the IEEE/ACM international conference on Automated software engineering, стр. 185--194. New York, NY, USA, ACM, (2010)
P. Carpenter. Ada Lett., XIX (3):
23--29(1999)ST: Vorgehensweise: Das Paper ordnet den Vorgang, wie man sicherheitskritische Anforderungen verifizieren kann, in einen Software Life-Cycle ein. Use-Cases werden mit Parametern für Daten versehen. Die Eingabedaten werden mit Hilfe eines Tools generiert per üblicher Ä-Klassenanalyse.
Eignung: Es ist nichts über die Testgüte zu finden (Abdeckungskriterium etc.). Außerdem wird kein Testmodell o.ä. erwähnt, welches alternative Ausführungspfade des Use Cases repräsentiert..
A. Anjos, D. Moreira, A. Mariz, и F. Nobre. Abstract Book of the XXIII IUPAP International Conference on Statistical Physics, Genova, Italy, (9-13 July 2007)
H. TARIQ, W. YANG, I. HAMEED, B. AHMED, и R. KHAN. IJIRIS:: International Journal of Innovative Research Journal in Information Security, Volume IV (Issue XII):
01-07(декабря 2017)1 Hugh A. Chipman, Edward I. George, and Robert E. McCulloch. “Bayesian CART Model Search.” Journal of the American Statistical Association, Vol. 93(443), pp 935–948, September 1998. 2 Sujata Garera, Niels Provos, Monica Chew, and Aviel D. Rubin. “A framework for detection and measurement of phishing attacks.” In Proceedings of the 2007 ACM workshop on Recurring malicious code - WORM ’07, page 1, 2007. 3 Abhishek Gattani, AnHai Doan, Digvijay S. Lamba, NikeshGarera, Mitul Tiwari, Xiaoyong Chai, Sanjib Das, Sri Subramaniam, AnandRajaraman, and VenkyHarinarayan. “Entity extraction, linking, classifica- tion, and tagging for social media.” Proceedings of the VLDB Endowment, Vol. 6(11), pp 1126–1137, August 2013. 4 David D. Lewis. Naive (Bayes) at forty: The independence assumption in information retrieval. pages 4–15. 1998. 5 Justin Ma, Lawrence K. Saul, Stefan Savage, and Geoffrey M. Voelker. “Learning to detect malicious URLs.” ACM Transactions on Intelligent Systems and Technology, Vol. 2(3), pp 1–24, April 2011. 6 FadiThabtah Maher Aburrous, M.A.Hossain, KeshavDahal. “Intelligent phishing detection system for e-banking using fuzzy data mining.” Expert Systems with Applications, Vol. 37(12), pp 7913–7921, Dec 2010. 7 AnkushMeshram and Christian Haas. “Anomaly Detection in Industrial. Networks using Machine Learning: A Roadmap.” In Machine Learning for Cyber Physical Systems, pages 65–72. Springer Berlin Heidelberg, Berlin, Heidelberg, 2017. 8 Xuequn Wang Nik Thompson,Tanya Jane McGill. “Security begins at home: Determinants of home computer and mobile device security behavior.” Computers & Security, Vol. 70, pp 376–391, Sep 2017. 9 Dan Steinberg and Phillip Colla. “CART: Classification and Regression Trees.” The Top Ten Algorithms in Data Mining, pp 179–201, 2009. 10 D. Teal. “Information security techniques including detection, interdiction and/or mitigation of memory injection attacks,” Google patents. Oct 2013. 11 Kurt Thomas, Chris Grier, Justin Ma, Vern Paxson, and Dawn Song. “Design and Evaluation of a Real-Time URL Spam Filtering Service.” In 2011 IEEE Symposium on Security and Privacy, pp 447–462. May 2011. 12 Sean Whalen, Nathaniel Boggs, and Salvatore J. Stolfo. “Model Aggregation for Distributed Content Anomaly Detection.” In Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop - AISec ’14, pp 61–71, New York, USA, 2014. ACM Press. 13 Ying Yang and Geoffrey I. Webb. “Discretization for Naive-Bayes learning: managing a discretization bias and variance.” Machine Learning, Vol. 74(1), pp 39–74, Jan 2009..
P. Pinheiro da Silva. Interactive Systems Design, Specification, and Verification, том 1946 из Lecture Notes in Computer Science, Springer, Berlin / Heidelberg, (2001)