1) Mathematical induction is a method of proof that can be used to prove statements for all positive integers. It involves showing that a statement is true for n=1, and assuming it is true for an integer k to prove it is true for k+1.
2) The document provides an example using mathematical induction to prove the formula Sn = n(n+1) for the sum of the first n even integers.
3) Finite differences are used to determine if a sequence has a quadratic model by seeing if the second differences are constant. The example finds the quadratic model n^2 for the sequence 1, 4, 9, 16, 25, 36.
Continuity says that the limit of a function at a point equals the value of the function at that point, or, that small changes in the input give only small changes in output. This has important implications, such as the Intermediate Value Theorem.
Mathematical Induction
CMSC 56 | Discrete Mathematical Structure for Computer Science
October 18, 2018
Instructor: Allyn Joy D. Calcaben
College of Arts & Sciences
University of the Philippines Visayas
Continuity says that the limit of a function at a point equals the value of the function at that point, or, that small changes in the input give only small changes in output. This has important implications, such as the Intermediate Value Theorem.
Mathematical Induction
CMSC 56 | Discrete Mathematical Structure for Computer Science
October 18, 2018
Instructor: Allyn Joy D. Calcaben
College of Arts & Sciences
University of the Philippines Visayas
A complete and enhanced presentation on mathematical induction and divisibility rules with out any calculation.
Here are some defined formulas and techniques to find the divisibility of numbers.
Successive Differentiation is the process of differentiating a given function successively times and the results of such differentiation are called successive derivatives. The higher order differential coefficients are of utmost importance in scientific and engineering applications.
Proofs Methods and Strategy
CMSC 56 | Discrete Mathematical Structure for Computer Science
September 10, 2018
Instructor: Allyn Joy D. Calcaben
College of Arts & Sciences
University of the Philippines Visayas
Propositional Equivalences
CMSC 56 | Discrete Mathematical Structure for Computer Science
August 23, 2018
Instructor: Allyn Joy D. Calcaben
College of Arts & Sciences
University of the Philippines Visayas
A complete and enhanced presentation on mathematical induction and divisibility rules with out any calculation.
Here are some defined formulas and techniques to find the divisibility of numbers.
Successive Differentiation is the process of differentiating a given function successively times and the results of such differentiation are called successive derivatives. The higher order differential coefficients are of utmost importance in scientific and engineering applications.
Proofs Methods and Strategy
CMSC 56 | Discrete Mathematical Structure for Computer Science
September 10, 2018
Instructor: Allyn Joy D. Calcaben
College of Arts & Sciences
University of the Philippines Visayas
Propositional Equivalences
CMSC 56 | Discrete Mathematical Structure for Computer Science
August 23, 2018
Instructor: Allyn Joy D. Calcaben
College of Arts & Sciences
University of the Philippines Visayas
RuleML2015: How to combine event stream reasoning with transactions for the...RuleML
Semantic Sensor Web is a new trend of research integrating
Semantic Web technologies with sensor networks. It uses Semantic Web
standards to describe both the data produced by the sensors, but also
the sensors and their networks, which enables interoperability of sensor
networks, and provides a way to formally analyze and reason about these
networks. Since sensors produce data at a very high rate, they require
solutions to reason efficiently about what complex events occur based on
the data captured. In this paper we propose T Rev as a solution to combine
the detection of complex events with the execution of transactions
for these domains. T Rev is an abstract logic to model and execute reactive
transactions. The logic is parametric on a pair of oracles defining the
basic primitives of the domain, which makes it suitable for a wide range
of applications. In this paper we provide oracle instantiations combining
RDF/OWL and relational database semantics for T Rev. Afterwards,
based on these oracles, we illustrate how T Rev can be useful for these
domains.
RuleML 2015: Semantics of Notation3 Logic: A Solution for Implicit Quantifica...RuleML
Since the development of Notation3 Logic, several years have
passed in which the theory has been refined and used in practice by different reasoning engines such as cwm, FuXi or EYE. Nevertheless, a clear model-theoretic definition of its semantics is still missing. This leaves room for individual interpretations and renders it difficult to make clear
statements about its relation to other logics such as DL or FOL or even about such basic concepts as correctness. In this paper we address one of the main open challenges: the formalization of implicit quantification.
We point out how the interpretation of implicit quantifiers differs in two of the above mentioned reasoning engines and how the specification, proposed in the W3C team submission, could be formalized. Our formalization is then put into context by integrating it into a model-theoretic definition of the whole language. We finish our contribution by arguing why universal quantification should be handled differently than currently
prescribed.
Big data, with its four main characteristics (Volume, Velocity,
Variety, and Veracity) pose challenges to the gathering, management, analytics, and visualization of events. These very same four characteristics, however, also hold a great promise in unlocking the story behind data. In this talk, we focus on the observation that event creation is guided by processes. For example, GPS information, emitted by buses in an urban setting follow the bus scheduled route. Also, RTLS information about the whereabouts of patients and nurses in a hospital is guided by the predefined schedule of work. With this observation at hand, we thoroughly seek a method for mining, not the data, but rather the rules that guide data creation and show how, by knowing such rules, big data tasks become more efficient and more effective. In particular, we demonstrate how, by knowing the rules that govern event creation, we can detect complex events sooner and make use of historical data to predict future behaviors.
RuleML 2015: Ontology Reasoning using Rules in an eHealth ContextRuleML
Traditionally, nurse call systems in hospitals are rather simple:
patients have a button next to their bed to call a nurse. Which specific
nurse is called cannot be controlled, as there is no extra information
available. This is different for solutions based on semantic knowledge:
if the state of care givers (busy or free), their current position, and for
example their skills are known, a system can always choose the best
suitable nurse for a call. In this paper we describe such a semantic nurse
call system implemented using the EYE reasoner and Notation3 rules.
The system is able to perform OWL-RL reasoning. Additionally, we use
rules to implement complex decision trees. We compare our solution to
an implementation using OWL-DL, the Pellet reasoner, and SPARQL
queries. We show that our purely rule-based approach gives promising
results. Further improvements will lead to a mature product which will
significantly change the organization of modern hospitals.
Challenge@RuleML2015 Transformation and aggregation preprocessing for top-k r...RuleML
In this paper we describe the KTIML team approach to RuleML 2015 Rule-based Recommender Systems for the Web of Data Challenge Track. The task is to estimate the top 5 movies for each user separately in a semantically enriched MovieLens 1M dataset. We have three results. Best is a domain specif-ic method like "recommend for all users the same set of movies from Spiel-berg". Our contributions are domain independent data mining methods tailored for top-k which combine second order logic data aggregations and transfor-mations of metadata, especially 5003 open data attributes and general GAP rules mining methods.
Challenge@rule ml2015 rule based recommender systems for the Web of DataRuleML
Augmenting a feature set using mappings to the Web of data
is an up-and-coming way to enrich data in the original dataset. Those
enrichments are valuable especially for the recent preference learning
algorithms and recommender systems. In this paper, we describe the
process of mapping and augmenting the movie ratings dataset Movi-
eTweetings from the perspective of RecSysRules 2015 Challenge. The
ad-hoc queries to DBpedia are used as an underlying concept. To the
best of our knowledge, there is no existing mapping dataset of movies
for MovieTweetings.We also provide a brief discussion about the benets
of the augmented feature set for an elementary rule-based representation
of the user preferences.
ملزمة الرياضيات للصف السادس الاحيائي الفصل الاولanasKhalaf4
طبعة جديدة ومنقحة
حل تمارين الكتاب
شرح المواضيع الرياضية بالتفصيل وبأسلوب واضح ومفهوم لجميع المستويات
حلول الاسألة الوزارية
اعداد الدكتور أنس ذياب خلف
email: anasdhyiab@gmail.com
ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022anasKhalaf4
طبعة جديدة ومنقحة
حل تمارين الكتاب
شرح المواضيع الرياضية بالتفصيل وبأسلوب واضح ومفهوم لجميع المستويات
حلول الاسألة الوزارية
اعداد الدكتور أنس ذياب خلف
email: anasdhyiab@gmail.com