Application Areas of Combinatorics, Especially Permutations and Combinations
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A Study on Social Network Analysis Through Data Mining Techniques – a Detailed Survey
ISSN (Online) 2278-1021 ISSN (Print) 2319-5940 IJARCCE International Journal of Advanced Research in Computer and Communication Engineering ICRITCSA M S Ramaiah Institute of Technology, Bangalore Vol. 5, Special Issue 2, October 2016 A Study on Social Network Analysis through Data Mining Techniques – A detailed Survey Annie Syrien1, M. Hanumanthappa2 Research Scholar, Department of Computer Science and Applications, Bangalore University, India1 Professor, Department of Computer Science and Applications, Bangalore University, India2 Abstract: The paper aims to have a detailed study on data collection, data preprocessing and various methods used in developing a useful algorithms or methodologies on social network analysis in social media. The recent trends and advancements in the big data have led many researchers to focus on social media. Web enabled devices is an another important reason for this advancement, electronic media such as tablets, mobile phones, desktops, laptops and notepads enable the users to actively participate in different social networking systems. Many research has also significantly shows the advantages and challenges that social media has posed to the research world. The principal objective of this paper is to provide an overview of social media research carried out in recent years. Keywords: data mining, social media, big data. I. INTRODUCTION Data mining is an important technique in social media in scrutiny and analysis. In any industry data mining recent years as it is used to help in extraction of lot of technique based reports become the base line for making useful information, whereas this information turns to be an decisions and formulating the decisions. This report important asset to both academia and industries. -
Geoseq2seq: Information Geometric Sequence-To-Sequence Networks
GEOSEQ2SEQ:INFORMATION GEOMETRIC SEQUENCE-TO-SEQUENCE NETWORKS Alessandro Bay Biswa Sengupta Cortexica Vision Systems Ltd. Noah’s Ark Lab (Huawei Technologies UK) London, UK Imperial College London, London, UK [email protected] ABSTRACT The Fisher information metric is an important foundation of information geometry, wherein it allows us to approximate the local geometry of a probability distribu- tion. Recurrent neural networks such as the Sequence-to-Sequence (Seq2Seq) networks that have lately been used to yield state-of-the-art performance on speech translation or image captioning have so far ignored the geometry of the latent embedding, that they iteratively learn. We propose the information geometric Seq2Seq (GeoSeq2Seq) network which abridges the gap between deep recurrent neural networks and information geometry. Specifically, the latent embedding offered by a recurrent network is encoded as a Fisher kernel of a parametric Gaus- sian Mixture Model, a formalism common in computer vision. We utilise such a network to predict the shortest routes between two nodes of a graph by learning the adjacency matrix using the GeoSeq2Seq formalism; our results show that for such a problem the probabilistic representation of the latent embedding supersedes the non-probabilistic embedding by 10-15%. 1 INTRODUCTION Information geometry situates itself in the intersection of probability theory and differential geometry, wherein it has found utility in understanding the geometry of a wide variety of probability models (Amari & Nagaoka, 2000). By virtue of Cencov’s characterisation theorem, the metric on such probability manifolds is described by a unique kernel known as the Fisher information metric. In statistics, the Fisher information is simply the variance of the score function. -
Data Mining and Soft Computing
© 2002 WIT Press, Ashurst Lodge, Southampton, SO40 7AA, UK. All rights reserved. Web: www.witpress.com Email [email protected] Paper from: Data Mining III, A Zanasi, CA Brebbia, NFF Ebecken & P Melli (Editors). ISBN 1-85312-925-9 Data mining and soft computing O. Ciftcioglu Faculty of Architecture, Deljl University of Technology Delft, The Netherlands Abstract The term data mining refers to information elicitation. On the other hand, soft computing deals with information processing. If these two key properties can be combined in a constructive way, then this formation can effectively be used for knowledge discovery in large databases. Referring to this synergetic combination, the basic merits of data mining and soft computing paradigms are pointed out and novel data mining implementation coupled to a soft computing approach for knowledge discovery is presented. Knowledge modeling by machine learning together with the computer experiments is described and the effectiveness of the machine learning approach employed is demonstrated. 1 Introduction Although the concept of data mining can be defined in several ways, it is clear enough to understand that it is related to information extraction especially in large databases. The methods of data mining are essentially borrowed from exact sciences among which statistics play the essential role. By contrast, soft computing is essentially used for information processing by employing methods, which are capable to deal with imprecision and uncertainty especially needed in ill-defined problem areas. In the former case, the outcomes are exact within the error bounds estimated. In the latter, the outcomes are approximate and in some cases they may be interpreted as outcomes from an intelligent behavior. -
FUNDAMENTALS of COMPUTING (2019-20) COURSE CODE: 5023 502800CH (Grade 7 for ½ High School Credit) 502900CH (Grade 8 for ½ High School Credit)
EXPLORING COMPUTER SCIENCE NEW NAME: FUNDAMENTALS OF COMPUTING (2019-20) COURSE CODE: 5023 502800CH (grade 7 for ½ high school credit) 502900CH (grade 8 for ½ high school credit) COURSE DESCRIPTION: Fundamentals of Computing is designed to introduce students to the field of computer science through an exploration of engaging and accessible topics. Through creativity and innovation, students will use critical thinking and problem solving skills to implement projects that are relevant to students’ lives. They will create a variety of computing artifacts while collaborating in teams. Students will gain a fundamental understanding of the history and operation of computers, programming, and web design. Students will also be introduced to computing careers and will examine societal and ethical issues of computing. OBJECTIVE: Given the necessary equipment, software, supplies, and facilities, the student will be able to successfully complete the following core standards for courses that grant one unit of credit. RECOMMENDED GRADE LEVELS: 9-12 (Preference 9-10) COURSE CREDIT: 1 unit (120 hours) COMPUTER REQUIREMENTS: One computer per student with Internet access RESOURCES: See attached Resource List A. SAFETY Effective professionals know the academic subject matter, including safety as required for proficiency within their area. They will use this knowledge as needed in their role. The following accountability criteria are considered essential for students in any program of study. 1. Review school safety policies and procedures. 2. Review classroom safety rules and procedures. 3. Review safety procedures for using equipment in the classroom. 4. Identify major causes of work-related accidents in office environments. 5. Demonstrate safety skills in an office/work environment. -
Mathematics 1
Mathematics 1 Mathematics Department Information • Department Chair: Friedrich Littmann, Ph.D. • Graduate Coordinator: Indranil Sengupta, Ph.D. • Department Location: 412 Minard Hall • Department Phone: (701) 231-8171 • Department Web Site: www.ndsu.edu/math (http://www.ndsu.edu/math/) • Application Deadline: March 1 to be considered for assistantships for fall. Openings may be very limited for spring. • Credential Offered: Ph.D., M.S. • English Proficiency Requirements: TOEFL iBT 71; IELTS 6 Program Description The Department of Mathematics offers graduate study leading to the degrees of Master of Science (M.S.) and Doctor of Philosophy (Ph.D.). Advanced work may be specialized among the following areas: • algebra, including algebraic number theory, commutative algebra, and homological algebra • analysis, including analytic number theory, approximation theory, ergodic theory, harmonic analysis, and operator algebras • applied mathematics, mathematical finance, mathematical biology, differential equations, dynamical systems, • combinatorics and graph theory • geometry/topology, including differential geometry, geometric group theory, and symplectic topology Beginning with their first year in residence, students are strongly urged to attend research seminars and discuss research opportunities with faculty members. By the end of their second semester, students select an advisory committee and develop a plan of study specifying how all degree requirements are to be met. One philosophical tenet of the Department of Mathematics graduate program is that each mathematics graduate student will be well grounded in at least two foundational areas of mathematics. To this end, each student's background will be assessed, and the student will be directed to the appropriate level of study. The Department of Mathematics graduate program is open to all qualified graduates of universities and colleges of recognized standing. -
Data Mining with Cloud Computing: - an Overview
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 5 Issue 1, January 2016 Data Mining with Cloud Computing: - An Overview Miss. Rohini A. Dhote Dr. S. P. Deshpande Department of Computer Science & Information Technology HOD at Computer Science Technology Department, HVPM, COET Amravati, Maharashtra, India. HVPM, COET Amravati, Maharashtra, India Abstract: Data mining is a process of extracting competitive environment for data analysis. The cloud potentially useful information from data, so as to makes it possible for you to access your information improve the quality of information service. The from anywhere at any time. The cloud removes the integration of data mining techniques with Cloud need for you to be in the same physical location as computing allows the users to extract useful the hardware that stores your data. The use of cloud information from a data warehouse that reduces the computing is gaining popularity due to its mobility, costs of infrastructure and storage. Data mining has attracted a great deal of attention in the information huge availability and low cost. industry and in society as a whole in recent Years Wide availability of huge amounts of data and the imminent 2. DATA MINING need for turning such data into useful information and Data Mining involves the use of sophisticated data knowledge Market analysis, fraud detection, and and tool to discover previously unknown, valid customer retention, production control and science patterns and Relationship in large data sets. These exploration. Data mining can be viewed as a result of tools can include statistical models, mathematical the natural evolution of information technology. -
Mathematics (MATH)
Course Descriptions MATH 1080 QL MATH 1210 QL Mathematics (MATH) Precalculus Calculus I 5 5 MATH 100R * Prerequisite(s): Within the past two years, * Prerequisite(s): One of the following within Math Leap one of the following: MAT 1000 or MAT 1010 the past two years: (MATH 1050 or MATH 1 with a grade of B or better or an appropriate 1055) and MATH 1060, each with a grade of Is part of UVU’s math placement process; for math placement score. C or higher; OR MATH 1080 with a grade of C students who desire to review math topics Is an accelerated version of MATH 1050 or higher; OR appropriate placement by math in order to improve placement level before and MATH 1060. Includes functions and placement test. beginning a math course. Addresses unique their graphs including polynomial, rational, Covers limits, continuity, differentiation, strengths and weaknesses of students, by exponential, logarithmic, trigonometric, and applications of differentiation, integration, providing group problem solving activities along inverse trigonometric functions. Covers and applications of integration, including with an individual assessment and study inequalities, systems of linear and derivatives and integrals of polynomial plan for mastering target material. Requires nonlinear equations, matrices, determinants, functions, rational functions, exponential mandatory class attendance and a minimum arithmetic and geometric sequences, the functions, logarithmic functions, trigonometric number of hours per week logged into a Binomial Theorem, the unit circle, right functions, inverse trigonometric functions, and preparation module, with progress monitored triangle trigonometry, trigonometric equations, hyperbolic functions. Is a prerequisite for by a mentor. May be repeated for a maximum trigonometric identities, the Law of Sines, the calculus-based sciences. -
Challenges in Bioinformatics for Statistical Data Miners
This article appeared in the October 2003 issue of the “Bulletin of the Swiss Statistical Society” (46; 10-17), and is reprinted by permission from its editor. Challenges in Bioinformatics for Statistical Data Miners Dr. Diego Kuonen Statoo Consulting, PSE-B, 1015 Lausanne 15, Switzerland [email protected] Abstract Starting with possible definitions of statistical data mining and bioinformatics, this article will give a general side-by-side view of both fields, emphasising on the statistical data mining part and its techniques, illustrate possible synergies and discuss how statistical data miners may collaborate in bioinformatics’ challenges in order to unlock the secrets of the cell. What is statistics and why is it needed? Statistics is the science of “learning from data”. It includes everything from planning for the collection of data and subsequent data management to end-of-the-line activities, such as drawing inferences from numerical facts called data and presentation of results. Statistics is concerned with one of the most basic of human needs: the need to find out more about the world and how it operates in face of variation and uncertainty. Because of the increasing use of statistics, it has become very important to understand and practise statistical thinking. Or, in the words of H. G. Wells: “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write”. But, why is statistics needed? Knowledge is what we know. Information is the communication of knowledge. Data are known to be crude information and not knowledge by itself. The way leading from data to knowledge is as follows: from data to information (data become information when they become relevant to the decision problem); from information to facts (information becomes facts when the data can support it); and finally, from facts to knowledge (facts become knowledge when they are used in the successful completion of the decision process). -
Mathematics: the Science of Patterns
_RE The Science of Patterns LYNNARTHUR STEEN forcesoutside mathematics while contributingto humancivilization The rapid growth of computing and applicationshas a rich and ever-changingvariety of intellectualflora and fauna. helpedcross-fertilize the mathematicalsciences, yielding These differencesin perceptionare due primarilyto the steep and an unprecedentedabundance of new methods,theories, harshterrain of abstractlanguage that separatesthe mathematical andmodels. Examples from statisiicalscienceX core math- rainforest from the domainof ordinaryhuman activity. ematics,and applied mathematics illustrate these changes, The densejungle of mathematicshas beennourished for millennia which have both broadenedand enrichedthe relation by challengesof practicalapplications. In recentyears, computers between mathematicsand science. No longer just the have amplifiedthe impact of applications;together, computation study of number and space, mathematicalscience has and applicationshave swept like a cyclone across the terrainof become the science of patterlls, with theory built on mathematics.Forces -unleashed by the interactionof theseintellectu- relations among patterns and on applicationsderived al stormshave changed forever and for the better the morpholo- from the fit betweenpattern and observation. gy of mathematics.In their wake have emergednew openingsthat link diverseparts of the mathematicalforest, making possible cross- fertilizationof isolatedparts that has immeasurablystrengthened the whole. MODERN MATHEMATICSJUST MARKED ITS 300TH BIRTH- -
Florida Course Code Directory Computer Science Course Information 2019-2020
FLORIDA COURSE CODE DIRECTORY COMPUTER SCIENCE COURSE INFORMATION 2019-2020 Section 1007.2616, Florida Statutes, was amended by the Florida Legislature to include the definition of computer science and a requirement that computer science courses be identified in the Course Code Directory published on the Department of Education’s website. DEFINITION: The study of computers and algorithmic processes, including their principles, hardware and software designs, applications, and their impact on society, and includes computer coding and computer programming. SECONDARY COMPUTER SCIENCE COURSES Middle and high schools in each district, including combination schools in which any of grades 6-12 are taught, must provide an opportunity for students to enroll in a computer science course. If a school district does not offer an identified computer science course the district must provide students access to the course through the Florida Virtual School (FLVS) or through other means. COURSE NUMBER COURSE TITLE 0200000 M/J Computer Science Discoveries 0200010 M/J Computer Science Discoveries 1 0200020 M/J Computer Science Discoveries 2 0200305 Computer Science Discoveries 0200315 Computer Science Principles 0200320 AP Computer Science A 0200325 AP Computer Science A Innovation 0200335 AP Computer Science Principles 0200435 PRE-AICE Computer Studies IGCSE Level 0200480 AICE Computer Science 1 AS Level 0200485 AICE Computer Science 2 A Level 0200490 AICE Information Technology 1 AS Level 0200495 AICE Information Technology 2 A Level 0200800 IB Computer -
Safety and Security Challenge
SAFETY AND SECURITY CHALLENGE TOP SUPERCOMPUTERS IN THE WORLD - FEATURING TWO of DOE’S!! Summary: The U.S. Department of Energy (DOE) plays a very special role in In fields where scientists deal with issues from disaster relief to the keeping you safe. DOE has two supercomputers in the top ten supercomputers in electric grid, simulations provide real-time situational awareness to the whole world. Titan is the name of the supercomputer at the Oak Ridge inform decisions. DOE supercomputers have helped the Federal National Laboratory (ORNL) in Oak Ridge, Tennessee. Sequoia is the name of Bureau of Investigation find criminals, and the Department of the supercomputer at Lawrence Livermore National Laboratory (LLNL) in Defense assess terrorist threats. Currently, ORNL is building a Livermore, California. How do supercomputers keep us safe and what makes computing infrastructure to help the Centers for Medicare and them in the Top Ten in the world? Medicaid Services combat fraud. An important focus lab-wide is managing the tsunamis of data generated by supercomputers and facilities like ORNL’s Spallation Neutron Source. In terms of national security, ORNL plays an important role in national and global security due to its expertise in advanced materials, nuclear science, supercomputing and other scientific specialties. Discovery and innovation in these areas are essential for protecting US citizens and advancing national and global security priorities. Titan Supercomputer at Oak Ridge National Laboratory Background: ORNL is using computing to tackle national challenges such as safe nuclear energy systems and running simulations for lower costs for vehicle Lawrence Livermore's Sequoia ranked No. -
History of Mathematics
History of Mathematics James Tattersall, Providence College (Chair) Janet Beery, University of Redlands Robert E. Bradley, Adelphi University V. Frederick Rickey, United States Military Academy Lawrence Shirley, Towson University Introduction. There are many excellent reasons to study the history of mathematics. It helps students develop a deeper understanding of the mathematics they have already studied by seeing how it was developed over time and in various places. It encourages creative and flexible thinking by allowing students to see historical evidence that there are different and perfectly valid ways to view concepts and to carry out computations. Ideally, a History of Mathematics course should be a part of every mathematics major program. A course taught at the sophomore-level allows mathematics students to see the great wealth of mathematics that lies before them and encourages them to continue studying the subject. A one- or two-semester course taught at the senior level can dig deeper into the history of mathematics, incorporating many ideas from the 19th and 20th centuries that could only be approached with difficulty by less prepared students. Such a senior-level course might be a capstone experience taught in a seminar format. It would be wonderful for students, especially those planning to become middle school or high school mathematics teachers, to have the opportunity to take advantage of both options. We also encourage History of Mathematics courses taught to entering students interested in mathematics, perhaps as First Year or Honors Seminars; to general education students at any level; and to junior and senior mathematics majors and minors. Ideally, mathematics history would be incorporated seamlessly into all courses in the undergraduate mathematics curriculum in addition to being addressed in a few courses of the type we have listed.