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The document analyzes different news articles covering the Boston Marathon bombings and notes their approaches. The Times article focuses only on facts without commentary. The Guardian live blog constantly updates with new information every few minutes and uses an emotional, sentimental tone in eyewitness quotes. Boston.com promotes donations and features posts about young victims with a realistic, sentimental tone.V3 gamingcasestudy
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This document describes a case study using the popular online video game YOURCraft. The game faces challenges like griefing and big data issues. The proposed solution is to host the game on AWS infrastructure for scalability, fault tolerance, and low costs. Splunk will be used to analyze game logs and player data from AWS storage to monitor player behavior and identify griefers. Dashboards and visualizations will provide insights to improve the user experience and identify monetization opportunities.ʹÓõçÄԵĽ¡¿µ¿¼Á¿
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The document describes a proposed mobile application called "Hit the Deal" that would allow users to view deals and offers from multiple deal sites in one place and make direct purchases using a stored credit card. It discusses the problems the app aims to solve around finding local deals and making easy purchases. The proposal compares "Hit the Deal" to other deal apps and sites that aggregate offers but lack personalization, gamification features, or direct in-app purchases. It outlines how "Hit the Deal" would differentiate itself through location-based deals, personalization of offers based on interests and categories, and a streamlined one-click purchase process using a stored app credit card.Final pink panthers_03_30
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The document summarizes a machine learning project analyzing data from the Titanic disaster to predict passenger survival. It describes various machine learning algorithms applied, including decision trees, logistic regression, random forests and conditional inference trees. Feature engineering improved predictions, such as using title and family size. While first class passengers had more survival chances, first class adult males had lower survival rates compared to expectations.Family pics2august014
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The document summarizes machine learning models developed to predict Titanic passenger survival from training data provided by Kaggle. Logistic regression and decision tree models were able to predict survival with over 75% accuracy. Random forest and conditional inference tree ensemble methods in the randomForest and party packages achieved the highest accuracy of over 81% on the competition test data. Feature engineering improved predictions by accounting for variables like passenger class, gender, age, fare paid, and whether traveling with family.Rsplit apply combine
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The document defines a function called covcor() that calculates and returns the covariance and correlation between variables in a data frame. The function takes a data frame as input, splits it by a grouping variable, applies covariance and correlation calculations to subsets of the data, and combines the results into an output data frame. Three methods for defining the covcor() function are presented: 1) Using subset() and merge(), 2) Using tapply(), and 3) Using ddply() from the plyr package. The function is demonstrated on orange tree data to calculate covariance and correlation between tree age and circumference for each tree. Transforming the circumference variable affects the covariance but not the correlation, demonstrating properties of these statistical measures.Cassandra NoSQL Tutorial
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VBA §Õ§Ý§ñ §é§Ñ§Û§ß§Ú§Ü§à§Ó 3rd Edition Steve Cummings
VBA §Õ§Ý§ñ §é§Ñ§Û§ß§Ú§Ü§à§Ó 3rd Edition Steve Cummings
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Bondarenko, Maria (2020, February). Representation of Motion and Direction in...MashaBondarenko4
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Bondarenko, Maria (2022). Representation of Motion and Direction in Russian Speakers: toward an Alternative Method of Teaching Russian Unprefixed Verbs of Motion. Paper presented at the annual conference of the AATSEEL, February 18¨C20, 2022, Philadelphia, USA. (ºÝºÝߣs)
_________________
Some of the ideas presented in this papers have been further developped in the publications:
Bondarenko, M. (2023). Learning motion verbs through a board game. Insights from a cognitive linguistics perspective. In S. Nuss, & V. Kogan (Eds.), Dynamic Teaching of Russian. Games and Gamification of Learning (pp. 107¨C137). Routledge.
Bondarenko, M. (2024). Structuralist roots of the traditional instruction of Russian verbs of motion. Does the Directionality-Based Approach fit to the Russian L2 classroom? DiSlaw ¨C Didaktik slawischer Sprachen, 6, 1¨C20. DOI: https://doi.org/10.48789/2024.1.1
Bondarenko, M. (2025). O¨´ ¡°directionnalit¨¦¡± nous m¨¨ne-t-elle? La conceptualisation structuraliste des verbes de d¨¦placement russes est-elle adapt¨¦e ¨¤ l¡¯enseignement? XVIIe Congr¨¨s international (extraordinaire) des slavistes. Livre des r¨¦sum¨¦s II. Litt¨¦rature. Slavistique, Paris, 2025, (Session 3.4.1, pp. 27¨C33) https://mks-paris.sciencesconf.org/data/pages/Litterature_Slavistique.pdf
Bondarenko, M. (2025). Searching for Alternative Approaches to Conceptualizing and Teaching Russian verbs of Motion: Bridging Linguistics and Teaching Practice (Editorial introduction). Russian Language Journal (Special issue), 76(1)
FactoryTalk View Site Edition §²§å§Ü§à§Ó§à§Õ§ã§ä§Ó§à §á§à§Ý§î§Ù§à§Ó§Ñ§ä§Ö§Ý§ñ §´§à§Þ 1 Allen-Bredley.
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§±§Ñ§â§Ñ§Õ§Ú§Ô§Þ§í §á§Ö§Õ§Ñ§Ô§à§Ô§Ú§Ü§Ú. §±§à§Ý§Ú§á§Ñ§â§Ñ§Õ§Ú§Ô§Þ§Ñ§Ý§î§ß§à§ã§ä§î §Ü§Ñ§Ü §Þ§Ö§ä§à§Õ§à§Ý§à§Ô§Ú§é§Ö§ã§Ü§Ú§Û §á§â§Ú§ß§è§Ú§áMore Related Content
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VBA §Õ§Ý§ñ §é§Ñ§Û§ß§Ú§Ü§à§Ó 3rd Edition Steve Cummings
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_________________
Some of the ideas presented in this papers have been further developped in the publications:
Bondarenko, M. (2023). Learning motion verbs through a board game. Insights from a cognitive linguistics perspective. In S. Nuss, & V. Kogan (Eds.), Dynamic Teaching of Russian. Games and Gamification of Learning (pp. 107¨C137). Routledge.
Bondarenko, M. (2024). Structuralist roots of the traditional instruction of Russian verbs of motion. Does the Directionality-Based Approach fit to the Russian L2 classroom? DiSlaw ¨C Didaktik slawischer Sprachen, 6, 1¨C20. DOI: https://doi.org/10.48789/2024.1.1
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- 12. §ï§Ü§Ù§Ñ§Þ§Ö§ß ready¨Cmade §Ü§Ñ§Ü§à§Ó§Ñ §à§ã§ß§à§Ó§ß§Ñ§ñ §è§Ö§Ý§î §á§à§ï§Ù§Ú§Ú? §à§ä§Ó§Ö§é§Ñ§Û§ä§Ö §á§â§Ñ§Ó§Ú§Ý§î§ß§à §Ú §ã§â§Ñ§Ù§å
- 17. §·§· §Ó§Ö§Ü ? §ä§Ú§â§Ö §Þ§Ö§Ø§Õ§å §á§à§Ý§à§Ó§Ú§ß§Ü§Ñ§Þ§Ú §Ó§à§á§â§à§ã§Ú§ä§Ö§Ý§î§ß§à§Ô§à §Ù§ß§Ñ§Ü§Ñ
- 19. §Ò§Ú§Ù§à§ß §Ó §à§Ò§à§Ù§Ö §ß§Ö§Ò§Ñ §æ§à§â§Þ§å§Ý§í §à§Ù§à§ß§à§Ó§í§Û §à§Ù§ß§à§Ò §Ù§Ý§à§Ò§í
- 20. §Ö§ã§Ý§Ú §ã§Þ§à§ä§â§Ö§ä§î §Ü§Ñ§Ü §á§Ý§Ñ§ß§Ú§â§å§Ö§ä §Ý§Ú§ã§ä §Þ§à§Ø§ß§à §à§ä§Õ§à§ç§ß§å§ä§î §à§ä §Ó§Ù§Ô§Ý§ñ§Õ§Ñ
- 21. §ß§à§ñ§Ò§â§î§ã§Ü§Ú§Û §á§à§Ý§Õ§Ö§ß§î §Ü§Ñ§Ý§î§ñ§ß §ß§Ñ§ã§ä§à§Ý§î§ß§í§ç §â§í§Ò§à§Ü §à§Ô§ß§Ö§ä§å§ê§Ú§ä§Ö§Ý§î §Ù§Ñ§á§â§ñ§ä§Ñ§ß§ß§í§Û
- 22. §Ý§Ö§Ô§é§Ö §á§ä§Ú§é§î§Ö §Ý§Ö§Ô§é§Ö §Ý§Ú§é§ß§à§ã§ä§Ú §Ý§Ú§é§ß§à §Ý§Ö§Ô§é§Ö §Ú §á§Ö§â§Ö§Ý?§ä§ß§Ö§Ö
- 23. §ß§Ñ §á§à§Ô§Ý§ñ§Õ§Ö§ß§î§Ö §Þ§Ú§â§Ñ §ß§Ö §á§à§Û§Õ§å §Ü§ä§à §Þ§Ö§ß§ñ §á§à§Õ§Ò§â§à§ã§Ú§ä §Õ§à §é§å§Õ§Ñ?
- 24. §ß§Ú§ß§Ñ§é?§Þ §ß§Ö§Ò§à §á§à§Ó§Ö§ã§Ú§Ý§Ñ §ã§Ö§Ò§ñ §Ú§Ù §æ§à§â§Þ§í §Ó§í§ä§Ñ§ã§Ü§Ú§Ó§Ñ§ñ §Þ§à§Ö§Û§Ó§ã§Ö§Û
- 25. §ã§Þ§Ö§â§ä§î §Ó§à §ã§ß§Ö §à§ß §Ó§ã§ä§â§Ö§ä§Ú§Ý §á§â§Ö§Ü§â§Ñ§ã§ß§à§Ô§à §Ñ§ß§Ô§Ö§Ý§Ñ §ß§à §à§ß §Ü §ã§à§Ø§Ñ§Ý§Ö§ß§Ú§ð §à§Ü§Ñ§Ù§Ñ§Ý§ã§ñ §¢§à§Ô§à§Þ
- 26. §á§à§ï§ä§í §ß§Ñ §Ò§å§Ü§Ó§å §Ñ §å§Þ§Ú§â§Ñ§ð§ä §á§Ö§â§Ó§í§Þ§Ú §Ñ§ß§è§Ö§Ý§î §ã§ä§Ñ§Ý §è§Ö§Ý§Ñ§ß§à§Þ §ß§Ö §á§à§Þ§à§Ô§Ý§à
- 27. §Þ§à§â§Ö §ã§Ú§ß§Ö§Ö §ß§Ö§ã§Ú§Ý§î§ß§à§Ö §ã§Ú§ß§Ö§Ö §á§à§ß§à§ã§Ú§Ý§Ú §ß§Ñ§ã§Ú§Ý§Ú§Ö §Þ§Ñ§Ý§à§ã§à§Ý§î§ß§à§Ö §Ý§Ñ§ã§Ü§à§Ó§í§Û §Þ§à§Ý§Ý§ð§ã§Ü
- 28. §ß§à§é§î§ð §Ù§Ö§Þ§ß§à§Û §Þ§ñ§é§Ú§Ü §ã§â§í§Ó§Ñ§Ö§ä§ã§ñ §ã §Ò§Ñ§Ý§Ü§à§ß§Ñ §Ú §à§ä§ã§Ü§Ñ§Ü§Ú§Ó§Ñ§Ö§ä §à§Ò§â§Ñ§ä§ß§à