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Data Mining & Business Intelligence | Tutorial #3 | Issues in Data Mining
 
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This video addresses the issues which are there involved in Data Mining system. Watch now! #RanjiRaj #DataMining #DMIssues Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj
Views: 2583 Ranji Raj
Data Mining: How You're Revealing More Than You Think
 
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Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 142122 SciShow
What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning
 
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What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning - SOCIAL MEDIA MINING definition - SOCIAL MEDIA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. The term "mining" is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to sift through vast quanitites of raw ore to find the precious minerals; likewise, social media "mining" requires human data analysts and automated software programs to sift through massive amounts of raw social media data (e.g., on social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, etc.) in order to discern patterns and trends. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs (or, for companies, new products, processes and services). Social media mining uses a range of basic concepts from computer science, data mining, machine learning and statistics. Social media miners develop algorithms suitable for investigating massive files of social media data. Social media mining is based on theories and methodologies from social network analysis, network science, sociology, ethnography, optimization and mathematics. It encompasses the tools to formally represent, measure, model, and mine meaningful patterns from large-scale social media data. In the 2010s, major corporations, as well as governments and not-for-profit organizations engage in social media mining to find out more about key populations of interest, which, depending on the organization carrying out the "mining", may be customers, clients, or citizens. As defined by Kaplan and Haenlein, social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Photobucket, or Picasa), news aggregation (Google reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch.tv), virtual worlds (Kaneva), social gaming (World of Warcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger). The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegree.com, was introduced in 1997. Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media.
Views: 728 The Audiopedia
Wikipedia MWdumper
 
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Wikipedia has over 4.45 million articles in about 32 million pages. This VM has been running for over 1 week now, taking gaps in between. Now is the time to break this process, as it is likely to take another few days / weeks if continued like this. Lets pause the VM and take a final snapshot ! VMware VM snapshots sometimes require immense hardware resources and time, especially on a huge VM like this one, Wikipedia. As we see 8 GB RAM is given to the VM, the Disk contention has suffered greatly during this process... CPU and RAM were relatively free, but disk was highly occupied with disk I/0 activity ranging between 1 to MB/sec throughout. Therefore, we shall look at installing local Wikipedia through a Big Data subsystem in the next activity. We shall bring in a "Mahout library", that works with HADOOP and HDFS, and then perform similar activity with parallel processing. To see how our local wikipedia looks as of now, lets open the web browser, and open the web page. Mahout is a scalable machine learning library that implements many different approaches to machine learning. The project currently contains implementations of algorithms for classification, clustering, frequent item set mining, genetic programming and collaborative filtering. Mahout is scalable along three dimensions: It scales to reasonably large data sets by leveraging algorithm properties or implementing versions based on Apache Hadoop. Snapshot is 85% complete now, and after this finishes, lets have a look at our local Wikipedia page. The whole idea is to manage huge sums of information. In this example, we saw that MediaWiki Inc. allows the public to download its database dumps. The english version of Wikipedia consists of a compressed file of 9.9 GB, which decompresses to over 44 GB XML file. This XML file has the structure and content of entire Wikipedia english TEXT pages. There is a seperate database for images, diagrams and photos. Alright, the FINAL snapshot is over, let see the state our VM now, and connect to it through the web browser. That is the URL, and we have the main page. Let give a search... Wikipedia on the internet is extensively CACHED, hence we get responses almost immediately. In a Virtualization environment, this may be slow. So lets stop the MWdumper from reading the wiki-dump. Now this is your local wikipedia. It doesn't end here. This ought to be used later for Data mining, and other project purposes. Thanks for Watching !!!
What is DATA STREAM MINING? What does DATA STREAM MINING mean? DATA STREAM MINING meaning
 
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What is DATA STREAM MINING? What does V mean? DATA STREAM MINING meaning - DATA STREAM MINING definition - DATA STREAM MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities. In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion. Often, concepts from the field of incremental learning are applied to cope with structural changes, on-line learning and real-time demands. In many applications, especially operating within non-stationary environments, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e. the goal of the prediction, the class to be predicted or the target value to be predicted, may change over time. This problem is referred to as concept drift. Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data. Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.
Views: 813 The Audiopedia
Don't Waste $1000 on Data Recovery
 
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Thanks to DeepSpar for sponsoring this video! Check out their RapidSpar Data Recovery Tool at http://geni.us/rapidspar RapidSpar is the first cloud-driven device built to help IT generalists and other non-specialized users recover client data from damaged or failing HDDs/SSDs Buy HDDs on Amazon: http://geni.us/sLlhDf Buy HDDs on Newegg: http://geni.us/a196 Linus Tech Tips merchandise at http://www.designbyhumans.com/shop/Linustechtips Linus Tech Tips posters at http://crowdmade.com/linustechtips Our Test Benches on Amazon: https://www.amazon.com/shop/linustechtips Our production gear: http://geni.us/cvOS Twitter - https://twitter.com/linustech Facebook - http://www.facebook.com/LinusTech Instagram - https://www.instagram.com/linustech Twitch - https://www.twitch.tv/linustech Intro Screen Music Credit: Title: Laszlo - Supernova Video Link: https://www.youtube.com/watch?v=PKfxm... iTunes Download Link: https://itunes.apple.com/us/album/sup... Artist Link: https://soundcloud.com/laszlomusic Outro Screen Music Credit: Approaching Nirvana - Sugar High http://www.youtube.com/approachingnir... Sound effects provided by http://www.freesfx.co.uk/sfx/
Views: 1648702 Linus Tech Tips
Geoff Webb - Analysis and Mining Large Data Sets
 
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Geoffrey I. Webb is Professor of Computer Science at Monash University, Founder and Director of Data Mining software development and consultancy company G. I. Webb and Associates, and Editor-in-Chief of the journal Data Mining and Knowledge Discovery. Before joining Monash University he was on the faculty at Griffith University from 1986 to 1988 and then at Deakin University from 1988 to 2002. Webb has published more than 180 scientific papers in the fields of machine learning, data science, data mining, data analytics, big data and user modeling. He is an editor of the Encyclopedia of Machine Learning. Webb created the Averaged One-Dependence Estimators machine learning algorithm and its generalization Averaged N-Dependence Estimators and has worked extensively on statistically sound association rule learning. Webb's awards include IEEE Fellow, the IEEE International Conference on Data Mining Outstanding Service Award, an Australian Research Council Outstanding Researcher Award and multiple Australian Research Council Discovery Grants. Webb is a Foundation Member of the Editorial Advisory Board of the journal Statistical Analysis and Data Mining, Wiley Inter Science. He has served on the Editorial Boards of the journals Machine Learning, ACM Transactions on Knowledge Discovery in Data,User Modeling and User Adapted Interaction,and Knowledge and Information Systems. https://en.wikipedia.org/wiki/Geoff_Webb http://www.infotech.monash.edu.au/research/profiles/profile.html?sid=4540&pid=122 http://www.csse.monash.edu.au/~webb Interviewed by Kevin Korb and Adam Ford Many thanks for watching! - Support me via Patreon: https://www.patreon.com/scifuture - Please Subscribe to this Channel: http://youtube.com/subscription_center?add_user=TheRationalFuture - Science, Technology & the Future website: http://scifuture.org
Social media data mining for counter-terrorism | Wassim Zoghlami | TEDxMünster
 
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Using public social media data from twitter and Facebook, actions and announcements of terrorists – in this case ISIS – can be monitored and even be predicted. With his project #DataShield Wassim shares his idea of having a tool to identify oncoming threats and attacks in order to protect people and to induce preventive actions. Wassim Zoghlami is a Tunisian Computer Engineering Senior focussing on Business Intelligence and ERP with a passion for data science, software life cycle and UX. Wassim is also an award winning serial entrepreneur working on startups in healthcare and prevention solutions in both Tunisia and The United States. During the past years Wassim has been working on different projects and campaigns about using data driven technology to help people working to uphold human rights and to promote civic engagement and culture across Tunisia and the MENA region. He is also the co-founder of the Tunisian Center for Civic Engagement, a strong advocate for open access to research, open data and open educational resources and one of the Global Shapers in Tunis. At TEDxMünster Wassim will talk about public social media data mining for counter-terrorism and his project idea DataShield. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 1976 TEDx Talks
Coding With Python :: Learn API Basics to Grab Data with Python
 
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Coding With Python :: Learn API Basics to Grab Data with Python This is a basic introduction to using APIs. APIs are the "glue" that keep a lot of web applications running and thriving. Without APIs much of the internet services you love might not even exist! APIs are easy way to connect with other websites & web services to use their data to make your site or application even better. This simple tutorial gives you the basics of how you can access this data and use it. If you want to know if a website has an api, just search "Facebook API" or "Twitter API" or "Foursquare API" on google. Some APIs are easy to use (like Locu's API which we use in this video) some are more complicated (Facebook's API is more complicated than Locu's). More about APIs: http://en.wikipedia.org/wiki/Api Code from the video: http://pastebin.com/tFeFvbXp If you want to learn more about using APIs with Django, learn at http://CodingForEntrepreneurs.com for just $25/month. We apply what we learn here into a Django web application in the GeoLocator project. The Try Django Tutorial Series is designed to help you get used to using Django in building a basic landing page (also known as splash page or MVP landing page) so you can collect data from potential users. Collecting this data will prove as verification (or validation) that your project is worth building. Furthermore, we also show you how to implement a Paypal Button so you can also accept payments. Django is awesome and very simple to get started. Step-by-step tutorials are to help you understand the workflow, get you started doing something real, then it is our goal to have you asking questions... "Why did I do X?" or "How would I do Y?" These are questions you wouldn't know to ask otherwise. Questions, after all, lead to answers. View all my videos: http://bit.ly/1a4Ienh Get Free Stuff with our Newsletter: http://eepurl.com/NmMcr The Coding For Entrepreneurs newsletter and get free deals on premium Django tutorial classes, coding for entrepreneurs courses, web hosting, marketing, and more. Oh yeah, it's free: A few ways to learn: Coding For Entrepreneurs: https://codingforentrepreneurs.com (includes free projects and free setup guides. All premium content is just $25/mo). Includes implementing Twitter Bootstrap 3, Stripe.com, django south, pip, django registration, virtual environments, deployment, basic jquery, ajax, and much more. On Udemy: Bestselling Udemy Coding for Entrepreneurs Course: https://www.udemy.com/coding-for-entrepreneurs/?couponCode=youtubecfe49 (reg $99, this link $49) MatchMaker and Geolocator Course: https://www.udemy.com/coding-for-entrepreneurs-matchmaker-geolocator/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Marketplace & Dail Deals Course: https://www.udemy.com/coding-for-entrepreneurs-marketplace-daily-deals/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Free Udemy Course (40k+ students): https://www.udemy.com/coding-for-entrepreneurs-basic/ Fun Fact! This Course was Funded on Kickstarter: http://www.kickstarter.com/projects/jmitchel3/coding-for-entrepreneurs
Views: 415478 CodingEntrepreneurs
Google's Deep Mind Explained! - Self Learning A.I.
 
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Subscribe here: https://goo.gl/9FS8uF Become a Patreon!: https://www.patreon.com/ColdFusion_TV Visual animal AI: https://www.youtube.com/watch?v=DgPaCWJL7XI Hi, welcome to ColdFusion (formally known as ColdfusTion). Experience the cutting edge of the world around us in a fun relaxed atmosphere. Sources: Why AlphaGo is NOT an "Expert System": https://googleblog.blogspot.com.au/2016/01/alphago-machine-learning-game-go.html “Inside DeepMind” Nature video: https://www.youtube.com/watch?v=xN1d3qHMIEQ “AlphaGo and the future of Artificial Intelligence” BBC Newsnight: https://www.youtube.com/watch?v=53YLZBSS0cc http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html http://www.ft.com/cms/s/2/063c1176-d29a-11e5-969e-9d801cf5e15b.html http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html#tables https://www.technologyreview.com/s/533741/best-of-2014-googles-secretive-deepmind-startup-unveils-a-neural-turing-machine/ https://medium.com/the-physics-arxiv-blog/the-last-ai-breakthrough-deepmind-made-before-google-bought-it-for-400m-7952031ee5e1 https://www.deepmind.com/ www.forbes.com/sites/privacynotice/2014/02/03/inside-googles-mysterious-ethics-board/#5dc388ee4674 https://medium.com/the-physics-arxiv-blog/the-last-ai-breakthrough-deepmind-made-before-google-bought-it-for-400m-7952031ee5e1#.4yt5o1e59 http://www.theverge.com/2016/3/10/11192774/demis-hassabis-interview-alphago-google-deepmind-ai https://en.wikipedia.org/wiki/Demis_Hassabis https://en.wikipedia.org/wiki/Google_DeepMind //Soundtrack// Disclosure - You & Me (Ft. Eliza Doolittle) (Bicep Remix) Stumbleine - Glacier Sundra - Drifting in the Sea of Dreams (Chapter 2) Dakent - Noon (Mindthings Rework) Hnrk - fjarlæg Dr Meaker - Don't Think It's Love (Real Connoisseur Remix) Sweetheart of Kairi - Last Summer Song (ft. CoMa) Hiatus - Nimbus KOAN Sound & Asa - This Time Around (feat. Koo) Burn Water - Hide » Google + | http://www.google.com/+coldfustion » Facebook | https://www.facebook.com/ColdFusionTV » My music | t.guarva.com.au/BurnWater http://burnwater.bandcamp.com or » http://www.soundcloud.com/burnwater » https://www.patreon.com/ColdFusion_TV » Collection of music used in videos: https://www.youtube.com/watch?v=YOrJJKW31OA Producer: Dagogo Altraide Editing website: www.cfnstudios.com Coldfusion Android Launcher: https://play.google.com/store/apps/details?id=nqr.coldfustion.com&hl=en » Twitter | @ColdFusion_TV
Views: 2989072 ColdFusion
HARD DRIVE Mining? This is getting ridiculous...
 
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Hard drive mining... could this be the solution to the GPU crisis?... For your unrestricted 30 days free trial, go to https://www.freshbooks.com/techtips and enter in “Linus Tech Tips” in the how you heard about us section. Get iFixit's Pro Tech Toolkit now for only $59.95 USD at https://www.ifixit.com/linus Buy HDDs on Amazon: http://geni.us/iJD6t Discuss on the forum: https://linustechtips.com/main/topic/910184-hard-drive-mining-this-is-getting-ridiculous/ Our Affiliates, Referral Programs, and Sponsors: https://linustechtips.com/main/topic/75969-linus-tech-tips-affiliates-referral-programs-and-sponsors Linus Tech Tips merchandise at http://www.designbyhumans.com/shop/LinusTechTips/ Linus Tech Tips posters at http://crowdmade.com/linustechtips Our production gear: http://geni.us/cvOS Get LTX 2018 tickets at https://www.ltxexpo.com/ Twitter - https://twitter.com/linustech Facebook - http://www.facebook.com/LinusTech Instagram - https://www.instagram.com/linustech Twitch - https://www.twitch.tv/linustech Intro Screen Music Credit: Title: Laszlo - Supernova Video Link: https://www.youtube.com/watch?v=PKfxmFU3lWY iTunes Download Link: https://itunes.apple.com/us/album/supernova/id936805712 Artist Link: https://soundcloud.com/laszlomusic Outro Screen Music Credit: Approaching Nirvana - Sugar High http://www.youtube.com/approachingnirvana Sound effects provided by http://www.freesfx.co.uk/s
Views: 2487625 Linus Tech Tips
What are Bloom Filters? - Hashing
 
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Bloom Filters are data structures used to efficiently answer queries when we do not have enough "search key" space to handle all possible queries. In this case, the "search key" is hashed, marked and then used later to check if it was searched earlier or not. Bloom Filters use hashing as this is an immutable function result, and marking the respective positions in the data structure guarantees that the next search for the same string will return true. This data structure has an error rate when returning 'true', and we look into how the number of hash functions effect it's performance. In practice, Bloom Filters can be used to check for membership and to avoid 'One Hit Wonders'. We talk about what are bloom filters, how do we use them and where can these filters be applied. An example would be tinyUrl, which can check if a url has been previously generated using a bloom filter, and regenerate it if the answer is positive. Bloom Filter: https://en.wikipedia.org/wiki/Bloom_filter Code: https://github.com/gkcs/Competitive-Programming/blob/master/src/main/java/main/java/course/BloomFilter.java References: Hashing: https://www.hackerearth.com/practice/data-structures/hash-tables/basics-of-hash-tables/tutorial/ FaceBook Talk: https://www.facebook.com/Engineering/videos/432864835468/ One Hit Wonder: https://en.wikipedia.org/wiki/One-hit_wonder Cache: https://en.wikipedia.org/wiki/Cache_(computing) LRU: https://en.wikipedia.org/wiki/Cache_replacement_policies#Least_recently_used_(LRU) My Social Media Links: https://www.facebook.com/gkcs0/ https://www.quora.com/profile/Gaurav-Sen-6 https://www.codechef.com/users/gkcs https://github.com/gkcs/Competitive-Programming/
Views: 8341 Gaurav Sen
Environmental Economics
 
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021 - Environmental Economics In this video Paul Andersen explains how economic models, like supply and demand, can be applied to environmental systems. The market forces will not protect environmental services until proper valuation and externalities are established. The wealth of a nation can be more accurately measured through the sustainability of the economic model. Do you speak another language? Help me translate my videos: http://www.bozemanscience.com/translations/ Music Attribution Intro Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License Outro Title: String Theory Artist: Herman Jolly http://sunsetvalley.bandcamp.com/track/string-theory All of the images are licensed under creative commons and public domain licensing: balin, jean victor. (2009). Cartoon cloud. Retrieved from https://commons.wikimedia.org/wiki/File:Cartoon_cloud.svg Contrast, H. (2008). Deutsch: Eine Fabrik in China am Ufer des Jangtse.English: A Factory in China at Yangtze River.Euskara: Landare industrialen ondoriz, aire kutsakorra.Nederlands: Industrie langs de Yangtse rivier in China. Retrieved from https://commons.wikimedia.org/wiki/File:Factory_in_China.jpg EPA. (2008). English: Diesel smoke from a big truck. Retrieved from https://commons.wikimedia.org/wiki/File:Diesel-smoke.jpg Evans, N. 17 crew; taken by either H. S. or R. (1972). العربية: صورة الكرة الزرقاء الشهيرة التي تعتبر أول صورة لمنظر الارض الكامل. إلتقطت الصورة في 7 ديسمبر 1972. Retrieved from https://commons.wikimedia.org/wiki/File:The_Earth_seen_from_Apollo_17.jpg Factory by Anonymous. (n.d.). Retrieved from https://openclipart.org/download/23962/Anonymous-Factory.svg Gizlog. (2011). English: Sigmund Freud Bobble Head/Wackelkopf. Retrieved from https://commons.wikimedia.org/wiki/File:Sigmund_Freud_Bobble_Head_Wackelkopf.JPG Zifan, A. (2015). English: Countries by GDP (PPP) per capita in 2014, based on data from the International Monetary Fund. Retrieved from https://commons.wikimedia.org/wiki/File:Countries_by_GDP_(PPP)_Per_Capita_in_2014.svg
Views: 82111 Bozeman Science
Hadoop Vs Traditional Database Systems | Hadoop Data Warehouse | Hadoop and ETL | Hadoop Data Mining
 
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http://www.edureka.co/hadoop Email Us: [email protected],phone : +91-8880862004 This short video explains the problems with existing database systems and Data Warehouse solutions, and how Hadoop based solutions solves these problems. Let's Get Going on our Hadoop Journey and Join our 'Big Data and Hadoop' course. - - - - - - - - - - - - - - How it Works? 1. This is a 10-Module Instructor led Online Course. 2. We have a 3-hour Live and Interactive Sessions every Sunday. 3. We have 4 hours of Practical Work involving Lab Assignments, Case Studies and Projects every week which can be done at your own pace. We can also provide you Remote Access to Our Hadoop Cluster for doing Practicals. 4. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 5. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, MapReduce, Advance MapReduce, PIG, HIVE, HBase, Zookeeper, SQOOP, Hadoop 2.0 , YARN etc. will be covered in the course. - - - - - - - - - - - - - - Course Objectives After the completion of the Hadoop Course at Edureka, you should be able to: Master the concepts of Hadoop Distributed File System. Understand Cluster Setup and Installation. Understand MapReduce and Functional programming. Understand How Pig is tightly coupled with Map-Reduce. Learn how to use Hive, How you can load data into HIVE and query data from Hive. Implement HBase, MapReduce Integration, Advanced Usage and Advanced Indexing. Have a good understanding of ZooKeeper service and Sqoop, Hadoop 2.0, YARN, etc. Develop a working Hadoop Architecture. - - - - - - - - - - - - - - Who should go for this course? This course is designed for developers with some programming experience (preferably Java) who are looking forward to acquire a solid foundation of Hadoop Architecture. Existing knowledge of Hadoop is not required for this course. - - - - - - - - - - - - - - Why Learn Hadoop? BiG Data! A Worldwide Problem? According to Wikipedia, "Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, It is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success! The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop, which is not less than a panacea for all those companies working with BIG DATA in a variety of applications and has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data. - - - - - - - - - - - - - - Some of the top companies using Hadoop: The importance of Hadoop is evident from the fact that there are many global MNCs that are using Hadoop and consider it as an integral part of their functioning, such as companies like Yahoo and Facebook! On February 19, 2008, Yahoo! Inc. established the world's largest Hadoop production application. The Yahoo! Search Webmap is a Hadoop application that runs on over 10,000 core Linux cluster and generates data that is now widely used in every Yahoo! Web search query. Opportunities for Hadoopers! Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself! Happy Hadooping! Please write back to us at [email protected] or call us at +91-8880862004 for more information. http://www.edureka.co/big-data-and-hadoop
Views: 14322 edureka!
Facebook's Cambridge Analytica data scandal, explained
 
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Cambridge Analytica improperly obtained data from as many as 50 million people. That's put Mark Zuckerberg on the defensive. The Verge's Silicon Valley editor Casey Newton reports. Subscribe: https://goo.gl/G5RXGs Check out our full video catalog: https://goo.gl/lfcGfq Visit our playlists: https://goo.gl/94XbKx Like The Verge on Facebook: https://goo.gl/2P1aGc Follow on Twitter: https://goo.gl/XTWX61 Follow on Instagram: https://goo.gl/7ZeLvX Read More: http://www.theverge.com
Views: 603429 The Verge
Bitcoin: How Cryptocurrencies Work
 
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Whether or not it's worth investing in, the math behind Bitcoin is an elegant solution to some complex problems. Hosted by: Michael Aranda Special Thanks: Dalton Hubble Learn more about Cryptography: https://www.youtube.com/watch?v=-yFZGF8FHSg ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters—we couldn't make SciShow without them! Shout out to Bella Nash, Kevin Bealer, Mark Terrio-Cameron, Patrick Merrithew, Charles Southerland, Fatima Iqbal, Benny, Kyle Anderson, Tim Curwick, Will and Sonja Marple, Philippe von Bergen, Bryce Daifuku, Chris Peters, Patrick D. Ashmore, Charles George, Bader AlGhamdi ---------- Like SciShow? Want to help support us, and also get things to put on your walls, cover your torso and hold your liquids? Check out our awesome products over at DFTBA Records: http://dftba.com/scishow ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://bitinfocharts.com/ https://chrispacia.wordpress.com/2013/09/02/bitcoin-mining-explained-like-youre-five-part-2-mechanics/ https://www.youtube.com/watch?v=Lx9zgZCMqXE https://www.youtube.com/watch?v=nQZUi24TrdI https://bitcoin.org/en/how-it-works http://www.forbes.com/sites/investopedia/2013/08/01/how-bitcoin-works/#36bd8b2d25ee http://www.makeuseof.com/tag/how-does-bitcoin-work/ https://blockchain.info/charts/total-bitcoins https://en.bitcoin.it/wiki/Controlled_supply https://www.bitcoinmining.com/ http://bitamplify.com/mobile/?a=news Image Sources: https://commons.wikimedia.org/wiki/File:Cryptocurrency_Mining_Farm.jpg
Views: 2634171 SciShow
What is CLUSTER ANALYSIS? What does CLUSTER ANALYSIS mean? CLUSTER ANALYSIS meaning & explanation
 
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What is CLUSTER ANALYSIS? What does CLUSTER ANALYSIS mean? CLUSTER ANALYSIS meaning - CLUSTER ANALYSIS definition - CLUSTER ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics. Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It is often necessary to modify data preprocessing and model parameters until the result achieves the desired properties. Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek ß????? "grape") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals. Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.
Views: 6578 The Audiopedia
Duke Pesta on Common Core – Six Years Later
 
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On March 2, 2016 Dr. Duke Pesta spoke on the dangers of Common Core and discussed the new federal law “Every Student Succeeds Act” (ESSA). No doubt one of the most outspoken critic of the Common Core State Standards, Dr. Pesta exposes the origins and dangers of the Common Core scheme. Through his and other's efforts, people are beginning to fight back on behalf of their children. From this video, you will learn about the government's overreach, its concealment and outright lies that drive Common Core. Throughout his presentation, Dr. Pesta discusses the current state of the fight, identifies new threats and updates us on the current condition of public education in America. With over 450 talks in 40 states behind him, he offers his perspective on the best ways to push back against this ploy to manipulate the heart and minds of our children. Speaker Bio: Dr. Duke Pesta Freedom Project Education Academic Director Dr. Duke Pesta received his M.A. in Renaissance Literature from John Carroll University and his Ph.D. in Shakespeare and Renaissance Literature from Purdue University. He has taught at major research institutions and small liberal arts colleges and currently is a professor of English at the University of Wisconsin, Oshkosh and the Academic Director of Freedom Project Education. This event was held at the Yorba Linda First Baptist Church in Yorba Linda, CA. and was sponsored by the Faithful Christian Servants, Adelphia Classical Christian Academy and Reclaim Public Education. For more information: Website: http://www.FaithfulChristianServants.com To learn more about Dr. Pesta's and the Freedom Project Education: Website: http://www.FPEusa.org Want to do something for your children? Complete an Opt Out Form: http://www.pacificjustice.org/california-common-core-data-opt-out-form.html For free legal advice on Common Core, contact Brad Dacus, Esq. Website: http://www.pacificJustice.org
Views: 46536 Costa Mesa Brief
Indexing Wikipedia as a Benchmark of Single Machine Performance Limits
 
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Presented by Paddy Mullen,Independent Contractor This talk walks through using the wikipedia_Solr and wikipedia_elasticsearch repositories to quickly get up to speed with search at scale. When choosing a search solution, a common question is "Can this architecture handle my volume of data", figuring out how to answer that problem without integrating with your existing document store saves a lot of time. If your document corpus is similar to Wikipedia's document corpus, you can save a lot of time using wikipedia_Solr/wikipedia_elasticsearch as comparison points. Wikipedia is a great source for a tutorial such as mine because of it's familiarity and free availability. The uncompressed Wikipedia data dump I used was 33GB, it had 12M documents. The documents can be further split into paragraphs and links to test search over a large number of small items. To add extra scale, prior revisions can be used bringing the corpus size into terabytes.
What is PREDICTIVE ANALYTICS? What does PREDICTIVE ANALYSIS mean? PREDICTIVE ANALYSIS meaning
 
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What is PREDICTIVE ANALYTICS? What does PREDICTIVE ANALYSIS mean? PREDICTIVE ANALYSIS meaning - PREDICTIVE ANALYTICS definition - PREDICTIVE ANALYTICS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement. Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, child protection, pharmaceuticals, capacity planning and other fields. One of the best-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, "Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions." In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization. Furthermore, the converted data can be used for closed-loop product life cycle improvement which is the vision of the Industrial Internet Consortium.
Views: 1244 The Audiopedia
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 145404 Siraj Raval
IDSS Distinguished Seminar Series: Matthew Salganik (Princeton University)
 
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Title: Wiki Surveys: Open and Quantifiable Social Data Collection Abstract: In the social sciences, there is a longstanding tension between data collection methods that facilitate quantification and those that are open to unanticipated information. Advances in technology now enable new, hybrid methods that can combine some of the benefits of both approaches. Drawing inspiration both from online information aggregation systems like Wikipedia and from traditional survey research, we propose a new class of research instruments called wiki surveys. Just as Wikipedia evolves over time based on contributions from participants, we envision an evolving survey driven by contributions from respondents. We develop three general principles that underlie wiki surveys: they should be greedy, collaborative, and adaptive. Building on these principles, we develop methods for data collection and data analysis for one type of wiki survey, a pairwise wiki survey. We then present results from www.allourideas.org, a free and open-source website we created that enables groups all over the world to deploy wiki surveys. To date, more than 7,000 wiki surveys have been created, and they have collected over 400,000 ideas and 10 million votes. We describe the methodological challenges involved in collecting and analyzing this type of data and present a case study of a wiki survey created by the New York City Mayor’s Office. The talk will end with some more general claims about social research in the digital age. [Joint work with Karen E.C. Levy]
DOW, GOLD & SILVER:  Markets Disconnect In 2019
 
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What will happen to the markets in 2019? The Dow Jones Index will likely continue to fall to new lows while the precious metals will disconnect and move higher. If the markets experience a crash, the fear could push investors into gold and silver, thus driving their prices to new highs.
Views: 10797 SRSrocco Report
Introduction to Text and Data Mining
 
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Heard about Text and Data Mining (TDM) and wondering if it might be a good fit for your research? Find out what text and data mining is and how it can usefully be applied in a research context. Also learn about data sources for text and data mining projects and support, tools, and resources for learning more.
Views: 55 UniSydneyLibrary
Bioinformatics part 1 What is Bioinformatics
 
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For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html Bioinformatics Listeni/ˌbaɪ.oʊˌɪnfərˈmætɪks/ is an interdisciplinary field that develops and improves on methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. Bioinformatics uses many areas of computer science, mathematics and engineering to process biological data. Complex machines are used to read in biological data at a much faster rate than before. Databases and information systems are used to store and organize biological data. Analyzing biological data may involve algorithms in artificial intelligence, soft computing, data mining, image processing, and simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics. Commonly used software tools and technologies in the field include Java, C#, XML, Perl, C, C++, Python, R, SQL, CUDA, MATLAB, and spreadsheet applications.[1][2][3] Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 213047 Shomu's Biology
What is DATA WAREHOUSE? What does DATA WAREHOUSE mean? DATA WAREHOUSE meaning & explanation
 
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What is DATA WAREHOUSE? What does DATA WAREHOUSE mean? DATA WAREHOUSE meaning - DATA WAREHOUSE definition - DATA WAREHOUSE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place and are used for creating analytical reports for knowledge workers throughout the enterprise. The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting. The typical Extract, transform, load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data. The main source of the data is cleansed, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata. A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to: Integrate data from multiple sources into a single database and data model. Mere congregation of data to single database so a single query engine can be used to present data is an ODS. Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long running, analysis queries in transaction processing databases. Maintain data history, even if the source transaction systems do not. Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger. Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data. Present the organization's information consistently. Provide a single common data model for all data of interest regardless of the data's source. Restructure the data so that it makes sense to the business users. Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems. Add value to operational business applications, notably customer relationship management (CRM) systems. Make decision–support queries easier to write. Optimized data warehouse architectures allow data scientists to organize and disambiguate repetitive data. The environment for data warehouses and marts includes the following: Source systems that provide data to the warehouse or mart; Data integration technology and processes that are needed to prepare the data for use; Different architectures for storing data in an organization's data warehouse or data marts; Different tools and applications for the variety of users; Metadata, data quality, and governance processes must be in place to ensure that the warehouse or mart meets its purposes. In regards to source systems listed above, Rainer states, "A common source for the data in data warehouses is the company's operational databases, which can be relational databases"....
Views: 1232 The Audiopedia
Excel Olympiad (plz help)
 
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EXCEL DATA MINING PROBLEM LINK WITH THE SEC MAIN PAGE: WWW.SEC.GOV LINK WITH COMPANY FILLINGS : http://www.sec.gov/edgar/searchedgar/companysearch.html LINK WITH XBRL DESCRIPTION: http://en.wikipedia.org/wiki/XBRL LINK WITH OXYGEN DOWNLOAD: http://oxygenxml.com/download_oxygenxml_editor.html LINK WITH MICROSOFT MAPPING INFORMATION: http://office.microsoft.com/en-us/excel-help/overview-of-xml-in-excel-HA010206396.aspx?CTT=5&origin=HP010206397
Views: 126 MarinosGo
What is DATA DREDGING? What does DATA DREDGING mean? DATA DREDGING meaning, definition & explanation
 
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What is DATA DREDGING? What does DATA DREDGING mean? DATA DREDGING meaning - DATA DREDGING definition - DATA DREDGING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data dredging (also data fishing, data snooping, and p-hacking) is the use of data mining to uncover patterns in data that can be presented as statistically significant, without first devising a specific hypothesis as to the underlying causality. The process of data mining involves automatically testing huge numbers of hypotheses about a single data set by exhaustively searching for combinations of variables that might show a correlation. Conventional tests of statistical significance are based on the probability that an observation arose by chance, and necessarily accept some risk of mistaken test results, called the significance. When large numbers of tests are performed, some produce false results, hence 5% of randomly chosen hypotheses turn out to be significant at the 5% level, 1% turn out to be significant at the 1% significance level, and so on, by chance alone. When enough hypotheses are tested, it is virtually certain that some falsely appear statistically significant, since almost every data set with any degree of randomness is likely to contain some spurious correlations. If they are not cautious, researchers using data mining techniques can be easily misled by these results. The multiple comparisons hazard is common in data dredging. Moreover, subgroups are sometimes explored without alerting the reader to the number of questions at issue, which can lead to misinformed conclusions.
Views: 1096 The Audiopedia
Multilingual Text Mining: Lost in Translation, Found in Native Language Mining - Rohini Srihari
 
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There has been a meteoric rise in the amount of multilingual content on the web. This is primarily due to social media sites such as Facebook, and Twitter, as well as blogs, discussion forums, and reader responses to articles on traditional news sites. Language usage statistics indicate that Chinese is a very close second to English, and could overtake it to become the dominant language on the web. It is also interesting to see the explosive growth in languages such as Arabic. The availability of this content warrants a discussion on how such information can be effectively utilized. Such data can be mined for many purposes including business-related competitive insight, e-commerce, as well as citizen response to current issues. This talk will begin with motivations for multilingual text mining, including commercial and societal applications, digital humanities applications such as semi-automated curation of online discussion forums, and lastly, government applications, where the value proposition (benefits, costs and value) is different, but equally compelling. There are several issues to be touched upon, beginning with the need for processing native language, as opposed to using machine translated text. In tasks such as sentiment or behaviour analysis, it can certainly be argued that a lot is lost in translation, since these depend on subtle nuances in language usage. On the other hand, processing native language is challenging, since it requires a multitude of linguistic resources such as lexicons, grammars, translation dictionaries, and annotated data. This is especially true for "resourceMpoor languages" such as Urdu, and Somali, languages spoken in parts of the world where there is considerable focus nowadays. The availability of content such as multilingual Wikipedia provides an opportunity to automatically generate needed resources, and explore alternate techniques for language processing. The rise of multilingual social media also leads to interesting developments such as code mixing, and code switching giving birth to "new" languages such as Hinglish, Urdish and Spanglish! This phenomena exhibits both pros and cons, in addition to posing difficult challenges to automatic natural language processing. But there is also an opportunity to use crowd-sourcing to preserve languages and dialects that are gradually becoming extinct. It is worthwhile to explore frameworks for facilitating such efforts, which are currently very ad hoc. In summary, the availability of multilingual data provides new opportunities in a variety of applications, and effective mining could lead to better cross-cultural communication. Questions Addressed (i) Motivation for mining multilingual text. (ii) The need for processing native language (vs. machine translated text). (iii) Multilingual Social Media: challenges and opportunities, e.g., preserving languages and dialects.
What Is A Multimedia Database?
 
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The extension of multimedia database systems. Googleusercontent search. Traditional databases contained 11 mar 2016 a multimedia database is that hosts one or more primary media file types such as. Multimedia is a combination of text, graphics, animations, audio and video converted from different formats into digital media. Characteristics of mdbmsdata types. A multimedia database (mmdb) is a collection of related data. Operations on data abstract. Mp3 17 sep 2008 organizationmm database system architecturemm service modelmultimedia data storage 19 dec 2012wei tsang ooimmdbms querying interface, indexing anda that is dedicated to the storage, management, and access of one or more media types, such as text, image, video, sound, diagram, etc from publisher multimedia management systems presents issues techniques used in building 10. Text audio graphic video animationmultimedia data typically means digital images, audio, video, animation and graphics together with text. A multimedia database is a that include one or more primary media file types such as. Multimedia database management system10. 10 multimedia database systems contents i4 lehrstuhl fuer multimedia database content and structure citeseerx. Multimedia database wikipedia multimedia wikipedia en. Multimedia databases are that contain and allow key data management operations with multimedia. The spatial, temporal, storage, retrieval, integration, and presentation requirements of multimedia data differ significantly abruce berra, 'multimedia database systems,' in lecture notes computer science, (advanced systems, edsbhargava n a management system (m dbms) should provide for the ef cient storage manipulation represented as text, images, voice, 28 feb 2011 purpose this study is to review current applications teaching learning, further discuss some issues a(mmdbms) must support types addition providing facilities traditional dbms functions controlled collection items such graphic objects, video audioMultimedia wikipediamultimedia slidesharemultimedia databasemultimedia tech faqintroduction youtubeigi global. Wikipedia wiki multimedia_database url? Q webcache. Multimedia database management systems acm digital library. The multimedia data include one or more primary media types such as text, images, graphic objects (including drawings, sketches and illustrations) animation sequences, audio video 20 dec 2015 database(mmdb)? Multimedia database is a collection of related. Multimedia database wikipediamultimedia slidesharemultimedia databasemultimedia the tech faqintroduction to multimedia youtubeigi global. Multimedia database management requirements citeseerxdistributed multimedia systems. The acquisition, generation a multimedia database management system (mm dbms) is framework that separate the and from application programs definition. Multimedia databases ieee xplore document. Multimedia database applications issues and concerns for multimedia systems where are we now? Itec.
Views: 419 Aile Aile
ROOT CANAL TREATMENT !!!
 
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ROOT CANAL TREATMENT !!! [ PART 1 ] Root canal treatment is needed when the pulp tissue is infected. It is a complicated procidure that has several steps! In this video you can see the part of the treatment where the pulp is devitalized. The nerve is being removed and in our next video you will see the final filling of the root canals. DENTAL ANESTHESIA TECHNIQUE - EXPLAINED : https://www.youtube.com/watch?v=ST9rtr9d9rw ROOT CANAL TREATMENT !!! [ PART 1 ] ------------------------------------------------------------------------------------------------------------------------------- If you like this video please don't forget to subscribe.Thank you :) FB Page : https://www.facebook.com/mr.dentisteducator/ Instagram : https://www.instagram.com/mr_dentist1/ Twitter : https://twitter.com/dentisteducator
Views: 1853516 Mr.Dentist
What is INFORMATION RETRIEVAL? What does INFORMATION RETRIEVAL mean? INFORMATION RETRIEVAL meaning
 
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✪✪✪ I MAKE CUTE BABIES ✪ https://amzn.to/2DqiynS ✪✪✪ What is INFORMATION RETRIEVAL? What does INFORMATION RETRIEVAL mean? INFORMATION RETRIEVAL meaning - INFORMATION RETRIEVAL definition - INFORMATION RETRIEVAL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Information retrieval (IR) is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on full-text or other content-based indexing. Automated information retrieval systems are used to reduce what has been called "information overload". Many universities and public libraries use IR systems to provide access to books, journals and other documents. Web search engines are the most visible IR applications. An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In information retrieval a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of relevancy. An object is an entity that is represented by information in a content collection or database. User queries are matched against the database information. However, as opposed to classical SQL queries of a database, in information retrieval the results returned may or may not match the query, so results are typically ranked. This ranking of results is a key difference of information retrieval searching compared to database searching. Depending on the application the data objects may be, for example, text documents, images, audio, mind maps or videos. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates or metadata. Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. The top ranking objects are then shown to the user. The process may then be iterated if the user wishes to refine the query.
Views: 12257 The Audiopedia
Learning from Bacteria about Social Networks
 
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Google Tech Talk (more info below) September 30, 2011 Presented by Eshel Ben-Jacob. ABSTRACT Scientific American placed Professor Eshel Ben-Jacob and Dr. Itay Baruchi's creation of a type of organic memory chip on its list of the year's 50 most significant scientific discoveries in 2007. For the last decade, he has pioneered the field of Systems Neuroscience, focusing first on investigations of living neural networks outside the brain. http://en.wikipedia.org/wiki/Eshel_Ben-Jacob Learning from Bacteria about Information Processing Bacteria, the first and most fundamental of all organisms, lead rich social life in complex hierarchical communities. Collectively, they gather information from the environment, learn from past experience, and make decisions. Bacteria do not store genetically all the information required to respond efficiently to all possible environmental conditions. Instead, to solve new encountered problems (challenges) posed by the environment, they first assess the problem via collective sensing, then recall stored information of past experience and finally execute distributed information processing of the 109-12 bacteria in the colony, thus turning the colony into super-brain. Super-brain, because the billions of bacteria in the colony use sophisticated communication strategies to link the intracellular computation networks of each bacterium (including signaling path ways of billions of molecules) into a network of networks. I will show illuminating movies of swarming intelligence of live bacteria in which they solve optimization problems for collective decision making that are beyond what we, human beings, can solve with our most powerful computers. I will discuss the special nature of bacteria computational principles in comparison to our Turing Algorithm computational principles, showing that we can learn from the bacteria about our brain, in particular about the crucial role of the neglected other side of the brain, distributed information processing of the astrocytes. Eshel Ben-Jacob is Professor of Physics of Complex Systems and holds the Maguy-Glass Chair in Physics at Tel Aviv University. He was an early leader in the study of bacterial colonies as the key to understanding larger biological systems. He maintains that the essence of cognition is rooted in the ability of bacteria to gather, measure, and process information, and to adapt in response. For the last decade, he has pioneered the field of Systems Neuroscience, focusing first on investigations of living neural networks outside the brain and later on analysis of actual brain activity. In 2007, Scientific American selected Ben-Jacob's invention, the first hybrid NeuroMemory Chip, as one of the 50 most important achievements in all fields of science and technology for that year. The NeuroMemory Chip entails imprinting multiple memories, based upon development of a novel, system-level analysis of neural network activity (inspired by concepts from statistical physics and quantum mechanics), ideas about distributed information processing (inspired by his research on collective behaviors of bacteria) and new experimental methods based on nanotechnology (carbon nanotubes). Prof. Ben-Jacob received his PhD in physics (1982) at Tel Aviv University, Israel. He served as Vice President of the Israel Physical Society (1999-2002), then as President of the Israel Physical Society (2002-2005), initiating the online magazine PhysicaPlus, the only Hebrew-English bilingual science magazine. The general principles he has uncovered have been examined in a wide range of disciplines, including their application to amoeboid navigation, bacterial colony competition, cell motility, epilepsy, gene networks, genome sequence of pattern-forming bacteria, network theory analysis of the immune system, neural networks, search, and stock market volatility and collapse. He has examined implications of bacterial collective intelligence for neurocomputing. His scientific findings have prompted studies of their implications for computing: using chemical "tweets" to communicate, millions of bacteria self-organize to form colonies that collaborate to feed and defend themselves, as in a sophisticated social network. This talk was hosted by Boris Debic, and arranged by Zann Gill and the Microbes Mind Forum.
Views: 27865 GoogleTechTalks
Vexor Navy Havens 20m Ticks - EVE Online Live Presented in 4k
 
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Vexor Navy Issue fit used on the show. [Vexor Navy Issue, Vexxed Moose] Omnidirectional Tracking Enhancer II Omnidirectional Tracking Enhancer II Drone Damage Amplifier II Drone Damage Amplifier II Drone Damage Amplifier II Drone Damage Amplifier II EM Ward Amplifier II Large Shield Extender II Large Shield Extender II 100MN Monopropellant Enduring Afterburner Drone Link Augmentor I Auto Targeting System I Medium Core Defense Field Extender I Medium Core Defense Field Extender I Medium Anti-EM Screen Reinforcer II Praetor II x7 http://store.markeedragon.com/affiliate.php?id=4&redirect=index.php?cat=4 Special Viewer Discount or Bonus. YOUR CHOICE! Want a bonus on your EVE new account or Plex? Use the discount code of "discount" and get 3% off your order. Or want 3.3% cash back for even more savings? Use bonus code "bonus" and get 3.3% credit in your account for future purchases This is for a limited time and the discount/bonus codes may be changed or removed at any time. The discount / bonus is provided by Markee Dragon Game Codes and we are an authorized CCP reseller. Codes delivered in 20 minutes or less. Want to Try EVE for free? Get it here: http://secure.eveonline.com/signup/?invc=d6baec26-231d-4ced-9cd2-1a8b3713d72d&action=buddy [!nojob] Want to get rid of your day job and be your own boss? Markee Dragon is sharing how he does it with a step by step guide to independence. Get it here: http://jedimarketingtricks.com/ultimate/ Join me and your favorite streamers! Put my 10+ years of experience to work for YOU. Learn how to start generating income while playing your favorite games! I include everything you need to know to get started PLUS plenty of support for when you get stuck! We do a monthly giveaway and you can get a free entry for it here: http://store.markeedragon.com/affiliate.php?id=4&redirect=index.php?cat=18 No purchase required. Join us for chat in Discord https://discord.gg/markeedragon Discord is what we use for in game chat and voice comms. This is a simulcast of http://twitch.tv/markeedragon . You can watch here live on YouTube and talk in chat. but for the giveaways mentioned on the show those currently only work in Twitch chat. You do not have to watch on Twitch. You only need to be in the Twitch chat to get in on the giveaways. WTFast is what I use to improve my connection to EVE. I get at least a 20% improvement at all times. Try it here: http://www.wtfast.com/markeedragon Videos How to convert Loyalty Points This video shows how we decided what items to use. Items Sold. https://www.youtube.com/watch?v=rnv4eW9hwP0 What worked Successful conversion of 1m LP to 1.5b ISK in 13 days. https://www.youtube.com/watch?v=eiGEj4XhKt0 Hauling Introduction https://www.youtube.com/watch?v=7NjY9aU-uBQ Hauling is a great secondary income. Market Blue Line Hauling is a great secondary income. EVE Sites Mentioned on the Show Faction Warfare See the tier listings here. http://www.factionwarfare.com/ LP Store Conversion See what LP items are currently worth on the market. https://www.fuzzwork.co.uk/lpstore/ Daopa's LP Stores Database LP store items information http://www.ellatha.com/eve/LP-Stores EVE Central Current values for most all items in game. http://eve-central.com/ EVE Assets Manager Find your stuff. Know where your money is sitting! http://eve.nikr.net/jeveasset EVE Markets Market history data. http://eve-markets.net/ EVEPraisal Quick values for your loot and other market actions. http://evepraisal.com/ EVE Maps All kinds of map related information. http://evemaps.dotlan.net/ EVE Vippy - Wormhole mapping and path tracking. http://www.eve-vippy.com EVE University EVE Wiki http://wiki.eveuniversity.org/Main_Page Gallente Militia Gillente FW Ship Fits http://gallentemilitia.blogspot.com/ Osmium Hands Down The Best Ship Fit Site / Tool. http://o.smium.org/ Live shows Schedule: https://docs.google.com/spreadsheets/d/1zQZoKQnzGRgWXBefzGKlyfDppGQWzJy6PafWm-4c4sQ/pubhtml Music by Monstercat http://www.monstercat.com
Views: 21486 Markee Dragon Gameplay
Empowering SysAdmins by Radically Simplifying Root Cause Analysis with Loom Systems Ops
 
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Introducing Loom Systems Ops, the cutting-edge IT Operations Analytics solution that radically simplifies root cause analysis by automatically analyzing machine data in real-time. * Ingests every type of log file out of the box, including from applications which were developed in-house - zero configuration required from the user. * Applies machine learning algorithms to learn the unique signature of the data in your organization so that when an issue starts manifesting it is detected automatically in real time. * Uses advanced analysis algorithms to automatically point the user to the root cause of the issue. * Recommends a simple and actionable resolution to the issue, in plain English. Built for low-touch operational simplicity and usability, our solution empowers IT, DevOps, System Admins and NOC teams by helping them get better results in a fraction of the time. Predicting and resolving IT incidents is a breeze with Loom Systems Ops. Learn more: www.loomsystems.com
Views: 2377 Loom Systems
European Union Targets YouTube With Internet Censorship Legislation Coming This January 2019
 
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Please watch: "Super Bowl 53 Blood Wolf Moon Meet The New England Patriots and the Los Angeles Rams On February 3rd in Atlanta" https://www.youtube.com/watch?v=GDENVHzJW18 --~-- It quietly went through the European Union Parliament on September 12th, 2018. This is how Wikipedia describes it. "The Directive on Copyright in the Digital Single Market 2016/0280(COD), also known as the EU Copyright Directive, is a proposed European Union directive intended to harmonise aspects of the European Union copyright law and moved towards a Digital Single Market. First introduced by the European Parliament Committee on Legal Affairs on 20 June 2018, the directive currently has been approved by the European Parliament on 12 September 2018, and will enter formal Trilogue discussions that are expected to conclude in January 2019. If formalised, each of the EU's member countries would then be required to enact laws to support the directive. The European Council describe their key goals as protecting press publications, reducing the "value gap" between the profits made by internet platforms and content creators, encouraging "collaboration" between these two groups, and creating copyright exceptions for text and data mining. The directive's specific proposals include giving press publishers direct copyright over use of their publications by internet platforms such as online news aggregators (Article 11) and requiring websites who primarily host content posted by users to take "effective and proportionate" measures to prevent unauthorized postings of copyrighted content or be liable for their users' actions (Article 13)." We have already seen how censorship can control a social media platform such as YouTube and Facebook. If we are to trust on-line A.I. enhanced algorithms to screen what we - as a group of individuals - can and cannot post on our social media platforms without risking getting hit and subsequently censored by Google or other search engines then this is the beginning of the end of Free Speech on the Internet. If the European Union can get away with imposing Draconian Internet Censorship Laws then we as a Human Race may well be whipped and beating into oblivion. That's my opinion. Who is going to take that away from me? #EuropeanUnionArt13 #YouTubePurge2 #InternetCensorship Music credit: Light Awash by Kevin MacLeod is licensed under a Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/) Source: http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1100175 Artist: http://incompetech.com/ Related Articles: https://www.theverge.com/2018/9/13/17854158/eu-copyright-directive-article-13-11-internet-censorship-google https://en.wikipedia.org/wiki/Directive_on_Copyright_in_the_Digital_Single_Market Related videos: UNIRock2 YouTube Channel CHANNELS DEMONETIZED OVER "DUPLICATE CONTENT" GLITCH | Big Problem for Creators https://www.youtube.com/watch?v=O58_BKHQxBw Please Subscribe and Follow Me @ Global Agenda Main Channel http://www.youtube.com/c/MarkCharles29 Global Agenda II http://www.youtube.com/c/GlobalAgenda Global Agenda on Twitter https://twitter.com/BD007Marky FAIR USE STATEMENT This video may contain copyrighted material the use of which has not been specifically authorized by the copyright owner. This material is being made available within this transformative or derivative work for the purpose of education, commentary and criticism, is being distributed without profit, and is believed to be "fair use" in accordance with Title 17 U.S.C. Section 107
Views: 416 Global Agenda
The Case for Small Data Management
 
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Abstract: Exabytes of data; several hundred thousand TPC-C transactions per second on a single computing core; scale-up to hundreds of cores and a dozen Terabytes of main memory; scale-out to thousands of nodes with close to Petabyte-sized main memories; and massively parallel query processing are a reality in data management. But, hold on a second: for how many users exactly? How many users do you know that really have to handle these kinds of massive datasets and extreme query workloads? On the other hand: how many users do you know that are fighting to handle relatively small datasets, say in the range of a few thousand to a few million rows per table? How come some of the most popular open source DBMS have hopelessly outdated optimizers producing inefficient query plans? How come people don’t care and love it anyway? Could it be that most of the world’s data management problems are actually quite small? How can we increase the impact of database research in areas when datasets are small? What are the typical problems? What does this mean for database research? We discuss research challenges, directions, and a concrete technical solution coined PDbF: Portable Database Files. This is an extended version of an abstract and Gong Show talk presented at CIDR 2015. This talk was held on March 6, 2015 at the German Database Conference BTW in Hamburg. http://www.btw-2015.de/?keynote_dittrich Short CV: Jens Dittrich is a Full Professor of Computer Science in the area of Databases, Data Management, and "Big Data" at Saarland University, Germany. Previous affiliations include U Marburg, SAP AG, and ETH Zurich. He is also associated to CISPA (Center for IT-Security, Privacy and Accountability). He received an Outrageous Ideas and Vision Paper Award at CIDR 2011, a BMBF VIP Grant, a best paper award at VLDB 2014, two CS teaching awards in 2011 and 2013, as well as several presentation awards including a qualification for the interdisciplinary German science slam finals in 2012 and three presentation awards at CIDR (2011, 2013, and 2015). His research focuses on fast access to big data including in particular: data analytics on large datasets, Hadoop MapReduce, main-memory databases, and database indexing. He has been a PC member and/or area chair of prestigious international database conferences such as PVLDB, SIGMOD, and ICDE. Since 2013 he has been teaching his classes on data management as flipped classrooms. See http://datenbankenlernen.de or http://youtube.com/jensdit for a list of freely available videos on database technology in German and English (about 80 videos in German and 80 in English so far). image credits: public domain http://commons.wikimedia.org/wiki/File:The_Blue_Marble.jpg CC, Laura Poitras / Praxis Films http://commons.wikimedia.org/wiki/File:Edward_Snowden-2.jpg http://creativecommons.org/licenses/by/3.0/legalcode istock, voyager624 http://www.istockphoto.com/stock-photo-20540898-blue-digital-tunnel.php?st=0d10b3d http://commons.wikimedia.org/wiki/Category:Egg_sandwich?uselang=de#mediaviewer/File:Sandwich_Huevo_-_Ventana.JPG http://creativecommons.org/licenses/by/3.0/legalcode زرشک CC BY-SA 3.0, http://creativecommons.org/licenses/by-sa/3.0/legalcode public domain, http://en.wikipedia.org/wiki/Tanker_%28ship%29#mediaviewer/File:Sirius_Star_2008b.jpg public domain, http://de.wikipedia.org/wiki/General_Dynamics_F-16#mediaviewer/File:General_Dynamic_F-16_USAF.jpg ©iStock.com: skynesher public domain, http://commons.wikimedia.org/wiki/File:Astronaut-EVA.jpg others: Jens Dittrich, http://datenbankenlernen.de
Life Inside a Secret Chinese Bitcoin Mine
 
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Subscribe to Motherboard Radio today! http://apple.co/1DWdc9d In October of last year Motherboard gained access to a massive, secretive Bitcoin mine housed within a repurposed factory in the Liaoning Province in rural northeast China. This is the infrastructure that keeps the digital currency’s decentralized network up and running, and its operators are profiting big time. The mine we visited is just one of six sites owned by a secretive group of four people, part of a colossal mining operation that, as of our visit, cumulatively generated 4,050 bitcoins a month, equivalent to a monthly gross of $1.5 million. Read more on Motherboard - http://bit.ly/Chinese-Bitcoin-Mine Up Next: The Beaver Slayers of Patagonia - http://bit.ly/Beaver-Slayers Subscribe to MOTHERBOARD: http://bit.ly/Subscribe-To-MOTHERBOARD Follow MOTHERBOARD Facebook: http://www.facebook.com/motherboardtv Twitter: http://twitter.com/motherboard Tumblr: http://motherboardtv.tumblr.com/ Instagram: http://instagram.com/motherboardtv More videos from the VICE network: https://www.fb.com/vicevideos
Views: 4206510 Motherboard
Student's t-test
 
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Excel file: https://dl.dropboxusercontent.com/u/561402/TTEST.xls In this video Paul Andersen explains how to run the student's t-test on a set of data. He starts by explaining conceptually how a t-value can be used to determine the statistical difference between two samples. He then shows you how to use a t-test to test the null hypothesis. He finally gives you a separate data set that can be used to practice running the test. Do you speak another language? Help me translate my videos: http://www.bozemanscience.com/translations/ Music Attribution Intro Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License Outro Title: String Theory Artist: Herman Jolly http://sunsetvalley.bandcamp.com/track/string-theory All of the images are licensed under creative commons and public domain licensing: 1.3.6.7.2. Critical Values of the Student’s-t Distribution. (n.d.). Retrieved April 12, 2016, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm File:Hordeum-barley.jpg - Wikimedia Commons. (n.d.). Retrieved April 11, 2016, from https://commons.wikimedia.org/wiki/File:Hordeum-barley.jpg Keinänen, S. (2005). English: Guinness for strenght. Retrieved from https://commons.wikimedia.org/wiki/File:Guinness.jpg Kirton, L. (2007). English: Footpath through barley field. A well defined and well used footpath through the fields at Nuthall. Retrieved from https://commons.wikimedia.org/wiki/File:Footpath_through_barley_field_-_geograph.org.uk_-_451384.jpg pl.wikipedia, U. W. on. ([object HTMLTableCellElement]). English: William Sealy Gosset, known as “Student”, British statistician. Picture taken in 1908. Retrieved from https://commons.wikimedia.org/wiki/File:William_Sealy_Gosset.jpg The T-Test. (n.d.). Retrieved April 12, 2016, from http://www.socialresearchmethods.net/kb/stat_t.php
Views: 445795 Bozeman Science
Causes and Effects of Climate Change | National Geographic
 
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What causes climate change (also known as global warming)? And what are the effects of climate change? Learn the human impact and consequences of climate change for the environment, and our lives. ➡ Subscribe: http://bit.ly/NatGeoSubscribe About National Geographic: National Geographic is the world's premium destination for science, exploration, and adventure. Through their world-class scientists, photographers, journalists, and filmmakers, Nat Geo gets you closer to the stories that matter and past the edge of what's possible. Get More National Geographic: Official Site: http://bit.ly/NatGeoOfficialSite Facebook: http://bit.ly/FBNatGeo Twitter: http://bit.ly/NatGeoTwitter Instagram: http://bit.ly/NatGeoInsta Causes and Effects of Climate Change | National Geographic https://youtu.be/G4H1N_yXBiA National Geographic https://www.youtube.com/natgeo
Views: 598819 National Geographic
WHAT IS THE ABYSS? EVIDENCE & THEORIES FOR NO MAN'S SKY'S NEXT UPDATE
 
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The New Upcoming Update for No Man's Sky... "The Abyss" is a very interesting topic, so here is a whole bunch of evidence for what The Abyss may be along with my reasoning for my stance on current theories. No Man's Sky Datamining Wiki Page https://nomanssky.gamepedia.com/Datamining Official 'The Abyss' Post https://www.nomanssky.com/2018/10/no-mans-sky-the-abyss/ Click here to Support Xaine's World on Patreon http://www.patreon.com/xainesworld/ Join the Xaine's World Discord https://discord.gg/M7AheT3 Submit Procedural Tech Data https://www.xainesworld.com/procedural-tech-data-submission/ Ket's Refiner Recipe Spreadsheet (Updated 1.55 but still 'mostly' accurate) https://docs.google.com/spreadsheets/d/1m3D-ElN7ek3Y0f-1XDt0IW2l6HxfXi5n5Yr7VLwLbg4/edit#gid=0 No Man's Sky Tools: • S-Class Module Mapped Planet Tool https://www.xainesworld.com/e5damagedtechmodules/ • Trade Goods Crafting Calculator/Shopping List Calc https://www.xainesworld.com/nmsshoppingcalc/ • Complexity Numbers List https://www.xainesworld.com/no-mans-sky-next-base-building-complexity-data/ No Man's Sky NEXT Written Guides: • Closed Infinite Refiner Loop Guide https://www.xainesworld.com/nextguides/infiniteloops No Man's Sky NEXT Guides: • Starship MEGA-GUIDE https://www.youtube.com/watch?v=EmZSz9IEBJE • Portable Refiner, Comprehensive Guide https://www.youtube.com/watch?v=tF_I2iSh-Fw • Artifacts, Comprehensive Guide https://www.youtube.com/watch?v=ngFw3gMnA8I • Nanite Gaining Guide https://www.youtube.com/watch?v=sE_vP5jpiZQ The Items Available to buy with Quicksilver https://www.youtube.com/watch?v=4LJ4uNtrugY Patch Notes Videos: • 1.61 https://youtu.be/OGTWfKx4HYI • 1.60 Extra https://www.youtube.com/watch?v=2Mm4DCSpyjk • 1.60 https://youtu.be/frUjiNdJnQ8 • 1.59 (Not So)-Hotfix https://www.youtube.com/watch?v=4WRzmULfGaQ • 1.58 https://youtu.be/Y8y5v7hvyNQ • 1.57 https://www.youtube.com/watch?v=yCeBn_R83N4 • 1.55 https://www.youtube.com/watch?v=RogCQwecmck • 1.54 https://www.youtube.com/watch?v=RTVTrE80rMw • 1.52, 1.52.1 and 1.52.2 https://youtu.be/dxMrvptiLJA • 1.51 https://www.youtube.com/watch?v=z1ITqYK17j0 • 1.5 https://youtu.be/SH8z6gWyjFQ Stream Schedule: Monday - N/A Tuesday - Detroit: Become Human Stream -- 5pm GMT/10am PST Wednesday - N/A Thursday - Detroit: Become Human Stream -- 5pm GMT/10am PST Friday - N/A Saturday - N/A Sunday - NMS Survival Stream -- 5pm GMT/10am PST Check me out on: • https://www.xainesworld.com • https://www.patreon.com/xainesworld • https://www.facebook.com/xainesworld • https://www.twitch.tv/xainekhlorik • https://twitter.com/XainesWorld • https://www.instagram.com/xainesworld 1.64 Patch Notes: https://steamcommunity.com/app/275850/discussions/0/1732089092447689430/ Latest changes on Experimental (10/10): • Fixed an issue where items could fail to display in the Quicksilver shop. • Fixed an issue where Community Research missions could be given deadlines in the distant future. • Fixed an issue where players could get trapped when getting out of some ship types. • Fixed an issue where players could fail to be given the blueprints for Hydroponic Trays. • Fixed an issue where players could fail to be given the blueprints for the plants required by the Farmer. • Fixed an issue where players could fail to learn the Circuit Board blueprint at the correct time. • Fixed an issue where NPC missions might return to a building that had no NPCs in them. • Fixed an issue where players could fail to learn the Glass blueprint at the correct time. • Fixed an issue where players could fail to learn the Frigate Fuel blueprints at the correct time. • Missions from the Mission Board or planetary NPCs will now display their mission title on the Galaxy Map instead of their current objective, to help manage destinations when multiple missions are active. Previous changes on Experimental (05/10): • Tessellation can now be enabled in the graphics options.
Views: 8910 Xaine's World
What is DATA WRANGLING? What does DATA WRANGLING mean? DATA WRANGLING meaning & explanation
 
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What is DATA WRANGLING? What does DATA WRANGLING mean? DATA WRANGLING meaning -DATA WRANGLING definition - DATA WRANGLING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data wrangling (sometimes referred to as data munging) is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. A data wrangler describes the person who performs these transformation operations. This may include further munging, data visualization, data aggregation, training a statistical model, as well as many other potential uses. Data munging as a process typically follows a set of general steps which begin with extracting the data in a raw form from the data source, "munging" the raw data using algorithms (e.g. sorting) or parsing the data into predefined data structures, and finally depositing the resulting content into a data sink for storage and future use. The "wrangler" non-technical term is often said to derive from work done by the United States Library of Congress's National Digital Information Infrastructure and Preservation Program (NDIIPP) and their program partner the Emory University Libraries based MetaArchive Partnership. The term "mung" has roots in munging as described in the Jargon File. The term "Data Wrangler" was also suggested as the best analogy to coder for code for someone working with data. The terms data wrangling and data wrangler had sporadic use in the 1990s and early 2000s. One of the earliest business mentions of data wrangling was in an article in Byte Magazine in 1997 (Volume 22 issue 4) referencing “Perl’s data wrangling services”. In 2001 it was reported that CNN hired “a dozen data wranglers” to help track down information for news stories. One of the first mentions of data wrangler in a scientific context was by Donald Cline during the NASA/NOAA Cold Lands Processes Experiment. Cline stated the data wranglers “coordinate the acquisition of the entire collection of the experiment data.” Cline also specifies duties typically handled by a storage administrator for working with large amounts of data. This can occur in areas like major research projects and the making of films with a large amount of complex computer-generated imagery. In research, this involves both data transfer from research instrument to storage grid or storage facility as well as data manipulation for re-analysis via high performance computing instruments or access via cyberinfrastructure-based digital libraries. The data transformations are typically applied to distinct entities (e.g. fields, rows, columns, data values etc.) within a data set, and could include such actions as extractions, parsing, joining, standardizing, augmenting, cleansing, consolidating and filtering to create desired wrangling outputs that can be leveraged downstream. The recipients could be individuals, such as data architects or data scientists who will investigate the data further, business users who will consume the data directly in reports, or systems that will further process the data and write it into targets such as data warehouses, data lakes or downstream applications. Depending on the amount and format of the incoming data, data wrangling has traditionally been performed manually (e.g. via spreadsheets such as Excel) or via hand-written scripts in languages such as Python or SQL. R, a language often used in data mining and statistical data analysis, is now also often used for data wrangling. On a film or television production utilizing digital cameras that are not tape based, a data wrangler is employed to manage the transfer of data from a camera to a computer and/or hard drive.....
Views: 3821 The Audiopedia
Multilingual Text Mining: Lost in Translation, Found in Native Language Mining - Rohini Srihari
 
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There has been a meteoric rise in the amount of multilingual content on the web. This is primarily due to social media sites such as Facebook, and Twitter, as well as blogs, discussion forums, and reader responses to articles on traditional news sites. Language usage statistics indicate that Chinese is a very close second to English, and could overtake it to become the dominant language on the web. It is also interesting to see the explosive growth in languages such as Arabic. The availability of this content warrants a discussion on how such information can be effectively utilized. Such data can be mined for many purposes including business-related competitive insight, e-commerce, as well as citizen response to current issues. This talk will begin with motivations for multilingual text mining, including commercial and societal applications, digital humanities applications such as semi-automated curation of online discussion forums, and lastly, government applications, where the value proposition (benefits, costs and value) is different, but equally compelling. There are several issues to be touched upon, beginning with the need for processing native language, as opposed to using machine translated text. In tasks such as sentiment or behaviour analysis, it can certainly be argued that a lot is lost in translation, since these depend on subtle nuances in language usage. On the other hand, processing native language is challenging, since it requires a multitude of linguistic resources such as lexicons, grammars, translation dictionaries, and annotated data. This is especially true for "resourceMpoor languages" such as Urdu, and Somali, languages spoken in parts of the world where there is considerable focus nowadays. The availability of content such as multilingual Wikipedia provides an opportunity to automatically generate needed resources, and explore alternate techniques for language processing. The rise of multilingual social media also leads to interesting developments such as code mixing, and code switching giving birth to "new" languages such as Hinglish, Urdish and Spanglish! This phenomena exhibits both pros and cons, in addition to posing difficult challenges to automatic natural language processing. But there is also an opportunity to use crowd-sourcing to preserve languages and dialects that are gradually becoming extinct. It is worthwhile to explore frameworks for facilitating such efforts, which are currently very ad hoc. In summary, the availability of multilingual data provides new opportunities in a variety of applications, and effective mining could lead to better cross-cultural communication. Questions Addressed (i) Motivation for mining multilingual text. (ii) The need for processing native language (vs. machine translated text). (iii) Multilingual Social Media: challenges and opportunities, e.g., preserving languages and dialects.
Views: 1428 UA German Department
Benchmarking Big Data Systems by YANPEI CHEN and GWEN SHAPIRA at Big Data Spain 2014
 
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http://www.bigdataspain.org Abstract: http://www.bigdataspain.org/2014/conference/five-pitfalls-for-benchmarking-big-data-systems You will never look at vendor benchmarks the same way after this presentation. Performance is an increasingly important attribute of Big Data systems as focus shifts from batch processing to real-time analysis and to consolidated multi-tenant systems. One of the little-understood challenges in scaling data. Session presented at Big Data Spain 2014 Conference 17th Nov 2014 Kinépolis Madrid Event promoted by: http://www.paradigmatecnologico.com
Views: 342 Big Data Spain
Amazing Things NLP Can Do!
 
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In this video I want to highlight a few of the awesome things that we can do with Natural Language Processing or NLP. NLP basically means getting a computer to understand text and help you with analysis. Some of the major tasks that are a part of NLP include: · Automatic summarization · Coreference resolution · Discourse analysis · Machine translation · Morphological segmentation · Named entity recognition (NER) · Natural language generation · Natural language understanding · Optical character recognition (OCR) · Part-of-speech tagging · Parsing · Question answering · Relationship extraction · Sentence breaking (also known as sentence boundary disambiguation) · Sentiment analysis · Speech recognition · Speech segmentation · Topic segmentation and recognition · Word segmentation · Word sense disambiguation · Lemmatization · Native-language identification · Stemming · Text simplification · Text-to-speech · Text-proofing · Natural language search · Query expansion · Automated essay scoring · Truecasing Let’s discuss some of the cool things NLP helps us with in life 1. Spam Filters – nobody wants to receive spam emails, NLP is here to help fight span and reduce the number of spam emails you receive. No it is not yet perfect and I’m sure we still all still receive some spam emails but imagine how many you’d get without NLP! 2. Bridging Language Barriers – when you come across a phrase or even an entire website in another language, NLP is there to help you translate it into something you can understand. 3. Investment Decisions – NLP has the power to help you make decisions for financial investing. It can read large amounts of text (such as news articles, press releases, etc) and can pull in the key data that will help make buy/hold/sell decisions. For example, it can let you know if there is an acquisition that is planned or has happened – which has large implications on the value of your investment 4. Insights – humans simply can’t read everything that is available to us. NLP helps us summarize the data we have and pull out meaningful information. An example of this is a computer reading through thousands of customer reviews to identify issues or conduct sentiment analysis. I’ve personally used NLP for getting insights from data. At work, we conducted an in depth interview which included several open ended response type questions. As a result we received thousands of paragraphs of data to analyze. It is very time consuming to read through every single answer so I created an algorithm that will categorize the responses into one of 6 categories using key terms for each category. This is a great time saver and turned out to be very accurate. Please subscribe to the YouTube channel to be notified of future content! Thanks! https://en.wikipedia.org/wiki/Natural_language_processing https://www.lifewire.com/applications-of-natural-language-processing-technology-2495544
Views: 6064 Story by Data
Delete GB WhatsApp NOW | GBWhatsApp can hack your Mobile Phone Data | GB WhatsApp Features in HINDI
 
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Hey Guys, In this video i'll explain you the truth behind GB Whatsapp or WhatsApp GOLD, WhatsApp Plus mobile app which provide more features than original WhatsApp version like Hide Online Status, Hide Typing status, Auto response, Theme change, WhatsApp Icon change etc, many people sharing download link of GBWhatsapp which is an illegal mobile app. You'll get to know is GBWhatsApp is secure in INDIA, is it legal or FAKE ? Queries Solved: 1) What is GBWhatsApp ? 2) GBWhatsapp Features in HINDI 3) GB WhatsApp Owner or Developer Details 4) GB WhatsApp or Dual WhatsApp Explained 5) What is difference between GBWhatsApp & Official WhatsApp Mobile App 6) WhatsApp Gold vs WhatsApp Plus vs GBWhatsApp in HINDI Social Links: [FOLLOW] Facebook: https://fb.com/SidTalk/ Twitter: https://twitter.com/Sid_Talk Instagram: https://instagram.com/Sid_Talk/ Google+: https://google.com/+SidTalk PS: Don't forget to SUBSCRIBE SidTalk for more Trusted & Awesome videos.
Views: 1991709 SidTalk
Addressing Big Data Challenges in the ICGC Project
 
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On January 31, Junjun Zhang presented "Addressing Big Data Challenges in the ICGC Project" to the Speaker Series. https://wiki.nci.nih.gov/display/CBIITSpeakers/CBIIT+Speaker+Series+Page
Views: 177 NCIwebinars
No Man's sky next Set of Issues
 
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Jaxxsins Trails video: https://www.youtube.com/watch?v=lbVA95F9jqI&t=1079s On post no man's sky industry: https://www.youtube.com/watch?v=Gsr7ZfBOtTc&t=6s Land vehicles post: https://www.reddit.com/r/NoMansSkyTheGame/comments/5fotwv/spoiler_all_of_the_leaked_information_about_land/ GDC award nominations 2017: http://www.forbes.com/sites/kevinmurnane/2017/01/06/here-are-the-nominees-for-the-2017-game-developers-choice-awards/#7450fd5224b1 Jonathan Blows Twitter comment: https://twitter.com/jonathan_blow/status/523319500184506368 Neogaf Data Mining E3 demo: http://www.neogaf.com/forum/showthread.php?t=1263009&page=4 Steam Page for NMS: http://store.steampowered.com/app/275850/ Proof that Sony interactive worked heavily with hellogames: https://en.wikipedia.org/wiki/No_Man's_Sky Actual Gamer demogrpahics: http://www.theesa.com/wp-content/uploads/2015/04/ESA-Essential-Facts-2015.pdf http://essentialfacts.theesa.com/Essential-Facts-2016.pdf On the subject of GDC corruption and how Jonathon blow is likely to win in his category: https://www.youtube.com/watch?v=1ON-oL4Mlks
Views: 75 TheBadcop69
Ashutosh Jadhav: Knowledge-driven Search Intent Mining
 
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http://www.knoesis.org/aboutus/thesis_defense#jadhav ABSTRACT: Understanding users’ latent intents behind search queries is essential for satisfying a user’s search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Consequently, there has been increasing attention on studying how to effectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domain-specific intent is still an open problem. Among all the topics available on the Internet, health is one of the most important in terms of impact on the user and it is one of the most frequently searched areas. This dissertation presents a knowledge-driven approach for domain-specific search intent mining with a focus on health-related search queries. First, we identified 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analyses of popular health websites, literature surveys, and an empirical study of search queries. We defined the problem of classifying millions of health search queries into zero or more intent classes as a multi-label classification problem. Popular machine learning approaches for multi-label classification tasks (namely, problem transformation and algorithm adaptation methods) were not feasible due to the limitation of label data creations and health domain constraints. Another challenge in solving the search intent identification problem was mapping terms used by laymen to medical terms. To address these challenges, we developed a semantics-driven, rule-based search intent mining approach leveraging rich background knowledge encoded in Unified Medical Language System (UMLS) and a crowd sourced encyclopedia (Wikipedia). The approach can identify search intent in a disease-agnostic manner and has been evaluated on three major diseases. While users often turn to search engines to learn about health conditions, a surprising amount of health information is also shared and consumed via social media, such as public social platforms like Twitter. Although Twitter is an excellent information source, the identification of informative tweets from the deluge of tweets is the major challenge. We used a hybrid approach consisting of supervised machine learning, rule-based classifiers, and biomedical domain knowledge to facilitate the retrieval of relevant and reliable health information shared on Twitter in real time. Furthermore, we extended our search intent mining algorithm to classify health-related tweets into health categories. Finally, we performed a large-scale study to compare health search intents and features that contribute in the expression of search intent from more than 100 million search queries from smarts devices (smartphones or tablets) and personal computers (desktops or laptops). SLIDES: http://www.slideshare.net/knoesis/ashutosh-thesis
Views: 189 Knoesis Center

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