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What is DATA REDUCTION? What does DATA REDUCTION mean? DATA REDUCTION meaning & explanation
 
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What is DATA REDUCTION? What does DATA REDUCTION mean? DATA REDUCTION meaning - DATA REDUCTION definition - DATA REDUCTION 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 reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. When information is derived from instrument readings there may also be a transformation from analog to digital form. When the data are already in digital form the 'reduction' of the data typically involves some editing, scaling, coding, sorting, collating, and producing tabular summaries. When the observations are discrete but the underlying phenomenon is continuous then smoothing and interpolation are often needed. Often the data reduction is undertaken in the presence of reading or measurement errors. Some idea of the nature of these errors is needed before the most likely value may be determined. An example in astronomy is the data reduction in the Kepler satellite. This satellite records 95-megapixel images once every six seconds, generating tens of megabytes of data per second, which is orders of magnitudes more than the downlink bandwidth of 550 KBps. The on-board data reduction encompasses co-adding the raw frames for thirty minutes, reducing the bandwidth by a factor of 300. Furthermore, interesting targets are pre-selected and only the relevant pixels are processed, which is 6% of the total. This reduced data is then sent to Earth where it is processed further. Research has also been carried out on the use of data reduction in wearable (wireless) devices for health monitoring and diagnosis applications. For example, in the context of epilepsy diagnosis, data reduction has been used to increase the battery lifetime of a wearable EEG device by selecting, and only transmitting, EEG data that is relevant for diagnosis and discarding background activity.
Views: 1034 The Audiopedia
What is EVOLUTIONARY DATA MINING? What does EVOLUTIONARY DATA MINING mean?
 
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What is EVOLUTIONARY DATA MINING? What does EVOLUTIONARY DATA MINING mean? EVOLUTIONARY DATA MINING meaning - EVOLUTIONARY DATA MINING definition - EVOLUTIONARY DATA 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 Evolutionary data mining, or genetic data mining is an umbrella term for any data mining using evolutionary algorithms. While it can be used for mining data from DNA sequences, it is not limited to biological contexts and can be used in any classification-based prediction scenario, which helps "predict the value ... of a user-specified goal attribute based on the values of other attributes." For instance, a banking institution might want to predict whether a customer's credit would be "good" or "bad" based on their age, income and current savings. Evolutionary algorithms for data mining work by creating a series of random rules to be checked against a training dataset. The rules which most closely fit the data are selected and are mutated. The process is iterated many times and eventually, a rule will arise that approaches 100% similarity with the training data. This rule is then checked against a test dataset, which was previously invisible to the genetic algorithm. Before databases can be mined for data using evolutionary algorithms, it first has to be cleaned, which means incomplete, noisy or inconsistent data should be repaired. It is imperative that this be done before the mining takes place, as it will help the algorithms produce more accurate results. If data comes from more than one database, they can be integrated, or combined, at this point. When dealing with large datasets, it might be beneficial to also reduce the amount of data being handled. One common method of data reduction works by getting a normalized sample of data from the database, resulting in much faster, yet statistically equivalent results. At this point, the data is split into two equal but mutually exclusive elements, a test and a training dataset. The training dataset will be used to let rules evolve which match it closely. The test dataset will then either confirm or deny these rules. Evolutionary algorithms work by trying to emulate natural evolution. First, a random series of "rules" are set on the training dataset, which try to generalize the data into formulas. The rules are checked, and the ones that fit the data best are kept, the rules that do not fit the data are discarded. The rules that were kept are then mutated, and multiplied to create new rules. This process iterates as necessary in order to produce a rule that matches the dataset as closely as possible. When this rule is obtained, it is then checked against the test dataset. If the rule still matches the data, then the rule is valid and is kept. If it does not match the data, then it is discarded and the process begins by selecting random rules again.
Views: 193 The Audiopedia
PCA For Dimensionality Reduction in Pattern Recognition, a slecture by Khalid Tahboub
 
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This is a slecture for Prof. Boutin's course on Statistical Pattern Recognition (ECE662) made by Purdue ECE student Khalid Tahboub. The complete slecture is posted at https://www.projectrhea.org/rhea/index.php/Pca_khalid To view other slectures on the same topic go to the ECE662 course wiki at https://www.projectrhea.org/rhea/index.php/2014_Spring_ECE_662_Boutin For more information about slectures, go to http://slectures.projectrhea.org
Views: 9539 Project Rhea
What is DATA CUBE? What does DATA CUBE mean? DATA CUBE meaning, definition & explanation
 
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What is DATA CUBE? What does DATA CUBE mean? DATA CUBE meaning - DATA CUBE definition - DATA CUBE 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 computer programming contexts, a data cube (or datacube) is a multi-dimensional array of values, commonly used to describe a time series of image data. The data cube is used to represent data along some measure of interest. Even though it is called a 'cube', it can be 1-dimensional, 2-dimensional, 3-dimensional, or higher-dimensional. Every dimension represents a new measure whereas the cells in the cube represent the facts of interest. The EarthServer initiative has established requirements which a datacube service should offer. Many high-level computer languages treat data cubes and other large arrays as single entities distinct from their contents. These languages, of which APL, IDL, NumPy, PDL, and S-Lang are examples, allow the programmer to manipulate complete film clips and other data en masse with simple expressions derived from linear algebra and vector mathematics. Some languages (such as PDL) distinguish between a list of images and a data cube, while many (such as IDL) do not. Array DBMSs (Database Management Systems) offer a data model which generically supports definition, management, retrieval, and manipulation of n-dimensional datacubes. This database category has been pioneered by the rasdaman system since 1994. Multi-dimensional arrays can meaningfully represent spatio-temporal sensor, image, and simulation data, but also statistics data where the semantics of dimensions is not necessarily of spatial or temporal nature. Generally, any kind of axis can be combined with any other into a datacube. In mathematics, a one-dimensional array corresponds to a vector, a two-dimensional array resembles a matrix; more generally, a tensor may be represented as an n-dimensional data cube. For a time sequence of color images, the array is generally four-dimensional, with the dimensions representing image X and Y coordinates, time, and RGB (or other color space) color plane. For example, the EarthServer initiative unites data centers from different continents offering 3-D x/y/t satellite image timeseries and 4-D x/y/z/t weather data for retrieval and server-side processing through the Open Geospatial Consortium WCPS geo datacube query language standard. A data cube is also used in the field of imaging spectroscopy, since a spectrally-resolved image is represented as a three-dimensional volume. In Online analytical processing (OLAP), data cubes are a common arrangement of business data suitable for analysis from different perspectives through operations like slicing, dicing, pivoting, and aggregation.
Views: 3901 The Audiopedia
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: 3535 Ranji Raj
Compare Search Engine Bias
 
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Search engines are the gatekeepers of online knowledge. They have the ability (in theory) to sway public opinion, even an entire democratic election. This is a proposal for a service that lets us study the biases of search engines with machine learning and data mining techniques. read more about this idea on medium: https://medium.com/opn-src-ideas/compare-search-engine-bias-2e1403b39986#.mmxw0z4mf Read about Search Neutrality https://en.wikipedia.org/wiki/Search_neutrality upcoming videos: https://workflowy.com/s/Re519yBl43 ⭐ Support this channel on Patreon: 💸 https://www.patreon.com/opensourceideas ⭐ Or donate bitcoin https://www.coinbase.com/sirjamesgray ⭐ Check out our Subreddit: https://www.reddit.com/r/OpenSourceIdeas/ ⭐ Follow on Twitter: 🐦 https://twitter.com/opn_src_ideas ⭐ Like on Facebook: https://www.facebook.com/opnsrcideas/ ⭐ Subscribe to this channel: http://bit.ly/25xHHTI About Jamie Gray: ⭐ My Site http://www.JamieGray.net ⭐ LinkedIn http://linkd.in/1xvKeLL ⭐ Twitter https://twitter.com/SirJamesGray ⭐ Instagram https://instagram.com/SirJamesGray ⭐ Tumblr http://SirJamesGray.tumblr.com ⭐ 500px https://500px.com/SirJamesGray ⭐ Spotify http://spoti.fi/1Ita20a ⭐ Snapchat (stories only) jamiegray https://www.snapchat.com/add/jamiegray
Views: 635 Open Source Ideas
What is INFORMATION RETRIEVAL? What does INFORMATION RETRIEVAL mean? INFORMATION RETRIEVAL meaning
 
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✪✪✪✪✪ WORK FROM HOME! Looking for WORKERS for simple Internet data entry JOBS. $15-20 per hour. SIGN UP here - http://jobs.theaudiopedia.com ✪✪✪✪✪ ✪✪✪✪✪ The Audiopedia Android application, INSTALL NOW - https://play.google.com/store/apps/details?id=com.wTheAudiopedia_8069473 ✪✪✪✪✪ 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: 13620 The Audiopedia
Rackspace Data Centers - The Difference
 
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At Rackspace, we leverage lean manufacturing concepts to maximize the customer experience through the reduction of disruptions and downtime. We call this operational discipline.
Views: 2298 Rackspace
What Is Tree Pruning In Data Mining
 
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Study of various decision tree pruning methods semantic scholar. 31 dec 2015 i understood what is decision trees and how it works with help of sunil sir's i couldnt understand what is pruning and how to do it in decision trees. I am however slightly uncertain in exactly how cv is used when pruning decision 4 mar 2016 lecture 77 (optional) tree algorithm and full of visualizations illustrations these techniques will behave on real data. Data training_imp_var,method there are several approaches to avoiding overfitting in building decision trees. Decision trees (part ii pruning the tree) ismll. What is tree pruning in data mining? Youtubepruning decision trees ibmwhat and how to do mining mapdata with. Pruning is a technique in machine learning that reduces the size of decision trees by removing sections tree provide little power to classify instances data mining induction learn simple and easy steps pruning performed order remove anomalies training 8 jul 2017. We may get a decision tree that might perform attribute selection measures, tree, post pruning, pre pruningdata mining is the extraction of hidden predictive information matteo matteucci retrieval & data mining• Usually based on statistical significance test. Pre pruning that stop growing the tree earlier, before it perfectly classifies this thesis presents algorithms for decision trees and lists are based should prove useful in practical data mining applications response to problem of overfitting nearly all modern adopt a strategy some sort. Test data keywords decision tree, tree pruning, miningdecision is one of the classification technique used in support system and i think have understood concepts between cross validation pruning. Wikipedia wiki pruning_(decision_trees)&sa u&ved 0ahukewjmxtqoi fvahutr48khypnaloqfggjmae&usg afqjcnhlpev_pbfseaco7iybewg5c15a3w"pruning (decision trees) wikipedia. Pruning (decision trees) wikipedia. Rpart rpart(promotion_name. Decision trees pruning matteo matteuccioverfitting of decision tree and in data mining techniques ijltet. Googleusercontent search. Data mining pruning (a decision tree, rules) [gerardnico]. Data mining cross validations and decision tree pruning (optional) algorithm university of washington. Many algorithms use a technique 26 nov 2008 lack of data points in the lower half diagram makes it difficult to predict correctly class labels that region. Insufficient number of 13 oct 2013 a decision tree is pruned to get (perhaps) that generalize better independent test data. Wikipedia wiki pruning_(decision_trees)&sa u&ved 0ahukewjmxtqoi fvahutr48khypnaloqfggjmae&usg afqjcnhlpev_pbfseaco7iybewg5c15a3w"pruning (decision trees) wikipedia pruning wikipedia en. Pruning is a technique in machine learning that reduces the size of decision trees by removing sections tree provide little power to classify instances. • Stop growing the tree when there is no data& regression• If a decision tree is decision tree pruning methodologies. D
Views: 272 Question Tags
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: 432402 CodingEntrepreneurs
Symmetric Key and Public Key Encryption
 
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Modern day encryption is performed in two different ways. Check out http://YouTube.com/ITFreeTraining or http://itfreetraining.com for more of our always free training videos. Using the same key or using a pair of keys called the public and private keys. This video looks at how these systems work and how they can be used together to perform encryption. Download the PDF handout http://itfreetraining.com/Handouts/Ce... Encryption Types Encryption is the process of scrambling data so it cannot be read without a decryption key. Encryption prevents data being read by a 3rd party if it is intercepted by a 3rd party. The two encryption methods that are used today are symmetric and public key encryption. Symmetric Key Symmetric key encryption uses the same key to encrypt data as decrypt data. This is generally quite fast when compared with public key encryption. In order to protect the data, the key needs to be secured. If a 3rd party was able to gain access to the key, they could decrypt any data that was encrypt with that data. For this reason, a secure channel is required to transfer the key if you need to transfer data between two points. For example, if you encrypted data on a CD and mail it to another party, the key must also be transferred to the second party so that they can decrypt the data. This is often done using e-mail or the telephone. In a lot of cases, sending the data using one method and the key using another method is enough to protect the data as an attacker would need to get both in order to decrypt the data. Public Key Encryption This method of encryption uses two keys. One key is used to encrypt data and the other key is used to decrypt data. The advantage of this is that the public key can be downloaded by anyone. Anyone with the public key can encrypt data that can only be decrypted using a private key. This means the public key does not need to be secured. The private key does need to be keep in a safe place. The advantage of using such a system is the private key is not required by the other party to perform encryption. Since the private key does not need to be transferred to the second party there is no risk of the private key being intercepted by a 3rd party. Public Key encryption is slower when compared with symmetric key so it is not always suitable for every application. The math used is complex but to put it simply it uses the modulus or remainder operator. For example, if you wanted to solve X mod 5 = 2, the possible solutions would be 2, 7, 12 and so on. The private key provides additional information which allows the problem to be solved easily. The math is more complex and uses much larger numbers than this but basically public and private key encryption rely on the modulus operator to work. Combing The Two There are two reasons you want to combine the two. The first is that often communication will be broken into two steps. Key exchange and data exchange. For key exchange, to protect the key used in data exchange it is often encrypted using public key encryption. Although slower than symmetric key encryption, this method ensures the key cannot accessed by a 3rd party while being transferred. Since the key has been transferred using a secure channel, a symmetric key can be used for data exchange. In some cases, data exchange may be done using public key encryption. If this is the case, often the data exchange will be done using a small key size to reduce the processing time. The second reason that both may be used is when a symmetric key is used and the key needs to be provided to multiple users. For example, if you are using encryption file system (EFS) this allows multiple users to access the same file, which includes recovery users. In order to make this possible, multiple copies of the same key are stored in the file and protected from being read by encrypting it with the public key of each user that requires access. References "Public-key cryptography" http://en.wikipedia.org/wiki/Public-k... "Encryption" http://en.wikipedia.org/wiki/Encryption
Views: 480184 itfreetraining
What is INSTANCE SELECTION? What does INSTANCE SELECTION mean? INSTANCE SELECTION meaning
 
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What is INSTANCE SELECTION? What does INSTANCE SELECTION mean? INSTANCE SELECTION meaning - INSTANCE SELECTION definition - INSTANCE SELECTION 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 Instance selection (or dataset reduction, or dataset condensation) is an important Data pre-processing step that can be applied in many Machine learning (or Data mining) tasks. Approaches for instance selection can be applied for reducing the original dataset to a manageable volume, leading to a reduction of the computational resources that are necessary for performing the learning process. Algorithms of instance selection can also be applied for removing noisy instances, before applying learning algorithms. This step can improve the accuracy in classification problems. Algorithm for instance selection should identify a subset of the total available data to achieve the original purpose of the data mining (or machine learning) application as if the whole data had been used. Considering this, the optimal outcome of IS would be the minimum data subset that can accomplish the same task with no performance loss, in comparison with the performance achieved when the task is performed using the whole available data. Therefore, every instance selection strategy should deal with a trade-off between the reduction rate of the dataset and the classification quality. The literature provides several differente algorithms for instance selection. They can be distinguished from each other according to several different criteria. Considering this, instance selection algorithms can be grouped in two main classes, according to what instances they select: algorithms that preserve the instances at the boundaries of classes and algorithms that preserve the internal instances of the classes. Within the category of algorithms that select instances at the boundaries it is possible to cite DROP3, ICF and LSBo. On the other hand, within the category of algorithms that select internal instances it is possible to mention ENN and LSSm. In general, algorithm such as ENN and LSSm are used for removing harmful (noisy) instances from the dataset. They do not reduce the data as the algorithms that select border instances, but they remove instances at the boundaries that have negative impact in the data ming task. They can be used bay other instance selection algorithms, as a filtering step. For example, the ENN algorithm is used by DROP3 as the first step, and the LSSm algorithm is used by LSBo. There is also another group os algorithms that adopt different selection criteria. For example, the algorithms LDIS and CDIS select the densest instances in a given arbitrary neighborhood. The selected instances can include both, border and internal instances. The LDIS and CDIS algorithms are very simple and select subsets that are very representative of the original dataset. Besides that, since they search by the representative instances in each class separately, they are faster (in terms of time complexity and effective running time) than other algorithms, such as DROP3 and ICF.
Views: 219 The Audiopedia
The best stats you've ever seen | Hans Rosling
 
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http://www.ted.com With the drama and urgency of a sportscaster, statistics guru Hans Rosling uses an amazing new presentation tool, Gapminder, to present data that debunks several myths about world development. Rosling is professor of international health at Sweden's Karolinska Institute, and founder of Gapminder, a nonprofit that brings vital global data to life. (Recorded February 2006 in Monterey, CA.) TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate. Follow us on Twitter http://www.twitter.com/tednews Checkout our Facebook page for TED exclusives https://www.facebook.com/TED
Views: 2866326 TED
What is DATA CLEANSING? What does DATA CLEANSING mean? DATA CLEANSING meaning & explanation
 
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What is DATA CLEANSING? What does DATA CLEANSING mean? DATA CLEANSING meaning - DATA CLEANSING definition - DATA CLEANSING 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 cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting. After cleansing, a data set should be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores. Data cleansing differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at the time of entry, rather than on batches of data. The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities. The validation may be strict (such as rejecting any address that does not have a valid postal code) or fuzzy (such as correcting records that partially match existing, known records). Some data cleansing solutions will clean data by cross checking with a validated data set. A common data cleansing practice is data enhancement, where data is made more complete by adding related information. For example, appending addresses with any phone numbers related to that address. Data cleansing may also involve activities like, harmonization of data, and standardization of data. For example, harmonization of short codes (st, rd, etc.) to actual words (street, road, etcetera). Standardization of data is a means of changing a reference data set to a new standard, ex, use of standard codes. Administratively, incorrect or inconsistent data can lead to false conclusions and misdirected investments on both public and private scales. For instance, the government may want to analyze population census figures to decide which regions require further spending and investment on infrastructure and services. In this case, it will be important to have access to reliable data to avoid erroneous fiscal decisions. In the business world, incorrect data can be costly. Many companies use customer information databases that record data like contact information, addresses, and preferences. For instance, if the addresses are inconsistent, the company will suffer the cost of resending mail or even losing customers. The profession of forensic accounting and fraud investigating uses data cleansing in preparing its data and is typically done before data is sent to a data warehouse for further investigation. There are packages available so you can cleanse/wash address data while you enter it into your system. This is normally done via an application programming interface (API)......
Views: 5534 The Audiopedia
Understanding Wavelets, Part 1: What Are Wavelets
 
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This introductory video covers what wavelets are and how you can use them to explore your data in MATLAB®. •Try Wavelet Toolbox: https://goo.gl/m0ms9d •Ready to Buy: https://goo.gl/sMfoDr The video focuses on two important wavelet transform concepts: scaling and shifting. The concepts can be applied to 2D data such as images. Video Transcript: Hello, everyone. In this introductory session, I will cover some basic wavelet concepts. I will be primarily using a 1-D example, but the same concepts can be applied to images, as well. First, let's review what a wavelet is. Real world data or signals frequently exhibit slowly changing trends or oscillations punctuated with transients. On the other hand, images have smooth regions interrupted by edges or abrupt changes in contrast. These abrupt changes are often the most interesting parts of the data, both perceptually and in terms of the information they provide. The Fourier transform is a powerful tool for data analysis. However, it does not represent abrupt changes efficiently. The reason for this is that the Fourier transform represents data as sum of sine waves, which are not localized in time or space. These sine waves oscillate forever. Therefore, to accurately analyze signals and images that have abrupt changes, we need to use a new class of functions that are well localized in time and frequency: This brings us to the topic of Wavelets. A wavelet is a rapidly decaying, wave-like oscillation that has zero mean. Unlike sinusoids, which extend to infinity, a wavelet exists for a finite duration. Wavelets come in different sizes and shapes. Here are some of the well-known ones. The availability of a wide range of wavelets is a key strength of wavelet analysis. To choose the right wavelet, you'll need to consider the application you'll use it for. We will discuss this in more detail in a subsequent session. For now, let's focus on two important wavelet transform concepts: scaling and shifting. Let' start with scaling. Say you have a signal PSI(t). Scaling refers to the process of stretching or shrinking the signal in time, which can be expressed using this equation [on screen]. S is the scaling factor, which is a positive value and corresponds to how much a signal is scaled in time. The scale factor is inversely proportional to frequency. For example, scaling a sine wave by 2 results in reducing its original frequency by half or by an octave. For a wavelet, there is a reciprocal relationship between scale and frequency with a constant of proportionality. This constant of proportionality is called the "center frequency" of the wavelet. This is because, unlike the sinewave, the wavelet has a band pass characteristic in the frequency domain. Mathematically, the equivalent frequency is defined using this equation [on screen], where Cf is center frequency of the wavelet, s is the wavelet scale, and delta t is the sampling interval. Therefore when you scale a wavelet by a factor of 2, it results in reducing the equivalent frequency by an octave. For instance, here is how a sym4 wavelet with center frequency 0.71 Hz corresponds to a sine wave of same frequency. A larger scale factor results in a stretched wavelet, which corresponds to a lower frequency. A smaller scale factor results in a shrunken wavelet, which corresponds to a high frequency. A stretched wavelet helps in capturing the slowly varying changes in a signal while a compressed wavelet helps in capturing abrupt changes. You can construct different scales that inversely correspond the equivalent frequencies, as mentioned earlier. Next, we'll discuss shifting. Shifting a wavelet simply means delaying or advancing the onset of the wavelet along the length of the signal. A shifted wavelet represented using this notation [on screen] means that the wavelet is shifted and centered at k. We need to shift the wavelet to align with the feature we are looking for in a signal.The two major transforms in wavelet analysis are Continuous and Discrete Wavelet Transforms. These transforms differ based on how the wavelets are scaled and shifted. More on this in the next session. But for now, you've got the basic concepts behind wavelets.
Views: 175369 MATLAB
What is DATA AGGREGATION? What does DATA AGGREGATION mean? DATA AGGREGATION meaning & explanation
 
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What is DATA AGGREGATION? What does DATA AGGREGATION mean? DATA AGGREGATION meaning - DATA AGGREGATION definition - DATA AGGREGATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data aggregation is the compiling of information from databases with intent to prepare combined datasets for data processing. The source information for data aggregation may originate from public records and criminal databases. The information is packaged into aggregate reports and then sold to businesses, as well as to local, state, and government agencies. This information can also be useful for marketing purposes. In the United States, many data brokers' activities fall under the Fair Credit Reporting Act (FCRA) which regulates consumer reporting agencies. The agencies then gather and package personal information into consumer reports that are sold to creditors, employers, insurers, and other businesses. Various reports of information are provided by database aggregators. Individuals may request their own consumer reports which contain basic biographical information such as name, date of birth, current address, and phone number. Employee background check reports, which contain highly detailed information such as past addresses and length of residence, professional licenses, and criminal history, may be requested by eligible and qualified third parties. Not only can this data be used in employee background checks, but it may also be used to make decisions about insurance coverage, pricing, and law enforcement. Privacy activists argue that database aggregators can provide erroneous information. The potential of the Internet to consolidate and manipulate information has a new application in data aggregation, also known as screen scraping. The Internet gives users the opportunity to consolidate their usernames and passwords, or PINs. Such consolidation enables consumers to access a wide variety of PIN-protected websites containing personal information by using one master PIN on a single website. Online account providers include financial institutions, stockbrokers, airline and frequent flyer and other reward programs, and e-mail accounts. Data aggregators can gather account or other information from designated websites by using account holders' PINs, and then making the users' account information available to them at a single website operated by the aggregator at an account holder's request. Aggregation services may be offered on a standalone basis or in conjunction with other financial services, such as portfolio tracking and bill payment provided by a specialized website, or as an additional service to augment the online presence of an enterprise established beyond the virtual world. Many established companies with an Internet presence appear to recognize the value of offering an aggregation service to enhance other web-based services and attract visitors. Offering a data aggregation service to a website may be attractive because of the potential that it will frequently draw users of the service to the hosting website. When it comes to compiling location information on local businesses, there are several major data aggregators that collect information such as the business name, address, phone number, website, description and hours of operation. They then validate this information using various validation methods. Once the business information has been verified to be accurate, the data aggregators make it available to publishers like Google and Yelp. When Yelp, for example, goes to update their Yelp listings, they will pull data from these local data aggregators. Publishers take local business data from different sources and compare it to what they currently have in their database. They then update their database it with what information they deem accurate. Financial institutions are concerned about the possibility of liability arising from data aggregation activities, potential security problems, infringement on intellectual property rights and the possibility of diminishing traffic to the institution's website. The aggregator and financial institution may agree on a data feed arrangement activated on the customer's request, using an Open Financial Exchange (OFX) standard to request and deliver information to the site selected by the customer as the place from which they will view their account data. Agreements provide an opportunity for institutions to negotiate to protect their customers' interests and offer aggregators the opportunity to provide a robust service.
Views: 3095 The Audiopedia
11. Introduction to Machine Learning
 
51:31
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: Eric Grimson In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 485510 MIT OpenCourseWare
Document Classification Using the Natural Language Toolkit
 
32:34
Wiki-to-Speech version of Ben Healey's talk at Kiwi PyCon 2011. This video is one in a series at http://bit.ly/openallure following the progress of the Wiki-to-Speech project. The voice used on this video is IVONA British English "Brian"
Views: 6114 John Graves
Nuclear Energy
 
09:06
025 - Nuclear Energy In this video Paul Andersen explains how nuclear energy is released during fission of radioactive uranium. Light water reactors, nuclear waste, and nuclear accidents are also discussed along with the future of nuclear energy. 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: Delphi234. (2014). English: History of nuclear power in the world. Data is from IAEA and EIA. Retrieved from https://commons.wikimedia.org/wiki/File:Nuclear_power_history.svg DOE. ([object HTMLTableCellElement]). English: Spent fuel pool at a nuclear power plant. http://www.ocrwm.doe.gov/curriculum/unit1/lesson3reading.shtml. Retrieved from https://commons.wikimedia.org/wiki/File:Fuel_pool.jpg File:Chernobyl Disaster.jpg. (2014, April 30). In Wikipedia, the free encyclopedia. Retrieved from https://en.wikipedia.org/w/index.php?title=File:Chernobyl_Disaster.jpg&oldid=606437678 Globe, D. (2011). English: The Fukushima I Nuclear Power Plant after the 2011 Tōhoku earthquake and tsunami. Reactor 1 to 4 from right to left. Retrieved from https://commons.wikimedia.org/wiki/File:Fukushima_I_by_Digital_Globe.jpg lightningBy ZaWertun. (n.d.). Retrieved from https://openclipart.org/detail/190134/lightning Spoon, S. (2011). English: en:International Nuclear Event Scale. Retrieved from https://commons.wikimedia.org/wiki/File:INES_en.svg UK, C. R. (2014). Diagram showing a lobectomy of the thyroid gland. Retrieved from https://commons.wikimedia.org/wiki/File:Diagram_showing_a_lobectomy_of_the_thyroid_gland_CRUK_067.svg Z22. (2014). English: The unit 2 of Three Mile Island Nuclear Generating Station closed since the accident in 1979. The cooling towers on the left. Retrieved from https://commons.wikimedia.org/wiki/File:Three_Mile_Island_Nuclear_Generating_Station_Unit_2.jpg
Views: 64523 Bozeman Science
Geology
 
11:04
003 - Geology In this video Paul Andersen explains how rock is formed and changed on the planet. The video begins with a brief description of rocks, minerals, and the rock cycle. Plate tectonics is used to describe structure near plate boundaries. Hot spots and natural hazards (like volcanos, earthquake, and tsunamis) are included. 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: Benbennick, David. English: This Is a Locator Map Showing Kalawao County in Hawaii. For More Information, See Commons:United States County Locator Maps., February 12, 2006. Own work: English: The maps use data from nationalatlas.gov, specifically countyp020.tar.gz on the Raw Data Download page. The maps also use state outline data from statesp020.tar.gz. The Florida maps use hydrogm020.tar.gz to display Lake Okeechobee. https://commons.wikimedia.org/wiki/File:Map_of_Hawaii_highlighting_Kalawao_County.svg. “Earth.” Wikipedia, the Free Encyclopedia, August 23, 2015. https://en.wikipedia.org/w/index.php?title=Earth&oldid=677456791. File:Hawaiien (volcano).svg, n.d. https://commons.wikimedia.org/wiki/File:Hawaiien_(volcano).svg. File:Structure Volcano Unlabeled.svg, n.d. https://commons.wikimedia.org/wiki/File:Structure_volcano_unlabeled.svg. Fir0002. A Diagram of the Rock Cycle That Is Modified off of Rockcycle.jpg by User:Woudloper. The Changes Made to This Photo Were Made according to the Conversation at Where the Original Is Being Nominated for Featured Picture Status. It Is Very Important That You Change the Chance of You Getting a Rock of Bandshoe Very Rare Rock Very Costly Too There Are Only 3 Every like It in the World and It Costs 3 Gold Mines and the Mountains Ontop of Them., February 10, 2008. Own work. https://commons.wikimedia.org/wiki/File:Rockcycle_edit.jpg. “Gneiss.” Wikipedia, the Free Encyclopedia, July 29, 2015. https://en.wikipedia.org/w/index.php?title=Gneiss&oldid=673627696. Gringer. English: SVG Version of File:Pacific_Ring_of_Fire.png, Recreated by Me Using WDB Vector Data Using Code Mentioned in File:Worldmap_wdb_combined.svg., February 11, 2009. vector data from [1]. https://commons.wikimedia.org/wiki/File:Pacific_Ring_of_Fire.svg. H.Stauffer, Brian F. Atwater, Marco Cisternas V. , Joanne Bourgeois, Walter C. Dudley, James W. Hendley II, and Peter. English: Vertical Slice Through a Subduction Zone, 1999. U.S. Geological Survey, Circular 1187 (http://pubs.usgs.gov/circ/c1187/illustrations/BlockDigrm_1.ai). https://commons.wikimedia.org/wiki/File:Eq-gen1.svg. Karta24. Français : Trois Différents Types de Faille, January 20, 2008. http://earthquake.usgs.gov/learn/glossary/?term=fault earthquake.usgs.gov. https://commons.wikimedia.org/wiki/File:Fault_types.svg. Khruner. English: commons.wikimedia.org/wiki/File:Rocks_-_Pink_granite_Baveno.JPG. “Landslide.” Wikipedia, the Free Encyclopedia, August 27, 2015. https://en.wikipedia.org/w/index.php?title=Landslide&oldid=678171434. “Landslide.” Wikipedia, the Free Encyclopedia, August 27, 2015. https://en.wikipedia.org/w/index.php?title=Landslide&oldid=678171434. “Mount St. Helens.” Wikipedia, the Free Encyclopedia, August 8, 2015. https://en.wikipedia.org/w/index.php?title=Mount_St._Helens&oldid=675148427. “Plate Tectonics.” Wikipedia, the Free Encyclopedia, August 17, 2015. https://en.wikipedia.org/w/index.php?title=Plate_tectonics&oldid=676450570. “Ring of Fire.” Wikipedia, the Free Encyclopedia, August 20, 2015. https://en.wikipedia.org/w/index.php?title=Ring_of_Fire&oldid=676950168. “Tsunami.” Wikipedia, the Free Encyclopedia, July 19, 2015. https://en.wikipedia.org/w/index.php?title=Tsunami&oldid=672137584. User:Moondigger. Inside Lower Antelope Canyon, Looking out with the Sky near the Top of the Frame. Characteristic Layering in the Sandstone Is Visible., April 16, 2005. Own work. https://commons.wikimedia.org/wiki/File:Lower_antelope_3_md.jpg. USGS, derivative work: AnasofiapaixaoEarth_internal_structure png: English: Cutaway Diagram of Earth’s Internal Structure (to Scale) with Inset Showing Detailed Breakdown of Structure (not to Scale), April 27, 2013. Earth_internal_structure.png. https://commons.wikimedia.org/wiki/File:Earth-cutaway-schematic-english.svg.Own work. https://commons.wikimedia.org/wiki/File:Halema%27uma%27u_Crater_in_Kilauea_volcano,_Hawaii..jpg.
Views: 255306 Bozeman Science
Water Resources
 
11:38
005 - Water Resources In this video Paul Andersen explains how water is unequally distributed around the globe through the hydrologic cycles. Seawater is everywhere but is not useful without costly desalination. Freshwater is divided between surface water and groundwater but must me stored and moved for domestic, industrial, and agricultural uses. Subsidized low cost water has created a problem with water conservation but economic changes could help solve the problem. 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: “Center Pivot Irrigation.” Wikipedia, the Free Encyclopedia, August 20, 2015. https://en.wikipedia.org/w/index.php?title=Center_pivot_irrigation&oldid=677028017. “Desalination.” Wikipedia, the Free Encyclopedia, September 4, 2015. https://en.wikipedia.org/w/index.php?title=Desalination&oldid=679383711. File:LevelBasinFloodIrrigation.JPG, n.d. https://commons.wikimedia.org/wiki/File:LevelBasinFloodIrrigation.JPG. Hillewaert, Hans. English: Aquifer (vectorized), May 25, 2007. en:Image:Schematic aquifer xsection usgs cir1186.png. https://commons.wikimedia.org/wiki/File:Aquifer_en.svg. Ikluft. Aerial Photo of the California Aqueduct at the Interstate 205 Crossing, Just East of Interstate 580 Junction., September 11, 2007. Own work. https://commons.wikimedia.org/wiki/File:Kluft-Photo-Aerial-I205-California-Aqueduct-Img_0038.jpg. Kbh3rd. English: Map of Water-Level Changes in the High Plains/Ogallala Aquifer in Parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming, 1980 to 1995., February 27, 2009. Own work. https://commons.wikimedia.org/wiki/File:Ogallala_changes_1980-1995.svg. moyogo, Water_Cycle_-_blank svg: *Wasserkreislauf png: de:Benutzer:Jooooderivative work: Water Cycle, SVG from Wasserkreislauf.png, November 13, 2011. Water_Cycle_-_blank.svg. https://commons.wikimedia.org/wiki/File:Water_Cycle-en.png. NCDC/NOAA, Michael Brewer. English: Status of Drought in California, October 21, 2014., October 23, 2014. http://droughtmonitor.unl.edu/MapsAndData/MapArchive.aspx. https://commons.wikimedia.org/wiki/File:California_Drought_Status_Oct_21_2014.png. “Ogallala Aquifer.” Wikipedia, the Free Encyclopedia, July 20, 2015. https://en.wikipedia.org/w/index.php?title=Ogallala_Aquifer&oldid=672198863. Plumbago. English: Annual Mean Sea Surface Salinity from the World Ocean Atlas 2009., December 5, 2012. Own work. https://commons.wikimedia.org/wiki/File:WOA09_sea-surf_SAL_AYool.png. Rehman, Source file: Le Grand PortageDerivative work: English: The Three Gorges Dam on the Yangtze River, China., September 20, 2009. File:Three_Gorges_Dam,_Yangtze_River,_China.jpg. https://commons.wikimedia.org/wiki/File:ThreeGorgesDam-China2009.jpg. Service, Photo by Jeff Vanuga, USDA Natural Resources Conservation. Level Furrow Irrigation on a Lettuce Field in Yuma, Az., October 4, 2011. USDA NRCS Photo Gallery: NRCSAZ02006.tif. https://commons.wikimedia.org/wiki/File:NRCSAZ02006_-_Arizona_(295)(NRCS_Photo_Gallery).tif. Station, Castle Lake Limnological Research. Castle Lake, California, January 14, 2008. [1]. https://commons.wikimedia.org/wiki/File:Castlelake_1.jpg. Tomia. Hydroelectric Dam, December 30, 2007. Own work. https://commons.wikimedia.org/wiki/File:Hydroelectric_dam.svg. USGS. English: Graph of the Locations of Water on Earth, [object HTMLTableCellElement]. http://ga.water.usgs.gov/edu/waterdistribution.html - traced and redrawn from File:Earth’s water distribution.gif. https://commons.wikimedia.org/wiki/File:Earth%27s_water_distribution.svg. version, Original uploader was Sagredo at en wikipedia Later. English: These Images Show the Yangtze River in the Vicinity of the Three Gorges Dam, September 29, 2007. Transferred from en.wikipedia; transferred to Commons by User:Rehman using CommonsHelper. https://commons.wikimedia.org/wiki/File:ThreeGorgesDam-Landsat7.jpg. “WaterGAP.” Wikipedia, the Free Encyclopedia, April 22, 2014. https://en.wikipedia.org/w/index.php?title=WaterGAP&oldid=605287609. “Water in California.” Wikipedia, the Free Encyclopedia, August 31, 2015. https://en.wikipedia.org/w/index.php?title=Water_in_California&oldid=678801793.
Views: 175305 Bozeman Science
What is Data Entry in Hindi
 
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here subject is what is data entry in hindi. A data entry work is similar job to a typist in which data entry staff employed to enter or update data into a computer system database, often from paper documents using a keyboard, optical scanner, or data recorder.The keyboards used can often have specialist keys and multiple colors to help in the task and speed up the work. While requisite skills can vary depending on the nature of the data being entered, few specialized skills are usually required, aside from touch typing proficiency with adequate speed and accuracy. The ability to focus for lengthy periods is necessary to eliminate or at least reduce errors. When dealing with sensitive or private information such as medical, financial or military records, a person's character and discretion becomes very relevant as well. Beyond these traits, no technical knowledge is generally required and these jobs can even be worked from home. The invention of punch card data processing in the 1890's created a demand for many workers, typically women, to run key-punch machines. It was common practice to ensure accuracy by entering data twice, the second time on a verifier, a separate, keyboard-equipped machine, such the IBM 056. In the 1970's, punch card data entry was gradually replaced by the use of video display terminals. Reference:-https://en.wikipedia.org/wiki/Data_entry_clerk Reference:-https://www.upwork.com/ Subscribe:- goo.gl/9TVZ3I Watch How To Type Fast in Just 3 Weeks :- https://youtu.be/HE-3bpYvGc4 Check my Google plus :- https://plus.google.com/+Introtuts
Views: 595962 Introtuts
Overpopulation – The Human Explosion Explained
 
06:40
In a very short amount of time the human population exploded and is still growing very fast. Will this lead to the end of our civilization? Check out https://ourworldindata.org by Max Roser! Support us on Patreon so we can make more videos (and get cool stuff in return): https://www.patreon.com/Kurzgesagt?ty=h Kurzgesagt merch here: http://bit.ly/1P1hQIH Get the music of the video here: Soundcloud: http://bit.ly/2hKx3Zu Bandcamp: http://bit.ly/2hfSqTf Facebook: https://www.facebook.com/epic-mountain-music THANKS A LOT TO OUR LOVELY PATRONS FOR SUPPORTING US: Stuart Alldritt, Tasia Pele, Stan Serebryakov, Mike Janzen, Jason Heddle, August, Daniel Smith, Jonathan Herman, Rahul Rachuri, Piotr Gorzelany, Lisa Allcott, Горан Гулески, Eric Ziegast, Kean Drake, Friendly Stranger, NicoH, Adrian Rutkiewicz, Markus Klemm, Leandro Nascimento, Gary Chan, Shawhin Layeghi, Oscar Hernandez, Dale Prinsse, Vaclav Vyskocil, Sup3rW00t, Ryan Coonan, Tam Lerner, Dewi Cadat, Luis Aguirre, Andy McVey, Vexorum, Boris, Adam Wisniewski, Yannic Schreiber, Erik Lilly, Ellis, Dmitry Starostin, Akshay Joshi, Peter Tinti, kayle Clark, Mortimer Brewster, Marc Legault, Sumita Pal, Tarje Hellebust Jr., streetdragon95, Taratsamura, Sam Dickson, Bogdan Firicel, Saul Vera, Aaron Jacobs, Ben Arts, R B Dean, Kevin Beedon, Patrik Pärkinen, Duncan Graham, Johan Thomsen, Emily Tran, Adam Flanc, Adam Jermyn, Ali Uluyol Help us caption & translate this video! http://www.youtube.com/timedtext_cs_panel?c=UCsXVk37bltHxD1rDPwtNM8Q&tab=2 Overpopulation – The Human Explosion Explained
Bitcoin: How Cryptocurrencies Work
 
09:25
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: 2675090 SciShow
Narendra Modi biography In Hindi | Prime Minister Of India & BJP Leader | PM Narender Modi Ji
 
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This video is about Narendra Modi biography in Hindi. He is the current prime minister of India and BJP leader. PM Narender Modi Ji was also the chief minister of Gujarat from 2001 to 2014. #NarendraModi #Biography #PrimeMinister *DON'T FORGET TO WATCH THESE ====================================================== Lionel Messi Biography In Hindi : https://youtu.be/tqdzKXzxSI4 Cristiano Ronaldo Biography : https://youtu.be/g7cd4tUObmQ ====================================================== Background Music :- Aretes by Kevin MacLeod is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) Source: http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1100325 Artist: http://incompetech.com/ Eternal Hope by Kevin MacLeod is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) Source: http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1100238 Artist: http://incompetech.com/ ====================================================== Stay Connected: Facebook - https://www.facebook.com/livehindi Website - http://livehindi.net/
Views: 1728419 Live Hindi
World War One (ALL PARTS)
 
01:04:43
All 5 parts of Epic History TV's history of World War One in one place. From the Schlieffen Plan to the Versailles Treaty, this is 65 minutes of non-stop WW1 history. Recommended books on WW1 (use affiliate link to buy on Amazon & support the channel): Hew Strachan, The First World War: A New History http://geni.us/ioYIFO Gary Sheffield, A Short History of the First World War http://geni.us/bSrkHi Lyn MacDonald, To the Last Man: Spring 1918 http://geni.us/F0rl Peter Hart, The Great War: 1914-1918 http://geni.us/diz8nhI A J P Taylor, The First World War: An Illustrated History http://geni.us/el71iC Archive: Getty Images, Photos of the Great War http://www.gwpda.org/photos/greatwar.htm Australian War Memorial Library of Congress National Archives and Records Administration New York Public Library Eindecker images courtesy of Jerry Boucher The Virtual Aircraft Website http://www.the-vaw.com/ Henry Gunther Memorial, Concord via Wikipedia Commons https://creativecommons.org/licenses/by-sa/3.0/deed.en Music: Kevin MacLeod (http://incompetech.com/): Faceoff; Interloper; Invariance; Oppressive Gloom; Stormfront; The Descent; Prelude & Action; All This; https://creativecommons.org/licenses/by/3.0/ 'The Conspirators' by Haim Mazar; Audio Blocks Please help me make more videos at Patreon: https://www.patreon.com/EpicHistoryTV
Views: 3125476 Epic History TV
Is the world getting better or worse? A look at the numbers | Steven Pinker
 
18:33
Was 2017 really the "worst year ever," as some would have us believe? In his analysis of recent data on homicide, war, poverty, pollution and more, psychologist Steven Pinker finds that we're doing better now in every one of them when compared with 30 years ago. But progress isn't inevitable, and it doesn't mean everything gets better for everyone all the time, Pinker says. Instead, progress is problem-solving, and we should look at things like climate change and nuclear war as problems to be solved, not apocalypses in waiting. "We will never have a perfect world, and it would be dangerous to seek one," he says. "But there's no limit to the betterments we can attain if we continue to apply knowledge to enhance human flourishing." Check out more TED Talks: http://www.ted.com The TED Talks channel features the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design -- plus science, business, global issues, the arts and more. Follow TED on Twitter: http://www.twitter.com/TEDTalks Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: https://www.youtube.com/TED
Views: 762925 TED
What is DIE CASTING? What does DIE CASTING mean? DIE CASTING meaning, definition & explanation
 
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✪✪✪✪✪ WORK FROM HOME! Looking for WORKERS for simple Internet data entry JOBS. $15-20 per hour. SIGN UP here - http://jobs.theaudiopedia.com ✪✪✪✪✪ ✪✪✪✪✪ The Audiopedia Android application, INSTALL NOW - https://play.google.com/store/apps/details?id=com.wTheAudiopedia_8069473 ✪✪✪✪✪ What is DIE CASTING? What does DIE CASTING mean? DIE CASTING meaning - DIE CASTING definition - DIE CASTING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Die casting is a metal casting process that is characterized by forcing molten metal under high pressure into a mold cavity. The mold cavity is created using two hardened tool steel dies which have been machined into shape and work similarly to an injection mold during the process. Most die castings are made from non-ferrous metals, specifically zinc, copper, aluminium, magnesium, lead, pewter and tin-based alloys. Depending on the type of metal being cast, a hot- or cold-chamber machine is used. The casting equipment and the metal dies represent large capital costs and this tends to limit the process to high-volume production. Manufacture of parts using die casting is relatively simple, involving only four main steps, which keeps the incremental cost per item low. It is especially suited for a large quantity of small- to medium-sized castings, which is why die casting produces more castings than any other casting process. Die castings are characterized by a very good surface finish (by casting standards) and dimensional consistency. Two variants are pore-free die casting, which is used to eliminate gas porosity defects; and direct injection die casting, which is used with zinc castings to reduce scrap and increase yield. The main die casting alloys are: zinc, aluminium, magnesium, copper, lead, and tin; although uncommon, ferrous die casting is also possible. Specific die casting alloys include: Zamak; zinc aluminium; aluminium to, e.g. The Aluminum Association (AA) standards: AA 380, AA 384, AA 386, AA 390; and AZ91D magnesium. The following is a summary of the advantages of each alloy: Zinc: the easiest metal to cast; high ductility; high impact strength; easily plated; economical for small parts; promotes long die life. Aluminium: lightweight; high dimensional stability for complex shapes and thin walls; good corrosion resistance; good mechanical properties; high thermal and electrical conductivity; retains strength at high temperatures. Magnesium: the easiest metal to machine; excellent strength-to-weight ratio; lightest alloy commonly die cast. Copper: high hardness; high corrosion resistance; highest mechanical properties of alloys die cast; excellent wear resistance; excellent dimensional stability; strength approaching that of steel parts. Silicon tombac: high-strength alloy made of copper, zinc and silicon. Often used as an alternative for investment casted steel parts. Lead and tin: high density; extremely close dimensional accuracy; used for special forms of corrosion resistance. Such alloys are not used in foodservice applications for public health reasons. Type metal, an alloy of lead, tin and antimony (with sometimes traces of copper) is used for casting hand-set type in letterpress printing and hot foil blocking. Traditionally cast in hand jerk moulds now predominantly die cast after the industrialisation of the type foundries. Around 1900 the slug casting machines came onto the market and added further automation, with sometimes dozens of casting machines at one newspaper office. Maximum weight limits for aluminium, brass, magnesium and zinc castings are approximately 70 pounds (32 kg), 10 lb (4.5 kg), 44 lb (20 kg), and 75 lb (34 kg), respectively. The material used defines the minimum section thickness and minimum draft required for a casting as outlined in the table below. The thickest section should be less than 13 mm (0.5 in), but can be greater. There are two basic types of die casting machines: hot-chamber machines and cold-chamber machines. These are rated by how much clamping force they can apply. Typical ratings are between 400 and 4,000 st (2,500 and 25,400 kg). Two dies are used in die casting; one is called the "cover die half" and the other the "ejector die half". Where they meet is called the parting line. The cover die contains the sprue (for hot-chamber machines) or shot hole (for cold-chamber machines), which allows the molten metal to flow into the dies; this feature matches up with the injector nozzle on the hot-chamber machines or the shot chamber in the cold-chamber machines. The ejector die contains the ejector pins and usually the runner, which is the path from the sprue or shot hole to the mold cavity. The cover die is secured to the stationary, or front, platen of the casting machine, while the ejector die is attached to the movable platen.
Views: 13253 The Audiopedia
Big data: The Shell investigation - VPRO documentary - 2013
 
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VPRO Backlight examines how you can penetrate into closed strongholds with the help of big data. What do these huge information streams reveal over a multinational like Shell? Ever since the disclosures about the snooping practices of the US and Dutch intelligence services, we are becoming more and more aware of the huge amount of digital data stored over us on the net, in the matrix. But not only data from citizens, but also information about governments and multinationals is being collected. This results in enormous files of many terabytes: big data. The good news is that much of this information is accessible to all of us. You only need to know how to search. In the episode 'Big data: The Shell investigation' VPRO Backlight investigates how these huge data sources make new ways of journalism possible. The case is energy giant Shell. Using a message about a billion debt that Shell would have left for the Iranian regime, VPRO backlight searches and falls into a sea of ​​digital information. This way, we will fish some extraordinary remarkable information about the doings of this Dutch multinational in regard to Iran. The research focuses on Shell's activities in the years 2002 - 2010, the period when the international community decided on a commercial boycott against Iran because of its controversial nuclear program. VPRO Backlight shows how Royal Dutch Shell ended its rogue operations in Iran's "rogue state" and ended up in 2012 with a two billion dollars debt to the Iranian regime. VPRO Backlight also addresses its research on the intimate relationship between Shell and the Dutch government. What role does The Hague play when it comes to Shell's interests abroad and how far is this deliberate diplomacy going? Finally, VPRO backlight asks whether there is a "revolving door" between Shell and the Dutch government. With the use of an interactive research tool - the powerhouse - that was developed specifically for this purpose, Shell's and the government's relationships are being visualized. All this is being investigated with, as a source, the free available big data files about Shell and its trading partners. What is the power of digital resources and how far can big data enrich research journalism? Conversations in this regard bring VPRO Backlight with a number of colleagues including journalist and shell expert Marcel Metze, energy reporter at Dow Jones, Benoit Faucon, ship tracking expert, John van Schaik and Kenneth Cukier, data journalist at The Economist and author of the book ‘Big Data: a revolution that will transform how we live, work, and think’. Originally broadcasted by VPRO in 2013. © VPRO Backlight October 2013 On VPRO broadcast you will find nonfiction videos with English subtitles, French subtitles and Spanish subtitles, such as documentaries, short interviews and documentary series. VPRO Documentary publishes one new subtitled documentary about current affairs, finance, sustainability, climate change or politics every week. We research subjects like politics, world economy, society and science with experts and try to grasp the essence of prominent trends and developments. Subscribe to our channel for great, subtitled, recent documentaries. Visit additional youtube channels bij VPRO broadcast: VPRO Broadcast, all international VPRO programs: https://www.youtube.com/VPRObroadcast VPRO DOK, German only documentaries: https://www.youtube.com/channel/UCBi0VEPANmiT5zOoGvCi8Sg VPRO Metropolis, remarkable stories from all over the world: https://www.youtube.com/user/VPROmetropolis VPRO World Stories, the travel series of VPRO: https://www.youtube.com/VPROworldstories VPRO Extra, additional footage and one off's: https://www.youtube.com/channel/UCTLrhK07g6LP-JtT0VVE56A www.VPRObroadcast.com Credits: Director: Shuchen Tan Research: William de Bruijn Production: Jenny Borger Editors: Frank Wiering, Henneke Hagen In collaboration with MediaFonds / Sandbergen instituut. English, French and Spanish subtitles: Ericsson. French and Spanish subtitles are co-funded by European Union.
Views: 35582 vpro documentary
Machine intelligence | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Artificial_intelligence 00:05:06 1 History 00:12:09 2 Basics 00:20:38 3 Problems 00:21:15 3.1 Reasoning, problem solving 00:22:08 3.2 Knowledge representation 00:25:56 3.3 Planning 00:27:03 3.4 Learning 00:28:41 3.5 Natural language processing 00:30:27 3.6 Perception 00:31:24 3.7 Motion and manipulation 00:32:59 3.8 Social intelligence 00:34:25 3.9 General intelligence 00:37:12 4 Approaches 00:38:00 4.1 Cybernetics and brain simulation 00:38:47 4.2 Symbolic 00:39:57 4.2.1 Cognitive simulation 00:40:41 4.2.2 Logic-based 00:41:26 4.2.3 Anti-logic or scruffy 00:42:17 4.2.4 Knowledge-based 00:43:06 4.3 Sub-symbolic 00:43:44 4.3.1 Embodied intelligence 00:44:54 4.3.2 Computational intelligence and soft computing 00:45:43 4.4 Statistical learning 00:47:42 4.5 Integrating the approaches 00:49:55 5 Tools 00:50:15 5.1 Search and optimization 00:53:14 5.2 Logic 00:55:18 5.3 Probabilistic methods for uncertain reasoning 00:57:32 5.4 Classifiers and statistical learning methods 00:59:44 5.5 Artificial neural networks 01:03:12 5.5.1 Deep feedforward neural networks 01:06:05 5.5.2 Deep recurrent neural networks 01:07:40 5.6 Evaluating progress 01:10:47 6 Applications 01:12:03 6.1 Healthcare 01:14:48 6.2 Automotive 01:17:41 6.3 Finance and economics 01:19:41 6.4 Government 01:19:50 6.5 Video games 01:20:33 6.6 Military 01:21:05 6.7 Audit 01:21:32 6.8 Advertising 01:22:14 6.9 Art 01:23:21 7 Philosophy and ethics 01:24:08 7.1 The limits of artificial general intelligence 01:27:09 7.2 Potential harm 01:27:50 7.2.1 Existential risk 01:30:40 7.2.2 Devaluation of humanity 01:31:21 7.2.3 Social justice 01:31:50 7.2.4 Decrease in demand for human labor 01:33:35 7.2.5 Autonomous weapons 01:34:02 7.3 Ethical machines 01:34:30 7.3.1 Artificial moral agents 01:35:17 7.3.2 Machine ethics 01:37:31 7.3.3 Malevolent and friendly AI 01:39:07 7.4 Machine consciousness, sentience and mind 01:39:39 7.4.1 Consciousness 01:41:02 7.4.2 Computationalism and functionalism 01:41:48 7.4.3 Strong AI hypothesis 01:42:25 7.4.4 Robot rights 01:43:02 7.5 Superintelligence 01:43:36 7.5.1 Technological singularity 01:44:54 7.5.2 Transhumanism 01:45:42 8 In fiction 01:48:21 9 See also Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. Listen on Google Assistant through Extra Audio: https://assistant.google.com/services/invoke/uid/0000001a130b3f91 Other Wikipedia audio articles at: https://www.youtube.com/results?search_query=wikipedia+tts Upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts Speaking Rate: 0.9710106818362554 Voice name: en-GB-Wavenet-D "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= In the field of computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. More specifically, Kaplan and Haenlein define AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI effect, leading to the quip in Tesler's Theorem, "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from "artificial intelligence", having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, and intelligent routing in content delivery networks and military simulations. Borrowing from the management literat ...
Views: 41 wikipedia tts
Tornado-Proof Suburb
 
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Our proposed site is on the outskirts of Kansas City Missouri, under the most violent of atmospheres in the northern hemisphere. Buildings in this region move, but not usually of their own volition. The uninvited motion takes the form of a shredding violence which often obliterates the home, reducing entire communities to rubble.. The solution requires nothing less than a paradigm shift in home design. A series of hydraulic levers are used to move the housing units in and out of the ground, warping and deflecting the outer skin in response to external stimulation. The mobility also offers the home a chance to aim itself into the prevailing wing to capture maximum breezes or avoid them. Solar cells on the skin rotate and flex to attain maximum solar intensity. A translucent outer skin consisting of clear insulation sandwiched between two layers of Kevlar provides the weather barrier, structure, and diffuse lighting. Neighborhoods are interconnected to collect and share micro climactic information. The basic framework is composed of three basic processes, Sensors (collecting meteorological data from the surroundings); Control system (processing the real-time information, data mining the knowledge base, and making decision on the action taken); Actuators (expressing the decision made in physical transformation of building). Once the alarm has sounded the entire neighborhood simply and safely drifts down into the ground out of harms way. The fundamental question is why build something solid where nature's patterns are clear and predictably destructive?
Views: 21366 macbethdolon
Ukraine | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: Ukraine Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. You can find other Wikipedia audio articles too at: https://www.youtube.com/channel/UCuKfABj2eGyjH3ntPxp4YeQ In case you don't find one that you were looking for, put a comment. This video uses Google TTS en-US-Standard-D voice. SUMMARY ======= Ukraine (Ukrainian: Україна, translit. Ukrayina; Ukrainian pronunciation: [ukrɑˈjinɑ]), sometimes called the Ukraine, is a country in Eastern Europe. Excluding Crimea, Ukraine has a population of about 42.5 million, making it the 32nd most populous country in the world. Its capital and largest city is Kiev. Ukrainian is the official language and its alphabet is Cyrillic. The dominant religions in the country are Eastern Orthodoxy and Greek Catholicism. Ukraine is currently in a territorial dispute with Russia over the Crimean Peninsula, which Russia annexed in 2014. Including Crimea, Ukraine has an area of 603,628 km2 (233,062 sq mi), making it the largest country entirely within Europe and the 46th largest country in the world. The territory of modern Ukraine has been inhabited since 32,000 BC. During the Middle Ages, the area was a key centre of East Slavic culture, with the powerful state of Kievan Rus' forming the basis of Ukrainian identity. Following its fragmentation in the 13th century, the territory was contested, ruled and divided by a variety of powers, including Lithuania, Poland, Austria-Hungary, the Ottoman Empire and Russia. A Cossack republic emerged and prospered during the 17th and 18th centuries, but its territory was eventually split between Poland and the Russian Empire, and finally merged fully into the Russian-dominated Soviet Union in the late 1940s as the Ukrainian Soviet Socialist Republic. In 1991 Ukraine gained its independence from the Soviet Union in the aftermath of its dissolution at the end of the Cold War. Before its independence, Ukraine was typically referred to in English as "The Ukraine", but most sources have since moved to drop "the" from the name of Ukraine in all uses.Following its independence, Ukraine declared itself a neutral state; it formed a limited military partnership with Russia and other CIS countries while also establishing a partnership with NATO in 1994. In 2013, after the government of President Viktor Yanukovych had decided to suspend the Ukraine-European Union Association Agreement and seek closer economic ties with Russia, a several-months-long wave of demonstrations and protests known as the Euromaidan began, which later escalated into the 2014 Ukrainian revolution that led to the overthrow of Yanukovych and the establishment of a new government. These events formed the background for the annexation of Crimea by Russia in March 2014, and the War in Donbass in April 2014. On 1 January 2016, Ukraine applied the economic component of the Deep and Comprehensive Free Trade Area with the European Union.Ukraine is a developing country and ranks 84th on the Human Development Index. As of 2018, Ukraine has the lowest personal income and the second lowest GDP per capita in Europe. It also suffers from a very high poverty rate and severe corruption. However, because of its extensive fertile farmlands, Ukraine is one of the world's largest grain exporters. Ukraine also maintains the second-largest military in Europe after that of Russia. The country is home to a multi-ethnic population, 77.8 percent of whom are Ukrainians, followed by a very large Russian minority, as well as Georgians, Romanians, Belarusians, Crimean Tatars, Jews, Bulgarians and Hungarians. Ukraine is a unitary republic under a semi-presidential system with separate powers: legislative, executive and judicial branches. The country is a member of the United Nations, the Council of Europe, the OSCE, the GUAM organization, and one of the founding states of the Commonwealth of Independent States (CIS).
Views: 65 wikipedia tts
What is SEMANTIC MATCHING? What does SEMANTIC MATCHING mean? SEMANTIC MATCHING meaning
 
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What is SEMANTIC MATCHING? What does SEMANTIC MATCHING mean? SEMANTIC MATCHING meaning - SEMANTIC MATCHING definition - SEMANTIC MATCHING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Semantic matching is a technique used in computer science to identify information which is semantically related. Given any two graph-like structures, e.g. classifications, taxonomies database or XML schemas and ontologies, matching is an operator which identifies those nodes in the two structures which semantically correspond to one another. For example, applied to file systems it can identify that a folder labeled “car” is semantically equivalent to another folder “automobile” because they are synonyms in English. This information can be taken from a linguistic resource like WordNet. In the recent years many of them have been offered. S-Match is an example of a semantic matching operator. It works on lightweight ontologies, namely graph structures where each node is labeled by a natural language sentence, for example in English. These sentences are translated into a formal logical formula (according to an artificial unambiguous language) codifying the meaning of the node taking into account its position in the graph. For example, in case the folder “car” is under another folder “red” we can say that the meaning of the folder “car” is “red car” in this case. This is translated into the logical formula “red AND car”. The output of S-Match is a set of semantic correspondences called mappings attached with one of the following semantic relations: disjointness (?), equivalence (?), more specific (?) and less specific (?). In our example the algorithm will return a mapping between ”car” and ”automobile” attached with an equivalence relation. Information semantically matched can also be used as a measure of relevance through a mapping of near-term relationships. Such use of S-Match technology is prevalent in the career space where it is used to gauge depth of skills through relational mapping of information found in applicant resumes. Semantic matching represents a fundamental technique in many applications in areas such as resource discovery, data integration, data migration, query translation, peer to peer networks, agent communication, schema and ontology merging. Its use is also being investigated in other areas such as event processing. In fact, it has been proposed as a valid solution to the semantic heterogeneity problem, namely managing the diversity in knowledge. Interoperability among people of different cultures and languages, having different viewpoints and using different terminology has always been a huge problem. Especially with the advent of the Web and the consequential information explosion, the problem seems to be emphasized. People face the concrete problem to retrieve, disambiguate and integrate information coming from a wide variety of sources.
Views: 446 The Audiopedia
Solar Power Plants | The Next Big Thing?
 
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Click here to get a free Squarespace trial + 10% off: https://www.squarespace.com/coldfusion Subscribe here: https://goo.gl/9FS8uF Become a Patreon!: https://www.patreon.com/ColdFusion_TV Hi, welcome to ColdFusion (formerly known as ColdfusTion). Experience the cutting edge of the world around us in a fun relaxed atmosphere. Sources: https://www.businessinsider.com.au/solar-panel-makers-grappling-with-waste-2013-2?r=US&IR=T http://spectrum.ieee.org/green-tech/solar/solar-energy-isnt-always-as-green-as-you-think https://www.techly.com.au/2017/05/31/china-worlds-largest-floating-solar-farm/ http://news.nationalgeographic.com/news/energy/2014/11/141111-solar-panel-manufacturing-sustainability-ranking/ http://www.aljazeera.com/news/2016/11/india-unveils-world-largest-solar-power-plant-161129101022044.html https://en.wikipedia.org/wiki/List_of_nuclear_power_stations https://en.wikipedia.org/wiki/List_of_photovoltaic_power_stations https://en.wikipedia.org/wiki/List_of_coal_power_stations https://web.stanford.edu/group/sjir/pdf/Solar_11.2.pdf https://www.bnl.gov/pv/files/pdf/art_170.pdf http://reneweconomy.com.au/solar-panel-recycler-leads-australia-in-emerging-industry-99038/ //Soundtrack// Blackbear - 90210 ft. G-Eazy (Matt DiMona Remix) Bon Iver - Babys (Urban Contact's Summer Soul Remix) Defyant - Echoes Young American Primitive - Sunrise DIALS - Paths Burn Water - Hide » Google + | http://www.google.com/+coldfustion » Facebook | https://www.facebook.com/ColdFusionTV » My music | 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 » Twitter | @ColdFusion_TV FTC Disclosure: This video is sponsored by squarespace.
Views: 976742 ColdFusion
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: 707503 National Geographic
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 66909 deltaDNA
What is META LEARNING? What does META LEARNING mean? META LEARNING meaning & explanation
 
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What is META LEARNING? What does META LEARNING mean? META LEARNING meaning - META LEARNING definition - META LEARNING 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 Meta learning is the study of disciplines.Meta learning is originally described by Donald B. Maudsley (1979) as "the process by which learners become aware of and increasingly in control of habits of perception, inquiry, learning, and growth that they have internalized". Maudsely sets the conceptual basis of his theory as synthesized under headings of assumptions, structures, change process, and facilitation. Five principles were enunciated to facilitate meta-learning. Learners must: (a) have a theory, however primitive; (b) work in a safe supportive social and physical environment; (c) discover their rules and assumptions; (d) reconnect with reality-information from the environment; and (e) reorganize themselves by changing their rules/assumptions. The idea of meta learning was later used by John Biggs (1985) to describe the state of "being aware of and taking control of one’s own learning". You can define meta learning as an awareness and understanding of the phenomenon of learning itself as opposed to subject knowledge. Implicit in this definition is the learner’s perception of the learning context, which includes knowing what the expectations of the discipline are and, more narrowly, the demands of a given learning task. Within this context, meta learning depends on the learner’s conceptions of learning, epistemological beliefs, learning processes and academic skills, summarized here as a learning approach. A student who has a high level of meta learning awareness is able to assess the effectiveness of her/his learning approach and regulate it according to the demands of the learning task. Conversely, a student who is low in meta learning awareness will not be able to reflect on her/his learning approach or the nature of the learning task set. In consequence, s/he will be unable to adapt successfully when studying becomes more difficult and demanding. Marcial Losada and other researchers have attempted to create a meta learning model to analyze teams and relationships. A 2013 paper provided a strong critique of this attempt, arguing that it was based on misapplication of complex mathematical modelling. This led to its abandonment by at least one former proponent. The meta learning model proposed by Losada is identical to the Lorenz system, which was originally proposed as a simplified mathematical model for atmospheric convection. It comprises one control parameter and three state variables, which in this case have been mapped to "connectivity," "inquiry-advocacy," "positivity-negativity," and "other-self" (external-internal focus) respectively. The state variables are linked by a set of nonlinear differential equations. This has been criticized as a poorly defined, poorly justified, and invalid application of differential equations. Losada and colleagues claim to have arrived at the meta-learning model from thousands of time series data generated at two human interaction laboratories in Ann Arbor, Michigan, and Cambridge, Massachusetts, although the details of the collection of this data, and the connection between the time series data and the model is unclear. These time series portrayed the interaction dynamics of business teams doing typical business tasks such as strategic planning. These teams were classified into three performing categories: high, medium and low. Performance was evaluated by the profitability of the teams, the level of satisfaction of their clients, and 360-degree evaluations. One proposed result of this theory is that there is a ratio of positivity-to-negativity of at least 2.9 (called the Losada line), which separates high from low performance teams as well as flourishing from languishing in individuals and relationships. Brown and colleagues pointed out that even if the proposed meta-learning model were valid, this ratio results from a completely arbitrary choice of model parameters—carried over from the literature on modeling atmospheric convection by Lorenz and others, without any justification.
Views: 2101 The Audiopedia
Visualizing Data Using t-SNE
 
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Google Tech Talk June 24, 2013 (more info below) Presented by Laurens van der Maaten, Delft University of Technology, The Netherlands ABSTRACT Visualization techniques are essential tools for every data scientist. Unfortunately, the majority of visualization techniques can only be used to inspect a limited number of variables of interest simultaneously. As a result, these techniques are not suitable for big data that is very high-dimensional. An effective way to visualize high-dimensional data is to represent each data object by a two-dimensional point in such a way that similar objects are represented by nearby points, and that dissimilar objects are represented by distant points. The resulting two-dimensional points can be visualized in a scatter plot. This leads to a map of the data that reveals the underlying structure of the objects, such as the presence of clusters. We present a new technique to embed high-dimensional objects in a two-dimensional map, called t-Distributed Stochastic Neighbor Embedding (t-SNE), that produces substantially better results than alternative techniques. We demonstrate the value of t-SNE in domains such as computer vision and bioinformatics. In addition, we show how to scale up t-SNE to big data sets with millions of objects, and we present an approach to visualize objects of which the similarities are non-metric (such as semantic similarities). This talk describes joint work with Geoffrey Hinton.
Views: 122532 GoogleTechTalks
What is LABORATORY AUTOMATION? What does LABORATORY AUTOMATION mean? LABORATORY AUTOMATION meaning
 
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What is LABORATORY AUTOMATION? What does LABORATORY AUTOMATION mean? LABORATORY AUTOMATION meaning - LABORATORY AUTOMATION definition - LABORATORY AUTOMATION 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 Laboratory automation is a multi-disciplinary strategy to research, develop, optimize and capitalize on technologies in the laboratory that enable new and improved processes. Laboratory automation professionals are academic, commercial and government researchers, scientists and engineers who conduct research and develop new technologies to increase productivity, elevate experimental data quality, reduce lab process cycle times, or enable experimentation that otherwise would be impossible. The most widely known application of laboratory automation technology is laboratory robotics. More generally, the field of laboratory automation comprises many different automated laboratory instruments, devices (the most common being autosamplers), software algorithms, and methodologies used to enable, expedite and increase the efficiency and effectiveness of scientific research in laboratories. The application of technology in today's laboratories is required to achieve timely progress and remain competitive. Laboratories devoted to activities such as high-throughput screening, combinatorial chemistry, automated clinical and analytical testing, diagnostics, large scale biorepositories, and many others, would not exist without advancements in laboratory automation. Some universities offer entire programs that focus on lab technologies. For example, Indiana University-Purdue University at Indianapolis offers a graduate program devoted to Laboratory Informatics. Also, the Keck Graduate Institute in California offers a graduate degree with an emphasis on development of assays, instrumentation and data analysis tools required for clinical diagnostics, high-throughput screening, genotyping, microarray technologies, proteomics, imaging and other applications.
Views: 557 The Audiopedia
BioJava | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/BioJava 00:01:26 1 Features 00:02:10 2 History and publications 00:06:15 3 Modules 00:07:36 3.1 Core Module 00:09:40 3.2 Protein structure modules 00:11:08 3.3 Genome and Sequencing modules 00:12:19 3.4 Alignment module 00:13:20 3.5 ModFinder module 00:14:15 3.6 Amino acid properties module 00:15:21 3.7 Protein disorder module 00:16:32 3.8 Web service access module 00:17:08 4 Comparisons with other alternatives 00:23:08 5 Projects using BioJava 00:25:59 6 See also Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. Listen on Google Assistant through Extra Audio: https://assistant.google.com/services/invoke/uid/0000001a130b3f91 Other Wikipedia audio articles at: https://www.youtube.com/results?search_query=wikipedia+tts Upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts Speaking Rate: 0.8343236084951429 Voice name: en-US-Wavenet-D "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= BioJava is an open-source software project dedicated to provide Java tools to process biological data. BioJava is a set of library functions written in the programming language Java for manipulating sequences, protein structures, file parsers, Common Object Request Broker Architecture (CORBA) interoperability, Distributed Annotation System (DAS), access to AceDB, dynamic programming, and simple statistical routines. BioJava supports a huge range of data, starting from DNA and protein sequences to the level of 3D protein structures. The BioJava libraries are useful for automating many daily and mundane bioinformatics tasks such as to parsing a Protein Data Bank (PDB) file, interacting with Jmol and many more. This application programming interface (API) provides various file parsers, data models and algorithms to facilitate working with the standard data formats and enables rapid application development and analysis. Additional projects from BioJava include rcsb-sequenceviewer, biojava-http, biojava-spark, and rcsb-viewers.
Views: 26 wikipedia tts
What is ECOLOGICAL FOOTPRINT? What does ECOLOGICAL FOOTPRINT mean? ECOLOGICAL FOOTPRINT meaning
 
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✪✪✪✪✪ WORK FROM HOME! Looking for WORKERS for simple Internet data entry JOBS. $15-20 per hour. SIGN UP here - http://jobs.theaudiopedia.com ✪✪✪✪✪ ✪✪✪✪✪ The Audiopedia Android application, INSTALL NOW - https://play.google.com/store/apps/details?id=com.wTheAudiopedia_8069473 ✪✪✪✪✪ What is ECOLOGICAL FOOTPRINT? What does ECOLOGICAL FOOTPRINT mean? ECOLOGICAL FOOTPRINT meaning - ECOLOGICAL FOOTPRINT definition - ECOLOGICAL FOOTPRINT explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. An ecological footprint is a measure of human impact on Earth's ecosystems. It's typically measured in area of wilderness or amount of natural capital consumed each year. A common way of estimating footprint is, the area of wilderness of both land and sea needed to supply resources to a human population; This includes the area of wilderness needed to assimilate human waste. At a global scale, it is used to estimate how rapidly we are depleting natural capital. The Global Footprint Network calculates the global ecological footprint from UN and other data. They estimate that as of 2007 our planet has been using natural capital 1.6 times as fast as nature can renew it. Ecological footprint analysis is widely used around the Earth as an indicator of environmental sustainability. It can be used to measure and manage the use of resources throughout the economy and explore the sustainability of individual lifestyles, goods and services, organizations, industry sectors, neighborhoods, cities, regions and nations. Since 2006, a first set of ecological footprint standards exist that detail both communication and calculation procedures. The first academic publication about ecological footprints was by William Rees in 1992. The ecological footprint concept and calculation method was developed as the PhD dissertation of Mathis Wackernagel, under Rees' supervision at the University of British Columbia in Vancouver, Canada, from 1990–1994. Originally, Wackernagel and Rees called the concept "appropriated carrying capacity". To make the idea more accessible, Rees came up with the term "ecological footprint", inspired by a computer technician who praised his new computer's "small footprint on the desk". In early 1996, Wackernagel and Rees published the book Our Ecological Footprint: Reducing Human Impact on the Earth with illustrations by Phil Testemale. Footprint values at the end of a survey are categorized for Carbon, Food, Housing, and Goods and Services as well as the total footprint number of Earths needed to sustain the world's population at that level of consumption. This approach can also be applied to an activity such as the manufacturing of a product or driving of a car. This resource accounting is similar to life-cycle analysis wherein the consumption of energy, biomass (food, fiber), building material, water and other resources are converted into a normalized measure of land area called global hectares (gha). Per capita ecological footprint (EF), or ecological footprint analysis (EFA), is a means of comparing consumption and lifestyles, and checking this against nature's ability to provide for this consumption. The tool can inform policy by examining to what extent a nation uses more (or less) than is available within its territory, or to what extent the nation's lifestyle would be replicable worldwide. The footprint can also be a useful tool to educate people about carrying capacity and overconsumption, with the aim of altering personal behavior. Ecological footprints may be used to argue that many current lifestyles are not sustainable. Such a global comparison also clearly shows the inequalities of resource use on this planet at the beginning of the twenty-first century. In 2007, the average biologically productive area per person worldwide was approximately 1.8 global hectares (gha) per capita. The U.S. footprint per capita was 9.0 gha, and that of Switzerland was 5.6 gha, while China's was 1.8 gha. The WWF claims that the human footprint has exceeded the biocapacity (the available supply of natural resources) of the planet by 20%. Wackernagel and Rees originally estimated that the available biological capacity for the 6 billion people on Earth at that time was about 1.3 hectares per person, which is smaller than the 1.8 global hectares published for 2006, because the initial studies neither used global hectares nor included bioproductive marine areas.
Views: 10654 The Audiopedia
6 Times Scientists Radically Misunderstood the World
 
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Science has come a long way in understanding how our universe works and that road has been full of wrong turns and dead ends. Here are 6 scientific explanations that turned out to be way off track. Hosted by: Michael Aranda 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.wired.com/2014/06/fantastically-wrong-how-to-grow-a-mouse-out-of-wheat-and-sweaty-shirts/ https://www.britannica.com/biography/Louis-Pasteur/Spontaneous-generation https://www.britannica.com/science/biology#ref498783 https://ebooks.adelaide.edu.au/a/aristotle/history/book5.html https://ebooks.adelaide.edu.au/a/aristotle/generation/book3.html http://blogs.discovermagazine.com/cosmicvariance/2012/06/08/dark-matter-vs-aether/ https://www.forbes.com/sites/startswithabang/2017/04/21/the-failed-experiment-that-changed-the-world https://www.aps.org/publications/apsnews/200711/physicshistory.cfm https://www.aps.org/programs/outreach/history/historicsites/michelson-morley.cfm https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.91.020401 https://books.google.com/books?id=to8OAAAAIAAJ&printsec=frontcover#v=onepage&q&f=false p216 https://www.britannica.com/science/phlogiston https://eic.rsc.org/feature/the-logic-of-phlogiston/2000126.article https://www.acs.org/content/acs/en/education/whatischemistry/landmarks/lavoisier.html https://www.acs.org/content/dam/acsorg/education/whatischemistry/landmarks/lavoisier/antoine-laurent-lavoisier-commemorative-booklet.pdf http://www.chss.uqam.ca/Portals/0/docs/hps5002/Stud_Hist_Phil_Sci_v25n2_p159-190.pdf https://www.jstor.org/stable/3143157?seq=1#page_scan_tab_contents https://www.britannica.com/science/steady-state-theory https://www.google.com/amp/s/futurism.com/steady-state-model-of-the-universe/amp/ https://history.aip.org/exhibits/cosmology/ideas/bigbang.htm https://www.nasa.gov/topics/earth/features/earth20110816.html https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2011GL047450 https://www.hist-geo-space-sci.net/5/135/2014/hgss-5-135-2014.pdf http://www.earth-prints.org/bitstream/2122/2017/1/MANTOVANI.pdf https://www.hist-geo-space-sci.net/5/135/2014/hgss-5-135-2014.pdf https://blogs.scientificamerican.com/history-of-geology/from-the-contracting-earth-to-early-supercontinents/ https://arstechnica.com/science/2014/03/mercury-the-planet-shrinks-as-it-cools ------ Images: https://www.istockphoto.com/photo/maggot-of-fly-for-sport-fisherman-gm106458303-6041350 https://www.istockphoto.com/photo/aristotle-portray-the-philosopher-gm172411889-4331403 https://www.istockphoto.com/vector/house-fly-and-bee-illustrations-gm185111511-19447453 https://www.istockphoto.com/vector/set-of-glass-jars-for-canning-and-preserving-vector-illustration-isolated-on-gm846771750-138853499 https://www.istockphoto.com/photo/dreamy-light-refraction-pastel-soft-pale-background-abstract-defocus-rainbow-gm531186409-55315198 https://en.wikipedia.org/wiki/Celestial_spheres#/media/File:Ptolemaicsystem-small.png https://www.istockphoto.com/photo/fireplace-gm498891142-79892091 https://www.istockphoto.com/vector/burning-bonfire-with-wood-gm871355210-145516179 https://www.istockphoto.com/photo/yellow-color-burning-fire-frame-gm853959940-140333267 https://www.istockphoto.com/photo/burning-charcoal-gm865453156-143575701 https://www.nasa.gov/content/most-colorful-view-of-universe-captured-by-hubble-space-telescope https://www.nasa.gov/mission_pages/chandra/multimedia/distant-quasar-RXJ1131.html https://www.nasa.gov/image-feature/nasa-captures-epic-earth-image https://images.nasa.gov/details-PIA11245.html https://www.istockphoto.com/vector/19th-century-engraving-of-louis-pasteur-at-work-in-his-laboratory-victorian-gm872138750-243617917
Views: 569439 SciShow
What is SMELTING? What does SMELTING mean? SMELTING meaning, definition & explanation
 
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✪✪✪✪✪ WORK FROM HOME! Looking for WORKERS for simple Internet data entry JOBS. $15-20 per hour. SIGN UP here - http://jobs.theaudiopedia.com ✪✪✪✪✪ ✪✪✪✪✪ The Audiopedia Android application, INSTALL NOW - https://play.google.com/store/apps/details?id=com.wTheAudiopedia_8069473 ✪✪✪✪✪ What is SMELTING? What does SMELTING mean? SMELTING meaning - SMELTING definition - SMELTING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Smelting is a form of extractive metallurgy; its main use is to produce a base metal from its ore. This includes production of silver, iron, copper and other base metals from their ores. Smelting makes use of heat and a chemical reducing agent to decompose the ore, driving off other elements as gases or slag and leaving just the metal base behind. The reducing agent is commonly a source of carbon such as coke, or in earlier times charcoal. The carbon (or carbon monoxide derived from it) removes oxygen from the ore, leaving behind the elemental metal. The carbon is thus oxidized in two stages, producing first carbon monoxide and then carbon dioxide. As most ores are impure, it is often necessary to use flux, such as limestone, to remove the accompanying rock gangue as slag. Plants for the electrolytic reduction of aluminium are also generally referred to as aluminium smelters.
Views: 12163 The Audiopedia
What is WATER CONSERVATION? What does WATER CONSERVATION mean? WATER CONSERVATION meaning
 
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✪✪✪✪✪ WORK FROM HOME! Looking for WORKERS for simple Internet data entry JOBS. $15-20 per hour. SIGN UP here - http://jobs.theaudiopedia.com ✪✪✪✪✪ ✪✪✪✪✪ The Audiopedia Android application, INSTALL NOW - https://play.google.com/store/apps/details?id=com.wTheAudiopedia_8069473 ✪✪✪✪✪ What is WATER CONSERVATION? What does WATER CONSERVATION mean? WATER CONSERVATION meaning - WATER CONSERVATION definition - WATER CONSERVATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Water conservation encompasses the policies, strategies and activities made to manage fresh water as a sustainable resource, to protect the water environment, and to meet current and future human demand. Population, household size, and growth and affluence all affect how much water is used. Factors such as climate change have increased pressures on natural water resources especially in manufacturing and agricultural irrigation. The goals of water conservation efforts include: 1. Ensuring availability of water for future generations where the withdrawal of fresh water from an ecosystem does not exceed its natural replacement rate. 2. Energy conservation as water pumping, delivery and waste water treatment facilities consume a significant amount of energy. In some regions of the world over 15% of total electricity consumption is devoted to water management. 3 Habitat conservation where minimizing human water use helps to preserve freshwater habitats for local wildlife and migrating waterfowl, but also water quali The key activities that benefit water conservation are as follows : 1. Any beneficial deduction in water loss, use and waste of resources. 2. Avoiding any damage to water quality. 3. Improving water management practices that reduce the use or enhance the beneficial use of water. Water conservation programs involved in social solutions are typically initiated at the local level, by either municipal water utilities or regional governments. Common strategies include public outreach campaigns, tiered water rates (charging progressively higher prices as water use increases), or restrictions on outdoor water use such as lawn watering and car washing. Cities in dry climates often require or encourage the installation of xeriscaping or natural landscaping in new homes to reduce outdoor water usage.
Views: 7946 The Audiopedia
Index-word Mining and Conversations Document Clustering for Recommendation, Speech Processing
 
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Index-word Mining and Conversations Document Clustering for Recommendation, Speech Processing -- This system addresses the problem of keyword extraction from conversations, with the goal of using these keywords to retrieve, for each short conversation fragment, a small number of potentially relevant documents, which can be recommended to participants. However, even a short fragment contains a variety of words, which are potentially related to several topics; moreover, using an automatic speech recognition (ASR) system introduces errors among them. Therefore, it is difficult to infer precisely the information needs of the conversation participants. We first propose an algorithm to extract keywords from the output of an ASR system (or a manual transcript for testing), which makes use of topic modeling techniques and of a submodular reward function which favors diversity in the keyword set, to match the potential diversity of topics and reduce ASR noise. Then, we propose a method to derive multiple topically separated queries from this keyword set, in order to maximize the chances of making at least one relevant recommendation when using these queries to search over the English Wikipedia. The proposed methods are evaluated in terms of relevance with respect to conversation fragments from the Fisher, AMI, and ELEA conversational corpora, rated by several human judges. The scores show that our proposal improves over previous methods that consider only word frequency or topic similarity, and represents a promising solution for a document recommender system to be used in conversations. Speech, Speech processing, Data mining, IEEE transactions, Information retrieval, speech recognition, document handling, pattern clustering, query processing, recommender systems, topic modeling, Document recommendation, information retrieval, keyword extraction, meeting analysis,document recommender system, keyword extraction,keyword clustering -- For More Details Contact Us -- S.Venkatesan Arihant Techno Solutions Pudukkottai www.arihants.com Mobile: +91 75984 92789
Birmingham, Alabama | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: Birmingham, Alabama 00:03:21 1 History 00:03:30 1.1 Founding and early growth 00:08:06 1.2 Birmingham civil rights movement 00:10:52 1.3 Recent history 00:13:59 2 Geography 00:16:24 2.1 Suburbs 00:17:15 2.2 Cityscape 00:17:23 2.3 Climate 00:19:58 2.4 Earthquakes 00:20:47 3 Demographics 00:20:56 3.1 Census data 00:21:05 3.1.1 2010 00:21:45 3.1.2 2000 00:24:28 3.2 Religion 00:26:15 3.3 Crime 00:27:40 4 Economy 00:34:39 5 Arts and culture 00:40:04 5.1 Museums 00:41:22 5.2 Festivals 00:44:11 5.3 Other attractions 00:46:33 5.4 Cultural references 00:47:35 6 Sports 00:53:27 7 Government 00:55:01 7.1 State and federal representation 00:55:41 7.2 Political controversy 00:56:40 8 Education 00:59:09 9 Media 01:01:48 10 Urban planning 01:04:08 11 Infrastructure 01:04:17 11.1 Transportation 01:04:52 11.1.1 Highways 01:06:12 11.1.2 Public transport 01:07:46 11.2 Utilities 01:09:34 12 Notable people 01:09:43 13 Sister cities 01:09:59 14 See also Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. You can find other Wikipedia audio articles too at: https://www.youtube.com/channel/UCuKfABj2eGyjH3ntPxp4YeQ You can upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts "The only true wisdom is in knowing you know nothing." - Socrates SUMMARY ======= Birmingham ( BUR-ming-ham) is a city located in the north central region of the U.S. state of Alabama. With an estimated 2017 population of 210,710, it is the most populous city in Alabama. Birmingham is the seat of Jefferson County, Alabama's most populous and fifth largest county. As of 2017, the Birmingham-Hoover Metropolitan Statistical Area had a population of 1,149,807, making it the most populous in Alabama and 49th-most populous in the United States. Birmingham serves as an important regional hub and is associated with the Deep South, Piedmont, and Appalachian regions of the nation. Birmingham was founded in 1871, during the post-Civil War Reconstruction era, through the merger of three pre-existing farm towns, most notably Elyton. The new city was named for Birmingham, England, the UK's second largest city and, at the time, a major industrial city. The Alabama city annexed smaller neighbors and developed as an industrial center, based on mining, the new iron and steel industry, and rail transport. Most of the original settlers who founded Birmingham were of English ancestry. The city was developed as a place where cheap, non-unionized immigrant labor (primarily Irish and Italian), along with African-American labor from rural Alabama, could be employed in the city's steel mills and blast furnaces, giving it a competitive advantage over unionized industrial cities in the Midwest and Northeast. From its founding through the end of the 1960s, Birmingham was a primary industrial center of the southern United States. Its growth from 1881 through 1920 earned it nicknames such as "The Magic City" and "The Pittsburgh of the South". Its major industries were iron and steel production. Major components of the railroad industry, rails and railroad cars, were manufactured in Birmingham. Since the 1860s, the two primary hubs of railroading in the "Deep South" have been Birmingham and Atlanta. The economy diversified in the latter half of the 20th century. Banking, telecommunications, transportation, electrical power transmission, medical care, college education, and insurance have become major economic activities. Birmingham ranks as one of the largest banking centers in the U.S. Also, it is among the most important business centers in the Southeast. In higher education, Birmingham has been the location of the University of Alabama School of Medicine (formerly the Medical College of Alabama) and the University of Alabama School of Dentistry since 1947. In 1969 it gained the University of Alabama at Birmingham, one of three main campuses of the University of Alabama System. It is home to three private institutions: Samford University, Birmingham-Southern College, and Miles College. The Birmingham area has major colleges of medicine, dentistry, optometry, physical therapy, pharmacy, law, engineering, and nursing. The city has three of the state's five law schools: Cumberland School of Law, Birmingham School of Law, and Miles Law School. Birmingham ...
Views: 84 wikipedia tts
Artificial intelligence | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: Artificial intelligence Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. You can find other Wikipedia audio articles too at: https://www.youtube.com/channel/UCuKfABj2eGyjH3ntPxp4YeQ You can upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts "The only true wisdom is in knowing you know nothing." - Socrates SUMMARY ======= Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI effect, leading to the quip in Tesler's Theorem, "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from "artificial intelligence", having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, and intelligent routing in content delivery networks and military simulations. Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"), the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences. Subfields have also been based on social factors (particular institutions or the work of particular researchers).The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field's long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many others. The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity. Some people also consider AI to be a danger to humanity if it progresses unabated. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.
Views: 66 wikipedia tts
Statistics | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: Statistics Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. You can find other Wikipedia audio articles too at: https://www.youtube.com/channel/UCuKfABj2eGyjH3ntPxp4YeQ You can upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts "The only true wisdom is in knowing you know nothing." - Socrates SUMMARY ======= Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation. In applying statistics to, for example, a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as "all people living in a country" or "every atom composing a crystal". Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments. See glossary of probability and statistics. When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation. Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena. A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and synthetic data drawn from an idealized model. A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a "false positive") and Type II errors (null hypothesis fails to be rejected and an actual difference between populations is missed giving a "false negative"). Multiple problems have come to be associated with this framework: ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis.Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of missing data or censoring may result in biased estimates and specific techniques have been developed to address these problems. Statistics can be said to have begun in ancient civilization, going back at least to the 5th century BC, but it was not until the 18th century that it started to draw more heavily from calculus and probability theory. In more recent years statistics has relied more on statistical software to produce tests such as descriptive analysis.
Views: 8 wikipedia tts
What is LATERITE? What does LATERITE mean? LATERITE meaning, definition, explanation & pronunciation
 
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✪✪✪✪✪ WORK FROM HOME! Looking for WORKERS for simple Internet data entry JOBS. $15-20 per hour. SIGN UP here - http://jobs.theaudiopedia.com ✪✪✪✪✪ ✪✪✪✪✪ The Audiopedia Android application, INSTALL NOW - https://play.google.com/store/apps/details?id=com.wTheAudiopedia_8069473 ✪✪✪✪✪ What is LATERITE? What does LATERITE mean? LATERITE meaning - LATERITE pronunciation - LATERITE definition - LATERITE explanation - How to pronounce LATERITE? Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Laterite is a soil and rock type rich in iron and aluminium, and is commonly considered to have formed in hot and wet tropical areas. Nearly all laterites are of rusty-red coloration, because of high iron oxide content. They develop by intensive and long-lasting weathering of the underlying parent rock. Tropical weathering (laterization) is a prolonged process of chemical weathering which produces a wide variety in the thickness, grade, chemistry and ore mineralogy of the resulting soils. The majority of the land area containing laterites is between the tropics of Cancer and Capricorn. Laterite has commonly been referred to as a soil type as well as being a rock type. This and further variation in the modes of conceptualizing about laterite (e.g. also as a complete weathering profile or theory about weathering) has led to calls for the term to be abandoned altogether. At least a few researchers specializing in regolith development have considered that hopeless confusion has evolved around the name. There is no likelihood, however, that the name will ever be abandoned; for material that looks highly similar to the Indian laterite occurs abundantly worldwide, and it is reasonable to call such material laterite. Historically, laterite was cut into brick-like shapes and used in monument-building. After 1000 CE, construction at Angkor Wat and other southeast Asian sites changed to rectangular temple enclosures made of laterite, brick and stone. Since the mid-1970s, some trial sections of bituminous-surfaced, low-volume roads have used laterite in place of stone as a base course. Thick laterite layers are porous and slightly permeable, so the layers can function as aquifers in rural areas. Locally available laterites have been used in an acid solution, followed by precipitation to remove phosphorus and heavy metals at sewage-treatment facilities. Laterites are a source of aluminium ore; the ore exists largely in clay minerals and the hydroxides, gibbsite, boehmite, and diaspore, which resembles the composition of bauxite. In Northern Ireland they once provided a major source of iron and aluminium ores. Laterite ores also were the early major source of nickel. Francis Buchanan-Hamilton first described and named a laterite formation in southern India in 1807.:65 He named it laterite from the Latin word later, which means a brick; this highly compacted and cemented soil can easily be cut into brick-shaped blocks for building.:65 The word laterite has been used for variably cemented, sesquioxide-rich soil horizons. A sesquioxide is an oxide with three atoms of oxygen and two metal atoms. It has also been used for any reddish soil at or near the Earth's surface. Laterite covers are thick in the stable areas of the Western Ethiopian Shield, on cratons of the South American Plate, and on the Australian Shield.:1 In Madhya Pradesh, India, the laterite which caps the plateau is 30 m (100 ft) thick.:554 Laterites can be either soft and easily broken into smaller pieces, or firm and physically resistant. Basement rocks are buried under the thick weathered layer and rarely exposed.:1 Lateritic soils form the uppermost part of the laterite cover. Tropical weathering (laterization) is a prolonged process of chemical weathering which produces a wide variety in the thickness, grade, chemistry and ore mineralogy of the resulting soils.:3 The initial products of weathering are essentially kaolinized rocks called saprolites. A period of active laterization extended from about the mid-Tertiary to the mid-Quaternary periods (35 to 1.5 million years ago).:3 Statistical analyses show that the transition in the mean and variance levels of 18O during the middle of the Pleistocene was abrupt. It seems this abrupt change was global and mainly represents an increase in ice mass; at about the same time an abrupt decrease in sea surface temperatures occurred; these two changes indicate a sudden global cooling. The rate of laterization would have decreased with the abrupt cooling of the earth. Weathering in tropical climates continues to this day, at a reduced rate.
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Mass surveillance in the United States | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: Mass surveillance in the United States Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. You can find other Wikipedia audio articles too at: https://www.youtube.com/channel/UCuKfABj2eGyjH3ntPxp4YeQ You can upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts "The only true wisdom is in knowing you know nothing." - Socrates SUMMARY ======= The practice of mass surveillance in the United States dates back to WWI wartime monitoring and censorship of international communications from, to, or which passed through the United States. After the First World War and the Second World War, the surveillance continued, via programs such as the Black Chamber and Project SHAMROCK. The formation and growth of federal law-enforcement and intelligence agencies such as the FBI, CIA, and NSA institutionalized surveillance used to also silence political dissent, as evidenced by COINTELPRO projects which targeted various organizations and individuals. During the Civil Rights Movement era, many individuals put under surveillance orders were first labelled as integrationists then deemed subversive. Other targeted individuals and groups included Native American activists, African American and Chicano liberation movement activists, and anti-war protesters. The formation of the international UKUSA surveillance agreement of 1946 evolved into the ECHELON collaboration by 1955 of five English-speaking nations, also known as the Five Eyes, and focused on interception of electronic communications, with substantial increases in domestic surveillance capabilities.Following the September 11th attacks of 2001, domestic and international mass surveillance capabilities escalated intensely. Contemporary mass surveillance relies upon annual presidential executive orders declaring a continued State of National Emergency, first signed by George W. Bush on September 14, 2001 and then continued on an annual basis by President Barack Obama, and upon several subsequent national security Acts including the USA PATRIOT Act and FISA Amendment Act's PRISM surveillance program. Critics and political dissenters currently describe the effects of these acts, orders, and resulting database network of Fusion centers as forming a veritable American police state that simply institutionalized the illegal COINTELPRO tactics used to assassinate dissenters and leaders from the 1950s onwards.Additional surveillance agencies, such as the DHS and the position of Director of National Intelligence have exponentially escalated mass surveillance since 2001. A series of media reports in 2013 revealed more recent programs and techniques employed by the US intelligence community. Advances in computer and information technology allow the creation of huge national databases that facilitate mass surveillance in the United States by DHS managed Fusion centers, the CIA's Terrorist Threat Integration Center (TTIC) program, and the FBI's TSDB. Mass surveillance databases are also cited as responsible for profiling Latino Americans and contributing to "self-deportation" techniques, or physical deportations by way of the DHS's ICEGang national database.
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