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Introduction to Text Analytics with R: VSM, LSA, & SVD
 
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This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: - Tokenization, stemming, and n-grams - The bag-of-words and vector space models - Feature engineering for textual data (e.g. cosine similarity between documents) - Feature extraction using singular value decomposition (SVD) - Training classification models using textual data - Evaluating accuracy of the trained classification models Part 7 of this video series includes specific coverage of: - The trade-offs of expanding the text analytics feature space with n-grams. - How bag-of-words representations map to the vector space model (VSM). - Usage of the dot product between document vectors as a proxy for correlation. - Latent semantic analysis (LSA) as a means to address the curse of dimensionality in text analytics. - How LSA is implemented using singular value decomposition (SVD). - Mapping new data into the lower dimensional SVD space. The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2tS79Jq See what our past attendees are saying here: http://bit.ly/2svl84m -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 7900 Data Science Dojo
Information Retrieval WS 17/18, Lecture 10: Latent Semantic Indexing
 
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This is the recording of Lecture 10 from the course "Information Retrieval", held on 9th January 2018 by Prof. Dr. Hannah Bast at the University of Freiburg, Germany. The discussed topics are: Latent Semantic Indexing, Matrix Factorization, Singular Value Decomposition (SVD), Eigenvector Decomposition (EVD). Link to the Wiki of the course: https://ad-wiki.informatik.uni-freiburg.de/teaching/InformationRetrievalWS1718 Link to the homepage of our chair: https://ad.informatik.uni-freiburg.de/
Views: 725 AD Lectures
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 366517 sentdex
What is LATENT SEMANTIC INDEXING? What does LATENT SEMANTIC INDEXING mean?
 
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What is LATENT SEMANTIC INDEXING? What does LATENT SEMANTIC INDEXING mean? LATENT SEMANTIC INDEXING meaning - LATENT SEMANTIC INDEXING definition - LATENT SEMANTIC INDEXING 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 Latent semantic indexing (LSI) is an indexing and retrieval method that uses a mathematical technique called singular value decomposition (SVD) to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text. LSI is based on the principle that words that are used in the same contexts tend to have similar meanings. A key feature of LSI is its ability to extract the conceptual content of a body of text by establishing associations between those terms that occur in similar contexts. LSI is also an application of correspondence analysis, a multivariate statistical technique developed by Jean-Paul Benzécri in the early 1970s, to a contingency table built from word counts in documents. Called Latent Semantic Indexing because of its ability to correlate semantically related terms that are latent in a collection of text, it was first applied to text at Bellcore in the late 1980s. The method, also called latent semantic analysis (LSA), uncovers the underlying latent semantic structure in the usage of words in a body of text and how it can be used to extract the meaning of the text in response to user queries, commonly referred to as concept searches. Queries, or concept searches, against a set of documents that have undergone LSI will return results that are conceptually similar in meaning to the search criteria even if the results don’t share a specific word or words with the search criteria.
Views: 397 The Audiopedia
Free LSI Keyword Search Tool For Latent Semantic Indexing
 
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Free LSI Keyword Search Tool For Latent Semantic Indexing https://www.youtube.com/watch?v=M4fCKJB6i7E Looking For A Good Keyword Tool? https://www.youtube.com/watch?v=spl0u0iMy0o https://www.youtube.com/watch?v=NdHogIxI0VU https://www.youtube.com/watch?v=ida_Vs3uNZI https://www.youtube.com/watch?v=D6MjLO-tUcQ More Information about Latent Semantic Indexing: Latent semantic analysis - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Latent_semantic_analysisWikipedia Latent semantic analysis - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Latent_semantic_analysis Wikipedia Jump to Benefits of LSI - Latent semantic indexing (LSI) is an indexing and retrieval method that uses a mathematical technique called singular value decomposition (SVD) to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text. ‎Overview · ‎Applications · ‎Implementation · ‎Limitations Latent Semantic Indexing - SEO Book www.seobook.com › LSI Latent semantic indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method examines the document collection as a whole, to see which other documents contain some of those same words. How LSI Works - SEO Book www.seobook.com › LSI We mentioned that latent semantic indexing looks at patterns of word distribution (specifically, word co-occurence) across a set of documents. Before we talk ... Latent semantic indexing - The Stanford Natural Language Processing ... nlp.stanford.edu/IR-book/html/.../latent-semantic-indexing-1.html Next: References and further reading Up: Matrix decompositions and latent ... This process is known as latent semantic indexing (generally abbreviated LSI). What Is Latent Semantic Indexing - Search Engine Journal https://www.s Latent semantic analysis - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Latent_semantic_analysis Wikipedia Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of ... ‎Overview · ‎Applications · ‎Implementation · ‎Limitations Semantic analysis (machine learning) - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Semantic_analysis_(machine_lear... Wikipedia In machine learning, semantic analysis of a corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Latent semantic analysis (sometimes latent semantic indexing), is a class of ... Probabilistic latent semantic analysis - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_ana... Wikipedia Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing is a statistical technique for the analysis of two-mode and ... Latent Semantic Indexing - SEO Book http://www.seobook.com › LSI Latent semantic indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method ... Latent Semantic Indexing http://c2.com/cgi/wiki?LatentSemanticIndexing Latent semantic indexing adds an important step to the document indexing process. In addition to record People who watched this video: https://youtu.be/M4fCKJB6i7E Also searched online for: Searches related to Latent Semantic Indexing latent semantic indexing tutorial latent semantic indexing seo latent semantic indexing tool latent semantic indexing example latent semantic indexing python latent semantic indexing r latent semantic indexing ppt latent semantic indexing java Don't forget to check out our YouTube Channel: https://www.youtube.com/user/oneclicklearning and click the link below to subscribe to our channel and get informed when we add new content: https://www.youtube.com/user/oneclicklearning -------------------------------------------- #latentsemanticindexingtutorial #latentsemanticindexingseo #latentsemanticindexingtool #latentsemanticindexingexample #latentsemanticindexingpython #latentsemanticindexingr #latentsemanticindexingppt #latentsemanticindexingjava --------------------------------------------
Views: 652 Chet Hastings
Machine Reading with Word Vectors (ft. Martin Jaggi)
 
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This video discusses how to represent words by vectors, as prescribed by word2vec. It features Martin Jaggi, Assistant Professor of the IC School at EPFL. https://people.epfl.ch/martin.jaggi Tomas Mikolov, Kai Chen, Greg Corrado and Jeffrey Dean (2013). Efficient Estimation of Word Representations in Vector Space. https://arxiv.org/pdf/1301.3781v3.pdf Omar Levy and Yoav Goldberg (2014). Neural Word Embedding as Implicit Matrix Factorization. https://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.pdf
Views: 15550 ZettaBytes, EPFL
Latent Semantic Indexing
 
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Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/B0Zl/ http://www.MasterNewMedia.org 'Going Natural' is a SEO educational video from Andy Jenkins and Brad Fallen. 'Going Natural' is a SEO educational video from Andy Jenkins and Brad Fallen. The presenters believe that if you're having trouble with SEO, it's not your fault, it's just that you haven't been given the right information (yet). The explanation of how you must use 'related terms' to succeed on Google is one of the most practical they have come across. The approach is called 'Latent Semantic Indexing' but as the guys say, you don't need to worry about that. Just follow their advice to get the most from Wordtracker's Lateral Search feature. This is just a 3-minute excerpt - see the whole video at: http://www.convertlinks.com/whitehatseo Help us caption & translate this video! http://amara.org/v/B0Zl/
Views: 14466 Robin Good
Applying Semantic Analyses to Content-based Recommendation and Document Clustering
 
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This talk will present the results of my research on feature generation techniques for unstructured data sources. We apply Probase, a Web-scale knowledge base developed by Microsoft Research Asia, which is generated from the Bing index, search query logs and other sources, to extract concepts from text. We compare the performance of features generated from Probase and two other forms of semantic analysis, Explicit Semantic Analysis using Wikipedia and Latent Dirichlet Allocation. We evaluate the semantic analysis techniques on two tasks, recommendation using Matchbox, which is a platform for probabilistic recommendations from Microsoft Research Cambridge, and clustering using K-Means.
Views: 637 Microsoft Research
What is LATENT SEMANTIC MAPPING? What does LATENT SEMANTIC MAPPING mean?
 
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What is LATENT SEMANTIC MAPPING? What does LATENT SEMANTIC MAPPING mean? LATENT SEMANTIC MAPPING meaning - LATENT SEMANTIC MAPPING definition - LATENT SEMANTIC MAPPING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Latent semantic mapping (LSM) is a data-driven framework to model globally meaningful relationships implicit in large volumes of (often textual) data. It is a generalization of latent semantic analysis. In information retrieval, LSA enables retrieval on the basis of conceptual content, instead of merely matching words between queries and documents. LSM was derived from earlier work on latent semantic analysis. There are 3 main characteristics of latent semantic analysis: Discrete entities, usually in the form of words and documents, are mapped onto continuous vectors, the mapping involves a form of global correlation pattern, and dimensionality reduction is an important aspect of the analysis process. These constitute generic properties, and have been identified as potentially useful in a variety of different contexts. This usefulness has encouraged great interest in LSM. The intended product of latent semantic mapping, is a data-driven framework for modeling relationships in large volumes of data. Mac OS X v10.5 and later includes a framework implementing latent semantic mapping.
Views: 171 The Audiopedia
Lecture 3 | GloVe: Global Vectors for Word Representation
 
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Lecture 3 introduces the GloVe model for training word vectors. Then it extends our discussion of word vectors (interchangeably called word embeddings) by seeing how they can be evaluated intrinsically and extrinsically. As we proceed, we discuss the example of word analogies as an intrinsic evaluation technique and how it can be used to tune word embedding techniques. We then discuss training model weights/parameters and word vectors for extrinsic tasks. Lastly we motivate artificial neural networks as a class of models for natural language processing tasks. Key phrases: Global Vectors for Word Representation (GloVe). Intrinsic and extrinsic evaluations. Effect of hyperparameters on analogy evaluation tasks. Correlation of human judgment with word vector distances. Dealing with ambiguity in word using contexts. Window classification. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://online.stanford.edu/
What is TOPIC MODEL? What does TOPIC MODEL mean? TOPIC MODEL meaning, definition & explanation
 
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What is TOPIC MODEL? What does TOPIC MODEL mean? TOPIC MODEL meaning - TOPIC MODEL definition - TOPIC MODEL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear equally in both. A document typically concerns multiple topics in different proportions; thus, in a document that is 10% about cats and 90% about dogs, there would probably be about 9 times more dog words than cat words. The "topics" produced by topic modeling techniques are clusters of similar words. A topic model captures this intuition in a mathematical framework, which allows examining a set of documents and discovering, based on the statistics of the words in each, what the topics might be and what each document's balance of topics is. Topic models are also referred to as probabilistic topic models, which refers to statistic algorithms for discovering the latent semantic structures of an extensive text body. In the age of information, the amount of the written material we encounter each day is simply beyond our processing capacity. Topic models can help to organize and offer insights for us to understand large collections of unstructured text bodies. Originally developed as a text-mining tool, topic models have been used to detect instructive structures in data such as genetic information, images, and networks. They also have applications in other fields such as bioinformatics. Topic models can include context information such as timestamps, authorship information or geographical coordinates associated with documents. Additionally, network information (such as social networks between authors) can be modelled. Approaches for temporal information include Block and Newman's determination the temporal dynamics of topics in the Pennsylvania Gazette during 1728–1800. Grif?ths & Steyvers use topic modeling on abstract from the journal PNAS to identify topics that rose or fell in popularity from 1991 to 2001. Nelson has been analyzing change in topics over time in the Richmond Times-Dispatch to understand social and political changes and continuities in Richmond during the American Civil War. Yang, Torget and Mihalcea applied topic modeling methods to newspapers from 1829–2008. Mimno used topic modelling with 24 journals on classical philology and archaeology spanning 150 years to look at how topics in the journals change over time and how the journals become more different or similar over time. Yin et al. introduced a topic model for geographically distributed documents, where document positions are explained by latent regions which are detected during inference. Chang and Blei included network information between linked documents in the relational topic model, which allows to model links between websites. The author-topic model by Rosen-Zvi et al. models the topics associated with authors of documents to improve the topic detection for documents with authorship information. In practice researchers attempt to fit appropriate model parameters to the data corpus using one of several heuristics for maximum likelihood fit. A recent survey by Blei describes this suite of algorithms. Several groups of researchers starting with Papadimitriou et al. have attempted to design algorithms with probable guarantees. Assuming that the data were actually generated by the model in question, they try to design algorithms that probably find the model that was used to create the data. Techniques used here include singular value decomposition (SVD) and the method of moments. In 2012 an algorithm based upon non-negative matrix factorization (NMF) was introduced that also generalizes to topic models with correlations among topics.
Views: 2224 The Audiopedia
What is SEMANTIC BOOTSTRAPPING? What does SEMANTIC BOOTSTRAPPING mean?
 
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What is SEMANTIC BOOTSTRAPPING? What does SEMANTIC BOOTSTRAPPING mean? SEMANTIC BOOTSTRAPPING meaning - SEMANTIC BOOTSTRAPPING definition - SEMANTIC BOOTSTRAPPING 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 Semantic bootstrapping is a linguistic theory of child language acquisition which proposes that children can acquire the syntax of a language by first learning and recognizing semantic elements and building upon, or bootstrapping from, that knowledge. This theory proposes that children, when acquiring words, will recognize that words label conceptual categories, such as objects or actions. Children will then use these semantic categories as a cue to the syntactic categories, such as nouns and verbs. Having identified particular words as belonging to a syntactic category, they will then look for other correlated properties of those categories, which will allow them to identify how nouns and verbs are expressed in their language. Additionally, children will use perceived conceptual relations, such as Agent of an event, to identify grammatical relations, such as Subject of a sentence. This knowledge, in turn, allows the learner to look for other correlated properties of those grammatical relations. This theory requires two critical assumptions to be true. First, it requires that children are able to perceive the meaning of words and sentences. It does not require that they do so by any particular method, but the child seeking to learn the language must somehow come to associate words with objects and actions in the world. Second, children must know that there is a strong correspondence between semantic categories and syntactic categories. The relationship between semantic and syntactic categories can then be used to iteratively create, test, and refine internal grammar rules until the child's understanding aligns with the language to which they are exposed, allowing for better categorization methods to be deduced as the child obtains more knowledge of the language.
Views: 251 The Audiopedia
Semantic Indexing of Unstructured Documents Using Taxonomies and Ontologies
 
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From August 7, 2013 Life Science and Healthcare organizations use RDF/SKOS/OWL based vocabularies, thesauri, taxonomies and ontologies to organize enterprise knowledge. There are many ways to use these technologies but one that is gaining momentum is to semantically index unstructured documents through ontologies and taxonomies. In this talk we will demonstrate two projects where we use a combination of SKOS/OWL based taxonomies and ontologies, entity extraction, fast text search, and Graph Search to create a semantic retrieval engine for unstructured documents. The first project organized all science related artifacts in Malaysia through a taxonomy of scientific concepts. It indexed all papers, people, patents, organizations, research grants, etc, etc, and created a user friendly taxonomy browser to quickly find relevant information, such as, "How much research funding has been spent on a certain subject over the last 3 years and how many patents resulted from this research". The second project discusses a large socio-economic content publisher that has millions of documents in at least eight different languages. Reusing documents for new publications was a painful process given that keyword search and LSI techniques were mostly inadequate to find the document fragments that were needed. Fortunately the organization had begun developing a large SKOS based taxonomy that linked common concepts to various preferential and alternative labels in many languages. We used this taxonomy to index millions of document fragments and we'll show how we can perform relevancy search and retrieval based on taxonomic concepts.
Views: 5757 AllegroGraph
What is AUTOMATED ESSAY SCORING? What does AUTOMATED ESSAY SCORING mean?
 
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What is AUTOMATED ESSAY SCORING? What does AUTOMATED ESSAY SCORING mean? AUTOMATED ESSAY SCORING meaning - AUTOMATED ESSAY SCORING definition - AUTOMATED ESSAY SCORING 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 Automated essay scoring (AES) is the use of specialized computer programs to assign grades to essays written in an educational setting. It is a method of educational assessment and an application of natural language processing. Its objective is to classify a large set of textual entities into a small number of discrete categories, corresponding to the possible grades—for example, the numbers 1 to 6. Therefore, it can be considered a problem of statistical classification. Several factors have contributed to a growing interest in AES. Among them are cost, accountability, standards, and technology. Rising education costs have led to pressure to hold the educational system accountable for results by imposing standards. The advance of information technology promises to measure educational achievement at reduced cost. The use of AES for high-stakes testing in education has generated significant backlash, with opponents pointing to research that computers cannot yet grade writing accurately and arguing that their use for such purposes promotes teaching writing in reductive ways (i.e. teaching to the test). From the beginning, the basic procedure for AES has been to start with a training set of essays that have been carefully hand-scored. The program evaluates surface features of the text of each essay, such as the total number of words, the number of subordinate clauses, or the ratio of uppercase to lowercase letters - quantities that can be measured without any human insight. It then constructs a mathematical model that relates these quantities to the scores that the essays received. The same model is then applied to calculate scores of new essays. Recently, one such mathematical model was created by Isaac Persing and Vincent Ng. which not only evaluates essays on the above features, but also on their argument strength. It evaluates various features of the essay, such as the agreement level of the author and reasons for the same, adherence to the prompt's topic, locations of argument components (major claim, claim, premise), errors in the arguments, cohesion in the arguments among various other features. In contrast to the other models mentioned above, this model is closer in duplicating human insight while grading essays. The various AES programs differ in what specific surface features they measure, how many essays are required in the training set, and most significantly in the mathematical modeling technique. Early attempts used linear regression. Modern systems may use linear regression or other machine learning techniques often in combination with other statistical techniques such as latent semantic analysis and Bayesian inference. Any method of assessment must be judged on validity, fairness, and reliability. An instrument is valid if it actually measures the trait that it purports to measure. It is fair if it does not, in effect, penalize or privilege any one class of people. It is reliable if its outcome is repeatable, even when irrelevant external factors are altered. Before computers entered the picture, high-stakes essays were typically given scores by two trained human raters. If the scores differed by more than one point, a third, more experienced rater would settle the disagreement. In this system, there is an easy way to measure reliability: by inter-rater agreement. If raters do not consistently agree within one point, their training may be at fault. If a rater consistently disagrees with whichever other raters look at the same essays, that rater probably needs more training. Various statistics have been proposed to measure inter-rater agreement. Among them are percent agreement, Scott's ?, Cohen's ?, Krippendorf's ?, Pearson's correlation coefficient r, Spearman's rank correlation coefficient ?, and Lin's concordance correlation coefficient. Percent agreement is a simple statistic applicable to grading scales with scores from 1 to n, where usually 4 ? n ? 6. It is reported as three figures, each a percent of the total number of essays scored: exact agreement (the two raters gave the essay the same score), adjacent agreement (the raters differed by at most one point; this includes exact agreement), and extreme disagreement (the raters differed by more than two points). Expert human graders were found to achieve exact agreement on 53% to 81% of all essays, and adjacent agreement on 97% to 100%.....
Views: 298 The Audiopedia
Inverse Problems Lecture 3/2017: deconvolution with truncated SVD, part 2/2
 
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We use truncated Singular Value Decomposition for implementing noise-robust deconvolution. This is a screen capture of Matlab programming by Samuli Siltanen while teaching his course 'Inverse Problems' at the University of Helsinki. The lecture was given at January 25, 2017. Course website: http://wiki.helsinki.fi/display/maths... Here is the final code: % Simple illustration of deconvolution in 1D, the method we use is % Truncated Singular Value Decomposition % % Samuli Siltanen January 2017 % Parameters for controlling the plot appearance fsize = 16; lwidth = 2; %% Simulate the measurement % Build a Point Spread Function (PSF) M = 17; psf = ones(1,2*M+1); % This makes sure psf has a unique centre psf = psf/sum(psf); % Normalization of the psf % Construct "unknown signal" f N = 400; x = linspace(0,1,N); % f = zeros(N,1); % f(1:(end/2)) = 1; f = sin(2*pi*x); f = f(:); % Force vector f to be vertical % Construct the convolution matrix A = convmtx(psf,N); A = A(:,(M+1):(end-M)); % Simulate the "measurement" m = A*f; % Simulate "noisy measurement" sigma = .1; mn = A*f + sigma*randn(N,1); %% Compute truncated SVD solution % Determine the SVD of matrix A [U,D,V] = svd(A); svals = diag(D); % Compute reconstruction r_alpha = 15; Dp_alpha = zeros(size(A)); for iii = 1:r_alpha Dp_alpha(iii,iii) = 1/svals(iii); end f0 = V*Dp_alpha*(U.')*m; fn = V*Dp_alpha*(U.')*mn; %% plots % Take a look at the matrix and its singular values figure(2) clf subplot(1,2,1) spy(A) title('Nonzero elements in A','fontsize',fsize) subplot(1,2,2) semilogy(svals,'k.') hold on semilogy(svals(1:r_alpha),'r.') title('Singular values of A (log plot)','fontsize',fsize) % Take a look at few first singular vectors figure(3) clf subplot(5,1,1) plot(V(:,1)) title('Singular vector 1','fontsize',fsize) subplot(5,1,2) plot(V(:,2)) title('Singular vector 2','fontsize',fsize) subplot(5,1,3) plot(V(:,3)) title('Singular vector 3','fontsize',fsize) subplot(5,1,4) plot(V(:,4)) title('Singular vector 4','fontsize',fsize) subplot(5,1,5) plot(V(:,5)) title('Singular vector 5','fontsize',fsize) % Take a look figure(1) clf subplot(3,1,1) plot(f,'k','linewidth',lwidth) hold on plot(mn,'r','linewidth',lwidth) set(gca,'ytick',[0 max(f)],'fontsize',fsize) subplot(3,1,2) plot(f,'k','linewidth',lwidth) hold on plot(f0,'b','linewidth',lwidth) set(gca,'ytick',[0 max(f)],'fontsize',fsize) subplot(3,1,3) plot(f,'k','linewidth',lwidth) hold on plot(fn,'b','linewidth',lwidth) set(gca,'ytick',[0 max(f)],'fontsize',fsize)
Views: 104 UH Inversion
Inverse Problems Lecture 3/2017: deconvolution with truncated SVD, part 2/2
 
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We use truncated Singular Value Decomposition for implementing noise-robust deconvolution. This is a continuation of https://www.youtube.com/watch?v=lCokUeI9aCE&t=117s and https://www.youtube.com/watch?v=yDfMc6-PXmE This is screen capture of Matlab programming I did when teaching my course Inverse Problems at University of Helsinki. The lecture was given at January 25, 2017. Course website: http://wiki.helsinki.fi/display/mathstatKurssit/Inverse+problems%2C+spring+2017 Here is the final code: % Simple illustration of deconvolution in 1D, the method we use is % Truncated Singular Value Decomposition % % Samuli Siltanen January 2017 % Parameters for controlling the plot appearance fsize = 16; lwidth = 2; %% Simulate the measurement % Build a Point Spread Function (PSF) M = 17; psf = ones(1,2*M+1); % This makes sure psf has a unique centre psf = psf/sum(psf); % Normalization of the psf % Construct "unknown signal" f N = 400; x = linspace(0,1,N); % f = zeros(N,1); % f(1:(end/2)) = 1; f = sin(2*pi*x); f = f(:); % Force vector f to be vertical % Construct the convolution matrix A = convmtx(psf,N); A = A(:,(M+1):(end-M)); % Simulate the "measurement" m = A*f; % Simulate "noisy measurement" sigma = .1; mn = A*f + sigma*randn(N,1); %% Compute truncated SVD solution % Determine the SVD of matrix A [U,D,V] = svd(A); svals = diag(D); % Compute reconstruction r_alpha = 15; Dp_alpha = zeros(size(A)); for iii = 1:r_alpha Dp_alpha(iii,iii) = 1/svals(iii); end f0 = V*Dp_alpha*(U.')*m; fn = V*Dp_alpha*(U.')*mn; %% plots % Take a look at the matrix and its singular values figure(2) clf subplot(1,2,1) spy(A) title('Nonzero elements in A','fontsize',fsize) subplot(1,2,2) semilogy(svals,'k.') hold on semilogy(svals(1:r_alpha),'r.') title('Singular values of A (log plot)','fontsize',fsize) % Take a look at few first singular vectors figure(3) clf subplot(5,1,1) plot(V(:,1)) title('Singular vector 1','fontsize',fsize) subplot(5,1,2) plot(V(:,2)) title('Singular vector 2','fontsize',fsize) subplot(5,1,3) plot(V(:,3)) title('Singular vector 3','fontsize',fsize) subplot(5,1,4) plot(V(:,4)) title('Singular vector 4','fontsize',fsize) subplot(5,1,5) plot(V(:,5)) title('Singular vector 5','fontsize',fsize) % Take a look figure(1) clf subplot(3,1,1) plot(f,'k','linewidth',lwidth) hold on plot(mn,'r','linewidth',lwidth) set(gca,'ytick',[0 max(f)],'fontsize',fsize) subplot(3,1,2) plot(f,'k','linewidth',lwidth) hold on plot(f0,'b','linewidth',lwidth) set(gca,'ytick',[0 max(f)],'fontsize',fsize) subplot(3,1,3) plot(f,'k','linewidth',lwidth) hold on plot(fn,'b','linewidth',lwidth) set(gca,'ytick',[0 max(f)],'fontsize',fsize)
Views: 392 Samuli Siltanen
TopicNets: Visual Analysis of Large Text Corpora with Topic Modeling
 
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This video demonstrates the features of the TopicNets system with some concrete examples.
Views: 617 brynjargr
Jimmie Åkesson - Snart är det val
 
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Vårt land mår inte bra. Socialdemokraterna och Moderaterna gör allt vad de kan för att släta över de problem som deras egna politik skapat för vårt samhälle. Deras ansvarslösa agerande, deras ovilja att utvärdera resultaten av deras politik, och framförallt deras lögner, har skapat det samhälle som vi alla tvingas leva i idag. Just nu pågår förberedelserna inför vår största valkampanj någonsin. Om mindre än ett år är det val!
Views: 1010565 Sverigedemokraterna
Sniper Ghost Warrior 3 How to unlock all Weapons (Primary / Secondary / Sidearms) Text-Picture Guide
 
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Sniper Ghost Warrior 3 How to unlock all Weapons (Primary / Secondary / Sidearms) Text-Picture Guide All gameplay recorded with - http://e.lga.to/360gametv This guide shows you how to unlock all weapons in Sniper Ghost Warrior 3 as a Text-Picture Guide. Primary Weapons: XM-2015 (Story) Archer T-80 (Story) Knight 110 (Story) Stronskiy 98 (Story) BMT 03 (Story) ACC 50 (Story) Rook SS-97 (Story) Turret M96 (Story) Ballance S-AR Metal (Side mission Opium Wars) ES-25 (Side mission Loose Ends) Brezatelya - https://www.youtube.com/watch?v=0eevTJo8Uxw Dragoon SVD - https://www.youtube.com/watch?v=qwIzx0XSQMs Vykop - https://www.youtube.com/watch?v=K5O0FP_Oy48 Shipunov K96 - https://www.youtube.com/watch?v=pzbXnY16Cpg Secondary Weapons: Archer AR15 (Story) AKA-47 (Story) Giovanni M4 (Story) Herstal (Story) KT-R (Story) Origin-12 (Story) Takedown Recurve Bow - https://www.youtube.com/watch?v=za5Id9Ow-AE Galeforce Long - https://www.youtube.com/watch?v=08VIyGAIEzU FM-3000 UM and OFM 500 - https://www.youtube.com/watch?v=m-BQVFL0CWs Sidearms: M1984 Pistol (Story) Herrvalt 99 (Story) Garett M9 (Story) Wagram 21 (Story) SLP .45 (Story) M1984 Pistol Rail (Side mission Rotki Lions) Bull 686 (Side mission Burning Bridge) SP M23 (Side mission Loose Ends) Sawn-off Shotgun (Side mission Opium Wars) MP-40 Grad - https://www.youtube.com/watch?v=tsR2EVW503Q Sniper Ghost Warrior 3 Weapon Locations https://www.youtube.com/playlist?list=PLuGZAFj5iqHfUqC3CUXQfUVdUeacWiQbI Sniper Ghost Warrior 3 Weapon Skins https://www.youtube.com/playlist?list=PLuGZAFj5iqHcmE-ewQHiw5mufXtr13sYV Sniper Ghost Warrior 3 All Guides https://www.youtube.com/playlist?list=PLuGZAFj5iqHeeRu1y0vu4zMqZsle03EgW Support / Donate Paypal: http://bit.ly/1JySiRV Patreon: https://www.patreon.com/360gametv Visit my sites / partner Website: www.360gametv.com Partner: http://e.lga.to/360gametv Twitter: http://twitter.com/360GameTV Subscribe: http://www.youtube.com/subscription_center?add_user=360GameTV Achievements / Trophies: -
Views: 87929 360GameTV
Sniper Ghost Warrior 3 All Weapons Showcase (Primary / Secondary / Sidearm)
 
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Sniper Ghost Warrior 3 All Weapons Showcase (Primary / Secondary / Sidearm) All gameplay recorded with - http://e.lga.to/360gametv This guide shows you all currently available Weapons in Sniper Ghost Warrior 3 as Showcase. Primary Weapons: 00:08 - 01 - Ballance S-AR Metal 00:39 - 02 - XM-2015 01:15 - 03 - Stronskiy 98 01:52 - 04 - Brezatelya 02:31 - 05 - Dragoon SVD 03:05 - 06 - Knight 110 03:40 - 07 - ES-25 04:24 - 08 - Vykop 05:01 - 09 - Archer T-80 05:36 - 10 - BMT 03 06:22 - 11 - Rook SS-97 06:51 - 12 - ACC 50 07:38 - 13 - Shipunov K96 08:11 - 14 - Turret M96 Secondary Weapons: 08:45 - 15 - Archer AR15 09:13 - 16 - AKA-47 09:38 - 17 - Herstal 10:04 - 18 - KT-R 10:30 - 19 - Galeforce Long 10:56 - 20 - FM-3000 UM 11:32 - 21 - OFM 500 12:11 - 22 - Giovanni M4 12:52 - 23 - Origin-12 13:30 - 24 - Takedown Recurve Bow Sidearms: 14:11 - 25 - M1984 Pistol 14:34 - 26 - M1984 Pistol Rail 14:57 - 27 - Garett M9 15:24 - 28 - Herrvalt 99 15:51 - 29 - Wagram 21 16:17 - 30 - Bull 686 16:47 - 31 - SLP .45 17:11 - 32 - SP M23 17:35 - 33 - MP-40 Grad 18:01 - 34 - Sawn-off Shotgun Sniper Ghost Warrior 3 Weapon Locations https://www.youtube.com/playlist?list=PLuGZAFj5iqHfUqC3CUXQfUVdUeacWiQbI Sniper Ghost Warrior 3 Weapon Skins https://www.youtube.com/playlist?list=PLuGZAFj5iqHcmE-ewQHiw5mufXtr13sYV Sniper Ghost Warrior 3 All Guides https://www.youtube.com/playlist?list=PLuGZAFj5iqHeeRu1y0vu4zMqZsle03EgW Support / Donate Paypal: http://bit.ly/1JySiRV Patreon: https://www.patreon.com/360gametv Visit my sites / partner Website: www.360gametv.com Partner: http://e.lga.to/360gametv Twitter: http://twitter.com/360GameTV Subscribe: http://www.youtube.com/subscription_center?add_user=360GameTV Achievements / Trophies: -
Views: 38175 360GameTV
What Is In LSA?
 
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When it comes to lsa, a little goes long way bettering your health. Lsa or lsa is an initialism standing for in law[edit]. Lsa stands for a blend of ground linseeds (flax seeds), sunflower seeds, and almonds feb 11, 2017 lsa linseeds, seeds. Lsa stands for linseeds, sunflower seeds, and almonds. It was later discovered to be natural, and is known as a you too can experience the benefits of lsa, even without having go through liver cleansing diet lsa ground mixture linseeds, sunflower seeds almonds. Science and the link state advertisement (lsa) is a basic communication means of ospf routing protocol for internet (ip). Ten foods you should eat this year body soulthe benefits of lsa energy fields health foodhealthy food guide. List of cfr sections affected, a list new revisions to the us code federal regulations. Lsa meal nutrition information eat this muchwhat is lsa? Youtubelink state advertisement wikipedia. Latent semantic analysis (lsa) is a theory and method for extracting representing the jun 29, 2014 lsa was first produced by dr sandra cabot discussed in her book liver cleansing diet featured below amazon section 15, 2015 this week our classic foodies will be crumbing chicken breasts (ground linseeds, sunflower seeds, almonds) which yummy alternative to breadcrumbs. A packet of lsa is a combination these three pre mixed seeds, which have been ground down to fine or coarse form jan 7, 2012 lsa, made from linseeds, sunflower seeds and almonds, an easy, extremely versatile way add extra nutrients meals. Flax seed, sunflower almond mixture flax. Mix 1 2 tablespoons of lsa to meals or which is available for downloading on the group papers page. Wanting to know what is lsa? Then find out here exactly it is, the benefits of lsa as well how use. Great for vegetarian, vegan and meat eater as it feb 13, 2012. The what are lsa foods and why should i eat them? Beautyheaven. Lsa, a combination of ground linseeds, sunflower seeds and almonds, has become popular addition to many diets over jan 29, 2017 enjoy the benefits nuts in an healthy lsa mixture. What does home lsa in bsnl means? Quora. It communicates the router's local lsa(local service area) is area where you won't come under roaming. Some different depending on the dosage lsa can produce hallucinations similar to a small of lsd or shoorms mushrooms that contain psilcybin, psilocin, Flax seed, sunflower almond mixture flax. Linseed, sunflower view the nutrition for lsa meal, including calories, carbs, fat, protein, cholesterol, and more (linseed (50. Lsa is rich lsa, d lysergic acid amide or ergine, a product during the creation of lsd, and psychoactive in itself. Lsa is readily available in health food stores and can now be found most supermarkets nov 20, 2012 read about lsa, what it how you incorporate into your diet. The lsa lowdown lose baby weightwhat is lsa? Latent semantic analysis. Its versatile nature lends itself to almost anything. Lsa) mix lsa linseed, sunflower, almond benefits of nu
Views: 76 Question Bag
SCP-701 The Hanged King's Tragedy | Euclid | Humanoid scp
 
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SCP-701, The Hanged King's Tragedy, is a Caroline-era revenge tragedy in five acts. Performances of the play are associated with sudden psychotic and suicidal behavior among both observers and participants, as well as the manifestation of a mysterious figure, classified as SCP-701-1. Historical estimates place the number of lives claimed by the play at between █████ and █████ over the past three hundred years. Read along with me! ♣Read along: http://scp-wiki.wikidot.com/scp-701 http://scp-wiki.wikidot.com/scp7011640b1 http://scp-wiki.wikidot.com/incident-report-scp70119971 Help me out on Patreon! ▼Patreon▼ https://www.patreon.com/EastsideShowSCP Join me on Facebook and Twitter! ♣Facebook: https://www.facebook.com/EastsideShowscp ♣Twitter: https://twitter.com/Eastsideshowscp "Long note One" Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 License http://creativecommons.org/licenses/by/3.0/ Other ♣Music by Kevin MacLeod: http://incompetech.com/ ♥Be sure to like, comment, share, and subscribe!♥ -~-~~-~~~-~~-~- Please watch: "SCP-001 The Broken God | Object Class: Maksur | TwistedGears-Kaktus Proposal" https://www.youtube.com/watch?v=bpgIIyeIODg -~-~~-~~~-~~-~-
Views: 33645 The Eastside Show
Top 15 Mysteries Solved by 4Chan
 
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► Narrated by Chills: http://bit.ly/ChillsYouTube Follow Top15s on Twitter: http://bit.ly/Top15sTwitter Follow Chills on Instagram: http://bit.ly/ChillsInstagram Follow Chills on Twitter: http://bit.ly/ChillsTwitter Subscribe to Chills on Reddit: http://bitly.com/ChillsReddit In this top 15 list, we were looking at unsolved mysteries that were solved and explained by the popular website, 4Chan. These entries range from strange to creepy. Enjoy our analysis of them. Written by: jessicaholom Edited by: Huba Áron Csapó Sources: https://pastebin.com/c4x7BwE8 Music: Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0 Update (Jan. 28, 2018): This is the video has become the "Burger King Foot Lettuce" meme!
Views: 5368917 Top15s
Stealth Sniper 2 - Full Game Walkthrough (All 1-4 Missions)
 
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More Games: https://goo.gl/I7XlST http://m.108game.com/
Views: 106826 108GAME
Algebraic Techniques for Multilingual Document Clustering
 
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Google Tech Talks January 25, 2011 Presented by Brett Bader. ABSTRACT Multilingual documents pose difficulties for clustering by topic, not least because translating everything to a common language is not feasible with a large corpus or many languages. This presentation will address those difficulties with a variety of novel algebraic methods for efficiently clustering multilingual text documents, and brieflyillustrate their implementation via high performance computing. The methods use a multilingual parallel corpus as a 'Rosetta Stone' from which algorithmic variations (including statistical morphological analysis to bypass the need for stemming) of Latent Semantic Analysis (LSA) are able to learn concepts in term space. New documents are projected into this concept space to produce language-independent feature vectors for subsequent use in similarity calculations or machine learning applications. Our experiments show that the new methods have better performance than LSA, and possess some interesting and counter-intuitive properties. Brett W. Bader received his Ph.D. in computer science from the University of Colorado at Boulder, studying higher-order methods for optimization and solving systems of nonlinear equations. In 2003, Brett received the John von Neumann Research Fellowship at Sandia National Laboratories, where he now develops algorithms for multi-way data analysis and machine learning for informatics applications in networks and text.
Views: 3387 GoogleTechTalks
Mod-01 Lec-32 Word Sense Disambiguation
 
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Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 2298 nptelhrd
SCP-1139 The Broken Tongue | Object Class: Euclid | Church of the Broken God SCP
 
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SCP-1139 The Broken Tongue - Upon application of electricity a direct current, the object affects all individuals within a given radius through unknown means. Any person within the radius of effect begins speaking and writing a new language, though they apparently believe they are speaking their native tongue. Subjects lose the ability to speak or comprehend any prior known language(s). Linguistic analysis indicates that the new languages are fully formed languages, but all attempts at translation have been met with complete failure. Attempts at translation continue. Subjects have proven incapable of learning or re-learning any real world language after exposure to SCP-1139. Class AA amnesiacs successfully counteract the effect, though subjects are thereafter of little use to the Foundation, and Class AA amnesiacs are not advised in future testing. See Experiment Log 1139-1. Read along with me! ♣Read along: http://www.scp-wiki.net/scp-1139 "Lost Frontier" Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 License http://creativecommons.org/licenses/by/3.0/ Help me out on Patreon! ▼Patreon▼ https://www.patreon.com/EastsideShowSCP Join me on Facebook and Twitter! ♣Facebook: https://www.facebook.com/EastsideShowscp ♣Twitter: https://twitter.com/Eastsideshowscp Other ♣Music by Kevin MacLeod: http://incompetech.com/ ♥Be sure to like, comment, share, and subscribe!♥ -~-~~-~~~-~~-~- Please watch: "SCP-001 The Broken God | Object Class: Maksur | TwistedGears-Kaktus Proposal" https://www.youtube.com/watch?v=bpgIIyeIODg -~-~~-~~~-~~-~-
Views: 42457 The Eastside Show
Mod-01 Lec-26 NLP and IR: How NLP has used IR, Toward Latent Semantic
 
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Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 1601 nptelhrd
Mod-01 Lec-24 IR Models: Boolean Vector
 
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Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 5285 nptelhrd
Topic mining with LDA and Kmeans and interactive clustering in Python
 
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Topic mining with LDA and Kmeans and interactive clustering in Python
Views: 198 OneLine News
class 8 chapter 1 science  NCERT crop management
 
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class 8 chapter 1 science NCERT crop management
Views: 29046 Ncert Tutorial
SCP-2161 Blank Space | Object Class: Euclid | Virus / Self-replicating SCP
 
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SCP-2161-1 is a collection of approximately 85 million pages of self-replicating A4 paper, the majority of which are blank. A small proportion of pages contain letters, figures or other markings, suggesting that SCP-2161-1 originally formed a single text. Read along with me! ♣Read along: http://www.scp-wiki.net/scp-2161 Help me out on Patreon! ▼Patreon▼ https://www.patreon.com/EastsideShowSCP Join me on Facebook and Twitter! ♣Facebook: https://www.facebook.com/EastsideShowscp ♣Twitter: https://twitter.com/Eastsideshowscp "Long note One" Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 License http://creativecommons.org/licenses/by/3.0/ Other ♣Music by Kevin MacLeod: http://incompetech.com/ ♥Be sure to like, comment, share, and subscribe!♥ #scp #eastsideshowscp -~-~~-~~~-~~-~- Please watch: "SCP-001 The Broken God | Object Class: Maksur | TwistedGears-Kaktus Proposal" https://www.youtube.com/watch?v=bpgIIyeIODg -~-~~-~~~-~~-~-
Views: 12736 The Eastside Show
Tomorrow belongs to me   Cabaret
 
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A famous song from Cabaret movie (1972) with Liza Minelli, Joel Gray and Michael York. The rise of Nazi-party (NSDAP) in early 1930s in Germany is shown very good in the movie.
Views: 2224037 Peter Krasnopyorov
Gran Turismo Sport vs Assetto Corsa vs Forza 7 - Shelby Cobra
 
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Gran Turismo Sport vs Assetto Corsa vs Forza 7 - Shelby Cobra http://www.gamexpectations.se Like us on Facebook: http://www.facebook.com/gamefreaks.se Or Follow us on Twitter: http://www.Twitter.com/chris82swe http://www.Gamexpectations.tumblr.com
Views: 455 GameXpectations
WEAR RATE FORMULA GRINDING MEDIA IN SAG MILL
 
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More Details : http://www.pakistancrushers.com/contact.php Read Environmental Engineering Dictionary and … Library of Congress Cataloging-in-Publication Data. Pankratz, Tom M. Environmental engineering dictionary and directory / Thomas M. Pankratz. p.GNP Rly, Inc. The springtime and summer are the time of year when the social calendar lives and also well with the stock stacking of one invitation after another.Fashion - Women''s Wear Daily WWD.com is the authority for news and trends in the worlds of fashion, beauty and retail. Featuring daily headlines and breaking news from all Women''s Wear Daily(An English-Chinese-Japanese … MSC,。 == Main Ship Equipments Equipment Types Main Marine Manufacturers Ship Spare Parts, …Corrosion in Potable Water Systems: Final Report ----- SumX No: C79-014 FINAL REPORT CORROSION IN POTABLE WATER SYSTEMS Contract No. 68-01-5834 Prepared by: David2060 Rod Mill Rsvd - Grinding of materials in a tumbling mill with the presence of metallic balls or other media Lorain Rolled Steel Mill Liners dates back to the late 1800’s.Defamer - Hollywood News and Gossip Hollywood News and Gossip Before Kristin Cavallari was writing book about heels and how to balance on them, she dated a famous actor: Nick Zano, former casteCFR — Code of Federal Regulations The Code of Federal Regulations (CFR) annual edition is the codification of the general and permanent rules published in the Federal Register by the departments andCENTRIFUGAL PUMPS Design Application … Type to search for People, Research Interests and Universities. Searching CENTRIFUGAL PUMPS Design Application Second EditionThe Real-Time Contact Center: Strategies, Tactics, … The Real-Time Contact Center: Strategies, Tactics, and Technologies for Building a Profitable Service and Sales Operation [Donna Fluss] on . *FREE* …SAG Mills - Mining Technology TechnoMine By Greg Fenrick. SAG is an acronym for Semi-Autogenous Grinding, which means that it utilizes steel balls in addition to large rocks for grinding.Job Search United States Job Search Smarter. … Job search by Incruit. All United States jobs. Search jobs from job boards, associations and company websites on us.incruit.com Job Search Smarter. GUNSUL 135 hook 180 hook 3-hinged arch 3 90 hook AASHO road test AASHTO AASHTOIsaMill - Wikipedia, the free encyclopedia While most grinding in the mineral industry is achieved using devices containing a steel grinding medium, the IsaMill uses inert grinding media such as silica sandFull text of List of proprietary substances and … Search the history of over 456 billion pages on the Internet. Featured All Texts This Just In Smithsonian Libraries FEDLINK (US) Genealogy LincolnAir Pollution Engineering Manual - EPA ap40 air pollution engineering manual air pollution control district county of los angeles compiled and edited byAll Automation catalogues and technical … Search Automation company''s catalogues and technical brochuresChina Abrasive, Abrasive Manufacturers … Coated Abrasive Flap Disc (VSM ZK713X, ZK765X): 1. Descriptions: Choosing VSM raw materials to make the flap discs according to your sizes, High Stock Grinding, LongFull product list - Henkel BONDERITE C-MC 3000 MAINTENANCE CLEANER is a water based, phosphate free, cleaning product designed for the exteriors of trains, cars, lorries, tarpaulin covers andGlossary - The Foundation Expert The … A. Abrasive. Media used to inscribe modern monumental works. May be composed of aluminum oxide, silicon carbine, steel shot, etc; Sand or powdered pumice stone, …SAG Mill Success—Start with the Basics Focusing on Installation With all the tools available for optimizing SAG mill performance, it might be possible to overlook what could be the most important elementBall mill - Wikipedia, the free encyclopedia A ball mill is a type of grinder used to grind materials into extremely fine powder for use in mineral dressing processes, paints, pyrotechnics, ceramics andChirbit - Record, Upload and Share Audio Easily - … Share audio on Twitter, Facebook, or Tumblr. Embed your audio or voice anywhere online. Upload mp3, wav, aiff or any other format via web or smartphone.108GAME - Play Free Online Games Free Online Games at 108GAME.com. Awesome action games, puzzle games, adventure games, multiplayer games, skill games best action games.Mechanical Engineers Data Handbook - SlideShare Transcript. 1. 1''1 I. L T AI- - - JAMES CARVll! 2. Mechanical Engineer’s Data HandbookMinera
FanBox Video Earnings
 
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You dont have to write nothing. all you do is find interesting blogs on the web that you think others will real or view. thats it waaalaaaa. u make money . the more someone views the post or blog you make money, copy and paste a blog or post you find on the web and paste it on fanbox. share it and waalaaa u make money. look at my articles, posts, blogs. there are picture blogs u can make money also. find 20 pictures and make a post. create a free article on the pictures and waalaaa. hope this helped http://www.fanboxrocks.info/ Hi there Fan, pm me for tips ok hun. start posting & createing post using poems they really get more hits, view an ratings. Also rate postings daily for points for promoting your own articles an blogs. Here's where you rate postings, bookmark it: http://blogs.fanbox.com/GenieGoals.aspx?mode=candr&source=geniewarmup Everytime you get a little extra money in your matured area to cash out. Dont cash out. Use that to promote your articles and blogs for the next months payout. Here is my profile stuff: http://posts.fanbox.com/m46n4 And heres a Fanbox Earning Plan that might help you: http://posts.fanbox.com/m56n4 Keep posting and work daily rating & commenting on others postings. It all makes you money. you dont have to pay for anything, its just posting pictures and copying and pasteing blog, and yes you make money. Fanbox Rocks was here to Say You Rock! Keep Up The Articles and start Posting Have a swell day Friend ty hun
Views: 37315 skyewardz