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Zack Witten: Extracting Structured Data from Legal Documents | PyData LA 2018
 
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PyData LA 2018 You’ll learn how to take a never-before-seen legal document, like a contract or a convertible note, and use machine learning to “read” the document and answer questions like “Who’s the investor” and “What interest rate did the parties agree to?” --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 911 PyData
text mining, web mining and sentiment analysis
 
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text mining, web mining
Views: 1632 Kakoli Bandyopadhyay
Informatica Big Data Edition: Social Data and Log File Processing on Hadoop
 
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Discover valuable customer insights from social data and web log clickstream analysis. Informatica PowerCenter Big Data Edition can process massive amounts of social data and log files on Hadoop.
Views: 3577 Informatica Support
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 78463 edureka!
Processing Audit log files with Hadoop and Hive
 
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This is an example of how you can use Hive to analyze audit logs. It is an example of how Hive excels at handling unstructured data.
Views: 1890 David Graesser
Data Analytic Experts - Harmonic
 
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Welcome to Harmonic Analytics. We are experts in business analytics and software development. We treat and interpret data to support smart decision making. Harmonic Data Analytics in Action The information our analysis unlocks gives you the knowledge you need to: increase efficiency, reduce costs and plan with confidence. We help clients across industry sectors to optimise business performance, forecast, manage their assets and improve return on investment, typically saving tens of millions per annum. Our highly qualified and experienced people are distinguished by their deep analytical ability and technical expertise combined with hands on commercial experience. Harmonic also has over 50 collective years of experience using the statistical language R. At Harmonic we pride ourselves on being diverse, vibrant and fun to work with. It's simply better together! ---- Data Analytic Experts - Harmonic is produced for Harmonic - Home - http://www.harmonic.co.nz Harmonic Analytics is a data science company. We provide custom business analytics, R Training and software tools for companies worldwide. Video is starring: - Rachel Prendergast - Communications Manager - Harmonic - http://www.harmonic.co.nz Related Videos: http://youtu.be/ckDVVeXhF8I Video Production by: 90 Seconds NZ - Online Video Production & Video Marketing - Auckland, NZ - http://90seconds.tv 90 Seconds is the Best Video Production company in New Zealand. We have produced over 3,000 online videos in Auckland, Wellington and Nationwide, making 90 Seconds the best online video marketers in NZ. As a leading Video Producer we enable any small business or corporate to get high quality, low cost, fast turn around, hassle-free videos for websites. 90 Seconds does Corporate Video Production and Video Marketing for New Zealands biggest brands and NZ Government agencies. We are also the NZ Small Business Video marketing experts and are a leading Tourism Video Production Company in NZ. 90 Seconds has a unique cloud video production platform that supports the entire production process enabling you to login and manage your video production online with 90 Seconds and collaborate with the 90 Seconds team and a crowd source community of approved video freelancers on the platform. 90 Seconds are a global Video Production company which started in NZ with bases in London, UK and Auckland, New Zealand, we also operate remotely in Australia, Singapore, Europe. We're a passionate crew and have built the business with a vision for changing the way media is produced and delivered globally, lead by entrepreneur Tim Norton
Views: 808 Harmonic Analytics
Predicting Users Behaviors Using Web Mining
 
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ChennaiSunday Systems Pvt.Ltd We are ready to provide guidance to successfully complete your projects and also download the abstract, base paper from our website IEEE 2014 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=1&catName=IEEE_2014-2015_Java_Projects IEEE 2014 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=20&catName=IEEE_2014-2015_DotNet_Projects Output Videos: https://www.youtube.com/channel/UCCpF34pmRlZbAsbkareU8_g/videos IEEE 2013 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=2&catName=IEEE_2013-2014_Java_Projects IEEE 2013 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=3&catName=IEEE_2013-2014_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UCpo4sL0gR8MFTOwGBCDqeFQ/videos IEEE 2012 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=26&catName=IEEE_2012-2013_Java_Projects Output Videos: https://www.youtube.com/user/siva6351/videos IEEE 2012 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=28&catName=IEEE_2012-2013_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UC4nV8PIFppB4r2wF5N4ipqA/videos IEEE 2011 Java Projects: http://chennaisunday.com/projectsNew.php?id=29&catName=IEEE_2011-2012_Java_Project IEEE 2011 Dotnet Projects: http://chennaisunday.com/projectsNew.php?id=33&catName=IEEE_2011-2012_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UCtmBGO0q5XZ5UsMW0oDhZ-A/videos IEEE PHP Projects: http://www.chennaisunday.com/projectsNew.php?id=41&catName=IEEE_PHP_Projects Output Videos: https://www.youtube.com/user/siva6351/videos Java Application Projects: http://www.chennaisunday.com/projectsNew.php?id=34&catName=Java_Application_Projects Dotnet Application Projects: http://www.chennaisunday.com/projectsNew.php?id=35&catName=Dotnet_Application_Projects Android Application Projects: http://www.chennaisunday.com/projectsNew.php?id=36&catName=Android_Application_Projects PHP Application Projects: http://www.chennaisunday.com/projectsNew.php?id=37&catName=PHP_Application_Projects Struts Application Projects: http://www.chennaisunday.com/projectsNew.php?id=38&catName=Struts_Application_Projects Java Mini Projects: http://www.chennaisunday.com/projectsNew.php?id=39&catName=Java_Mini_Projects Dotnet Mini Projects: http://www.chennaisunday.com/projectsNew.php?id=40&catName=Dotnet_Mini_Projects -- *Contact * * P.Sivakumar MCA Director Chennai Sunday Systems Pvt Ltd Phone No: 09566137117 No: 1,15th Street Vel Flats Ashok Nagar Chennai-83 Landmark R3 Police Station Signal (Via 19th Street) URL: www.chennaisunday.com Map View: http://chennaisunday.com/locationmap.php
Views: 828 Chennai Sunday
Data Duplication Removal Using File Checksum
 
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Get this software with source codes and explanation at http://nevonprojects.com/data-duplication-removal-using-file-checksum/
Views: 4907 Nevon Projects
How to do real-time Twitter Sentiment Analysis (or any analysis)
 
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This tutorial video covers how to do real-time analysis alongside your streaming Twitter API v1.1 feed. In this case, for example, we use the Sentdex Sentiment Analysis API, http://sentdex.com/sentiment-analysis-api/, though you can use ANY API like this, or just your own custom function too. If you don't already have a twitter stream set up, here is some sample code and tutorial video for it: http://sentdex.com/sentiment-analysisbig-data-and-python-tutorials-algorithmic-trading/how-to-use-the-twitter-api-1-1-to-stream-tweets-in-python/ Sentdex.com Facebook.com/sentdex Twitter.com/sentdex
Views: 72155 sentdex
Open source natural language processing projects
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-mapreduce/
Views: 24 PHD PROJECTS
K Means Clustering Algorithm | K Means Example in Python | Machine Learning Algorithms | Edureka
 
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** Python Training for Data Science: https://www.edureka.co/python ** This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session: 1. What is Clustering? 2. Types of Clustering 3. What is K-Means Clustering? 4. How does a K-Means Algorithm works? 5. K-Means Clustering Using Python Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm Subscribe to our channel to get video updates. Hit the subscribe button above. How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Programmatically download and analyze data 2. Learn techniques to deal with different types of data – ordinal, categorical, encoding 3. Learn data visualization 4. Using I python notebooks, master the art of presenting step by step data analysis 5. Gain insight into the 'Roles' played by a Machine Learning Engineer 6. Describe Machine Learning 7. Work with real-time data 8. Learn tools and techniques for predictive modeling 9. Discuss Machine Learning algorithms and their implementation 10. Validate Machine Learning algorithms 11. Explain Time Series and its related concepts 12. Perform Text Mining and Sentimental analysis 13. Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Review Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."
Views: 48027 edureka!
SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations
 
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The author's personal website: http://dongyu.tech More information please refer to the paper: http://dongyu.name/papers/tvcg_2016_dongyu_smartadp.pdf Online System: http://smartadp.chinacloudapp.cn/ Abstract: The problem of formulating solutions immediately and comparing them rapidly for billboard placements has plagued advertising planners for a long time, owing to the lack of efficient tools for in-depth analyses to make informed decisions. In this study, we attempt to employ visual analytics that combines the state-of-the-art mining and visualization techniques to tackle this problem using large-scale GPS trajectory data. In particular, we present SmartAdP, an interactive visual analytics system that deals with the two major challenges including finding good solutions in a huge solution space and comparing the solutions in a visual and intuitive manner. An interactive framework that integrates a novel visualization-driven data mining model enables advertising planners to effectively and efficiently formulate good candidate solutions. In addition, we propose a set of coupled visualizations: a solution view with metaphor-based glyphs to visualize the correlation between different solutions; a location view to display billboard locations in a compact manner; and a ranking view to present multi-typed rankings of the solutions. This system has been demonstrated using case studies with a real-world dataset and domain-expert interviews. Our approach can be adapted for other location selection problems such as selecting locations of retail stores or restaurants using trajectory data.
Views: 48 Dongyu Liu
Data-driven Personas: Constructing Archetypal Users with Clickstreams and User Telemetry
 
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Data-driven Personas: Constructing Archetypal Users with Clickstreams and User Telemetry Xiang Zhang, Hans-Frederick Brown, Anil Shankar CHI '16: ACM Conference on Human Factors in Computing Systems Session: Representing User Experience Abstract User Experience (UX) research teams following a user centered design approach harness personas to better understand a user's workflow by examining that user's behavior, goals, needs, wants, and frustrations. To create target personas these researchers rely on workflow data from surveys, self-reports, interviews, and user observation. However, this data not directly related to user behavior, weakly reflects a user's actual workflow in the product, is costly to collect, is limited to a few hundred responses, and is outdated as soon as a persona's workflows evolve. To address these limitations we present a quantitative bottom-up data-driven approach to create personas. First, we directly incorporate user behavior via clicks gathered automatically from telemetry data related to the actual product use in the field; since the data collection is automatic it is also cost effective. Next, we aggregate 3.5 million clicks from 2400 users into 39,000 clickstreams and then structure them into 10 workflows via hierarchical clustering; we thus base our personas on a large data sample. Finally, we use mixed models, a statistical approach that incorporates these clustered workflows to create five representative personas; updating our mixed model ensures that these personas remain current. We also validated these personas with our product's user behavior experts to ensure that workflows and the persona goals represent actual product use. DOI:: http://dx.doi.org/10.1145/2858036.2858523 WEB:: https://chi2016.acm.org/ Recorded at the 2016 CHI Conference on Human Factors in Computing Systems in San Jose, CA, United States, May 7-12, 2016
Views: 675 ACM SIGCHI
Factorization Machines, Visual Analytics, and Personalized Marketing
 
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Competition in customer experience management has never been as challenging as it is now. Customers spend more money in aggregate, but less per brand. The average size of a single purchase has decreased, partly because competitive offers are just one click away. Predicting offer relevance to potential (and existing) customers plays a key role in segmentation strategies, increasing macro- and micro-conversion rates, and the average order size. This session (and the associated white paper) covers the following topics: factorization machines and how they support personalized marketing; how SAS® Visual Data Mining and Machine Learning with SAS® Customer Intelligence 360 support building and deploying factorization machines with digital experiences; and a step-by-step demonstration and business use case for the sas.com bookstore. Presenter: Suneel Grover is an Advisory Solutions Architect supporting digital intelligence, marketing analytics and multi-channel marketing at SAS. By providing client-facing services for SAS in the areas of predictive analytics, digital analytics, visualization and data-driven integrated marketing, Grover provides technical consulting support in industry verticals such as media, entertainment, hospitality, communications, and sports. In addition to his role at SAS, Grover is a professorial lecturer at The George Washington University (GWU) in Washington DC, teaching in the Masters of Science in Business Analytics graduate program within the School of Business and Decision Science. Grover has an MBA in Marketing Research & Decision Science from The George Washington University (GWU), and an MS in Integrated Marketing Analytics from New York University (NYU). Presentation Outline 00:15 – The Romance of Being Digital – Why? 01:50 – The Reality of Customer Experiences – Content Shock & Micro-Moments 03:38 – Do Brands Need to Adapt? – To Capture The Attention Of Consumers 04:50 – Every Brand Offers A Digital Experience – Recommendation Systems Play A Role 05:35 – SAS Communities Example 07:25 – Analytically-Driven Marketing – Various Flavors 09:58 – Measuring Consumer Interest In Products & Services – Recommendation Systems Require More Than Just Statistical Models 11:25 – Before You Do Analysis – You Need Data 13:20 – There Are A Few Challenges – Every Recommendation System Must Overcome 17:17 – Factorization Machines – Algorithmic Firepower For Personalized Marketing 18:23 – SAS Customer Intelligence 360 and SAS Viya – An End-To-End Solution 20:48 – SAS Customer Intelligence 360 and SAS Viya – The Bridge For Machine Learning Within Marketing 21:45 – Factorization Machines, Visual Analytics, and Personalized Marketing – Show Me The Demo View White Paper and Full Demo Factorization Machines, Visual Analytics, and Personalized Marketing – https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2019/3087-2019.pdf SAS Customer Intelligence 360 Meets SAS Viya: Show me the demo (full demo) – https://blogs.sas.com/content/customeranalytics/2019/03/28/sas-customer-intelligence-360-meets-sas-viya-show-me-the-demo/ For additional content from SAS Global Forum 2019, visit https://www.sas.com/en_us/events/sas-global-forum/virtual.html Learn More about SAS Software SAS® Customer Intelligence 360 – https://support.sas.com/en/software/customer-intelligence-360.html SAS Viya – https://www.sas.com/en_us/software/viya.html SAS® Visual Data Mining and Machine Learning – https://www.sas.com/en_us/software/visual-data-mining-machine-learning.html SAS® Intelligent Decisioning – https://www.sas.com/en_us/software/intelligent-decisioning.html SUBSCRIBE TO THE SAS USERS YOUTUBE CHANNEL https://www.youtube.com/channel/UCWOfmTlbeesYiDJNflqsWQA?sub_confirmation=1 ABOUT SAS SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS®, you can discover insights from your data and make sense of it all. Identify what’s working and fix what isn’t. Make more intelligent decisions. And drive relevant change. CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 103 SAS Users
Internet privacy | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Internet_privacy 00:01:34 1 Levels of privacy 00:05:52 2 Risks to Internet privacy 00:11:07 2.1 HTTP cookies 00:18:30 2.2 Flash cookies 00:20:52 2.3 Evercookies 00:22:12 2.3.1 Anti-fraud uses 00:22:46 2.3.2 Advertising uses 00:23:32 2.3.3 Criticism 00:24:08 2.4 Device fingerprinting 00:26:45 2.4.1 Sentinel Advanced Detection Analysis and Predator Tracking (A.D.A.P.T.) 00:28:04 2.4.2 Canvas fingerprinting 00:28:32 2.5 Photographs on the Internet 00:32:46 2.5.1 Google Street View 00:36:00 2.6 Search engines 00:44:56 2.6.1 Privacy focused search engines/browsers 00:47:52 2.7 Privacy issues of social networking sites 00:52:01 2.8 Internet service providers 00:55:59 2.9 HTML5 00:59:19 2.10 Big Data 01:01:39 2.11 Other potential Internet privacy risks 01:04:34 2.12 Reduction of risks to Internet privacy 01:05:35 2.13 Noise Society – Protection through Information Overflow 01:06:55 3 Public views 01:08:49 3.1 Concerns of Internet privacy and real life implications 01:16:17 4 Laws and regulations 01:16:27 4.1 Global privacy policies 01:18:47 4.2 Data protection regulation 01:21:23 4.3 Internet privacy in China 01:25:41 4.4 Internet privacy in Sweden 01:29:56 4.5 Internet privacy in the United States 01:31:38 5 Legal threats 01:33:50 6 Children and Internet Privacy 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: 0.8669684245251084 Voice name: en-US-Wavenet-B "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= Internet privacy involves the right or mandate of personal privacy concerning the storing, repurposing, provision to third parties, and displaying of information pertaining to oneself via the Internet. Internet privacy is a subset of data privacy. Privacy concerns have been articulated from the beginnings of large-scale computer sharing.Privacy can entail either Personally Identifying Information (PII) or non-PII information such as a site visitor's behavior on a website. PII refers to any information that can be used to identify an individual. For example, age and physical address alone could identify who an individual is without explicitly disclosing their name, as these two factors are unique enough to identify a specific person typically. Some experts such as Steve Rambam, a private investigator specializing in Internet privacy cases, believe that privacy no longer exists; saying, "Privacy is dead – get over it". In fact, it has been suggested that the "appeal of online services is to broadcast personal information on purpose." On the other hand, in his essay The Value of Privacy, security expert Bruce Schneier says, "Privacy protects us from abuses by those in power, even if we're doing nothing wrong at the time of surveillance."
Views: 9 wikipedia tts
Build Adobe Analytics SDR in R using RSiteCatalyst and Tidyverse
 
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Greetings R and Analytics Ninjas. In this fairly quick example, I am creating a simple SDR with R using R Studio, RSiteCatalyst, Tidyverse, among other packages. It only takes a couple minutes to setup and get the script to run and it takes about 10-20 minutes to export a significant amount of implementation data into an excel file. Very cool. Credit goes out to originally Randy Zwitch for his work and contributions to RSiteCatalyst and the article on the 60 second SDR below, as-well-as Tim Wilson and his blogging and articles, as-well-as this REALLY cool SDR builder with the R Script that came from his repository. Make sure to like, share and subscribe for more content. Thanks for watching. Github Repository: https://github.com/gilliganondata/adobe_analytics_audit_doc Randy Zwitch's article on 60-second SDR: https://www.r-bloggers.com/adobe-analytics-implementation-documentation-in-60-seconds/ Tim Wilson's Article on GA Audit - that I saw this Adobe Analytics Audit link in (shown in video briefly): https://analyticsdemystified.com/google-analytics/r-interested-auditing-google-analytics-data-collection/ Thanks for watching!
Blackhat 2012 EUROPE - Data Mining a Mountain of Zero Day Vulnerabilities
 
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This video is part of the Infosec Video Collection at SecurityTube.net: http://www.securitytube.net Blackhat 2012 EUROPE - Data Mining a Mountain of Zero Day Vulnerabilities Every day, software developers around the world, from Bangalore to Silicon Valley, churn out millions of lines of insecure code. We used static binary analysis on thousands of applications submitted to us by large enterprises, commercial software vendors, open source projects, and software outsourcers, to create an anonymized vulnerability data set. By mining this data we can answer some interesting questions. Which industries have the most secure and least secure code? What types of mistakes do developers make most often? Which languages and platforms have the apps with the most vulnerabilities? Should you be most worried of internally built apps, open source, commercial software, or outsourcers? These questions and many more will be answered as we tunnel through zero day mountain. https://media.blackhat.com/bh-eu-12/Wysopal/bh-eu-12-Wysopal-State_of_Software_Security-WP.pdf https://media.blackhat.com/bh-eu-12/Wysopal/bh-eu-12-Wysopal-State_of_Software_Security-Slides.pdf
Views: 1411 SecurityTubeCons
The Connected Vehicle: How Analytics Drives Telematics Value
 
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http://www.sas.com/automotive Learn how SAS' Internet of Things technology is turning mundane telematics trouble codes into real value in the automotive and trucking industries. When everything is connected, we need answers, we need the Analytics of Things. SAS AUTOMOTIVE SOLUTIONS Drive better decisions with the world’s best analytics. SAS has automotive solutions for: * Sales & Marketing * Product & Process Quality * Aftermarket Service * Credit & Finance * Supply & Demand Planning * And more... LEARN MORE ABOUT SAS SOLUTIONS FOR AUTOMOTIVE http://www.sas.com/en_us/industry/automotive.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 27902 SAS Software
Elasticsearch: Analyzing Log Data Using ELK
 
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This video is a sample from Skillsoft's video course catalog. After watching this video, you will be able to demonstrate how to use ELK analysis to monitor and analyze log data. Skillsoft is the global leader in eLearning. We train more professionals than any other company and we are trusted by the world's leading organizations, including 65 percent of the Fortune 500. At Skillsoft, our mission is to build beautiful technology and engaging content. Our 165,000+ courses, videos and books are accessed more than 130 million times every month, in 160 countries and 29 languages. With 100% cloud access, anytime, anywhere.
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