PPT-Mining the Dark

W eb Drugs and Fake IDs Andres Baravalle Mauro Sanchez Lopez Outline Synopsis and introduction Surface web deep web and dark web Dark markets Going undercover in Agora Results What now. This . powerpoint. will show you the basics of dark matter and dark energy . Their place in the universe . By Jordan . Ilori. . DARK MATTER. Dark matter is a type of matter hypothesized in astronomy and cosmology to account for a large part of the mass that appears to be missing from the universe. Dark matter cannot be seen directly with telescopes; evidently it neither emits nor absorbs light or other electromagnetic radiation at any significant level. Instead, the existence and properties of dark matter are inferred from its gravitational effects on visible matter, radiation, and the large-scale structure of the universe. According to the Planck mission team, and based on the standard model of cosmology, the total mass–energy of the known universe contains 4.9% ordinary matter, 26.8% dark matter and 68.3% dark energy. Thus, dark matter is estimated to constitute 84.5% of the total matter in the universe and 26.8% of the total content of the universe.. Course Introduction. Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . 1. Overview . This presentation is for chapter 16 which discuss :. Chapter . 16: Text Mining for Translational . Bioinformatics. 1- terminologies.. 2- definitions.. 2-uses cases and applications.. 3-evaluation techniques and evaluation metrics.. 26/11/14. Scott Dennis – Appin Mine. Location – Appin Area 9. Page . 2. Appin Area 7. Current LW blocks. Appin Area 9. Future LW blocks . Location - STIS holes. Page . 3. STIS 2 – Build Site. STIS 2 – 3 x laterals. Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. Emre Eftelioglu. 1. What is Knowledge Discovery in Databases?. Data mining is actually one step of a larger process known as . knowledge discovery in databases. (KDD).. The KDD process model consists of six phases. Editor-In Chief. Samuel . Frimpong. Professor and . Chair. Robert H. . Quenon. Endowed Chair. Department of Mining . & Nuclear . Engineering. Missouri University of . Science . and Technology. USA. Discovering Business Rules From Event Logs. Marlon Dumas. University of Tartu, Estonia. With contributions from . Luciano. . García-Bañuelos. , . Fabrizio. . Maggi. & . Massimiliano. de . Leoni. Lisa Randall. Harvard University. @. lirarandall. . What do we know about dark matter?. It has gravitational interactions—of matter!. Gravitational . lensing. Rotation curves in galaxies. Detailed measurements of energy abundances—total and normal matter. (Part 1). Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. By: Jim Martin. 2. Criminal activity that is spanned by the Dark Web . includes: . theft . of intellectual . property . financial fraud . hacking . and . terrorism.   . Organizations . must take proactive steps to be prepared for these threats.  In order to be ready, companies need to be well informed about this type of criminal activity in order to prevent and overcome any potential threats. . Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. http://www.cs.uic.edu/~. liub. CS583, Bing Liu, UIC. 2. General Information. Instructor: Bing Liu . Email: liub@cs.uic.edu . Tel: (312) 355 1318 . Office: SEO 931 . Lecture . times: . 9:30am-10:45am. REVIEWED BROAD-BASED BLACK ECONOMIC EMPOWERMENT CHARTER FOR THE SOUTH AFRICAN MINING AND MINERALS INDUSTRY, 2016 ("MINING CHARTER 3. "). PRESENTATION PREPARED FOR . SAIMM – RESPONSIBILITIES PLACED ON OEMs AND SERVICE PROVIDERS.