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Data Analytics - Descriptive , Predictive and Prescriptive Analytics
 
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@ Members ~ This video would let you know about rising importance of Analytics where by we are covering all 4 Branches of Analytics like Financial Analytics , Risk Based Analytics , Cash Flow Analytics and Data Analytics. Video would also let you know about 3 types of Analytics covering Descriptive Analytics , Predictive Analytics and Prescriptive Analytics. You are most welcome to connect with us at 91-9899242978 (Handheld) , Skype ~ Rahul5327 , Twitter @ Rahulmagan8 , [email protected] , [email protected] or visit our website - www.treasuryconsulting.in
Webinar: Understanding Real World Evidence - A Registry Study Perspective
 
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While randomized controlled clinical trials (RCTs) remain the gold standard for assessing the safety and efficacy of pharmaceutical medicines, there is a growing need to generate real-world evidence data, adding value and differentiation to the product profile.
Views: 1241 medpaceinc
The Data-Mining Revolution: MUM prepares students for the skills and jobs of the future
 
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http://www.mum.edu Prof. Anil Maheshwari, Ph.D., discusses the new immersion program Maharishi University of Management has just launched to train students in the next wave of data-mining software. In today's data-driven economy there is an urgent need for more sophisticated software programs to mine and better utilize data coming in over multiple platforms from diverse sectors of the economy, not only for business, but also for higher education. To help Maharishi University of Management students build essential skills in analytics technology, we recently joined the IBM Academic Initiative, which offers participating schools no-charge access to IBM software, discounted hardware, course materials, training and curriculum development—over 6,000 universities and 30,000 faculty members worldwide are members of the program. "We are using industrial strength tools such as IBM SPSS Modeler," Dr. Maheshwari said, "along with open-source tools, to provide our students a strong data-mining toolkit to engage with Big Data, and generate interesting insights and new knowledge." Students will learn more than just how to operate the software, but how to use it effectively as a business tool. Dr. Maheshwari said, "Our students will have end-to-end skills to discern what is the business problem, what is the data being generated, how do I mine the data, how do I generate intelligence out of it and feed it back to the business so the business can actually benefit from it. That whole cycle is what we're training, not just the tool itself." Industry analysts have identified predictive analytics as the fastest growing software category for company spending. They also expect that the need for staff with these capabilities will outpace available skill sets in many organizations. This will mean that expertise in data mining and predictive analytics will be highly sought after for years to come. "Having this kind of software suite on their resumes can be a big advantage for our students headed for IT/management jobs," said Dr. Maheshwari. For more videos about MUM, visit our Video Café: http://www.mum.edu/video-cafe At MUM, Consciousness-Based education connects everything you learn to the underlying wholeness of life. So each class becomes relevant, because the knowledge of that subject is connected with your own inner intelligence. You study traditional subjects, but you also systematically cultivate your inner potential developing your creativity and learning ability. Your awareness expands, improving your ability to see the big picture, and to relate to others. Maharishi University of Management (MUM) offers undergraduate and graduate degree programs in the arts, sciences, business, and humanities. The University is accredited through the doctoral level by the Higher Learning Commission. Founded in 1971 by Maharishi Mahesh Yogi, the University features Consciousness-Based education to develop students' inner potential. All students and faculty practice the Transcendental Meditation technique, which extensive published research has found boosts learning ability, improves brain functioning, and reduces stress. Maharishi University uses the block system in which each student takes one course at a time. Students report they learn more without the stress of taking 4-5 courses at once. The University has a strong focus on sustainability and natural health, and serves organic vegetarian meals. The B.S. in Sustainable Living is MUM's most popular undergraduate major. Maharishi University of Management: http://www.mum.edu Consciousness-Based education: http://www.mum.edu/cbe BS Sustainable Living: http://www.mum.edu/sustainable_living/ Transcendental Meditation: http://www.mum.edu/tm Research: http://www.mum.edu/tm_research Block system: http://www.mum.edu/cbe/block Sustainability: http://www.mum.edu/sustainability Natural health: http://www.mum.edu/cbe/natural_health Organic veg meals: http://www.mum.edu/campus/dining
Cisco Helps Mining Companies Streamline Operations
 
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Mining companies looking to continually seeking new ways to streamline and improve operations. As they evolve from traditional manual processes for monitoring, production control and ore tracking to advanced technologies that automate and integrate these processes across their company, they count on Cisco for the right solutions and services. See some specific use cases for the mining industry. By gaining more visibility into each step of production, companies can more accurately monitor output, equipment, workers, and security for better and faster decision-making. Cisco solutions for mining provide proven technologies that enable immediate and sustainable operational improvements, as well as a platform to integrate advanced technologies. Subscribe to Cisco's YouTube channel: http://cs.co/Subscribe.
Views: 14416 Cisco
NASA Perspectives on Deep Learning
 
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In this video from the HPC User Forum in Milwaukee, Nikunj Oza from NASA Ames presents: NASA Perspectives on Deep Learning. This talk will give a broad overview of work at NASA in the space of data sciences, data mining, machine learning, and related areas at NASA. This will include work within the Data Sciences Group at NASA Ames, together with other groups at NASA and university and industry partners. We will delineate our thoughts on the roles of NASA, academia, and industry in advancing machine learning to help with NASA problems. Bio: Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center and the Discovery of Precursors to Safety Incidents (DPSI) team which applies data mining to aviation safety. Dr. Oza’s 40+ research papers represent his research interests which include data mining, fault detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley. and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley. Learn more: https://ti.arc.nasa.gov/profile/oza/ and http://hpcuserforum.com Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Views: 988 RichReport
John Boswell (SAS Institute) - The Global Industry Perspective
 
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Presentation by John Boswell (SAS Institute) on the global industry perspective at the LIBER-C4C workshop 'the perfect swell' on text and data mining, held in London on 27 September, 2013.
Building Explainable Machine Learning Systems: The Good, the Bad, and the Ugly
 
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This meetup was held in New York City on 30th April. Abstract: The good news is building fair, accountable, and transparent machine learning systems is possible. The bad news is it’s harder than many blogs and software package docs would have you believe. The truth is nearly all interpretable machine learning techniques generate approximate explanations, that the fields of eXplainable AI (XAI) and Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) are very new, and that few best practices have been widely agreed upon. This combination can lead to some ugly outcomes! This talk aims to make your interpretable machine learning project a success by describing fundamental technical challenges you will face in building an interpretable machine learning system, defining the real-world value proposition of approximate explanations for exact models, and then outlining the following viable techniques for debugging, explaining, and testing machine learning models: *Model visualizations including decision tree surrogate models, individual conditional expectation (ICE) plots, partial dependence plots, and residual analysis. *Reason code generation techniques like LIME, Shapley explanations, and Treeinterpreter. *Sensitivity Analysis. Plenty of guidance on when, and when not, to use these techniques will also be shared, and the talk will conclude by providing guidelines for testing generated explanations themselves for accuracy and stability. Open source examples (with lots of comments and helpful hints) for building interpretable machine learning systems are available to accompany the talk at: https://github.com/jphall663/interpretable_machine_learning_with_python Bio: Patrick Hall is senior director for data science products at H2O.ai where he focuses mainly on model interpretability. Patrick is also currently an adjunct professor in the Department of Decision Sciences at George Washington University, where he teaches graduate classes in data mining and machine learning. Prior to joining H2O.ai, Patrick held global customer facing roles and research and development roles at SAS Institute. Navdeep Gill Navdeep Gill is a Software Engineer & Data Scientist at H2O.ai where he focuses on model interpretability, GPU accelerated machine learning, and automated machine learning. He graduated from California State University, East Bay with a M.S. degree in Computational Statistics, B.S. in Statistics, and a B.A. in Psychology (minor in Mathematics). During his education, he gained interests in machine learning, time series analysis, statistical computing, data mining, and data visualization. Before joining H2O.ai, he worked at Cisco Systems, focusing on data science and software development. Before stepping into industry he worked in various Neuroscience labs as a researcher/analyst. These labs were at institutions such as California State University, East Bay, University of California, San Francisco, and Smith Kettlewell Eye Research Institute. His work across these labs varied from behavioral, electrophysiology, and functional magnetic resonance imaging research. Connect with Navdeep on Twitter @Navdeep_Gill_.
Views: 4314 H2O.ai
From satellite to soil: perspectives from end-users
 
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End-users perspective: Keith Norman (Velcourt) – Earth observation, precision farming – the sky’s the limit!; Andrew Richards (Agrii) – Using environmental data to improve season management of wheat; Sergio Moreno Rojas (G’s Growers) – Salad and vegetable crop monitoring using remote sensing; David Gardner (Innovation for Agriculture) – Opportunities and future potential for satellite and sensor technologies in livestock agriculture. Find out more about our industry activities: http://ow.ly/u21n302t8LV
Views: 1812 The Royal Society
DNAlytics - Data mining service for personalized medicine
 
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http://www.dnalytics.com - DNAlytics aims at being a reference European partner for the analytical / computational needs of the healthcare industry in the field of personalized medicine, helping patients benefit from it. In order to materialize this vision, DNAlytics exploits its expertise in Data Mining / Machine Learning, Statistics, Intensive Computing and Web technologies.
Views: 3884 Thibault Helleputte
Artificial Intelligence - Annual Meeting 2018 | Documentary (Advexon) #Advexon
 
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About conference Hear, Explore and learn the latest research. Present before distinguished global audience. Collaborate, build partnerships and experience USA. Join the global academic community. Conference Series LLC Ltd. invites all the participants across the globe to attend the 5th International conference on Artificial Intelligence during April 16-17, 2018 at Las Vegas, Nevada, USA. Artificial Intelligence 2018 includes prompt keynote presentations, Oral talks, Poster presentations and Exhibitions. Artificial Intelligence 2018 aims in proclaim knowledge and share new ideas amongst the professionals, industrialists and students from research area of Artificial Intelligence to share their research experiences and indulge in interactive discussions at the event. This scientific gathering guarantees that offering the thoughts and ideas will enable and secure you the theme “Surging into the future of Artificial Intelligence”. Artificial Intelligence is the latest trending technology in many fields especially in industries like manufacturing, control systems, Data mining, etc. The current era fully rolled out with many new Artificial Intelligence technologies. In such case more Software companies and industries were newly introduced within market which obviously shows the market growth of Artificial Intelligence. While analyzing the revenue growth of Artificial Intelligence, it highly developed from $150 billion USD to $250 billion USD since from 2010-2015. And the annual growth percentage increases from 20-55 percentages, which clearly shows that Software technology contains huge scope in coming years. Due to incredible technology development, the industries are trying to reduce man power where they trying to increase automation function in various sectors. Now artificial intelligence is used in each and every company where machines are involved and some or other process is involved. Many fields like robotics, mechatronics, control systems, electronics, wireless, laser technology, automotive motors are depended only on this Automation functions. The conference organizers aim is to gather the researcher’s academicians and scientists from the field of Data Mining and Artificial Intelligence community to create an approach towards global exchange of information on technological advances, new scientific innovations, and the effectiveness of various regulatory programs towards artificial Intelligence. Target Audience: Artificial Intelligence Lab Directors/Associates, Robotics Lab Directors Head of the Departments from the field of Artificial Intelligence, Automation & Robotics, Data Mining, Mechatronics, Control systems According to TechSci Research report, "United States Artificial Intelligence Market, By Application, By Region, By End User Competition Forecast & Opportunities, 2011-2021", the artificial intelligence market in the US is projected to grow at a CAGR of 75% during 2016 - 2021 on account of growing artificial intelligence technology adoption in consumer electronic devices, research and developmental activities in healthcare industry, unmanned aerial vehicles, autonomous cars, etc. This research evaluates enterprise robotics in the United States including companies, technologies, and solutions across industry verticals and applications. The report includes forecasts by industry vertical/application for 2017 through 2021. Leading industry verticals are beginning to see improved operational efficiency through the introduction of robotics and Artificial Intelligence (AI). Robotics investment in many industries represents a substantial capital expenditure with the potential to dramatically reduce operational expenses through resource optimization, quality improvement, and waste reduction. Robotics in business will accelerate as less expensive hardware and improvements in AI lead to improved cost structures and increased integration with enterprise software systems respectively. The massive amount of data generated by robotics will create opportunities for data analytics and AI-enabled decision support systems. Emerging areas for enterprise robotics include Robotics as a Service, Cloud Robotics, and General Purpose Robotics. * Subscribe for more Scientific & Technological Videos * Like & Share * go to our website http://www.advexon.com * Share your ideas and comment artificial intelligence ai national geographic documentary bbc documentary (Advexon)
Views: 21914 ADVEXON TV
Biomedical Big Data Revolution | Dr. Stefan Bekiranov | TEDxRVA
 
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Find a cure for cancer from the comfort of your living room while in your PJs. It’s more possible today than it was a short time ago. We are currently undergoing a revolution in the field of biomedical research that will enable tailoring preventative strategies and therapies directly for each patient--Precision medicine. Systems Biologist, Stefan Bekiranov talks about what’s driving this revolution and how researchers are finding potential cures to diseases such as cancer at a faster rate than ever before. This talk was given at a local TEDx event, produced independently of the TED Conferences. It was filmed and edited by Tijo Media at the Carpenter Theatre at Dominion Arts Center in Richmond, VA. #medicalresearch #UVA #biomedical #bigdata #cancer #research #medicine After receiving his Bachelor of Science in Electrical Engineering from UCLA, Dr. Stefan Bekiranov worked as a microwave engineer at Raytheon Electromagnetic Systems Division in Santa Barbara. He received his PhD in theoretical condensed matter physics from the University of California at Santa Barbara and went on to do postdoctoral research in statistical/condensed matter physics at the University of Maryland. After that, Dr. Bekiranov conducted more postdoctoral research in computational biology at The Rockefeller University. He pioneered the analysis of high-resolution genomic tiling array data as a Bioinformatics Staff Scientist at Affymetrix. He is now an Associate Professor at the University of Virginia School of Medicine working in the fields of epigenomics and systems biology and has published over 50 papers in peer-reviewed journals. The ultimate goal of his work is to arrive at improved therapeutic targets to treat and hopefully, one day, cure cancer. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 16099 TEDx Talks
Experiences and lessons in developing industry-strength machine learning and data min.. (KDD 2012)
 
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Experiences and lessons in developing industry-strength machine learning and data mining software KDD 2012 Chih-Jen Lin Traditionally academic machine learning and data mining researchers focus on proposing new algorithms. The task of implementing these methods is often left to companies that are developing software packages. However, the gap between the two sides has caused some problems. First, the practical deployment of new algorithms still involves some challenging issues that need to be studied by researchers. Second, without further investigation after publishing their papers, researchers have neither the opportunity to work with real problems nor see how their methods are used. We discuss the experiences in developing two machine learning packages LIBSVM and LIBLINEAR, that are widely used in both academia and industry. We demonstrate that the interaction with users leads us to identify some important research problems. For example, the decision to study and then support multi-class SVM was essential in the early stage of developing LIBSVM. The birth of LIBLINEAR was driven by the need to classify large-scale documents in Internet companies. For fast training of large-scale problems, we had to create new algorithms other than those used in LIBSVM for kernel SVM. We present some practical use of LIBLINEAR for Internet applications. Finally, we give lessons learned and future perspectives for developing industry-strength machine learning and data mining software.
Data Mining - Warwick Graco - ATO
 
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Dr Warwick Graco, Senior Director Operational Analytics, Australian Taxation Office sat down with ADMA and IAPA to discuss the importance of data in public sector. He's also the Convenor, Whole of Government Data Analytics Centre of Excellence (AGIMO). For more insights go to http://admadataday.com.au/
Views: 400 ADMA
Harvesting NEO Riches: A Mining Engineer's Perspective
 
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Professor Leslie Gertsch from Missouri University of Science and Technology presents "Harvesting NEO Riches: A Mining Engineer's Perspective" at the Keck Institute for Space Studies, August 11, 2014
Views: 232 KISSCaltech
Data mining of social | Anna Dubovik | TEDxYouth@Tomsk
 
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Do You know what is really interesting for everyone? Future. In 1968, Arthur C. Clarke in his novel "2001: A Space Odyssey" predicted boom of space traveling and the development of artificial intelligence. He described the future in many ways and it has become real. In 2001, the computer was able to beat a man at chess, and in the early 2000s the company started to create a space tourism industry. What is the forecast of today? TEDYouth 2015 is an opportunity for young people to think about the world in 2035, to create their own view of future reality. Tomsk is among more than 150 cities around the globe gathered to explore the event's theme, "Made in the Future" on the 14-15 November 2015. During these two days more than 900 speakers of various professions will talk about their version of the key changes that await humanity in the perspective of 15-20 years. Graduated Skolkovo Institute of Science and Technology, analyst in the Information-Analytical Center of Moscow City Health Departments, Big Data analyst This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 412 TEDx Talks
Data Mining | What is Data Mining - Imarticus
 
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Know more about Data Mining and its applications. The Post Graduate Program in Data Analytics is a 500+ hour program covering foundational concepts and hands-on learning of leading analytical tools, such as SAS, R, Python, Hive, Spark and Tableau as well as functional analytics across many domains. Over the course of 3 semesters, candidates will not only gain theoretical knowledge of data science tools, but also gain exposure to business perspectives and industry best practices through guest lectures and project submissions. Click here to know more about the Program http://imarticus.org/post-graduate-program-in-data-analytics Imarticus Learning is a professional education institute focused on bridging the gap between industry & academia by offering certified industry-endorsed courses in Financial Services, Business Analysis, Business Analytics & Wealth Management. Visit: http://www.imarticus.org
Views: 343 Imarticus Learning
Nathalie Henry Riche: Researchers developing new ways to visualize complex data
 
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Data-driven storytelling is becoming more pervasive with the help of sophisticated visualization tools. Tools that allow us to visualize complex data add more than decoration to our work, says Microsoft researcher Nathalie Riche. “Visualization helps you answer questions you did not even know you had. So it’s about generating hypotheses,” Riche said during her presentation at this year’s Women in Data Science conference at Stanford University. “Of course, you still need statistics and all those complex algorithms to actually answer those questions and really know if this is significant or not. The pattern is in the data,” she says. As an example, Riche showed a table containing four series of numbers. When computing basic statistics about the numbers using measures like standard deviation and regression, they appear to be equivalent and it would appear that the x and y coordinates are essentially the same. Yet when plugged into a basic visualization tool, it’s apparent that they are not. The tool Riche used in her demonstration is decades old. But her research is aimed at laying the groundwork for the development of advanced visualization applications that are simple enough to include in programs like Excel or the more sophisticated Power BI, she says. A tool in Excel called Power Map allows users to plot geographic and temporal data on a representation of a 3D globe or a custom map. A new custom visualization tool in Power BI lets a user animate each data point in a set of data. And Microsoft researchers are currently exploring ways to use virtual reality to visualize data in 3D, Riche says. Data visualization is a powerful way to tell stories, Riche says. “You can actually communicate a message very effectively with visualization. And in fact, those stories with data are everywhere.” As part of their research, Riche and her colleagues look for the most effective way to tell a story with visualization.
Maria Eskevich: Stakeholders in academic publishing
 
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Stakeholders in academic publishing: Text and data mining perspective and potential, Maria Eskevich, Radboud University Lecture held during the 20th International Conference on Electronic Publishing entitled "Positioning and Power in Academic Publishing: Players, Agents and Agendas", June 7-9 2016 at Göttingen State and University Library. Session 8 (9 June 2016, 11.15 – 12.45), Data mining and knowledge discovery, Session Chair: Pierre Mournier Further information: http://elpub.net/ Abstract: In this paper we discuss the concept of open access in academic publishing with the focus on the right to mine the data once the right to read is granted. Thus we envisage the roles and types of the stakeholders in academic publishing from the perspective of the potential text and data mining (TDM) applications. Further on, we briefly introduce FutureTDM project that aims to improve TDM uptake in Europe.
New Challenges in Big Data: Technical Perspectives
 
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Abstract: Big Data has been a buzz word as an emerging technology. Yet there seems no killing application and it is unclear what new challenges are. One main reason for this is that Big Data is a methodological discipline applied to a wide range of domains, and its individual application may not look technologically substantial though it improves the quality of services. However, Big Data technology has its own technical depth and nontrivial challenges. In this talk, we first clarify new challenges in Big Data processing that are distinct from conventional parallel processing. After that, we introduce several research projects in the data mining lab at Pohang University of Science and Technology (POSTECH), including PubMed relevance feedback search, blackbox video search, novel recommendation, and timing when to recommend. Bio: Hwanjo Yu received his PhD in Computer Science at the University of Illinois at Urbana-Champaign in June 2004 under the supervision of Prof. Jiawei Han. From July 2004 to January 2008, he was an assistant professor at the University of Iowa. He is now an associate professor at POSTECH in South Korea. He developed influential algorithms and systems in the areas of data mining, database, and machine learning, including (1) algorithms for classifying without negative data (PEBL, SVMC), (2) privacy-preserving SVM algorithms (PP-SVM), (3) SVM-JAVA: an educational java open source for SVM, (4) RefMed: the relevance feedback search engine for PubMed, and (5) TurboGraph: a fast parallel graph engine handling billion-scale graphs in a single PC. His methods and algorithms were published in prestigious journals and conferences, including ACM SIGMOD, ACM SIGKDD, IEEE ICDE, IEEE ICDM, ACM CIKM, etc.
Views: 133 bioCADDIE Project
Media Ethics Initiative Speaker Series: danah boyd on Hacking Big Data
 
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Hacking Big Data: Discovering Vulnerabilities in a Sociotechnical Society http://www.mediaethicsinitiative.org Speaker: danah boyd Principal Researcher at Microsoft Research and the Founder of Data & Society Data-driven and algorithmic systems increasingly underpin many decision-making systems, shaping where law enforcement are stationed and what news you are shown on social media. The design of these systems is inscribed with organizational and cultural values. Often, these systems depend on the behavior of everyday people, who may not act as expected. Meanwhile, adversarial actors also seek to manipulate the data upon which these systems are built for personal, political, and economic reasons. In this talk, Danah will unpack some of the unique cultural challenges presented by “big data” and machine learning, raising critical questions about fairness and accountability. She will describe how those who are manipulating media for lulz are discovering the attack surfaces of new technical systems and how their exploits may undermine many aspects of society that we hold dear. Above all, she will argue that we need to develop more sophisticated ways of thinking about technology before jumping to hype and fear. danah boyd is a principal researcher at Microsoft Research, the founder and president of Data & Society, and a visiting professor at New York University. Her research is focused on addressing social and cultural inequities by understanding the relationship between technology and society. Her most recent books – “It’s Complicated: The Social Lives of Networked Teens” and “Participatory Culture in a Networked Age” – examine the intersection of everyday practices and social media. She is a 2011 Young Global Leader of the World Economic Forum, a member of the Council on Foreign Relations, a director of both Crisis Text Line and Social Science Research Council, and a trustee of the National Museum of the American Indian. She received a bachelor’s degree in computer science from Brown University, a master’s degree from the MIT Media Lab, and a Ph.D in Information from the University of California, Berkeley. This event is co-sponsored by the Global Media Industry Speaker Series
Alejandro Jaimes - CONFERENZA ANNUALE DI TOP-IX 2011
 
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Data as a Medium: A Human-Centered Perspective Alejandro (Alex) Jaimes is Senior Research Scientist at Yahoo! Research where he is leading new initiatives at the intersection of web-scale data analysis and user understanding (user engagement & improving user experience). Dr. Jaimes is the founder of the ACM Multimedia Interactive Art program, Industry Track chair for ACM RecSys 2010 and UMAP 2009, and panels chair for KDD 2009. He was program co- chair of ACM Multimedia 2008, co-editor of the IEEE Trans. on Multimedia Special issue on Integration of Context and Content for Multimedia Management (2008), and a founding member of the IEEE CS Taskforce on Human-Centered Computing. His work has led to over 70 technical publications in international conferences and journals, and to numerous contributions to MPEG-7. He has been granted several patents, and serves in the program committee of several international conferences. He has been an invited speaker at Practitioner Web Analytics 2010, CIVR 2010, ECML-PKDD 2010 and KDD 2009 and (Industry tracks), ACM Recommender Systems 2008 (panel), DAGM 2008 (keynote), 2007 ICCV Workshop on HCI, and several others. Before joining Yahoo! Dr. Jaimes was a visiting professor at U. Carlos III in Madrid and founded and managed the User Modeling and Data Mining group at Telefónica Research. Prior to that Dr. Jaimes was Scientific Manager at IDIAP-EPFL (Switzerland), and was previously at Fuji Xerox (Japan), IBM TJ Watson (USA), IBM Tokyo Research Laboratory (Japan), Siemens Corporate Research (USA), and AT&T Bell Laboratories (USA). Dr. Jaimes received a Ph.D. in Electrical Engineering (2003) and a M.S. in Computer Science from Columbia U. (1997) in NYC.
Views: 82 topixtube
Why the Deadly Asbestos Industry is Still Alive and Well
 
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Despite irrefutable scientific evidence calling out the dangers of asbestos, 2 million tons of the carcinogen are exported every year to the developing world, where it's often handled with little to no regulation. For this episode of VICE Reports, correspondent Milène Larsson traveled to the world's largest asbestos mine in the eponymous town of Asbest, Russia, to meet workers whose livelihoods revolve entirely around the dangerous mineral. Surprisingly, the risks associated with asbestos mining didn't seem to worry the inhabitants; in fact, asbestos is the city's pride, celebrated with monuments, songs, and even its own museum. Larsson then visits Libby, Montana, another mining town almost on the other side of the globe, where the effects of asbestos exposure are undeniable: 400 townspeople have died from asbestos-related diseases, and many more are slowly choking to death. Why is the deadly industry of mining and selling asbestos still alive and well? Click here to subscribe to VICE: http://bit.ly/Subscribe-to-VICE Check out our full video catalog: http://bit.ly/VICE-Videos Videos, daily editorial and more: http://vice.com More videos from the VICE network: https://www.fb.com/vicevideos Like VICE on Facebook: http://fb.com/vice Follow VICE on Twitter: http://twitter.com/vice Read our Tumblr: http://vicemag.tumblr.com Follow us on Instagram: http://instagram.com/vice Check out our Pinterest: https://pinterest.com/vicemag
Views: 1317096 VICE
Study a specialization in Data Science at Tilburg University
 
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'Data Scientist: The Sexiest Job of the 21st Century' Today’s society operates on large amounts of data. Industry, governments and academia are asked to provide insight into these data. But how do we deal with such large amounts of data? What techniques do we use to mine the data? What are the legal and ethical aspects regarding these data sets? And what economic value can be found in big data? The MSc specialization Data Science: Business and Governance trains students to become Data Scientists that can address these questions. The Harvard Business Review calls the job of Data Scientist "the sexiest job of the 21st century"! Tilburg University offers a wide range of complementary expertise, including techniques for data mining, pattern recognition, business analytics, visualization and process analytics; as well as knowledge on law, regulation, ethics and entrepreneurship. The MSc specialization consists of courses in methods of analysis, together with economic and management as well as legal, ethical and methodological perspectives on data, all of them taught by experts in these fields. The Master’s specialization Data Science: Business and Governance offers (constitutes/ consists of) a well-balanced mixture of theoretical and practical (elective) courses. These elements combine to make this specialization unique in Europe and possibly even in the world: Four schools (Tilburg School of Economics and Management, Tilburg School of Law, Tilburg School of Social and Behavioral Sciences, and the Tilburg School of Humanities) work together in offering the best possible training for the job of the future, that of Data Scientist.
Views: 7014 TilburgUniversity
Math and Big Data
 
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The ability to collect and interpret ever-growing amounts of data is playing an increasingly important role in scientific and technological progress. New challenges and opportunities abound from big data problems in bioinformatics, computer vision, geophysics, high energy physics, industrial processes, microarrays, networks, neuroscience, object recognition, sensors, scientific computation, signal processing, social science, and other fields. These challenges and opportunities are leading to new analysis, theory, and simulation in diverse areas: algebra, dynamical systems, graph theory, harmonic analysis, linear algebra, machine learning, numerical analysis, optimization, statistics, and topology, etc. At the SIAM Annual Meeting held in Minneapolis in July 2012, a panel with representatives from academia, government labs and industry, discussed the role of mathematics, computational science, and statistics in `Big Data.'
Big Data Analytics
 
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http://www.ibm.com/software/data/bigdata/ Deepak Rangarao, IBM Client Technical Specialist, takes us through a demo showing Big Data Analytics in action in the Telco industry. IBM's Big Data platform is at the heart of the solution that includes a real time dashboard and data mining from Netezza, InfoSphere Streams processing and scoring CDR's, BigInsights and ad-hoc analysis using BigSheets. Video produced, directed and edited by Gary Robinson, contact robinsg at us.ibm.com Music Track title: Clouds, composer: Dmitriy Lukyanov, publisher:Shockwave-Sound.Com Royalty Free
Views: 11914 IBM Analytics
Some Thoughts on Industrial R&D
 
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This talk looks back over a 40 year career which revolved primarily around industrial R&D. I will share some of my perspectives on working with teams of great people and choosing the right projects with a goal to have major impact on both business and society. Specifically, I will share my experience in initiating, participating in, and managing early research and development in machine vision, computer-based manufacturing, robotics and control. In the course of a career, an industry researcher might take on responsibilities for research and technology management. I will outline insights and lessons learned in leading computer research activities in AI, data mining, advanced computing methods, machine perception, manufacturing systems, advanced web technologies, and robotics that added value to GM's business.
Views: 104 ACMatOakland
Actuarial Research and Predictive Analytics
 
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SOA Managing Director of Research Dale Hall discusses actuarial research involving predictive analytics and modeling. Visit http://www.soa.org/research/topics/pred-analytics-topic-landing/.
Views: 958 SocietyofActuaries
Management Information Systems | MIS in hindi
 
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In this video we discuss what is Management information system? Elements of Management information system Please like, share and subscribe my channel. Click here for subscribe... https://www.youtube.com/channel/UCrFBvmpqjx7AyAsIjPM8w9g
Views: 78782 DigiHunt
Curtin's Digital Mineral Library: how it can work for you!
 
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Explore some more: http://jdlc.curtin.edu.au/ Why open data is critical to the mineral and mining industry? The John de Laeter Centre for Isotope Research (JDLC), headquartered at Curtin University, is a Perth-based multi-institutional research infrastructure centre providing the academic, resources and environmental research sector with advanced analytical facilities and expertise. Research Data Australia: https://researchdata.ands.org.au/tima-energy-dispersive-mineralogical-classification/549527
Views: 205 Curtin University
What is Analytics and How Analytics work
 
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What is Analytics ? Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight. Firms may commonly apply analytics to business data, to describe, predict, and improve business performance. Specifically, arenas within analytics include enterprise decision management, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics. Analytics vs. analysis Analytics is a multi-dimensional discipline. There is extensive use of mathematics and statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data - data analysis. The insights from data are used to recommend action or to guide decision making rooted in business context. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology. There is a pronounced tendency to use the term analytics in business settings e.g. text analytics vs. the more generic text mining to emphasize this broader perspective. There is an increasing use of the term advanced analytics,typically used to describe the technical aspects of analytics, especially predictive modeling, machine learning techniques, and neural networks. What is Data Analytics ? Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories. Data analytics is distinguished from data mining by the scope, purpose and focus of the analysis. Data miners sort through huge data sets using sophisticated software to identify undiscovered patterns and establish hidden relationships. Data analytics focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher.
Views: 272 Eway Biz
Interactive Data Analysis - Jeffrey Heer - May 23, 2013
 
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This talk is part of the symposium, "Data Visualization from Data to Discovery: Art Center + Caltech + JPL", May 23, 2013 | Beckman Auditorium | Caltech, Pasadena, CA, USA | http://www.hi.jpl.nasa.gov/datavis Interactive Data Analysis Data analysis is a complex process with frequent shifts among data formats and models, and among textual and graphical media. We are investigating how to better support this lifecycle of analysis by identifying critical bottlenecks and developing new methods at the intersection of data visualization, machine learning and computer systems. Can we empower users to transform and clean data without programming? Can we design scalable representations and systems to visualize and query big data in real-time? How might we enable domain experts to guide machine learning methods to produce better models? Jeffrey Heer presents selected projects that attempt to address these challenges and introduce new tools for interactive visual analysis. Jeffrey Heer is an Assistant Professor of Computer Science at Stanford University, where he works on human-computer interaction, visualization and social computing. His research investigates the perceptual, cognitive and social factors involved in making sense of large data collections, resulting in new interactive systems for visual analysis and communication. The visualization tools developed by his lab (D3, Protovis, Flare, Prefuse) are used by researchers, companies and thousands of data enthusiasts around the world. His group has received Best Paper and Honorable Mention awards at the premier venues in Human-Computer Interaction and Information Visualization (ACM CHI, ACM UIST, IEEE InfoVis, IEEE VAST). In 2009 Jeff was named to MIT Technology Review's TR35; in 2012 he was named a Sloan Foundation Research Fellow. He holds BS, MS and PhD degrees in Computer Science from the University of California, Berkeley. About the symposium: Nearly every scientific and engineering endeavor faces a fundamental challenge to see and extract insights from data. Effective Data Science and Visualization can lead to new discoveries. Together, we at Caltech, NASA JPL, and Art Center represent the same convergence of science, engineering and design that drives new Big Data-powered discovery. On May 23, 2013, industry leaders visited Pasadena for a series of talks to inspire, unite and challenge our community to re-examine our practices, and our perspectives. Guests included: * Fernanda Viégas & Martin Wattenberg | Co-leaders, Google Data Visualization Group * Jer Thorp | Co-founder, The Office for Creative Research * Golan Levin | Director, Carnegie Mellon Studio for Creative Inquiry * Eric Rodenbeck | Founder, Stamen Design * Jeff Heer | Assistant Professor, Stanford University * Anja-Silvia Goeing | Privatdozent, University of Zurich and Lecturer of History and History of Science, Caltech See http://www.hi.jpl.nasa.gov/datavis for more information. Produced in association with Caltech Academic Media Technologies. © 2013 California Institute of Technology.
Views: 11928 caltech
Qiang Yang: When Transfer Learning Meets Deep Learning
 
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Abstract: Deep learning has achieved great success as evidenced by many challenging applications. However, deep learning developed so far has some inherent limitations. In particular, deep learning is not yet adaptable to different domains and cannot handle small data. In this talk, I will give an overview of how transfer learning can help alleviate these problems. In particular, I will survey some recent progress on integrating deep learning and transfer learning together and show some interesting applications in sentiment analysis, image processing and dialog systems. Bio: Qiang Yang is the head of Computer Science and Engineering Department at Hong Kong University of Science and Technology (HKUST), where he is a New Bright Endowed Chair Professor of Engineering and the founding director of HKUST’s Big Data Institute. His research interest is artificial intelligence, including machine learning, data mining and planning. He is a fellow of AAAI, IEEE, IAPR and AAAS. He received his PhD from the Department of Computer Science at the University of Maryland, College Park in 1989 and had been a faculty member at the University of Waterloo between 1989 and 1995. He was a professor and NSERC Industrial Research Chair at Simon Fraser University in Canada from 1995 to 2001. He had been the founding director of the Huawei's Noah's Ark Research Lab between 2012 and 2015. He was the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST) and the founding Editor in Chief of IEEE Transactions on Big Data (IEEE TBD). He has served as a PC Chair or General Chair of several international conferences, including ACM KDD, IJCAI, RecSys, IUI and ICCBR. In 2017, he received the ACM SIGKDD Distinguished Service Award. He is currently the President of IJCAI and a council member of AAAI.
Views: 480 UMD CS
Big Data in Health research by Ronald Stolk
 
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This eScience talk was presented at 4th National eScience Symposium Astronomy Session 13 October 2016, Amsterdam ArenA https://www.esciencecenter.nl/event/program/4th-national-escience-symposium/ SPEAKER: Ronald Stolk - University of Groningen TALK: Big Data in Health research ABSTRACT: Like other parts of society, health care is increasingly data-driven. Diagnostic procedures advance rapidly, resulting in data-intensive output of genomics, imaging, biomarkers, and others. Information in electronic patient records gets better structured, like symptoms, life style, family history. Moreover, people collect health related data themselves by wearable devices, mobile health apps and online programs. These data are valuable for scientific research, even more when combined with data collected in clinical studies or biobanks, but not easily available. From a Big Data perspective the challenge is to handle the Variety rather than the Volume of health data. In addition, health related data requires control of privacy and security. Within the Netherlands recently all major stakeholders have joined forces in a proposal for a Dutch national infrastructure for personalized medicine & health research: Health-RI. It connects all high-end resources available (biobanks, clinical data, experimental facilities) through a linked-data ‘backbone’ into a single binding framework. Health-RI offers a frontier playground for all Dutch science & innovation projects in the area of health research, be it in human biology, citizen science, clinical or industrial research. ABOUT: Ronald Stolk is Professor of clinical epidemiology, Director “Research Data & Biobanking” of the UMCG and Board Member of several organizations on Big Data in Health such as NFU data4lifesciences, BBMRI, and DTL/ELIXIR.
EMBA in Strategic Mining Management | Sauder School of Business at UBC, Vancouver, Canada
 
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Designed exclusively for the mining industry, this first-of-its-kind executive business degree was developed in consultation with mining professionals for mining professionals. The new EMBA in Strategic Mining Management harnesses the world-renowned expertise in business education of the Sauder School of Business and North America’s largest and most advanced centre for mining engineering education and research, the Keevil Institute of Mining Engineering, to train highly skilled executives in the fields of Operations Management and Executive Management. More info: http://www.sauder.ubc.ca/Programs/Executive%20Education/EMBA%20in%20Strategic%20Mining%20Management
IoT Big Data Stream Mining (Part 3)
 
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Authors: Latifur Khan, Department of Computer Science, Erik Jonsson School of Engineering & Computer Science, The University of Texas at Dallas João Gama, Laboratory of Artificial Intelligence and Decision Support, University of Porto Albert Bifet, Telecom ParisTech Abstract: The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in IoT stream mining. This tutorial is a gentle introduction to mining IoT big data streams. The first part introduces data stream learners for classification, regression, clustering, and frequent pattern mining. The second part deals with scalability issues inherent in IoT applications, and discusses how to mine data streams on distributed engines such as Spark, Flink, Storm, and Samza. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 261 KDD2016 video
David Hand at NTTS 2015
 
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David Hand is Senior Research Investigator and Emeritus Professor of Mathematics at Imperial College, London, and Chief Scientific Advisor to Winton Capital Management. He is a Fellow of the British Academy, and a recipient of the Guy Medal of the Royal Statistical Society. He has served (twice) as President of the Royal Statistical Society, is on the Board of the UK Statistics Authority, and chairs the Governing Board of the UK’s Administrative Data Research Network. He has published 300 scientific papers and 26 books. He has broad research interests in areas including classification, data mining, and the foundations of statistics. His applications interests include psychology, official statistics, and the retail credit industry - he and his research group won the 2012 Credit Collections and Risk Award for Contributions to the Credit Industry. He was made OBE for services to research and innovation in 2013. His two most recent books, The Improbability Principle and The Wellbeing of Nations, were published in 2014.
Views: 264 Estat NTTS
Use of Big and Real-World Data by Pharma: More than Data Warehousing
 
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Real-world data—a subset of big data—is clinical data used for decision-making that did not come from a clinical trial. According to Aaron Galaznik, MD, senior director, real-world data and analytics at Pfizer, Inc, once integrated, the plethora of datasets will allow for a holistic view of disease processes and drivers that will identify patterns of use and opportunities to improve care in different patient populations. The synergistic combination of clinical and real-world data will ultimately improve the personalization of care patients receive. For more information, please visit http://www.npcnow.org/issues/comparative-effectiveness-research.
Views: 2118 npcnow
IoT Big Data Stream Mining (KDD 2016)
 
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IoT Big Data Stream Mining KDD 2016 Gianmarco De Francisci Morales Albert Bifet Latifur Khan Joao Gama Wei Fan The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in IoT stream mining. This tutorial is a gentle introduction to mining IoT big data streams. The first part introduces data stream learners for classification, regression, clustering, and frequent pattern mining. The second part deals with scalability issues inherent in IoT applications, and discusses how to mine data streams on distributed engines such as Spark, Flink, Storm, and Samza.
Academic Publishing in Europe 9 - Redefining the Scientific Record - Day 1
 
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Welcome and Opening: "Redefining the Scientific Record" Dr. Elisabeth Niggemann, Director General, Deutsche Nationalbibliothek, Leipzig und Frankfurt am Main   Keynote 1 Sander Dekker, State Secretary, Ministry of Education, Culture and Science, The Hague, The Netherlands   Keynote 2: Can Creators and Curators Redefine the Scientific Record? Professor Dr. David Black, Secretary General of ICSU (International Council for Science), Paris, and Chair of Organic Chemistry, University of New South Wales, Sydney There has alway been a crucial linkage between creators and curators of scientific knowledge. The creators need the curators so that they can establish their reputations, and the curators need the steady generation of new scientific knowledge. The advent of the Internet has changed the balance of the relationship between creators and curators, but the interdependency remains. There is also a greater call for open access to publication, and this brings a need for more appropriate controls. The talk will deal with aspects of the relationship from the point of view of a researcher, and also describe steps being taken by the International Council for Science to deal with aspects of open access, and the use of metrics for the assessment of quality.   Keynote 3 The Future of Academic Publishing: The Chemists' Point of View Professor Dr. Wolfram Koch,Executive Director, German Chemical Society (GDCh), Frankfurt am Main The academic publishing landscape is rapidly changing. Among the buzz words are open access, big data, and quality control. The Gesellschaft Deutscher Chemiker has very recently published a position paper "On the Future of Scientific Publishing" focusing in particular on open access. The main points of this position paper will be presented. In addition, some of the aspects which make chemistry special in scientific publishing will be addressed. Among these are the dominant role of Learned Societies as publishers, the importance of intellectual property rights and of the chemical and pharmaceutical industry, the special requirements of chemistry due to the importance of structural information, and finally the widespread conservatism among chemists when it comes to changes of the existing system.   Buffet Lunch   Keynote 4: CHORUS: A Solution for Public Access Dr. H. Frederick Dylla, Executive Director and CEO, American Institute of Physics, College Park, MD   Session: Big Data: Researchers' Perspectives on Hype, Challenges, Opportunities and Imperatives Chair: Jon Treadway, Senior Analyst, Science & Scholarly,Holtzbrinck Publishing Group, London   "Big data" has become a buzz phrase in fields from policy-making to advertising. Sharing, creation and analysis of data has the potential to transform the scientific record and further research, but grappling with "big data" is complex and challenging.   Invited Speakers:   Big Data and International Scientific Collaboration: Challenges and Opportunities Dr. Simon Hodson, Executive Director CODATA (ICSU Committee on Data for Science and Technology), Paris (accepted provisionally)   Publishing Large BioImage Datasets with Bio-Formats and OMERO: A Report from the Real World Dr. Jason Swedlow, Professor of Quantitative Cell Biology, University of Dundee and President, Glencoe Software (confirmed)   Science Funding and Science Policy: Big Data as a Tool for Supporting the Research Funding Process Christian Herzog, ÜberResearch GmbH, Köln   Coffee & Tea and Networking   Session: All about Metrics Chair: Mayur Amin, Senior Vice President of Research, Elsevier, Oxford   Altmetrics - building a broader Picture of Impact Dr. Paul Groth, Assistant Professor, Knowledge Representation & Reasoning Group, Department of Computer Science & the Network Institute, Free University of Amsterdam   Altmetrics - from Hype to Opportunity (Mike Taylor goes beyond the noise to present seven distinct uses for Altmetrics) Mike Taylor, Technology Research Specialist, Elsevier Labs, Oxford   The 'Finch Report' and the Transition to OA: Long Term Monitoring of Progress Michael Jubb, Executive Director, Research Information Network (RIN), London   APE Lecture Guest Lecture Professor Dr. Bernhard Sabel, Editor-in-Chief, "Restorative Neurology and Neuroscience", Medical Faculty, Otto-v.-Guericke Universität Magdeburg The Psychology of Innovation. Where Academia and Business meet or not.
Data & Analytics in the Law - NYC - Sep 27 2017
 
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Today Artificial Intelligence (AI), Blockchain, smart contracts, machine learning are top of mind and the legal profession is no exception. Law firms are hiring Chief Data Scientists; in-house departments are pressured to do more with less; and there have been two legal blockchain projects recently announced. By crunching data and using automation, lawyers can improving efficiency and accuracy and delivering better services to clients. Hear from experts on how your organization can harness information; produce analytics; and benefit from innovation in the law. 00:27 Welcome – Mary Juetten, Evolve Law 03:27 Darwin Talk – AI: An Historical Perspective – Dean Sonderegger, Wolters Kluwer 13:05 Expert Panel: Data & Analytics in the Law Moderator – Mary Juetten, Evolve Law Bennett Collen, Cognate Houman Shadab – New York Law School, Clause.io Susan Chazin, Wolters Kluwer Aaron Wright, Cardozo Law VENUE SPONSOR Cardozo Law - https://www.cardozo.yu.edu/ SPONSOR Wolters Kluwer - http://wolterskluwer.com/ ABOUT EVOLVE LAW Evolve Law brings together legal tech companies, attorneys, in-house counsel, entrepreneurs, and law schools for events centered around product demos, education, and discussion around the future of law. http://evolvelawnow.com #evolvelawlive #3539
Views: 469 Evolve the Law
From information society to knowledge society
 
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Marc Bertin is assistant professor at the University of Toulouse and he uses text and data mining to study scientific papers. Text and data mining can help us move from an information society to a knowledge society, but not without open access to research papers.
Views: 407 OpenMinTeD
I'm just not that good at coding
 
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If you ever feel that you will never be able to learn to code then watch this. You can buy me a coffee here if you like this channel https://www.ko-fi.com/S6S7LZYP TEDx Talk https://www.youtube.com/watch?v=0tqq66zwa7g Research Growth mindset on google Some learning resources Amazon (affiliate links) Hello World - Being Human in the Age of Algorithms https://amzn.to/2qJUjds Machine Learning for absolute beginners https://amzn.to/2K4dur4 Introduction to Statistical Learning https://amzn.to/2PwBI3o A First Course in Machine Learning https://amzn.to/2DlRqae The Elements of Statistical Learning: Data Mining, Inference, and Prediction https://amzn.to/2DHexwG Artificial Intelligence: A Modern Approach https://amzn.to/2PYzbOB Machine Learning: A Probabilistic Perspective https://amzn.to/2DqtEtQ Pattern Recognition and Machine Learning https://amzn.to/2DGtT4P Information Theory, Inference and Learning Algorithms https://amzn.to/2RX7nrd Make your Own Neural Network https://amzn.to/2DGG5T1 The Master Algorithm https://amzn.to/2DGgfia
Views: 4623 Python Programmer
What is the future for the commercial drone industry?
 
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#commercialdrones #dronecybersecurity Trusted rollout for commercial drones hinges on regulation, cyber security, connectivity, and being able to process data from drones in a standardized way. This is a key technological development we should be expecting, with commercial drones flying beyond the line of sight, either manually or in autonomous mode. Find out more with Peter Richardson, Research Director at Counterpoint. For more information on Drone Cybersecurity, visit our webpage: https://www.gemalto.com/iot/inspired/commercial-drone#key-factors.
Views: 1295 Gemalto
Master Innovation Research Informatics - Data Mining and Business Intelligence - FIB
 
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FIB Master's Degrees are official university studies within the framework of the European Higher Education Area (EHEA). Your degree is acknowledged all across the globe and it meets EU’s requirements. More information at: http://masters.fib.upc.edu/ The master empowers graduates with solid knowledge and hands-on experience on the techniques to manage, analyze and extract hidden knowledge from Big Data ensembles, either structured and unstructured, and to build adaptive Analytic systems able to exploit that knowledge in modern organizations. In particular the master addresses the new challenges of the smart society bloom: fraud detection, bioinformatics, extracting information from open linked data, real time analysis of sensor data and social networks, and customer relationship management,
Views: 2017 mediafib
An Interview: Professor meets a Scientist at Cyberc 2017
 
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Visit www.Cyberc.org and submit your research paper to attend this great event. CyberC (International Conference on Cyber-enabled distributed computing and knowledge discovery) is an international conference on cyber-enabled technology. It covers cyber-networks, data mining, cyber security, distributed computing, mobile computing, cognitive computing, cloud computing, computing tools, applications, and system performance. CyberC offers a forum for presentation and discussion of innovative ideas, research results, applications, and experience for network-enabled distributed computing and knowledge discovery technologies. This video is an interview and the background information is below. Dr. Chih-Lin I , Chief Scientist, Wireless Technologies, China Mobile Research Institute Chih-Lin I received her Ph.D. degree in electrical engineering from Stanford University. She has been working at multiple world-class companies and research institutes leading the R&D, including AT&T Bell Labs; Director of AT&T HQ, Director of ITRI Taiwan, and VPGD of ASTRI Hong Kong. She received the IEEE Trans. COM Stephen Rice Best Paper Award, is a winner of the CCCP National 1000 Talent Program, and has won the 2015 Industrial Innovation Award of IEEE Communication Society for Leadership and Innovation in Next-Generation Cellular Wireless Networks. In 2011, she joined China Mobile as its Chief Scientist of wireless technologies, established the Green Communications Research Center, and launched the 5G Key Technologies R&D. She is spearheading major initiatives including 5G, C-RAN, high energy efficiency system architectures, technologies and devices; and green energy. Keynote Topic in CyberC 2017: SDN/NFV via SBA&CUDU Abstract: From Green to Soft, the revolutionary path towards future 5G has been charted out. Since its proposal in 2012, SDN/NFV has been viewed as an essential element towards this end. In this talk, we will share CMCC’s endeavor on the path towards SOFT 5G. In particular, it will be presented how the philosophy of SDN/NFV has been adopted and implemented in our networks, from SBA-based cthe ore network to CU-DU-based radio access networks. In fact, C-RAN, which has been proposed by CMCC in 2009, has conceived the NFV idea since its birth. The achievements at the earlier stage will be introduced, including the deployment of centralization, trials on CoMP, some pioneering work on RAN virtualization. Then, the latest progress on RAN cloudification/virtualization will be detailed from various perspectives such as hypervisor, HW platform and MANO systems. Finally other key 5G components such as big data analytics, mobile edge computing etc. will also be touched. Anup Kumar, PhD, Professor, University of Louisville, Kentucky, USA Anup Kumar completed his Ph.D. from North Carolina State University and is currently a Professor of CECS Department at the University of Louisville. He is also the Director of Mobile Information Network and Distributed Systems (MINDS) Lab. His research interests include web services, wireless networks, distributed system modeling, and simulation. He has co-edited a book titled, “Handbook of Mobile Systems: Applications ands Services” published by CRC press in 2012. He is a Senior Member of IEEE. Topic: Access Control Security – Why and How Access Control Policies are Tested and Verified? Abstract: SDN/NFV, cloud, and many other online systems relies Access control (AC) to protect the secret financial, enterprise, organization, healthcare, defense, and various IT resources/services. In order to protect the classified resources, the security specialist needs to compose a set of AC policies (e.g., in XACML policies) to prevent unintended access. However, the current AC policies are composed and deployed into an AC system without comprehensive security tests and verifications. This results in many AC flaws (e.g., information or service leaks) in the systems and these AC flaws are normally hidden from us until observable damages (e.g., secret data leakage) are caused. This paves the way for cybersecurity hackers or insiders to steal the IT assets by exploring the access control weakness. Recently NIST has released several specifications in order to help government and enterprises to enhance the nation's critical access control security, such as NIST SP 800-192: Verification and Test Methods for Access Control Policies/Models. As stated by NIST, many of the access control incidents (e.g., data breaches, insiders) are caused by misconfigured access control policies. In this talk, we will explore the state-of-art access control policy testing and verification approaches. Examples are (i) Access Control Polity Tool and (ii) Security Policy Tool, which respectively delivers a solution for testing, analyzing, inspecting, and correcting the access control flaws. Primary information can be found at http://csrc.nist.gov/groups/SNS/acpt/acpt-beta.html and https://securitypolicytool.com .
Big Data Deep Learning Challenges and Perspectives    | IEEE | IEEE projects 2014
 
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Deep learning is currently an extremely active research area in machine learning and pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language processing. With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. In this paper, we provide a brief overview of deep learning, and highlight current research efforts and the challenges to big data, as well as the future trends. Graphical Abstract. Graphical Abstract. Published in: Access, IEEE (Volume:2 ) Date of Publication: 2014 Page(s): 514 - 525 ISSN : 2169-3536 INSPEC Accession Number: 14326272 DOI: 10.1109/ACCESS.2014.2325029 Date of Publication : 16 May 2014 Date of Current Version : 29 May 2014 Issue Date : 2014 Sponsored by : IEEE Publisher: IEEE Mob: 9985613074, 7396708351 Site: www.newieeeprojects.com Email: [email protected]
Views: 177 Renown Technologies
1/19 Aviv Zohar- “Bitcoin and the Blockchain. A new computational perspective on money”
 
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Speaker: Aviv Zohar, Senior Lecturer, School of Engineering and Computer Science, Hebrew University Title: “Bitcoin and the Blockchain. A new computational perspective on money” Abstract: Bitcoin and other modern cryptocurrency systems bring forth new paradigms for digital asset transmission and management. To do so they integrate advances from several areas in computer science and build upon decades of progress in the field. Recent research into their inner workings highlights several obstacles that still need to be overcome if they are to reach global adoption levels. The talk will review some of these challenges and survey emerging approaches to overcome them. Bio: Aviv Zohar is a faculty member at the Benin School of Computer Science and Engineering, in the Hebrew University of Jerusalem. His research revolves around the study of multi-agent systems including topics on the border of economics and computer science. Recently he has been studying cryptocurrencies such as Bitcoin and other permissionless distributed ledger systems.
Market Research, Analysis and Industry Engagement - Tim Evans
 
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Market Research, Analysis and Industry Engagement presentation by Tim Evans at the bluebox 2013 Innovation and Technology Transfer Workshop.
Views: 243 qutbluebox
'BREAKDOWN TOMORROW'🔸3 EXPERTS PREDICT END OF BEAR MARKET🔸 FACEBOOK ENTERS CRYPTO🔸 1BIL XRP MOVED
 
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Views: 6175 CryptoRevolution