Search results “Data mining knowledge discovery springer”
What is KNOWLEDGE DISCOVERY? What does KNOWLEDGE DISCOVERY mean? KNOWLEDGE DISCOVERY meaning - KNOWLEDGE DISCOVERY definition - KNOWLEDGE DISCOVERY explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. nowledge discovery describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. It is often described as deriving knowledge from the input data. Knowledge discovery developed out of the data mining domain, and is closely related to it both in terms of methodology and terminology. The most well-known branch of data mining is knowledge discovery, also known as knowledge discovery in databases (KDD). Just as many other forms of knowledge discovery it creates abstractions of the input data. The knowledge obtained through the process may become additional data that can be used for further usage and discovery. Often the outcomes from knowledge discovery are not actionable, actionable knowledge discovery, also known as domain driven data mining, aims to discover and deliver actionable knowledge and insights. Another promising application of knowledge discovery is in the area of software modernization, weakness discovery and compliance which involves understanding existing software artifacts. This process is related to a concept of reverse engineering. Usually the knowledge obtained from existing software is presented in the form of models to which specific queries can be made when necessary. An entity relationship is a frequent format of representing knowledge obtained from existing software. Object Management Group (OMG) developed specification Knowledge Discovery Metamodel (KDM) which defines an ontology for the software assets and their relationships for the purpose of performing knowledge discovery of existing code. Knowledge discovery from existing software systems, also known as software mining is closely related to data mining, since existing software artifacts contain enormous value for risk management and business value, key for the evaluation and evolution of software systems. Instead of mining individual data sets, software mining focuses on metadata, such as process flows (e.g. data flows, control flows, & call maps), architecture, database schemas, and business rules/terms/process.
Views: 1749 The Audiopedia
2018 HOLZINGER Machine Learning Research Topics
Andreas Holzinger promotes a synergistic approach by integration of two areas to understand intelligence to realize context-adaptive systems: Human-Computer Interaction (HCI) & Knowledge Discovery/Data Mining (KDD). Andreas has pioneered in interactive machine learning (iML) with the human-in-the-loop. Andreas Holzingers’ goal is to augment human intelligence with artificial intelligence to help to solve problems in health informatics. Due to raising legal and privacy issues in the European Union glass box AI approaches will become important in the future to be able to make decisions transparent, re-traceable, thus understandable. Andreas Holzingers’ aim is to explain why a machine decision has been made, paving the way towards explainable AI. 00:34 [1] June-Goo Lee, Sanghoon Jun, Young-Won Cho, Hyunna Lee, Guk Bae Kim, Joon Beom Seo & Namkug Kim 2017. Deep learning in medical imaging: general overview. Korean journal of radiology, 18, (4), 570-584, doi:10.3348/kjr.2017.18.4.570. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5447633/ 01:26 [2] Andreas Holzinger 2017. Introduction to Machine Learning and Knowledge Extraction (MAKE). Machine Learning and Knowledge Extraction, 1, (1), 1-20, doi:10.3390/make1010001. https://www.mdpi.com/2504-4990/1/1/1 03:21 [3] Andreas Holzinger 2013. Human–Computer Interaction and Knowledge Discovery (HCI-KDD): What is the benefit of bringing those two fields to work together? In: Cuzzocrea, Alfredo, Kittl, Christian, Simos, Dimitris E., Weippl, Edgar & Xu, Lida (eds.) Multidisciplinary Research and Practice for Information Systems, Springer Lecture Notes in Computer Science LNCS 8127. Heidelberg, Berlin, New York: Springer, pp. 319-328, doi:10.1007/978-3-642-40511-2_22. https://link.springer.com/chapter/10.1007/978-3-642-40511-2_22 04:00 [4] Andreas Holzinger & Klaus-Martin Simonic (eds.) 2011. Information Quality in e-Health. Lecture Notes in Computer Science LNCS 7058, Heidelberg, Berlin, New York: Springer, doi:10.1007/978-3-642-25364-5. https://www.springer.com/de/book/9783642253638 04:26 [5] Andreas Holzinger, Matthias Dehmer & Igor Jurisica 2014. Knowledge Discovery and interactive Data Mining in Bioinformatics - State-of-the-Art, future challenges and research directions. Springer/Nature BMC Bioinformatics, 15, (S6), I1, doi:10.1186/1471-2105-15-S6-I1. https://www.ncbi.nlm.nih.gov/pubmed/25078282 04:40 [6] Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams & Nando De Freitas 2016. Taking the human out of the loop: A review of Bayesian optimization. Proceedings of the IEEE, 104, (1), 148-175, doi:10.1109/JPROC.2015.2494218. https://www.semanticscholar.org/paper/Taking-the-Human-Out-of-the-Loop%3A-A-Review-of-Shahriari-Swersky/5ba6dcdbf846abb56bf9c8a060d98875ae70dbc8 05:10 [7a] Quoc V. Le, Marc'aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeff Dean & Andrew Y. Ng 2011. Building high-level features using large scale unsupervised learning. arXiv:1112.6209.05:16 https://arxiv.org/abs/1112.6209 [7b] Quoc V. Le. Building high-level features using large scale unsupervised learning. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013. IEEE, 8595-8598, doi:10.1109/ICASSP.2013.6639343. https://ieeexplore.ieee.org/abstract/document/6639343 05:24 [8] Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau & Sebastian Thrun 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, (7639), 115-118, doi:10.1038/nature21056. https://cs.stanford.edu/people/esteva/nature [9] Alex Krizhevsky, Ilya Sutskever & Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In: Pereira, Fernando, Burges, Christopher .J.C., Bottou, Leon & Weinberger, Kilian Q., eds. Advances in neural information processing systems (NIPS 2012), 2012 Lake Tahoe. NIPS, 1097-1105. https://github.com/abhshkdz/papers/blob/master/reviews/imagenet-classification-with-deep-convolutional-neural-networks.md 06:15 [10] Randy Goebel, Ajay Chander, Katharina Holzinger, Freddy Lecue, Zeynep Akata, Simone Stumpf, Peter Kieseberg & Andreas Holzinger. Explainable AI: the new 42? Springer Lecture Notes in Computer Science LNCS 11015, 2018 Cham. Springer, 295-303, doi:10.1007/978-3-319-99740-7_21. https://link.springer.com/chapter/10.1007/978-3-319-99740-7_21 06:45 [11] Zhangzhang Si & Song-Chun Zhu 2013. Learning and-or templates for object recognition and detection. IEEE transactions on pattern analysis and machine intelligence, 35, (9), 2189-2205, doi:10.1109/TPAMI.2013.35. https://ieeexplore.ieee.org/document/6425379 About the concept of the human-in-the-loop: [1] Andreas Holzinger 2016. Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop? Brain Informatics, 3, (2), 119-131, doi:10.1007/s40708-016-0042-6. https://link.springer.com/article/10.1007/s40708-016-0042-6 https://hci-kdd.org http://www.aholzinger.at
Views: 215 Andreas Holzinger
The difference between structured and unstructured data
http://www.perfectsearchcorp.com -- Lynn Bendixsen, Software Engineer at Perfect Search, explains the difference between structured and unstructured data.
Views: 4100 PerfectSearchCorp
Do you KnowDis? ...Knowledge Discovery in Organizations
Laqua, S., Sasse, M.A., Gates, C., and Greenspan, S. (2011). Do you KnowDis? A User Study of a Knowledge Discovery Tool for Organizations. To be presented at CHI 2011, Vancouver, BC, Canada. See: http://sec.cs.ucl.ac.uk/projects/knowdis/
Views: 157 chi2011madness
Data Mining Lecture 2 Part 1
Statistics Principles : Birthday Paradox + Coupon Collector
Views: 373 Utah Data
Pocket Data Mining
http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-319-02710-4 Pocket Data Mining PDM is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. Related publications: Stahl F., Gaber M. M., Bramer M., and Yu P. S, Distributed Hoeffding Trees for Pocket Data Mining, Proceedings of the 2011 International Conference on High Performance Computing & Simulation (HPCS 2011), Special Session on High Performance Parallel and Distributed Data Mining (HPPD-DM 2011), July 4 -- 8, 2011, Istanbul, Turkey, IEEE press. http://eprints.port.ac.uk//3523 Stahl F., Gaber M. M., Bramer M., Liu H., and Yu P. S., Distributed Classification for Pocket Data Mining, Proceedings of the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS 2011), Warsaw, Poland, 28-30 June, 2011, Lecture Notes in Artificial Intelligence LNAI, Springer Verlag. http://eprints.port.ac.uk/3524/ Stahl F., Gaber M. M., Bramer M., and Yu P. S., Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments, Proceedings of the IEEE 22nd International Conference on Tools with Artificial Intelligence (ICTAI 2010), Arras, France, 27-29 October, 2010. http://eprints.port.ac.uk/3248/
Views: 2958 Mohamed Medhat Gaber
[ACSIC Speaker Series #5] Writing Research Papers for Premier Forums in Knowledge and Data Engine...
Time: Jan. 22nd, 10:00--11:30am, EST Title:  Writing Research Papers for Premier Forums in Knowledge and Data Engineering Presenter: Xindong Wu is a Professor of Computer Science at the University of Vermont (USA), and a Fellow of the IEEE and the AAAS. He holds a PhD in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, Big Data analytics, knowledge engineering, and Web systems. He has published over 370 refereed papers in these areas in various journals and conferences, including IEEE TPAMI, TKDE, ACM TOIS, KAIS, DMKD, IJCAI, AAAI, ICML, KDD, ICDM, and WWW, as well as 40 books and conference proceedings. He is Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), Editor-in-Chief of Knowledge and Information Systems (KAIS, by Springer), and Editor-in-Chief of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE, by the IEEE Computer Society) between January 1, 2005 and December 31, 2008. He has served as Program Committee Chair/Co-Chair for ICDM '03 (the 2003 IEEE International Conference on Data Mining), KDD-07 (the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), CIKM 2010 (the 19th ACM Conference on Information and Knowledge Management), and ASONAM 2014 (the 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining). Professor Wu is the 2004 ACM SIGKDD Service Award winner and the 2006 IEEE ICDM Outstanding Service Award winner. He received the 2012 IEEE Computer Society Technical Achievement Award "for pioneering contributions to data mining and applications", and the 2014 IEEE ICDM 10-Year Highest-Impact Paper Award.
Views: 1600 Acsic People
Providing insight into the structure of scientific papers
How is a scientific paper structured and how related is it to other papers? These are some of the things that Iana Atanassova of the University of Bourgogne Franche-Comte (Besancon, France) focuses on in her research. She uses text and data mining to study full-text scientific articles. Studying these papers can be a challenge, as they are usually in a format that is hard to process. For more info about text and data mining, visit www.openminted.eu
Views: 148 OpenMinTeD
FDP on Data Mining - Tools and Research Issues by Dr A V KrishnaPrasad
FDP on Data Mining - Tools and Research Issues by Dr A V KrishnaPrasad
What is AGENT MINING? What does AGENT MINING mean? AGENT MINING meaning, definition & explanation
What is AGENT MINING? What does AGENT MINING mean? AGENT MINING meaning - AGENT MINING definition - AGENT MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Agent mining is an interdisciplinary area that synergizes multiagent systems with data mining and machine learning. The interaction and integration between multiagent systems and data mining have a long history. The very early work on agent mining focused on agent-based knowledge discovery, agent-based distributed data mining, and agent-based distributed machine learning, and using data mining to enhance agent intelligence. The International Workshop on Agents and Data Mining Interaction has been held for more than 10 times, co-located with the International Conference on Autonomous Agents and Multi-Agent Systems. Several proceedings are available from Springer Lecture Notes in Computer Science.
Views: 57 The Audiopedia
Day2 JustAlhaji x264
Reda Alhajj Department of Computer Science, the University of Calgary CANADA Title Facilitating Big Data Analysis Using Limited Computing Resources Abstract The rapid development in technology and social media has gradually shifted the focus in research, industry and community from traditional into dynamic environments where creativity and innovation dominate various aspects of the daily life. This facilitated the automated collection and storage of huge amount of data which is necessary for effective decision making. The value of data is increasingly realized and there is a tremendous need for effective techniques to maintain and handle the collected data starting from storage to processing and analysis leading to knowledge discovery. This talk will focus on techniques and structures which could maximize the benefit from data beyond what is traditionally supported. We emphasize on data intensive domains which require developing and utilizing advance computational techniques for informative discoveries. We describe some of our accomplishments, ongoing research and future research plans. The notion of big data will be addressed to show how it is possible to process incrementally available big data using limited computing resources. The benefit of various data mining and network modeling mechanisms for data analysis and prediction will be addressed with emphasize on some practical applications ranging from forums and reviews to social media as effective means for communication, sharing and discussion leading to collaborative decision making and shaping of future plans. Biography Reda Alhajj is a professor in the Department of Computer Science at the University of Calgary. He published over 500 papers in refereed international journals, conferences and edited books. He served on the program committee of several international conferences. He is founding editor in chief of the Springer premier journal “Social Networks Analysis and Mining”, founding editor-inchief of Springer Series “Lecture Notes on Social Networks”, founding editor-in-chief of Springer journal “Network Modeling Analysis in Health Informatics and Bioinformatics”, founding coeditor-in-chief of Springer “Encyclopedia on Social Networks Analysis and Mining”, founding steering chair of the flagship conference “IEEE/ACM International Conference on Advances in Social Network Analysis and Mining”, and three accompanying symposiums FAB, FOSINT-SI and HI-BI-BI. He is member of the editorial board of the Journal of Information Assurance and Security, Journal of Data Mining and Bioinformatics, Journal of Data Mining, Modeling and Management; he has been guest editor of a number of special issues and edited a number of conference proceedings. Dr. Alhajj's primary work and research interests focus on various aspects of data science and big data with emphasis on areas like: (1) scalable techniques and structures for data management and mining, (2) social network analysis with applications in computational biology and bioinformatics, homeland security, etc., (3) sequence analysis with emphasis on domains like financial, weather, traffic, energy, etc., (4) XML, schema integration and reengineering. He currently leads a large research group of PhD and MSc candidates. He received best graduate supervision award and community service award at the University of Calgary. He recently mentored a number of successful teams, including SANO who ranked first in the Microsoft Imagine Cup Competition in Canada and received KFC Innovation Award in the World Finals held in Russia, TRAK who ranked in the top 15 teams in the open data analysis competition in Canada, Go2There who ranked first in the Imagine Camp competition organized by Microsoft Canada, Funiverse who ranked first in Microsoft Imagine Cup Competition in Canada. http://www.tophpc.com
Views: 9 TopHPC Office
A constraint-based local search backend for MiniZinc
by Gustav Björdal, Jean-Noël Monette , Pierre Flener, Justin Pearson http://link.springer.com/article/10.1007%2Fs10601-015-9184-z#page-1
Data mining
Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1629 Audiopedia
Symmetry Group-based Learning for Regularity Discovery from Real World Patterns
Google Tech Talks December 15, 2008 ABSTRACT We explore a formal and computational characterization of real world regularity using discrete symmetry groups (hierarchy) as a theoretical basis, embedded in a well-defined Bayesian framework. Our existing work on "A Computational Model for Periodic Pattern Perception Based on Frieze and Wallpaper Groups" (TPAMI 2004), 'Near-regular texture analysis and manipulation' (SGIGRAPH 2004), and "A Lattice-based MRF Model for Dynamic Near-regular Texture Tracking" (PAMI 2007) already demonstrate the power of such a formalization on a diverse set of real problems, such as texture analysis, synthesis, tracking, perception and manipulation in terms of regularity. Symmetry and symmetry group detection from real world data turns out to be a very challenging problem that has been puzzling computer vision researchers for the past 40 years. Our novel formalization will lead the way to a more robust and comprehensive algorithmic treatment of the whole regularity spectrum, from regular (perfect symmetry), near-regular (deviations from symmetry), to various types of irregularities. The recent results of the proposed methodology will be illustrated in this talk by several real world applications such as deformed lattice detection, rotation and glide-reflection detection, gait recognition, grid-cell clustering, symmetry of dance, automatic geo-tagging and image de-fencing. Speaker: Yanxi Liu Yanxi Liu received her B.S. degree in physics/electrical engineering and her Ph.D. degree in computer science for group theory applications in robotics (UMass Amherst). Her postdoctoral training was performed in LIFIA/IMAG, Grenoble, France. She spent one year at DIMACS (NSF center for Discrete Mathematics and Theoretical Computer Science) with an NSF research-education fellowship award. Before joining the Departments of Computer Science and Engineering and Electrical Engineering at Penn State in Fall 2006 as a tenured faculty member, Dr. Liu had been with the faculty of the Robotics Institute of Carnegie Mellon University, and affiliated with the Machine Learning Department of CMU. She is also an adjunct associate professor in the Radiology Department of University of Pittsburgh. Dr. Liu is the co-director of the Laboratory for Perception, Action, and Cognition (LPAC) at Penn State (http://vision.cse.psu.edu/). Dr. Liu's research interests span a wide range of applications in computer vision and pattern recognition, computer graphics, medical image analysis and robotics, with two main research themes: computational (a)symmetry and discriminative subspace learning. With her colleagues, Dr. Liu won first place in the clinical science category and the best paper overall at the Annual Conference of Plastic and Reconstructive Surgeons for their work on "Measurement of Asymmetry in Persons with Facial Paralysis." Dr. Liu chaired the First International Workshop on Computer Vision for Biomedical Image Applications (CVBIA) in conjunction with ICCV 2005 in Beijing, and co-edited the book: "CVBIA: Current Techniques and Future Trends," Springer-Verlag LNCS 3765. Dr. Liu serves as an area chair/reviewer/committee member/panelist for all major journals, conferences, and NIH/NSF panels in computer vision, computer graphics, pattern recognition, biomedical image analysis, and machine learning. She has served as a chartered NIH study section member. She is a senior member of IEEE and the IEEE Computer Society.
Views: 8493 GoogleTechTalks
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
Authors: Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, Noemie Elhadad Abstract: In machine learning often a tradeoff must be made between accuracy and intelligibility. More accurate models such as boosted trees, random forests, and neural nets usually are not intelligible, but more intelligible models such as logistic regression, naive-Bayes, and single decision trees often have significantly worse accuracy. This tradeoff sometimes limits the accuracy of models that can be applied in mission-critical applications such as healthcare where being able to understand, validate, edit, and trust a learned model is important. We present two case studies where high-performance generalized additive models with pairwise interactions (GA2Ms) are applied to real healthcare problems yielding intelligible models with state-of-the-art accuracy. In the pneumonia risk prediction case study, the intelligible model uncovers surprising patterns in the data that previously had prevented complex learned models from being fielded in this domain, but because it is intelligible and modular allows these patterns to be recognized and removed. In the 30-day hospital readmission case study, we show that the same methods scale to large datasets containing hundreds of thousands of patients and thousands of attributes while remaining intelligible and providing accuracy comparable to the best (unintelligible) machine learning methods. ACM DL: http://dl.acm.org/citation.cfm?id=2788613 DOI: http://dx.doi.org/10.1145/2783258.2788613
Recent Advances in Data Assimilation
The improvement in numerical weather prediction in the last three decades is due to improvements in atmospheric models, observations and data assimilation (the science of combining forecasts and observations to create model initial conditions). In recent years, the Ensemble Kalman filter has become the most advanced approach for performing data assimilation. I will introduce data assimilation, Ensemble Kalman filters and new advances that extend their usefulness. In addition, I will show application of these algorithms to real and simulated observation examples, illustrating the potential of these new approaches. The results include seven years of global ocean data assimilation, estimation of surface carbon, heat and moisture fluxes from atmospheric data assimilation, and a comparison of fourdimensional variational data assimilation (4D-Var) and an Ensemble Kalman filter for a simple "coupled ocean-atmosphere model". About Dr. Eugenia Kalnay Eugenia Kalnay is a Distinguished University Professor in the Department of Atmospheric and Oceanic Science at the University of Maryland. For ten years she served as Director of the Environmental Modeling Center within the National Weather Service, which is a pioneer in both the fundamental science and practical applications of numerical weather prediction. In 2009, Dr. Kalnay won the prestigious World Meteorological Organization IMO Prize. Her work on the impact of land use on climate change was chosen by Discovery Magazine as one of the top 100 science results of the year, and her seminal paper on reanalysis is the most cited paper in geosciences. Dr. Kalnay was the first woman to obtain a doctorate from the MIT Department of Meteorology (1971) and the first female professor there. Her current research interests lie in data assimilation, numerical weather prediction, coupled ocean-atmosphere modeling and climate change. This lecture is part of the seminar series at NASA Jet Propulsion Laboratory's Center for Climate Sciences. Lecture Date: February 15, 2012 http://climatesciences.jpl.nasa.gov
Views: 2918 JPLClimateSciences
"A Systems Approach to Data Privacy in the Biomedical Domain" (CRCS Lunch Seminar)
CRCS Privacy and Security Lunch Seminar (Wednesday, May 13, 2009) Speaker: Bradley Malin Title: A Systems Approach to Data Privacy in the Biomedical Domain Abstract: The healthcare community has made considerable strides in the development and deployment of information systems, with particular gains in electronic health records and cheap genome sequencing technologies. Given the recent passage of the American Recovery and Reinvestment Act of 2009, and the HITECH Act in particular, advancement and adoption of such systems is expected to grow at unprecedented rates. The quantity of patient-level data that will be generated is substantial and can enable more cost-effective care as well as support a host of secondary uses, such biomedical research and biosurveillance. At the same time, care must be taken to ensure that such records are accessed and shared without violating a patient's privacy rights. The construction and application of data privacy technologies in the biomedical domain is a complex endeavor and requires the resolution of often competing computational, organizational, regulatory, and scientific needs. In this talk, I will introduce how the Vanderbilt Health Information Privacy Laboratory builds and applies data privacy solutions to support various biomedical settings. Our solutions are rooted in computational formalisms, but are driven by real world requirements and, as such, draw upon various tools and techniques from a number of fields, including cryptography, databases and data mining, public policy, risk analysis, and statistics. Beyond a high-level overview, I will delve into recent research on how we are measuring and mitigating privacy risks when sharing patient-level data from electronic medical and genomic records from the Vanderbilt University Medical Center to local researchers and an emerging de-identified repository at the National Institutes of Health. Bio: Brad Malin is an Assistant Professor of Biomedical Informatics in the School of Medicine and an Assistant Professor of Computer Science in the School of Engineering at Vanderbilt University. He is the founder and director of the Vanderbilt Health Information Privacy Laboratory (HIPLab), which focuses on basic and applied research in a number of health-related areas, including primary care and secondary sharing of patient-specific clinical and genomic data. His research has received several awards of distinction from the American and International Medical Informatics Associations and the HIPLab is currently supported by grant funding from the National Science Foundation, National Institutes of Health, and Veterans Health Administration. For the past several years, he has directed a data privacy research and consultation team for the Electronic Medical Records and Genomics (eMERGE) project, a consortium sponsored by the National Human Genome Research Institute. He has served as a program committee member and workshop chair for numerous conferences on data mining, privacy, and medical informatics. He has also edited several volumes for Springer Lecture Notes in Computer Science, a special issue for the journal Data and Knowledge Engineering, and is currently on the editorial board of the journal Transactions on Data Privacy. He received a bachelor's in biology (2000), master's in knowledge discovery and data mining (2002), master's in public policy & management (2003), and a doctorate in computation, organizations & society (2006) from the School of Computer Science at Carnegie Mellon University. His home on the web can be found at http://www.hiplab.org/people/malin
Views: 203 Harvard's CRCS
XSearch Demo
XSearch is a meta-search engine that reads the description of an underlying search source, queries that source, analyzes the returned results in various ways and also exploits the availability of semantic repositories. The key features of XSearch are: (a) Textual clustering of the results. (b) Named Entity Recognition (NER) of the results. (c) Faceted search-like exploration of the results. (d) On-click semantic exploration of a Knowledge Base. (e) Entity discovery and exploration during plain web browsing. XSearch has been designed and developed by the Information Systems Laboratory of FORTH-ICS (http://www.ics.forth.gr/isl) More information and live demos van be found at http://www.ics.forth.gr/isl/X-Search/ More information about XSearch can be found in the following scientific publications: • P. Fafalios, P. Papadakos and Y. Tzitzikas, Enriching Textual Search Results at Query Time using Entity Mining, Linked Data and Link Analysis, International Journal of Semantic Computing, Vol. 08, No. 04 (2014), World Scientific (ISSN: 1793-7108). • P. Fafalios and Y. Tzitzikas, Exploratory Professional Search through Semantic Post-Analysis of Search Results, Professional Search in the Modern World, Lecture Notes in Computer Science, Vol. 8830, Springer, 2014. • P. Fafalios and Y. Tzitzikas, Post-Analysis of Keyword-based Search Results using Entity Mining, Linked Data and Link Analysis at Query Time, IEEE 8th International Conference on Semantic Computing (ICSC'14), Newport Beach, California, USA, June 2014. • P. Fafalios and Y. Tzitzikas, X-ENS: Semantic Enrichment of Web Search Results at Real-Time, Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (demo paper), SIGIR 2013, Dublin, Ireland, 28 July - 1 August 2013. • P. Fafalios, M. Salampasis and Y. Tzitzikas, Exploratory Patent Search with Faceted Search and Configurable Entity Mining, Proceedings of the 1st International Workshop on Integrating IR technologies for Professional Search, in conjunction with the 35th European Conference on Information Retrieval (ECIR'13), Moscow, Russia, March 2013. • P. Fafalios, I. Kitsos, Y. Marketakis, C. Baldassarre, M. Salampasis and Y. Tzitzikas, Web Searching with Entity Mining at Query Time, Proceedings of the 5th Information Retrieval Facility Conference, IRFC 2012, Vienna, Austria, July 2012.
Views: 45 Yannis Marketakis
Data Reduction
Data Reduction via a Highly Exothermic Reaction
Views: 723 Joe Huffman
Improvisation in Frequent Pattern Mining Technique
Author Sagar Gajera, Manmay Badheka, L J Institute of Engineering and Technology, Ahmedabad, India {sdgajera14, manmaybadheka}@gmail.com ABSTRACT In Present, so many techniques are available which can be applicable to wide range of datasets. They provide effective way to mine frequent pattern from the datasets. Most of them use different kind of data structures for the processing which provide variations in requirement of time and space. Generally, traditional techniques are restricted to the narrow area or provide effective results only in the specific environment. So, it requires continuous optimization and updation. Dynamic data structure and mapping shows more effectiveness compared to the traditional techniques in terms of time and space requirement for processing. Aspire Research Foundation The International Conference on Data Engineering and Communication Technology-ICDECT will be held during March 10 -11, 2016 at LAVASA, Pune. ICDECT, is to bring together innovative academics and industrial experts in the field of Computer Science and Electronics Engineering to a common forum. ICDECT will provide an Excellent international forum for sharing knowledge and results in Computer Science and Electronics Engineering. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet the share cutting-edge development in the field. The primary goal of the conference is to promote research and developmental activities in Computer Science and Electronics Engineering. Another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Computer Science and Electronics Engineering and related areas. All Accepted & Registered Papers will be published in AISC Series of Springer http://www.springer.com/series/11156 ** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink ** Aspire Research Foundation http://aspire-research.org/ ICDECT 2016 http://icdect.com/ Aspire Team http://aspire-research.org/innerhome.jsp?page=our_team.html Advisory Commitee http://aspire-research.org/innerhome.jsp?page=advisory_commitee.html -- ------------------------------------------------------------------------------------------------------- Regards Ganesh Khedkar Director Aspire Research Foundation Email:[email protected] http://aspire-research.org/
Networking Knowledge - The world of STM Publishing
We believe that policy makers and the public at large would benefit from a simple and clear description of the role of the scientific publisher in today's system of scholarly communication. This video, along with one other Networking Knowledge video, explains simply how publishers add value to scholarly communication and invest in making scientific information more accessible and discoverable. View them. Use them. Embed them. **** STM hereby grants any person a worldwide, non-exclusive, royalty-free, licence to use, reproduce, distribute, display, broadcast, transmit by wire or wireless means, communicate to members of the public and perform the cinematograph film, including the sound-track embodied therein, in the two videos entitled Networking Knowledge -- The world of STM publishing and Networking Knowledge -- It´s all about discoverability (the "videos"), including, without limitation, for promoting and redistributing part or all of the videos and any part thereof in any media formats and through any media channels, provided the following conditions are met: (i) the copyright subsisting in the videos and STM's ownership is acknowledged, as well as the producer Thank You & Good Night productions S.a.r.l. (ii) the videos are not denigrated or mutilated; (iii) dubbing, reproducing the sound-track or musical score alone, translating, introducing sub-titles is only permitted with the prior written authority of STM (iv) Where permission is given, any adaptation remains true to the original videos. STM grants the above licence "as is" without any warranty of fitness for purpose. Please note that although the content displayed in the videos is not considered harmful to persons under age, approval may be required under local law before distributing or otherwise communicating the videos in some countries of the world to all or some audiences. No animals or plants were harmed in the making of this video. Trademarks, service marks and logos ("Marks") used in the video, are owned by or licensed to STM, subject to copyright and other intellectual property rights under the law.
May 23, 2017-PAKDD PAISI in Jeju, KR
The following two papers from Computer Crime Investigation Lab, Dep. of Information Management, Central Police University, Taiwan, R.O.C. ~~ 1. Da-Yu Kao, Benjamaporn Kluaypa, Hung-Chih Lin, “The Cyberbullying Assessment of Capable Guardianship in Routine Activity Theory,” Intelligence and Security Informatics: 12th Pacific Asia Workshop PAISI 2017 Proceedings, LNCS 10241, Springer International Publishing, pp. 3-14, Jeju, South Korea, May 23-26, 2017. (ISSN: 0302-9743) (EI) 2. Shou-Ching Hsiao and Da-Yu Kao, “Differentiating the Investigation Response Process of Cyber Security Incident for LEAs,” Intelligence and Security Informatics: 12th Pacific Asia Workshop PAISI 2017 Proceedings, LNCS 10241, Springer International Publishing, pp. 34-48, Jeju, South Korea, May 23-26, 2017. (ISSN: 0302-9743) (EI) (MOST 105-2221-E-015-001-)
Views: 21 Dayu Kao
Krishna Dronamraju | Foundation for Genetic Research | USA | Omics International
Title: Applications of genomics and future prospects 2nd International Conference on Genomics & Pharmacogenomics, September 08-10, 2014, Doubletree by Hilton Hotel Raleigh-Brownstone-University, USA OMICS International: http://omicsonline.org Vaccine Conferences: http://conferenceseries.com/immunology-conferences.php Global Medical Conferences: http://conferenceseries.com Global Pharmaceutical Conferences: http://pharmaceuticalconferences.com Global Cancer Conferences: http://cancersummit.org Global Diabetes Conferences: http://diabetesexpo.com Global Dental Conferences: http://dentalcongress.com Global Nursing Conferences: http://nursingconference.com Abstract: Applications of genomics include clinical genetics, disease gene discovery, identifying gene regulatory systems, and Evolutionary analysis. The two areas of paramount interest are (a) vaccine development for preventing infections which are major killers such as the malaria parasites and HIV virus, and (b) cancer genomics. A comprehensive analysis of the cancer genome remains a daunting challenge. There is no single technology at present that will detect all the types of abnormality-deletions, rearrangements, point mutations, frame shift insertions, amplifications, imprinting, and epigenetic changes implicated in cancer. Microarrays and gene chip analysis, however, are beginning to unveil some key genomic drivers. Many clinical trials now include genomic profiles of cancer patients as prognostic and diagnostic indicators, which are playing an increasing role in defining pharmacogenomics. Genomic profiles are useful in monitoring where and how the cancer genome has been hit during molecularly targeted therapies. Data-Mining and sharing these data should eventually help to better integrate the genotypic and phenotypic changes that occur in a during cancer’s progression. Genetic variability revealed in the newly sequenced malaria genomes indicates new challenges in efforts to eradicate the parasite, but also offers a clearer and more detailed picture of its genetic composition, providing an initial roadmap in the development of pharmaceuticals and vaccines to combat malaria. Recent studies have focused attention on Plasmodium vivax, a species of malaria that afflicts humans and the most prevalent human malaria parasite outside Africa, and Plasmodium cynomolgi, a close relative that infects Asian Old World monkeys. However, what has been revealed is both bad and good. The bad news is that there is significantly more genetic variation in P. vivax than what had been expected, which increases the likelihood of the pathogen evading whatever drugs and vaccines we may use. The good news is that we have gained greater knowledge of the challenges we will be facing and we can move forward with more confidence in designing more effective remedies. Research at The Sanger Institute in the U.K. has produced the world’s largest resource of malaria parasite genomic data, comprising over 3,000 genomes to date. The level of detail, both genomic and geographical, afforded by this project has enabled new levels of genetic analysis. The relationship between clinical research and genomics is at a turning point in malaria, offering much promise for future advances. Biography: Krishna Dronamraju is a geneticist and author of many books in genetics, medical genetics and history of science. He worked with Victor McKusick in medical genetics at the Johns Hopkins University School of Medicine, and recently edited, with Clair Francomano(eds), Victor McKusick and the History of Medical Genetics, published by Springer, New York, 2012. He was a member of the U.S. Presidential delegation led by President Bill Clinton to India to promote cooperation in science and technology in 2000. He served on the Recombinant DNA Advisory Committee of the U.S. National Institutes of Health, and the National Advisory Committee of the U.S. Secretary of Agriculture. He is a visiting Professor of the University of Paris, and an Honorary Research Fellow of University of London. His books include: Haldane, Mayr and Beanbag Genetics, Malaria: Genetic and Evolutionary Aspects, Infectious Disease and Host-Pathogen Evolution, The History and Development of Human Genetics, and (ed) Selected Genetic Papers of JBS Haldane.
Views: 159 OMICS International
Query Model for Image Search based on User Clicks and NN Features - Dmitry Krivokon
Yandex School of Data Analysis Conference Machine Learning: Prospects and Applications https://yandexdataschool.com/conference We consider a problem of improving the quality of a query-based image search engine by using user click data. The primary purpose of an image search engine (SE) is to assist in finding images that are relevant to the text query entered by a user (such SEs should not be confused with content base image retrieval [10]). The resulting images ideally should be sorted by their relevance in descending order, hence the main task of the SE is to determine the relevance (or rank [3]) of a particular image to the particular query. A lot of information about image relevance to the query can be deduced from the actions performed by a user while browsing the results of the SE. User clicks on a specific result can be considered as a strong signal of the image relevance. The abundance of this data in large SEs leaves a lot of space for different strategies for its adaptation to the ranking problem [2, 4]. We propose to use click data to construct a vector space representation of a query based on the content of the images on which the user clicked viewing the results of the search engine for the query. Document [8] and query models [9] are popular means to solve classification and cluster- ing problems, however, we apply our technique to directly compare a query and some image to understand their “similarity” to each other. To represent the content of an image we use one of the final layers of deep convolutional neural network [6] trained on the standard ImageNet data- set [1]. Essentially this representation is just a 100-dimensional vector of real values. Usage of this type of features became a generally accepted practice in various tasks of image classification and recognition. Besides, they are also generally used as the basis for image search engines that find images visually similar to the “query” image [10]. Such successful applications motivated our approach. Our query model is constructed by aggregating the feature vectors of all clicked images for a particular query. Having such a model, which by design resides in the same vector space as the image features used for its construction, allows for calculation of the direct distance between a query and a specific image. This distance can be used as a feature in a search engine ranker [2] or can be used to re-rank the top ranked images returned by such ranker [2, 5]. Having huge amounts of historical click data allows mitigation of the negative effects of noise that is naturally present in user data and even in the responses of the neural network. In addition, we use not only the features of a specific image on which users clicked but also the features of the image duplicates [7] found in the search engine database. That leads to even better quality of the resulting model. We analyze and compare several aggregation strategies and show the performance of our approach on standard type datasets by measuring NDCG [3] and MSE metrics. 1. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. 2012. 2. Jain, Vidit, and Manik Varma. “Learning to re-rank: query-dependent image re-ranking using click data.” Proceedings of the 20th international conference on World wide web. ACM, 2011. 3. Burges, Christopher JC. “From ranknet to lambdarank to lambdamart: An overview.” Learning 11 (2010): 23-581. 4. Joachims, Thorsten. “Optimizing search engines using clickthrough data.” Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2002. 5. Mei, Tao, et al. “Multimedia search reranking: A literature survey.” ACM Computing Surveys (CSUR) 46.3 (2014): 38. 6. LeCun, Yann, et al. “Gradient-based learning applied to document rec- ognition.” Proceedings of the IEEE 86.11 (1998): 2278-2324. 7. Ke, Yan, et al. “Efficient near-duplicate detection and sub-image retriev- al.” ACM Multimedia. Vol. 4. No. 1. 2004. 8. Mikolov, Tomas, et al. “Efficient estimation of word representations in vector space.” arXiv preprint arXiv:1301.3781 (2013). 9. Luo, Cheng, et al. “Query Ambiguity Identification Based on User Behavior Information.” Information Retrieval Technology. Springer Inter- national Publishing, 2014. 36-47. 10. Smeulders, Arnold WM, et al. “Content-based image retrieval at the end of the early years.” Pattern Analysis and Machine Intelligence, IEEE Transactions on 22.12 (2000): 1349-1380.
Methods for efficient statistical inference of stochastic dynamical systems
At the Conference on Mathematical Modeling and Control of Communicable Diseases, Professor Aaron A. King of the University of Michigan, United States, discusses "Methods for efficient statistical inference of stochastic dynamical systems". The conference was held in Rio de Janeiro in January, 2016. For more information, please visit the event website: http://math-epidemics.emap.fgv.br/
Views: 61 FGV Brazil
CLEI 2015 Dia 3 Deep Learning for Multimedia Data   teaching Computers to Sense
Deep Learning for Multimedia Data: Teaching Computers to Sense Omar Florez Intel Labs. California USA email: [email protected] Schedule:Wed [email protected]:00, Room: A For the past few years, deep learning has been making rapid progress in both techniques and applications; significant performance gains were reported using deep learning in automatic speech recognition and image recognition over hand-optimized feature representations. Advances in smartphones, tablets, and wearables have made possible to sense a rich collection of user data, examples include audio, images, and video. This information allows us to infer user contexts such as faces, semantic locations, activities, and mood states enabling better and personalized user experiences. This explains the growing interest of industry (Intel, Facebook, Google, NVIDIA, Spotify, Netflix, Baidu, etc.) trying to take advantage of deep learning capabilities in recent years. In several image and speech tasks, the success of deep learning is due to its ability to learn representations from noisy and unstructured data. Context sensing faces the same problem therefore we believe applications of deep learning in this domain can be advantageous. During this talk we will try bringing together researchers and applicants to discuss some of the deep learning algorithms and capabilities for multimedia and context domains as well as explore possible new research areas. Short Biography Dr. Omar U. Florez is a Research Scientist at the Anticipatory Computing Group at Intel Labs (California, USA). He graduated from Universidad Nacional de San Agustin, Peru in 2007 and received his Ph.D. in Computer Science at Utah State University in 2013. He is a recipient of an Innovation Award on Large-Scale Analytics by IBM Research, and the organizer of the NSF-funded Broader Participation in Data Mining workshop at KDD in 2014, which for first time funded the attendance of under-represented researchers worldwide. He is also the co-founder of South Americans in Computing. Dr. Florez's research interests cover statistical machine learning, recommender systems, and deep learning for multimedia data. He has 20+ academic publications and journals in ACM, IEEE, and Springer.
Views: 280 CLEI 2015

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