Slides for:
Rabia Haq, Michael L. Nelson: Using timed-release cryptography to mitigate the preservation risk of embargo periods. 2009 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 183-192.
Introduction to homomorphic encryption, encryption which allows computations on ciphertext. An overview of key aspects and the ideas that allow these schemes to work is given, as well as examples of how to apply it.
Christoph Matthies (@chrisma0), Hubert Hesse (@hubx), Robert Lehmann (@rlehmann)
This presentation deals with RealmDB, which is a convenient replacement for SQLite & Core Data in mobile development.
Presentation by Anton Minashkin (Software Engineer, GlobalLogic, Lviv), delivered at Mobile TechTalk Lviv on April 28, 2015.
More details - http://globallogic.com.ua/mobile-techtalk-lviv-2015-report
Shai Halevi discusses new ways to protect cloud data and security. Presented at "New Techniques for Protecting Cloud Data and Security" organized by the New York Technology Council.
This presentation helps discover some of the specific features of Java 8, (in particular, atomics and parallelism) and start using them effectively.
This presentation by Maksym Voronyi (Software Engineer, GlobalLogic) was delivered at Java.io 3.0 conference in Kharkiv on March 24, 2016, and GlobalLogic Mykolaiv Java Conference on June 11, 2016.
Cloud computing is an ever-growing field in today‘s era.With the accumulation of data and the
advancement of technology,a large amount of data is generated everyday.Storage, availability and security of
the data form major concerns in the field of cloud computing.This paper focuses on homomorphic encryption,
which is largely used for security of data in the cloud.Homomorphic encryption is defined as the technique of
encryption in which specific operations can be carried out on the encrypted data.The data is stored on a remote
server.The task here is operating on the encrypted data.There are two types of homomorphic encryption, Fully
homomorphic encryption and patially homomorphic encryption.Fully homomorphic encryption allow arbitrary
computation on the ciphertext in a ring, while the partially homomorphic encryption is the one in which
addition or multiplication operations can be carried out on the normal ciphertext.Homomorphic encryption
plays a vital role in cloud computing as the encrypted data of companies is stored in a public cloud, thus taking
advantage of the cloud provider‘s services.Various algorithms and methods of homomorphic encryption that
have been proposed are discussed in this paper
Introduction to homomorphic encryption, encryption which allows computations on ciphertext. An overview of key aspects and the ideas that allow these schemes to work is given, as well as examples of how to apply it.
Christoph Matthies (@chrisma0), Hubert Hesse (@hubx), Robert Lehmann (@rlehmann)
This presentation deals with RealmDB, which is a convenient replacement for SQLite & Core Data in mobile development.
Presentation by Anton Minashkin (Software Engineer, GlobalLogic, Lviv), delivered at Mobile TechTalk Lviv on April 28, 2015.
More details - http://globallogic.com.ua/mobile-techtalk-lviv-2015-report
Shai Halevi discusses new ways to protect cloud data and security. Presented at "New Techniques for Protecting Cloud Data and Security" organized by the New York Technology Council.
This presentation helps discover some of the specific features of Java 8, (in particular, atomics and parallelism) and start using them effectively.
This presentation by Maksym Voronyi (Software Engineer, GlobalLogic) was delivered at Java.io 3.0 conference in Kharkiv on March 24, 2016, and GlobalLogic Mykolaiv Java Conference on June 11, 2016.
Cloud computing is an ever-growing field in today‘s era.With the accumulation of data and the
advancement of technology,a large amount of data is generated everyday.Storage, availability and security of
the data form major concerns in the field of cloud computing.This paper focuses on homomorphic encryption,
which is largely used for security of data in the cloud.Homomorphic encryption is defined as the technique of
encryption in which specific operations can be carried out on the encrypted data.The data is stored on a remote
server.The task here is operating on the encrypted data.There are two types of homomorphic encryption, Fully
homomorphic encryption and patially homomorphic encryption.Fully homomorphic encryption allow arbitrary
computation on the ciphertext in a ring, while the partially homomorphic encryption is the one in which
addition or multiplication operations can be carried out on the normal ciphertext.Homomorphic encryption
plays a vital role in cloud computing as the encrypted data of companies is stored in a public cloud, thus taking
advantage of the cloud provider‘s services.Various algorithms and methods of homomorphic encryption that
have been proposed are discussed in this paper
A three-part presentation on the Swift programming language:
• An introduction to Swift for Objective-C developers
• Changes in Swift 2
• What's coming in Swift 2.2 & 3.0
Building a Big Data Machine Learning PlatformCliff Click
H2O - It's open source, in-memory, big data, clustered computing - Math At Scale. We got the Worlds Fastest Logistic Regression (by a lot!), world's first (and fastest) distributed Gradient Boosted Method (GBM), plus Random Forest, PCA, KMeans++, etc... R's "plyr" style data munging at-scale, including ddply (Group-By for you SQL'rs) and much of R's expressive coding style.
We built H2O, an open-source platform for working with in-memory distributed data. Then we built on top of H2O state-of-the-art predictive modeling and analytics (e.g. GLM & Logistic Regression, GBM, Random Forest, Neural Nets, PCA to name a few) that's 1000x faster than the disk-bound alternatives, and 100x faster than R (we love R but it's tooo slow on big data!). We can run R expressions on tera-scale datasets, or munge data from Scala & Python. We're building our newest algorithms in a few weeks, start to finish, because the platform makes Big Math easy. We routinely test on 100G datasets, have customers using 1T datasets.
This talk is about the platform, coding style & API that lets us seamlessly deal with datasets from 1K to 1TB without changing a line of code, lets us use clusters ranging from your laptop to 100 server clusters with many many TB of ram and hundreds of CPUs.
ASFWS 2012 - Hash-flooding DoS reloaded: attacks and defenses par Jean-Philip...Cyber Security Alliance
At 28c3, Klink and Waelde demonstrated that a number of technologies (PHP, .NET, Ruby, Java, etc.) remained vulnerable to the decade-old hash-flooding DoS attacks. These attacks work by enforcing worst-case insert time in hash tables by sending many inputs hashing to the same value (a “multicollision”). Many vendors fixed the issue by replacing the weak deterministic hash function with stronger and randomized hash functions. In this presentation, we will show examples of such stronger randomized hash functions that fail to protect against hash-flooding, by presenting “universal multicollision” attacks based on differential cryptanalysis techniques. We will present demos showing how to exploit these attacks to DoS a Ruby on Rails application, as well as the latest Java OpenJDK; two technologies that chose to “fix” hash-flooding by using the MurmurHash hash functions. Finally, we will describe a reliable fix to hash-flooding with the SipHash family of pseudorandom functions: SipHash provides the adequate cryptographic strength to mitigate hash-flooding, yet is competitive in performance with the non-cryptographic hashes.
Rust: Reach Further (from QCon Sao Paolo 2018)nikomatsakis
Rust is a new programming language that is growing rapidly. Rust's goal is to support a high-level coding style while offering performance comparable to C and C++ as well as minimal runtime requirements -- it does not require a runtime or garbage collector, and you can even choose to forego the standard library. At the same time, Rust offers strong support for parallel programming, including guaranteed freedom from data-races (something that GC’d languages like Java or Go do not provide).
Rust’s slim runtime requirements make it an ideal choice for integrating into other languages and projects. Anywhere that you could integrate a C or C++ library, you can choose to use Rust instead. Mozilla, for example, has rewritten a portion of the Firefox web browser in Rust -- while keeping the rest in C++. There are also projects for writing native extensions to Python, Ruby, and Node in Rust, as well as a recent effort to have the Rust compiler generate WebAssembly.
This talk will cover some of the highlights of Rust's design, and show how Rust's type system not only supports different parallel styles but also encourages users to write code that is amenable to parallelization. I'll also talk a bit about some of the experiences of using Rust in production, as well as how to integrate Rust into existing projects written in different languages.
Santa Fe Complex
March 13, 2009
Martin Klein, Frank McCown,
Joan Smith, Michael L. Nelson
Department of Computer Science
Old Dominion University
Norfolk VA
A three-part presentation on the Swift programming language:
• An introduction to Swift for Objective-C developers
• Changes in Swift 2
• What's coming in Swift 2.2 & 3.0
Building a Big Data Machine Learning PlatformCliff Click
H2O - It's open source, in-memory, big data, clustered computing - Math At Scale. We got the Worlds Fastest Logistic Regression (by a lot!), world's first (and fastest) distributed Gradient Boosted Method (GBM), plus Random Forest, PCA, KMeans++, etc... R's "plyr" style data munging at-scale, including ddply (Group-By for you SQL'rs) and much of R's expressive coding style.
We built H2O, an open-source platform for working with in-memory distributed data. Then we built on top of H2O state-of-the-art predictive modeling and analytics (e.g. GLM & Logistic Regression, GBM, Random Forest, Neural Nets, PCA to name a few) that's 1000x faster than the disk-bound alternatives, and 100x faster than R (we love R but it's tooo slow on big data!). We can run R expressions on tera-scale datasets, or munge data from Scala & Python. We're building our newest algorithms in a few weeks, start to finish, because the platform makes Big Math easy. We routinely test on 100G datasets, have customers using 1T datasets.
This talk is about the platform, coding style & API that lets us seamlessly deal with datasets from 1K to 1TB without changing a line of code, lets us use clusters ranging from your laptop to 100 server clusters with many many TB of ram and hundreds of CPUs.
ASFWS 2012 - Hash-flooding DoS reloaded: attacks and defenses par Jean-Philip...Cyber Security Alliance
At 28c3, Klink and Waelde demonstrated that a number of technologies (PHP, .NET, Ruby, Java, etc.) remained vulnerable to the decade-old hash-flooding DoS attacks. These attacks work by enforcing worst-case insert time in hash tables by sending many inputs hashing to the same value (a “multicollision”). Many vendors fixed the issue by replacing the weak deterministic hash function with stronger and randomized hash functions. In this presentation, we will show examples of such stronger randomized hash functions that fail to protect against hash-flooding, by presenting “universal multicollision” attacks based on differential cryptanalysis techniques. We will present demos showing how to exploit these attacks to DoS a Ruby on Rails application, as well as the latest Java OpenJDK; two technologies that chose to “fix” hash-flooding by using the MurmurHash hash functions. Finally, we will describe a reliable fix to hash-flooding with the SipHash family of pseudorandom functions: SipHash provides the adequate cryptographic strength to mitigate hash-flooding, yet is competitive in performance with the non-cryptographic hashes.
Rust: Reach Further (from QCon Sao Paolo 2018)nikomatsakis
Rust is a new programming language that is growing rapidly. Rust's goal is to support a high-level coding style while offering performance comparable to C and C++ as well as minimal runtime requirements -- it does not require a runtime or garbage collector, and you can even choose to forego the standard library. At the same time, Rust offers strong support for parallel programming, including guaranteed freedom from data-races (something that GC’d languages like Java or Go do not provide).
Rust’s slim runtime requirements make it an ideal choice for integrating into other languages and projects. Anywhere that you could integrate a C or C++ library, you can choose to use Rust instead. Mozilla, for example, has rewritten a portion of the Firefox web browser in Rust -- while keeping the rest in C++. There are also projects for writing native extensions to Python, Ruby, and Node in Rust, as well as a recent effort to have the Rust compiler generate WebAssembly.
This talk will cover some of the highlights of Rust's design, and show how Rust's type system not only supports different parallel styles but also encourages users to write code that is amenable to parallelization. I'll also talk a bit about some of the experiences of using Rust in production, as well as how to integrate Rust into existing projects written in different languages.
Santa Fe Complex
March 13, 2009
Martin Klein, Frank McCown,
Joan Smith, Michael L. Nelson
Department of Computer Science
Old Dominion University
Norfolk VA
Mathematics & Computer Science Seminar
Emory University
October 2, 2009
Martin Klein & Michael L. Nelson
Department of Computer Science
Old Dominion University
Norfolk VA
LANL Research Library
March 12, 2009
Martin Klein & Michael L. Nelson
Department of Computer Science
Old Dominion University
Norfolk VA
www.cs.odu.edu/~{mklein,mln}
A set of slides we've used in various presentations to show that replaying an experience via archived web pages is more compelling than reading a summary of the event.
Elliptic Curve Cryptography and Zero Knowledge ProofArunanand Ta
Elliptic Curve Cryptography and Zero Knowledge Proof
Presentation by Nimish Joseph, at College of Engineering Cherthala, Kerala, India, during Faculty Development Program, on 06-Nov-2013
Event description:
Why Tangent Works chooses Julia: The Two Language Problem
TIM: Automatic Model Building for Energy Industry
Julia and its major differences to other technical computing languages (R, Matlab, ...)
- Why is vectorized code fast?
- Why is it not as fast as it could be?
Speaker:
Ján Dolinský, Tangent Works (www.tangent.works)
Language of the event: Julia, Slovak & English
------------------------------------
PyData Bratislava [Python Data Enthusiasts and Users, Data Scientists & Statisticians of all levels from Slovakia]
------------------------------------
--
This meetup group is for Data Scientists, Statisticians, Economists and Data Enthusiasts using Python for data analysis and data visualization. The goals are to provide Python enthusiasts a place to share ideas and learn from each other about how best to apply the language and tools to ever-evolving challenges in the vast realm of data management, processing, analytics, and visualization.
--
PyData is a group for users and developers of data analysis tools to share ideas and learn from each other. We gather to discuss how best to apply Python tools, as well as those using R and Julia, to meet the evolving challenges in data management, processing, analytics, and visualization. PyData groups, events, and conferences aim to provide a venue for users acrossall the various domains of data analysis to share their experiences and their techniques. PyData is organized by NumFOCUS.org, a 501(c)3 non-profit in the United States.
------------------------------------
Our Facebook group here: https://www.facebook.com/groups/1813599648877946/
Our Twitter account here: https://twitter.com/PyDataBA
Our LinkedIn group here: https://www.linkedin.com/groups/13506080
All materials from previous meetups on GitHub here: https://github.com/GapData/PyDataBratislava
Recordings of previous meetups on our YouTube here: https://www.youtube.com/watch?v=XYpKpmapqjI&list=PLISV6olKXnd9pE-KPtPgwwLe6qPXvb9K7
------------------------------------
Organizers:
GapData Institute (https://www.gapdata.org/) (GDI) is a nonprofit nonpartisan research institution harnessing power of data & wisdom of economics for public good.
|| Data. Think. Change. ||
--
NumFOCUS (http://www.numfocus.org/) is a 501(c)(3) nonprofit that supports and promotes world-class, innovative, open source scientific computing. The mission of NumFOCUS is to promote sustainable high-level programming languages, open code development, and reproducible scientific research. We accomplish this mission through our educational programs and events as well as through fiscal sponsorship of open source data science projects. We aim to increase collaboration and communication within the scientific computing community.
Challenging Web-Scale Graph Analytics with Apache Spark with Xiangrui MengDatabricks
Graph analytics has a wide range of applications, from information propagation and network flow optimization to fraud and anomaly detection. The rise of social networks and the Internet of Things has given us complex web-scale graphs with billions of vertices and edges. However, in order to extract the hidden gems within those graphs, you need tools to analyze the graphs easily and efficiently.
At Spark Summit 2016, Databricks introduced GraphFrames, which implemented graph queries and pattern matching on top of Spark SQL to simplify graph analytics. In this talk, you'll learn about work that has made graph algorithms in GraphFrames faster and more scalable. For example, new implementations like connected components have received algorithm improvements based on recent research, as well as performance improvements from Spark DataFrames. Discover lessons learned from scaling the implementation from millions to billions of nodes; compare its performance with other popular graph libraries; and hear about real-world applications.
Challenging Web-Scale Graph Analytics with Apache SparkDatabricks
Graph analytics has a wide range of applications, from information propagation and network flow optimization to fraud and anomaly detection. The rise of social networks and the Internet of Things has given us complex web-scale graphs with billions of vertices and edges. However, in order to extract the hidden gems within those graphs, you need tools to analyze the graphs easily and efficiently.
At Spark Summit 2016, Databricks introduced GraphFrames, which implemented graph queries and pattern matching on top of Spark SQL to simplify graph analytics. In this talk, you’ll learn about work that has made graph algorithms in GraphFrames faster and more scalable. For example, new implementations like connected components have received algorithm improvements based on recent research, as well as performance improvements from Spark DataFrames. Discover lessons learned from scaling the implementation from millions to billions of nodes; compare its performance with other popular graph libraries; and hear about real-world applications.
Também conhecido como o “Time Lock Puzzle”, o LCS35 é um desafio em forma de criptografia projetado em 1999 pelo pesquisador Ron Rivest, do Instituto de Tecnologia de Massachusetts (MIT). Quando este problema matemático for resolvido, uma cápsula do tempo de chumbo será aberta no MIT.
O puzzle envolve a divisão de um número incrivelmente enorme, por um número que é apenas um pouco menor que o da conta (mas ainda com mais de 600 dígitos).
Ninguém sabe o que está dentro da capsula e, segundo os dados de Rivest, estima-se que levaria cerca de 35 anos para que o enigma seja resolvido. Os interessados ainda terão de esperar para descobrir o que de fato tem na capsula.
Web-Scale Graph Analytics with Apache® Spark™Databricks
Graph analytics has a wide range of applications, from information propagation and network flow optimization to fraud and anomaly detection. The rise of social networks and the Internet of Things has given us complex web-scale graphs with billions of vertices and edges. However, in order to extract the hidden gems within those graphs, you need tools to analyze the graphs easily and efficiently.
At Spark Summit 2016, Databricks introduced GraphFrames, which implemented graph queries and pattern matching on top of Spark SQL to simplify graph analytics. In this talk, you’ll learn about work that has made graph algorithms in GraphFrames faster and more scalable. For example, new implementations like connected components have received algorithm improvements based on recent research, as well as performance improvements from Spark DataFrames. Discover lessons learned from scaling the implementation from millions to billions of nodes; compare its performance with other popular graph libraries; and hear about real-world applications.
Web Archiving in the Year eaee1902f186819154789ee22ca30035Michael Nelson
(Web Archiving in the Year 2025)
My Vision for Trustworthy
Web Archiving in 2025
Michael L. Nelson
@phonedude_mln
with: Scott Ainsworth, Sawood Alam, Mohamed Aturban, John Berlin, Justin Brunelle, Kritika Garg, Hussam Hallak, Himarsha Jayanetti, Mat Kelly, Michele C. Weigle
@WebSciDL
Trust in Web Archives Panel, 2021 Web Archiving Conference
2021-06-16
Uncertainty in replaying archived Twitter pagesMichael Nelson
Michael L. Nelson
@phonedude_mln
with: Sawood Alam, Kritika Garg, Himarsha Jayanetti,
Shawn M. Jones, Nauman Siddique, Michele C. Weigle
@WebSciDL
Ethics and Archiving the Web: How to ethically collect and use web archives
2021-03-30
Web Archives at the Nexus of Good Fakes and Flawed OriginalsMichael Nelson
Michael L. Nelson
Old Dominion University
Web Science & Digital Libraries Research Group @WebSciDL, @phonedude_mln
Drexel CCI IS Department Distinguished Speaker Series, 2020-03-09
Web Archives at the Nexus of Good Fakes and Flawed OriginalsMichael Nelson
Web Archives at the Nexus of Good Fakes and Flawed Originals
Michael L. Nelson
Old Dominion University
Web Science & Digital Libraries Research Group
@WebSciDL, @phonedude_mln
With:
ODU: Michele C. Weigle, John Berlin, Mohamed Aturban, Justin Whitlock
LANL: Martin Klein, DANS: Herbert Van de Sompel
CNI Spring 2019 Membership Meeting, 2019-04-09,
@phonedude_mln, @WebSciDL
Blockchain Can Not Be Used To Verify Replayed Archived Web PagesMichael Nelson
Blockchain Can Not Be Used To Verify Replayed Archived Web Pages
Michael L. Nelson
Old Dominion University
Web Science & Digital Libraries Research Group
@WebSciDL, @phonedude_mln
With:
ODU: Michele C. Weigle, Mohamed Aturban
Los Alamos National Laboratory: Herbert Van de Sompel, Martin Klein
CNI Fall 2018 Membership Meeting, 2018-12-11,
@phonedude_mln, @WebSciDL
Blockchain Can Not Be Used To Verify Replayed Archived Web PagesMichael Nelson
Blockchain Can Not Be Used To Verify Replayed Archived Web Pages
Michael L. Nelson
Old Dominion University
Web Science & Digital Libraries Research Group
@WebSciDL, @phonedude_mln
With:
ODU: Michele C. Weigle, Mohamed Aturban
Los Alamos National Laboratory: Herbert Van de Sompel, Martin Klein
Weaponized Web Archives: Provenance Laundering of Short Order Evidence Michael Nelson
Weaponized Web Archives: Provenance Laundering of Short Order Evidence
Michael L. Nelson
Old Dominion University
Web Science & Digital Libraries Research Group
@WebSciDL, @phonedude_mln
With:
ODU: Michele C. Weigle, Mohamed Aturban, John Berlin, Sawood Alam, Plinio Vargas
Los Alamos National Laboratory: Herbert Van de Sompel, Martin Klein
Weaponized Web Archives: Provenance Laundering of Short Order Evidence Michael Nelson
Michael L. Nelson
Old Dominion University
Web Science & Digital Libraries Research Group
@WebSciDL, @phonedude_mln
With:
ODU: Michele C. Weigle, Mohamed Aturban, John Berlin, Sawood Alam, Plinio Vargas
Los Alamos National Laboratory: Herbert Van de Sompel, Martin Klein
ODU Computer Science Colloquium 2018-04-06
based on a 2018-03-23 presentation at the National Forum on Ethics and Archiving the Web
Weaponized Web Archives: Provenance Laundering of Short Order Evidence Michael Nelson
Michael L. Nelson
Old Dominion University
Web Science & Digital Libraries Research Group
@WebSciDL, @phonedude_mln
With:
ODU: Michele C. Weigle, Mohamed Aturban, John Berlin, Sawood Alam, Plinio Vargas
Los Alamos National Laboratory: Herbert Van de Sompel, Martin Klein
National Forum on Ethics and Archiving the Web
2018-03-23, #eaw18, @phonedude_mln
Web Archiving Activities of ODU’s Web Science and Digital Library Research G...Michael Nelson
Michael L. Nelson
@phonedude_mln
Michele C. Weigle
@weiglemc
National Symposium on Web Archiving Interoperability
2017-02-21
Many projects joint with LANL
Funding from NSF, IMLS, NEH, and AMF
Summarizing archival collections using storytelling techniquesMichael Nelson
Summarizing archival collections using storytelling techniques
Yasmin AlNoamany
Michele C. Weigle
Michael L. Nelson
Old Dominion University
Web Science & Digital Libraries Research Group
www.cs.odu.edu/~mln/
@phonedude_mln
Research Funded by IMLS LG-71-15-0077-15
Dodging the Memory Hole
Los Angeles, CA, 2016-10-14
The Memento Protocol and Research Issues With Web ArchivingMichael Nelson
Michael L. Nelson
Old Dominion University
Web Science & Digital Libraries Research Group
www.cs.odu.edu/~mln/
University of Virginia Colloquium
2016-09-12
Combining Heritrix and PhantomJS for Better Crawling of Pages with JavascriptMichael Nelson
Justin F. Brunelle
Michele C. Weigle
Michael L. Nelson
Web Science and Digital Libraries Research Group
Old Dominion University
@WebSciDL
IIPC 2016
Reykjavik, Iceland, April 11, 2016
Storytelling for Summarizing Collections in Web ArchivesMichael Nelson
Yasmin AlNoamany
Michele C. Weigle
Michael L. Nelson
Old Dominion University
Web Science and Digital Libraries Group
@WebSciDL
This work is supported in part by IMLS LG-71-15-0077
CNI Spring 2016
2016-04-05
Yasmin AlNoamany
Michele C. Weigle
Michael L. Nelson
Old Dominion University
Web Science and Digital Libraries Group
ws-dl.cs.odu.edu
@WebSciDL
This work is supported in part by IMLS LG-71-15-0077
Old Dominion University ECE Department Colloquium
2015-11-13
@WebSciDL PhD Student Project Reviews August 5&6, 2015Michael Nelson
Herbert Van de Sompel (LANL) visisted the Web Science & Digital Libraries Group @ ODU on August 5--7, 2015. The seven PhD students who were in town at that time reviewed their current status for him.
Evaluating the Temporal Coherence of Archived PagesMichael Nelson
Evaluating the Temporal Coherence of Archived Pages
Scott G. Ainsworth
Michael L. Nelson
Herbert Van De Sompel
IIPC 2015
April 27–May 1, 2015
Stanford University