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Data Compression: Dictionary-Based Coding 2 / 37 Dictionary-Based Coding Dictionary-Based Coding
Dictionary-based Coding already coded not yet coded search buffer look-ahead buffer cursor (N symbols) (L symbols) We know the past but cannot control it. We control the future but... Last Lecture Last Lecture: Predictive Lossless Coding Predictive Lossless Coding Simple and effective way to exploit dependencies between neighboring symbols / samples Optimal predictor: Conditional mean (requires storage of large tables) Affine and Linear Prediction Simple structure, low-complex implementation possible Optimal prediction parameters are given by solution of Yule-Walker equations Works very well for real signals (e.g., audio, images, ...) Efficient Lossless Coding for Real-World Signals Affine/linear prediction (often: block-adaptive choice of prediction parameters) Entropy coding of prediction errors (e.g., arithmetic coding) Using marginal pmf often already yields good results Can be improved by using conditional pmfs (with simple conditions) Heiko Schwarz (Freie Universität Berlin) — Data Compression: Dictionary-based Coding 2 / 37 Dictionary-based Coding Dictionary-Based Coding Coding of Text Files Very high amount of dependencies Affine prediction does not work (requires linear dependencies) Higher-order conditional coding should work well, but is way to complex (memory) Alternative: Do not code single characters, but words or phrases Example: English Texts Oxford English Dictionary lists less than 230 000 words (including obsolete words) On average, a word contains about 6 characters Average codeword length per character would be limited by 1 -
Butler Lampson, Martin Abadi, Michael Burrows, Edward Wobber
Outline • Chapter 19: Security (cont) • A Method for Obtaining Digital Signatures and Public-Key Cryptosystems Ronald L. Rivest, Adi Shamir, and Leonard M. Adleman. Communications of the ACM 21,2 (Feb. 1978) – RSA Algorithm – First practical public key crypto system • Authentication in Distributed Systems: Theory and Practice, Butler Lampson, Martin Abadi, Michael Burrows, Edward Wobber – Butler Lampson (MSR) - He was one of the designers of the SDS 940 time-sharing system, the Alto personal distributed computing system, the Xerox 9700 laser printer, two-phase commit protocols, the Autonet LAN, and several programming languages – Martin Abadi (Bell Labs) – Michael Burrows, Edward Wobber (DEC/Compaq/HP SRC) Oct-21-03 CSE 542: Operating Systems 1 Encryption • Properties of good encryption technique: – Relatively simple for authorized users to encrypt and decrypt data. – Encryption scheme depends not on the secrecy of the algorithm but on a parameter of the algorithm called the encryption key. – Extremely difficult for an intruder to determine the encryption key. Oct-21-03 CSE 542: Operating Systems 2 Strength • Strength of crypto system depends on the strengths of the keys • Computers get faster – keys have to become harder to keep up • If it takes more effort to break a code than is worth, it is okay – Transferring money from my bank to my credit card and Citibank transferring billions of dollars with another bank should not have the same key strength Oct-21-03 CSE 542: Operating Systems 3 Encryption methods • Symmetric cryptography – Sender and receiver know the secret key (apriori ) • Fast encryption, but key exchange should happen outside the system • Asymmetric cryptography – Each person maintains two keys, public and private • M ≡ PrivateKey(PublicKey(M)) • M ≡ PublicKey (PrivateKey(M)) – Public part is available to anyone, private part is only known to the sender – E.g. -
QATAR ASW ONLINE SCRIPT V.8.0 Pages
“DEXIGN THE FUTURE: Innovation for Exponential Times” ARNOLD WASSERMAN COMMENCEMENT ADDRESS VIRGINIA COMMONWEALTH UNIVERSITY IN QATAR 2 MAY 2016 مساء الخير .Good afternoon It is an honor to be invited to address you on this important day. Today you artists and designers graduate into the future. Do you think about the future? Of course you do. But how do you think about the future? Is the future tangible - or is it intangible ? Is it a subject for rational deliberation? Or is it an imagined fantasy that forever recedes before us? Is the future simply whatever happens to us next? Or is it something we deliberately create? Can we Dexign the Future? What I would like us to think about for the next few moments is how creative professionals like yourselves might think about the future. What I have to say applies as much to art as it does to design. Both are technologies of creative innovation. I will take the liberty here of calling it all design. My speech is also available online. There you can follow a number of links to what I will talk about here. The future you are graduating into is an exponential future. What that means is that everything is happening faster and at an accelerating rate. For example, human knowledge is doubling every 12 months. And that curve is accelerating. An IBM researcher foresees that within a few years knowledge will be doubling every 12 hours as we build out the internet of things globally. This acceleration of human knowledge implies that the primary skill of a knowledge worker like yourself is research, analysis and synthesis of the not-yet-known. -
ACM Honors Eminent Researchers for Technical Innovations That Have Improved How We Live and Work 2016 Recipients Made Contribu
Contact: Jim Ormond 212-626-0505 [email protected] ACM Honors Eminent Researchers for Technical Innovations That Have Improved How We Live and Work 2016 Recipients Made Contributions in Areas Including Big Data Analysis, Computer Vision, and Encryption NEW YORK, NY, May 3, 2017 – ACM, the Association for Computing Machinery (www.acm.org), today announced the recipients of four prestigious technical awards. These leaders were selected by their peers for making significant contributions that have had far-reaching impact on how we live and work. The awards reflect achievements in file sharing, broadcast encryption, information visualization and computer vision. The 2016 recipients will be formally honored at the ACM Awards Banquet on June 24 in San Francisco. The 2016 Award Recipients include: • Mahadev Satyanarayanan, Michael L. Kazar, Robert N. Sidebotham, David A. Nichols, Michael J. West, John H. Howard, Alfred Z. Spector and Sherri M. Nichols, recipients of the ACM Software System Award for developing the Andrew File System (AFS). AFS was the first distributed file system designed for tens of thousands of machines, and pioneered the use of scalable, secure and ubiquitous access to shared file data. To achieve the goal of providing a common shared file system used by large networks of people, AFS introduced novel approaches to caching, security, management and administration. AFS is still in use today as both an open source system and as the file system in commercial applications. It has also inspired several cloud-based storage applications. The 2016 Software System Award recipients designed and built the Andrew File System in the 1980s while working as a team at the Information Technology Center (ITC), a partnership between Carnegie Mellon University and IBM. -
Rivest, Shamir, and Adleman Receive 2002 Turing Award, Volume 50
Rivest, Shamir, and Adleman Receive 2002 Turing Award Cryptography and Information Se- curity Group. He received a B.A. in mathematics from Yale University and a Ph.D. in computer science from Stanford University. Shamir is the Borman Profes- sor in the Applied Mathematics Department of the Weizmann In- stitute of Science in Israel. He re- Ronald L. Rivest Adi Shamir Leonard M. Adleman ceived a B.S. in mathematics from Tel Aviv University and a Ph.D. in The Association for Computing Machinery (ACM) has computer science from the Weizmann Institute. named RONALD L. RIVEST, ADI SHAMIR, and LEONARD M. Adleman is the Distinguished Henry Salvatori ADLEMAN as winners of the 2002 A. M. Turing Award, Professor of Computer Science and Professor of considered the “Nobel Prize of Computing”, for Molecular Biology at the University of Southern their contributions to public key cryptography. California. He earned a B.S. in mathematics at the The Turing Award carries a $100,000 prize, with University of California, Berkeley, and a Ph.D. in funding provided by Intel Corporation. computer science, also at Berkeley. As researchers at the Massachusetts Institute of The ACM presented the Turing Award on June 7, Technology in 1977, the team developed the RSA 2003, in conjunction with the Federated Computing code, which has become the foundation for an en- Research Conference in San Diego, California. The tire generation of technology security products. It award was named for Alan M. Turing, the British mathematician who articulated the mathematical has also inspired important work in both theoret- foundation and limits of computing and who was a ical computer science and mathematics. -
Compact E-Cash and Simulatable Vrfs Revisited
Compact E-Cash and Simulatable VRFs Revisited Mira Belenkiy1, Melissa Chase2, Markulf Kohlweiss3, and Anna Lysyanskaya4 1 Microsoft, [email protected] 2 Microsoft Research, [email protected] 3 KU Leuven, ESAT-COSIC / IBBT, [email protected] 4 Brown University, [email protected] Abstract. Efficient non-interactive zero-knowledge proofs are a powerful tool for solving many cryptographic problems. We apply the recent Groth-Sahai (GS) proof system for pairing product equations (Eurocrypt 2008) to two related cryptographic problems: compact e-cash (Eurocrypt 2005) and simulatable verifiable random functions (CRYPTO 2007). We present the first efficient compact e-cash scheme that does not rely on a ran- dom oracle. To this end we construct efficient GS proofs for signature possession, pseudo randomness and set membership. The GS proofs for pseudorandom functions give rise to a much cleaner and substantially faster construction of simulatable verifiable random functions (sVRF) under a weaker number theoretic assumption. We obtain the first efficient fully simulatable sVRF with a polynomial sized output domain (in the security parameter). 1 Introduction Since their invention [BFM88] non-interactive zero-knowledge proofs played an important role in ob- taining feasibility results for many interesting cryptographic primitives [BG90,GO92,Sah99], such as the first chosen ciphertext secure public key encryption scheme [BFM88,RS92,DDN91]. The inefficiency of these constructions often motivated independent practical instantiations that were arguably conceptually less elegant, but much more efficient ([CS98] for chosen ciphertext security). We revisit two important cryptographic results of pairing-based cryptography, compact e-cash [CHL05] and simulatable verifiable random functions [CL07], that have very elegant constructions based on non-interactive zero-knowledge proof systems, but less elegant practical instantiations. -
Reducing Redundancy in Data Organization and Arithmetic Calculation for Stencil Computations
Reducing Redundancy in Data Organization and Arithmetic Calculation for Stencil Computations Kun Li Liang Yuan Yunquan Zhang Institute of Computing Technology, Institute of Computing Technology, Institute of Computing Technology, Chinese Academy of Sciences Chinese Academy of Sciences Chinese Academy of Sciences University of Chinese Academy of Beijing, China Beijing, China Sciences [email protected] [email protected] Beijing, China [email protected] Yue Yue Hang Cao Pengqi Lu Institute of Computing Technology, Institute of Computing Technology, Institute of Computing Technology, Chinese Academy of Sciences Chinese Academy of Sciences Chinese Academy of Sciences University of Chinese Academy of University of Chinese Academy of University of Chinese Academy of Sciences Sciences Sciences Beijing, China Beijing, China Beijing, China [email protected] [email protected] [email protected] Abstract ACM Reference Format: Stencil computation is one of the most important kernels Kun Li, Liang Yuan, Yunquan Zhang, Yue Yue, Hang Cao, and Pengqi in various scientific and engineering applications. A variety Lu. 2021. Reducing Redundancy in Data Organization and Arith- metic Calculation for Stencil Computations. In Proceedings of ACM of work has focused on vectorization techniques, aiming at Conference (Conference’21). ACM, Seoul, South Korea, 12 pages. exploiting the in-core data parallelism. Briefly, they either https://doi.org/10.1145/nnnnnnn.nnnnnnn incur data alignment conflicts or hurt the data locality when integrated with tiling. In this paper, a novel transpose lay- out is devised to preserve the data locality for tiling in the 1 Introduction data space and reduce the data reorganization overhead for Stencil is one of the most important kernels widely used vectorization simultaneously. -
Simula Mother Tongue for a Generation of Nordic Programmers
Simula! Mother Tongue! for a Generation of! Nordic Programmers! Yngve Sundblad HCI, CSC, KTH! ! KTH - CSC (School of Computer Science and Communication) Yngve Sundblad – Simula OctoberYngve 2010Sundblad! Inspired by Ole-Johan Dahl, 1931-2002, and Kristen Nygaard, 1926-2002" “From the cold waters of Norway comes Object-Oriented Programming” " (first line in Bertrand Meyer#s widely used text book Object Oriented Software Construction) ! ! KTH - CSC (School of Computer Science and Communication) Yngve Sundblad – Simula OctoberYngve 2010Sundblad! Simula concepts 1967" •# Class of similar Objects (in Simula declaration of CLASS with data and actions)! •# Objects created as Instances of a Class (in Simula NEW object of class)! •# Data attributes of a class (in Simula type declared as parameters or internal)! •# Method attributes are patterns of action (PROCEDURE)! •# Message passing, calls of methods (in Simula dot-notation)! •# Subclasses that inherit from superclasses! •# Polymorphism with several subclasses to a superclass! •# Co-routines (in Simula Detach – Resume)! •# Encapsulation of data supporting abstractions! ! KTH - CSC (School of Computer Science and Communication) Yngve Sundblad – Simula OctoberYngve 2010Sundblad! Simula example BEGIN! REF(taxi) t;" CLASS taxi(n); INTEGER n;! BEGIN ! INTEGER pax;" PROCEDURE book;" IF pax<n THEN pax:=pax+1;! pax:=n;" END of taxi;! t:-NEW taxi(5);" t.book; t.book;" print(t.pax)" END! Output: 7 ! ! KTH - CSC (School of Computer Science and Communication) Yngve Sundblad – Simula OctoberYngve 2010Sundblad! -
John Mccarthy
JOHN MCCARTHY: the uncommon logician of common sense Excerpt from Out of their Minds: the lives and discoveries of 15 great computer scientists by Dennis Shasha and Cathy Lazere, Copernicus Press August 23, 2004 If you want the computer to have general intelligence, the outer structure has to be common sense knowledge and reasoning. — John McCarthy When a five-year old receives a plastic toy car, she soon pushes it and beeps the horn. She realizes that she shouldn’t roll it on the dining room table or bounce it on the floor or land it on her little brother’s head. When she returns from school, she expects to find her car in more or less the same place she last put it, because she put it outside her baby brother’s reach. The reasoning is so simple that any five-year old child can understand it, yet most computers can’t. Part of the computer’s problem has to do with its lack of knowledge about day-to-day social conventions that the five-year old has learned from her parents, such as don’t scratch the furniture and don’t injure little brothers. Another part of the problem has to do with a computer’s inability to reason as we do daily, a type of reasoning that’s foreign to conventional logic and therefore to the thinking of the average computer programmer. Conventional logic uses a form of reasoning known as deduction. Deduction permits us to conclude from statements such as “All unemployed actors are waiters, ” and “ Sebastian is an unemployed actor,” the new statement that “Sebastian is a waiter.” The main virtue of deduction is that it is “sound” — if the premises hold, then so will the conclusions. -
The Basic Principles of Data Compression
The Basic Principles of Data Compression Author: Conrad Chung, 2BrightSparks Introduction Internet users who download or upload files from/to the web, or use email to send or receive attachments will most likely have encountered files in compressed format. In this topic we will cover how compression works, the advantages and disadvantages of compression, as well as types of compression. What is Compression? Compression is the process of encoding data more efficiently to achieve a reduction in file size. One type of compression available is referred to as lossless compression. This means the compressed file will be restored exactly to its original state with no loss of data during the decompression process. This is essential to data compression as the file would be corrupted and unusable should data be lost. Another compression category which will not be covered in this article is “lossy” compression often used in multimedia files for music and images and where data is discarded. Lossless compression algorithms use statistic modeling techniques to reduce repetitive information in a file. Some of the methods may include removal of spacing characters, representing a string of repeated characters with a single character or replacing recurring characters with smaller bit sequences. Advantages/Disadvantages of Compression Compression of files offer many advantages. When compressed, the quantity of bits used to store the information is reduced. Files that are smaller in size will result in shorter transmission times when they are transferred on the Internet. Compressed files also take up less storage space. File compression can zip up several small files into a single file for more convenient email transmission. -
Variations on the Sensitivity Conjecture
THEORY OF COMPUTING LIBRARY GRADUATE SURVEYS 4 (2011), pp. 1–27 www.theoryofcomputing.org Variations on the Sensitivity Conjecture Pooya Hatami Raghav Kulkarni Denis Pankratov Received: November 2, 2010; published: June 22, 2011. Abstract: The sensitivity of a Boolean function f of n Boolean variables is the maximum over all inputs x of the number of positions i such that flipping the i-th bit of x changes the value of f (x). Permitting to flip disjoint blocks of bits leads to the notion of block sensitivity, known to be polynomially related to a number of other complexity measures of f , including the decision-tree complexity, the polynomial degree, and the certificate complexity. A long-standing open question is whether sensitivity also belongs to this equivalence class. A positive answer to this question is known as the Sensitivity Conjecture. We present a selection of known as well as new variants of the Sensitivity Conjecture and point out some weaker versions that are also open. Among other things, we relate the problem to Communication Complexity via recent results by Sherstov (QIC 2010). We also indicate new connections to Fourier analysis. ACM Classification: F.0, F.1, F.2 AMS Classification: 68-02, 68Q05, 68Q25 Key words and phrases: sensitivity, block sensitivity, complexity measures of Boolean functions 1 The Sensitivity Conjecture For Boolean strings x;y 2 f0;1gn let x ⊕ y 2 f0;1gn denote the coordinate-wise exclusive or of x and y. n th Let ei 2 f0;1g denote an n-bit Boolean string whose i bit is 1 and the rest of the bits are 0: Let f : f0;1gn ! f0;1g be a Boolean function. -
Annual Report
ANNUAL REPORT 2019FISCAL YEAR ACM, the Association for Computing Machinery, is an international scientific and educational organization dedicated to advancing the arts, sciences, and applications of information technology. Letter from the President It’s been quite an eventful year and challenges posed by evolving technology. for ACM. While this annual Education has always been at the foundation of exercise allows us a moment ACM, as reflected in two recent curriculum efforts. First, “ACM’s mission to celebrate some of the many the ACM Task Force on Data Science issued “Comput- hinges on successes and achievements ing Competencies for Undergraduate Data Science Cur- creating a the Association has realized ricula.” The guidelines lay out the computing-specific over the past year, it is also an competencies that should be included when other community that opportunity to focus on new academic departments offer programs in data science encompasses and innovative ways to ensure at the undergraduate level. Second, building on the all who work in ACM remains a vibrant global success of our recent guidelines for 4-year cybersecu- the computing resource for the computing community. rity curricula, the ACM Committee for Computing Edu- ACM’s mission hinges on creating a community cation in Community Colleges created a related cur- and technology that encompasses all who work in the computing and riculum targeted at two-year programs, “Cybersecurity arena” technology arena. This year, ACM established a new Di- Curricular Guidance for Associate-Degree Programs.” versity and Inclusion Council to identify ways to create The following pages offer a sampling of the many environments that are welcoming to new perspectives ACM events and accomplishments that occurred over and will attract an even broader membership from the past fiscal year, none of which would have been around the world.