Ieee Richard W. Hamming Medal Recipients
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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 -
Investigating Statistical Privacy Frameworks from the Perspective of Hypothesis Testing 234 Able Quantity
Investigating Statistical Privacy Frameworks from the Perspective of Hypothesis Testing 234 able quantity. Only a limited number of previous works which can provide a natural and interpretable guide- [29, 34, 35] have investigated the question of how to line for selecting proper privacy parameters by system select a proper value of ǫ, but these approaches ei- designers and researchers. Furthermore, we extend our ther require complicated economic models or lack the analysis on unbounded DP to bounded DP and the ap- analysis of adversaries with arbitrary auxiliary informa- proximate (ǫ, δ)-DP. tion (see Section 2.3 for more details). Our work is in- Impact of Auxiliary Information. The conjecture spired by the interpretation of differential privacy via that auxiliary information can influence the design of hypothesis testing, initially introduced by Wasserman DP mechanisms has been made in prior work [8, 28, 32, and Zhou [27, 30, 60]. However, this interpretation has 33, 39, 65]. We therefore investigate the adversary’s ca- not been systematically investigated before in the con- pability based on hypothesis testing under three types text of our research objective, i.e., reasoning about the of auxiliary information: the prior distribution of the in- choice of the privacy parameter ǫ (see Section 2.4 for put record, the correlation across records, and the corre- more details). lation across time. Our analysis demonstrates that the We consider hypothesis testing [2, 45, 61] as the tool auxiliary information indeed influences the appropriate used by the adversary to infer sensitive information of selection of ǫ. The results suggest that, when possible an individual record (e.g., the presence or absence of and available, the practitioners of DP should explicitly a record in the database for unbounded DP) from the incorporate adversary’s auxiliary information into the outputs of privacy mechanisms. -
2008 Annual Report
2008 Annual Report NATIONAL ACADEMY OF ENGINEERING ENGINEERING THE FUTURE 1 Letter from the President 3 In Service to the Nation 3 Mission Statement 4 Program Reports 4 Engineering Education 4 Center for the Advancement of Scholarship on Engineering Education 6 Technological Literacy 6 Public Understanding of Engineering Developing Effective Messages Media Relations Public Relations Grand Challenges for Engineering 8 Center for Engineering, Ethics, and Society 9 Diversity in the Engineering Workforce Engineer Girl! Website Engineer Your Life Project Engineering Equity Extension Service 10 Frontiers of Engineering Armstrong Endowment for Young Engineers-Gilbreth Lectures 12 Engineering and Health Care 14 Technology and Peace Building 14 Technology for a Quieter America 15 America’s Energy Future 16 Terrorism and the Electric Power-Delivery System 16 U.S.-China Cooperation on Electricity from Renewables 17 U.S.-China Symposium on Science and Technology Strategic Policy 17 Offshoring of Engineering 18 Gathering Storm Still Frames the Policy Debate 20 2008 NAE Awards Recipients 22 2008 New Members and Foreign Associates 24 2008 NAE Anniversary Members 28 2008 Private Contributions 28 Einstein Society 28 Heritage Society 29 Golden Bridge Society 29 Catalyst Society 30 Rosette Society 30 Challenge Society 30 Charter Society 31 Other Individual Donors 34 The Presidents’ Circle 34 Corporations, Foundations, and Other Organizations 35 National Academy of Engineering Fund Financial Report 37 Report of Independent Certified Public Accountants 41 Notes to Financial Statements 53 Officers 53 Councillors 54 Staff 54 NAE Publications Letter from the President Engineering is critical to meeting the fundamental challenges facing the U.S. economy in the 21st century. -
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. -
Lzw Compression and Decompression
LZW COMPRESSION AND DECOMPRESSION December 4, 2015 1 Contents 1 INTRODUCTION 3 2 CONCEPT 3 3 COMPRESSION 3 4 DECOMPRESSION: 4 5 ADVANTAGES OF LZW: 6 6 DISADVANTAGES OF LZW: 6 2 1 INTRODUCTION LZW stands for Lempel-Ziv-Welch. This algorithm was created in 1984 by these people namely Abraham Lempel, Jacob Ziv, and Terry Welch. This algorithm is very simple to implement. In 1977, Lempel and Ziv published a paper on the \sliding-window" compression followed by the \dictionary" based compression which were named LZ77 and LZ78, respectively. later, Welch made a contri- bution to LZ78 algorithm, which was then renamed to be LZW Compression algorithm. 2 CONCEPT Many files in real time, especially text files, have certain set of strings that repeat very often, for example " The ","of","on"etc., . With the spaces, any string takes 5 bytes, or 40 bits to encode. But what if we need to add the whole string to the list of characters after the last one, at 256. Then every time we came across the string like" the ", we could send the code 256 instead of 32,116,104 etc.,. This would take 9 bits instead of 40bits. This is the algorithm of LZW compression. It starts with a "dictionary" of all the single character with indexes from 0 to 255. It then starts to expand the dictionary as information gets sent through. Pretty soon, all the strings will be encoded as a single bit, and compression would have occurred. LZW compression replaces strings of characters with single codes. It does not analyze the input text. -
Digital Communication Systems 2.2 Optimal Source Coding
Digital Communication Systems EES 452 Asst. Prof. Dr. Prapun Suksompong [email protected] 2. Source Coding 2.2 Optimal Source Coding: Huffman Coding: Origin, Recipe, MATLAB Implementation 1 Examples of Prefix Codes Nonsingular Fixed-Length Code Shannon–Fano code Huffman Code 2 Prof. Robert Fano (1917-2016) Shannon Award (1976 ) Shannon–Fano Code Proposed in Shannon’s “A Mathematical Theory of Communication” in 1948 The method was attributed to Fano, who later published it as a technical report. Fano, R.M. (1949). “The transmission of information”. Technical Report No. 65. Cambridge (Mass.), USA: Research Laboratory of Electronics at MIT. Should not be confused with Shannon coding, the coding method used to prove Shannon's noiseless coding theorem, or with Shannon–Fano–Elias coding (also known as Elias coding), the precursor to arithmetic coding. 3 Claude E. Shannon Award Claude E. Shannon (1972) Elwyn R. Berlekamp (1993) Sergio Verdu (2007) David S. Slepian (1974) Aaron D. Wyner (1994) Robert M. Gray (2008) Robert M. Fano (1976) G. David Forney, Jr. (1995) Jorma Rissanen (2009) Peter Elias (1977) Imre Csiszár (1996) Te Sun Han (2010) Mark S. Pinsker (1978) Jacob Ziv (1997) Shlomo Shamai (Shitz) (2011) Jacob Wolfowitz (1979) Neil J. A. Sloane (1998) Abbas El Gamal (2012) W. Wesley Peterson (1981) Tadao Kasami (1999) Katalin Marton (2013) Irving S. Reed (1982) Thomas Kailath (2000) János Körner (2014) Robert G. Gallager (1983) Jack KeilWolf (2001) Arthur Robert Calderbank (2015) Solomon W. Golomb (1985) Toby Berger (2002) Alexander S. Holevo (2016) William L. Root (1986) Lloyd R. Welch (2003) David Tse (2017) James L. -
Cryptography: DH And
1 ì Key Exchange Secure Software Systems Fall 2018 2 Challenge – Exchanging Keys & & − 1 6(6 − 1) !"#ℎ%&'() = = = 15 & 2 2 The more parties in communication, ! $ the more keys that need to be securely exchanged Do we have to use out-of-band " # methods? (e.g., phone?) % Secure Software Systems Fall 2018 3 Key Exchange ì Insecure communica-ons ì Alice and Bob agree on a channel shared secret (“key”) that ì Eve can see everything! Eve doesn’t know ì Despite Eve seeing everything! ! " (alice) (bob) # (eve) Secure Software Systems Fall 2018 Whitfield Diffie and Martin Hellman, 4 “New directions in cryptography,” in IEEE Transactions on Information Theory, vol. 22, no. 6, Nov 1976. Proposed public key cryptography. Diffie-Hellman key exchange. Secure Software Systems Fall 2018 5 Diffie-Hellman Color Analogy (1) It’s easy to mix two colors: + = (2) Mixing two or more colors in a different order results in + + = the same color: + + = (3) Mixing colors is one-way (Impossible to determine which colors went in to produce final result) https://www.crypto101.io/ Secure Software Systems Fall 2018 6 Diffie-Hellman Color Analogy ! # " (alice) (eve) (bob) + + $ $ = = Mix Mix (1) Start with public color ▇ – share across network (2) Alice picks secret color ▇ and mixes it to get ▇ (3) Bob picks secret color ▇ and mixes it to get ▇ Secure Software Systems Fall 2018 7 Diffie-Hellman Color Analogy ! # " (alice) (eve) (bob) $ $ Mix Mix = = Eve can’t calculate ▇ !! (secret keys were never shared) (4) Alice and Bob exchange their mixed colors (▇,▇) (5) Eve will -
Randomized Lempel-Ziv Compression for Anti-Compression Side-Channel Attacks
Randomized Lempel-Ziv Compression for Anti-Compression Side-Channel Attacks by Meng Yang A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master of Applied Science in Electrical and Computer Engineering Waterloo, Ontario, Canada, 2018 c Meng Yang 2018 I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Abstract Security experts confront new attacks on TLS/SSL every year. Ever since the compres- sion side-channel attacks CRIME and BREACH were presented during security conferences in 2012 and 2013, online users connecting to HTTP servers that run TLS version 1.2 are susceptible of being impersonated. We set up three Randomized Lempel-Ziv Models, which are built on Lempel-Ziv77, to confront this attack. Our three models change the determin- istic characteristic of the compression algorithm: each compression with the same input gives output of different lengths. We implemented SSL/TLS protocol and the Lempel- Ziv77 compression algorithm, and used them as a base for our simulations of compression side-channel attack. After performing the simulations, all three models successfully pre- vented the attack. However, we demonstrate that our randomized models can still be broken by a stronger version of compression side-channel attack that we created. But this latter attack has a greater time complexity and is easily detectable. Finally, from the results, we conclude that our models couldn't compress as well as Lempel-Ziv77, but they can be used against compression side-channel attacks. -
Principles of Communications ECS 332
Principles of Communications ECS 332 Asst. Prof. Dr. Prapun Suksompong (ผศ.ดร.ประพันธ ์ สขสมปองุ ) [email protected] 1. Intro to Communication Systems Office Hours: Check Google Calendar on the course website. Dr.Prapun’s Office: 6th floor of Sirindhralai building, 1 BKD 2 Remark 1 If the downloaded file crashed your device/browser, try another one posted on the course website: 3 Remark 2 There is also three more sections from the Appendices of the lecture notes: 4 Shannon's insight 5 “The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point.” Shannon, Claude. A Mathematical Theory Of Communication. (1948) 6 Shannon: Father of the Info. Age Documentary Co-produced by the Jacobs School, UCSD- TV, and the California Institute for Telecommunic ations and Information Technology 7 [http://www.uctv.tv/shows/Claude-Shannon-Father-of-the-Information-Age-6090] [http://www.youtube.com/watch?v=z2Whj_nL-x8] C. E. Shannon (1916-2001) Hello. I'm Claude Shannon a mathematician here at the Bell Telephone laboratories He didn't create the compact disc, the fax machine, digital wireless telephones Or mp3 files, but in 1948 Claude Shannon paved the way for all of them with the Basic theory underlying digital communications and storage he called it 8 information theory. C. E. Shannon (1916-2001) 9 https://www.youtube.com/watch?v=47ag2sXRDeU C. E. Shannon (1916-2001) One of the most influential minds of the 20th century yet when he died on February 24, 2001, Shannon was virtually unknown to the public at large 10 C. -
Marconi Society - Wikipedia
9/23/2019 Marconi Society - Wikipedia Marconi Society The Guglielmo Marconi International Fellowship Foundation, briefly called Marconi Foundation and currently known as The Marconi Society, was established by Gioia Marconi Braga in 1974[1] to commemorate the centennial of the birth (April 24, 1874) of her father Guglielmo Marconi. The Marconi International Fellowship Council was established to honor significant contributions in science and technology, awarding the Marconi Prize and an annual $100,000 grant to a living scientist who has made advances in communication technology that benefits mankind. The Marconi Fellows are Sir Eric A. Ash (1984), Paul Baran (1991), Sir Tim Berners-Lee (2002), Claude Berrou (2005), Sergey Brin (2004), Francesco Carassa (1983), Vinton G. Cerf (1998), Andrew Chraplyvy (2009), Colin Cherry (1978), John Cioffi (2006), Arthur C. Clarke (1982), Martin Cooper (2013), Whitfield Diffie (2000), Federico Faggin (1988), James Flanagan (1992), David Forney, Jr. (1997), Robert G. Gallager (2003), Robert N. Hall (1989), Izuo Hayashi (1993), Martin Hellman (2000), Hiroshi Inose (1976), Irwin M. Jacobs (2011), Robert E. Kahn (1994) Sir Charles Kao (1985), James R. Killian (1975), Leonard Kleinrock (1986), Herwig Kogelnik (2001), Robert W. Lucky (1987), James L. Massey (1999), Robert Metcalfe (2003), Lawrence Page (2004), Yash Pal (1980), Seymour Papert (1981), Arogyaswami Paulraj (2014), David N. Payne (2008), John R. Pierce (1979), Ronald L. Rivest (2007), Arthur L. Schawlow (1977), Allan Snyder (2001), Robert Tkach (2009), Gottfried Ungerboeck (1996), Andrew Viterbi (1990), Jack Keil Wolf (2011), Jacob Ziv (1995). In 2015, the prize went to Peter T. Kirstein for bringing the internet to Europe. Since 2008, Marconi has also issued the Paul Baran Marconi Society Young Scholar Awards. -
An Economic Analysis of Privacy Protection and Statistical Accuracy As Social Choices
An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices John M. Abowd and Ian M. Schmutte August 15, 2018 Forthcoming in American Economic Review Abowd: U.S. Census Bureau HQ 8H120, 4600 Silver Hill Rd., Washington, DC 20233, and Cornell University, (email: [email protected]); Schmutte: Department of Eco- nomics, University of Georgia, B408 Amos Hall, Athens, GA 30602 (email: [email protected]). Abowd and Schmutte acknowledge the support of Alfred P. Sloan Foundation Grant G-2015-13903 and NSF Grant SES-1131848. Abowd acknowledges direct support from NSF Grants BCS-0941226, TC-1012593. Any opinions and conclusions are those of the authors and do not represent the views of the Census Bureau, NSF, or the Sloan Foundation. We thank the Center for Labor Economics at UC–Berkeley and Isaac Newton Institute for Mathematical Sciences, Cambridge (EPSRC grant no. EP/K032208/1) for support and hospitality. We are extremely grateful for very valuable com- ments and guidance from the editor, Pinelopi Goldberg, and six anonymous referees. We acknowl- edge helpful comments from Robin Bachman, Nick Bloom, Larry Blume, David Card, Michael Castro, Jennifer Childs, Melissa Creech, Cynthia Dwork, Casey Eggleston, John Eltinge, Stephen Fienberg, Mark Kutzbach, Ron Jarmin, Christa Jones, Dan Kifer, Ashwin Machanavajjhala, Frank McSherry, Gerome Miklau, Kobbi Nissim, Paul Oyer, Mallesh Pai, Jerry Reiter, Eric Slud, Adam Smith, Bruce Spencer, Sara Sullivan, Salil Vadhan, Lars Vilhuber, Glen Weyl, and Nellie Zhao along with seminar and conference participants at the U.S. Census Bureau, Cornell, CREST, George Ma- son, Georgetown, Microsoft Research–NYC, University of Washington Evans School, and SOLE. -
Fall 2016 Dear Electrical Engineering Alumni and Friends, This Past
ABBAS EL GAMAL Fortinet Founders Chair of the Department of Electrical Engineering Hitachi America Professor Fall 2016 Dear Electrical Engineering Alumni and Friends, This past academic year was another very successful one for the department. We made great progress toward implementing the vision of our strategic plan (EE in the 21st Century, or EE21 for short), which I outlined in my letter to you last year. I am also proud to share some of the exciting research in the department and the significant recognitions our faculty have received. I will first briefly describe the progress we have made toward implementing our EE21 plan. Faculty hiring. The top priority in our strategic plan is hiring faculty with complementary vision and expertise and who enhance our faculty diversity. This past academic year, we conducted a junior faculty broad area search and participated in a School of Engineering wide search in the area of robotics. I am happy to report that Mary Wootters joined our faculty in September as an assistant professor jointly with Computer Science. Mary’s research focuses on applying probability to coding theory, signal processing, and randomized algorithms. She also explores quantum information theory and complexity theory. Mary was previously an NSF postdoctoral fellow in the CS department at Carnegie Mellon University. The robotics search yielded two top candidates. I will report on the final results of this search in my next year’s letter. Reinventing the undergraduate curriculum. We continue to innovate our undergraduate curriculum, introducing two new, exciting project-oriented courses: EE107: Embedded Networked Systems and EE267: Virtual Reality.