Comparing Cryptosystems  Which is more secure? Affine or a Vigenere ciphers? 7. Which is more secure? Vigenere or Hill ciphers? 8. Could a computer with 4 processors perform 200 Rotation encryptions or 100 Autokey encryptions faster? 9. Could a computer with 4 processors perform 200 Rotation encryptions or 100 Affine encryptions faster? 10. Which has a larger keyspace? Hill over or Vigenere over ? 6.
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Comparing Cryptosystems 1. 2. 3. 4. 5.  Among Rotation, Affine, and Autokey, which is most resistant to a known-plaintext attack? Among Affine, Autokey, and Vigenere, which can encrypt the fastest? Among Affine, Autokey, and Vigenere, which can decrypt the fastest? Which has a larger keyspace? Affine over or Vigenere over ? Which has a larger keysize? Substitution over or rotation over
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bond 1.00% 0.99% 0.95% 0.89% 0.81% 0.71% 0.59% 0.45% 0.31% 0.16% 0.00% s&p500 0.00% 0.16% 0.31% 0.45% 0.59% 0.71% 0.81% 0.89% 0.95% 0.99% 1.00% 100.080 100.750 100.000 100.280 100.692 100.025 100.091 100.081 100.081 109.817 109.100 109.240 100.110 100.617 100.000 100.264 100.582 100.004 100.069 100.058 100.057 108.012 108.530 108.577 100.141 100.452 100.000 100.217 100.495 100.000 100.066 100.038 100.040 106.049 107.784 107.718 100.157 100.276 100.000 100.137 100.467 100.021 100.093 100.021 100.040 104.041 106.995 106.790 100.146 100.143 100.000 100.051 100.607 100.087 100.162 100.027 100.082 102.318 106.548 106.179 100.131 100.151 100.000 100.079 101.156 100.242 100.288 100.120 100.210 101.614 107.355 106.789 100.170 100.359 100.000 100.370 102.251 100.462 100.425 100.338 100.415 103.003 110.991 110.180 100.263 100.665 100.000 100.559 103.114 100.517 100.396 100.477 100.496 106.862 119.270 118.149 100.400 100.834 100.088 100.000 102.362 100.471 100.434 100.517 100.467 111.456 135.055 133.594 101.346 101.361 101.198 100.000 101.509 101.255 101.536 101.334 101.324 114.216 170.918 170.345 100.402 100.427 100.310 100.000 100.405 100.132 100.288 100.150 100.296 107.440 142.314 125.169 BEKK DIAGONAL - VT BEKK SCALAR BEKK SCALAR - VT DCC-ASY-GEN DCC-ASYMMETRIC DCC-GENERALIZED DCC-IMA DCC-MR DCC-RANK FIXED (all sample) OGARCH 1 OGARCH 2 Average 100.159 100.403 100.000 100.033 101.093 100.147 100.204 100.142 100.174 106.648 120.267 118.258 Table 157: sample variance of minimum variance portfolios subject to a required return of 1 (sample: 1001-end) 39
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bond 1.00% 0.99% 0.95% 0.89% 0.81% 0.71% 0.59% 0.45% 0.31% 0.16% 0.00% s&p500 0.00% 0.16% 0.31% 0.45% 0.59% 0.71% 0.81% 0.89% 0.95% 0.99% 1.00% 100.511 100.931 100.000 100.253 100.625 100.402 100.458 112.560 104.676 120.472 111.264 100.462 101.745 100.557 101.014 100.000 100.176 100.481 100.330 100.364 112.188 104.607 117.156 109.198 100.442 101.654 100.592 101.162 100.000 100.112 100.330 100.260 100.276 111.464 104.427 113.237 106.905 100.480 101.546 100.561 101.393 100.000 100.062 100.170 100.168 100.190 110.313 104.131 108.851 104.490 100.536 101.422 100.392 101.578 100.000 100.017 100.005 100.045 100.093 108.717 103.743 104.476 102.268 100.524 101.277 100.328 101.471 100.012 100.079 100.000 100.073 100.105 106.990 103.500 101.517 101.107 100.512 101.239 100.636 101.218 100.000 100.390 100.411 100.524 100.441 105.665 103.734 102.748 102.429 100.644 101.479 100.674 100.912 100.000 100.859 101.066 101.155 101.006 104.578 104.064 110.842 106.870 100.639 101.836 100.076 100.534 100.000 101.284 101.439 101.407 101.385 104.130 103.176 121.875 112.237 100.183 102.002 100.601 100.815 101.616 101.368 101.574 101.333 101.520 110.939 101.852 123.302 114.554 100.000 101.743 100.498 101.415 101.151 100.000 100.297 100.245 100.081 114.037 107.580 109.876 107.572 100.197 102.290 EXP-SM MA(20) MA(100) DCC-IMA (recursive) DCC-MR (recursive) DCC-ASY (recursive) DCC-RANK (recursive) OGARCH1 (recursive) OGARCH2 (recursive) FIXED (sample 1-1000) FIXED (daily update) DCC-ASY-GEN (recursive) DCC-GEN (recursive) Average 100.238 100.874 100.000 100.165 100.328 100.287 100.285 108.954 103.868 111.942 106.909 100.165 101.400 Table 158: sample variance of minimum variance portfolios subject to a required return of 1 (sample: 1001-end) 42
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Real Numbers & Their Properties Order And Absolute Value Exponents & Polynomials Factoring Polynomials Rational & Radical Expressions 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500
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1.5 Spectra: Spectrum channel Vin H channel 2.5 Spectra: V channel Spectra: channel H 4.0 Spectra: channelHVchannel 6.0 Spectra: channel V H 20 channel H 10 10 50 50 50 50 50 50 100 100 100 10 100 100 100 150 150 150 150 150 10 50 50 50 5 100 0 100 V 150 0 150 -5 150 10 5 5 100 0 0 -5 0 150 -5 -10 200 200 200 200 200 200 -10 200 -10 200 200 -10 -15 250 250 250 300 300 300 350 350 350 -10 250 250 250 -15 300 -20 300 300 350 350 350 -20 -15 250 250 250 -20 300 300 -25 300 -25 350 -30 350 350 -30 400 -35 400 -35 400 450 450 -20 -20 -25 -30 -30 400 400 400 450 450 450 -30 400 400 400 450 450 -40 -35 -40 120 20 20 60 4080 6010080 120 100 spectrum in V channel 040429 exp1, cut2, rad#74=180o 180o 40 120 20 450 -40 40 80 40 100 60 80 120 100 Spectrum in H channel Spectra: channel V 040429 exp1, cut3, az#28=180o 50 6020 120 20 40 80 60100 80120100 Spectra: V channel Spectra: channel H 040429, exp1, cut4, rad#342=180o 20 50 50 100 40 50 10 100 100100 20 60 120 20 40 2060 40 80 60 100 80 120 100 Spectra:H channel channel V 040429 exp1, cut5, az#296=180o 120 -40 20 40 60 80 Spectra: channel H V 100 120 50 10 50 50 5 H 150 150150 150 0 0 100100 100 100 100 0 -5 150 150 -5 150 50 5 5 100 chan 10 10 50 50 -40 450 0 Range gates, (1gate = 1km/4) 00 0.5 150150 -5 150 -10 200 -10 200 200200 200 200 -10 200 200 200200 -15 250 -10 250 250250 300 300300 300 -20 -15 250 250 250 250250 -20 -20 300 300 300 -15 250 -20 -25300300 -25 300 -25 350 350350 350 350 350 350 -30350350 -30 -35 400400 -35 -40 -40 350 -30 400 -30 400 400400 450 450450 20 40 60 80 100 040429 exp1 cut1 rad#120=180o 120 20 20 4040 60 60 80 100 100 120 120 040429 exp1, cut2, rad#74=180o 450 -40 400 400 -35 400 450 -40 450 450 20 40 20 60 40 80 60 80100100120120 040429 exp1, cut3, az#28=180o 450450 20 2040 40 60 60 8080 100 100 120 040429, exp1, cut4, rad#342=180o 400 450 20 20 4040 60 60 80 100 100 120 120 040429 exp1, cut5, az#296=180o 20 40 60
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The City University of New York 2016-2017 Year-End Financial Report Expenditures Comparison: Percent of Total Expenditure by College FY2016 Expenditures Adjunct/ PS Regular Temp Service Summer FY2017 Expenditures Total PS OTPS Total Exp PS Regular Adjunct / Summe r Temp Service Total PS Total Proj. Exp OTPS Baruch College Brooklyn College City College Hunter College John Jay College Lehman College Medgar Evers College NYC College of Technology Queens College College of Staten Island York College Graduate Center CUNY School of Law School of Journalism School of Professional Studies School of Public Health 79.5% 78.6% 81.0% 76.5% 73.0% 79.5% 79.4% 69.5% 7.6% 6.1% 3.9% 9.4% 8.7% 7.3% 7.8% 15.0% 2.6% 4.9% 3.1% 4.1% 3.7% 2.4% 3.6% 2.1% 89.7% 89.6% 88.0% 90.1% 85.5% 89.2% 90.8% 86.7% 10.3% 10.4% 12.0% 9.9% 14.5% 10.8% 9.2% 13.3% 100% 100% 100% 100% 100% 100% 100% 100% 81.2% 79.5% 81.4% 76.9% 74.5% 79.8% 78.0% 72.2% 8.0% 6.8% 5.1% 10.2% 9.6% 8.6% 9.5% 17.2% 2.5% 5.0% 3.3% 4.6% 3.9% 2.7% 4.6% 2.1% 91.7% 91.3% 89.8% 91.7% 87.9% 91.1% 92.1% 91.5% 8.3% 8.7% 10.2% 8.3% 12.1% 8.9% 7.9% 8.5% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 77.9% 76.1% 77.2% 58.2% 70.0% 57.3% 62.4% 7.7% 8.2% 9.4% 0.5% 1.4% 7.2% 12.4% 3.6% 5.0% 2.6% 10.5% 4.2% 5.2% 4.7% 89.2% 89.3% 89.2% 69.2% 75.6% 69.7% 79.5% 10.8% 10.7% 10.8% 30.8% 24.4% 30.3% 20.5% 100% 100% 100% 100% 100% 100% 100% 79.3% 78.2% 78.9% 58.3% 72.8% 74.5% 62.3% 8.3% 10.0% 9.9% 0.6% 2.3% 9.4% 14.7% 4.0% 4.1% 2.7% 12.7% 5.4% 7.8% 5.5% 91.6% 92.3% 91.4% 71.6% 80.5% 91.7% 82.5% 8.4% 7.7% 8.6% 28.4% 19.5% 8.3% 17.5% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% - - - - - - 79.3% 0.5% 6.0% 85.9% 14.1% 100.0% Senior College Total 75.2% 7.3% 4.2% 86.6% 13.4% 100.0% 76.5% 8.3% 4.5% 89.3% 10.7% 100.0% BMCC Bronx CC Guttman CC Hostos CC Kingsborough CC LaGuardia CC Queensborough CC 63.6% 77.2% 62.7% 72.3% 71.7% 69.4% 74.5% 11.6% 7.8% 2.4% 7.4% 10.1% 10.2% 10.2% 3.0% 4.1% 4.0% 3.3% 6.9% 4.6% 3.2% 78.2% 89.1% 69.1% 83.0% 88.7% 84.2% 87.9% 21.8% 10.9% 30.9% 17.0% 11.3% 15.8% 12.1% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 65.9% 78.6% 64.0% 74.7% 73.2% 72.6% 76.2% 11.6% 8.0% 2.7% 7.4% 9.7% 10.3% 9.7% 3.0% 4.2% 4.4% 3.4% 6.8% 4.7% 3.0% 80.5% 90.9% 71.2% 85.5% 89.8% 87.6% 88.9% 19.5% 9.1% 28.8% 14.5% 10.2% 12.4% 11.1% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Community College Total 70.3% 9.7% 4.2% 84.1% 15.9% 100.0% 72.4% 9.6% 4.1% 86.1% 13.9% 100.0% 9 University Total 73.7% 8.0% 4.2% 85.9% 14.1% 100.0% 75.3% 8.7% 4.4% 88.4% 11.6% 100.0%
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MatrixResults.txt MatrixData1.txt 4 3.5 2.4 1.0 -4.6 -1.7 0.0 4.7 2.1 6.6 4.8 -5.0 1.6 2.1 -8.5 3.5 -7.4 MatrixData2.txt 4 -6.4 1.2 6.1 -4.7 0.1 7.4 -0.5 5.3 4.2 0.0 8.4 -8.5 5.3 -7.1 6.6 1.2 CHAPTER 8 – Multidimensional Arrays MATRIX MATRIX A: A: 3.500 -1.700 -1.700 6.600 6.600 2.100 2.400 0.000 0.000 4.800 4.800 -8.500 1.000 4.700 4.700 -5.000 -5.000 3.500 -4.600 2.100 2.100 1.600 1.600 -7.400 MATRIX MATRIX B: B: -6.400 -6.400 0.100 0.100 4.200 5.300 5.300 1.200 1.200 7.400 7.400 0.000 -7.100 -7.100 6.100 6.100 -0.500 -0.500 8.400 6.600 6.600 -4.700 -4.700 5.300 5.300 -8.500 1.200 1.200 DIAGONAL DIAGONAL OF OF B: B: -6.400 -6.400 SUM (A+B): SUM (A+B): -2.900 -2.900 -1.600 -1.600 10.800 10.800 7.400 7.400 7.400 8.400 8.400 1.200 1.200 3.600 3.600 7.400 7.400 4.800 4.800 -15.600 7.100 7.100 4.200 4.200 3.400 3.400 10.100 -9.300 -9.300 7.400 7.400 -6.900 -6.900 -6.200 DIFFERENCE DIFFERENCE (A-B): (A-B): 9.900 1.200 9.900 1.200 -1.800 -7.400 -1.800 -7.400 2.400 4.800 -3.200 -1.400 -3.200 -1.400 -5.100 -5.100 5.200 5.200 -13.400 -3.100 -3.100 0.100 0.100 -3.200 -3.200 10.100 -8.600 -8.600 MATRIX MATRIX PRODUCT PRODUCT (A*B): (A*B): -42.340 54.620 -42.340 54.620 41.750 -16.950 -54.280 32.080 -54.280 32.080 -38.810 -7.840 -38.810 -7.840 -1.810 -1.810 42.970 6.420 6.420 -2.380 -2.380 -17.750 -17.750 -29.440 38.840 38.840 -93.550 -93.550 SCALAR SCALAR PRODUCT PRODUCT (5*B): (5*B): -32.000 6.000 0.500 37.000 0.500 37.000 21.000 0.000 21.000 0.000 26.500 -35.500 26.500 -35.500 30.500 -2.500 -2.500 42.000 42.000 33.000 33.000 -23.500 26.500 26.500 -42.500 -42.500 6.000 6.000 INVERSE OF A: -0.027 0.120 -0.027 0.120 0.147 -0.004 0.147 -0.004 0.057 0.197 0.057 0.197 -0.149 0.132 -0.149 0.132 0.169 0.169 -0.046 -0.046 0.013 0.013 0.107 0.107 0.088 0.088 -0.102 -0.102 0.023 0.023 0.018 0.018 PRODUCT PRODUCT OF OF A A AND AND ITS ITS INVERSE: INVERSE: 1.000 0.000 0.000 1.000 0.000 0.000 0.000 1.000 -0.000 -0.000 0.000 1.000 -0.000 0.000 1.000 0.000 0.000 -0.000 -0.000 -0.000 17
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Security and performance   point for the first 9 correct point for the next 20 correct 1 point for each correct answer beyond 29 (Rotation does not count) (Assume you only ever store the encryption key and never modify it for quick decryption) Key siz e Substitution over Rotation over Autokey over One-time-pad over Affine over Vigenere over Hill over Keyspa ce Encrypti on runtime Decrypti on runtime pairs needed Resistant to Frequency Analysis?
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 In Europe during the Renaissance, cryptography was becoming a routine diplomatic tool  Cryptanalysis was moving into the West during the 15th century time  By the 18th century the telegraph was invented. During the Civil War U.S. Military Telegraph Corps used route ciphers.  Many cryptanalysists invented different ciphers between the 15th and 18th century periods:  Giovanni Battista Porta (1535-1615) invented the earliest digraphic cipher.  Blaise de Vigenere (1523-1596) invented the first acceptable autokey cipher system. Also the Vigenere Square.  Francis Bacon (1561-1626) invented the Bilateral cipher.  Thomas Jefferson (1743-1826) invented the wheel cipher.  Charles Wheatstone invented the digraphic cipher (playfair cipher).
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Purchasing Card Restricted (Federal Funded) Purchasing Card Policy page 11 2CFR 200 subpart E-200.407, items that require prior written approval of the cognizant agency before purchasing                       §200.201 Use of grant agreements (including fixed amount awards), cooperative agreements, and contracts, paragraph (b)(5) §200.306 Cost sharing or matching §200.307 Program income §200.308 Revision of budget and program plans §200.332 Fixed amount sub-awards §200.413 Direct costs, paragraph (c) §200.430 Compensation—personal services, paragraph (h) §200.431 Compensation—fringe benefits §200.438 Entertainment costs §200.439 Equipment and other capital expenditures §200.440 Exchange rates §200.441 Fines, penalties, damages and other settlements §200.442 Fund raising and investment management costs §200.445 Goods or services for personal use §200.447 Insurance and indemnification §200.454 Memberships, subscriptions, and professional activity costs, paragraph (c) §200.455 Organization costs §200.456 Participant support costs §200.458 Pre-award costs §200.462 Rearrangement and reconversion costs §200.467 Selling and marketing costs §200.474 Travel costs
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15.18 CL5 versus multiplicative affine logic Affine logic is a variation of the famous linear logic. Multiplicative affine logic is obtained from system G1 (see Episode 4) by deleting Contraction (as for linear logic, it further deletes Weakening as well). Our CL5 is also obtained by deleting Contraction from a deductive system for classical logic, and it is natural to ask how the two compare. Here is the answer: Fact 15.3. Every formula provable in multiplicative affine logic is also provable in CL5, but not vice versa: some formulas provable in CL5 are not provable in affine logic. Blass’s principle ( ( P  Q )  ( R  S ))  ( (P  R )  (Q  S )) proven on slide 15.13 is an example of a formula provable in CL5 but not in affine logic. In fact, one can show that any proof of Blass’s principle in G1 would require using not only Contraction, but also Weakening. On the other hand, our CL5-proof of it used neither Weakening nor Contraction (nor Duplication).
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Substitution Cryptosystems  How many possible keys does an affine cipher on have? 7. Encrypt using a rotation cipher over with . 8. Encrypt using an affine cipher over with 9. Cipher X consists of a rotation, and then an affine cipher. What type of cipher is X? 10. Cipher Y is a substitution cipher over . Cipher consists of applying Y twenty-four times. What type of cipher is X? Be as specific as possible. 6.
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  Student #1 Student #2 Student #3 Student #4 Student #5 Student #6 Student #7 Student #8 Student #9 Student #10 Student #11 Student #12 Student #13 Student #14 Student #15   Student #16   Student #17   Student #18   Student #19   Student #20   Student #21     Techniq ue   Desig n   Presenta tion   TOTA L POIN TS 87   Creativ ity And Conce pt 87 73 100 87% 93 100 100 93 80 87 93 80 87 87 87 100 73 80 87 93 87 87 87 100 100 100 100 100 87 87 87 100 97%   85%   90%   83%   90%   100%   100 100 100 100 87 87 87 87 100 100 100 100 87 93 100 93 80 87 93 93 100 100 100 100 67 87 87 87 100%   82% 80 87 93 80 85% 87 87 93 80 87% 100 100 100 100 100% 67 93 93 87 85% 87 100 100 93 95% 87 100 100 100 97% 90% 100%   87%   100%   93%   88% The results show an overall above average achievement and consistency within each category of scoring. Based on the data, emphasis will continue in areas of technique and design. The high scores in the presentation area are credited to the formal critiques and informal presentations conducted in each design course. The rubric is shared with the students throughout the semester, making each student aware of the assessment areas and criteria. Every required deliverable is compared against the rubric by the student to determine the scoring possibilities. Students are given immediate feedback by all evaluators at the reception. A discussion by students and faculty also takes place in a classroom critique following the reception.
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