CCR (Corners of Circumscribing Coordinate Rectangle) (rnd-f, rnd-g, then lin-d) f=MinVecX≡(minXx1..minXxn) g≡MaxVecX≡(maxXx1..maxXxn), d≡(g-f)/|g-f| Sequence thru main diagonal pairs, {f, g} lexicographically. For each, create d. Notes: No calculation required to find f and g (assuming f1=MnVec RnGp>4 none start  g1=MxVec RnGp>4 Sub 0 7 vir18... Clus1 1 47 ver30 Sub 0 53 ver49.. Clus2 0 74 set14 SubClus1 Lin>4 none f2=0001 RnGp>4 none CCR-1. Do SpS((x-f)o(x-f)) round gap analysis CCR-2. Do SpS((x-g)o(x-g)) rnd gap analysis. CCR-3. Do SpS((xod)) linear gap analysis. SubCluster2 g2=1110 RnGp>4 none This ends SubClus2 = 47 setosa only Lin>4 none f1=0000 RnGp>4 none g1=1111 RnGp>4 none Lin>4 none f3=0010 RnGp>4 none f2=0001 RnGp>4 none g2=1110 RnGp>4 none Lin>4 none Lin>4 none f3=0010 RnGp>4 none g3=1101 RnGp>4 none Lin>4 none f4=0011 RnGp>4 none f4=0011 RnGp>4 none g4=1100 RnGp>4 none Lin>4 none f5=0100 RnGp>4 none g5=1011 RnGp>4 none Lin>4 none g3=1101 RnGp>4 none g4=1100 RnGp>4 none Lin>4 none f5=0100 RnGp>4 none g5=1011 RnGp>4 none Lin>4 none f6=0101 RnGp>4 none g6=1010 RnGp>4 none f6=0101 RnGp>4 1 19 set26 0 28 ver49 0 31 set42 0 31 ver8 0 32 set36 0 32 ver44 1 35 ver11 0 41 ver13 ver49 0.0 19.8 3.9 21.3 3.9 7.2 Lin>4 none f7=0110 RnGp>4 none g7=1001 RnGp>4 none Lin>4 none f8=0111 RnGp>4 none g8=1000 RnGp>4 none Lin>4 none f7=0110 RnGp>4 1 28 ver13 0 33 vir49 f8=0111 RnGp>4 none set42 ver8 19.8 3.9 0.0 21.6 21.6 0.0 10.4 23.9 21.8 1.4 23.8 4.6 set36 ver44 ver11 21.3 3.9 7.2 10.4 21.8 23.8 23.9 1.4 4.6 0.0 24.2 27.1 24.2 0.0 3.6 27.1 3.6 0.0 g6=1010 RnGp>4 none g7=1001 RnGp>4 none g8=1000 RnGp>4 none Lin>4 none Lin>4 none Lin>4 none MaxVecX and MinVecX have been calculated and residualized when PTreeSetX was captured.) 3. (and 2.?) may be unproductive in finding new subclusters (either because 1 finds almost all or because 2 and/or 3 find the same ones) and could be skipped (very likely if dimension is high, since the main diagonal corners are typically far from X, in a high dimensional vector space and thus the radii of a round gap is large and large radii rnd gaps are near linear, suggesting a will find all the subclusters that b and c would find. 2. good!, else setosa/versicolor+virginica are not separated! 3. is unproductive, suggesting productive to calculate 1., 2. but having done that, 3. will probably not be productive. Next consider only 3. to see if it is as productive as 1.+2. Subc2.1 ver49 ver8 ver44 ver11 CCR is as good as the combo (projection on d appears to be as accurate as the combination of square length of f and of g). This is probably because the round gaps (centered at the corners) are nearly linear by the time they get to the set X itself. To compare the time costs, we note: Combo (p-x)o(p-x) = pop + xox2xop = pop + k=1..nxk2 + k=1..n(-2pk)xk has n multiplications in the second term, n scalar multiplications and n additions in the third term. For both p=f and p=g, then, it takes 2n multiplications, 2n scalar multiplications and 2n additions. For CCR, xod = k=1..n(dk)xk involves n scalar mults and n additions. It appears to be cheaper (timewise) This ends SubClus1 = 95 ver and vir samples only
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CCR(fgd) (Corners of Circumscribing Coordinate Rectangle) f1=minVecX≡(minXx1..minXxn) (0000) g1=MaxVecX≡(MaxXx1..MaxXxn) (1111), d=(g-f)/|g-f| Sequence thru main diagonal pairs, {f, g} lexicographically. For each, create d. f1=MnVec RnGp>4 none start  g1=MxVec RnGp>4 Sub 0 7 vir18... Clus1 1 47 ver30 Sub 0 53 ver49.. Clus2 0 74 set14 SubClus1 Lin>4 none f2=0001 RnGp>4 none CCR(f) Do SpS((x-f)o(x-f)) round gap analysis CCR(g) Do SpS((x-g)o(x-g)) round gap analysis. CCR(d) Do SpS((xod)) linear gap analysis. Notes: No calculation required to find f and g (assuming MaxVecX and minVecX have been calculated and residualized when PTreeSetX was captured.) SubCluster2 g2=1110 RnGp>4 none This ends SubClus2 = 47 setosa only Lin>4 none f1=0000 RnGp>4 none g1=1111 RnGp>4 none Lin>4 none f3=0010 RnGp>4 none f2=0001 RnGp>4 none g2=1110 RnGp>4 none Lin>4 none Lin>4 none f3=0010 RnGp>4 none g3=1101 RnGp>4 none Lin>4 none f4=0011 RnGp>4 none f4=0011 RnGp>4 none g4=1100 RnGp>4 none Lin>4 none f5=0100 RnGp>4 none g5=1011 RnGp>4 none Lin>4 none If the dimension is high, since the main diagonal corners are liekly far from X and thus the large radii make the round gaps nearly linear. g3=1101 RnGp>4 none g4=1100 RnGp>4 none Lin>4 none f5=0100 RnGp>4 none g5=1011 RnGp>4 none Lin>4 none f6=0101 RnGp>4 none g6=1010 RnGp>4 none f6=0101 RnGp>4 1 19 set26 0 28 ver49 0 31 set42 0 31 ver8 0 32 set36 0 32 ver44 1 35 ver11 0 41 ver13 ver49 0.0 19.8 3.9 21.3 3.9 7.2 Lin>4 none f7=0110 RnGp>4 none g7=1001 RnGp>4 none Lin>4 none f8=0111 RnGp>4 none f7=0110 RnGp>4 1 28 ver13 0 33 vir49 f8=0111 RnGp>4 none set42 ver8 19.8 3.9 0.0 21.6 21.6 0.0 10.4 23.9 21.8 1.4 23.8 4.6 g6=1010 RnGp>4 none g7=1001 RnGp>4 none g8=1000 RnGp>4 none g8=1000 RnGp>4 none Lin>4 none set36 ver44 ver11 21.3 3.9 7.2 10.4 21.8 23.8 23.9 1.4 4.6 0.0 24.2 27.1 24.2 0.0 3.6 27.1 3.6 0.0 This ends SubClus1 = 95 ver and vir samples only Lin>4 none Lin>4 none Lin>4 none Subc2.1 ver49 ver8 ver44 ver11
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RC4 Security  claimed secure against known attacks  There are some attacks, none practical  result is very non-linear  since RC4 is a stream cipher, must never reuse a key  There are concerns with WEP, but due to key handling rather than RC4 itself
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Ron’s Code or Rivest Codes Scorecard Description RC2 RC4 RC5 RC6 Timeline 1987 1987 1994 1998 Type of Algorithm Block cipher Stream cipher Block cipher Block cipher Key size (in bits) 40 and 64 1 - 256 0 to 2040 bits (128 suggested) 128, 192, or 256 Variable key-size block cipher that was designed as a "drop-in" replacement for DES. Use Most widely used stream cipher based on a variable key-size Vernam stream cipher. It is often used in file encryption products and secure communications, such as within SSL. The cipher can be expected to run very quickly in software and is considered secure. © 2012 Cisco and/or its affiliates. All rights reserved. A fast block cipher that has a variable block size and key size. It can be used as a dropin replacement for DES if the block size is set to 64-bit. An AES finalist (Rijndael won). A 128-bit to 256- bit block cipher that was designed by Rivest, Sidney, and Yin and is based on RC5. Its main design goal was to meet the requirement of AES. 104
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d3 Sub 0 Clus1 1 Sub 0 Clus2 0 10 19 30 69 set23...50set+vir39 set25 ver49...50ver_49vir vir19 d5 (f5=vir23, g5=set14) none f5 none g5 none d5 (f5=vir32, g5=set14) none f5 none g5 none d5 (f5=vir6, g5=set14) none f5 none g5 none (d1+d3)/sqr(2) clus1 none (d1+d3)/sqr(2) clus2: ver49 ver8 ver44 ver11 0 57.3 ver49 0.0 3.9 3.9 7.2 0 58.0 ver8 3.9 0.0 1.4 4.6 0 58.7 ver44 3.9 1.4 0.0 3.6 1 60.1 ver11 7.2 4.6 3.6 0.0 0 64.3 ver10 none (d3+d4)/sqr(2) clus1 none (d3+d4)/sqr(2) clus2 none (d1+d3+d4)/sqr(3) clus1 1 44.5 set19 0 55.4 vir39 (d1+d3+d4)/sqr(3) clus2 none (d1+d2+d3+d4)/sqr(4) clus1 (d1+d2+d3+d4)/sqr(4) clus2 none d5 (f5=vir19, g5=set14) none f5 1 0.0 vir19 clus2 0 4.1 vir23 g5 none d5 (f5=vir18, g5=set14) none f5 1 0.0 vir18 clus2 1 4.1 vir32 0 8.2 vir6 g5 none
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WEP (continued) • RC4 issues – RC4 uses a pseudo random number generator (PRNG) to create the keystream • PRNG does not create a true random number – First 256 bytes of the RC4 cipher can be determined • By bytes in the key itself – RC4 source code (or a derivation) has been revealed • Attackers can see how the keystream itself is generated • WEP attack tools – AirSnort, Aircrack, ChopChop WEP Cracker, and WEP Crack CWSP Guide to Wireless Security 36
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Model Structure Tree Structure Specified for the Nested Logit Model Sample proportions are marginal, not conditional. Choices marked with * are excluded for the IIA test. ----------------+----------------+----------------+----------------+------+--Trunk (prop.)|Limb (prop.)|Branch (prop.)|Choice (prop.)|Weight|IIA ----------------+----------------+----------------+----------------+------+--Trunk{1} 1.00000|TRAVEL 1.00000|PRIVATE .55714|AIR .27619| 1.000| | | |CAR .28095| 1.000| | |PUBLIC .44286|TRAIN .30000| 1.000| | | |BUS .14286| 1.000| ----------------+----------------+----------------+----------------+------+--+---------------------------------------------------------------+ | Model Specification: Table entry is the attribute that | | multiplies the indicated parameter. | +--------+------+-----------------------------------------------+ | Choice |******| Parameter | | |Row 1| GC TTME INVT INVC A_AIR | | |Row 2| AIR_HIN1 A_TRAIN TRA_HIN3 A_BUS BUS_HIN4 | +--------+------+-----------------------------------------------+ |AIR | 1| GC TTME INVT INVC Constant | | | 2| HINC none none none none | |CAR | 1| GC TTME INVT INVC none | | | 2| none none none none none | |TRAIN | 1| GC TTME INVT INVC none | | | 2| none Constant HINC none none | |BUS | 1| GC TTME INVT INVC none | | | 2| none none none Constant HINC | +---------------------------------------------------------------+
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Summary RSA’s RC4 is used in many security protocols including WEP and SSL WEP is inherently weak but the weakness is not due to RC4 encryption TKIP and other similar key rotation schemes correct the problem with WEP while retaining RC4 stream cipher RC5 is the most well-known block cipher RC5 is a parameterized algorithm with a variable block size, variable key size and a variable number of rounds This work is supported by the National Science Foundation under Grant Number DUE0302909. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of the National Science Foundation.
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Why Deep Learning? Different Classifiers on the MNIST Database Linear classifier Pairwise linear classifier None Deskewing Error rate (%) 7.6[9] K-Nearest Neighbors K-NN with non-linear deformation (P2DHMDM) None Shiftable edges 0.52[18] Boosted Stumps Product of stumps on Haar features None Haar features 0.87[19] Non-linear classifier 40 PCA + quadratic classifier None Support vector machine Virtual SVM, deg-9 poly, 2pixel jittered None Neural network 2-layer 784-800-10 None None Neural network 2-layer 784-800-10 elastic distortions None 1.6[21] 0.7[21] Deep neural network 6-layer 784-2500-2000-15001000-500-10 elastic distortions None 0.35[22] Convolutional neural network 6-layer 784-40-80-500-10002000-10 None Expansion of the training data 0.31[15] Convolutional neural network 6-layer 784-50-100-500-100010-10 None Expansion of the training data 0.27[16] Convolutional neural network Committee of 35 CNNs, 1-20P-40-P-150-10 elastic distortions Width normalizations 0.23[8] Convolutional neural network Committee of 5 CNNs, 6-layer 784-50-100-500-1000-10-10 None Expansion of the training data 0.21[17] Distortion https://en.wikipedia.org/wiki/MNIST_database 9 Preprocessing None 3.3[9] 0.56[20] Deskewing P Classifier DE E Type
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Concerns About ALN Learning  Concerns About the Explosion of ALN in Education  Concerns About Residency Living & Learning on Campus  Concerns About Impersonality and Becoming Irrevocably Orwellian  Concerns About Making ALN Learning Too Easy  Concerns About Making ALN Learning Too Hard  Concerns About Corporate Influences on Traditional Missions  Concerns About Library Services  Concerns About Academic Standards and Student Ethics  Concerns About Messaging Overload  Concerns About Faculty Efficiency and Burnout  Concerns About Misleading and Fraudulent Web Sites  Concerns About CyberPsychology  Concerns About Computer Services and Network Reliability  Concerns About Faculty Resistance to Change  Concerns About Effectiveness of Learning Technologies in Large Classes 1-58
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SecureX Solutions • Secure Edge and Branch – The goal of the Cisco secure edge and branch is to deploy devices and systems to detect and block attacks and exploits, and prevent intruder access. With firewall and intrusion prevention in standalone and integrated deployment options, organizations can avoid attacks and meet compliance requirements. • Secure Email and Web – Cisco secure email and web solutions reduce costly downtime associated with email-based spam, viruses, and web threats, and are available in a variety of form factors, including on-premise appliances, cloud services, and hybrid security deployments with centralized management. • Secure Access – Secure access technologies are put in place to enforce network security policies, secure user and host access controls, and control network access based on dynamic conditions. • Secure Mobility – Cisco secure mobility solutions promote highly secure mobile connectivity with VPN, wireless security, and remote workforce security solutions that extend network access safely and easily to a wide range of users and devices. • Secure Data Center and Virtualization – Cisco secure data center and virtualization solutions protect high-value data and data center resources with threat defense, secure virtualization, segmentation and policy control. © 2012 Cisco and/or its affiliates. All rights reserved. 31
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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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