Datasets:

mnlp-nsoai

/

rag-text-chunks

like 0

capturing the variations in the target output random variables of interest (e. g. , classes). The optimization effect is more difficult to tease out because the top two layers of a deep neural net can just overfit the training set whether the lower layers compute useful features or not, but there are

ieee_xplore

these factors would typically be relevant for any particular example, justifying sparsity of representation. These factors are expected to be related to simple (e. g. , linear) depen-dencies, with subsets of these explaining different random variables of interest (inputs, tasks) and varying in struc-

ieee_xplore

domain, while only unlabeled data DTare available in the target domain. More specifically, let the source domain data beDS={(xS1, yS1), . . . , ( xSn1, ySn1)}, w h e r e xSi∈Xis the input and ySi∈Yis the corresponding output. Similarly, let the target domain data be DT={xT1, . . . , xTn2}, w h e r et h e

ieee_xplore

the distance (measured w. r. t. the MMD) between the projected source and target domain data while maximizing the embeddeddata variance. By virtue of the kernel trick, it can be shown that the MMD distance in Section III-A can be written as tr (KL), where K=[φ(x i)⊤φ(xj)], a n d Lij=1/n2 1ifxi, xj∈XS,

ieee_xplore

(i, j)∈Nmij[W⊤K]i−[W⊤K]j2 =tr(W⊤KLKW). (11) B. Formulation and Optimization Procedure Combining all three objectives, we thus want to find a W that maximizes (10) while simultaneously minimizing (5) and(11). The final optimization problem can be written as min Wtr(W⊤KLKW )+μtr(W⊤W)+λ n2tr(W⊤KLKW)

ieee_xplore

208 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 22, NO. 2, FEBRUARY 2011 0 5 10 15 20 25 30 35 40 45 50 551. 52. 54. 58153055100 # DimensionsAED ( unit: m)KPCA SSTCATCA TCARed uced SSA (a)0 100 200 300 400 500 600 700 800 9002345678 # Unlabeled Data in the Tar get Domain for Trainin gAED ( unit: m)RLSR

ieee_xplore

72. 33 (2. 32) 75. 86 (1. 55) 76. 70 (1. 05) 81. 59 (1. 36) 90. 51 (0. 70) TCAlinear 10 70. 41 (6. 84) 87. 67 (2. 12) 82. 83 (3. 07) 84. 51 (5. 04) 89. 79 (2. 54) 88. 67 (3. 01) kernel 20 69. 04 (5. 07) 81. 52 (8. 86) 83. 86 (3. 24) 82. 23 (2. 64) 89. 73 (4. 04) 92. 03 (2. 05) 30 69. 01 (2. 39) 77. 26 (15. 81)

ieee_xplore

84. 44 (2. 29) 79. 81 (4. 20) 91. 81 (2. 38) 92. 23 (2. 41) Laplacian 10 69. 12 (13. 27) 78. 68 (8. 23) 79. 58 (8. 12) 69. 02 (5. 31) 82. 29 (2. 57) 90. 01 (1. 02) kernel 20 69. 37 (11. 22) 79. 57 (4. 31) 78. 71 (9. 22) 74. 71 (3. 01) 85. 59 (1. 59) 92. 68 (1. 12) 30 68. 58 (11. 21) 80. 43 (4. 64) 76. 69 (9. 52)

ieee_xplore

74. 40 (2. 49) 87. 89 (2. 22) 92. 63 (0. 84) RBF 10 74. 88 (3. 51) 82. 51 (7. 65) 78. 34 (6. 58) 81. 65 (4. 08) 82. 69 (2. 24) 89. 15 (0. 69) kernel 20 72. 60 (5. 60) 77. 47 (2. 62) 78. 09 (6. 88) 79. 54 (1. 89) 83. 51 (3. 32) 90. 77 (0. 83) 30 71. 64 (5. 49) 77. 62 (3. 75) 80. 11 (7. 73) 79. 50 (1. 91) 83. 71 (2. 27)

ieee_xplore

91. 58 (0. 64) SSTCAlinear 10 68. 64 (3. 00) 75. 11 (11. 93) 81. 46 (3. 59) 73. 75 (7. 55) 85. 99 (3. 22) 91. 38 (2. 22) kernel 20 64. 28 (3. 48) 60. 69 (14. 87) 77. 45 (5. 30) 78. 19 (4. 17) 86. 71 (3. 36) 91. 81 (2. 13) 30 65. 08 (3. 12) 66. 30 (16. 74) 77. 98 (4. 19) 72. 79 (5. 75) 85. 81 (3. 23) 93. 38 (2. 02)

ieee_xplore

Laplacian 10 75. 29 (3. 92) 79. 84 (5. 03) 72. 70 (10. 69) 73. 10 (2. 10) 85. 26 (2. 25) 91. 72 (0. 64) kernel 20 71. 99 (4. 73) 81. 77 (3. 44) 72. 99 (9. 99) 74. 94 (2. 28) 84. 28 (1. 27) 92. 47 (0. 74) 30 69. 71 (4. 99) 82. 09 (4. 42) 72. 34 (10. 82) 74. 67 (1. 79) 85. 30 (1. 80) 92. 73 (0. 76) RBF 10

ieee_xplore

73. 76 (3. 25) 74. 50 (7. 85) 78. 51 (7. 50) 77. 61 (1. 49) 83. 09 (. 0287) 90. 35 (1. 18) kernel 20 70. 87 (7. 51) 75. 49 (6. 67) 79. 28 (7. 20) 79. 46 (1. 27) 80. 02 (. 0287) 90. 62 (0. 83) 30 70. 16 (5. 98) 77. 03 (5. 56) 79. 06 (7. 60) 79. 88 (1. 52) 81. 30 (. 0287) 90. 21 (0. 96) KPCAlinear 10 68. 66 (6. 59)

ieee_xplore

88. 26 (5. 85) 68. 59 (10. 00) 81. 42 (6. 67) 87. 33 (3. 56) 91. 24 (1. 84) kernel 20 69. 18 (6. 27) 82. 59 (7. 07) 71. 46 (7. 41) 80. 22 (3. 81) 89. 49 (3. 34) 93. 44 (1. 92) 30 70. 55 (2. 81) 80. 94 (11. 63) 78. 90 (8. 33) 77. 92 (4. 32) 91. 36 (1. 51) 93. 66 (1. 81) Laplacian 10 44. 43 (8. 01) 81. 52 (9. 00)

ieee_xplore

54. 42 (7. 33) 80. 37 (. 0252) 58. 87 (4. 97) 58. 47 (2. 35) kernel 20 49. 08 (10. 46) 55. 67 (6. 35) 50. 42 (1. 01) 72. 67 (. 0252) 75. 71 (6. 83) 73. 94 (3. 75) 30 45. 24 (8. 17) 63. 13 (7. 76) 50. 43 (1. 03) 69. 36 (. 0252) 75. 07 (10. 64) 74. 18 (4. 24) RBF 10 53. 82 (6. 23) 78. 50 (4. 23) 51. 64 (2. 11)

ieee_xplore

79. 92 (. 0252) 57. 84 (3. 74) 56. 82 (2. 03) kernel 20 47. 66 (8. 19) 60. 94 (10. 97) 50. 49 (1. 00) 79. 37 (. 0252) 67. 73 (5. 53) 62. 36 (3. 84) 30 47. 82 (8. 37) 69. 13 (9. 66) 51. 86 (3. 82) 72. 31 (. 0252) 67. 66 (4. 48) 64. 76 (5. 14) SCL all+50 68. 29 (1. 22) 72. 38 (2. 36) 75. 87 (1. 48) 76. 73 (1. 00)

ieee_xplore

81. 60 (1. 35) 90. 61 (0. 64) KMMlinear kernel all 69. 81 (1. 27) 72. 86 (1. 53) 75. 29 (1. 85) 76. 38 (1. 32) 78. 17 (1. 29) 88. 06 (1. 33) Laplacian kernel all 69. 64 (1. 27) 73. 10 (1. 67) 76. 62 (1. 23) 75. 83 (1. 27) 77. 81 (1. 21) 85. 92 (0. 70) RBF kernel all 69. 65 (1. 24) 73. 07 (1. 48) 76. 63 (1. 14)

ieee_xplore

fects of randomness are seen to be controlled. Some results oftheparticleswarmoptimizer, usingmodificationsderivedfromtheanalysis, arepresented;theseresultssuggestmethodsforal- tering the original algorithm in ways that eliminate some prob- lemsandincreasetheoptimizationpoweroftheparticleswarm. II. A

ieee_xplore

choice. The probabilities for messages are implicitly determined bystating oura priori knowledge of the enemy's language habits, thetactical situation (which will influence the probable content of the message) and any special information we may have regarding the cryptogram. . . . . , ~. , . . . ----::;:;~

ieee_xplore

integers. Thus, for d=5, we might have 2 3 1 54as the permutation. This means that: mim2mam4m6m6m7ms mg mlO. . . becomes ~ma mi m«m4m7msm6mIDmg. . . . Sequential application of two or more transpositions will be called compound transposition. Ifthe periods are dl, d2, "', d. it isdear that the result is

ieee_xplore

6. Matrix System? One method ofn-gram substitution is to operate on successive n-grams with a matrix having an inverse. The letters are assumed numbered from oto25, making them elements of an algebraic ring. From the n-gram m, m~. . . m;of message, the matrix ajjgives an n-gram of cryptogram "e, =Lau m,

ieee_xplore

674 BELL SYSTEM TECHNICAL JOURNAL bethesame as theMspace, i. e. that thesystem beendomorphic. The fractional transposition is as homogeneous as the ordinary transposition without being endomorphic. The proper definition is the following: A cipher Tispure if for every T, , T, , T»there is a T. such that

ieee_xplore

identifying Bwith the message gives the second result. The last result fol­ lows from [fB(M) ~HB(K, M) =HE(K)+HB. K(M) and the fact that HB, K(M) =0 since KandEuniquely determine M. Since the message and key are chosen independently we have: H(M, K)=H(M)+H(K). Furthermore, H(M, K)=H(E, K)=H(E)+HE(K),

ieee_xplore

tions of the system. The lower limit is achieved if all the systems R, S, . . . , Ugo to completely different cryptogram spaces. This theorem is also proved by the general inequalities governing equivocation, HAB)sH(B) ::::;H(A)+HA(B). Weidentify Awith the particular system being used and Bwith the key

ieee_xplore

for 0 and 1, and successive letters chosen independently. We have HB(M) =HB(K) = -L: P(E)PB(K) logP. (K) The probability that Econtains exactly sO's in a particular permutation is: !(p. qN-. +q'pN-. ) ""-, \ r-, \ r-, \<, "-<, \<, . . . . . . . P"'11, q"'1~ \r-, . . . . . . . \i""""--. r-. . I f\r--r-. \ --\.

ieee_xplore

692 BELL SYSTEM TECHNICAL JOURNAL mkeys from high probability messages each with probabilityf. Hence the equivocation is: T k(k)(S)m( s)~m -L: - 1 - - InlogmSkm_1 m T T We wish to find a simple approximation to this when kis large. Ifthe expected value of m, namely iii=SkiT, is»1, thevariation of log m

ieee_xplore

each L, weighted in accordance with its Pi. The mean equivocation char­ acteristic will be a line somewhere in the midst of these ridges and may not give a very complete picture of the situation. This is shown in Fig. 11. A similar effect occurs if the system is not pure butmade up of several systems

ieee_xplore

secrecy" afforded by the system. For a simple substitution on English the work and equivocation char­ acteristics would be somewhat as shown in Fig, 12. The dotted portion of. :". . . . . . -. \, , , , , , , \, . , ', I, . , . •, , . I•, , , •, , , , , , , , , , , . , , . Fig. 12-Typical work and equivocation characteristics.

ieee_xplore

therefore be complex in the kj, and involve many of them. Otherwise the enemy can solve the simple ones and then the more complex ones by sub­ stitution. From thepoint of view of increasing confusion, it is desirable to have the f. involve several mi, especially ifthese are not adjacent and hence less

ieee_xplore

(e-mail: cheewooi@iastate. edu; liu@iastate. edu; gmani@iastate. edu). Digital Object Identifier 10. 1109/TPWRS. 2008. 2002298Since the 1970s, the control center framework has gradually evolved from a closed monolithic structure to a more opennetworked environment. With the recent trend of using stan-

ieee_xplore

, represents the level of impact on a power system when a substation is removed, i. e. , electrically disconnected, by switching actions due to the attack. The impact caused by an attack through an access point will be evaluated by a logic- and power flow-based procedure. The steady state probabilities

ieee_xplore

bound on transmission rate of 2, 3, and 4 b/s/Hz. It follows also from the above that there is a fundamental tradeoff between constellation size, diversity, and the trans-mission rate. We relate this tradeoff to the trellis complexityof the code. Lemma 3. 3. 1: The constraint length of an -space–time

ieee_xplore

uses the RBF kernel, whose two optimal hyperparame- tersσandλ(the regularization parameter to balance the training and testing errors) can be determined by fivefold cross validation in the range σ=[2−3, 2−2, . . . , 24]and λ=[10−2, 10−1, . . . , 104]. 4) For the 1-D CNN, we use one convolutional block,

ieee_xplore

including a 1-D convolutional layer with a filter size of 128, a BN layer, a ReLU activation layer, and a softmax layer with the size of P, w h e r e Pdenotes the dimension of network output. 3https://www. csie. ntu. edu. tw/ ∼cjlin/libsvm/TABLE IV GENERAL NETWORK CONFIGURATION IN EACH LAYER OF OURFUNET.

ieee_xplore