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07/16/2019 09:32 city county stamps 3
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07/16/2019 09:32 CLOSING 2,119
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07/16/2019 09:29 origi 164
07/16/2019 09:29 adjustme 5,420
07/16/2019 09:29 file contac 1,009
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07/16/2019 09:25 Status Date 33
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07/16/2019 09:25 mortgage type 14
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07/16/2019 09:25 second Term 1
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07/16/2019 09:25 sale 46
07/16/2019 09:25 sECONDARY REGIST 1
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07/16/2019 09:24 refinance purpos 4
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07/16/2019 09:24 rescission 2
07/16/2019 09:24 rate locked 25
07/16/2019 09:24 line 1010 38
07/16/2019 09:24 rate lock time 76
07/16/2019 09:24 rate lock buyside 156
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07/16/2019 09:24 cLTV 38
07/16/2019 09:24 rans Details Inte 14
07/16/2019 09:24 than 32
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07/16/2019 09:24 edisclosedt 37
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07/16/2019 09:24 qm s 328
07/16/2019 09:24 condotel 13
07/16/2019 09:24 proce 96
07/16/2019 09:24 purchase advi 190
07/16/2019 09:24 purchase Date 33
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07/16/2019 09:24 property taxe 7
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07/16/2019 09:23 prope 622
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07/16/2019 09:23 product rate 23
07/16/2019 09:23 product cod 12
07/16/2019 09:23 private mortgage insuran 3
07/16/2019 09:23 present Address 9
07/16/2019 09:23 property add 54
07/16/2019 09:23 prepayment term 1
07/16/2019 09:23 Assets Net Worth of Busi 1
07/16/2019 09:23 VAELIG.X71 1
07/16/2019 09:23 prepayment 50
07/16/2019 09:23 prepayme 50
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07/16/2019 09:23 poo 64
07/16/2019 09:23 pmt 123
07/16/2019 09:23 pend 236
07/16/2019 09:23 pled 1
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07/16/2019 09:23 phone number 49
07/16/2019 09:23 Mortgage insurance 77
07/16/2019 09:23 pes 44
07/16/2019 09:23 Residual 6
07/16/2019 09:23 person 44
07/16/2019 09:22 period 95
07/16/2019 09:22 percen 231
07/16/2019 09:22 payoff 71
07/16/2019 09:22 paymenttable.customize 1
07/16/2019 09:22 paymentdate 1
07/16/2019 09:22 payment adjustment 12
07/16/2019 09:22 paymen 832
07/16/2019 09:22 aus.x6 22
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07/16/2019 09:22 party ty 93
07/16/2019 09:22 va number 8
07/16/2019 09:22 parsed 4
07/16/2019 09:22 par lock 2,570
07/16/2019 09:22 p and I 767
07/16/2019 09:22 ownership pe 3
07/16/2019 09:22 owned 20
07/16/2019 09:22 out 174
07/16/2019 09:22 originating company 2
07/16/2019 09:22 originati 89
07/16/2019 09:22 ULDD.LoanIdentifierValueType 1
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07/16/2019 09:22 ori 265
07/16/2019 09:22 order date 17
07/16/2019 09:22 open-end 2
07/16/2019 09:22 lender paid 189
07/16/2019 09:22 officer 178
07/16/2019 09:22 of 1,560
07/16/2019 09:21 obligate 277
07/16/2019 09:21 ob 718
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07/16/2019 09:21 Credit Reference 2
07/16/2019 09:21 nstitution 38
07/16/2019 09:21 investor 642
07/16/2019 09:21 notices.x57 1
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07/16/2019 09:21 notices.x 96
07/16/2019 09:21 notic 127
07/16/2019 09:21 complian 47
07/16/2019 09:21 note date 9
07/16/2019 09:21 notary fee 16
07/16/2019 09:21 no prov 21
07/16/2019 09:21 no escrow 7
07/16/2019 09:21 anti-steering 196
07/16/2019 09:21 no co 334
07/16/2019 09:21 licens 462
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07/16/2019 09:21 next expected milestone 1
07/16/2019 09:21 newhud2.x4404 1
07/16/2019 09:21 newhud2.x33 91
07/16/2019 09:21 property stree 21
07/16/2019 09:21 File Cont 1,009
07/16/2019 09:20 newhud2.X300 8
07/16/2019 09:20 MI coverage 1
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07/16/2019 09:20 negative amortization 15
07/16/2019 09:20 multifamil 2
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07/16/2019 09:20 ms.statu 3
07/16/2019 09:20 ms.dur 1
07/16/2019 09:20 optiona 13
07/16/2019 09:20 co-borrower 70
07/16/2019 09:20 ms.date 2
07/16/2019 09:20 mortgage t 542
07/16/2019 09:20 mortgage property city 1
07/16/2019 09:20 mortgage prope 32
07/16/2019 09:20 loan or 478
07/16/2019 09:20 mortgage ins 97
07/16/2019 09:20 mortgage delinquen 6
07/16/2019 09:20 mortgage count 13
07/16/2019 09:20 TPO.X90 1
07/16/2019 09:20 mornet.x76 2
07/16/2019 09:19 sco 737
07/16/2019 09:19 monthly premium 8
07/16/2019 09:19 coup 107
07/16/2019 09:19 month remainin 8
07/16/2019 09:19 vi 8,253
07/16/2019 09:19 monetary 3
07/16/2019 09:19 modific 4
07/16/2019 09:19 ml 395
07/16/2019 09:19 milestone completion 1
07/16/2019 09:19 present housing expense 5
07/16/2019 09:19 mi cert 2
07/16/2019 09:19 message 103
07/16/2019 09:19 mers mortgagee 1
07/16/2019 09:19 closing cit 71
07/16/2019 09:19 mcawpur.x9 1
07/16/2019 09:19 may 26
07/16/2019 09:19 maximum loan amoun 2
07/16/2019 09:19 maturity dat 3
07/16/2019 09:19 Seller C 1,366
07/16/2019 09:19 manufactured secured 1
07/16/2019 09:19 manufactured home land 1
07/16/2019 09:19 mac 338
07/16/2019 09:19 Loan Processor 21
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07/16/2019 09:19 low income 29
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07/16/2019 09:19 log.mt.nam 1
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07/16/2019 09:19 i 32,630
07/16/2019 09:19 log.ms. 11
07/16/2019 09:18 lockrate.denial 4
07/16/2019 09:18 lockrate.3045 1
07/16/2019 09:18 lock rate comments 52
07/16/2019 09:18 lock exp 317
07/16/2019 09:18 lock ca 234
07/16/2019 09:18 lock (Most r 5,533
07/16/2019 09:18 lock (Most 5,533
07/16/2019 09:18 location 5
07/16/2019 09:18 loanteammember.name 1
07/16/2019 09:18 loan submit 1
07/16/2019 09:18 loan servicer id 1
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07/16/2019 09:18 loan officer name 5
07/16/2019 09:18 loan n 1,906
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07/16/2019 09:18 loan info refi 14
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07/16/2019 09:18 fees interest 17
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07/16/2019 09:18 loan info chann 1
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07/16/2019 09:17 act 1,396
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07/16/2019 09:16 housing expen 18
07/16/2019 09:16 li 9,654
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07/16/2019 09:16 hud 6,442
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07/16/2019 09:15 guarantee amount 5
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07/16/2019 09:15 ad 11,474
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07/16/2019 09:14 X 17,090
07/16/2019 09:14 Un 6,079
07/16/2019 09:14 Rate N 12,539
07/16/2019 09:14 Rate Lock Request L 7,896
07/16/2019 09:14 expenses proposed taxes 1
07/16/2019 09:14 Rate Lock Reques 7,896
07/16/2019 09:14 expenses housin 10
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07/16/2019 09:13 current team member 2
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07/16/2019 09:13 NEWHUD 5,803
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07/16/2019 09:13 credit score method 2
07/16/2019 09:13 fannie mae case 2
07/16/2019 09:13 NEWH 5,803
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07/16/2019 09:11 rat 14,117
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07/16/2019 09:11 Wire Transfer 13
07/16/2019 09:11 prequal 149
07/16/2019 09:11 VETERAN TYPE 3
07/16/2019 09:11 qu 8,901
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07/16/2019 09:11 Underwriting Risk Assess Type 7
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07/16/2019 09:10 Shipping Pool 2
07/16/2019 09:10 SYS.X1 82
07/16/2019 09:10 BORPCC 1
07/16/2019 09:10 SP0003 1
07/16/2019 09:10 m a 23,365
07/16/2019 09:10 SECONDARY FINANCING 1
07/16/2019 09:10 DEN 3,299
07/16/2019 09:10 SCORE 564
07/16/2019 09:10 Rate and term 4
07/16/2019 09:10 Rate Lock Curr 197
07/16/2019 09:10 Rate No Closing 15
07/16/2019 09:10 REPORTED 24
07/16/2019 09:10 Purchase Price Net 1
07/16/2019 09:10 lockra 12,421
07/16/2019 09:10 Payout 5 Desc 1
07/16/2019 09:10 PROCESSOR 25
07/16/2019 09:10 lockr 12,421
07/16/2019 09:10 PRICING 3
07/16/2019 09:10 PARTICIPATION 3
07/16/2019 09:10 lock t 13,777
07/16/2019 09:10 Note rate 6
07/16/2019 09:10 lock request rate 7,896
07/16/2019 09:10 Note Da 16
07/16/2019 09:10 NEWHUD2.x134 11
07/16/2019 09:10 Debt to income 8
07/16/2019 09:09 lock d 12,581
07/16/2019 09:09 conversion 7
07/16/2019 09:09 ACH Authorization 7
07/16/2019 09:09 CONDITIO 202
07/16/2019 09:09 lock C 13,781
07/16/2019 09:09 lock (previous 6,770
07/16/2019 09:09 Mornet case 1
07/16/2019 09:09 cd3.x290 1
07/16/2019 09:09 Monthly Principal and Int 11
07/16/2019 09:09 lock ( 12,310
07/16/2019 09:09 Mi company name ty 3
07/16/2019 09:09 lo lock 13,781
07/16/2019 09:09 fees lin 1,276
07/16/2019 09:09 Maximum L 79
07/16/2019 09:09 Lock expiration dat 27
07/16/2019 09:09 ju 10,039
07/16/2019 09:09 agreement 27
07/16/2019 09:09 Loan Snapshot 39
07/16/2019 09:09 inte 571
07/16/2019 09:09 j 10,866
07/16/2019 09:09 Liabilities 42
07/16/2019 09:09 Lenders 28
07/16/2019 09:09 Late Charge 11
07/16/2019 09:09 Other Description 85
07/16/2019 09:09 is 6,828
07/16/2019 09:09 LOCK type 265
07/16/2019 09:09 LENDER ID 285
07/16/2019 09:09 LEGAL 27
07/16/2019 09:09 LE1.x 129
07/16/2019 09:09 LE1. 129
07/16/2019 09:08 Jurisdiction 45
07/16/2019 09:08 est c 9,318
07/16/2019 09:08 AUSF.X12 1
07/16/2019 09:08 InterestRate 3
07/16/2019 09:08 Interest Only Indicator 3
07/16/2019 09:08 eques 8,313
07/16/2019 09:08 Intend 7
07/16/2019 09:08 eq 8,706
07/16/2019 09:08 1003 page 1 1
07/16/2019 09:08 Initial Escrow Payment 3
07/16/2019 09:08 Hours Of Opera 8
07/16/2019 09:08 Fee Line 1 4,339
07/16/2019 09:08 Home 202
07/16/2019 09:08 Trans Details Loan Purp 18
07/16/2019 09:08 HUD 92900 LT Ri 1
07/16/2019 09:08 ab000 9
07/16/2019 09:08 Trans Details Amor 26
07/16/2019 09:08 HMDA age 3
07/16/2019 09:08 Title fee 50
07/16/2019 09:08 HMDA X24 1
07/16/2019 09:08 HMDA Profil 5
07/16/2019 09:08 TR0005 2
07/16/2019 09:08 Gross 36
07/16/2019 09:08 N 28,769
07/16/2019 09:08 GMI 1
07/16/2019 09:08 GF 476
07/16/2019 09:08 File Contacts Title Co email 1
07/16/2019 09:08 Fees Line 8 622
07/16/2019 09:08 TSU 20
07/16/2019 09:08 Fee Plan 9
07/16/2019 09:08 Subsidy 17
07/16/2019 09:08 FV.X397 1
07/16/2019 09:08 State Transfer 6
07/16/2019 09:08 FR03 9
07/16/2019 09:07 vend. 991
07/16/2019 09:07 FEMA 5
07/16/2019 09:07 ca 2,298
07/16/2019 09:07 FE021 16
07/16/2019 09:07 Trans Details Est C 118
07/16/2019 09:07 Escrow Dat 57
07/16/2019 09:07 Source 74
07/16/2019 09:07 EDS.X1 1
07/16/2019 09:07 Settlement Provider 49
07/16/2019 09:07 file contacts 977
07/16/2019 09:07 new 5,903
07/16/2019 09:07 EDISC 47
07/16/2019 09:07 Scenario 75
07/16/2019 09:07 SYS.X257 1
07/16/2019 09:07 Do Not Wish Indicator 4
07/16/2019 09:07 1100 117
07/16/2019 09:07 MAX23K.X117 1
07/16/2019 09:07 social security number 11
07/16/2019 09:07 Date issued 9
07/16/2019 09:07 Amortization type 10
07/16/2019 09:07 DL code 2
07/16/2019 09:07 SUBORDINATE 56
07/16/2019 09:07 900 147
07/16/2019 09:07 SPOU 25
07/16/2019 09:07 DISCLOSURE.X25 11
07/16/2019 09:07 Rest 314
07/16/2019 09:07 Currentdate 1
07/16/2019 09:07 Contact Type 68
07/16/2019 09:07 Refin 78
07/16/2019 09:07 Commissions 8
07/16/2019 09:07 Recordin 31
07/16/2019 09:07 Close Date 50
07/16/2019 09:07 Case File ID 3
07/16/2019 09:06 Can 607
07/16/2019 09:06 CR0015 1
07/16/2019 09:06 CONS 285
07/16/2019 09:06 Property Zip 43
07/16/2019 09:06 Property State 51
07/16/2019 09:06 Prof 524
07/16/2019 09:06 CANCEL 34
07/16/2019 09:06 Borrower Credit Report Authorization Met 1
07/16/2019 09:06 Borr dur 1
07/16/2019 09:06 re88395.x122 1
07/16/2019 09:06 Prequal ARM Index 1
07/16/2019 09:06 Borr Not Hisp 2
07/16/2019 09:06 Borr Employer Month 4
07/16/2019 09:06 Borr Empl 147
07/16/2019 09:06 BE009 4
07/16/2019 09:06 Appli 220
07/16/2019 09:06 AUSTRACKING. 192
07/16/2019 09:06 Payout 6 Amount 1
07/16/2019 09:06 ARM initial 1
07/16/2019 09:06 APPROVAL 61
07/16/2019 09:06 Loan Maturity Date 1
07/16/2019 09:06 Owner Title Policy 3
07/16/2019 09:06 Organizatio 43
07/16/2019 09:06 paid by the broker 1
07/16/2019 09:05 ULDD.TotalMortgaged 1
07/16/2019 09:05 decision making 16
07/16/2019 09:05 30 days 7
07/16/2019 09:05 bankrupt 13
07/16/2019 09:05 NEWHUD2.X96 9
07/16/2019 09:05 Rate Lock Cu 1,189
07/16/2019 09:05 confirmed) 1,141
07/16/2019 09:05 1003 number 1
07/16/2019 09:05 .HMDA 1
07/16/2019 09:04 loan purpose code 1
07/16/2019 09:04 si 9,846
07/16/2019 09:04 sel 5,207
07/16/2019 09:04 se 17,176
07/16/2019 09:04 ro 5,923
07/16/2019 09:04 revi 7,266
07/16/2019 09:04 request 8,467
07/16/2019 09:04 credit r 517
07/16/2019 09:04 rate se 10,206
07/16/2019 09:04 rate adju 9,568
07/16/2019 09:04 USDA 224
07/16/2019 09:04 que 8,514
07/16/2019 09:04 Request is For 19
07/16/2019 09:04 Monthly PI 2
07/16/2019 09:04 previous 7,185
07/16/2019 09:03 oc 16,742
07/16/2019 09:03 mo 11,608
07/16/2019 09:03 persona 23
07/16/2019 09:03 Midd 5
07/16/2019 09:03 Other 1,226
07/16/2019 09:03 Mi T 1,135
07/16/2019 09:03 lock req 8,183
07/16/2019 09:03 lock e 13,956
07/16/2019 09:03 lock 3 7,441
07/16/2019 09:03 estimate 346
07/16/2019 09:03 lock (Most rec 5,598
07/16/2019 09:03 l 32,481
07/16/2019 09:03 Manufactured Secured Property Type 1
07/16/2019 09:03 ice 6,668
07/16/2019 09:03 fe 8,873
07/16/2019 09:03 des 5,647
07/16/2019 09:03 d o b 17,538
07/16/2019 09:03 d o 28,421
07/16/2019 09:03 Assump 24
07/16/2019 09:03 com 4,438
07/16/2019 09:03 base rate 9,191
07/16/2019 09:03 adju 9,985
07/16/2019 09:03 a 33,202
07/16/2019 09:03 W 8,733
07/16/2019 09:03 T 33,245
07/16/2019 09:03 S 33,233
07/16/2019 09:02 USDA - Have You Ever Ob 4
07/16/2019 09:02 Re 20,830
07/16/2019 09:02 Rate Lock R 13,776
07/16/2019 09:02 MORNET. 120
07/16/2019 09:02 Rate Loc 13,782
07/16/2019 09:02 Closing Disclos 1,016
07/16/2019 09:02 Rate Lo 13,892
07/16/2019 09:02 REQ 8,802
07/16/2019 09:02 report dat 14
07/16/2019 09:02 Price Lock 5,213
07/16/2019 09:02 File Contacts Closer 6
07/16/2019 09:02 Previou 7,185
07/16/2019 09:02 Previo 7,185
07/16/2019 09:02 Prev A 7,228
07/16/2019 09:02 Log.Ms 11
07/16/2019 09:02 name 1,053
07/16/2019 09:02 Lock Request 8,176
07/16/2019 09:02 Lo 20,100
07/16/2019 09:02 CONST.X57 1
07/16/2019 09:02 Job 16
07/16/2019 09:02 Insurance Mtg Ins Periodic Fa 2
07/16/2019 09:02 Es 17,173
07/16/2019 09:02 apprais 142
07/16/2019 09:01 De 18,199
07/16/2019 09:01 LoanTeamMember.U 1
3 of 34,519 Fields in Encompass 18.4 contain 'city county stamps'
Type Field ID Format Opts Description First Chng
Standard 647 DECIMAL_2 Fees City/County/Stamps Borr
Standard S32DISC.X37 YN 2 Sec 32 City/County Tax/Stamps Lender/Brkr Rec Portion
Standard S32DISC.X86 YN 2 Sec 32 City/County Tax/Stamps To Be Financed
Tuesday, July 16 2019 09:32:09
© 2013-2019 EncompDEV


Finished the 2009 Buller Gorge Recreational Walk in 03:32:13 in F3544 class!
She came 71st in class and 365th overall!
No photos of Nicky Baird in 2009 , we may still be uploading/processing, check back later.


text:
package org.archive.tnh.nutch;

import java.util.*;
import java.io.*;
import java.util.logging.Logger;

import org.apache.hadoop.io.*;
import org.apache.hadoop.fs.*;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.mapred.lib.*;

import org.apache.nutch.parse.ParseText;

/**
 * Hack to read the parsed text from a Nutch(WAX) segment.  This way
 * TNH can search indexes created by Nutch(WAX) and also generate
 * snippets based on the parsed text from the segments.
 *
 *
 */
public class Segments
{
  public static final Logger LOG = Logger.getLogger( Segments.class.getName() );

  public Map<String,Map<String,Segment>> perCollectionSegments = new HashMap<String,Map<String,Segment>>( );

  public Segments( String segmentsPath )
    throws IOException
  {
    Configuration conf = new Configuration();
    FileSystem fs = FileSystem.getLocal(conf);

    Path[] collectionDirs = getPaths( fs.listStatus(new Path(segmentsPath), getPassDirectoriesFilter(fs)) );

    if ( collectionDirs == null || collectionDirs.length == 0 )
      {
        LOG.warning( "No per-collection segment directories under: " + segmentsPath );
        return ;
      }

    for ( Path collectionDir : collectionDirs )
      {
        Path[] segmentDirs = getPaths( fs.listStatus( collectionDir, getPassDirectoriesFilter(fs) ) );

        if ( segmentDirs == null )
          {
            continue ;
          }

        Map<String,Segment> collectionSegments = new HashMap<String,Segment>( );
        for ( Path segmentDir : segmentDirs )
          {
            collectionSegments.put( segmentDir.getName(), new Segment( fs, segmentDir, conf ) );
          }

        perCollectionSegments.put( collectionDir.getName(), collectionSegments );
      }
  }

  public String getParseText( String collection, String segment, String key )
  {
    Map<String,Segment> collectionSegments = this.perCollectionSegments.get( collection );

    if ( collectionSegments == null )
      {
        LOG.warning( "Collection not found: " + collection );
        return "";
      }

    Segment s = collectionSegments.get( segment );

    if ( s == null )
      {
        LOG.warning( "Segment not found: " + collection + "/" + segment );
        return "";
      }

    try
      {
        String value = s.get( key );

        if ( value == null )
          {
            LOG.warning( "No value found for key: " + collection + "/" + segment + ": " + key );
            return "";
          }
        return value;
      }
    catch ( IOException ioe )
      {
        LOG.warning( "IOException retrieving key: " + collection + "/" + segment + ": " + key );
        return "";
      }
  }

  /* Copied from Nutch's HadoopFSUtil */
  public static Path[] getPaths( FileStatus[] stats )
  {
    if ( stats == null) return null;

    if (stats.length == 0)
      {
        return new Path[0];
      }

    Path[] res = new Path[stats.length];
    for (int i = 0; i < stats.length; i++)
      {
        res[i] = stats[i].getPath();
      }
    return res;
  }

  public static PathFilter getPassDirectoriesFilter( final FileSystem fs )
  {
    return new PathFilter()
      {
        public boolean accept(final Path path)
        {
          try
            {
              return fs.getFileStatus(path).isDir();
            }
          catch (IOException ioe)
            {
              return false;
            }
        }
    };
  }

}

class Segment
{
  public static final Partitioner PARTITIONER = new HashPartitioner();

  public MapFile.Reader[] parseTextReaders;

  public Segment( FileSystem fs, Path segmentDir, Configuration conf)
    throws IOException
  {
    this.parseTextReaders = MapFileOutputFormat.getReaders(fs, new Path(segmentDir, "parse_text"), conf);
  }

  public String get( String key ) throws IOException
  {
    Writable w = MapFileOutputFormat.getEntry( this.parseTextReaders, PARTITIONER, new Text(key), new ParseText() );

    if ( w == null ) return null;

    return w.toString();
  }

}

redpajama_set_name: RedPajamaGithub


text:
\section{Paper Plan}

\section{Introduction}

According to a 2013 Pew Internet Project study of 2076 people \cite{duggan2013cell}, 91\% of American adults own
a cellphone. Increasingly, people are using their phones to access and store sensitive data. The
same study found that 81\% of cellphone owners use their mobile device for texting, 52\% use it for email, 49\% use it
for maps (enabling location services), and 29\% use it for online banking. And yet, securing the data is often not taken
seriously because of an inaccurate estimation of risk as discussed in \cite{egelman2014you}. In particular, several
studies have shown that a large percentage of smartphone owners do not lock their phone: 57\% in \cite{harbach2014sa},
33\% in \cite{van2013modifying}, 39\% in \cite{egelman2014you}, and 48\% in this study.

Active authentication is an approach of monitoring the behavioral biometric characteristics of a user's interaction with
the device for the purpose of securing the phone when the point-of-entry locking mechanism fails or is absent.  In
recent years, continuous authentication has been explored extensively on desktop computers, based either on a single
biometric modality like mouse movement \cite{shen2012effectiveness} or a fusion of multiple modalities like keyboard
dynamics, mouse movement, web browsing, and stylometry \cite{fridman2013decision}. Unlike physical biometric devices
like fingerprint scanners or iris scanners, these systems rely on computer interface hardware like the keyboard and
mouse that are already commonly available with most computers.

In this paper, we consider the problem of active authentication on mobile devices, where the variety of available sensor
data is much greater than on the desktop, but so is the variety of behavioral profiles, device form factors, and
environments in which the device is used. Active authentication is the approach of verifying a user's identity
continuously based on various sensors commonly available on the device. We study four representative modalities of
stylometry (text analysis), application usage patterns, web browsing behavior, and physical location of the
device. These modalities were chosen, in part, due to their relatively low power consumption. In the remainder of the
paper these four modalities will be referred to as {\scshape\textsf{text}}{}, {\scshape\textsf{app}}{}, {\scshape\textsf{web}}{}, and {\scshape\textsf{location}}{}, respectively. We
consider the trade-off between intruder detection time and detection error as measured by false accept rate (FAR) and
false reject rate (FRR). The analysis is performed on a dataset collected by the authors of 200 subjects using their
personal Android mobile device for a period of at least 30 days. To the best of our knowledge, this dataset is the first
of its kind studied in active authentication literature, due to its large size \cite{derawi2010unobtrusive}, the
duration of tracked activity \cite{li2014active}, and the absence of restrictions on usage patterns and on the form
factor of the mobile device. The geographical colocation of the participants, in particular, makes the dataset a good
representation of an environment such as a closed-world organization where the unauthorized user of a particular device
will most likely come from inside the organization.

We propose to use decision fusion in order to asynchronously integrate the four modalities and make serial
authentication decisions. While we consider here a specific set of binary classifiers, the strength of our
decision-level approach is that additional classifiers can be added without having to change the basic fusion
rule. Moreover, it is easy to evaluate the marginal improvement of any added classifier to the overall performance of
the system. We evaluate the multimodal continuous authentication system by characterizing the error rates of local
classifier decisions, fused global decisions, and the contribution of each local classifier to the fused decision. The
novel aspects of our work include the scope of the dataset, the particular portfolio of behavioral biometrics in the
context of mobile devices, and the extent of temporal performance analysis.

The remainder of the paper is structured as follows. In \secref{related-work}, we discuss the related work on multimodal
biometric systems, active authentication on mobile devices, and each of the four behavioral biometrics considered in
this paper. In \secref{dataset}, we discuss the 200 subject dataset that we collected and analyzed. In
\secref{algorithms}, we discuss four biometric modalities, their associated classifiers, and the decision fusion
architecture. In \secref{results}, we present the performance of each individual classifier, the performance of the
fusion system, and the contribution of each individual classifier to the fused decisions.

\section{Related Work}\label{sec:related-work}

\subsection{Multimodal Biometric Systems}

The window of time based on which an active authentication system is tasked with making a binary decision is relatively
short and thus contains a highly variable set of biometric information. Depending on the task the user is engaged in,
some of the biometric classifiers may provide more data than others. For example, as the user chats with a friend via
SMS, the text-based classifiers will be actively flooded with data, while the web browsing based classifiers may only
get a few infrequent events. This motivates the recent work on multimodal authentication systems where the decisions of
multiple classifiers are fused together \cite{sim2007continuous}. In this way, the verification process is more robust
to the dynamic nature of human-computer interaction. The current approaches to the fusion of classifiers
center around max, min, median, or majority vote combinations \cite{kittler1998combining}. When neural networks are used
as classifiers, an ensemble of classifiers is constructed and fused based on different initialization of the neural
network \cite{chen2013optimal}.

Several active authentication studies have utilized multimodal biometric systems but have all, to the best of our
knowledge: (1) considered a smaller pool of subjects, (2) have not characterized the temporal performance of intruder
detection, and (3) have shown overall significantly worse performance than that achieved in our study.

Our approach in this paper is to apply the Chair-Varshney optimal fusion rule \cite{chair-varshney-1986-optimal} for the
combination of available multimodal decisions. The strength of the decision-level fusion approach is that an arbitrary
number of classifiers can be added without re-training the classifiers already in the system. This modular design allows
for multiple groups to contribute drastically different classification schemes, each lowering the error rate of the
global decision.

\subsection{Mobile Active Authentication}

With the rise of smartphone usage, active authentication on mobile devices has begun to be studied in the last few
years. The large number of available sensors makes for a rich feature space to explore. Ultimately, the question is the
one that we ask in this paper: what modality contributes the most to a decision fusion system toward the goal of fast,
accurate verification of identity? Most of the studies focus on a single modality. For example, gait pattern was
considered in \cite{derawi2010unobtrusive} achieving an EER of 0.201 (20.1\%) for 51 subjects during two short sessions,
where each subject was tasked with walking down a hallway. Some studies have incorporated multiple modalities. For
example, keystroke dynamics, stylometry, and behavioral profiling were considered in \cite{saevanee2014text} achieving
an EER of 0.033 (3.3\%) from 30 simulated users. The data for these users was pieced together from different
datasets. To the best of our knowledge, the dataset that we collected and analyzed is unique in all its key aspects: its
size (200 subjects), its duration (30+ days), and the size of the portfolio of modalities that were all tracked
concurrently with a synchronized timestamp.

\subsection{Stylometry, Web Browsing, Application Usage, Location}

Stylometry is the study of linguistic style. It has been extensively applied to the problems of authorship attribution,
identification, and verification. See \cite{brocardo2013authorship} for a thorough summary of stylometric studies in
each of these three problem domains along with their study parameters and the resulting accuracy. These studies
traditionally use large sets of features (see Table II in \cite{abbasi2008writeprints}) in combination with support
vector machines (SVMs) that have proven to be effective in high dimensional feature space \cite{mcdonald:12}, even in
cases when the number of features exceeds the number of samples. Nevertheless, with these approaches, often more than
500 words are required in order to achieve adequately low error rates \cite{fridman2014multimodal}. This makes them
impractical for the application of real-time active authentication on mobile devices where text data comes in short
bursts. While the other three modalities are not well investigated in the context of active authentication, this is not
true for stylometry. Therefore, for this modality, we don't reinvent the wheel, and implement the n-gram analysis
approach presented in \cite{brocardo2013authorship} that has been shown to work sufficiently well on short blocks of
texts.

Web browsing, application usage, and location have not been studied extensively in the context of active
authentication. The following is a discussion of the few studies that we are aware of. Web browsing behavior has been
studied for the purpose of understanding user behavior, habits, and interests \cite{yampolskiy2008behavioral}. Web
browsing as a source for behavioral biometric data was considered in \cite{abramson2013user} to achieve average
identification FAR/FRR of 0.24 (24\%) on a dataset of 14 desktop computer users.  Application usage was considered in
\cite{li2014active}, where cellphone data (from 2004) from the MIT Reality Mining project \cite{eagle2009inferring} was
used to achieve 0.1 (10\%) EER based on a portfolio of metrics including application usage, call patterns, and
location. Application usage and movements patterns have been studied as part of behavioral profiling in cellular
networks \cite{sun2004mobility, hall2005anomaly, li2014active}. However, these approaches use position data of lower
resolution in time and space than that provided by GPS on smartphones. To the best of our knowledge, GPS traces have not
been utilized in literature for continuous authentication.

\section{Dataset}\label{sec:dataset}

The dataset used in this work contains behavioral biometrics data for 200 subjects. The collection of the data was
carried out by the authors over a period of 5 months. The requirements of the study were that each subject was a student
or employee of Drexel University and was an owner and an active user of an Android smartphone or tablet. The number of
subjects with each major Android version and associated API level are listed in \tabref{android-versions}. Nexus 5 was
the most popular device with 10 subjects using it. Samsung Galaxy S5 was the second most popular device with 6 subjects
using it.

\begin{table}[h]
  \centering
  \begin{tabular}{| l | l | l |}
    \hline
    \textbf{Android Version} & \textbf{API Level} & \textbf{Subjects} \\\hline
    4.4 & 19 & 143\\\hline
    4.1 & 16 & 16\\\hline
    4.3 & 18 & 15\\\hline
    4.2 & 17 & 9\\\hline
    4.0.4 & 15 & 5\\\hline
    2.3.6 & 10 & 4\\\hline
    4.0.3& 15 & 3\\\hline
    2.3.5& 10 & 3\\\hline
    2.2 & 8 & 2\\\hline
  \end{tabular}
  \caption{The Android version and API level of the 200 devices that were part of the study.}
  \label{tab:android-versions}
\end{table}

A tracking application was installed on each subject's device and operated for a period of at least 30 days until the
subject came in to approve the collected data and get the tracking application uninstalled from their device.  The
following data modalities were tracked with 1-second resolution:

\begin{itemize}
\item Text typed via soft keyboard.
\item Apps visited.
\item Websites visited.
\item Location (based on GPS or WiFi).
\end{itemize}

The key characteristics of this dataset are its large size (200 users), the duration of tracked activity (30+ days), and
the geographical colocation of its participants in the Philadelphia area. Moreover, we did not place any restrictions on
usage patterns, on the type of Android device, and on the Android OS version (see \tabref{android-versions}).

There were several challenges encountered in the collection of the data. The biggest problem was battery drain. Due to
the long duration of the study, we could not enable modalities whose tracking proved to be significantly draining of battery
power. These modalities include front-facing video for eye tracking and face recognition, gyroscope, accelerometer, and
touch gestures. Moreover, we had to reduce GPS sampling frequency to once per minute on most of the devices.

\begin{table}[h]
  \centering
  \begin{tabular}{| l | l | }
    \hline
    \textbf{Event} & \textbf{Frequency} \\\hline
    Text & 23,254,478\\\hline
    App & 927,433\\\hline
    Web & 210,322\\\hline
    Location & 143,875\\\hline
  \end{tabular}
  \caption{The number of events in the dataset associated with each of the four modalities considered in this
    paper. A {\scshape\textsf{text}}{} event refers to a single character entered on the soft keyboard. An {\scshape\textsf{app}}{} events refers to a new
    app receiving focus. A {\scshape\textsf{web}}{} event refers to a new url entered in the url box. A {\scshape\textsf{location}}{} event refers to a
    new sample of the device location either from GPS or WiFi.}
  \label{tab:event-count}
\end{table}

\tabref{event-count} shows statistics on each of the four investigated modalities in the corpus. The table contains data
aggregated over all 200 users. The ``frequency'' here is a count of the number of instances of an action associated with
that modality. As stated previously, the four modalities will be referred to as {\scshape\textsf{text}}{}, {\scshape\textsf{app}}{}, {\scshape\textsf{web}}{}, and
``location.''  For {\scshape\textsf{text}}{}, the action is a single keystroke on the soft keyboard. For {\scshape\textsf{app}}{}, the action is opening
or bringing focus to a new app. For {\scshape\textsf{web}}{}, the action is visiting a new website. For {\scshape\textsf{location}}{}, no explicitly
action is taken by the user. Rather, location is sampled regularly at intervals of 1 minute when GPS is enabled. As
\tabref{event-count} suggests, {\scshape\textsf{text}}{} events fire 1-2 orders of magnitude more frequently than the other
three.

\begin{figure}[h]
  \centering
  \includegraphics[width=\columnwidth]{images/stats-active-duration/user-activeduration-hours.pdf}
  \caption{The duration of time (in hours) that each of the 200 users actively interacted with their device..}
  \label{fig:active-duration}
\end{figure}

The data for each user is processed to remove idle periods when the device is not active. The threshold for what is
considered an idle period is 5 minutes. For example, if the time between event A and event B is 20 minutes, with no
other events in between, this 20 minutes is compressed down to 5 minutes. The date and time of the event are not changed
but the timestamp used in dividing the dataset for training and testing (see \secref{training-char-testing}) is updated
to reflect the new time between event A and event B. This compression of idle times is performed in order to regularize
periods of activity for cross validation that utilizes time-based windows as described in
\secref{training-char-testing}. The resulting compressed timestamps are referred to as ``active
interaction''. \figref{active-duration} shows the duration (in hours) of active interaction for each of the 200 users
ordered from least to most active.

\begin{table*}[!htbp]
  \centering
  \subfloat[]{\label{tab:keys-per-app}
    \begin{tabular}{| l | l | }
      \hline
      \textbf{App Name} & \textbf{Keys Per App} \\\hline
      com.android.sms & 5,617,297\\\hline
      com.android.mms & 5,552,079\\\hline
      com.whatsapp & 4,055,622\\\hline
      com.facebook.orca & 1,252,456\\\hline
      com.google.android.talk & 1,147,295\\\hline
      com.infraware.polarisviewer4 & 990,319\\\hline
      com.android.chrome & 417,165\\\hline
      com.facebook.katana & 405,267\\\hline
      com.snapchat.android & 377,840\\\hline
      com.google.android.gm & 271,570\\\hline
      com.htc.sense.mms & 238,300\\\hline
      com.tencent.mm & 221,461\\\hline
      com.motorola.messaging & 203,649\\\hline
      com.android.calculator2 & 167,435\\\hline
      com.verizon.messaging.vzmsgs & 137,339\\\hline
      com.groupme.android & 134,896\\\hline
      com.handcent.nextsms & 123,065\\\hline
      com.jb.gosms & 118,316\\\hline
      com.sonyericsson.conversations & 114,219\\\hline
      com.twitter.android & 92,605\\\hline
    \end{tabular}
  }
  \hspace{0.3in}
  \subfloat[]{\label{tab:top-app}
    \begin{tabular}{| l | l | }
      \hline
      \textbf{App Name} & \textbf{Visits} \\\hline
      TouchWiz home & 101,151\\\hline
      WhatsApp & 64,038\\\hline
      Messaging & 60,015\\\hline
      Launcher & 39,113\\\hline
      Facebook & 38,591\\\hline
      Google Search & 32,947\\\hline
      Chrome & 32,032\\\hline
      Snapchat & 23,481\\\hline
      System UI & 22,772\\\hline
      Phone & 19,396\\\hline
      Gmail & 19,329\\\hline
      Messages & 19,154\\\hline
      Contacts & 18,668\\\hline
      Hangouts & 17,209\\\hline
      Home & 16,775\\\hline
      HTC Sense & 16,325\\\hline
      YouTube & 14,552\\\hline
      Xperia Home & 13,639\\\hline
      Instagram & 13,146\\\hline
      Settings & 12,675\\\hline
    \end{tabular}
  }
  \hspace{0.3in}
  \subfloat[]{\label{tab:top-web}
    \begin{tabular}{| l | l | }
      \hline
      \textbf{Website Domain} & \textbf{Visits} \\\hline
      www.google.com & 19,004\\\hline
      m.facebook.com & 9,300\\\hline
      www.reddit.com & 4,348\\\hline
      forums.huaren.us & 3,093\\\hline
      learn.dcollege.net & 2,133\\\hline
      en.m.wikipedia.org & 1,825\\\hline
      mail.drexel.edu & 1,520\\\hline
      one.drexel.edu & 1,472\\\hline
      login.drexel.edu & 1,462\\\hline
      likes.com & 1,361\\\hline
      mail.google.com & 1,292\\\hline
      i.imgur.com & 1,132\\\hline
      www.amazon.com & 1,079\\\hline
      netcontrol.irt.drexel.edu & 1,049\\\hline
      www.facebook.com & 903\\\hline
      banner.drexel.edu & 902\\\hline
      m.hupu.com & 824\\\hline
      t.co & 801\\\hline
      duapp2.drexel.edu & 786\\\hline
      m.ign.com & 725\\\hline
    \end{tabular}
  }
  \caption{Top 20 apps ordered by text entry and visit frequency and top 20 websites ordered by visit frequency. These
    tables are provided to give insight into the structure and content of the dataset.}
  \label{tab:top-20}
\end{table*}

\tabref{top-20} shows three top-20 lists: (1) the top-20 apps based on the amount of text that was typed inside each
app, (2) the top-20 apps based on the number of times they received focused, and (3) the top-20 website domains based on
the number of times a website associated with that domain was visited. These are aggregate measures across the dataset
intended to provide an intuition about its structure and content, but the top-20 list is the same as that used for the
the classifier model based on the {\scshape\textsf{web}}{} and {\scshape\textsf{app}}{} features in \secref{algorithms}.

\begin{figure}[h]
  \centering
  \includegraphics[width=\columnwidth]{images/gps/3-philadelphia/heatmap-compressed.png}
  \caption{An aggregate heatmap showing a selection from the dataset of GPS locations in the Philadelphia area.}
  \label{fig:location-heatmap}
\end{figure}

\figref{location-heatmap} shows a heat map visualization of a selection from the dataset of GPS locations in the
Philadelphia area. The subjects in the study resided in Philadelphia but traveled all over United States and the
world. There are two key characteristics of the GPS location data. First, it is relatively unique to each individual
even for people living in the same area of a city. Second, outside of occasional travel, it does not vary significantly
from day to day. Human beings are creatures of habit, and in as much as location is a measure of habit, this idea is
confirmed by the location data of the majority of the subjects in the study.

\section{Classification and Decision Fusion}\label{sec:algorithms}

\subsection{Features and Classifiers}

The four distinct biometric modalities considered in our analysis are (1) text entered via soft keyboard, (2)
applications used, (3) websites visited, and (4) physical location of the device as determined from GPS (when outdoors)
or WiFi (when indoors). We refer to these four modalities as {\scshape\textsf{text}}{}, {\scshape\textsf{app}}{}, {\scshape\textsf{web}}{}, and {\scshape\textsf{location}}{},
respectively. In this section we discuss the features that were extracted from the raw data of each modality, and the
classifiers that were used to map these features into binary decision space.

A binary classifier is constructed for each of the 200 users and 4 modalities. In total, there are 800 classifiers, each
producing either a probability that a user is valid $P(H_1)$ (or a binary decision of 0 (invalid) or 1 (valid). The
first class ($H_1$) for each classifier is trained on the valid user's data and the second class ($H_0$) is trained on
the other 199 users' data. The training process is described in more detail in \secref{training-char-testing}. For
{\scshape\textsf{app}}{}, {\scshape\textsf{web}}{}, and {\scshape\textsf{location}}{}, the classifier takes a single instance of the event and produces a probability. For multiple
events of the same modality, the set of probabilities is fused across time using maximum likelihood:

\begin{equation}\label{eq:max-likelihood}
H^* = \argmax_{i\in\{0,1\}} \prod_{x_t\in \Omega}P(x_t|H_i),
\end{equation}

\noindent where $\Omega = \{x_t | T_{\mbox{current}} - T(x_t) \leq \omega\}$, $\omega$ is a fixed window size in seconds,
$T(x_t)$ is the timestamp of event $x_t$, and $T_{\mbox{current}}$ is the current timestamp. The process of fusing classifier
scores across time is illustrated in \figref{fusion-across-time-and-classifiers}.

\subsubsection{Text}

As \tabref{keys-per-app} indicates, the apps into which text was entered on mobile devices varied, but the activity in
majority of the cases was communication via SMS, MMS, WhatsApp, Facebook, Google Hangouts, and other chat
apps. Therefore, {\scshape\textsf{text}}{} events fired in short bursts. The tracking application captured the keys that were
touched on the keyboard and not the autocorrected result. Therefore, the majority of the typed messages had a lot of
misspellings and words that were erased in the final submitted message. In the case of SMS, we also were able to record
the submitted result. For example, an SMS text that was submitted as ``\texttt{Sorry couldn't call back.}'' had
associated with it the following recorded keystrokes: ``\texttt{Sprry coyld cpuldn't vsll back.}'' Classification based
on the actual typed keys in principle is a better representation of the person's linguistic style. It captures unique typing
idiosyncrasies that autocorrect can conceal. As discussed in \secref{related-work}, we implemented a one-feature n-gram
classifier from \cite{brocardo2013authorship} that has been shown to work well on short messages. It works by
analyzing the presence or absence of n-grams with respect to the training set.

\subsubsection{App and Web}

The {\scshape\textsf{app}}{} and {\scshape\textsf{web}}{} classifier models we construct are identical in their structure. For the {\scshape\textsf{app}}{} modality we use the
app name as the unique identifier and count the number of times a user visits each app in the training set. For the {\scshape\textsf{web}}{}
modality we use the domain of the URL as the unique identifier and count the number of times a user visits each domain
in the training set. Note that, for example, ``m.facebook.com'' is a considered a different domain than
``www.facebook.com'' because the subdomain is different. In this section we refer to the app name and the web domain as
an ``entity''. \tabref{top-app} and \tabref{top-web} show the top entities aggregated across all 200 users for {\scshape\textsf{app}}{}
and {\scshape\textsf{web}}{} respectively.

For each user, the classification model for the valid class is constructed by determining the top 20 entities visited by
that user in the training set. The quantity of visits is then normalized so that the 20 frequency values sum to 1. The
classification model for the invalid class is constructed by counting the number of visit by the other 199 users to
those same 20 domains, such that for each of those domains we now have a probability that a valid user visits it and an
invalid user visits it. The evaluation for each user given the two empirical distributions is performed by the maximum
likelihood product in \eqref{max-likelihood}. Entities that do not appear in the top 20 are considered outliers and are
ignored in this classifier.

\subsubsection{Location}

Location is specified as a pair of values: latitude and longitude. Classification is performed using support vector
machines (SVMs) \cite{abe2010support} with the radial basis function (RBF) as the kernel function. The SVM produces a
classification score for each pair of latitude and longitude. This score is calibrated to form a probability using Platt
scaling \cite{niculescu2005predicting} which requires an extra logistic regression on the SVM scores via an additional
cross-validation on the training data. All of the code in this paper is written by the authors except for the SVM
classifier. Since the authentication system is written in C++, we used the Shark 3.0 machine learning library for the
SVM implementation.

\subsection{Decision Fusion}\label{sec:decision-fusion}

\begin{figure*}[!htbp]
  \centering
  \includegraphics[width=\textwidth]{images/diagrams/fusion-across-time-and-classifiers.pdf}
  \caption{The fusion architecture across time and across classifiers. The {\scshape\textsf{text}}{}, {\scshape\textsf{app}}{}, {\scshape\textsf{web}}{}, and {\scshape\textsf{location}}{}
    boxes indicate a firing of a single event associated with each of those modalities. Multiple classifier scores from
    the same modality are fused via \eqref{max-likelihood} to produce a single local binary decision. Local binary
    decisions from each of the four modalities are fused via \eqref{fusion-rule} to produce a single global binary
    decision.}
  \label{fig:fusion-across-time-and-classifiers}
\end{figure*}

Decision fusion with distributed sensors is described by Tenney and Sandell in \cite{Tenney1981} who studied a parallel
decision architecture. As described in \cite{Kam1991}, the system comprises of $n$ local detectors, each making a
decision about a binary hypothesis $(H_0,H_1)$, and a decision fusion center (DFC) that uses these local decisions
$\{u_1,u_2,...,u_n \}$ for a global decision about the hypothesis. The $i^{th}$ detector collects $K$ observations
before it makes its decision, $u_i$. The decision is $u_i =1$ if the detector decides in favor of $H_1$ and $u_i = -1$
if it decides in favor of $H_0$. The DFC collects the $n$ decisions of the local detectors and uses them in order to
decide in favor of $H_0 (u = -1)$ or in favor of $H_1 (u = 1)$.  Tenney and Sandell \cite{Tenney1981} and Reibman and
Nolte \cite{Reibman1987} studied the design of the local detectors and the DFC with respect to a Bayesian cost, assuming
the observations are independent conditioned on the hypothesis. The ensuing formulation derived the local and DFC
decision rules to be used by the system components for optimizing the system-wide cost. The resulting design requires
the use of likelihood ratio tests by the decision makers (local detectors and DFC) in the system. However the thresholds
used by these tests require the solution of a set of nonlinear coupled differential equations. In other words, the
design of the local decision makers and the DFC are co-dependent. In most scenarios the resulting complexity renders the
quest for an optimal design impractical.

Chair and Varshney in \cite{chair-varshney-1986-optimal} developed the optimal fusion rule when the local detectors are
fixed and local observations are statistically independent conditioned on the hypothesis. Data Fusion Center is optimal
given the performance characteristics of the local fixed decision makers. The result is a suboptimal (since local
detectors are fixed) but computationally efficient and scalable design. In this study we use the Chair-Varshney
formulation. The parallel distributed fusion scheme (see \figref{fusion-across-time-and-classifiers}) allows each
classifier to observe an event, minimize the local risk and make a local decision over the set of hypothesis, based on
only its own observations. Each classifier sends out a decision of the form:

\begin{equation}
  u_i =
  \begin{cases}
    1, & \mbox{if $H_1$ is decided}\\
    -1, & \mbox{if $H_0$ is decided}
  \end{cases}
\end{equation}

The fusion center combines these local decisions by minimizing the global Bayes' risk. The optimum decision rule
performs the following likelihood ratio test
\begin{eqnarray}\label{eq:Chair_Varshney_LRT}
\frac{P(u_1,...,u_n|H_1)}{P(u_1,...,u_n|H_0)} \underset{H_0}{\overset{H_1}{\gtrless}}  \frac{P_0}{P_1} = \tau
\end{eqnarray}
where the a priori probabilities of the binary hypotheses $H_1$ and $H_0$ are $P_1$ and $P_0$ respectively. In this case
the general fusion rule proposed in \cite{chair-varshney-1986-optimal} is

\begin{equation}\label{eq:fusion-rule}
  f(u_1, ..., u_n) =
  \begin{cases}
    1, & \mbox{if } a_0+\sum_{i=0}^n a_i u_i > 0\\
    -1, & \mbox{otherwise}
  \end{cases}
\end{equation}
with $P^M_i , P^F_i$ representing the \emph{False Rejection Rate} (FRR) and \emph{False Acceptance Rate} (FAR) of the $i^{th}$ classifier respectively. The optimum weights minimizing the global probability of error are given by

\begin{align}
  a_0 &= \log{\frac{P_1}{P_0}} \label{eq:const_weight}\\
  a_i &= \label{eq:nonconst_weight}
  \begin{cases}
    \log{\frac{1-P^M_i}{P^F_i}}, & \mbox{if } u_i=1\\
    \log{\frac{1-P^F_i}{P^M_i}}, & \mbox{if } u_i=-1
  \end{cases}
\end{align}

The threshold in \eqref{Chair_Varshney_LRT} requires knowledge of the a priori probabilities of the hypotheses. In
practice, these probabilities are not available, and the threshold $\tau$ is determined using different considerations
such as fixing the probability of false alarm or false rejection as is done in \secref{results-fusion}.

\section{Results}\label{sec:results}

\subsection{Training, Characterization, Testing}\label{sec:training-char-testing}

The data of each of the 200 users' active interaction with the mobile device was divided into 5 equal-size folds (each
containing 20\% time span of the full set). We performed training of each classifier on the first three folds (60\%). We
then tested their performance on the fourth fold. This phase is referred to as ``characterization'', because its sole
purpose is to form estimates of FAR and FRR for use by the fusion algorithm. We then tested the performance of the
classifiers, individually and as part of the fusion system, on the fifth fold. This phase is referred to as ``testing''
since this is the part that is used for evaluation the performance of the individual classifiers and the fusion system. The
three phases of training, characterization, and testing as they relate to the data folds are shown in
\figref{training-char-testing-phases}.

\begin{itemize}
\item Training on folds 1, 2, 3.\\ Characterization on fold 4.\\ Testing on fold 5.
\item Training on folds 2, 3, 4.\\ Characterization on fold 5.\\ Testing on fold 1.
\item Training on folds 3, 4, 5.\\ Characterization on fold 1.\\ Testing on fold 2.
\item Training on folds 4, 5, 1.\\ Characterization on fold 2.\\ Testing on fold 3.
\item Training on folds 5, 1, 2.\\ Characterization on fold 3.\\ Testing on fold 4.
\end{itemize}

\begin{figure}[h]
  \centering
  \includegraphics[width=\columnwidth]{images/diagrams/training-characterization-testing.pdf}
  \caption{The three phases of processing the data to determine the individual performance of each classifiers and the
    performance of the fusion system that combines some subset of these classifiers.}
  \label{fig:training-char-testing-phases}
\end{figure}

The common evaluation method used with each classifier for data fusion was measuring the averaged error rates across
five experiments; In each experiment, data of 3 folds was taken for training, 1 fold for characterization, and 1 for
testing. The FAR and FRR computed during characterization were taken as input for the fusion system as a measurement of
the expected performance of the classifiers. Therefore each experiment consisted of three phases: 1) train the
classifier(s) using the training set, 2) determine FAR and FRR based on the training set, and 3) classify the windows in
the test set.

\subsection{Performance: Individual Classifiers}\label{sec:results-individual}

\begin{figure}[h]
  \centering
  \includegraphics[width=\columnwidth]{images/perf-individual-histogram/far-with-errorbars.pdf}\\\vspace{0.2in}
  \includegraphics[width=\columnwidth]{images/perf-individual-histogram/frr-with-errorbars.pdf}
  \caption{FAR and FRR performance of the individual classifiers associated with each of the four modalities. Each bar
    represent the average error rate for a given module and time window. Each of the 200 users has 2 classifiers for
    each modality, so each bar provides a value that was averaged over 200 individual error rates. The error bar
    indicate the standard deviation across these 200 values.}
  \label{fig:far-frr-individual}
\end{figure}

The conflicting objectives of an active authentication system are of response-time and performance. The less the system
waits before making an authentication decision, the higher the expected rate of error. As more behavioral biometric data
trickles in, the system can, on average, make a classification decision with greater certainty.

This pattern of decreased error rates with an increased decision window can be observed in \figref{far-frr-individual}
that shows (for 10 different time windows) the FAR and FRR of the 4 classifiers averaged over the 200 users with the
error bars indicating the standard deviation. The ``testing fold'' (see \secref{training-char-testing}) is used for
computing these error rates. The ``characterization fold'' does not affect these results, but is used only for FAR/FRR
estimation required by the decision fusion center in \secref{results-fusion}.

The ``time before decision'' is the time between the first event indicating activity and the first decision produced by the
fusion system. This metric can be thought of as ``decision window size''. Events older than the time range covered by
the time-window are disregarded in the classification. If no event associated with the modality under consideration
fires in a specific time window, no error is added to the average.

\begin{table}[h]
  \centering
  \begin{tabular}{| l | l | }
    \hline
    \textbf{Event} & \textbf{Firing Rate (per hour)}\\\hline
    Text & 557.8\\\hline
    App & 23.2\\\hline
    Web & 5.6\\\hline
    Location & 3.5\\\hline
  \end{tabular}
  \caption{The rates at which an event associated with each modality ``fires'' per hour. On average, GPS location is
    provided only 3.5 times an hour.}
  \label{tab:firing-rate}
\end{table}

There are two notable observations about the FAR/FRR plots in \figref{far-frr-individual}. First, the location modality
provides the lowest error rates even though on average across the dataset it fires only 3.5 times an hour as shown in
\tabref{firing-rate}. This means that classification on a single GPS coordinate is sufficient to correctly verify the
user with an FAR of under 0.1 and an FRR of under 0.05. Second, the text modality converges to an FAR of 0.16 and an FRR
of 0.11 after 30 minutes which is one of the worse performers of the four modalities, even though it fires 557.8 times
an hour on average. At the 30 minute mark, that firing rate equates to an average text block size of 279 characters. An
FAR/FRR of 0.16/0.11 with 279 characters blocks improves on the error rates achieved in \cite{brocardo2013authorship}
with 500 character blocks which in turn improved on the errors rates achieved in prior work for blocks of small text
(see \cite{brocardo2013authorship} for a full reference list on short-text stylometric analysis).

\subsection{Performance: Decision Fusion}\label{sec:results-fusion}

\begin{figure}[h]
  \centering
  \includegraphics[width=\columnwidth]{images/firing-histogram/firing-histogram.pdf}
  \caption{The distribution of the number of events that fire within a given time window. This is a long tail
    distribution as non-zero probabilities of event frequencies above 13 extend to over 100. These outliers are excluded
    from this histogram plot in order to highlight the high-probability frequencies. Time windows in which no events
    fire are not included in this plot.}
  \label{fig:firing-histogram}
\end{figure}

The events associated with each of the 4 modalities fire at very different rates as shown in
\tabref{firing-rate}. Moreover, text events fire in bursts, while the location events fire at regularly spaced intervals
when GPS signal is available. The app and web events fire at varying degrees of burstiness depending on the user.
\figref{firing-histogram} shows the distribution of the number of events that fire within each of the time windows. An
important takeaway from these distributions is that most events come in bursts followed by periods of inactivity. This
results in the counterintuitive fact that the 1 minute, 10 minute, and 30 minute windows have a similar distribution on
the number of events that fire within them. This is why the decrease in error rates attained from waiting longer for a
decision is not as significant as might be expected.

Asynchronous fusion of classification of events from each of the four modalities is robust to the irregular rates at
which events fire. The decision fusion rule in \eqref{fusion-rule} utilizes all the available biometric data, weighing
each classifier according to its prior performance. \figref{fusion-performance-pareto} shows the receiver operating
characteristic (ROC) curve trading off between FAR and FRR by varying the threshold parameter $\tau$ in \eqref{Chair_Varshney_LRT}.

\begin{figure}[h]
  \centering
  \includegraphics[width=\columnwidth]{images/perf-fused/pareto.pdf}
  \caption{The performance of the fusion system with 4 classifiers on the 200 subject dataset. The ROC curve shows the
    tradeoff between FAR and FRR achieved by varying the threshold parameter $a_0$ in \eqref{fusion-rule}.}
  \label{fig:fusion-performance-pareto}
\end{figure}

As the size of the decision window increases, the performance of the fusion system improves, dropping from an equal
error rate (EER) of 0.05 using the 1 minute window to below 0.01 EER using the 30 minute window.

\subsection{Contribution of Local Classifiers to Global Decision}\label{sec:contribution}

\begin{figure}[h]
  \centering
  \includegraphics[width=\columnwidth]{images/perf-contribution/contribution.pdf}
  \caption{Relative contribution of each of the 4 classifiers computed according to \eqref{relative-contribution}.}
  \label{fig:individual-contributions}
\end{figure}

The performance of the fusion system that utilizes all four modalities of {\scshape\textsf{text}}{}, {\scshape\textsf{app}}{}, {\scshape\textsf{web}}{}, and {\scshape\textsf{location}}{}
is described in the previous section. Besides this, we are able to use the fusion system to characterize the
contribution of each of the local classifiers to the global decision. This is the central question we consider in the
paper: what biometric modality is most helpful in verifying a person's identity under a constraint of a specific time
window before the verification decision must be made? We measure the contribution $C_i$ of each of the four
classifiers by evaluating the performance of the system with and without the classifier, and computing the contribution
by:

\begin{equation}\label{eq:relative-contribution}
  C_i=\frac{E_i - E}{E_i}
\end{equation}

\noindent where $E$ is the error rate computed by averaging FAR and FRR of the fusion system using the full portfolio of
4 classifiers, $E_i$ is the error rate of the fusion system using all but the $i$-th classifier, and $C_i$ is
the relative contribution of the $i$-th classifier as shown in \figref{individual-contributions}. We consider the
contribution of each classifier under three time windows of 1 minute, 10 minutes, and 30 minutes. Location contributes
the most in all three cases, with the second biggest contributor being web browsing. Text contributes the least for the
small window of 1 minute, but improve for the large windows. App usage is the least predictable contributor. One
explanation for the {\scshape\textsf{app}}{} modality contributing significantly under the short decision window is that the first app
opened in a session is a strong and frequent indicator of identity. Therefore, its contribution is high for short
decision windows.

\section{Conclusion}\label{sec:conclusion}

In this work, we proposed a parallel binary decision-level fusion architecture for classifiers based on four biometric
modalities: text, application usage, web browsing, and location. Using this fusion method we addressed the problem of
active authentication and characterized its performance on a real-world dataset of 200 subjects, each using their
personal Android mobile device for a period of at least 30 days. The authentication system achieved an equal error rate
(ERR) of 0.05 (5\%) after 1 minute of user interaction with the device, and an EER of 0.01 (1\%) after 30 minutes. We
showed the performance of each individual classifier and its contribution to the fused global decision. The
location-based classifier, while having the lowest firing rate, contributes the most to the performance of the fusion
system.

\vspace{0.3in}

\noindent\textbf{Lex Fridman} is a Postdoctoral Associate at the Massachusetts Institute of Technology. He received his
BS, MS, and PhD from Drexel University. His research interests include machine learning, decision fusion, and numerical
optimization.

\noindent\textbf{Steven Weber} received the BS degree in 1996 from Marquette University in Milwaukee, Wisconsin, and the
MS and PhD degrees from the University of Texas at Austin in 1999 and 2003, respectively. He joined the Department of
Electrical and Computer Engineering at Drexel University in 2003, where he is currently an associate professor. His
research interests are centered around mathematical modeling of computer and communication networks, specifically
streaming multimedia and ad hoc networks. He is a senior member of the IEEE.

\noindent\textbf{Rachel Greenstadt} is an Associate Professor of Computer Science at Drexel University, where she
research the privacy and security properties of intelligent systems and the economics of electronic privacy and
information security. Her work is at "layer 8" of the network -- analyzing the content. She is a member of the DARPA
Computer Science Study Group and she runs the Privacy, Security, and Automation Laboratory (PSAL) which is a vibrant
group of ten researchers. The privacy research community has recognized her scholarship with the PET Award for
Outstanding Research in Privacy Enhancing Technologies, the NSF CAREER Award, and the Andreas Pfitzmann Best Student
Paper Award.

\noindent\textbf{Moshe Kam} received his BS in electrical engineering from Tel Aviv University in 1976 and MSc and PhD
from Drexel University in 1985 and 1987, respectively. He is the Dean of the Newark College of Engineering at the New
Jersey Institute of Technology, and had served earlier (2007-2014) as the Robert Quinn professor and Department Head of
Electrical and Computer Engineering at Drexel University. His professional interests are in system theory, detection and
estimation, information assurance, robotics, navigation, and engineering education. In 2011 he served as President and
CEO of IEEE, and at present he is member of the Boards of Directors of ABET and the United Engineering Foundation (UEF).
He is a fellow of the IEEE ``for contributions to the theory of decision fusion and distributed detection.''

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Q: how to make stand-alone setup file of a mfc project including all data files I have a project in VC++ MFC and works fine with the .mdb files. But just copying the project's  .exe file on other system does not let the project work as it searches for the same path as mentioned in the code for the .mdb files and fails to find one. Also, apart from .mdb files, theres a need for certain .ocx files and io library suite to be registered in the system prior to the project's execution. How to overcome this problem?

A: You need to wrap up all the files into a package also known as installer. One of the most popular (and free) at the moment is Inno Setup. This will produce a single exe file that you will be deploying to users / other machines. You need to make sure that you include all the needed files, libraries etc in your setup.

redpajama_set_name: RedPajamaStackExchange


Blue Dart Express Limited, South Asia's number one express air and integrated transportation, distribution and logistics company, today declared its financial results for the quarter and twelve months period ended December 31, 2012, at its Board Meeting held in Mumbai.
The company posted Rs. 146.91 crores profit after tax for the twelve months ended December 31, 2012, an increase of Rs. 24.67 million growth in profit over 2011. Income from operations for the twelve months ended December 31, 2012 stood at Rs. 1,716.63 crores.
The Company has changed its Accounting year to commence from 1st April of every year and to end on 31st March of the following year, to proactively comply with the proposed Companies Bill 2012. Consequent to this, the current accounting period would be for the fifteen month period from January 1, 2012 to March 31, 2013.
Blue Dart Express Ltd., South Asia's premier express air and integrated transportation & distribution company, offers secure and reliable delivery of consignments to over 33,739 locations in India. As part of the DP DHL Group (DHL Express, DHL Global Forwarding & DHL Supply Chain), Blue Dart accesses the largest and most comprehensive express and logistics network worldwide, covering over 220 countries and territories and offers an entire spectrum of distribution services including air express, freight forwarding supply chain solutions and customs clearance.


text:
Sting sells all of his songs to Universal
by Adrian Porter | Feb 11, 2022 | Recent News | 0 comments
Sting sold his entire catalog of singers, including solo works and Police material, to Universal Music Group (UMG).
The Tyneside-born singer-songwriter had a backing track with songs like "Every Breath You Take" and "Roxanne".
He has had solo success with songs like "Fields Of Gold" and has won 17 Grammys and three Brit Awards.
The move is the publishing group's latest major acquisition, having struck a deal with Bob Dylan in 2020.
It also comes amid a booming record industry for a year, with record labels and investment firms grabbing the rights to songs and recordings from David Bowie, Fleetwood Mac, and Tina Turner, as well as Blondie, Taylor Swift, David. Guetta and Shakira.
"It's important for me to have a respected and respected career, not only to connect with longtime fans in new ways but also to introduce my songs to new audiences, musicians, and generations," Sting said of most of her Career Part of the music label Universal.
Throughout my career, I've had a long and successful relationship with UMG as a label partner under the watchful eye of Lucian (Grainge) so it was only natural to house it all in one reliable house, as I have done myself. back to the workshop. , ready for the next chapter".
"Crossing Global Culture"
Sting, aptly named Gordon Sumner, was the mainstay, bassist, and lead singer-songwriter for The Police in the late 1970s and early 1980s before becoming a solo artist. The British band won Song of the Year at the 1984 Grammys for their stirring classic Every Breath You Take.
Sir Lucian Grainge, head of Universal Music Group, said that having had the "privilege" of working with Sting for more than 20 years, he was "delighted" to develop their relationship to now releasing music.
"Sting is a songwriting genius whose music has permeated global culture," he said. "We are honored that if UMPG is selected for its music release, all of Sting's work as a composer and recording artist will reside within the UMG family.
"It's a responsibility we don't take lightly and a great validation of what we've built for artists at UMG."
Mark Savage wrote in January after the Dylan Universal deal was completed that the idea in these markets was "to give the artist and their property immediate financial security and the holders are hoping for new rights to the evolving classic." to enjoy new streams and revenue streams through film and television licenses, merchandise, covers, and copyrights."
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text:
The influence of stage-dependent dispersal on the population dynamics of three amphipod species
Pablo Munguia, Coleman Mackie, Don R. Levitan
In metapopulations, the maintenance of local populations can depend on source-sink dynamics, where populations with positive growth rate seed populations with negative growth rate. The pattern and probability of successful dispersal among habitats can therefore be crucial in determining whether local populations will become rare or increase in abundance. We present here data on the dispersal strategy and population dynamics of three marine amphipods living in pen shells (Atrina rigida) in the Gulf of Mexico. The three amphipod species in this study disperse at different life stages. Neomegamphopus hiatus and Melita nitida disperse as adults, while Bemlos unicornis disperses as juveniles. The two species that disperse as adults have the highest initial population sizes when a new shell becomes available, likely caused by the arriving females releasing their brood into these recently occupied shells. This dispersal pattern results in initially higher population growth, but fewer occupied shells, as noted by their clumped distribution. In contrast, the species that disperses as juveniles accumulates more slowly and more evenly across habitats, eventually dominating the other two in terms of numerical abundance. The metapopulation dynamics of the three species seems to be highly dependent on the life history stage involved in dispersal.
Acknowledgments We would like to thank D. Ferrell, N. Fogarty, K. Lotterhos, T. Miller, and C. terHorst for comments on earlier versions of the manuscript. Many thanks to B. Ehrmann, A. Schweikart, C. Scott, C. Stokes, and E. Yambaski for Weld assistance. This research was partially supported by a Gramlin Award, Florida State University. The experiments in this study comply with the current laws of the United States.
Abundance-distribution relationship
Benthic invertebrates
Life history stage
Metapopulation
10.1007/s00442-007-0762-7Licence: In Copyright
Dive into the research topics of 'The influence of stage-dependent dispersal on the population dynamics of three amphipod species'. Together they form a unique fingerprint.
shell (arthropods) Agriculture & Biology 100%
amphipod Earth & Environmental Sciences 84%
Amphipoda Agriculture & Biology 79%
population dynamics Earth & Environmental Sciences 69%
shell Earth & Environmental Sciences 57%
metapopulation Earth & Environmental Sciences 44%
Atrina rigida Agriculture & Biology 38%
source-sink dynamics Earth & Environmental Sciences 28%
Munguia, P., Mackie, C., & Levitan, D. R. (2007). The influence of stage-dependent dispersal on the population dynamics of three amphipod species. Oecologia, 153(3), 533-541. https://doi.org/10.1007/s00442-007-0762-7
Munguia, Pablo ; Mackie, Coleman ; Levitan, Don R. / The influence of stage-dependent dispersal on the population dynamics of three amphipod species. In: Oecologia. 2007 ; Vol. 153, No. 3. pp. 533-541.
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title = "The influence of stage-dependent dispersal on the population dynamics of three amphipod species",
abstract = "In metapopulations, the maintenance of local populations can depend on source-sink dynamics, where populations with positive growth rate seed populations with negative growth rate. The pattern and probability of successful dispersal among habitats can therefore be crucial in determining whether local populations will become rare or increase in abundance. We present here data on the dispersal strategy and population dynamics of three marine amphipods living in pen shells (Atrina rigida) in the Gulf of Mexico. The three amphipod species in this study disperse at different life stages. Neomegamphopus hiatus and Melita nitida disperse as adults, while Bemlos unicornis disperses as juveniles. The two species that disperse as adults have the highest initial population sizes when a new shell becomes available, likely caused by the arriving females releasing their brood into these recently occupied shells. This dispersal pattern results in initially higher population growth, but fewer occupied shells, as noted by their clumped distribution. In contrast, the species that disperses as juveniles accumulates more slowly and more evenly across habitats, eventually dominating the other two in terms of numerical abundance. The metapopulation dynamics of the three species seems to be highly dependent on the life history stage involved in dispersal.",
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Munguia, P, Mackie, C & Levitan, DR 2007, 'The influence of stage-dependent dispersal on the population dynamics of three amphipod species', Oecologia, vol. 153, no. 3, pp. 533-541. https://doi.org/10.1007/s00442-007-0762-7
The influence of stage-dependent dispersal on the population dynamics of three amphipod species. / Munguia, Pablo; Mackie, Coleman; Levitan, Don R.
In: Oecologia, Vol. 153, No. 3, 09.2007, p. 533-541.
T1 - The influence of stage-dependent dispersal on the population dynamics of three amphipod species
AU - Munguia, Pablo
AU - Mackie, Coleman
AU - Levitan, Don R.
N1 - Funding Information: Acknowledgments We would like to thank D. Ferrell, N. Fogarty, K. Lotterhos, T. Miller, and C. terHorst for comments on earlier versions of the manuscript. Many thanks to B. Ehrmann, A. Schweikart, C. Scott, C. Stokes, and E. Yambaski for Weld assistance. This research was partially supported by a Gramlin Award, Florida State University. The experiments in this study comply with the current laws of the United States.
N2 - In metapopulations, the maintenance of local populations can depend on source-sink dynamics, where populations with positive growth rate seed populations with negative growth rate. The pattern and probability of successful dispersal among habitats can therefore be crucial in determining whether local populations will become rare or increase in abundance. We present here data on the dispersal strategy and population dynamics of three marine amphipods living in pen shells (Atrina rigida) in the Gulf of Mexico. The three amphipod species in this study disperse at different life stages. Neomegamphopus hiatus and Melita nitida disperse as adults, while Bemlos unicornis disperses as juveniles. The two species that disperse as adults have the highest initial population sizes when a new shell becomes available, likely caused by the arriving females releasing their brood into these recently occupied shells. This dispersal pattern results in initially higher population growth, but fewer occupied shells, as noted by their clumped distribution. In contrast, the species that disperses as juveniles accumulates more slowly and more evenly across habitats, eventually dominating the other two in terms of numerical abundance. The metapopulation dynamics of the three species seems to be highly dependent on the life history stage involved in dispersal.
AB - In metapopulations, the maintenance of local populations can depend on source-sink dynamics, where populations with positive growth rate seed populations with negative growth rate. The pattern and probability of successful dispersal among habitats can therefore be crucial in determining whether local populations will become rare or increase in abundance. We present here data on the dispersal strategy and population dynamics of three marine amphipods living in pen shells (Atrina rigida) in the Gulf of Mexico. The three amphipod species in this study disperse at different life stages. Neomegamphopus hiatus and Melita nitida disperse as adults, while Bemlos unicornis disperses as juveniles. The two species that disperse as adults have the highest initial population sizes when a new shell becomes available, likely caused by the arriving females releasing their brood into these recently occupied shells. This dispersal pattern results in initially higher population growth, but fewer occupied shells, as noted by their clumped distribution. In contrast, the species that disperses as juveniles accumulates more slowly and more evenly across habitats, eventually dominating the other two in terms of numerical abundance. The metapopulation dynamics of the three species seems to be highly dependent on the life history stage involved in dispersal.
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KW - Benthic invertebrates
KW - Life history stage
KW - Metapopulation
JO - Oecologia
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Munguia P, Mackie C, Levitan DR. The influence of stage-dependent dispersal on the population dynamics of three amphipod species. Oecologia. 2007 Sep;153(3):533-541. https://doi.org/10.1007/s00442-007-0762-7


that precedent by the acquisition of more Cherokee lands.
cherokee land, his conclusion forbode's the appropriation.
evolution into herdsmen and cultivators instead of hunters.
be an infringement upon their sovereignty.
in expanding the State of Georgia into cherokee lands.
Indians can be explained by this.
trust and believe we are acting for their greatest good."
sovereignty of the Cherokee Indians.
the soverign to negotiate by treaty with the subject. . .
were subjects to the united states, which is not factual.
the irreverance of Indian Lands.
Indians west of the Mississippi.


text:
Siagon Backpacker Street
Maya Bay, part of the Hat Noppharat Thara-Mu Ko Phi Phi National Park, had remained open year round to cater to tourist demand since a Hollywood crew set foot there in 1999 to film the dark backpacker.
A former student from Norwich who went missing in Vietnam has been found dead. 22-year-old Aiden Webb was last seen on June 3 when he set off to climb the Fansipan Mountain northwest of the country. T.
Typhoon Damrey has killed at least 27 people after pummelling central and southern Vietnam, just days before Donald Trump heads to the country for the APEC summit of Asia-Pacific leaders. Damrey reach.
However, with the northern Naxi city of Lijiang having become China's hottest holiday destination for tour groups, nearby Dali a laidback retreat for younger backpackers. more popular route towards.
Parers King Island Hotel Ocean Dunes King Island Hotel 7 Main St, Currie, Victoria 7256. www.oceandunes.com.au Formerly known as Parers King Island, the newly
More videos of the Khaosan : Rambuttri walk (street near khaosan) , Water festival (Songkran) at the Khaosan road, New Year eve at the Khaosan, A Night walk in the Khaosan Why stay in the Khaosan area : A very lively atmosphere with travellers from all over the world, probably one of the craziest place on earth, especially at night.
A backpacker from Norfolk, who died while climbing the highest mountain in Vietnam, fell to his death after slipping down a waterfall his family have confirmed. 22-year-old Aiden Webb tried to climb t.
The body of a 22-year-old backpacker was found in an apparent murder-suicide. Blake was from Leicestershire and had been planning to travel Vietnam after her time in Australia, according to the Ind.
Bordering Vietnam, Guangxi is convenient for tourists crossing overland. is a small but peaceful town with stunning country scenery. The town's West Street, lined with western cafes, restaurants an.
The city's restaurant and bar scene is catered towards older travellers rather than the raucous backpackers who dominate other Southeast. This city in South Central Vietnam is filled with French co.
Cheap Cruises From Charleston 2019 One of the South's loveliest and best preserved cities, Charleston is an 18th century. Departing: April 10, 2019; Starting at:
In recent weeks, the terms and scope of the council's proposed ban have been hotly debated, including at a multi-hour public hearing before the council April 5, during a street protest on Broadway and.
The company likened Cartographer to an indoor version of its Street View technology for creating highly. worth of LIDAR and other sensor-based data sets collected from its backpackers. "Our focus i.
She said the latest trends in travel destinations include going to "exotic, far-flung places" like Vietnam, Bali, Iceland and even Antarctica. (Those former backpackers already have been to Europe and.
I kept company with bedbugs, ate on the street and sometimes slept there. You may have to find the local backpackers' cafe, put up a notice and wait for one. There are very few things you can't buy.
Food in Hanoi. I grew up on Vietnamese food. After the war in Vietnam, a lot of refugees went to Poland and opened small Vietnamese diners. The food was cheap and tasty, so my family was going there at least once a week.
Don't forget to visit the page : Khaosan Road – Survival Guide. (About the famous backpacker area in Bangkok). Bangkok – Khaosan area Khaosan road, Rambuttri street, Public boat pier 13, Bus stations, Post office, vegetarian restaurants, Tourist.
Source: joyfull / Shutterstock.com BANGKOK'S world-famous culinary experience looks set to change dramatically today as Thailand's ruling junta placed a complete ban on street food stalls. Khaosan.
Grace Taylor, the 21-year-old backpacker from Swanage who has gone missing in Thailand. Pic: Facebook. IN the latest case of a British backpacker gone missing, the missing traveler in question has tur.
Last week, @Slate published an article that said backpackers don't need to filter stream water. That's simply not true. https://t.co/8JJPQ6Rihj pic.twitter.com/UHa1mgmXeu "Even water that appears pure.
International Hotel Management School Solo Updated world stock indexes. Get an overview of major world indexes, current values and stock market data. Cheap Cruises From
For example, an outdoor gear and apparel retailer might be able to identify Solo Backpackers and Gadget Enthusiasts as distinct personas they can market to with different messages.
Vacation Homes Bend Oregon We Oregonians take great pride in our independence, so much so that we occasionally tell the federal government to go
"The study confirms that wage theft is endemic among international students, backpackers and other temporary migrants. The proportions for Taiwan and Vietnam were 79 per cent and 75 per cent respec.
Prime Minister Nguyen Tan Dung has approved the construction of Samsung Electronics Vietnam (SEV)'s $300 million research and development (R&D) centre in Hanoi's Hoang Mai district. The PM asked the H.
But in just four short years, the slow trickle of backpackers has turned into a steady stream of. It was here that local villages were forced to find shelter during the Vietnam War, as American pla.
It's exactly 10 years since what is widely believed to be the UK's first internet café, Cyberia, opened in a back street in. Natasha Lakovic, Vietnam (from the UK) I'm currently reading this in the.
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Dorset House Backpackers →


I've been a bit preoccupied with thoughts of ice cream as of late. With the warmer weather I was reminded that finally, finally, we have a kitchen with amble counter and storage space to comfortably operate and store a homemade ice cream machine. I've been wanting one for years but the size of our old apartment's kitchen shattered my dreams of cool, creamy homemade ice cream confections.
These thoughts were only strengthened when Dan's sister and her daughter took us to a delicious little ice cream parlor in West Chester, Pennsylvania a few Saturday's ago. I snapped a few shots of the quaint shop to share with you all.
Scooped Ice Cream is a small ice cream parlor that offers housemade ice cream and gelato at their two locations in downtown West Chester and in Chester Springs, where the ice cream is made. The shop's use of local ingredients (including cream, fruits and chocolate) sourced from a certified free-range and organic farm in Lancaster, Pa is evident in the quality of the ice cream. My two scoops of Mint Chip were so creamy and absolutely delicious they left me craving another visit in the near future.
Scooped offers an exciting and somewhat unusual variety of flavors that change daily and seasonally. When we visited, there seemed to be the traditional varieties available and less of the more exotic flavors, but all seemed enticing. I tasted a few, including a raspberry chocolate chip, but settled on the tried and true mint chocolate chip.
Other flavors that the shop has made are Candied Bacon in Maple ice cream, Bailey's Irish Cream, and Cookie Monster. Dan's sister raved about their Cantaloupe ice cream offered in the warmer summer months. Unfortunately, there didn't seem to be any gelato offered when I was there. I suppose that only means that I need to go back for another visit! According to Scoops twitter account, which posts updates on the changing flavors, Coconut Macaroon, Chocolate Hazelnut and Espresso are amongst their gelato flavors. According to a commenter on FourSquare, Scooped also makes a Chocolate Caramel Sea Salt ice cream. In all honesty, I might actually stalk a business for a taste of that ice cream!
The West Chester location was perfectly quaint and everything you'd expect and hope an ice cream parlor to look like. According to this article, the location is said to have once been a speakeasy. Even better.
So even though I don't have the capabilities of making my own ice creams and gelati yet, I find comfort knowing that Scooped is there for me when I am in need.
Yay! I've worked here for the last 3 summers, and it's always nice to see such a great review!
My name is Amy and this, dear readers, is my blog. By day, I am a research librarian for an architectural stained glass studio, by night, a self taught jewelry artisan and budding sewist. Helen Ethel Studio chronicles my adventures and misadventures in crafting and baking in a little apartment just outside of Philadelphia, occupied by me, my husband, and a little Beagle named Heidi.


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