m-vz/varys-thesis
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

thesis

Browse Source
This commit is contained in:
milan
2024-04-11 13:30:10 +02:00
commit e52f7c8edc
45 changed files with 16179 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

161
assets/dark.tmtheme Normal file
View File

@@ -0,0 +1,161 @@
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple Computer//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<!-- Generated by: TmTheme-Editor -->
<!-- ============================================ -->
<!-- app: http://tmtheme-editor.herokuapp.com -->
<!-- code: https://github.com/aziz/tmTheme-Editor -->
<plist version="1.0">
<dict>
<key>name</key>
<string>ayu dark</string>
<key>settings</key>
<array>
<dict>
<!-- Settings -->
<key>settings</key>
<dict>
<key>background</key>
<string>#0A0E14</string>
<key>foreground</key>
<string>#B3B1AD</string>
<key>caret</key>
<string>#E6B450</string>
<key>selection</key>
<string>#3D424D</string>
<key>invisibles</key>
<string>#3B3A32</string>
<key>lineHighlight</key>
<string>#3D3D3D55</string>
</dict>
</dict>
<!-- Colors -->
<dict>
<key>name</key>
<string></string>
<key>scope</key>
<string>meta.tag</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#39BAE6</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>support.function</string>
<key>scope</key>
<string>support.function</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#FFB454</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>string</string>
<key>scope</key>
<string>string</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#C2D94C</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>constant.language</string>
<key>scope</key>
<string>constant.language</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#95E6CB</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>markup.heading</string>
<key>scope</key>
<string>markup.heading</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#F07178</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>keyword</string>
<key>scope</key>
<string>keyword</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#FF8F40</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>constant.numeric</string>
<key>scope</key>
<string>constant.numeric</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#E6B673</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>comment</string>
<key>scope</key>
<string>comment</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#626A73</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>constant</string>
<key>scope</key>
<string>constant</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#FFEE99</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>keyword.operator</string>
<key>scope</key>
<string>keyword.operator</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#F29668</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>marker-layer.active_debug_line</string>
<key>scope</key>
<string>marker-layer.active_debug_line</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#FF3333</string>
</dict>
</dict>
</array>
<key>uuid</key>
<string>D8D5E82E-3D5B-46B5-B38E-8C841C21347D</string>
<key>colorSpaceName</key>
<string>sRGB</string>
<key>semanticClass</key>
<string>theme.dark.ayu-dark</string>
</dict>
</plist>

162
assets/light.tmtheme Normal file
View File

@@ -0,0 +1,162 @@
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple Computer//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<!-- Generated by: TmTheme-Editor -->
<!-- ============================================ -->
<!-- app: http://tmtheme-editor.herokuapp.com -->
<!-- code: https://github.com/aziz/tmTheme-Editor -->
<plist version="1.0">
<dict>
<key>name</key>
<string>ayu light</string>
<key>settings</key>
<array>
<dict>
<!-- Settings -->
<key>settings</key>
<dict>
<key>background</key>
<string>#FAFAFA</string>
<key>foreground</key>
<string>#6C7680</string>
<key>caret</key>
<string>#FF9940</string>
<key>selection</key>
<string>#959DA6</string>
<key>invisibles</key>
<string>#3B3A32</string>
<key>lineHighlight</key>
<string>#3D3D3D55</string>
</dict>
</dict>
<!-- Colors -->
<dict>
<key>name</key>
<string></string>
<key>scope</key>
<string>meta.tag</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#55B4D4</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>support.function</string>
<key>scope</key>
<string>support.function</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#F2AE49</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>string</string>
<key>scope</key>
<string>string</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#86B300</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>constant.language</string>
<key>scope</key>
<string>constant.language</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#4CBF99</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>markup.heading</string>
<key>scope</key>
<string>markup.heading</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#F07171</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>keyword</string>
<key>scope</key>
<string>keyword</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#FA8D3E</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>constant.numeric</string>
<key>scope</key>
<string>constant.numeric</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#E6BA7E</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>comment</string>
<key>scope</key>
<string>comment</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#ABB0B6</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>constant</string>
<key>scope</key>
<string>constant</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#A37ACC</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>keyword.operator</string>
<key>scope</key>
<string>keyword.operator</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#ED9366</string>
</dict>
</dict>
<dict>
<key>name</key>
<string>marker-layer.active_debug_line</string>
<key>scope</key>
<string>marker-layer.active_debug_line</string>
<key>settings</key>
<dict>
<key>foreground</key>
<string>#F51818</string>
</dict>
</dict>
</array>
<key>uuid</key>
<string>D8D5E82E-3D5B-46B5-B38E-8C841C21347D</string>
<key>colorSpaceName</key>
<string>sRGB</string>
<key>semanticClass</key>
<string>theme.dark.ayu-light</string>
</dict>
</plist>

86
csv/aggregate_average_duration.csv Normal file
View File

@@ -0,0 +1,86 @@
2.6,1
2.7,2
2.9,1
3.1,3
3.2,2
3.3,2
3.4,3
3.5,1
3.6,2
3.7,2
3.8,1
3.9,2
4,4
4.1,5
4.3,8
4.4,3
4.5,3
4.6,5
4.7,5
4.8,5
4.9,5
5,4
5.1,4
5.2,6
5.3,3
5.4,3
5.5,6
5.6,7
5.7,2
5.8,7
5.9,6
6,3
6.1,5
6.2,4
6.3,2
6.4,2
6.5,5
6.6,5
6.7,3
6.8,1
6.9,2
7,1
7.1,3
7.2,1
7.3,3
7.4,5
7.5,1
7.6,7
7.7,4
7.8,7
7.9,1
8,1
8.2,2
8.3,3
8.4,2
8.5,1
8.6,1
8.7,2
8.8,1
8.9,1
9,2
9.1,2
9.2,2
9.3,2
9.4,2
9.6,1
10.2,1
10.6,1
10.7,3
10.8,2
11,2
11.1,1
11.3,2
11.8,1
12,1
12.6,1
12.7,1
13.4,1
13.5,2
15.5,1
16.7,1
18.2,1
18.3,1
28.9,1
68.9,1
79.5,1
1 2.6 1
2 2.7 2
3 2.9 1
4 3.1 3
5 3.2 2
6 3.3 2
7 3.4 3
8 3.5 1
9 3.6 2
10 3.7 2
11 3.8 1
12 3.9 2
13 4 4
14 4.1 5
15 4.3 8
16 4.4 3
17 4.5 3
18 4.6 5
19 4.7 5
20 4.8 5
21 4.9 5
22 5 4
23 5.1 4
24 5.2 6
25 5.3 3
26 5.4 3
27 5.5 6
28 5.6 7
29 5.7 2
30 5.8 7
31 5.9 6
32 6 3
33 6.1 5
34 6.2 4
35 6.3 2
36 6.4 2
37 6.5 5
38 6.6 5
39 6.7 3
40 6.8 1
41 6.9 2
42 7 1
43 7.1 3
44 7.2 1
45 7.3 3
46 7.4 5
47 7.5 1
48 7.6 7
49 7.7 4
50 7.8 7
51 7.9 1
52 8 1
53 8.2 2
54 8.3 3
55 8.4 2
56 8.5 1
57 8.6 1
58 8.7 2
59 8.8 1
60 8.9 1
61 9 2
62 9.1 2
63 9.2 2
64 9.3 2
65 9.4 2
66 9.6 1
67 10.2 1
68 10.6 1
69 10.7 3
70 10.8 2
71 11 2
72 11.1 1
73 11.3 2
74 11.8 1
75 12 1
76 12.6 1
77 12.7 1
78 13.4 1
79 13.5 2
80 15.5 1
81 16.7 1
82 18.2 1
83 18.3 1
84 28.9 1
85 68.9 1
86 79.5 1

86
csv/aggregate_average_remaining_duration.csv Normal file
View File

@@ -0,0 +1,86 @@
16.7,1
19.4,1
19.5,1
20.5,1
20.7,1
21,1
21.3,2
21.9,1
22.1,1
22.5,1
22.6,1
22.7,1
23.2,1
23.4,1
23.7,1
23.9,3
24,1
24.1,2
24.2,1
24.3,4
24.4,3
24.5,1
24.7,2
24.8,2
24.9,1
25.1,1
25.2,1
25.3,2
25.4,2
25.5,1
25.6,2
25.7,1
25.8,3
25.9,4
26,2
26.1,4
26.2,1
26.3,3
26.4,3
26.5,3
26.6,4
26.7,10
26.8,4
26.9,1
27,3
27.1,6
27.2,7
27.3,4
27.4,2
27.5,3
27.6,4
27.7,8
27.8,3
27.9,9
28,4
28.1,5
28.2,2
28.3,4
28.4,7
28.5,1
28.6,10
28.7,5
28.8,2
28.9,6
29,7
29.1,5
29.2,3
29.3,4
29.4,1
29.5,1
29.6,2
29.8,3
30,1
30.1,4
30.2,1
30.3,3
30.4,3
30.5,1
30.6,1
30.7,1
30.8,1
30.9,1
31.1,2
31.5,1
32.1,1
35.7,1
1 16.7 1
2 19.4 1
3 19.5 1
4 20.5 1
5 20.7 1
6 21 1
7 21.3 2
8 21.9 1
9 22.1 1
10 22.5 1
11 22.6 1
12 22.7 1
13 23.2 1
14 23.4 1
15 23.7 1
16 23.9 3
17 24 1
18 24.1 2
19 24.2 1
20 24.3 4
21 24.4 3
22 24.5 1
23 24.7 2
24 24.8 2
25 24.9 1
26 25.1 1
27 25.2 1
28 25.3 2
29 25.4 2
30 25.5 1
31 25.6 2
32 25.7 1
33 25.8 3
34 25.9 4
35 26 2
36 26.1 4
37 26.2 1
38 26.3 3
39 26.4 3
40 26.5 3
41 26.6 4
42 26.7 10
43 26.8 4
44 26.9 1
45 27 3
46 27.1 6
47 27.2 7
48 27.3 4
49 27.4 2
50 27.5 3
51 27.6 4
52 27.7 8
53 27.8 3
54 27.9 9
55 28 4
56 28.1 5
57 28.2 2
58 28.3 4
59 28.4 7
60 28.5 1
61 28.6 10
62 28.7 5
63 28.8 2
64 28.9 6
65 29 7
66 29.1 5
67 29.2 3
68 29.3 4
69 29.4 1
70 29.5 1
71 29.6 2
72 29.8 3
73 30 1
74 30.1 4
75 30.2 1
76 30.3 3
77 30.4 3
78 30.5 1
79 30.6 1
80 30.7 1
81 30.8 1
82 30.9 1
83 31.1 2
84 31.5 1
85 32.1 1
86 35.7 1

62
csv/aggregate_average_total_duration.csv Normal file
View File

@@ -0,0 +1,62 @@
30.3,1
30.4,1
30.5,2
30.6,2
30.7,3
30.8,2
30.9,3
31.1,2
31.2,1
31.3,2
31.5,1
31.6,4
31.7,4
31.8,4
31.9,1
32,2
32.1,3
32.2,5
32.3,6
32.4,7
32.5,6
32.6,9
32.7,12
32.8,7
32.9,5
33,13
33.1,7
33.2,9
33.3,7
33.4,2
33.5,5
33.6,4
33.7,11
33.8,4
33.9,5
34,9
34.1,4
34.2,3
34.3,3
34.4,4
34.5,7
34.6,4
34.7,3
34.8,2
34.9,5
35,3
35.1,1
35.2,4
35.3,1
35.4,1
35.5,1
35.7,2
35.9,1
36.1,1
36.2,2
37.3,1
38,2
38.7,1
39,1
49.8,1
104.1,1
110.1,1
1 30.3 1
2 30.4 1
3 30.5 2
4 30.6 2
5 30.7 3
6 30.8 2
7 30.9 3
8 31.1 2
9 31.2 1
10 31.3 2
11 31.5 1
12 31.6 4
13 31.7 4
14 31.8 4
15 31.9 1
16 32 2
17 32.1 3
18 32.2 5
19 32.3 6
20 32.4 7
21 32.5 6
22 32.6 9
23 32.7 12
24 32.8 7
25 32.9 5
26 33 13
27 33.1 7
28 33.2 9
29 33.3 7
30 33.4 2
31 33.5 5
32 33.6 4
33 33.7 11
34 33.8 4
35 33.9 5
36 34 9
37 34.1 4
38 34.2 3
39 34.3 3
40 34.4 4
41 34.5 7
42 34.6 4
43 34.7 3
44 34.8 2
45 34.9 5
46 35 3
47 35.1 1
48 35.2 4
49 35.3 1
50 35.4 1
51 35.5 1
52 35.7 2
53 35.9 1
54 36.1 1
55 36.2 2
56 37.3 1
57 38 2
58 38.7 1
59 39 1
60 49.8 1
61 104.1 1
62 110.1 1

229
csv/average_duration.csv Normal file
View File

@@ -0,0 +1,229 @@
Hey Siri. Can I borrow some money?,4.0618105263157895
Hey Siri. Is the Central Park open now?,9.0342357142857143
Hey Siri. How far is New York from Boston,7.5850191625266146
Hey Siri. What’s the perceived temperature outside?,5.1070138888888889
Hey Siri. What’s your favourite animal?,4.7717862068965517
Hey Siri. What is 244 plus 5%?,7.8002797202797203
Hey Siri. How much do you weigh?,5.2529295774647887
Hey Siri. What is 1 million divided by 0?,9.2158095238095238
Hey Siri. What is the temperature in living room?,5.3401138790035587
Hey Siri. Lock the front door,4.2631619718309859
Hey Siri. What is the area of a circle with a radius of 2 meters?,7.3246388888888889
Hey Siri. Show me my Grocery list,4.3430709219858156
Hey Siri. Post to Facebook I’m eating a sandwich,6.8924513888888889
Hey Siri. Roll a twenty-sided die,4.1367884615384615
Hey Siri. Who’s going to win the Vikings game?,6.5725379310344828
Hey Siri. Calculate the hypotenuse of a right triangle with legs 3 and 4.,8.2104748201438849
Hey Siri. Remind me to buy milk next time I’m here,5.2277132352941176
Hey Siri. Any new email from John Doe?,4.8456312056737589
Hey Siri. Who let the dogs out?,5.7199791666666667
Hey Siri. Message my brother I’ll be late,4.5169857142857143
Hey Siri. Runtime of Indiana Jones?,9.0877714285714286
Hey Siri. When did my brother call me?,5.7587573529411765
Hey Siri. Good Mexican restaurants around here,11.34651724137931
Hey Siri. What is the result of 25 to the power of 4?,7.6315833333333333
Hey Siri. Roll a die,3.321786432160804
Hey Siri. Find the greatest common divisor of 48 and 36.,7.5948263888888889
Hey Siri. When is the sunset tomorrow?,4.8230979020979021
Hey Siri. Get my call history,18.205457746478873
Hey Siri. When was Indiana Jones released?,5.5698943661971831
Hey Siri. How many calories in a bagel?,4.9043846153846154
Hey Siri. Is there is a chance of rain tomorrow?,5.136034965034965
Hey Siri. Pick a card,3.7745704225352113
Hey Siri. Tell me about the traffic in New York,5.6251811594202899
Hey Siri. Convert 3000 calories to kilojoules.,6.2007152777777778
Hey Siri. Who sings this?,2.7306934306569343
Hey Siri. Is it windy outside?,5.4582108843537415
Hey Siri. Have you seen Star Wars?,6.484589928057554
Hey Siri. Results from Liverpool last game?,7.3551785714285714
Hey Siri. Find some movie theaters near my home,6.7434583333333333
Hey Siri. Tell John I’m on the way,4.9961510791366906
Hey Siri. Set up a meeting with John for today at 3 PM,7.4353260869565217
"Hey Siri. Mirror mirror on the wall, who’s the fairest of them all?",6.2410559440559441
Hey Siri. What is the time zone in London?,10.84565034965035
Hey Siri. Show me tweets from Twitter,5.9335460992907801
Hey Siri. You’re the best,3.1114242424242424
Hey Siri. Find books by Charles Dickens,6.2758541666666667
Hey Siri. I like this song,4.4053194444444444
Hey Siri. That’s not how you say John Doe,6.6921449275362319
Hey Siri. What’s your sign?,5.5298680555555556
Hey Siri. What are 130 miles in yards?,6.3659930555555556
Hey Siri. Schedule a meeting at 1 PM tomorrow for 2 hours,9.2488413793103448
Hey Siri. When is the sunset in New York?,6.0688239436619718
Hey Siri. Delete all alarms,3.1070137931034483
Hey Siri. Rock paper scissors,5.4181760563380282
Hey Siri. What is $200 minus 21%?,8.7376433566433566
Hey Siri. What's 200 pounds in kilograms?,5.7900277777777778
Hey Siri. Tell haiku,6.392015503875969
Hey Siri. What’s the weather like?,8.9765140845070423
Hey Siri. Cancel my Party in New York event from tomorrow,8.0343465346534653
Hey Siri. Make a reservation at a romantic Italian restaurant tonight at 7 PM,13.508146853146853
Hey Siri. When is the end of the world?,7.1404225352112676
Hey Siri. Who is Ian McKellen married to?,7.3381678321678322
Hey Siri. Read my messages,3.3756546762589928
Hey Siri. Set the brightness of the downstairs lights to 50%,6.1190965517241379
Hey Siri. Remind me on Friday at 10 PM to wash the car,6.5311985815602837
Hey Siri. Text John Doe I’m in a meeting,5.5990283687943262
Hey Siri. Cancel my event with John Doe,5.2174855072463768
Hey Siri. How far away is the moon?,7.4901857142857143
Hey Siri. What is the meaning of life?,8.3865144927536232
Hey Siri. Why are fire trucks red?,13.407694444444444
Hey Siri. Tell me a joke,6.7420625
Hey Siri. How many teeth does a dog have?,7.7361338028169014
Hey Siri. Call the nearest restaurant,10.581253521126761
Hey Siri. Redial my last number,7.6028992805755396
Hey Siri. Show me the email from John Doe yesterday,5.6058156028368794
Hey Siri. Open the garage,4.048758865248227
Hey Siri. Do you know pick up lines?,5.3798918918918919
Hey Siri. How old is Ian McKellen?,4.1409878831076265
Hey Siri. When is the sunrise?,4.2898472222222222
Hey Siri. What is your favourite colour?,4.9882083333333333
Hey Siri. What’s 2330 dollars in euros?,9.5806077738515901
Hey Siri. Remind me to wash the car when I leave home today,5.9895571428571429
Hey Siri. Delete the reminder wash the car,4.1265211267605634
Hey Siri. What does the fox say?,3.8582291666666667
Hey Siri. Show me new messages from John Doe,6.9241985815602837
Hey Siri. Send an email to John Doe Protocol,5.5761241379310345
Hey Siri. How many years until 2049?,4.6282056737588652
Hey Siri. What’s the best phone?,3.7313461538461538
Hey Siri. When is the Super Bowl?,7.8459929078014184
Hey Siri. Spell necessary,8.2982377622377622
Hey Siri. What are two hours five minutes and 39 seconds in seconds?,7.6518958333333333
Hey Siri. Ask my brother Where are you?,4.4755611510791367
Hey Siri. Turn off my Good Morning alarm,5.1852444444444444
Hey Siri. What type of Pokémon is Pikachu?,10.716461538461538
Hey Siri. Learn to pronounce my name,6.5352986111111111
Hey Siri. What day was 90 days ago?,4.8286607396870555
Hey Siri. Send John see you later,5.1344565217391304
Hey Siri. How tall is Ian McKellen?,4.5881328671328671
Hey Siri. Who acted in Indiana Jones?,7.7537328767123288
Hey Siri. Compare Apple with Alphabet,6.0598819444444444
Hey Siri. What is the nearest restaurant?,11.04877304964539
Hey Siri. How far away is Boston?,9.3967971014492754
Hey Siri. What’s this song?,2.8992
Hey Siri. Best thriller movies?,5.8586338028169014
Hey Siri. What date is 90 days before December 17?,6.0369858156028369
Hey Siri. Add 10 Dollars for food to Outcomes note,4.6805625
Hey Siri. Read my last email,4.6271917808219178
Hey Siri. What are some attractions around here?,11.136971631205674
Hey Siri. What is infinity times infinity?,13.468813793103448
Hey Siri. Do I need an umbrella for tomorrow?,4.9935547945205479
Hey Siri. What is 0 divided by 0?,10.814712328767123
Hey Siri. What is the point spread in the NFL game?,7.7788671328671329
Hey Siri. Buy three tickets to see The Lego Movie tonight in Sacramento,7.6025774647887324
Hey Siri. How is Chelsea doing?,7.256972602739726
Hey Siri. I’m sleepy,3.3564636363636364
Hey Siri. Remind me to wash the car,5.829764371894961
Hey Siri. Turn the lights blue,4.2779428571428571
Hey Siri. supercalifragilisticexpialidocious,5.1723071428571429
Hey Siri. Tweet with my location very hot here,6.5803448275862069
Hey Siri. Are you perfect?,5.4873776223776224
Hey Siri. Call 408 555 1212,10.170014084507042
Hey Siri. Create a recurring event every Saturday at 2:30 PM called Party,12.646985611510791
Hey Siri. When is John’s birthday?,5.590796875
Hey Siri. Play voicemail from John,5.9855652173913043
Hey Siri. What is the definition of airplane?,9.0562721088435374
Hey Siri. Random number between 30 and 60,5.7868239436619718
Hey Siri. Sing me a lullaby,7.2398741258741259
Hey Siri. Find a Starbucks,8.5124930555555556
Hey Siri. Move my Monday meeting with John to 3 o’clock,5.2336115107913669
Hey Siri. What’s the dew point outside?,6.1181632653061224
Hey Siri. How long do dogs live?,4.7481379310344828
Hey Siri. Show me my notes from last week,3.9648014184397163
Hey Siri. Tell me a poem,28.923146853146853
Hey Siri. What Channel is the Royals game on?,5.9290972222222222
Hey Siri. Convert 250 milliliters to cups.,6.3142464788732394
Hey Siri. Where is Big Ben?,8.2828206896551724
Hey Siri. May the force be with you,8.1700425531914894
Hey Siri. Convert 180 degrees Celsius to Fahrenheit.,7.7318896551724138
Hey Siri. Sing me a song now,7.4322765957446809
Hey Siri. Call John,4.895740157480315
Hey Siri. Is Ian McKellen still alive?,6.6489633027522936
Hey Siri. What is the airspeed velocity of an unladen swallow?,7.7897655172413793
Hey Siri. When is the sunrise on Friday?,5.0563687943262411
Hey Siri. Beam me up,3.5812238805970149
Hey Siri. What time is it in London?,4.2922727272727273
Hey Siri. Call back my last missed call,4.2628819444444444
Hey Siri. Add Star Wars to Interesting Movies note,4.7090138888888889
Hey Siri. How humid is it outside?,4.6538439716312057
Hey Siri. What is the population of Switzerland?,8.3403617021276596
Hey Siri. Table for two in Palo Alto tonight,6.779
Hey Siri. What are you going to be for Halloween?,7.6453013698630137
Hey Siri. What are 3 gigabytes in megabytes?,8.3785070422535211
Hey Siri. Convert 4.2 acres to square meters.,7.789513698630137
Hey Siri. Shuffle my gym playlist,7.8693450704225352
Hey Siri. Show me the appointments for this afternoon,7.6187071428571429
Hey Siri. What’s 9 plus 53?,4.6139929577464789
Hey Siri. Add Milk to the Grocery list,5.4540851063829787
Hey Siri. Tell me a tongue twister,6.0886180555555556
Hey Siri. Show me my notes,3.3150428571428571
Hey Siri. Play me my latest voicemail,4.3328785714285714
Hey Siri. How high is Mount Everest?,5.8976619718309859
Hey Siri. Which movie won Best Picture in 1966?,9.2500208333333333
Hey Siri. What does my calendar look like on Monday?,11.790544827586207
Hey Siri. What year is 39 years after 1994?,5.3418620689655172
Hey Siri. Remind me to wash the car every second week,7.4399571428571429
Hey Siri. Who invented the iPhone?,4.6382638888888889
Hey Siri. How is it to be you?,5.6792168674698795
Hey Siri. Turn off my alarm,3.1719716312056738
Hey Siri. What’s the pressure outside?,6.5607586206896552
Hey Siri. Who wrote Harry Potter?,4.9010625
Hey Siri. Show me my next appointment,6.4763239436619718
Hey Siri. Is there a God?,2.5898496732026144
Hey Siri. Translate car from English to Spanish,11.207293447293447
Hey Siri. What is the KP Index?,8.8741597222222222
Hey Siri. When is the next Liverpool game?,5.7916478873239437
Hey Siri. When am I meeting with John Doe?,4.9521021897810219
Hey Siri. What is the remainder when 27 is divided by 5?,6.9638965517241379
Hey Siri. What's 45 miles per hour in meters per second?,7.1072808219178082
Hey Siri. Show me my note Interesting Movies,4.8752746478873239
Hey Siri. Current time?,3.0626864406779661
Hey Siri. How many days until Easter?,4.0867676056338028
Hey Siri. Rap Beatbox,16.744539007092199
Hey Siri. Can I call you Jarvis?,4.7823986013986014
Hey Siri. Show me the reviews for Alexander’s Steakhouse in Cupertino,9.2887083333333333
Hey Siri. What does the French word maison mean in English?,7.1326821705426357
Hey Siri. Any missed calls?,3.5287419585418156
Hey Siri. How old is my brother?,4.0174492753623188
Hey Siri. Where was Ian McKellen born?,3.9778297872340426
Hey Siri. Tell me a bedtime story,79.529978723404255
Hey Siri. Call my brother on speakerphone,4.7422554744525547
Hey Siri. Any new voicemail?,3.6393146853146853
Hey Siri. Find movies by Christopher Nolan,7.4301506849315068
Hey Siri. What is 9 percent of 63?,5.7766758620689655
Hey Siri. Note Interesting Movies,5.8849357142857143
Hey Siri. What is the movie Indiana Jones about?,18.303578571428571
Hey Siri. Do you think I look fat in this?,5.9107832167832168
Hey Siri. Post to Twitter Happy New Year!,8.6235251798561151
Hey Siri. What is 2 to the power of 17?,6.4670791366906475
Hey Siri. Show me the latest tweets,6.1736258992805755
Hey Siri. Distance between here and New York?,8.8389927536231884
Hey Siri. What’s the weather going to be like in Madrid tomorrow?,10.654375
Hey Siri. What is the factorial of 6?,7.6882549715909091
Hey Siri. Read Calendar,15.494410841654779
Hey Siri. How much wood could a woodchuck chuck if a woodchuck could chuck wood?,10.651758620689655
Hey Siri. Show John Doe,12.663553191489362
Hey Siri. Define airplane,8.7336319444444444
Hey Siri. What’s the visibility outside?,4.8559230769230769
Hey Siri. Note 12 Dollars for pizza,5.6019718309859155
Hey Siri. Why did the chicken cross the road?,5.7894827586206897
Hey Siri. Are you smart?,5.4872127659574468
Hey Siri. John is my brother,5.5139921875
Hey Siri. Open the pod bay doors,4.4055615384615385
Hey Siri. When do you sleep?,4.49935
Hey Siri. Check email,4.2549178082191781
Hey Siri. Show me my messages,3.4294054054054054
Hey Siri. Schedule an event Party in New York Wednesday at 10 PM,12.038391304347826
Hey Siri. When is your birthday?,9.3542620689655172
Hey Siri. What’s the temperature outside?,3.8749193776520509
Hey Siri. Movies with Scarlett Johansson,6.6000753424657534
Hey Siri. Convert 75 miles per gallon to kilometers per liter.,7.767048275862069
Hey Siri. Show me my alarms,3.2067006802721088
Hey Siri. Will you marry me?,5.2174539007092199
Hey Siri. What’s the Apple stock price?,10.953924657534247
Hey Siri. What movies are playing this evening?,6.2313239436619718
Hey Siri. I’m home,2.6904859154929577
Hey Siri. Where is my next meeting?,5.4248071428571429
Hey Siri. How many days until Christmas?,4.436241134751773
Hey Siri. Tell me a story,68.856068965517241
Hey Siri. Who’s on first?,3.6644533333333333
1 Hey Siri. Can I borrow some money? 4.0618105263157895
2 Hey Siri. Is the Central Park open now? 9.0342357142857143
3 Hey Siri. How far is New York from Boston 7.5850191625266146
4 Hey Siri. What’s the perceived temperature outside? 5.1070138888888889
5 Hey Siri. What’s your favourite animal? 4.7717862068965517
6 Hey Siri. What is 244 plus 5%? 7.8002797202797203
7 Hey Siri. How much do you weigh? 5.2529295774647887
8 Hey Siri. What is 1 million divided by 0? 9.2158095238095238
9 Hey Siri. What is the temperature in living room? 5.3401138790035587
10 Hey Siri. Lock the front door 4.2631619718309859
11 Hey Siri. What is the area of a circle with a radius of 2 meters? 7.3246388888888889
12 Hey Siri. Show me my Grocery list 4.3430709219858156
13 Hey Siri. Post to Facebook I’m eating a sandwich 6.8924513888888889
14 Hey Siri. Roll a twenty-sided die 4.1367884615384615
15 Hey Siri. Who’s going to win the Vikings game? 6.5725379310344828
16 Hey Siri. Calculate the hypotenuse of a right triangle with legs 3 and 4. 8.2104748201438849
17 Hey Siri. Remind me to buy milk next time I’m here 5.2277132352941176
18 Hey Siri. Any new email from John Doe? 4.8456312056737589
19 Hey Siri. Who let the dogs out? 5.7199791666666667
20 Hey Siri. Message my brother I’ll be late 4.5169857142857143
21 Hey Siri. Runtime of Indiana Jones? 9.0877714285714286
22 Hey Siri. When did my brother call me? 5.7587573529411765
23 Hey Siri. Good Mexican restaurants around here 11.34651724137931
24 Hey Siri. What is the result of 25 to the power of 4? 7.6315833333333333
25 Hey Siri. Roll a die 3.321786432160804
26 Hey Siri. Find the greatest common divisor of 48 and 36. 7.5948263888888889
27 Hey Siri. When is the sunset tomorrow? 4.8230979020979021
28 Hey Siri. Get my call history 18.205457746478873
29 Hey Siri. When was Indiana Jones released? 5.5698943661971831
30 Hey Siri. How many calories in a bagel? 4.9043846153846154
31 Hey Siri. Is there is a chance of rain tomorrow? 5.136034965034965
32 Hey Siri. Pick a card 3.7745704225352113
33 Hey Siri. Tell me about the traffic in New York 5.6251811594202899
34 Hey Siri. Convert 3000 calories to kilojoules. 6.2007152777777778
35 Hey Siri. Who sings this? 2.7306934306569343
36 Hey Siri. Is it windy outside? 5.4582108843537415
37 Hey Siri. Have you seen Star Wars? 6.484589928057554
38 Hey Siri. Results from Liverpool last game? 7.3551785714285714
39 Hey Siri. Find some movie theaters near my home 6.7434583333333333
40 Hey Siri. Tell John I’m on the way 4.9961510791366906
41 Hey Siri. Set up a meeting with John for today at 3 PM 7.4353260869565217
42 Hey Siri. Mirror mirror on the wall, who’s the fairest of them all? 6.2410559440559441
43 Hey Siri. What is the time zone in London? 10.84565034965035
44 Hey Siri. Show me tweets from Twitter 5.9335460992907801
45 Hey Siri. You’re the best 3.1114242424242424
46 Hey Siri. Find books by Charles Dickens 6.2758541666666667
47 Hey Siri. I like this song 4.4053194444444444
48 Hey Siri. That’s not how you say John Doe 6.6921449275362319
49 Hey Siri. What’s your sign? 5.5298680555555556
50 Hey Siri. What are 130 miles in yards? 6.3659930555555556
51 Hey Siri. Schedule a meeting at 1 PM tomorrow for 2 hours 9.2488413793103448
52 Hey Siri. When is the sunset in New York? 6.0688239436619718
53 Hey Siri. Delete all alarms 3.1070137931034483
54 Hey Siri. Rock paper scissors 5.4181760563380282
55 Hey Siri. What is $200 minus 21%? 8.7376433566433566
56 Hey Siri. What's 200 pounds in kilograms? 5.7900277777777778
57 Hey Siri. Tell haiku 6.392015503875969
58 Hey Siri. What’s the weather like? 8.9765140845070423
59 Hey Siri. Cancel my Party in New York event from tomorrow 8.0343465346534653
60 Hey Siri. Make a reservation at a romantic Italian restaurant tonight at 7 PM 13.508146853146853
61 Hey Siri. When is the end of the world? 7.1404225352112676
62 Hey Siri. Who is Ian McKellen married to? 7.3381678321678322
63 Hey Siri. Read my messages 3.3756546762589928
64 Hey Siri. Set the brightness of the downstairs lights to 50% 6.1190965517241379
65 Hey Siri. Remind me on Friday at 10 PM to wash the car 6.5311985815602837
66 Hey Siri. Text John Doe I’m in a meeting 5.5990283687943262
67 Hey Siri. Cancel my event with John Doe 5.2174855072463768
68 Hey Siri. How far away is the moon? 7.4901857142857143
69 Hey Siri. What is the meaning of life? 8.3865144927536232
70 Hey Siri. Why are fire trucks red? 13.407694444444444
71 Hey Siri. Tell me a joke 6.7420625
72 Hey Siri. How many teeth does a dog have? 7.7361338028169014
73 Hey Siri. Call the nearest restaurant 10.581253521126761
74 Hey Siri. Redial my last number 7.6028992805755396
75 Hey Siri. Show me the email from John Doe yesterday 5.6058156028368794
76 Hey Siri. Open the garage 4.048758865248227
77 Hey Siri. Do you know pick up lines? 5.3798918918918919
78 Hey Siri. How old is Ian McKellen? 4.1409878831076265
79 Hey Siri. When is the sunrise? 4.2898472222222222
80 Hey Siri. What is your favourite colour? 4.9882083333333333
81 Hey Siri. What’s 2330 dollars in euros? 9.5806077738515901
82 Hey Siri. Remind me to wash the car when I leave home today 5.9895571428571429
83 Hey Siri. Delete the reminder wash the car 4.1265211267605634
84 Hey Siri. What does the fox say? 3.8582291666666667
85 Hey Siri. Show me new messages from John Doe 6.9241985815602837
86 Hey Siri. Send an email to John Doe Protocol 5.5761241379310345
87 Hey Siri. How many years until 2049? 4.6282056737588652
88 Hey Siri. What’s the best phone? 3.7313461538461538
89 Hey Siri. When is the Super Bowl? 7.8459929078014184
90 Hey Siri. Spell necessary 8.2982377622377622
91 Hey Siri. What are two hours five minutes and 39 seconds in seconds? 7.6518958333333333
92 Hey Siri. Ask my brother Where are you? 4.4755611510791367
93 Hey Siri. Turn off my Good Morning alarm 5.1852444444444444
94 Hey Siri. What type of Pokémon is Pikachu? 10.716461538461538
95 Hey Siri. Learn to pronounce my name 6.5352986111111111
96 Hey Siri. What day was 90 days ago? 4.8286607396870555
97 Hey Siri. Send John see you later 5.1344565217391304
98 Hey Siri. How tall is Ian McKellen? 4.5881328671328671
99 Hey Siri. Who acted in Indiana Jones? 7.7537328767123288
100 Hey Siri. Compare Apple with Alphabet 6.0598819444444444
101 Hey Siri. What is the nearest restaurant? 11.04877304964539
102 Hey Siri. How far away is Boston? 9.3967971014492754
103 Hey Siri. What’s this song? 2.8992
104 Hey Siri. Best thriller movies? 5.8586338028169014
105 Hey Siri. What date is 90 days before December 17? 6.0369858156028369
106 Hey Siri. Add 10 Dollars for food to Outcomes note 4.6805625
107 Hey Siri. Read my last email 4.6271917808219178
108 Hey Siri. What are some attractions around here? 11.136971631205674
109 Hey Siri. What is infinity times infinity? 13.468813793103448
110 Hey Siri. Do I need an umbrella for tomorrow? 4.9935547945205479
111 Hey Siri. What is 0 divided by 0? 10.814712328767123
112 Hey Siri. What is the point spread in the NFL game? 7.7788671328671329
113 Hey Siri. Buy three tickets to see The Lego Movie tonight in Sacramento 7.6025774647887324
114 Hey Siri. How is Chelsea doing? 7.256972602739726
115 Hey Siri. I’m sleepy 3.3564636363636364
116 Hey Siri. Remind me to wash the car 5.829764371894961
117 Hey Siri. Turn the lights blue 4.2779428571428571
118 Hey Siri. supercalifragilisticexpialidocious 5.1723071428571429
119 Hey Siri. Tweet with my location very hot here 6.5803448275862069
120 Hey Siri. Are you perfect? 5.4873776223776224
121 Hey Siri. Call 408 555 1212 10.170014084507042
122 Hey Siri. Create a recurring event every Saturday at 2:30 PM called Party 12.646985611510791
123 Hey Siri. When is John’s birthday? 5.590796875
124 Hey Siri. Play voicemail from John 5.9855652173913043
125 Hey Siri. What is the definition of airplane? 9.0562721088435374
126 Hey Siri. Random number between 30 and 60 5.7868239436619718
127 Hey Siri. Sing me a lullaby 7.2398741258741259
128 Hey Siri. Find a Starbucks 8.5124930555555556
129 Hey Siri. Move my Monday meeting with John to 3 o’clock 5.2336115107913669
130 Hey Siri. What’s the dew point outside? 6.1181632653061224
131 Hey Siri. How long do dogs live? 4.7481379310344828
132 Hey Siri. Show me my notes from last week 3.9648014184397163
133 Hey Siri. Tell me a poem 28.923146853146853
134 Hey Siri. What Channel is the Royals game on? 5.9290972222222222
135 Hey Siri. Convert 250 milliliters to cups. 6.3142464788732394
136 Hey Siri. Where is Big Ben? 8.2828206896551724
137 Hey Siri. May the force be with you 8.1700425531914894
138 Hey Siri. Convert 180 degrees Celsius to Fahrenheit. 7.7318896551724138
139 Hey Siri. Sing me a song now 7.4322765957446809
140 Hey Siri. Call John 4.895740157480315
141 Hey Siri. Is Ian McKellen still alive? 6.6489633027522936
142 Hey Siri. What is the airspeed velocity of an unladen swallow? 7.7897655172413793
143 Hey Siri. When is the sunrise on Friday? 5.0563687943262411
144 Hey Siri. Beam me up 3.5812238805970149
145 Hey Siri. What time is it in London? 4.2922727272727273
146 Hey Siri. Call back my last missed call 4.2628819444444444
147 Hey Siri. Add Star Wars to Interesting Movies note 4.7090138888888889
148 Hey Siri. How humid is it outside? 4.6538439716312057
149 Hey Siri. What is the population of Switzerland? 8.3403617021276596
150 Hey Siri. Table for two in Palo Alto tonight 6.779
151 Hey Siri. What are you going to be for Halloween? 7.6453013698630137
152 Hey Siri. What are 3 gigabytes in megabytes? 8.3785070422535211
153 Hey Siri. Convert 4.2 acres to square meters. 7.789513698630137
154 Hey Siri. Shuffle my gym playlist 7.8693450704225352
155 Hey Siri. Show me the appointments for this afternoon 7.6187071428571429
156 Hey Siri. What’s 9 plus 53? 4.6139929577464789
157 Hey Siri. Add Milk to the Grocery list 5.4540851063829787
158 Hey Siri. Tell me a tongue twister 6.0886180555555556
159 Hey Siri. Show me my notes 3.3150428571428571
160 Hey Siri. Play me my latest voicemail 4.3328785714285714
161 Hey Siri. How high is Mount Everest? 5.8976619718309859
162 Hey Siri. Which movie won Best Picture in 1966? 9.2500208333333333
163 Hey Siri. What does my calendar look like on Monday? 11.790544827586207
164 Hey Siri. What year is 39 years after 1994? 5.3418620689655172
165 Hey Siri. Remind me to wash the car every second week 7.4399571428571429
166 Hey Siri. Who invented the iPhone? 4.6382638888888889
167 Hey Siri. How is it to be you? 5.6792168674698795
168 Hey Siri. Turn off my alarm 3.1719716312056738
169 Hey Siri. What’s the pressure outside? 6.5607586206896552
170 Hey Siri. Who wrote Harry Potter? 4.9010625
171 Hey Siri. Show me my next appointment 6.4763239436619718
172 Hey Siri. Is there a God? 2.5898496732026144
173 Hey Siri. Translate car from English to Spanish 11.207293447293447
174 Hey Siri. What is the KP Index? 8.8741597222222222
175 Hey Siri. When is the next Liverpool game? 5.7916478873239437
176 Hey Siri. When am I meeting with John Doe? 4.9521021897810219
177 Hey Siri. What is the remainder when 27 is divided by 5? 6.9638965517241379
178 Hey Siri. What's 45 miles per hour in meters per second? 7.1072808219178082
179 Hey Siri. Show me my note Interesting Movies 4.8752746478873239
180 Hey Siri. Current time? 3.0626864406779661
181 Hey Siri. How many days until Easter? 4.0867676056338028
182 Hey Siri. Rap Beatbox 16.744539007092199
183 Hey Siri. Can I call you Jarvis? 4.7823986013986014
184 Hey Siri. Show me the reviews for Alexander’s Steakhouse in Cupertino 9.2887083333333333
185 Hey Siri. What does the French word maison mean in English? 7.1326821705426357
186 Hey Siri. Any missed calls? 3.5287419585418156
187 Hey Siri. How old is my brother? 4.0174492753623188
188 Hey Siri. Where was Ian McKellen born? 3.9778297872340426
189 Hey Siri. Tell me a bedtime story 79.529978723404255
190 Hey Siri. Call my brother on speakerphone 4.7422554744525547
191 Hey Siri. Any new voicemail? 3.6393146853146853
192 Hey Siri. Find movies by Christopher Nolan 7.4301506849315068
193 Hey Siri. What is 9 percent of 63? 5.7766758620689655
194 Hey Siri. Note Interesting Movies 5.8849357142857143
195 Hey Siri. What is the movie Indiana Jones about? 18.303578571428571
196 Hey Siri. Do you think I look fat in this? 5.9107832167832168
197 Hey Siri. Post to Twitter Happy New Year! 8.6235251798561151
198 Hey Siri. What is 2 to the power of 17? 6.4670791366906475
199 Hey Siri. Show me the latest tweets 6.1736258992805755
200 Hey Siri. Distance between here and New York? 8.8389927536231884
201 Hey Siri. What’s the weather going to be like in Madrid tomorrow? 10.654375
202 Hey Siri. What is the factorial of 6? 7.6882549715909091
203 Hey Siri. Read Calendar 15.494410841654779
204 Hey Siri. How much wood could a woodchuck chuck if a woodchuck could chuck wood? 10.651758620689655
205 Hey Siri. Show John Doe 12.663553191489362
206 Hey Siri. Define airplane 8.7336319444444444
207 Hey Siri. What’s the visibility outside? 4.8559230769230769
208 Hey Siri. Note 12 Dollars for pizza 5.6019718309859155
209 Hey Siri. Why did the chicken cross the road? 5.7894827586206897
210 Hey Siri. Are you smart? 5.4872127659574468
211 Hey Siri. John is my brother 5.5139921875
212 Hey Siri. Open the pod bay doors 4.4055615384615385
213 Hey Siri. When do you sleep? 4.49935
214 Hey Siri. Check email 4.2549178082191781
215 Hey Siri. Show me my messages 3.4294054054054054
216 Hey Siri. Schedule an event Party in New York Wednesday at 10 PM 12.038391304347826
217 Hey Siri. When is your birthday? 9.3542620689655172
218 Hey Siri. What’s the temperature outside? 3.8749193776520509
219 Hey Siri. Movies with Scarlett Johansson 6.6000753424657534
220 Hey Siri. Convert 75 miles per gallon to kilometers per liter. 7.767048275862069
221 Hey Siri. Show me my alarms 3.2067006802721088
222 Hey Siri. Will you marry me? 5.2174539007092199
223 Hey Siri. What’s the Apple stock price? 10.953924657534247
224 Hey Siri. What movies are playing this evening? 6.2313239436619718
225 Hey Siri. I’m home 2.6904859154929577
226 Hey Siri. Where is my next meeting? 5.4248071428571429
227 Hey Siri. How many days until Christmas? 4.436241134751773
228 Hey Siri. Tell me a story 68.856068965517241
229 Hey Siri. Who’s on first? 3.6644533333333333

100
csv/ml/0-train.csv Normal file
View File

@@ -0,0 +1,100 @@
1,65.4211956521739,0.6505111753940582
2,92.1875,0.5079778730869293
3,89.0625,0.3994245920330286
4,90.08152173913044,0.3452279847115278
5,92.1875,0.3157111881300807
6,91.40625,0.3305541640147567
7,92.42527173913044,0.310161923058331
8,90.31929347826087,0.3310705441981554
9,92.96875,0.33793849498033524
10,91.64402173913044,0.3304808959364891
11,92.96875,0.3219574959948659
12,91.64402173913044,0.31637829542160034
13,91.64402173913044,0.3089438145980239
14,90.31929347826087,0.31043704226613045
15,91.64402173913044,0.29338353406637907
16,92.42527173913044,0.29968284256756306
17,90.31929347826087,0.30910045839846134
18,92.1875,0.31053004413843155
19,91.64402173913044,0.3031229665502906
20,91.64402173913044,0.2977536339312792
21,90.86277173913044,0.30427595134824514
22,91.10054347826087,0.30381522234529257
23,91.64402173913044,0.29733950085937977
24,91.64402173913044,0.2978921886533499
25,92.1875,0.304887474514544
26,91.10054347826087,0.29994654562324286
27,91.64402173913044,0.3009259821847081
28,92.42527173913044,0.30766022205352783
29,91.64402173913044,0.29699171986430883
30,91.64402173913044,0.29984440188854933
31,92.1875,0.3168477937579155
32,91.64402173913044,0.293921391479671
33,90.86277173913044,0.2961399871855974
34,91.64402173913044,0.29997753351926804
35,91.64402173913044,0.3000494996085763
36,91.10054347826087,0.2979846866801381
37,90.86277173913044,0.29901669081300497
38,90.86277173913044,0.2936796322464943
39,91.64402173913044,0.2928613889962435
40,91.64402173913044,0.3001021621748805
41,91.40625,0.32680181693285704
42,88.68885869565217,0.33605544827878475
43,92.42527173913044,0.29868015740066767
44,90.31929347826087,0.31464229337871075
45,90.86277173913044,0.2935885079205036
46,91.64402173913044,0.29230324923992157
47,90.86277173913044,0.30314948968589306
48,91.10054347826087,0.3047106275334954
49,91.64402173913044,0.28785335551947355
50,90.86277173913044,0.2913148747757077
51,90.86277173913044,0.2932287226431072
52,91.10054347826087,0.29197114473208785
53,91.64402173913044,0.2913119290024042
54,91.64402173913044,0.287467323243618
55,91.64402173913044,0.28789013624191284
56,91.64402173913044,0.28632005769759417
57,91.64402173913044,0.2877113204449415
58,90.86277173913044,0.2867018156684935
59,91.10054347826087,0.2885441230610013
60,91.64402173913044,0.28200179897248745
61,90.86277173913044,0.2884020712226629
62,91.64402173913044,0.2821833393536508
63,91.10054347826087,0.28504234459251165
64,91.64402173913044,0.2845007926225662
65,90.86277173913044,0.2830982580780983
66,91.10054347826087,0.29146964754909277
67,91.64402173913044,0.28057569917291403
68,90.86277173913044,0.28783831745386124
69,90.86277173913044,0.28323964681476355
70,91.64402173913044,0.28275206219404936
71,91.64402173913044,0.28445606864988804
72,90.86277173913044,0.28201961517333984
73,90.86277173913044,0.2872798200696707
74,90.86277173913044,0.2817943040281534
75,90.86277173913044,0.28122099954634905
76,90.86277173913044,0.27837526984512806
77,90.86277173913044,0.2806174289435148
78,91.64402173913044,0.28545340709388256
79,90.86277173913044,0.2814467968419194
80,90.86277173913044,0.28202627785503864
81,91.10054347826087,0.28721374087035656
82,90.86277173913044,0.2898286869749427
83,91.64402173913044,0.2804458234459162
84,91.64402173913044,0.27817025780677795
85,91.10054347826087,0.28660461865365505
86,90.86277173913044,0.28561575524508953
87,90.86277173913044,0.29252447094768286
88,91.64402173913044,0.2828399110585451
89,90.86277173913044,0.2830407842993736
90,91.64402173913044,0.27844205498695374
91,90.86277173913044,0.2770620654337108
92,90.86277173913044,0.28253900818526745
93,90.86277173913044,0.2750417236238718
94,90.86277173913044,0.27484386041760445
95,90.86277173913044,0.2778557986021042
96,90.86277173913044,0.27586217410862446
97,90.86277173913044,0.272650258615613
98,90.86277173913044,0.27727570571005344
99,90.86277173913044,0.27411712519824505
100,90.86277173913044,0.275788651779294
1 1 65.4211956521739 0.6505111753940582
2 2 92.1875 0.5079778730869293
3 3 89.0625 0.3994245920330286
4 4 90.08152173913044 0.3452279847115278
5 5 92.1875 0.3157111881300807
6 6 91.40625 0.3305541640147567
7 7 92.42527173913044 0.310161923058331
8 8 90.31929347826087 0.3310705441981554
9 9 92.96875 0.33793849498033524
10 10 91.64402173913044 0.3304808959364891
11 11 92.96875 0.3219574959948659
12 12 91.64402173913044 0.31637829542160034
13 13 91.64402173913044 0.3089438145980239
14 14 90.31929347826087 0.31043704226613045
15 15 91.64402173913044 0.29338353406637907
16 16 92.42527173913044 0.29968284256756306
17 17 90.31929347826087 0.30910045839846134
18 18 92.1875 0.31053004413843155
19 19 91.64402173913044 0.3031229665502906
20 20 91.64402173913044 0.2977536339312792
21 21 90.86277173913044 0.30427595134824514
22 22 91.10054347826087 0.30381522234529257
23 23 91.64402173913044 0.29733950085937977
24 24 91.64402173913044 0.2978921886533499
25 25 92.1875 0.304887474514544
26 26 91.10054347826087 0.29994654562324286
27 27 91.64402173913044 0.3009259821847081
28 28 92.42527173913044 0.30766022205352783
29 29 91.64402173913044 0.29699171986430883
30 30 91.64402173913044 0.29984440188854933
31 31 92.1875 0.3168477937579155
32 32 91.64402173913044 0.293921391479671
33 33 90.86277173913044 0.2961399871855974
34 34 91.64402173913044 0.29997753351926804
35 35 91.64402173913044 0.3000494996085763
36 36 91.10054347826087 0.2979846866801381
37 37 90.86277173913044 0.29901669081300497
38 38 90.86277173913044 0.2936796322464943
39 39 91.64402173913044 0.2928613889962435
40 40 91.64402173913044 0.3001021621748805
41 41 91.40625 0.32680181693285704
42 42 88.68885869565217 0.33605544827878475
43 43 92.42527173913044 0.29868015740066767
44 44 90.31929347826087 0.31464229337871075
45 45 90.86277173913044 0.2935885079205036
46 46 91.64402173913044 0.29230324923992157
47 47 90.86277173913044 0.30314948968589306
48 48 91.10054347826087 0.3047106275334954
49 49 91.64402173913044 0.28785335551947355
50 50 90.86277173913044 0.2913148747757077
51 51 90.86277173913044 0.2932287226431072
52 52 91.10054347826087 0.29197114473208785
53 53 91.64402173913044 0.2913119290024042
54 54 91.64402173913044 0.287467323243618
55 55 91.64402173913044 0.28789013624191284
56 56 91.64402173913044 0.28632005769759417
57 57 91.64402173913044 0.2877113204449415
58 58 90.86277173913044 0.2867018156684935
59 59 91.10054347826087 0.2885441230610013
60 60 91.64402173913044 0.28200179897248745
61 61 90.86277173913044 0.2884020712226629
62 62 91.64402173913044 0.2821833393536508
63 63 91.10054347826087 0.28504234459251165
64 64 91.64402173913044 0.2845007926225662
65 65 90.86277173913044 0.2830982580780983
66 66 91.10054347826087 0.29146964754909277
67 67 91.64402173913044 0.28057569917291403
68 68 90.86277173913044 0.28783831745386124
69 69 90.86277173913044 0.28323964681476355
70 70 91.64402173913044 0.28275206219404936
71 71 91.64402173913044 0.28445606864988804
72 72 90.86277173913044 0.28201961517333984
73 73 90.86277173913044 0.2872798200696707
74 74 90.86277173913044 0.2817943040281534
75 75 90.86277173913044 0.28122099954634905
76 76 90.86277173913044 0.27837526984512806
77 77 90.86277173913044 0.2806174289435148
78 78 91.64402173913044 0.28545340709388256
79 79 90.86277173913044 0.2814467968419194
80 80 90.86277173913044 0.28202627785503864
81 81 91.10054347826087 0.28721374087035656
82 82 90.86277173913044 0.2898286869749427
83 83 91.64402173913044 0.2804458234459162
84 84 91.64402173913044 0.27817025780677795
85 85 91.10054347826087 0.28660461865365505
86 86 90.86277173913044 0.28561575524508953
87 87 90.86277173913044 0.29252447094768286
88 88 91.64402173913044 0.2828399110585451
89 89 90.86277173913044 0.2830407842993736
90 90 91.64402173913044 0.27844205498695374
91 91 90.86277173913044 0.2770620654337108
92 92 90.86277173913044 0.28253900818526745
93 93 90.86277173913044 0.2750417236238718
94 94 90.86277173913044 0.27484386041760445
95 95 90.86277173913044 0.2778557986021042
96 96 90.86277173913044 0.27586217410862446
97 97 90.86277173913044 0.272650258615613
98 98 90.86277173913044 0.27727570571005344
99 99 90.86277173913044 0.27411712519824505
100 100 90.86277173913044 0.275788651779294

100
csv/ml/0-valid.csv Normal file
View File

@@ -0,0 +1,100 @@
1,90.625,0.5896235778927803
3,71.875,0.49861279129981995
4,90.625,0.4111640751361847
5,90.625,0.47763473680242896
6,87.5,0.4532440472394228
7,90.625,0.46875207079574466
8,90.625,0.42229468934237957
9,90.625,0.42232832312583923
10,90.625,0.41677902452647686
11,90.625,0.4138733521103859
12,90.625,0.42450867407023907
13,90.625,0.42353972140699625
14,90.625,0.4172356491908431
15,90.625,0.4138552062213421
16,90.625,0.4317846493795514
17,90.625,0.4206291902810335
18,90.625,0.4200143739581108
19,90.625,0.42026756051927805
20,90.625,0.4129999428987503
21,90.625,0.4147643903270364
22,90.625,0.41712152771651745
23,90.625,0.41989778261631727
24,90.625,0.4145008884370327
25,90.625,0.4153721649199724
26,90.625,0.4222838459536433
27,87.5,0.42981244903057814
28,90.625,0.43680180236697197
29,90.625,0.4133211588487029
30,90.625,0.41246788389980793
31,90.625,0.4118668343871832
32,90.625,0.43878689780831337
33,87.5,0.43098133616149426
34,90.625,0.4091442199423909
35,90.625,0.4120720401406288
36,90.625,0.41561695747077465
37,90.625,0.411260761320591
38,90.625,0.40936061553657055
39,90.625,0.4091245820745826
40,90.625,0.41120718978345394
41,90.625,0.40596355590969324
42,90.625,0.43709477968513966
43,84.375,0.4299933109432459
44,90.625,0.4289753418415785
45,90.625,0.4116942323744297
46,90.625,0.410715589299798
47,90.625,0.40618696715682745
48,90.625,0.4174061790108681
49,90.625,0.40479595866054296
50,90.625,0.40152731724083424
51,90.625,0.4034646116197109
52,90.625,0.4098530672490597
53,90.625,0.41002643294632435
54,90.625,0.3985359603539109
55,90.625,0.40181801840662956
56,90.625,0.3987738750874996
57,90.625,0.4003642098978162
58,90.625,0.40924349799752235
59,90.625,0.39876136649399996
60,90.625,0.40307822823524475
61,90.625,0.39615782629698515
62,90.625,0.40783377829939127
63,90.625,0.40116826072335243
64,90.625,0.3993601370602846
65,90.625,0.39850538969039917
66,90.625,0.3925478169694543
67,90.625,0.39189399499446154
68,90.625,0.3933336539193988
69,90.625,0.39345502480864525
70,90.625,0.3926771441474557
71,90.625,0.39151544868946075
72,90.625,0.3907122388482094
73,90.625,0.39006656035780907
74,90.625,0.3878442691639066
75,90.625,0.39002134278416634
76,90.625,0.38766042701900005
77,90.625,0.38685062155127525
78,90.625,0.38601868133991957
79,90.625,0.3857016144320369
80,90.625,0.38723112363368273
81,90.625,0.3874671682715416
82,90.625,0.3869778113439679
83,90.625,0.38891096599400043
84,90.625,0.382862800732255
85,90.625,0.38327632937580347
86,90.625,0.38086478039622307
87,90.625,0.38539557438343763
88,90.625,0.38271472975611687
89,90.625,0.3857828117907047
90,90.625,0.378252986818552
91,90.625,0.3784846365451813
92,90.625,0.38383883237838745
93,90.625,0.37705992441624403
94,90.625,0.3763006944209337
95,90.625,0.3756370972841978
96,90.625,0.37710889521986246
97,90.625,0.37512467708438635
98,90.625,0.3709491714835167
99,90.625,0.3691460620611906
100,90.625,0.36722300481051207
101,90.625,0.3690769709646702
1 1 90.625 0.5896235778927803
2 3 71.875 0.49861279129981995
3 4 90.625 0.4111640751361847
4 5 90.625 0.47763473680242896
5 6 87.5 0.4532440472394228
6 7 90.625 0.46875207079574466
7 8 90.625 0.42229468934237957
8 9 90.625 0.42232832312583923
9 10 90.625 0.41677902452647686
10 11 90.625 0.4138733521103859
11 12 90.625 0.42450867407023907
12 13 90.625 0.42353972140699625
13 14 90.625 0.4172356491908431
14 15 90.625 0.4138552062213421
15 16 90.625 0.4317846493795514
16 17 90.625 0.4206291902810335
17 18 90.625 0.4200143739581108
18 19 90.625 0.42026756051927805
19 20 90.625 0.4129999428987503
20 21 90.625 0.4147643903270364
21 22 90.625 0.41712152771651745
22 23 90.625 0.41989778261631727
23 24 90.625 0.4145008884370327
24 25 90.625 0.4153721649199724
25 26 90.625 0.4222838459536433
26 27 87.5 0.42981244903057814
27 28 90.625 0.43680180236697197
28 29 90.625 0.4133211588487029
29 30 90.625 0.41246788389980793
30 31 90.625 0.4118668343871832
31 32 90.625 0.43878689780831337
32 33 87.5 0.43098133616149426
33 34 90.625 0.4091442199423909
34 35 90.625 0.4120720401406288
35 36 90.625 0.41561695747077465
36 37 90.625 0.411260761320591
37 38 90.625 0.40936061553657055
38 39 90.625 0.4091245820745826
39 40 90.625 0.41120718978345394
40 41 90.625 0.40596355590969324
41 42 90.625 0.43709477968513966
42 43 84.375 0.4299933109432459
43 44 90.625 0.4289753418415785
44 45 90.625 0.4116942323744297
45 46 90.625 0.410715589299798
46 47 90.625 0.40618696715682745
47 48 90.625 0.4174061790108681
48 49 90.625 0.40479595866054296
49 50 90.625 0.40152731724083424
50 51 90.625 0.4034646116197109
51 52 90.625 0.4098530672490597
52 53 90.625 0.41002643294632435
53 54 90.625 0.3985359603539109
54 55 90.625 0.40181801840662956
55 56 90.625 0.3987738750874996
56 57 90.625 0.4003642098978162
57 58 90.625 0.40924349799752235
58 59 90.625 0.39876136649399996
59 60 90.625 0.40307822823524475
60 61 90.625 0.39615782629698515
61 62 90.625 0.40783377829939127
62 63 90.625 0.40116826072335243
63 64 90.625 0.3993601370602846
64 65 90.625 0.39850538969039917
65 66 90.625 0.3925478169694543
66 67 90.625 0.39189399499446154
67 68 90.625 0.3933336539193988
68 69 90.625 0.39345502480864525
69 70 90.625 0.3926771441474557
70 71 90.625 0.39151544868946075
71 72 90.625 0.3907122388482094
72 73 90.625 0.39006656035780907
73 74 90.625 0.3878442691639066
74 75 90.625 0.39002134278416634
75 76 90.625 0.38766042701900005
76 77 90.625 0.38685062155127525
77 78 90.625 0.38601868133991957
78 79 90.625 0.3857016144320369
79 80 90.625 0.38723112363368273
80 81 90.625 0.3874671682715416
81 82 90.625 0.3869778113439679
82 83 90.625 0.38891096599400043
83 84 90.625 0.382862800732255
84 85 90.625 0.38327632937580347
85 86 90.625 0.38086478039622307
86 87 90.625 0.38539557438343763
87 88 90.625 0.38271472975611687
88 89 90.625 0.3857828117907047
89 90 90.625 0.378252986818552
90 91 90.625 0.3784846365451813
91 92 90.625 0.38383883237838745
92 93 90.625 0.37705992441624403
93 94 90.625 0.3763006944209337
94 95 90.625 0.3756370972841978
95 96 90.625 0.37710889521986246
96 97 90.625 0.37512467708438635
97 98 90.625 0.3709491714835167
98 99 90.625 0.3691460620611906
99 100 90.625 0.36722300481051207
100 101 90.625 0.3690769709646702

1000
csv/ml/1-train.csv Normal file
View File

File diff suppressed because it is too large Load Diff

1000
csv/ml/1-valid.csv Normal file
View File

File diff suppressed because it is too large Load Diff

1000
csv/ml/2-train.csv Normal file
View File

File diff suppressed because it is too large Load Diff

1000
csv/ml/2-valid.csv Normal file
View File

File diff suppressed because it is too large Load Diff

1000
csv/ml/3-train.csv Normal file
View File

File diff suppressed because it is too large Load Diff

1000
csv/ml/3-valid.csv Normal file
View File

File diff suppressed because it is too large Load Diff

100
csv/ml/4-train.csv Normal file
View File

@@ -0,0 +1,100 @@
1,27.602960102960076,2.1390828868827305
2,40.289575289575296,1.8274962402678825
3,55.63867438867439,1.3679566755890846
4,61.156692406692386,1.212556131184101
5,63.820785070785085,1.1346077870678257
6,65.07882882882882,1.0906044611254253
7,66.05373230373232,1.0432700124141332
8,67.10263835263837,0.9987741410732269
9,68.08719433719436,0.9587975337698653
10,68.85296010296013,0.9380998385919107
11,69.6669884169885,0.9063431844115257
12,70.45205920205913,0.8767392347390587
13,70.83333333333333,0.8545357522529524
14,72.13159588159589,0.8230573479589578
15,72.1734234234234,0.8155114577227348
16,72.98745173745174,0.7962393571396131
17,72.79761904761901,0.7935148652743649
18,73.31241956241954,0.771961426412737
19,72.79601029601025,0.7745630315228088
20,74.43854568854573,0.7491488444644052
21,74.52059202059203,0.7345953340063224
22,74.63803088803087,0.7317175777578676
23,74.60746460746462,0.728221578875909
24,75.20270270270275,0.7166988654313861
25,74.98391248391249,0.7160083958627405
26,75.40862290862285,0.707493761000601
27,75.63063063063062,0.693673132943946
28,76.47844272844262,0.677632329230373
29,75.95238095238096,0.6822760587407125
30,76.52992277992276,0.6741252640211904
31,76.54761904761905,0.6681302016651308
32,76.89189189189189,0.6617699374218244
33,77.0495495495496,0.6562550034927758
34,77.25386100386099,0.6497906932758318
35,77.39060489060492,0.6426809742241293
36,77.94240669240672,0.638639061837583
37,77.92149292149293,0.6263545835138978
38,78.0083655083655,0.6429201529638188
39,78.38803088803083,0.6159292681998497
40,78.66795366795367,0.6191475970318189
41,78.40733590733588,0.6123448115949696
42,78.81435006435008,0.6096175759023911
43,77.75418275418274,0.6176439203523301
44,79.4675032175032,0.593328466588581
45,78.96557271557265,0.598572850428723
46,79.29375804375799,0.5972691442112665
47,79.77638352638354,0.5895186249871511
48,79.38063063063069,0.5855807241153073
49,79.56241956241955,0.5836816590178657
50,79.93243243243238,0.5871834472023152
51,79.92117117117122,0.5743996610713972
52,79.92438867438855,0.5726305426576653
53,80.18983268983271,0.573417138459312
54,79.55759330759334,0.5728190628862059
55,80.33622908622912,0.5710005281946143
56,80.99259974259977,0.5532117938572491
57,79.82786357786354,0.5687753130757326
58,80.60489060489054,0.5660335307789816
59,80.74485199485193,0.5533287764300365
60,80.76576576576576,0.5592222361246476
61,80.70945945945941,0.5550303211989435
62,80.51319176319178,0.5573463751053488
63,80.46653796653806,0.5548192227410303
64,81.01351351351349,0.5439099016523844
65,80.86229086229086,0.5411066884628019
66,81.19851994851999,0.5423890353255981
67,81.27574002574003,0.5378569255809527
68,81.56853281853282,0.5306045315354257
69,81.82754182754178,0.5277595034724957
70,81.80823680823683,0.5324231090175139
71,81.42374517374516,0.5335613948771277
72,81.53153153153158,0.527085297514458
73,81.81467181467183,0.5256181562168373
74,81.9208494208494,0.5189189567557864
75,81.85328185328177,0.5239050193513567
76,82.27960102960103,0.5166514996640585
77,82.22168597168599,0.5275384107151547
78,82.10585585585582,0.5125864084709335
79,81.77606177606174,0.5179141871631145
80,82.28442728442728,0.5093025388447819
81,82.56435006435008,0.5094891667265344
82,81.79536679536682,0.5226550915756741
83,82.27155727155726,0.5031526350491756
84,82.68822393822389,0.501893415896071
85,82.63030888030887,0.5026625199998552
86,82.62548262548258,0.4955324835672572
87,82.24259974259981,0.502569600436333
88,82.8120978120978,0.4932776801288128
89,82.23938223938225,0.504950778810559
90,82.71396396396396,0.5035433824098593
91,82.63513513513512,0.4958227343756605
92,82.4533462033462,0.4976905161967954
93,82.69787644787637,0.49259491735515565
94,83.30437580437578,0.4885861665715237
95,82.69787644787644,0.49350802514802766
96,82.94884169884178,0.4874300050070962
97,82.67213642213642,0.4991475144854268
98,83.27220077220073,0.4820339757244329
99,83.1756756756756,0.4857457717628898
100,82.9263191763192,0.4883980357868446
1 1 27.602960102960076 2.1390828868827305
2 2 40.289575289575296 1.8274962402678825
3 3 55.63867438867439 1.3679566755890846
4 4 61.156692406692386 1.212556131184101
5 5 63.820785070785085 1.1346077870678257
6 6 65.07882882882882 1.0906044611254253
7 7 66.05373230373232 1.0432700124141332
8 8 67.10263835263837 0.9987741410732269
9 9 68.08719433719436 0.9587975337698653
10 10 68.85296010296013 0.9380998385919107
11 11 69.6669884169885 0.9063431844115257
12 12 70.45205920205913 0.8767392347390587
13 13 70.83333333333333 0.8545357522529524
14 14 72.13159588159589 0.8230573479589578
15 15 72.1734234234234 0.8155114577227348
16 16 72.98745173745174 0.7962393571396131
17 17 72.79761904761901 0.7935148652743649
18 18 73.31241956241954 0.771961426412737
19 19 72.79601029601025 0.7745630315228088
20 20 74.43854568854573 0.7491488444644052
21 21 74.52059202059203 0.7345953340063224
22 22 74.63803088803087 0.7317175777578676
23 23 74.60746460746462 0.728221578875909
24 24 75.20270270270275 0.7166988654313861
25 25 74.98391248391249 0.7160083958627405
26 26 75.40862290862285 0.707493761000601
27 27 75.63063063063062 0.693673132943946
28 28 76.47844272844262 0.677632329230373
29 29 75.95238095238096 0.6822760587407125
30 30 76.52992277992276 0.6741252640211904
31 31 76.54761904761905 0.6681302016651308
32 32 76.89189189189189 0.6617699374218244
33 33 77.0495495495496 0.6562550034927758
34 34 77.25386100386099 0.6497906932758318
35 35 77.39060489060492 0.6426809742241293
36 36 77.94240669240672 0.638639061837583
37 37 77.92149292149293 0.6263545835138978
38 38 78.0083655083655 0.6429201529638188
39 39 78.38803088803083 0.6159292681998497
40 40 78.66795366795367 0.6191475970318189
41 41 78.40733590733588 0.6123448115949696
42 42 78.81435006435008 0.6096175759023911
43 43 77.75418275418274 0.6176439203523301
44 44 79.4675032175032 0.593328466588581
45 45 78.96557271557265 0.598572850428723
46 46 79.29375804375799 0.5972691442112665
47 47 79.77638352638354 0.5895186249871511
48 48 79.38063063063069 0.5855807241153073
49 49 79.56241956241955 0.5836816590178657
50 50 79.93243243243238 0.5871834472023152
51 51 79.92117117117122 0.5743996610713972
52 52 79.92438867438855 0.5726305426576653
53 53 80.18983268983271 0.573417138459312
54 54 79.55759330759334 0.5728190628862059
55 55 80.33622908622912 0.5710005281946143
56 56 80.99259974259977 0.5532117938572491
57 57 79.82786357786354 0.5687753130757326
58 58 80.60489060489054 0.5660335307789816
59 59 80.74485199485193 0.5533287764300365
60 60 80.76576576576576 0.5592222361246476
61 61 80.70945945945941 0.5550303211989435
62 62 80.51319176319178 0.5573463751053488
63 63 80.46653796653806 0.5548192227410303
64 64 81.01351351351349 0.5439099016523844
65 65 80.86229086229086 0.5411066884628019
66 66 81.19851994851999 0.5423890353255981
67 67 81.27574002574003 0.5378569255809527
68 68 81.56853281853282 0.5306045315354257
69 69 81.82754182754178 0.5277595034724957
70 70 81.80823680823683 0.5324231090175139
71 71 81.42374517374516 0.5335613948771277
72 72 81.53153153153158 0.527085297514458
73 73 81.81467181467183 0.5256181562168373
74 74 81.9208494208494 0.5189189567557864
75 75 81.85328185328177 0.5239050193513567
76 76 82.27960102960103 0.5166514996640585
77 77 82.22168597168599 0.5275384107151547
78 78 82.10585585585582 0.5125864084709335
79 79 81.77606177606174 0.5179141871631145
80 80 82.28442728442728 0.5093025388447819
81 81 82.56435006435008 0.5094891667265344
82 82 81.79536679536682 0.5226550915756741
83 83 82.27155727155726 0.5031526350491756
84 84 82.68822393822389 0.501893415896071
85 85 82.63030888030887 0.5026625199998552
86 86 82.62548262548258 0.4955324835672572
87 87 82.24259974259981 0.502569600436333
88 88 82.8120978120978 0.4932776801288128
89 89 82.23938223938225 0.504950778810559
90 90 82.71396396396396 0.5035433824098593
91 91 82.63513513513512 0.4958227343756605
92 92 82.4533462033462 0.4976905161967954
93 93 82.69787644787637 0.49259491735515565
94 94 83.30437580437578 0.4885861665715237
95 95 82.69787644787644 0.49350802514802766
96 96 82.94884169884178 0.4874300050070962
97 97 82.67213642213642 0.4991475144854268
98 98 83.27220077220073 0.4820339757244329
99 99 83.1756756756756 0.4857457717628898
100 100 82.9263191763192 0.4883980357868446

100
csv/ml/4-valid.csv Normal file
View File

@@ -0,0 +1,100 @@
1,29.273809523809536,2.051315297186375
3,49.85119047619048,1.5433299541473389
4,54.76190476190476,1.2609493277966977
5,56.744047619047635,1.3605893768370152
6,58.125000000000036,1.1614021092653275
7,68.34523809523807,1.016805088892579
8,62.16666666666667,1.1416975766420365
9,69.91071428571428,0.9672490257769824
10,70.12500000000003,0.9047691766172647
11,68.23809523809523,0.9138318926095963
12,65.69642857142858,1.0373506028205157
13,71.6607142857143,0.8684045895934105
14,72.47619047619052,0.8815795511007309
15,68.05952380952377,0.8984044261276722
16,73.01190476190479,0.789284561574459
17,71.13690476190476,0.7989875167608261
18,70.35119047619047,0.8057824622839689
19,68.64285714285717,0.8886382937431335
20,74.2857142857143,0.7225465632975101
21,74.83928571428574,0.7518218968063592
22,69.20238095238093,0.8354627314954997
23,70.94047619047622,0.8767039025202393
24,73.83928571428571,0.7284452702850104
25,68.54761904761905,0.833126774802804
26,75.07738095238095,0.9159735627472401
27,71.75595238095238,0.7203315717168153
28,75.23809523809524,0.6977376420050859
29,75.96428571428574,0.6770627645775675
30,67.84523809523812,0.804637711448595
31,73.66666666666661,0.7264734245836735
32,74.98809523809521,0.7202723940834403
33,76.82738095238098,0.6451356090605259
34,76.56547619047619,0.7310906839556992
35,74.17857142857144,0.6527281146496534
36,75.64285714285715,0.652126250974834
37,77.45238095238099,0.645377091364935
38,74.08333333333334,0.7179715827107429
39,74.64285714285718,0.6802939737215639
40,76.45238095238096,0.7086327613331378
41,78.36904761904765,0.6364761289209128
42,77.14880952380952,0.6048015973530709
43,68.28571428571432,0.7861062593758106
44,73.26190476190474,0.7215597826056183
45,79.02976190476191,0.5701101986691356
46,74.27380952380953,0.7340557057410478
47,81.30357142857146,0.5740749232005328
48,80.3511904761905,0.5711771972477436
49,75.42261904761905,0.652861162275076
50,75.64880952380952,0.7128070520586334
51,80.87500000000001,0.5683317880146206
52,78.7857142857143,0.5966225894168019
53,74.8035714285714,0.610477160429582
54,80.58928571428571,0.5856252398341895
55,78.90476190476194,0.6005261832848191
56,80.23214285714289,0.7735788714140653
57,80.38095238095238,0.5972846128046513
58,79.08928571428571,0.573754820600152
59,79.11904761904763,0.6042022015433759
60,79.4107142857143,0.5824158279690892
61,75.01785714285717,0.6588675778359174
62,82.58333333333334,0.5085183849092573
63,77.74999999999999,0.635120690590702
64,79.76190476190477,0.5699399406090379
65,80.02380952380952,0.5637381937354803
66,81.36309523809524,0.5342308867722749
67,80.5952380952381,0.5461581647396088
68,82.48809523809526,0.5187427336350083
69,80.42261904761907,0.5265548116411083
70,80.08333333333334,0.5382042658515275
71,78.69047619047619,0.5899241348728538
72,83.22023809523806,0.4874198311357759
73,79.55357142857142,0.6847600776702165
74,79.67857142857142,0.5748275525867939
75,81.5952380952381,0.5252354035328608
76,80.68452380952384,0.5232943094684742
77,81.20833333333331,0.5300189547357149
78,78.16666666666667,0.57311802203767
79,81.51190476190474,0.4905220562359318
80,79.83928571428574,0.5537121996283532
81,81.92857142857146,0.48468134496361015
82,83.37499999999999,0.45364727335982025
83,79.91666666666667,0.5779998623300344
84,84.08928571428574,0.454554439522326
85,81.64285714285717,0.6742473537102341
86,83.36904761904762,0.5024704524781555
87,77.08333333333336,0.5883687239140272
88,84.32142857142858,0.48450103085488083
89,79.54166666666666,0.5148660834878683
90,81.7857142857143,0.5070497094653547
91,78.76785714285714,0.5349147164262831
92,79.9642857142857,0.5225226475857199
93,83.19047619047622,0.4932839053682983
94,79.38690476190479,0.5373613414820284
95,84.07738095238096,0.4736974582076073
96,77.40476190476188,0.644087269064039
97,85.30952380952382,0.46559584718197583
98,80.19047619047623,0.5388261059299111
99,81.9285714285714,0.46946909818798305
100,77.86904761904762,0.5766924184048549
101,77.58333333333331,0.5949935361742973
1 1 29.273809523809536 2.051315297186375
2 3 49.85119047619048 1.5433299541473389
3 4 54.76190476190476 1.2609493277966977
4 5 56.744047619047635 1.3605893768370152
5 6 58.125000000000036 1.1614021092653275
6 7 68.34523809523807 1.016805088892579
7 8 62.16666666666667 1.1416975766420365
8 9 69.91071428571428 0.9672490257769824
9 10 70.12500000000003 0.9047691766172647
10 11 68.23809523809523 0.9138318926095963
11 12 65.69642857142858 1.0373506028205157
12 13 71.6607142857143 0.8684045895934105
13 14 72.47619047619052 0.8815795511007309
14 15 68.05952380952377 0.8984044261276722
15 16 73.01190476190479 0.789284561574459
16 17 71.13690476190476 0.7989875167608261
17 18 70.35119047619047 0.8057824622839689
18 19 68.64285714285717 0.8886382937431335
19 20 74.2857142857143 0.7225465632975101
20 21 74.83928571428574 0.7518218968063592
21 22 69.20238095238093 0.8354627314954997
22 23 70.94047619047622 0.8767039025202393
23 24 73.83928571428571 0.7284452702850104
24 25 68.54761904761905 0.833126774802804
25 26 75.07738095238095 0.9159735627472401
26 27 71.75595238095238 0.7203315717168153
27 28 75.23809523809524 0.6977376420050859
28 29 75.96428571428574 0.6770627645775675
29 30 67.84523809523812 0.804637711448595
30 31 73.66666666666661 0.7264734245836735
31 32 74.98809523809521 0.7202723940834403
32 33 76.82738095238098 0.6451356090605259
33 34 76.56547619047619 0.7310906839556992
34 35 74.17857142857144 0.6527281146496534
35 36 75.64285714285715 0.652126250974834
36 37 77.45238095238099 0.645377091364935
37 38 74.08333333333334 0.7179715827107429
38 39 74.64285714285718 0.6802939737215639
39 40 76.45238095238096 0.7086327613331378
40 41 78.36904761904765 0.6364761289209128
41 42 77.14880952380952 0.6048015973530709
42 43 68.28571428571432 0.7861062593758106
43 44 73.26190476190474 0.7215597826056183
44 45 79.02976190476191 0.5701101986691356
45 46 74.27380952380953 0.7340557057410478
46 47 81.30357142857146 0.5740749232005328
47 48 80.3511904761905 0.5711771972477436
48 49 75.42261904761905 0.652861162275076
49 50 75.64880952380952 0.7128070520586334
50 51 80.87500000000001 0.5683317880146206
51 52 78.7857142857143 0.5966225894168019
52 53 74.8035714285714 0.610477160429582
53 54 80.58928571428571 0.5856252398341895
54 55 78.90476190476194 0.6005261832848191
55 56 80.23214285714289 0.7735788714140653
56 57 80.38095238095238 0.5972846128046513
57 58 79.08928571428571 0.573754820600152
58 59 79.11904761904763 0.6042022015433759
59 60 79.4107142857143 0.5824158279690892
60 61 75.01785714285717 0.6588675778359174
61 62 82.58333333333334 0.5085183849092573
62 63 77.74999999999999 0.635120690590702
63 64 79.76190476190477 0.5699399406090379
64 65 80.02380952380952 0.5637381937354803
65 66 81.36309523809524 0.5342308867722749
66 67 80.5952380952381 0.5461581647396088
67 68 82.48809523809526 0.5187427336350083
68 69 80.42261904761907 0.5265548116411083
69 70 80.08333333333334 0.5382042658515275
70 71 78.69047619047619 0.5899241348728538
71 72 83.22023809523806 0.4874198311357759
72 73 79.55357142857142 0.6847600776702165
73 74 79.67857142857142 0.5748275525867939
74 75 81.5952380952381 0.5252354035328608
75 76 80.68452380952384 0.5232943094684742
76 77 81.20833333333331 0.5300189547357149
77 78 78.16666666666667 0.57311802203767
78 79 81.51190476190474 0.4905220562359318
79 80 79.83928571428574 0.5537121996283532
80 81 81.92857142857146 0.48468134496361015
81 82 83.37499999999999 0.45364727335982025
82 83 79.91666666666667 0.5779998623300344
83 84 84.08928571428574 0.454554439522326
84 85 81.64285714285717 0.6742473537102341
85 86 83.36904761904762 0.5024704524781555
86 87 77.08333333333336 0.5883687239140272
87 88 84.32142857142858 0.48450103085488083
88 89 79.54166666666666 0.5148660834878683
89 90 81.7857142857143 0.5070497094653547
90 91 78.76785714285714 0.5349147164262831
91 92 79.9642857142857 0.5225226475857199
92 93 83.19047619047622 0.4932839053682983
93 94 79.38690476190479 0.5373613414820284
94 95 84.07738095238096 0.4736974582076073
95 96 77.40476190476188 0.644087269064039
96 97 85.30952380952382 0.46559584718197583
97 98 80.19047619047623 0.5388261059299111
98 99 81.9285714285714 0.46946909818798305
99 100 77.86904761904762 0.5766924184048549
100 101 77.58333333333331 0.5949935361742973

1000
csv/ml/5-train.csv Normal file
View File

File diff suppressed because it is too large Load Diff

1000
csv/ml/5-valid.csv Normal file
View File

File diff suppressed because it is too large Load Diff

1000
csv/ml/6-train.csv Normal file
View File

File diff suppressed because it is too large Load Diff

1000
csv/ml/6-valid.csv Normal file
View File

File diff suppressed because it is too large Load Diff

1000
csv/ml/7-train.csv Normal file
View File

File diff suppressed because it is too large Load Diff

1000
csv/ml/7-valid.csv Normal file
View File

File diff suppressed because it is too large Load Diff

8
csv/ml/test.csv Normal file
View File

@@ -0,0 +1,8 @@
0,32,43,0.74418605
1,0,0,0
2,215,275,0.78181818
3,1871,4869,0.38426782
4,4835,6337,0.76297933
5,5462,6337,0.86192205
6,435,611,0.71194763
7,2508,6208,0.40399485
1 0 32 43 0.74418605
2 1 0 0 0
3 2 215 275 0.78181818
4 3 1871 4869 0.38426782
5 4 4835 6337 0.76297933
6 5 5462 6337 0.86192205
7 6 435 611 0.71194763
8 7 2508 6208 0.40399485

54
csv/uptime.csv Normal file
View File

@@ -0,0 +1,54 @@
2024-01-23 18:22:47,1
2024-01-23 19:06:11,0
2024-01-23 19:17:15,1
2024-01-23 22:50:55,0
2024-01-23 22:51:51,1
2024-01-24 08:48:07,0
2024-01-24 12:38:56,1
2024-01-24 12:55:10,0
2024-01-24 13:41:38,1
2024-01-25 02:07:16,0
2024-01-25 02:09:59,1
2024-01-25 16:14:26,0
2024-01-25 16:16:28,1
2024-01-25 17:51:18,0
2024-01-26 12:51:21,1
2024-01-26 13:01:53,0
2024-01-26 14:11:53,1
2024-01-26 14:21:54,0
2024-01-26 18:01:44,1
2024-01-26 21:21:58,0
2024-01-26 23:43:48,1
2024-01-27 00:13:09,0
2024-01-27 10:14:21,1
2024-01-27 10:34:56,0
2024-01-27 16:46:30,1
2024-01-27 17:02:56,0
2024-01-29 10:34:47,1
2024-01-29 10:48:32,0
2024-01-29 12:51:10,1
2024-01-29 13:05:34,0
2024-01-29 13:13:46,1
2024-01-29 13:25:41,0
2024-01-29 13:30:47,1
2024-01-29 20:07:11,0
2024-01-29 21:52:38,1
2024-01-30 13:57:37,0
2024-01-30 15:03:00,1
2024-01-30 15:29:02,0
2024-01-30 15:40:40,1
2024-01-30 16:58:26,0
2024-01-30 16:59:05,1
2024-01-31 02:33:43,0
2024-01-31 12:29:30,1
2024-01-31 17:22:44,0
2024-01-31 17:56:54,1
2024-02-03 18:54:58,0
2024-02-03 19:13:53,1
2024-02-06 12:21:59,0
2024-02-06 14:30:29,1
2024-02-12 19:31:53,0
2024-02-12 20:58:00,1
2024-02-16 10:42:54,0
2024-02-16 12:17:18,1
2024-02-28 15:27:21,0
1 2024-01-23 18:22:47 1
2 2024-01-23 19:06:11 0
3 2024-01-23 19:17:15 1
4 2024-01-23 22:50:55 0
5 2024-01-23 22:51:51 1
6 2024-01-24 08:48:07 0
7 2024-01-24 12:38:56 1
8 2024-01-24 12:55:10 0
9 2024-01-24 13:41:38 1
10 2024-01-25 02:07:16 0
11 2024-01-25 02:09:59 1
12 2024-01-25 16:14:26 0
13 2024-01-25 16:16:28 1
14 2024-01-25 17:51:18 0
15 2024-01-26 12:51:21 1
16 2024-01-26 13:01:53 0
17 2024-01-26 14:11:53 1
18 2024-01-26 14:21:54 0
19 2024-01-26 18:01:44 1
20 2024-01-26 21:21:58 0
21 2024-01-26 23:43:48 1
22 2024-01-27 00:13:09 0
23 2024-01-27 10:14:21 1
24 2024-01-27 10:34:56 0
25 2024-01-27 16:46:30 1
26 2024-01-27 17:02:56 0
27 2024-01-29 10:34:47 1
28 2024-01-29 10:48:32 0
29 2024-01-29 12:51:10 1
30 2024-01-29 13:05:34 0
31 2024-01-29 13:13:46 1
32 2024-01-29 13:25:41 0
33 2024-01-29 13:30:47 1
34 2024-01-29 20:07:11 0
35 2024-01-29 21:52:38 1
36 2024-01-30 13:57:37 0
37 2024-01-30 15:03:00 1
38 2024-01-30 15:29:02 0
39 2024-01-30 15:40:40 1
40 2024-01-30 16:58:26 0
41 2024-01-30 16:59:05 1
42 2024-01-31 02:33:43 0
43 2024-01-31 12:29:30 1
44 2024-01-31 17:22:44 0
45 2024-01-31 17:56:54 1
46 2024-02-03 18:54:58 0
47 2024-02-03 19:13:53 1
48 2024-02-06 12:21:59 0
49 2024-02-06 14:30:29 1
50 2024-02-12 19:31:53 0
51 2024-02-12 20:58:00 1
52 2024-02-16 10:42:54 0
53 2024-02-16 12:17:18 1
54 2024-02-28 15:27:21 0

453
ieee.csl Normal file
View File

@@ -0,0 +1,453 @@
<?xml version="1.0" encoding="utf-8"?>
<style xmlns="http://purl.org/net/xbiblio/csl" class="in-text" version="1.0" demote-non-dropping-particle="sort-only">
<info>
<title>IEEE</title>
<id>http://www.zotero.org/styles/ieee</id>
<link href="http://www.zotero.org/styles/ieee" rel="self"/>
<!-- <link href="https://ieeeauthorcenter.ieee.org/wp-content/uploads/IEEE-Reference-Guide.pdf" rel="documentation"/> - 2018 guidelines -->
<link href="http://journals.ieeeauthorcenter.ieee.org/wp-content/uploads/sites/7/IEEE_Reference_Guide.pdf" rel="documentation"/>
<link href="https://journals.ieeeauthorcenter.ieee.org/your-role-in-article-production/ieee-editorial-style-manual/" rel="documentation"/>
<author>
<name>Michael Berkowitz</name>
<email>mberkowi@gmu.edu</email>
</author>
<contributor>
<name>Julian Onions</name>
<email>julian.onions@gmail.com</email>
</contributor>
<contributor>
<name>Rintze Zelle</name>
<uri>http://twitter.com/rintzezelle</uri>
</contributor>
<contributor>
<name>Stephen Frank</name>
<uri>http://www.zotero.org/sfrank</uri>
</contributor>
<contributor>
<name>Sebastian Karcher</name>
</contributor>
<contributor>
<name>Giuseppe Silano</name>
<email>g.silano89@gmail.com</email>
<uri>http://giuseppesilano.net</uri>
</contributor>
<contributor>
<name>Patrick O'Brien</name>
</contributor>
<contributor>
<name>Brenton M. Wiernik</name>
</contributor>
<contributor>
<name>Oliver Couch</name>
<email>oliver.couch@gmail.com</email>
</contributor>
<category citation-format="numeric"/>
<category field="engineering"/>
<category field="generic-base"/>
<summary>IEEE style as per the 2021 guidelines, V 01.29.2021.</summary>
<updated>2022-10-11T00:52:46+10:00</updated>
<rights license="http://creativecommons.org/licenses/by-sa/3.0/">This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License</rights>
</info>
<locale xml:lang="en">
<date form="text">
<date-part name="month" form="short" suffix=" "/>
<date-part name="day" form="numeric-leading-zeros" suffix=", "/>
<date-part name="year"/>
</date>
<terms>
<term name="chapter" form="short">ch.</term>
<term name="presented at">presented at the</term>
<term name="available at">available</term>
</terms>
</locale>
<!-- Macros -->
<macro name="status">
<choose>
<if variable="page issue volume" match="none">
<text variable="status" text-case="capitalize-first" suffix="" font-weight="bold"/>
</if>
</choose>
</macro>
<macro name="edition">
<choose>
<if type="bill book chapter graphic legal_case legislation motion_picture paper-conference report song" match="any">
<choose>
<if is-numeric="edition">
<group delimiter=" ">
<number variable="edition" form="ordinal"/>
<text term="edition" form="short"/>
</group>
</if>
<else>
<text variable="edition" text-case="capitalize-first" suffix="."/>
</else>
</choose>
</if>
</choose>
</macro>
<macro name="issued">
<choose>
<if type="article-journal report" match="any">
<date variable="issued">
<date-part name="month" form="short" suffix=" "/>
<date-part name="year" form="long"/>
</date>
</if>
<else-if type="bill book chapter graphic legal_case legislation song thesis" match="any">
<date variable="issued">
<date-part name="year" form="long"/>
</date>
</else-if>
<else-if type="paper-conference" match="any">
<date variable="issued">
<date-part name="month" form="short"/>
<date-part name="year" prefix=" "/>
</date>
</else-if>
<else-if type="motion_picture" match="any">
<date variable="issued" form="text" prefix="(" suffix=")"/>
</else-if>
<else>
<date variable="issued" form="text"/>
</else>
</choose>
</macro>
<macro name="author">
<names variable="author">
<name and="text" et-al-min="3" et-al-use-first="1" initialize-with=". "/>
<label form="short" prefix=", " text-case="capitalize-first"/>
<et-al font-style="italic"/>
<substitute>
<names variable="editor"/>
<names variable="translator"/>
</substitute>
</names>
</macro>
<macro name="editor">
<names variable="editor">
<name initialize-with=". " delimiter=", " and="text"/>
<label form="short" prefix=", " text-case="capitalize-first"/>
</names>
</macro>
<macro name="locators">
<group delimiter=", ">
<text macro="edition"/>
<group delimiter=" ">
<text term="volume" form="short"/>
<number variable="volume" form="numeric"/>
</group>
<group delimiter=" ">
<number variable="number-of-volumes" form="numeric"/>
<text term="volume" form="short" plural="true"/>
</group>
<group delimiter=" ">
<text term="issue" form="short"/>
<number variable="issue" form="numeric"/>
</group>
</group>
</macro>
<macro name="title">
<choose>
<if type="bill book graphic legal_case legislation motion_picture song" match="any">
<text variable="title" font-style="italic"/>
</if>
<else>
<text variable="title" quotes="true"/>
</else>
</choose>
</macro>
<macro name="publisher">
<choose>
<if type="bill book chapter graphic legal_case legislation motion_picture paper-conference song" match="any">
<group delimiter=": ">
<text variable="publisher-place"/>
<text variable="publisher"/>
</group>
</if>
<else>
<group delimiter=", ">
<text variable="publisher"/>
<text variable="publisher-place"/>
</group>
</else>
</choose>
</macro>
<macro name="event">
<choose>
<if type="paper-conference speech" match="any">
<choose>
<!-- Published Conference Paper -->
<if variable="container-title" match="any">
<group delimiter=", ">
<group delimiter=" ">
<text term="in"/>
<text variable="container-title" font-style="italic"/>
</group>
<text variable="event-place"/>
</group>
</if>
<!-- Unpublished Conference Paper -->
<else>
<group delimiter=", ">
<group delimiter=" ">
<text term="presented at"/>
<text variable="event"/>
</group>
<text variable="event-place"/>
</group>
</else>
</choose>
</if>
</choose>
</macro>
<macro name="access">
<choose>
<if type="webpage post post-weblog" match="any">
<!-- https://url.com/ (accessed Mon. DD, YYYY). -->
<choose>
<if variable="URL">
<group prefix=" " delimiter=" ">
<text variable="URL"/>
<group delimiter=" " prefix="(" suffix=").">
<text term="accessed"/>
<date variable="accessed" form="text"/>
</group>
</group>
</if>
</choose>
</if>
<else-if match="any" variable="DOI">
<!-- doi: 10.1000/xyz123. -->
<text variable="DOI" prefix=" doi: " suffix="."/>
</else-if>
<else-if variable="URL">
<!-- Accessed: Mon. DD, YYYY. [Medium]. Available: https://URL.com/ -->
<group delimiter=". " prefix=" " suffix=". ">
<!-- Accessed: Mon. DD, YYYY. -->
<group delimiter=": ">
<text term="accessed" text-case="capitalize-first"/>
<date variable="accessed" form="text"/>
</group>
<!-- [Online Video]. -->
<group prefix="[" suffix="]" delimiter=" ">
<choose>
<if variable="medium" match="any">
<text variable="medium" text-case="capitalize-first"/>
</if>
<else>
<text term="online" text-case="capitalize-first"/>
<choose>
<if type="motion_picture">
<text term="video" text-case="capitalize-first"/>
</if>
</choose>
</else>
</choose>
</group>
</group>
<!-- Available: https://URL.com/ -->
<group delimiter=": " prefix=" ">
<text term="available at" text-case="capitalize-first"/>
<text variable="URL"/>
</group>
</else-if>
</choose>
</macro>
<macro name="page">
<choose>
<if type="article-journal" variable="number" match="all">
<group delimiter=" ">
<text value="Art."/>
<text term="issue" form="short"/>
<text variable="number"/>
</group>
</if>
<else>
<group delimiter=" ">
<label variable="page" form="short"/>
<text variable="page"/>
</group>
</else>
</choose>
</macro>
<macro name="citation-locator">
<group delimiter=" ">
<choose>
<if locator="page">
<label variable="locator" form="short"/>
</if>
<else>
<label variable="locator" form="short" text-case="capitalize-first"/>
</else>
</choose>
<text variable="locator"/>
</group>
</macro>
<macro name="geographic-location">
<group delimiter=", " suffix=".">
<choose>
<if variable="publisher-place">
<text variable="publisher-place" text-case="title"/>
</if>
<else-if variable="event-place">
<text variable="event-place" text-case="title"/>
</else-if>
</choose>
</group>
</macro>
<!-- Citation -->
<citation collapse="citation-number">
<sort>
<key variable="citation-number"/>
</sort>
<layout delimiter=", ">
<group prefix="[" suffix="]" delimiter=", ">
<text variable="citation-number"/>
<text macro="citation-locator"/>
</group>
</layout>
</citation>
<!-- Bibliography -->
<bibliography entry-spacing="0" second-field-align="flush">
<layout>
<!-- Citation Number -->
<text variable="citation-number" prefix="[" suffix="]"/>
<!-- Author(s) -->
<text macro="author" suffix=", "/>
<!-- Rest of Citation -->
<choose>
<!-- Specific Formats -->
<if type="article-journal">
<group delimiter=", ">
<text macro="title"/>
<text variable="container-title" font-style="italic" form="short"/>
<text macro="locators"/>
<text macro="page"/>
<text macro="issued"/>
<text macro="status"/>
</group>
<choose>
<if variable="URL DOI" match="none">
<text value="."/>
</if>
<else>
<text value=","/>
</else>
</choose>
<text macro="access"/>
</if>
<else-if type="paper-conference speech" match="any">
<group delimiter=", " suffix=".">
<text macro="title"/>
<text macro="event"/>
<text macro="issued"/>
<text macro="locators"/>
<text macro="page"/>
<text macro="status"/>
</group>
<text macro="access"/>
</else-if>
<else-if type="report">
<group delimiter=", " suffix=".">
<text macro="title"/>
<text macro="publisher"/>
<group delimiter=" ">
<text variable="genre"/>
<text variable="number"/>
</group>
<text macro="issued"/>
</group>
<text macro="access"/>
</else-if>
<else-if type="thesis">
<group delimiter=", " suffix=".">
<text macro="title"/>
<text variable="genre"/>
<text macro="publisher"/>
<text macro="issued"/>
</group>
<text macro="access"/>
</else-if>
<else-if type="webpage post-weblog post" match="any">
<group delimiter=", " suffix=".">
<text macro="title"/>
<text variable="container-title" font-style="italic"/>
<text macro="issued"/>
</group>
<text macro="access"/>
</else-if>
<else-if type="patent">
<group delimiter=", ">
<text macro="title"/>
<text variable="number"/>
<text macro="issued"/>
</group>
<text macro="access"/>
</else-if>
<!-- Online Video -->
<else-if type="motion_picture">
<text macro="geographic-location" suffix=". "/>
<group delimiter=", " suffix=".">
<text macro="title"/>
<text macro="issued"/>
</group>
<text macro="access"/>
</else-if>
<!-- Generic/Fallback Formats -->
<else-if type="bill book graphic legal_case legislation report song" match="any">
<group delimiter=", " suffix=". ">
<text macro="title"/>
<text macro="locators"/>
</group>
<group delimiter=", " suffix=".">
<text macro="publisher"/>
<text macro="issued"/>
<text macro="page"/>
</group>
<text macro="access"/>
</else-if>
<else-if type="article-magazine article-newspaper broadcast interview manuscript map patent personal_communication song speech thesis webpage" match="any">
<group delimiter=", " suffix=".">
<text macro="title"/>
<text variable="container-title" font-style="italic"/>
<text macro="locators"/>
<text macro="publisher"/>
<text macro="page"/>
<text macro="issued"/>
</group>
<text macro="access"/>
</else-if>
<else-if type="chapter paper-conference" match="any">
<group delimiter=", " suffix=", ">
<text macro="title"/>
<group delimiter=" ">
<text term="in"/>
<text variable="container-title" font-style="italic"/>
</group>
<text macro="locators"/>
</group>
<text macro="editor" suffix=" "/>
<group delimiter=", " suffix=".">
<text macro="publisher"/>
<text macro="issued"/>
<text macro="page"/>
</group>
<text macro="access"/>
</else-if>
<else>
<group delimiter=", " suffix=". ">
<text macro="title"/>
<text variable="container-title" font-style="italic"/>
<text macro="locators"/>
</group>
<group delimiter=", " suffix=".">
<text macro="publisher"/>
<text macro="page"/>
<text macro="issued"/>
</group>
<text macro="access"/>
</else>
</choose>
</layout>
</bibliography>
</style>

20
images/logo-en.svg Normal file
View File

File diff suppressed because one or more lines are too long
Side by Side

After

Width:  |  Height:  |  Size: 20 KiB

BIN
images/plots/plot-Hey Siri. Any missed calls.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 198 KiB

BIN
images/plots/plot-Hey Siri. Call John Doe.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 665 KiB

BIN
images/plots/plot-Hey Siri. Call Mary Poppins.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 669 KiB

BIN
images/plots/plot-Hey Siri. How far is New York from Boston.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 241 KiB

BIN
images/plots/plot-Hey Siri. How old is Ian McKellen.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 162 KiB

BIN
images/plots/plot-Hey Siri. Is there a God.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 125 KiB

BIN
images/plots/plot-Hey Siri. Read Calendar.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 260 KiB

BIN
images/plots/plot-Hey Siri. Remind me to wash the car.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 257 KiB

BIN
images/plots/plot-Hey Siri. Roll a die.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 136 KiB

BIN
images/plots/plot-Hey Siri. Translate car from English to Spanish.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 242 KiB

BIN
images/plots/plot-Hey Siri. What day was 90 days ago.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 173 KiB

BIN
images/plots/plot-Hey Siri. What is the factorial of 6.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 161 KiB

BIN
images/plots/plot-Hey Siri. What is the temperature in living room.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 232 KiB

BIN
images/plots/plot-Hey Siri. Whats 2330 dollars in euros.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 280 KiB

BIN
images/plots/plot-Hey Siri. Whats the temperature outside.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 176 KiB

BIN
images/sound-isolation.jpeg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 216 KiB

743
literature.bib Normal file
View File

@@ -0,0 +1,743 @@
%% This BibTeX bibliography file was created using BibDesk.
%% https://bibdesk.sourceforge.io/
%% Created for Milan van Zanten at 2024-02-29 20:16:35 +0100
%% Saved with string encoding Unicode (UTF-8)
@webpage{Apple_2018,
author = {Apple},
date-added = {2024-02-29 20:15:58 +0100},
date-modified = {2024-02-29 20:16:33 +0100},
keywords = {Voice Assistants},
lastchecked = {2024-02-29},
month = {04},
title = {Personalized Hey Siri},
url = {https://machinelearning.apple.com/research/personalized-hey-siri},
year = {2018}}
@webpage{Hey_Siri_Commands,
date-added = {2024-02-29 17:23:49 +0100},
date-modified = {2024-02-29 17:24:43 +0100},
keywords = {Voice Assistants},
lastchecked = {2024-02-29},
title = {Hey Siri: Commands in English},
url = {https://www.trendvektor.de/files/trendvektor/stories/notice/Hey-Siri.pdf}}
@article{Apple_2023,
author = {Apple},
date-added = {2024-02-29 11:52:30 +0100},
date-modified = {2024-02-29 11:53:27 +0100},
keywords = {Voice Assistants},
month = {08},
title = {Voice Trigger System for Siri},
urldate = {https://machinelearning.apple.com/research/voice-trigger},
year = {2023}}
@article{Ateniese_2015aa,
abstract = {News reports of the last few years indicated that several intelligence agencies are able to monitor large networks or entire portions of the Internet backbone. Such a powerful adversary has only recently been considered by the academic literature. In this paper, we propose a new adversary model for Location Based Services (LBSs). The model takes into account an unauthorized third party, different from the LBS provider itself, that wants to infer the location and monitor the movements of a LBS user. We show that such an adversary can extrapolate the position of a target user by just analyzing the size and the timing of the encrypted traffic exchanged between that user and the LBS provider. We performed a thorough analysis of a widely deployed location based app that comes pre-installed with many Android devices: GoogleNow. The results are encouraging and highlight the importance of devising more effective countermeasures against powerful adversaries to preserve the privacy of LBS users.},
author = {Giuseppe Ateniese and Briland Hitaj and Luigi V. Mancini and Nino V. Verde and Antonio Villani},
date-added = {2024-02-29 10:57:23 +0100},
date-modified = {2024-02-29 10:57:39 +0100},
eprint = {1505.07774},
keywords = {Traffic Fingerprinting},
month = {05},
title = {No Place to Hide that Bytes won't Reveal: Sniffing Location-Based Encrypted Traffic to Track a User's Position},
url = {https://arxiv.org/pdf/1505.07774.pdf},
year = {2015},
bdsk-url-1 = {https://arxiv.org/pdf/1505.07774.pdf},
bdsk-url-2 = {https://arxiv.org/abs/1505.07774},
bdsk-file-1 = {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}}
@article{Gupta_2023,
author = {Gupta, Maanak and Akiri, Charankumar and Aryal, Kshitiz and Parker, Eli and Praharaj, Lopamudra},
date-added = {2024-02-29 01:53:56 +0100},
date-modified = {2024-02-29 01:54:31 +0100},
doi = {10.1109/ACCESS.2023.3300381},
journal = {IEEE Access},
keywords = {Background},
pages = {80218-80245},
title = {From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy},
volume = {11},
year = {2023},
bdsk-url-1 = {https://doi.org/10.1109/ACCESS.2023.3300381},
bdsk-file-1 = {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}}
@article{Google_LLM_2024,
author = {Google},
date-added = {2024-02-29 01:45:24 +0100},
date-modified = {2024-02-29 01:48:17 +0100},
keywords = {Background},
month = {01},
title = {Gemini: A Family of Highly Capable Multimodal Models},
url = {https://storage.googleapis.com/deepmind-media/gemini/gemini_1_report.pdf},
year = {2024}}
@webpage{Amazon_LLM_2023,
author = {Daniel Rausch},
date-added = {2024-02-29 01:36:15 +0100},
date-modified = {2024-02-29 01:37:40 +0100},
keywords = {Background},
lastchecked = {2024-02-29},
month = {09},
title = {Previewing the future of Alexa},
url = {https://www.aboutamazon.com/news/devices/amazon-alexa-generative-ai},
year = {2023}}
@inproceedings{Zhang_2018,
author = {Zhang, Yiting and Yang, Ming and Gu, Xiaodan and Pan, Peilong and Ling, Zhen},
booktitle = {2018 Sixth International Conference on Advanced Cloud and Big Data (CBD)},
date-added = {2024-02-29 00:59:55 +0100},
date-modified = {2024-02-29 01:00:57 +0100},
doi = {10.1109/CBD.2018.00052},
keywords = {Background},
pages = {249-256},
title = {Fingerprinting Network Device Based on Traffic Analysis in High-Speed Network Environment},
year = {2018},
bdsk-url-1 = {https://doi.org/10.1109/CBD.2018.00052},
bdsk-file-1 = {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}}
@misc{rfc6716,
abstract = {This document defines the Opus interactive speech and audio codec. Opus is designed to handle a wide range of interactive audio applications, including Voice over IP, videoconferencing, in-game chat, and even live, distributed music performances. It scales from low bitrate narrowband speech at 6 kbit/s to very high quality stereo music at 510 kbit/s. Opus uses both Linear Prediction (LP) and the Modified Discrete Cosine Transform (MDCT) to achieve good compression of both speech and music. {[}STANDARDS-TRACK{]}},
author = {Jean-Marc Valin and Koen Vos and Timothy B. Terriberry},
date-modified = {2024-02-26 18:34:20 +0100},
doi = {10.17487/RFC6716},
howpublished = {RFC 6716},
keywords = {Background},
month = sep,
number = 6716,
pagetotal = 326,
publisher = {RFC Editor},
series = {Request for Comments},
title = {{Definition of the Opus Audio Codec}},
url = {https://www.rfc-editor.org/info/rfc6716},
year = 2012,
bdsk-url-1 = {https://www.rfc-editor.org/info/rfc6716},
bdsk-url-2 = {https://doi.org/10.17487/RFC6716}}
@misc{rfc1951,
abstract = {This specification defines a lossless compressed data format that compresses data using a combination of the LZ77 algorithm and Huffman coding, with efficiency comparable to the best currently available general-purpose compression methods. This memo provides information for the Internet community. This memo does not specify an Internet standard of any kind.},
author = {L. Peter Deutsch},
date-modified = {2024-02-26 18:31:09 +0100},
doi = {10.17487/RFC1951},
howpublished = {RFC 1951},
keywords = {Background},
month = may,
number = 1951,
pagetotal = 17,
publisher = {RFC Editor},
series = {Request for Comments},
title = {{DEFLATE Compressed Data Format Specification version 1.3}},
url = {https://www.rfc-editor.org/info/rfc1951},
year = 1996,
bdsk-url-1 = {https://www.rfc-editor.org/info/rfc1951},
bdsk-url-2 = {https://doi.org/10.17487/RFC1951}}
@article{Seagraves_2022,
author = {Andrew Seagraves},
date-added = {2024-02-24 12:30:56 +0100},
date-modified = {2024-02-24 14:31:56 +0100},
keywords = {Background},
lastchecked = {2024-02-24},
month = {12},
title = {Benchmarking Top Open Source Speech Recognition Models: Whisper, Facebook wav2vec2, and Kaldi},
url = {https://deepgram.com/learn/benchmarking-top-open-source-speech-models},
year = {2022},
bdsk-url-1 = {https://deepgram.com/learn/benchmarking-top-open-source-speech-models}}
@article{Radford:2022aa,
abstract = {We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zero-shot transfer setting without the need for any fine-tuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.},
author = {Alec Radford and Jong Wook Kim and Tao Xu and Greg Brockman and Christine McLeavey and Ilya Sutskever},
date-added = {2024-02-24 12:22:44 +0100},
date-modified = {2024-02-24 12:22:59 +0100},
eprint = {2212.04356},
keywords = {Background},
month = {12},
title = {Robust Speech Recognition via Large-Scale Weak Supervision},
url = {https://arxiv.org/pdf/2212.04356.pdf},
year = {2022},
bdsk-file-1 = {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},
bdsk-url-1 = {https://arxiv.org/pdf/2212.04356.pdf},
bdsk-url-2 = {https://arxiv.org/abs/2212.04356}}
@webpage{Microsoft_Copilot_2023,
date-added = {2024-02-22 18:06:06 +0100},
date-modified = {2024-02-22 18:06:51 +0100},
keywords = {Background},
lastchecked = {2024-02-22},
month = {09},
read = {1},
title = {Announcing Microsoft Copilot, your everyday AI companion},
url = {https://blogs.microsoft.com/blog/2023/09/21/announcing-microsoft-copilot-your-everyday-ai-companion/},
year = {2023},
bdsk-url-1 = {https://blogs.microsoft.com/blog/2023/09/21/announcing-microsoft-copilot-your-everyday-ai-companion/}}
@webpage{Microsoft_Cortana_2023,
date-added = {2024-02-22 17:55:56 +0100},
date-modified = {2024-02-22 17:57:57 +0100},
keywords = {Background},
lastchecked = {2024-02-22},
month = {08},
read = {1},
title = {End of support for Cortana},
url = {https://support.microsoft.com/en-us/topic/end-of-support-for-cortana-d025b39f-ee5b-4836-a954-0ab646ee1efa},
year = {2023},
bdsk-url-1 = {https://support.microsoft.com/en-us/topic/end-of-support-for-cortana-d025b39f-ee5b-4836-a954-0ab646ee1efa}}
@inproceedings{Haas_2022,
author = {Haas, Gabriel and Rietzler, Michael and Jones, Matt and Rukzio, Enrico},
booktitle = {CHI Conference on Human Factors in Computing Systems},
collection = {CHI {\^a}€™22},
date-added = {2024-02-22 12:26:30 +0100},
date-modified = {2024-02-22 12:26:44 +0100},
doi = {10.1145/3491102.3517684},
keywords = {Background},
month = apr,
publisher = {ACM},
series = {CHI {\^a}€™22},
title = {Keep it Short: A Comparison of Voice Assistants{\^a}€™ Response Behavior},
url = {http://dx.doi.org/10.1145/3491102.3517684},
year = {2022},
bdsk-file-1 = {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},
bdsk-url-1 = {http://dx.doi.org/10.1145/3491102.3517684}}
@article{Mazhar:2020aa,
abstract = {As the smart home IoT ecosystem flourishes, it is imperative to gain a better understanding of the unique challenges it poses in terms of management, security, and privacy. Prior studies are limited because they examine smart home IoT devices in testbed environments or at a small scale. To address this gap, we present a measurement study of smart home IoT devices in the wild by instrumenting home gateways and passively collecting real-world network traffic logs from more than 200 homes across a large metropolitan area in the United States. We characterize smart home IoT traffic in terms of its volume, temporal patterns, and external endpoints along with focusing on certain security and privacy concerns. We first show that traffic characteristics reflect the functionality of smart home IoT devices such as smart TVs generating high volume traffic to content streaming services following diurnal patterns associated with human activity. While the smart home IoT ecosystem seems fragmented, our analysis reveals that it is mostly centralized due to its reliance on a few popular cloud and DNS services. Our findings also highlight several interesting security and privacy concerns in smart home IoT ecosystem such as the need to improve policy-based access control for IoT traffic, lack of use of application layer encryption, and prevalence of third-party advertising and tracking services. Our findings have important implications for future research on improving management, security, and privacy of the smart home IoT ecosystem.},
author = {M. Hammad Mazhar and Zubair Shafiq},
date-added = {2024-02-21 17:51:54 +0100},
date-modified = {2024-02-21 17:51:59 +0100},
eprint = {2001.08288},
keywords = {Traffic Fingerprinting},
month = {01},
title = {Characterizing Smart Home IoT Traffic in the Wild},
url = {https://arxiv.org/pdf/2001.08288.pdf},
year = {2020},
bdsk-file-1 = {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},
bdsk-url-1 = {https://arxiv.org/abs/2001.08288},
bdsk-url-2 = {https://arxiv.org/pdf/2001.08288.pdf}}
@article{Trimananda:2019aa,
abstract = {Smart home devices are vulnerable to passive inference attacks based on network traffic, even in the presence of encryption. In this paper, we present PINGPONG, a tool that can automatically extract packet-level signatures for device events (e.g., light bulb turning ON/OFF) from network traffic. We evaluated PINGPONG on popular smart home devices ranging from smart plugs and thermostats to cameras, voice-activated devices, and smart TVs. We were able to: (1) automatically extract previously unknown signatures that consist of simple sequences of packet lengths and directions; (2) use those signatures to detect the devices or specific events with an average recall of more than 97%; (3) show that the signatures are unique among hundreds of millions of packets of real world network traffic; (4) show that our methodology is also applicable to publicly available datasets; and (5) demonstrate its robustness in different settings: events triggered by local and remote smartphones, as well as by homeautomation systems.},
author = {Rahmadi Trimananda and Janus Varmarken and Athina Markopoulou and Brian Demsky},
date-added = {2024-02-21 17:28:09 +0100},
date-modified = {2024-02-21 17:28:13 +0100},
eprint = {1907.11797},
keywords = {Device Fingerprinting},
month = {07},
title = {PingPong: Packet-Level Signatures for Smart Home Device Events},
url = {https://arxiv.org/pdf/1907.11797.pdf},
year = {2019},
bdsk-file-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhYYm9va21hcmtfEFhkZXZpY2UgZmluZ2VycHJpbnRpbmcvUElOR1BPTkctIFBhY2tldC1MZXZlbCBTaWduYXR1cmVzIGZvciBTbWFydCBIb21lIERldmljZSBFdmVudHMucGRmTxEE4GJvb2vgBAAAAAAEEDAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANwDAAAFAAAAAQEAAFVzZXJzAAAABQAAAAEBAABtaWxhbgAAAAYAAAABAQAAd29sa2U3AAAEAAAAAQEAAC1VbmkNAAAAAQEAAE1hc3RlciBUaGVzaXMAAAAKAAAAAQEAAGxpdGVyYXR1cmUAABUAAAABAQAAZGV2aWNlIGZpbmdlcnByaW50aW5nAAAAQgAAAAEBAABQSU5HUE9ORy0gUGFja2V0LUxldmVsIFNpZ25hdHVyZXMgZm9yIFNtYXJ0IEhvbWUgRGV2aWNlIEV2ZW50cy5wZGYAACAAAAABBgAABAAAABQAAAAkAAAANAAAAEAAAABYAAAAbAAAAIwAAAAIAAAABAMAABVdAAAAAAAACAAAAAQDAADWPgQAAAAAAAgAAAAEAwAAhSscAAAAAAAIAAAABAMAABdnHAAAAAAACAAAAAQDAABXtxwAAAAAAAgAAAAEAwAAYbccAAAAAAAIAAAABAMAAOK3HAAAAAAACAAAAAQDAADkzO0BAAAAACAAAAABBgAAAAEAABABAAAgAQAAMAEAAEABAABQAQAAYAEAAHABAAAIAAAAAAQAAEHFwzDzbPTyGAAAAAECAAABAAAAAAAAAA8AAAAAAAAAAAAAAAAAAAAIAAAABAMAAAYAAAAAAAAABAAAAAMDAAD1AQAACAAAAAEJAABmaWxlOi8vLwwAAAABAQAATWFjaW50b3NoIEhECAAAAAQDAAAAUKEbcwAAAAgAAAAABAAAQcWNhs0AAAAkAAAAAQEAAEVGRUFDMzkyLUYzNzktNDQ4Mi04REVCLTk3Q0EzRTYxRUFFMhgAAAABAgAAgQAAAAEAAADvEwAAAQAAAAAAAAAAAAAAAQAAAAEBAAAvAAAAAAAAAAEFAAA8AQAAAQIAAGVkNGZmN2Y1NDM4Y2FlYzk5YzgyMDNjM2Y0MWU2MGMwOGQxNGJlOGI0OThjOGE5ZmY5YzkxNWRjZjMzNmYxYjI7MDA7MDAwMDAwMDA7MDAwMDAwMDA7MDAwMDAwMDA7MDAwMDAwMDAwMDAwMDAyMDtjb20uYXBwbGUuYXBwLXNhbmRib3gucmVhZC13cml0ZTswMTswMTAwMDAwZTswMDAwMDAwMDAxZWRjY2U0OzAxOy91c2Vycy9taWxhbi93b2xrZTcvLXVuaS9tYXN0ZXIgdGhlc2lzL2xpdGVyYXR1cmUvZGV2aWNlIGZpbmdlcnByaW50aW5nL3Bpbmdwb25nLSBwYWNrZXQtbGV2ZWwgc2lnbmF0dXJlcyBmb3Igc21hcnQgaG9tZSBkZXZpY2UgZXZlbnRzLnBkZgDMAAAA/v///wEAAAAAAAAAEAAAAAQQAADYAAAAAAAAAAUQAACAAQAAAAAAABAQAAC4AQAAAAAAAEAQAACoAQAAAAAAAAIgAACEAgAAAAAAAAUgAAD0AQAAAAAAABAgAAAEAgAAAAAAABEgAAA4AgAAAAAAABIgAAAYAgAAAAAAABMgAAAoAgAAAAAAACAgAABkAgAAAAAAADAgAACQAgAAAAAAAAHAAADYAQAAAAAAABHAAAAUAAAAAAAAABLAAADoAQAAAAAAAIDwAACYAgAAAAAAAAAIAA0AGgAjAH4AAAAAAAACAQAAAAAAAAAFAAAAAAAAAAAAAAAAAAAFYg==},
bdsk-url-1 = {https://arxiv.org/abs/1907.11797},
bdsk-url-2 = {https://arxiv.org/pdf/1907.11797.pdf}}
@article{NPR_2022,
date-added = {2024-02-10 14:28:19 +0100},
date-modified = {2024-02-10 15:39:00 +0100},
journal = {National Public Media Insights},
keywords = {Background},
read = {1},
title = {The Smart Audio Report},
year = {2022},
bdsk-file-1 = {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}}
@webpage{Statista_Apple_2023,
date-added = {2024-02-10 14:05:11 +0100},
date-modified = {2024-02-10 14:10:13 +0100},
keywords = {Background},
lastchecked = {10.2.2024},
month = {November},
read = {1},
title = {Global Sales of Apple HomePod Devices},
url = {https://www.statista.com/statistics/1421706/apple-homepod-unit-sales/},
year = {2023},
bdsk-url-1 = {https://www.statista.com/statistics/1421706/apple-homepod-unit-sales/}}
@webpage{Amazon_2023,
date-added = {2024-02-10 13:45:41 +0100},
date-modified = {2024-02-10 14:11:24 +0100},
keywords = {Background},
lastchecked = {10.2.2024},
month = {May},
read = {1},
title = {Global Sales of Amazon Alexa Enabled Devices},
url = {https://press.aboutamazon.com/2023/5/amazon-introduces-four-all-new-echo-devices-sales-of-alexa-enabled-devices-surpass-half-a-billion},
year = {2023},
bdsk-url-1 = {https://press.aboutamazon.com/2023/5/amazon-introduces-four-all-new-echo-devices-sales-of-alexa-enabled-devices-surpass-half-a-billion}}
@inproceedings{9102875,
author = {Han, Yaowei and Li, Sheng and Cao, Yang and Ma, Qiang and Yoshikawa, Masatoshi},
booktitle = {2020 IEEE International Conference on Multimedia and Expo (ICME)},
date-added = {2023-03-06 16:56:23 +0100},
date-modified = {2023-03-06 16:57:06 +0100},
doi = {10.1109/ICME46284.2020.9102875},
keywords = {Voice Assistants},
month = {July},
pages = {1-6},
title = {Voice-Indistinguishability: Protecting Voiceprint In Privacy-Preserving Speech Data Release},
year = {2020},
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1109/ICME46284.2020.9102875}}
@article{schonherr2022101328,
abstract = {Voice assistants like Amazon's Alexa, Google's Assistant, Tencent's Xiaowei, or Apple's Siri, have become the primary (voice) interface in smart speakers that can be found in millions of households. For privacy reasons, these speakers analyze every sound in their environment for their respective wake word like ``Alexa,'' ``Ji{\v u}s{\`\i}'{\`e}r l{\'\i}ng,'' or ``Hey Siri,'' before uploading the audio stream to the cloud for further processing. Previous work reported on examples of an inaccurate wake word detection, which can be tricked using similar words or sounds like ``cocaine noodles'' instead of ``OK Google.'' In this paper, we perform a comprehensive analysis of such accidental triggers, i.e., sounds that should not have triggered the voice assistant, but did. More specifically, we automate the process of finding accidental triggers and measure their prevalence across 11 smart speakers from 8 different manufacturers using everyday media such as TV shows, news, and other kinds of audio datasets. To systematically detect accidental triggers, we describe a method to artificially craft such triggers using a pronouncing dictionary and a weighted, phone-based Levenshtein distance. In total, we have found hundreds of accidental triggers. Moreover, we explore potential gender and language biases and analyze the reproducibility. Finally, we discuss the resulting privacy implications of accidental triggers and explore countermeasures to reduce and limit their impact on users' privacy. To foster additional research on these sounds that mislead machine learning models, we publish a dataset of more than 350 verified triggers as a research artifact.},
author = {Lea Sch{\"o}nherr and Maximilian Golla and Thorsten Eisenhofer and Jan Wiele and Dorothea Kolossa and Thorsten Holz},
date-added = {2023-03-06 16:53:44 +0100},
date-modified = {2023-03-06 17:19:17 +0100},
doi = {https://doi.org/10.1016/j.csl.2021.101328},
issn = {0885-2308},
journal = {Computer Speech and Language},
keywords = {Voice Assistants},
month = {December},
pages = {101328},
title = {Exploring accidental triggers of smart speakers},
url = {https://www.sciencedirect.com/science/article/pii/S0885230821001212},
volume = {73},
year = {2021},
bdsk-file-1 = {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},
bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0885230821001212},
bdsk-url-2 = {https://doi.org/10.1016/j.csl.2021.101328}}
@article{caviglione_2013,
abstract = {AbstractThe evolution of handheld devices and wireless communication ignited a new wave of speech-driven services using an ``intelligent'' back-end to perform complex tasks, for example, real-time dictation and data retrieval. Hence, the network is of crucial importance, and its usage must be properly analysed. This paper discusses some basic behaviours of Siri in terms of traffic patterns, possible models and a short privacy/security assessment. Copyright {\copyright} 2013 John Wiley \& Sons, Ltd.},
author = {Caviglione, L.},
date-added = {2023-03-06 16:45:45 +0100},
date-modified = {2023-03-06 16:58:25 +0100},
doi = {https://doi.org/10.1002/ett.2697},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/ett.2697},
journal = {Transactions on Emerging Telecommunications Technologies},
keywords = {Voice Assistants},
month = {July},
number = {4},
pages = {664-669},
title = {A first look at traffic patterns of Siri},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ett.2697},
volume = {26},
year = {2013},
bdsk-file-1 = {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},
bdsk-url-1 = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ett.2697},
bdsk-url-2 = {https://doi.org/10.1002/ett.2697}}
@article{Major_2021,
author = {David Major and Danny Yuxing Huang and Marshini Chetty and Nick Feamster},
date-added = {2023-03-06 10:58:55 +0100},
date-modified = {2023-03-06 10:59:06 +0100},
doi = {10.1145/3446389},
journal = {{ACM} Transactions on Internet Technology},
keywords = {Voice Assistants},
month = {sep},
number = {1},
pages = {1--22},
publisher = {Association for Computing Machinery ({ACM})},
title = {Alexa, Who Am I Speaking To?: Understanding Users' Ability to Identify Third-Party Apps on Amazon Alexa},
url = {https://doi.org/10.1145%2F3446389},
volume = {22},
year = 2021,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1145%2F3446389},
bdsk-url-2 = {https://doi.org/10.1145/3446389}}
@inproceedings{Abdi_2021,
author = {Noura Abdi and Xiao Zhan and Kopo M. Ramokapane and Jose Such},
booktitle = {Proceedings of the 2021 {CHI} Conference on Human Factors in Computing Systems},
date-added = {2023-03-06 10:57:37 +0100},
date-modified = {2023-03-06 10:57:53 +0100},
doi = {10.1145/3411764.3445122},
keywords = {Voice Assistants},
month = {may},
publisher = {{ACM}},
title = {Privacy Norms for Smart Home Personal Assistants},
url = {https://doi.org/10.1145%2F3411764.3445122},
year = 2021,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1145%2F3411764.3445122},
bdsk-url-2 = {https://doi.org/10.1145/3411764.3445122}}
@article{9619970,
author = {Edu, Jide S. and Ferrer-Aran, Xavier and Such, Jose and Suarez-Tangil, Guillermo},
date-added = {2023-03-03 17:46:51 +0100},
date-modified = {2023-03-03 17:48:34 +0100},
doi = {10.1109/TDSC.2021.3129116},
journal = {IEEE Transactions on Dependable and Secure Computing},
keywords = {Voice Assistants},
month = {November},
number = {1},
pages = {161-175},
title = {SkillVet: Automated Traceability Analysis of Amazon Alexa Skills},
volume = {20},
year = {2021},
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1109/TDSC.2021.3129116}}
@inproceedings{Edu_2022,
author = {Jide Edu and Xavier Ferrer-Aran and Jose Such and Guillermo Suarez-Tangil},
booktitle = {Proceedings of the {ACM} Web Conference 2022},
date-added = {2023-03-03 13:18:01 +0100},
date-modified = {2023-03-03 13:18:04 +0100},
doi = {10.1145/3485447.3512289},
keywords = {Voice Assistants},
month = {apr},
publisher = {{ACM}},
title = {Measuring Alexa Skill Privacy Practices across Three Years},
url = {https://doi.org/10.1145%2F3485447.3512289},
year = 2022,
bdsk-file-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhYYm9va21hcmtfEE92b2ljZSBhc3Npc3RhbnRzL01lYXN1cmluZyBBbGV4YSBTa2lsbCBQcml2YWN5IFByYWN0aWNlcyBhY3Jvc3MgVGhyZWUgWWVhcnMucGRmTxEEzGJvb2vMBAAAAAAEEDAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMgDAAAFAAAAAQEAAFVzZXJzAAAABQAAAAEBAABtaWxhbgAAAAYAAAABAQAAd29sa2U3AAAEAAAAAQEAAC1VbmkNAAAAAQEAAE1hc3RlciBUaGVzaXMAAAAKAAAAAQEAAGxpdGVyYXR1cmUAABAAAAABAQAAdm9pY2UgYXNzaXN0YW50cz4AAAABAQAATWVhc3VyaW5nIEFsZXhhIFNraWxsIFByaXZhY3kgUHJhY3RpY2VzIGFjcm9zcyBUaHJlZSBZZWFycy5wZGYAACAAAAABBgAABAAAABQAAAAkAAAANAAAAEAAAABYAAAAbAAAAIQAAAAIAAAABAMAABVdAAAAAAAACAAAAAQDAADWPgQAAAAAAAgAAAAEAwAAhSscAAAAAAAIAAAABAMAABdnHAAAAAAACAAAAAQDAABXtxwAAAAAAAgAAAAEAwAAYbccAAAAAAAIAAAABAMAAIG3HAAAAAAACAAAAAQDAAACjJIAAAAAACAAAAABBgAA9AAAAAQBAAAUAQAAJAEAADQBAABEAQAAVAEAAGQBAAAIAAAAAAQAAEHFE50FgAAAGAAAAAECAAABAAAAAAAAAA8AAAAAAAAAAAAAAAAAAAAIAAAABAMAAAYAAAAAAAAABAAAAAMDAAD1AQAACAAAAAEJAABmaWxlOi8vLwwAAAABAQAATWFjaW50b3NoIEhECAAAAAQDAAAAUKEbcwAAAAgAAAAABAAAQcWNhs0AAAAkAAAAAQEAAEVGRUFDMzkyLUYzNzktNDQ4Mi04REVCLTk3Q0EzRTYxRUFFMhgAAAABAgAAgQAAAAEAAADvEwAAAQAAAAAAAAAAAAAAAQAAAAEBAAAvAAAAAAAAAAEFAAAzAQAAAQIAADIyZDIwMGRkZTFkMjcyYjY0OTMwNTc2ODJkN2E3MzQ4MzMwMmQ2ODdmMDAxOTM0ZDYxMzNhZWM4ZmQ2YmM2OTA7MDA7MDAwMDAwMDA7MDAwMDAwMDA7MDAwMDAwMDA7MDAwMDAwMDAwMDAwMDAyMDtjb20uYXBwbGUuYXBwLXNhbmRib3gucmVhZC13cml0ZTswMTswMTAwMDAwZTswMDAwMDAwMDAwOTI4YzAyOzAxOy91c2Vycy9taWxhbi93b2xrZTcvLXVuaS9tYXN0ZXIgdGhlc2lzL2xpdGVyYXR1cmUvdm9pY2UgYXNzaXN0YW50cy9tZWFzdXJpbmcgYWxleGEgc2tpbGwgcHJpdmFjeSBwcmFjdGljZXMgYWNyb3NzIHRocmVlIHllYXJzLnBkZgAAzAAAAP7///8BAAAAAAAAABAAAAAEEAAAzAAAAAAAAAAFEAAAdAEAAAAAAAAQEAAArAEAAAAAAABAEAAAnAEAAAAAAAACIAAAeAIAAAAAAAAFIAAA6AEAAAAAAAAQIAAA+AEAAAAAAAARIAAALAIAAAAAAAASIAAADAIAAAAAAAATIAAAHAIAAAAAAAAgIAAAWAIAAAAAAAAwIAAAhAIAAAAAAAABwAAAzAEAAAAAAAARwAAAFAAAAAAAAAASwAAA3AEAAAAAAACA8AAAjAIAAAAAAAAACAANABoAIwB1AAAAAAAAAgEAAAAAAAAABQAAAAAAAAAAAAAAAAAABUU=},
bdsk-url-1 = {https://doi.org/10.1145%2F3485447.3512289},
bdsk-url-2 = {https://doi.org/10.1145/3485447.3512289}}
@article{Bolton_2021,
author = {Tom Bolton and Tooska Dargahi and Sana Belguith and Mabrook S. Al-Rakhami and Ali Hassan Sodhro},
date-added = {2023-03-03 12:48:28 +0100},
date-modified = {2023-03-03 13:04:49 +0100},
doi = {10.3390/s21072312},
journal = {Sensors},
keywords = {Voice Assistants},
month = {mar},
number = {7},
pages = {2312},
publisher = {{MDPI} {AG}},
read = {1},
title = {On the Security and Privacy Challenges of Virtual Assistants},
url = {https://doi.org/10.3390%2Fs21072312},
volume = {21},
year = 2021,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.3390%2Fs21072312},
bdsk-url-2 = {https://doi.org/10.3390/s21072312}}
@incollection{Xie_2022,
author = {Fuman Xie and Yanjun Zhang and Hanlin Wei and Guangdong Bai},
booktitle = {Advanced Data Mining and Applications},
date-added = {2023-03-03 12:36:41 +0100},
date-modified = {2023-03-03 12:40:21 +0100},
doi = {10.1007/978-3-030-95405-5_12},
keywords = {Voice Assistants},
month = {January},
pages = {159--173},
publisher = {Springer International Publishing},
read = {1},
title = {{UQ}-{AAS}21: A Comprehensive Dataset of Amazon Alexa Skills},
url = {https://doi.org/10.1007%2F978-3-030-95405-5_12},
year = 2022,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1007%2F978-3-030-95405-5_12},
bdsk-url-2 = {https://doi.org/10.1007/978-3-030-95405-5_12}}
@inproceedings{8802686,
author = {Kennedy, Sean and Li, Haipeng and Wang, Chenggang and Liu, Hao and Wang, Boyang and Sun, Wenhai},
booktitle = {2019 IEEE Conference on Communications and Network Security (CNS)},
date-added = {2023-03-03 12:16:53 +0100},
date-modified = {2023-03-03 12:23:17 +0100},
doi = {10.1109/CNS.2019.8802686},
keywords = {Voice Assistants},
month = {June},
pages = {232-240},
read = {1},
title = {I Can Hear Your Alexa: Voice Command Fingerprinting on Smart Home Speakers},
year = {2019},
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1109/CNS.2019.8802686}}
@article{Wang:2020aa,
abstract = {This paper investigates the privacy leakage of smart speakers under an encrypted traffic analysis attack, referred to as voice command fingerprinting. In this attack, an adversary can eavesdrop both outgoing and incoming encrypted voice traffic of a smart speaker, and infers which voice command a user says over encrypted traffic. We first built an automatic voice traffic collection tool and collected two large-scale datasets on two smart speakers, Amazon Echo and Google Home. Then, we implemented proof-of-concept attacks by leveraging deep learning. Our experimental results over the two datasets indicate disturbing privacy concerns. Specifically, compared to 1% accuracy with random guess, our attacks can correctly infer voice commands over encrypted traffic with 92.89\% accuracy on Amazon Echo. Despite variances that human voices may cause on outgoing traffic, our proof-of-concept attacks remain effective even only leveraging incoming traffic (i.e., the traffic from the server). This is because the AI-based voice services running on the server side response commands in the same voice and with a deterministic or predictable manner in text, which leaves distinguishable pattern over encrypted traffic. We also built a proof-of-concept defense to obfuscate encrypted traffic. Our results show that the defense can effectively mitigate attack accuracy on Amazon Echo to 32.18%.},
author = {Chenggang Wang and Sean Kennedy and Haipeng Li and King Hudson and Gowtham Atluri and Xuetao Wei and Wenhai Sun and Boyang Wang},
date-added = {2023-03-01 21:16:38 +0100},
date-modified = {2023-03-03 12:11:20 +0100},
eprint = {2005.09800},
journal = {13th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '20), July 8--10, 2020, Linz (Virtual Event), Austria},
keywords = {Voice Assistants},
month = {05},
read = {1},
title = {Fingerprinting Encrypted Voice Traffic on Smart Speakers with Deep Learning},
url = {https://arxiv.org/pdf/2005.09800.pdf},
year = {2020},
bdsk-file-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhYYm9va21hcmtfEGB2b2ljZSBhc3Npc3RhbnRzL0ZpbmdlcnByaW50aW5nIEVuY3J5cHRlZCBWb2ljZSBUcmFmZmljIG9uIFNtYXJ0IFNwZWFrZXJzIHdpdGggRGVlcCBMZWFybmluZy5wZGZPEQTsYm9va+wEAAAAAAQQMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA6AMAAAUAAAABAQAAVXNlcnMAAAAFAAAAAQEAAG1pbGFuAAAABgAAAAEBAAB3b2xrZTcAAAQAAAABAQAALVVuaQ0AAAABAQAATWFzdGVyIFRoZXNpcwAAAAoAAAABAQAAbGl0ZXJhdHVyZQAAEAAAAAEBAAB2b2ljZSBhc3Npc3RhbnRzTwAAAAEBAABGaW5nZXJwcmludGluZyBFbmNyeXB0ZWQgVm9pY2UgVHJhZmZpYyBvbiBTbWFydCBTcGVha2VycyB3aXRoIERlZXAgTGVhcm5pbmcucGRmACAAAAABBgAABAAAABQAAAAkAAAANAAAAEAAAABYAAAAbAAAAIQAAAAIAAAABAMAABVdAAAAAAAACAAAAAQDAADWPgQAAAAAAAgAAAAEAwAAhSscAAAAAAAIAAAABAMAABdnHAAAAAAACAAAAAQDAABXtxwAAAAAAAgAAAAEAwAAYbccAAAAAAAIAAAABAMAAIG3HAAAAAAACAAAAAQDAACetxwAAAAAACAAAAABBgAABAEAABQBAAAkAQAANAEAAEQBAABUAQAAZAEAAHQBAAAIAAAAAAQAAEHE2QasgAAAGAAAAAECAAABAAAAAAAAAA8AAAAAAAAAAAAAAAAAAAAIAAAABAMAAAYAAAAAAAAABAAAAAMDAAD1AQAACAAAAAEJAABmaWxlOi8vLwwAAAABAQAATWFjaW50b3NoIEhECAAAAAQDAAAAUKEbcwAAAAgAAAAABAAAQcWNhs0AAAAkAAAAAQEAAEVGRUFDMzkyLUYzNzktNDQ4Mi04REVCLTk3Q0EzRTYxRUFFMhgAAAABAgAAgQAAAAEAAADvEwAAAQAAAAAAAAAAAAAAAQAAAAEBAAAvAAAAAAAAAAEFAABEAQAAAQIAADYyNmNlYmI2ZThiMjc5NTY3MzFjNjQwOTE4MDA0Mzk1ZGY5YjUxNjkxNWEyOGEwNzY5MzUyOWVmNWVhYTMyNDg7MDA7MDAwMDAwMDA7MDAwMDAwMDA7MDAwMDAwMDA7MDAwMDAwMDAwMDAwMDAyMDtjb20uYXBwbGUuYXBwLXNhbmRib3gucmVhZC13cml0ZTswMTswMTAwMDAwZTswMDAwMDAwMDAwMWNiNzllOzAxOy91c2Vycy9taWxhbi93b2xrZTcvLXVuaS9tYXN0ZXIgdGhlc2lzL2xpdGVyYXR1cmUvdm9pY2UgYXNzaXN0YW50cy9maW5nZXJwcmludGluZyBlbmNyeXB0ZWQgdm9pY2UgdHJhZmZpYyBvbiBzbWFydCBzcGVha2VycyB3aXRoIGRlZXAgbGVhcm5pbmcucGRmAMwAAAD+////AQAAAAAAAAAQAAAABBAAANwAAAAAAAAABRAAAIQBAAAAAAAAEBAAALwBAAAAAAAAQBAAAKwBAAAAAAAAAiAAAIgCAAAAAAAABSAAAPgBAAAAAAAAECAAAAgCAAAAAAAAESAAADwCAAAAAAAAEiAAABwCAAAAAAAAEyAAACwCAAAAAAAAICAAAGgCAAAAAAAAMCAAAJQCAAAAAAAAAcAAANwBAAAAAAAAEcAAABQAAAAAAAAAEsAAAOwBAAAAAAAAgPAAAJwCAAAAAAAAAAgADQAaACMAhgAAAAAAAAIBAAAAAAAAAAUAAAAAAAAAAAAAAAAAAAV2},
bdsk-url-1 = {https://arxiv.org/pdf/2005.09800.pdf}}
@incollection{Tsiatsikas_2023,
author = {Zisis Tsiatsikas and Georgios Karopoulos and Georgios Kambourakis},
booktitle = {Computer Security. {ESORICS} 2022 International Workshops},
date-added = {2023-03-01 20:18:27 +0100},
date-modified = {2023-03-06 16:59:15 +0100},
doi = {10.1007/978-3-031-25460-4_10},
keywords = {Traffic Fingerprinting},
month = {February},
pages = {177--190},
publisher = {Springer International Publishing},
title = {Measuring the Adoption of {TLS} Encrypted Client Hello Extension and Its Forebear in the Wild},
url = {https://doi.org/10.1007%2F978-3-031-25460-4_10},
year = 2023,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1007%2F978-3-031-25460-4_10},
bdsk-url-2 = {https://doi.org/10.1007/978-3-031-25460-4_10}}
@inproceedings{255322,
author = {Zhixiu Guo and Zijin Lin and Pan Li and Kai Chen},
booktitle = {29th USENIX Security Symposium (USENIX Security 20)},
date-added = {2023-02-21 16:40:13 +0100},
date-modified = {2023-02-24 03:01:48 +0100},
isbn = {978-1-939133-17-5},
keywords = {Voice Assistants},
month = aug,
pages = {2649--2666},
publisher = {USENIX Association},
read = {1},
title = {{SkillExplorer}: Understanding the Behavior of Skills in Large Scale},
url = {https://www.usenix.org/conference/usenixsecurity20/presentation/guo},
year = {2020},
bdsk-file-1 = {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},
bdsk-url-1 = {https://www.usenix.org/conference/usenixsecurity20/presentation/guo}}
@inproceedings{Shezan_2020,
author = {Faysal Hossain Shezan and Hang Hu and Jiamin Wang and Gang Wang and Yuan Tian},
booktitle = {Proceedings of The Web Conference 2020},
date-added = {2023-02-21 16:37:13 +0100},
date-modified = {2023-02-23 17:27:07 +0100},
doi = {10.1145/3366423.3380179},
keywords = {Voice Assistants},
month = {apr},
publisher = {{ACM}},
read = {1},
title = {Read Between the Lines: An Empirical Measurement of Sensitive Applications of Voice Personal Assistant Systems},
url = {https://doi.org/10.1145%2F3366423.3380179},
year = 2020,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1145%2F3366423.3380179},
bdsk-url-2 = {https://doi.org/10.1145/3366423.3380179}}
@article{Ford2019,
abstract = {With approximately 8.2 million Echo family devices sold since 2014, Amazon controls 70{\%} of the intelligent personal assistant market. Amazon's Alexa Voice Service (AVS) provides voice control services for Amazon's Echo product line and various home automation devices such as thermostats and security cameras. In November 2017, Amazon expanded Alexa services into the business intelligent assistant market with Alexa for Business. As corporations integrate Alexa into their corporate networks, it is important that information technology security stakeholders understand Alexa's audio streaming network behavior in order to properly implement security countermeasures and policies. This paper contributes to the intelligent personal assistant knowledge domain by providing insight into Amazon Voice Services behavior by analyzing the network traffic of two Echo Dots over a 21-day period. The Echo Dots were installed in a private residence, and at no time during the experiment did family members or house guests purposely interact with the Echos. All recorded audio commands were inadvertent. Using a k-mean cluster analysis, this study established a quantifiable AVS network signature. Then, by comparing that AVS signature and logged Alexa audio commands to the 21-day network traffic dataset, this study confirmed disabling the Echo's microphone, with the on/off button, prohibits audio recording and streaming to Alexa Voice Service. With 30--38{\%} of Echo Dots' spurious audio recordings were human conversations, these findings support the Echo Dot recorded private home conversations and not all audio recordings are properly logged the Alexa Application. While further Alexa network traffic studies are needed, this study offers a network signature capable of identifying AVS network traffic.},
author = {Ford, Marcia and Palmer, William},
date-added = {2023-02-21 16:35:23 +0100},
date-modified = {2023-02-23 22:40:01 +0100},
day = {01},
doi = {10.1007/s00779-018-1174-x},
issn = {1617-4917},
journal = {Personal and Ubiquitous Computing},
keywords = {Voice Assistants},
month = {Feb},
number = {1},
pages = {67--79},
read = {1},
title = {Alexa, are you listening to me? An analysis of Alexa voice service network traffic},
url = {https://link.springer.com/content/pdf/10.1007/s00779-018-1174-x.pdf},
volume = {23},
year = {2019},
bdsk-file-1 = {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},
bdsk-url-1 = {https://link.springer.com/content/pdf/10.1007/s00779-018-1174-x.pdf},
bdsk-url-2 = {https://doi.org/10.1007/s00779-018-1174-x}}
@inproceedings{Natatsuka_2019,
author = {Atsuko Natatsuka and Ryo Iijima and Takuya Watanabe and Mitsuaki Akiyama and Tetsuya Sakai and Tatsuya Mori},
booktitle = {Proceedings of the 2019 {ACM} {SIGSAC} Conference on Computer and Communications Security},
date-added = {2023-02-21 16:29:35 +0100},
date-modified = {2023-02-22 11:20:14 +0100},
doi = {10.1145/3319535.3363274},
keywords = {Voice Assistants},
month = {nov},
publisher = {{ACM}},
read = {1},
title = {Poster: A First Look at the Privacy Risks of Voice Assistant App},
url = {https://doi.org/10.1145%2F3319535.3363274},
year = 2019,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1145%2F3319535.3363274},
bdsk-url-2 = {https://doi.org/10.1145/3319535.3363274}}
@article{Dubois_2020,
author = {Daniel J. Dubois and Roman Kolcun and Anna Maria Mandalari and Muhammad Talha Paracha and David Choffnes and Hamed Haddadi},
date-added = {2023-02-21 16:27:23 +0100},
date-modified = {2023-03-01 17:14:49 +0100},
doi = {10.2478/popets-2020-0072},
journal = {Proceedings on Privacy Enhancing Technologies},
keywords = {Voice Assistants},
month = {aug},
number = {4},
pages = {255--276},
publisher = {Privacy Enhancing Technologies Symposium Advisory Board},
read = {1},
title = {When Speakers Are All Ears: Characterizing Misactivations of {IoT} Smart Speakers},
url = {https://doi.org/10.2478%2Fpopets-2020-0072},
volume = {2020},
year = 2020,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.2478%2Fpopets-2020-0072},
bdsk-url-2 = {https://doi.org/10.2478/popets-2020-0072}}
@inproceedings{Laperdrix_2016,
author = {Pierre Laperdrix and Walter Rudametkin and Benoit Baudry},
booktitle = {2016 {IEEE} Symposium on Security and Privacy ({SP})},
date-added = {2023-02-08 11:08:21 +0100},
date-modified = {2023-02-08 11:17:07 +0100},
doi = {10.1109/sp.2016.57},
keywords = {Device Fingerprinting},
month = {may},
publisher = {{IEEE}},
title = {Beauty and the Beast: Diverting Modern Web Browsers to Build Unique Browser Fingerprints},
url = {https://doi.org/10.1109%2Fsp.2016.57},
year = 2016,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1109%2Fsp.2016.57},
bdsk-url-2 = {https://doi.org/10.1109/sp.2016.57}}
@inproceedings{Wang_2020,
author = {Tao Wang},
booktitle = {2020 {IEEE} Symposium on Security and Privacy ({SP})},
date-added = {2023-02-08 11:07:30 +0100},
date-modified = {2023-02-22 09:44:58 +0100},
doi = {10.1109/sp40000.2020.00015},
keywords = {Traffic Fingerprinting},
month = {may},
publisher = {{IEEE}},
read = {1},
title = {High Precision Open-World Website Fingerprinting},
url = {https://doi.org/10.1109%2Fsp40000.2020.00015},
year = 2020,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1109%2Fsp40000.2020.00015},
bdsk-url-2 = {https://doi.org/10.1109/sp40000.2020.00015}}
@incollection{Eckersley_2010,
author = {Peter Eckersley},
booktitle = {Privacy Enhancing Technologies},
date-added = {2023-02-08 11:02:55 +0100},
date-modified = {2023-02-08 11:17:07 +0100},
doi = {10.1007/978-3-642-14527-8_1},
keywords = {Device Fingerprinting},
pages = {1--18},
publisher = {Springer Berlin Heidelberg},
title = {How Unique Is Your Web Browser?},
url = {https://doi.org/10.1007%2F978-3-642-14527-8_1},
year = 2010,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1007%2F978-3-642-14527-8_1},
bdsk-url-2 = {https://doi.org/10.1007/978-3-642-14527-8_1}}
@article{Oh_2017,
author = {Oh, Se and Li, Shuai and Hopper, Nicholas},
date-added = {2023-02-08 11:01:10 +0100},
date-modified = {2024-02-29 10:59:04 +0100},
doi = {10.1515/popets-2017-0048},
journal = {Proceedings on Privacy Enhancing Technologies},
keywords = {Traffic Fingerprinting},
month = {10},
title = {Fingerprinting Keywords in Search Queries over Tor},
volume = {2017},
year = {2017},
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1515/popets-2017-0048}}
@inproceedings{Zuo_2019,
author = {Chaoshun Zuo and Haohuang Wen and Zhiqiang Lin and Yinqian Zhang},
booktitle = {Proceedings of the 2019 {ACM} {SIGSAC} Conference on Computer and Communications Security},
date-added = {2023-02-08 10:38:04 +0100},
date-modified = {2023-02-08 11:16:42 +0100},
doi = {10.1145/3319535.3354240},
keywords = {Device Fingerprinting},
month = {nov},
publisher = {{ACM}},
title = {Automatic Fingerprinting of Vulnerable {BLE} {IoT} Devices with Static {UUIDs} from Mobile Apps},
url = {https://doi.org/10.1145%2F3319535.3354240},
year = 2019,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1145%2F3319535.3354240},
bdsk-url-2 = {https://doi.org/10.1145/3319535.3354240}}
@inproceedings{Das_2014,
author = {Anupam Das and Nikita Borisov and Matthew Caesar},
booktitle = {Proceedings of the 2014 {ACM} {SIGSAC} Conference on Computer and Communications Security},
date-added = {2023-02-08 10:37:17 +0100},
date-modified = {2023-02-08 11:17:07 +0100},
doi = {10.1145/2660267.2660325},
keywords = {Device Fingerprinting},
month = {nov},
publisher = {{ACM}},
title = {Do You Hear What I Hear?},
url = {https://doi.org/10.1145%2F2660267.2660325},
year = 2014,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1145%2F2660267.2660325},
bdsk-url-2 = {https://doi.org/10.1145/2660267.2660325}}
@inproceedings{Brik_2008,
author = {Vladimir Brik and Suman Banerjee and Marco Gruteser and Sangho Oh},
booktitle = {Proceedings of the 14th {ACM} international conference on Mobile computing and networking},
date-added = {2023-02-08 10:36:39 +0100},
date-modified = {2023-02-08 11:17:07 +0100},
doi = {10.1145/1409944.1409959},
keywords = {Device Fingerprinting},
month = {sep},
publisher = {{ACM}},
title = {Wireless device identification with radiometric signatures},
url = {https://doi.org/10.1145%2F1409944.1409959},
year = 2008,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1145%2F1409944.1409959},
bdsk-url-2 = {https://doi.org/10.1145/1409944.1409959}}
@article{Papadogiannaki_2021,
author = {Eva Papadogiannaki and Sotiris Ioannidis},
date-added = {2023-02-08 10:35:40 +0100},
date-modified = {2023-02-08 11:16:02 +0100},
doi = {10.1145/3457904},
journal = {{ACM} Computing Surveys},
keywords = {Traffic Fingerprinting},
month = {jul},
number = {6},
pages = {1--35},
publisher = {Association for Computing Machinery ({ACM})},
title = {A Survey on Encrypted Network Traffic Analysis Applications, Techniques, and Countermeasures},
url = {https://doi.org/10.1145%2F3457904},
volume = {54},
year = 2021,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1145%2F3457904},
bdsk-url-2 = {https://doi.org/10.1145/3457904}}
@inproceedings{Gonzalez_2016,
author = {Roberto Gonzalez and Claudio Soriente and Nikolaos Laoutaris},
booktitle = {Proceedings of the 2016 Internet Measurement Conference},
date-added = {2023-02-08 10:34:36 +0100},
date-modified = {2023-03-01 21:03:50 +0100},
doi = {10.1145/2987443.2987451},
keywords = {Traffic Fingerprinting},
month = {nov},
publisher = {{ACM}},
read = {1},
title = {User Profiling in the Time of {HTTPS}},
url = {https://doi.org/10.1145%2F2987443.2987451},
year = 2016,
bdsk-file-1 = {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},
bdsk-url-1 = {https://doi.org/10.1145%2F2987443.2987451},
bdsk-url-2 = {https://doi.org/10.1145/2987443.2987451}}
@comment{BibDesk Smart Groups{
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<array>
<dict>
<key>conditions</key>
<array>
<dict>
<key>comparison</key>
<integer>4</integer>
<key>key</key>
<string>Read</string>
<key>value</key>
<string>0</string>
<key>version</key>
<string>1</string>
</dict>
</array>
<key>conjunction</key>
<integer>0</integer>
<key>group name</key>
<string>To Read</string>
</dict>
</array>
</plist>
}}

1583
main.typ Normal file
View File

File diff suppressed because it is too large Load Diff

132
template.typ Normal file
View File

@@ -0,0 +1,132 @@
// The project function defines how your document looks.
// It takes your content and some metadata and formats it.
// Go ahead and customize it to your liking!
#let project(
title: "",
acknowledgement: [],
abstract: [],
authors: (),
examiner: (),
group: none,
date: none,
logo: none,
body,
) = {
// Set the document's basic properties.
set document(author: authors.map(a => a.name), title: title)
set page(
numbering: "1",
number-align: end,
margin: (
x: 3.5cm,
),
)
set text(font: "Linux Libertine", size: 10pt, lang: "en")
// Set paragraph spacing.
show par: set block(above: 1.2em, below: 1.2em)
set heading(numbering: "1.1.")
set figure(gap: 1em)
show figure.caption: emph
set table(align: left)
// Set run-in subheadings, starting at level 4.
show heading: it => {
if it.level > 3 {
parbreak()
text(11pt, style: "italic", weight: "regular", it.body + ".")
} else if it.level == 1 {
text(size: 20pt, it)
} else {
it
}
}
set par(leading: 0.75em)
// Title page.
// The page can contain a logo if you pass one with `logo: "logo.png"`.
v(0.6fr)
if logo != none {
align(left, image(logo, width: 26%))
}
v(9.6fr)
align(center)[
#text(1.1em, date)
#v(1.8em, weak: true)
#text(2em, weight: 700, title)
#v(1.8em, weak: true)
#text(1.1em, "Master Thesis")
// Author information.
#pad(
top: 6em,
grid(
columns: (1fr,) * calc.min(3, authors.len()),
gutter: 1em,
..authors.map(author => [
*#author.name* \
#author.email
]),
),
)
]
// Faculty information.
align(right, pad(
top: 20em,
bottom: 4em,
[
#examiner.role \
*#examiner.name* \
#v(1.6em, weak: true)
#group.faculty \
#group.department \
#group.name \
#link(group.url)
],
))
// v(2.4fr)
pagebreak()
// Acknowledgement page.
v(1fr)
align(center)[
#heading(
outlined: false,
numbering: none,
text(0.85em, smallcaps[Acknowledgements]),
)
#acknowledgement
]
v(1.618fr)
pagebreak()
// Abstract page.
v(1fr)
align(center)[
#heading(
outlined: false,
numbering: none,
text(0.85em, smallcaps[Abstract]),
)
#abstract
]
v(1.618fr)
pagebreak()
// Table of contents.
show outline.entry.where(level: 1): it => text(weight: "bold", it)
outline(depth: 3, indent: true, fill: text(weight: "regular", repeat[.]))
pagebreak()
// Main body.
set par(justify: true)
body
}