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101
dev/developers/test-cases/index.html
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@ -2050,9 +2050,9 @@
<tr>
<td>Phone Bluetooth</td>
<td>Doryab</td>
<td>N</td>
<td>N</td>
<td>N</td>
<td>Y</td>
<td>Y</td>
<td>Y</td>
</tr>
<tr>
<td>Phone Bluetooth</td>
@ -2216,7 +2216,7 @@
<h2 id="calls">Calls<a class="headerlink" href="#calls" title="Permanent link">&para;</a></h2>
<p>Due to the difference in the format of the raw data for iOS and Android the following is the expected results
the <code>phone_calls.csv</code>. </p>
<p>Description:</p>
<p>Description</p>
<ul>
<li>One missed episode, one outgoing episode and one incoming episode on Friday night, morning, afternoon and evening</li>
<li>There is at least one episode of each type of phone calls on each day</li>
@ -2227,7 +2227,7 @@ the <code>phone_calls.csv</code>. </p>
<li>There is one outcoming episode before, during or after the <code>event</code></li>
<li>There is one missed episode before, during or after the <code>event</code></li>
</ul>
<p>Data format:</p>
<p>Data format</p>
<table>
<thead>
<tr>
@ -2252,9 +2252,9 @@ the <code>phone_calls.csv</code>. </p>
</tr>
</tbody>
</table>
<p>Note:
<p>Note
When generating test data, all traces for iOS device need to be unique otherwise the episode with duplicate trace will be dropped </p>
<p>Checklist:</p>
<p>Checklist</p>
<table>
<thead>
<tr>
@ -2311,7 +2311,7 @@ When generating test data, all traces for iOS device need to be unique otherwise
</table>
<h2 id="screen">Screen<a class="headerlink" href="#screen" title="Permanent link">&para;</a></h2>
<p>Due to the difference in the format of the raw screen data for iOS and Android the following is the expected results the <code>phone_screen.csv</code>. </p>
<p>Description:</p>
<p>Description</p>
<ul>
<li>The screen data file contains data for 4 days.</li>
<li>The screen data contains 1 record to represent an <code>unlock</code>
@ -2324,7 +2324,7 @@ When generating test data, all traces for iOS device need to be unique otherwise
<code>night</code></li>
<li>One episode that crossing two <code>30-min</code> segments</li>
</ul>
<p>Data format:</p>
<p>Data format</p>
<table>
<thead>
<tr>
@ -2419,32 +2419,79 @@ When generating test data, all traces for iOS device need to be unique otherwise
iOS for testing empty data files</li>
</ul>
<h2 id="bluetooth">Bluetooth<a class="headerlink" href="#bluetooth" title="Permanent link">&para;</a></h2>
<p>Description </p>
<ul>
<li>The raw Bluetooth data file contains data for 1 day.</li>
<li>The raw Bluetooth data contains at least 2 records for each
<code>epoch</code>. Each <code>epoch</code> has a record with a <code>timestamp</code> for the
beginning boundary for that <code>epoch</code> and a record with a
<code>timestamp</code> for the ending boundary for that <code>epoch</code>. (e.g. For
the <code>morning</code> epoch there is a record with a <code>timestamp</code> for
<code>6:00AM</code> and another record with a <code>timestamp</code> for <code>11:59:59AM</code>.
These are to test edge cases)</li>
<li>An option of 5 Bluetooth devices are randomly distributed
throughout the data records.</li>
<li>There is one raw Bluetooth data file each, for testing both iOS
and Android data formats.</li>
<li>There is also an additional empty data file for both android and
iOS for testing empty data files.</li>
<li>The 4-day raw data is contained in <code>phone_bluetooth_raw.csv</code></li>
<li>One episode for each daily segment (<code>night</code>, <code>morning</code>, <code>afternoon</code> and <code>evening</code>)</li>
<li>Two episodes locate in the same 30-min segment (<code>Fri 23:38:45.789</code> and <code>Fri 23:59:59.465</code>)</li>
<li>Two episodes locate in the same daily segment (<code>Fri 00:00:00.798</code> and <code>Fri 00:49:04.132</code>)</li>
<li>One episode before the time switch (<code>Sun 00:24:00.000</code>) and one episode after the time switch (<code>Sun 17:32:00.000</code>)</li>
</ul>
<p>Checklist</p>
<table>
<thead>
<tr>
<th>time segment</th>
<th>single tz</th>
<th>multi tz</th>
<th>platform</th>
</tr>
</thead>
<tbody>
<tr>
<td>30min</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
</tr>
<tr>
<td>morning</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
</tr>
<tr>
<td>daily</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
</tr>
<tr>
<td>threeday</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
</tr>
<tr>
<td>weekend</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
</tr>
<tr>
<td>beforeMarchEvent</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
</tr>
<tr>
<td>beforeNovemberEvent</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
</tr>
</tbody>
</table>
<h2 id="wifi">WIFI<a class="headerlink" href="#wifi" title="Permanent link">&para;</a></h2>
<p>There are two wifi features (<code>phone wifi connected</code> and <code>phone wifi visible</code>). The raw test data are seperatly stored in the <code>phone_wifi_connected_raw.csv</code> and <code>phone_wifi_visible_raw.csv</code>.</p>
<p>Description: </p>
<p>Description</p>
<ul>
<li>One episode for each <code>epoch</code> (<code>night</code>, <code>morining</code>, <code>afternoon</code> and <code>evening</code>)</li>
<li>Two two episodes in the same time segment (<code>daily</code> and <code>30-min</code>)</li>
<li>Two episodes around the transition of <code>epochs</code> (e.g. one at the end of <code>night</code> and one at the beginning of <code>morning</code>) </li>
<li>One episode before and after the time switch on Sunday</li>
</ul>
<p>phone wifi connected:</p>
<p>phone wifi connected</p>
<p>Checklist</p>
<table>
<thead>
@ -2612,13 +2659,13 @@ When generating test data, all traces for iOS device need to be unique otherwise
<h2 id="conversation">Conversation<a class="headerlink" href="#conversation" title="Permanent link">&para;</a></h2>
<p>The 4-day raw conversation data is contained in <code>phone_conversation_raw.csv</code>. The different <code>inference</code> records are
randomly distributed throughout the <code>epoch</code>. </p>
<p>Description:
<p>Description
- One episode for each daily segment (<code>night</code>, <code>morning</code>, <code>afternoon</code> and <code>evening</code>) on each day
- Two episodes near the transition of the daily segment, one starts at the end of the afternoon, <code>Fri 17:10:00</code> and another one starts at the beginning of the evening, <code>Fri 18:01:00</code>
- One episode across two segments, <code>daily</code> and <code>30-mins</code>, (from <code>Fri 05:55:00</code> to <code>Fri 06:00:41</code>)
- Two episodes locate in the same daily segment (<code>Sat 12:45:36</code> and <code>Sat 16:48:22</code>)
- One episode before the time switch, <code>Sun 00:15:06</code>, and one episode after the time switch, <code>Sun 06:01:00</code></p>
<p>Data format:</p>
<p>Data format</p>
<table>
<thead>
<tr>

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dev/features/phone-bluetooth/index.html
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@ -1995,6 +1995,7 @@
<ul>
<li>Devices are classified as belonging to the participant (<code>own</code>) or to other people (<code>others</code>) using k-means based on the number of times and the number of days each device was detected across each participant&rsquo;s dataset. See <a href="../../citation#doryab-bluetooth">Doryab et al</a> for more details.</li>
<li>If ownership cannot be computed because all devices were detected on only one day, they are all considered as <code>other</code>. Thus <code>all</code> and <code>other</code> features will be equal. The likelihood of this scenario decreases the more days of data you have.</li>
<li>When searching for the most frequent device across 30-minute segments, the search range is equivalent to the sum of all segments of the same time period. For instance, the <code>countscansmostfrequentdeviceacrosssegments</code> for the time segment (<code>Fri 00:00:00, Fri 00:29:59</code>) will get the count in that segment of the most frequent device found within all (<code>00:00:00, 00:29:59</code>) time segments. To find <code>countscansmostfrequentdeviceacrosssegments</code> for <code>other</code> devices, the search range needs to filter out all <code>own</code> devices. But no need to do so for <code>countscansmostfrequentdeviceacrosssedataset</code>. The most frequent device across the dataset stays the same for <code>countscansmostfrequentdeviceacrossdatasetall</code>, <code>countscansmostfrequentdeviceacrossdatasetown</code> and <code>countscansmostfrequentdeviceacrossdatasetother</code>. Same rule applies to the least frequent device across the dataset. </li>
<li>The most and least frequent devices will be the same across time segment instances and across the entire dataset when every time segment instance covers every hour of a dataset. For example, daily segments (00:00 to 23:59) fall in this category but morning segments (06:00am to 11:59am) or periodic 30-minute segments don&rsquo;t.</li>
</ul>
<details class="info"><summary>Example</summary><details class="example"><summary>Simplified raw bluetooth data</summary><p>The following is a simplified example with bluetooth data from three days and two time segments: morning and afternoon. There are two <code>own</code> devices: <code>5C836F5-487E-405F-8E28-21DBD40FA4FF</code> detected seven times across two days and <code>499A1EAF-DDF1-4657-986C-EA5032104448</code> detected eight times on a single day.

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