1 Timeline Releases and How to Access Them. Current nikeukoutlet.me- nikeukoutlet.me?bundle=true. Complete. This tutorial has been partially updated for use with Timeline version 2.x. timeline//nikeukoutlet.me CAS/ICSC/SSC-7 / DOC Timeline of Significant Events . modelling experiments – e.g., OSSEs – still needs to be made more.
timeline 2.3.1. Experimental
For the exact format of such XML files, refer to the documentation on event sources. Note that loading XML files is only one way in which you can add events to timelines. Looking at the previous timeline, it is obvious that the lower band looks denser, and it will become a lot denser a lot quicker than the upper band should we add more events. Usually, a lower band usually acts as a zoomed-out overview for an upper band and it does not have to show as much detail as the upper band.
Change the lower band to be an overview band:. For more background on how a timeline is initialized including how to override defaults check out Understanding Initialization. That's it for getting started. To continue with this tutorial check out creating Hot Zones when your events get too cramped.
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Together these and other performance interfaces define performance metrics that describe the Performance Timeline of a web application. For example, the following script shows how a developer can access the Performance Timeline to obtain performance metrics related to the navigation of the document, resources on the page, and developer scripts:. Alternatively, instead of processing metrics at a predefined time, or having to periodically poll the timeline for new metrics, the developer may also observe the Performance Timeline and be notified of new performance metrics via a Performance Observer:.
The Performance Timeline enables the user agent and application developers to access, instrument, and retrieve various performance metrics from the full lifecycle of a web application. The PerformanceEntry interface hosts the performance data of various metrics. Instances of this interface are serialized as a map with entries for each of the serializable attributes. This extends the Performance interface [ HR-TIME-2 ] and hosts performance related attributes and methods used to retrieve the performance metric data from the Performance Timeline.
This method returns a PerformanceEntryList object that contains a list of PerformanceEntry objects, sorted in chronological order with respect to startTime , that match the following criteria:. The PerformanceObserver interface can be used to observe the Performance Timeline and be notified of new performance entries as they are recorded by the user agent. CET because these data can be analyzed along with the collected tweets.
Overall, the precision of the HR measurement is not crucial. However, for the purpose of the experiment, the tendency of the HR change at the moment of sentiment extraction is important.
In the case of a systematic error occurring during the HR measurement, we would still get useful data collections. Only random errors or systematic errors appearing only in certain parts of the HR data could cause difficult data interpretation.
Considering the lawsuit to Fitbit regarding the product accuracy  we took into account information from many sources  ,  , . One of the sources  providing a lot of information about Fitbit is cited but seems to be anecdote research.
However, the results from  show that the mean absolute percentage error was 6. The precision of the HR measurement for the entire testing interval was determined as an average difference of 3.
The HR values were produced over This precision is also confirmed by the results of  , which presents the mean absolute percentage error of 3. Both twitter corpora have the same description of columns in the provided datasets:. The positive and negative sentiment is not extracted by machine learning methods; it is evaluated by the participant and recorded using the positive and negative hashtags.
The process of writing tweets experienced from the first version of the experiment realized and described in . The first crucial problem was a distribution of tweets during the day. For this purpose, the daily windows were used. More specifically, a tweet should have been written in the following times: The exact time points were not strictly enforced, but the participant had a tendency to keep them.
As we can see the tweets are distributed evenly from 9: The values close to midnight are lowered by the fact that tweets were usually recorded after the midnight of the next day. Lower values are expected from 7: Since the experimental data is collected from three different data source systems working in different time zones, it is necessary to clarify which time zone was used during each data processing phase see Table 4.
The time zone matrix for a particular data source system and specific data processing phases. All three sources Table 4 were carefully investigated and based on the knowledge of the particular activity the time zone was determined. Table 4 clearly explains several phases when data was extracted and preprocessed and provides information what time zone is expected to be used during data descriptive analysis to avoid misunderstanding and pitfalls.
Since two different wearables for the HR measurement were used, the experiment was performed in two periods, each with duration of 50 days:. The wearables broadly available on the market were chosen. The main requirements were defined as follows:. During the market research and parameters evaluation, 30 devices from 16 companies were taken into account and two of them complied with the requirements Figs. Fitbit Charge HR wearable from Fitbit company. Picture sourced from Fitbit press release kit: Basis Peak wearable from Basis company.
The sentiment of the participant was expressed by writing short texts — tweets. The social network Twitter was used as a recording medium. It provides the ability to record the text together with its timestamp using a mobile phone with social media account and several appropriate applications. On the other hand, Twitter uses character limit for text entry. The text message was extended by two hash-tags i.
The first one was used to identify the tweets related to the experiment:. The second pair of hashtags identifies participant's positive or negative mood during the tweet recording:. It means 23 tweets were expected during a working day and 21 tweets were recorded during a weekday. However, at least 20 tweets per day were required. This section introduces the work with the common timeline for the obtained data. This idea was already described in our previous publication .
All servers providing particular web services Twitter, Fitbit, Basis are connected to the Internet and thus we assume their current time is synchronized through the network time protocol NTP or simple network time protocol SNTP . NTP can usually set the time within tens of milliseconds over the public Internet and can achieve better than one millisecond accuracy in local area networks under ideal conditions.
The datasets for both experiments were acquired from four different data sources, so it would be convenient to have them in one dataset for each experiment.
It is suitable to merge the data to have heart rate data and corresponding sentiment data together. It means that for each tweet date and time attribute the entry in the heart rate data has to be found. We looked for the lowest absolute time differences.
Performance Timeline Level 2
Facebook Timeline. Timelines are populated by categorical additions. Facebook and other social networks perform social experiments with their users. nikeukoutlet.me?bundle=true http:// nikeukoutlet.me?bundle=true. Vertical timeline for nikeukoutlet.me - - a CSS package on npm - nikeukoutlet.me