Difference between revisions of "Winter Milestone 5 @niranga"

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== System ==
 
  
'''Introducing the workers skill to the Boomerang Ranking'''
 
 
 
=== Brief introduction of the system ===
 
 
Boomerang, the rating system that uses in Daemo decide what are tasks that workers going to get in their future and what type of workers, requesters going to get for their task. Requesters' ratings of workers of tasks are used to give early access to workers that requester rates highly. However, Boomerang still can recommend a worker to the requester who are not familiar with the posted task because Boomerang doesn't consider the workers skill level related to that particular task when recommend.
 
 
For example:-
 
 
* Requester post two tasks to the platform (Image tagging, Translation)
 
 
* When the requester posts the tasks Boomerang recommend the higher rating workers first
 
 
* However, among those workers, there can be workers who are not familiar with those tasks because those workers previously have worked with the requester on some other type of tasks( Surveys, Bookkeeping, etc.)
 
 
* So, if the requester selects one of the higher rating worker not familiar with the task, there can be a quality issue.
 
 
=== How is the system solving critical problems ===
 
To motivate continued participation, Twitch provides both
 
instant and aggregated feedback to the user. An instant feedback display shows how many other users agreed via a
 
fadeout as the lock screen disappears (Figure 4) or how the
 
user’s contributions apply to the whole (Figure 5).
 
Aggregated data is also available via a web application,
 
allowing the user to explore all data that the system has
 
collected. For example, Figure 2 shows a human generated
 
map from the Census application.
 
To address security concerns, users are allowed to either
 
disable or keep their existing Android passcode while using
 
Twitch. If users do not wish to answer a question, they may
 
skip Twitch by selecting ‘Exit’ via the options menu. This
 
design decision has been made to encourage the user to give
 
Twitch an answer, which is usually faster than exiting.
 
Future designs could make it easier to skip a task, for
 
example through a swipe-up.
 
 
=== Introducing modules of the system ===
 
Below, we introduce the three main crowdsourcing
 
applications that Twitch supports. The first, Census,
 
attempts to capture local knowledge. The following two,
 
Image Voting and Structuring the Web, draw on creative
 
and topical expertise. These three applications are bundled
 
into one Android package, and each can be accessed
 
interchangeably through Twitch's settings menu.
 
 
=== Module 1: Census ===
 
 
==== Problem/Limitations ====
 
Despite progress in producing effective understanding of
 
static elements of our physical world — routes, businesses
 
and points of interest — we lack an understanding of
 
human activity. How busy is the corner cafe at 2pm on
 
Fridays? What time of day do businesspeople clear out of
 
the downtown district and get replaced by socializers?
 
Which neighborhoods keep high-energy activities going
 
until 11pm, and which ones become sleepy by 6pm? Users
 
could take advantage of this information to plan their
 
commutes, their social lives and their work.
 
 
==== Module preview ====
 
Existing crowdsourced techniques such as Foursquare are
 
too sparse to answer these kinds of questions: the answers
 
require at-the-moment, distributed human knowledge. We
 
envision that twitch crowdsourcing can help create a
 
human-centered equivalent of Google Street View, where a
 
user could browse typical crowd activity in an area. To do
 
so, we ask users to answer one of several questions about the world around them each time they unlock their phone.
 
Users can then browse the map they are helping create.
 
 
==== System details ====
 
Census is the default crowdsourcing task in Twitch. It
 
collects structured information about what people
 
experience around them. Each Census unlock screen
 
consists of four to six tiles (Figures 1 and 3), each task
 
centered around questions such as:
 
• How many people are around you?
 
• What kinds of attire are nearby people wearing?
 
• What are you currently doing?
 
• How much energy do you have right now?
 
While not exhaustive, these questions cover several types of
 
information that a local census might seek to provide. Two
 
of the four questions ask users about the people around
 
them, while the other two ask about users themselves; both
 
of which they are uniquely equipped to answer. Each
 
answer is represented graphically; for example, in case of
 
activities, users have icons for working, at home, eating,
 
travelling, socializing, or exercising.
 
To motivate continued engagement, Census provides two
 
modes of feedback. Instant feedback (Figure 4) is a brief
 
Android popup message that appears immediately after the
 
user makes a selection. It reports the percentage of
 
responses in the current time bin and location that agreed
 
with the user, then fades out within two seconds. It is
 
transparent to user input, so the user can begin interacting
 
with the phone even while it is visible. Aggregated report
 
allows Twitch users to see the cumulative effect of all
 
users’ behavior. The data is bucketed and visualized on a
 
map (Figure 2) on the Twitch homepage. Users can filter
 
the data based on activity type or time of day.
 

Latest revision as of 00:34, 14 February 2016