Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userWorking in Pairs: Understanding the Effects of Worker Interactions in Crowdwork
81
0
0.0
(
0
)
Added by
Chien-Ju Ho
Publication date
2018
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Crowdsourcing has gained popularity as a tool to harness human brain power to help solve problems that are difficult for computers. Previous work in crowdsourcing often assumes that workers complete crowdwork independently. In this paper, we relax the independent property of crowdwork and explore how introducing direct, synchronous, and free-style interactions between workers would affect crowdwork. In particular, motivated by the concept of peer instruction in educational settings, we study the effects of peer communication in crowdsourcing environments. In the crowdsourcing setting with peer communication, pairs of workers are asked to complete the same task together by first generating their initial answers to the task independently and then freely discussing the tasks with each other and updating their answers after the discussion. We experimentally examine the effects of peer communication in crowdwork on various common types of tasks on crowdsourcing platforms, including image labeling, optical character recognition (OCR), audio transcription, and nutrition analysis. Our experiment results show that the work quality is significantly improved in tasks with peer communication compared to tasks where workers complete the work independently. However, participating in tasks with peer communication has limited effects on influencing workers independent performance in tasks of the same type in the future.
rate research
Read More
Program tracing, or mentally simulating a program on concrete inputs, is an important part of general program comprehension. Programs involve many kinds of virtual state that must be held in memory, such as variable/value pairs and a call stack. In this work, we examine the influence of short-term working memory (WM) on a persons ability to remember program state during tracing. We first confirm that previous findings in cognitive psychology transfer to the programming domain: people can keep about 7 variable/value pairs in WM, and people will accidentally swap associations between variables due to WM load. We use a restricted focus viewing interface to further analyze the strategies people use to trace through programs, and the relationship of tracing strategy to WM. Given a straight-line program, we find half of our participants traced a program from the top-down line-by-line (linearly), and the other half start at the bottom and trace upward based on data dependencies (on-demand). Participants with an on-demand strategy made more WM errors while tracing straight-line code than with a linear strategy, but the two strategies contained an equal number of WM errors when tracing code with functions. We conclude with the implications of these findings for the design of programming tools: first, programs should be analyzed to identify and refactor human-memory-intensive sections of code. Second, programming environments should interactively visualize variable metadata to reduce WM load in accordance with a persons tracing strategy. Third, tools for program comprehension should enable externalizing program state while tracing.
The gig economy has transformed the ways in which people work, but in many ways these markets stifle the growth of workers and the autonomy and protections that workers have grown to expect. We explored the viability of a worker centric peer economy--a system wherein workers benefit as well as consumers-- and conducted ethnographic field work across fields ranging from domestic labor to home health care. We discovered seven facets that system designers ought to consider when designing a labor market for gig workers, consisting principally of the following: constructive feedback, assigning work fairly, managing customer expectations, protecting vulnerable workers, reconciling worker identities, assessing worker qualifications, & communicating worker quality. We discuss these considerations and provide guidance toward the design of a mutually beneficial market for gig workers.
We qualitatively assess and map the relative contribution of pre-processing and cluster related processes to the build-up of A963, a massive cluster at z=0.2 showing an unusually high fraction of star forming galaxies in its interior. We use Voronoi binning of positions of cluster members on the plane of the sky in order to map the 2D variations of galaxy properties in the centre and infall region of A963. We map four galaxy parameters (fraction of star forming galaxies, specific star formation rate, HI deficiency and age of the stellar population) based on full SED fitting, 21cm imaging and optical spectroscopy. We find an extended region dominated by passive galaxies along a north-south axis crossing the cluster centre, possibly associated with known filaments of the large-scale structure. There are signs that the passive galaxies in this region were quenched long before their arrival in the vicinity of the cluster. Contrary to that, to the east and west of the cluster centre lie regions of recent accretion dominated by gas rich, actively star forming galaxies not associated with any substructure or filament. The few passive galaxies in this region appear to be recently quenched, and some gas rich galaxies show signs of ongoing ram-pressure stripping. We report the first tentative observations at 21cm of ongoing ram-pressure stripping at z=0.2, as well as observed inflow of low-entropy gas into the cluster along filaments of the large-scale structure. The observed galaxy content of A963 is a result of strongly anisotropic accretion of galaxies with different properties. Gas rich, star forming galaxies are being accreted from the east and west of the cluster and these galaxies are being quenched at r<R200, while the bulk of the accretion, containing multiple groups, happens along the north-south axis and brings mostly passive galaxies.
With the growing ubiquity of wearable devices, sensed physiological responses provide new means to connect with others. While recent research demonstrates the expressive potential for biosignals, the value of sharing these personal data remains unclear. To understand their role in communication, we created Significant Otter, an Apple Watch/iPhone app that enables romantic partners to share and respond to each others biosignals in the form of animated otter avatars. In a one-month study with 20 couples, participants used Significant Otter with biosignals sensing OFF and ON. We found that while sensing OFF enabled couples to keep in touch, sensing ON enabled easier and more authentic communication that fostered social connection. However, the addition of biosignals introduced concerns about autonomy and agency over the messages they sent. We discuss design implications and future directions for communication systems that recommend messages based on biosignals.
Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning. To understand how Auto-ML tools are used in practice today, we performed a qualitative study with participants ranging from novice hobbyists to industry researchers who use Auto-ML tools. We present insights into the benefits and deficiencies of existing tools, as well as the respective roles of the human and automation in ML workflows. Finally, we discuss design implications for the future of Auto-ML tool development. We argue that instead of full automation being the ultimate goal of Auto-ML, designers of these tools should focus on supporting a partnership between the user and the Auto-ML tool. This means that a range of Auto-ML tools will need to be developed to support varying user goals such as simplicity, reproducibility, and reliability.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
