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Parity Queries for Binary Classification
[article]
2019
arXiv
pre-print
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Consider a query-based data acquisition problem that aims to recover the values of k binary variables from parity (XOR) measurements of chosen subsets of the variables. ...
In particular, the necessary and sufficient sample complexity required for recovering all k variables with high probability is n = c_0 max{k, (k log k)/d̅} and the sample complexity for recovering a fixed ...
For real crowdsourced labeling problems the probability that a worker provides an incorrect answer changes depending on the query difficulty. ...
arXiv:1809.00901v2
fatcat:rwl2wudwqfhphhsi32dovzzos4
Big Data, Deep Learning – At the Edge of X-Ray Speaker Analysis
[chapter]
2017
Lecture Notes in Computer Science
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Then, gamified dynamic cooperative crowdsourcing turn its labelling into an entertaining experience, while reducing the amount of required labels to a minimum by learning alongside the target task also ...
If only a fraction of these data would be shared and labelled reliably, 'x-ray'-alike automatic speaker analysis could be around the corner for next gen human-computer interaction, mobile health applications ...
Then, gamified dynamic cooperative crowdsourcing aim at turning its labelling into an entertaining experience, while reducing the amount of required labels to a minimum by learning alongside the target ...
doi:10.1007/978-3-319-66429-3_2
fatcat:5ku3hm464jdftnrxczx7d2yicy
Three recent trends in Paralinguistics on the way to omniscient machine intelligence
2018
Journal on Multimodal User Interfaces
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Further, gamified crowdsourcing combined with human-machine cooperative learning turns the annotation process into an entertaining experience, while reducing the manual labelling effort to a minimum. ...
A major effort could thus be invested in efficient labelling and sharing of these. ...
Next presented, gamified dynamic cooperative crowdsourcing aims at turning its labelling into an entertaining experience, while reducing the amount of required labels to a minimum by learning alongside ...
doi:10.1007/s12193-018-0270-6
fatcat:cqlvp4ozmbe6zponscyplel45i
On Content-centric Wireless Delivery Networks
[article]
2014
arXiv
pre-print
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A network architecture that enables wireless network crowdsourcing for content delivery is then described, followed by an exemplary campus wireless network that encompasses the above concepts. ...
Towards this end, we first review some of the recent advancements in Information Centric Networking (ICN) which provides the basis on how media contents can be labeled, distributed, and placed across the ...
Layer Network Coding for Wireless Cooperative Networks in 2010. ...
arXiv:1410.5257v1
fatcat:r5a3rsv4hrg5dnksmi3ycfcb5u
Towards content-driven reputation for collaborative code repositories
2012
Proceedings of the Eighth Annual International Symposium on Wikis and Open Collaboration - WikiSym '12
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To prevent detrimental contributions enabled by crowdsourcing, reputation is one proposed solution. ...
To prevent detrimental contributions enabled by crowdsourcing, reputation is one proposed solution. ...
This finding motivates investigation into this parity across a broader set of cooperative settings and should serve as notice to researchers that cooperative behaviors are not fixed across the "peer production ...
doi:10.1145/2462932.2462950
dblp:conf/wikis/WestL12
fatcat:rm6moahq4bdxbn4i3uabotjqpq
Decision Learning : Data analytic learning with strategic decision making
2016
IEEE Signal Processing Magazine
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On one hand, requesters typically have a very low budget for each task in microtask crowdsourcing. ...
One key factor for the success of supervised and semisupervised learning is a large-scale labeled data set [1] , [2] . ...
In summary, users' decisions and actions affect each other in an ever-changing fashion for user-generated data applications. ...
doi:10.1109/msp.2015.2479895
fatcat:njhu7af2kbf7xlfox2vm6kabca
Descriptive Metadata for Field Books: Methods and Practices of the Field Book Project
2013
D-Lib Magazine
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Furthermore, with the integral relationship between specimens and field notes museum descriptive practices are well-suited for providing some consistency and parity with descriptions for specimens. ...
For person records (Figure 7), names in the "Primary Name" field are taken from VIAF, SIRIS, or are formed following Name Authority Cooperative (NACO) standards. ...
doi:10.1045/november2013-nakasone
fatcat:lcdkgv2of5ebdookklztuscdmy
Tag Prediction at Flickr: a View from the Darkroom
[article]
2017
arXiv
pre-print
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data for training. ...
As such, we advocate for the approach of harnessing user-generated data in large-scale systems. ...
For COCO, the ImageNet and YFCC pre-trained models achieve parity performance when the latter is trained with 2,000 examples per tag. ...
arXiv:1612.01922v3
fatcat:y5ek65nfancwpfg3ddk4txc7wq
Emerging Wireless Sensor Networks and Internet of Things Technologies—Foundations of Smart Healthcare
2020
Sensors
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A particular attention is devoted to crowdsourcing/crowdsensing, envisaged as tools for the rapid collection of massive quantities of medical data. ...
This work provides an extensive survey on emerging IoT communication standards and technologies suitable for smart healthcare applications. ...
Only then the harvested medical data would deserve the label "big". ...
doi:10.3390/s20133619
pmid:32605071
pmcid:PMC7374296
fatcat:wqhioh3lsrbx5fxk54k6m4puui
Intersectional AI Is Essential
2019
Journal of Science and Technology of the Arts
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This paper calls for the application of intersectional strategies to artificial intelligence at every level, from data to design to implementation, from technologist to user. ...
Although it promises parity and efficiency, its computational processes mirror biases of existing power even as often-proprietary data practices and cultural perceptions of computational magic obscure ...
through Amazon's crowdsourcing labor platform, Amazon Mechanical Turk, to apply labels to the images. ...
doi:10.7559/citarj.v11i2.665
fatcat:fduxkwsxovcsfb4fgyehftz65y
A Survey on Bias in Visual Datasets
[article]
2021
arXiv
pre-print
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To this end, this work aims to: i) describe the biases that can affect visual datasets; ii) review the literature on methods for bias discovery and quantification in visual datasets; iii) discuss existing ...
A key conclusion of our study is that the problem of bias discovery and quantification in visual datasets is still open and there is room for improvement in terms of both methods and the range of biases ...
Acknowledgements We would like to thank Alaa Elobaid, Miriam Fahimi and Giorgos Kordopatis-Zilos for the fruitful discussions. ...
arXiv:2107.07919v1
fatcat:ibhsebbwwzaw3n5rdedmv3a6we
Multimodal machine translation through visuals and speech
2020
Machine Translation
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The paper concludes with a discussion of directions for future research in these areas: the need for more expansive and challenging datasets, for targeted evaluations of model performance, and for multimodality ...
This survey reviews the major data resources for these tasks, the evaluation campaigns concentrated around them, the state of the art in end-to-end and pipeline approaches, and also the challenges in performance ...
We would also like to thank Maarit Koponen for her valuable feedback and her help in establishing our discussions of machine translation evaluation. ...
doi:10.1007/s10590-020-09250-0
fatcat:jod3ghcsnnbipotcqp6sme4lna
Fairness-Aware PAC Learning from Corrupted Data
[article]
2021
arXiv
pre-print
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While many approaches have been developed for training fair models from data, little is known about the robustness of these methods to data corruption. ...
can in some situations force any learner to return an overly biased classifier, regardless of the sample size and with or without degrading accuracy, and that the strength of the excess bias increases for ...
survey/crowdsourcing experiment. ...
arXiv:2102.06004v2
fatcat:n2pcxaougffulewkxl3uw6mt2q
A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges
[article]
2022
arXiv
pre-print
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We believe that this technical review can help to promote a sustainable development of AI R&D activities for the research community. ...
Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which is attracting increasing attention because of its promises to bring vast benefits for consumers and businesses ...
Data sharing across parties/organizations will lead to privacy concerns which would limit the cooperation for sustainable development of AI. ...
arXiv:2205.03824v1
fatcat:q6wti44kaffnbcjy4jtyhkekb4
Considerations for a More Ethical Approach to Data in AI: On Data Representation and Infrastructure
2020
Frontiers in Big Data
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Research suggests that centralized approaches result in poor representation, and as AI is now integrated more in daily life, there is a need for efforts to improve on this. ...
Data shapes the development of Artificial Intelligence (AI) as we currently know it, and for many years centralized networking infrastructures have dominated both the sourcing and subsequent use of such ...
semi-automatic labeling (Wang et al., 2019c) . ...
doi:10.3389/fdata.2020.00025
pmid:33693398
pmcid:PMC7931893
fatcat:jdmkycn7prdrbaxcn3epxzk6x4
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