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Parity Queries for Binary Classification [article]

Hye Won Chung, Ji Oon Lee, Doyeon Kim, Alfred O. Hero
2019 arXiv   pre-print
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]

Björn W. Schuller
2017 Lecture Notes in Computer Science  
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

Björn W. Schuller, Yue Zhang, Felix Weninger
2018 Journal on Multimodal User Interfaces  
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]

Hui Liu, Zhiyong Chen, Xiaohua Tian, Xinbing Wang, Meixia Tao
2014 arXiv   pre-print
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

Andrew G. West, Insup Lee
2012 Proceedings of the Eighth Annual International Symposium on Wikis and Open Collaboration - WikiSym '12  
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

Yan Chen, Chunxiao Jiang, Chih-Yu Wang, Yang Gao, K.J. Ray Liu
2016 IEEE Signal Processing Magazine  
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

Sonoe Nakasone, Carolyn Sheffield
2013 D-Lib Magazine  
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]

Kofi Boakye, Sachin Farfade, Hamid Izadinia, Yannis Kalantidis, and Pierre Garrigues
2017 arXiv   pre-print
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

Gordana Gardašević, Konstantinos Katzis, Dragana Bajić, Lazar Berbakov
2020 Sensors  
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

Sarah Ciston
2019 Journal of Science and Technology of the Arts  
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]

Simone Fabbrizzi, Symeon Papadopoulos, Eirini Ntoutsi, Ioannis Kompatsiaris
2021 arXiv   pre-print
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

Umut Sulubacak, Ozan Caglayan, Stig-Arne Grönroos, Aku Rouhe, Desmond Elliott, Lucia Specia, Jörg Tiedemann
2020 Machine Translation  
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]

Nikola Konstantinov, Christoph H. Lampert
2021 arXiv   pre-print
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]

Zhenghua Chen, Min Wu, Alvin Chan, Xiaoli Li, Yew-Soon Ong
2022 arXiv   pre-print
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

Alice Baird, Björn Schuller
2020 Frontiers in Big Data  
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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