Reliable Crowdsourcing for Multi-Class Labeling Using Coding Theory

Abstract. Because of the downlink bandwidth bottleneck and power limitation on satellite, the demands for low power cost high performance on-board payload data processing which can reduce the volume of communication data are growing as well. This paper propos es a high efficiency architecture for on-board hyperspectral image classification in a Zynq Soc to achieve real-time performance. The Hamming-distance based Support vector machine (SVM) is adopted to get a high accuracy and low energy consumption for multi-class classification. The sequential control and the computing data path are realized in ARM processor and Programmable logic respectively. By the pipelined computing data path, a satisfying speedup is reached and thus lowers the energy consumption. The experiments on real hyperspectral image datasets demonstrate that our architecture can achieve 97.8% overall accuracy, 2.5~330x speed up and 11~835x energy saving compared with different state-of-art embedded platforms. For the AVIRIS spectrometer in real NASA application, it can realize real-time image classification.

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“…In our research we will focus on this approach owing to its higher accuracy than previously described approaches [8,12].…”

Section: Multi-classifier By Hamming-distance Decisionmentioning

confidence: 99%

High Efficiency On-Board Hyperspectral Image Classification with Zynq SoC

Wang

Ali

et al. 2016

MATEC Web of Conferences

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“…When team members are rational and knowledgeable about the given problem domain, the team decision theory provides well-developed insights [14]. Since the emergence of high-performance computing and networking infrastructure, new paradigms for user participation, such as crowdsourcing, have arisen for distributed inference tasks [14]. Therefore, crowdsourcing can be defined as a distributed approach for complex problems through an open user participation paradigm.…”

Section: Crowdsourcingmentioning

confidence: 99%

Stochastic Decision Making for Adaptive Crowdsourcing in Medical Big-Data Platforms

Kim

Lee

2015

IEEE Trans. Syst. Man Cybern, Syst.

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This paper proposes two novel algorithms for adaptive crowdsourcing in 60-GHz medical imaging big-data platforms, namely, a max-weight scheduling algorithm for medical cloud platforms and a stochastic decision-making algorithm for distributed power-and-latency-aware dynamic buffer management in medical devices. In the first algorithm, medical cloud platforms perform a joint queue-backlog and rate-aware scheduling decisions for matching deployed access points (APs) and medical users where APs are eventually connected to medical clouds. In the second algorithm, each scheduled medical device computes the amounts of power allocation to upload its own medical data to medical big-data clouds with stochastic decision making considering joint energy-efficiency and buffer stability optimization. Through extensive simulations, the proposed algorithms are shown to achieve the desired results.Index Terms-60 GHz, dynamic buffering, IEEE 802.11ad, medical big-data platforms, stochastic decision making.

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“…The best phoneme transcription is then the transcription whose error-correcting code, c mj , best matches the distinctive feature labels that were actually provided by the labelers. Redundancy in this way permits us to acquire more accurate transcriptions, because even a crowd worker who is wrong about every single phoneme is often, nevertheless, right about many of the distinctive features (Vempaty et al 2014). …”

Section: Crowdsourcing With Binary Error Correcting Codesmentioning

confidence: 99%

Models of dataset size, question design, and cross-language speech perception for speech crowdsourcing applications

Hasegawa‐Johnson

Cole

Jyothi

et al. 2015

Laboratory Phonology

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Transcribers make mistakes. Workers recruited in a crowdsourcing marketplace, because of their varying levels of commitment and education, make more mistakes than workers in a controlled laboratory setting. Methods for compensating transcriber mistakes are desirable because, with such methods available, crowdsourcing has the potential to significantly increase the scale of experiments in laboratory phonology. This paper provides a brief tutorial on statistical learning theory, introducing the relationship between dataset size and estimation error, then presents a theoretical description and preliminary results for two new methods that control labeler error in laboratory phonology experiments. First, we discuss the method of crowdsourcing over error-correcting codes. In the error-correcting-code method, each difficult labeling task is first factored, by the experimenter, into the product of several easy labeling tasks (typically binary). Factoring increases the total number of tasks, nevertheless it results in faster completion and higher accuracy, because workers unable to perform the difficult task may be able to meaningfully contribute to the solution of each easy task. Second, we discuss the use of explicit mathematical models of the errors made by a worker in the crowd. In particular, we introduce the method of mismatched crowdsourcing, in which workers transcribe a language they do not understand, and an explicit mathematical model of second-language phoneme perception is used to learn and then compensate their transcription errors. Though introduced as technologies that increase the scale of phonology experiments, both methods have implications beyond increased scale. The method of easy questions permits us to probe the perception, by untrained listeners, of complicated phonological models; examples are provided from the prosody of English and Hindi. The method of mismatched crowdsourcing permits us to probe, in more detail than ever before, the perception of phonetic categories by listeners with a different phonological system.

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Reliable Crowdsourcing for Multi-Class Labeling Using Coding Theory

Cited by 58 publications

References 20 publications

High Efficiency On-Board Hyperspectral Image Classification with Zynq SoC

High Efficiency On-Board Hyperspectral Image Classification with Zynq SoC

Stochastic Decision Making for Adaptive Crowdsourcing in Medical Big-Data Platforms

Models of dataset size, question design, and cross-language speech perception for speech crowdsourcing applications

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