Kendall’s Tau

Puka, Llukan

doi:10.1007/978-3-642-04898-2_324

Cited by 43 publications

(26 citation statements)

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“…To compare the different methods (landscape connectivity or network approaches) or the results obtained for different seasons, the distance between distributions of normalized activities (i.e rankings) can be assessed with the Kendall's τ coefficient 29 . A value close to 1 means that the administrative units are ordered in the same way, while a value close to 0 means that there is no concordance in the rankings.…”

Section: Identification Of High Potential Areasmentioning

confidence: 99%

Mapping livestock movements in Sahelian Africa

Jahel

Lenormand

Seck³

et al. 2020

Sci Rep

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in the dominant livestock systems of Sahelian countries herds have to move across territories. their mobility is often a source of conflict with farmers in the areas crossed, and helps spread diseases such as Rift Valley fever. Knowledge of the routes followed by herds is therefore core to guiding the implementation of preventive and control measures for transboundary animal diseases, land use planning and conflict management. However, the lack of quantitative data on livestock movements, together with the high temporal and spatial variability of herd movements, has so far hampered the production of fine resolution maps of animal movements. This paper proposes a general framework for mapping potential paths for livestock movements and identifying areas of high animal passage potential for those movements. the method consists in combining the information contained in livestock mobility networks with landscape connectivity, based on different mobility conductance layers. We illustrate our approach with a livestock mobility network in Senegal and Mauritania in the 2014 dry and wet seasons.

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Section: Identification Of High Potential Areasmentioning

confidence: 99%

Mapping livestock movements in Sahelian Africa

Jahel

Lenormand

Seck³

et al. 2020

Sci Rep

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“…For the evaluation, we consider the correlation between the salient object ranking based on the gray-level saliency values provided by an algorithm and that based on the ground truth gray-level saliencies of type γ by computing Kendall rank-correlation coefficient (Kendall's τ b ) [49] between them. In Figures 11(a), (b), and (c), the Kendall's tau (τ γ b ) values for the existing methods considering the three different ground truths are shown.…”

Section: Salient Object Ranking Correlationmentioning

confidence: 99%

“…Further, let the set of gray-level saliencies of all the corresponding objects estimated by an algorithm be R. The gray-level object saliency estimated by an algorithm for an object o is given in (9), and therefore, S o ∈ R, ∀o. Kendall's tau τ γ b for each type of ground truth is calculated between R and ργ using the standard way of Kendall's tau computation as given in [49].…”

Section: Salient Object Ranking Correlationmentioning

confidence: 99%

Evaluating salient object detection in natural images with multiple objects having multi‐level saliency

Yildirim¹,

Sen

Kankanhalli

et al. 2020

IET image process

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Salient object detection is evaluated using binary ground truth with the labels being salient object class and background. In this paper, we corroborate based on three subjective experiments on a novel image dataset that objects in natural images are inherently perceived to have varying levels of importance. Our dataset, named SalMoN (saliency in multi-object natural images), has 588 images containing multiple objects. The subjective experiments performed record spontaneous attention and perception through eye fixation duration, point clicking and rectangle drawing. As object saliency in a multi-object image is inherently multilevel, we propose that salient object detection must be evaluated for the capability to detect all multi-level salient objects apart from the salient object class detection capability. For this purpose, we generate multi-level maps as ground truth corresponding to all the dataset images using the results of the subjective experiments, with the labels being multi-level salient objects and background. We then propose the use of mean absolute error, Kendall's rank correlation and average area under precision-recall curve to evaluate existing salient object detection methods on our multi-level saliency ground truth dataset. Approaches that represent saliency detection on images as local-global hierarchical processing of a graph perform well in our dataset.

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“…Spearman's rho is often considered as the regular Pearson's product moment correlation coefficient, in terms of the proportion of variability accounted for, whereas τ measures the difference between the probabilities of the datasets when they are in the same and different order (Puka, 2011). An extensive study for the relationship between r s and τ can be found in (Fredricks and Nelsen, 2007).…”

Section: Correlation Coefficientmentioning

confidence: 99%

Secure rank correlation computation for IoT applications

Wong

2017

IJSTM

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The internet of things (IoT) is emerging as the next mega-trend technology across the business spectrum. Due to significant advances in sensor and RFID technologies, smart devices are now connected via the internet and able to communicate with each other without human intervention. Hence, the amount of data collected about individuals has increased dramatically. The ability of machines and sensor devices to collect, transmit and share data has raised privacy concerns in IoT applications. Recently, privacy concerns about data collected by IoT applications have received an increasing amount of attention. Therefore, a series of security mechanisms must be available for data privacy protection. In this paper, we propose two secure correlation computation protocols for IoT applications. We concentrate on the asymmetric setting where only one party receives the computation result. We incorporate homomorphic cryptosystem into our computation to support operations in an encrypted form. Two experimental results in this paper demonstrate the performance of both computation protocols.

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Kendall’s Tau

Cited by 43 publications

References 0 publications

Mapping livestock movements in Sahelian Africa

Mapping livestock movements in Sahelian Africa

Evaluating salient object detection in natural images with multiple objects having multi‐level saliency

Secure rank correlation computation for IoT applications

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