Testing the Validity of CV for Single-Plant Yield in the Absence of Competition as a Homeostasis Index

The coefficient of variation (CV) of yield may functionally be related to the mean. The expected exponential CV decline with increasing mean, i.e., the Taylor’s power law (TPL), is not always valid. Removal of this scale dependency allows for a scale-independent assessment of stability. The objective of this study was to interpret the validity of the homeostasis index (HI), i.e., the inverse CV value, suggested in breeding under nil competition as a selection criterion for progeny lines that oppose the acquired interplant variation. Data concerning the single-plant yield of various crops under a nil-competition regime were studies against the theoretical background of the above hypothesis. Simple correlations between logarithms of variances and respective means were used to assess the reliability of CV as a stability statistic in breeding trials under nil competition. A total of 8 of the 24 case analyses revealed a systematic variance dependence on the mean. The impact was more prevalent in experiments with extensive spatial heterogeneity and high CV scores. Conversion of variance to remove systematic dependence did not validate the CV~mean negative relationship. Because of variance dependence, caution is needed when interpreting the HI as a stability index. Misuse of the HI may entail the risk of bias, upgrading or downgrading a progeny line in its ability to withstand acquired dissimilarity between plants. Testing the validity of the variance seems necessary, and the calculation of HI on a converted variance may enhance the accuracy of identifying the most promising progeny lines

A Recommendation Specific Human Activity Recognition Dataset with Mobile Device’s Sensor Data

Part 5: Energy Efficiency and Artificial Intelligence (ΕΕΑΙ 2021) WorkshopInternational audienceHuman activity recognition is a challenging field that grabbed considerable research attention in the last decade. Two types of models can be used for such predictions, those which use visual data and those which use data from inertial sensors. To improve the classification algorithms in the sensor category, a new dataset has been created, targeting more realistic activities, during which the user may be more prompt to receive and act upon a recommendation. Contrary to previous similar datasets, which were collected with the device in the user’s pockets or strapped to their waist, the introduced dataset presents activities during which the user is looking on the screen, and thus most likely interacts with the device. The dataset from an initial sample of 31 participants was gathered using a mobile application that prompted users to do 10 different activities following specific guidelines. Finally, towards evaluating the resulting data, a brief classification benchmarking was performed with two other datasets (i.e., WISDM and Actitracker datasets) by employing a Convolutional Neural Network model. The results acquired demonstrate a promising performance of the model tested, as well as a high quality of the dataset created, which is available online on Zenodo