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Communication is arguably the most important way to enable cooperation among multiple robots. In numerous such settings, robots exchange local sensor measurements to form a global perception of the environment. One example of this setting is adaptive multi-robot informative path planning, where robots’ local measurements are “fused” using probabilistic techniques (e.g., Gaussian process models) for more accurate prediction of the underlying ambient phenomena. In an adversarial setting, in which we assume a malicious entity–-the adversary-–can modify data exchanged during inter-robot communications, these cooperating robots become vulnerable to data integrity attacks. Such attacks on a multi-robot informative path planning system may, for example, replace the original sensor measurements with fake measurements to negatively affect achievable prediction accuracy. In this paper, we study how such an adversary may design data integrity attacks using a Generative Adversarial Network (GAN). Results show the GAN-based techniques learning spatial patterns in training data to produce fake measurements that are relatively undetectable yet significantly degrade prediction accuracy. 

This study aims to identify the most significant features in physiological signals representing a biphasic pattern in the menstrual cycle using circular statistics which is an appropriate analytic method for the interpretation of data with a periodic nature. The results can be used empirically to determine menstrual phases. A non-uniform pattern was observed in ovulating subjects, with a significant periodicity (p $<$0.05) in mean temperature, heart rate (HR), Inter-beat Interval (IBI), mean tonic component of Electrodermal Activity (EDA), and signal magnitude area (SMA) of the EDA phasic component in the frequency domain. In contrast, non-ovulating cycles displayed a more uniform distribution (p $>$0.05). There was a significant difference between ovulating and non-ovulating cycles (p $<$0.05) in temperature, IBI, and EDA but not in mean HR. Selected features were used in training an Autoregressive Integrated Moving Average (ARIMA) model, using data from at least one cycle of a subject, to predict the behavior of the signal in the last cycle. By iteratively retraining the algorithm on a per-day basis, the mean temperature, HR, IBI and EDA tonic values of the next day were predicted with root mean square error (RMSE) of 0.13 ± 0.07 (C°), 1.31 ± 0.34 (bpm), 0.016 ± 0.005 (s) and 0.17 ± 0.17 (μS), respectively.