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Hierarchical federated learning (HFL) is a promising distributed deep learning model training paradigm, but it has crucial security concerns arising from adversarial attacks. This research investigates and assesses the security of HFL using a novel methodology by focusing on its resilience against adversarial attacks inference-time and training-time. Through a series of extensive experiments across diverse datasets and attack scenarios, we uncover that HFL demonstrates robustness against untargeted training-time attacks due to its hierarchical structure. However, targeted attacks, particularly backdoor attacks, exploit this architecture, especially when malicious clients are positioned in the overlapping coverage areas of edge servers. Consequently, HFL shows a dual nature in its resilience, showcasing its capability to recover from attacks thanks to its hierarchical aggregation that strengthens its suitability for adversarial training, thereby reinforcing its resistance against inference-time attacks. These insights underscore the necessity for balanced security strategies in HFL systems, leveraging their inherent strengths while effectively mitigating vulnerabilities.

Sound event detection and sound event localization requires different features from audio input signals. While sound event detection mainly relies on time-frequency patterns to distinguish different event classes, sound event localization uses magnitude or phase differences between microphones to estimate source directions. Therefore, we propose a two-step system to do sound event localization and detection. In the first step, we detect the sound events and estimate the directions-of-arrival separately. In the second step, we combine the results of the event detector and direction-of-arrival estimator together. The obtained results show a significant improvement over the baseline solution for sound event localization and detection in DCASE 2019 task 3 challenge. Using the evaluation dataset, the proposed system achieved an F1 score of 93.4% for sound event detection and an error of 5.4 degrees for direction-of-arrival estimation, while the winning solution achieved an F1 score of 94.7% and an angle error of 3.7 degrees respectively., Comment: 5 pages

Mn$_3$GaC undergoes a ferromagnetic to antiferromagnetic, volume discontinuous cubic-cubic phase transition as a function of temperature, pressure and magnetic field. Through a series of temperature dependent x-ray absorption fine structure spectroscopy experiments at the Mn K and Ga K edge, it is shown that the first order magnetic transformation in Mn$_3$GaC is entirely due to distortions in Mn sub-lattice and with a very little role for Mn-C interactions. The distortion in Mn sub-lattice results in long and short Mn-Mn bonds with the longer Mn-Mn bonds favoring ferromagnetic interactions and the shorter Mn-Mn bonds favoring antiferromagnetic interactions. At the first order transition, the shorter Mn-Mn bonds exhibit an abrupt decrease in their length resulting in an antiferromagnetic ground state and a strained lattice., Comment: Accepted in J. Appl. Phys. Please contact authors for supplementary information

This work proposes a quantitative metric to analyze potential reusability of a BPEL (Business Process Execution Language) Process. The approach is based on Description and Logic Mismatch Probability of a BPEL Process that will be reused within potential contexts. The mismatch probabilities have been consolidated to a metric formula for quantifying the probability of potential reuse of BPEL processes. An initial empirical evaluation suggests that the proposed metric properly predict potential reusability of BPEL processes. According to the experiment, there exists a significant statistical correlation between results of the metric and the experts judgements. This indicates a predictive dependency between the proposed metric and potential reusability of BPEL processes as a measuring stick for this phenomena. If future studies ascertain these findings by replicating this experiment, the practical implications of such a metric are early detection of the design flaws and aiding architects to judge various design alternatives., Comment: This the extended version of the conference paper that Published in: WETSoM '10 Proceedings of the 2010 ICSE Workshop on Emerging Trends in Software Metrics Pages 67-74, ACM New York, NY, USA \c{opyright}2010