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Mass gatherings (such as Hajj/Umrah), owing to their immensity, often present a variety of difficulties to the attendees. To have a comprehensive understanding of the difficulties as well as their potential remedies, surveying a good number of attendees is unavoidable and this can be facilitated through engaging data collectors. A crucial part here is identifying the integrity of the data collectors, which is yet to be explored in the literature to the best of our knowledge. To address this gap, in this study, we first perform a mass-scale data collection over Hajj/Umrah pilgrims through online (n=236) and in-person (n=752) surveys, where we cover a substantial part (n=712) through paid data collectors (n=53). We critically investigate data collection activities of the data collectors through focused group discussions involving expert reviewers. We explore two computing techniques to unveil the integrity of the data collectors from two different perspectives. Our study finds out influential (religious and socio-geographical) aspects that impact the data collection process. Besides, we find that the collaborative participation of expert reviewers is obligatory to scrutinize the data collectors' integrity. Additionally, our explored computing techniques, namely conflict analysis and learning-based analysis, can identify up to 74% and 99% of the unreliable data collectors respectively. We observe that, although these computing-based filtering can indicate the integrity up to a certain level, human-in-the-loop is unavoidable for concluding on the integrity. To the best of our knowledge, this is the first study of its kind (i.e., integrity analysis of the data collectors) in the literature.

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Using low-cost piezoelectric sensors to sense structural vibration exhibits a great potential in augmenting structural engineering, which is yet to be explored in the literature to the best of our knowledge. Examples of such unexplored augmentation include classifying diverse structures (such as building, flyover, foot overbridge, etc.), and relating extents of vibration generated at different heights of a structure with the associated heights. Accordingly, to explore these aspects, we develop a low-cost piezoelectric sensor based vibration sensing system aiming to remotely collect real vibration data from diversified civil structures. We dig into our collected sensed data to classify five different types of structures through rigorous statistical and machine learning based analyses. Our analyses achieve a classification accuracy of up to 97% with an F1 score of 0.97. Nonetheless, in the rarely-explored time domain, our analyses reveal a novel modality of relationship between extents of vibration generated at different heights of a structure and the associated heights, which was explored in the frequency domain earlier in the literature with expensive sensors.

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