How Internet of Things responds to the COVID-19 pandemic
Bibliographic record
Abstract
The cornovirus disease (COVID-19) pandemic has had a severe impact on our daily lives. As a result, there has been an increasing demand for technological solutions to overcome such challenges. The Internet of Things (IoT) has recently emerged to improve many aspects of human’s day-to-day activities and routines. IoT makes it easier to follow the safety guidelines and precautions provided by the World Health Organization (WHO). Prior reports have shown that the world nowadays may need more IoT facilities than ever before. However, little is known about the reaction of the IoT community towards defeating the COVID-19 pandemic, technologies being used, solutions being provided, and how our societies perceive the IoT means available to them. In this paper, we conduct an empirical study to investigate the IoT response to the COVID-19 pandemic. In particular, we study the characteristics of the IoT solutions hosted on a large online IoT community (i.e., Hackster.io) throughout the year of 2020. The study: (a) explores the proportion, types, and nations of IoT solutions/engineers that contributed to defeating COVID-19, (b) characterizes the complexity of COVID-19 IoT solutions, and (c) identifies how IoT solutions are perceived by the surrounding community. Our results indicate that IoT engineers have been actively working towards providing solutions to help their societies, especially in the most affected nations. Our findings (i) provide insights into the aspects IoT practitioners need to pay more attention to when developing IoT solutions for COVID-19 and to (ii) outlines the common IoT solutions and technologies available to humans to deal with the current challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".