SmartEye: Real-time and efficient cloud image sharing for disaster environments
Bibliographic record
Abstract
Rapid disaster relief is important to save human lives and reduce property loss. With the wide use of smartphones and their ubiquitous easy access to the Internet, sharing and uploading images to the cloud via smartphones offer a nontrivial opportunity to provide information of disaster zones. However, due to limited available bandwidth and energy, smartphone-based crowdsourcing fails to support the real-time data analytics. The key to efficiently and timely share and analyze the images is to determine the value/worth of the images based on their significance and redundancy, and only upload those valuable and unique images. In this paper, we propose a near-realtime and cost-efficient scheme, called SmartEye, in the cloud-assisted disaster environment. The idea behind SmartEye is to implement QoS-aware in-network deduplication over DiffServ in the software-defined networks (SDN). Due to the ease of use, simplicity and scalability, DiffServ supports the in-network deduplication to meet the needs of differentiated QoS. SmartEye aggregates the flows with similar features via a semantic hashing, and provides communication services for the aggregated, not a single, flow. To achieve these goals, we leverage two main optimization schemes, including semantic hashing and space-efficient filters. Efficient image sharing is helpful to disaster detection and scene recognition. To demonstrate the feasibility of SmartEye, we conduct two real-world case studies in which the loss in Typhoon Haiyan (2013) and Hurricane Sandy (2012) can be identified in a timely fashion by analyzing massive data consisting of more than 22 million images using our SmartEye system. Extensive experimental results illustrate that SmartEye is efficient and effective to achieve real-time analytics in disasters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".