In-Route Task Selection in Spatial Crowdsourcing
Bibliographic record
Abstract
Consider a city’s road network and a worker who is traveling on a given path from a starting point s to a destination d (e.g., from school or work to home) in said network. Consider further that there is a set of tasks in the network available to be performed, where each such task takes a certain amount of time to be completed and yields a positive reward if completed, and, finally, that the worker is willing to deviate from his/her path as long as the travel time to the selected tasks plus the time taken for completing them does not exceed a given time budget. We call this problem the In-Route Task Selection (IRTS) problem and consider two variants thereof. In the first one, named IRTS-SP, we assume that the worker only specifies s and d and he/she wants to consider alternative paths that deviate (cost-wise) as little as possible from the cost of the shortest path connecting s and d . In the second variant, named IRTS-PP, we assume that the worker has a preferred path from s to d and wants to travel along that one path for as long as possible. The latter is practically relevant in cases where the worker has a path other than the shortest one that is more desirable for non-objective reasons, e.g., availability of public transit, bicycle-friendliness or perceived safety. Common to both variants though, we assume that the worker wants to maximize the rewards collected by completing tasks. Clearly, there are now two conflicting criteria for the worker to contemplate when considering which tasks to perform: minimizing path deviation and maximizing collected reward. In this context, we investigate both IRTS variants using the skyline paradigm in order to obtain the set of non-dominated solutions w.r.t. the tradeoffs between earned rewards and deviation from either the cost of the shortest path, in the case of IRTS-SP, or the actual preferred path, in the case of IRTS-PP. Returning the skyline set of solutions to workers is of practical interest as it empowers them, e.g., it allows them to decide, at query time, which tasks suit them better. We propose exact and heuristic approaches in order to solve both variants of the IRTS problem. Our experiments, using real city-scale datasets, show that while the exact approaches serve as benchmarks, they do not scale due to the NP-hardness of the problems. The overall best heuristic approach, on the other hand, can solve relatively large instances of the IRTS problems within practical query processing time, e.g., at par with less effective greedy heuristics, while still producing very good approximate skyline sets, e.g., often yielding less than 10% relative error w.r.t. the exact solution.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".