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We consider the issue of strategic behaviour in various peer-assessment tasks, including peer grading of exams or homeworks and peer review in hiring or promotions. When a peer-assessment task is competitive (e.g., when students are graded on a curve), agents may be incentivized to misreport evaluations in order to improve their own final standing. Our focus is on designing methods for detection of such manipulations. Specifically, we consider a setting in which agents evaluate a subset of their peers and output rankings that are later aggregated to form a final ordering. In this paper, we investigate a statistical framework for this problem and design a principled test for detecting strategic behaviour. We prove that our test has strong false alarm guarantees and evaluate its detection ability in practical settings. For this, we design and execute an experiment that elicits strategic behaviour from subjects and release a dataset of patterns of strategic behaviour that may be of independent interest. We then use the collected data to conduct a series of real and semi-synthetic evaluations that demonstrate a strong detection power of our test.
With more than 500 million daily tweets from over 330 million active users, Twitter constantly attracts malicious users aiming to carry out phishing and malware-related attacks against its user base. It therefore becomes of paramount importance to as
Direct scattering transform of nonlinear wave fields with solitons may lead to anomalous numerical errors of soliton phase and position parameters. With the focusing one-dimensional nonlinear Schrodinger equation serving as a model, we investigate th
We report that Ly$alpha$-emitting galaxies (LAEs) may not faithfully trace the cosmic web of neutral hydrogen (HI), but their distribution is likely biased depending on the viewing direction. We calculate the cross-correlation (CCF) between galaxies
Adversarial evaluation stress tests a models understanding of natural language. While past approaches expose superficial patterns, the resulting adversarial examples are limited in complexity and diversity. We propose human-in-the-loop adversarial ge
Recently, the object detection based on deep learning has proven to be vulnerable to adversarial patch attacks. The attackers holding a specially crafted patch can hide themselves from the state-of-the-art person detectors, e.g., YOLO, even in the ph