We present Reusable Motion prior (ReMP), an effective motion prior that can accurately track the temporal evolution of motion in various downstream tasks. Inspired by the success of foundation models, we argue that a ...
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Network security is a crucial component of Information Technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of ne...
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Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers' opinions on using such platform. Sen...
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Malware had been a problem for quite some times since it spreads easily and can cause various problems. Currently, malware is also one of the big threats for internet users. With a huge number of internet users today,...
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In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained...
With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performan...
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Counterfactuals, or modified inputs that lead to a different outcome, are an important tool for understanding the logic used by machine learning classifiers and how to change an undesirable classification. Even if a c...
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Counterfactuals, or modified inputs that lead to a different outcome, are an important tool for understanding the logic used by machine learning classifiers and how to change an undesirable classification. Even if a counterfactual changes a classifier's decision, however, it may not affect the true underlying class probabilities, i.e. the counterfactual may act like an adversarial attack and "fool" the classifier. We propose a new framework for creating modified inputs that change the true underlying probabilities in a beneficial way which we call Trustworthy Actionable Perturbations (TAP). This includes a novel verification procedure to ensure that TAP change the true class probabilities instead of acting adversarially. Our framework also includes new cost, reward, and goal definitions that are better suited to effectuating change in the real world. We present PAC-learnability results for our verification procedure and theoretically analyze our new method for measuring reward. We also develop a methodology for creating TAP and compare our results to those achieved by previous counterfactual methods. Copyright 2024 by the author(s)
Gastroenteritis is a common gastrointestinal disorder with varying degrees of severity, including cases without dehydration, mild dehydration, moderate dehydration, and severe dehydration. This research focuses on the...
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In gene expression analysis, understanding a biological event that is observed at some time instance often requires capturing genes whose expression levels modulate before and after the event. Such genes are expected ...
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In the modern era, an increasing number of diseases are emerging because of human lifestyle choices and bacterial transmission. Gastritis, characterized by inflammation in the stomach lining leading to frequent abdomi...
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