别名
发表于 2025-3-28 18:20:18
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侵略
发表于 2025-3-28 20:38:09
Estimating Difficulty Score of Visual Search in Images for Semi-supervised Object Detection,thers. However, this is quite challenging for computers as it is a subjective task which may be influenced by human emotional factors. Instead of focusing on how the models make reactions on datasets, our method has a capability of assigning scores to samples respectively within a dataset that estim
BROW
发表于 2025-3-28 22:55:14
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FRAUD
发表于 2025-3-29 04:27:53
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阻塞
发表于 2025-3-29 08:21:00
Finding Diachronic Objects of Drifting Descriptions by Similar Mentions, as the same over time and diversity descriptions that record actions on different objects. This research finds diachronic objects to extract a document subset of drift descriptions. We assumed that a diachronic object would be mentioned similarly and have different time-distribution appearances. Co
ARY
发表于 2025-3-29 15:15:21
A Max-Min Conflict Algorithm for the Stable Marriage Problem,ge problem. We solve the problem in terms of a constraint satisfaction problem, i.e. find a complete assignment for men in which every man is assigned to a woman so that the assignment does not contain any blocking pairs. To do this, we apply a local search method in which a max-conflict heuristic i
老巫婆
发表于 2025-3-29 15:43:46
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Memorial
发表于 2025-3-29 23:34:32
Marine Vertebrate Predator Detection and Recognition in Underwater Videos by Region Convolutional Nare the results of these methods on real data and discuss their strengths and weaknesses. We build a dataset using footage captured from representative environment of the wild and devise a data model with three classes (seal, dolphin, background). Following this, we train R-CNN, Fast R-CNN and Faste
针叶树
发表于 2025-3-30 00:21:20
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语言学
发表于 2025-3-30 04:02:28
,Adaptive Database’s Performance Tuning Based on Reinforcement Learning,. With the hundreds of parameters to be considered under the diverse application configurations, business logic and software technology, getting a true global optimum setting is difficult for a DB administrator. We propose a novel approach based on Reinforcement Learning to tune a DB adaptively with