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This dataset provides labeled humor detection from product question answering systems. The dataset contains 3 csv files: Humorous.csv containing the humorous product questions, Non-humorous-unbiased.csv containing the non-humorous prodcut questions from the same products as the humorous one, and, Non-humorous-biased.csv containing the non-humorous prodcut questions from randomly selected products. Each file has the question, product description, image url to the product, and a label if the question is humorous or not.

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A framework integrating the Budyko model has been developed to distinguish between rainfed and irrigated cropland areas across Africa. This expands on remote sensing land cover products available for agricultural water studies in Africa and thereby helps address the need for deeper insights into cropland patterns. Validation against an independent dataset revealed an overall accuracy of 73% with high precision and specificity scores. These results validate the framework’s effectiveness in identifying irrigated areas while minimizing errors in misclassifying rainfed areas as irrigated.

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Largest Visual Anomaly detection dataset containing objects from 12 classes in 3 domains across 10,821(9,621 normal and 1,200 anomaly) images. Both image and pixel level annotations are provided.

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This dataset provides a set of non-shoppable items, which are items that can't be purchased via a virtual assistant (love, vampires, etc). In addition, for each non-shoppable item, the dataset contains humorous responses generated by two different large language models, and a template-based generation solution that uses a commonsense knowledge graph called ConceptNet. Finally, each row contains a score provided by human annotators that judge how funny each response is. The columns provided for each datapoint are as follows: question- purchase request of the non-shoppable item answer- a generated response model- the name of the model or solution that generated the response nonshoppableitem- the name of the non shoppable item relation- commonsense relation that was used to generate the response in the template-based approach promptortemplate- the prompt that was used to generate the responses score- the average response score of the human evaluation

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The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Approximate Monte Carlo (AMC) method seed Privacy Protected Microdata File (PPMF0) and PPMF replicates (PPMF1, PPMF2, ..., PPMF25) are a set of microdata files intended for use in estimating the magnitude of error(s) introduced by the 2020 Decennial Census Disclosure Avoidance System (DAS) into the Redistricting and DHC products. The PPMF0 was created by executing the 2020 DAS TopDown Algorithm (TDA) using the confidential 2010 Census Edited File (CEF) as the initial input; the replicates were then created by executing the 2020 DAS TDA repeatedly with the PPMF0 as its initial input. Inspired by analogy to the use of bootstrap methods in non-private contexts, U.S. Census Bureau (USCB) researchers explored whether simple calculations based on comparing each PPMFi to the PPMF0 could be used to reliably estimate the scale of errors introduced by the 2020 DAS, and generally found this approach worked[...]

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The IMOS Ship of Opportunity Underway CO2 Measurements group is a research and data collection project working within the IMOS Ship of Opportunity Multi-Disciplinary Underway Network sub-facility. The CO2 group sample critical regions of the Southern Ocean and the Australian shelf waters have a major impact on CO2 uptake by the ocean. These are regions where biogeochemical cycling is predicted to be particularly sensitive to a changing climate. The pCO2 Underway System measures the fugacity of carbon dioxide (fCO2) along with other variables such as sea surface salinity (SSS) and sea surface temperature (SST) using an automated system. The data represented by this record are presented in delayed mode. The RV Aurora Australis provides data on surface ocean CO2 for the Southern Ocean, while the Southern Surveyor and its replacement in 2014, RV Investigator, covers shelf and offshore waters around Australia from the tropics to the sea-ice edge. The RV L'Astrolabe is also used to c[...]

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A large scale multimodal dataset for Autonomous Trucking. Sensor data was recorded with a heavy truck from MAN equipped with 6 lidars, 6 radars, 4 cameras and a high-precision GNSS. MAN TruckScenes allows the research community to come into contact with truck-specific challenges, such as trailer occlusions, novel sensor perspectives, and terminal environments for the first time. It comprises more than 740 scenes of 20s each within a multitude of different environmental conditions. Bounding boxes are available for 27 object classes, 15 attributes, and a range of more than 230m. The scenes are tagged according to 34 distinct scene tags, and all objects are tracked throughout the scene to promote a wide range of applications.

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Cryo-electron tomography (cryoET) is a powerful technique for visualizing 3D structures of cellular macromolecules at near atomic resolution in their native environment. Observing the inner workings of cells in context enables better understanding about the function of healthy cells and the changes associated with disease. However, the analysis of cryoET data remains a significant bottleneck, particularly the annotation of macromolecules within a set of tomograms, which often requires a laborious and time-consuming process of manual labelling that can take months to complete. Given the current success of machine learning (ML) methods for image analysis, it seems likely that ML will have a significant impact on resolving this bottleneck. The scientific community has expressed the need to encourage further ML algorithm development by providing large training sets of annotated cryoET data in standardized formats. In response to this, we (the Chan Zuckerberg Institute for Advanced Bio[...]

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This dataset provides extra annotations on top of the publicly released Topical-Chat dataset(https://github.com/alexa/Topical-Chat) which will help in reproducing the results in our paper "Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems" (https://arxiv.org/abs/2005.12529?context=cs.CL). The dataset contains 5 files: train.json, validfreq.json, validrare.json, testfreq.json and testrare.json. Each of these files will have additional annotations on top of the original Topical-Chat dataset. These specific annotations are: dialogue act annotations and knowledge sentence annotations. The annotations were computed automatically using off the shelf models which are mentioned in the README.txt

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Sentence classification datatasets with ASR Errors.