VALUE is a Video-And-Language Understanding Evaluation benchmark to test models that are generalizable to diverse tasks, domains, and datasets. It is an assemblage of 11 VidL (video-and-language) datasets over 3 popular tasks: (i) text-to-video retrieval; (ii) video question answering; and (iii) video captioning. VALUE benchmark aims to cover a broad range of video genres, video lengths, data volumes, and task difficulty levels. Rather than focusing on single-channel videos with visual information only, VALUE promotes models that leverage information from both video frames and their associated subtitles, as well as models that share knowledge across multiple tasks. The datasets used for the VALUE benchmark are: TVQA, TVR, TVC, How2R, How2QA, VIOLIN, VLEP, YouCook2 (YC2C, YC2R), VATEX

CValues is a Chinese human values evaluation benchmark designed to assess the alignment of Chinese Large Language Models (LLMs) with human values. Let me provide you with more details: 1. Purpose and Context: - With the rapid evolution of large language models, there is a growing concern that they may pose risks or have negative social impacts. - CValues focuses on evaluating the alignment ability of Chinese LLMs in terms of both safety and responsibility criteria. - Previous work mainly assessed LLMs based on knowledge and reasoning abilities, but CValues specifically targets human values alignment, especially in a Chinese context. 2. Data Collection: - The benchmark involves manually collecting adversarial safety prompts across 10 scenarios and inducing responsibility prompts from 8 domains using input from professional experts. 3. Evaluation Methods: - Human Evaluation: Experts assess the alignment of Chinese LLMs with human values. - Automatic Evaluation: Multi-choice prompts are constructed for automatic assessment. 4. Findings: - Most Chinese LLMs perform well in terms of safety. - However, there is room for improvement in terms of responsibility. - Both automatic and human evaluations are crucial for assessing human values alignment. Source: Conversation with Bing, 3/18/2024 (1) [2307.09705] CValues: Measuring the Values of Chinese Large Language .... https://arxiv.org/abs/2307.09705. (2) VALUE - GitHub Pages. https://value-benchmark.github.io/. (3) Benchmarking 101: Definition, Types, Benefits and How to Use Them - Databox. https://databox.com/what-are-benchmarks. (4) Compare and Conquer: 12 Types of Benchmarking for Measuring ... - Databox. https://databox.com/benchmarking-types. (5) [2307.09705] CValues: Measuring the Values of Chinese Large Language .... https://ar5iv.labs.arxiv.org/html/2307.09705. (6) undefined. https://doi.org/10.48550/arXiv.2307.09705.

The dataset contains 3 million attribute-value annotations across 1257 unique categories created from 2.2 million cleaned Amazon product profiles. It is a large, multi-sourced, diverse dataset for product attribute extraction study.

We present a new large-scale human value dataset called ValueNet, which contains human attitudes on 21,374 text scenarios. The dataset is organized in ten dimensions that conform to the basic human value theory in intercultural research.

The dataset contains constructed multi-modal features (visual and textual), pseudo-labels (on heritage values and attributes), and graph structures (with temporal, social, and spatial links) constructed using User-Generated Content data collected from Flickr social media platform in three global cities containing UNESCO World Heritage property (Amsterdam, Suzhou, Venice). The motivation of data collection in this project is to provide datasets that could be both directly applicable for ML communities as test-bed, and theoretically informative for heritage and urban scholars to draw conclusions on for planning decision-making.

ABSTRACT Development of the Complex-Valued (CV) deep learning architectures has enabled us to exploit the amplitude and phase components of the CV Synthetic Aperture Radar (SAR) data. However, most of the available annotated SAR datasets provide only the amplitude information (Only detected SAR data) and disregard the phase information. The lack of high-quality and large-scale annotated CV-SAR datasets is a significant challenge for developing CV deep learning algorithms in remote sensing. In order to tackle this problem, a large-scale semantically annotated CV-SAR dataset is developed using the Single Look Complex (SLC) StripMap (SM) Sentinel-1 (S1) SAR data in two polarization channels (HH and HV) for Complex-Valued Deep Learning applications (S1SLC_CVDL). The S1SLC_CVDL dataset comprises 276,571 CV-SAR patches (100×100 pixel), derived from three scenes acquired over Chicago and Houston in the Uniate States, and Sao Paulo in Brazil in May 2021. These three scenes are selected to cover different landcovers including various vegetation covers, constructed areas and water bodies. The CV-SAR patches in this dataset are semantically annotated in 7 different classes, including, Agricultural fields (AG), Forest and Woodlands (FR), High Density Urban Areas (HD), High Rise Buildings (HR), Low Density Urban Areas (LD), Industrial Regions (IR), and Water Regions (WR). Refer to the cited articles for more information about the dataset and the selected S1 scenes. Overall, the S1SLC_CVDL dataset provides semantically annotated CV-SAR data which can serve as a valuable resource for researchers and practitioners in the field of CV deep architecture developments for remote sensing applications. Instructions: The S1SLC_CVDL dataset comprises 276,571 patches (100×100 pixel) of Single Look Complex (SLC) StripMap (SM) Sentinel-1 (S1) CV-SAR data, derived from three scenes acquired over Chicago and Houston in the Uniate States, and Sao Paulo in Brazil in May 2021. Refer to the cited articles for more information about the dataset and the selected S1 scenes. The S1SCL_CVDL.zip file, includes three subfolders (one for each S1 scene, Chicago, Houston, and Sao Paulo) containing the patches in two polarization channels (HH and HV) and the semantic labels of the corresponding patches. The data and label files are in .npy format and can be loaded into the python environment, using the “numpy.load(‘path to the file’)” function. The semantic labels are provided as the numeric format as following: Agricultural fields (AG) Forest and Woodlands (FR) High Density Urban Areas (HD) High Rise Buildings (HR) Low Density Urban Areas (LD) Industrial Regions (IR) Water Regions (WR) For example, if the ith element of the label file is “1”, it indicates that the ith element in the corresponding data file for HH and HV polarization channels is from Agricultural fields (AG) class. References Please cite the following articles if you find the dataset useful: R. M. Asiyabi, M. Datcu, A. Anghel, H. Nies, " Complex-Valued End-to-end Deep Network with Coherency Preservation for Complex-Valued SAR Data Reconstruction and Classification," in IEEE Transactions on Geoscience and Remote Sensing, 2023. R. M. Asiyabi and M. Datcu, "Earth Observation Semantic Data Mining: Latent Dirichlet Allocation-Based Approach," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 2607-2620, 2022, doi: 10.1109/JSTARS.2022.3159277. Funding Agency: European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Number: 860370

Description We propose a new database for information extraction from historical handwritten documents. The corpus includes 5,393 finding aids from six different series, dating from the 18th-20th centuries. Finding aids are handwritten documents that contain metadata describing older archives. They are stored in the National Archives of France and are used by archivists to identify and find archival documents. Each document is annotated at page-level, and contains seven fields to retrieve. The localization of each field is not available in such a way that this dataset encourages research on segmentation-free systems for information extraction. The dataset is available at https://zenodo.org/record/7868059 ## Details for each series and entity type | Series | Train | Validation | Test | Total (%) | | ------------------- | ----- | ---------- | ---- | --------: | | E series | 322 | 64 | 79 | 8.6 | | L series | 38 | 8 | 4 | 0.9 | | M series | 128 | 21 | 27 | 3.3 | | X1a series | 2209 | 491 | 469 | 58.8 | | Y series | 940 | 205 | 196 | 24.9 | | Douët s'Arcq series | 141 | 22 | 29 | 3.5 | | Total | 3778 | 811 | 804 | 100 | | Entities | Train | Validation | Test | Total (%) | | -------------- | ----- | ---------- | ----- | --------: | | date | 8406 | 1814 | 1799 | 10.4 | | title | 35531 | 7495 | 8173 | 44.5 | | serie | 3168 | 664 | 676 | 3.9 | | analysis | 25988 | 5130 | 5602 | 31.9 | | volume_number | 3913 | 808 | 813 | 4.8 | | article_number | 3181 | 665 | 678 | 3.9 | | arrangement | 644 | 122 | 153 | 0.8 | | Total | 80831 | 16698 | 17894 | 100 | ## Data encoding Transcriptions with entities are encoded in the labels.json JSON file. Special tokens are used to represent named entities. Please not that there are only opening NER tokens: each entity spans all words until the next entity starts. | Entities | Special token | Symbol unicode | | -------------- | ------------- | -------- | | date | ⓓ | \u24d3 | | title | ⓘ | \u24d8 | | serie | ⓢ | \u24e2 | | analysis | ⓒ | \u24d2 | | volume_number | ⓟ | \u24df | | article_number | ⓐ | \u24d0 | | arrangement | ⓥ | \u24e5 | # Cite us! The dataset is presented in details in the following article: bib @article{simara2023, author = {Solène Tarride and Mélodie Boillet and Jean-François Moufflet and Christopher Kermorvant}, title = {SIMARA: a database for key-value information extraction from full-page handwritten documents}, year = {2023}, journal={Proceedings of the 17th International Conference on Document Analysis and Recognition}, }

ValueConsistency is a dataset of both controversial and uncontroversial questions in English, Chinese, German, and Japanese for topics from the U.S., China, Germany, and Japan. It was generated via prompting by GPT-4 and validated manually. You can find details about how we made the dataset in the linked paper and in our code base. Curated by: Jared Moore, Tanvi Desphande, Diyi Yang Language(s) (NLP): English, Chinese (Mandarin), German, Japanese License: MIT

The datasets contains 1,420 human annotated product offers, systematically selected from the Web Data Commons Product Matching Corpus, featuring 24,582 annotated attribute-value pairs, making it a valuable resource for both product attribute-value extraction and product matching tasks. The normalized gold standard contains the standardized attribute value pairs as described below.

This dataset provides simulated flood inundation maps of Abu Dhabi's coast under 174 different shoreline protection scenarios. The maps were produced with a high-fidelity physics-based hydrodynamic simulator under a 0.5-meter sea level rise projection. The details of the hydrodynamic model are reported in [1]. The coastline was partitioned into 17 segments. The input protection scenarios were encoded as 17-dimensional binary vectors, with 1 indicating the placement of containments and 0 otherwise. The output inundation depth maps are named according to protection scenarios. Each map is a 1024x1024 matrix containing the estimated peak water level values (in meters) for selected 12066 locations along the coastline of Abu Dhabi. The mapping from gird indices to the original geographical coordinates of these locations is provided in a separate file (ad_grid_1024.npy). A Jupyter notebook with a sample Python code is included for visualizing the data entries (see Data_Visualization.ipynb). [1] Chow, Aaron C. H., and Jiayun Sun. 2022. "Combining Sea Level Rise Inundation Impacts, Tidal Flooding and Extreme Wind Events along the Abu Dhabi Coastline" Hydrology 9, no. 8: 143. https://doi.org/10.3390/hydrology9080143

This is a corpus of about 500 computer vision datasets, from which the authors sampled 114 dataset publications across different vision tasks and coded for themes through both structured and qualitative content analysis. This work most closely pairs with the following research question: How do dataset developers in CV and NLP research, describe and motivate the decisions that go into their creation?

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The dataset contains 3 million attribute-value annotations across 1257 unique categories created from 2.2 million cleaned Amazon product profiles. It is a large, multi-sourced, diverse dataset for product attribute extraction study.

Multilingual human VALUE(MVALUE) is a multilingual dataset covering 7 concepts of human values: morality, deontology, utilitarianism, fairness, truthfulness, toxicity and harmfulness, each concept subset of it includes positive and negative texts that represent the two opposing directions of the concept. We performed translation on collected human value datasets from English into 15 non-English languages using Google Translate. These languages belong to various language families, including Indo-European (Catalan, French, Indonesian, Portuguese, Spanish), NigerCongo (Chichewa, Swahili), Dravidian (Tamil, Telugu), Uralic (Finnish, Hungarian), Sino-Tibetan (Chinese), Japonic (Japanese), Koreanic (Korean) and Austro-Asiatic (Vietnamese). In detail, we collected three subsets of the ETHICS dataset (Hendrycks et al.,2021) for morality, deontology, and utilitarianism. Regarding fairness, truthfulness, toxicity, and harmfulness, we chose the StereoSet (Nadeem et al., 2021), TruthfulQA (Lin et al., 2022), REALTOXICITYPROMPTS (Gehman et al., 2020), AdvBench (Zou et al., 2023b) dataset, respectively. Some of these dataset are naturally composed of positive and negative text pairs, we reformatted the rest ones. Refer to Appendix A of our paper for detailed definitions, data splits, and examples of each human value of MVALUE dataset.

The Touché23-ValueEval Dataset is a collection of arguments used for identifying human values behind those arguments. It was created by collecting 9324 arguments from 6 diverse sources, including religious texts, political discussions, free-text arguments, newspaper editorials, and online democracy platforms. Each argument was annotated by 3 crowdworkers for 54 values. This dataset extends the Webis-ArgValues-22 and is designed to investigate approaches for the automated detection of human values behind arguments. Despite the classification difficulty increasing due to the label distribution, the larger dataset allows for training better models.

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The EQN framework is a micro-emotion annotation and detection system that realizes the automatic micro-emotion annotation of text with energy level scores for the first time. The text emotion datasets it annotates are no longer simple single-label or multi-label, but macro-emotions and micro-emotions with continuous values ​​of emotion intensity. The labeling of emotion datasets has changed from discrete to continuous. It plays an important role in the subtle research of emotions in fields such as emotional computing, human-computer alignment, humanoid robots, and psychology.