Omkar2118/datasets huggingface dataset:
ACOS
Full name: Aspect Category Opinion Sentiment

Most of the aspect based sentiment analysis research aims at identifying the sentiment polarities toward some explicit aspect terms while ignores implicit aspects in text. To capture both explicit and implicit aspects, we focus on aspect-category based sentiment analysis, which involves joint aspect category detection and category-oriented sentiment classification. However, currently only a few simple studies have focused on this problem. The shortcomings in the way they defined the task make their approaches difficult to effectively learn the inner-relations between categories and the inter-relations between categories and sentiments. In this work, we re-formalize the task as a category-sentiment hierarchy prediction problem, which contains a hierarchy output structure to first identify multiple aspect categories in a piece of text, and then predict the sentiment for each of the identified categories. Specifically, we propose a Hierarchical Graph Convolutional Network (Hier-GCN), where a lower-level GCN is to model the inner-relations among multiple categories, and the higher-level GCN is to capture the inter-relations between aspect categories and sentiments. Extensive evaluations demonstrate that our hierarchy output structure is superior over existing ones, and the Hier-GCN model can consistently achieve the best results on four benchmarks.

Introducing paper: Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network
ASQP
Full name: Aspect Sentiment Quad Prediction

Aspect-based sentiment analysis (ABSA) typically focuses on extracting aspects and predicting their sentiments on individual sentences such as customer reviews. Recently, another kind of opinion sharing platform, namely question answering (QA) forum, has received increasing popularity, which accumulates a large number of user opinions towards various aspects. This motivates us to investigate the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair. Unlike review sentences, a QA pair is composed of two parallel sentences, which requires interaction modeling to align the aspect mentioned in the question and the associated opinion clues in the answer. To this end, we propose a model with a specific design of cross-sentence aspect-opinion interaction modeling to address this task. The proposed method is evaluated on three real-world datasets and the results show that our model outperforms several strong baselines adopted from related state-of-the-art models.

Introducing paper: Aspect-based Sentiment Analysis in Question Answering Forums
ASTE
Full name: Aspect Sentiment Triplet Extraction

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from “Waiters are very friendly and the pasta is simply average” could be (‘Waiters’, positive, ‘friendly’). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.

Introducing paper: Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis
TASD
Full name: Target Aspect Sentiment Detection

Aspect-based sentiment analysis (ABSA) aims to detect the targets (which are composed by continuous words), aspects and sentiment polarities in text. Published datasets from SemEval-2015 and SemEval-2016 reveal that a sentiment polarity depends on both the target and the aspect. However, most of the existing methods consider predicting sentiment polarities from either targets or aspects but not from both, thus they easily make wrong predictions on sentiment polarities. In particular, where the target is implicit, i.e., it does not appear in the given text, the methods predicting sentiment polarities from targets do not work. To tackle these limitations in ABSA, this paper proposes a novel method for target-aspect-sentiment joint detection. It relies on a pre-trained language model and can capture the dependence on both targets and aspects for sentiment prediction. Experimental results on the SemEval-2015 and SemEval-2016 restaurant datasets show that the proposed method achieves a high performance in detecting target-aspect-sentiment triples even for the implicit target cases; moreover, it even outperforms the state-of-the-art methods for those subtasks of target-aspect-sentiment detection that they are competent to.
HR-Avenue

The human-Related version of the CUHK Avenue dataset, first presented by Morais et al. in the paper "Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos".

Introducing paper: Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos
Watkins Marine Mammal Sounds
Full name: Watkins Marine Mammal Sound Database

One of the founding fathers of marine mammal bioacoustics, William Watkins, carried out pioneering work with William Schevill at the Woods Hole Oceanographic Institution for more than four decades, laying the groundwork for our field today. One of the lasting achievements of his career was the Watkins Marine Mammal Sound Database, a resource that contains approximately 2000 unique recordings of more than 60 species of marine mammals (Table 1). Recordings were made by Watkins and Schevill as well as many others, including G. C. Ray, D. Wartzok, D. and M. Caldwell, K. Norris, and T. Poulter. Most of these have been digitized, along with approximately 15,000 annotated digital sound clips.

The Watkins database has enormous historical and scientific value. The recordings provide sounds professionally identified as produced by particular marine mammal species in defined geographic regions (Figure 1) during specific seasons, which can be used as reference datasets for marine mammal detections from the growing amounts of passive acoustic monitoring (PAM) data that are being collected worldwide. In addition, the archive contains recordings that span seven decades, from the 1940's to the 2000's, and includes the very first recordings of 51 species of marine mammals. These data provide a rich resource to efforts aimed at examining long-term changes in vocal production that may be related to changes in ambient noise levels, as well as serve as a voucher collection for many species. We have made this resource fully accessible online, as was Watkins' goal (see crediting information below). The final product enables investigators, educators, students, and the public worldwide to freely and easily access acoustic samples from identified species of marine mammals, and place these samples in a geographic and temporal context. The physical collection has been donated to the New Bedford Whaling Museum.
Speech Accent Archive
Full name: The Speech Accent Archive

The speech accent archive uniformly presents a large set of speech samples from a variety of language backgrounds. Native and non-native speakers of English read the same paragraph and are carefully transcribed. The archive is used by people who wish to compare and analyze the accents of different English speakers.
HR-UBnormal

The Human Related version of UBnormal ("UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection," Acsintoae et al.) was introduced by Flaborea et al. in the paper "Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection".

Introducing paper: Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
NaSGEC

**NaSGEC** is a new dataset to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains. Previous CGEC research primarily focuses on correcting texts from a single domain, especially learner essays.

Source: [NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts](https://arxiv.org/pdf/2305.16023v1.pdf)

Image Source: [NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts](https://arxiv.org/pdf/2305.16023v1.pdf)

Introducing paper: NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts
CN-Celeb-AV

**CN-Celeb-AV** is a multi-genre AVPR dataset collected 'in the wild'. This dataset contains more than 420k video segments from 1,136 persons from public media.

Source: [CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition](https://arxiv.org/pdf/2305.16049v1.pdf)

Image Source: [CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition](https://arxiv.org/pdf/2305.16049v1.pdf)

Introducing paper: CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition
FishEye8K
Full name: FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. The dataset will be available on the GitHub (https://github.com/MoyoG/FishEye8K) with PASCAL VOC, MS COCO, and YOLO annotation formats.

Introducing paper: FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection
SHD
Full name: Spiking Heidelberg Digits

The Spiking Heidelberg Digits (SHD) dataset is an audio-based classification dataset of 1k spoken digits ranging from __zero__ to __nine__ in the English and German languages. The audio waveforms have been converted into spike trains using an artificial model of the inner ear and parts of the ascending auditory pathway. The SHD dataset has 8,156 training and 2,264 test samples. A full description of the dataset and how it was created can be found in the paper below. Please cite this paper if you make use of the dataset.

Cramer, B.; Stradmann, Y.; Schemmel, J.; and Zenke, F. "The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks". IEEE Transactions on Neural Networks and Learning Systems 33, 2744–2757, 2022.

Introducing paper: The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks
MADDPG AND P2P-VFRL FOR MINIMIZING AOI IN NTN NETWORK UNDER CSI UNCERTAINTY

MADDPG AND P2P-VFRL FOR MINIMIZING AOI IN NTN NETWORK UNDER CSI UNCERTAINTY