Omkar2118/datasets huggingface dataset:
Animal3D

Accurately estimating the 3D pose and shape is an essential step towards understanding animal behavior, and can potentially benefit many downstream applications, such as wildlife conservation. However, research in this area is held back by the lack of a comprehensive and diverse dataset with high-quality 3D pose and shape annotations. In this paper, we propose Animal3D, the first comprehensive dataset for mammal animal 3D pose and shape estimation. Animal3D consists of 3379 images collected from 40 mammal species, high-quality annotations of 26 keypoints, and importantly the pose and shape parameters of the SMAL model. All annotations were labeled and checked manually in a multi-stage process to ensure highest quality results. Based on the Animal3D dataset, we benchmark representative shape and pose estimation models at: (1) supervised learning from only the Animal3D data, (2) synthetic to real transfer from synthetically generated images, and (3) fine-tuning human pose and shape estimation models. Our experimental results demonstrate that predicting the 3D shape and pose of animals across species remains a very challenging task, despite significant advances in human pose estimation. Our results further demonstrate that synthetic pre-training is a viable strategy to boost the model performance. Overall, Animal3D opens new directions for facilitating future research in animal 3D pose and shape estimation, and is publicly available.

Introducing paper: Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape
BIPIA
Full name: Benchmark of Indirect Prompt Injection Attacks

Recent advancements in large language models (LLMs) have led to their adoption across various applications, notably in combining LLMs with external content to generate responses. These applications, however, are vulnerable to indirect prompt injection attacks, where malicious instructions embedded within external content compromise LLM's output, causing their responses to deviate from user expectations. Despite the discovery of this security issue, no comprehensive analysis of indirect prompt injection attacks on different LLMs is available due to the lack of a benchmark. Furthermore, no effective defense has been proposed.
We introduce the first benchmark of indirect prompt injection attack, BIPIA, to measure the robustness of various LLMs and defenses against indirect prompt injection attacks. We hope that our benchmark and defenses can inspire future work in this important area.

Introducing paper: Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models
LEARNING STYLE IDENTIFICATION
Full name: Learning Style Identification Using Semi-Supervised Self-Taught Labeling

The dataset was collected from two courses offered on the University of Jordan's E-learning Portal during the second semester of 2020, namely "Computer Skills for Humanities Students" (CSHS) and "Computer Skills for Medical Students" (CSMS). Over the sixteen-week duration of each course, students participated in various activities such as reading materials, video lectures, assignments, and quizzes. To preserve student privacy, the log activity of each student was anonymized. Data was aggregated from multiple sources, including the Moodle learning management system and the student information system, and consolidated into a single database.
The dataset contains information on the number of learners and events for each course, as well as their launch and end dates. CSHS had 1749 learners and 1,139,810 events from January 21, 2020 to May 20, 2020, while CSMS had 564 learners and 484,410 events during the same period. The dataset is based on the Filder and Silverman learning style model (FSLSM), which captures students' preferences regarding each dimension of the model. At the beginning of each course, students completed the Index of Learning Styles (ILS) questionnaire, a 44-item questionnaire that gauges learners' personal preferences for each dimension by assigning values ranging from -11 to +11 per dimension based on the responses to eleven questions per dimension. The ILS questionnaire allowed us to capture students' preferences regarding the FSLSM dimensions.

Introducing paper: Learning Style Identification Using Semi-Supervised Self-Taught Labeling
Radio-Freqency Ultrasound volume dataset for pre-clinical liver tumors

A total of 227 cross sectional images (20 x 54 mm with a resolution of 289 x 648 pixels) of hind-leg xenograft tumors from 29 mice were obtained with 1mm step-wise movement of the array mounted on a manual positioning device. The whole tumor volume was acquired using a diagnostic ultrasound system with a 10 MHz linear transducer and 50 MHz sampling.

The dataset images are in Analyze 7.5 file format consisting of two files: a header with information about the size and number of voxels in each dimension (filename extension .hdr), and the actual data in binary format (filename extension .img). The images in the dataset are formatted according to the following protocol: CASE NUMBER | FILE TYPE (LOC OR SEG) | FILE EXTENSION | where loc is the original image and seg is the associated segmentation mask. The volumetric data are categorised into progressive versus non-progressive cases in response to chemotherapy treatment.

The example_case folder contains an output of the 3D Multi-fractal Nakagami Feature descriptor (MNF) algorithm when applied to one of the liver tumour cases, see below. The group of slice images for a particular case where transformed to corresponding Nakagami shape and scale parametric images, and animated GIF images representing the fractal slice maps and a video of the corresponding fractal volume maps were estimated and included as well.

Introducing paper: Quantification of Ultrasonic Texture heterogeneity via Volumetric Stochastic Modeling for Tissue Characterization