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Predict Students' Dropout and Academic Success
A dataset created from a higher education institution (acquired from several disjoint databases) related to students enrolled in different undergraduate degrees, such as agronomy, design, education, nursing, journalism, management, social service, and technologies. The dataset includes information known at the time of student enrollment (academic path, demographics, and social-economic factors) and the students' academic performance at the end of the first and second semesters. The data is used to build classification models to predict students' dropout and academic sucess. The problem is formulated as a three category classification task, in which there is a strong imbalance towards one of the classes.
Product Classification and Clustering
This dataset was collected from PriceRunner, a popular product comparison platform. It includes 35311 product offers from 10 categories, provided by 306 different merchants. This dataset offers an ideal ground for evaluating classification, clustering, and entity matching algorithms. Although it contains product-related data, it can still be applied to any problem involving text/short-text mining.
Multivariate Gait Data
Bilateral (left, right) joint angle (ankle, knee, hip) times series data collected from 10 healthy subjects under 3 walking conditions (unbraced, knee braced, ankle braced). For each condition, each subject’s data consists of 10 consecutive gait cycles.
RealWaste
An image classification dataset of waste items across 9 major material types, collected within an authentic landfill environment.
Recipe Reviews and User Feedback
The "Recipe Reviews and User Feedback Dataset" is a comprehensive repository of data encompassing various aspects of recipe reviews and user interactions. It includes essential information such as the recipe name, its ranking on the top 100 recipes list, a unique recipe code, and user details like user ID, user name, and an internal user reputation score. Each review comment is uniquely identified with a comment ID and comes with additional attributes, including the creation timestamp, reply count, and the number of up-votes and down-votes received. Users' sentiment towards recipes is quantified on a 1 to 5 star rating scale, with a score of 0 denoting an absence of rating. This dataset is a valuable resource for researchers and data scientists, facilitating endeavors in sentiment analysis, user behavior analysis, recipe recommendation systems, and more. It offers a window into the dynamics of recipe reviews and user feedback within the culinary website domain.
Bengali Hate Speech Detection Dataset
The dataset can be used for hate speech detection in Bengali social media texts. The dataset is categorized into political, personal, geopolitical, religious, and gender abusive hates that are either directed or generalized towards a specific person, entity, or group. The data and lexicons contain content that is racist, sexist, homophobic, and offensive in many different ways. The dataset is collected and subsequently annotated only for research-related purposes. Besides, authors don't take any liability if some statements contain very offensive and hateful statements that are either directed towards a specific person or entity or generalized towards a group. Therefore, please use it at your risk.
Sundanese Twitter Dataset
This dataset contains tweet of the second-largest local language in Indonesia and is used for emotion classification.
Inflation Research Abstracts Classification
This data set contains scientific papers abstracts from economics inflation. The task is to classify them according to their machine learning methodologies inclusion.
HLS-CMDS: Heart and Lung Sounds Dataset Recorded from a Clinical Manikin using Digital Stethoscope
This dataset contains 535 recordings of heart and lung sounds captured using a digital stethoscope from a clinical manikin, including both individual and mixed recordings of heart and lung sounds; 50 heart sounds, 50 lung sounds, and 145 mixed sounds. For each mixed sound, the corresponding source heart sound (145 recordings) and source lung sound (145 recordings) were also recorded. It includes recordings from different anatomical chest locations, with normal and abnormal sounds. Each recording has been filtered to highlight specific sound types, making it valuable for artificial intelligence (AI) research and applications.
COREVQA
Recently, many benchmarks and datasets have been developed to evaluate Vision-Language Models (VLMs) using visual question answering (VQA) pairs, and models have shown significant accuracy improvements. However, these benchmarks rarely test the model's ability to accurately complete visual entailment, for instance, accepting or refuting a hypothesis based on the image. To address this, we propose COREVQA (Crowd Observations and Reasoning Entailment), a benchmark of 5608 image and synthetically generated true/false statement pairs, with images derived from the CrowdHuman dataset, to provoke visual entailment reasoning on challenging crowded images. Our results show that even the top-performing VLMs achieve accuracy below 80%, with other models performing substantially worse (39.98%-69.95%). This significant performance gap reveals key limitations in VLMs’ ability to reason over certain types of image–question pairs in crowded scenes.
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