Machine Learning
Artificial Intelligence (AI) has increasingly influenced modern society, recently in particular through significant advancements in Large Language Models (LLMs). However, high computational and storage demands of LLMs still limit their deployment in resource-constrained environments. Knowledge distillation addresses this challenge by training a smaller language model (student) from a larger one (teacher). Previous research has introduced several distillation methods for both generating training data and training the student model.
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Shape completion remains a fundamental challenge in computer vision and image processing, particularly for tasks involving hand-drawn sketches and occluded objects. Traditional deep learning methods such as Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) often suffer from high computational costs and poor generalization on sparse, abstract structures.
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27 Views
- This dataset comprises license plate information captured by Automatic Number Plate Recognition (ANPR) devices as vehicles either entered or left the smart village area of Alpujarra, which encompasses the towns of Pampaneira, Capileira, and Bubión.
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75 Views
The growing adoption of declarative software specification languages, coupled with their inherent difficulty in debugging, has underscored the need for effective and automated repair techniques applicable to such languages. Researchers have recently explored various methods to automatically repair declarative software specifications, such as template-based repair, feedback-driven iterative repair, and bounded exhaustive approaches. The latest developments in Large Language Models (LLMs) provide new opportunities for the automatic repair of declarative specifications.
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227 Views
The TUROS-TS encompasses 5,357 Google Street View images with 8,775 traffic sign instances covering 9 categories and 28 classes. Three subsets of the dataset were created: test (10%-1050 images 579), validation (20% -1050 images), and training (70% - 3728 images). It is available upon request. If you want to train and test the data set. Please send an email to afef.zwidi@regim.usf.tn
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58 Views
This dataset was collected to support research on the screening and diagnosis of Diabetic Peripheral Neuropathy (DPN) and Cardiac Autonomic Neuropathy (CAN) using wearable sensor technology. It includes synchronized data from gait analysis and physiological signals such as electrocardiogram (ECG), heart rate variability (HRV), and inertial measurement units (IMUs) obtained from individuals with and without DPN and CAN.
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164 Views
Clean energy technologies, encompassing renewable resources like solar, wind, and hydropower, are essential in the global effort to reduce greenhouse gas emissions and combat climate change. As the globe prepares to transition away from fossil fuels, understanding the factors and parameters influencing the penetration of clean energy into existing energy markets has become a critical step. Controversies surrounding the environmental impacts of renewable technologies, variability in market structures, and economic pressures on clean energy companies can complicate this transition.
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85 Views
This dataset provides electromagnetic spectrum feature data for target recognition in combat formations, supporting both closed and open set scenarios. It includes three subsets: a closed set with known target types, open set 1 with one unknown target type, and open set 2 with multiple unknown target types. Each dataset contains extracted target features, adjacency matrices representing communication links, and ground truth labels. The dataset covers radar and communication attributes, including carrier frequency, pulse characteristics, modulation types, power, and movement parameters.
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An automatic waste classification system embedded with higher accuracy and precision of convolution neural network (CNN) model can significantly the reduce manual labor involved in recycling. The ConvNeXt architecture has gained remarkable improvements in image recognition. A larger dataset, called TrashNeXt, comprising 23,625 images across nine categories has been introduced in this study by combining and thoroughly analyzing various pre-existing datasets.
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336 Views