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Found 10 papers, 3 papers with code
A State-of-the-practice Release-readiness Checklist for Generative AI-based Software Products
This paper investigates the complexities of integrating Large Language Models (LLMs) into software products, with a focus on the challenges encountered for determining their readiness for release.
Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks
By leveraging operational data as a foundation for the agent's actions, we enhance the explainability of the agent's actions, foster more robust recommendations, and minimize error.
Exploring Multilingual Text Data Distillation
In the paper, we propose several data distillation techniques for multilingual text classification datasets using language-model-based learning methods.
Green synthesis of silver nanoparticles using Curcuma longa flower extract and antibacterial activity
The GAgNP's were found to contain crystalline silver through XRD, and the particles were confirmed to be homogeneous and spherical with a size of approximately 5 nm, as evidenced by UV-Visible spectroscopy, XRD, and HR-TEM.
Using dynamic circles and squares to visualize spatio-temporal variation
Visualizations such as bar charts, scatter plots, and objects on geographical maps often convey critical information, including exact and relative numeric values, using shapes.
Exploring Explainability Methods for Graph Neural Networks
With the growing use of deep learning methods, particularly graph neural networks, which encode intricate interconnectedness information, for a variety of real tasks, there is a necessity for explainability in such settings.
Image Inpainting using Partial Convolution
Image Inpainting is one of the very popular tasks in the field of image processing with broad applications in computer vision.
HinFlair: pre-trained contextual string embeddings for pos tagging and text classification in the Hindi language
Multi-lingual language models include Indian languages like Hindi, Telugu, Bengali in their training corpus, but they often fail to represent the linguistic features of these languages as they are not the primary language of the study.
BioNerFlair: biomedical named entity recognition using flair embedding and sequence tagger
Results: We introduce BioNerFlair, a method to train models for biomedical named entity recognition using Flair plus GloVe embeddings and Bidirectional LSTM-CRF based sequence tagger.
E-LSTM at SemEval-2019 Task 3: Semantic and Sentimental Features Retention for Emotion Detection in Text
This paper discusses the solution to the problem statement of the SemEval19: EmoContext competition which is {''}Contextual Emotion Detection in Texts{''}.
