Generating poetry using machine and deep learning techniques has been a challenging and exciting topic of research in recent years. It has significance fi cance in natural language processing and computational linguis...
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Generating poetry using machine and deep learning techniques has been a challenging and exciting topic of research in recent years. It has significance fi cance in natural language processing and computational linguistics. This study introduces an innovative approach to generate high-quality Pashto poetry by leveraging two pre- trained transformer models, LaMini-Cerebras-590M and bloomz-560m. The models were trained on an extensive new and quality Pashto poetry dataset to learn the underlying complex patterns and structures. The trained models are then used to generate new Pashto poetry by providing them with a seed text or prompt. To evaluate the quality of the generated poetry, we conducted both subjective and objective evaluations, including human evaluation. The experimental results demonstrate that the proposed approach can generate Pashto poetry that is comparable in quality to human-generated poetry. The study provides a valuable contribution to the fi eld of Pashto language and poetry generation and has potential applications in natural language processing and computational linguistics.
Multi-exposure image fusion is a cost-effective method to improve the dynamic range of images. For the problems of inadequate detail extraction in bright and dark areas and the over simplicity of existing fusion rules...
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Handwritten text analysis for author identification is a significant but difficult topic with wide applications across several areas. The effectiveness of deep learning models, notably AlexNet and ResNet-101, in ident...
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Person Re-Identification (ReID) is a crucial task in computer vision that aims to match persons captured from non-overlapping camera views. In this paper, to alleviate the impacts of background and occlusion, we propo...
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With the increasing growth of electronic medical data, the difficulties of data sharing among medical institutions and the leakage of data privacy have become the focus of the public and medical workers. The blockchai...
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Federated learning has emerged as an efficient way to exploit distributed data in recent years. It allows multiple client nodes to collaboratively train an optimized machine learning model without revealing the partic...
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Vertebrate neurons are highly dynamic cells that undergo several alterations in their functioning and physiologies in adaptation to various external *** particular,how these neurons respond to physical exercise has lo...
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Vertebrate neurons are highly dynamic cells that undergo several alterations in their functioning and physiologies in adaptation to various external *** particular,how these neurons respond to physical exercise has long been an area of active *** of the vertebrate locomotor system’s adaptability suggest multiple mechanisms are involved in the regulation of neuronal activity and properties during *** this brief review,we highlight recent results and insights from the field with a focus on the following mechanisms:(a)alterations in neuronal excitability during acute exercise;(b)alterations in neuronal excitability after chronic exercise;(c)exercise-induced changes in neuronal membrane properties via modulation of ion channel activity;(d)exercise-enhanced dendritic plasticity;and(e)exercise-induced alterations in neuronal gene expression and protein *** hope is to update the community with a cellular and molecular understanding of the recent mechanisms underlying the adaptability of the vertebrate locomotor system in response to both acute and chronic physical exercise.
Underwater vision is typically more difficult to tackle than open-air vision due to the degraded visibility and geometrical distortion, which impedes the development of underwater machine vision. Hence, we propose a j...
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Knowledge distillation is a popular technique for model compression. However, since knowledge distillation originated in image classification, it primarily concentrates on classification tasks in object detection and ...
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Knowledge distillation is a popular technique for model compression. However, since knowledge distillation originated in image classification, it primarily concentrates on classification tasks in object detection and often overlooks regression tasks. Emphasizing the classification task while neglecting the regression task can lead to skewed predictions about the model's learning condition in knowledge distillation methods. To address this, we propose Task Integration Distillation (TID), a method that integrates both classification and regression tasks, enhancing the model's ability to accurately capture the learning condition. Inspired by real-world teaching strategies and the concept of learning conditions, TID emphasizes the importance of features derived from both tasks, ensuring a balanced consideration of key and weak areas. We quantify and map the outputs of the two tasks from the object detector onto the feature map. Through this output mapping, we identify key areas and weak areas of the features to assess the model's current learning state. Extensive experiments demonstrate that TID consistently outperforms existing methods, with a notable increase of about 2.0% in mean Average Precision (mAP) over recent feature decoupling and distillation approaches.
With the rapid increase in the number of various types of software, the importance of software quality has become increasingly prominent, and software testing has become an indispensable part of the software developme...
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