Academic Articles & Research Papers

Local and temporal predicates in distributed systems

The definitions of the predicates Possibly φ and Definitely φ, where φ is a global predicate of a distributed computation, lead to the definitions of two predicate transformers P and D . We show that P plays the same role with respect to ti...

Transformers in Natural Language Processing

Natural Language Processing

This book chapter focuses on natural language processing (NLP) and covers various applications such as classifying messages as spam or ham, sentiment analysis, and the use of Bidirectional Encoder Representations from Transformers (BERT). T...

Natural Language Processing

In the modern age of information and analytics, natural language processing (NLP) is one of the most important technologies out there. Making sense of complex structures in language and deriving insights and actions from it are crucial from...

Reactive Power Optimization Control for Renewable Energy in Distribution Networks Considering Active Power Uncertainties

A dual-layer robust optimization control method based on the droop principle of reactive power and voltage is proposed for renewable energy in distribution networks, considering active power uncertainties. First, in the reference-point opti...

A Review of Power System Fault Diagnosis with Spiking Neural P Systems

With the advancement of technologies it is becoming imperative to have a stable, secure and uninterrupted supply of power to electronic systems as well as to ensure the identification of faults occurring in these systems quickly and efficie...

Research on Personalized Recommendation Algorithm Integrating Cross-Grained Sentiment and Rating Interaction Features

To investigate the impact of cross-grained sentiments on user feature representation and address the issue of data sparsity, this paper proposes a Personalized Recommendation Algorithm Integrating Cross-Grained Sentiment and Rating Interact...

TransKGQA: Enhanced Knowledge Graph Question Answering With Sentence Transformers

Knowledge Graph Question Answering (KGQA) plays a crucial role in extracting valuable insights from interconnected information. Existing methods, while commendable, face challenges such as contextual ambiguity and limited adaptability to di...

Natural language processing with transformers: a review

Natural language processing (NLP) tasks can be addressed with several deep learning architectures, and many different approaches have proven to be efficient. This study aims to briefly summarize the use cases for NLP tasks along with the ma...

Machine Unlearning: A Perspective, Taxonomy, and Benchmark Evaluation

Machine Learning (ML) models trained on large-scale datasets learn useful predictive patterns, but they may also memorize undesired information, leading to risks such as information leakage, bias, copyright violations, and privacy attacks. ...

Information Extraction From FDA Drug Labeling to Enhance Product-Specific Guidance Assessment Using Natural Language Processing

Towards the objectives of the UnitedStates Food and Drug Administration (FDA) generic drug science and research program, it is of vital importance in developing product-specific guidances (PSGs) with recommendations that can facilitate and ...

Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine

Pre-trained language models (PLM) based on transformer neural networks developed in the field of natural language processing (NLP) offer great opportunities to improve automatic content analysis in communication science, especially for the ...

Analyzing Sentiments in eLearning: A Comparative Study of Bangla and Romanized Bangla Text Using Transformers

In the modern world, learning is becoming increasingly critical due to rapid technological breakthroughs, which highlight the need for continuous skill development in both the personal and professional spheres. As a result, eLearning is a c...

Menstrual Health Education Using a Specialized Large Language Model in India: Development and Evaluation Study of MenstLLaMA

Abstract BackgroundThe quality and accessibility of menstrual health education (MHE) in low- and middle-income countries, including India, remain inadequate due to persistent challenges (eg, poverty, social stigma, and gender in...

BERT applications in natural language processing: a review

BERT (Bidirectional Encoder Representations from Transformers) has revolutionized Natural Language Processing (NLP) by significantly enhancing the capabilities of language models. This review study examines the complex nature of BERT, inclu...

Analyzing Brain Tumor Classification Techniques: A Comprehensive Survey

Classification of brain tumour is critical in medical image analysis and diagnosis as it aids in determining the affinity of cases to select treatment paths. This feature underscores the importance of accurate classification of brain tumour...

HuggingFace's Transformers: State-of-the-art Natural Language Processing

Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to ...

Transformers: State-of-the-Art Natural Language Processing

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu...

Transformer in Computer Vision

Transformer is widely used in Natural Language Processing (NLP), in which numerous papers have been proposed. Recently, the transformer has been borrowed for many computer vision tasks. However, there are few papers to give a comprehensive ...

UPANets: Learning from the Universal Pixel Attention Neworks

With the successful development in computer vision, building a deep convolutional neural network (CNNs) has been mainstream, considering the character of shared parameters in a convolutional layer. Stacking convolutional layers into a deep ...