Academic Articles & Research Papers

E2ETrADS: end-to-end transformer based autonomous driving system for adverse weather conditions

Abstract Adverse weather conditions, such as snow, heavy rain, fog, or limited illumination, pose significant challenges for autonomous vehicles (AVs) by degrading sensor reliability. This paper introduces E2ETrADS, an end-to-end transforme...

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...

Research on the Configuration of a 100% Green Electricity Supplied Zero-Carbon Integrated Energy Station

In the context of rapid growth in renewable energy installations and increasingly severe consumption issues, this paper designs a 100% green electricity supplied zero-carbon integrated energy station. It aims to analyze its configuration fo...

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...

Bridging Deep Learning and Quantum Computing for EEG-Driven P300 Targeted ASD Response Detection

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose diagnosis relies heavily on subjective behavioral assessments. Electroencephalography (EEG) provides an inexpensive and non-invasive method of objective biomarker detect...

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...

Hybrid spatio-temporal transformer and variational autoencoder framework for advanced sports injury prediction and analysis

Sports injuries significantly affect sports performance and overall health, making using accurate and reliable prognostic models imperative. Conventional algorithms for injury prediction, e.g., random forest and XGBoost, generally cannot un...

A Unified Transformer–BDI Architecture for Financial Fraud Detection: Distributed Knowledge Transfer Across Diverse Datasets

Financial fraud detection is a critical application area within the broader domains of cybersecurity and intelligent financial analytics. With the growing volume and complexity of digital transactions, the traditional rule-based and shallow...

Small Object Detection Algorithm Based on Improved YOLOv8 for Remote Sensing

Due to the limitations of small targets in remote sensing images, such as background noise, poor information, and so on, the results of commonly used detection algorithms in small target detection is not satisfactory. To improve the accurac...

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...

Enhancing sentiment classification on small datasets through data augmentation and transfer learning

Abstract Small-scale sentiment classification often suffers from data scarcity, which limits the generalization ability of the models. This study evaluates and compares the effectiveness of three data augmentation strategies: Easy Data Augm...

A Multi-Stage NLP Framework for Knowledge Discovery from Crop Disease Research Literature

Extracting and organizing knowledge from the agricultural crop disease research literature are challenging tasks because of the heterogeneous terminologies, complicated symptom descriptions, and unstructured nature of scientific documents. ...

D‐BERT: Incorporating dependency‐based attention into BERT for relation extraction

Abstract Relation extraction between entity pairs is an increasingly critical area in natural language processing. Recently, the pre‐trained bidirectional encoder representation from transformer (BERT) performs excellently on the text class...

Sequence Decision Transformer for Adaptive Traffic Signal Control

Urban traffic congestion poses significant economic and environmental challenges worldwide. To mitigate these issues, Adaptive Traffic Signal Control (ATSC) has emerged as a promising solution. Recent advancements in deep reinforcement lear...

Advancements in natural language processing: Implications, challenges, and future directions

This research delves into the latest advancements in Natural Language Processing (NLP) and their broader implications, challenges, and future directions. With the ever-increasing volume of text data generated daily from diverse sources, ext...