Large Language Models (LLMs) such as GPTs and LLaMa have ushered in a revolution in
machine intelligence, owing to their exceptional capabilities in a wide range of machine …

Convolutional Recurrent Neural Network (CRNN) based architecture is an attractive branch
of Optical Character Recognition (OCR) studies. OCR is the process for transforming the …

Ultra-high frequency radio frequency identification (UHF RFID) technology has been widely
used in many areas, and RFID localization becomes a research hotspot. There are many …

Performance prediction is an important research facet of educational data mining. Most
models extract student behavior features from campus card data for prediction. However …

Recognizing the emotional state of people is a basic but challenging task in video
understanding. In this paper, we propose a new task in this field, named Pairwise Emotional …

DNNs are ubiquitous on edge devices nowadays. With its increasing importance and use
cases, it's not likely to pack all DNNs into device memory and expect that each inference has …

To reduce neural network parameter counts and improve sound event detection
performance, we propose a multiscale time-frequency convolutional recurrent neural …

In order to gain competitive advantage, technology opportunity detection in the latest and
fast-growing areas has been becoming an important research issue. However, current …

Abstract Recurrent Neural Networks (RNN) or their variants, eg GRU and LSTM, have been
widely adopted for image captioning. In an RNN, the production of a caption is driven by a …

Requirements are usually" hand-written" and suffers from several problems like redundancy
and inconsistency. The problems of redundancy and inconsistency between requirements or …