Navigation systems help users access unfamiliar environments. Current technological
advancements enable users to encapsulate these systems in handheld devices, which …

Deep learning is a quite useful and proliferating technique of machine learning. Various
applications, such as medical images analysis, medical images processing, text …

The predominant paradigm for using machine learning models on a device is to train a
model in the cloud and perform inference using the trained model on the device. However …

Human activity recognition is a core problem in intelligent automation systems due to its far-
reaching applications including ubiquitous computing, health-care services, and smart …

With the rapid development of the stock markets in developing countries, determining how to
efficiently detect stock price manipulation activities to protect the interests of ordinary …

The superconducting LHC magnets are coupled with an electronic monitoring system which
records and analyzes voltage time series reflecting their performance. A currently used …

The paper extends the recent star reachability method to verify the robustness of recurrent
neural networks (RNNs) for use in safety-critical applications. RNNs are a popular machine …

In order to currently motivate a wide range of various interactions between power network
operators and electricity customers, residential load forecasting plays an increasingly …

State-of-the-art neural network language models (NNLMs) represented by long short term
memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly …

Accurate short-term electric load forecasting is significant for the smart grid. It can reduce
electric power consumption and ensure the balance between power supply and demand. In …