Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit
about one-third of greenhouse gases. In the last few years, machine learning has achieved …

Abstract Multi-Source Information Fusion (MSIF), as a comprehensive interdisciplinary field
based on modern information technology, has gained significant research value and …

Transfer learning (TL) has been successfully applied to many real-world problems that
traditional machine learning (ML) cannot handle, such as image processing, speech …

Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

The vast proliferation of sensor devices and Internet of Things enables the applications of
sensor-based activity recognition. However, there exist substantial challenges that could …

Recent advancements in deep learning architecture have increased its utility in real-life
applications. Deep learning models require a large amount of data to train the model. In …

This survey investigates current techniques for representing qualitative data for use as input
to neural networks. Techniques for using qualitative data in neural networks are well known …

Conjecture about the weak replicability in social sciences has made scholars eager to
quantify the scale and scope of replication failure for a discipline. Yet small-scale manual …

Data collection is a major bottleneck in machine learning and an active research topic in
multiple communities. There are largely two reasons data collection has recently become a …

The security issue impacting the Internet-of-Things (IoT) paradigm has recently attracted
significant attention from the research community. To this end, several surveys were put …