Synthetic data consists of artificially generated data. When data are scarce, or of poor
quality, synthetic data can be used, for example, to improve the performance of machine …

Large datasets are necessary for deep learning as the performance of the algorithms used
increases as the size of the dataset increases. Poor data management practices and the low …

Introduction/Purpose. Behavior mapping is a crucial practice to capture precise data on
human activities. Over the years, technological advancements have improved reliable data …

Existing building performance models (existing BPMs) often lack the capability for
addressing human-building interactions in future buildings or buildings under design …

One of the approaches to deal with the small dataset problem is synthetic data generation.
Kernel density estimation is a common method to approximate the underlying probability …

Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of
machine learning models on networked devices (eg, mobile devices, IoT edge nodes). It …

The number of reliable samples obtained in early decision-making activity is usually
relatively small. Due to variable distribution and incomplete structure of tiny datasets, it is …

In the realm of data-driven systems, understanding and controlling biases in datasets
emerges as a critical challenge. These biases, defined in this study as systematic …

There is a lot of confusion and ambiguity regarding the quantification of the Quality of
Service (QoS) of a system, especially for cyber-physical systems (CPS) involved in …

Utility-rich (eg, more attractive or safer) route planning on city-scale road networks is a
common yet time-consuming task. Although both travel time and utility on edges are time …