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Random forest (RF) methodology is one of the most popular machine learning techniques for prediction problems. In this article, we discuss some cases where random forests may suffer and propose a novel generalized RF method, namely…

To improve accuracy and speed of regressions and classifications, we present a data-based prediction method, Random Bits Regression (RBR). This method first generates a large number of random binary intermediate/derived features based on…

Stochastic rounding (SR) is a probabilistic method used to round numbers to floating-point and fixed-point representations. In length $n$ summation, the worst-case error of SR grows as $\sqrt{n}$ with high probability, unlike for standard…

Providing the ability to any law enforcement officer to remotely transfer an image from any suspect computer directly to a forensic laboratory for analysis, can only help to greatly reduce the time wasted by forensic investigators in…

Reconfigurable Intelligent Surfaces (RISs) promise improved, secure and more efficient wireless communications. We propose and demonstrate how to exploit the diversity offered by RISs to generate and select easily differentiable radio maps…

Random sampling is an essential tool in the processing and transmission of data. It is used to summarize data too large to store or manipulate and meet resource constraints on bandwidth or battery power. Estimators that are applied to the…

Inspired by sample splitting and the reusable holdout introduced in the field of differential privacy, we consider selective inference with a randomized response. We discuss two major advantages of using a randomized response for model…

Although Raman spectroscopy is widely used for the investigation of biomedical samples and has a high potential for use in clinical applications, it is not common in clinical routines. One of the factors that obstruct the integration of…

Efficiently finding safe and feasible trajectories for mobile objects is a critical field in robotics and computer science. In this paper, we propose SIL-RRT*, a novel learning-based motion planning algorithm that extends the RRT* algorithm…

This study explores stochastic resonance (SR) in a Schmitt trigger circuit and its application to weak signal detection. SR, a phenomenon where noise synchronizes with weak signals to enhance detectability, was demonstrated using a…

With the growing importance of personal identification and authentication in todays highly advanced world where most business and personal tasks are being replaced by electronic means, the need for a technology that is able to uniquely…

Synthetic aperture radar technology is crucial for high-resolution imaging under various conditions; however, the acquisition of real-world synthetic aperture radar data for deep learning-based automatic target recognition remains…

The characterization and monitoring of buildings is an issue that has attracted the interest of many sectors over the last two decades. With the increasing use of permanent, continuous and real-time networks, ambient vibrations can provide…

Respondent-driven sampling (RDS) is an approach to sampling design and analysis which utilizes the networks of social relationships that connect members of the target population, using chain-referral methods to facilitate sampling. RDS…

The Reservoir Computing (RC) paradigm posits that sufficiently complex physical systems can be used to massively simplify pattern recognition tasks and nonlinear signal prediction. This work demonstrates how random topological magnetic…

Temporal alignment of multiple signals through time warping is crucial in many fields, such as classification within speech recognition or robot motion learning. Almost all related works are limited to data in Euclidean space. Although an…

Modern statistical estimation is often performed in a distributed setting where each sample belongs to a single user who shares their data with a central server. Users are typically concerned with preserving the privacy of their samples,…

When it is not feasible to conduct randomized controlled trials (RCTs), the use of external control arms based on real-world data (RWD) may be a viable option. However, challenges arising from data heterogeneity must be addressed to ensure…

The sample selection approach is very popular in learning with noisy labels. As deep networks learn pattern first, prior methods built on sample selection share a similar training procedure: the small-loss examples can be regarded as clean…

Fingerprinting is a popular indoor localization technique since it can utilize existing infrastructures (e.g., access points). However, its site survey process is a labor-intensive and time-consuming task, which limits the application of…