Saied Hemati

S. Hemati, Krishna R. Kalari, H. R. Tizhoosh

Digital pathology is revolutionizing the field of pathology by enabling the digitization, storage, and analysis of tissue samples as whole slide images (WSIs). WSIs are gigapixel files that capture the intricate details of tissue samples, providing a rich source of information for diagnostic and research purposes. However, due to their enormous size, representing these images as one compact vector is essential for many computational pathology tasks, such as search and retrieval, to ensure efficiency and scalability. Most current methods are "patch-oriented," meaning they divide WSIs into smaller patches for processing, which prevents a holistic analysis of the entire slide. Additionally, the necessity for compact representation is driven by the expensive high-performance storage required for WSIs. Not all hospitals have access to such extensive storage solutions, leading to potential disparities in healthcare quality and accessibility. This paper provides an overview of existing set-based approaches to single-vector WSI representation, highlighting the innovations that allow for more efficient and effective use of these complex images in digital pathology, thus addressing both computational challenges and storage limitations.

Niloofar Hezarjaribi, Zhila Esna Ashari, James F. Frenzel et al.

This article presents PerSense, a framework to estimate human personality traits based on expressed texts and to use them for commonsense reasoning analysis. The personality assessment approaches include an aggregated Probability Density Functions (PDF), and Machine Learning (ML) models. Our goal is to demonstrate the feasibility of using machine learning algorithms on personality trait data to predict humans' responses to open-ended commonsense questions. We assess the performance of the PerSense algorithms for personality assessment by conducting an experiment focused on Neuroticism, an important personality trait crucial in mental health analysis and suicide prevention by collecting data from a diverse population with different Neuroticism scores. Our analysis shows that the algorithms achieve comparable results to the ground truth data. Specifically, the PDF approach achieves 97% accuracy when the confidence factor, the logarithmic ratio of the first to the second guess probability, is greater than 3. Additionally, ML approach obtains its highest accuracy, 82.2%, with a multilayer Perceptron classifier. To assess the feasibility of commonsense reasoning analysis, we train ML algorithms to predict responses to commonsense questions. Our analysis of data collected with 300 participants demonstrate that PerSense predicts answers to commonsense questions with 82.3% accuracy using a Random Forest classifier.

Saied Hemati

This paper investigates hardware cyber-security risks associated with channel decoders, which are commonly acquired as a black box in semiconductor industry. It is shown that channel decoders are potentially attractive targets for hardware cyber-security attacks and can be easily embedded with malicious blocks. Several attack scenarios are considered in this work and suitable methods for mitigating the risks are proposed. These methods are based on randomizing the inputs of the channel decoder to obstruct the communications between attackers and the malicious blocks, ideally without changing the decoding performance.