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This research examines the effectiveness of OpenAI's GPT models as independent evaluators of text summaries generated by six transformer-based models from Hugging Face: DistilBART, BERT, ProphetNet, T5, BART, and PEGASUS. We evaluated these summaries based on essential properties of high-quality summary - conciseness, relevance, coherence, and readability - using traditional metrics such as ROUGE and Latent Semantic Analysis (LSA). Uniquely, we also employed GPT not as a summarizer but as an evaluator, allowing it to independently assess summary quality without predefined metrics. Our analysis revealed significant correlations between GPT evaluations and traditional metrics, particularly in assessing relevance and coherence. The results demonstrate GPT's potential as a robust tool for evaluating text summaries, offering insights that complement established metrics and providing a basis for comparative analysis of transformer-based models in natural language processing tasks., Comment: 10 pages, 5 figures

Wrist accelerometers for assessing hallmark measures of physical activity (PA) are rapidly growing with the advent of smartwatch technology. Given the growing popularity of wrist-worn accelerometers, there needs to be a rigorous evaluation for recognizing (PA) type and estimating energy expenditure (EE) across the lifespan. Participants (66% women, aged 20-89 yrs) performed a battery of 33 daily activities in a standardized laboratory setting while a tri-axial accelerometer collected data from the right wrist. A portable metabolic unit was worn to measure metabolic intensity. We built deep learning networks to extract spatial and temporal representations from the time-series data, and used them to recognize PA type and estimate EE. The deep learning models resulted in high performance; the F1 score was: 0.82, 0.81, and 95 for recognizing sedentary, locomotor, and lifestyle activities, respectively. The root mean square error was 1.1 (+/-0.13) for the estimation of EE.

A Master of Science thesis in Computer Engineering by Mamoun Al-Mardini entitled, "A Framework for Screening and Classifying Obstructive Sleep Apnea Using Smartphones," submitted in June 2013. Thesis advisor is Dr. Fadi Aloul and Co-advisor is Dr. Assim Sagahyroon. Available are both soft and hard copies of the thesis., Obstructive sleep apnea (OSA) is a serious sleep disorder which is characterized by frequent obstruction of the upper airway, often resulting in oxygen desaturation. The serious negative impact of OSA on human health makes monitoring and diagnosing it a necessity. Currently, polysomnography is considered the golden standard for diagnosing OSA, which requires an expensive attended overnight stay at a hospital with considerable wiring between the human body and the system. In the proposed research, we implement a reliable, comfortable, inexpensive, and easily available portable device that allows users to apply the OSA test at home without the need for attended overnight tests. The design takes advantage of a smatrphone's built-in sensors, pervasiveness, computational capabilities, and user-friendly interface to screen OSA. We use three main sensors to extract physiological signals from patients which are (1) an oximeter to measure the oxygen level, (2) a microphone to record the respiratory efforts, and (3) an accelerometer to detect the body's movements. The collected signals are then analyzed on the phone to deduce if the patient is suffering from OSA. In the proposed system, we have developed an Android application that is able to record and extract the physiological signals from the patients and analyze them solely on the smartphone without the need for any external resources. The smartphone is able to analyze the oximeter and accelerometer reading. Most health applications use smartphones to collect physiological readings, and then off load them to an external server for analysis. However, in this work we developed an integrated environment that collects and processes data on the smartphone, including the signal processing functions that analyze the recorded respiratory efforts. Finally, we examine our system's ability to screen the disease when compared to the golden standard by testing it on 17 samples. The results showed that 100% of patients were correctly i, College of Engineering, Department of Computer Science and Engineering