Forget and Rewire (FaR) methodology has demonstrated strong resilience against Bit-Flip Attacks (BFAs) on Transformer-based models by obfuscating critical parameters through dynamic rewiring of linear layers. However, the application of FaR introduces non-negligible performance and memory overheads, primarily due to the runtime modification of activation pathways and the lack of hardware-level optimization. To overcome these limitations, we propose FaRAccel, a novel hardware accelerator architecture implemented on FPGA, specifically designed to offload and optimize FaR operations. FaRAccel integrates reconfigurable logic for dynamic activation rerouting, and lightweight storage of rewiring configurations, enabling low-latency inference with minimal energy overhead. We evaluate FaRAccel across a suite of Transformer models and demonstrate substantial reductions in FaR inference latency and improvement in energy efficiency, while maintaining the robustness gains of the original FaR methodology. To the best of our knowledge, this is the first hardware-accelerated defense against BFAs in Transformers, effectively bridging the gap between algorithmic resilience and efficient deployment on real-world AI platforms.

Opioid-induced adverse events pose significant risks to hospitalized patients. However, there is a limited understanding of which patients on general care floors are at risk for Opioid-Induced Respiratory Depression (OIRD). This study aims to bridge that knowledge gap by utilizing the advancements of AI to interpret Electronic Medical Records (EMRs). Recently, Large Language Models (LLMs) have gained attention for their exceptional capabilities in understanding human language, which makes them crucial for AI systems in healthcare that focus on clinical narratives. In this study, we extracted 2,663 hospitalized adult patient records from UC Davis Medical Center archives between January 2010 and April 2020 to identify patients at high risk of OIRD. For this purpose, we employed clinical language models (BioBERT, ClinicalBERT, and GatorTron) and fine-tuned them on the OIRD dataset. Additionally, we leveraged the capabilities of GPT-4, a state-of-the-art LLM, to select the most informative risk factors and enhance the accuracy of the predictive models.

This paper presents a novel Electrodermal Activ-ity (EDA) signal acquisition system, designed to address the challenges of stress monitoring in contemporary society, where stress affects one in four individuals. Our system focuses on enhancing the accuracy and efficiency of EDA measurements, a reliable indicator of stress. Traditional EDA monitoring solutions often grapple with trade-offs between sensor placement, cost, and power consumption, leading to compromised data accuracy. Our innovative design incorporates an adaptive gain mechanism, catering to the broad dynamic range and high-resolution needs of EDA data analysis. The performance of our system was extensively tested through simulations and a custom Printed Circuit Board (PCB), achieving an error rate below 1 % and maintaining power consumption at a mere$\mathbf{700}\mu \mathbf{A}$under a 3.$7\mathbf{V}$power supply. This research contributes significantly to the field of wearable health technology, offering a robust and efficient solution for long-term stress monitoring.

In the realm of physiological-based affective computing, significant progress has been witnessed in machine learning over the last two decades. Nevertheless, the lack of consistency in measurement tools and data organization across diverse datasets poses a challenge when integrating new datasets for algorithm testing, research, and result comparison across multiple datasets. Despite the expansion of artificial intelligence-driven affective computing, a notable gap remains in the form of a comprehensive toolkit tailored for both machine learning researchers and psychologists who are new to the field of machine learning. In response to these challenges, we introduce a Python toolkit designed to fulfill two key roles: establishing a standardized benchmark for affective computing datasets and offering an all-encompassing toolkit for machine learning in physiological signal based affective computing. This toolkit encompasses vital components essential to the machine learning process, encompassing tasks like dataset integration and interpretation, signal preprocessing, feature derivation, post-processing, classification models, and evaluation metrics. Our proposed toolkit is designed for working with seven publicly available datasets, embracing five different modalities and incorporating twenty diverse machine learning models spanning from conventional options like the support vector machine (SVM) to cutting-edge deep learning models. To the best of our knowledge, the proposed toolkit stands as the pioneering initiative for creating a standardized dataset benchmarking system and a comprehensive solution tailored for machine learning applications in affective computing. The open-source codebase for the proposed toolkit is accessible via https://github.com/rjfang/pyAffeCT.

In modern society, people are exposed to various stressors and negative emotions daily and they may cause mental and physical diseases such as depression, anxiety, high blood pressure, heart attacks, and stroke. Therefore, this paper delves into the potential of modern wearable technologies as a tool for real-time health monitoring. The advent of ubiquitous sensing has ushered in an era where physiological and behavioral measurements can be continuously recorded in daily life. One significant physiological marker is the Galvanic Skin Response (GSR), which exhibits noteworthy changes under different emotional states. We propose a machine learning-based emotion recognition framework. It includes a preprocessing stage that eliminates noise and extracts 87 features from the GSR data. To account for individual differences in physiological responses, we also introduce a novel normalization procedure per subject. Finally, a subset of dominant and discriminative features enhances the proposed framework’s performance. We conducted experiments on two datasets, the wearable stress and affect detection dataset (WESAD) for stress detection, and the multimodal MAHNOB-HCI dataset for emotion recognition. The results show that the Leave-One-Out method is capable of detecting stress with 97.03% accuracy. Moreover, the proposed method classifies arousal and valence with an accuracy of 82.20% and 82.57%, respectively.

Physiological signal-based pattern recognition has progressed significantly, such as automated pain assessment and stress detection. Public datasets provide a research platform to conduct machine learning studies. However, models trained from public datasets easily overfit that specific dataset and do not apply to unseen data collected in real-life scenarios. This paper proposes to use the transfer learning-based domain generalization technique to generalize the models to solve this issue. Data from different training domains are generalized, i.e., the dissimilarity is minimized by the proposed approach such that the model trained is generalized. We proved that the generalized model is more adaptive to new unseen data. Experiments have been done on the BioVid heat pain dataset and WESAD stress dataset, and results showed that our proposed methods significantly improve the model performance on new unseen data.

Stress detection is an emerging field. WESAD is a commonly used public dataset for automated stress detection. It contains physiological signals including ECG, EDA, EMG, ACC, BVP, EDA, and skin temperature. The time window approach is used to extract features from time-series physiological signals. We find in previous studies that a 60-second time window with a 0.25-second window shift is widely used, but such window settings may cause redundancy and over-fitting. Thus, we propose to use (1) new window settings and (2) normalization per subject to tackle this problem. The experiment results show that our proposed methods significantly increase the classification performance.

Stress is an inevitable part of our lives in modern society since in many situations people are exposed to various stressors daily. According to studies, long-term stress can cause mental and physical diseases such as depression, anxiety, high blood pressure, heart attacks, and stroke. Therefore, stress detection is one of the crucial areas of study to maintain a healthy life. Recently, by developing commercial wearable technologies, real-time and continuous data collection for personal stress monitoring becomes more feasible. Under stress conditions, there are notable changes in physiological signals such as heart rate, respiration, perspiration, and eye pupil dilation. Previous studies have shown that Electrodermal Activity (EDA), also known as Galvanic Skin Response (GSR), can identify stress. EDA measures changes in perspiration by detecting the changes in the electrical conductivity of the skin. This paper focuses on stress detection using only EDA wearable sensors and applied machine learning techniques. First, 87 different features are extracted from EDA signals. Then, the data are normalized per subject because of differences in individuals’ physiological responses. Finally, five dominant features in stress detection are selected. We used a publicly available dataset, namely, the wearable stress and affect detection dataset (WESAD) in this study. The results show that the One-Leave-Out method is capable of detecting stress with 97.03% accuracy.