Chee Siang Ang

Saber Mirzaee Bafti, Chee Siang Ang, Gianluca Marcelli et al.

A diversified dataset is crucial for training a well-generalized supervised computer vision algorithm. However, in the field of microbiology, generation and annotation of a diverse dataset including field-taken images are time consuming, costly, and in some cases impossible. Image to image translation frameworks allow us to diversify the dataset by transferring images from one domain to another. However, most existing image translation techniques require a paired dataset (original image and its corresponding image in the target domain), which poses a significant challenge in collecting such datasets. In addition, the application of these image translation frameworks in microbiology is rarely discussed. In this study, we aim to develop an unpaired GAN-based (Generative Adversarial Network) image to image translation model for microbiological images, and study how it can improve generalization ability of object detection models. In this paper, we present an unpaired and unsupervised image translation model to translate laboratory-taken microbiological images to field images, building upon the recent advances in GAN networks and Perceptual loss function. We propose a novel design for a GAN model, BioGAN, by utilizing Adversarial and Perceptual loss in order to transform high level features of laboratory-taken images into field images, while keeping their spatial features. The contribution of Adversarial and Perceptual loss in the generation of realistic field images were studied. We used the synthetic field images, generated by BioGAN, to train an object-detection framework, and compared the results with those of an object-detection framework trained with laboratory images; this resulted in up to 68.1% and 75.3% improvement on F1-score and mAP, respectively. Codes is publicly available at https://github.com/Kahroba2000/BioGAN.

A. Xygkou-Tsiamoulou, Alexandra Covaci, Zeqi Jia et al.

As humanity pivots toward long-duration interplanetary travel, the psychological constraints of Isolated and Confined Environments (ICE) emerge as a primary mission risk. This paper presents COSMIC (COmpanion System for Mission Interaction and Communication) representing the inaugural investigation into the deployment of a high-fidelity, emotionally intelligent AI companion in an analog astronaut setting. By integrating a Large Language Model (LLM) architecture with a diffusion-based digital avatar interface, COSMIC transcends traditional task-oriented automation to provide longitudinal affective support. We detail a modular system architecture designed for temporal continuity through short- and long-term memory systems and outline a robust naturalistic observational framework for evaluating psychological resilience at the LunAres Research Station. This work constitutes the first formal submission in the field to evaluate the efficacy of state-of-the-art generative AI and synthesized visual empathy in mitigating the effects of extreme isolation.

Ben Nicholls, Chee Siang Ang, Eiman Kanjo et al.

Mindless eating, or the lack of awareness of the food we are consuming, has been linked to health problems attributed to unhealthy eating behaviour, including obesity. Traditional approaches used to moderate eating behaviour often rely on inaccurate self-logging, manual observations or bulky equipment. Overall, there is a need for an intelligent and lightweight system which can automatically monitor eating behaviour and provide feedback. In this paper, we investigate: i) the development of an automated system for detecting eating behaviour using wearable Electromyography (EMG) sensors, and ii) the application of such a system in combination with real time wristband haptic feedback to facilitate mindful eating. Data collected from 16 participants were used to develop an algorithm for detecting chewing and swallowing. We extracted 18 features from EMG and presented those features to different classifiers. We demonstrated that eating behaviour can be automatically assessed accurately using the EMG-extracted features and a Support Vector Machine (SVM): F1-Score=0.94 for chewing classification, and F1-Score=0.86 for swallowing classification. Based on this algorithm, we developed a system to enable participants to self-moderate their chewing behaviour using haptic feedback. An experiment study was carried out with 20 additional participants showing that participants exhibited a lower rate of chewing when haptic feedback delivered in forms of wristband vibration was used compared to a baseline and non-haptic condition (F (2,38)=58.243, p<0.001). These findings may have major implications for research in eating behaviour, providing key new insights into the impacts of automatic chewing detection and haptic feedback systems on moderating eating behaviour with the aim to improve health outcomes.