A new artificial intelligence machine learning model is capable of accurately diagnosing certain illnesses nearly every time by simply looking at a patient's tongue. The novel technology, while state-of-the-art, draws its inspiration from medical approaches utilized by humans for over 2,000 years. When it comes to diagnosing ailments, traditional Chinese medicine and other practices often turn to the tongue for clues. Based on its color, shape, and thickness, the muscle can reveal a number of possible health issues--from cancer, to diabetes, to even asthma and gastrointestinal issues. Now, after more than two millennia of peering into patient mouths for answers, doctors may soon receive a second opinion from artificial eyes powered by machine learning.

A continual learning (CL) model is desired for remote sensing image analysis because of varying camera parameters, spectral ranges, resolutions, etc. There exist some recent initiatives to develop CL techniques in this domain but they still depend on massive labelled samples which do not fully fit remote sensing applications because ground truths are often obtained via field-based surveys. This paper addresses this problem with a proposal of unsupervised flat-wide learning approach (UNISA) for unsupervised few-shot continual learning approaches of remote sensing image scene classifications which do not depend on any labelled samples for its model updates. UNISA is developed from the idea of prototype scattering and positive sampling for learning representations while the catastrophic forgetting problem is tackled with the flat-wide learning approach combined with a ball generator to address the data scarcity problem. Our numerical study with remote sensing image scene datasets and a hyperspectral dataset confirms the advantages of our solution. Source codes of UNISA are shared publicly in \url{https://github.com/anwarmaxsum/UNISA} to allow convenient future studies and reproductions of our numerical results.

Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information. It consists of several subtasks, such as emotion recognition in conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal sentiment analysis (MSA). However, unifying all subtasks in sentiment analysis presents numerous challenges, including modality alignment, unified input/output forms, and dataset bias. To address these challenges, we propose a Task-Specific Prompt method to jointly model subtasks and introduce a multimodal generative framework called UniSA. Additionally, we organize the benchmark datasets of main subtasks into a new Sentiment Analysis Evaluation benchmark, SAEval. We design novel pre-training tasks and training methods to enable the model to learn generic sentiment knowledge among subtasks to improve the model's multimodal sentiment perception ability. Our experimental results show that UniSA performs comparably to the state-of-the-art on all subtasks and generalizes well to various subtasks in sentiment analysis.

University of South Australia's Habib Habibullah says their algorithm could be applied in many environments, including industrial warehouses where robots are commonly used, for robotic fruit picking, packing and pelletizing, and also for restaurant robots An algorithm developed by researchers at the University of South Australia (UniSA) aims to help robots avoid humans and other obstacles in their path while taking the fastest, safest route to their destination. The researchers based their model on the best elements of existing algorithms and used it to create a TurtleBot able to avoid collisions by adjusting its speed and direction. They performed simulations in nine different scenarios and found their model outperformed the online collision avoidance algorithms Dynamic Window Approach and Artificial Potential Field. Said UniSA's Habib Habibullah, "Our proposed method sometimes took a longer path, but it was faster and safer, avoiding all collisions."

Become an expert and make a difference to society. The University of South Australia (UniSA) is Australia's University of Enterprise. We are South Australia's largest university and one of the very best young universities in the world. At UniSA, we are authentic, resilient, and influential - and we deliver results. We pride ourselves on our dynamic and agile culture, which embraces challenges and thrives on breaking new ground.

New research proves that eyes might in fact be windows to the soul. Over the past few years, eye tracking technology has emerged as a field of much academic and corporate interest. With major acquisitions by Apple (SMI) and Oculus (The Eye Tribe), it's clear that major international companies regard eye tracking technology as an vital facet of Industry 4.0 -- particularly in its integration with virtual and augmented reality technologies (VR/AR), which involve persistent interaction with the human eye. In the study, researchers tracked 42 participants' eye movements while going about their day on a university campus, and matrixed these findings against user questionnaires. The results assert that machine learning can in fact deduce important personality traits with appropriate datasets -- with the algorithm reliably identifying four of the "Big Five" human personality traits: agreeableness, conscientiousness, extroversion, and neuroticism. In a statement from UniSA, Senior Lecturer of Psychology Dr. Tobias Loetscher explained that this research establishes a meaningful link between our eye motions and our innate and learned characteristics: People are always looking for improved, personalised [sic] services.