Delays in public transport are common, often impacting users through prolonged travel times and missed transfers. Existing solutions for handling delays remain limited; backup plans based on historical data miss opportunities for earlier arrivals, while snapshot planning accounts for current delays but not future ones. With the growing availability of live delay data, users can adjust their journeys in real-time. However, the literature lacks a framework that fully exploits this advantage for system-scale dynamic replanning. To address this, we formalise the dynamic replanning problem in public transport routing and propose two solutions: a "pull" approach, where users manually request re-planning, and a novel "push" approach, where the server proactively monitors and adjusts journeys. Our experiments show that the push approach outperforms the pull approach, achieving significant speedups. The results also reveal substantial arrival time savings enabled by dynamic replanning.

Robots zipping down footpaths may sound futuristic, but they are increasingly being put to work making deliveries around the world – though a legal minefield and cautious approach to new tech means they are largely absent in Australia. Retail and food businesses have been using robots for a variety of reasons, with hazard detection robots popping up in certain Woolworths stores and virtual waiters taking dishes from kitchens in understaffed restaurants to hungry diners in recent years. Overseas, in jurisdictions such as California, robots are far more visible in everyday life. Following on from the first wave of self-driving car trials in cities such as San Francisco, humans now also share footpaths with robots. Likened to lockers on wheels, companies including Serve Robotics and Coco have partnered with Uber Eats and Doordash, which have armies of robots travelling along footpaths in Los Angeles delivering takeaway meals and groceries.

Spoken language assessment (SLA) systems restrict themselves to evaluating the pronunciation and oral fluency of a speaker by analysing the read and spontaneous spoken utterances respectively. The assessment of language grammar or vocabulary is relegated to written language assessment (WLA) systems. Most WLA systems present a set of sentences from a curated finite-size database of sentences thereby making it possible to anticipate the test questions and train oneself. In this paper, we propose a novel end-to-end SLA system to assess language grammar from spoken utterances thus making WLA systems redundant; additionally, we make the assessment largely unteachable by employing a large language model (LLM) to bring in variations in the test. We further demonstrate that a hybrid automatic speech recognition (ASR) with a custom-built language model outperforms the state-of-the-art ASR engine for spoken grammar assessment.

For autonomous robots navigating in urban environments, it is important for the robot to stay on the designated path of travel (i.e., the footpath), and avoid areas such as grass and garden beds, for safety and social conformity considerations. This paper presents an autonomous navigation approach for unknown urban environments that combines the use of semantic segmentation and LiDAR data. The proposed approach uses the segmented image mask to create a 3D obstacle map of the environment, from which, the boundaries of the footpath is computed. Compared to existing methods, our approach does not require a pre-built map and provides a 3D understanding of the safe region of travel, enabling the robot to plan any path through the footpath. Experiments comparing our method with two alternatives using only LiDAR or only semantic segmentation show that overall our proposed approach performs significantly better with greater than 91% success rate outdoors, and greater than 66% indoors. Our method enabled the robot to remain on the safe path of travel at all times, and reduced the number of collisions.

Kate Crawford, one of the world's pre-eminent scholars on the social and political implications of artificial intelligence, is being watched. She has arrived at our meeting point outside an anonymous inner-Sydney building before me and, while she waits on the footpath, is twice questioned by people who seem to be security staff. A woman is the first to come out of the building. Are you meeting someone here, she asks, do you have an appointment? He asks Crawford if there's anything she needs.