--Federated Learning (FL) remains highly vulnerable to poisoning attacks, especially under real-world hyper-heterogeneity, where clients differ significantly in data distributions, communication capabilities, and model architectures. Such heterogeneity not only undermines the effectiveness of aggregation strategies but also makes attacks more difficult to detect. Furthermore, high-dimensional models expand the attack surface. T o address these challenges, we propose Horus, a heterogeneity-oblivious robust FL framework centered on low-rank adaptations (LoRAs). Rather than aggregating full model parameters, Horus inserts LoRAs into empirically stable layers and aggregates only LoRAs to reduce the attack surface. We uncover a key empirical observation that the input projection (LoRA-A) is markedly more stable than the output projection (LoRA-B) under heterogeneity and poisoning. Leveraging this, we design a Heterogeneity-Oblivious Poisoning Score using the features from LoRA-A to filter poisoned clients. For the remaining benign clients, we propose projection-aware aggregation mechanism to preserve collaborative signals while suppressing drifts, which reweights client updates by consistency with the global directions. Extensive experiments across diverse datasets, model architectures, and attacks demonstrate that Horus consistently outperforms state-of-the-art baselines in both robustness and accuracy. Federated Learning (FL) has gained significant traction as a privacy-preserving paradigm for distributed training, enabling clients to collaboratively learn a global model without sharing their raw data [12], [20]. However, the decentralized nature of FL inherently introduces serious security vulnerabilities, making it susceptible to poisoning attacks, in which attackers inject malicious data or local updates. Such attacks pose a particularly insidious threat, as they can stealthily degrade or manipulate the global model over time [29]. For example, perturbing a federated model deployed in vehicular systems could autonomously start the vehicle or execute an emergency brake, thereby endangering human lives and compromising property safety [24].

-- Mixed Reality (MR) interfaces have been extensively explored for controlling mobile robots, but there is limited research on their application to managing teams of robots. This paper presents HORUS: Holistic Operational Reality for Unified Systems, a Mixed Reality interface offering a comprehensive set of tools for managing multiple mobile robots simultaneously. HORUS enables operators to monitor individual robot statuses, visualize sensor data projected in real time, and assign tasks to single robots, subsets of the team, or the entire group, all from a Mini-Map (Ground Station). The interface also provides different teleoperation modes: a mini-map mode that allows teleoperation while observing the robot model and its transform on the mini-map, and a semi-immersive mode that offers a flat, screen-like view in either single or stereo view (3D). We conducted a user study in which participants used HORUS to manage a team of mobile robots tasked with finding clues in an environment, simulating search and rescue tasks. This study compared HORUS's full-team management capabilities with individual robot teleoperation. The experiments validated the versatility and effectiveness of HORUS in multi-robot coordination, demonstrating its potential to advance human-robot collaboration in dynamic, team-based environments.

NASA's Artemis program aims to send astronauts to the south pole of the Moon, where water in the form of ice has been confirmed, rather than near the equator as with the Apollo mission. The dark areas are likely to contain more ice than the others but also to be dangerous for the astronauts venturing there. A team of researchers studied these areas using deep learning, the study entitled "Cryogeomorphic Characterization of Shadowed Regions in the Artemis Exploration Zone" was published in Geophysical Research Letters. For the first Artemis lunar missions, the selected astronauts (one man and one woman) will fly to the south pole of the moon. This region has a great potential, it is thought to have the greatest abundance of water ice because it has craters where the sun's rays never penetrate, their temperature is estimated at -170 .

There is a substantial market foreseen for autonomous UAVs including the delivery business where a number of startups are beginning to establish themselves [1]. As son as a number of such aircraft inhabit the airspace, autonomous operation with in-flight correction, rather than direct control, is expected to be the operational modus of choice. In the European airspace the SORA (Specific Operations Risk Assessment) process, whilst designed to enable, or at least not to hinder innovation in this market, are explicit on the safety demands on UAVs [2]. As a result, adhering to these specifications comes at considerable cost. Roughly broken down, an autonomous UAV consists of an airframe, propulsion and base station and flight controllers. Whereas airframe and propulsion require a certain co-design/co-specification effort, Figure 1: HORUS Mounted on a small Drone. Two of the three GPS under the SORA regime, at least for aircraft of similar weight antennas are visible. HORUS itself is the small, credit-card sized class, flight controllers for out-of-sight operation can be PCB on the bottom.

The Moon’s polar regions are home to craters and other depressions that never receive sunlight. Today, a group of researchers led by the Max Planck Institute for Solar System Research (MPS) in Germany presents the highest-resolution images to date covering 17 such craters in the journal Nature Communications. Craters of this type could contain frozen water, making them attractive targets for future lunar missions, and the researchers focused further on relatively small and accessible craters surrounded by gentle slopes. In fact, three of the craters have turned out to lie within the just-announced mission area of NASA's Volatiles Investigating Polar Exploration Rover (VIPER), which is scheduled to touch down on the Moon in 2023. Imaging the interior of permanently shadowed craters is difficult, and efforts so far have relied on long exposure times resulting in smearing and lower resolution. By taking advantage of reflected sunlight from nearby hills and a novel image processing method, the researchers have now produced images at 1-2 meters per pixel, which is at or very close to the best capability of the cameras.

Automatically monitoring and supporting healthy lifestyle is a recent research trend, fostered by the availability of low-cost monitoring devices, and it can significantly contribute to the prevention of chronic diseases deriving from incorrect diet and lack of physical activity. In this talk I will present the HORUS.AI platform: an AI-based platform built upon the integration of semantic web technologies and persuasive techniques for motivating people to adopt healthy lifestyle or for supporting them to cope with the self-management of chronic diseases. The platform collects data from users' devices, explicit users' inputs, or from the external environment (e.g. Interactive dialogues are used for proposing set of challenges to users that, through a mobile application, are able to provide the required information and to receive contextual motivational messages helping them to achieve the proposed goals. HORUS.AI is constituted by two main layers: the Knowledge and the Dialog-Based Persuasive layers.

Since 2016, 4Tel Pty Ltd of Newcastle, Australia, has been investing in the development of artificial intelligence for application in the rail industry generally. As a part of this activity, 4Tel has a research and development contract with the University of Newcastle Robotics Laboratory known as NUBots, where 4Tel is their Platinum Sponsor. The work is being conducted under Project HORUS, which seeks to develop an Advanced Driver Advisory System (ADAS) using real-time sensors and software to assist a driver in the safe operation of a locomotive. As the technical basis to this work, 4Tel has selectively applied modern autonomous car technology to achieve very sophisticated artificial, intelligence based, ADAS functionality. For safe and efficient operations, a locomotive needs to know exactly where it is, recognise the objects around it, and continuously monitor the authorised route for normal operations.

Since 2016, 4Tel Pty Ltd of Newcastle, Australia, has been investing in the development of artificial intelligence for application in the rail industry generally. As a part of this activity, 4Tel has a research and development contract with the University of Newcastle Robotics Laboratory known as NUBots, where 4Tel is their Platinum Sponsor. The work is being conducted under Project HORUS, which seeks to develop an Advanced Driver Advisory System (ADAS) using real-time sensors and software to assist a driver in the safe operation of a locomotive. As the technical basis to this work, 4Tel has selectively applied modern autonomous car technology to achieve very sophisticated artificial, intelligence based, ADAS functionality. For safe and efficient operations, a locomotive needs to know exactly where it is, recognise the objects around it, and continuously monitor the authorised route for normal operations.

The world is getting better at combining machine learning and computer vision, but it's not just cars and drones that benefit from that. For instance, the same technology could be used to dramatically improve the lives of people with visual impairments, enabling them to be more independent. One of the startups looking to do just that is Eyra, which is showing off a wearable called Horus that could help the blind "see." I got to try out the prototype hardware at TechCrunch Disrupt, and while the design is close to being done, the current device is still rough around the edges. The starting point is a pair of Aftershokz bone-conduction headphones with a camera module attached to the right hand side.

A new wearable aid for the blind and visually impaired people uses machine learning and artificial intelligence to better analyze fed data from cameras and sensors. The device is being developed by Swiss startup Eyra, and is named Horus, after the Egyptian god. Its an apt symbol since stories tell us that Horus lost his eye in a fight only to have it restored by another god. Horus is a wrap-around headband equipped with two cameras to watch for what's in front of the user. The images seen are narrated through earpieces that directly stimulate the tiny bones in the ear, with a technology called bone conduction.