No matter how you use your iPhone, Apple's huge iOS 18 update is certain to bring some big changes to your favourite apps. Revealed at the company's annual Worldwide Developers Conference (WWDC) this week, Apple's latest update will hit devices in autumn this year. From the controversial ability to hide apps to the ability to generate new emojis with AI, iOS 18 is set to roll out with a truly bewildering amount of changes. But the software update won't just give you new features, - it will also bring the new'Apple Intelligence' AI integration to compatible phones. If you're feeling overwhelmed by the absolute mountain of new content in this huge update, here's MailOnline's guide to the biggest features to look out for.

This level of autonomy, in Space exploration, particularly the long-term habitation of conjunction with collaboration between astronauts and robots, planetary surfaces, requires significant technological advances, is pivotal for the successful construction of structures and the with a strong focus on collaboration between robots and astronauts accomplishment of mission-specific tasks. This paper presents where the modularity and autonomy of space robots will the development of the CISRU suite, the preparation of field stand out, allowing them to perform different tasks [2], [3].

With the growing concern about the security and privacy of smart grid systems, cyberattacks on critical power grid components, such as state estimation, have proven to be one of the top-priority cyber-related issues and have received significant attention in recent years. However, cyberattack detection in smart grids now faces new challenges, including privacy preservation and decentralized power zones with strategic data owners. To address these technical bottlenecks, this paper proposes a novel Federated Learning-based privacy-preserving and communication-efficient attack detection framework, known as FedDiSC, that enables Discrimination between power System disturbances and Cyberattacks. Specifically, we first propose a Federated Learning approach to enable Supervisory Control and Data Acquisition subsystems of decentralized power grid zones to collaboratively train an attack detection model without sharing sensitive power related data. Secondly, we put forward a representation learning-based Deep Auto-Encoder network to accurately detect power system and cybersecurity anomalies. Lastly, to adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs, we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD to improve its communication efficiency. Extensive simulations of the proposed framework on publicly available Industrial Control Systems datasets demonstrate that the proposed framework can achieve superior detection accuracy while preserving the privacy of sensitive power grid related information. Furthermore, we find that the gradient quantization scheme utilized improves communication efficiency by 40% when compared to a traditional federated learning approach without gradient quantization which suggests suitability in a real-world scenario.

As Smart Meters are collecting and transmitting household energy consumption data to Retail Energy Providers (REP), the main challenge is to ensure the effective use of fine-grained consumer data while ensuring data privacy. In this manuscript, we tackle this challenge for energy load consumption forecasting in regards to REPs which is essential to energy demand management, load switching and infrastructure development. Specifically, we note that existing energy load forecasting is centralized, which are not scalable and most importantly, vulnerable to data privacy threats. Besides, REPs are individual market participants and liable to ensure the privacy of their own customers. To address this issue, we propose a novel horizontal privacy-preserving federated learning framework for REPs energy load forecasting, namely FedREP. We consider a federated learning system consisting of a control centre and multiple retailers by enabling multiple REPs to build a common, robust machine learning model without sharing data, thus addressing critical issues such as data privacy, data security and scalability. For forecasting, we use a state-of-the-art Long Short-Term Memory (LSTM) neural network due to its ability to learn long term sequences of observations and promises of higher accuracy with time-series data while solving the vanishing gradient problem. Finally, we conduct extensive data-driven experiments using a real energy consumption dataset. Experimental results demonstrate that our proposed federated learning framework can achieve sufficient performance in terms of MSE ranging between 0.3 to 0.4 and is relatively similar to that of a centralized approach while preserving privacy and improving scalability.

With growing security and privacy concerns in the Smart Grid domain, intrusion detection on critical energy infrastructure has become a high priority in recent years. To remedy the challenges of privacy preservation and decentralized power zones with strategic data owners, Federated Learning (FL) has contemporarily surfaced as a viable privacy-preserving alternative which enables collaborative training of attack detection models without requiring the sharing of raw data. To address some of the technical challenges associated with conventional synchronous FL, this paper proposes FeDiSa, a novel Semi-asynchronous Federated learning framework for power system faults and cyberattack Discrimination which takes into account communication latency and stragglers. Specifically, we propose a collaborative training of deep auto-encoder by Supervisory Control and Data Acquisition sub-systems which upload their local model updates to a control centre, which then perform a semi-asynchronous model aggregation for a new global model parameters based on a buffer system and a preset cut-off time. Experiments on the proposed framework using publicly available industrial control systems datasets reveal superior attack detection accuracy whilst preserving data confidentiality and minimizing the adverse effects of communication latency and stragglers. Furthermore, we see a 35% improvement in training time, thus validating the robustness of our proposed method.

Rapid urbanisation in Nairobi, Kenya's capital city, has meant there's been huge growth in the number of vehicles on roads. Today, Nairobi is one of the world's most congested cities. Kenya Urban Roads Authority (KURA) Director General Silas Kinoti has said intelligent infrastructure is helping transport networks to become more connected in an attempt to identify ways of improving experience for everyone on the road. Bird's eye view tech aims to unlock Nairobi traffic jams according to @KURAroads Director General @MuriraKinoti who believes that construction of many roads is a milestone yes but not a solution to nerve-racking snarl ups pic.twitter.com/WGHhRk9N3X He said Kenya will be seeking to emulate on their foreign counterparts like Germany to initiate usage of Artificial Intelligence(AI) to optimise traffic light control and reduce the waiting time at an intersection. "There are real world projects around the globe and the applications are continuously expanding. Artificial Intelligence (AI) will be key to help us with the data which would identify patterns that would not have been seen without AI. Through continuous learning, we're able to constantly update the traffic patterns and thus traffic flow. Road Traffic monitoring involves the collection of data describing the characteristic of vehicles and their movement through road networks. Such data may be used for one of these purposes such as law enforcement, congestion and incident detection and increasing road capacity. The roads in Nairobi carry more than 60 per cent of more than two million registered vehicles, resulting in tangles of traffic stretching for miles. Earlier today, KURA top management team inspected the dualling of the Eastern Bypass Project and appreciated the progress achieved. Once the road is complete, traffic jams will be reduced and improve connectivity.@PDUDelivery "KURA being an expert in Intelligent Traffic System (ITS) with an example being Yaya Centre,we will have cameras, signals and censors in all arms of the junctions.

Athens, Greece – "Let's go see something that looks really nice," says Anastasios Salis, head of information and communications technology at the Greek Migration and Asylum Ministry in Athens, before entering an airtight room sealed behind two interlocking doors, accessible only with an ID card and fingerprint scan. Beyond these doors is the ministry's newly-installed centralised surveillance room. The front wall is covered by a vast screen. More than a dozen rectangles and squares display footage from three refugee camps already connected to the system. Another screen shows the playground and another the inside of one of the containers where people socialise.

Abstract: To build a robust secure solution for smart city's IOT network from any Cyber-attacks using Artificial Intelligence (AI). In Smart City's IOT network, data collected from different log collectors or direct sources from cloud or edge should harness the potential of AI. The smart city command and control center team will leverage these models and deploy it in different city's IOT network to help on intrusion prediction, network packet surge, potential botnet attacks from external network. Some of the vital use cases considered based on the users of command-and-control center. Keywords-Artificial Intelligence, Internet of Things, Smart City, IOT Security, Smart City command and control center I. INTRODUCTION The Internet of Things market will grow from 170 Billion devices (as on 2017) to 561 Billion devices by 2020 as reply.com It will bring more niche devices like Smart Home appliances, Smart Home Security, Digital Assistants and Home Robots from different providers.

The University of Waterloo is showcasing the operation of a driverless, autonomous shuttle research program that will transport students and staff around campus. The demonstration of the shuttle, dubbed "WATonoBus" by the research team, is the first of its kind at a Canadian academic institution and marks a significant milestone in a multi-year initiative to demonstrate and integrate autonomous transportation onto the campus. This milestone features another significant technological advancement, as the shuttle becomes the first in the country to operate remotely over Rogers 5G network, thanks to a Rogers partnership agreement with the University to advance 5G research in the Toronto-Waterloo tech-corridor. This past September, as Rogers expanded Canada's largest and most reliable 5G network to reach more communities, it lit up the University's 5G Smart Campus to support researchers developing 5G applications and use cases in a real-world setting. The shuttle's 5-stop, 2.7-kilometre journey around the Waterloo main campus, intersecting with the campus light rail transit stop, holds the potential to help reshape how entire communities move around their urban spaces.

According to JLR, it will span 12km of public roads, combining smart junctions and connected car parks to facilitate the harnessing of valuable sensor data and offer the unique ability to trial new technologies. Aptly named the Future Mobility Campus Ireland (FMCI), the facility will be equipped with sensors throughout the site, along with high-accuracy location systems, a data management and control centre and self-driving prototype vehicles. Russel Vickers (pictured above), chief executive officer at the FMCI, explained: "The smart-city zone provides a first-class facility for global companies to work together and develop world-leading technology, from autonomous vehicles to connected infrastructure. The testbed provides an opportunity to test in the real world and help answer some of the questions posed by the future of mobility in a collaborative and efficient way." As part of the trials, the company's all-electric performance SUV, the Jaguar I-PACE, will be deployed for testing.