Air pollution in cities, especially NO\textsubscript{2}, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities. Here, we demonstrate how city CCTV cameras can act as a pseudo-NO\textsubscript{2} sensors. Using a predictive graph deep model, we utilised traffic flow from London's cameras in addition to environmental and spatial factors, generating NO\textsubscript{2} predictions from over 133 million frames. Our analysis of London's mobility patterns unveiled critical spatiotemporal connections, showing how specific traffic patterns affect NO\textsubscript{2} levels, sometimes with temporal lags of up to 6 hours. For instance, if trucks only drive at night, their effects on NO\textsubscript{2} levels are most likely to be seen in the morning when people commute. These findings cast doubt on the efficacy of some of the urban policies currently being implemented to reduce pollution. By leveraging existing camera infrastructure and our introduced methods, city planners and policymakers could cost-effectively monitor and mitigate the impact of NO\textsubscript{2} and other pollutants.

Abstract-- This paper investigates the utility of Neural Radiance Fields (NeRF) models in extending the regions of operation of a mobile robot, controlled by Image-Based Visual Servoing (IBVS) via static CCTV cameras. Using NeRF as a 3Drepresentation prior, the robot's footprint may be extrapolated geometrically and used to train a CNN-based network to extract it online from the robot's appearance alone. The resulting footprint results in a tighter bound than a robot-wide bounding box, allowing the robot's controller to prescribe more optimal trajectories and expand its safe operational floor area. Visual servoing is a robotics technique that provides control Figure 1: [4] controls the robot based on its bounding box (yellow) based on visual feedback from external cameras. When checking if a trajectory [1], the field has evolved to encompass various methodologies is safe, its box must stay within the drivable region (blue).

Thousands of people catching trains in the United Kingdom likely had their faces scanned by Amazon software as part of widespread artificial intelligence trials, new documents reveal. The image recognition system was used to predict travelers' age, gender, and potential emotions--with the suggestion that the data could be used in advertising systems in the future. During the past two years, eight train stations around the UK--including large stations such as London's Euston and Waterloo, Manchester Piccadilly, and other smaller stations--have tested AI surveillance technology with CCTV cameras with the aim of alerting staff to safety incidents and potentially reducing certain types of crime. The extensive trials, overseen by rail infrastructure body Network Rail, have used object recognition--a type of machine learning that can identify items in videofeeds--to detect people trespassing on tracks, monitor and predict platform overcrowding, identify antisocial behavior ("running, shouting, skateboarding, smoking"), and spot potential bike thieves. Separate trials have used wireless sensors to detect slippery floors, full bins, and drains that may overflow.

More than 100 days of nationwide protests in Iran have demonstrated the greatest pushback against the decades-old regime and its repressive policies, showing the world that the people demand rights they have long been denied. Iran has embraced artificial intelligence (AI) as a way to significantly improve its state surveillance networks, allowing the repressive regime to further crack down on perceived offenses. "The Iran regime is certainly joining rogue leaders of the world in redefining and modernizing their modes of suppression," Lisa Daftari, a Middle East expert and editor-in-chief of The Foreign Desk, told Fox News Digital. "Unfortunately, just as the Iranian people are finding innovative ways of using social media, streaming and VPNs to get their message out, the regime is also taking advantage of technological advances to continue their reign of brutality." "The regime in Iran is using surveillance technology to identify'transgressors,'" Daftari said.

One of the most neglected sources of energy loss is streetlights which generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of the operation, streetlights are frequently seen being turned ON during the day and OFF in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight 'ON' and 'OFF' to save energy consumption costs. According to the aforementioned approach, geolocation sensor data could be utilized to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. The validity of the models is guaranteed with the use of assessment matrices. The suggested concept is straightforward, economical, energy-efficient, long-lasting, and more resilient than conventional alternatives.

This month, French lawmakers are expected to pass legislation for the 2024 Paris Olympics, which, for the first time in France's history, will permit mass video surveillance powered by artificial intelligence (AI) systems. When governments embark on the slippery slope towards the expansion of surveillance powers, it has damning consequences for fundamental human rights, including the rights to privacy, equality and non-discrimination, as well as freedom of expression and peaceful assembly. Under the guise of ensuring security and fighting terrorism, the French authorities will be able to monitor the movements of millions of people from around the world, whether they are heading to or near stadiums, or using public transportation leading in or out of the premises of the grand sporting event. The need for security during the game is understandable, but transparency and legal justification are needed at every step of the way. Any proposal concerning security must comply with fundamental rights.

Calculating an Air Quality Index (AQI) typically uses data streams from air quality sensors deployed at fixed locations and the calculation is a real time process. If one or a number of sensors are broken or offline, then the real time AQI value cannot be computed. Estimating AQI values for some point in the future is a predictive process and uses historical AQI values to train and build models. In this work we focus on gap filling in air quality data where the task is to predict the AQI at 1, 5 and 7 days into the future. The scenario is where one or a number of air, weather and traffic sensors are offline and explores prediction accuracy under such situations. The work is part of the MediaEval'2022 Urban Air: Urban Life and Air Pollution task submitted by the DCU-Insight-AQ team and uses multimodal and crossmodal data consisting of AQI, weather and CCTV traffic images for air pollution prediction.

Lawyer and developer Matthew Butterick announced last month that he'd teamed up with the Joseph Saveri Law Firm to investigate Copilot. They wanted to know if and how the software infringed upon the legal rights of coders by scraping and emitting their work without proper attribution under current open-source licenses. Now, the firm has filed a class-action lawsuit in the District Court of Northern California in San Francisco. "We are challenging the legality of GitHub Copilot," Butterick said. "This is the first step in what will be a long journey. As far as we know, this is the first class-action case in the US challenging the training and output of AI systems. It will not be the last. AI systems are not exempt from the law. Those who create and operate these systems must remain accountable," he continued in a statement.

Joe Levy is working hard to help avoid another mass school shooting tragedy. He says his technology, designed to spot a gun using existing CCTV cameras, could make a critical difference in future life-or-death situations. Seventeen people died in 2018 when a 19-year-old student opened fire at Stoneman Douglas High School, in Parkland, Florida, USA. Fifteen died in the Columbine High School massacre, near Denver, Colorado, in 1999 when a 17-year-old and an 18-year-old shot fellow students. And 22 people died in May of this year when an 18-year-old rampaged through the Robb Elementary School, in Uvalde, Texas – one of the worst school shootings in US history.

Wisdomise Data Scientist - Hamid Reza Mazandarani, shares his insight on a very intriguing topic within the world of AI. One of the most important elements to blockchain technology is distribution of control, ownership, and learning. Achieving distributed network architecture means high security and fair access for the masses among many other important concepts. Adding Artificial Intelligence (AI) to a decentralized ecosystem has the potential to elevate its capabilities alongside the utility for users. But how can we go about to accomplish this, and what must we keep a look out for?