Robotics, machine learning, data science, and mathematical modelling are just some of the tools that a group of researchers will use to forecast environmental changes across Antarctica as part of a seven-year research project. To be led by Monash University, the Securing Antarctica's Environmental Future (SAEF) project will involve 30 Australian and overseas organisations, including Queensland University of Technology (QUT), University of Wollongong, University of New South Wales, James Cook University, University of Adelaide, the South Australian Museum, and the Western Australian Museum. According to QUT Institute for Future Environments executive director Kerrie Wilson, who will form part of the program's leadership team, the research aims to "bring new perspectives to Antarctic conservation". "Antarctica is facing unprecedented threats from climate change, fishing, visitation, and other human activities. Safeguarding its future will require new ideas, and collaborations between different fields of science," she told ZDNet.

Along with my CTO, Ido Ivri, I Co-Founded Zencity to help local governments make data-driven decisions based on their communities' priorities when creating policies and communicating them to their residents. The Zencity platform gathers and analyzes millions of anonymized, aggregated data points of community feedback from channels like social media, local broadcast media, and government customer service channels (such as 311 and call centers) and turns them into actionable insights about community trends and priorities for local government decision-makers. We analyze these millions of unstructured data points by using advanced AI and NLP algorithms to make the data structured and actionable for these organizations. The algorithms automatically classify data by relevance to the different departments in city hall and then run a sentiment analysis to determine if the data reflects positive, negative, or neutral feedback on a city-related topic. As trends emerge throughout cities or regions, our platform sends alerts to city officials so that they can take immediate action and be proactive.

A Russian rocket used to launch a scientific satellite into space has broken apart after nine years in orbit - leaving a dozens of pieces of debris around the Earth. The Fregat-SB is a type of space tug and its upper stage was left floating after it helped deliver the Spektr-R satellite in 2011, according to Roscosmos. Spektr-R was a radio telescope launched by the Russian space agency but it stopped responding to ground control last year and was declared dead in May 2019. Roscosmos confirmed the breakdown of the rocket happened on May 8 between 06:00 and 07:00 BST somewhere above the Indian ocean. About two-thirds of the satellites orbiting the Earth are dead - about 3,000 of about 4,500 objects - and pose a'very big danger' to the planet - this also includes parts of the Russian rocket that disintegrated (artist's impression) The Russian space agency is studying data to find out how many parts it broke up into and where they are currently orbiting the planet.

Animosity deepened in early January when a U.S. drone strike in Baghdad killed top Iranian military commander Qassem Soleimani. Later that day, Iran's armed forces shot down a Ukrainian airliner, killing all 176 people aboard, in what the military later acknowledged was a mistake.

When it comes to AI, Elon Musk has a name in treating it like some aliens' attack or God's wrath upon us, even without an exaggeration. Speaking at MIT in 2014, he called AI humanity's "biggest existential threat" and compared it to "summoning the demon." He is very optimistic about all other technologies like neurotechnology, self-driving cars, Mars colonization, and others, but Artificial Intelligence always seems to scare him. I wonder if it is something so dreadful as he says it is. Today, we will talk about the things he has said in the past years about AI implications and sees if they are convincing enough.

The long-simmering debate over facial recognition technology is taking on new urgency during the pandemic, as companies rush to pitch face-scanning systems to track the movements of Covid-19 patients. That's playing out in California, where state legislators on Tuesday will debate legislation that would regulate the use of the technology. Its most controversial element: It would permit companies and public agencies to feed people's facial data into a recognition system without their consent if there is probable cause to believe they've engaged in criminal activity. The bill isn't specifically meant for the coronavirus response, but if enacted, could shape the way that people with Covid-19 and their contacts are tracked and traced in the coming months. The legislation has won the support of Microsoft, but it has garnered opposition from more than 40 civil rights and privacy groups and from 18 public health scholars.

This paper reviews current literature in the field of predictive maintenance from the system point of view. We differentiate the existing capabilities of condition estimation and failure risk forecasting as currently applied to simple components, from the capabilities needed to solve the same tasks for complex assets. System-level analysis faces more complex latent degradation states, it has to comprehensively account for active maintenance programs at each component level and consider coupling between different maintenance actions, while reflecting increased monetary and safety costs for system failures. As a result, methods that are effective for forecasting risk and informing maintenance decisions regarding individual components do not readily scale to provide reliable sub-system or system level insights. A novel holistic modeling approach is needed to incorporate available structural and physical knowledge and naturally handle the complexities of actively fielded and maintained assets.

This paper presents channel-aware adversarial attacks against deep learning-based wireless signal classifiers. There is a transmitter that transmits signals with different modulation types. A deep neural network is used at each receiver to classify its over-the-air received signals to modulation types. In the meantime, an adversary transmits an adversarial perturbation (subject to a power budget) to fool receivers into making errors in classifying signals that are received as superpositions of transmitted signals and adversarial perturbations. First, these evasion attacks are shown to fail when channels are not considered in designing adversarial perturbations. Then realistic attacks are presented by considering channel effects from the adversary to each receiver. After showing that a channel-aware attack is selective (i.e., it affects only the receiver whose channel is considered in the perturbation design), a broadcast adversarial attack is presented by crafting a common adversarial perturbation to simultaneously fool classifiers at different receivers. The major vulnerability of modulation classifiers to over-the-air adversarial attacks is shown by accounting for different levels of information available about channel, transmitter input, and classifier model. Finally, a certified defense based on randomized smoothing that augments training data with noise is introduced to make modulation classifier robust to adversarial perturbations.

We provide an insight into the open data resources pertinent to the study of the spread of Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behaviour, regional mortality rates, and effectiveness of government measures. Open data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, at a world scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 data-sets at a country-wide level (i.e. China, Italy, Spain, France, Germany, U.S., etc.). In an attempt to facilitate the rapid response to the study of the seasonal behaviour of Covid-19, we enumerate the main open resources in terms of weather and climate variables. CONCO-Team: The authors of this paper belong to the CONtrol COvid-19 Team, which is composed of different researches from universities of Spain, Italy, France, Germany, United Kingdom and Argentina. The main goal of CONCO-Team is to develop data-driven methods for the better understanding and control of the pandemic.

As humanity struggles to contain the global Covid-19 infection, prophylactic actions are grandly slowed down by the shortage of testing kits. Governments have taken several measures to work around this shortage: the FDA has become more liberal on the approval of Covid-19 tests in the US. In the UK emergency measures allowed to increase the daily number of locally produced test kits to 100,000. China has recently launched a massive test manufacturing program. However, all those efforts are very insufficient and many poor countries are still under threat. A popular method for reducing the number of tests consists in pooling samples, i.e. mixing patient samples and testing the mixed samples once. If all the samples are negative, pooling succeeds at a unitary cost. However, if a single sample is positive, failure does not indicate which patient is infected. This paper describes how to optimally detect infected patients in pools, i.e. using a minimal number of tests to precisely identify them, given the a priori probabilities that each of the patients is healthy. Those probabilities can be estimated using questionnaires, supervised machine learning or clinical examinations. The resulting algorithms, which can be interpreted as informed divide-and-conquer strategies, are non-intuitive and quite surprising. They are patent-free. Co-authors are listed in alphabetical order.