Geo-tagged tweets can potentially help with sensing the interaction of people with their surrounding environment. Based on this hypothesis, this paper makes use of geotagged tweets in order to ascertain various land uses with a broader goal to help with urban/city planning. The proposed method utilises supervised learning to reveal spatial land use within cities with the help of Twitter activity signatures. Specifically, the technique involves using tweets from three cities of Australia namely Brisbane, Melbourne and Sydney. Analytical results are checked against the zoning data provided by respective city councils and a good match is observed between the predicted land use and existing land zoning by the city councils. We show that geo-tagged tweets contain features that can be useful for land use identification.

Former CDC Director Dr. Tom Frieden says the new strain increases the urgency for vaccines and wearing masks. The Food and Drug Administration (FDA) on Friday issued an alert about the impact viral mutations of the coronavirus may have, including the potential to result in false negative tests. The variant, B.1.1.7 was first discovered in the U.K. several weeks ago, and has been confirmed in over 50 cases in the U.S. so far. "The Food and Drug Administration is alerting clinical laboratory staff and health care providers that the FDA is monitoring the potential impact of viral mutations, including an emerging variant from the United Kingdom known as the B.1.1.7 variant, on authorized SARS-CoV-2 molecular tests, and that false negative results can occur with any molecular test for the detection of SARS-CoV-2 if a mutation occurs on the part of the virus's genome assessed by that test," the FDA said. "The SARS-CoV-2 virus can mutate over time, like all viruses, resulting in genetic variation in the population of circulating viral strains, as seen with the B.1.1.7 variant."

The U.S.-China relationship will be the great geopolitical rivalry of the early 21st century, and every facet of the competition will involve the two big powers' capabilities in science and technology. Figures from across the political spectrum worry about a technology race with China, and many Americans fear that China has already surpassed us in such frontier technologies as artificial intelligence and 5G broadband communications. "China has stolen a march and is now leading in 5G," then-Attorney General William Barr declared in a recent keynote speech at a Justice Department conference on China. Graham Allison of Harvard University warns that China "is currently on a trajectory to overtake the United States in the decade ahead" in artificial intelligence. The conventional wisdom about China's supposed advantages in AI and 5G shows how easy it is for incomplete understanding of technologies to lead to misjudgments and policy mistakes.

In 1964, the Civil Rights Act barred the humans who made hiring decisions from discriminating on the basis of sex or race. Now, software often contributes to those hiring decisions, helping managers screen résumés or interpret video interviews. That worries some tech experts and civil rights groups, who cite evidence that algorithms can replicate or magnify biases shown by people. In 2018, Reuters reported that Amazon scrapped a tool that filtered résumés based on past hiring patterns because it discriminated against women. Legislation proposed in the New York City Council seeks to update hiring discrimination rules for the age of algorithms.

The coronavirus test being provided daily to tens of thousands of residents in Los Angeles and other parts of California may be producing inaccurate results, according to a warning from federal officials that could raise questions about the accuracy of infection data shaping the pandemic response. The guidance from the Food and Drug Administration warns healthcare providers and patients that the test made by Curative, a year-old Silicon Valley start-up that supplies the oral-swab tests at L.A.'s 10 drive-through testing sites, carries a "risk of false results, particularly false negative results." To reduce the risk of false negatives, the Curative test should be used only on "symptomatic individuals within 14 days of COVID-19 symptom onset," and the swab should be observed and directed by a healthcare worker, the FDA said. The guidance, issued Monday, repeats the instructions that the FDA issued when the test was first granted an emergency-use authorization. The FDA warning appears to sharply contradict Los Angeles Mayor Eric Garcetti, who in April made coronavirus testing available to anyone, regardless of symptoms.

Recommender systems play a crucial role in helping users to find their interested information in various web services such as Amazon, YouTube, and Google News. Various recommender systems, ranging from neighborhood-based, association-rule-based, matrix-factorization-based, to deep learning based, have been developed and deployed in industry. Among them, deep learning based recommender systems become increasingly popular due to their superior performance. In this work, we conduct the first systematic study on data poisoning attacks to deep learning based recommender systems. An attacker's goal is to manipulate a recommender system such that the attacker-chosen target items are recommended to many users. To achieve this goal, our attack injects fake users with carefully crafted ratings to a recommender system. Specifically, we formulate our attack as an optimization problem, such that the injected ratings would maximize the number of normal users to whom the target items are recommended. However, it is challenging to solve the optimization problem because it is a non-convex integer programming problem. To address the challenge, we develop multiple techniques to approximately solve the optimization problem. Our experimental results on three real-world datasets, including small and large datasets, show that our attack is effective and outperforms existing attacks. Moreover, we attempt to detect fake users via statistical analysis of the rating patterns of normal and fake users. Our results show that our attack is still effective and outperforms existing attacks even if such a detector is deployed.

After affecting the world in unexpected ways, COVID-19 has started mutating which is evident with the insurgence of its new variants. The governments, hospitals, schools, industries, and humans, in general, are looking for a potential solution in the vaccine which will eventually be available but its timeline for eradicating the virus is yet unknown. Several researchers have encouraged and recommended the use of good practices such as physical healthcare monitoring, immunity-boosting, personal hygiene, mental healthcare, and contact tracing for slowing down the spread of the virus. In this article, we propose the use of wearable/mobile sensors integrated with the Internet of Everything to cover the spectrum of good practices in an automated manner. We present hypothetical frameworks for each of the good practice modules and propose the COvid-19 Resistance Framework using the Internet of Everything (CORFIE) to tie all the individual modules in a unified architecture. We envision that CORFIE would be influential in assisting people with the new normal for current and future pandemics as well as instrumental in halting the economic losses, respectively. We also provide potential challenges and their probable solutions in compliance with the proposed CORFIE.

Undirected probabilistic graphical models represent the conditional dependencies, or Markov properties, of a collection of random variables. Knowing the sparsity of such a graphical model is valuable for modeling multivariate distributions and for efficiently performing inference. While the problem of learning graph structure from data has been studied extensively for certain parametric families of distributions, most existing methods fail to consistently recover the graph structure for non-Gaussian data. Here we propose an algorithm for learning the Markov structure of continuous and non-Gaussian distributions. To characterize conditional independence, we introduce a score based on integrated Hessian information from the joint log-density, and we prove that this score upper bounds the conditional mutual information for a general class of distributions. To compute the score, our algorithm sing estimates the density using a deterministic coupling, induced by a triangular transport map, and iteratively exploits sparse structure in the map to reveal sparsity in the graph. For certain non-Gaussian datasets, we show that our algorithm recovers the graph structure even with a biased approximation to the density. Among other examples, we apply sing to learn the dependencies between the states of a chaotic dynamical system with local interactions.

In their Policy Forum “How to fix the GDPR's frustration of global biomedical research” (2 October 2020, p. [40][1]), J. Bovenberg et al. argue that the biomedical research community has struggled to share data outside the European Union as a result of the EU's General Data Protection Regulation (GDPR), which strictly limits the international transfer of personal data. However, they do not acknowledge the law's flexibility, and their solutions fail to recognize the importance of multilateral efforts to raise standards for global data-sharing. Bovenberg et al. express concern about the thwarting of “critical data flows” in biomedical research. However, the limited number of critical commentaries ([ 1 ][2], [ 2 ][3]) and registered complaints ([ 3 ][4]) indicate that hindered data exchange may not be a substantial global problem. Moreover, the authors concede that during the COVID-19 pandemic, data transfers remain ongoing because transfers “necessary for important reasons of public interest” are already provided in the law [([ 4 ][5]), Article 49(1)(d)]. The European Data Protection Board (EDPB) has cautioned that transfers according to this derogation shall not become the rule in practice ([ 5 ][6]), but this conditional support for international COVID-19 data sharing shows that the law already provides suitable flexibility. This flexibility also shows the EDPB's recognition of the pressing social need that biomedical research represents for the global research community during the COVID-19 pandemic, while also seeking to ensure that this remains the exception and not the beginning of a normalized practice. Bovenberg et al. contend that pseudonymized data should not be considered personal data in the hands of an entity that does not possess the key needed for re-identification. This proposal runs against well-established guidance in EU member states such as Ireland ([ 6 ][7]) and Germany ([ 7 ][8]), and it does not take into account the cases in which identifiers remain attached to transferred biomedical data or in which data could be identified without a key. Bovenberg et al. also neglect to state that the GDPR has special principles and safeguards for particularly sensitive re-identifiable data, not just for the protection of privacy but also for the security and integrity of health research data—aims that align with all high-quality scientific research. Respecting these standards (both technical and organizational) is fundamental to ensuring better data security and accuracy in the transferring of huge datasets of sensitive health data that are essential to global collaboration [([ 4 ][5]), Articles 5 and 9, Recitals 53 and 54, and ([ 8 ][9])]. Thus, these rules should not be subject to exemptions, which would result from not classifying pseudonymized data as personal data. The purpose of the GDPR's strict rules is to ensure that when personal data are transferred to non-EU countries, the level of protection ensured in the European Union is not undermined. The EU's Court of Justice decisions ([ 9 ][10], [ 10 ][11]) make it clear that ensuring an adequate level of protection in non-EU countries, especially independent oversight and judicial remedies—which the Court found lacking in the United States—is a matter of fundamental rights. This discrepancy is an opportunity for non-EU countries, including the United States, to raise their data protection standards to the level of the European Union's, not for the European Union to decrease its own standards in a regulatory race to the bottom. We encourage research organizations and country delegations to work with the European Commission, national data protection authorities, and the EDPB to craft interoperable rules on data sharing applicable for biomedical research in ways that do not undermine fundamental rights owed to data subjects. 1. [↵][12]1. R. Eiss , Nature 584, 498 (2020). [OpenUrl][13] 2. [↵][14]1. R. Becker et al ., J. Med. Internet Res. 22, e19799 (2020). [OpenUrl][15] 3. [↵][16]1. A. Jelinek , EDPB response letter to Mark W. Libby, Chargé d'Affaires, United States Mission to the European Union (2020); [https://edpb.europa.eu/sites/edpb/files/files/file1/edpb\_letter\_out2020-0029\_usmission\_covid19.pdf][17]. 4. [↵][18]GDPR (2016); . 5. [↵][19]EDPB, “Guidelines 03/2020 on the processing of data concerning health for the purpose of scientific research in the context of the COVID-19 outbreak” (2020). 6. [↵][20]Data Protection Commission, “Guidance on Anonymisation and Pseudonymisation” (2019); [www.dataprotection.ie/sites/default/files/uploads/2019-06/190614%20Anonymisation%20and%20Pseudonymisation.pdf][21]. 7. [↵][22]German Federal Ministry of the Interior, Building and Community, “Draft for a Code of Conduct on the use of GDPR compliant pseudonymisation” (2019); [www.gdd.de/downloads/aktuelles/whitepaper/Data\_Protection\_Focus\_Group-Draft\_CoC\_Pseudonymisation\_V1.0.pdf][23]. 8. [↵][24]1. D. Anderson et al ., Int. Data Privacy L. 10, 180 (2020). [OpenUrl][25] 9. [↵][26]Case C-362/14 Maximilian Schrems v. Data Protection Commissioner (Court of Justice of the EU, 2015). 10. [↵][27]Case C-311/18 Data Protection Commissioner v. Facebook Ireland Limited and Maximillian Schrems (Court of Justice of the EU, 2020). [1]: http://www.sciencemag.org/content/370/6512/40 [2]: #ref-1 [3]: #ref-2 [4]: #ref-3 [5]: #ref-4 [6]: #ref-5 [7]: #ref-6 [8]: #ref-7 [9]: #ref-8 [10]: #ref-9 [11]: #ref-10 [12]: #xref-ref-1-1 "View reference 1 in text" [13]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D584%26rft.spage%253D498%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [14]: #xref-ref-2-1 "View reference 2 in text" [15]: {openurl}?query=rft.jtitle%253DJ.%2BMed.%2BInternet%2BRes.%26rft.volume%253D22%26rft.spage%253De19799%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [16]: #xref-ref-3-1 "View reference 3 in text" [17]: https://edpb.europa.eu/sites/edpb/files/files/file1/edpb_letter_out2020-0029_usmission_covid19.pdf [18]: #xref-ref-4-1 "View reference 4 in text" [19]: #xref-ref-5-1 "View reference 5 in text" [20]: #xref-ref-6-1 "View reference 6 in text" [21]: http://www.dataprotection.ie/sites/default/files/uploads/2019-06/190614%20Anonymisation%20and%20Pseudonymisation.pdf [22]: #xref-ref-7-1 "View reference 7 in text" [23]: http://www.gdd.de/downloads/aktuelles/whitepaper/Data_Protection_Focus_Group-Draft_CoC_Pseudonymisation_V1.0.pdf [24]: #xref-ref-8-1 "View reference 8 in text" [25]: {openurl}?query=rft.jtitle%253DInt.%2BData%2BPrivacy%2BL.%26rft.volume%253D10%26rft.spage%253D180%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [26]: #xref-ref-9-1 "View reference 9 in text" [27]: #xref-ref-10-1 "View reference 10 in text"

Low-income commuters who rely on public transit face many challenges—multiple transfers, long waits, and off-hour travel—that aren't measured in the usual ridership surveys. Vanessa Frias-Martinez, a computer scientist at the University of Maryland, College Park, wants to ease their commute by harnessing two hot trends in computer science, cloud computing and artificial intelligence (AI), which Congress now hopes to scale up dramatically for U.S. scientists. With support from the National Science Foundation, including an NSF-funded effort called CloudBank that subsidizes access to commercial cloud services, Frias-Martinez plans to track the movements of thousands of Baltimore residents while protecting their privacy. And by applying AI algorithms to the large data sets, she hopes to identify ways to eliminate transit bottlenecks and improve service. Frias-Martinez predicts CloudBank “will flatten the steep learning curve” for first-time cloud users like her. Congress has now embraced a plan to ensure there are many more. The National Artificial Intelligence Initiative Act (NAIIA) of 2020, which became law last week, aims to bolster AI activities at more than a dozen agencies. Its directives include a study of how to create a national research cloud that would build on CloudBank. It also calls for an expansion of a network of research institutes launched last summer, and the creation of a White House AI office and an advisory committee to monitor those efforts. “It's the closest thing to a national strategy on AI from the United States to be formally endorsed by Congress,” says Tony Samp, a former congressional staffer turned high-tech lobbyist for DLA Piper. He and others say the new law is meant to keep the country at the forefront of global AI research in the face of growing investments by other countries. The NAIIA authorizes spending but doesn't appropriate money. If funded, however, it would significantly ramp up federal AI investments. It authorizes $4.8 billion for NSF over the next 5 years, with another $1.15 billion for the Department of Energy (DOE) and $390 million for National Institute of Standards and Technology (NIST). NSF, which funds the vast majority of federally supported AI academic research, estimates it spent $510 million on AI in 2020, so the NAIIA would roughly double that effort. The military is also upping its AI game. The NAIIA is appended to the National Defense Authorization Act, a 4500-page bill providing annual policy guidance to the Department of Defense that survived a presidential veto. This year's version of the must-pass bill raises the stature of the Pentagon's Joint Artificial Intelligence Center formed in 2018 and gives it new authority to use AI to improve combat readiness and fight wars. The NAIIA both codifies what some federal agencies are already doing and gives them an extensive to-do list. For example, it endorses NSF's network of seven AI research institutes, launched last summer with help from the U.S. Department of Agriculture and in partnership with industry, and backs similar centers at DOE and the Department of Commerce—which includes NIST and the National Oceanic and Atmospheric Administration. The NSF institutes, each funded at roughly $20 million over 5 years, will support research in applying AI to a variety of topics including weather forecasting, sustainable agriculture, drug discovery, and cosmology. NSF is already soliciting proposals for a second round of multidisciplinary institutes, and many AI advocates would like to see its growth continue. A white paper for President-elect Joe Biden, for example, calls for an initial investment of $1 billion, and a 2019 community road map envisions each institute supporting 100 faculty members, 200 AI engineers, and 500 students. Their popularity has revived a recurring debate about how to grow such an initiative without hurting the core NSF research programs that support individual investigators. “We're very proud of the institutes, which have gotten a lot of attention, and we think they can be wonderfully transformational,” says Margaret Martonosi, head of NSF's Computing and Information Science and Engineering (CISE) directorate. But Martonosi also notes that CISE spends even more on its core programs—and still rejects more good proposals than it funds. Cloud computing could also boost AI, because it enables researchers to compile and analyze the huge data sets required to train AI algorithms. It, too, gets a big shoutout in the new law, which directs the NSF director and the president's science adviser to assemble a 12-member task force to study the feasibility of a National Research Resource (NRR). Such a national cloud would scale up what CloudBank is now doing and give researchers the tools to analyze large public data sets containing, say, anonymized government health records or satellite data. “At present, only a handful of companies can afford the substantial computational resources required to develop and train the machine learning models underlying today's AI,” says Stanford University's John Etchemendy. “What's more, the large data troves required to train these algorithms are for the most part controlled by either industry or government. Academic researchers struggle to gain access to both.” Etchemendy, a former longtime provost, and computer scientist Fei-Fei Li direct Stanford's Institute for Human-Centered Artificial Intelligence and co-authored a proposal for an NRR that legislators used as a template in the NAIIA. Columbia University computer scientist Jeannette Wing, whose resume includes leading NSF's computing directorate and running Microsoft's research shop, would like to see “all universities use the cloud routinely for all research and all educational activities.” Scientists who continue to rely on their own institutional computing resources, expertise, and support staff, she believes, will find it increasingly difficult to keep pace with competitors who can address cutting-edge research questions via the cloud. Creating such a ubiquitous network, which she calls an academic cloud, won't be easy. “Current commercial cloud providers have interfaces and services that are not nontechie friendly and price points that are out of line for academics,” she explains. But she thinks those problems can be solved. How a national cloud would be structured or managed poses another challenge. Some have suggested linking it to DOE's network of national labs, or to the supercomputing centers that DOE and NSF support. Etchemendy hopes the government will decide to contract with commercial cloud services such as Amazon Web Services, Google Cloud, Microsoft Azure, and IBM Cloud rather than starting from scratch. “The commercial cloud providers are doing the innovation, and they invest massive amounts of money to keep it up-to-date,” he says. “It would be a huge mistake to build a facility like a supercomputer center because it would be obsolete within a few years.” Even if the spending levels authorized by the new law are aspirational, AI advocates say the act demonstrates the remarkable support that the field now enjoys. “There was a real sense of urgency on this issue,” Samp says. “I also think [the NAIIA] provides a foundation for years to come.”