"Anonymised" data lies at the core of everything from modern medical research to personalised recommendations and modern AI techniques. Unfortunately, according to a paper, successfully anonymising data is practically impossible for any complex dataset. An anonymised dataset is supposed to have had all personally identifiable information removed from it, while retaining a core of useful information for researchers to operate on without fear of invading privacy. For instance, a hospital may remove patients' names, addresses and dates of birth from a set of health records in the hope researchers may be able to use the large sets of records to uncover hidden links between conditions. But in practice, data can be deanonymised in a number of ways.

The federal business watchdog will reportedly find that Facebook deceived users about how it handled phone numbers it asked for as part of a security feature and provided insufficient information about how to turn off a facial recognition tool for photos.

We propose a novel neural topic model in the Wasserstein autoencoders (WAE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training leads to significantly more coherent topics. To measure the diversity of the produced topics, we propose a simple topic uniqueness metric. Together with the widely used coherence measure NPMI, we offer a more wholistic evaluation of topic quality. Experiments on several real datasets show that our model produces significantly better topics than existing topic models.

Transportation systems can be conceptualized as an instrument of spreading people and resources over the territory, playing an important role in developing sustainable cities. The current rationale of transport provision is based on population demand, disregarding land use and socioeconomic information. To meet the challenge to promote a more equitable resource distribution, this work aims at identifying and describing patterns of urban services supply, their accessibility, and household income. By using a multidimensional approach, the spatial inequalities of a large city of the global south reveal that the low-income population has low access mainly to hospitals and cultural centers. A low-income group presents an intermediate level of accessibility to public schools and sports centers, evidencing the diverse condition of citizens in the peripheries. These complex outcomes generated by the interaction of land use and public transportation emphasize the importance of comprehensive methodological approaches to support decisions of urban projects, plans and programs. Reducing spatial inequalities, especially providing services for deprived groups, is fundamental to promote the sustainable use of resources and optimize the daily commuting.

In this May 7, 2018, file photo Microsoft CEO Satya Nadella looks on during a video as he delivers the keynote address at Build, the company's annual conference for software developers in Seattle. Microsoft is paying more than $25 million to settle federal corruption charges involving a bribery scheme in its Hungary office and three other foreign subsidiaries, the U.S. Securities and Exchange Commission said Monday, July 22, 2019. NEW YORK – Microsoft is paying more than $25 million to settle federal corruption charges involving a bribery scheme in Hungary and other foreign offices. The U.S. Securities and Exchange Commission said Microsoft will pay about $16.6 million to settle charges that it violated the Foreign Corrupt Practices Act. While the case centered on Hungary, the SEC said it also found improprieties at Microsoft offices in Saudi Arabia, Thailand and Turkey.

With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments. Machines capable of responding and interacting with humans in helpful ways have become ubiquitous. We now expect them to discuss with us the more delicate questions in our world, and they should do so armed with effective arguments. But what makes an argument more persuasive? What will convince you? In this paper, we present a new data set, IBM-EviConv, of pairs of evidence labeled for convincingness, designed to be more challenging than existing alternatives. We also propose a Siamese neural network architecture shown to outperform several baselines on both a prior convincingness data set and our own. Finally, we provide insights into our experimental results and the various kinds of argumentative value our method is capable of detecting.

NEW DELHI: RecruVia, a legal tech and recruitment startup has secured an undisclosed amount of funding from a clutch of foreign and Indian-based investors. The company will utilise the fresh funds towards completing the development of its software-as-a-service (SaaS) self-learning recruitment search engine that will have the ability to search through large amounts of data (Curriculum Vitae/Job Description) to give job-seekers and end employers results based on key hiring parameters, while taking into account their individual preferences. RecruVia's clients include Uber, Khaitan & Co, Integreon, IndusLaw, Phoenix Legal and others, according to a release. Sachin Sukumar, founder, RecruVia said, "Our venture has a single aim – to make the search for the right legal candidate or job easy and provide a'fit for purposeness' to those recruiting / being recruited. The development of a machine learning based web-services engine is akin to a Google Maps API at the backend. This would be able to run on our marketplace as well as assist law firms and recruiters in finding the right fit of candidates while sifting through large number of CVs they already have or receive."

We'll be talking about everyone's favorite topic at the moment: facial recognition. First San Francisco, Somerville ... now Oakland: California's Oakland has become the third US city to ban its local government using facial recognition technology, after its council passed an ordinance this week. Council member Rebecca Kaplan submitted the ordinance for city officials to consider earlier this year in June. The document describes the shortcomings of the technology and why it should be banned. "The City of Oakland should reject the use of this flawed technology on the following basis: 1) systems rely on biased datasets with high levels of inaccuracy; 2) a lack of standards around the use and sharing of this technology; 3) the invasive nature of the technology; 4) and the potential abuses of data by our government that could lead to persecution of minority groups," according to the ordinance.

Accurately forecasting urban development and its environmental and climate impacts critically depends on realistic models of the spatial structure of the built environment, and of its dependence on key factors such as population and economic development. Scenario simulation and sensitivity analysis, i.e., predicting how changes in underlying factors at a given location affect urbanization outcomes at other locations, is currently not achievable at a large scale with traditional urban growth models, which are either too simplistic, or depend on detailed locally-collected socioeconomic data that is not available in most places. Here we develop a framework to estimate, purely from globally-available remote-sensing data and without parametric assumptions, the spatial sensitivity of the (\textit{static}) rate of change of urban sprawl to key macroeconomic development indicators. We formulate this spatial regression problem as an image-to-image translation task using conditional generative adversarial networks (GANs), where the gradients necessary for comparative static analysis are provided by the backpropagation algorithm used to train the model. This framework allows to naturally incorporate physical constraints, e.g., the inability to build over water bodies. To validate the spatial structure of model-generated built environment distributions, we use spatial statistics commonly used in urban form analysis. We apply our method to a novel dataset comprising of layers on the built environment, nightlighs measurements (a proxy for economic development and energy use), and population density for the world's most populous 15,000 cities.

The financial services industry has seen an explosive growth in Artificial Intelligence (AI) to supplement, and often supplant, existing processes both customer-facing and internal. Given the potential created by rapid advancements in AI sophistication and functionality, more and more financial services firms are leveraging the technology to deploy new use cases for improved decision-making processes – particularly in the areas of anti-money laundering, fraud prevention, risk management, and lending. While the first wave of AI was generally focused on automating manually-intensive and repetitive tasks, banks are now turning to machine learning systems (ML) to uncover more dynamic ways of interpreting their vast swaths of customer data. Whereas AI, at a fundamental level, permits a machine to imitate intelligent human behavior, ML is a specific application (or subset) of AI that enables systems automatically to learn and improve – e.g., reduce errors or maximize the likelihood that their predictions will be true – without being explicitly programmed to make such adjustments. This development has an exciting potential to expand the products available to underbanked communities and improve services and customer experience as a whole.