Stagnation in the proportion of women in the workplace and women's declining representation in politics, coupled with greater inequality in access to health and education, offset improvements in wage equality and the number of women in professional positions, leaving the global gender gap only slightly reduced in 2018. This is according to the Forum's Global Gender Gap Report 2018, published today. According to the report, the world has closed 68% of its gender gap, as measured across four key pillars: economic opportunity; political empowerment; educational attainment; and health and survival. While only a marginal improvement on 2017, the move is nonetheless welcome as 2017 was the first year since the report was first published in 2006 that the gap between men and women widened. At the current rate of change, the data suggest that it will take 108 years to close the overall gender gap and 202 years to bring about parity in the workplace.

Understanding the loss surface of neural networks is essential for the design of models with predictable performance and their success in applications. Experimental results suggest that sufficiently deep and wide neural networks are not negatively impacted by suboptimal local minima. Despite recent progress, the reason for this outcome is not fully understood. Could deep networks have very few, if at all, suboptimal local optima? or could all of them be equally good? We provide a construction to show that suboptimal local minima (i.e. non-global ones), even though degenerate, exist for fully connected neural networks with sigmoid activation functions. The local minima obtained by our proposed construction belong to a connected set of local solutions that can be escaped from via a non-increasing path on the loss curve. For extremely wide neural networks with two hidden layers, we prove that every suboptimal local minimum belongs to such a connected set. This provides a partial explanation for the successful application of deep neural networks. In addition, we also characterize under what conditions the same construction leads to saddle points instead of local minima for deep neural networks.

Binary data matrices can represent many types of data such as social networks, votes or gene expression. In some cases, the analysis of binary matrices can be tackled with nonnegative matrix factorization (NMF), where the observed data matrix is approximated by the product of two smaller nonnegative matrices. In this context, probabilistic NMF assumes a generative model where the data is usually Bernoulli-distributed. Often, a link function is used to map the factorization to the $[0,1]$ range, ensuring a valid Bernoulli mean parameter. However, link functions have the potential disadvantage to lead to uninterpretable models. Mean-parameterized NMF, on the contrary, overcomes this problem. We propose a unified framework for Bayesian mean-parameterized nonnegative binary matrix factorization models (NBMF). We analyze three models which correspond to three possible constraints that respect the mean-parametrization without the need for link functions. Furthermore, we derive a novel collapsed Gibbs sampler and a collapsed variational algorithm to infer the posterior distribution of the factors. Next, we extend the proposed models to a nonparametric setting where the number of used latent dimensions is automatically driven by the observed data. We analyze the performance of our NBMF methods in multiple datasets for different tasks such as dictionary learning and prediction of missing data. Experiments show that our methods provide similar or superior results than the state of the art, while automatically detecting the number of relevant components.

Binary classification rules based on covariates typically depend on simple loss functions such as zero-one misclassification. Some cases may require more complex loss functions. For example, individual-level monitoring of HIV-infected individuals on antiretroviral therapy (ART) requires periodic assessment of treatment failure, defined as having a viral load (VL) value above a certain threshold. In some resource limited settings, VL tests may be limited by cost or technology, and diagnoses are based on other clinical markers. Depending on scenario, higher premium may be placed on avoiding false-positives which brings greater cost and reduced treatment options. Here, the optimal rule is determined by minimizing a weighted misclassification loss/risk. We propose a method for finding and cross-validating optimal binary classification rules under weighted misclassification loss. We focus on rules comprising a prediction score and an associated threshold, where the score is derived using an ensemble learner. Simulations and examples show that our method, which derives the score and threshold jointly, more accurately estimates overall risk and has better operating characteristics compared with methods that derive the score first and the cutoff conditionally on the score especially for finite samples.

Thispaper studies the question on whether machines can be rational. It observes the existing reasons why humans are not rational which is due to imperfect and limited information, limited and inconsistent processing power through the brain and the inability to optimize decisions and achieve maximum utility. It studies whether these limitations of humans are transferred to the limitations of machines. The conclusion reached is that even though machines are not rational advances in technological developments make these machines more rational. It also concludes that machines can be more rational than humans. Introduction Oneof the most interesting concepts invented by humans is rationality (Anand, 1993; Marwala, 2014&2015).

Faculty of Computing, Poznan University of Technology, Poland, agnieszka.lawrynowicz@cs.put.poznan.pl Abstract Ontology authoring is a complex process, where commonly the automated reasoner is invoked for verification of newly introduced changes, therewith amounting to a time-consuming test-last approach. Test-Driven Development (TDD) for ontology authoring is a recent test-first approach that aims to reduce authoring time and increase authoring efficiency. Current TDD testing falls short on coverage of OWL features and possible test outcomes, the rigorous foundation thereof, and evaluations to ascertain its effectiveness. We aim to address these issues in one instantiation of TDD for ontology authoring. We first propose a succinct, logic-based model of TDD testing and present novel TDD algorithms so as to cover also any OWL 2 class expression for the TBox and for the principal ABox assertions, and prove their correctness. The algorithms use methods from the OWL API directly such that reclassification is not necessary for test execution, therewith reducing ontology authoring time. The algorithms were implemented in TDDonto2, a Protégé plugin. TDDonto2 was evaluated on editing efficiency and by users. The editing efficiency study demonstrated that it is faster than a typical ontology authoring interface, especially for medium size and large ontologies. The user evaluation demonstrated that modellers make significantly less errors with TDDonto2 compared to the standard Protégé interface and complete their tasks better using less time. Thus, the results indicate that Test-Driven Development is a promising approach in an ontology development methodology. Keywords:Ontology Engineering, Test-Driven Development, OWL 1. Introduction Ontology engineering is facilitated by methods and methodologies, andtooling support for them. The methodologies are mostly information system-like, high-level directions, such as variants on waterfall and lifecycle development [1, 2], although more recently, notions of Agile development are being ported to the ontology development setting, e.g., [3, 4], including testing in some form [5, 6, 7, 8].

The 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2018) was held at Concordia University in Montreal, Canada, June 25–28, 2018. This report summarizes the The 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2018) was held at Concordia University in Montreal, Canada, June 25–28, 2018.  IEA/AIE 2018 continued the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas including engineering, science, industry, automation a robotics, business and finance, medicine and biomedicine, bioinformatics, cyberspace, and human-machine interactions.

Most research into wild marine mammals occurs in wealthy countries. Amazingly, in 2018, we have still have very little idea what species are present, let alone the population size / health status / behaviour, etc. in many parts of the world. A solid first step to address this problem is to conduct a rapid assessment survey to determine which species of marine mammals are present in a given area. The idea of a rapid assessment survey is fairly straightforward: you take a boat out and survey the entire coastline of a country using visual observers to record the number and species of any whales and dolphins encountered. As well as being large, surface present and often charismatic animals, and so possible to detect visually at relatively long ranges, dolphins and whales are also highly vocal, using sound to communicate and some species hunt/sense their surroundings with a sophisticated a bio-sonar.

If you really want to know how to future-proof your career, your best bet is the World Economic Forum (WEF) Future of Jobs Report 2018. The report confirmed most of the things we already knew: that automation and machine learning are set to create as many jobs as they displace, that the gig economy and flexible contract work will become standard, and that knowledge of data science is going to be a key differentiator in the job market over the next few years. With more than two years having passed since the first Future of Jobs report, there have been some new developments. With the mainstreaming of chatbots and other consumer-facing artificial intelligence (AI), there's more of an understanding of how machine learning might integrate into our society. Now that we've had some time to get to know Sophia, Alexa, Pepper and the rest, there are noticeably fewer "Are robots coming to steal your job?" clickbait articles in the media.

REFUNITE, based in California, is trialing the app in Uganda where it has launched a pilot project involving 5,000 refugees – mainly form South Sudan and Democratic Republic of Congo. It hopes to scale up to 25,000 refugees within two years.