A growing specter in the rise of machine learning is whether the decisions made by machine learning models are fair. While research is already underway to formalize a machine-learning concept of fairness and to design frameworks for building fair models with sacrifice in accuracy, most are geared toward either supervised or unsupervised learning. Yet two observations inspired us to wonder whether semi-supervised learning might be useful to solve discrimination problems. First, previous study showed that increasing the size of the training set may lead to a better trade-off between fairness and accuracy. Second, the most powerful models today require an enormous of data to train which, in practical terms, is likely possible from a combination of labeled and unlabeled data. Hence, in this paper, we present a framework of fair semi-supervised learning in the pre-processing phase, including pseudo labeling to predict labels for unlabeled data, a re-sampling method to obtain multiple fair datasets and lastly, ensemble learning to improve accuracy and decrease discrimination. A theoretical decomposition analysis of bias, variance and noise highlights the different sources of discrimination and the impact they have on fairness in semi-supervised learning. A set of experiments on real-world and synthetic datasets show that our method is able to use unlabeled data to achieve a better trade-off between accuracy and discrimination.

Airlines need to construct crew pairings to cover their flights. A pairing is a sequence of flights starting and finishing at a base and satisfying complex collective agreement constraints. For major airlines which handle more than 10k flights on a weekly basis, this becomes an important and difficult problem to solve. Efficient solutions are required since savings as low as 1% represent many dozens of millions saved every year. The complexity of the problem lies in the large number of possible pairings, and the selection of the set of pairings of minimal cost, which is a large integer programming problem impossible to solve with standard solvers (Elhallaoui et al., 2005; Kasirzadeh et al., 2017). In our review of related work, we address some advanced optimization techniques that reduce the number of variables and the number of constraints to solve it. The main drawback of these techniques, however, is that they require days to compute, while airlines are often given all the scheduling data only a few days before having to build the schedule. The objective of this paper is to use machine learning (ML) techniques to improve the algorithmic efficiency and solve this problem in a more feasible time horizon. Unfortunately, solving the problem with ML alone seems out of reach.

In 2020, the economy is once again teetering on the edge of what some are calling a fiscal cliff. In the midst of COVID-19, a global economic crisis is threatening the livelihoods of small business owners everywhere. In ordinary times, 50 percent of small businesses go out of business in the first 5 years. In today's extraordinary times, nearly 7.5 million ( 25 percent) of small businesses in the U.S. alone have been at risk of closing permanently in a matter of months (Source: Main Street America's Small Business Survey 2020). To that end, our company immediately took steps at the outset of the pandemic to help consumers and small businesses access desperately-needed capital through four key initiatives and AI-driven innovations, such as Intuit Aid Assist and the QuickBooks Capital for Paycheck Protection Program.

As Doctor Who, Tom Baker fought Daleks and Cybermen, robot mummies and gothic monsters -- but his latest'creature feature' has taken the form of an accolade. Australian palaeontologists have named a newly-found species of trilobite -- a segmented sea creature from 450 million years ago -- in honour of the actor. Trilobites loosely resemble woodlice -- and their closest living relatives include lobsters, crabs and scorpions. They fell extinct around 251.9 million years ago. The fossil -- 'Gravicalymene bakeri' -- was found preserved in shale rocks in Northern Tasmania that date back to the so-called'Late Ordovician' period.

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Our approach is validated using all deployed smart contracts on the blockchain and demonstrates scalability and concrete effectiveness. The threat to some of these smart contracts presented by our tools is overwhelming in financial terms, especially considering the high precision of warnings in a manually-inspected sample. Gas-focused vulnerabilities are likely to become more relevant in the foreseeable future. Gas (or a quantity like it) is fundamental in blockchain computation and is, for example, included in the design of the upcoming Facebook Libra. Computation under gas constraints requires different coding styles than in traditional programming domains--a simple linear loop over a data structure may render a contract vulnerable!

Search and recommendation systems, such as search engines, recruiting tools, online marketplaces, news, and social media, output ranked lists of content, products, and sometimes, people. Credit ratings, standardized tests, risk assessments output only a score, but are also used implicitly for ranking. Bias in such ranking systems, especially among the top ranks, can worsen social and economic inequalities, polarize opinions, and reinforce stereotypes. On the other hand, a bias correction for minority groups can cause more harm if perceived as favoring group-fair outcomes over meritocracy. In this paper, we study a trade-off between individual fairness and group fairness in ranking. We define individual fairness based on how close the predicted rank of each item is to its true rank, and prove a lower bound on the trade-off achievable for simultaneous individual and group fairness in ranking. We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous individual and group fairness guarantees comparable to the lower bound we prove. Our algorithm can be used to both pre-process training data as well as post-process the output of existing ranking algorithms. Our experimental results show that our algorithm performs better than the state-of-the-art fair learning to rank and fair post-processing baselines.

As per the records of theWorld Health Organization, the first formally reported incidence of Zika virus occurred in Brazil in May 2015. The disease then rapidly spread to other countries in Americas and East Asia, affecting more than 1,000,000 people. Zika virus is primarily transmitted through bites of infected mosquitoes of the species Aedes (Aedes aegypti and Aedes albopictus). The abundance of mosquitoes and, as a result, the prevalence of Zika virus infections are common in areas which have high precipitation, high temperature, and high population density.Nonlinear spatio-temporal dependency of such data and lack of historical public health records make prediction of the virus spread particularly challenging. In this article, we enhance Zika forecasting by introducing the concepts of topological data analysis and, specifically, persistent homology of atmospheric variables, into the virus spread modeling. The topological summaries allow for capturing higher order dependencies among atmospheric variables that otherwise might be unassessable via conventional spatio-temporal modeling approaches based on geographical proximity assessed via Euclidean distance. We introduce a new concept of cumulative Betti numbers and then integrate the cumulative Betti numbers as topological descriptors into three predictive machine learning models: random forest, generalized boosted regression, and deep neural network. Furthermore, to better quantify for various sources of uncertainties, we combine the resulting individual model forecasts into an ensemble of the Zika spread predictions using Bayesian model averaging. The proposed methodology is illustrated in application to forecasting of the Zika space-time spread in Brazil in the year 2018.

Anomaly detection in time series data is a significant problem faced in many application areas such as manufacturing, medical imaging and cyber-security. Recently, Generative Adversarial Networks (GAN) have gained attention for generation and anomaly detection in image domain. In this paper, we propose a novel GAN-based unsupervised method called TAnoGan for detecting anomalies in time series when a small number of data points are available. We evaluate TAnoGan with 46 real-world time series datasets that cover a variety of domains. Extensive experimental results show that TAnoGan performs better than traditional and neural network models.

This article proposes an efficient heuristic in accelerating the column generation by parallel resolution of pricing problems for aircrafts in the tail assignment problem (TAP). The approach is able to achieve considerable improvement in resolution time for real life test instances from two major Indian air carriers. The different restrictions on individual aircraft for maintenance routing as per aviation regulatory bodies are considered in this paper. We also present a variable fixing heuristic to improve the integrality of the solution. The hybridization of constraint programming and column generation was substantial in accelerating the resolution process.