Since the world learned of state-sponsored campaigns to spread disinformation on social media and sway the 2016 election, Twitter has scrambled to rein in the bots and trolls polluting its platform. But when it comes to the larger problem of automated accounts on Twitter designed to spread spam and scams, inflate follower counts, and game trending topics, one study argues that the company still isn't keeping up with the deluge of garbage and abuse. In fact, the paper's two researchers write that with a machine learning approach they developed themselves, they could identify abusive accounts in far greater volumes and faster than Twitter does--often flagging the accounts months before Twitter spotted and banned them. In an 16-month study of 1.5 billion tweets, Zubair Shafiq, a computer science professor at the University of Iowa, and his graduate student Shehroze Farooqi, identified more than 167,000 apps using Twitter's API to automate bot accounts that spread tens of millions of tweets pushing spam, links to malware, and astroturfing campaigns. They write that more than 60 percent of the time, Twitter waited for those apps to send more than 100 tweets before identifying them as abusive; the researchers' own detection method had flagged the vast majority of the malicious apps after just a handful of tweets.

When Claire Bell became pregnant she paid for a test that would indicate whether the baby had Down's Syndrome - and agreed to be screened for some other rare conditions at the same time. Not long afterwards, writes the BBC's Charlotte Hayward, she received what appeared to be terrible news. For five years, Claire Bell's husband was treated for two types of cancer. When it finally came to an end the couple decided to try having a baby through IVF, using some sperm her husband had had frozen and stored before he had chemotherapy. On the first round, at the age of 41, she became pregnant - and felt incredibly lucky. "It was this miraculous pregnancy," she says.

Link prediction requires predicting which new links are likely to appear in a graph. Being able to predict unseen links with good accuracy has important applications in several domains such as social media, security, transportation, and recommendation systems. A common approach is to use features based on the common neighbors of an unconnected pair of nodes to predict whether the pair will form a link in the future. In this paper, we present an approach for link prediction that relies on higher-order analysis of the graph topology, well beyond common neighbors. We treat the link prediction problem as a supervised classification problem, and we propose a set of features that depend on the patterns or motifs that a pair of nodes occurs in. By using motifs of sizes 3, 4, and 5, our approach captures a high level of detail about the graph topology within the neighborhood of the pair of nodes, which leads to a higher classification accuracy. In addition to proposing the use of motif-based features, we also propose two optimizations related to constructing the classification dataset from the graph. First, to ensure that positive and negative examples are treated equally when extracting features, we propose adding the negative examples to the graph as an alternative to the common approach of removing the positive ones. Second, we show that it is important to control for the shortest-path distance when sampling pairs of nodes to form negative examples, since the difficulty of prediction varies with the shortest-path distance. We experimentally demonstrate that using off-the-shelf classifiers with a well constructed classification dataset results in up to 10 percentage points increase in accuracy over prior topology-based and feature learning methods.

Music genre can be hard to describe: many factors are involved, such as style, music technique, and historical context. Some genres even have overlapping characteristics. Looking for a better understanding of how music genres are related to musical harmonic structures, we gathered data about the music chords for thousands of popular Brazilian songs. Here, 'popular' does not only refer to the genre named MPB (Brazilian Popular Music) but to nine different genres that were considered particular to the Brazilian case. The main goals of the present work are to extract and engineer harmonically related features from chords data and to use it to classify popular Brazilian music genres towards establishing a connection between harmonic relationships and Brazilian genres. We also emphasize the generalisation of the method for obtaining the data, allowing for the replication and direct extension of this work. Our final model is a combination of multiple classification trees, also known as the random forest model. We found that features extracted from harmonic elements can satisfactorily predict music genre for the Brazilian case, as well as features obtained from the Spotify API. The variables considered in this work also give an intuition about how they relate to the genres.

We consider the problem of learning a binary classifier from only positive and unlabeled observations (PU learning). Although recent research in PU learning has succeeded in showing theoretical and empirical performance, most existing algorithms need to solve either a convex or a non-convex optimization problem and thus are not suitable for large-scale datasets. In this paper, we propose a simple yet theoretically grounded PU learning algorithm by extending the previous work proposed for supervised binary classification (Sriperumbudur et al., 2012). The proposed PU learning algorithm produces a closed-form classifier when the hypothesis space is a closed ball in reproducing kernel Hilbert space. In addition, we establish upper bounds of the estimation error and the excess risk. The obtained estimation error bound is sharper than existing results and the excess risk bound does not rely on an approximation error term. To the best of our knowledge, we are the first to explicitly derive the excess risk bound in the field of PU learning. Finally, we conduct extensive numerical experiments using both synthetic and real datasets, demonstrating improved accuracy, scalability, and robustness of the proposed algorithm.

Abstract-- In this paper we propose the use of multiple local binary patterns(LBPs) to effectively classify land use images. We use the UC Merced 21 class land use image dataset. Task is challenging for classification as the dataset contains intra class variability and inter class similarities. Our proposed method of using multi-neighborhood LBPs combined with nearest neighbor classifier is able to achieve an accuracy of 77.76%. Further class wise analysis is conducted and suitable suggestion are made for further improvements to classification accuracy. INTRODUCTION The world is changing rapidly, new technology and infrastructure is resulting in faster growth. To meet the demands of the growing populations, cities are expanding and land use pattern are changing to accommodate the needs.

Recent years have witnessed an increased focus on interpretability and the use of machine learning to inform policy analysis and decision making. This paper applies machine learning to examine travel behavior and, in particular, on modeling changes in travel modes when individuals are presented with a novel (on-demand) mobility option. It addresses the following question: Can machine learning be applied to model individual taste heterogeneity (preference heterogeneity for travel modes and response heterogeneity to travel attributes) in travel mode choice? This paper first develops a high-accuracy classifier to predict mode-switching behavior under a hypothetical Mobility-on-Demand Transit system (i.e., stated-preference data), which represents the case study underlying this research. We show that this classifier naturally captures individual heterogeneity available in the data. Moreover, the paper derives insights on heterogeneous switching behaviors through the generation of marginal effects and elasticities by current travel mode, partial dependence plots, and individual conditional expectation plots. The paper also proposes two new model-agnostic interpretation tools for machine learning, i.e., conditional partial dependence plots and conditional individual partial dependence plots, specifically designed to examine response heterogeneity. The results on the case study show that the machine-learning classifier, together with model-agnostic interpretation tools, provides valuable insights on travel mode switching behavior for different individuals and population segments. For example, the existing drivers are more sensitive to additional pickups than people using other travel modes, and current transit users are generally willing to share rides but reluctant to take any additional transfers.

Motivating study and statistical approaches Crohn's disease (CD) is a chronic debilitating condition characterized by periods of inflammatory activity in the bowel that causes symptoms such as abdominal pain, diarrhea, andweight loss. Pharmacologic treatment for CD includes medications such as steroids, immunomodulating drugs, and biological therapy. Despite these available medications, many people with CD are escalated to surgical interventions from small to extensive resections of the bowel or colon (Gomollón et al., 2016). Previous studies have estimated that up to 50% of patients with CD undergo surgery within 10 years after diagnosis; however, surgical rates have decreased over time, possibly due to the introduction of modern treatments such as thiopurines and anti-TNF (Lakatos et al., 2012; Ramadas et al., 2010). The aim of this study is to determine whether clinical and demographic characteristics observed at the time of diagnosis can be used to predict the occurrence of major abdominal surgery within 5 years, with the goal of personalized disease management.

Sparsity promoting norms are frequently used in high dimensional regression. A limitation of Lasso-type estimators is that the regulariza-tion parameter depends on the noise level which varies between datasets and experiments. Esti-mators such as the concomitant Lasso address this dependence by jointly estimating the noise level and the regression coefficients. As sample sizes are often limited in high dimensional regimes, simplified heteroscedastic models are customary. However, in many experimental applications , data is obtained by averaging multiple measurements. This helps reducing the noise variance, yet it dramatically reduces sample sizes, preventing refined noise modeling. In this work, we propose an estimator that can cope with complex heteroscedastic noise structures by using non-averaged measurements and a con-comitant formulation. The resulting optimization problem is convex, so thanks to smoothing theory, it is amenable to state-of-the-art proximal coordinate descent techniques that can leverage the expected sparsity of the solutions. Practical benefits are demonstrated on simulations and on neuroimaging applications.

The early detection of infectious disease outbreaks is a crucial task to protect population health. To this end, public health surveillance systems have been established to systematically collect and analyse infectious disease data. A variety of statistical tools are available, which detect potential outbreaks as abberations from an expected endemic level using these data. Here, we develop the first supervised learning approach based on hidden Markov models for disease outbreak detection, which leverages data that is routinely collected within a public health surveillance system. We evaluate our model using real Salmonella and Campylobacter data, as well as simulations. In comparison to a state-of-the-art approach, which is applied in multiple European countries including Germany, our proposed model reduces the false positive rate by up to 50% while retaining the same sensitivity. We see our supervised learning approach as a significant step to further develop machine learning applications for disease outbreak detection, which will be instrumental to improve public health surveillance systems.