With the omnipresence of the term artificial intelligence (AI) and the increased popularity of deep learning, a lot of security practitioners are being lured into believing that these approaches are the magic silver bullet we have been waiting for to solve all of our security challenges. But deep learning -- or any other machine learning (ML) approach -- is just a tool. And it's not a tool we should use on its own. We need to incorporate expert knowledge for the algorithms to reveal actual security insights. Before continuing this post, I will stop using the term artificial intelligence and revert back to using the term machine learning.

In tropical regions, populations continue to suffer morbidity and mortality from malaria and arboviral diseases. In Kedougou (Senegal), these illnesses are all endemic due to the climate and its geographical position. The co-circulation of malaria parasites and arboviruses can explain the observation of coinfected cases. Indeed there is strong resemblance in symptoms between these diseases making problematic targeted medical care of coinfected cases. This is due to the fact that the origin of illness is not obviously known. Some cases could be immunized against one or the other of the pathogens, immunity typically acquired with factors like age and exposure as usual for endemic area. Then, coinfection needs to be better diagnosed. Using data collected from patients in Kedougou region, from 2009 to 2013, we adjusted a multinomial logistic model and selected relevant variables in explaining coinfection status. We observed specific sets of variables explaining each of the diseases exclusively and the coinfection. We tested the independence between arboviral and malaria infections and derived coinfection probabilities from the model fitting. In case of a coinfection probability greater than a threshold value to be calibrated on the data, duration of illness above 3 days and age above 10 years-old are mostly indicative of arboviral disease while body temperature higher than 40{\textdegree}C and presence of nausea or vomiting symptoms during the rainy season are mostly indicative of malaria disease.

The Internet has enabled the emergence of massive online collaborative projects. As the number of contributors to these projects grows, it becomes increasingly important to understand and predict whether the edits that users make will eventually impact the project positively. Existing solutions either rely on a user reputation system or consist of a highly-specialized predictor tailored to a specific peer-production system. In this work, we explore a different point in the solution space, which does not involve any content-based feature of the edits. To this end, we formulate a statistical model of edit outcomes. We view each edit as a game between the editor and the component of the project. We posit that the probability of a positive outcome is a function of the editor's skill, of the difficulty of editing the component and of a user-component interaction term. Our model is broadly applicable, as it only requires observing data about who makes an edit, what the edit affects and whether the edit survives or not. Then, we consider Wikipedia and the Linux kernel, two examples of large-scale collaborative projects, and we seek to understand whether this simple model can effectively predict edit survival: in both cases, we provide a positive answer. Our approach significantly outperforms those based solely on user reputation and bridges the gap with specialized predictors that use content-based features. Furthermore, inspecting the model parameters enables us to discover interesting structure in the data. Our method is simple to implement, computationally inexpensive, and it produces interpretable results; as such, we believe that it is a valuable tool to analyze collaborative systems.

Data augmentation is a widely used technique in many machine learning tasks, such as image classification, to virtually enlarge the training dataset size and avoid overfitting. Traditional data augmentation techniques for image classification tasks create new samples from the original training data by, for example, flipping, distorting, adding a small amount of noise to, or cropping a patch from an original image. In this paper, we introduce a simple but surprisingly effective data augmentation technique for image classification tasks. With our technique, named SamplePairing, we synthesize a new sample from one image by overlaying another image randomly chosen from the training data (i.e., taking an average of two images for each pixel). By using two images randomly selected from the training set, we can generate $N^2$ new samples from $N$ training samples. This simple data augmentation technique significantly improved classification accuracy for all the tested datasets; for example, the top-1 error rate was reduced from 33.5% to 29.0% for the ILSVRC 2012 dataset with GoogLeNet and from 8.22% to 6.93% in the CIFAR-10 dataset. We also show that our SamplePairing technique largely improved accuracy when the number of samples in the training set was very small. Therefore, our technique is more valuable for tasks with a limited amount of training data, such as medical imaging tasks.

This article describes Team Kernel Glitches' solution to the National Institute of Justice's (NIJ) Real-Time Crime Forecasting Challenge. The goal of the NIJ Real-Time Crime Forecasting Competition was to maximize two different crime hotspot scoring metrics for calls-for-service to the Portland Police Bureau (PPB) in Portland, Oregon during the period from March 1, 2017 to May 31, 2017. Our solution to the challenge is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian processes with autoregressive smoothing kernels in a regularized supervised learning framework. Our model can be understood as an approximation to the popular log-Gaussian Cox Process model: we discretize the spatiotemporal point pattern and learn a log intensity function using the Poisson likelihood and highly efficient gradient-based optimization methods. Model hyperparameters including quality of RKHS approximation, spatial and temporal kernel lengthscales, number of autoregressive lags, bandwidths for smoothing kernels, as well as cell shape, size, and rotation, were learned using crossvalidation. Resulting predictions exceeded baseline KDE estimates by 0.157. Performance improvement over baseline predictions were particularly large for sparse crimes over short forecasting horizons.

Deep learning research in medicine is a bit like the Wild West at the moment; sometimes you find gold, sometimes a giant steampunk spider-bot causes a ruckus. This has derailed my series on whether AI will be replacing doctors soon, as I have felt the need to focus a bit more on how to assess the quality of medical AI research. I wanted to start closing out my series on the role of AI in medicine. What has happened instead is that several papers have claimed to beat doctors, and have failed to justify these claims. Despite this, and despite not going through peer review, the groups involved have issued press releases about their achievements, marketing the results direct to the public and the media. I don't think this is malicious. I think there is a cultural divide between the machine learning and the medical communities, a different way of doing research, and a different level of evidence required for making strong claims. If I have to be honest, I think the machine learning community has a fair bit to learn from medical research in this regard. Last time I made a set of three rules about how to assess medical AI research, and the third was a glib recommendation to "actually read the paper".

One of the things I am trying to do this year is some more technical posts (following up on some issues I have noticed at the intersection between medicine and machine learning). This is the first in a little mini-series on performance testing. Medical research has a different way of doing things, being more cautious about making claims and a bit more rigorous in justifying them, both of which are useful ideas to apply more broadly in machine learning (particularly at the applied end). While performance testing is often considered basic knowledge, one of my supervisors/colleagues is a bit of a ROC expert so I hope I can pass on some new ways of looking at things that are interesting even for some of the more knowledgeable folks around here.

In this webinar, Jan 11, DataRobot will show how automated machine learning can be used to reduce false positive rates, thereby improving the efficiency of AML transaction monitoring and reducing costs.

We can see that Ridge and Lasso is performing far better than Linear Regression when the correlation exists in the dataset. We can tune our penalty parameter further and try to find best value of RMSE. Here, 0.01 is the best value I got for lambda. So, we can use these regression methods when the variables are highly correlated. Hope this article was useful in understanding Bias-Variance trade-off, Lasso and Ridge Regression. Please feel free to comment, give feedback and share this article if you found it useful. You can find the original article on my blog here.

Microscopic interpretation of stained smears is one of the most operator-dependent and time intensive activities in the clinical microbiology laboratory. Here, we investigated application of an automated image acquisition and convolutional neural network (CNN)-based approach for automated Gram stain classification. Using an automated microscopy platform, uncoverslipped slides were scanned with a 40x dry objective, generating images of sufficient resolution for interpretation. We collected 25,488 images from positive blood culture Gram stains prepared during routine clinical workup. These images were used to generate 100,213 crops containing Gram-positive cocci in clusters, Gram-positive cocci in chains/pairs, Gram-negative rods, or background (no cells).