In this file photo taken on March 21, 2018 A laptop showing the Facebook logo is held alongside a Cambridge Analytica sign at the entrance to the building housing the offices of Cambridge Analytica, in central London. LONDON – Britain's Information Commissioner has slapped Facebook with a fine of 500,000 pounds ($644,000) – the maximum possible – for its behavior in the Cambridge Analytica scandal. The ICO's investigation found that between 2007 to 2014, Facebook processed the personal information of users unfairly by giving app developers access to their information without informed consent. The fine was the maximum allowed under the law at the time the breach occurred. Had the scandal taken place after new EU data protection rules went into effect, the amount would have been far higher.

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University of Cambridge researchers used machine learning to attempt to define why children struggle in school. Scientists at the University of Cambridge in the U.K. have used machine learning to identify why children struggle at school, through an analysis of 550 students having difficulty. The team fed their algorithm cognitive testing information from each child, which demonstrated how the children best fit within four clusters of problems. Two clusters identified working memory skills and processing sounds in words as problem areas, while the other two defined broad cognitive difficulties in many areas and typical cognitive tests for the subjects' age. Cambridge's Joni Holmes says the results suggest "children who are finding the same subjects difficult could be struggling for very different reasons, which has important implications for selecting appropriate interventions."

Variable speed limits (VSL) control is a flexible way to improve traffic condition,increase safety and reduce emission. There is an emerging trend of using reinforcement learning technique for VSL control and recent studies have shown promising results. Currently, deep learning is enabling reinforcement learning to develope autonomous control agents for problems that were previously intractable. In this paper, we propose a more effective deep reinforcement learning (DRL) model for differential variable speed limits (DVSL) control, in which the dynamic and different speed limits among lanes can be imposed. The proposed DRL models use a novel actor-critic architecture which can learn a large number of discrete speed limits in a continues action space. Different reward signals, e.g. total travel time, bottleneck speed, emergency braking, and vehicular emission are used to train the DVSL controller, and comparison between these reward signals are conducted. We test proposed DRL baased DVSL controllers on a simulated freeway recurrent bottleneck. Results show that the efficiency, safety and emissions can be improved by the proposed method. We also show some interesting findings through the visulization of the control policies generated from DRL models.

The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings. Despite the significant increase in adoption of EHR systems, this data remains largely unexplored, but presents a rich data source for knowledge discovery from patient health histories in tasks such as understanding disease correlations and predicting health outcomes. However, the heterogeneity, sparsity, noise, and bias in this data present many complex challenges. This complexity makes it difficult to translate potentially relevant information into machine learning algorithms. In this paper, we propose a computational framework, Patient2Vec, to learn an interpretable deep representation of longitudinal EHR data which is personalized for each patient. To evaluate this approach, we apply it to the prediction of future hospitalizations using real EHR data and compare its predictive performance with baseline methods. Patient2Vec produces a vector space with meaningful structure and it achieves an AUC around 0.799 outperforming baseline methods. In the end, the learned feature importance can be visualized and interpreted at both the individual and population levels to bring clinical insights.

A current research trend in neurocomputing involves the design of novel artificial neural networks incorporating the concept of time into their operating model. In this paper, a novel architecture that employs stigmergy is proposed. Computational stigmergy is used to dynamically increase (or decrease) the strength of a connection, or the activation level, of an artificial neuron when stimulated (or released). This study lays down a basic framework for the derivation of a stigmergic NN with a related training algorithm. To show its potential, some pilot experiments have been reported. The XOR problem is solved by using only one single stigmergic neuron with one input and one output. A static NN, a stigmergic NN, a recurrent NN and a long short-term memory NN have been trained to solve the MNIST digits recognition benchmark.

The normalized sequence of moments characterizes the law of any finite-dimensional random variable. We prove an analogous result for path-valued random variables, that is stochastic processes, by using the normalized sequence of signature moments. We use this to define a metric for laws of stochastic processes. This metric can be efficiently estimated from finite samples, even if the stochastic processes themselves evolve in high-dimensional state spaces. As an application, we provide a non-parametric two-sample hypothesis test for laws of stochastic processes.

Human activity recognition (HAR) is a classification task that aims to classify human activities or predict human behavior by means of features extracted from sensors data. Typical HAR systems use wearable sensors and/or handheld and mobile devices with built-in sensing capabilities. Due to the widespread use of smartphones and to the inclusion of various sensors in all contemporary smartphones (e.g., accelerometers and gyroscopes), they are commonly used for extracting and collecting data from sensors and even for implementing HAR systems. When using mobile devices, e.g., smartphones, HAR systems need to deal with several constraints regarding battery, computation and memory. These constraints enforce the need of a system capable of managing its resources and maintain acceptable levels of classification accuracy. Moreover, several factors can influence activity recognition, such as classification models, sensors availability and size of data window for feature extraction, making stable accuracy a difficult task. In this paper, we present a semi-supervised classifier and a study regarding the influence of hyperparameter configuration in classification accuracy, depending on the user and the activities performed by each user. This study focuses on sensing data provided by the PAMAP2 dataset. Experimental results show that it is possible to maintain classification accuracy by adjusting hyperparameters, like window size and windows overlap factor, depending on user and activity performed. These experiments motivate the development of a system able to automatically adapt hyperparameter settings for the activity performed by each user.

Security-critical applications such as malware, fraud, or spam detection, require machine learning models that operate on examples from constrained discrete domains. In these settings, gradient-based attacks that rely on adding perturbations often fail to produce adversarial examples that meet the domain constraints, and thus are not effective. We introduce a graphical framework that (1) formalizes existing attacks in discrete domains, (2) efficiently produces valid adversarial examples with guarantees of minimal cost, and (3) can accommodate complex cost functions beyond the commonly used p-norm. We demonstrate the effectiveness of this method by crafting adversarial examples that evade a Twitter bot detection classifier using a provably minimal number of changes.

In natural language processing, a lot of the tasks are successfully solved with recurrent neural networks, but such models have a huge number of parameters. The majority of these parameters are often concentrated in the embedding layer, which size grows proportionally to the vocabulary length. We propose a Bayesian sparsification technique for RNNs which allows compressing the RNN dozens or hundreds of times without time-consuming hyperparameters tuning. We also generalize the model for vocabulary sparsification to filter out unnecessary words and compress the RNN even further. We show that the choice of the kept words is interpretable. 1 Introduction Recurrent neural networks (RNNs) are among the most powerful models for natural language processing, speech recognition, question-answering systems (Chan et al., 2016; Ha et al., 2017; Wu et al., 2016; Ren et al., 2015).