There is a need for affordable, widely deployable maternal-fetal ECG monitors to improve maternal and fetal health during pregnancy and delivery. Based on the diffusion-based channel selection, here we present the mathematical formalism and clinical validation of an algorithm capable of accurate separation of maternal and fetal ECG from a two channel signal acquired over maternal abdomen.

One of the most typical tasks in machine learning is classification tasks. It may seem that evaluating the effectiveness of such a model is easy. Let's assume that we have a model which, based on historical data, calculates if a client will pay back credit obligations. We evaluate 100 bank customers and our model correctly guesses in 93 instances. That may appear to be a good result – but is it really?

Missingness was an important issue with this dataset. I ignored certain missing items, such as a house not being available on Zillow, under the assumption that they item got dropped for technical reasons in the web scraping process. But others I was less confident about.

Regularized discriminant analysis (RDA), proposed by Friedman (1989), is a widely popular classifier that lacks interpretability and is impractical for high-dimensional data sets. Here, we present an interpretable and computationally efficient classifier called high-dimensional RDA (HDRDA), designed for the small-sample, high-dimensional setting. For HDRDA, we show that each training observation, regardless of class, contributes to the class covariance matrix, resulting in an interpretable estimator that borrows from the pooled sample covariance matrix. Moreover, we show that HDRDA is equivalent to a classifier in a reduced-feature space with dimension approximately equal to the training sample size. As a result, the matrix operations employed by HDRDA are computationally linear in the number of features, making the classifier well-suited for high-dimensional classification in practice. We demonstrate that HDRDA is often superior to several sparse and regularized classifiers in terms of classification accuracy with three artificial and six real high-dimensional data sets. Also, timing comparisons between our HDRDA implementation in the sparsediscrim R package and the standard RDA formulation in the klaR R package demonstrate that as the number of features increases, the computational runtime of HDRDA is drastically smaller than that of RDA.

When acting, agents may deviate from the optimal plan, either because they are not perfect optimizers or because they interleave multiple unrelated tasks. In this paper, we detect such deviations by analyzing a set of observations and a monitored goal to determine if an observed agent's actions contribute towards achieving the goal. We address this problem without pre-defined static plan libraries, and instead use a planning domain definition to represent the problem and the expected agent behavior. At the core of our approach, we exploit domain-independent heuristics for estimating the goal distance, incorporating the concept of landmarks (actions which all plans must undertake if they are to achieve the goal). We evaluate the resulting approach empirically using several known planning domains, and demonstrate that our approach effectively detects such deviations.

Unsolicited or unwanted messages is a byproduct of virtually every popular social media website. Spammers have become increasingly proficient at bypassing conventional spam filters, prompting a stronger effort to develop new methods that accurately detect spam while simultaneously acting as a more robust classifier against users that modify their behavior in order to avoid detection. This paper shows the usefulness of a relational model that works in conjunction with an independent model. First, an independent model is built using features that characterize individual comments and users, capturing the cases where spam is obvious. Second, a relational model is built, taking advantage of the interconnected nature of users and their comments. By feeding our initial predictions from the independent model into the relational model, we can start to propagate information about spammers and spam comments to jointly infer the labels of all spam comments at the same time. This allows us to capture the obfuscated spam comments missed by the independent model that are only found by looking at the relational structure of the social network. The results from our experiments demonstrates the viability of our method, and shows that models utilizing the underlying structure of the social network are more effective at detecting spam than ones that do not.

We present a framework for automated analysis and categorization of .onion websites in the darkweb to facilitate analyst situational awareness of new content that emerges from this dynamic landscape. Over the last two years, our team has developed a large-scale darkweb crawling infrastructure called OnionCrawler that acquires new onion domains on a daily basis, and crawls and indexes millions of pages from these new and previously known .onion sites. It stores this data into a research repository designed to help better understand Tor’s hidden service ecosystem. The analysis component of our framework is called Automated Tool for Onion Labeling (ATOL), which introduces a two-stage thematic labeling strategy: (1) it learns descriptive and discriminative keywords for different categories, and (2) uses these terms to map onion site content to a set of thematic labels. We also present empirical results of ATOL and our ongoing experimentation with it, as we have gained experience applying it to the entirety of our darkweb repository, now over 70 million indexed pages. We find that ATOL can perform site-level thematic label assignment more accurately than keywordbased schemes developed by domain experts — we expand the analyst-provided keywords using an automatic keyword discovery algorithm, and get 12% gain in accuracy by using a machine learning classification model. We also show how ATOL can discover categories on previously unlabeled onions and discuss applications of ATOL in supporting various analyses and investigations of the darkweb.

Recent developments in SCADA (Supervisory Control and Data Acquisition) systems for physical infrastructure, such as high pressure gas pipeline systems and electric grids, have generated enormous amounts of time series data. This data brings great opportunities for advanced knowledge discovery and data mining methods to identify system failures faster and earlier than operation experts. This paper presents our effort in collaboration with a utility company to solve a grand challenge; namely, to use advanced data mining methods to detect leaks on a high pressure gas transmission system. Leak detection models with unsupervised learning tasks were developed analyzing billions of data records to identify leaks of different sizes and impacts, with very low false positive rates. In particular, our solution was able to identify small leaks leading to rupture events. The model also identified small leaks not identifiable with current detection systems. Such high-fidelity early identification enables operation personnel to take preventive measures against possible catastrophic events. We then formulate several generic detection methods with models derived from time series anomaly detection methods. We show that our leak detection models are superior to the SCADA alarm system, a mass balance model and other generic time series anomaly detection models in terms of both detection accuracy and computation time.

Money laundering is a major global problem, enabling criminal organisations to hide their ill-gotten gains and to finance further operations. Prevention of money laundering is seen as a high priority by many governments, however detection of money laundering without prior knowledge of predicate crimes remains a significant challenge. Previous detection systems have tended to focus on individuals, considering transaction histories and applying anomaly detection to identify suspicious behaviour. However, money laundering involves groups of collaborating individuals and evidence of money laundering may only be apparent when the collective behaviour of these groups is considered. In this paper we describe a detection system that is capable of analysing group behaviour, using a combination of network analysis and supervised learning. This system is designed for real-world application and operates on networks consisting of millions of interacting parties. Evaluation of the system using real-world data indicates that suspicious activity is successfully detected. Importantly, the system exhibits a low rate of false positives, and is therefore suitable for use in a live intelligence environment.

In this thesis we present the novel semi-supervised network-based algorithm P-Net, which is able to rank and classify patients with respect to a specific phenotype or clinical outcome under study. The peculiar and innovative characteristic of this method is that it builds a network of samples/patients, where the nodes represent the samples and the edges are functional or genetic relationships between individuals (e.g. similarity of expression profiles), to predict the phenotype under study. In other words, it constructs the network in the "sample space" and not in the "biomarker space" (where nodes represent biomolecules (e.g. genes, proteins) and edges represent functional or genetic relationships between nodes), as usual in state-of-the-art methods. To assess the performances of P-Net, we apply it on three different publicly available datasets from patients afflicted with a specific type of tumor: pancreatic cancer, melanoma and ovarian cancer dataset, by using the data and following the experimental set-up proposed in two recently published papers [Barter et al., 2014, Winter et al., 2012]. We show that network-based methods in the "sample space" can achieve results competitive with classical supervised inductive systems. Moreover, the graph representation of the samples can be easily visualized through networks and can be used to gain visual clues about the relationships between samples, taking into account the phenotype associated or predicted for each sample. To our knowledge this is one of the first works that proposes graph-based algorithms working in the "sample space" of the biomolecular profiles of the patients to predict their phenotype or outcome, thus contributing to a novel research line in the framework of the Network Medicine.