Papers.

Predicting Sales Email Responders using a Natural Language Model

Parser Macros for Scala

Online Learning of Eigenvectors.

Obey: Code Health for Scala.Meta

New Challenges of Democracy in Serbia: Global Norms in Local Perspective

Network Analysis of the International Conference Utilizing Social Media: the Case of Internet Research Conference(IR15)

Modeling and Analyzing IMA Architectures with AADL, From Modeling to Safety Evaluation and Code Generation: A Case-Study

Safety-Critical Systems for the aerospace domain are becoming extremely software-reliant, with tight coupling with hardware and network elements. At the same time, these have to demonstrate conformance with stringent standards so as to ensure a sufficient level of safety. In this presentation, we report on recent advances in the SAE Architecture Analysis and Design Language -- AADL -- standard and supporting tools for the modeling of avionics system and software. SAE AS2-C committee pushed forward various standard documents 1) to model avionics system compatible with the Integrated Modular Avionics (IMA) paradigm, and 2) to support the analysis of safety properties by modeling the effects of faults and errors on architecture through the Error Modeling annex language. The proposed approach allows for an efficient modeling of the various aspects of a system, ranging from high-level architecture down to precise defects mode. Using OSATE analysis facilities, designer can generate from models analysis reports conformant to SAE ARP 4761, such as the Functional Hazard Assessment (FHA), Fault-Tree Analysis (FTA) or Failure Mode and Effects Analysis (FMEA). In a second phase, using Ocarina, one can generate the configuration of an ARINC653 APEX to set up run-time elements (partitions, time and memory budgets,communication ports QoS policies), but also the configuration of health monitoring policies. We illustrate how those elements are applied on the Software Health Management unit an ADIRU unit exposed as part of the incident report by the ATSB. This public case study allows for a precise assessment of the AADLv2 new capabilities.

Macros in sbt: Problem solved!

Liquid: Unifying Nearline and Offline Big Data Integration

Learning Spark: Lightning-Fast Big Data Analytics

JQuery Pocket Primer

Influence of spectral cues on the perception of pitch height

HTML5 Canvas

High Resolution: Bringing a Sharper Focus to Precision Medicine Through Design

High-Power Flow Batteries, Part II: Flow Field Modeling And Simulation

Heisenberg Was on the Write Track

Hadoop Application Architectures: Designing Real-World Big Data Applications

Gradients for the Loss ! Towards Provable Gradient Algorithms for Non-Convex Problems

Finding Money in the Haystack: Information Retrieval at Bloomberg

Exploring the Social and Ethical Issues in Data Linkage

Evaluating and Mitigating the Impact of Complexity in Software Models

Software Engineering Institute

Distributed Stochastic Variance Reduced Gradient Methods.

Distributed Online Latent Dirichlet Allocation with Apache Spark

Distantly Supervised Information Extraction Using Bootstrapped Patterns

Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares Algorithms.

D3 Data-Driven Documents Pocket Primer

Creating Games to Get Students

Coordination Avoidance in Distributed Databases

Communication Lower Bounds for Statistical Estimation Problems via a Distributed Data Processing Inequality.

Common Information Models for an Open, Analytical, and Agile World

Bootstrapped Self Training for Knowledge Base Population

Back to the Future: Malware Detection with Temporally Consistent Labels

Atomic Scala

Apache Solr Enterprise Search Server, Third Edition

Standard reference book on Apache Solr, covering schema design, indexing, searching, faceting, relevancy tuning, SolrCloud distributed search, and deployment/scaling of Solr as an enterprise search server.

Advanced Analytics with Spark: Patterns for Learning from Data at Scale

A Survey of Online Experiment Design with the Stochastic Multi-Armed Bandit.

A Spectrum-based Regularization Approach to Linear Inverse Problems: Models, Learned Parameters and Algorithms

In this thesis, we study the problem of recovering signals, in particular images, that approximately satisfy severely ill-conditioned or underdetermined linear systems. For example, such a linear system may represent a set of under-sampled and noisy linear measurements. It is well-known that the quality of the recovery critically depends on the choice of an appropriate regularization model that incorporates prior information about the target solution. Two of the most successful regularization models are the Tikhonov and Total Variation (TV) models, each of which is used in a wide range of applications. We design and investigate a class of spectrum-based models that generalize and improve upon both the Tikhonov and the TV methods, as well as their combinations or so-called hybrids. The proposed models contain "spectrum parameters" that are learned from training data sets through solving optimization problems. This parameter-learning feature gives these models the flexibility to adapt to desired target solutions. We devise efficient algorithms for all the proposed models and conduct comprehensive numerical experiments to evaluate their performance as compared to established models. Numerical results show a generally superior quality in recovered images by our approach from under-sampled linear measurements. Using the proposed algorithms, one can often obtain much improved quality at a moderate increase in computational time.

A Represention Theorem for Second-Order Functionals

A Demonstration of the BigDAWG Polystore System

A Multi-Cultural Repository of Automatically Discovered Linguistic and Conceptual Metaphors

In this article, we present details about our ongoing work towards building a repository of Linguistic and Conceptual Metaphors. This resource is being developed as part of our research effort into the large-scale detection of metaphors from unrestricted text. We have stored a large amount of automatically extracted metaphors in American English, Mexican Spanish, Russian and Iranian Farsi in a relational database, along with pertinent metadata associated with these metaphors. A substantial subset of the contents of our repository has been systematically validated via rigorous social science experiments. Using information stored in the repository, we are able to posit certain claims in a cross-cultural context about how peoples in these cultures (America, Mexico, Russia and Iran) view particular concepts related to Governance and Economic Inequality through the use of metaphor. Researchers in the field can use this resource as a reference of typical metaphors used across these cultures. In addition, it can be used to recognize metaphors of the same form or pattern, in other domains of research.

Automatic Expansion of the MRC Psycholinguistic Database Imageability Ratings

Recent studies in metaphor extraction across several languages (Broadwell et al., 2013; Strzalkowski et al., 2013) have shown that word imageability ratings are highly correlated with the presence of metaphors in text. Information about imageability of words can be obtained from the MRC Psycholinguistic Database (MRCPD) for English words and Léxico Informatizado del Español Programa (LEXESP) for Spanish words, which is a collection of human ratings obtained in a series of controlled surveys. Unfortunately, word imageability ratings were collected for only a limited number of words: 9,240 words in English, 6,233 in Spanish; and are unavailable at all in the other two languages studied: Russian and Farsi. The present study describes an automated method for expanding the MRCPD by conferring imageability ratings over the synonyms and hyponyms of existing MRCPD words, as identified in Wordnet. The result is an expanded MRCPD+ database with imagea-bility scores for more than 100,000 words. The appropriateness of this expansion process is assessed by examining the structural coherence of the expanded set and by validating the expanded lexicon against human judgment. Finally, the performance of the metaphor extraction system is shown to improve significantly with the expanded database. This paper describes the process for English MRCPD+ and the resulting lexical resource. The process is analogous for other languages.

New Directions for Language Resource Development and Distribution

DysList: An Annotated Resource of Dyslexic Errors

GraphX: Graph Processing in a Distributed Dataflow Framework

Online Topic-aware Influence Maximization Queries

Follow the money: A study of cashtags on Twitter

The popularity of Twitter goes beyond trending topics, world events, memes, and popular hashtags. Recently a new way of sharing financial information is taking place in social media under the name of cashtags , stock ticker symbols that are prefixed with a dollar sign. In this paper we present an exploratory analysis of cashtags on Twitter. Specifically, we investigate how widespread cashtags are, what stock symbols are tweeted more often, and which users tweet about cashtags in general. We analyze relationships among cashtags and study hashtags in the context of cashtags. Finally, we compare tweet performance to stock market performance. We conclude that cashtags, in particular in combination with other cashtags or hashtags, can be very useful for analyzing financial information and provide new insights into stocks and companies.

Security, Trust, and Regulatory Aspects of Cloud Computing in Business Environments

Machine learning for neuroimaging with scikit-learn

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

h2o: R Interface for the 'H2O' Scalable Machine Learning Platform

R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).

Discovering Conceptual Metaphors using Source Domain Spaces

This article makes two contributions towards the use of lexical resources and corpora; specifically making use of them for gaining access to and using word associations. The direct application of our approach is for detecting linguistic and conceptual metaphors automatically in text. We describe our method of building conceptual spaces, that is, defining the vocabulary that characterizes a Source Domain (e.g., Disease) of a conceptual metaphor (e.g., Poverty is a Disease). We also describe how these conceptual spaces are used to group linguistic metaphors into conceptual metaphors. Our method works in multiple languages, including English, Spanish, Russian and Farsi. We provide details of how our method can be evaluated and evaluation results that show satisfactory performance across all languages.