Binci
Binci is a utility that allows you to easily containerize your development workflow using Docker. Simply put, it’s like having a cleanroom for all of your development processes which contain services (like databases) without needing to setup and maintain these environments manually. …

CryptoNN
Emerging neural networks based machine learning techniques such as deep learning and its variants have shown tremendous potential in many application domains. However, they raise serious privacy concerns due to the risk of leakage of highly privacy-sensitive data when data collected from users is used to train neural network models to support predictive tasks. To tackle such serious privacy concerns, several privacy-preserving approaches have been proposed in the literature that use either secure multi-party computation (SMC) or homomorphic encryption (HE) as the underlying mechanisms. However, neither of these cryptographic approaches provides an efficient solution towards constructing a privacy-preserving machine learning model, as well as supporting both the training and inference phases. To tackle the above issue, we propose a CryptoNN framework that supports training a neural network model over encrypted data by using the emerging functional encryption scheme instead of SMC or HE. We also construct a functional encryption scheme for basic arithmetic computation to support the requirement of the proposed CryptoNN framework. We present performance evaluation and security analysis of the underlying crypto scheme and show through our experiments that CryptoNN achieves accuracy that is similar to those of the baseline neural network models on the MNIST dataset. …

Real-Time Predictive Analytics
It is when a predictive model (built/fitted on a set of aggregated data) is deployed to perform run-time prediction on a continuous stream of event data to enable decision making in real-time. In order to achieve this, there are two aspects involved. One, the predictive model built by a Data Scientist via a stand-alone tool (R, SAS, SPSS, etc.) has to be exported in a consumable format (PMML is a preferred method across machine learning environments these days; we have done this and also via other formats). Second, a streaming operational analytics platform has to consume the model (PMML or other format) and translate it into the necessary predictive function (via open-source jPMML or Cascading Pattern or Zementis’ commercial licensed UPPI or other interfaces), and also feed the processed streaming event data (via a stream processing component in CEP or similar) to compute the predicted outcome. This deployment of a complex predictive model, from its parent machine learning environment to an operational analytics environment, is one possible route in order to successfully achieve a continuous run-time prediction on streaming event data in real-time. …

KnowBias
We introduce KnowBias, a system for detecting the degree of political bias in textual content such as social media posts and news articles. In the space of scalable text classification, a common problem is domain mismatch, where easily accessible training data (i.e., tweets) does not correspond in format to the desired testing domain (i.e., longer form article content). While universal text encoders such as word or sentence embeddings could be leveraged to train target agnostic classifiers, such schemes result in poor performance on long-form articles. Our key insight is that long-form articles are a mix of neutral and political sentences, while tweets are concentrated with opinion. We propose a two-step classification system that first automatically filters out neutral sentences from the input text document at evaluation time, and then the resulting text is input into a polarity classifier. We evaluate our two-step approach using a variety of test suites, including a set of tweets and long-form articles where annotations were crowd-sourced to decrease label noise, measuring accuracy and Spearman-rho rank correlation. In practice, KnowBias achieves a high accuracy of 86% (rho = 0.65) on these tweets and 75% (rho = 0.69) on long-form articles. …