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Machine Learning (ML) algorithms have shown quite promising applications in smart meter data analytics enabling intelligent energy management systems for the Advanced Metering Infrastructure (AMI). One of the major challenges in developing ML applications for the AMI is to preserve user privacy while allowing active end-users participation. This paper addresses this challenge and proposes Differential Privacy-enabled AMI with Federated Learning (DP-AMI-FL), framework for ML-based applications in the AMI. This framework provides two layers of privacy protection: first, it keeps the raw data of consumers hosting ML applications at edge devices (smart meters) with Federated Learning (FL), and second, it obfuscates the ML models using Differential Privacy (DP) to avoid privacy leakage threats on the models posed by various inference attacks. The framework is evaluated by analyzing its performance on a use case aimed to improve Short-Term Load Forecasting (STLF) for residential consumers having smart meters and home energy management systems. Extensive experiments demonstrate that the framework when used with Long Short-Term Memory (LSTM) recurrent neural network models, achieves high forecasting accuracy while preserving users data privacy. 

Access and reuse of authoritative phylogenetic knowledge have been a longstanding challenges in the evolutionary biology community — leading to a number of research efforts (e.g. focused on interoperation, standardization of formats, and development of minimum reporting requirements). The Phylotastic project was launched to provide an answer to such challenges — as an architectural concept collaboratively designed by evolutionary biologists and computer scientists. This paper describes the first comprehensive implementation of the Phylotastic architecture, based on an open platform for Web services composition. The implementation provides a portal, which composes Web services along a fixed collection of workflows, as well as an interface to allow users to develop novel workflows. The Web services composition is guided by automated planning algorithms and built on a Web services registry and an execution monitoring engine. The platform provides resilience through seamless automated recovery from failed services. 

Online social media is being widely used by social scientists to study human behavior. Researchers have explored different feature extraction (FE) and classification techniques to perform sentiment analysis, topic identification, etc. Most studies tend to evaluate FE and classification methods using only one particular class of datasets---well-defined with little/no noise or with well-defined noise. For instance, when the datasets under study have different noise characteristics, various FE and/or classification methods may fail to identify a given topic. In this paper, we fill this gap by quantitatively comparing multiple FE methods and classifiers using three different datasets (two moderator-controlled blogs and one single-authored personal blogs) related to Autism Spectrum Disorder (ASD). Our result shows that no particular combination of FE and classifier is the best overall, but choosing the right ones can improve accuracy by over 30%. 

A comprehensive phylogeny of species, i.e., a tree of life, has potential uses in a variety of contexts, including research, education, and public policy. Yet, accessing the tree of life typically requires special knowledge, complex software, or long periods of training. The Phylotastic project aims make it as easy to get a phylogeny of species as it is to get driving directions from mapping software. In prior work, we presented a design for an open system to validate and manage taxon names, find phylogeny resources, extract subtrees matching a user’s taxon list, scale trees to time, and integrate related resources such as species images. Here, we report the implementation of a set of tools that together represent a robust, accessible system for on-the-fly delivery of phylogenetic knowledge. This set of tools includes a web portal to execute several customizable workflows to obtain species phylogenies (scaled by geologic time and decorated with thumbnail images); more than 30 underlying web services (accessible via a common registry); and code toolkits in R and Python (allowing others to develop custom applications using Phylotastic services). The Phylotastic system, accessible via http://www.phylotastic.org , provides a unique resource to access the current state of phylogenetic knowledge, useful for a variety of cases in which a tree extracted quickly from online resources (as distinct from a tree custom-made from character data) is sufficient, as it is for many casual uses of trees identified here.