DeepSoft: A vision for a deep model of software

Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification problems, as opposed to predicting future events. We present a vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling software and its development process to predict future risks and recommend interventions. DeepSoft, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term temporal dependencies that occur in software evolution. Such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction. DeepSoft provides a new approach for research into modeling of source code, risk prediction and mitigation, developer modeling, and automatically generating code patches from bug reports.Comment: FSE 201

Recognition of cooking activities through air quality sensor data for supporting food journaling

Abstract Unhealthy behaviors regarding nutrition are a global risk for health. Therefore, the healthiness of an individual's nutrition should be monitored in the medium and long term. A powerful tool for monitoring nutrition is a food diary; i.e., a daily list of food taken by the individual, together with portion information. Unfortunately, frail people such as the elderly have a hard time filling food diaries on a continuous basis due to forgetfulness or physical issues. Existing solutions based on mobile apps also require user's effort and are rarely used in the long term, especially by elderly people. For these reasons, in this paper we propose a novel architecture to automatically recognize the preparation of food at home in a privacy-preserving and unobtrusive way, by means of air quality data acquired from a commercial sensor. In particular, we devised statistical features to represent the trend of several air parameters, and a deep neural network for recognizing cooking activities based on those data. We collected a large corpus of annotated sensor data gathered over a period of 8 months from different individuals in different homes, and performed extensive experiments. Moreover, we developed an initial prototype of an interactive system for acquiring food information from the user when a cooking activity is detected by the neural network. To the best of our knowledge, this is the first work that adopts air quality sensor data for cooking activity recognition

Platform Infrastructure for Agile Software Estimation

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumSoftware acquisition reform is a hot topic in the DoD, but the oversight community is struggling to adapt to changes. I diagnose some issues with the current state of business in the Software Acquisition Pathway and propose a system called Overlord to increase the level of automation in software program management and oversight. My goal is to make life easier for software developers, program managers, and members of the oversight community.Approved for public release; distribution is unlimited

Platform Infrastructure for Agile Software Estimation

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumSoftware acquisition reform is a hot topic in the DoD, but the oversight community is struggling to adapt to changes. I diagnose some issues with the current state of business in the Software Acquisition Pathway and propose a system called Overlord to increase the level of automation in software program management and oversight. My goal is to make life easier for software developers, program managers, and members of the oversight community.Approved for public release; distribution is unlimited

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Ten Quick Tips for Using a Raspberry Pi

Much of biology (and, indeed, all of science) is becoming increasingly computational. We tend to think of this in regards to algorithmic approaches and software tools, as well as increased computing power. There has also been a shift towards slicker, packaged solutions--which mirrors everyday life, from smart phones to smart homes. As a result, it's all too easy to be detached from the fundamental elements that power these changes, and to see solutions as "black boxes". The major goal of this piece is to use the example of the Raspberry Pi--a small, general-purpose computer--as the central component in a highly developed ecosystem that brings together elements like external hardware, sensors and controllers, state-of-the-art programming practices, and basic electronics and physics, all in an approachable and useful way. External devices and inputs are easily connected to the Pi, and it can, in turn, control attached devices very simply. So whether you want to use it to manage laboratory equipment, sample the environment, teach bioinformatics, control your home security or make a model lunar lander, it's all built from the same basic principles. To quote Richard Feynman, "What I cannot create, I do not understand".Comment: 12 pages, 2 figure

Toxicological and ecotoxicological risk‐based prioritization of pharmaceuticals in the natural environment

Approximately 1500 active pharmaceutical ingredients are currently in use; however, the environmental occurrence and impacts of only a small proportion of these have been investigated. Recognizing that it would be impractical to monitor and assess all pharmaceuticals that are in use, several previous studies have proposed the use of prioritization approaches to identify substances of most concern so that resources can be focused on these. All of these previous approaches suffer from limitations. In the present study, the authors draw on experience from previous prioritization exercises and present a holistic approach for prioritizing pharmaceuticals in the environment in terms of risks to aquatic and soil organisms, avian and mammalian wildlife, and humans. The approach considers both apical ecotoxicological endpoints as well as potential nonapical effects related to the therapeutic mode of action. Application of the approach is illustrated for 146 active pharmaceuticals that are used either in the community or in hospital settings in the United Kingdom. Using the approach, 16 compounds were identified as a potential priority. These substances include compounds belonging to the antibiotic, antidepressant, anti‐inflammatory, antidiabetic, antiobesity, and estrogen classes as well as associated metabolites. In the future, the prioritization approach should be applied more broadly around the different regions of the world. Environ Toxicol Chem 2016;9999:1–10. © 2016 SETA

Image processing for the extraction of nutritional information from food labels

Current techniques for tracking nutritional data require undesirable amounts of either time or man-power. People must choose between tediously recording and updating dietary information or depending on unreliable crowd-sourced or costly maintained databases. Our project looks to overcome these pitfalls by providing a programming interface for image analysis that will read and report the information present on a nutrition label directly. Our solution involves a C++ library that combines image pre-processing, optical character recognition, and post-processing techniques to pull the relevant information from an image of a nutrition label. We apply an understanding of a nutrition label\u27s content and data organization to approach the accuracy of traditional data-entry methods. Our system currently provides around 80% accuracy for most label images, and we will continue to work to improve our accuracy