Adversarial Unsupervised Representation Learning for Activity Time-Series

Sufficient physical activity and restful sleep play a major role in the prevention and cure of many chronic conditions. Being able to proactively screen and monitor such chronic conditions would be a big step forward for overall health. The rapid increase in the popularity of wearable devices provides a significant new source, making it possible to track the user's lifestyle real-time. In this paper, we propose a novel unsupervised representation learning technique called activity2vec that learns and "summarizes" the discrete-valued activity time-series. It learns the representations with three components: (i) the co-occurrence and magnitude of the activity levels in a time-segment, (ii) neighboring context of the time-segment, and (iii) promoting subject-invariance with adversarial training. We evaluate our method on four disorder prediction tasks using linear classifiers. Empirical evaluation demonstrates that our proposed method scales and performs better than many strong baselines. The adversarial regime helps improve the generalizability of our representations by promoting subject invariant features. We also show that using the representations at the level of a day works the best since human activity is structured in terms of daily routinesComment: Accepted at AAAI'19. arXiv admin note: text overlap with arXiv:1712.0952

Context-driven progressive enhancement of mobile web applications: a multicriteria decision-making approach

Personal computing has become all about mobile and embedded devices. As a result, the adoption rate of smartphones is rapidly increasing and this trend has set a need for mobile applications to be available at anytime, anywhere and on any device. Despite the obvious advantages of such immersive mobile applications, software developers are increasingly facing the challenges related to device fragmentation. Current application development solutions are insufficiently prepared for handling the enormous variety of software platforms and hardware characteristics covering the mobile eco-system. As a result, maintaining a viable balance between development costs and market coverage has turned out to be a challenging issue when developing mobile applications. This article proposes a context-aware software platform for the development and delivery of self-adaptive mobile applications over the Web. An adaptive application composition approach is introduced, capable of autonomously bypassing context-related fragmentation issues. This goal is achieved by incorporating and validating the concept of fine-grained progressive application enhancements based on a multicriteria decision-making strategy

Projectional Editors for JSON-Based DSLs

Augmenting text-based programming with rich structured interactions has been explored in many ways. Among these, projectional editors offer an enticing combination of structure editing and domain-specific program visualization. Yet such tools are typically bespoke and expensive to produce, leaving them inaccessible to many DSL and application designers. We describe a relatively inexpensive way to build rich projectional editors for a large class of DSLs -- namely, those defined using JSON. Given any such JSON-based DSL, we derive a projectional editor through (i) a language-agnostic mapping from JSON Schemas to structure-editor GUIs and (ii) an API for application designers to implement custom views for the domain-specific types described in a schema. We implement these ideas in a prototype, Prong, which we illustrate with several examples including the Vega and Vega-Lite data visualization DSLs.Comment: To appear at VL/HCC 202

Context-aware access to heterogeneous resources through on-the-fly mashups

Current scenarios for app development are characterized by rich resources that often overwhelm the final users, especially in mobile app usage situations. It is therefore important to define design methods that enable dynamic filtering of the pertinent resources and appropriate tailoring of the retrieved content. This paper presents a design framework based on the specification of the possible contexts deemed relevant to a given application domain and on their mapping onto an integrated schema of the resources underlying the app. The context and the integrated schema enable the instantiation at runtime of templates of app pages in function of the context characterizing the user’s current situation of use

The Environmental Conditions, Treatments, and Exposures Ontology (ECTO): connecting toxicology and exposure to human health and beyond.

BACKGROUND: Evaluating the impact of environmental exposures on organism health is a key goal of modern biomedicine and is critically important in an age of greater pollution and chemicals in our environment. Environmental health utilizes many different research methods and generates a variety of data types. However, to date, no comprehensive database represents the full spectrum of environmental health data. Due to a lack of interoperability between databases, tools for integrating these resources are needed. In this manuscript we present the Environmental Conditions, Treatments, and Exposures Ontology (ECTO), a species-agnostic ontology focused on exposure events that occur as a result of natural and experimental processes, such as diet, work, or research activities. ECTO is intended for use in harmonizing environmental health data resources to support cross-study integration and inference for mechanism discovery. METHODS AND FINDINGS: ECTO is an ontology designed for describing organismal exposures such as toxicological research, environmental variables, dietary features, and patient-reported data from surveys. ECTO utilizes the base model established within the Exposure Ontology (ExO). ECTO is developed using a combination of manual curation and Dead Simple OWL Design Patterns (DOSDP), and contains over 2700 environmental exposure terms, and incorporates chemical and environmental ontologies. ECTO is an Open Biological and Biomedical Ontology (OBO) Foundry ontology that is designed for interoperability, reuse, and axiomatization with other ontologies. ECTO terms have been utilized in axioms within the Mondo Disease Ontology to represent diseases caused or influenced by environmental factors, as well as for survey encoding for the Personalized Environment and Genes Study (PEGS). CONCLUSIONS: We constructed ECTO to meet Open Biological and Biomedical Ontology (OBO) Foundry principles to increase translation opportunities between environmental health and other areas of biology. ECTO has a growing community of contributors consisting of toxicologists, public health epidemiologists, and health care providers to provide the necessary expertise for areas that have been identified previously as gaps

EXTREMO: An Eclipse plugin for modelling and meta-modelling assistance

Modelling is a core activity in software development paradigms like Model-driven Engineering (MDE). Therefore, the quality of (meta-)models is crucial for the success of software projects. However, many times, modelling becomes a purely manual activity, which does not take advantage of information embedded in heterogeneous information sources, such as XML documents, ontologies, or other models and meta-models. In order to improve this situation, we present EXTREMO, an Eclipse plugin aimed at gathering the information stored in heterogeneous sources in a common data model, to facilitate the reuse of information chunks in the model being built. The tool covers the steps needed to incorporate this knowledge within an external modelling tool, supporting the uniform query of the heterogeneous sources and the evaluation of constraints. Flexibility of the main features (e.g., supported data formats, queries)is achieved by means of extensible mechanisms. To illustrate the usefulness of EXTREMO, we describe a practical case study in the financial domain and evaluate its performance and scalabilityThis work was partially supported by the Ministry of Education of Spain (FPU grant FPU13/02698), the Spanish Ministry of Science (RTI2018-095255-B-I00), and the R&D programme of the Madrid Region (S2018/TCS-4314