Type-Directed Program Transformations for the Working Functional Programmer

We present preliminary research on Deuce+, a set of tools integrating plain text editing with structural manipulation that brings the power of expressive and extensible type-directed program transformations to everyday, working programmers without a background in computer science or mathematical theory. Deuce+ comprises three components: (i) a novel set of type-directed program transformations, (ii) support for syntax constraints for specifying "code style sheets" as a means of flexibly ensuring the consistency of both the concrete and abstract syntax of the output of program transformations, and (iii) a domain-specific language for specifying program transformations that can operate at a high level on the abstract (and/or concrete) syntax tree of a program and interface with syntax constraints to expose end-user options and alleviate tedious and potentially mutually inconsistent style choices. Currently, Deuce+ is in the design phase of development, and discovering the right usability choices for the system is of the highest priority

Customer churn prediction in telecom using machine learning and social network analysis in big data platform

Customer churn is a major problem and one of the most important concerns for large companies. Due to the direct effect on the revenues of the companies, especially in the telecom field, companies are seeking to develop means to predict potential customer to churn. Therefore, finding factors that increase customer churn is important to take necessary actions to reduce this churn. The main contribution of our work is to develop a churn prediction model which assists telecom operators to predict customers who are most likely subject to churn. The model developed in this work uses machine learning techniques on big data platform and builds a new way of features' engineering and selection. In order to measure the performance of the model, the Area Under Curve (AUC) standard measure is adopted, and the AUC value obtained is 93.3%. Another main contribution is to use customer social network in the prediction model by extracting Social Network Analysis (SNA) features. The use of SNA enhanced the performance of the model from 84 to 93.3% against AUC standard. The model was prepared and tested through Spark environment by working on a large dataset created by transforming big raw data provided by SyriaTel telecom company. The dataset contained all customers' information over 9 months, and was used to train, test, and evaluate the system at SyriaTel. The model experimented four algorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree "GBM" and Extreme Gradient Boosting "XGBOOST". However, the best results were obtained by applying XGBOOST algorithm. This algorithm was used for classification in this churn predictive model.Comment: 24 pages, 14 figures. PDF https://rdcu.be/budK

Frex: dependently-typed algebraic simplification

We present an extensible, mathematically-structured algebraic simplification library design. We structure the library using universal algebraic concepts: a free algebra -- fral -- and a free extension -- frex -- of an algebra by a set of variables. The library's dependently-typed API guarantees simplification modules, even user-defined ones, are terminating, sound, and complete with respect to a well-specified class of equations. Completeness offers intangible benefits in practice -- our main contribution is the novel design. Cleanly separating between the interface and implementation of simplification modules provides two new modularity axes. First, simplification modules share thousands of lines of infrastructure code dealing with term-representation, pretty-printing, certification, and macros/reflection. Second, new simplification modules can reuse existing ones. We demonstrate this design by developing simplification modules for monoid varieties: ordinary, commutative, and involutive. We implemented this design in the new Idris2 dependently-typed programming language, and in Agda

Health Figures: An Open Source JavaScript Library for Health Data Visualization

The way we look at data has a great impact on how we can understand it, particularly when the data is related to health and wellness. Due to the increased use of self-tracking devices and the ongoing shift towards preventive medicine, better understanding of our health data is an important part of improving the general welfare of the citizens. Electronic Health Records, self-tracking devices and mobile applications provide a rich variety of data but it often becomes difficult to understand. We implemented the hFigures library inspired on the hGraph visualization with additional improvements. The purpose of the library is to provide a visual representation of the evolution of health measurements in a complete and useful manner. We researched the usefulness and usability of the library by building an application for health data visualization in a health coaching program. We performed a user evaluation with Heuristic Evaluation, Controlled User Testing and Usability Questionnaires. In the Heuristics Evaluation the average response was 6.3 out of 7 points and the Cognitive Walkthrough done by usability experts indicated no design or mismatch errors. In the CSUQ usability test the system obtained an average score of 6.13 out of 7, and in the ASQ usability test the overall satisfaction score was 6.64 out of 7. We developed hFigures, an open source library for visualizing a complete, accurate and normalized graphical representation of health data. The idea is based on the concept of the hGraph but it provides additional key features, including a comparison of multiple health measurements over time. We conducted a usability evaluation of the library as a key component of an application for health and wellness monitoring. The results indicate that the data visualization library was helpful in assisting users in understanding health data and its evolution over time.Comment: BMC Medical Informatics and Decision Making 16.1 (2016

UTP Knowledge Based Healthcare Information Services

This report is an extension of the proposal that was submitted for approval as a Final Year Project. It outlines the implementation of UTP Knowledge Based Healthcare Information Services. UTP Knowledge Based Healthcare Information Services will be developed to enhance the current system's functionalities and address issues such as redundancy while providing informative and feedback to system users, who are the staff of UTP Health Clinic, students and lecturer's, to provide a better standard of clinical services. Among the benefits attainable by the UTP Health Clinic with the UTP Knowledge Based Healthcare Information Services are productivity, time savings, improved quality of care and profitability. For implementation, a basis for literature review is determined and steps to conduct this scientific research is determined to derive a hypothesis that builds on the works of others and relating its concepts to implement UTP Knowledge Based Healthcare Information Services. The methodology of the project is discussed where a complete research of the component-based software engineering research is done based on the UTP Knowledge Based Healthcare Information Services case. Finally, the purpose of gathering data together with its conclusions and recommendations are briefly described in this report

Morpheus: Automated Safety Verification of Data-Dependent Parser Combinator Programs

Parser combinators are a well-known mechanism used for the compositional construction of parsers, and have shown to be particularly useful in writing parsers for rich grammars with data-dependencies and global state. Verifying applications written using them, however, has proven to be challenging in large part because of the inherently effectful nature of the parsers being composed and the difficulty in reasoning about the arbitrarily rich data-dependent semantic actions that can be associated with parsing actions. In this paper, we address these challenges by defining a parser combinator framework called Morpheus equipped with abstractions for defining composable effects tailored for parsing and semantic actions, and a rich specification language used to define safety properties over the constituent parsers comprising a program. Even though its abstractions yield many of the same expressivity benefits as other parser combinator systems, Morpheus is carefully engineered to yield a substantially more tractable automated verification pathway. We demonstrate its utility in verifying a number of realistic, challenging parsing applications, including several cases that involve non-trivial data-dependent relations

Absolute electrical impedance tomography (aEIT) guided ventilation therapy in critical care patients: simulations and future trends

Thoracic electrical impedance tomography (EIT) is a noninvasive, radiation-free monitoring technique whose aim is to reconstruct a cross-sectional image of the internal spatial distribution of conductivity from electrical measurements made by injecting small alternating currents via an electrode array placed on the surface of the thorax. The purpose of this paper is to discuss the fundamentals of EIT and demonstrate the principles of mechanical ventilation, lung recruitment, and EIT imaging on a comprehensive physiological model, which combines a model of respiratory mechanics, a model of the human lung absolute resistivity as a function of air content, and a 2-D finite-element mesh of the thorax to simulate EIT image reconstruction during mechanical ventilation. The overall model gives a good understanding of respiratory physiology and EIT monitoring techniques in mechanically ventilated patients. The model proposed here was able to reproduce consistent images of ventilation distribution in simulated acutely injured and collapsed lung conditions. A new advisory system architecture integrating a previously developed data-driven physiological model for continuous and noninvasive predictions of blood gas parameters with the regional lung function data/information generated from absolute EIT (aEIT) is proposed for monitoring and ventilator therapy management of critical care patients