Blazes: Coordination Analysis for Distributed Programs

Distributed consistency is perhaps the most discussed topic in distributed systems today. Coordination protocols can ensure consistency, but in practice they cause undesirable performance unless used judiciously. Scalable distributed architectures avoid coordination whenever possible, but under-coordinated systems can exhibit behavioral anomalies under fault, which are often extremely difficult to debug. This raises significant challenges for distributed system architects and developers. In this paper we present Blazes, a cross-platform program analysis framework that (a) identifies program locations that require coordination to ensure consistent executions, and (b) automatically synthesizes application-specific coordination code that can significantly outperform general-purpose techniques. We present two case studies, one using annotated programs in the Twitter Storm system, and another using the Bloom declarative language.Comment: Updated to include additional materials from the original technical report: derivation rules, output stream label

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio

An Overview of Ontology Application for Policy-Based Management using POPPET

The use of ontology to describe the key concepts and their interrelationships within a particular area has become a widely recognised and advantageous means of sharing information about the structure of knowledge within a domain. Ontologies provide a way of integrating structured data within an application. This report provides an overview of how the ACCENT policy-based management system [1] was significantly re-engineered to utilise an ontology in place of previously hard-coded, domain-specific information within its user interface. In order to successfully integrate the ontology with the policy system, a new framework named POPPET was developed, responsible for parsing and querying ontological data. Although a substantial alteration in technical structure, the process vastly generalises the policy system, enabling adaptation for policy management within any custom domain. An introduction to the concepts and motivation for ontology creation using OWL is presented, together with general background to the ACCENT policy system. A technical overview is then given covering the developed ontologies, the POPPET system design, and the policy system re-engineering process. Finally, a comparison is made between the new and old policy system structures, and the impact on system performance is evaluated

Linear Haskell: practical linearity in a higher-order polymorphic language

Linear type systems have a long and storied history, but not a clear path forward to integrate with existing languages such as OCaml or Haskell. In this paper, we study a linear type system designed with two crucial properties in mind: backwards-compatibility and code reuse across linear and non-linear users of a library. Only then can the benefits of linear types permeate conventional functional programming. Rather than bifurcate types into linear and non-linear counterparts, we instead attach linearity to function arrows. Linear functions can receive inputs from linearly-bound values, but can also operate over unrestricted, regular values. To demonstrate the efficacy of our linear type system - both how easy it can be integrated in an existing language implementation and how streamlined it makes it to write programs with linear types - we implemented our type system in GHC, the leading Haskell compiler, and demonstrate two kinds of applications of linear types: mutable data with pure interfaces; and enforcing protocols in I/O-performing functions

A heuristic-based approach to code-smell detection

Encapsulation and data hiding are central tenets of the object oriented paradigm. Deciding what data and behaviour to form into a class and where to draw the line between its public and private details can make the difference between a class that is an understandable, flexible and reusable abstraction and one which is not. This decision is a difficult one and may easily result in poor encapsulation which can then have serious implications for a number of system qualities. It is often hard to identify such encapsulation problems within large software systems until they cause a maintenance problem (which is usually too late) and attempting to perform such analysis manually can also be tedious and error prone. Two of the common encapsulation problems that can arise as a consequence of this decomposition process are data classes and god classes. Typically, these two problems occur together – data classes are lacking in functionality that has typically been sucked into an over-complicated and domineering god class. This paper describes the architecture of a tool which automatically detects data and god classes that has been developed as a plug-in for the Eclipse IDE. The technique has been evaluated in a controlled study on two large open source systems which compare the tool results to similar work by Marinescu, who employs a metrics-based approach to detecting such features. The study provides some valuable insights into the strengths and weaknesses of the two approache

Ontology Stack for A Policy Wizard

An ontology provides a common vocabulary through which to share information in a particular area of knowledge, including the key terms, their semantic interconnections and certain rules of inference. The ACCENT policy-based management system uses a policy description language called APPEL and supports policy document formation through the use of a comprehensive user interface wizard. Through the use of OWL (the Web Ontology Language), the core aspects of APPEL have been captured and defined in an ontology. Assigned the acronym genpol, this ontology describes the policy language independent of any user interface or domain-specific policy information. A further ontology has been developed to define common interface features implemented by the policy wizard [17]. This ontology, referred to as wizpol, directly extends genpol. It provides additional information to the language itself, whilst retaining freedom from any domain-specific policy details. Combined, both genpol and wizpol act as a base for defining further domain-specific ontologies which may describe policy options tailored for a particular application. This report presents a technical overview of both the generic policy language ontology (genpol) and the wizard policy ontology (wizpol), expressed in the form of graphical depictions of OWL classes and properties