Towards sound refactoring in erlang

Erlang is an actor-based programming language used extensively for building concurrent, reactive systems that are highly available and suff er minimum downtime. Such systems are often mission critical, making system correctness vital. Refactoring is code restructuring that improves the code but does not change behaviour. While using automated refactoring tools is less error-prone than performing refactorings manually, automated refactoring tools still cannot guarantee that the refactoring is correct, i.e., program behaviour is preserved. This leads to lack of trust in automated refactoring tools. We rst survey solutions to this problem proposed in the literature. Erlang refactoring tools as commonly use approximation techniques which do not guarantee behaviour while some other works propose the use of formal methodologies. In this work we aim to develop a formal methodology for refactoring Erlang code. We study behavioural preorders, with a special focus on the testing preorder as it seems most suited to our purpose.peer-reviewe

Equivalence proofs for Erlang refactoring

Erlang is an actor-based programming language used extensively for building concurrent, reactive systems that are highlighly available and suffer minimum downtime. Such systems are often mission critical, making system correctness vital. In industrial-scale systems, correctness is usually ascertained through testing, a lightweight verification technique trading analysis completeness for scalability. In such cases, a system is deemed correct whenever it “passes” a suite of tests, each checking for the correct functionality of a particular aspect of a system. This is also true for large Erlang systems: even when correctness specifications are provided, it is commonplace for Erlang developers to use testing tools, automating test-case generation from these specifications.peer-reviewe

The influences and consequences of being digitally connected and/or disconnected to travellers

© 2017, The Author(s).Technological progress and tourism have worked in tandem for many years. Connectivity is the vehicle that drove the goal of technologically enhanced tourism experiences forward. This study, through an exploratory qualitative research identifies the factors that boost and/or distract travellers from obtaining a digitally enhanced tourism experience. Four factors can boost and/or distract travellers from being connected: (1) hardware and software, (2) needs and contexts, (3) openness to usage, and (4) supply and provision of connectivity. The research also analyses the positive and/or negative consequences that arise from being connected or disconnected. A Connected/Disconnected Consequences Model illustrates five forms of positive and/or negative consequences: (1) availability, (2) communication, (3) information obtainability, (4) time consumption, and (5) supporting experiences. A better understanding of the role and consequence of connectivity during the trip can enhance traveller experience

Face2Text revisited: Improved data set and baseline results

Current image description generation models do not transfer well to the task of describing human faces. To encourage the development of more human-focused descriptions, we developed a new data set of facial descriptions based on the CelebA image data set. We describe the properties of this data set, and present results from a face description generator trained on it, which explores the feasibility of using transfer learning from VGGFace/ResNet CNNs. Comparisons are drawn through both automated metrics and human evaluation by 76 English-speaking participants. The descriptions generated by the VGGFace-LSTM + Attention model are closest to the ground truth according to human evaluation whilst the ResNet-LSTM + Attention model obtained the highest CIDEr and CIDEr-D results (1.252 and 0.686 respectively). Together, the new data set and these experimental results provide data and baselines for future work in this area.Comment: 7 pages, 5 figures, 4 tables, to appear in LREC 2022 (P-VLAM workshop

Automated segmentation of microtomography imaging of Egyptian mummies

Propagation Phase Contrast Synchrotron Microtomography (PPC-SRμCT) is the gold standard for non-invasive and non-destructive access to internal structures of archaeological remains. In this analysis, the virtual specimen needs to be segmented to separate different parts or materials, a process that normally requires considerable human effort. In the Automated SEgmentation of Microtomography Imaging (ASEMI) project, we developed a tool to automatically segment these volumetric images, using manually segmented samples to tune and train a machine learning model. For a set of four specimens of ancient Egyptian animal mummies we achieve an overall accuracy of 94–98% when compared with manually segmented slices, approaching the results of off-the-shelf commercial software using deep learning (97–99%) at much lower complexity. A qualitative analysis of the segmented output shows that our results are close in terms of usability to those from deep learning, justifying the use of these techniques.peer-reviewe