An evaluation of e-learning standards

The aim of this investigation is to perform an independent study of the various emerging elearning standards. This paper presents a summary of these standards in order to make them more accessible and understandable, and provide preliminary evidence as to their utility and adoption by the various UK higher and further education institutions. Recently there have been efforts to define standards for the elearning contents and elearning components like the IEEELOM, UKLOM, IMS, SCORM and OKI. Since it was not possible to cover all the standards in detail within the time available, so our independent study focuses on eight standards Although the results of the preliminary study suggest that the eight standards considered in the study may help interoperability, accessibility and reusability of the elearning content and elearning components, but it is yet to be seen how many of these are actually followed at UK higher education institutions

The problem of labels in e-assessment of diagrams

In this short paper we explore a problematic aspect of automated assessment of diagrams. Diagrams have partial and sometimes inconsistent semantics. Typically much of the meaning of diagram resides in the labels, however, the choice of labeling is largely unrestricted. This means a correct solution may utilise differing yet semantically equivalent labels to the specimen solution. With human marking this problem can be easily overcome. Unfortunately with e-assessment this is challenging. We empirically explore the scale of the problem of synonyms by analysing 160 student solutions to a UML task. From this we find that cumulative growth of synonyms only shows a limited tendency to reduce at the margin. This finding has significant implications for the ease in which we may develop future e-assessment systems of diagrams, in that the need for better algorithms for assessing label semantic similarity becomes inescapable

A wide band gap metal-semiconductor-metal nanostructure made entirely from graphene

A blueprint for producing scalable digital graphene electronics has remained elusive. Current methods to produce semiconducting-metallic graphene networks all suffer from either stringent lithographic demands that prevent reproducibility, process-induced disorder in the graphene, or scalability issues. Using angle resolved photoemission, we have discovered a unique one dimensional metallic-semiconducting-metallic junction made entirely from graphene, and produced without chemical functionalization or finite size patterning. The junction is produced by taking advantage of the inherent, atomically ordered, substrate-graphene interaction when it is grown on SiC, in this case when graphene is forced to grow over patterned SiC steps. This scalable bottomup approach allows us to produce a semiconducting graphene strip whose width is precisely defined within a few graphene lattice constants, a level of precision entirely outside modern lithographic limits. The architecture demonstrated in this work is so robust that variations in the average electronic band structure of thousands of these patterned ribbons have little variation over length scales tens of microns long. The semiconducting graphene has a topologically defined few nanometer wide region with an energy gap greater than 0.5 eV in an otherwise continuous metallic graphene sheet. This work demonstrates how the graphene-substrate interaction can be used as a powerful tool to scalably modify graphene's electronic structure and opens a new direction in graphene electronics research.Comment: 11 pages, 7 figure

Symmetry breaking in commensurate graphene rotational stacking; a comparison of theory and experiment

Graphene stacked in a Bernal configuration (60 degrees relative rotations between sheets) differs electronically from isolated graphene due to the broken symmetry introduced by interlayer bonds forming between only one of the two graphene unit cell atoms. A variety of experiments have shown that non-Bernal rotations restore this broken symmetry; consequently, these stacking varieties have been the subject of intensive theoretical interest. Most theories predict substantial changes in the band structure ranging from the development of a Van Hove singularity and an angle dependent electron localization that causes the Fermi velocity to go to zero as the relative rotation angle between sheets goes to zero. In this work we show by direct measurement that non-Bernal rotations preserve the graphene symmetry with only a small perturbation due to weak effective interlayer coupling. We detect neither a Van Hove singularity nor any significant change in the Fermi velocity. These results suggest significant problems in our current theoretical understanding of the origins of the band structure of this material.Comment: 7 pages, 6 figures, submitted to PR

Silicon intercalation into the graphene-SiC interface

In this work we use LEEM, XPEEM and XPS to study how the excess Si at the graphene-vacuum interface reorders itself at high temperatures. We show that silicon deposited at room temperature onto multilayer graphene films grown on the SiC(000[`1]) rapidly diffuses to the graphene-SiC interface when heated to temperatures above 1020. In a sequence of depositions, we have been able to intercalate ~ 6 ML of Si into the graphene-SiC interface.Comment: 6 pages, 8 figures, submitted to PR

Show, Not Tell: The Contingency Role of Infographics Versus Text in the Differential Effects of Message Strategies on Optimistic Bias

© The Author(s) 2019. Using an online between-subject experiment, this study tested the effects of message framing (gain vs. loss), reference point (self vs. other), and modality (text vs. infographics) in the scenario of recycling promotion. The findings identified that modality determines under what circumstances message strategies make a difference in risk perception and optimistic bias. In particular, only when paired with infographics and other-referencing point are loss-framed messages more effective than gain-framed messages in increasing risk perception and reducing the self-other gap in perceived benefits. Moreover, risk perception variables and the self-other risk perceptual gap were significant predictors of promoted behavioral intentions

Constructing Search Spaces for Search-Based Software Testing Using Neural Networks

A central requirement for any Search-Based Software Testing (SBST) technique is a convenient and meaningful fitness landscape. Whether one follows a targeted or a diversification driven strategy, a search landscape needs to be large, continuous, easy to construct and representative of the underlying property of interest. Constructing such a landscape is not a trivial task often requiring a significant manual effort by an expert. We present an approach for constructing meaningful and convenient fitness landscapes using neural networks (NN) – for targeted and diversification strategies alike. We suggest that output of an NN predictor can be interpreted as a fitness for a targeted strategy. The NN is trained on a corpus of execution traces and various properties of interest, prior to searching. During search, the trained NN is queried to predict an estimate of a property given an execution trace. The outputs of the NN form a convenient search space which is strongly representative of a number of properties. We believe that such a search space can be readily used for driving a search towards specific properties of interest. For a diversification strategy, we propose the use of an autoencoder; a mechanism for compacting data into an n-dimensional “latent” space. In it, datapoints are arranged according to the similarity of their salient features. We show that a latent space of execution traces possesses characteristics of a convenient search landscape: it is continuous, large and crucially, it defines a notion of similarity to arbitrary observations

Patients' experiences of illness, operation and outcome with reference to gastro-oesophageal reflux disease.

Background. Describing the illness-story from a patient perspective could increase understanding of living with a chronic disease for health professionals and others, facilitate decision-making about treatment and enhance information about the outcome from a patient perspective. Aim. To illuminate patients' illness experiences of having a gastro-oesophageal reflux disease (GORD), going through surgery and the outcome. Methods. Twelve patients were interviewed 5 years after having had the operation; six patients had had fundoplication via laparoscopy and six via open surgery. Each patient was asked to talk openly about their experiences, thoughts, feelings and consequences of living with the illness, going through surgery and the period from surgery to the day of interview. A qualitative content analysis was performed concerning the context of the data and its meaning. Findings. Three central categories were identified and nine subcategories: living with GORD- symptoms of the disease affecting daily living, taking medicines, work, family and social life; concerns related to surgery- decision-making about the operation, influence by physicians; life after the operation- outcomes and consequences, side-effects and complications of the operation, sick leave, information and sharing experiences with future patients. All patients were free from symptoms of the illness after surgery independent of type of surgery, but side-effects from surgical treatment varied individually. Interviewees would have liked information concerning side-effects after surgery from previous patients. Conclusions. This study contributes to knowledge about patients' long-term suffering, their control of symptoms and how they have tried to cure themselves, but also about their concerns about surgery and the importance of surgical treatment to their quality of life. They wanted information about treatment, outcome and consequences, not only from a health care perspective but also from previous patients having had the same treatment

Mutation-aware fault prediction

We introduce mutation-aware fault prediction, which leverages additional guidance from metrics constructed in terms of mutants and the test cases that cover and detect them. We report the results of 12 sets of experiments, applying 4 di↵erent predictive modelling techniques to 3 large real world systems (both open and closed source). The results show that our proposal can significantly (p 0.05) improve fault prediction performance. Moreover, mutation based metrics lie in the top 5% most frequently relied upon fault predictors in 10 of the 12 sets of experiments, and provide the majority of the top ten fault predictors in 9 of the 12 sets of experiments.http://www0.cs.ucl.ac.uk/staff/F.Sarro/resource/papers/ISSTA2016-Bowesetal.pd

RAMESES publication standards: realist syntheses

PMCID: PMC3558331This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited