The economic impact of consumer copyright exceptions: a literature review

Advances in the technology available to consumers have fundamentally altered the relationship between authors, rights-holders and consumers with regard to copyrighted creative works. The copyright system in the UK is undergoing a gradual process of reform to reflect this new reality. In 2006, Andrew Gowers, a former editor of the Financial Times, presented a report into the state of intellectual property in the U.K. Among his policy recommendations were three proposed changes to the copyright exceptions system which alter the way in which consumers can interact with copyrighted works. Gowers proposed introducing copyright exceptions for: - Format shifting, for instance the transfer of a piece of music from a CD to an mp3 player. - Parody, caricature and pastiche. - Creative, transformative or derivative works. In our context, this definition includes user-generated content. Our review examines the existing literature on the possible economic effects of these proposed changes to the copyright exceptions system, specifically whether the introduction of these proposed changes would cause economic damage to rights-holders. Whilst the economic issues surrounding copyright infringement via file-sharing and commercial "mash-ups" are interesting and important, our review is focused solely on copyright

Information systems project maturity framework for level 2 compliance

Chapter 1 unpacked the problem identified by the Standish Chaos Reports (2014), where it appears that projects across the globe are often not managed successfully for earned value. A general overview placed software project management in context while proposing that a focus on process management using the PMIS emplacement may alleviate many of the challenges faced. Chapter 1 also explained the problems and resultant inability to unlock capability maturity requirements needed to move out of CM L1 behaviourSchool of Computin

Using educational analytics to improve test performance

Learning analytics are being used in many educational applications in order to help students and Faculty. In our work we use predictive analytics, using student behaviour to predict the likely performance of end of semester final grades with a system we call PredictED. The main contribution of our approach is that our intervention automatically emailed students on a regular basis, with our prediction for the outcome of their exam performance. We targeted first year, first semester University students who often struggle with making the transition into University life where they are given much more responsibility for things like attending class, completing assignments, etc. The form of student behaviour that we used is students’ levels and types of engagement with the University’s Virtual Learning Environment (VLE), Moodle. We mined the Moodle access log files for a range of parameters based on temporal as well as content access, and use machine learning techniques to predict likely pass/fail, on a weekly basis throughout the semester using logs and outcomes from previous years as training material. We chose ten first-year modules with reasonably high failure rates, large enrolments and stability of module content across the years to implement an early warning system on. From these modules 1,558 students were registered for one of these modules. They were offered the chance to opt into receiving weekly email alerts warning them about their likely outcome. Of these 75% or 1,181 students opted into this service. Pre-intervention there were no differences between participants and non-participants on a number of measures related to previous academic record. However, post- intervention the first-attempt final grade performance yielded nearly 3% improvement (58.4% to 61.2%) on average for those who opted in. This tells us that providing weekly guidance and personalised feedback to vulnerable first year students, automatically generated from monitoring of their online behaviour, has a significant positive effect on their exam performance

Differential progression of cognitive and mood-related phenotypes in a mouse model of chronic depression

Depressive disorders remain highly prevalent and lack effective treatments. Mounting evidence suggests that disrupted cognitive processes, such as uninhibited negative thought patterns (“rumination”), play a significant role in the development of Major Depressive Disorder (MDD), either alongside or preceding serotonin-related mood imbalances. However, the underlying mechanisms of prodromal cognitive symptoms necessitate further investigation. This study explored the differential progression of cognitive and mood-related phenotypes in BALB/c mice subjected to the unpredictable chronic mild stress (UCMS) model. Following two UCMS durations—short-term (2–3 weeks) and long-term (5–6 weeks)—we observed the emergence of distinct symptom sets. Differences in neurobiological processes and substrates were also seen, including alterations to regional metabolic activity. Additionally, sex differences in behavioural measures were identified, in line with previous research pointing to sex-specific vulnerabilities to chronic stress. Altogether, our findings demonstrate the emergence of cognitive deficits associated with dysregulated inhibitory mechanisms in the early stages of depressive-like symptom onset, and may inform the development of preventive treatment strategies

Student data: data is knowledge – putting the knowledge back in the students’ hands

Learning Management Systems are integral technologies within higher education institutions. These tools automatically amass large amounts of log data relating to student activities. The field of learning analytics uses data from learning management systems (LMSs) and student information systems to track student progress and predict future performance in order to enhance learning environments (Siemens, 2011). The aim of this paper is to describe a project where we utilized a system developed in Dublin City University to use information about student engagement with our LMS, Moodle, to create a model predicting pass or failure in certain modules. The project is divided into three distinct phases. An initial investigation was completed analyzing Moodle activity for the last six years. The purpose of this exercise was to determine automatically if “trends” could be identified linking Moodle engagement with student attainment. This was done by training a machine learning classifier to map student online behaviour, against outcomes. Once the classifier was trained, several modules were identified as suitable for building a predictor of student exam success.Ten modules were identified for semester 1 with a further seven identified for semester 2. The second phase involved analyzing current students’ engagement with these modules and sending students information about the predictions of their attainment for the module, based on their Moodle engagement. At this stage concerns were raised within the university that the data that we share with the students could actually have the opposite effect to what we are after, i.e. the student may look at the data and think that there is no point in putting in more effort as ‘I’m too far behind already’. Dietz-Uhler and Hurn refer to this as “instead of being a constructive tool, feedback becomes a prophet of failure” (Dietz-Uhler, 2013). This contention was addressed by conducting an online survey with students in an effort to explore their experiences of being provided with feedback regarding their engagement with the LMS. The third and final phase of this project was the development of a dashboard for lecturers to enable monitoring of their students’ engagement with their module on Moodle. This enables lecturers to have an overview of how students are engaging with their course on Moodle and quickly identify students who are not engaging with the LMS and who are potentially at risk of failure or non-completion. There are numerous examples of the use of learning analytics in higher education. This study focuses on the provision of data obtained through learning analytics to the student and qualitative analysis that was conducted in relation to this data. This research adds to the existing research into learning analytics being used for student retention

Optimizing astrophotonic spatial reformatters using simulated on-sky performance

One of the most useful techniques in astronomical instrumentation is image slicing. It enables a spectrograph to have a more compact angular slit, whilst retaining throughput and increasing resolving power. Astrophotonic components like the photonic lanterns and photonic reformatters can be used to replace bulk optics used so far. This study investigates the performance of such devices using end-to-end simulations to approximate realistic on-sky conditions. It investigates existing components, tries to optimize their performance and aims to understand better how best to design instruments to maximize their performance. This work complements the recent work in the field and provides an estimation for the performance of the new components.Comment: Conference proceedings in SPIE 2018 Austin Texa

The clinicomolecular landscape of de novo versus relapsed stage IV metastatic breast cancer

Background: de novo metastatic breast cancer (dnMBC) is responsible for 6–10% of breast cancer presentations with increasing incidence and has remained resistant to detection by mammography screening. Recent publications hypothesized that in addition to poor screening uptake, the presentation of dnMBC may be due to its unfavourable biology which remains unknown at the molecular level. Here we investigated the tumour biology of dnMBC in the form of clinicopathology, genomic alterations and differential gene expression to create a comparative landscape of de novo versus relapsed metastatic breast cancer (rMBC). Additionally, to address the current screening limitations, we conducted a preliminary biomarker investigation for early dnMBC detection. Methods: In this retrospective case-control study, gene expression and clinical data were accessed from the Cancer Genome Atlas (TCGA) for primary tumours of treatment-naïve patients with dnMBC (n = 17), rMBC (n = 49), and normal tissue (n = 113). The clinical and histological data were assessed categorically using Fisher's Exact-Test for significance (p < .05), or continuously using the Mann-Whitney Test (p < .05) where appropriate. The differential gene expression analysis was performed using EdgeR's negative binomial distribution model with a false discovery rate (FDR) <0.05. The resulting gene list was analysed manually for roles in metastasis as well as ontologically using STRING-DB with FDR <0.05. Results: dnMBCs showed improved median survival vs rMBC (36 vs. 12 months). dnMBCs were more likely to be hormone receptor positive, less likely to be triple negative with lower histological lymphocytic infiltrate. In terms of genome alterations, dnMBCs had 4-fold increased PTEN mutations and poor survival with ABL2 and GATA3 alterations. Expression-wise, dnMBCs down-regulated TNFa, IL-17 signalling, and chemotaxis, while up-regulating steroid biosynthesis, cell migration, and cell adhesion. Biomarker analysis detected pre-existing and novel breast cancer biomarkers. Conclusion: The comparative tumour landscape revealed significant clinical, pathological and molecular differences between dnMBC and rMBC, indicating that dnMBC may be a separate biological entity to rMBC at the primary level with differing paths to metastasis. Additionally, we provided a list of potential serum biomarkers that may be useful in detecting dnMBC in its pre-metastatic window if such a window exists