Publishing more than reviewing? Some ethical musings on the sustainability of the peer review process

Based on our editorial experience, and acknowledging the regular editor grievances about reviewer disengagement at professional meeting and conferences, in this article we argue that the review system is in need of significant repair. We argue that this has emerged because an audit culture in academia and individual incentives (like reduced teaching loads or publication bonuses) have eroded the willingness of individuals to engage in the collective enterprise of peer-reviewing each others’ work on a quid pro quo basis. In response to this, we emphasise why it is unethical for potential reviewers to disengage from the review process, and outline the implications for our profession if colleagues publish more than they review. Designed as a political intervention in response to reviewer disengagement, we aim to ‘politicise’ the review process and its consequences for the sustainability of the scholarly community. We propose three pathways towards greater reviewer engagement: (i) senior scholars setting the right kind of ‘reviewer’ example; (ii) journals introducing recognition awards to foster a healthy reviewer progression path and (iii) universities and accreditation bodies moving to explicitly recognise reviewing in workload models and evaluations. While all three proposals have merit, the latter point is especially powerful in fostering reviewer engagement as it aligns individual and institutional goals in ‘measurable’ ways. In this way, ironically, the audit culture can be subverted to address the imbalance between individual and collective goals.

Generic SDE and GA-based workload modeling for cloud systems

Workload models are typically built based on user and application behavior in a system, limiting them to specific domains. Undoubtedly, such a practice creates a dilemma in a cloud computing (cloud) environment, where a wide range of heterogeneous applications are running and many users have access to these resources. The workload model in such an infrastructure must adapt to the evolution of the system configuration parameters, such as job load fluctuation. The aim of this work is to propose an approach that generates generic workload models (1) which are independent of user behavior and the applications running in the system, and can fit any workload domain and type, (2) model sharp workload variations that are most likely to appear in cloud environments, and (3) with high degree of fidelity with respect to observed data, within a short execution time. We propose two approaches for workload estimation, the first being a Hull-White and Genetic Algorithm (GA) combination, while the second is a Support Vector Regression (SVR) and Kalman-filter combination. Thorough experiments are conducted on real CPU and throughput datasets from virtualized IP Multimedia Subsystem (IMS), Web and cloud environments to study the efficiency of both propositions. The results show a higher accuracy for the Hull-White-GA approach with marginal overhead over the SVR-Kalman-Filter combination.

Pedagogy empowered by technology

With the increasing ubiquity of web-based tools to facilitate learning and teaching, educators across universities worldwide are now required to prepare and deliver online programs. This requirement may be part of, or in addition to their face-to-face delivery workloads, or as part of migrating programs to be delivered ‘purely online’. In moving towards these new approaches to teaching and learning, there are a number of competing and significant challenges facing staff: There is no one universal definition of online learning; Existing workload models represent traditional forms of content delivery; Prestige of research over teaching still largely exists across the sector (Bradwell, 2009; Keengwe & Kidd, 2010; HEA, 2014; OECD, 2005; O’Connor, 2009; Woodley, Funk & Curran, 2013). With digital skill-building very much on the Irish national agenda for higher education (National Forum, 2015), institutions are now facing important decisions around how best to support staff and foster cultural change towards new technologically-enhanced learning paradigms. This position paper draws on research undertaken at local, national and international levels and is focussed around providing an underpinning for the following: a) Working definitions of what constitutes various forms of online delivery b) Policy documentation around workload models c) Recommendations for future directions. This paper aims to provide a reference point for academics, sessional staff and heads of school regarding current best practice and recommendations for online teaching and learning in higher education.

Various Workload Models and the Preseason Are Associated With Injuries in Professional Female Cyclists

Purpose: To determine if workload and seasonal periods (preseason vs in season) are associated with the incidence of injuries and illnesses in female professional cyclists. Methods: Session rating of perceived exertion was used to quantify internal workload and was collected from 15 professional female cyclists, from 33 athlete seasons. One week (acute) workload, 4 weeks (chronic) workload, and 3 acute:chronic workload models were analyzed. Two workload models are based on moving averages of the ratios, the acute:chronic workload ratio (ACWR), and the ACWR uncoupled (ACWRuncoup). The difference between both is the chronic load; in ACWR, the acute load is part of the chronic load, and in ACWRuncoup, the acute and chronic load are uncoupled. The third workload model is based on exponentially weighted moving averages of the ratios. In addition, the athlete season is divided into the preseason and in season. Results: Generalized estimating equations analysis was used to assess the associations between the workload ratios and the occurrence of injuries and illnesses. High values of acute workload (P = .048), ACWR (P = .02), ACWRuncoup (P = .02), exponentially weighted moving averages of the ratios (P = .01), and the in season (P = .0001) are significantly associated with the occurrence of injury. No significant associations were found between the workload models, the seasonal periods, and the occurrence of illnesses. Conclusions: These findings suggest the importance of monitoring workload and workload ratios in female professional cyclists to lower the risk of injuries and therefore improve their performances. Furthermore, these results indicate that, in the preseason, additional stressors occur, which could lead to an increased risk of injuries.

Characterization and modeling of an edge computing mixed reality workload

The edge computing paradigm comes with a promise of lower application latency compared to the cloud. Moreover, offloading user device computations to the edge enables running demanding applications on resource-constrained mobile end devices. However, there is a lack of workload models specific to edge offloading using applications as their basis.In this work, we build upon the reconfigurable open-source mixed reality (MR) framework MR-Leo as a vehicle to study resource utilisation and quality of service for a time-critical mobile application that would have to rely on the edge to be widely deployed. We perform experiments to aid estimating the resource footprint and the generated load by MR-Leo, and propose an application model and a statistical workload model for it. The idea is that such empirically-driven models can be the basis of evaluations of edge algorithms within simulation or analytical studies.A comparison with a workload model used in a recent work shows that the computational demand of MR-Leo exhibits very different characteristics from those assumed for MR applications earlier.

Adaptive aiding with an individualized workload model based on psychophysiological measures

Potential benefits of technology such as automation are oftentimes negated by improper use and application. Adaptive systems provide a means to calibrate the use of technological aids to the operator’s state, such as workload state, which can change throughout the course of a task. Such systems require a workload model which detects workload and specifies the level at which aid should be rendered. Workload models that use psychophysiological measures have the advantage of detecting workload continuously and relatively unobtrusively, although the inter-individual variability in psychophysiological responses to workload is a major challenge for many models. This study describes an approach to workload modeling with multiple psychophysiological measures that was generalizable across individuals, and yet accommodated inter-individual variability. Under this approach, several novel algorithms were formulated. Each of these underwent a process of evaluation which included comparisons of the algorithm’s performance to an at-chance level, and assessment of algorithm robustness. Further evaluations involved the sensitivity of the shortlisted algorithms at various threshold values for triggering an adaptive aid.