Theorising the ‘Security Influencer’ : speaking security, terror and Muslims on social media during the Manchester bombings

Security studies literature neglects social media’s potential for lay actors to become influential within security debates. This article develops the concept of ‘security influencers’, bringing literature from marketing into the security debate to understand how social media enables individuals to ‘speak’ and contest security and how lay actors exert influence. Methodologically, this article applies a multi-methods approach to 27,367 tweets to identify and analyse the top four most influential actors in 48 hours following the 2017 bombings by keywords ‘Manchester’ and ‘Muslims’. This article builds a typology of security influencers nuancing definitions of the passive ‘security broadcaster’ and the active ‘security engager’, both of which emerge from obscurity or influence within non-security domains. Furthermore, a dichotomy emerges within influential messages and contestation; messages discussing Muslims in banal terms as diverse individuals register high levels of agreement, whereas those discussing Islam as a world religion receive more hostility and contestation

Manifestations of hard and soft technologies in immersive spaces

Immersive spaces are innately flexible. However, for learners, some constraints and scaffolding may often be valuable. This paper looks at immersive spaces as soft and hard technologies. Soft technologies are technologies enabling creative and flexible use, while hard technologies embed processes that limit creativity but provide efficiency and freedom from error. Technologies may be softened or hardened by assembly. For instance, if your Learning Management System (LMS) has no wiki, then it may be softened by adding one from outside the system. If your wiki has no assessment management system, then it may be hardened by using a LMS. For learning, the intrinsically soft nature of immersive spaces sometimes requires scaffolded hardening. This paper provides an example of an ongoing project that realizes these soft and hard technologies in an immersive virtual space and discusses the rich potential of such spaces for technology assembly

Learning analytics beyond the LMS: The connected learning analytics toolkit

We present a Connected Learning Analytics (CLA) toolkit, which enables data to be extracted from social media and imported into a Learning Record Store (LRS), as defined by the new xAPI standard. A number of implementation issues are discussed, and a mapping that will enable the consistent storage and then analysis of xAPI verb/object/activity statements across different social media and online environments is introduced. A set of example learning activities are proposed, each facilitated by the Learning Analytics beyond the LMS that the toolkit enables

Association of Early Interventions With Birth Outcomes and Child Linear Growth in Low-Income and Middle-Income Countries:Bayesian Network Meta-analyses of Randomized Clinical Trials

Importance:The first 1000 days of life represent a critical window for child development. Pregnancy, exclusive breastfeeding (EBF) period (0-6 months), and complementary feeding (CF) period (6-24 months) have different growth requirements, so separate considerations for intervention strategies are needed. No synthesis to date has attempted to quantify the associations of interventions under multiple domains of micronutrient and balanced energy protein and food supplements, deworming, maternal education, water sanitation, and hygiene across these 3 life periods with birth and growth outcomes. Objective: To determine the magnitude of association of interventions with birth and growth outcomes based on randomized clinical trials (RCTs) conducted in low-income and middle-income countries (LMICs) using Bayesian network meta-analyses. Data Sources: MEDLINE, Embase, and Cochrane databases were searched from their inception up to August 14, 2018. Study Selection: Included were LMIC-based RCTs of interventions provided to pregnant women, infants (0-6 months), and children (6-24 months). Data Extraction and Synthesis: Two independent reviewers used a standardized data extraction and quality assessment form. Random-effects network meta-analyses were performed for each life period. Effect sizes are reported as odds ratios (ORs) and mean differences (MeanDiffs) for dichotomous and continuous outcomes, with 95% credible intervals (CrIs). This study calculated probabilities of interventions being superior to standard of care by at least a minimal clinically important difference. Main Outcomes and Measures. The study compared ORs on preterm birth and MeanDiffs on birth weight for pregnancy, length for age (LAZ) for EBF, and height for age (HAZ) for CF. Results: Among 302 061 participants in 169 randomized clinical trials, the network meta-analyses found several nutritional interventions that demonstrated greater association with improved birth and growth outcomes compared with standard of care. For instance, compared with standard of care, maternal supplements of multiple micronutrients showed reduced odds for preterm birth (OR, 0.54; 95% CrI, 0.27-0.97) and improved mean birth weight (MeanDiff, 0.08 kg; 95% CrI, 0.00-0.17 kg) but not LAZ during EBF (MeanDiff, −0.02; 95% CrI, −0.18 to 0.14). Supplementing infants and children with multiple micronutrients showed improved LAZ (MeanDiff, 0.20; 95% CrI, 0.03-0.35) and HAZ (MeanDiff, 0.14; 95% CrI, 0.02-0.25). The study found that pregnancy interventions generally had higher probabilities of a minimal clinically importance difference than the interventions for the EBF or CF in the first 1000 days of life. Conclusions and Relevance: These analyses highlight the importance of intervening early for child development, during pregnancy if possible. Results of this study suggest that there is a need to combine interventions from multiple domains and test for their effectiveness as a package

Deep convolutional neural networks for Bearings failure predictionand temperature correlation

Rolling elements bearings (REBs) is one of the most sensitive components and the common failure unit in mechanical equipment. Bearings failure prognostics, which aims to achieve an effective way to handle the increasing requirements for higher reliability and in the same time reduce unnecessary costs, has been an area of extensive research. The accurate prediction of bearings Remaining Useful Life (RUL) is indispensable for safe and lifetime-optimized operations. To monitor this vital component and planning repair work, a new intelligent method based on Wavelet Packet Decomposition (WPD) and deep learning networks is proposed in this paper. Firstly, features extraction from WPD used as input data. Secondly, these selected features are fed into deep Convolutional Neural Networks (CNNs) to construct the Health Indicator (HI). This study focuses on analysing the relationships such as correlations between the HI and temperature. We develop a solution for the Connectiomics contest dataset of bearings under different operating conditions and severity of defects. The performance of the proposed method is verified by four bearing data sets collected from experimental setup called “PRONOSTIA”. The results show that the health indicator obtains fairly high monotonicity and correlation values and it is beneficial to bearing life prediction. In addition, it is experimentally demonstrated that the proposed method is able to achieve better performance than a traditional neural network based method

The role and challenges of cluster randomised trials for global health

Evaluating whether an intervention works when trialled in groups of individuals can pose complex challenges for clinical research. Cluster randomised controlled trials involve the random allocation of groups or clusters of individuals to receive an intervention, and they are commonly used in global health research. In this paper, we describe the potential reasons for the increasing popularity of cluster trials in low-income and middle-income countries. We also draw on key areas of global health research for an assessment of common trial planning practices, and we address their methodological shortcomings and pitfalls. Lastly, we discuss alternative approaches for population-level intervention trials that could be useful for research undertaken in low-income and middle-income countries for situations in which the use of cluster randomisation might not be appropriate

Facilitating new forms of discourse for learning and teaching: harnessing the power of Web 2.0 practices

When asked what they would find most helpful to enable them to use technologies more in their teaching, most teachers say "give me examples, in my subject area" and "point me to relevant people I can discuss these issues with". Web 2.0 technologies - with their emphasis on sharing, networking and user production - seem to offer a potential solution. However uptake and use of web 2.0 sites such as blogs, social networking and wikis by teachers for sharing and discussing practice has being marginal so far. This paper focuses on work we are undertaking as part of the OU Learning Design Initiative (http://ouldi.open.ac.uk) and the Hewlett-funded Olnet initiative (http://olnet.org). A key focus of our work is the development of tools, methods and approaches to support the design of innovative learning activities and Open Educational Resources (OER). In this paper I want to focus on one strand of our work; namely how to leverage technologies to promote better sharing and discussing of learning and teaching ideas and designs

Behavioural cloning of teachers for automatic homework selection

© Springer Nature Switzerland AG 2019. We describe a machine-learning system for supporting teachers through the selection of homework assignments. Our system uses behavioural cloning of teacher activity to generate personalised homework assignments for students. Classroom use is then supported through additional mechanisms to combine these predictions into group assignments. We train and evaluate our system against 50,065 homework assignments collected over two years by the Isaac Physics platform. We use baseline policies incorporating expert curriculum knowledge for evaluation and find that our technique improves on the strongest baseline policy by 18.5% in Year 1 and by 13.3% in Year 2.Cambridge Assessmen

Speculative Futures on ChatGPT and Generative Artificial Intelligence (AI): A collective reflection from the educational landscape

While ChatGPT has recently become very popular, AI has a long history and philosophy. This paper intends to explore the promises and pitfalls of the Generative Pre-trained Transformer (GPT) AI and potentially future technologies by adopting a speculative methodology. Speculative future narratives with a specific focus on educational contexts are provided in an attempt to identify emerging themes and discuss their implications for education in the 21st century. Affordances of (using) AI in Education (AIEd)and possible adverse effects are identified and discussed which emerge from the narratives. It is argued that now is the best of times to define human vs AI contribution to education because AI can accomplish more and more educational activities that used to be the prerogative of human educators. Therefore, it is imperative to rethink the respective roles of technology and human educators in education with a future-oriented mindset