The interaction between supportive and unsupportive manager behaviors on employee work attitudes

Purpose: To use Social Exchange Theory (SET) to examine a model where supportive (SMB) and unsupportive (UMB) manager behaviors interact to predict employees’ engagement, job satisfaction and turnover intention. Design/Methodology: A cross-sectional online survey collected data from 252 UK based employees of a global data management company. Findings: Factor analysis confirmed manager behaviors to consist of two constructs: supportive and unsupportive behaviors. Structural equation modelling indicated SMB predicted job satisfaction and turnover intentions, but not engagement. Job satisfaction, but not engagement, mediated the SMB-turnover intention relationship. UMB only predicted job dissatisfaction. Neither job satisfaction nor engagement mediated the UMB-turnover intention relationship. UMB undermined the positive relationship between SMB and turnover intention. Implications: The behaviors assessed can be integrated into various stages of a manager’s development process to serve as guidelines of good practice. Crucially, findings suggest managers can exhibit both supportive and unsupportive behaviors, and that consistency in behaviors is important. The study also provides evidence that supportive managers can help reduce turnover intention through job satisfaction. Originality/value: SET was used as a framework for SMB, UMB and engagement. To our knowledge this is the first study to examine the interaction between SMB and UMB

PROGRAML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations

Machine learning (ML) is increasingly seen as a viable approach for building compiler optimization heuristics, but many ML methods cannot replicate even the simplest of the data flow analyses that are critical to making good optimization decisions. We posit that if ML cannot do that, then it is insufficiently able to reason about programs. We formulate data flow analyses as supervised learning tasks and introduce a large open dataset of programs and their corresponding labels from several analyses. We use this dataset to benchmark ML methods and show that they struggle on these fundamental program reasoning tasks. We propose PROGRAML - Program Graphs for Machine Learning - a language-independent, portable representation of program semantics. PROGRAML overcomes the limitations of prior works and yields improved performance on downstream optimization tasks.ISSN:2640-349

Correlation techniques applied to antenna pattern measurement

A correlation processor based on the excellent periodic autocorrelation properties of maximal-length pseudorandom binary sequences has been used in antenna pattern measurements to resolve the direct (wanted) path from any unwanted multipath components. A simple implementation of the technique has been used to make measurements in a controlled environment; the results show that the multipath effects are almost completely eliminated and an accurate pattern measurement is obtained

Synthesizing benchmarks for predictive modeling

Predictive modeling using machine learning is an effective method for building compiler heuristics, but there is a shortage of benchmarks. Typical machine learning experiments outside of the compilation field train over thousands or millions of examples. In machine learning for compilers, however, there are typically only a few dozen common benchmarks available. This limits the quality of learned models, as they have very sparse training data for what are often high-dimensional feature spaces. What is needed is a way to generate an unbounded number of training programs that finely cover the feature space. At the same time the generated programs must be similar to the types of programs that human developers actually write, otherwise the learning will target the wrong parts of the feature space. We mine open source repositories for program fragments and apply deep learning techniques to automatically construct models for how humans write programs. We sample these models to generate an unbounded number of runnable training programs. The quality of the programs is such that even human developers struggle to distinguish our generated programs from hand-written code. We use our generator for OpenCL programs, CLgen, to automatically synthesize thousands of programs and show that learning over these improves the performance of a state of the art predictive model by 1.27×. In addition, the fine covering of the feature space automatically exposes weaknesses in the feature design which are invisible with the sparse training examples from existing benchmark suites. Correcting these weaknesses further increases performance by 4.30×

Outdoor learning spaces: the case of forest school

© 2017 The Author. Area published by John Wiley & Sons Ltd on behalf of Royal Geographical Society (with the Institute of British Geographers). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.This paper contributes to the growing body of research concerning use of outdoor spaces by educators, and the increased use of informal and outdoor learning spaces when teaching primary school children. The research takes the example of forest school, a form of regular and repeated outdoor learning increasingly common in primary schools. This research focuses on how the learning space at forest school shapes the experience of children and forest school leaders as they engage in learning outside the classroom. The learning space is considered as a physical space, and also in a more metaphorical way as a space where different behaviours are permitted, and a space set apart from the national curriculum. Through semi-structured interviews with members of the community of practice of forest school leaders, the paper seeks to determine the significance of being outdoors on the forest school experience. How does this learning space differ from the classroom environment? What aspects of the forest school learning space support pupils’ experiences? How does the outdoor learning space affect teaching, and the dynamics of learning while at forest school? The research shows that the outdoor space provides new opportunities for children and teachers to interact and learn, and revealed how forest school leaders and children co-create a learning environment in which the boundaries between classroom and outdoor learning, teacher and pupil, are renegotiated to stimulate teaching and learning. Forest school practitioners see forest school as a separate learning space that is removed from the physical constraints of the classroom and pedagogical constraints of the national curriculum to provide a more flexible and responsive learning environment.Peer reviewe

Project MOSI: rationale and pilot-study results of an initiative to help protect zoo animals from mosquito-transmitted pathogens and contribute data on mosquito spatio–temporal distribution change

Mosquito-borne pathogens pose major threats to both wildlife and human health and, largely as a result of unintentional human-aided dispersal of their vector species, their cumulative threat is on the rise. Anthropogenic climate change is expected to be an increasingly significant driver of mosquito dispersal and associated disease spread. The potential health implications of changes in the spatio-temporal distribution of mosquitoes highlight the importance of ongoing surveillance and, where necessary, vector control and other health-management measures. The World Association of Zoos and Aquariums initiative, Project MOSI, was established to help protect vulnerable wildlife species in zoological facilities from mosquito-transmitted pathogens by establishing a zoo-based network of fixed mosquito monitoring sites to assist wildlife health management and contribute data on mosquito spatio-temporal distribution changes. A pilot study for Project MOSI is described here, including project rationale and results that confirm the feasibility of conducting basic standardized year-round mosquito trapping and monitoring in a zoo environment

Function Merging by Sequence Alignment

Resource-constrained devices for embedded systems are becoming increasingly important. In such systems, memory is highly restrictive, making code size in most cases even more important than performance. Compared to more traditional platforms, memory is a larger part of the cost and code occupies much of it. Despite that, compilers make little effort to reduce code size. One key technique attempts to merge the bodies of similar functions. However, production compilers only apply this optimization to identical functions, while research compilers improve on that by merging the few functions with identical control-flow graphs and signatures. Overall, existing solutions are insufficient and we end up having to either increase cost by adding more memory or remove functionality from programs. We introduce a novel technique that can merge arbitrary functions through sequence alignment, a bioinformatics algorithm for identifying regions of similarity between sequences. We combine this technique with an intelligent exploration mechanism to direct the search towards the most promising function pairs. Our approach is more than 2.4x better than the state-of-the-art, reducing code size by up to 25%, with an overall average of 6%, while introducing an average compilation-time overhead of only 15%. When aided by profiling information, this optimization can be deployed without any significant impact on the performance of the generated code

Diffusion of e-health innovations in 'post-conflict' settings: a qualitative study on the personal experiences of health workers.

BACKGROUND: Technological innovations have the potential to strengthen human resources for health and improve access and quality of care in challenging 'post-conflict' contexts. However, analyses on the adoption of technology for health (that is, 'e-health') and whether and how e-health can strengthen a health workforce in these settings have been limited so far. This study explores the personal experiences of health workers using e-health innovations in selected post-conflict situations. METHODS: This study had a cross-sectional qualitative design. Telephone interviews were conducted with 12 health workers, from a variety of cadres and stages in their careers, from four post-conflict settings (Liberia, West Bank and Gaza, Sierra Leone and Somaliland) in 2012. Everett Roger's diffusion of innovation-decision model (that is, knowledge, persuasion, decision, implementation, contemplation) guided the thematic analysis. RESULTS: All health workers interviewed held positive perceptions of e-health, related to their beliefs that e-health can help them to access information and communicate with other health workers. However, understanding of the scope of e-health was generally limited, and often based on innovations that health workers have been introduced through by their international partners. Health workers reported a range of engagement with e-health innovations, mostly for communication (for example, email) and educational purposes (for example, online learning platforms). Poor, unreliable and unaffordable Internet was a commonly mentioned barrier to e-health use. Scaling-up existing e-health partnerships and innovations were suggested starting points to increase e-health innovation dissemination. CONCLUSIONS: Results from this study showed ICT based e-health innovations can relieve information and communication needs of health workers in post-conflict settings. However, more efforts and investments, preferably driven by healthcare workers within the post-conflict context, are needed to make e-health more widespread and sustainable. Increased awareness is necessary among health professionals, even among current e-health users, and physical and financial access barriers need to be addressed. Future e-health initiatives are likely to increase their impact if based on perceived health information needs of intended users