Mobility, Career Pathways, and the Landscape of Employer and Youth Engagement in the South

It's tough for a southern kid born at the bottom of the income ladder to get ahead. Overcoming regional economic hardship, long-tolerated racial inequity and subpar education infrastructure is almost impossible. But there is progress. This issue brief examines two key elements connecting southern young adults with rewarding employment opportunities: employer and youth engagement. The brief offers a framework to assess the preconditions for effectively engaging employers and young adults and identifies examples of promising efforts. It also considers what philanthropy can do to reinforce the importance of employer and youth engagement and expand the use of both in the South

On the Relation Between Mobile Encounters and Web Traffic Patterns: A Data-driven Study

Mobility and network traffic have been traditionally studied separately. Their interaction is vital for generations of future mobile services and effective caching, but has not been studied in depth with real-world big data. In this paper, we characterize mobility encounters and study the correlation between encounters and web traffic profiles using large-scale datasets (30TB in size) of WiFi and NetFlow traces. The analysis quantifies these correlations for the first time, across spatio-temporal dimensions, for device types grouped into on-the-go Flutes and sit-to-use Cellos. The results consistently show a clear relation between mobility encounters and traffic across different buildings over multiple days, with encountered pairs showing higher traffic similarity than non-encountered pairs, and long encounters being associated with the highest similarity. We also investigate the feasibility of learning encounters through web traffic profiles, with implications for dissemination protocols, and contact tracing. This provides a compelling case to integrate both mobility and web traffic dimensions in future models, not only at an individual level, but also at pairwise and collective levels. We have released samples of code and data used in this study on GitHub, to support reproducibility and encourage further research (https://github.com/BabakAp/encounter-traffic).Comment: Technical report with details for conference paper at MSWiM 2018, v3 adds GitHub lin

FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices

Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impediment to deployment on low-end devices. To address this challenge, recent literature focused on compressing neural network size to improve performance. We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time. Rather, extreme run-time nonlinearities exist over the network configuration space. Hence, we propose a novel framework, called FastDeepIoT, that uncovers the non-linear relation between neural network structure and execution time, then exploits that understanding to find network configurations that significantly improve the trade-off between execution time and accuracy on mobile and embedded devices. FastDeepIoT makes two key contributions. First, FastDeepIoT automatically learns an accurate and highly interpretable execution time model for deep neural networks on the target device. This is done without prior knowledge of either the hardware specifications or the detailed implementation of the used deep learning library. Second, FastDeepIoT informs a compression algorithm how to minimize execution time on the profiled device without impacting accuracy. We evaluate FastDeepIoT using three different sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus. FastDeepIoT further reduces the neural network execution time by $48\%$ to $78\%$ and energy consumption by $37\%$ to $69\%$ compared with the state-of-the-art compression algorithms.Comment: Accepted by SenSys '1

Educational Technology Topic Guide

This guide aims to contribute to what we know about the relationship between educational technology (edtech) and educational outcomes by addressing the following overarching question: What is the evidence that the use of edtech, by teachers or students, impacts teaching and learning practices, or learning outcomes? It also offers recommendations to support advisors to strengthen the design, implementation and evaluation of programmes that use edtech. We define edtech as the use of digital or electronic technologies and materials to support teaching and learning. Recognising that technology alone does not enhance learning, evaluations must also consider how programmes are designed and implemented, how teachers are supported, how communities are developed and how outcomes are measured (see http://tel.ac.uk/about-3/, 2014). Effective edtech programmes are characterised by: a clear and specific curriculum focus the use of relevant curriculum materials a focus on teacher development and pedagogy evaluation mechanisms that go beyond outputs. These findings come from a wide range of technology use including: interactive radio instruction (IRI) classroom audio or video resources accessed via teachers’ mobile phones student tablets and eReaders computer-assisted learning (CAL) to supplement classroom teaching. However, there are also examples of large-scale investment in edtech – particularly computers for student use – that produce limited educational outcomes. We need to know more about: how to support teachers to develop appropriate, relevant practices using edtech how such practices are enacted in schools, and what factors contribute to or mitigate against successful outcomes. Recommendations: 1. Edtech programmes should focus on enabling educational change, not delivering technology. In doing so, programmes should provide adequate support for teachers and aim to capture changes in teaching practice and learning outcomes in evaluation. 2. Advisors should support proposals that further develop successful practices or that address gaps in evidence and understanding. 3. Advisors should discourage proposals that have an emphasis on technology over education, weak programmatic support or poor evaluation. 4. In design and evaluation, value-for-money metrics and cost-effectiveness analyses should be carried out

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version