4,273 research outputs found
MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications
Mobile smartphones along with embedded sensors have become an efficient
enabler for various mobile applications including opportunistic sensing. The
hi-tech advances in smartphones are opening up a world of possibilities. This
paper proposes a mobile collaborative platform called MOSDEN that enables and
supports opportunistic sensing at run time. MOSDEN captures and shares sensor
data across multiple apps, smartphones and users. MOSDEN supports the emerging
trend of separating sensors from application-specific processing, storing and
sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing
the efforts in developing novel opportunistic sensing applications. MOSDEN has
been implemented on Android-based smartphones and tablets. Experimental
evaluations validate the scalability and energy efficiency of MOSDEN and its
suitability towards real world applications. The results of evaluation and
lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing,
2014. arXiv admin note: substantial text overlap with arXiv:1310.405
Resilient seed systems for climate change adaptation and sustainable livelihoods in the East Africa sub-region: Report of training workshop, Addis Ababa Ethiopia, 17-21 September 2019
Bioversity International is implementing a Dutch-supported project entitled: Resilient seed systems for climate change adaptation and sustainable livelihoods in the East Africa sub-region. This work aims to boost timely and affordable access to good-quality seed for a portfolio of crops / varieties for millions of women and men farmers’ and their communities across east Africa.
A first project training: i) contextualized farmer varietal selection, ii) provided practical demonstrations of tools for climate-change analysis, iii) introduced policy issues associated with managing crop diversity, iv) outlined characterization and evaluation of genetic resources, and v) articulated associated gender issues, and issues related to disseminating elite materials. The training concluded with a contextualizing field trip.
In the workshop evaluation, 98% participants declared their overall satisfaction level to be high (74%) or medium (24%), indicating the training furnished them with good ideas for networking and using the tools and methods they learned about
Managing big data experiments on smartphones
The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones
Explicit diversification of event aspects for temporal summarization
During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness
Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
(abridged for arXiv) With the first direct detection of gravitational waves,
the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has
initiated a new field of astronomy by providing an alternate means of sensing
the universe. The extreme sensitivity required to make such detections is
achieved through exquisite isolation of all sensitive components of LIGO from
non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to
a variety of instrumental and environmental sources of noise that contaminate
the data. Of particular concern are noise features known as glitches, which are
transient and non-Gaussian in their nature, and occur at a high enough rate so
that accidental coincidence between the two LIGO detectors is non-negligible.
In this paper we describe an innovative project that combines crowdsourcing
with machine learning to aid in the challenging task of categorizing all of the
glitches recorded by the LIGO detectors. Through the Zooniverse platform, we
engage and recruit volunteers from the public to categorize images of glitches
into pre-identified morphological classes and to discover new classes that
appear as the detectors evolve. In addition, machine learning algorithms are
used to categorize images after being trained on human-classified examples of
the morphological classes. Leveraging the strengths of both classification
methods, we create a combined method with the aim of improving the efficiency
and accuracy of each individual classifier. The resulting classification and
characterization should help LIGO scientists to identify causes of glitches and
subsequently eliminate them from the data or the detector entirely, thereby
improving the rate and accuracy of gravitational-wave observations. We
demonstrate these methods using a small subset of data from LIGO's first
observing run.Comment: 27 pages, 8 figures, 1 tabl
Innovation Initiatives in Large Software Companies: A Systematic Mapping Study
To keep the competitive advantage and adapt to changes in the market and
technology, companies need to innovate in an organised, purposeful and
systematic manner. However, due to their size and complexity, large companies
tend to focus on maintaining their business, which can potentially lower their
agility to innovate. This study aims to provide an overview of the current
research on innovation initiatives and to identify the challenges of
implementing the initiatives in the context of large software companies. The
investigation was performed using a systematic mapping approach of published
literature on corporate innovation and entrepreneurship. Then it was
complemented with interviews with four experts with rich industry experience.
Our study results suggest that, there is a lack of high quality empirical
studies on innovation initiative in the context of large software companies. A
total of 7 studies are conducted in such context, which reported 5 types of
initiatives: intrapreneurship, bootlegging, internal venture, spin-off and
crowdsourcing. Our study offers three contributions. First, this paper
represents the map of existing literature on innovation initiatives inside
large companies. The second contribution is to provide an innovation initiative
tree. The third contribution is to identify key challenges faced by each
initiative in large software companies. At the strategic and tactical levels,
there is no difference between large software companies and other companies. At
the operational level, large software companies are highly influenced by the
advancement of Internet technology. Large software companies use open
innovation paradigm as part of their innovation initiatives. We envision a
future work is to further empirically evaluate the innovation initiative tree
in large software companies, which involves more practitioners from different
companies
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