Unmanned Aerial Systems for Rapid Mapping - UASRapidMap 2013 4th JRC ECML Crisis Management Technology Workshop

The 4th JRC ECML Crisis Management Technology Workshop on Unmanned Aerial Systems for Rapid Mapping was co-organised by the European Commission Joint Research Centre and the United Nations Institute for Training and Research - Operational Satellite Applications Programme (UNITAR - UNOSAT). It took place in Geneva & Dardagny, Switzerland from 11 to 13 September 2013. 74 participants from UN and EC stakeholders, NGOs, civil protection bodies, academia, and industry attended the workshop. The workshop's purpose was to present, demonstrate, and explore the state-of-the-art and future potential of unmanned aerial systems for rapid mapping applications in the context of humanitarian crisis aid and natural disaster relief operations. Main impressions from the workshop were the diversity in technological solutions for various practical uses, the rapid turnaround time from flight to having useable data at hand in the field and a reality check on what are still challenges related to flight permissions. Within the European Union a process on the harmonisation of the diverse regulations for UAS operations and the introduction of UAS into the civil airspace is ongoing. The UAS technology will most likely make a large impact on data collection in future emergency situations. In addition, based on what was demonstrated, the tools are also useful for disaster risk reduction activities.JRC.G.2-Global security and crisis managemen

Constructing grassroots innovations for sustainability

One of the cleavages within sustainable development is division between grassroots environmental action, often deemed good on participation terms, and green innovation, usually centred on technologies in firms and deemed good for ecological modernisation. This special section is dedicated to an obvious and missing connection: grassroots innovation for sustainability. Grassroots innovations typically involve networks of activists and organisations generating novel bottom-up solutions for sustainable development; solutions that respond to the local situation and the interests and values of the communities involved (Seyfang and Smith, 2007). What they share is commitment on the part of those involved towards openness and inclusion in the processes of innovation and the outputs of innovation. Research is still needed that considers whether and how grassroots innovators network with one another; the extent to which movements for grassroots innovation approaches exist and how they operate; whether and how innovations diffuse through processes of replication, scaling-up, and translation into institutions; and whether or not these developments constitute alternative pathways for sustainability. The empirical contributions in this special section consider the dilemmas of going to scale, the challenges of moving from innovation to institutionalisation, and the risks of capture and instrumentality when grassroots innovations encounter more powerful political economies of conventional innovation systems (see also Smith et al., 2013). A recurring theme is diversity in innovation for sustainability; which might be served best by resisting pressures to mainstream, yet simultaneously generates accusations of marginality. In highlighting these themes and introducing the special section, we use a particular example, the Brighton Earthship, and which all contributing authors visited as part of a research workshop on grassroots innovation held at Sussex University in May 2012 and that led to the papers here

Automatic transcription of multi-genre media archives

This paper describes some recent results of our collaborative work on developing a speech recognition system for the automatic transcription or media archives from the British Broadcasting Corporation (BBC). The material includes a wide diversity of shows with their associated metadata. The latter are highly diverse in terms of completeness, reliability and accuracy. First, we investigate how to improve lightly supervised acoustic training, when timestamp information is inaccurate and when speech deviates significantly from the transcription, and how to perform evaluations when no reference transcripts are available. An automatic timestamp correction method as well as a word and segment level combination approaches between the lightly supervised transcripts and the original programme scripts are presented which yield improved metadata. Experimental results show that systems trained using the improved metadata consistently outperform those trained with only the original lightly supervised decoding hypotheses. Secondly, we show that the recognition task may benefit from systems trained on a combination of in-domain and out-of-domain data. Working with tandem HMMs, we describe Multi-level Adaptive Networks, a novel technique for incorporating information from out-of domain posterior features using deep neural network. We show that it provides a substantial reduction in WER over other systems including a PLP-based baseline, in-domain tandem features, and the best out-of-domain tandem features.This research was supported by EPSRC Programme Grant EP/I031022/1 (Natural Speech Technology).This paper was presented at the First Workshop on Speech, Language and Audio in Multimedia, August 22-23, 2013; Marseille. It was published in CEUR Workshop Proceedings at http://ceur-ws.org/Vol-1012/

Evolution of plant phenotypes, from genomes to traits

Connecting genotype to phenotype is a grand challenge of biology. Over the past 50 years, there have been numerous and powerful advances to meet this challenge, including next-generation sequencing approaches (Jackson et al. 2011), molecular genetic mapping techniques, computational modeling, and the integration of evolutionary theory and tools. In plants, the long history of domestication and breeding has provided multiple insights into the genotype–phenotype equation (Meyer and Purugganan 2013; Olsen and Wendel 2013). Domestication and breeding provide unique systems with which to study the evolution of traits and adaptation to new environments. At present, agriculture faces unprecedented challenges, with the need to continue to increase food quality and food production for a population that will likely exceed 9 billion by 2050, combined with the urgent need to make agriculture more sustainable in an environment that will be altered by climate change (Diouf 2009). Crop wild relatives, however, have evolved under ecological settings that often are more extreme than those under cultivation and thus represent a reservoir of useful adaptive traits. This genetic diversity has mostly been untapped because of a lack of appropriate tools, both at the genetic level and in describing plant phenotypes and adaptation (Mace et al. 2013). In this context, crop improvement needs to undergo a qualitative leap forward by exploiting the knowledge from the interface of the fields of molecular evolution, bioinformatics, plant physiology, and genetics. With the objective of reviewing the most recent advances and identifying unanswered questions at this interface, a group of scientists met in Barcelona in March 2015 for a workshop organized by B-Debate (www.bdebate.org) and the Center for Research in Agricultural Genomics (CRAG, www.cragenomica.es), with the support of the US National Science Foundation. The meeting was divided into three scientific sessions. The first concentrated on the mechanisms that generate genomic diversity in plants, with a particular emphasis on transposable elements and polyploidy, while the second and third sessions were devoted to the evolution of plant phenotypes in wild and domesticated species, and to domestication and plant improvement processes, respectively

Learning from implementation of community selection in Zambia, Solomon Islands, and Bangladesh AAS hubs

The CGIAR Research Program on Aquatic Agricultural Systems (AAS) is a research in development program which aims to foster innovation to respond to community needs, and through networking and social learning to bring about development outcomes and impact at scale. It aims to reach the poorest and most vulnerable communities that are dependent upon aquatic agricultural systems. AAS uses monitoring and evaluation to track progress along identified impact pathways for accountability and learning. This report presents an evaluation of the recommended method for selecting communities during the participatory planning process, referred to as AAS “hub rollout,” in the first year of program implementation

Automatic Quality Estimation for ASR System Combination

Recognizer Output Voting Error Reduction (ROVER) has been widely used for system combination in automatic speech recognition (ASR). In order to select the most appropriate words to insert at each position in the output transcriptions, some ROVER extensions rely on critical information such as confidence scores and other ASR decoder features. This information, which is not always available, highly depends on the decoding process and sometimes tends to over estimate the real quality of the recognized words. In this paper we propose a novel variant of ROVER that takes advantage of ASR quality estimation (QE) for ranking the transcriptions at "segment level" instead of: i) relying on confidence scores, or ii) feeding ROVER with randomly ordered hypotheses. We first introduce an effective set of features to compensate for the absence of ASR decoder information. Then, we apply QE techniques to perform accurate hypothesis ranking at segment-level before starting the fusion process. The evaluation is carried out on two different tasks, in which we respectively combine hypotheses coming from independent ASR systems and multi-microphone recordings. In both tasks, it is assumed that the ASR decoder information is not available. The proposed approach significantly outperforms standard ROVER and it is competitive with two strong oracles that e xploit prior knowledge about the real quality of the hypotheses to be combined. Compared to standard ROVER, the abs olute WER improvements in the two evaluation scenarios range from 0.5% to 7.3%

Dimensions of professional competences for interventions towards sustainability

This paper investigates sustainability competences through the eyes of professional practitioners in the field of sustainability and presents empirical data that have been created using an action research approach. The design of the study consists of two workshops, in which professional practitioners in interaction with each other and the facilitators are invited to explore and reflect on the specific knowledge, skills, attitudes and behaviours necessary to conduct change processes successfully towards sustainability in a variety of business and professional contexts. The research focuses on the competences associated with these change processes to devise, propose and conduct appropriate interventions that address sustainability issues. Labelled ‘intervention competence’, this ability comprises an interlocking set of knowledge, skills, attitudes and behaviours that include: appreciating the importance of (trying to) reaching decisions or interventions; being able to learn from lived experience of practice and to connect such learning to one’s own scientific knowledge; being able to engage in political-strategic thinking, deliberations and actions, related to different perspectives; the ability for showing goal-oriented, adequate action; adopting and communicating ethical practices during the intervention process; being able to cope with the degree of complexity, and finally being able to translate stakeholder diversity into collectively produced interventions (actions) towards sustainability. Moreover, this competence has to be practised in contexts of competing values, non-technical interests and power relations. The article concludes with recommendations for future research and practice