Bayesian keys: biological identification on mobile devices

A Bayesian key is a computer-aided method for biological identification. A traditional biological key is a series of branching questions which must be answered in order to arrive at a correct identification. But these keys can be cumbersome, error-prone, and do not match users' approach to the task. Multi-access keys based on Bayesian statistics promise quicker and more robust identification that matches the users' task. We are developing these for the web and for mobile devices

Quantum identification system

A secure quantum identification system combining a classical identification procedure and quantum key distribution is proposed. Each identification sequence is always used just once and new sequences are ``refuelled'' from a shared provably secret key transferred through the quantum channel. Two identification protocols are devised. The first protocol can be applied when legitimate users have an unjammable public channel at their disposal. The deception probability is derived for the case of a noisy quantum channel. The second protocol employs unconditionally secure authentication of information sent over the public channel, and thus it can be applied even in the case when an adversary is allowed to modify public communications. An experimental realization of a quantum identification system is described.Comment: RevTeX, 4 postscript figures, 9 pages, submitted to Physical Review

Identity in research infrastructure and scientific communication: Report from the 1st IRISC workshop, Helsinki Sep 12-13, 2011

Motivation for the IRISC workshop came from the observation that identity and digital identification are increasingly important factors in modern scientific research, especially with the now near-ubiquitous use of the Internet as a global medium for dissemination and debate of scientific knowledge and data, and as a platform for scientific collaborations and large-scale e-science activities.

The 1 1/2 day IRISC2011 workshop sought to explore a series of interrelated topics under two main themes: i) unambiguously identifying authors/creators & attributing their scholarly works, and ii) individual identification and access management in the context of identity federations. Specific aims of the workshop included:

• Raising overall awareness of key technical and non-technical challenges, opportunities and developments.
• Facilitating a dialogue, cross-pollination of ideas, collaboration and coordination between diverse – and largely unconnected – communities.
• Identifying & discussing existing/emerging technologies, best practices and requirements for researcher identification.

This report provides background information on key identification-related concepts & projects, describes workshop proceedings and summarizes key workshop findings

Practical identification key for 14 Rubiaceae weed species of western and central Africa

Des adventices de la famille des rubiacées ont été identifiées en Afrique de l'Ouest et du Centre. Une clé de détermination comprend 8 caractères de l'appareil végétatif. Le tableau de ces critères, la démarche de la clé sont présentés, sans faire appel à une description complète des plante

The role of automated feedback in training and retaining biological recorders for citizen science

The rapid rise of citizen science, with lay people forming often extensive biodiversity sensor networks, is seen as a solution to the mismatch between data demand and supply while simultaneously engaging citizens with environmental topics. However, citizen science recording schemes require careful consideration of how to motivate, train, and retain volunteers. We evaluated a novel computing science framework that allowed for the automated generation of feedback to citizen scientists using natural language generation (NLG) technology. We worked with a photo-based citizen science program in which users also volunteer species identification aided by an online key. Feedback is provided after photo (and identification) submission and is aimed to improve volunteer species identification skills and to enhance volunteer experience and retention. To assess the utility of NLG feedback, we conducted two experiments with novices to assess short-term (single session) and longer-term (5 sessions in 2 months) learning, respectively. Participants identified a specimen in a series of photos. One group received only the correct answer after each identification, and the other group received the correct answer and NLG feedback explaining reasons for misidentification and highlighting key features that facilitate correct identification. We then developed an identification training tool with NLG feedback as part of the citizen science program BeeWatch and analyzed learning by users. Finally, we implemented NLG feedback in the live program and evaluated this by randomly allocating all BeeWatch users to treatment groups that received different types of feedback upon identification submission. After 6 months separate surveys were sent out to assess whether views on the citizen science program and its feedback differed among the groups. Identification accuracy and retention of novices were higher for those who received automated feedback than for those who received only confirmation of the correct identification without explanation. The value of NLG feedback in the live program, captured through questionnaires and evaluation of the online photo-based training tool, likewise showed that the automated generation of informative feedback fostered learning and volunteer engagement and thus paves the way for productive and long-lived citizen science projects

Digital Image Analysis of Actinomycetes Colonies as a Potential Aid for Rapid Taxonomic Identification

High frequency isolation of actinomycetes poses a challenge for the taxonomists hence simple and rapid identification methods are required. Our work to catalogue biodiversity of actinomycetes of Goa yielded several distinct morphotypes. After their tentative identification, the feasibility to distinguish these using digital image analyses (DIA) was explored. Digital images of wild colony morphotypes were processed using public domain SCION image analysis software. DIA revealed some intricate digital characters. A combination of these with standard morphological and microscopic characters could be potentially useful for preparing a digital identification key of the actinomycetes strains with potential application in rapid taxonomic identification