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

Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network

Disguised face identification (DFI) is an extremely challenging problem due to the numerous variations that can be introduced using different disguises. This paper introduces a deep learning framework to first detect 14 facial key-points which are then utilized to perform disguised face identification. Since the training of deep learning architectures relies on large annotated datasets, two annotated facial key-points datasets are introduced. The effectiveness of the facial keypoint detection framework is presented for each keypoint. The superiority of the key-point detection framework is also demonstrated by a comparison with other deep networks. The effectiveness of classification performance is also demonstrated by comparison with the state-of-the-art face disguise classification methods.Comment: To Appear in the IEEE International Conference on Computer Vision Workshops (ICCVW) 201

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

A multiprocess quality model: identification of of key processes in the integration approach

In this paper we investigate the use of multiprocess quality model in the adoption of process improvement frameworks. We analyze an improvement effort based on multiple process quality models adoption. At present, there is a possibility of a software development organization to adopt multi-quality and improvement models in order to remain competitive in the IT market place. Various quality models emerge to satisfy different improvement objective such as to improve capability of models, quality management and serve as IT government purpose. The heterogeneity characteristics of the models require further research on dealing with multiple process models at a time. We discuss on the concept of software process and overview on software maintenance and evolution which are important elements in the quality models. The concepts related to process quality model and improvement models are discussed. The research outlined in this paper shows that software processes, maintenance, evolution, quality and improvement have become really important in software engineering. The synergy among the multi-focused process quality model is examined with respect to process improvement. The research outcome is to determine key processes vital to the implementation of multi-process quality model

Proxy Controls and Panel Data

We present a flexible approach to the identification and estimation of causal objects in nonparametric, non-separable models with confounding. Key to our analysis is the use of `proxy controls': covariates that do not satisfy a standard `unconfoundedness' assumption but are informative proxies for variables that do. Our analysis applies to both cross-sectional and panel models. Our identification results motivate a simple and `well-posed' nonparametric estimator and we analyze its asymptotic properties. In panel settings, our methods provide a novel approach to the difficult problem of identification with non-separable general heterogeneity and fixed $T$. In panels, observations from different periods serve as proxies for unobserved heterogeneity and our key identifying assumptions follow from restrictions on the serial dependence structure. We apply our methodology to two empirical settings. We estimate causal effects of grade retention on cognitive performance using cross-sectional variation and we estimate a structural Engel curve for food using panel data.Comment: 76 pages, 1 table, 1 figur