Web-based information systems development and dynamic organisational change: the need for emergent development tools

This paper considers contextual issues relating to the problem of developing web-based information systems in and for emergent organisations. It postulates that the methods available suffer because of sudden and unexpected changing characteristics within the organisation. The Theory of Deferred Action is used as the basis for the development of an emergent development tool. Many tools for managing change in a continuously changing organisation are susceptible to inadequacy. The insights proposed are believed to assist designers in developing functional and relevant approaches within dynamic organisational contexts

C2AE: Class Conditioned Auto-Encoder for Open-set Recognition

Models trained for classification often assume that all testing classes are known while training. As a result, when presented with an unknown class during testing, such closed-set assumption forces the model to classify it as one of the known classes. However, in a real world scenario, classification models are likely to encounter such examples. Hence, identifying those examples as unknown becomes critical to model performance. A potential solution to overcome this problem lies in a class of learning problems known as open-set recognition. It refers to the problem of identifying the unknown classes during testing, while maintaining performance on the known classes. In this paper, we propose an open-set recognition algorithm using class conditioned auto-encoders with novel training and testing methodology. In contrast to previous methods, training procedure is divided in two sub-tasks, 1. closed-set classification and, 2. open-set identification (i.e. identifying a class as known or unknown). Encoder learns the first task following the closed-set classification training pipeline, whereas decoder learns the second task by reconstructing conditioned on class identity. Furthermore, we model reconstruction errors using the Extreme Value Theory of statistical modeling to find the threshold for identifying known/unknown class samples. Experiments performed on multiple image classification datasets show proposed method performs significantly better than state of the art.Comment: CVPR2019 (Oral

Changes in forward light scatter parameters as a function of refractive error in young adults

Background/aims Some aspects of visual performance worsen with increasing myopia. Whilst the underlying causes are not always clear, reduction in retinal image quality is often attributed to structural changes in the posterior myopic eye. Forward light scatter, originating principally from the cornea and lens, is known to produce veiling glare which subsequently reduces retinal image contrast. It is therefore of interest to investigate whether forward light scatter varies with refractive error. Methods Thirteen young-adult subjects (18–25 years), with mean spherical errors (MSE ± sd, D) RE, − 1.69 ± 2.02 (range 0.38 to − 4.75); LE, − 1.91 ± 1.94 (range 0.50 to − 4.63) underwent binocular assessment of forward light scatter using the AVOT light scatter test. Five glare annuli, with effective eccentricities ranging from 2 to 10°, were used to estimate parameters, k and n, which define the light scatter function of the eye. These were then used to calculate the area under the light scatter function (k′) and the total volume of light scatter (k″). Results Significant correlation was found between increasing myopia and k′ values (RE, p  0.05 for both eyes). Axial length was also not correlated with any of the light scatter parameters measured. Conclusion The preliminary data from this study provide evidence that some light scatter parameters may be correlated with refractive error. Further studies are needed to characterize how changes in the anterior media of the eye, and inclusion of a wider range of refractive errors, may affect forward light scatter

Revisiting lepton flavor violation in supersymmetric type II seesaw

In view of the recent measurement of reactor mixing angle $\theta_{13}$ and updated limit on $BR(\mu \to e \gamma)$ by the MEG experiment, we re-examine the charged lepton flavor violations in a framework of supersymmetric type II seesaw mechanism. Supersymmetric type II seesaw predicts strong correlation between $BR(\mu \to e \gamma)$ and $BR(\tau \to \mu \gamma)$ mainly in terms of the neutrino mixing angles. We show that such a correlation can be determined accurately after the measurement of $\theta_{13}$. We compute different factors which can affect this correlation and show that the mSUGRA-like scenarios, in which slepton masses are taken to be universal at the high scale, predicts $3.5 \lesssim BR(\tau \to \mu \gamma)/BR(\mu \to e \gamma) \lesssim 30$ for normal hierarchical neutrino masses. Any experimental indication of deviation from this prediction would rule out the minimal models of supersymmetric type II seesaw. We show that the current MEG limit puts severe constraints on the light sparticle spectrum in mSUGRA model if the seesaw scale lies within $10^{13}$-$10^{15}$ GeV. It is shown that these constraints can be relaxed and relatively light sparticle spectrum can be obtained in a class of models in which the soft mass of triplet scalar is taken to be non-universal at the high scale.Comment: Minor changes in text; accepted for publication in Phys. Rev.

Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201