Interrelation of families of points of high order on the Edwards curve over a prime field

We propose a modification of the addition law on the Edwards curve over a prime field. We prove three theorems on properties of coordinates of high-order points and on a degenerate pair of twisted curves. We propose an algorithm for reconstructing all unknown points kP of the Edwards curve when only 1/8 of the points are known.     

Adaptive methods of solving inverse problems for improvement of fidelity of molecular DNA computations

Elaboration of methods of monitoring of biochemical reactions with DNA strands is necessary to solve one of the main problems in creation of biocomputers—improvement of fidelity of molecular DNA computations. In this paper, the results of solution of inverse two-parameter problems of laser Raman spectroscopy on determination of the types and concentration of DNA nitrogenous bases in multicomponent solutions are presented. Comparative analysis of the three used methods of solving these problems has demonstrated convincing advantages of artificial neural networks and of the method of projection to latent structures. Use of adaptive methods allowed achieving the accuracy of determining the concentration of each base in two-component solutions about 0.2–0.4 g/L.     

Depth-Based Classification Method Underlain by a Remote Concentration Measure for Processing Asymmetric Data

A depth-based classification method underlain by a remote concentration measure for processing asymmetric data is developed and investigated. The motivation for the construction of the method was the inefficient use of affine invariant classifiers in combination with depth functions vanishing outside the convex hull of data. The idea of the proposed method is to map a remote space using a remote concentration measure, the Stahel–Donoho remoteness measure, and a corrected remoteness measure.     

Recognition of Actions of Medical Workers on the Basis of Accelerometer Readings with the Use of a Deep Belief Network

This paper analyzes a real set of large-volume medico-statistical data used for the recognition of actions of medical workers on the basis of accelerometer readings at definite moments of time. During the recognition process, a deep belief network was applied to unlabeled data, and then it was supervisedly learned using the error backpropagation method. The obtained results demonstrate higher recognition accuracy in comparison with basic methods. A considerable improvement in medical staff productivity is also achieved.     

Privacy Preserving Geo-Linkage in the Big Urban Data Era

Big data technologies and a range of Government open data initiatives provide the basis for discovering new insights into cities; how they are planned, how they managed and the day-to-day challenges they face in health, transport and changing population profiles. The Australian Urban Research Infrastructure Network (AURIN – www.aurin.org.au) project is one example of such a big data initiative that is currently running across Australia. AURIN provides a single gateway providing online (live) programmatic access to over 2000 data sets from over 70 major and typically definitive data-driven organizations across federal and State government, across industry and across academia. However whilst open (public) data is useful to bring data-driven intelligence to cities, more often than not, it is the data that is not-publicly accessible that is essential to understand city challenges and needs. Such sensitive (unit-level) data has unique requirements on access and usage to meet the privacy and confidentiality demands of the associated organizations. In this paper we highlight a novel geo-privacy supporting solution implemented as part of the AURIN project that provides seamless and secure access to individual (unit-level) data from the Department of Health in Victoria. We illustrate this solution across a range of typical city challenges in localized contexts around Melbourne. We show how unit level data can be combined with other data in a privacy-protecting manner. Unlike other secure data access and usage solutions that have been developed/deployed, the AURIN solution allows any researcher to access and use the data in a manner that meets all of the associated privacy and confidentiality concerns, without obliging them to obtain ethical approval or any other hurdles that are normally put in place on access to and use of sensitive data. This provides a paradigm shift in secure access to sensitive data with geospatial content.     

Towards the generation of dialogue acts in socio-affective ECAs: a corpus-based prosodic analysis

We present a corpus-based prosodic analysis with the aim of uncovering the relationship between dialogue acts, personality and prosody in view to providing guidelines for the ECA Greta’s text-to-speech system. The corpus used is the SEMAINE corpus, featuring four different personalities, further annotated for dialogue acts and prosodic features. In order to show the importance of the choice of dialogue act taxonomy, two different taxonomies were used, the first corresponding to Searle’s taxonomy of speech acts and the second, inspired by Bunt’s DIT++, including a division of directive acts into finer categories. Our results show that finer-grained distinctions are important when choosing a taxonomy. We also show with some preliminary results that the prosodic correlates of dialogue acts are not always as cited in the literature and prove more complex and variable. By studying the realisation of different directive acts, we also observe differences in the communicative strategies of the ECA depending on personality, in view to providing input to a speech system.