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.     

An improved algorithm of median flow for visual object tracking and its implementation on ARM platform

An improved algorithm of median flow used for visual object tracking is described. The improvement consists in adaptive selection of aperture window size and number of pyramid levels at optical flow estimation. It can increase the tracking efficiency as compared to the basic algorithm, especially when dealing with small and low-contrast objects. The proposed version of the algorithm has been implemented using OpenCV library and tested on OMAP 35x EVM and BeagleBoard-xM based on Texas Instruments OMAP3530 and DM3730 processors, respectively. Analysis of improved median flow was performed over actual video sequences. The results obtained show versatility and computational robustness of the algorithm, which makes it promising for embedded application based on ARM processors.     

Spatial Water Use efficiency Index towards resource sustainability: application in the island of Crete,Greece

This paper introduces a new index to test water resource sustainability in regions where groundwater is the main source of water supply. The Spatial Water Use efficiency Index (SWUI) is a geographical information system (GIS) environment index which expresses the ratio of potential available water volume (groundwater recharge minus water needs) to the respective water needs. Its low and/or negative values indicate water stress. SWUI is applied in the island of Crete, Greece, where water needs are mainly covered by groundwater abstractions. The annual water mass balance is estimated using the RIBASIM (River Basin Simulation) model. The total freshwater needs in the Cretan region reach 535 hm³ annually according to the model's simulation. SWUI with values ranging from ? 0.8 to 214.3 has proved to be an important visual tool towards depicting the low efficiency of the east part of the island to cover water demand and a useful decision-making tool in the sustainability evaluation of groundwater aquifers.