An innovative clustering technique for unsupervized learning in the context of remotely sensed earth resources data analysis

A new clustering technique, based on the concept of immediato neighbourhood, with a novel capability to self-learn the number of clusters expected in the unsupervized environment, has been developed. The method compares favourably with other clustering schemes based on distance measures, both in terms of conceptual innovations and computational economy. Test implementation of the scheme using C-l flight line training sample data in a simulated unsupervized mode has brought out the efficacy of the technique. The technique can easily be implemented as a front end to established pattern classification systems with supervized learning capabilities to derive unified learning systems capable of operating in both supervized and unsupervized environments. This makes the technique an attractive proposition in the context of remotely sensed earth resources data analysis wherein it is essential to have such a unified learning system capability.

On the integration of self-tracking data amongst quantified self members

Self-tracking, the process of recording one's own behaviours, thoughts and feelings, is a popular approach to enhance one's self-knowledge. While dedicated self-tracking apps and devices support data collection, previous research highlights that the integration of data constitutes a barrier for users. In this study we investigated how members of the Quantified Self movement---early adopters of self-tracking tools---overcome these barriers. We conducted a qualitative analysis of 51 videos of Quantified Self presentations to explore intentions for collecting data, methods for integrating and representing data, and how intentions and methods shaped reflection. The findings highlight two different intentions---striving for self-improvement and curiosity in personal data---which shaped how these users integrated data, i.e. the effort required. Furthermore, we identified three methods for representing data---binary, structured and abstract---which influenced reflection. Binary representations supported reflection-in-action, whereas structured and abstract representations supported iterative processes of data collection, integration and reflection. For people tracking out of curiosity, this iterative engagement with personal data often became an end in itself, rather than a means to achieve a goal. We discuss how these findings contribute to our current understanding of self-tracking amongst Quantified Self members and beyond, and we conclude with directions for future work to support self-trackers with their aspirations.

The logic of data-sense: Thinking through Learning Personalisation

Big Data and Learning Analytics’ promise to revolutionise educational institutions, endeavours, and actions through more and better data is now compelling. Multiple, and continually updating, data sets produce a new sense of ‘personalised learning’. A crucial attribute of the datafication, and subsequent profiling, of learner behaviour and engagement is the continual modification of the learning environment to induce greater levels of investment on the parts of each learner. The assumption is that more and better data, gathered faster and fed into ever-updating algorithms, provide more complete tools to understand, and therefore improve, learning experiences through adaptive personalisation. The argument in this paper is that Learning Personalisation names a new logistics of investment as the common ‘sense’ of the school, in which disciplinary education is ‘both disappearing and giving way to frightful continual training, to continual monitoring'.

Reputation model based on rating data and application in recommender systems

This thesis introduced two novel reputation models to generate accurate item reputation scores using ratings data and the statistics of the dataset. It also presented an innovative method that incorporates reputation awareness in recommender systems by employing voting system methods to produce more accurate top-N item recommendations. Additionally, this thesis introduced a personalisation method for generating reputation scores based on users' interests, where a single item can have different reputation scores for different users. The personalised reputation scores are then used in the proposed reputation-aware recommender systems to enhance the recommendation quality.

Urban Growth Analysis Using Spatial and Temporal Data

Urban growth identification, quantification, knowledge of rate and the trends of growth would help in regional planning for better infrastructure provision in environmentally sound way. This requires analysis of spatial and temporal data, which help in quantifying the trends of growth on spatial scale. Emerging technologies such as Remote Sensing, Geographic Information System (GIS) along with Global Positioning System (GPS) help in this regard. Remote sensing aids in the collection of temporal data and GIS helps in spatial analysis. This paper focuses on the analysis of urban growth pattern in the form of either radial or linear sprawl along the Bangalore - Mysore highway. Various GIS base layers such as builtup areas along the highway, road network, village boundary etc. were generated using collateral data such as the Survey of India toposheet, etc. Further, this analysis was complemented with the computation of Shannon's entropy, which helped in identifying prevalent sprawl zone, rate of growth and in delineating potential sprawl locations. The computation Shannon's entropy helped in delineating regions with dispersed and compact growth. This study reveals that the Bangalore North and South taluks contributed mainly to the sprawl with 559% increase in built-up area over a period of 28 years and high degree of dispersion. The Mysore and Srirangapatna region showed 128% change in built-up area and a high potential for sprawl with slightly high dispersion. The degree of sprawl was found to be directly proportional to the distances from the cities.

Spectral and thermal data on polystyrene peroxide

Poly(styrene peroxide) has been prepared and characterized. Nuclear magnetlc resonance (NMR) spectra Of the polymer show the shift Of aliphatic protons. Differential scanning calorimetric (DSC) and differential thermal analysis (DTA) results show anexothermic peak around 110 OC which is characteristic of peroxide decomposition.

Principals of audit: Testing, data and ‘implicated advocacy’

Historically, school leaders have occupied a somewhat ambiguous position within networks of power. On the one hand, they appear to be celebrated as what Ball (2003) has termed the ‘new hero of educational reform'; on the other, they are often ‘held to account’ through those same performative processes and technologies. These have become compelling in schools and principals are ‘doubly bound’ through this. Adopting a Foucauldian notion of discursive production, this paper addresses the ways that the discursive ‘field’ of ‘principal’ (within larger regimes of truth such as schools, leadership, quality and efficiency) is produced. It explores how individual principals understand their roles and ethics within those practices of audit emerging in school governance, and how their self-regulation is constituted through NAPLAN – the National Assessment Program, Literacy and Numeracy. A key effect of NAPLAN has been the rise of auditing practices that change how education is valued. Open-ended interviews with 13 primary and secondary school principals from Western Australia, South Australia and New South Wales asked how they perceived NAPLAN's impact on their work, their relationships within their school community and their ethical practice.

Multicarrier analysis of magnetotransport data at low and high electric fields

We present some results on multicarrier analysis of magnetotransport data, Both synthetic as well as data from narrow gap Hg0.8Cd0.2Te samples are used to demonstrate applicability of various algorithms vs. nonlinear least square fitting, Quantitative Mobility Spectrum Analysis (QMSA) and Maximum Entropy Mobility Spectrum Analysis (MEMSA). Comments are made from our experience oil these algorithms, and, on the inversion procedure from experimental R/sigma-B to S-mu specifically with least square fitting as an example. Amongst the conclusions drawn are: (i) Experimentally measured resistivity (R-xx, R-xy) should also be used instead of just the inverted conductivity (sigma(xx), sigma(xy)) to fit data to semiclassical expressions for better fits especially at higher B. (ii) High magnetic field is necessary to extract low mobility carrier parameters. (iii) Provided the error in data is not large, better estimates to carrier parameters of remaining carrier species can be obtained at any stage by subtracting highest mobility carrier contribution to sigma from the experimental data and fitting with the remaining carriers. (iv)Even in presence of high electric field, an approximate multicarrier expression can be used to guess the carrier mobilities and their variations before solving the full Boltzmann equation.

Using a glow graph to represent data flow and dependency in event logs

The idea of extracting knowledge in process mining is a descendant of data mining. Both mining disciplines emphasise data flow and relations among elements in the data. Unfortunately, challenges have been encountered when working with the data flow and relations. One of the challenges is that the representation of the data flow between a pair of elements or tasks is insufficiently simplified and formulated, as it considers only a one-to-one data flow relation. In this paper, we discuss how the effectiveness of knowledge representation can be extended in both disciplines. To this end, we introduce a new representation of the data flow and dependency formulation using a flow graph. The flow graph solves the issue of the insufficiency of presenting other relation types, such as many-to-one and one-to-many relations. As an experiment, a new evaluation framework is applied to the Teleclaim process in order to show how this method can provide us with more precise results when compared with other representations.

Assimilation of remote sensing and hydrological data using adaptive filtering techniques for watershed modelling

The knowledge of hydrological variables (e. g. soil moisture, evapotranspiration) are of pronounced importance in various applications including flood control, agricultural production and effective water resources management. These applications require the accurate prediction of hydrological variables spatially and temporally in watershed/basin. Though hydrological models can simulate these variables at desired resolution (spatial and temporal), often they are validated against the variables, which are either sparse in resolution (e. g. soil moisture) or averaged over large regions (e. g. runoff). A combination of the distributed hydrological model (DHM) and remote sensing (RS) has the potential to improve resolution. Data assimilation schemes can optimally combine DHM and RS. Retrieval of hydrological variables (e. g. soil moisture) from remote sensing and assimilating it in hydrological model requires validation of algorithms using field studies. Here we present a review of methodologies developed to assimilate RS in DHM and demonstrate the application for soil moisture in a small experimental watershed in south India.

Inferring neuronal network connectivity from spike data: A temporal data mining approach

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent d evelopments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.

The reproducibility of acquiring three dimensional gait and plantar pressure data using established protocols in participants with and without type 2 diabetes and foot ulcers

Background Several prospective studies have suggested that gait and plantar pressure abnormalities secondary to diabetic peripheral neuropathy contributes to foot ulceration. There are many different methods by which gait and plantar pressures are assessed and currently there is no agreed standardised approach. This study aimed to describe the methods and reproducibility of three-dimensional gait and plantar pressure assessments in a small subset of participants using pre-existing protocols. Methods Fourteen participants were conveniently sampled prior to a planned longitudinal study; four patients with diabetes and plantar foot ulcers, five patients with diabetes but no foot ulcers and five healthy controls. The repeatability of measuring key biomechanical data was assessed including the identification of 16 key anatomical landmarks, the measurement of seven leg dimensions, the processing of 22 three-dimensional gait parameters and the analysis of four different plantar pressures measures at 20 foot regions. Results The mean inter-observer differences were within the pre-defined acceptable level (<7 mm) for 100 % (16 of 16) of key anatomical landmarks measured for gait analysis. The intra-observer assessment concordance correlation coefficients were > 0.9 for 100 % (7 of 7) of leg dimensions. The coefficients of variations (CVs) were within the pre-defined acceptable level (<10 %) for 100 % (22 of 22) of gait parameters. The CVs were within the pre-defined acceptable level (<30 %) for 95 % (19 of 20) of the contact area measures, 85 % (17 of 20) of mean plantar pressures, 70 % (14 of 20) of pressure time integrals and 55 % (11 of 20) of maximum sensor plantar pressure measures. Conclusion Overall, the findings of this study suggest that important gait and plantar pressure measurements can be reliably acquired. Nearly all measures contributing to three-dimensional gait parameter assessments were within predefined acceptable limits. Most plantar pressure measurements were also within predefined acceptable limits; however, reproducibility was not as good for assessment of the maximum sensor pressure. To our knowledge, this is the first study to investigate the reproducibility of several biomechanical methods in a heterogeneous cohort.

Combinatorial Design of Secure Lightweight Data Dispersal Schemes (Work in Progress)

Dispersing a data object into a set of data shares is an elemental stage in distributed communication and storage systems. In comparison to data replication, data dispersal with redundancy saves space and bandwidth. Moreover, dispersing a data object to distinct communication links or storage sites limits adversarial access to whole data and tolerates loss of a part of data shares. Existing data dispersal schemes have been proposed mostly based on various mathematical transformations on the data which induce high computation overhead. This paper presents a novel data dispersal scheme where each part of a data object is replicated, without encoding, into a subset of data shares according to combinatorial design theory. Particularly, data parts are mapped to points and data shares are mapped to lines of a projective plane. Data parts are then distributed to data shares using the point and line incidence relations in the plane so that certain subsets of data shares collectively possess all data parts. The presented scheme incorporates combinatorial design theory with inseparability transformation to achieve secure data dispersal at reduced computation, communication and storage costs. Rigorous formal analysis and experimental study demonstrate significant cost-benefits of the presented scheme in comparison to existing methods.

A cloud-based intelligent computing system for contextual exploration on personal sleep-tracking data using association rule mining

With the development of wearable and mobile computing technology, more and more people start using sleep-tracking tools to collect personal sleep data on a daily basis aiming at understanding and improving their sleep. While sleep quality is influenced by many factors in a person’s lifestyle context, such as exercise, diet and steps walked, existing tools simply visualize sleep data per se on a dashboard rather than analyse those data in combination with contextual factors. Hence many people find it difficult to make sense of their sleep data. In this paper, we present a cloud-based intelligent computing system named SleepExplorer that incorporates sleep domain knowledge and association rule mining for automated analysis on personal sleep data in light of contextual factors. Experiments show that the same contextual factors can play a distinct role in sleep of different people, and SleepExplorer could help users discover factors that are most relevant to their personal sleep.