Journalism, media & communication network: Final report 2015

The JoMeC Network project had three key objectives. These were to: 1. Benchmark the pedagogical elements of journalism, media and communication (JoMeC) programs at Australian universities in order to develop a set of minimum academic standards, to be known as Threshold Learning Outcomes (TLOs), which would applicable to the disciplines of Journalism, Communication and/or Media Studies, and Public Relations; 2. Build a learning and teaching network of scholars across the JoMeC disciplines to support collaboration, develop leadership potential among educators, and progress shared priorities; 3. Create an online resources hub to support learning and teaching excellence and foster leadership in learning and teaching in the JoMeC disciplines. In order to benchmark the pedagogical elements of the JoMeC disciplines, the project started with a comprehensive review of the disciplinary settings of journalism, media and communication-related programs within Higher Education in Australia plus an analysis of capstone units (or subjects) offered in JoMeC-related degrees. This audit revealed a diversity of degree titles, disciplinary foci, projected career outcomes and pedagogical styles in the 36 universities that offered JoMeC-related degrees in 2012, highlighting the difficulties of classifying the JoMeC disciplines collectively or singularly. Instead of attempting to map all disciplines related to journalism, media and communication, the project team opted to create generalised TLOs for these fields, coupled with detailed TLOs for bachelor-level qualifications in three selected JoMeC disciplines: Journalism, Communication and/or Media Studies, and Public Relations. The initial review’s outcomes shaped the methodology that was used to develop the TLOs. Given the complexity of the JoMeC disciplines and the diversity of degrees across the network, the project team deployed an issue-framing process to create TLO statements. This involved several phases, including discussions with an issue-framing team (an advisory group of representatives from different disciplinary areas); research into accreditation requirements and industry-produced materials about employment expectations; evaluation of learning outcomes from universities across Australia; reviews of scholarly literature; as well as input from disciplinary leaders in a variety of forms. Draft TLOs were refined after further consultation with industry stakeholders and the academic community via email, telephone interviews, and meetings and public forums at conferences. This process was used to create a set of common TLOs for JoMeC disciplines in general and extended TLO statements for the specific disciplines of Journalism and Public Relations. A TLO statement for Communication and/or Media Studies remains in draft form. The Australian and New Zealand Communication Association (ANZCA) and Journalism Education and Research Association of Australian (JERAA) have agreed to host meetings to review, revise and further develop the TLOs. The aim is to support the JoMeC Network’s sustainability and the TLOs’ future development and use.

Electrical switching and thermal studies on Ge22Te78-xIx chalcohalide glasses: The effect of iodine on network-topology

Investigations on the electrical switching behavior and thermal studies using Alternating Differential Scanning Calorimetry have been undertaken on bulk, melt-quenched Ge22Te78-,Is (3 <= x <= 10) chalcohalide glasses. All the glasses studied have been found to exhibit memory-type electrical switching. The threshold voltages of Ge22Te78-I-x(x) glasses have been found to increase with the addition of iodine and the composition dependence of threshold voltages of Ge22Te78-xIx glasses exhibits a cusp at 5 at.% of iodine. Also, the variation with composition of the glass transition temperature (Tg) of Ge22Te78-I-x(x) glasses, exhibits a broad hump around this composition. Based on the present results, the composition x = 5 has been identified as the inverse rigidity percolation threshold at which Ge22Te78-I-x(x) glassy system exhibits a change from a stressed rigid amorphous solid to a flexible polymeric glass. Further, a sharp minimum is seen in the composition dependence of non-reversing enthalpy (Delta H-nr) of Ge22Te78-I-x(x) glasses at x = 5, which is suggestive of a thermally reversing window at this composition. (C) 2007 Elsevier Ltd. All rights reserved.

Enhancing sensor network lifetime using interactive communication

We are concerned with maximizing the lifetime of a data-gathering wireless sensor network consisting of set of nodes directly communicating with a base-station. We model this scenario as the m-message interactive communication between multiple correlated informants (sensor nodes) and a recipient (base-station). With this framework, we show that m-message interactive communication can indeed enhance network lifetime. Both worst-case and average-case performances are considered.

System-level analysis of network interfaces for hierarchical MPSoCs

Network Interfaces (NIs) are used in Multiprocessor System-on-Chips (MPSoCs) to connect CPUs to a packet switched Network-on-Chip. In this work we introduce a new NI architecture for our hierarchical CoreVA-MPSoC. The CoreVA-MPSoC targets streaming applications in embedded systems. The main contribution of this paper is a system-level analysis of different NI configurations, considering both software and hardware costs for NoC communication. Different configurations of the NI are compared using a benchmark suite of 10 streaming applications. The best performing NI configuration shows an average speedup of 20 for a CoreVA-MPSoC with 32 CPUs compared to a single CPU. Furthermore, we present physical implementation results using a 28 nm FD-SOI standard cell technology. A hierarchical MPSoC with 8 CPU clusters and 4 CPUs in each cluster running at 800MHz requires an area of 4.56mm2.

GMM based Bayesian approach to speech enhancement in signal transform domain

Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator. The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.

High pressure studies on the electrical resistivity of As-Te-bond Si glasses and the effect of network topological thresholds

The variation of resistivity in an amorphous As30Te70-xSix system of glasses with high pressure has been studied for pressures up to 8 GPa. It is found that the electrical resistivity and the conduction activation energy decrease continuously with increase in pressure, and samples become metallic in the pressure range 1.0-2.0 GPa. Temperature variation studies carried out at a pressure of 0.92 GPa show that the activation energies lie in the range 0.16-0.18eV. Studies on the composition/average co-ordination number (r) dependence of normalized electrical resistivity at different pressures indicate that rigidity percolation is extended, the onset of the intermediate phase is around (r) = 2.44, and completion at (r) = 2.56, respectively, while the chemical threshold is at (r) = 2.67. These results compare favorably with those obtained from electrical switching and differential scanning calorimetric studies.

Network flow-control using asynchronous stochastic approximation

We propose several stochastic approximation implementations for related algorithms in flow-control of communication networks. First, a discrete-time implementation of Kelly's primal flow-control algorithm is proposed. Convergence with probability 1 is shown, even in the presence of communication delays and stochastic effects seen in link congestion indications. This ensues from an analysis of the flow-control algorithm using the asynchronous stochastic approximation (ASA) framework. Two relevant enhancements are then pursued: a) an implementation of the primal algorithm using second-order information, and b) an implementation where edge-routers rectify misbehaving flows. Next, discretetime implementations of Kelly's dual algorithm and primaldual algorithm are proposed. Simulation results a) verifying the proposed algorithms and, b) comparing the stability properties are presented.

Network topological thresholds in gallium doped As-Te glasses Electrical and thermal investigations

Electrical switching and differential scanning calorimetric studies are undertaken on bulk As20Te80-xGax glasses, to elucidate the network topological thresholds. It is found that these glasses exhibit a single glass transition (T-g) and two crystallization reactions (T-cl & T-c2) upon heating. It is also found that there is only a marginal change in T-g with the addition of up to about 10% of Ga; around this composition an increase is seen in 7, which culminates in a local maximum around x = 15. The decrease exhibited in T, beyond this composition, leads to a local minimum at x = 17.5. Further, the As20Te80-xGax glasses are found to exhibit memory type electrical switching. The switching voltages (VT) increase with the increase in gallium content and a local maximum is seen in V-tau around x = 15. VT is found to decrease with x thereafter, exhibiting a local minimum around x = 17.5. The composition dependence of T-cl is found to be very similar to that of V-T of As20Te80-xGax glasses. Based on the present results, it is proposed that the composition x = 15 and x = 17.5 correspond to the rigidity percolation and chemical thresholds, respectively, of As20Te80-xGax glasses. (c) 2007 Elsevier B.V. All rights reserved.

Bayesian estimation of small effects in exercise and sports science

The aim of this paper is to provide a Bayesian formulation of the so-called magnitude-based inference approach to quantifying and interpreting effects, and in a case study example provide accurate probabilistic statements that correspond to the intended magnitude-based inferences. The model is described in the context of a published small-scale athlete study which employed a magnitude-based inference approach to compare the effect of two altitude training regimens (live high-train low (LHTL), and intermittent hypoxic exposure (IHE)) on running performance and blood measurements of elite triathletes. The posterior distributions, and corresponding point and interval estimates, for the parameters and associated effects and comparisons of interest, were estimated using Markov chain Monte Carlo simulations. The Bayesian analysis was shown to provide more direct probabilistic comparisons of treatments and able to identify small effects of interest. The approach avoided asymptotic assumptions and overcame issues such as multiple testing. Bayesian analysis of unscaled effects showed a probability of 0.96 that LHTL yields a substantially greater increase in hemoglobin mass than IHE, a 0.93 probability of a substantially greater improvement in running economy and a greater than 0.96 probability that both IHE and LHTL yield a substantially greater improvement in maximum blood lactate concentration compared to a Placebo. The conclusions are consistent with those obtained using a ‘magnitude-based inference’ approach that has been promoted in the field. The paper demonstrates that a fully Bayesian analysis is a simple and effective way of analysing small effects, providing a rich set of results that are straightforward to interpret in terms of probabilistic statements.

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.

Optimizing a piezometric network in the estimation of the groundwater budget: a case study from a crystalline-rock watershed in southern India

An estimate of the groundwater budget at the catchment scale is extremely important for the sustainable management of available water resources. Water resources are generally subjected to over-exploitation for agricultural and domestic purposes in agrarian economies like India. The double water-table fluctuation method is a reliable method for calculating the water budget in semi-arid crystalline rock areas. Extensive measurements of water levels from a dense network before and after the monsoon rainfall were made in a 53 km(2)atershed in southern India and various components of the water balance were then calculated. Later, water level data underwent geostatistical analyses to determine the priority and/or redundancy of each measurement point using a cross-validation method. An optimal network evolved from these analyses. The network was then used in re-calculation of the water-balance components. It was established that such an optimized network provides far fewer measurement points without considerably changing the conclusions regarding groundwater budget. This exercise is helpful in reducing the time and expenditure involved in exhaustive piezometric surveys and also in determining the water budget for large watersheds (watersheds greater than 50 km(2)).

Rotorcraft parameter estimation using radial basis function neural network

Increased emphasis on rotorcraft performance and perational capabilities has resulted in accurate computation of aerodynamic stability and control parameters. System identification is one such tool in which the model structure and parameters such as aerodynamic stability and control derivatives are derived. In the present work, the rotorcraft aerodynamic parameters are computed using radial basis function neural networks (RBFN) in the presence of both state and measurement noise. The effect of presence of outliers in the data is also considered. RBFN is found to give superior results compared to finite difference derivatives for noisy data. (C) 2010 Elsevier Inc. All rights reserved.

Accelerator Mass Spectrometry and Bayesian Data Analysis

Accelerator mass spectrometry (AMS) is an ultrasensitive technique for measuring the concentration of a single isotope. The electric and magnetic fields of an electrostatic accelerator system are used to filter out other isotopes from the ion beam. The high velocity means that molecules can be destroyed and removed from the measurement background. As a result, concentrations down to one atom in 10^16 atoms are measurable. This thesis describes the construction of the new AMS system in the Accelerator Laboratory of the University of Helsinki. The system is described in detail along with the relevant ion optics. System performance and some of the 14C measurements done with the system are described. In a second part of the thesis, a novel statistical model for the analysis of AMS data is presented. Bayesian methods are used in order to make the best use of the available information. In the new model, instrumental drift is modelled with a continuous first-order autoregressive process. This enables rigorous normalization to standards measured at different times. The Poisson statistical nature of a 14C measurement is also taken into account properly, so that uncertainty estimates are much more stable. It is shown that, overall, the new model improves both the accuracy and the precision of AMS measurements. In particular, the results can be improved for samples with very low 14C concentrations or measured only a few times.

Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.

Teacher peer support in social network sites

This paper describes the types of support that teachers are accessing through the Social Network Site (SNS) 'Facebook'. It describes six ways in which teachers support one another within online groups. It presents evidence from a study of a large, open group of teachers online over a twelve week period, repeated with multiple groups a year later over a one week period. The findings suggest that large open groups in SNSs can be a useful source of pragmatic advice for teachers but that these groups are rarely a place for reflection on or feedback about teaching practice.