Producing the NAPLAN Machine: A Schizoanalytic Cartography

Everything revolves around desiring-machines and the production of desire… Schizoanalysis merely asks what are the machinic, social and technical indices on a socius that open to desiring-machines (Deleuze & Guattari, 1983, pp. 380-381). Achievement tests like NAPLAN are fairly recent, yet common, education policy initiatives in much of the Western world. They intersect with, use and change pre-existing logics of education, teaching and learning. There has been much written about the form and function of these tests, the ‘stakes’ involved and the effects of their practice. This paper adopts a different “angle of vision” to ask what ‘opens’ education to these regimes of testing(Roy, 2008)? This paper builds on previous analyses of NAPLAN as a modulating machine, or a machine characterised by the increased intensity of connections and couplings. One affect can be “an existential disquiet” as “disciplinary subjects attempt to force coherence onto a disintegrating narrative of self”(Thompson & Cook, 2012, p. 576). Desire operates at all levels of the education assemblage, however our argument is that achievement testing manifests desire as ‘lack’; seen in the desire for improved results, the desire for increased control, the desire for freedom, the desire for acceptance to name a few. For Deleuze and Guattari desire is irreducible to lack, instead desire is productive. As a productive assemblage, education machines operationalise and produce through desire; “Desire is a machine, and the object of the desire is another machine connected to it”(Deleuze & Guattari, 1983, p. 26). This intersection is complexified by the strata at which they occur, the molar and molecular connections and flows they make possible. Our argument is that when attention is paid to the macro and micro connections, the machines built and disassembled as a result of high-stakes testing, a map is constructed that outlines possibilities, desires and blockages within the education assemblage. This schizoanalytic cartography suggests a new analysis of these ‘axioms’ of testing and accountability. It follows the flows and disruptions made possible as different or altered connections are made and as new machines are brought online. Thinking of education machinically requires recognising that “every machine functions as a break in the flow in relation to the machine to which it is connected, but at the same time is also a flow itself, or the production of flow, in relation to the machine connected to it”(Deleuze & Guattari, 1983, p. 37). Through its potential to map desire, desire-production and the production of desire within those assemblages that have come to dominate our understanding of what is possible, Deleuze and Guattari’s method of schizoanalysis provides a provocative lens for grappling with the question of what one can do, and what lines of flight are possible.

Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning

Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

A penalty-based genetic algorithm for the migration cost-aware virtual machine placement problem in cloud data centers

In the past few years, the virtual machine (VM) placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used in today's cloud data centers as they do not consider the migration cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the migration cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm (GA) for the VM placement problem that considers the migration cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

An Evaluation of Inventory Management and Performance in Indian Machine Tool SMEs: An Exploratory Study

Inventory Management (IM) plays a decisive role in the enhancement of efficiency and competitiveness of manufacturing enterprises. Therefore, major manufacturing enterprises are following IM practices as a strategy to improve efficiency and achieve competitiveness. However, the spread of IM culture among Small and Medium Enterprises (SMEs) is limited due to lack of initiation, expertise and financial limitations in developed countries, leave alone developing countries. With this backdrop, this paper makes an attempt to ascertain the role and importance of IM practices and performance of SMEs in the machine tools industry of Bangalore, India. The relationship between inventory management practices and inventory cost are probed based on primary data gathered from 91 SMEs. The paper brings out that formal IM practices have a positive impact on the inventory performance of SMEs.

Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine

In this paper, downscaling models are developed using a support vector machine (SVM) for obtaining projections of monthly mean maximum and minimum temperatures (T-max and T-min) to river-basin scale. The effectiveness of the model is demonstrated through application to downscale the predictands for the catchment of the Malaprabha reservoir in India, which is considered to be a climatically sensitive region. The probable predictor variables are extracted from (1) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1978-2000, and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 1978-2100. The predictor variables are classified into three groups, namely A, B and C. Large-scale atmospheric variables Such as air temperature, zonal and meridional wind velocities at 925 nib which are often used for downscaling temperature are considered as predictors in Group A. Surface flux variables such as latent heat (LH), sensible heat, shortwave radiation and longwave radiation fluxes, which control temperature of the Earth's surface are tried as plausible predictors in Group B. Group C comprises of all the predictor variables in both the Groups A and B. The scatter plots and cross-correlations are used for verifying the reliability of the simulation of the predictor variables by the CGCM3 and to Study the predictor-predictand relationships. The impact of trend in predictor variables on downscaled temperature was studied. The predictor, air temperature at 925 mb showed an increasing trend, while the rest of the predictors showed no trend. The performance of the SVM models that are developed, one for each combination of predictor group, predictand, calibration period and location-based stratification (land, land and ocean) of climate variables, was evaluated. In general, the models which use predictor variables pertaining to land surface improved the performance of SVM models for downscaling T-max and T-min

Prediction of Ultimate Capacity of Laterally Loaded Piles in Clay: A Relevance Vector Machine Approach

This study investigates the potential of Relevance Vector Machine (RVM)-based approach to predict the ultimate capacity of laterally loaded pile in clay. RVM is a sparse approximate Bayesian kernel method. It can be seen as a probabilistic version of support vector machine. It provides much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. RVM model outperforms the two other models based on root-mean-square-error (RMSE) and mean-absolute-error (MAE) performance criteria. It also stimates the prediction variance. The results presented in this paper clearly highlight that the RVM is a robust tool for prediction Of ultimate capacity of laterally loaded piles in clay.

Experimental and Analytical Investigations of the actions taking place in a Synchronous machine during the starting period when the starting Is effected by means of alternating current

The literature on the subject of the present investigation is somewhat meagre. A rotary converter or synchronous motor no! provided with any special starting devices forms, when started from the alternating current side, a type of induction motor whoso Htator is provided with a polyphase winding, and whoso rotor has a single-phase (or single magnetic axis) winding.

Process studies of odour emissions from effluent ponds using machine-based odour measurement

Replicable experimental studies using a novel experimental facility and a machine-based odour quantification technique were conducted to demonstrate the relationship between odour emission rates and pond loading rates. The odour quantification technique consisted of an electronic nose, AromaScan A32S, and an artificial neural network. Odour concentrations determined by olfactometry were used along with the AromaScan responses to train the artificial neural network. The trained network was able to predict the odour emission rates for the test data with a correlation coefficient of 0.98. Time averaged odour emission rates predicted by the machine-based odour quantification technique, were strongly correlated with volatile solids loading rate, demonstrating the increased magnitude of emissions from a heavily loaded effluent pond. However, it was not possible to obtain the same relationship between volatile solids loading rates and odour emission rates from the individual data. It is concluded that taking a limited number of odour samples over a short period is unlikely to provide a representative rate of odour emissions from an effluent pond. A continuous odour monitoring instrument will be required for that more demanding task.

Natural Frequencies And Transient Responses Of 3-Phase Rotating Machine Windings

The analysis of transient electrical stresses in the insulation of high voltage rotating machines is rendered difficult because of the existence of capacitive and inductive couplings between phases. The Published theories ignore many of the couplings between phases to obtain the solution. A new procedure is proposed here to determine the transient voltage distribution on rotating machine windings. All the significicant capacitive and inductive couplings between different sections in a phase and between different sections in different phases have been considered in this analysis. The experimental results show good correlation with those computed.

Bi-criteria single machine scheduling problem with a learning effect: Aneja-Nair method to obtain the set of optimal sequences

In this paper, we consider the bi-criteria single machine scheduling problem of n jobs with a learning effect. The two objectives considered are the total completion time (TC) and total absolute differences in completion times (TADC). The objective is to find a sequence that performs well with respect to both the objectives: the total completion time and the total absolute differences in completion times. In an earlier study, a method of solving bi-criteria transportation problem is presented. In this paper, we use the methodology of solvin bi-criteria transportation problem, to our bi-criteria single machine scheduling problem with a learning effect, and obtain the set of optimal sequences,. Numerical examples are presented for illustrating the applicability and ease of understanding.

Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. Conclusion The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.

Design and Development of an In-Vehicle Human Machine Interface to Improve Fuel Efficiency & Safety

Road transport plays a significant role in various industries and mobility services around the globe and has a vital impact on our daily lives. However it also has serious impacts on both public health and the environment. In-vehicle feedback systems are a relatively new approach to encouraging driver behaviour change for improving fuel efficiency and safety in automotive environments. While many studies claim that the adoption of eco-driving practices, such as eco-driving training programs and in-vehicle feedback to drivers, has the potential to improve fuel efficiency, limited research has integrated safety and eco-driving. Therefore, this research seeks to use human factors related theories and practices to inform the design and evaluation of an in-vehicle Human Machine Interface (HMI) providing real-time driver feedback with the aim of improving both fuel efficiency and safety.

Behavior Of High-Voltage Machine Insulation System In The Presence Of Thermal And Electrical Stresses

This paper presents the results on a resin-rich machine insulation system subjected to varying stresses such as electrical (2.6 to 13.3 MV/m) and thermal (40 to 155° C) acting together. Accelerated electro-thermal aging experiments subsequently have been performed to understand the insulation degradation The interpretations are based on several measured properties like capacitance, loss tangent, ac resistance, leakage current, and partial discharge quantities. The results indicate that the changes in properties are not significant below a certain temperature for any applied stress, Beyond this temperature large variations are observed even for low electrical stresses. Electrothermal aging studies reveal that the acceleration of the insulation degradation and the ultimate time to failure depends on the relative values of temperature and voltage stresses. At lower temperatures, below critical, material characteristics of the system predominate whereas beyond this temperature, other phenomena come into play causing insulation deterioration. During aging under combined stresses, it appears that the prevailing temperature of the system has a significant role in the insulation degradation and ultimate failure.