Skill Formation Strategies for Sustaining 'The Drive to Maturity' in Pakistan.

This paper outlines some problems in the articulation of a national skill formation strategy seeking to sustain ‘the drive to maturity’ of the Pakistan economy. We examine the thought of two economists—Adam Smith and Amartya Sen—to identify market-, society-, and state-related skills that they theorise as necessary for sustaining an economy’s ‘drive to maturity’. We then briefly outline Michel Foucault’s social theory to contextualise these skill formation paradigms within the institutional structure characteristic of mature capitalism. We argue that integration within global capitalist order leaves little room for the articulation of such a skill formation national strategy. Pakistan is therefore likely to share the fate of the majority of the under-developed countries which are experiencing de-skilling and detechonolgising

An OpenMP Based Approach for Parallelization and Performance Evaluation of k-Means Algorithm

In today’s digital world, the volume of data is drastically increasing due to the continuous flow of data from various heterogenous sources such as WWW, social media, environmental sensors, huge enterprise data warehouses, bioinformatic labs etc. to name a few. This results in creation of many high-volume datasets in various domains. Processing such large datasets is a tedious task, therefore they need to be categorized into smaller subsets using various supervised or unsupervised classification techniques. Clustering is the process of statistically analyzing and categorizing data objects with similarity, into substantially homogeneous groups, called data clusters. k-Means is the most common, simple and popular clustering technique, due to its ease of implementation, usability and wide range of applications. One of the issues associated with the k-Means algorithm is that it suffers from the scalability problem due to which, its performance degrades as the dataset sizes grow.  In order to address this issue, we have presented an OpenMP based parallelized k-means algorithm which results in better computational cost as compared with its sequential counterpart. Computational performance results of both sequential and OpenMP based k-means algorithms are illustrated and compared

HISTOPATHOLOGY IMAGE CLASSIFICATION USING HYBRID PARALLEL STRUCTURED DEEP-CNN MODELS

The healthcare industry is one of the many out there that could majorly benefit from advancement in the technology it utilizes. Artificial intelligence (AI) technologies are especially integral and specifically deep learning (DL); a highly useful data-driven technology. It is applied in a variety of different methods but it mainly depends on the structure of the available data. However, with varying applications, this technology produces data in different contexts with particular connotations. Reports which are the images of scans play a great role in identifying the existence of the disease in a patient. Further, the automation in processing these images using technology like CNN-based models makes it highly efficient in reducing human errors otherwise resulting in large data. Hence this study presents a hybrid deep learning architecture to classify the histopathology images to identify the presence of cancer in a patient. Further, the proposed models are parallelized using the TensorFlow-GPU framework to accelerate the training of these deep CNN (Convolution Neural Networks) architectures. This study uses the transfer learning technique during training and early stopping criteria are used to avoid overfitting during the training phase. these models use LSTM parallel layer imposed in the model to experiment with four considered architectures such as MobileNet, VGG16, and ResNet with 101 and 152 layers. The experimental results produced by these hybrid models show that the capability of Hybrid ResNet101 and Hybrid ResNet152 architectures are highly suitable with an accuracy of 90% and 92%. Finally, this study concludes that the proposed Hybrid ResNet-152 architecture is highly efficient in classifying the histopathology images. The proposed study has conducted a well-focused and detailed experimental study which will further help researchers to understand the deep CNN architectures to be applied in application development

Skill Formation Strategies for Sustaining ‘The Drive to Maturity’ in Pakistan

Skill formation is a multi-faceted process. Skills are necessarily (by definition) instrumental—i.e. means for the achievement of a metaphysically defined objective. In Modernity,1 this metaphysical presupposed ‘rational’ purpose of existence (both individual and societal) is freedom [Kant (2001)]. In the history of Modernity, the primary source of the growth of freedom has been capital accumulation.2 A nation committed to Modernity (‘Enlightened Moderation’) is necessarily committed to articulating a skill formation strategy which can transform ‘human being’ into ‘human capital’. This transformation requires three distinct types of skills: individual, communitarian and political. This is because capitalism is not just a ‘lifeworld’ in the Habermasian sense but a system [Foucault (1976)]. Capitalist individuality requires a prioritisation of the preference for preference itself (‘choice’) over all preferences. This is necessary for the internalisation of capitalist norms (the commitment to profit/utility maximisation and competition to achieve this end). Capitalist individuality must also posses the skills which allow it to rationally identify and pursue its interest in the market and in the firm. It must also have the selfdiscipline to function as a diligent and co-operative participant in the capitalist work process

Numerical Investigation of Transient Temperature and Residual Stresses in Thin Dissimilar Aluminium Alloy Plates

AbstractThe purpose of the present investigation is to assess transient temperature and residual stresses in Gas Metal Arc Welding (GMAW) of dissimilar aluminium alloys (AA). A moving heat source model based on Goldak's double – ellipsoid heat flux distribution is utilised in finite element simulation of welding process. To solve the three dimensional thermal and mechanical equations, ANSYS Workbench software was used. Element death and birth code was written for modelling the amount of material added during the analysis. Effects of conduction, convection and radiation were considered in transient thermal analysis. Temperature dependent properties as thermal conductivity, heat capacity, yield stress, elastic modulus and thermal expansion were employed in the welding simulations. Based on the results, it was found that lower temperature and higher residual stresses were generated in AA 6061-T6 plate as compared to AA 5052-H32 plate

Governing the Labour Market: The Impossibility of Corporatist Reforms

This paper argues that a return to corporatist governance structures is impossible in Pakistan. Section 1 outlines neo-classical labour market regulation rationalities presented by Hayek, Wieser, and Sen. Section 2 compares and contrasts Fordist and Post-Fordist modes of labour market regulation. And Section 3 seeks to establish the impossibility of institutionalising corporatist governance structures in the labour markets of Pakistan. Neo-classical theory sees relations between labour and the representatives of capital (‘managers’) as relations created spontaneously by individuals in the pursuit of their rational self-interest. The capitalist individual, be he labourer or manager, defines ‘maximisation of utility’ as his ‘rational self interest’, and order within the labour market requires a reconciliation of individual (the labourer’s) and aggregate (the manager’s) utility maximisation (with aggregate utility maximisation being represented by shareholders value). Labour market order is thus impeded if

A Fuzzy Approach for Feature Evaluation and Dimensionality Reduction to Improve the Quality of Web Usage Mining Results

The explosive growth in the information available on the Web has necessitated the need for developing Web personalization systems that understand user preferences to dynamically serve customized content to individual users. Web server access logs contain substantial data about the accesses of users to a Web site. Hence, if properly exploited, the log data can reveal useful information about the navigational behaviour of users in a site. In order to reveal the information about user preferences from, Web Usage Mining is being performed. Web Usage Mining is the application of data mining techniques to web usage log repositories in order to discover the usage patterns that can be used to analyze the user’s navigational behavior. WUM contains three main steps: preprocessing, knowledge extraction and results analysis. During the preprocessing stage, raw web log data is transformed into a set of user profiles. Each user profile captures a set of URLs representing a user session. Clustering can be applied to this sessionized data in order to capture similar interests and trends among users’ navigational patterns. Since the sessionized data may contain thousands of user sessions and each user session may consist of hundreds of URL accesses, dimensionality reduction is achieved by eliminating the low support URLs. Very small sessions are also removed in order to filter out the noise from the data. But direct elimination of low support URLs and small sized sessions may results in loss of a significant amount of information especially when the count of low support URLs and small sessions is large. We propose a fuzzy solution to deal with this problem by assigning weights to URLs and user sessions based on a fuzzy membership function. After assigning the weights we apply a "Fuzzy c-Mean Clustering" algorithm to discover the clusters of user profiles. In this paper, we describe our fuzzy set theoretic approach to perform feature selection (or dimensionality reduction) and session weight assignment. Finally we compare our soft computing based approach of dimensionality reduction with the traditional approach of direct elimination of small sessions and low support count URLs. Our results show that fuzzy feature evaluation and dimensionality  reduction results in better performance and validity indices for the discovered clusters