Kerala Libraries Network (KELNET): a Proposal

Visualizes the conceptual framework and propose the development of a Kerala Library Network (KELNET) by exploring and exploiting the available and the existing social infrastructures, social softwares, open standards and technologies

Content creation and E-learning in Indian Languages : a model

In the era of E-publishing and E-learning, numerous universities and cultural organizations around the world have launched initiatives to develop tools for multilingual learning and web publishing and have given preference to local content. India has different languages and different culture. Most of the knowledge and information related to people, culture, science and philosophy of India is available in Indian languages, which will be useful for learning and developing knowledge base. In India E-learning systems and online courses are already started, but as a multi lingual country, which gives importance to education through regional languages, there should be facilities for multi lingual E-learning. This paper covers the issues of Indian language knowledge base/content base, its requirement, and its implication in e learning. An Integrated multi lingual E-learning system for India is proposed in this paper, where importance given to multi lingual course content creation

Scaling Reinforcement Learning Paradigms for Motor Control

Reinforcement learning offers a general framework to explain reward related learning in artificial and biological motor control. However, current reinforcement learning methods rarely scale to high dimensional movement systems and mainly operate in discrete, low dimensional domains like game-playing, artificial toy problems, etc. This drawback makes them unsuitable for application to human or bio-mimetic motor control. In this poster, we look at promising approaches that can potentially scale and suggest a novel formulation of the actor-critic algorithm which takes steps towards alleviating the current shortcomings. We argue that methods based on greedy policies are not likely to scale into high-dimensional domains as they are problematic when used with function approximation – a must when dealing with continuous domains. We adopt the path of direct policy gradient based policy improvements since they avoid the problems of unstabilizing dynamics encountered in traditional value iteration based updates. While regular policy gradient methods have demonstrated promising results in the domain of humanoid notor control, we demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. Based on this, it is proved that Kakade’s ‘average natural policy gradient’ is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges with probability one to the nearest local minimum in Riemannian space of the cost function. The algorithm outperforms nonnatural policy gradients by far in a cart-pole balancing evaluation, and offers a promising route for the development of reinforcement learning for truly high-dimensionally continuous state-action systems. Keywords: Reinforcement learning, neurodynamic programming, actorcritic methods, policy gradient methods, natural policy gradien

Secondary arm coarsening and microsegregation in superalloy PWA-1480 single crystals: Effect of low gravity

Single crystal specimens of nickel base superalloy PWA-1480 were directionally solidified on ground and during low gravity (20 sec) and high gravity (90 sec) parabolic maneuver of KC-135 aircraft. Thermal profiles were measured during solidification by two in-situ thermocouples positioned along the sample length. The samples were quenched during either high or low gravity cycles so as to freeze the structures of the mushy zone developing under different gravity levels. Microsegregation was measured by examining the solutal profiles on several transverse cross-sections across primary dendrites along their length in the quenched mushy zone. Effect of gravity level on secondary arm coarsening kinetics and microsegregation have been investigated. The results indicate that there is no appreciable difference in the microsegregation and coarsening kinetics behavior in the specimens grown under high or low gravity. This suggests that short duration changes in gravity/levels (0.02 to 1.7 g) do not influence convection in the interdendritic region. Examination of the role of natural convection, in the melt near the primary dendrite tips, on secondary arm spacings requires low gravity periods longer than presently available on KC-135. Secondary arm coarsening kinetics show a reasonable fit with the predictions from a simple analytical model proposed by Kirkwood for a binary alloy

The Role of the document delivery service at an evolving research library in Saudi Arabia

Purpose – This purpose of this study was to assess the effectiveness of the document delivery service according to user perception, and a usage analysis was done to inform collection building and refining. This is especially important in a high-calibre research community, where the usage, interests and research groups and disciplines are still evolving. Design/methodology/approach – To collect the responses of document delivery service users, an online questionnaire was used with 12 multiple-choice questions and two open-ended questions. The questionnaires were sent only to the users of this service, and the responses were collected anonymously. Two surveys were conducted, in 2010 and 2013, with the same questions. The responses are displayed graphically prepared to compare the results. There were 71 responses in 2010 and 95 in 2013. Findings – In both surveys, the majority of users rated the service, staff behaviour, speed, quality, etc. with a high satisfaction level. Document delivery order statistics are a major decision-making tool, in addition to usage analysis, for developing a better, economical and highly utilized collection in brand new libraries. Originality/value – This is the first published study of user perception of document delivery in Saudi Arabi

Deferring the learning for better generalization in radial basis neural networks

Proceeding of: International Conference Artificial Neural Networks — ICANN 2001. Vienna, Austria, August 21–25, 2001The level of generalization of neural networks is heavily dependent on the quality of the training data. That is, some of the training patterns can be redundant or irrelevant. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. Nevertheless, generalization is carried out independently of the novel patterns to be approximated. In this paper, we present a learning method that automatically selects the most appropriate training patterns to the new sample to be predicted. The proposed method has been applied to Radial Basis Neural Networks, whose generalization capability is usually very poor. The learning strategy slows down the response of the network in the generalisation phase. However, this does not introduces a significance limitation in the application of the method because of the fast training of Radial Basis Neural Networks

Optimization of Drilling Process Parameters on Die Steel (H13) using Carbide Coated Drill by Design of Experiment Concept

This experimental work presents the optimization of process parameter of surface roughness with using coated carbide drill on H13 steel. Taguchi design of experiments was implemented for executing the process parameter of Drilling process on H13 steel plates. The drilling parameters including 2 Factors such as spindle speed (rpm) and feed rate (mm/min) are optimized using response performance characteristic of surface roughness of H13 die steel plates.H13 steel play an important role in many applications such as Shaft, axle, gears and fasteners due to their strength to weight ratio. The process parameters of spindle speed and feed rate are influenced by machining accuracy during drilling process. The main objectives of experimental works have been identified by lower roughness during drilling process of H13 steel plates. Orthogonal array (L16) of Taguchi Design of experiments and Analysis of Variance (ANOVA) are utilized to analyze the effect of drilling parameters on Quality of drilled holes. The result of experiments indicate is a dominating parameter of surface roughness of H 13 steel plates in Drilling process

Electronic Theses and Dissertations and Academia: A Preliminary Study From India

This paper outlines the current state of doctoral theses collections in India, their usage, problems with access, and the academic and research community’s attitude towards digital archiving and electronic publishing in Indian universities

An efficient approach based on trust and reputation for secured selection of grid resources

Security is a principal concern in offering an infrastructure for the formation of general-purpose computational grids. A number of grid implementations have been devised to deal with the security concerns by authenticating the users, hosts and their interactions in an appropriate fashion. Resource management systems that are sophisticated and secured are inevitable for the efficient and beneficial deployment of grid computing services. The chief factors that can be problematic in the secured selection of grid resources are the wide range of selection and the high degree of strangeness. Moreover, the lack of a higher degree of confidence relationship is likely to prevent efficient resource allocation and utilisation. In this paper, we present an efficient approach for the secured selection of grid resources, so as to achieve secure execution of the jobs. This approach utilises trust and reputation for securely selecting the grid resources. To start with, the self-protection capability and reputation weightage of all the entities are computed, and based on those values, the trust factor (TF) of all the entities are determined. The reputation weightage of an entity is the measure of both the user’s feedback and other entities’ feedback. Those entities with higher TF values are selected for the secured execution of jobs. To make the proposed approach more comprehensive, a novel method is employed for evaluating the user’s feedback on the basis of the existing feedbacks available regarding the entities. This approach is proved to be scalable for an increased number of user jobs and grid entities. The experimentation portrays that this approach offers desirable efficiency in the secured selection of grid resources