Effect of chromium on the mechanical properties of powder-processed Fe–0.45 wt.% P alloys

The present investigation shows the role of chromium in Fe–P binary and Fe–P–Cr ternary alloys. The compositions are characterized in terms of microstructure, porosity content, hardness and tensile properties. The alloys were made using a hot powder forging technique. In this process mild steel encapsulated powders were hot forged into slabs. Then the slabs were hot rolled and annealed to relieve the residual stresses. Densifications as high as 98.9% of theoretical density have been realized. Microstructures of these alloys consist of single-phase ferrite only. Both Fe–0.45P and Fe–0.45P–3Cr alloys showed very high strength. As forged and hot rolled Fe–0.45P alloy showed low elongation. It was observed that, the addition of Cr to Fe–P based alloys caused an increase in strength associated with the reduction in ductility. Alloys developed in the present investigation were capable of hot working to very thin gage of sheets and wires.     

Domain Adaptation for POS Tagging with Contrastive Monotonic Chunk-wise Attention

Neural Processing Letters - Part of Speech (POS) tagging is a sequential labelling task and one of the core applications of Natural Language Processing. It has been a challenging problem for the...     

FRAPP: a framework for high-accuracy privacy-preserving mining

To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of individual data records have been proposed recently. In this paper, we present FRAPP, a generalized matrix-theoretic framework of random perturbation, which facilitates a systematic approach to the design of perturbation mechanisms for privacy-preserving mining. Specifically, FRAPP is used to demonstrate that (a) the prior techniques differ only in their choices for the perturbation matrix elements, and (b) a symmetric positive-definite perturbation matrix with minimal condition number can be identified, substantially enhancing the accuracy even under strict privacy requirements. We also propose a novel perturbation mechanism wherein the matrix elements are themselves characterized as random variables, and demonstrate that this feature provides significant improvements in privacy at only a marginal reduction in accuracy. The quantitative utility of FRAPP, which is a general-purpose random-perturbation-based privacy-preserving mining technique, is evaluated specifically with regard to association and classification rule mining on a variety of real datasets. Our experimental results indicate that, for a given privacy requirement, either substantially lower modeling errors are incurred as compared to the prior techniques, or the errors are comparable to those of direct mining on the true database. A partial and preliminary version of this paper appeared in the Proc. of the 21st IEEE Intl. Conf. on Data Engineering (ICDE), Tokyo, Japan, 2005, pgs. 193–204.     

A Formal Methodology for Detecting Managerial Vulnerabilities and Threats in an Enterprise Information System

From information security point of view, an enterprise is considered as a collection of assets and their interrelationships. These interrelationships may be built into the enterprise information infrastructure, as in the case of connection of hardware elements in network architecture, or in the installation of software or in the information assets. As a result, access to one element may enable access to another if they are connected. An enterprise may specify conditions on the access of certain assets in certain mode (read, write etc.) as policies. The interconnection of assets, along with specified policies, may lead to managerial vulnerabilities in the enterprise information system. These vulnerabilities, if exploited by threats, may cause disruption to the normal functioning of information systems. This paper presents a formal methodology for detection of managerial vulnerabilities of, and threats to, enterprise information systems in linear time.     

Visual perception and mixed-initiative interaction for assisted visualization design

This paper describes the integration of perceptual guidelines from human vision with an AI-based mixed-initiative search strategy. The result is a visualization assistant called ViA, a system that collaborates with its users to identify perceptually salient visualizations for large, multidimensional datasets. ViA applies knowledge of low-level human vision to: (1) evaluate the effectiveness of a particular visualization for a given dataset and analysis tasks; and (2) rapidly direct its search towards new visualizations that are most likely to offer improvements over those seen to date. Context, domain expertise, and a high-level understanding of a dataset are critical to identifying effective visualizations. We apply a mixed-initiative strategy that allows ViA and its users to share their different strengths and continually improve ViA's understanding of a user's preferences. We visualize historical weather conditions to compare ViA's search strategy to exhaustive analysis, simulated annealing, and reactive tabu search, and to measure the improvement provided by mixed-initiative interaction. We also visualize intelligent agents competing in a simulated online auction to evaluate ViA's perceptual guidelines. Results from each study are positive, suggesting that ViA can construct high-quality visualizations for a range of real-world datasets.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

Survey of denoising,segmentation and classification of magnetic resonance imaging for prostate cancer

Multimedia Tools and Applications - Prostate cancer (PCa) has become the second most dreadful cancer in men after lung cancer. Traditional approaches used for treatment of PCa were manual, time...     

Aim4GDI: Facilitating the Synthesis of GDI Resources through Mapping and Superimpositions of Metadata Summaries

In the world of geospatial data infrastructures, geoportals are developed to facilitate access and use of geospatial resources, including data. The design and implementation of effective and efficient geoportals are becoming crucial. Providing possibilities for users to organize and integrate available resources can arguably enhance the process of data discovery and spatial analysis required, and hence, is the focus of this paper. We argue that the synthesis of summaries of distributed datasets, map and feature services through the geoportal can present a coherent view to support data discovery. For this purpose, the atlas metaphor is used as an indexing server and integration interface of summaries. Thematic maps and a storyteller view are accessible through the atlas metaphor. They can be used to provide a supporting context for data discovery and access purposes.

Seating discomfort for tractor operators – a critical review

A large number of studies have been conducted and researchers have suggested several criteria for evaluating discomfort and the suitability of a tractor seat in a given working condition. The studies have led to various parameters, viz. the body pressure distributed under and supporting both the buttocks, thighs and the back of an operator, control of posture in static or dynamic condition, ride vibration, exposure time on task and other factors. But in the absence of a more definitive and the most logical criteria particularly from biomechanical viewpoint, the researchers will continue to design conditions and procedures to understand the seat dynamics and evaluate the seating discomfort. Therefore, this paper reviews the research information available in this regard and attempts to set the most appropriate procedure for assessment of seating discomfort during tractor driving.

Relevance to industry

This paper attempts to project the most appropriate method of assessment and selection of tractor seats from engineering and biomechanical view point which could be adopted by the tractor seat manufacturers. It is designed to enhance the feeling of comfort, safety, convenience, and results in higher work output from the operator.