In the present scenario, smart devices are using flexible wearable wireless technologies to enable interaction among people and their surrounding equipments. Due to the commercialization of these flexible electronic devices and systems, these systems require the integration of flexible antennas. The compactness and flexibility of the antenna are the major objectives of the current research. This paper highlights the comparative study of a small-sized swastika-shaped slotted patch ultra-wide-band (UWB) antenna. The designed microstrip patch antenna is simulated using Rogers RT 5880 as a substrate mounted on the partial ground structure. A rectangular substrate with a size of 24.56 × 22.48 mm × 1.575 mm is placed between the partial ground and patch. The dimensions of the radiating patch are 13 mm × 10.5 mm × 0.035 mm. The resonant frequencies of the designed antenna are 4.43 GHz and 8.84 GHz and have a large fractional bandwidth of 110% in the range of 3.1 to 10.7 GHz. The maximum observed gain and directivity of the proposed antenna are 5.9 dB and 6.8 dBi at 4.43 GHz. The simulation of the presented antenna design is performed in CST Microwave Studio 2018. The proposed antenna performance metrics are comparatively analyzed using different substrate materials like Rogers RT 5880, Jeans, and rubber substrates. The developed dual-band antenna can be useful for WBAN communication applications in the UWB band.

Design and comparative analysis of compact flexible UWB antenna using different substrate materials for WBAN applications

Both information systems IS outsourcing and knowledge management are well-established business phenomena. The integration of shared knowledge in an IS outsourcing arrangement, represents the blending of organisational knowledge with external knowledge sources. In an attempt to provide tactical mechanisms for creating and managing shared knowledge in organisations embarking on IS outsourcing arrangements, this article focuses on the design and application of a knowledge framework for IS outsourcing, with the purpose of guiding organisations in their knowledge exchange planning through concrete mechanisms, practical steps and validation. Key considerations for IS outsourcing is mapped to critical success factors, each associated with a set of knowledge requirements and knowledge flows to support the successful achievement of a specific critical success factor. An associated assessment tool was designed to identify knowledge exchange mechanisms and potential issues and gaps in current or future IS outsource arrangements.

Framework for Managing Shared Knowledge in an Information Systems Outsourcing Context

The equivalent term for data visualization is ‘visual communication,’ representing data visually. The theme of data visualization is to convey information clearly, efficiently to users by using graphics, and it helps to have an inner view of data. Data visualization mesmerizes users by changing tedious data into a visually colorful tale. For this, we need data visualization tools that are popular in understanding data easily and visually. When analyzing data, data visualization is one of the steps that aroused to present the data to the users. Big data has come up considerably in recent times and has become an integral part of modern database research. In fact, there is a need for analyzing large amount of data which is temporal. Other characteristics of these datasets are that these are dynamic, noisy and heterogeneous in nature. As a result, to transform these different types of datasets into an accessible and understandable format, we need big data visualization tools. Traditionally, in the beginning, visualization in big data area was done by traditional systems but this approach was not capable of handling variety in datasets. Also, these traditional systems are restricted to deal with the datasets of small size. So, here comes the need for modern systems to analyze variety of datasets in big data, and this modern system gives awareness of modern data visualization tools that supports a variety of big datasets. Henceforth, the comparative analysis on visualization tools and challenges allows user to go with the best visualization tool for analyzing the big data based on the nature of the dataset. Since their inception, several tools have been proposed to model and analyze the process of data visualization in big datasets. In this chapter, we propose to analyze these tools in the form of their strengths and weaknesses. Also, we have planned to discuss in detail their applications and suitability in dealing with different situations (Caldarola, Picariello, & Rinaldi in Experiences in wordnet visualization with labeled graph database. Berlin: Springer, pp. 80–99, [1], Caldarola, Picariello, Rinaldi, & Sacco in Exploration and visualization of big graphs—The DBpedia case study. KDIR (IC3K 2016), pp. 257–264, [2], Caldarola, & Rinaldi in Improving the visualization of wordnet large lexical database through semantic tag clouds. IEEE, pp. 34–41 [3]).

Comparative Analysis of Tools for Big Data Visualization and Challenges

Organisations are increasingly outsourcing information systems (IS) to external service providers. These IS outsourcing decisions are driven by multiple organisational factors such as outsource vendor expertise and knowledge, process performance improvement due to better IS, and enabling the organisation’s ability to focus on its core capabilities. In order to harnass such vendor knowledge to achieve business outcomes, the importance of a shared knowledge asset base, between the client organisation and outsource vendor, is emphasised. However, outcomes from IS outsourcing remain poor despite consideration of experience and research. Therefore, the aim of this study is to design and propose a knowledge asset management implementation framework that may be applied in IS outsourcing projects. The proposed framework was evaluated by an experienced programme director and its applicability was tested against a large scale IS outsourcing project. The purpose of such a framework is to enable organisations to manage and institutionalise knowledge assets that are created during the IS outsourcing project and to ensure that the organisation may gain the benefit from such knowledge assets as an outcome of the IS outsourcing arrangement.

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A knowledge asset management implementation framework for information systems outsourcing projects

Data mining techniques are used to generate information from enormous amount of raw data collected from different sources so that prediction of future events can be made. Rough set theory, which is used to perform data mining for knowledge acquisition has imitations and hence is not efficient in handling heterogeneous real datasets. In this paper, we use a neighbourhood based rough set model and propose a method to determine reduced neighbourhood subsets derived from samples of the universal set. We compare the accuracy and coverage of the computations obtained by using parallel rough set-based methods using the conventional MapReduce technique. The results provide strong evidence of reduced reasoning time in both the cases. Although the subset formation method defines a range of values to which the rules give a better result of the computational analysis, the covering method reduces the number of rules at some cost of the values computed.

Neighbourhood rough set model for knowledge acquisition using MapReduce

Cloud computing represents a paradigm shift - a transition from computing-as-a-product to computing-as-aservice. It can be applied to a range of areas, including ecommerce, health, education, communities, etc which are emerging as the important sectors in today's market. Day-byday more knowledge is added to the internet and is shared amongst the users in addition to the official use over the cloud. As a result the energy consumption by the networks is increasing and it needs to be managed. This usage can be brought into account for measuring and hence conserving the energy. The consumption is all together considered for the processing, storage and transport of the knowledge granules over the cloud. Since the data accessed in the cloud is “ondemand”, the prediction techniques like those using rough sets can be used to minimize the transfer of data over the cloud networks. The data over the cloud can be procured with the help of rough set based methods efficiently which can help in conserving the energy. Recently, the basic rough set theory has been extended to the notion of neighbourhood based rough sets where information systems with heterogeneous features are prevalent. In this paper, we propose a neighbourhood based rough set approach for knowledge acquisition using MapReduce from Big Data.

Neighbourhood systems based knowledge acquisition using MapReduce from Big Data over cloud computing

Computing and Network Sustainability

Knowledge assets (KAs) are the main focus for knowledge management as these are considered most valuable in this knowledge-driven economy. However, the tasks of understanding, valuation, and management of knowledge assets have been daunting in contrast to their physical counterparts. There have been no generally agreed frameworks for carrying out the above tasks. Organizations strive to minimize the cost of creating and managing knowledge assets and to maximize value addition from knowledge assets. Also, they provide knowledge services to its internal and external customers using knowledge assets and processes with the help of knowledge workers. Hence, efficient management of knowledge assets is often the central theme in most organizational knowledge management (KM) programs. In this paper, we discuss knowledge asset management from multiple perspectives. We propose a set of generic steps involved in knowledge asset management (KAM) and also propose an architectural design for managing knowledge assets in organizational settings.

An Architectural Design for Knowledge Asset Management System

Weak supervision (WS) is a rich set of techniques that produce pseudolabels by aggregating easily obtained but potentially noisy label estimates from a variety of sources. WS is theoretically well understood for binary classification, where simple approaches enable consistent estimation of pseudolabel noise rates. Using this result, it has been shown that downstream models trained on the pseudolabels have generalization guarantees nearly identical to those trained on clean labels. While this is exciting, users often wish to use WS for structured prediction, where the output space consists of more than a binary or multi-class label set: e.g. rankings, graphs, manifolds, and more. Do the favorable theoretical properties of WS for binary classification lift to this setting? We answer this question in the affirmative for a wide range of scenarios. For labels taking values in a finite metric space, we introduce techniques new to weak supervision based on pseudo-Euclidean embeddings and tensor decompositions, providing a nearly-consistent noise rate estimator. For labels in constant-curvature Riemannian manifolds, we introduce new invariants that also yield consistent noise rate estimation. In both cases, when using the resulting pseudolabels in concert with a flexible downstream model, we obtain generalization guarantees nearly identical to those for models trained on clean data. Several of our results, which can be viewed as robustness guarantees in structured prediction with noisy labels, may be of independent interest. Empirical evaluation validates our claims and shows the merits of the proposed method.

Lifting Weak Supervision To Structured Prediction

The scope and significance of learning is increasingly being seen beyond its traditional context in educational institutions such as universities. Now learning is an integral part of organizational development process for all sectors including business, industry and government. Project-based learning, on-job training, online education/training and digital learning have become the norms to either replace or augment traditional class-room based learning. Apart from taking corporate learning initiatives, enterprise-wide knowledge management programs are being designed and deployed to remain competitive.

H.R. Vishwakarma

Papers

Neighbourhood systems based knowledge acquisition using MapReduce from Big Data over cloud computing

Computing and Network Sustainability

An Architectural Design for Knowledge Asset Management System

Lifting Weak Supervision To Structured Prediction

A framework for enhancing quality of learning and knowledge management programmes using analytics