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Article
Publication date: 26 March 2021

Azra Nazir, Roohie Naaz Mir and Shaima Qureshi

Natural languages have a fundamental quality of suppleness that makes it possible to present a single idea in plenty of different ways. This feature is often exploited in the…

268

Abstract

Purpose

Natural languages have a fundamental quality of suppleness that makes it possible to present a single idea in plenty of different ways. This feature is often exploited in the academic world, leading to the theft of work referred to as plagiarism. Many approaches have been put forward to detect such cases based on various text features and grammatical structures of languages. However, there is a huge scope of improvement for detecting intelligent plagiarism.

Design/methodology/approach

To realize this, the paper introduces a hybrid model to detect intelligent plagiarism by breaking the entire process into three stages: (1) clustering, (2) vector formulation in each cluster based on semantic roles, normalization and similarity index calculation and (3) Summary generation using encoder-decoder. An effective weighing scheme has been introduced to select terms used to build vectors based on K-means, which is calculated on the synonym set for the said term. If the value calculated in the last stage lies above a predefined threshold, only then the next semantic argument is analyzed. When the similarity score for two documents is beyond the threshold, a short summary for plagiarized documents is created.

Findings

Experimental results show that this method is able to detect connotation and concealment used in idea plagiarism besides detecting literal plagiarism.

Originality/value

The proposed model can help academics stay updated by providing summaries of relevant articles. It would eliminate the practice of plagiarism infesting the academic community at an unprecedented pace. The model will also accelerate the process of reviewing academic documents, aiding in the speedy publishing of research articles.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 14 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 3 July 2020

Azra Nazir, Roohie Naaz Mir and Shaima Qureshi

The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud…

286

Abstract

Purpose

The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant. But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015. Thus, it is high time to explore a different approach to tackle this issue, keeping in view the characteristics and needs of the two fields. Processing at the Edge can boost applications with real-time deadlines while complementing security.

Design/methodology/approach

This review paper contributes towards three cardinal directions of research in the field of DL for IoT. The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices, forming the realm of the things for IoT. The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques. An appropriate combination of these techniques, including regularization, quantization, and pruning, can aid in building an effective compression pipeline for establishing DL models for IoT use-cases. The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.

Findings

DL models are growing deeper with every passing year. Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm. It is realized that a vertically partitioned compressed deep model can handle the trade-off between size, accuracy, communication overhead, bandwidth utilization, and latency but at the expense of an additionally considerable memory footprint. To reduce the memory budget, we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks. However, the critical point between accuracy and size for such models needs further investigation.

Originality/value

To the best of our knowledge, no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum. Besides covering techniques and frameworks that have tried to bring inference to the Edge, the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT. The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 24 March 2021

Azra Rafique, Kanwal Ameen and Alia Arshad

The purpose of this study is to examine the evidence-based use patterns of Higher Education Commission (HEC) subscribed e-books databases by the academic community at institutions…

845

Abstract

Purpose

The purpose of this study is to examine the evidence-based use patterns of Higher Education Commission (HEC) subscribed e-books databases by the academic community at institutions of higher education in Pakistan. The study also investigates the differences in usage based on points of access, scholarly disciplines and gender of users.

Design/methodology/approach

A transaction log analysis (TLA) method was used to explore the use patterns of HEC e-books databases at the University of the Punjab, utilizing the SAWMILL analytical software and MS Excel.

Findings

The results of the study showed that the use of e-books was at a growing stage at the university. Male boarding students of the university were more active users of e-books as compared to their female counterparts. The Central Library of the University of the Punjab, the Department of Zoology and the Institute of Communication Studies were the most frequently used access points for the e-book users, and the Faculties of Sciences, Life Sciences, Economics and Management Sciences had the most active e-book readers. Furthermore, it was found that the HEC e-books databases were only being used during the four months of summer vacations at the university.

Research limitations/implications

The study provides only descriptive use frequencies rather than a deep log analysis of e-books usage.

Practical implications

This research provides important practical implications for examining the evidence-based use patterns of e-books databases' users at the higher educational level. The research suggests that HEC should maintain subscriptions of the most required e-books databases and that the information professionals should conduct orientations and information literacy programs to enhance the utilization of these subscribed databases among female boarder students and those faculties where they were being used less frequently.

Originality/value

The study is the second part of the first phase of a Ph.D. project. This is the first large scale study conducted in a developing country which reports the close to actual, approximate use patterns of e-books based on raw transaction logs of local cache servers at the higher academic level.

Details

Library Hi Tech, vol. 41 no. 2
Type: Research Article
ISSN: 0737-8831

Keywords

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Article
Publication date: 22 November 2024

Amirhossein Termebaf Shirazi, Zahra Zamani Miandashti and Seyed Alireza Momeni

Additive manufacturing offers the ability to produce complex, flexible structures from materials like thermoplastic polyurethane (TPU) for energy-absorption applications. However…

42

Abstract

Purpose

Additive manufacturing offers the ability to produce complex, flexible structures from materials like thermoplastic polyurethane (TPU) for energy-absorption applications. However, selecting optimal structural parameters to achieve desired mechanical responses remains a challenge. This study aims to investigate the influence of key structural characteristics on the energy absorption and dissipation behavior and the deformation process of 3D-printed flexible TPU line-oriented structures.

Design/methodology/approach

Samples with varying line orientations and infill densities were fabricated using material extrusion and subjected to quasi-static compression tests. The design of experiments methodology explored the significance of design variables and their interaction effects on energy absorption and dissipation.

Findings

The results revealed a statistically significant interaction between infill density and orientation, highlighting their combined influence; however, the effect was less pronounced compared to infill density alone. For low-density structures, changing the orientation from 0°/90° to 45°/−45° and increasing infill density enhanced energy absorption and dissipation, while high-density structures exhibited unique energy absorption behavior influenced by deformation patterns and heterogeneity levels. This study facilitates the prediction of mechanical responses and selection of suitable TPU line-oriented printed parts for energy absorbing applications.

Originality/value

To the best of the authors’ knowledge, the present work have investigated for the first time the energy-related responses of flexible line-oriented TPU structures highlighting the distinction between the low and high density structures.

Graphical abstarct

Details

Rapid Prototyping Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1355-2546

Keywords

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