open research
Recently Published Documents


TOTAL DOCUMENTS

742
(FIVE YEARS 400)

H-INDEX

35
(FIVE YEARS 13)

2022 ◽  
Author(s):  
Paula Delgado-Santos ◽  
Giuseppe Stragapede ◽  
Ruben Tolosana ◽  
Richard Guest ◽  
Farzin Deravi ◽  
...  

The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasising critical aspects such as demographics, health and body features, activity and behaviour recognition, etc. In addition, we review popular metrics in the literature to quantify the degree of privacy, and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions are presented for further advancements in the field.


2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Juliana Elisa Raffaghelli ◽  
Stefania Manca

Purpose Although current research has investigated how open research data (ORD) are published, researchers' behaviour of ORD sharing on academic social networks (ASNs) remains insufficiently explored. The purpose of this study is to investigate the connections between ORDs publication and social activity to uncover data literacy gaps.Design/methodology/approach This work investigates whether the ORDs publication leads to social activity around the ORDs and their linked published articles to uncover data literacy needs. The social activity was characterised as reads and citations, over the basis of a non-invasive approach supporting this preliminary study. The eventual associations between the social activity and the researchers' profile (scientific domain, gender, region, professional position, reputation) and the quality of the ORD published were investigated to complete this picture. A random sample of ORD items extracted from ResearchGate (752 ORDs) was analysed using quantitative techniques, including descriptive statistics, logistic regression and K-means cluster analysis.Findings The results highlight three main phenomena: (1) Globally, there is still an underdeveloped social activity around self-archived ORDs in ResearchGate, in terms of reads and citations, regardless of the published ORDs quality; (2) disentangling the moderating effects over social activity around ORD spots traditional dynamics within the “innovative” practice of engaging with data practices; (3) a somewhat similar situation of ResearchGate as ASN to other data platforms and repositories, in terms of social activity around ORD, was detected.Research limitations/implications Although the data were collected within a narrow period, the random data collection ensures a representative picture of researchers' practices.Practical implications As per the implications, the study sheds light on data literacy requirements to promote social activity around ORD in the context of open science as a desirable frontier of practice.Originality/value Researchers data literacy across digital systems is still little understood. Although there are many policies and technological infrastructure providing support, the researchers do not make an in-depth use of them.Peer reviewThe peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2021-0255.


2022 ◽  
pp. 1249-1274
Author(s):  
Ramgopal Kashyap

A large vault of terabytes of information created every day from present-day data frameworks and digital innovations, for example, the internet of things and distributed computing. Investigation of this enormous information requires a ton of endeavors at different dimensions to separate learning for central leadership. An examination is an ebb-and-flow territory of innovative work. The fundamental goal of this paper is to investigate the potential effect of enormous information challenges, open research issues, and different instruments related to it. Subsequently, this article gives a stage to study big data at various stages. It opens another skyline for analysts to build up the arrangement in light of the difficulties, and open research issues. The article comprehended that each large information stage has its core interest. Some of this is intended for bunch handling while some are great at constant scientific. Each large information stage likewise has explicit usefulness. Unique procedures were utilized for the investigation.


Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 280
Author(s):  
Uzoma Rita Alo ◽  
Friday Onwe Nkwo ◽  
Henry Friday Nweke ◽  
Ifeanyi Isaiah Achi ◽  
Henry Anayo Okemiri

The COVID-19 Pandemic has punched a devastating blow on the majority of the world’s population. Millions of people have been infected while hundreds of thousands have died of the disease throwing many families into mourning and other psychological torments. It has also crippled the economy of many countries of the world leading to job losses, high inflation, and dwindling Gross Domestic Product (GDP). The duo of social distancing and contact tracing are the major technological-based non-pharmaceutical public health intervention strategies adopted for combating the dreaded disease. These technologies have been deployed by different countries around the world to achieve effective and efficient means of maintaining appropriate distance and tracking the transmission pattern of the diseases or identifying those at high risk of infecting others. This paper aims to synthesize the research efforts on contact tracing and social distancing to minimize the spread of COVID-19. The paper critically and comprehensively reviews contact tracing technologies, protocols, and mobile applications (apps) that were recently developed and deployed against the coronavirus disease. Furthermore, the paper discusses social distancing technologies, appropriate methods to maintain distances, regulations, isolation/quarantine, and interaction strategies. In addition, the paper highlights different security/privacy vulnerabilities identified in contact tracing and social distancing technologies and solutions against these vulnerabilities. We also x-rayed the strengths and weaknesses of the various technologies concerning their application in contact tracing and social distancing. Finally, the paper proposed insightful recommendations and open research directions in contact tracing and social distancing that could assist researchers, developers, and governments in implementing new technological methods to combat the menace of COVID-19.


Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 196
Author(s):  
Nancy A Angel ◽  
Dakshanamoorthy Ravindran ◽  
P M Durai Raj Vincent ◽  
Kathiravan Srinivasan ◽  
Yuh-Chung Hu

Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects.


Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 60-67
Author(s):  
Dr.M. Krishnamurthy ◽  
Dr. Bhalachandra S. Deshpande ◽  
Dr.C. Sajana

Open Access is a synergised global movement using Internet to provide equal access to knowledge that once hid behind the subscription paywalls. Many new models for scholarly communication have emerged in recent past. One among them is institutional or digital repositories which archive the scholarly content of an organization. While the concept of Open Access opened new arena for institutional or digital repositories in the form of Open repositories. Likewise, the Open repositories for Research Data Management (RDM) are initiative to organize, store, cite, preserve, and share the collected data derived from the research. There are many multidisciplinary and subject specific open repositories for RDM offering exquisite features for perpetual management of research data. The objective of the present study is to evaluate features of popular Open Data Repositories-Zenodo, FigShare, Harvard Dataverse and Mendeley Data. The evaluation provided insights about the key features of the selected Open Data Repositories and which enable us to select the best among them. Zenodo provides maximum data upload limit. While the major features required by a researcher like DOI, File Types, citation support, licenses, search (metadata harvesting) are provided by all three repositories.


2021 ◽  
pp. 1-11
Author(s):  
Oscar Herrera ◽  
Belém Priego

Traditionally, a few activation functions have been considered in neural networks, including bounded functions such as threshold, sigmoidal and hyperbolic-tangent, as well as unbounded ReLU, GELU, and Soft-plus, among other functions for deep learning, but the search for new activation functions still being an open research area. In this paper, wavelets are reconsidered as activation functions in neural networks and the performance of Gaussian family wavelets (first, second and third derivatives) are studied together with other functions available in Keras-Tensorflow. Experimental results show how the combination of these activation functions can improve the performance and supports the idea of extending the list of activation functions to wavelets which can be available in high performance platforms.


2021 ◽  
Vol 11 (24) ◽  
pp. 11957
Author(s):  
Andrea Agiollo ◽  
Andrea Omicini

The application of Artificial Intelligence to the industrial world and its appliances has recently grown in popularity. Indeed, AI techniques are now becoming the de-facto technology for the resolution of complex tasks concerning computer vision, natural language processing and many other areas. In the last years, most of the the research community efforts have focused on increasing the performance of most common AI techniques—e.g., Neural Networks, etc.—at the expenses of their complexity. Indeed, many works in the AI field identify and propose hyper-efficient techniques, targeting high-end devices. However, the application of such AI techniques to devices and appliances which are characterised by limited computational capabilities, remains an open research issue. In the industrial world, this problem heavily targets low-end appliances, which are developed focusing on saving costs and relying on—computationally—constrained components. While some efforts have been made in this area through the proposal of AI-simplification and AI-compression techniques, it is still relevant to study which available AI techniques can be used in modern constrained devices. Therefore, in this paper we propose a load classification task as a case study to analyse which state-of-the-art NN solutions can be embedded successfully into constrained industrial devices. The presented case study is tested on a simple microcontroller, characterised by very poor computational performances—i.e., FLOPS –, to mirror faithfully the design process of low-end appliances. A handful of NN models are tested, showing positive outcomes and possible limitations, and highlighting the complexity of AI embedding.


Sign in / Sign up

Export Citation Format

Share Document