scholarly journals Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Data ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 87 ◽  
Author(s):  
Viktoriia Shubina ◽  
Sylvia Holcer ◽  
Michael Gould ◽  
Elena Simona Lohan
Keyword(s):  
Mobile Devices ◽  
Large Scale ◽  
Data Science ◽  
Research Field ◽  
Mobile Internet ◽  
Contact Tracing ◽  
Future Research ◽  
Location Tracking ◽  
Proximity Detection ◽  
User Location

Some of the recent developments in data science for worldwide disease control have involved research of large-scale feasibility and usefulness of digital contact tracing, user location tracking, and proximity detection on users’ mobile devices or wearables. A centralized solution relying on collecting and storing user traces and location information on a central server can provide more accurate and timely actions than a decentralized solution in combating viral outbreaks, such as COVID-19. However, centralized solutions are more prone to privacy breaches and privacy attacks by malevolent third parties than decentralized solutions, storing the information in a distributed manner among wireless networks. Thus, it is of timely relevance to identify and summarize the existing privacy-preserving solutions, focusing on decentralized methods, and analyzing them in the context of mobile device-based localization and tracking, contact tracing, and proximity detection. Wearables and other mobile Internet of Things devices are of particular interest in our study, as not only privacy, but also energy-efficiency, targets are becoming more and more critical to the end-users. This paper provides a comprehensive survey of user location-tracking, proximity-detection, and digital contact-tracing solutions in the literature from the past two decades, analyses their advantages and drawbacks concerning centralized and decentralized solutions, and presents the authors’ thoughts on future research directions in this timely research field.

scholarly journals Understanding the phenomenology of reading through modelling

Semantic Web ◽  
2020 ◽  
pp. 1-27
Author(s):  
Alessio Antonini ◽  
Mari Carmen Suárez-Figueroa ◽  
Alessandro Adamou ◽  
Francesca Benatti ◽  
François Vignale ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Conceptual Model ◽  
Large Scale ◽  
Research Field ◽  
Future Research ◽  
Ongoing Research ◽  
Reading Experience ◽  
Macro Scale ◽  
Support Of Research ◽  
Data Investigation

Large scale cultural heritage datasets and computational methods for the Humanities research framework are the two pillars of Digital Humanities (DH), a research field aiming to expand Humanities studies beyond specific sources and periods to address macro-scale research questions on broad human phenomena. In this regard, the development of machine-readable semantically enriched data models based on a cross-disciplinary “language” of phenomena is critical for achieving the interoperability of research data. This paper reports on, documents, and discusses the development of a model for the study of reading experiences as part of the EU JPI-CH project Reading Europe Advanced Data Investigation Tool (READ-IT). Through the discussion of the READ-IT ontology of reading experience, this contribution will highlight and address three challenges emerging from the development of a conceptual model for the support of research on cultural heritage. Firstly, this contribution addresses modelling for multi-disciplinary research. Secondly, this work describes the development of an ontology of reading experience, under the light of the experience of previous projects, and of ongoing and future research developments. Lastly, this contribution addresses the validation of a conceptual model in the context of ongoing research, the lack of a consolidated set of theories and of a consensus of domain experts.

scholarly journals Potential reduction in transmission of COVID-19 by digital contact tracing systems

2020 ◽  
Author(s):  
Michael J Plank ◽  
Alex James ◽  
Audrey Lustig ◽  
Nicholas Steyn ◽  
Rachelle N Binny ◽  
...  
Keyword(s):  
Uptake Rate ◽  
Process Model ◽  
Reproduction Number ◽  
Contact Tracing ◽  
Location Tracking ◽  
Public Health Agencies ◽  
Proximity Detection ◽  
Health Agencies ◽  
Alternative Approaches ◽  
Age Structured

Digital tools are being developed to support contact tracing as part of the global effort to control the spread of COVID-19. These include smartphone apps, Bluetooth-based proximity detection, location tracking, and automatic exposure notification features. Evidence on the effectiveness of alternative approaches to digital contact tracing is so far limited. We use an age-structured branching process model of the transmission of COVID-19 in different settings to estimate the potential of manual contact tracing and digital tracing systems to help control the epidemic. We investigate the effect of the uptake rate and proportion of contacts recorded by the digital system on key model outputs: the effective reproduction number, the mean outbreak size after 30 days, and the probability of elimination. We show that effective manual contact tracing can reduce the effective reproduction number from 2.4 to around 1.5. The addition of a digital tracing system with a high uptake rate over 75% could further reduce the effective reproduction number to around 1.1. Fully automated digital tracing without manual contact tracing is predicted to be much less effective. We conclude that, for digital tracing systems to make a significant contribution to the control of COVID-19, they need be designed in close conjunction with public health agencies to support and complement manual contact tracing by trained professionals.

scholarly journals Addressing confirmation bias in middle school data science education

2022 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Sarai Hedges ◽  
Kim Given
Keyword(s):  
Middle School ◽  
Science Education ◽  
Middle School Students ◽  
Large Scale ◽  
Data Science ◽  
Social Issues ◽  
Confirmation Bias ◽  
Future Research ◽  
School Students ◽  
Pull Out

<p style='text-indent:20px;'>More research is needed involving middle school students' engagement in the statistical problem-solving process, particularly the beginning process steps: formulate a question and make a plan to collect data/consider the data. Further, the increased availability of large-scale electronically accessible data sets is an untapped area of study. This interpretive study examined middle school students' understanding of statistical concepts involved in making a plan to collect data to answer a statistical question within a social issue context using data available on the internet. Student artifacts, researcher notes, and audio and video recordings from nine groups of 20 seventh-grade students in two gifted education pull-out classes at a suburban middle school were used to answer the study research questions. Data were analyzed using a priori codes from previously developed frameworks and by using an inductive approach to find themes.</p><p style='text-indent:20px;'>Three themes that emerged from data related to confirmation bias. Some middle school students held preconceptions about the social issues they chose to study that biased their statistical questions. This in turn influenced the sources of data students used to answer their questions. Confirmation bias is a serious issue that is exacerbated due to endless sources of data electronically available. We argue that this type of bias should be addressed early in students' educational experiences. Based on the findings from this study, we offer recommendations for future research and implications for statistics and data science education.</p>

How do mid-level managers experience data science disruptions? An in-depth inquiry through interpretative phenomenological analysis (IPA)

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Atri Sengupta ◽  
Shashank Mittal ◽  
Kuchi Sanchita
Keyword(s):  
Interpretative Phenomenological Analysis ◽  
Large Scale ◽  
Data Science ◽  
Phenomenological Analysis ◽  
Small Sample ◽  
Process Models ◽  
Organizational Level ◽  
Future Research ◽  
Content Type ◽  
Extant Literature

PurposeRapid advancement of data science has disrupted both business and employees in organizations. However, extant literature primarily focuses on the organizational level phenomena, and has almost ignored the employee/individual perspective. This study thereby intends to capture the experiences of mid-level managers about these disruptions vis a vis their corresponding actions.Design/methodology/approachIn a small-sample qualitative research design, Interpretative Phenomenological Analysis (IPA) was adopted to capture this individual-level phenomenon. Twelve mid-level managers from large-scale Indian organizations that have extensively adopted data science tools and techniques participated in a semi-structured and in-depth interview process.FindingsOur findings unfolded several perspectives gained from their experiences, leading thereby to two emergent person-job (mis)fit process models. (1) Managers, who perceived demands-abilities misfit (D-A misfit) as a growth-alignment opportunity vis a vis their corresponding actions, which effectively trapped them into a vicious cycle; and (2) the managers, who considered D-A misfit as a psychological strain vis a vis their corresponding actions, which engaged them into a benevolent cycle.Research limitations/implicationsThe present paper has major theoretical and managerial implications in the field of human resource management and business analytics.Practical implicationsThe findings advise managers that the focus should be on developing an organizational learning eco-system, which would enable mid-level managers to gain their confidence and control over their job and work environment in the context of data science disruptions. Importantly, organizations should facilitate integrated workplace learning (both formal and informal) with an appropriate ecosystem to help mid-level managers to adapt to the data-science disruptions.Originality/valueThe present study offers two emergent cyclic models to the existing person–job fit literature in the context of data science disruptions. A scant attention of the earlier researchers on how individual employees actually experience disruption, and the corresponding IPA method used in the present study may add significant value to the extant literature. Further, it opens a timely and relevant future research avenues in the context of data science disruptions.

scholarly journals A Survey on Machine Reading Comprehension—Tasks, Evaluation Metrics and Benchmark Datasets

2020 ◽  
Vol 10 (21) ◽  
pp. 7640
Author(s):  
Changchang Zeng ◽  
Shaobo Li ◽  
Qin Li ◽  
Jie Hu ◽  
Jianjun Hu
Keyword(s):  
Reading Comprehension ◽  
Language Processing ◽  
Large Scale ◽  
Human Performance ◽  
Research Field ◽  
Evaluation Metrics ◽  
Future Research ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Machine Reading

Machine Reading Comprehension (MRC) is a challenging Natural Language Processing (NLP) research field with wide real-world applications. The great progress of this field in recent years is mainly due to the emergence of large-scale datasets and deep learning. At present, a lot of MRC models have already surpassed human performance on various benchmark datasets despite the obvious giant gap between existing MRC models and genuine human-level reading comprehension. This shows the need for improving existing datasets, evaluation metrics, and models to move current MRC models toward “real” understanding. To address the current lack of comprehensive survey of existing MRC tasks, evaluation metrics, and datasets, herein, (1) we analyze 57 MRC tasks and datasets and propose a more precise classification method of MRC tasks with 4 different attributes; (2) we summarized 9 evaluation metrics of MRC tasks, 7 attributes and 10 characteristics of MRC datasets; (3) We also discuss key open issues in MRC research and highlighted future research directions. In addition, we have collected, organized, and published our data on the companion website where MRC researchers could directly access each MRC dataset, papers, baseline projects, and the leaderboard.

scholarly journals Archival Data Thinking and Practices for Social Media Collections

2021 ◽  
Author(s):  
Lizhou Fan
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Data Science ◽  
Hate Speech ◽  
Research Direction ◽  
Data Driven ◽  
Future Research ◽  
Archival Data ◽  
Data Collections ◽  
Rapid Processing

In the Web 2.0 Era, most social media archives are born digital and large-scale. With an increasing need for processing them at a fast speed, researchers and archivists have started applying data science methods in managing social media data collections. However, many of the current computational or data-driven archival processing methods are missing the critical background understandings like “why we need to use computational methods,” and “how to evaluate and improve data-driven applications.” As a result, many computational archival science (CAS) attempts, with comparatively narrow scopes and low efficiencies, are not sufficiently holistic. In this talk, we first introduce the proposed concept of “Archival Data Thinking” that highlights the desirable comprehensiveness in mapping data science mindsets to archival practices. Next, we examine several examples of implementing “Archival Data Thinking” in processing two social media collections: (i) the COVID-19 Hate Speech Twitter Archive (CHSTA) and (ii) the Counter-anti-Asian Hate Twitter Archive (CAAHTA), both of which are with millions of records and their metadata, and needs for rapid processing. Finally, as a future research direction, we briefly discuss the standards and infrastructures that can better support the implementation of “Archival Data Thinking”.

Sentiment Analysis of Users’ Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset (Preprint)

2021 ◽  
Author(s):  
Kashif Ahmad ◽  
Firoj Alam ◽  
Junaid Qadir ◽  
Basheer Qolomony ◽  
Imran Khan ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Mobile Applications ◽  
Large Scale ◽  
Benchmark Dataset ◽  
Contact Tracing ◽  
Future Research ◽  
Crowd Sourcing ◽  
Large Scale Dataset ◽  
And Performance ◽  
Different Sources

BACKGROUND Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. Thanks to digital technologies, such as smartphones and wearable devices, contacts of COVID-19 patients can be easily traced and informed about their potential exposure to the virus. To this aim, several interesting mobile applications have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community’s response to the applications by analyzing information from different sources, such as news and users’ reviews of the applications. However, to the best of our knowledge, there is no existing solution that automatically analyzes users’ reviews and extracts the evoked sentiments. OBJECTIVE In this paper, we analyze how AI models can help in automatically extract and classify the polarity of users’ sentiments and propose a sentiment analysis framework to automatically analyze users’ reviews on COVID-19 contact tracing mobile applications. METHODS we propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users’ reviews. In detail, we collected and annotated a large-scale dataset of Android and iOS mobile application users’ reviews for COVID-19 contact tracing. After manually analyzing and annotating users’ reviews, we employed both classical (i.e., Naïve Bayes, SVM, Random Forest) and deep learning (i.e., fastText, and different transformers) methods for classification experiments. This resulted in eight different classification models. RESULTS We employed eight different methods on three different tasks achieving up to an average F1-Scores 94.8% indicating the feasibility of automatic sentiment analysis of users’ reviews on the COVID-19 contact tracing applications. Moreover, the crowd-sourcing activity resulted in a large-scale benchmark dataset composed of 34,534 reviews manually annotated from the contract tracing applications of 46 distinct countries. CONCLUSIONS The existing literature mostly relies on the manual/exploratory analysis of users’ reviews on the application, which is a tedious and time-consuming process. Moreover, in the existing studies, generally, data from fewer applications are analyzed. In this work, we showed that automatic sentiment analysis can help in analyzing users’ responses to the application more quickly with significant accuracy. Moreover, we also provided a large-scale benchmark dataset composed of 34,534 reviews from 47 different applications. We believe the presented analysis and the dataset will support future research on the topic.

scholarly journals Business intelligence and big data in hospitality and tourism: a systematic literature review

2018 ◽  
Vol 30 (12) ◽  
pp. 3514-3554 ◽  
Author(s):  
Marcello Mariani ◽  
Rodolfo Baggio ◽  
Matthias Fuchs ◽  
Wolfram Höepken
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Business Intelligence ◽  
Data Science ◽  
Academic Research ◽  
Research Field ◽  
Future Research ◽  
Content Type ◽  
Hospitality And Tourism

PurposeThis paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research.Design/methodology/approachThe study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization.FindingsFindings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research.Research limitations/implicationsThis work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed.Originality/valueThis study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data.

scholarly journals Data science approaches to confronting the COVID-19 pandemic: a narrative review

2021 ◽  
Vol 380 (2214) ◽  
Author(s):  
Qingpeng Zhang ◽  
Jianxi Gao ◽  
Joseph T. Wu ◽  
Zhidong Cao ◽  
Daniel Dajun Zeng
Keyword(s):  
Infectious Disease ◽  
Disease Surveillance ◽  
Large Scale ◽  
Data Science ◽  
Drug Repurposing ◽  
Epidemiological Studies ◽  
Contact Tracing ◽  
Social Media Analytics ◽  
Computer Scientists ◽  
Disease Epidemics

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale ‘big data’ generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

Change of Attitude, Technology and Practice: Identifying the Change for Increased Value Creation with Customer Co-creation

2017 ◽  
Vol 5 (1) ◽  
pp. 70-82
Author(s):  
Soumi Paul ◽  
Paola Peretti ◽  
Saroj Kumar Datta
Keyword(s):  
Social Media ◽  
New Product Development ◽  
Value Creation ◽  
Large Scale ◽  
Customer Orientation ◽  
Customer Relationships ◽  
Future Research ◽  
Management Competencies ◽  
Business Decisions ◽  
Prime Concern

Building customer relationships and customer equity is the prime concern in today’s business decisions. The emergence of internet, especially social media like Facebook and Twitter, changed traditional marketing thought to a great extent. The importance of customer orientation is reflected in the axiom, “The customer is the king”. A good number of organizations are engaging customers in their new product development activities via social media platforms. Co-creation, a new perspective in which customers are active co-creators of the products they buy and use, is currently challenging the traditional paradigm. The concept of co-creation involving the customer’s knowledge, creativity and judgment to generate value is considered not only an upcoming trend that introduces new products or services but also fitting their need and increasing value for money. Knowledge and innovation are inseparable. Knowledge management competencies and capacities are essential to any organization that aspires to be distinguished and innovative. The present work is an attempt to identify the change in value creation procedure along with one area of business, where co-creation can return significant dividends. It is on extending the brand or brand category through brand extension or line extension. This article, through an in depth literature review analysis, identifies the changes in every perspective of this paradigm shift and it presents a conceptual model of company-customer-brand-based co-creation activity via social media. The main objective is offering an agenda for future research of this emerging trend and ensuring the way to move from theory to practice. The paper acts as a proposal; it allows the organization to go for this change in a large scale and obtain early feedback on the idea presented. 

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