A Blockchain based Architecture for the Detection of Fake Sensing in Mobile Crowdsensing

© 2019 University of Split, FESB. With the emergence of mobile crowdsensing (MCS), we now have the possibility of leveraging the sensing capabilities of mobile devices to collect information and intelligence about cities and events. Despite the promise that MCS brings, this new concept opens the door to a multitude of security and privacy threats and attacks. Indeed, the human involvement in the crowdsensing process and the openness of this process to any participant, render the task of securing MCS environments very challenging. In this work, we propose a Blockchain-based hybrid architecture for the detection and prevention of fake sensing activities in MCS. Our architecture leverages the capabilities of the Blockchain network and introduces a new role to the MCS architecture to ensure the validation of the collected information. Combining both data quality metrics along with behavioral analysis based participants\u27 reliability scoring, our solution is able to detect variations in behavior and quality of contributions. The proposed solution was tested with real life data collected from 200 mobile users, over the span of 2 years, and the results obtained are very promising

Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions

News creation and consumption has been changing since the advent of social media. An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. Most platforms were used to transmit relevant news, guidelines and precautions to people. According to WHO, uncontrolled conspiracy theories and propaganda are spreading faster than the COVID-19 pandemic itself, creating an infodemic and thus causing psychological panic, misleading medical advises, and economic disruption. Accordingly, discussions have been initiated with the objective of moderating all COVID-19 communications, except those initiated from trusted sources such as the WHO and authorized governmental entities. This paper presents a large-scale study based on data mined from Twitter. Extensive analysis has been performed on approximately one million COVID-19 related tweets collected over a period of two months. Furthermore, the profiles of 288,000 users were analyzed including unique users profiles, meta-data and tweets context. The study noted various interesting conclusions including the critical impact of the (1) exploitation of the COVID-19 crisis to redirect readers to irrelevant topics and (2) widespread of unauthentic medical precautions and information. Further data analysis revealed the importance of using social networks in a global pandemic crisis by relying on credible users with variety of occupations, content developers and influencers in specific fields. In this context, several insights and findings have been provided while elaborating computing and non-computing implications and research directions for potential solutions and social networks management strategies during crisis periods.Comment: 11 pages, 10 figures, Journal Articl

Ontology based recommender system using social network data

Online Social Network (OSN) is considered a key source of information for real-time decision making. However, several constraints lead to decreasing the amount of information that a researcher can have while increasing the time of social network mining procedures. In this context, this paper proposes a new framework for sampling Online Social Network (OSN). Domain knowledge is used to define tailored strategies that can decrease the budget and time required for mining while increasing the recall. An ontology supports our filtering layer in evaluating the relatedness of nodes. Our approach demonstrates that the same mechanism can be advanced to prompt recommendations to users. Our test cases and experimental results emphasize the importance of the strategy definition step in our social miner and the application of ontologies on the knowledge graph in the domain of recommendation analysis

The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

Mobile learning perception and interest among higher education distance learners in Asia

This study, as a part of Asia e University mobile Learning project (AeUmL), aimed to investigate the higher education distance learners’ conceptualization and the level of interest towards having the opportunity to learn while mobile. It also addressed participants’ access level and frequency use of the technologies typically employed in mobile learning. Quantitative data were collected from 112 survey respondents enrolled in AeU Post Graduate Programs in Kuala Lumpur center. Results yielded a mixed response in terms of student perception on various examples of mobile learning while their interest level and attitudes towards having the option of mobile learning were reported very high. Students’ participation rate in utilizing mobile technologies and electronic learning activities were analyzed. Future research implications and issues surrounding the development of mobile learning in Asian higher education are also discussed

A Blockchain based Architecture for the Detection of Fake Sensing in Mobile Crowdsensing

With the emergence of mobile crowdsensing (MCS), we now have the possibility of leveraging the sensing capabilities of mobile devices to collect information and intelligence about cities and events. Despite the promise that MCS brings, this new concept opens the door to a multitude of security and privacy threats and attacks. Indeed, the human involvement in the crowdsensing process and the openness of this process to any participant, render the task of securing MCS environments very challenging. In this work, we propose a Blockchain-based hybrid architecture for the detection and prevention of fake sensing activities in MCS. Our architecture leverages the capabilities of the Blockchain network and introduces a new role to the MCS architecture to ensure the validation of the collected information. Combining both data quality metrics along with behavioral analysis based participants\u27 reliability scoring, our solution is able to detect variations in behavior and quality of contributions. The proposed solution was tested with real life data collected from 200 mobile users, over the span of 2 years, and the results obtained are very promising

Anticipated time to seek medical advice for possible ovarian cancer symptoms and perceived barriers to early presentation among Palestinian women: a national cross-sectional study

Abstract Background Several factors contribute to delayed presentation with ovarian cancer (OC) symptoms including poor symptom awareness and barriers to seeking help. This study explored the anticipated time to seek medical advice for possible OC symptoms and its association with OC symptom awareness. In addition, it examined perceived barriers that may delay help-seeking among Palestinian women. Methods A cross-sectional study was conducted among adult women (≥ 18 years) recruited from hospitals, primary healthcare centers, and public spaces in 11 Palestinian governorates. A modified version of the OC awareness measure was used to collect data in face-to-face interviews. The questionnaire comprised three sections: sociodemographic details, awareness of 11 OC symptoms and time to seek medical advice, and barriers to early presentation. Results Of 6095 participants approached, 5618 completed the OCAM (response rate = 92.1%). The proportion of participants who would immediately seek medical advice for a possible OC symptom varied based on the symptom’s nature. For OC symptoms with pain, the proportion that reported immediate seeking of medical advice ranged from 7.9% for ‘persistent low back pain’ to 13.6% for ‘persistent pain in the pelvis’. For non-specific potential OC symptoms, the proportion that reported immediate seeking of medical advice ranged from 2.3% for ‘feeling full persistently’ to 15.8% for ‘increased abdominal size on most days’. Good OC symptom awareness was associated with higher likelihood of seeking medical advice within a week from recognizing 10 out of 11 OC symptoms. Emotional barriers were the most common barriers with ‘feeling scared’ as the most reported barrier (n = 1512, 52.4%). Displaying good OC symptom awareness was associated with a lower likelihood of reporting ≥ 4 emotional barriers (OR = 0.61, 95% CI: 0.38–0.98). Conclusion Participants with good OC symptom awareness were more likely to seek medical advice earlier and to display fewer emotional barriers. Establishing educational interventions to raise OC awareness may help in promoting earlier help-seeking and, thus, facilitate earlier diagnosis and improved prognosis

Additional file 1 of Anticipated time to seek medical advice for possible ovarian cancer symptoms and perceived barriers to early presentation among Palestinian women: a national cross-sectional study

Additional file 1: Supplementary table 1. Characteristics of study participants