Security and Privacy Challenges in Healthcare Minitrack Overview

Digitizing healthcare services can provide many new benefits and opportunities. However, it can also introduce new research challenges in terms of protecting the security and privacy of patient data and electronic health records. The global COVID-19 pandemic and the increasing security incidents and breaches put patient information at risk, and organizations are under pressure to enhance the credibility and reliability of the health facilities and databases they operate. This minitrack encourages research in emerging problems and opportunities for security and privacy in healthcare. It addresses new approaches and strategies to improve the capabilities for protecting healthcare information and reducing misinformation, especially surrounding the COVID-19 pandemic. © 2023 IEEE Computer Society. All rights reserved.

Cloud Computing Continuum Research Topics and Challenges. A Multi-source Analysis

While the emergence of COVID-19 [1] has put major cloud service providers around the world to the test, the pandemic has also provided a strong impetus for the adoption and deployment of cloud computing: the transition to a remote workforce, entertainment, e-commerce, and especially remote education have affected the cloud industry and how providers are responding to the sudden and significant increase in demand for cloud solutions and services. Obviously, while highlighting the robustness of the public cloud, the pandemic-induced situation also highlights several important research challenges that need to be addressed. This paper presents a multi-source based analysis for the identification of cloud computing research challenges as part of the road mapping methodology followed in the HUB4CLOUD project. The analysis consists of an in-depth study of several sources including analysis of the international context, analysis of academic venues, interviews with relevant stakeholders and existing funded projects. The paper also provides an overview of the main research topics identified and proposes next steps for the utilization of these finding in the development of a Cloud Computing research roadmap. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Overcoming data collection challenges and establishing trustworthiness: The need for flexibility and responsiveness in research

An increasingly multicultural Aotearoa early childhood education (ECE) landscape forms the context for my doctoral study in progress. My research explores the culturally embedded and negotiated environmental identities of a growing number of migrant Indian teachers. This article documents my experiences of confronting and navigating the unexpected while planning and conducting the data collection for my research. The primary challenges were access to participants as well as participant dropouts. I discuss how I mitigated these challenges by employing an alternate sampling method as well as accounting for participant attrition and trustworthiness of data. The modification strategies highlight flexibility and responsiveness as critical research tools. This article has implications for early career researchers intending to plan or begin their research in the light of any future disruptions, such as the current Covid-19 climate. © 2022, Wilf Malcolm Institute of Educational Research. All rights reserved.

A Survey of Deep Learning on COVID-19 Identification Through X-Ray Images

New Coronavirus 2019 (COVID-19) is a virus that causes severe pneumonia and affects many organs of the body. This infection was initially discovered in one of the cities in the Republic of China, Wuhan, in December 2019 and since then has been spread throughout the globe as a global pandemic. To prevent the virus from spreading, positive cases must be identified early and infected persons must be treated as soon as possible. As new instances emerge regularly, many developing countries are experiencing COVID-19 testing kit scarcity because the demand for testing kits has soared. As an alternative, radiological imaging techniques such as X-ray images have been proven to help in COVID-19 diagnosis because images from X-ray provide valuable information about the COVID-19 virus disease. This paper presents a survey of Deep learning-based methods in identifying COVID-19 with X-ray input images, and classifies these images into several categories, namely: no findings, normal, COVID, and pneumonia. Several studies have been included with details about their datasets, methodologies, and findings. A total of thirteen popular datasets and fifteen articles are reviewed in this paper. Research challenges and recommendations for future research directions are also provided as an evaluation of previous research. Search for research articles in well-known digital libraries, namely Scopus, IEEE Xplore, Springer, and ScienceDirect, was carried out to obtain a list of studies relevant to the scope of research. Related articles that have a high impact are considered in the list of studies. Also, in selecting studies related to the research scope, we apply some inclusion and exclusion criteria. The list of studies used in subsequent research is imported to the library. Then, studies that did not match the criteria for inclusion were eliminated. The clinical application of artificial intelligence, i.e., DL in diagnosing COVID-19, is promising, and further research is needed. Convolutional Neural Network (CNN) approaches could be used in collaboration through X-ray pictures to identify diseases quickly and accurately, reducing the shortage of testing equipment and their restrictions. It is expected that this work can help researchers understand the general picture and existing research gaps to decide on the appropriate architecture and approach in developing deep learning-based covid identification research. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Artificial Intelligence Based Model for Covid-19 Detection by Analyzing X-Ray Images: Researches, challenges and Future Directions

The interdependence between artificial intelligence and medical services allows for saving time and devices with a high degree of accuracy in medical analyses. Because medical imaging devices and computers are available in hospitals, this method of diagnosing COVID-19 infection is less expensive and faster than traditional methods. And our goal in this research is to shed light on research efforts in the field of exploiting artificial intelligence, software, and algorithms that allow the machine to learn by itself to detect the COVID-19 virus taken from Medical chest x-rays, comparing research conducted in this area over the past two years, this paper reviews 40 published research papers, studied and categorized based on specific criteria, to obtain a comprehensive view of the use of deep learning and machine learning models that predict COVID-19 infection And future aspirations for the possibility of applying this method of diagnosis on a global scale in hospitals and health care centers. © 2022 IEEE.

COVID-19 Spread Prediction and Its Impact on the Stock market price

Predicting the Covid-19 spread and its impact on the stock market is an important research challenge these days. In order to obtain the best forecasting model, we have exploited neuro-evolutionary technique Cartesian genetic programming evolved artificial neural network (CGPANN) based solution to predict the future cases of COVID-19 up to 6-days in advance. This helps authorities and paramedical staff to take precautionary measures on time which helps in counteracting the spreading of the virus. The rising number of COVID cases has caused a significant impact on the stock market. CGPANN being the best performer for the time series prediction model seems ideal for the case under consideration. The proposed model achieved an accuracy as high as 98% predicting COVID-19 cases for the next six days. When compared with other contemporary models CGPANN seems to perform well ahead in terms of accuracy. © 2022 IEEE.

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

The number of connected mobile devices and Internet of Things (IoT) is growing around us, rapidly. Since most of people's daily activities are relying on these connected things or devices. Specifically, this past year (with COVID-19) changed daily life in abroad and this is increased the use of IoT-enabled technologies in the health sector, work, and play. Further, the most common service via using these technologies is the localization/positioning service for different applications including: geo-tagging, billing, contact tracing, health-care system, point-of-interest recommendations, social networking, security, and more. Despite the availability of a large number of localization solutions in the literature, the precision of localization cannot meet the needs of consumers. For that reason, this paper provides an in-depth investigation of the existing technologies and techniques in the localization field, within the IoT era. Furthermore, the benefits and drawbacks of each technique with enabled technologies are illustrated and a comparison between the utilized technologies in the localization is made. The paper as a guideline is also going through all of the metrics that may be used to assess the localization solutions. Finally, the state-of-the-art solutions are examined, with challenges and perspectives regarding indoors/outdoors environments are demonstrated. © 2022

Tweet Credibility Detection for COVID-19 Tweets using Text and User Content Features

The deadly COVID-19 pandemic is currently sweeping the globe, and millions of people have been exposed to false information about the disease, its remedies, prevention, and origins. During such perilous times, the propagation of fake news and misinformation can have serious implications, causing widespread panic and exacerbating the pandemic's threat. This increasing threat factor has given rise to considerable research challenges. This article is mainly concerned about fake news identification and experimentation is specifically performed considering COVID-19 fake news as a case study. Fake news is spread intentionally to mislead the people and therefore we need to identify user's involvement and it's correlation with additional features. The aim of this research is to develop a model that can predict the essence of a tweet given as an input with the help of multiple features. Our strategy is to make use of the tweet's text as well as the user's metadata and develops a model using natural processing technique and deep learning method. In this process, we have analyzed the behavior of the accounts, observed the impact of the various factors that can lead to fake news. The experimental analysis shows that hybrid model with text and content features have generated a benchmark result than the existing state of art techniques. We have obtained a best F1-score of 0.976 during the experimentation. © 2022. All Rights Reserved.

Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings

Growing polarization of the news media has been blamed for fanning disagreement, controversy and even violence. Early identification of polarized topics is thus an urgent matter that can help mitigate conflict. However, accurate measurement of topic-wise polarization is still an open research challenge. To address this gap, we propose Partisanship-aware Contextualized Topic Embeddings (PaCTE), a method to automatically detect polarized topics from partisan news sources. Specifically, utilizing a language model that has been finetuned on recognizing partisanship of the news articles, we represent the ideology of a news corpus on a topic by corpus-contextualized topic embedding and measure the polarization using cosine distance. We apply our method to a dataset of news articles about the COVID19 pandemic. Extensive experiments on different news sources and topics demonstrate the efficacy of our method to capture topical polarization, as indicated by its effectiveness of retrieving the most polarized topics. © 2021 Association for Computational Linguistics.

AFRICA TOURISM IN CHANGE: PAST AND PRESENT

Globally it is apparent that tourism exists in a state of continual change that impacts destinations and the tourism system. The nexus of "tourism and change" requires research perspectives at different scales of analysis. In addition, it demands the extended application of historical perspectives in order to inform contemporary debates and practices. Arguably, change in tourism in sub-Saharan Africa is not a new phenomenon. Over recent decades, however, several events and processes have intensified the shifting complexions of African tourism, most recently the COVID-19 pandemic. It is argued that the pandemic poses a host of new challenges for research concerning tourism and change in Africa. Welcome signs exist of an emerging African scholarship that is engaged and addresses several of the challenges caused by the COVID-19 crisis. This emergent strand of writings includes works on both Africa tourism in change, past and present.

IoT Based Health Monitoring System and Its Challenges and Opportunities

With the incarnation of novel COVID-19, health care is getting more preference in each country. IoT-based health monitoring systems might be the best option to monitor infected patients and be helpful for elderly population. In this paper, analyzed different IoT-based health monitoring systems and their challenges. Searched through established journal and conference databases using specific keywords to find scholarly works to conduct the analysis. Investigated unique articles related to this analysis. The selected papers were then sifted through to understand their contributions/research focus. Then tried to find their research gap and challenges, created them into opportunities and proposed a GSM-based offline health monitoring system that will conduct with the healthcare providers through communication networks. Hopefully, this model will work as an absolute pathway for the researchers to establish a sustainable IoT-based health monitoring system for humankind. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Has COVID-19 halted winter-spring wildfires in the Mediterranean? Insights for wildfire science under a pandemic context.

Wildfires in the Mediterranean are strongly tied to human activities. Given their particular link with humans, which act as both initiators and suppressors, wildfire hazard is highly sensitive to socioeconomic changes and patterns. Many researchers have prompted the perils of sustaining the current management policy, the so-called 'total fire exclusion'. This policy, coupled to increasingly fire-prone weather conditions, may lead to more hazardous fires in the mid-long run. Under this framework, the irruption of the COVID-19 pandemic adds to the ongoing situation. Facing the lack of an effective treatment, the only alternative was the implementation of strict lockdown strategies. The virtual halt of the system undoubtedly affected economic and social behavior, triggering cascading effects such as the drop in winter-spring wildfire activity. In this work, we discuss the main impacts, challenges and consequences that wildfire science may experience due to the pandemic situation, and identify potential opportunities for wildfire management. We investigate the recent evolution of burned area (retrieved from the MCD64A1 v006 MODIS product) in the EU Mediterranean region (Portugal, Spain, France, Italy and Greece) to ascertain to what extent the 2020 winter-spring season was impacted by the public health response to COVID-19 (curfews and lockdowns). We accounted for weather conditions (characterized using the 6-month Standardized Precipitation Evapotranspiration Index; SPEI6) to disregard possible weather effects mediating fire activity. Our results suggest that, under similar drought-related circumstances (SPEI6 ≈ -0.7), the expected burned area in 2020 during the lockdown period in the EU (March-May) would lay somewhere within the range of 38,800 ha ± 18,379 ha. Instead, the affected area stands one order of magnitude below average (3325 ha). This stresses the need of considering the social dimension in the analysis of current and future wildfire impacts in the Mediterranean region.

Covid-19 pandemic research opportunity: Is the Middle East & North Africa (MENA) missing out?

BACKGROUND: The Covid-19 pandemic has caused fear and panic worldwide, forcing healthcare systems to disregard conventional practices and adopt innovation to contain the infection and death. Globally, there has been a rapid proliferation of research studies and clinical trials assessing risks, infectivity and treatment. METHODS: This review assesses the opportunities and challenges in the Middle East North Africa (MENA) region to engage in the conduct of high quality clinical trials during the Covid-19 pandemic. RESULTS: Opportunities are abundant for conducting clinical trials in MENA countries, including substantial cost savings, academic health centers, integrated health information systems, international accreditation, and international collaborations. Yet, the MENA region has missed out on opportunities to advance patient research during prior infectious disease outbreaks caused by the Severe Acute Respiratory Syndrome, Ebola, and the Middle East Respiratory Syndrome, as evidenced by the lack of concerted research and clinical trials from the region. A large vulnerable population, especially the poor expatriate work force, the current isolation of the health centers, and the lack of an expert network or field trained task force, all contribute to challenges preventing the formation of a pan Arab research enterprise for epidemics. CONCLUSION: Quality clinical research is critical during public health emergencies to identify treatments and solutions. The efficient conduct of clinical trials requires innovative strategies in research design, approval, and dissemination. Many countries in the MENA region have an opportunity to quickly ramp up research capacity and contribute significantly to the fight against the Covid-19 global threat.