15,534 research outputs found
Securing NextG networks with physical-layer key generation: A survey
As the development of next-generation (NextG) communication networks continues, tremendous devices are accessing the network and the amount of information is exploding. However, with the increase of sensitive data that requires confidentiality to be transmitted and stored in the network, wireless network security risks are further amplified. Physical-layer key generation (PKG) has received extensive attention in security research due to its solid information-theoretic security proof, ease of implementation, and low cost. Nevertheless, the applications of PKG in the NextG networks are still in the preliminary exploration stage. Therefore, we survey existing research and discuss (1) the performance advantages of PKG compared to cryptography schemes, (2) the principles and processes of PKG, as well as research progresses in previous network environments, and (3) new application scenarios and development potential for PKG in NextG communication networks, particularly analyzing the effect and prospects of PKG in massive multiple-input multiple-output (MIMO), reconfigurable intelligent surfaces (RISs), artificial intelligence (AI) enabled networks, integrated space-air-ground network, and quantum communication. Moreover, we summarize open issues and provide new insights into the development trends of PKG in NextG networks
A Privacy Calculus Perspective
Sandhu, R. K., Vasconcelos-Gomes, J., Thomas, M. A., & Oliveira, T. (2023). Unfolding the Popularity of Video Conferencing Apps: A Privacy Calculus Perspective. International Journal Of Information Management, 68(February), 1-17. [102569]. https://doi.org/10.1016/j.ijinfomgt.2022.102569. Funding: This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC).Videoconferencing (VC) applications (apps) have surged in popularity as an alternative to face-to-face communications especially during the COVID-19 pandemic. Although VC apps offer myriad benefits, it has caught much media attention owing to concerns of privacy infringements. This study examines the key determinants of working professional’s intentions to use VC apps in the backdrop of this conflicting duality. A conceptual research model is proposed that is based on theoretical foundations of privacy calculus and extended with conceptualizations of mobile users’ information privacy concerns (MUIPC), trust, technicality, ubiquity, as well as theoretical underpinnings of social presence theory. Structural equation modelling (SEM) is used to empirically test the model using data collected from 487 working professionals. For researchers, the study offers insights on the extent to which social richness and technological capabilities afforded by the virtual environment serve as predictors of the continuance intentions of using VC apps. Researchers may also find the model applicable to other studies of surveillance-based technologies. For practitioners, key recommendations pivotal to the design and development mobile video-conferencing apps are presented to ensure higher acceptance and continued usage of VC apps in professional settings.preprintauthorsversionepub_ahead_of_prin
Challenges in the Design and Implementation of IoT Testbeds in Smart-Cities : A Systematic Review
Advancements in wireless communication and the increased accessibility to low-cost sensing and data processing IoT technologies have increased the research and development of urban monitoring systems. Most smart city research projects rely on deploying proprietary IoT testbeds for indoor and outdoor data collection. Such testbeds typically rely on a three-tier architecture composed of the Endpoint, the Edge, and the Cloud. Managing the system's operation whilst considering the security and privacy challenges that emerge, such as data privacy controls, network security, and security updates on the devices, is challenging. This work presents a systematic study of the challenges of developing, deploying and managing urban monitoring testbeds, as experienced in a series of urban monitoring research projects, followed by an analysis of the relevant literature. By identifying the challenges in the various projects and organising them under the V-model development lifecycle levels, we provide a reference guide for future projects. Understanding the challenges early on will facilitate current and future smart-cities IoT research projects to reduce implementation time and deliver secure and resilient testbeds
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Landfill site trees: Potential source or sink of greenhouse gases?
Tree stems can transport greenhouse gases (GHGs) produced belowground to the atmosphere. Previous studies in natural wetland and upland ecosystems have quantified tree stem fluxes of methane (CH4), carbon dioxide (CO2) and nitrous oxide (N2O). However, tree stem GHG fluxes have not previously been measured in the context of managed environments. The work presented in this thesis aimed to quantify GHG fluxes from tree stems on closed landfill sites.
To investigate the potential for trees growing on closed landfill sites to act as conduits for GHGs produced belowground to the atmosphere, GHG fluxes were measured from tree stem and soil surfaces. In situ measurements from a closed landfill site in the UK were examined for spatial and temporal patterns and evaluated against data from a comparable non-landfill area. Measurements were also conducted from landfill sites in the UK with varying management practices and different tree species present. The resulting flux values were scaled up to estimate the magnitude of tree stem GHG fluxes from closed landfills at a national level.
The findings presented here show evidence of tree mediated GHG transport on closed landfill sites and temporal variations in fluxes from tree stems were also observed, with generally higher fluxes in the summer months. Stem CH4 fluxes varied between trees growing on landfill sites with different management practices. Additionally, stem N2O fluxes displayed spatial patterns, with decreasing emissions at increased height from the forest floor, indicating an underground source. Evidence suggested that GHG fluxes from closed landfills are influenced by factors including the quantity of GHG produced in the waste (linked to the age of the site), the susceptibility of the area to waterlogging and landfill management techniques put in place upon closure (for example, clay caps, cover soils and gas extraction). Upscaled CH4 and N2O flux values from tree stems on closed landfill sites corresponded to less than 1% of the total CH4 and N2O emissions reported from UK landfills in 2020.
Overall, results indicated that measuring soil fluxes alone from forested landfill sites would result in an underestimation of the total surface fluxes. However, the emission rates from tree stems on closed landfills observed in this thesis do not exceed those in natural ecosystems. Therefore, with careful planning and management, the recommendation is that trees can be planted on closed landfill sites in the UK without emitting atypical levels of GHGs. However, including gas fluxes from tree stems on closed landfills would increase the accuracy of GHG budgets at national and global levels
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GATEKEEPER’s Strategy for the Multinational Large-Scale Piloting of an eHealth Platform: Tutorial on How to Identify Relevant Settings and Use Cases
Background:
The World Health Organization’s strategy toward healthy aging fosters person-centered integrated care sustained by eHealth systems. However, there is a need for standardized frameworks or platforms accommodating and interconnecting multiple of these systems while ensuring secure, relevant, fair, trust-based data sharing and use. The H2020 project GATEKEEPER aims to implement and test an open-source, European, standard-based, interoperable, and secure framework serving broad populations of aging citizens with heterogeneous health needs.
Objective:
We aim to describe the rationale for the selection of an optimal group of settings for the multinational large-scale piloting of the GATEKEEPER platform.
Methods:
The selection of implementation sites and reference use cases (RUCs) was based on the adoption of a double stratification pyramid reflecting the overall health of target populations and the intensity of proposed interventions; the identification of a principles guiding implementation site selection; and the elaboration of guidelines for RUC selection, ensuring clinical relevance and scientific excellence while covering the whole spectrum of citizen complexities and intervention intensities.
Results:
Seven European countries were selected, covering Europe’s geographical and socioeconomic heterogeneity: Cyprus, Germany, Greece, Italy, Poland, Spain, and the United Kingdom. These were complemented by the following 3 Asian pilots: Hong Kong, Singapore, and Taiwan. Implementation sites consisted of local ecosystems, including health care organizations and partners from industry, civil society, academia, and government, prioritizing the highly rated European Innovation Partnership on Active and Healthy Aging reference sites. RUCs covered the whole spectrum of chronic diseases, citizen complexities, and intervention intensities while privileging clinical relevance and scientific rigor. These included lifestyle-related early detection and interventions, using artificial intelligence–based digital coaches to promote healthy lifestyle and delay the onset or worsening of chronic diseases in healthy citizens; chronic obstructive pulmonary disease and heart failure decompensations management, proposing integrated care management based on advanced wearable monitoring and machine learning (ML) to predict decompensations; management of glycemic status in diabetes mellitus, based on beat to beat monitoring and short-term ML-based prediction of glycemic dynamics; treatment decision support systems for Parkinson disease, continuously monitoring motor and nonmotor complications to trigger enhanced treatment strategies; primary and secondary stroke prevention, using a coaching app and educational simulations with virtual and augmented reality; management of multimorbid older patients or patients with cancer, exploring novel chronic care models based on digital coaching, and advanced monitoring and ML; high blood pressure management, with ML-based predictions based on different intensities of monitoring through self-managed apps; and COVID-19 management, with integrated management tools limiting physical contact among actors.
Conclusions:
This paper provides a methodology for selecting adequate settings for the large-scale piloting of eHealth frameworks and exemplifies with the decisions taken in GATEKEEPER the current views of the WHO and European Commission while moving forward toward a European Data Space
Sociodemographic, nutritional and health status factors associated with adherence to Mediterranean diet in an agricultural Moroccan adult's population
Background. Numerous studies have demonstrated beneficial effects of adherence to the Mediterranean diet (MD) on many chronic diseases, including chronic kidney disease (CKD).
Objective. The aim of this study was to assess the adherence of a rural population to the Mediterranean diet, to identify the sociodemographic and lifestyle determinants and to analyze the association between adherence to MD and CKD.
Material and Methods. In a cross-sectional study, data on sociodemographic, lifestyle factors, clinical, biochemical parameters and diet were collected on a sample of 154 subjects. Adherence to MD was assessed according to a simplified MD score based on the daily frequency of intake of eight food groups (vegetables, legumes, fruits, cereal or potatoes, fish, red meat, dairy products and MUFA/SFA), using the sex specific sample medians as cut-offs. A value of 0 or 1 was assigned to consumption of each component according to its presumed detrimental or beneficial effect on health.
Results. According to the simplified MD score, the study data show that high adherence (44.2%) to MD was characterized by intakes high in vegetables, fruits, fish, cereals, olive oil, and low in meat and moderate in dairy. Furthermore, several factors such as age, marital status, education level, and hypertension status were associated with the adherence to MD in the study population. The majority of subjects with CKD have poor adherence to the MD compared to non-CKD with a statistically insignificant difference.
Conclusions. In Morocco, maintaining the traditional MD pattern play crucial role for public health. More research is needed in this area to precisely measure this association
Improving diagnostic procedures for epilepsy through automated recording and analysis of patients’ history
Transient loss of consciousness (TLOC) is a time-limited state of profound cognitive impairment characterised by amnesia, abnormal motor control, loss of responsiveness, a short duration and complete recovery. Most instances of TLOC are caused by one of three health conditions: epilepsy, functional (dissociative) seizures (FDS), or syncope. There is often a delay before the correct diagnosis is made and 10-20% of individuals initially receive an incorrect diagnosis. Clinical decision tools based on the endorsement of TLOC symptom lists have been limited to distinguishing between two causes of TLOC. The Initial Paroxysmal Event Profile (iPEP) has shown promise but was demonstrated to have greater accuracy in distinguishing between syncope and epilepsy or FDS than between epilepsy and FDS. The objective of this thesis was to investigate whether interactional, linguistic, and communicative differences in how people with epilepsy and people with FDS describe their experiences of TLOC can improve the predictive performance of the iPEP. An online web application was designed that collected information about TLOC symptoms and medical history from patients and witnesses using a binary questionnaire and verbal interaction with a virtual agent. We explored potential methods of automatically detecting these communicative differences, whether the differences were present during an interaction with a VA, to what extent these automatically detectable communicative differences improve the performance of the iPEP, and the acceptability of the application from the perspective of patients and witnesses. The two feature sets that were applied to previous doctor-patient interactions, features designed to measure formulation effort or detect semantic differences between the two groups, were able to predict the diagnosis with an accuracy of 71% and 81%, respectively. Individuals with epilepsy or FDS provided descriptions of TLOC to the VA that were qualitatively like those observed in previous research. Both feature sets were effective predictors of the diagnosis when applied to the web application recordings (85.7% and 85.7%). Overall, the accuracy of machine learning models trained for the threeway classification between epilepsy, FDS, and syncope using the iPEP responses from patients that were collected through the web application was worse than the performance observed in previous research (65.8% vs 78.3%), but the performance was increased by the inclusion of features extracted from the spoken descriptions on TLOC (85.5%). Finally, most participants who provided feedback reported that the online application was acceptable. These findings suggest that it is feasible to differentiate between people with epilepsy and people with FDS using an automated analysis of spoken seizure descriptions. Furthermore, incorporating these features into a clinical decision tool for TLOC can improve the predictive performance by improving the differential diagnosis between these two health conditions. Future research should use the feedback to improve the design of the application and increase perceived acceptability of the approach
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
The State of Algorithmic Fairness in Mobile Human-Computer Interaction
This paper explores the intersection of Artificial Intelligence and Machine
Learning (AI/ML) fairness and mobile human-computer interaction (MobileHCI).
Through a comprehensive analysis of MobileHCI proceedings published between
2017 and 2022, we first aim to understand the current state of algorithmic
fairness in the community. By manually analyzing 90 papers, we found that only
a small portion (5%) thereof adheres to modern fairness reporting, such as
analyses conditioned on demographic breakdowns. At the same time, the
overwhelming majority draws its findings from highly-educated, employed, and
Western populations. We situate these findings within recent efforts to capture
the current state of algorithmic fairness in mobile and wearable computing, and
envision that our results will serve as an open invitation to the design and
development of fairer ubiquitous technologies.Comment: arXiv admin note: text overlap with arXiv:2303.1558
Machine learning and mixed reality for smart aviation: applications and challenges
The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency
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