ABSTRACT
Tracking the evolution of clusters in social media streams is becoming increasingly important for many applications, such as early detection and monitoring of natural disasters or pandemics. In contrast to clustering on a static set of data, streaming data clustering does not have a global view of the complete data. The local (or partial) view in a high-speed stream makes clustering a challenging task. In this paper, we propose a novel density peak based algorithm, TStream, for tracking the evolution of clusters and outliers in social media streams, via the evolutionary actions of cluster adjustment, emergence, disappearance, split, and merge. TStream is based on a temporal decay model and text stream summarisation. The decay model captures the decreasing importance of textual documents over time. The stream summarisation compactly represents them with the help of cells (aka micro-clusters) in the memory. We also propose a novel efficient index called shared dependency tree (aka SD-Tree) based on the ideas of density peak and shared dependency. It maintains the dynamic dependency relationships in TStream and thereby improves the overall efficiency. We conduct extensive experiments on five real datasets. TStream outperforms the existing state-of-the-art solutions based on MStream, MStreamF, EDMStream, OSGM, and EStream, in terms of cluster mapping measure (CMM) by up to 17.8%, 18.6%, 6.9%, 16.4%, and 20.1%, respectively. It is also significantly more efficient than MStream, MStreamF, OSGM, and EStream, in terms of response time and throughput.
ABSTRACT
Tracking the evolution of clusters in social media streams is becoming increasingly important for many applications, such as early detection and monitoring of natural disasters or pandemics. In contrast to clustering on a static set of data, streaming data clustering does not have a global view of the complete data. The local (or partial) view in a high-speed stream makes clustering a challenging task. In this paper, we propose a novel density peak based algorithm, <monospace>TStream</monospace>, for tracking the evolution of clusters and outliers in social media streams, via the evolutionary actions of cluster adjustment, emergence, disappearance, split, and merge. <monospace>TStream</monospace> is based on a temporal decay model and text stream summarisation. The decay model captures the decreasing importance of textual documents over time. The stream summarisation compactly represents them with the help of cells (aka micro-clusters) in the memory. We also propose a novel efficient index called shared dependency tree (aka SD-Tree) based on the ideas of density peak and shared dependency. It maintains the dynamic dependency relationships in <monospace>TStream</monospace> and thereby improves the overall efficiency. We conduct extensive experiments on five real datasets. <monospace>TStream</monospace> outperforms the existing state-of-the-art solutions based on <monospace>MStream</monospace>, <monospace>MStreamF</monospace>, <monospace>EDMStream</monospace>, <monospace>OSGM</monospace>, and <monospace>EStream</monospace>, in terms of cluster mapping measure (CMM) by up to 17.8%, 18.6%, 6.9%, 16.4%, and 20.1%, respectively. It is also significantly more efficient than <monospace>MStream</monospace>, <monospace>MStreamF</monospace>, <monospace>OSGM</monospace>, and <monospace>EStream</monospace>, in terms of response time and throughput. IEEE
ABSTRACT
The COVID-19 pandemic continues to affect the daily life of college students, impacting their social life, education, stress levels and overall mental well-being. We study and assess behavioral changes of N=180 undergraduate college students one year prior to the pandemic as a baseline and then during the first year of the pandemic using mobile phone sensing and behavioral inference. We observe that certain groups of students experience the pandemic very differently. Furthermore, we explore the association of self-reported COVID-19 concern with students' behavior and mental health. We find that heightened COVID-19 concern is correlated with increased depression, anxiety and stress. We evaluate the performance of different deep learning models to classify student COVID-19 concerns with an AUROC and F1 score of 0.70 and 0.71, respectively. Our study spans a two-year period and provides a number of important insights into the life of college students during this period. © 2022 Owner/Author.
ABSTRACT
The novel coronavirus was renamed as coronavirus disease 2019 (COVID-19) by the world health organization, began its spread in December 2019, in the city of Wuhan, China. Global bodies and governments weren't prepared to handle the impact of the virus on society. Nepal's landlocked nation encountered its incident confirmed case of COVID-19 during the first week of January, with the primary host being a student with a travel history from its place of inception. The nation is deficient in its health resources. The country mainly focused on the stringent implementation of washing of hands, wearing masks, restricting general movement, and maintaining social distancing in public. The disease transmission reached to the third stage, which began within three months after the confirmation of the first case of COVID-19. The lack of tropical hospitals, laboratory and diagnostic facilities added to the challenges faced by the country. This paper is a comprehensive review of the overall preparation and steps taken by the federal system of Nepal to combat the virus's effects till the third stage of transmission. It concludes with the practical limitations faced by the governing authorities of the nation while implementing these measures.
ABSTRACT
Background: The novel coronavirus disease outbreak of 2019 was declared as a public health emergency by the World Health Organization. At present, the virus has spread throughout the world, leading to millions of cases and is further increasing. Objective: The main objective of this study is to review the impact of Corona Virus Disease 2019 (COVID-19) on the mental health of frontline workers, isolated and quarantined people and the general population. Methods: The relevant articles were extracted from PubMed, Web of Science and Science Direct database by using the keywords “Mental health”, “COVID-19”, “Impact of COVID-19”, “Frontline workers”, “Quarantine”, “Isolation”, “Immunity” and “Economy”. The retrieved articles were included in the study based on inclusion criteria to perform the review. All the selected scientific articles were critically reviewed and the information is summarized in this narrative review. Results: The majority of the studies stated that frontline health workers were at an increased risk of depression. The infected, suspects and quarantined people were reported with high stress, post-traumatic stress disorder, and suicidal thoughts. The pandemic has devastated the world’s economy, which has severely impacted global mental health. Conclusion: Mental health should be taken into account, and necessary interventional initiatives need to be considered both by the health authorities and the government to minimize the adversity of the consequences. The pandemic may disappear with the discovery of new vaccines or medications, but its negative impact on mental health may persist, particularly among vulnerable populations. Thus, mental health must be a matter of concern in the present scenario. © 2020 Bentham Science Publishers.
ABSTRACT
It is argued that the COVID-19 pandemic has given the global adventure tourism industry an opportunity to reset. The adventure travel sector has the opportunity for turning its attention away from haphazard development to one that repositions itself as a major partner in contributing to sustainable and mindful travel.