arXiv subjects

Shakila Khan Rumi

Publications and source records attributed to Shakila Khan Rumi.

6 recordsLinked to original sources

arXiv2025

Exploring Emotional and Social Dynamics in Mobile Usage During Home Confinement

Home confinement, a situation experienced by individuals for reasons ranging from medical quarantines, rehabilitation needs, disability accommodations, and remote working, is a common yet impactful aspect of modern life. While essential in various scenarios, confinement within the home environment can profoundly influence mental well-being and digital device usage. Using the COVID-19 lockdown as a case study, this research explores the emotional and social effects of prolonged home confinement on mobile device usage. We conducted an in-situ study with 32 participants, analyzing three weeks of mobile usage data to assess emotional well-being and social dynamics in restricted environments. Our findings reveal that app usage patterns serve as strong indicators of emotional states, offering insights into how digital interactions can reflect and influence well-being during isolation. This study highlights the potential for developing targeted interventions and support systems for individuals in long-term home confinement, including those with chronic illness, recovery needs, or permanent remote work situations.

cs.HC↗

arXiv2021

Multi-officer Routing for Patrolling High Risk Areas Jointly Learned from Check-ins, Crime and Incident Response Data

A well-crafted police patrol route design is vital in providing community safety and security in the society. Previous works have largely focused on predicting crime events with historical crime data. The usage of large-scale mobility data collected from Location-Based Social Network, or check-ins, and Point of Interests (POI) data for designing an effective police patrol is largely understudied. Given that there are multiple police officers being on duty in a real-life situation, this makes the problem more complex to solve. In this paper, we formulate the dynamic crime patrol planning problem for multiple police officers using check-ins, crime, incident response data, and POI information. We propose a joint learning and non-random optimisation method for the representation of possible solutions where multiple police officers patrol the high crime risk areas simultaneously first rather than the low crime risk areas. Later, meta-heuristic Genetic Algorithm (GA) and Cuckoo Search (CS) are implemented to find the optimal routes. The performance of the proposed solution is verified and compared with several state-of-art methods using real-world datasets.

cs.SI↗

arXiv2021

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

cs.LG↗

arXiv2020

Modelling Memory for Individual Re-identification in Decentralised Mobile Contact Tracing Applications

In 2020 the coronavirus outbreak changed the lives of people worldwide. After an initial time period in which it was unclear how to battle the virus, social distancing has been recognised globally as an effective method to mitigate the disease spread. This called for technological tools such as Mobile Contact Tracing Applications (MCTA), which are used to digitally trace contacts among people, and in case a positive case is found, people with the application installed which had been in contact will be notified. De-centralised MCTA may suffer from a novel kind of privacy attack, based on the memory of the human beings, which upon notification of the application can identify who is the positive individual responsible for the notification. Our results show that it is indeed possible to identify positive people among the group of contacts of a human being, and this is even easier when the sociability of the positive individual is low. In practice, our simulation results show that identification can be made with an accuracy of more than 90% depending on the scenario. We also provide three mitigation strategies which can be implemented in de-centralised MCTA and analyse which of the three are more effective in limiting this novel kind of attack.

cs.CY↗

arXiv2020

Modelling Regional Crime Risk using Directed Graph of Check-ins

The location-based social network, Foursquare, reflects the human activities of a city. The mobility dynamics inferred from Foursquare helps us understanding urban social events like crime In this paper, we propose a directed graph from the aggregated movement between regions using Foursquare data. We derive region risk factor from the movement direction, quantity and crime history in different periods of the day. Later, we propose a new set of features, DIrected graph Flow FEatuRes (DIFFER) which are associated with region risk factor. The reliable correlations between DIFFER and crime count are observed. We verify the effectiveness of the DIFFER in monthly crime count using Linear, XGBoost, and Random Forest regression in two cities, Chicago and New York City.

cs.SI↗

arXiv2020

Exploring the Impact of COVID-19 Lockdown on Social Roles and Emotions while Working from Home

In the opening months of 2020, COVID-19 changed the way for which people work, forcing more people to work from home. This research investigates the impact of COVID-19 on five researchers' work and private roles, happiness, and mobile and desktop activity patterns. Desktop and smartphone application usage were gathered before and during COVID-19. Individuals' roles and happiness were captured through experience sampling. Our analysis show that researchers tend to work more during COVID-19 resulting an imbalance of work and private roles. We also found that as working styles and patterns as well as individual behaviour changed, reported valence distribution was less varied in the later weeks of the pandemic when compared to the start. This shows a resilient adaptation to the disruption caused by the pandemic.

cs.HC↗