Flora Dilys Salim

Flora Dilys Salim
RMIT University | RMIT · Computer Science and Information Technology, School of Science

BComp (Hons1), PhD (Computer Science)

About

239
Publications
79,730
Reads
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2,094
Citations
Introduction
A/Prof Flora Salim is a Deputy Director of the RMIT Centre for Information Discovery and Data Analytics. She leads the Context Recognition and Urban Intelligence group. Her research interests include Time-Series Data Mining, Spatio-Temporal and Trajectory Data Mining, Human Mobility, Context and Behaviour Modelling, Cross-Domain Data Fusion, and Applied Machine Learning. She was a Humboldt Fellow, Victoria Fellow, RMIT Vice-Chancellor's Awardee for Research Excellence 2016, an ARC APDI Fellow.
Additional affiliations
October 2008 - December 2011
RMIT University
Position
  • Research Associate
Description
  • Developed novel research outputs, a new cloud-based software service that integrates design tools with engineering analysis tools for performance-based design and multi-criteria optimisation, in collaboration with QUT and Project Services.
July 2005 - August 2008
Monash University (Australia)
Position
  • Context-Aware Computing Framework for Cooperative Intersection Collision Avoidance Systems
Description
  • PhD project, funded by APA scholarship and DSTC top-up scholarship.

Publications

Publications (239)
Article
Full-text available
We propose a model for clustering data with spatiotemporal intervals, which is a type of spatiotemporal data associated with a start-and an end-point. This model can be used to effectively evaluate clusters of spatiotemporal interval data, which signifies an event at a particular location that stretches over a period of time. Our work aims to deal...
Article
Full-text available
Existing journey planners and route recommenders mainly focus on calculating the shortest path with minimum distance or travel time. However, elderly people and those with special needs (i.e. those in wheelchairs or walking with sticks) often prefer a safer and more gentle journey. Given that their route options are affected by accessibility issues...
Article
This paper aims to observe and recognize transition times, when human activities change. No generic method has been proposed for extracting transition times at different levels of activity granularity. Existing work in human behavior analysis and activity recognition has mainly used predefined sliding windows or fixed segments, either at low-level,...
Conference Paper
Full-text available
A fundamental challenge in real-time labelling of activity data is user burden. The Experience Sampling Method (ESM) is widely used to obtain such labels for sensor data. However, in an in-situ deployment, it is not feasible to expect users to precisely label the start and end time of each event or activity. For this reason, time-point based experi...
Article
Full-text available
Understanding and predicting human mobility is vital to a large number of applications, ranging from recommendations to safety and urban service planning. In some travel applications, the ability to accurately predict the user's future trajectory is vital for delivering high quality of service. The accurate prediction of detailed trajectories would...
Preprint
Full-text available
Digital Assistants (DAs) can support workers in the workplace and beyond. However, target user needs are not fully understood, and the functions that workers would ideally want a DA to support require further study. A richer understanding of worker needs could help inform the design of future DAs. We investigate user needs of future workplace DAs u...
Article
Full-text available
Digital Assistants (DAs) can support workers in the workplace and beyond. However, target user needs are not fully understood, and the functions that workers would ideally want a DA to support require further study. A richer understanding of worker needs could help inform the design of future DAs. We investigate user needs of future workplace DAs u...
Preprint
Self-Supervised Learning (SSL) is a new paradigm for learning discriminative representations without labelled data and has reached comparable or even state-of-the-art results in comparison to supervised counterparts. Contrastive Learning (CL) is one of the most well-known approaches in SSL that attempts to learn general, informative representations...
Preprint
Full-text available
With the introduction of machine learning in high-stakes decision making, ensuring algorithmic fairness has become an increasingly important problem to solve. In response to this, many mathematical definitions of fairness have been proposed, and a variety of optimisation techniques have been developed, all designed to maximise a defined notion of f...
Article
The recent COVID-19 pandemic changed radically the world and how people interact, move and behave. Following a lockdown that was imposed worldwide, although with different timing, Mobile Contact Tracing Apps (MCTA) were proposed to digitally trace contacts between individuals, while releasing gradually mobility constraints mandated to contain the d...
Preprint
Full-text available
Notifications are one of the most prevailing mechanisms on smartphones and personal computers to convey timely and important information. Despite these benefits, smartphone notifications demand individuals' attention and can cause stress and frustration when delivered at inopportune timings. This paper investigates the effect of individuals' smartp...
Conference Paper
Many real-world ubiquitous applications, such as parking recommendations and air pollution monitoring, benefit significantly from accurate long-term spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency structure between the spatial and temporal domains, as well as the contextual information. Recent studies have revealed the po...
Article
Full-text available
This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patte...
Article
As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality. While tremendous progress has been made to model high-level spatio-temporal regularities with d...
Preprint
Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in the field of computer vision, speech, natural language processing (NLP), and recently, with other types of modalities, including time series from sensors. The popularity of self-supervised learning is driven by the fact that traditional models typically require...
Article
Full-text available
We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia. The data capture contained two elements: First, a 5-month longitudinal field study In-Gauge using two outdoor weather stations, as well as indoor weather stations in 17 classrooms and temperature sensors on the vents of occupant-controlled room air-condition...
Article
This article explores the design, implementation, and querying of a prototype system for automated spatial reasoning for geospatial intelligence applications, called NEXUS. The system combines multiple different reasoning components that can support a wide range of spatiotemporal queries. Fundamental to the requirements of intelligence analysts is...
Article
Full-text available
Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to be promising for spatio-temporal-based applications such as trajectory prediction, events generation, and time-series data imputation. While several reviews for GANs in c...
Preprint
Full-text available
Many real-world ubiquitous applications, such as parking recommendations and air pollution monitoring, benefit significantly from accurate long-term spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency between spatial and temporal domains, contextual information, and inherent pattern in the data. Recent studies have revealed t...
Conference Paper
Full-text available
Many real-world ubiquitous applications, such as parking recommendations and air pollution monitoring, benefit significantly from accurate long-term spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency between spatial and temporal domains, contextual information, and inherent pattern in the data. Recent studies have revealed t...
Preprint
While predictive models are a purely technological feat, they may operate in a social context in which benign engineering choices entail unexpected real-life consequences. Fairness -- pertaining both to individuals and groups -- is one of such considerations; it surfaces when data capture protected characteristics of people who may be discriminated...
Preprint
Full-text available
Disentangled representation learning offers useful properties such as dimension reduction and interpretability, which are essential to modern deep learning approaches. Although deep learning techniques have been widely applied to spatio-temporal data mining, there has been little attention to further disentangle the latent features and understandin...
Article
Full-text available
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...
Preprint
Seating location in the classroom can affect student engagement, involvement, attention and academic performance by providing better visibility, improved movement, and discussion participation. For example, sitting at the front of the classroom is a popular choice for students pursuing good academic performance. This research investigates how indiv...
Preprint
As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality. While tremendous progress has been made to model high-level spatio-temporal regularities with d...
Preprint
Existing human mobility forecasting models follow the standard design of the time-series prediction model which takes a series of numerical values as input to generate a numerical value as a prediction. Although treating this as a regression problem seems straightforward, incorporating various contextual information such as the semantic category in...
Preprint
Human mobility prediction is a core functionality in many location-based services and applications. However, due to the sparsity of mobility data, it is not an easy task to predict future POIs (place-of-interests) that are going to be visited. In this paper, we propose MobTCast, a Transformer-based context-aware network for mobility prediction. Spe...
Preprint
Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In the literature, the fusion of multi-source time-series has been achieved either by using ensemble learning models which ignore temporal patterns and correlation within features or by defining a fixed-size window to select specific pa...
Preprint
Full-text available
App usage prediction is important for smartphone system optimization to enhance user experience. Existing modeling approaches utilize historical app usage logs along with a wide range of semantic information to predict the app usage; however, they are only effective in certain scenarios and cannot be generalized across different situations. This pa...
Preprint
Multivariate time series (MTS) prediction plays a key role in many fields such as finance, energy and transport, where each individual time series corresponds to the data collected from a certain data source, so-called channel. A typical pipeline of building an MTS prediction model (PM) consists of selecting a subset of channels among all available...
Conference Paper
Full-text available
Conventional optical holography can address only the amplitude and phase information of an optical beam, leaving the 3D vectorial feature of light inaccessible. We demonstrate 3D vectorial holography where an arbitrary 3D vectorial field distribution on a wavefront can be precisely reconstructed using the machine-learning inverse design based on mu...
Article
Traditional occupant behavior modeling has been studied at the building level, and it has become an important factor in the investigation of building energy consumption. However, studies modeling occupant behaviors at the urban scale are still limited. Recent work has revealed that urban big data can enable occupant behavior modeling at the urban s...
Preprint
Inferring human mental state (e.g., emotion, depression, engagement) with sensing technology is one of the most valuable challenges in the affective computing area, which has a profound impact in all industries interacting with humans. The self-report survey is the most common way to quantify how people think, but prone to subjectivity and various...
Preprint
Full-text available
Existing parking recommendation solutions mainly focus on finding and suggesting parking spaces based on the unoccupied options only. However, there are other factors associated with parking spaces that can influence someone's choice of parking such as fare, parking rule, walking distance to destination, travel time, likelihood to be unoccupied at...
Preprint
Full-text available
Managing individuals' attention and interruptibility is still a challenging task in the field of human-computer interaction. Individuals' intrinsic interruptibility preferences are often established for and across different social roles and life domains, which have not yet been captured by modeling short-term opportunities alone. This paper investi...
Preprint
Full-text available
To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor data, if modelled correctly, can be used to generate a situational awareness map. Existing techniques apply traditional supervised learning methods onto historical data, c...
Preprint
The usage of smartphone-collected respiratory sound, trained with deep learning models, for detecting and classifying COVID-19 becomes popular recently. It removes the need for in-person testing procedures especially for rural regions where related medical supplies, experienced workers, and equipment are limited. However, existing sound-based diagn...
Preprint
Full-text available
We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia. The data capture contained two elements: First, a 5-month longitudinal field study In-Gauge using two outdoor weather stations, as well as indoor weather stations in 17 classrooms and temperature sensors on the vents of occupant-controlled room air-condition...
Chapter
Urban flow forecasting is a challenging task, given the inherent periodic characteristics of urban flow patterns. To capture the periodicity, existing urban flow prediction approaches are often designed with closeness, period, and trend components extracted from the urban flow sequence. However, these three components are often considered separatel...
Preprint
Full-text available
We propose flexgrid2vec, a novel approach for image representation learning. Existing visual representation methods suffer from several issues, including the need for highly intensive computation, the risk of losing in-depth structural information, and the specificity of the method to certain shapes or objects. flexgrid2vec converts an image to a l...
Article
Full-text available
To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor data, if modelled correctly, can be used to generate a situational awareness map. Existing techniques apply traditional supervised learning methods onto historical data, c...
Article
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...
Preprint
Full-text available
We propose an uncertainty-aware service approach to provide drone-based delivery services called Drone-as-a-Service (DaaS) effectively. Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts. The proposed DaaS service approach consists of three components: scheduling, rou...
Article
Full-text available
We propose an uncertainty-aware service approach to provide drone-based delivery services called Drone-as-a-Service (DaaS) effectively. Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts. The proposed DaaS service approach consists of three components: scheduling, rou...
Article
The HVAC (Heating, Ventilation and Air Conditioning) system is an important part of a building, which constitutes up to 40% of building energy usage. The main purpose of HVAC, maintaining appropriate thermal comfort, is crucial for the best energy usage. Additionally, thermal comfort is also important for well-being, health, and work productivity....
Preprint
Change Point Detection techniques aim to capture changes in trends and sequences in time-series data to describe the underlying behaviour of the system. Detecting changes and anomalies in the web services, the trend of applications usage can provide valuable insight towards the system, however, many existing approaches are done in a supervised mann...
Conference Paper
In a modern smart building, many aspects of the use can be monitored using sensing technologies. This enables a high number of data-driven applications used for many tasks, such as indoor comfort, energy efficiency, and space utilization. Open data sharing enables more robust data-driven applications for optimizing building operations. To enable su...
Preprint
Traffic flow prediction is a crucial task in enabling efficient intelligent transportation systems and smart cities. Although there has been rapid progress in this area in the last few years, given the major advances of deep learning techniques, it remains a challenging task because of the inherent periodic characteristics of traffic flow sequence....
Article
Full-text available
The study of student engagement has attracted growing interests to address problems such as low academic performance, disaffection, and high dropout rates. Existing approaches to measuring student engagement typically rely on survey-based instruments. While effective, those approaches are time-consuming and labour-intensive. Meanwhile, both the res...
Article
Extracting informative and meaningful temporal segments from high-dimensional wearable sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as Human Activity Recognition (HAR), trajectory prediction, gesture recognition, and lifelogging. In this paper, we propose ESPRESSO (Entropy and ShaPe awaRe timE-Series Se...
Preprint
Full-text available
This paper investigates the Cyber-Physical behavior of users in a large indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the mall operators. Our analysis shows that many users exhibit a high correlation between their cyber activities and their physical context. To find this correlation, we propo...
Preprint
Full-text available
Generative Adversarial Networks (GANs) have shown remarkable success in the computer vision area for producing realistic-looking images. Recently, GAN-based techniques are shown to be promising for spatiotemporal-based applications such as trajectory prediction, events generation and time-series data imputation. While several reviews for GANs in co...
Article
Full-text available
The Travelling Officer Problem (TOP) is a graph-based orienteering problem for modelling the patrolling routines of a parking officer monitoring an area. Recently, a spatio-temporal probabilistic model was built for TOP to estimate the leaving probability of parking cars, and relevant algorithms were applied to search for the optimal path for a par...
Article
Full-text available
Nowadays the ever-increasing energy consumption in buildings has caused supply shortages and adverse environmental impacts. The accurate prediction of energy consumption in smart buildings may help to monitor and control energy usage. As energy consumption is inevitably affected by exogenous factors such as temperature and wind speed, it is fundame...