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
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
In this paper, we propose Global Context Convolutional Network (GCCN) for visual recognition. GCCN computes global features representing contextual information across image patches. These global
We propose a novel approach for visual representation learning called Signature-Graph Neural Networks (SGN). SGN learns latent global structures that augment the feature representation of Convolu
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 eithe
Methods and systems are disclosed for scheduling a task of a user based on a cyber - physical - social ( CPS ) context of activities . The present disclosure is directed to increasing the efficie
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
The automatic generation of long and coherent medical reports given medical images (e.g. Chest X-ray and Fundus Fluorescein Angiography (FFA)) has great potential to support clinical practice. Res
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
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 spac
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
Managing individuals’ attention and interruptibility is still a challenging task in the field of human-computer interaction. Individuals’ intrinsic interruptibility preferences are often establ
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 (pl
To model and forecast ight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor data, if modelled
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 out
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 prog
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
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
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 de
With the development of sensing and wireless communication technologies, recent years have witnessed a wide spectrum of vehicles including taxis, buses and logistical vans, equipped with a number
Finding the shortest route between a pair of origin and destination is known to be a crucial and challenging task in intelligent transportation systems. Current methods assume fixed travel time be
A well-planned patrol route plays a crucial role in increasing public security. Most of the existing studies designed the patrol route in a static manner. Situations when rerouting of patrol path
We introduce the novel research problem of task recognition in daily life.We recognize tasks such as project management, planning, meal-breaks, communication, documentation, and family care. We c
Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity an
Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity an
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
We present DroTrack, a high-speed visual singleobject tracking framework for drone-captured video sequences. Most of the existing object tracking methods are designed to tackle well-known challen
OVer the past decade, there has been an upward trend for the adoption of open-plan offices [1], [2], [3] in the corporate sector. This trend shift is often associated with a drive to save space,
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
Existing research on parking availability sensing mainly relies on extensive contextual and historical information. In practice, the availability of such information is a challenge as it requires
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
Smartphones, wearables, and Internet of Things (IoT) devices produce a wealth of data that cannot be accumulated in a centralized repository for learning supervised models due to privacy, bandwid
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
Due to the increasing nature of flexible work and the recent requirements from COVID-19 restrictions, workplaces are becoming more hybrid (i.e. allowing workers to work between traditional office
Network alignment is useful for multiple applications that require increasingly large graphs to be processed. Existing research approaches this as an optimization problem or computes the similarit
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 da
A critical problem in time series analysis is change point detection, which identifies the times when the underlying distribution of a time series abruptly changes. However, several shortcomings l
voice text transcription, the voice segment cut in the previous step is transcribed into a complete meeting text; The third part is the extraction of the meeting minutes, extracting meaningful key
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 spatiotemporal probabilistic m
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
The smart parking system is one of the most important problems in smart cities. Recently, an increasing number of sensors installed in parking spaces provide big spatio-temporal data which be used
The study of urban dynamics is to understand and analyse the dynamic properties of a city in the spatio-temporal domain. It includes the daily routines of the inhabitants, their movement patterns, g
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 amongst others things for indoor comfort
The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. Th
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 R
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. Ou
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
Smartphones, wearables, and Internet of Things (IoT) devices produce a wealth of data that cannot be accumulated in a centralized repository for learning supervised models due to privacy, bandwid
Many new tools for improving the design and operation of buildings try to realize the potential of big data. In particular, data is an important element for occupant-centric design and operation as
One of the core challenges in open-plan workspaces is to ensure a good level of concentration for the workers while performing their tasks. Hence, being able to infer concentration levels of worker
In the last four decades several methods have been used to model occupants’ presence and actions (OPA) in buildings according to different purposes, available computational power, and technical s
The three-dimensional (3D) vectorial nature of electromagnetic waves of light has not only played a fundamental role in science but also driven disruptive applications in optical display, microscop
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
Intelligent assistants can serve many purposes, including entertainment (e.g. playing music), home automation, and task management (e.g. timers, reminders). The role of these assistants is evolvin
The prediction of flight delays plays a significantly important role for airlines and travellers because flight delays cause not only tremendous economic loss but also potential security risks. In
On-ground aircraft trajectory information plays a key role in airport situations awareness prediction and management. Airport administration needs to arrange and schedule the time and order of ai
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 d
Workplace occupancy detection is becoming increasingly important in large Activity Based Work (ABW) environments as it helps building and office management understand the utilisation and potentia
The process of automatic generation of a road map from GPS trajectories, called map inference, remains a challenging task to perform on a geospatial data from a variety of domains as the majority
Call and messaging logs from mobile devices have been used to predict human personality traits successfully in re-cent years. However, the widely available accelerometer data is not yet utilized for
Deep learning has been extended to a number of new domains with critical success, though some traditional orienteering problems such as the Travelling Salesman Problem (TSP) and its variants are
Call and messaging logs from mobile devices have been used to predict human personality traits successfully in re-cent years. However, the widely available accelerometer data is not yet utilized for
Location Based Social Network, Foursquare helps us to understand the human movement of a city. It provides data that characterises the volume of movements across regions and Places of Interests (
The process of automatic generation of a road map from GPS trajectories, called map inference, remains a challenging task to perform on a geospatial data from a variety of domains as the majority
The prediction of flight delays plays a significantly important role for airlines and travellers because flight delays cause not only tremendous economic loss but also potential security risks. In
User mobility is the movement of individuals from one place to another. Trip is a segment of user mobility. A context is any information that can be used to characterise the situation of an entity
Call and messaging logs from mobile devices have been used to predict human personality traits successfully in re-cent years. However, the widely available accelerometer data is not yet utilized for
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 dep
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 spatiotemporal probabilistic mo
On-ground aircraft trajectory information plays a key role in airport situations awareness prediction and management. Airport administration needs to arrange and schedule the time and order of ai
Intelligent assistants can serve many purposes, including entertainment (e.g. playing music), home automation, and task management (e.g. timers, reminders). The role of these assistants is evolvin
A critical problem in time series analysis is change point detection, which identifies the times when the underlying distribution of a time series abruptly changes. However, several shortcomings l
Multivariate time series (MTS) prediction plays a significant role in many practical data mining applications, such as finance, energy supply, and medical care domains. Over the years, various predi
The usage of smart devices is an integral element in our daily life. With the richness of data streaming from sensors embedded in these smart devices, the applications of ubiquitous computing are li
Given the source and destination locations of n group members and a set of required point of interest (POI) types such as restaurants and shopping centers, a Group Trip Scheduling (GTS) query sched
The proliferation of Internet of Things (IoT) has led to the emergence of enabling many interesting applications within the realm of several domains including smart cities. However, the accumulatio
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 thi
Understanding how tasks progress over time enables digital assistants to help with current activities and support future activities. Imbuing assistants with the ability to track task progress requ
In this paper, we propose and demonstrate a novel technique for true random number generator (TRNG) application using GeSe-based Ovonic threshold switching (OTS) selector devices. The inherent varia
Recently human activity recognition has encouraged a great deal of interest due to its impact on various areas of application. As human’s brain own ability to recognize the actions relies upon
User mobility is the movement of individuals from one place to another. Trip is a segment of user mobility. A context is any information that can be used to characterise the situation of an entity
Human occupancy information is crucial for any modern Building Management System (BMS). Implementing pervasive sensing and leveraging Carbon Dioxide data from BMS sensor, we present large Room Uti
Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - H
Location-Based Social Networks (LBSN) provides unprecedented opportunities to tackle various social problems. In this study, we identify a number of crime-prediction-specific dynamic features whi
Traffic congestion causes heavily energy consumption, carbon dioxide emission and air pollution in cities, which is usually created by cars searching on-street parking spaces. Drivers are likely t
Energy consumption prediction typically corresponds to a multivariate time series prediction task where different channels in the multivariate time series represent energy consumption data and va
In this paper, we focus on simultaneous inference of transportation modes and human activities in daily life via modelling and inference from multivariate time series data, which are streamed from
In this paper, we focus on designing and developing ProMETheus, an intelligent system for meeting minutes generated from audio data. The first task in ProMETheus is to recognize the speakers from
The global urbanization imposes unprecedented pressure on urban infrastructure and public resources. The population explosion has made it challenging to satisfy the daily needs of urban residents.
We address the problem of identifying in-app user actions from Web access logs when the content of those logs is both encrypted (through HTTPS) and also contains automated Web accesses. We nd th
The increasing popularity of smart mobile phones and their powerful sensing capabilities have enabled the collection of rich contextual information and mobile phone usage records through the devic
Policy makers and urban planners around the world are encouraging people to use active transport by providing more easily accessible facilities for active transport users. However, trip planning
This paper investigates the Cyber-Physical behavior of a user in a large indoor shopping center by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the center operato
The developments in sensing modalities and computing platforms enable many new sensing technologies and data sources for monitoring occupant presence and actions. The wealth of data opens new opp
In this paper, we address the neighborhood identication problem in the presence of a large number of heterogeneous contextual features. We formulate our research as a problem of queue wait time p
The increasing popularity of smart mobile phones and their powerful sensing capabilities have enabled the collection of rich contex- tual information and mobile phone usage records through the dev
Understanding the association between customer demographics and behaviour is critical for operators of indoor retail spaces. This study explores such an association based on a combined understandi
Nowadays, Location-Based Social Networks (LBSN) collect a vast range of information which can help us to understand the regional dynamics (i.e. human mobility) across an entire city. LBSN provides
We propose a novel approach for enabling trustworthy, privacy-enhanced and personalized location based services (LBSs) that find nearby points of interests (POIs) such as restaurants, ATM booths, a
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 t
Recently, door access control with Internet of Things (IoT) has become increasingly popular in the field of security. However, conventional approaches such as video-based or biological informatio
The on-street parking system is an indispensable part of civil projects, which provides travellers and shoppers with parking spaces. With the recent in-ground sensors deployed throughout the city
Research on building and room occupancy counting is becoming more important. By understanding and knowing the numbers of people within a building, the heating, cooling, lighting control, building e
Smart cities and smart environments – as enabled by pervasive computing technologies – can make us become smarter, relieve us from many customary activities and even take over some boring and re
Traditionally, recommender systems modelled the physical and cyber contextual influence on people’s moving, querying, and browsing behaviours in isolation. Yet, searching, querying and moving beh
Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - H
We propose a novel approach for enabling trustworthy, privacy-enhanced and personalized location based services (LBSs) that find nearby points of interests (POIs) such as restaurants, ATM booths, a
We address the problem of identifying in app user actions from Web access logs when the content of those logs is both encrypted (through HTTPS) and also contains auto mated Web accesses. Wend th
In this paper, we address the neighborhood identication problem in the presence of a large number of heterogeneous contextual features. We formulate our research as a problem of queue wait time p
Understanding the association between customer demographics and behaviour is critical for operators of indoor retail spaces. This study explores such an association based on a combined understandi
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 t
The increasing popularity of smart mobile phones and their powerful sensing capabilities have enabled the collection of rich contex- tual information and mobile phone usage records through the dev
Energy consumption prediction typically corresponds to a multivariate time series prediction task where different channels in the multivariate time series represent energy consumption data and va
The study of user mobility is to understand and analyse the movement of individuals in the spatial and temporal domains. Mobility analytics and trip planning are two vital components of user mobilit
Recent advances in communication, sensors and processors have made pervasive systems more computationally powerful and increasingly popular. These systems are deployed everywhere all the time while
With advancement in sensors and the Internet of Things, gathering spatiotemporal information from one’s surroundings has become more convenient. There are multiple phenomenological behaviours,
Recent advances in the Internet of Things (IoT) have changed the way we interact with the world. The ability to monitor and manage objects in the physical world electronically makes it possible to b
Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - H
Mining time series data is a difficult process due to the lag factor and different time of data arrival. In this paper, we present Seasonal Decomposition for Human Occupancy Counting (SD-HOC), a c
Understanding and predicting human mobility is a key problem in different applications. Existing works on human mobility prediction mainly focus on the prediction of the next location (or a set o
We propose a multi-resolution selective ensemble extreme learning machine (MRSE-ELM) method for time-series prediction with the application to the next-step and next-day electricity consumption p
This paper presents a method to automatically estimate parameters for density-based clustering based on data distribution. It also includes several techniques for visualizing the clusters over a
Rooms and buildings in public and private institutions are usually secured with door access control, which is prone to intrusion, particularly when the key or access card falls to unauthorised pa
This paper addresses the problem of taxi-passenger queue context prediction using neighborhood based methods. We capture the taxi drivers’ knowledge based on how they move in terms of temporal
In this paper,we focus on developing a model and system for predicting the city foot traffic. We utilise historical records of pedestrian counts captured with thermal and laser-based sensors insta
The taxi and passenger queue contexts indicate the various states of queues related to taxis and passengers (i.e. taxis are waiting for passengers, passengers are waiting for taxis, both are waiting
Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide -
Human occupancy information is crucial for any modern Building Management System (BMS). Implement- ing pervasive sensing and leveraging Carbon Dioxide data from BMS sensor, we present Carbon Dioxide
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. thos
The ever increasing size of graphs makes them difficult to query and store. In this paper, we present Shrink, a compression method that reduces the size of the graph while preserving the distances
We analyze 18 million rows of Wi-Fi access logs collected over a one year period from over 120,000 anonymized users at an inner-city shopping mall. The anonymized dataset gathered from an optin sy
Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the
Currently, large amounts of Wi-Fi access logs are collected in diverse indoor environments, but cannot be widely used for ne-grained spatio-temporal analysis due to coarse positioning. We present
The on-street parking system is an indispensable part of civil projects, which provides travellers and shoppers with parking spaces. With the recent in-ground sensors deployed throughout the city
Understanding human mobility is the key problem in many applications such as location-based services and recommen- dation systems. The mobility of a smartphone user can be modeled by a movement g
Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide -
Event detection on Twitter has attracted active research. Although existing work considers the semantic topic structure of documents for event detection, the topic dynamics and the semantic consis
Since the early days of automotive entertainment, music has played a crucial role in establishing pleasurable driving experiences. Future autonomous driving technologies will relieve the driver f
In this paper, Extreme Learning Machine (ELM) is shown to be a powerful tool for electricity consumption prediction, demonstrated by its competitive prediction accuracy and superior computational
In this paper, we present Ubiquitous daTa Exploration (UTE), a mobile sensor data collection, annotation and exploration platform. Our platform facilitates rapid prototyping of data mining experi
Multi-activity recognition in the urban environment is a challenging task. This is largely attributed to the influence of urban dynamics, the variety of the label sets, and the heterogeneous natur
This paper presents AmPost, a prototype of an interactive audio poster, which integrates ink-jet printed sonic and tactile elements along with textual and graphical information. This enables user
With advancement in sensors and Internet of Things, gathering spatiotemporal information from one’s surroundings has become easier, to an extent that we can start to use sensor data to infer in
Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to th
In this paper, the Multi-Objective Time-Dependent Orienteering Problem (MOTDOP) is investigated. Time-dependent travel time and multiple preferences are two of the most important factors in practi
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 evaluat
With growing urban populations, the World Health Organization has highlighted the importance of urban design for everyone. It is widely recognized that quality of life in the urban environment could
Pervasive and mobile computing technologies can make our everyday living environments and our cities “smart”, i.e., capable of reaching awareness of physical and social processes and of dynam
In many real-world applications, one needs to deal with a large multi-silo problem with interdependent silos. In order to investigate the interdependency between silos (subproblems), the Travelin
Urban planners and policy makers often rely on data visualization and spatial data mapping tools to perceive the overall urban trends. The accumulation of historical and real-time urban data from
Internet of Things (IoT) represents a cyberphysical world where physical things are interconnected on the Web. This paper presents an architecture designed for Energy-efficient Inter-organization
We analyze 18 million rows of Wi-Fi access logs collected over a one year period from over 120,000 anonymized users at an inner-city shopping mall. The anonymized dataset gathered from an optin sy
With today’s ubiquitous computing technologies, our daily activities are continuously traced by smartphones in our pockets and more of our everyday things are now connected to the Internet. This
For workers in extreme environments, such as firefighters, thermal protective clothing is essential to protect them from exposures to high heat and life threatening risks. This study will investi
This paper presents a smartphone app connected to a sensor cloud for spatio-temporal management and 3D visualization of data from ad-hoc wireless sensor networks (WSN) and Internet of Things (IoT
Elderly people are prone to fall due to the high rate of risk factors associated with ageing. Existing fall detection sys- tems are mostly designed for a constrained environment, where various as
When users consult a trip planner, map or navigation sys- tem for directions, they are presented with several route options which often are evaluated based on shortest path or shortest duration.
Human activity modeling from large-scale sensor data is an emerging domain. We present a framework to classify days into two groups: weekends and weekdays. The data collected by Device Analyzer,
Most Augmented Reality applications feature an information overlay over physical objects that consists of text, images, and animated 3-D models. We go a step further by using an AR-menu and linki