Clifton Phua

NCS Group · Smart & Safe City Centre of Excellence

In this paper we present the problems associated with acquisition of ground truth, which is a critical step in facilitating accurate and automated care in Assisted Living Facilities. The approach permits both bottom up and top down methods of reasoning about data. The tradeoffs between granularity of ground truth acquisition and its impact on the d...

The open-source Global Database of Events, Language, and Tone (GDELT) is the most comprehensive and updated Big Data source of important terms extracted from international news articles . We focus only on GDELT's Singapore events to better understand the data quality of its news articles, accuracy of its term extraction, and potential for predictio...

Using Graham's rules on picking stocks has been proven to generate profits for value investors. The authors propose using 3D subspace clustering to generate rules to pick potential undervalued stocks; 3D subspace clustering is effective in handling high-dimensional financial data and is adaptive to new data. In addition, its results aren't influenc...

Click fraud--the deliberate clicking on advertisements with no real interest on the product or service offered--is one of the most daunting problems in online advertising. Building an effective fraud detection method is thus pivotal for online advertising businesses. We organized a Fraud Detection in Mobile Advertising (FDMA) 2012 Competition, open...

The technological advances in smartphones and their widespread use has resulted in the big volume and varied types of mobile data which we have today. Location prediction through mobile data mining leverages such big data in applications such as traffic planning, location-based advertising, intelligent resource allocation; as well as in recommender...

With growing aging population around the world, there is urgent need of effective solution to improve the quality of life of demented elderly. In order to provide real-time monitoring and timely assistance to elderly in their home settings, we designed and developed a system employing model-based scenario verification, sensing-assisted activity rec...

This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the...

Background: With an ever-growing ageing population, dementia is fast becoming the chronic disease of the 21st century. Elderly people affected with dementia progressively lose their autonomy as they encounter problems in their Activities of Daily Living (ADLs). Hence, they need supervision and assistance from their family members or professional c...

In this work, we presented the strategies and techniques that we have developed for predicting the near-future churners and win-backs for a telecom company. On a large-scale and real-world database containing customer profiles and some transaction data from a telecom company, we first analyzed the data schema, developed feature computation strategi...

This poster presents an integrated framework to enable using standard non-sequential machine learning tools for accurate multi-modal activity recognition. Our framework contains simple pre- and post-classification strategies such as class-imbalance correction on the learning data using structure preserving oversampling, leveraging the sequential na...

This paper presents an integrated framework to enable using standard non-sequential machine learning tools for accurate multi-modal activity recognition. We develop a novel framework that contains simple pre- and post-classification strategies to improve the overall performance. We achieve this through class-imbalance correction on the learning dat...

Activity recognition within ambient environments is a highly non-trivial process. Such procedures can be managed using rule based systems in monitoring human behavior. However, designing and verification of such systems is laborious and time-consuming. We present a rule verification system that uses model checking techniques to ensure rule validity...

To enhance ICP monitoring of Traumatic Brain Injury (TBI) patients, much research effort has been attracted to the development auto-alarming systems and forecasting methods to predict impending intracranial hypertension episodes. Nevertheless, the performance of the proposed methods are often limited by the presence of artifacts in the ICP signal....

Most smart home based monitoring / assistive systems that attempt to recognize activities within a smart home are targeted towards living-alone elderly, and stop at providing instantaneous coarse grained information such as room-occupancy or provide specific programmed reminders for taking medication etc. In our work, we target multiple residents,...

Due to the rapidly aging population around the world, senile dementia is growing into a prominent problem in many societies. To monitor the elderly dementia patients so as to assist them in carrying out their basic Activities of Daily Living (ADLs) independently, sensors are deployed in their homes. The sensors generate a stream of context informat...

Multiple people activity recognition system is an essential step in Ambient Assisted Living system development. A possible approach for multiple people is to take an existing system for single person activity recognition and extend it to the case of multiple people. One approach is Multiple Hypothesis Tracking (MHT) which provides capabilities of m...

Providing cognitive assistance to Alzheimer’s patients in smart homes is a field of research that receives a lot of attention lately. The recognition of the patient’s behavior when he carries out some activities in a smart home is primordial in order to give adequate assistance at the opportune moment. To address this challenging issue, we present...

Monitoring and timely intervention are extremely important in the continuous management of health and wellness among all segments of the population, but particularly among those with mild dementia. In relation to this, we prescribe three design principles for the construction of services and applications. These are ambient intelligence, service con...

This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the...

Using ambient intelligence to assist people with dementia in carrying out their Activities of Daily Living (ADLs) independently in smart home environment is an important research area, due to the projected increasing number of people with dementia. We present herein, a system and algorithms for the automated recognition of ADLs; the ADLs are in ter...

Providing cognitive assistance in smart homes is a field of research that receives a lot of attention lately. In order to give adequate assistance at the opportune moment, we need to recognize the observed behavior when the patient carries out some activities in a smart home. To address this challeng- ing issue, we present a formal activity recogni...

People with dementia lose their ability to learn, solve problems, and communicate. And they are all around us. To potentially replace some of their diminished memory and problem-solving abilities, erroneous-plan recognition (EPR) aims to detect defects or faults in the execution of correct plans by the dementia patient, and send timely audio and vi...

In this paper we propose an efficient and incremental plan recognition method for cognitive assistance. We design our unique method based on graph matching and heuristic chaining rules in order to deal with interleaved and sequential activities. The finding of this research work is to be applied to predict abnormal behavior of the users, and optimi...

This paper describes a rapid technique: communal analysis suspicion scoring (CASS), for generating numeric suspicion scores on streaming credit applications based on implicit links to each other, over both time and space. CASS includes pair-wise communal scoring of identifier attributes for applications, definition of categories of suspiciousness f...

Gigabytes of personal data are stored electronically by businesses, governments, universities, hospitals, and organisations on their customers, citizens, students, patients, and employees respectively. These personal data include social security numbers (or identity card numbers), names, addresses, dates-of-birth, driver's licence numbers, credit c...

Security data mining, a form of countermeasure, is the use of large-scale data analytics to dynamically detect a small number of adversaries who are constantly changing. It encompasses data-and results-related safeguards; and is relevant across multiple domains such as financial, insurance, and health. With reference to security data mining, there...

The work is motivated by the expanding demand and limited supply of long-term personal care for People with Dementia (PwD), and assistive technology as an alternative. Telecare allows PwD to live in the comfort of their homes for a longer time. It is challenging to have remote care in smart homes with ambient intelligence, using devices, networks,...

The objective is to measure utility of real-time commercial decision making. It is important due to a higher possibility of mistakes in real-time decisions, problems with recording actual occurrences, and significant costs associated with predictions produced by algorithms. The first contribution is to use overall utility and represent individual u...

Almost every person has a life-long personal name which is officially recognised and has only one correct version in their language. Each personal name typically has two components/parts: a first name (also known as given, fore, or Christian name) and a last name (also known as family name or surname). Both these name components are strongly influe...

Automated adversarial detection systems can fail when under attack by adversaries. As part of a re- silient data stream mining system to reduce the pos- sibility of such failure, adaptive spike detection is attribute ranking and selection without class-labels. The first part of adaptive spike detection requires weighing all attributes for spiky-nes...

This paper is on adaptive real-time searching of credit application data streams for identity crime with many search parameters. Specifically, we concentrated on handling our domain-specific adversarial activity problem with the adaptive Communal Analysis Suspicion Scoring (CASS) algorithm. CASS's main novel theoretical contribution is in the formu...

The purpose of this paper is to outline some of the major developments of an identity crime/fraud stream mining system. Communal detection is about finding real communities of interest. The algorithm itself is unsupervised, single-pass, differentiates between normal and anomalous links, and mitigates the suspicion of normal links with a dynamic glo...

Identity crime has increased enormously over the recent years. Spike detection is important because it highlights sudden and sharp rises in intensity relative to the current identity attribute value (which can be indicative of abuse). This paper proposes the new spike analysis framework for monitoring sparse personal identity streams. For each iden...

This paper describes a technique for generating numeric suspicion scores on credit applications based on implicit links to each other, and over time and space. Its contributions include pair-wise communal scoring of identifier attributes for applications, definition of categories of suspiciousness for application-pairs, smoothed k-wise scoring of m...

This paper proposes an innovative fraud detection method, built upon existing fraud detection research and Minority Report, to deal with the data mining problem of skewed data distributions. This method uses backpropagation (BP), together with naive Bayesian (NB) and C4.5 algorithms, on data partitions derived from minority oversampling with replac...

This paper proposes an innovative fraud detection method, built upon existing fraud detection research and Minority Report, to deal with the data mining problem of skewed data distributions. This method uses backpropagation (BP), together with naive Bayesian (NB) and C4.5 algorithms, on data partitions derived from minority oversampling with replac...

This paper outlines an approach that we are taking for eldercare applications in the smart home, involving cognitive errors and their compensation. Our approach involves high-level modeling of daily activities of the elderly by breaking down these activities into smaller units, which can then be automatically recognized at a low-level by collection...