Virtual Visit of External Examiner 

Date: 6th & 7th December 2021 

Platform: Microsoft Teams

Programme: Bachelor of Science (Honours) Applied Mathematics with Computing 

External Examiner: Prof. Dr. Huang Huang-Nan (Tunghai University, Taiwan)

Programme: Bachelor of Science (Honours) Financial Mathematics

 External Examiner: Prof. Dr. Yang Hailiang (The University of Hong Kong)

Opening Meeting

Meeting with Staff

Research Talk by Prof. Dr. Yang Hailiang

Meeting with Students (AM)

Meeting with Students (FM)

Exit Meeting

Virtual Visit of External Examiner for Bachelor of Science (Honours) Actuarial Science

Prof. Dr. Yam Sheung Chi Phillip

(The Chinese University of Hong Kong)

Date: 29th & 30th November 2021

Platform: Zoom

Opening Meeting

Meeting with Students

Meeting with Staff

Exit Meeting

Research Talk by Invited Speaker from Universiti Teknologi PETRONAS

Multi-EigenSpot: A Novel Eigenspace-Based Method for Detecting Multiple Space-Time Disease Clusters

Date	21 October 2021 (Thursday)
Time	10.00am – 11.00am
Platform	Microsoft Teams
Speaker	Assoc. Prof. Dr Hanita binti Daud Fundamental and Applied Sciences Department Universiti Teknologi PETRONAS Perak, Malaysia
Organizer/  Co-organizer	Department of Mathematical and Actuarial Sciences (DMAS) Centre for Mathematical Sciences (CMS)

Abstract:

Detecting the potential space-time disease clusters is necessary for conducting surveillance and implementing disease prevention policies. In disease surveillance, space-time cluster detection aims to detect the sub-regions in the spatiotemporal space, where the observed disease count is higher than what is to be expected if no risk factor exists. The state-of-the-art method for this problem is the space-time scan statistic (SaTScan). This method is based on the Maximum Likelihood Estimation (MLE) which put some constraints on the distribution of the data such as Poisson or Gaussian counts that are valid only for the laboratory data and not necessarily valid for the non-traditional data sources. Addressing this problem, an Eigenspace-based method called an EigenSpot has been recently proposed as a nonparametric solution for space-time cluster detection. However, the main problem with the EigenSpot method is that it can detect a single cluster only and cannot be adapted for detecting multiple clusters. This is an important limitation, since multiple hotspots may have occurred in the study area, sometimes at the same level of importance. Addressing this issue, this study proposes an extension of the EigenSpot method, called a Multi-EigenSpot that is able to handle multiple hotspots by iteratively removing previously detected hotspots and re-running the algorithm until no more hotspots are found. The proposed method uses the expected disease counts as the baseline information instead of the population counts and hence allows for removing the previously detected cluster by replacing the observed counts by the respective expected counts. The proposed method approximates multiple clusters automatically through a visualization tool. In addition, the proposed method can be adapted for detecting clusters with unknown population counts which is often the case in the least developed countries. A comprehensive experimental evaluation, both on simulated and real-world datasets reveals that the proposed method not only provides a nonparametric solution to multiple clusters detection problem but also characterizes relatively higher efficiency than the state-of-the-art methods.

Thank you for the wonderful sharing!

Research Talk by Invited Speaker from Multimedia University Melaka, Malaysia

Swarm Intelligence Mimicking to Solve Complex Problems

Date	10 September 2021 (Friday)
Time	10.00am– 11.00am
Platform	Microsoft Teams
Speaker	Dr Nor Azlina Binti Ab Aziz Faculty of Engineering and Technology Multimedia University Melaka, Malaysia
Organizer/  Co-organizer	Department of Mathematical and Actuarial Sciences (DMAS) Centre for Mathematical Sciences (CMS)

Abstract:

Swarm intelligence (SI) belongs to the family of computational intelligence algorithms. The algorithms from SI family are mostly inspired from nature and work based on the concept of collaboration and information sharing. Among the popular SI algorithms are Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and many others. These algorithms share the same framework but differ from each other on how the agents look for a solution which is dependent on their source of inspiration. During this session, the broad concept of SI will be discussed, followed by some of the popular SI algorithms and an example of application for solving a real-world problem. This session aims to attract the interest of new researchers in this field and as a channel of discussion among existing SI’s researchers.

Thank you for the informative sharing!

Research Talk by Invited Speaker from Universiti Tun Hussein Onn Malaysia

Curve Fitting with First-Order Linear Differential Equation

Date	20 August 2021 (Friday)
Time	10.00am– 11.00am
Platform	Microsoft Teams
Speaker	Dr Kek Sie Long PhD, CQRM Department of Mathematics and Statistics Universiti Tun Hussein Onn Malaysia Pagoh Campus, Muar, Johor, Malaysia
Organizer/  Co-organizer	Department of Mathematical and Actuarial Sciences (DMAS) Centre for Mathematical Sciences (CMS)

Abstract:

In this talk, the curve fitting, which is handled by using the first-order linear differential equation, is discussed. For this purpose, the coefficient of proportionality is determined from the set of data points. For the constant coefficient, the measures of the central tendency, which are mean, median, and mode, are calculated. By applying these constant coefficients and the varying coefficients, the first-order linear differential equation exists and solvable. On this basis, the Euler method is applied to approximate the solution of the differential equations. For illustration, some sets of data points and their curves are observed, while the best fitting of these curves is demonstrated by using the method proposed. The results show that the performance efficiency of the method proposed is satisfied within a given tolerance. In conclusion, applying the linear differential equation to fit the curve of data points provides an effective alternative approach for the curve fitting problem.

Thank you for the educational sharing!

Research Talk by Invited Speaker from Universiti Sains Malaysia

Tight Bounds on the Burning Numbers of Path Forests and Spiders

Date	13 August 2021 (Friday)
Time	10.00am– 11.00am
Platform	Microsoft Teams
Speaker	Dr Teh Wen Chean School of Mathematical Sciences Universiti Sains Malaysia Pulau Pinang, Malaysia
Organizer/  Co-organizer	Department of Mathematical and Actuarial Sciences (DMAS) Centre for Mathematical Sciences (CMS)

Abstract:

In 2016, Bonato, Janssen, and Roshanbin introduced graph burning as a discrete process that models the spread of social contagion. Although the burning process is a simple algorithm, the problem of determining the least number of rounds needed to completely burn a graph is NP-complete even for elementary graph structures like starlike trees, also called spiders. An open burning number conjecture states that every connected graph of order m2 can be burned in at most m rounds. Attempts to prove the conjecture have resulted in various upper bounds for the burning number and verification of the conjecture for various classes of graphs, including spiders. In this seminar, we will present a detailed outline about our contribution towards the burning number conjecture, which results from a collaborative work with Ta Sheng Tan. Together, we found a tight upper bound on the order of a spider for it to be burned within a given number of rounds. Our result showed that the tight bound depends on the structure of the spiders, namely the number of arms. Additionally, an analogous corresponding tight upper bound for path forests was obtained, thus completing a previously known partial result. This suggests new perspective towards the burning number conjecture via characteristics of graphs. We will end the presentation by briefly mentioning the speaker's ongoing work as appropriate. This presentation is intended to facilitate readers interested in our contribution and as an invitation for potential collaboration into this work.

Thank you for the interesting sharing!

Research Talk by Invited Speaker from Universiti Malaya

Modelling and forecasting stock volatility and return: A new approach based on quantile Rogers-Satchell volatility measure with asymmetric bilinear CARR model

Date	6 August 2021 (Friday)
Time	11.00am– 12.00pm
Platform	Microsoft Teams
Speaker	Assoc Prof Dr Ng Kok Haur Institute of Mathematical Sciences Faculty of Science Universiti Malaya Kuala Lumpur, Malaysia
Organizer/  Co-organizer	Department of Mathematical and Actuarial Sciences (DMAS) Centre for Mathematical Sciences (CMS)

Abstract:

Volatility of asset prices in financial market is not directly observable. Return-based models have been proposed to estimate the volatility using daily closing prices. Recently, many new range-based volatility measures were proposed to estimate the volatility directly. This study proposes quantile Rogers-Satchell (QRS) measure to ensure robustness to extreme prices. We add an efficient term to correct the downward bias of Rogers-Satchell (RS) measure and provide scaling factors for different interquantile range levels to ensure unbiasedness of QRS. Simulation studies confirm the efficiency of QRS measure relative to the intraday squared returns and RS measures in the presence of extreme prices. To smooth out the noises, QRS measures are fitted to the conditional autoregressive range model with different asymmetric mean functions. By comparing to two realised volatility measures as proxies for the unobserved true volatility, result from Standard and Poor 500 index shows that QRS volatility estimates using asymmetric bilinear mean function provide the best in-sample model fit based on two robust loss functions. These fitted volatilities are then incorporated into return models to capture the heteroskedasticity of returns. Model with a constant mean, Student-t errors and QRS estimates gives the best-in-sample fit. Different value-at-risk (VaR) and conditional VaR forecasts based on the best return model are also provided and tested.

Thank you for the wonderful sharing!

Research Talk by Invited Speaker from Universiti Malaysia Terrengganu (UMT)

Ordered Weighted Averaging Operators: Modelling the Majority Concept based on Cardinality

Date	30 July 2021 (Friday)
Time	11.00am– 12.00pm
Platform	Microsoft Teams
Speaker	Dr. Binyamin Yusoff Mathematical Sciences Field Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terrenganu (UMT) Terrengganu, Malaysia
Organizer/  Co-organizer	Department of Mathematical and Actuarial Sciences (DMAS) Centre for Mathematical Sciences (CMS)

Abstract:

Aggregation of several input values into a single output value is crucial in numerous mathematical models. The problems of aggregation are very broad and heterogeneous. This talk will particularly focus on the ordered weighted averaging (OWA) operators, a specific topic of aggregation under the finite number of real inputs. The OWA operators are a parameterized family of mean operators. Recent developments of OWA, especially under the cardinality-dependent aggregation operators, or the so-called majority-additive OWA, will be presented. The MA-OWA operator generalizes the simple arithmetic mean, and it is known as the arithmetic mean of arithmetic means. Moreover, it can control the effect of extreme values in arithmetic mean that may cause biased results. The other variants of this aggregation operator, namely selective majority additive (SMA-OWA), selective aggregated majority (SAM-OWA) and weighted SAM-OWA with additional characteristic of cardinality relevance factor (CRF) will also be highlighted. Finally, the application of these aggregation operators in group decision-making problem will be presented to conclude this talk.

Thank you for the enlighten sharing!