Undergraduate Research (UResearch) in Computing Programme

What is the objective of the programme?

The programme offers an opportunity for undergraduate students in the BSc (Computer Science), BSc (Computing & Law), BSc (Information Systems) and BSc (Software Engineering) degree programmes to experience academic research in Computing, as well as prepare them for graduate studies.

It allows students to experience first-hand the challenges and exhilaration of research, discovery and innovation, and enriches their academic learning by working at the frontiers of computing research.

Who are eligible?

Third/fourth-year students in the BSc (Computer Science), BSc (Information Systems) and BSc (Software Engineering) degree programmes with GPA >= 3.4 and passion for research. Students must be able to complete the UResearch Programme requirements before they graduate.

Strongly motivated students in these degree programmes with a lower GPA may self-recommend by demonstrating their ability and passion for research through other means (e.g., research experience and strong recommendation of a faculty member).

Programme Coordinator


SUN Jun

Professor of Computer Science; Co-Director, Centre for Research for Intelligent Software Engineering; Lead Principal Investigator; Lee Kuan Yew Fellow

Email: junsun@smu.edu.sg

If you are interested in UResearch or have questions about projects or eligibility, please email the programme coordinator. 

Why should I join the programme?

Students enrolled in the programme will gain cutting-edge knowledge as well as experience on how research is conducted.

This experience will prepare them well for technology R&D jobs as well as graduate studies.

In particular, the following are put in place to motivate and support students in undertaking this programme:

  • Students may apply to work as research interns for up to 3 months during Summer (May - Aug).
    This can be used to fulfil the internship requirement.
  • A research excellence award will be given to students with excellent performance.
  • Students will receive a certificate from the school upon completing the UResearch Programme (IS 470, IS 472 and 1 PhD Course, or CS 470, CS 472 and 1 PhD Course).
  • Financial support for one conference trip if a research paper is published (at a tier-2 or above conference) as the result of the programme.

What are the requirements for the UResearch programme?

These courses can be mapped to students’ degree requirements as below:


BSc (Computer Science)StudentsBSc (IS) : InformationSystems StudentsBSc (Computing & Law) StudentsBSc (Software Engineering) Students

PhD course

CS Electives

IS Depth Electives

Computing Electives

Free Elective

IS470/CS470

IS472/CS472

CS Project Experience

IS Project Experience

C & L Project Experience

Free Elective

IS471/CS471

CS Elective

IS Depth Elective

Computing Elective

Free Elective

How do I identify a research topic and a supervisor?

A list of broad research projects proposed by faculty members will be listed online. A student is expected to identify relevant projects based on his/her interest and passion. Afterwards, the student meets the corresponding faculty member and secures his/her agreement to be the project supervisor before enrolling in the programme.

Frequently Asked Questions

Q: How long does this programme last?

A: This programme will last about one year (first regular term for IS470/CS470 and second regular term for IS472/CS472 and 1 PhD course)

Q: How many CUs can I take in the terms that I‘m undertaking the programme?

A: It is recommended that you do not take more than 4 CUs per term (including the UResearch Programme course(s)) as the workload is expected to be heavy.

Q: Do I need to pay additional fees for the PhD course?

A: No additional fees required; only e$20 will be deducted for enrolment for each of the UResearch Programme courses.

Q: What if I do not wish to continue with the programme after my IS470/CS470?

A: There is no penalty for dropping out of the programme. Your IS470/CS470 can still count towards your IS or CS Depth elective.

If you are still keen to continue with your IS470/CS470 project, or do another research study, you can do IS471/CS471 instead. However, you will not earn the UResearch Programme certificate nor map the 2 research courses (IS470/CS470 and IS471/CS471) towards your Information Systems(IS)/Smart-City Management & Technology(SMT)/Computer Science(CS) Project Experience.

UResearch Publication

Here are a list of publications contributed by our SCIS undergraduate students.

UResearch Projects from IS470/IS471/IS472 or CS470/CS471/CS472

Here are examples of research projects past and present worked on by students & faculty in IS470/IS471/IS472 or CS470/CS471/CS472 sorted by calendar year:

Cloud, edge and fog computing: Trends and case studies
Publication
As it is done today, an informal – solely based on experts’ intuition – evaluation of profitability of adopting cloud services is undependable and not scalable as there are many conflicting factors and constraints such evaluation should account for. The revenue from service tenants and the cost of implementing the service architecture are the leading service factors that drive profitability. Cloud service architectures also need to handle a growing number of tenants with increasingly diverse requirements which must be weighed against the capabilities and costs of various service architectures, particularly single- versus multi-tenanted models. We believe a conceptual model enumerating the many decisions and factors affecting profitability of various cloud service offering strategies, and explicating dependencies among those factors is the first step to set up a ground for systematic analysis of service profitability. Based on such a model, we can define methods and implement tools to aid service providers in evaluating and selecting service offering strategies. In this work, we present a model of cloud service profitability, as well as an example of a method and tool that our model facilitates.
Working Period
AY2021-22, Term 1
Student(s)
  • LIM Geok Shan
  • Masayoshi OGAWA
Project Supervisor(s)
The efficacy of collaborative authoring of video scene descriptions
Publication
The majority of online video contents remain inaccessible to people with visual impairments due to the lack of audio descriptions to depict the video scenes. Content creators have traditionally relied on professionals to author audio descriptions, but their service is costly and not readily-available. We investigate the feasibility of creating more cost-effective audio descriptions that are also of high quality by involving novices. Specifically, we designed, developed, and evaluated ViScene, a web-based collaborative audio description authoring tool that enables a sighted novice author and a reviewer either sighted or blind to interact and contribute to scene descriptions (SDs)—text that can be transformed into audio through text-to-speech. Through a mixed-design study with N = 60 participants, we assessed the quality of SDs created by sighted novices with feedback from both sighted and blind reviewers. Our results showed that with ViScene novices could produce content that is Descriptive, Objective, Referable, and Clear at a cost of i.e., US$2.81pvm to US$5.48pvm, which is 54% to 96% lower than the professional service. However, the descriptions lacked in other quality dimensions (e.g., learning, a measure of how well an SD conveys the video’s intended message). While professional audio describers remain the gold standard, for content creators who cannot afford it, ViScene offers a cost-effective alternative, ultimately leading to a more accessible medium.
Working Period
AY2021-22, Term 1
Student(s)
  • Jolene LOH
  • TAN Huei Suen
  • Joshua TSENG
  • Ian Luke CHAN
Project Supervisor(s)
Project Reviewer(s)
Visionary caption: Improving the accessibility of presentation slides through highlighting visualization
Publication
Presentation slides are widely used in occasions such as academic talks and business meetings. Captions placed on slides support deaf and hard of hearing (DHH) people to understand spoken contents, but simultaneously comprehending and associating visual contents on slides and caption text could be challenging. In this paper, we design and develop a visualization technique to highlight and associate chart on a slide and numerical data in caption. We first conduct a small formative study with people with and without hearing impairments to assess the value of the visualization technique using a lo-fidelity video prototype. We then develop Visionary Caption, a visualization technique that uses natural language processing to automatically highlight visual content and numerical phrases, and show the association between them. We present a scenario and personas to showcase the potential utility of Visionary Caption and guide its future development.
Working Period
AY2021-22, Term 1
Student(s)
  • Carmen YIP Ji Yan
  • CHONG Jie Mi
  • KWEK Sin Yee
Project Supervisor(s)
EtherLearn: Decentralizing learning via blockchain
Publication
In institutes of higher learning, most of the time course material development and delivery follow a centralized model which is fully lecturer-controlled. In this model, engaging students as partners in learning is a challenging problem as: 1) students are usually hesitant to contribute due to the fear of getting it wrong, 2) not much incentive for them to put in the extra effort, and 3) current online learning systems lack adequate facilities to support seamless and anonymous interactions between students. In this work, we propose EtherLearn, a blockchain based peer-learning system to distribute the control of how course material and formative assessments could be developed and delivered over the set of stakeholders in the particular course. EtherLearn leverages features of the rising blockchain technology, e.g., decentralization, anonymity, transparency and security to address the aforementioned concerns in university learning environments. To this end, we have successfully implemented a proof of concept for EtherLearn based on the Ethereum blockchain network. We have also conducted preliminary evaluations to demonstrate that it can be useful in decentralizing learning resource creation and student sharing in an encouraging teaching and learning environment.
Working Period
AY2021-22, Term 1
Student(s)
  • Joel YANG Tian Jun
Project Supervisor(s)
Profiling Student Learning from Q&A Interactions in Online Discussion Forums
Publication
The last two decades have witnessed an explosive growth in technology adoption in education. Proliferation of digital learning resources through Massive Open Online Courses (MOOCs) and social media platforms coupled with significantly lowered cost of learning has brought and is continuing to take education to every doorstep globally. In recent years, the use of asynchronous online discussion forums has become pervasive in tertiary education institutions. Online discussion forums are widely used for facilitating interactions both during the lesson time and beyond. Numerous prior studies have reported benefits of using online discussion forums including enhanced quality of learning, improved level of thinking beyond the classroom, collaborative knowledge building, and enhanced participation by shy or intimidated students. By monitoring and analyzing students’ activities in online discussion forums, instructors can intervene and manage students’ learning. For the instructor to employ appropriate intervention measures, both quantitative and qualitative analyses of students’ participation are important. To mitigate the challenge of the sheer volume of conversation threads in online discussion forums, we present a text mining approach to profiling student learning based on Q&A interactions. Firstly, we perform text classification to categorize conversations into two categories: non-programming-related and programming-related. Secondly, from the programming-related conversation threads, our method categorizes students into four participation proficiency types based on their Q&A activities. Next, our method determines whether a student adopts more explicit or implicit expression behavior in Q&A activities. We evaluate our approach on the second-year computing course, Web Application Development II. Finally, we share the lessons learned in this teaching process.
Working Period
AY2021-22, Term 1
Student(s)
  • ONG De Lin
Uncovering patterns in reviewers’ feedback to scene description authors
Publication
Audio descriptions (ADs) can increase access to videos for blind people. Researchers have explored different mechanisms for generating ADs, with some of the most recent studies involving paid novices; to improve the quality of their ADs, novices receive feedback from reviewers. However, reviewer feedback is not instantaneous. To explore the potential for real-time feedback through automation, in this paper, we analyze 1,120 comments that 40 sighted novices received from a sighted or a blind reviewer. We find that feedback patterns tend to fall under four themes: (i) Quality; commenting on different AD quality variables, (ii) Speech Act; the utterance or speech action that the reviewers used, (iii) Required Action; the recommended action that the authors should do to improve the AD, and (iv) Guidance; the additional help that the reviewers gave to help the authors. We discuss which of these patterns could be automated within the review process as design implications for future AD collaborative authoring systems.
Working Period
AY2021-22, Term 1
Student(s)
  • Jolene LOH
  • TAN Huei Suen
  • Joshua TSENG
Project Supervisor(s)
Project Reviewer(s)
Facilitating Multi-Tenancy Research with an Open-Source Application Repository
Multi-tenancy designs allow service providers to maximize profits by looking into ways to reduce costs while selling the service to a large number of tenants. We first conduct a systematic literature review on the current state of multitenancy research. The systematic literature review consists of the following steps: search execution, paper screening, filtering on relevance, completeness and recency, and categorization. Through our literature review, we observe that existing multitenancy research is challenging to replicate due to limited disclosure of source codes. As such, we propose an open-source software-as-a-service (SaaS) application based on key architectural multi-tenancy features identified in our literature review. This application is built to accommodate different isolation techniques in multitenancy. We hope this application provides the necessary building blocks for the community to further validate their research in multi-tenancy.
Working Period
AY2019-20, Term 2
Student(s)
  • LIM Geok Shan
Project Supervisor(s)
Differential Testing of Blockchain Implementations for Smart Contracts and Fuzz Testing of Blockchain Implementations

A smart contract is an executable code that runs on the blockchain to facilitate, execute and enforce the terms of an agreement. A smart contract can execute the terms in a contract or agreement automatically when the specific conditions are met. Hence, resulting in a lower transaction fee when compared to the traditional method of executing a contract or terms of agreement (Alharby and Moorsel, 2017). Smart contracts are believed to revolutionize many industries in the near future and Ethereum is the most popular blockchain implementation that supports smart contracts.

In contrast to traditional programs, a deployed smart contract cannot be patched easily. If a bug is found in the smart contract or its underlying semantics provided by the blockchain implementation, it will result in the system being vulnerable to attacks. Thus, it is extremely important to ensure that smart contracts are executed correctly. This requires both the smart contract and the blockchain implementation to be bug-free. To achieve that, this study will be testing smart contract transactions on both the Conflux and Ethereum blockchain and analyse the possible differences in the test results.

Working Period
AY2019-20, Term 2
Student(s)
  • LIM Jia Wei
Project Supervisor(s)
Applying Surgical Prediction Model to Robust Surgery Scheduling
This project focuses on the development of a baseline lower bound optimization model to maximize utilization of the operating theatres in a public hospital for multiple medical disciplines. We built upon a previous work which output was the predicted surgical durations which in turn be used as an input for this project. For the purpose of building a lower bound through a deterministic model, we model the duration of the surgery of a discipline based on the mean surgical time from the previous surgical prediction model. We then provide a linear programming model to consider the scheduling of surgeries under the conditions of multiple operating theatres, multiple disciplines and across a planning horizon. Through literature review, to our best knowledge, this is the first work handling scheduling of operating theatres across multiple disciplines. The ability to handle multiple disciplines is an important step to a practical solution in the real world where public hospitals share resources in order to optimize them.
Working Period
AY2019-20, Term 2
Student(s)
  • LIU Mengru
Project Supervisor(s)
Evaluating the impact of technology on the operational efficiency in an elder caregiving organisation
The rapidly rising ageing population is catching up in the world and in Singapore. Alarmingly, in 2019, United Nations (UN) has predicted that by year 2050, about 47% of Singapore's total population will consist of seniors at least 65 years old [1]. While many efforts have been mostly targeted to provide accessible elderly social and care services, and assistance schemes, there is still an annual increase of approximately 5% in the number of elderly suffering from depression and seniors who face the greater issue of social isolation. In fact, operators and welfare groups have been trying to inculcate a social community network through befriending programs, whereby volunteers befriend and visit the seniors. This helps to provide social and psycho-emotional support to seniors through the interactions and quality time spent. Staff or volunteers are stationed at community care centres or Senior Activity Centres (SACs) to interact and serve the needs of the elderly. However, these elder caregiving organisations may often find that their staff are overwhelmed with many paper-based administrations duties such as attendance tracking, leading to lesser time spent on interacting with seniors. Thus, this paper presents and discusses the findings of deploying technology solutions in an elder caregiving organisation in Singapore. Specifically, we evaluate the operational efficiency of eight SACs operated by this elder caregiving organisation, with regards to the use of technology to help free up paper-based and manual duties done by the staff.
Working Period
AY2019-20, Term 2
Student(s)
  • GOH Jin Qiang
Project Supervisor(s)
  • TAN Hwee Pink (Former Faculty)
Project Reviewer(s)
Incremental Few-Shot Learning
Catastrophic forgetting has limited the effectiveness of learning novel classes while maintaining performance on base classes using a few supervisory examples. Fewshot learning tends to focus on the ability of the model to learn novel classes without much research into the performance of old classes. We attempt to understand the limitations of current few-shot learning methods to mitigate the poor performance on previously learnt classes. We also investigate the ramifications of current few-shot learning experimental set up and propose the additional evaluation of performance on the full set of test data not just the classification over classes present in the support set sample. Lastly, we use methods present in other domains and show that the utilisation of said methods improves the performance of incremental few-shot learning models on existing datasets.
Working Period
AY2019-20, Term 2
Student(s)
  • YEO Qi Xun
Project Supervisor(s)
Deep Activity Recommendation in Online Social Collaborative Platform
With the proliferation of online social collaborative platforms (such as GitHub, StackOverflow, etc.), users are increasingly adopting and engaging in various activities. For instance, a user may perform an "answer" and/or "favorite" question (i.e., item) in Stack Overflow. To improve user experiences, it is desirable to have a recommender system that can suggest the item and the type of activities to be performed on the suggested items. In this work, we propose a deep recommender system approach using multi-task learning by modifying some of the existing state-of-the-art model to learn a shared representation across multiple activities. We evaluate our proposed approach on Stack Overflow, and the experiments show promising potentials where our proposed approach outperforms Factorization Machine (baseline) and outperforms state-of-the-art in one of the experiments.
Working Period
AY2019-20, Term 2
Student(s)
  • HEE Ming Shan
Project Supervisor(s)
Query Formulation for Content Augmentation: A Case Study on Academic Slides
In this research, we study the problem of augmenting documents with additional information of various modalities obtained from the Web.As an exemplifying use case, we attempt augmentation of the slides associated with academic courses, in order to provide students with additional resources to understand the materials.Within an information retrieval framework, we distil the problem to query formulation from the slides, analytically investigating the effect of surrounding context on a query.This is showcased with an end-to-end tool that automates the entire process of query formulation, Web extraction, query ranking and multimodal result display.
Working Period
AY2019-20, Term 1
Student(s)
  • YEO Qi Xun
Project Supervisor(s)
Project Reviewer(s)
Evaluating Trustworthiness of Stack Overflow's Answer Using Contextual Expertise

Stack Overflow (SO) is the most popular Q&A website where developers can share their expertise by being the answerer and/or the questioner of a technical question. While receiving answers from the community helps to boost productivity and reduce the time-to-market for applications, the answers might not be provided by a relevant expert and could in turn promote security issues for real-life applications. In a recent study, researchers have identified security vulnerabilities in the suggested code of SO answers, which eventually found their way into applications installed by millions every day.

To mitigate the identified problem, SO uses the "Reputation" mechanism that measures the amount of trust the community has in a user. Users are able to earn reputation points by having his answer up-voted and/or having his answer accepted by the thread starter.However, a limitation to a user's reputation is that it is an aggregated across all domains and could hence be unrepresentative of the user's domain expertise for a particular question. As a result, there will be discrepancies between a user's reputation and the quality of their answers when answering questions out of their expertise.

In this work, we are proposing a new trust mechanism, “Contextual Expertise". Contextual Expertise measures the trustworthiness of a user's answer by evaluating his domain expertise using his activities across multiple platforms (i.e. SO and GitHub). We achieved a better accuracy in predicting whether the user's answer contains a security vulnerability.

Working Period
AY2019-20, Term 1
Student(s)
  • HEE Ming Shan
Project Supervisor(s)
Project Reviewer(s)
SmartBFA: On the feasibility of using commodity smartphones to gather wheelchair accessibility information
The Smart Barrier-Free Access (SmartBFA) project aims to identify barrier-free access routes throughout Singapore, to help maintain mobility independence among the physically disabled, and to promote an inclusive society. Currently, SmartBFA uses custom Internet of Things (IoT) devices to gather crowdsourced sensor data on accessibility - such as bumps and inclines. However, the current implementation is not very scalable as it requires custom IoT devices to be retrofitted onto the wheelchairs of the participants. In this project, we explore the feasibility of using commodity smartphones to crowdsource similar wheelchair accessibility information from the participants. Many smartphones today are already equipped with multi-modal sensors, such as GPS, accelerometer, gyroscope and magnetometer. Through experiments, we evaluate the accuracy of similar accessibility indices that are acquired through smartphones, and show that these smartphones can potentially be used to replace the custom IoT devices that are used to collect accessibility information. This can thus allow the SmartBFA project to be scaled across Singapore.
Working Period
AY2019-20, Term 1
Student(s)
  • Randy LAI Yonghao
Project Supervisor(s)
  • TAN Hwee Xian (Former Faculty)
Project Reviewer(s)
  • LI Yingjiu (Former Faculty)
Implementation and Evaluation of IoT Testbed
In choosing the right connectivity option among Wi-Fi, Bluetooth, SigFox, and LoRa for IoT systems, the trade-off between maximum range, rate of transmitting and receiving data and power consumption must be considered in conjunction with the application requirements. In this project, we performed extensive experiments on LoRa and Sigfox in the SMU campus to better understand the actual performance and tradeoffs in a smart campus scenario. These are chosen as they are longer range and lower power consumption over Wi-Fi and Bluetooth render them suitable for long-term, in-site smart campus IoT applications. In addition, these emerging technologies are less studied as compared to Wi-Fi and Bluetooth. In this presentation, I will discuss my experiments, and share interesting findings based on the actual performance and trade-offs with LoRa and SigFox in relation to other reported findings.
Working Period
AY2019-20, Term 1
Student(s)
  • Daryln TOO Wei Jian
Project Supervisor(s)
  • TAN Hwee Pink (Former Faculty)
Project Reviewer(s)
  • TAN Hwee Xian (Former Faculty)
Designing an Eyes-Free Conversational Microtask Interface
Crowd work’s excessive reliance on GUI-based interaction hinders its accessibility, disproportionately preventing people with visual impairments to work online. To explore the potential of using non-visual modalities and non-graphical interfaces to perform online work, we iteratively designed and developed a voice-based conversational interface to perform three online micro-tasks; survey, sentiment analysis, and audio transcription. As an initial study, we recruited 17 participants (5 participants for pilot study) without visual impairments toconduct usability testing to assess user’s cognitive workload, adaptability, and quantitative performance in using our prototype task interfaces.We also collected qualitative feedback on the prototypes from our participants via interviews. Through the quantitative analysis of the usability testing data and the heuristic evaluation with the qualitative interview feedback, we summarized design insights of the task interfaces. We found that, for instance, the disparity between task types and challenges in mixing two different user intents in a single communication channel (e.g., distinguishing command intents against audio transcription intents in a single speech-recognition channel). We conclude by compiling a list of design suggestions for future iterations of the voice-based conversational interface for the common crowdsourcing work.
Working Period
AY2019-20, Term 1
Student(s)
  • Jerry TOHVAN
Project Supervisor(s)
Project Reviewer(s)
  • LI Yingjiu (Former Faculty)
Design of Techniques to Visualize Potentially Erroneous Map Data
We propose novel methods to visualize occluded (and potentially corrupted) graph nodes and edges that represent 2.5D geographical structure (e.g., multi-floor indoor maps). We designed two visualization techniques—color and micro-animation (repel)—to signify the graph elements. To assess the usability of our visualization techniques, we conducted user studies. Based on our user studies’ results, we showed that (i) the non-expert users can identify occluded and potentially erroneous elements using our techniques, (ii) the color interface enables faster interaction compared to the repel interface (thought the interaction speed declined as the graph size grew), (iii) the user could acquire nodes faster with the repel interface, and (iv) user feels that the technique would encourage higher volunteered geographic information’s contribution. These techniques could extend the existing 2D map editing tools to enable the user to visualize and edit potentially 2.5D erroneous data.
Working Period
AY2019-20, Term 1
Student(s)
  • Solomon TEO Kok How
Project Supervisor(s)
Project Reviewer(s)
  • LI Yingjiu (Former Faculty)
Understanding the Implications of Platforms for Urban Logistics
Collaborative Urban Delivery Optimisation (CUDO) is introducing a delivery route optimisation to improve the delivery arrangement of urban retail mall logistics across Singapore. CUDO is a platform that is able to automatically plan and coordinate urban mall delivery to reduce and remove waiting time spent on queuing, reduce the time spent on roads in between delivery destinations to allow for more deliveries to be fulfilled with less time, increasing the efficiency of urban logistics. This paper aims to understand the implications of coordinating urban delivery at retail malls by comparing real uncoordinated scenarios with calculated coordinated simulations. Through primary research methods including data collection, interviews, meetings and through secondary research including IP searches, online literature reviews and governmental press releases, information will be gathered. These information will be analysed to understand the implications a route optimisation system through simulations and detailed analysis. The results show there they are efficiency improvements. This paper ends with recommendations for CUDO to harness the network effect in the urban logistics to further improve the overall efficiencies for multiple related stakeholders.
Working Period
AY2019-20, Term 1
Student(s)
  • KOH Zhi Rong
Project Supervisor(s)
Project Reviewer(s)
Quantum Inspired Optimization Methods Research and Benchmarking
Classical heuristic search algorithms (such as Simulated Annealing) for solving combinatorial optimization problems are prone to leading the traveller into local minimums which are not the global minimum. In this project, we explore the concept of Quantum Annealing (QA) that begins with the solution simultaneously occupying many coordinates, making use of the quantum effect of superposition. This idea makes use of the adiabatic theorem of computation and quantum tunnelling to gradually increase the probability of finding the solution in states corresponding to deep minima, as annealing progresses. The project aims to explore the performance of Simulated Quantum Annealing (SQA) and compare it with Fujitsu's Digital Annealer for solving standard TSPLIB problems. Through this research, we hope to better understand if quantum inspired algorithms can provide advantages as a general-purpose solver whether in terms of speed & scale, or in the quality of solution obtained.
Working Period
AY2019-20, Term 1
Student(s)
  • LAM Ying Sheng
Project Supervisor(s)
Project Reviewer(s)
Data Analytics Approach to the Study of Mental Health Issues
According to Samaritans of Singapore (SOS), a suicide prevention agency, suicide is the leading cause of death for Singapore’s millennials aged 20 to 29. The common struggles cited by youth include academic and work pressure, relationship problems at home, school, or workplace. Mental health is still rarely discussed in the public. This situation is exacerbated as victims of mental health problems may feel embarrassed or uncomfortable with reaching out for mental health help. As such, through research and data analysis, this project seeks to explore the patterns and sources of stress, anxiety, and depression. From there, we aim to identify how relevant mental health help can be served in an appropriate and less uncomfortable manner.
Working Period
AY2018-19, Term 2
Student(s)
  • Gladys NG Hui Li
Project Supervisor(s)
Project Reviewer(s)
A Machine Learning Approach to Topic Analysis – A Case of Depression and Suicide YouTube Videos
The increasing popularity of social media platforms in recent years drastically changed the way people communicate and interact with one another. With hundreds of social media platforms, millions of everyday social media users around the world contribute content of various types and forms. YouTube has emerged as the go-to platform for video content sharing globally. Its comments and replies feature allows users to engage themselves in discussions with other users. In this project, we analyze social conversations from YouTube platform with regards to depression and suicide-related content. We develop a web application that can help visualize information extracted from YouTube - providing a platform for interested users to acquire insights from any specified YouTube video URLs.
Working Period
AY2018-19, Term 2
Student(s)
  • Bernadine LYE Su Hui
Project Supervisor(s)
Project Reviewer(s)
A Mobile Gratitude Journal Application for Stress Management
Gratitude, the feeling of appreciation or thanks has gained significant attention in the field of positive psychology. Numerous research studies have shown that those who are habitually grateful are happier than those who are not. In this project, we develop a mobile gratitude journal application designed for busy professionals and students. Recent reports show that mobile applications account for some 50% of the Internet traffic around the world. As smartphones and mobile applications increasingly become pervasive, we aim to bring gratitude journal onto a mobile platform for easy access. We will present our thought process behind designing a digital gratitude journal and discuss technical details of the solution architecture.
Working Period
AY2018-19, Term 2
Student(s)
  • Martius LIM Jia Hao
Project Supervisor(s)
Project Reviewer(s)
Listen, Nudge & Empower – Data Analytics Approach to the Analysis of Digital Gratitude Journal Entries
Recent statistics from the World Health Organization (WHO) show that some 800,000 individuals become victims of suicide annually. Based on these alarming statistics, there is a pressing need to encourage Singaporeans to adopt healthier habits to cope with anxiety, stress, and depression. In this project, we develop a software module for analyzing digital gratitude journal entries. The mobile gratitude journal application collects journal entries from users. Our software module is designed to analyze these entries to detect mood changes and stress.
Working Period
AY2018-19, Term 2
Student(s)
  • Eugene CHOY Wen Jia
Project Supervisor(s)
Project Reviewer(s)
A Web Plug-In for Mood Monitoring, Analysis & Content Recommendation
Over the years, the Internet has become ubiquitous and pervasive. As increasingly more contents become available in the World Wide Web, the use of web browsing applications (‘web browsers’) will only increase. In this project, we develop a Google Chrome web plug-in for mood monitoring, analysis and content recommendation. We will present the prototype application’s architecture, data collected from the user interface, web browsing data, analysis and methods for making relevant content recommendations.
Working Period
AY2018-19, Term 2
Student(s)
  • Jane SEAH Hing Kid
Project Supervisor(s)
Project Reviewer(s)
  • LI Yingjiu (Former Faculty)
Library Package for General Multiple Objective Optimization
This research aims to explore and successfully develop a generic version of library package that can optimize with single or multiple objective linear problem. The library support stochastic independent scenarios and constraints and allow the user to specify their different objective functions without each objective’s weightage. One application instance will be quarterly budgeting problem where the model tries to maximize the investment return while minimize the capital requirements and the risk involved for each quarter. Another example will be assignment problem such as shared transportation where the model tries to maximize the profit, passenger experience and minimize waiting time. Besides those, this library can also address other type of problems. The library comes in two programming language, Java and Python. For Java, we will be using lp_solve, a library to solve Mixed Integer programming problem. For Python, we will be using linprog from SciPy library which provides efficient numerical solution for numerical integration and optimization.
Working Period
AY2018-19, Term 2
Student(s)
  • POH Boon Keat
Project Supervisor(s)
Project Reviewer(s)
Vessel coordination in the Maritime Context through Multi-agent Pathfinding
Multi-agent Pathfinding in the maritime context is a unique problem in which there are many unique constraints imposed upon vessel movements. They can be broadly described as one of two types: constraints imposed due to static environmental features and other dynamic constraints imposed to regulate vessel movement near other vessels. In this work, an extension of the Multi-agent A*-Search algorithm is explored to better model vessel behaviour.
Working Period
AY2018-19, Term 2
Student(s)
  • Gabriel Manuel SIDIK
Project Supervisor(s)
Microservices-based Architecture for Financial Organization

The purpose of this project is to research, design and prototype microservices-based architecture for financial organizations with large transaction volumes and complex internal operations.

The presentation will first discuss about the current industry trend of IT systems development and deployment with microservices-based architecture, with further elaborations on how it can be potentially implemented for IT systems in financial organizations. Comparisons will be drawn between a monolithic system and microservices in terms of architecture designs and performance considerations. Lastly, there will be discussions on concrete steps in decomposing a monolithic application into microservices-based application.

A prototype of microservice, based on the Master Data Management (MDM) Service of SMU tBank, will be demonstrated in the presentation and performance measures will be used to compare the characteristics of different architectural approaches, such as CPU, memory and disk usages.

Working Period
AY2018-19, Term 2
Student(s)
  • TAN Chee Wei
Project Supervisor(s)
Project Reviewer(s)
Quantum consensus

Classical permissioned distributed systems achieve consensus slowly. This is due in part to the quadratic messaging system utilised for its 3-phase agreement protocol, which achieves consensus in O(n2) time-complexity (Castro & Liskov, 1999). Current proposed quantum byzantine agreements present consensus resolution of up to O(1) time-complexity, with varying pros and cons (Ben-Or & Hassidim, 2005). The paper describes how we could utilise various aspects of quantum computing to instantly determine agreement, or disagreement, of proposed values between participants of a distributed network. We also explore the concept of private verifiability – how the network can come into consensus despite the presence of private, obfuscated data – without relying on zero knowledge proofs, which can be computationally expensive.

In this paper, we propose a novel consensus mechanism utilising quantum entanglement of photonic qubits to enhance the 3-phase agreement protocol by removing the need for multicast responses but instead, exploit the property of quantum entanglement to ensure that in each phase, only one multicast is required. This is made feasible with the recent revelation in which single-photon qubits can achieve a coherence time of >100ms, enough for data travel to most locations (K¨orber, et al., 2017). We also leverage the E91 quantum key distribution protocol, which requires photonic entanglement, to securely transmit values and preventing a man-in-the-middle attacks or system disturbance (Ekert, 1991). Finally, we also leverage the quantum entanglement of photonic qubits to achieve a verifiable “bridge” for private transactions between a subset private network and it’s corresponding consortium network

Working Period
AY2018-19, Term 2
Student(s)
  • Jorden SEET Shi Yuan
Project Supervisor(s)
Decision Framework For Evaluating DEX Protocols (ERC20 Tokens)
The advent of Distributed Ledger Technologies (DLTs) and Smart Contracts (SC) brought in the possibility of creating a new form of financial channel that is a game-changer to the traditional financial markets. Organisations like ICHX seek to leverage upon these technologies to connect the investors to businesses. Such a channel is made possible with the introduction of Security Tokens (STO). ICHX’s iSTOX platform allows companies to digitally represent their equity using STO. Investors can easily find potential STOs which they are interested in and purchase them through the iSTOX platform. The platform creates a marketplace which facilitates price discovery and enabling a seamless transfer of equity from the company to investor or vice versa. Given the cryptographic and decentralised properties of DLT, such a solution promises security and legality benefits over traditional trading platforms. A key enabler to facilitate the exchange of STO is the development of Decentralised Exchange (DEX) protocols. While the DLT scene may seem exciting for the years to come, there is a lack of fundamental practices in technical evaluation on DEX protocols. Especially when it comes to technical adoption, it is imperative for organisation to adopt a solution based on its needs instead of following the hype. This problem is further exacerbated when there is a myriad of DEX solutions. ICHX highlights that there is a lack of guided practice when evaluating these protocols. Therefore, this article aims to provide a comprehensive study on comparison practices and propose a decision framework for evaluating DEX protocols. The decision framework breaks down into multiple areas of concern with each area attributed with a score. The scoring metric is highly customisable to align to the adopter’s requirement and provide a quantitative summary as a mean of comparison. To put the framework to its test, it will be used by ICHX to evaluate multiple DEX protocols for their adoption. The framework’s evaluation process will cover everything from the deployment to the actual use-cases being simulated on the deployment. The added input from ICHX provides real-life adoption inputs to further improve the framework.
Working Period
AY2018-19, Term 2
Student(s)
  • TAN Kee Hock
Project Supervisor(s)
Analyzing personalities and user behaviour on Instagram
Instagram is a fast-growing social media platform with several media sharing and interaction features. Its content covers both images and videos that can be tagged or liked. This has also made Instagram an ideal platform to study behavior related to rich media sharing. This study will focus on analyzing the personality traits of Singapore Instagram users, and relating these traits with media content sharing behavior. Personality traits are known to be important attributes that determine individuals’ academic performance, work performance, subjective well-beings, and health. Studies of personality traits on social media are difficult to conduct because personality traits are latent attributes. Using personality and social media data collected from research participants, this study also proposes a personality prediction model that would predict a given user's Big-5 Personality based on their Instagram profile.
Working Period
AY2018-19, Term 2
Student(s)
  • Jonathan CHEW Yee Long
Project Supervisor(s)
Inclusive ageing of community-dwelling seniors

The ageing population is rapidly increasing, both in Singapore and worldwide. Due to the shortage of healthcare professionals and institutionalized care, there is a pertinent need for seniors to age-in-place - safely and in the familiarity of their neighborhoods. In addition, changing family structures and heavy work commitments of family members, coupled with the desire for more personal space and independence, have resulted in a significant proportion of seniors who live alone at home.

In this project, we study two programmes that are established by a social enterprise to improve the wellbeing of community-dwelling seniors. We analyze seven months of weekly survey data from the community care programme and befriending programme, comprising a total of 191 clients and 39 volunteers in the south-westernmost fringe of the Central Region of Singapore. Preliminary results show that: (i) there is an unbalanced distribution of clients, whereby 25% volunteers care for more than 67% of the clients; (ii) the average client duration in the community care programme is 18 months; and (iii) befrienders with less clients appear to be paired with clients who will require more emotional needs.

Working Period
AY2018-19, Term 2
Student(s)
  • GOH Jin Qiang
Project Supervisor(s)
  • TAN Hwee Xian (Former Faculty)
Project Reviewer(s)
Detection of mild cognitive impairments in elderly using sensor data
Mild Cognitive Impairment (MCI) causes a slight but noticeable and measurable decline in one’s cognitive abilities, including memory and thinking skills. While MCI results in behavioural changes, such changes are not severe enough to interfere with daily life or independent function. However, early intervention is still important in prescribing timely treatment in order to prevent the problem from worsening, given the increase in ageing population in Singapore in which such healthcare problems can put a strain on our facilities and budget. In this research, we explore and analyse the data collected from sensors that are deployed in the homes of more than 50 seniors. Our aim is to use a combination of sensor and survey data to differentiate between seniors who are cognitively healthy, versus seniors who have MCI.
Working Period
AY2018-19, Term 2
Student(s)
  • Debbie LEE Shan Ying
Project Supervisor(s)
  • TAN Hwee Xian (Former Faculty)
Project Reviewer(s)
Investigating the Social Participation Performance of Wheelchair and Personal Mobility Device Users

For wheelchair users, social participation and physical mobility play a significant part in determining their quality of life outcomes. However, little is known about how wheelchair users move about and engage in social interactions in their life-spaces. In this project, we investigate the social participation performance of the wheelchair users based on a combination of geolocational and lifestyle survey data collected over a period of 3 months.

This paper adopts a multi-variate approach combining geolocational travel patterns and various factors (independence, willingness, self-perception, etc.), deviating from the univariate approach taken by previous studies. We provide profiles of wheelchair users by combining these factors in an empirical study-based analysis. With the geolocational data of our volunteers, we can demonstrate the influences of other attributes that affect the social participation performance of wheelchair users with regards to life-space mobility.

Working Period
AY2018-19, Term 2
Student(s)
  • YIN Yukun
Project Supervisor(s)
Understanding the implications of designing voice-based forums for work permit holders in Singapore
Singapore’s employment policies provide minimal legal protections for the transient Work Permit (WP) holders, resulting in issues such as exploitation, wage theft and lack of due treatment after a workplace accident. Currently, recommended mitigations to curb these issues mostly revolve around educating WP holders on awareness of their rights, but these measures are not tangible in providing evidential proof to safeguard the workers and are thus, limited. In this project, I aim to explore how technology can assist WP holders in preventing the onset of employment-related issues. Firstly, we conducted a survey study to understand how migrant workers seek assistance in resolving their employment issues, their literacy proficiency as well as how frequently they use computing devices. We identified that WP holders have limited to zero access of mobile data when repatriated, and none took measures (such as documenting their work hours) as proof against exploitation. Based on this result and my conversation with members of NGO, we thought voice-based and chatbot-based technologies could be useful to support WP Holders to self-document their employment records (e.g., tracking of work hours and consequently wages). In the second part of the project, I designed two lo-fidelity prototypes of potentially useful technologies: (i) an automatically-generated report through guided speech and (ii) a wage theft chatbot are useful mitigations. Because I thought that the wage theft chatbot would be a more viable and potentially useful solution, I developed a working prototype that requests for location-based check-in and check-out during work hours with the Telegram chatbot API. While the formal evaluation of location-based “clock-ins” as employment record documentation remains as a future work, the nature of the employment policies render it difficult to fully combat wage exploitation issues. Evaluation of the usability and effectiveness of these technologies towards WP holders remain future work.
Working Period
AY2018-19, Term 2
Student(s)
  • SIN Jean Min
Project Supervisor(s)
Project Reviewer(s)
JioApp : Recommendations for Group Meetups
A common problem with group meetups for meals is the difficulty in coming to a common consensus on the food location to meet, in part due to the varying preferences among the attendees. Towards recommending food establishments to a group that balances the preferences of members, we develop JioApp that runs on both iOS and Android. Powering the group recommendations is a machine learning algorithm that aggregates users' preferences as trained from their individual feedbacks. Recommendations are then dynamically updated based on the incremental additions of the preference indications of all users in a particular group. With the usage of the platform, experiments are continuously run to further improve the performance of the recommendations over time.
Working Period
AY2018-19, Term 2
Student(s)
  • ONG Rong Sheng
Project Supervisor(s)
Project Reviewer(s)
  • TAN Hwee Xian (Former Faculty)
Discourse Analysis in Classroom Interactions
An important factor affecting students’ learning journey in both online and offline platforms of education is interaction. Interaction in learning settings is a necessary and fundamental process for knowledge acquisition and cognitive development. Classroom interactions are identified as the means through which learners acquire knowledge, develop skills, build relationships and further their understanding of the topic of study. Our main interest is the quality of interactions as it affects the quality of learning. Several frameworks were proposed with the aim to identify the impact of interactions on students' outcomes. Our focus is on analysing the behavioural aspects and the interpersonal aspects in online interactions. With the help of the Penn Discourse Treebank (PDTB) 2.0 from the University of Pennsylvania which supports the extraction of useful features pertaining to syntax, semantics and discourse all at once, we have used NLP, text mining and information extraction techniques to analyse the interactions and generate learning profile of the students.
Working Period
AY2018-19, Term 2
Student(s)
  • Mallika Nitin GOKARN
Project Supervisor(s)
Data Mining Approach to the Study of Depression and Suicide in Singapore

The increasing popularity of social media platforms in the last decade or so has changed the way people communicate and interact with one another. With over 900 active social media platforms globally, millions of everyday social media users are contributing to content creation for various purposes. Especially in recent years, Reddit community has grown significantly around the world. In Singapore, Reddit has grown to be a go-to platform for sharing information and voicing opinions. This study focuses on the phenomenon of suicide and depression in Singapore. Recent studies have shown that Twitter, for example, can be used to prevent online suicide. Such studies have identified terms that are used by suicidal individuals. In our study, we further this investigation with a focus on Singapore society. We built a custom Singlish corpus and performed natural language processing to analyze text indicative of depression and suicide. With this study, we hope to help make a happier society where early detection of depression and suicidal behavior can be made and proper interventions employed in a timely manner.

Working Period
AY2018-19, Term 1
Student(s)
  • Jane SEAH Hing Kid
Project Supervisor(s)
Pin Similarity Networks

Pinterest is a social media platform where users save images as pins and categorise them into boards. Each board groups pins that a user deems similar. Our problem of interest is recommending relevant pins to a user so as to increase the engagement level. Traditional approaches presume a single standard of image similarity. Hypothetically, on Pinterest each user creates boards based on her own unique notion of similarity, as similar pins can be grouped differently by two different users. We investigate this hypothesis by comparing neural networks that condition pin similarity on the context of the user with those that learn a generic notion of similarity.

Working Period
AY2018-19, Term 1
Student(s)
  • LE VAN Tuan Leong
Project Supervisor(s)
Surgical duration prediction model for robust optimization under uncertainty

Hospitals have been trying to improve the utilization of operating theatres (OTs) as they affect patient satisfaction, surgery throughput, revenues and costs. An existing surgical prediction model used post-surgery data showed promising results but contained key predictors which were absent during surgical listing. This study aims to provide an implementable surgical duration prediction model which bridges the “research-to-practice” gap. To build an implementable model, we seek to understand the listing process where patient is scheduled for a surgery to gain insights on domain knowledge and to isolate unavailable data. We used these insights found for feature engineering of key predictors and proposed new implementable prediction models. We tested our proposed models and the experimental results showed that our proposed models fared similarly to existing model and it can be implemented in practice as the predictors used are now available during listing.

Working Period
AY2018-19, Term 1
Student(s)
  • GAN Tang Chow Jerald
Project Supervisor(s)
Interaction Analysis in Classroom Discussion

In today’s day and age information and communications technology has permeated into almost every activity of our daily life. This extends to education delivery assisted by these devices. The main challenge with in-class discussions is that they are conducted orally, giving rise to difficulty in retaining uncovered knowledge. The most common way to ensure that this knowledge is not lost is to record the entire discussion, transcribe and subsequently analyse it. Using speech recognition, this effort of transcribing can be reduced and finally eliminated. Interaction analysis is the area of research that analyses the discussions to discover the behaviour of participants and topics of interests. Using a Teaching Assistant for the capture of such knowledge is painstaking, tedious, and mundane for the TA, while not providing many insights to the faculty. Therefore, automated interaction analysis can be used to facilitate the faculty’s understanding of classroom interactions along three dimensions namely; participation of students, the evolution of topics and peer dynamics. For our study, we used classroom discussions which are converted into text by Liveclass App developed internally. We applied Natural Language Processing, and text mining techniques to process the discussions and analyse interactions in the three dimensions. Lastly, we evaluated the topics with human gold truth and generated visual reports for faculty in the previously mentioned aspects.

Working Period
AY2018-19, Term 1
Student(s)
  • Mallika Nitin GOKARN
Understanding the Implications of Designing Technologies for Work Permit Holders in Singapore: A Formative Study

In Singapore, 788,500 foreign unskilled and semiskilled workers, also known as Work Permit (WP) Holders, are hired on a 2-year contractual basis to alleviate the manual labour shortage in industries like construction and marine. While the WP Holders benefit relatively high-income jobs offered in Singapore compared to their home countries (mainly Bangladesh, Myanmar, India, China and Sri Lanka), they have lower salaries compared to local workers, fewer rights, and typically work in places with the highest risk of injuries. These issues seem to remain to despite of the recent efforts by Ministry of Manpower (MOM) and Non-Governmental Organisations (NGO). In this research, we seek to understand how computer-mediated support can improve accessibility and empower migrant communities. We conduct a formative semi-structured interview study with 2 local NGO representatives and a survey study with 22 WP Holders, of which 6 experience employment-related issues. The results of the study have shown that the abovementioned problems remain for the WP Holders despite of both technical and non-technical support provided to the WP Holders. Based on the insights gained through the study, we list design implications for future technologies to mitigate the challenges that WP Holders face.

Working Period
AY2018-19, Term 1
Student(s)
  • SIN Jean Min
Project Supervisor(s)
Question answering

The amount of structured knowledge on the web is growing and this has led to the creation of large knowledge bases (KB) such as Freebase. This has sparked the interest in developing methods to allow users to access these resources. Knowledge Based Question Answering systems provide users an intuitive access to information via natural language by encapsulating the technical complexities of data modelling, vocabularies and query languages.

Representations of the KBs are often learned only from the QA training data, leading to the following problems. First, the global information of the KB is insufficient. This means that for a question answer pair in the training data, there may be a more suitable answer from the global KB information. However, current KB-QA systems may not be able to learn this. Second, there is the problem of out-of-vocabulary (OOV) as the testing data contains answer entities that was not observed in the training data, thus the performance of the KB-QA system would be negatively affected.

This research aims to successfully learn the semantic vectors that model the entities and relationships of the KB to mitigate the effects of the above-mentioned problems.

Working Period
AY2017-18, Term 2
Student(s)
  • TAN Chang Sheng
Project Supervisor(s)
Research on Natural Language Processing for a Taskbot application

In an increasing digitised world, an event registration chatbot (“chatbot”) serves as a beneficial use case to provide users with a means of automated querying and receiving of information related to events. A key technology enabling such a chatbot is Natural Language Processing, specifically in the realm of sequence classification and labelling. This research project studies the use of LSTM in sequence classification and labelling, with a direct application in TensorFlow to provide the chatbot with the ability to (1) process Natural Language input, (2) retrieve the desired information, and (3) return the information in a structured format.

Working Period
AY2017-18, Term 2
Student(s)
  • Jaren LIM Jian Quan
Project Supervisor(s)
Survey of Fintech Payment Industry

In this study, we dive into an understanding of what the FinTech Payments industry is going through. These companies are becoming prevalent in the current world. In the last decade, there has been a transformational change in the way we deal with payments. Innovative FinTech Payment Companies are up and about providing alternative ways that we have not imagined payment services could provide. In this paper, we take a look into what these companies are from three different categories, studying how these companies work and function to understand the impacts they would have on the payment industry. Next, we take a look at a response regulatory bodies have implemented on the changing payment industry. The paper focuses on the new Payment Services Directive 2 (PSD2) implemented by European Union in 2018 which promotes the concept of “Open Banking” where banks are pressured to share their information through services for other companies to utilize as well as the impacts it has on the payment industry. In view of the rapid changes happening, what can banks do towards this? We propose in the paper as well, a recommended approach that banks should take in order to keep competing in this changing industry.

Working Period
AY2017-18, Term 2
Student(s)
  • TOH Hong Ren
Project Supervisor(s)
FoodRecce: Personalized Food Reconnaissance While on the Move

An everyday dilemma close to many Singaporeans' heart is deciding where and what to eat for a particular meal. Being a food haven, Singapore offers a great diversity of options. FoodRecce is an Android application that allows users to survey their immediate locality for desired eateries, by recommending food places and exhibiting their established information and photos obtained from various online sources. The personalized recommendation is powered by a machine learning algorithm, which is dynamically updated based on implicit feedback from users’ actions through an intuitive swipe-based interface. The underlying principle is to correlate the preferences of users who are similar and likely to love eateries enjoyed by other similar users. With a platform to run controlled experiments, the recommendations are continually evaluated and refined, so as to improve the performance and user experience over time.

Working Period
AY2017-18, Term 2
Student(s)
  • Ong Rong Sheng
Project Supervisor(s)