Digital Commons Network^™

Detecting Foot Strikes During Running With Earbuds, Changshuo Hu, Thivya Kandappu, Jake Stuchbury-Wass, Yang Liu, Anthony Tang, Cecelia Mascolo, Dong Ma

Research Collection School Of Computing and Information Systems

Running is a widely embraced form of aerobic exercise, offering various physical and mental benefits. However, improper running gaits (i.e., the way of foot landing) can pose safety risks and impact running efficiency. As many runners lack the knowledge or continuous attention to manage their foot strikes during running, in this work, we present a portable and non-invasive running gait monitoring system. Specifically, we leverage the in-ear microphone on wireless earbuds to capture the vibrations generated by foot strikes. Landing with different parts of the foot (e.g., forefoot and heel) generates distinct vibration patterns, and thus we utilize machine learning …

Gts: Gpu-Based Tree Index For Fast Similarity Search, Yifan Zhu, Ruiyao Ma, Baihua Zheng, Xiangyu Ke, Lu Chen, Yunjun Gao

Research Collection School Of Computing and Information Systems

Similarity search, the task of identifying objects most similar to a given query object under a specific metric, has gathered significant attention due to its practical applications. However, the absence of coordinate information to accelerate similarity search and the high computational cost of measuring object similarity hinder the efficiency of existing CPU-based methods. Additionally, these methods struggle to meet the demand for high throughput data management. To address these challenges, we propose GTS, a GPU-based tree index designed for the parallel processing of similarity search in general metric spaces, where only the distance metric for measuring object similarity is known. …

Jollygesture: Exploring Dual-Purpose Gestures In Vr Presentations, Gun Woo Warren Park, Anthony Tang, Fanny Chevalier

Research Collection School Of Computing and Information Systems

Virtual reality (VR) offers new opportunities for presenters to use expressive body language to engage their audience. Yet, most VR presentation systems have adopted control mechanisms that mimic those found in face-to-face presentation systems. We explore the use of gestures that have dual-purpose: first, for the audience, a communicative purpose; second, for the presenter, a control purpose to alter content in slides. To support presenters, we provide guidance on what gestures are available and their effects. We realize our design approach in JollyGesture, a VR technology probe that recognizes dual-purpose gestures in a presentation scenario. We evaluate our approach through …

How Is Our Mobility Affected As We Age? Findings From A 934 Users Field Study Of Older Adults Conducted In An Urban Asian City, Yi Zhen Tan, Ngoc Doan Thu Tran, Sapphire Lin, Fang Zhao, Yee Sien Ng, Dong Ma, Jeonggil Ko, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

In this paper, we analyze the results of a large study involving 934 older adults living in an urban Asian city that collected their mobility patterns, in the form of logged GPS data, along with a multitude of demographic and health data. We show that mobility, in terms of average distance travelled per day, is greatly affected by age and by employment status. In addition, other factors such as type of day, household size, physical and financial conditions and the onset of retirement also play a significant role in determining the mobility of an individual. These results will have high …

Usability Versus Collectibility In Nft: The Case Of Web3 Domain Names, Ping Fan Ke, Yi Meng Lau

Research Collection School Of Computing and Information Systems

This study examines the market’s inclination towards usability and collectibility aspects of Non-Fungible Tokens (NFTs) within Web3 domain name marketplaces, drawing insights from resale records. Our findings reveal a prevailing preference for usability, as evidenced by consistently higher average resale prices observed for Ethereum Name Service (ENS) domains compared to Linagee Name Registrar (LNR) domains. However, domains with diminished usability, such as those containing non-ASCII characters, tend to attract investors due to their enhanced collectibility. Our analysis on the effect from previous resale suggests a potential aversion towards second-hand acquisitions among NFT investors when value derives primarily from usability, while …

Poster: Towards Efficient Spatio-Temporal Video Grounding In Pervasive Mobile Devices, Dulanga Kaveesha Weerakoon Mudiyanselage, Vigneshwaran Subbaraju, Joo Hwee Lim, Archan Misra

Research Collection School Of Computing and Information Systems

As the use of pervasive devices expands into complex collaborative tasks such as cognitive assistants and interactive AR/VR companions, they are equipped with a myriad of sensors facilitating natural interactions, such as voice commands. Spatio-Temporal Video Grounding (STVG), the task of identifying the target object in the field-of-view referred to in a language instruction, is a key capability needed for such systems. However, current STVG models tend to be resource-intensive, relying on multiple cross-attentional transformers applied to each video frame. This results in runtime complexity that increases linearly with video length. Furthermore, deploying these models on mobile devices while maintaining …

Poster: Profiling Event Vision Processing On Edge Devices, Ila Nitin Gokarn, Archan Misra

Research Collection School Of Computing and Information Systems

As RGB camera resolutions and frame-rates improve, their increased energy requirements make it challenging to deploy fast, efficient, and low-power applications on edge devices. Newer classes of sensors, such as the biologically inspired neuromorphic event-based camera, capture only changes in light intensity per-pixel to achieve operational superiority in sensing latency (O(μs)), energy consumption (O(mW)), high dynamic range (140dB), and task accuracy such as in object tracking, over traditional RGB camera streams. However, highly dynamic scenes can yield an event rate of up to 12MEvents/second, the processing of which could overwhelm …

Predicting Mild Cognitive Impairment Through Ambient Sensing And Artificial Intelligence, Ah-Hwee Tan, Weng Yan Ying, Budhitama Subagdja, Anni Huang, Shanthoshigaa D, Tony Chin-Ian Tay, Iris Rawtaer

Research Collection School Of Computing and Information Systems

This paper reports an emerging application leveraging ambient and artificial intelligence techniques for in-home sensing and cognitive health assessment. The application involves a prospective longitudinal study, wherein non-pervasive sensing devices are installed in homes of over 63 real users undergoing clinical cognitive assessment, and digital signals of the users’ activities and behaviour are transmitted to a central cloud-based data server for further processing and analysis. Based on the sensor readings, we identify a set of digital biomarkers covering four key aspects of daily living, namely physical, activity, cognitive, and sleep, and develop a suite of customized feature extraction methods for …

Try It Together: Transcribing Your Audio With Whisper Api, Bella Ratmelia

2024 AI for Research Week

In this hands-on session, we will explore using the Whisper API to transcribe audio recordings from interviews, focus groups, and speeches. The session will delve into best practices and address common issues that may arise during the transcription process.

Academic Search And Discovery Tools In The Age Of Ai And Large Language Models: An Overview Of The Space, Aaron Tay

2024 AI for Research Week

In the ever-evolving landscape of academic research, “AI tools” for literature search and synthesis are currently getting a lot of attention. These tools promise to ramp up productivity, enabling us to accomplish more in less time or absorb more knowledge without drowning in endless reading. With the sheer number of these systems increasing daily, it's natural to wonder: are they really worth our time and money? And if they are, how should we go about picking the right one from the multitude of options?

In this talk, I will share my views on how the space has developed over two …

Q&A With Professor Lily Kong, Royal Geographical Society

Oral History Collection

SMU President Professor Lily Kong has been conferred the Royal Geographical Society (with the institute of British Geographers) Victoria Medal 2024 for conspicuous merit in social and cultural geography and in urban research. The Medal recognises Prof Kong’s exceptional intellectual leadership, demonstrated through her scholarship and contributions to the internationalisation of geography. She is also the first Asian woman, in over 120 years, to be accorded this honour. In an interview, Prof Kong shared about her role and work as the first Singaporean woman to be appointed President of any university in Singapore, as well as the highlights of her …

Specifying And Estimating Vector Autoregressions Using Their Eigensystem Representation, Leo Krippner

Sim Kee Boon Institute for Financial Economics

This article introduces the principles and mechanics of the eigensystem vector autoregression (EVAR) framework, where a VAR may be specified and estimated directly via its eigenvalue and eigenvector parameters. Using explicit constraints on the eigensystem permits control of a VAR ís allowable dynamics, which is illustrated empirically with standard and time-varying VAR estimations specified to be always non-explosive.

Getting Published And Raising Research Visibility, Pin Pin Yeo

Research Collection Library

Embarking on publishing your research can be exciting yet daunting. This seminar will guide you through crafting a compelling paper, navigating the submission process, and enhancing your paper's visibility. Learn what editors and reviewers seek, how to select the right journal, and how to publicise your research effectively. Whether you're a seasoned researcher or new to publishing, join us to amplify the impact of your work and share your discoveries with the world.

Academic Literature Review In Age Of Ai And Large Language Models​, Aaron Tay

Research Collection Library

Explore the evolving landscape of academic research with a focus on open data and AI advancements, particularly in natural language processing. Join us for a practical presentation on leveraging emerging tools for literature review. Discover platforms like Connected Papers, ResearchRabbit, and Litmaps, offering paper exploration and recommendations based on initial 'seed papers.' Dive into AI-enhanced search engines like Elicit, Scispace, Semantic Scholar, and Scite.ai, powered by Large Language Models such as BERT and GPT. Learn about the latest developments, strengths, and weaknesses of these tools, and how they reshape literature review methods, from tool selection to query input techniques.

Streamlined Workflow For Qualitative Data Analysis With Whisper And Atlas.Ti, Bella Ratmelia, Bryan Leow, Danping Dong

Research Collection Library

Qualitative research, often reliant on interviews and focus groups, stands to benefit significantly from the latest advancements and innovations in AI. This workshop presents a practical and efficient research workflow with two powerful tools—Whisper and ATLAS.ti—to streamline the process of qualitative data analysis.

Whisper is an open-source machine learning model for speech recognition and transcription by OpenAI. With the ability to achieve human-level accuracy in speech recognition, it significantly reduces the time and effort required for transcribing, saving your time for more valuable work.

Moving beyond transcription, the workshop presents ATLAS.ti, a well-established qualitative data analysis tool. Learn how to …

When Market Forces Backfire: Mandatory Esg Disclosure And Corporate Innovation, Andi Zhang

Dissertations and Theses Collection (Open Access)

Mandatory ESG disclosure makes it possible to incorporate ESG information into stock prices, incentivizing firms to “do good”. This channel, however, may lead to suboptimal investments, according to disclosure theories. This study investigates the changes in firms’ investment in innovation activities following the staggered introduction of mandatory ESG disclosure around the world. Using a sample of corporate patents filed by listed firms across 58 countries from 2000 to 2022, I find that the introduction of mandatory ESG disclosure is associated with less corporate innovation. The effect is mainly driven by countries that mandate ESG disclosure within corporate financial reports, when …

Make Revocation Cheaper: Hardware-Based Revocable Attribute-Based Encryption, Xiaoguo Li, Guomin Yang, Tao Xiang, Shengmin Xu, Bowen Zhao, Robert H. Deng, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

As an advanced one-to-many public key encryption system, attribute-based encryption (ABE) is widely believed to be a promising technology for achieving flexible and fine-grained access control of encrypted data on untrusted storage servers (e.g., public cloud servers). However, user revocation in ABE is a critical but challenging problem, and designing efficient revocable ABE has been an active research topic in the past decade. Almost all the existing revocable ABE schemes incorporate a timestamp in the encryption algorithm such that revoked users cannot decrypt ciphertexts generated in future time intervals. To prevent revoked users from decrypting past ciphertexts, the storage server …

A Tale Of Two Signals: Partner Csr Versus Csi And Alliance Formation, Qiwen Yu, Ilya R. P. Cuypers, Heli Wang

Research Collection Lee Kong Chian School Of Business

This study outlines two signaling mechanisms—trust and spillover—through which a potential partner’s corporate social responsibility (CSR) and corporate social irresponsibility (CSI) affect alliance formation. Extending a key insight in signaling theory that positive and negative signals are conceptually distinct, we propose that which mechanism is dominant in explaining alliance formation varies between CSR and CSI. Specifically, we argue that the dominant signaling mechanism for CSR is the trust mechanism, through which CSR signals the moral character of a potential partner, which is used by the focal firm to infer the partner’s trustworthiness. In contrast, CSI negatively affects alliance formation primarily …

Bridging The Chasm Between Intentions And Behaviors: Developing And Testing A Construal Level Theory Of Internalwhistle-Blowing, Abhijeet K. Vadera, Ann E. Tenbrunsel, Kristina A. Diekmann

Research Collection Lee Kong Chian School Of Business

The recent wave of corporate scandals has necessitated a more systematic investigation of internal whistle-blowing as a potential way to prevent wrongdoing. Our understanding of whistle-blowing, however, has been hampered by a deep chasm that exists between employees’ intent to blow the whistle and their whistle-blowing behaviors. We argue that to fully bridge this gap, we need to consider employees’ cognitive states at the time of whistle-blowing intentions versus behaviors and to link these cognitive states to the ethical systems within the organization’s ethical infrastructure to understand which systems are more effective in cultivating whistle-blowing intentions and which systems help …

Intriguing Properties Of Data Attribution On Diffusion Models, Xiaosen Zheng, Tianyu Pang, Chao Du, Jing Jiang, Min Lin

Research Collection School Of Computing and Information Systems

Data attribution seeks to trace model outputs back to training data. With the recent development of diffusion models, data attribution has become a desired module to properly assign valuations for high-quality or copyrighted training samples, ensuring that data contributors are fairly compensated or credited. Several theoretically motivated methods have been proposed to implement data attribution, in an effort to improve the trade-off between computational scalability and effectiveness. In this work, we conduct extensive experiments and ablation studies on attributing diffusion models, specifically focusing on DDPMs trained on CIFAR-10 and CelebA, as well as a Stable Diffusion model LoRA-finetuned on ArtBench. …

Collaborative Deep Reinforcement Learning For Solving Multi-Objective Vehicle Routing Problems, Yaoxin Wu, Mingfeng Fan, Zhiguang Cao, Ruobin Gao, Yaqing Hou, Guillaume Sartoretti

Research Collection School Of Computing and Information Systems

Existing deep reinforcement learning (DRL) methods for multi-objective vehicle routing problems (MOVRPs) typically decompose an MOVRP into subproblems with respective preferences and then train policies to solve corresponding subproblems. However, such a paradigm is still less effective in tackling the intricate interactions among subproblems, thus holding back the quality of the Pareto solutions. To counteract this limitation, we introduce a collaborative deep reinforcement learning method. We first propose a preference-based attention network (PAN) that allows the DRL agents to reason out solutions to subproblems in parallel, where a shared encoder learns the instance embedding and a decoder is tailored for …

Deep Reinforcement Learning Guided Improvement Heuristic For Job Shop Scheduling, Cong Zhang, Zhiguang Cao, Wen Song, Yaoxin Wu, Jie Zhang

Research Collection School Of Computing and Information Systems

Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics. However, their performance is still far from optimality, mainly because the underlying graph representation scheme is unsuitable for modelling partial solutions at each construction step. This paper proposes a novel DRL-guided improvement heuristic for solving JSSP, where graph representation is employed to encode complete solutions. We design a Graph-Neural-Network-based representation scheme, consisting of two modules to effectively capture the information of dynamic topology and different types of nodes in graphs encountered during the improvement process. To speed up solution evaluation during improvement, …

Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang

Research Collection School Of Computing and Information Systems

Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems, etc. Modern graph link prediction methods often employ a contrastive approach to learn robust node representations, where negative sampling is pivotal. Typical negative sampling methods aim to retrieve hard examples based on either predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space. …

An Adaptive Large Neighborhood Search For The Multi-Vehicle Profitable Tour Problem With Flexible Compartments And Mandatory Customers, Vincent F. Yu, Nabila Yuraisyah Salsabila, Aldy Gunawan, Anggun Nurfitriani Handoko

Research Collection School Of Computing and Information Systems

The home-refill delivery system is a business model that addresses the concerns of plastic waste and its impact on the environment. It allows customers to pick up their household goods at their doorsteps and refill them into their own containers. However, the difficulty in accessing customers’ locations and product consolidations are undeniable challenges. To overcome these issues, we introduce a new variant of the Profitable Tour Problem, named the multi-vehicle profitable tour problem with flexible compartments and mandatory customers (MVPTPFC-MC). The objective is to maximize the difference between the total collected profit and the traveling cost. We model the proposed …

Breathpro: Monitoring Breathing Mode During Running With Earables, Changshuo Hu, Thivya Kandappu, Yang Liu, Cecilia Mascolo, Dong Ma

Research Collection School Of Computing and Information Systems

Running is a popular and accessible form of aerobic exercise, significantly benefiting our health and wellness. By monitoring a range of running parameters with wearable devices, runners can gain a deep understanding of their running behavior, facilitating performance improvement in future runs. Among these parameters, breathing, which fuels our bodies with oxygen and expels carbon dioxide, is crucial to improving the efficiency of running. While previous studies have made substantial progress in measuring breathing rate, exploration of additional breathing monitoring during running is still lacking. In this work, we fill this gap by presenting BreathPro, the first breathing mode monitoring …

Swapvid: Integrating Video Viewing And Document Exploration With Direct Manipulation, Taichi Murakami, Kazuyuki Fujita, Kotaro Hara, Kazuki Takashima, Yoshifumi Kitamura

Research Collection School Of Computing and Information Systems

Videos accompanied by documents—document-based videos—enable presenters to share contents beyond videos and audience to use them for detailed content comprehension. However, concurrently exploring multiple channels of information could be taxing. We propose SwapVid, a novel interface for viewing and exploring document-based videos. SwapVid seamlessly integrates a video and a document into a single view and lets the content behaves as both video and a document; it adaptively switches a document-based video to act as a video or a document upon direct manipulation (e.g., scrolling the document, manipulating the video timeline). We conducted a user study with twenty participants, comparing SwapVid …

Evaluation Of Orca 2 Against Other Llms For Retrieval Augmented Generation, Donghao Huang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

This study presents a comprehensive evaluation of Microsoft Research’s Orca 2, a small yet potent language model, in the context of Retrieval Augmented Generation (RAG). The research involved comparing Orca 2 with other significant models such as Llama-2, GPT-3.5-Turbo, and GPT-4, particularly focusing on its application in RAG. Key metrics, included faithfulness, answer relevance, overall score, and inference speed, were assessed. Experiments conducted on high-specification PCs revealed Orca 2’s exceptional performance in generating high quality responses and its efficiency on consumer-grade GPUs, underscoring its potential for scalable RAG applications. This study highlights the pivotal role of smaller, efficient models like …

Unveiling Code Pre-Trained Models: Investigating Syntax And Semantics Capacities, Wei Ma, Shangqing Liu, Mengjie Zhao, Xiaofei Xie, Wenhang Wang, Qiang Hu, Jie Zhang, Liu Yang

Research Collection School Of Computing and Information Systems

Code models have made significant advancements in code intelligence by encoding knowledge about programming languages. While previous studies have explored the capabilities of these models in learning code syntax, there has been limited investigation on their ability to understand code semantics. Additionally, existing analyses assume the number of edges between nodes at the abstract syntax tree (AST) is related to syntax distance, and also often require transforming the high-dimensional space of deep learning models to a low-dimensional one, which may introduce inaccuracies. To study how code models represent code syntax and semantics, we conduct a comprehensive analysis of 7 code …

Financial Literacy And Ipo Underpricing, Xiaoran Jia, Kiridaran G. Kanagaretnam, Chee Yeow Lim, Gerald J. Lobo

Research Collection School Of Accountancy

Using an international sample of IPO firms and two country-level measures of financial literacy, we find strong evidence that financial literacy is negatively associated with IPO underpricing. In cross-sectional analyses, we find that the effect of financial literacy in reducing IPO underpricing is more pronounced when the information environment is less transparent. Employing path analysis, we document that information friction, firm transparency, and stock market participation are mechanisms that mediate this relationship. Our study contributes to and extends the literature by providing strong evidence that citizens' financial literacy has an important and consistent influence on IPO underpricing.

Online Control Of Adaptive Large Neighborhood Search Using Deep Reinforcement Learning, Reijnen Reijnen, Yingqian Zhang, Hoong Chuin Lau, Zaharah Bukhsh

Research Collection School Of Computing and Information Systems

The Adaptive Large Neighborhood Search (ALNS) algorithm has shown considerable success in solving combinatorial optimization problems (COPs). Nonetheless, the performance of ALNS relies on the proper configuration of its selection and acceptance parameters, which is known to be a complex and resource-intensive task. To address this, we introduce a Deep Reinforcement Learning (DRL) based approach called DR-ALNS that selects operators, adjusts parameters, and controls the acceptance criterion throughout the search. The proposed method aims to learn, based on the state of the search, to configure ALNS for the next iteration to yield more effective solutions for the given optimization problem. …