Physical Sciences and Mathematics Commons^™

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 …

Efficient Cross-Modal Video Retrieval With Meta-Optimized Frames, Ning Han, Xun Yang, Ee-Peng Lim, Hao Chen, Qianru Sun

Research Collection School Of Computing and Information Systems

Cross-modal video retrieval aims to retrieve semantically relevant videos when given a textual query, and is one of the fundamental multimedia tasks. Most top-performing methods primarily leverage Vision Transformer (ViT) to extract video features [1]-[3]. However, they suffer from the high computational complexity of ViT, especially when encoding long videos. A common and simple solution is to uniformly sample a small number (e.g., 4 or 8) of frames from the target video (instead of using the whole video) as ViT inputs. The number of frames has a strong influence on the performance of ViT, e.g., using 8 frames yields better …

Enhancing Code Vulnerability Detection Via Vulnerability-Preserving Data Augmentation, Shangqing Liu, Wei Ma, Jian Wang, Xiaofei Xie, Ruitao Feng, Yang Liu

Research Collection School Of Computing and Information Systems

Source code vulnerability detection aims to identify inherent vulnerabilities to safeguard software systems from potential attacks. Many prior studies overlook diverse vulnerability characteristics, simplifying the problem into a binary (0-1) classification task for example determining whether it is vulnerable or not. This poses a challenge for a single deep-learning based model to effectively learn the wide array of vulnerability characteristics. Furthermore, due to the challenges associated with collecting large-scale vulnerability data, these detectors often overfit limited training datasets, resulting in lower model generalization performance. To address the aforementioned challenges, in this work, we introduce a fine-grained vulnerability detector namely FGVulDet. …

Applicability And Challenges Of Indoor Localization Using One-Sided Round Trip Time Measurements, Quang Hai Truong, Xi Kai Justin Lam, Guru Anand Anish, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Radio Frequency fingerprinting, based on WiFi or cellular signals, has been a popular approach for localization. However, adoptions in real-world applications have confronted with challenges due to low accuracy, especially in crowded environments. The received signal strength (RSS) could be easily interfered by a large number of other devices or strictly depends on physical surrounding environments, which may cause localization errors of a few meters. On the other hand, the fine time measurement (FTM) round-trip time (RTT) has shown compelling improvement in indoor localization with ~1-2 meter accuracy in both 2D and 3D environments [13]. This method relies on the …

Fully Automated Selfish Mining Analysis In Efficient Proof Systems Blockchains, Krishnendu Chatterjee, Amirali Ebrahimzadeh, Mehrdad Karrabi, Krzysztof Pietrzak, Michelle Yeo, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We study selfish mining attacks in longest-chain blockchains like Bitcoin, but where the proof of work is replaced with efficient proof systems - like proofs of stake or proofs of space - and consider the problem of computing an optimal selfish mining attack which maximizes expected relative revenue of the adversary, thus minimizing the chain quality. To this end, we propose a novel selfish mining attack that aims to maximize this objective and formally model the attack as a Markov decision process (MDP). We then present a formal analysis procedure which computes an ϵ-tight lower bound on the optimal expected …

Neuron Sensitivity Guided Test Case Selection, Dong Huang, Qingwen Bu, Yichao Fu, Yuhao Qing, Xiaofei Xie, Junjie Chen, Heming Cui

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) have been widely deployed in software to address various tasks (e.g., autonomous driving, medical diagnosis). However, they can also produce incorrect behaviors that result in financial losses and even threaten human safety. To reveal and repair incorrect behaviors in DNNs, developers often collect rich, unlabeled datasets from the natural world and label them to test DNN models. However, properly labeling a large number of datasets is a highly expensive and time-consuming task. To address the above-mentioned problem, we propose NSS, Neuron Sensitivity Guided Test Case Selection, which can reduce the labeling time by selecting valuable test …

Criticality Aware Canvas-Based Visual Perception At The Edge, Ila Gokarn

Research Collection School Of Computing and Information Systems

Efficient and effective machine perception remains a formidable challenge in sustaining high fidelity and high throughput of perception tasks on affordable edge devices. This is especially due to the continuing increase in resolution of sensor streams (e.g., video input streams generated by 4K/8K cameras and neuromorphic event cameras that produce ≥ 10 MEvents/second) and computational complexity of Deep Neural Network (DNN) models, which overwhelms edge platforms, adversely impacting machine perception efficiency. Given the insufficiency of the available computation resources, a question then arises on whether selected regions/components of the perception task can be prioritized (and executed preferentially) to achieve highest …

Unmasking The Lurking: Malicious Behavior Detection For Iot Malware With Multi-Label Classification, Ruitao Feng, Sen Li, Sen Chen, Mengmeng Ge, Xuewei Li, Xiaohong Li

Research Collection School Of Computing and Information Systems

Current methods for classifying IoT malware predominantly utilize binary and family classifications. However, these outcomes lack the detailed granularity to describe malicious behavior comprehensively. This limitation poses challenges for security analysts, failing to support further analysis and timely preventive actions. To achieve fine-grained malicious behavior identification in the lurking stage of IoT malware, we propose MaGraMal. This approach, leveraging masked graph representation, supplements traditional classification methodology, empowering analysts with critical insights for rapid responses. Through the empirical study, which took three person-months, we identify and summarize four fine-grained malicious behaviors during the lurking stage, constructing an annotated dataset. Our evaluation …

Let’S Think Outside The Box: Exploring Leap-Of-Thought In Large Language Models With Multimodal Humor Generation, Shanshan Zhong, Zhongzhan Huang, Shanghua Gao, Wushao Wen, Liang Lin, Marinka Zitnik, Pan Zhou

Research Collection School Of Computing and Information Systems

Chain-of-Thought (CoT) [2, 3] guides large language models (LLMs) to reason step-by-step, and can motivate their logical reasoning ability. While effective for logical tasks, CoT is not conducive to creative problem-solving which often requires out-of-box thoughts and is crucial for innovation advancements. In this paper, we explore the Leap-of-Thought (LoT) abilities within LLMs — a nonsequential, creative paradigm involving strong associations and knowledge leaps. To this end, we study LLMs on the popular Oogiri game which needs participants to have good creativity and strong associative thinking for responding unexpectedly and humorously to the given image, text, or both, and thus …

Learning Dynamic Multimodal Network Slot Concepts From The Web For Forecasting Environmental, Social And Governance Ratings, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Dynamic multimodal networks are networks with node attributes from different modalities where the at- tributes and network relationships evolve across time, i.e., both networks and multimodal attributes are dynamic; for example, dynamic relationship networks between companies that evolve across time due to changes in business strategies and alliances, which are associated with dynamic company attributes from multiple modalities such as textual online news, categorical events, and numerical financial-related data. Such information can be useful in predictive tasks involving companies. Environmental, social, and gov- ernance (ESG) ratings of companies are important for assessing the sustainability risks of companies. The process of …

Few-Shot Learner Parameterization By Diffusion Time-Steps, Zhongqi Yue, Pan Zhou, Richang Hong, Hanwang Zhang, Sun Qianru

Research Collection School Of Computing and Information Systems

Even when using large multi-modal foundation models, few-shot learning is still challenging—if there is no proper inductive bias, it is nearly impossible to keep the nuanced class attributes while removing the visually prominent attributes that spuriously correlate with class labels. To this end, we find an inductive bias that the time-steps of a Diffusion Model (DM) can isolate the nuanced class attributes, i.e., as the forward diffusion adds noise to an image at each time-step, nuanced attributes are usually lost at an earlier time-step than the spurious attributes that are visually prominent. Building on this, we propose Time-step Few-shot (TiF) …

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. …

To Protect Or To Hide: An Investigation On Corporate Redacted Disclosure Motives Under New Fast Act Regulation, Yan Ma, Qian Mao, Nan Hu

Research Collection School Of Computing and Information Systems

China adopted amendments allowing companies to redact filings without prior approval in 2016. Leveraging this change as a quasi-nature experiment, we explore whether managers utilize redacted information to withhold bad information in the more lenient regulatory environment. Our investigation uncovers a significant shift in managerial behavior: Since 2016, managers incline to employ redactions to obscure negative news rather than safeguarding proprietary data. Furthermore, we find that the poorer firm performance and a higher cost of equity are associated with the redacted disclosures after 2016, suggesting that investors perceive an increase in firm-specific risk attributed to withholding bad news through redactions.

Friendly Sharpness-Aware Minimization, Tao Li, Pan Zhou, Zhengbao He, Xinwen Cheng, Xiaolin Huang

Research Collection School Of Computing and Information Systems

Sharpness-Aware Minimization (SAM) has been instrumental in improving deep neural network training by minimizing both training loss and loss sharpness. Despite the practical success, the mechanisms behind SAM’s generalization enhancements remain elusive, limiting its progress in deep learning optimization. In this work, we investigate SAM’s core components for generalization improvement and introduce “Friendly-SAM” (F-SAM) to further enhance SAM’s generalization. Our investigation reveals the key role of batch-specific stochastic gradient noise within the adversarial perturbation, i.e., the current minibatch gradient, which significantly influences SAM’s generalization performance. By decomposing the adversarial perturbation in SAM into full gradient and stochastic gradient noise components, …

Diffusion Time-Step Curriculum For One Image To 3d Generation, Xuanyu Yi, Zike Wu, Qingshan Xu, Pan Zhou, Joo Hwee Lim, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Score distillation sampling (SDS) has been widely adopted to overcome the absence of unseen views in reconstructing 3D objects from a single image. It leverages pretrained 2D diffusion models as teacher to guide the reconstruction of student 3D models. Despite their remarkable success, SDS-based methods often encounter geometric artifacts and texture saturation. We find out the crux is the overlooked indiscriminate treatment of diffusion time-steps during optimization: it unreasonably treats the studentteacher knowledge distillation to be equal at all time-steps and thus entangles coarse-grained and fine-grained modeling. Therefore, we propose the Diffusion Time-step Curriculum one-image-to-3D pipeline (DTC123), which involves both …

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 …

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 …

Ethical Considerations Toward Protestware, Marc Cheong, Raula Kula, Christoph Treude

Research Collection School Of Computing and Information Systems

This article looks into possible scenarios where developers might consider turning their free and open source software into protestware. Using different frameworks commonly used in artificial intelligence (AI) ethics, we extend the applications of AI ethics to the study of protestware.

Consistent3d: Towards Consistent High-Fidelity Text-To-3d Generation With Deterministic Sampling Prior, Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Score distillation sampling (SDS) and its variants have greatly boosted the development of text-to-3D generation, but are vulnerable to geometry collapse and poor textures yet. To solve this issue, we first deeply analyze the SDS and find that its distillation sampling process indeed corresponds to the trajectory sampling of a stochastic differential equation (SDE): SDS samples along an SDE trajectory to yield a less noisy sample which then serves as a guidance to optimize a 3D model. However, the randomness in SDE sampling often leads to a diverse and unpredictable sample which is not always less noisy, and thus is …

Closest Pairs Search Over Data Stream, Rui Zhu Zhu, Bin Wang, Xiaochun Yang, Baihua Zheng

Research Collection School Of Computing and Information Systems

��-closest pair (KCP for short) search is a fundamental problem in database research. Given a set of��-dimensional streaming data S, KCP search aims to retrieve �� pairs with the shortest distances between them. While existing works have studied continuous 1-closest pair query (i.e., �� = 1) over dynamic data environments, which allow for object insertions/deletions, they require high computational costs and cannot easily support KCP search with �� > 1. This paper investigates the problem of KCP search over data stream, aiming to incrementally maintain as few pairs as possible to support KCP search with arbitrarily ��. To achieve this, we …

Improving Interpretable Embeddings For Ad-Hoc Video Search With Generative Captions And Multi-Word Concept Bank, Jiaxin Wu, Chong-Wah Ngo, Wing-Kwong Chan

Research Collection School Of Computing and Information Systems

Aligning a user query and video clips in cross-modal latent space and that with semantic concepts are two mainstream approaches for ad-hoc video search (AVS). However, the effectiveness of existing approaches is bottlenecked by the small sizes of available video-text datasets and the low quality of concept banks, which results in the failures of unseen queries and the out-of-vocabulary problem. This paper addresses these two problems by constructing a new dataset and developing a multi-word concept bank. Specifically, capitalizing on a generative model, we construct a new dataset consisting of 7 million generated text and video pairs for pre-training. To …

Try It Together - Qualitative Coding With Atlas.Ti, Danping Dong, Bryan Leow

AI for Research Week

This hands-on session introduces Atlas.ti, a well-established qualitative data analysis tool for analyzing your transcripts and textual data. The session will cover coding data, extracting insights, creating visualizations, and exploring the tool's latest AI features.

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

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

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 …

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.

Using Pre-Trained Models For Vision-Language Understanding Tasks, Rui Cao

Dissertations and Theses Collection (Open Access)

In recent years, remarkable progress has been made in Artificial Intelligence (AI), with an increasing focus on integrating AI systems into people’s daily lives. In the context of our diverse world, research attention has shifted towards applying AI to multimodal understanding tasks. This thesis specifically addresses two key modalities, namely, vision and language, and explores Vision-Language Understanding (VLU).

In the past, addressing VLU tasks involved training distinct models from scratch using task-specific data. However, limited by the amount of training data, models may easily overfit the training data and fail to generalize. A recent breakthrough is the development of Pre-trained …

Enabling And Optimizing Multi-Modal Sense-Making For Human-Ai Interaction Tasks, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage

Dissertations and Theses Collection (Open Access)

The rapid pace of adoption of mixed-reality in tandem with advances in NLP and computer vision have opened up unprecedented opportunities for more naturalistic interaction interfaces which underpin Human-AI collaborative applications such as spatial computing and interactive conversational agents. One notable example is the emergence of interactive virtual assistants, which facilitate more natural communication of instructions and queries through modalities like voice and text. This trend is driving the development of innovative ubiquitous, mixed-reality computing applications. Such interactive, natural communication is also critical to support advances in human-robot interactive co-working, across a variety of industrial, commercial and home environments. Conventional …

Enabling Criticality-Aware Optimized Machine Perception At The Edge, Ila Nitin Gokarn

Dissertations and Theses Collection (Open Access)

Cyber-physical systems and applications have fundamentally changed people and processes in the way they interact with the physical world, ushering in the fourth industrial revolution. Supported by a variety of sensors, hardware platforms, artificial intelligence and machine learning models, and systems frameworks, CPS applications aim to automate and ease the burden of repetitive, laborious, or unsafe tasks borne by humans. Machine visual perception, encompassing tasks such as object detection, object tracking and activity analysis, is a key technical enabler of such CPS applications. Efficient execution of such machine vision perception tasks on resource-constrained edge devices, especially in terms of ensuring …

Improving The Performance Of Wi-Fi Indoor Localization In Both Dense And Unknown Environments, Quang Truong Hai

Dissertations and Theses Collection (Open Access)

Indoor localization is important for various pervasive applications, garnering considerable research attention over recent decades. Despite numerous proposed solutions, the practical application of these methods in real-world environments with high applicability remains challenging. One compelling use case for building owners is the ability to track individuals as they navigate through the building, whether for security, customer analytics, space utilization planning, or other management purposes. However, this task becomes exceedingly difficult in environments with hundreds or thousands of people in motion. Conversely, the need to track oneself’s location is also meaningful from the perspective of individuals traversing in crowded spaces. These …

Ethical Imperatives In Ai-Driven Educational Assessment: Framework And Implications, Ming Soon Tristan Lim

Dissertations and Theses Collection (Open Access)

This dissertation embarks on an extensive exploration of the ethical challenges emerging from the integration of AI in educational assessments. It uncovers the complex interplay between AI and the ethical imperatives these technologies pose within educational assessments.

Amidst the rapid development of AI-enabled educational technologies, such as Ubiquitous, Adaptive, and Immersive technologies, this research identifies a notable gap in literature specifically concerning the ethical imperatives and implications of AI in educational assessments. Addressing this gap, the dissertation has three primary objectives: to comprehend and analyze the underpinning educational technologies driving assessments, to elucidate the intricate relationship between AI, ethics, and …