Digital Commons Network^™

Deep Graph Anomaly Detection: A Survey And New Perspectives, Hezhe Qiao, Hanghang Tong, Nanyang Technological University, Irwin King, Charu Aggarwal, Guansong Pang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD), which aims to identify unusual graph instances (e.g., nodes, edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its significance in a wide range of applications. Deep learning approaches, graph neural networks (GNNs) in particular, have been emerging as a promising paradigm for GAD, owing to its strong capability in capturing complex structure and/or node attributes in graph data. Considering the large number of methods proposed for GNN-based GAD, it is of paramount importance to summarize the methodologies and findings in the existing GAD studies, so that we can pinpoint effective model …

Boosting Symbolic Execution For Vulnerability Detection, Haoxin Tu

Dissertations and Theses Collection (Open Access)

Software systems written by humans tend to be unreliable and insecure, hence, bugs or vulnerabilities in them are inevitable. Symbolic execution has shown considerable potential in detecting diverse types of software bugs and also vulnerabilities that have severe security implications. However, existing symbolic execution engines still suffer from at least three fundamental limitations in memory modeling, path exploration, and structured input generation, which significantly impede existing engines from efficiently and effectively detecting software bugs and vulnerabilities.

The objective of this dissertation is to boost existing symbolic execution engines by designing a new memory model, two new path exploration strategies, and …

Memory-Efficient Graph Processing On Gpus: Reducing Intermediate Data Structure Overhead, Chang Ye

Dissertations and Theses Collection (Open Access)

The increasing scale of real-world graphs in domains such as fraud detection, community detection, and biological analysis demands high-throughput, memory-efficient graph processing solutions. GPUs offer massive parallelism for accelerating such workloads, and numerous frameworks have been developed to leverage their computational power. These frameworks primarily focus on optimizing scheduling to better align graph processing with GPU architectures. It performs well for algorithms with low memory demands, such as BFS, SSSP, and PageRank. However, for algorithms that require substantial memory, such as label propagation, and subgraph counting, the limited memory capacity of GPUs often becomes a significant bottleneck.

This dissertation addresses …

Impact Of Aigc Application On E-Commerce: A Study In The Scenario Of Product Display And Recommendation, Ya Shen

Dissertations and Theses Collection (Open Access)

Artificial Intelligence Generated Content is widely expected to enhance efficiency and lower operation cost for e-commerce. This study aims to investigate on how AIGC technology would impact on the online shopping behavior and conversion rate, specifically in the scenario of product display and recommendations.

Experiment 1 is to evaluate which scenario would have better efficiency on e-commerce platforms when using AIGC. Using an A/B testing methodology, the effects of AIGC application were measured among key factors of six product display and recommendation scenario on Vipshop.com. Results show that AIGC application on model image has the greatest impact, increasing CTR by …

The Moderating Role Of Trait Online Disinhibition In Exacerbating The Online Victim-Bully Cycle, Sandeeshwara K Tennakoon Appuhamillage Kasturiratna, Andree Hartanto

Research Collection School of Social Sciences

The growth of the internet and social media as a communication platform has increased the incidence of cyberbullying victimisation. While there has been increasing research interest on the antecedents of cyberbullying perpetration, little is known on why some individuals who themself experience cyber victimisation are more likely to become cyberbullying perpetrators. Considering the large number of individuals who experience cyberbullying victimisation in the current, highly digitalised world, this trend of victims turning into bullies poses a worrisome possibility and deserves research attention. One promising individual difference that could potentially moderate the relationship between cyberbullying victimisation can lead to cyberbullying perpetration …

Reimagining Academic Assessment In The Age Of Ai, Matthew Hammerton

Research Collection School of Social Sciences

In ‘Reimagining Academic Assessment in the Age of AI’, Matthew Hammerton examines the challenges and opportunities posed by generative AI for higher education assessment. He critiques common responses like banning AI, reverting to in-class exams, or abandoning essays altogether, arguing that they fail to preserve the deeper pedagogical goals of higher order, independent thinking. Instead, Hammerton proposes a guiding principle of intellectual responsibility: students should be accountable for explaining and defending each major choice in their work—regardless of whether they use AI tools. To operationalise this, he advocates for reintegrating oral examinations (‘vivas’) alongside written essays. In this model, students …

The Moderating Role Of Digital Multi-Sided Platform On The Firm Value Of Traditional Value-Chain Firms, Chee Pheng Lim

Dissertations and Theses Collection (Open Access)

This dissertation examines the strategic value and performance implications for traditional value-chain firms (TVCFs) that adopt digital multi-sided platforms (DMSPs) to augment their existing businesses. DMSPs facilitate interactions among multiple user groups through digital intermediaries, generating self-reinforcing network effects that enhance value creation, reduce operational and transaction costs, and enable collective innovation. Despite the prevalence of platform-based firms, 60% of global unicorns and 7 of the world’s 10 most valuable companies operate digital platforms, empirical research on the value-creation mechanisms of DMSPs remains limited. Anchored in the resource-based view and platform theory, this study investigates how DMSP moderates the relationship …

A Settlement By “Any Other Name”: Evaluating The Sustainability Of Settlement Mechanisms, Dorcas Quek Anderson

Research Collection Yong Pung How School Of Law

Private settlements have been consistently encouraged by the Singapore civil justice system. Depending on the context, the settlement may take on many forms, including consent orders and settlement agreements. This begs the question of whether the exact form of settlement matters, or whether a settlement “by any other name” suffices. This article proposes the concept of “sustainable settlements” – comprising the measures of ease of enforcement, finality, and confidentiality – to holistically appraise the effectiveness of settlement mechanisms. The assessment of six settlement mechanisms using these measures clearly indicates that not every mechanism is equally sustainable. Exploring settlement amidst the …

What Are The Limits To Smart Innovation? The Digital Underground Initiative And New Frontiers Of “Smartness” In Singapore, Prerona Das, Orlando Woods, Lily Kong

Research Collection College of Integrative Studies

This paper argues that the emergence of new frontiers of smart transformation, such as underground spaces, can pose significant challenges to the implementation of “smart” technological solutions. Exploring the case of Singapore’s Digital Underground initiative, we show that elements such as material complexity, co-ordination between stakeholders, and the availability of appropriate regulatory and administrative frameworks can problematize the implementation of subterranean smart initiatives. By considering multistake-holder perspectives on these “more-than-technological” factors, this paper offers lessons for urban planners, policymakers, and developers in cities around the world that are keen to leverage technology to unlock the potential of underground space.

Optimal Abort Policy For Mission-Critical Systems Under Imperfect Condition Monitoring, Qiuzhuang Sun, Jiawen Hu, Zhi-Sheng Ye

Research Collection College of Integrative Studies

Although most on-demand mission-critical systems are engineered to be reliable to support critical tasks, occasional failures may still occur during missions. To increase system survivability, a common practice is to abort the mission before an imminent failure. We consider optimal mission abort for a system whose deterioration follows a general three-state (normal, defective, failed) semi-Markov chain. The failure is assumed self-revealed, whereas the healthy and defective states have to be inferred from imperfect condition-monitoring data. Because of the non-Markovian process dynamics, optimal mission abort for this partially observable system is an intractable stopping problem. For a tractable solution, we introduce …

Iot Sensors As A Tool For Assessing Spatiotemporal Risk To Extreme Heat, Kijin Seong, Seung Jun Choi, Junfeng Jiao

Research Collection College of Integrative Studies

The safety of urban populations sensitive to extreme heat is under increasing threat. Few studies examine the potential benefits of deploying IoT environmental sensors in the urban context and their integration with large-scale human activity data. This paper examines the deployment of IoT sensors in high-resolution extreme heat risk assessment in the case of Seoul, South Korea. This study conducted spatiotemporal analysis on heat exposure with IoT sensors, compared it with an existing land surface temperature map for validation, combined it with human activity data for risk assessment, and finally discussed the benefits of IoTs in detecting abnormal weather events. …

The Cool, Quiet City Machine Learning Competition: Overview And Results, Clayton Miller, M. Ibrahim, I.S. Akbar, B. Picchetti, Y.X. Chua, M. Frei, F. Biljecki, A. Chong, M. Quintana, C. Fu

Research Collection College of Integrative Studies

The prediction of thermal and noise-based preferences in the urban context is valuable in characterizing interventions to mitigate the challenges of health, productivity, and satisfaction of urban dwellers. The growth of crowd-sourced data and data-driven techniques provides an opportunity to increase the understanding of which machine learning models are most accurate and applicable for this context. This paper outlines the results of a machine learning competition aiming to enhance the accuracy of predicting human comfort in the city context. The competition asks contestants to use contextual data to predict noise distraction and thermal preference in various indoor and outdoor spaces. …

Recurrent Autoregressive Linear Model For Next-Basket Recommendation, Tereza Zmeskalova, Antoine Ledent, Martin Spisak, Pavel Kordik, Rodrigo Alves

Research Collection School Of Computing and Information Systems

Next-basket recommendation aims to predict the (sets of) items that a user is most likely to purchase during their next visit, capturing both short-term sequential patterns and long-term user preferences. However, effectively modeling these dynamics remains a challenge for traditional methods, which often struggle with interpretability and computational efficiency, particularly when dealing with intricate temporal dependencies and inter-item relationships. In this paper, we propose ReALM, a Recurrent Autoregressive Linear Model that explicitly captures temporal item-to-item dependencies across multiple time steps. By leveraging a recurrent loss function and a closed-form optimization solution, our approach offers both interpretability and scalability while maintaining …

Storage Location Optimization In Automated Storage And Retrieval Systems: A Deep Reinforcement Learning Approach, Lingjun Wang, Aldy Gunawan, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

This study investigates the optimization of storage location in automated storage and retrieval systems (AS/RS). We introduce an optimization approach based on the Deep Q-Network (DQN) algorithm to enhance warehouse task efficiency and minimize stacker travel during storage and retrieval. To accelerate the algorithm training process, we integrate a prioritized experience replay mechanism. Furthermore, we decouple action selection from value estimation within the DQN framework to address the issue of value overestimation. The proposed model is evaluated against three heuristic methods. The experimental results demonstrate that our approach significantly outperforms these baselines.

Learning Orientation Field For Osm-Guided Autonomous Navigation, Yuming Huang, Wei Gao, Zhiyuan Zhang, Maani Ghaffari, Dezhen Song, Cheng-Zhong Xu, Hui Kong

Research Collection School Of Computing and Information Systems

OpenStreetMap (OSM) has gained popularity recently in autonomous navigation due to its public accessibility, lower maintenance costs, and broader geographical coverage. However, existing methods often struggle with noisy OSM data and incomplete sensor observations, leading to inaccuracies in trajectory planning. These challenges are particularly evident in complex driving scenarios, such as at intersections or facing occlusions. To address these challenges, we propose a robust and explainable two-stage framework to learn an Orientation Field (OrField) for robot navigation by integrating LiDAR scans and OSM routes. In the first stage, we introduce a novel representation, OrField, which can provide orientations for each …

Probabilistic Modeling, Learnability And Uncertainty Estimation For Interaction Prediction In Movie Rating Datasets, Jennifer Poernomo, Nicole Gabrielle Lee Tan, Rodrigo Alves, Antoine Ledent

Research Collection School Of Computing and Information Systems

In this paper, we examine the hypothesis that the interactions recorded in many Recommendation Systems datasets are distributed according to a low-rank distribution, i.e. a mixture of factorizable distributions. Surprisingly, we find that on several popular datasets, a simple non-negative matrix factorization method equals or outperforms more modern methods such as LightGCN, which indicates that the sampling distribution over interactions is indeed low-rank. Furthermore, we mathematically prove that low-rank distributions are learnable with a sparse number of observations (where m/n and r refer to the number of users/items and the non-negative rank respectively) both in terms of the total variation …

Privacy-Preserving Ridge Regression Over Encrypted Data Under Multiple Keys, Yanling Li, Junzuo Lai, Meng Sun, Beibei Song, Robert H. Deng

Research Collection School Of Computing and Information Systems

With the increase of private data being collected by data owners, it has been a trend for data owners to store the data on cloud computing platforms. The huge amounts of data in cloud servers bring fresh development opportunities to machine learning, which is applied to build a high-quality machine learning model based on a large training dataset. However, to ensure the privacy of data and facilitate retrieval, data owners often upload encrypted data under their public keys. But it creates new challenges for machine learning to learn a predictive model over these encrypted data under different keys. Most existing …

An Efficient Security-Enhanced Accountable Access Control For Named Data Networking, Jianfei Sun, Yuxian Li, Xuehuan Yang, Guomin Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Named Data Networking (NDN) is embraced as the crucial implementation of Information-Centric Networking (ICN), enhancing content distribution and caching efficiency through edge routers. However, existing NDN architectures face significant security and privacy challenges, including: (a) a lack of secure and efficient access control; (b) inadequate support for flexible and selective content management by content publishers; (c) insufficient implementation of accountability and privilege revocation mechanisms. To handle these challenges, we propose ESAS, the first-ever Efficient Security-enhanced Accountable Access Control Scheme for NDN. Specifically, our ESAS incorporates anonymous authentication using group signatures at network routers to prevent unauthorized access, employs key-aggregation-based access …

From Release To Adoption: Challenges In Reusing Pre-Trained Ai Models For Downstream Developers, Peerachai Banyongrakkul, Mansooreh Zahedi, Patanamon Thongtanunam, Christoph Treude, Haoyu Gao

Research Collection School Of Computing and Information Systems

Pre-trained models (PTMs) have gained widespread popularity and achieved remarkable success across various fields, driven by their groundbreaking performance and easy accessibility through hosting providers. However, the challenges faced by downstream developers in reusing PTMs in software systems are less explored. To bridge this knowledge gap, we qualitatively created and analyzed a dataset of 840 PTM-related issue reports from 31 OSS GitHub projects. We systematically developed a comprehensive taxonomy of PTM-related challenges that developers face in downstream projects. Our study identifies seven key categories of challenges that downstream developers face in reusing PTMs, such as model usage, model performance, and …

Apidocbooster: An Extract-Then-Abstract Framework Leveraging Large Language Models For Augmenting Api Documentation, Chengran Yang, Jiakun Liu, Bowen Xu, Christoph Treude, Yunbo Lyu, Junda He, Ming Li, David Lo

Research Collection School Of Computing and Information Systems

API documentation is often the most trusted resource for programming. Many approaches have been proposed to augment API documentation by summarizing complementary information from external resources such as Stack Overflow. Existing extractive-based summarization approaches excel in producing faithful summaries that accurately represent the source content without input length restrictions. Nevertheless, they suffer from inherent readability limitations. On the other hand, our empirical study on the abstractive-based summarization method, i.e., GPT-4, reveals that GPT-4 can generate coherent and concise summaries but presents limitations in terms of informativeness and faithfulness. We introduce APIDocBooster, an extract-then-abstract framework that seamlessly fuses the advantages of …

Static Analysis As A Feedback Loop: Enhancing Llm-Generated Code Beyond Correctness, Scott Blyth, Sherlock Licorish, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have demonstrated impressive capabilities in code generation, achieving high scores on benchmarks such as HumanEval and MBPP. However, these benchmarks primarily assess functional correctness and neglect broader dimensions of code quality, including security, reliability, readability, and maintainability. In this work, we systematically evaluate the ability of LLMs to generate high-quality code across multiple dimensions using the PythonSecurityEval benchmark. We introduce an iterative static analysis-driven prompting algorithm that leverages Bandit and Pylint to identify and resolve code quality issues. Our experiments with GPT-4o show substantial improvements: security issues reduced from >40% to 13%, readability violations from >80% …

Educator Perceptions Of Devops Teaching Recommendations And Their Alignment With Common Challenges, Marcelo Romulo Fernandes, Pablo Paiva, Samuel Lucas De Moura Ferino, Roberta Coelho, Christoph Treude, Eduardo Aranha, Uirá Kulesza

Research Collection School Of Computing and Information Systems

DevOps education presents unique pedagogical challenges due to the diversity of tools, rapid technological change, and the multidisciplinary nature of the field. Although previous work has proposed recommendations to address these challenges, it is unclear how educators perceive these recommendations and whether they align with the challenges encountered in practice. In this paper, we present a quantitative and qualitative methods study involving 11 DevOps educators who interacted with Improve, a tool that presents a curated set of educational challenges and recommendations derived from previous literature. Educators indicated which recommendations they already use, which they intend to use, and which challenges …

Studying Satd In Drone Systems With Human-Ai Collaboration, Leevi Rantala, Lwin Khin Shar, Mäntylä Mika V., Wei Minn, Naing Tun Yan

Research Collection School Of Computing and Information Systems

Background: Self-Admitted Technical Debt (SATD) refers to sub-optimal solutions that developers acknowledge within the source code. SATD research originated on Java projects but is expanding to other domains. We focus on SATD in drones, which are used for various critical tasks.Aims: The primary objective is to investigate SATD in drone systems. The second aim is to explore the integration of AI and human collaboration for SATD labelling and classification.Method: We conducted a sample study of SATD comments in drone systems (14 open source, 4 SDKs) to analyse the quantity and types of SATD comments present. Our study incorporates collaboration between …

Large Lithium-Ion Battery Model For Secure Shared E-Bike Battery In Smart Cities, Donghui Ding, Zhao Li, Linhao Luo, Ming Jin, Bin Zhu, Yichen Zhong, Junhao Hu, Peng Cai, Huiqi Hu

Research Collection School Of Computing and Information Systems

Electric bikes powered by lithium-ion batteries are increasingly used in smart cities to promote sustainable mobility and efficient delivery services. However, limited battery range and slow plug-in charging remain key challenges. Shared electric bike battery systems, facilitated by battery swapping stations, offer a promising solution by enabling quick and efficient battery replacements. However, their success hinges on accurate anomaly detection, battery health estimation and remain range prediction. These tasks remain challenging due to data scarcity, battery diversity and environmental variability. Here we show that a large-scale lithium-ion battery model trained on over ten million battery time series data enables robust …

Stylegan-∞: Extending Stylegan To Arbitrary-Ratio Translation With Stylebook, Yihua Dai, Tianyi Xiang, Bailin Deng, Yong Du, Hongmin Cai, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Although pre-trained large-scale generative models StyleGAN series have proven to be effective in various editing and translation tasks, they are limited to pre-defined fixed aspect ratio. To overcome this limitation, we propose StyleGAN-∞, a model that enables pre-trained StyleGAN to perform arbitrary-ratio conditional synthesis. Our key insight is to distill the expressive StyleGAN features into a StyleBook, such that an arbitrary-ratio condition can be translated to other forms by properly assembling pre-defined StyleBook vectors. To learn and leverage the StyleBook, we employ a network with three distinct stages, each corresponding to StyleBook extraction, StyleBook correspondence learning, and arbitrary-ratio synthesis. Extensive …

Preference-Based Deep Reinforcement Learning For Historical Route Estimation, Boshen Pan, Yaoxin Wu, Zhiguang Cao, Yaqing Hou, Guangyu Zou, Qiang Zhang

Research Collection School Of Computing and Information Systems

Recent Deep Reinforcement Learning (DRL) techniques have advanced solutions to Vehicle Routing Problems (VRPs). However, many of these methods focus exclusively on optimizing distance-oriented objectives (i.e., minimizing route length), often overlooking the implicit drivers' preferences for routes. These preferences, which are crucial in practice, are challenging to model using traditional DRL approaches. To address this gap, we propose a preference-based DRL method characterized by its reward design and optimization objective, which is specialized to learn historical route preferences. Our experiments demonstrate that the method aligns generated solutions more closely with human preferences. Moreover, it exhibits strong generalization performance across a …

Dgl: Dynamic Global-Local Information Aggregation For Scalable Vrp Generalization With Self-Improvement Learning, Yubin Xiao, Yuesong Wu, Rui Cao, Di Wang, Zhiguang Cao, Xuan Wu, Peng Zhao, Yuanshu Li, You Zhou, Yuan Jiang

Research Collection School Of Computing and Information Systems

The Vehicle Routing Problem (VRP) is a critical combinatorial optimization problem with wide-reaching real-world applications, particularly in logistics, transportation. While neural network-based VRP solvers have shown impressive results on test instances similar to training data, their performance often degrades when faced with varying scales and unseen distributions, limiting their practical applicability. To overcome these limitations, we introduce DGL (Dynamic Global-Local Information Aggregation), a novel model that combines global and local information to effectively solve VRPs. DGL dynamically adjusts local node selections within a localized range, capturing local invariance across problems of different scales and distributions, thereby enhancing generalization. At the …

Coupling Category Alignment For Graph Domain Adaptation, Nan Yin, Xiao Teng, Zhiguang Cao, Mengzhu Wang

Research Collection School Of Computing and Information Systems

Graph domain adaptation (GDA), which transfers knowledge from a labeled source domain to an unlabeled target graph domain, attracts considerable attention in numerous fields. However, existing methods commonly employ message-passing neural networks (MPNNs) to learn domain-invariant representations by aligning the entire domain distribution, inadvertently neglecting category-level distribution alignment and potentially causing category confusion. To address the problem, we propose an effective framework named Coupling Category Alignment (CoCA) for GDA, which effectively addresses the category alignment issue with theoretical guarantees. CoCA incorporates a graph convolutional network branch and a graph kernel network branch, which explore graph topology in implicit and explicit …

Map As A By-Product: Collective Landmark Mapping From Imu Data And User-Provided Texts In Situated Tasks, Ryo Yonetani, Kotaro Hara

Research Collection School Of Computing and Information Systems

This paper presents Collective Landmark Mapper, a novel map-as-a-by-product system for generating semantic landmark maps of indoor environments. Consider users engaged in situated tasks that require them to navigate these environments and regularly take notes on their smartphones. Collective Landmark Mapper exploits the smartphone's IMU data and the user's free text input during these tasks to identify a set of landmarks encountered by the user. The identified landmarks are then aggregated across multiple users to generate a unified map representing the positions and semantic information of all landmarks. In developing the proposed system, we focused specifically on retail applications and …

Towards Multimodal Emotional Support Conversation Systems, Yuqi Chu, Lizi Liao, Zhiyuan Zhou, Chong-Wah Ngo, Richang Hong

Research Collection School Of Computing and Information Systems

The integration of conversational artificial intelligence (AI) into mental health care promises a new horizon for therapist-client interactions, aiming to closely emulate the depth and nuance of human conversations. Despite the potential, the current landscape of conversational AI is markedly limited by its reliance on single-modal data, constraining the systems’ ability to empathize and provide effective emotional support. This limitation stems from a paucity of resources that encapsulate the multimodal nature of human communication essential for therapeutic counseling. To address this gap, we introduce the Multimodal Emotional Support Conversation (MESC) dataset, a first-of-its-kind resource enriched with comprehensive annotations across text, …