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Sticky Information Technology Investment: Theory And Empirical Evidence, Peng Liang, Hasan Cavusoglu, Nan Hu Mar 2025

Sticky Information Technology Investment: Theory And Empirical Evidence, Peng Liang, Hasan Cavusoglu, Nan Hu

Research Collection School Of Computing and Information Systems

This article provides a new way of thinking about managerial discretion in information technology (IT) investment decisions. We delve into the existence, antecedents, and consequences of sticky IT investment behavior, an understudied managerial deliberate resource commitment decision in response to changes in sales. Guided by downsizing theory, we initially theorize and find that IT investments exhibit stickiness: IT investments move downward less for sales decreases than they move upward for equivalent increases. Then drawing upon agency theory, adjustment costs theory, and managerial expectations theory—which influence managers’ motivation for downsizing—we predict and demonstrate that managers’ empire-building incentives, their avoidance of adjustment …

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Gender Biases Within Artificial Intelligence And Chatgpt: Evidence, Sources Of Biases, And Solutions, Qi Hui Jerlyn Ho, Andree Hartanto, Andrew Koh, Nadyanna M. Majeed Mar 2025

Gender Biases Within Artificial Intelligence And Chatgpt: Evidence, Sources Of Biases, And Solutions, Qi Hui Jerlyn Ho, Andree Hartanto, Andrew Koh, Nadyanna M. Majeed

Research Collection School of Social Sciences

The growing adoption of Artificial Intelligence (AI) in various sectors has introduced significant benefits, but also raised concerns over biases, particularly in relation to gender. Despite AI's potential to enhance sectors like healthcare, education, and business, it often mirrors reality and its societal prejudices and can manifest itself through unequal treatment in hiring decisions, academic recommendations, or healthcare diagnostics, systematically disadvantaging women. This paper explores how AI systems and chatbots, notably ChatGPT, can perpetuate gender biases due to inherent flaws in training data, algorithms, and user feedback loops. This problem stems from several sources, including biased training datasets, algorithmic design …

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Private Reachability Queries On Structured Encrypted Temporal Bipartite Graphs, Yulin Wu, Lanxiang Chen, Gaolin Chen, Yi Mu, Robert H. Deng Mar 2025

Private Reachability Queries On Structured Encrypted Temporal Bipartite Graphs, Yulin Wu, Lanxiang Chen, Gaolin Chen, Yi Mu, Robert H. Deng

Research Collection School Of Computing and Information Systems

A temporal bipartite graph is a graph model that incorporates time-related information into its edges, making it suitable for modeling real-world phenomena like disease outbreaks. However, this temporal information is often sensitive. To protect the privacy of graph data, researchers have explored various approaches to preserve privacy in graph queries, with reachability queries being popular and fundamental as they determine the possibility of reaching one node from others in a graph. While privacy-preserving reachability queries have been extensively studied, existing efforts often overlook the valuable attribute information present in both edges and nodes of the graphs. Moreover, reachability queries on …

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Cachealarm: Monitoring Sensitive Behaviors Of Android Apps Using Cache Side Channel, Jianwen Tian, Haoyu Ma, Debin Gao, Xiaohui Kuang Mar 2025

Cachealarm: Monitoring Sensitive Behaviors Of Android Apps Using Cache Side Channel, Jianwen Tian, Haoyu Ma, Debin Gao, Xiaohui Kuang

Research Collection School Of Computing and Information Systems

Malware attack has been a serious threat to the security and privacy of both individual and corporation users of the Android platform. Business entities seek to protect themselves by means of monitoring privacy-related sensitive behaviors conducted on company-issued Android devices. However, due to Android’s own access control and privacy protection policies, this is difficult to be done with third-party apps using only normal privileges. Existing works proposed using side-channel readings from leaky APIs and system virtual files to speculate runtime app behaviors, which could be unreliable due to future system updates (that ban exploited resources), hardware jittering, etc. In this …

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Personamagic: Stage-Regulated High-Fidelity Face Customization With Tandem Equilibrium, Xinzhe Li, Jiahui Zhan, Shengfeng He, Yangyang Xu, Junyu Dong, Huaidong Zhang, Yong Du Mar 2025

Personamagic: Stage-Regulated High-Fidelity Face Customization With Tandem Equilibrium, Xinzhe Li, Jiahui Zhan, Shengfeng He, Yangyang Xu, Junyu Dong, Huaidong Zhang, Yong Du

Research Collection School Of Computing and Information Systems

Personalized image generation has made significant strides in adapting content to novel concepts. However, a persistent challenge remains: balancing the accurate reconstruction of unseen concepts with the need for editability according to the prompt, especially when dealing with the complex nuances of facial features. In this study, we delve into the temporal dynamics of the text-to-image conditioning process, emphasizing the crucial role of stage partitioning in introducing new concepts. We present PersonaMagic, a stage-regulated generative technique designed for high-fidelity face customization. Using a simple MLP network, our method learns a series of embeddings within a specific timestep interval to capture …

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An Aspect Performance-Aware Hypergraph Neural Network For Review-Based Recommendation, Junrui Liu, Tong Li, Di Wu, Zifang Tang, Yuan Fang, Zhen Yang Mar 2025

An Aspect Performance-Aware Hypergraph Neural Network For Review-Based Recommendation, Junrui Liu, Tong Li, Di Wu, Zifang Tang, Yuan Fang, Zhen Yang

Research Collection School Of Computing and Information Systems

Online reviews allow consumers to provide detailed feedback on various aspects of items. Existing methods utilize these aspects to model users' fine-grained preferences for specific item features through graph neural networks. We argue that the performance of items on different aspects is important for making precise recommendations, which has not been taken into account by existing approaches, due to lack of data. In this paper, we propose an aspect performance-aware hypergraph neural network (APH) for the review-based recommendation, which learns the performance of items from the conflicting sentiment polarity of user reviews. Specifically, APH comprehensively models the relationships among users, …

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Food Self-Sufficiency And Building-Integrated Urban Agriculture: Lessons From Singapore, Tomoki Fujii, Christoph Waibel, Xinyi Du, Zhongming Shi Mar 2025

Food Self-Sufficiency And Building-Integrated Urban Agriculture: Lessons From Singapore, Tomoki Fujii, Christoph Waibel, Xinyi Du, Zhongming Shi

Research Collection School Of Economics

Singapore aims to achieve 30 percent food self-sufficiency by 2030, known as the 30-by-30 target, and this paper reviews Singapore’s changing landscape for urban agriculture and its recent progress towards the 30-by-30 goal, highlighting key challenges such as land constraints, high production costs, and resource limitations. Building-integrated agriculture (BIA), which utilizes building surfaces such as rooftops, façades, and balconies for food production within urban environments, is examined as a potential way to increase self-sufficiency through a simulation. Despite the BIA’s potential, practical issues—including regulatory concerns and infrastructure limitations—must be addressed before it can be implemented at scale. Insights from Singapore’s …

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Unpacking Singapore’S Leasehold Relativity Table: An Empirical And Legal Analysis, Koon Shing Kwong, Jing Rong Goh, Seng Wei, Edward Ti Mar 2025

Unpacking Singapore’S Leasehold Relativity Table: An Empirical And Legal Analysis, Koon Shing Kwong, Jing Rong Goh, Seng Wei, Edward Ti

Research Collection School Of Economics

In Singapore, most land is state-owned, with the state generally issuing leasehold estates via state leases of not more than 99 years1, depending on the intended land use. Naturally, the value of a leasehold estate, which erodes over time as the lease approaches the end of its term, is a key component of the premium charged for lease renewals, or the tax imposed for permission given in relation to a development that would increase the value of the land. By law, the state valuation of leasehold land is prescribed by a leasehold relativity table colloquially known as ‘Bala’s Curve’ or …

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Deepvmunprotect: Neural Network-Based Recovery Of Vm‑Protected Android Apps For Semantics‑Aware Malware Detection, Xin Zhao, Mu Zhang, Xiaopeng Ke, Yu Pan, Yue Duan, Sheng Zhong, Fengyuan Xu Mar 2025

Deepvmunprotect: Neural Network-Based Recovery Of Vm‑Protected Android Apps For Semantics‑Aware Malware Detection, Xin Zhao, Mu Zhang, Xiaopeng Ke, Yu Pan, Yue Duan, Sheng Zhong, Fengyuan Xu

Research Collection School Of Computing and Information Systems

The emerging virtual machine-based Android packers render existing unpacking techniques ineffective. The state-of-the-art unpacker falls short because it relies on unreliable heuristics and manually crafted semantic models. Hence, it cannot precisely recover app semantics necessary for malware detection. In this paper, we propose DeepVMUnProtect, a deep learning-based approach to automatically and accurately capture the semantics of VM-packed code, so as to facilitate semantic-based Android malware classification. Experiments have shown that DeepVMUnProtect outperforms the state-of-the-art tool on recovering opcode semantics in Qihoo(58.3%), Baidu(47.5%) and NMMP (58.8%) respectively, and can enable semantics-aware malware detection which prior work fails to do.

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Respear: Earable-Based Robust Respiratory Rate Monitoring, Yang Liu, Kayla-Jade Butkow, Jake Stuchbury-Wass, Adam Pullin, Dong Ma, Cecilia Masolo Mar 2025

Respear: Earable-Based Robust Respiratory Rate Monitoring, Yang Liu, Kayla-Jade Butkow, Jake Stuchbury-Wass, Adam Pullin, Dong Ma, Cecilia Masolo

Research Collection School Of Computing and Information Systems

Respiratory rate (RR) monitoring is integral to understanding physical and mental health and tracking fitness. Existing studies have demonstrated the feasibility of RR monitoring under specific user conditions (e.g., while remaining still, or while breathing heavily). Yet, performing accurate, continuous and non-obtrusive RR monitoring across diverse daily routines and activities remains challenging. In this work, we present RespEar, an earable-based system for robust RR monitoring. By leveraging the unique properties of in-ear microphones in earbuds, RespEar enables the use of Respiratory Sinus Arrhythmia (RSA) and Locomotor Respiratory Coupling (LRC), physiological couplings between cardiovascular activity, gait and respiration, to indirectly determine …

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Forward-Secure Hierarchical Delegable Signature For Smart Homes, Jianfei Sun, Guowen Xu, Yang Yang, Xuehuan Yang, Xiaoguo Li, Cong Wu, Zhen Liu, Guomin Yang, Robert H. Deng Mar 2025

Forward-Secure Hierarchical Delegable Signature For Smart Homes, Jianfei Sun, Guowen Xu, Yang Yang, Xuehuan Yang, Xiaoguo Li, Cong Wu, Zhen Liu, Guomin Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Aiming to provide people with great convenience and comfort, smart home systems have been deployed in thousands of homes. In this paper, we focus on handling the security and privacy issues in such a promising system by customizing a new cryptographic primitive to provide the following security guarantees: (1) fine-grained, privacy-preserving authorization for smart home users and integrity protection of communication contents; (2) flexible self-sovereign permission delegation; (3) forward security of previous messages. To our knowledge, no previous system has been designed to consider these three security and privacy requirements simultaneously. To tackle these challenges, we put forward the first-ever …

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Adaptive Deviation Learning For Visual Anomaly Detection With Data Contamination, Aanindya Sundar Das, Guansong Pang, Monowar Bhuyan Mar 2025

Adaptive Deviation Learning For Visual Anomaly Detection With Data Contamination, Aanindya Sundar Das, Guansong Pang, Monowar Bhuyan

Research Collection School Of Computing and Information Systems

Visual anomaly detection targets to detect images that notably differ from normal pattern, and it has found extensive application in identifying defective parts within the manufacturing industry. These anomaly detection paradigms predominantly focus on training detection models using only clean, unlabeled normal samples, assuming an absence of contamination; a condition often unmet in real-world scenarios. The performance of these methods significantly depends on the quality of the data and usually decreases when exposed to noise. We introduce a systematic adaptive method that employs deviation learning to compute anomaly scores end-to-end while addressing data contamination by assigning relative importance to the …

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Generalization Analysis For Deep Contrastive Representation Learning, Minh Hieu Nong, Antoine Ledent, Yunwen Lei, Cheng Yeaw Ku Mar 2025

Generalization Analysis For Deep Contrastive Representation Learning, Minh Hieu Nong, Antoine Ledent, Yunwen Lei, Cheng Yeaw Ku

Research Collection School Of Computing and Information Systems

In this paper, we present generalization bounds for the unsupervised risk in the Deep Contrastive Representation Learning framework, which employs deep neural networks as representation functions. We approach this problem from two angles. On the one hand, we derive a parameter-counting bound that scales with the overall size of the neural networks. On the other hand, we provide a norm-based bound that scales with the norms of neural networks’ weight matrices. Ignoring logarithmic factors, the bounds are independent of k, the size of the tuples provided for contrastive learning. To the best of our knowledge, this property is only shared …

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Revisiting Sentiment Analysis For Software Engineering In The Era Of Large Language Models, Ting Zhang, Ivana Clairine Irsan, Thung Ferdian, David Lo Mar 2025

Revisiting Sentiment Analysis For Software Engineering In The Era Of Large Language Models, Ting Zhang, Ivana Clairine Irsan, Thung Ferdian, David Lo

Research Collection School Of Computing and Information Systems

Software development involves collaborative interactions where stakeholders express opinions across various platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. For software products, analyzing the sentiment of user feedback, e.g., reviews, comments, and forum posts can provide valuable insights into user satisfaction and areas for improvement. This can guide the development of future updates and features. However, accurately identifying sentiments in software engineering datasets remains challenging.This study investigates bigger large language models (bLLMs) in addressing the labeled data shortage that hampers fine-tuned smaller large language models (sLLMs) in software …

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Understanding Individual Agent Importance In Multi-Agent System Via Counterfactual Reasoning, Jianming Chen, Yawen Wang, Junjie Wang, Xiaofei Xie, Jun Hu, Qing Wang, Fanjiang Xu Mar 2025

Understanding Individual Agent Importance In Multi-Agent System Via Counterfactual Reasoning, Jianming Chen, Yawen Wang, Junjie Wang, Xiaofei Xie, Jun Hu, Qing Wang, Fanjiang Xu

Research Collection School Of Computing and Information Systems

Explaining multi-agent systems (MAS) is urgent as these systems become increasingly prevalent in various applications. Previous work has provided explanations for the actions or states of agents, yet falls short in understanding the black-boxed agent's importance within a MAS and the overall team strategy. To bridge this gap, we propose EMAI, a novel agent-level explanation approach that evaluates the individual agent's importance. Inspired by counterfactual reasoning, a larger change in reward caused by the randomized action of agent indicates its higher importance. We model it as a MARL problem to capture interactions across agents. Utilizing counterfactual reasoning, EMAI learns the …

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How To Securely Delegate And Revoke Partial Authorization Credentials, Meng Sun, Junzuo Lai, Wei Wu, Ye Yang, Cheng-Kang Chu, Robert H. Deng Mar 2025

How To Securely Delegate And Revoke Partial Authorization Credentials, Meng Sun, Junzuo Lai, Wei Wu, Ye Yang, Cheng-Kang Chu, Robert H. Deng

Research Collection School Of Computing and Information Systems

An attribute-based credential (ABC) system allows a user, obtaining a credential on a set of attributes from an issuer, to anonymously prove a subset of attributes to a service provider. Nowadays, delegation is an important requirement of ABC, which allows a user to delegate his credentials to other users. However, traditional delegatable ABC systems only support delegating a credential with all attributes. In many scenarios, an appropriate delegation is a user can delegate his credential on parts of attributes to others. Another requirement is revocation of credentials in case of unexpected events. In this article, we propose a delegatable and …

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Dualopt: A Dual Divide-And-Optimize Algorithm For The Large-Scale Traveling Salesman Problem, Shipei Zhou, Yuandong Ding, Chi Zhang, Zhiguang Cao, Yan Jin Mar 2025

Dualopt: A Dual Divide-And-Optimize Algorithm For The Large-Scale Traveling Salesman Problem, Shipei Zhou, Yuandong Ding, Chi Zhang, Zhiguang Cao, Yan Jin

Research Collection School Of Computing and Information Systems

This paper proposes a dual divide-and-optimize algorithm (DualOpt) for solving the large-scale traveling salesman problem (TSP). DualOpt combines two complementary strategies to improve both solution quality and computational efficiency. The first strategy is a grid-based divide-and-conquer procedure that partitions the TSP into smaller subproblems, solving them in parallel and iteratively refining the solution by merging nodes and partial routes. The process continues until only one grid remains, yielding a high-quality initial solution. The second strategy involves a path-based divide-and-optimize procedure that further optimizes the solution by dividing it into sub-paths, optimizing each using a neural solver, and merging them back …

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Leveraging Constraint Violation Signals For Action Constrained Reinforcement Learning, Janaka Chathuranga Brahmanage, Jiajing Ling, Akshat Kumar Mar 2025

Leveraging Constraint Violation Signals For Action Constrained Reinforcement Learning, Janaka Chathuranga Brahmanage, Jiajing Ling, Akshat Kumar

Research Collection School Of Computing and Information Systems

In many RL applications, ensuring an agent’s actions adhere to constraints is crucial for safety. Most previous methods in Action-Constrained Reinforcement Learning (ACRL) employ a projection layer after the policy network to correct the action. However projection-based methods suffer from issues like the zero gradient problem and higher runtime due to the usage of optimization solvers. Recently methods were proposed to train generative models to learn a differentiable mapping between latent variables and feasible actions to address this issue. However, generative models require training using samples from the constrained action space, which itself is challenging. To address such limitations, first, …

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Simulation-Free Hierarchical Latent Policy Planning For Proactive Dialogues, Tao He, Lizi Liao, Yixin Cao, Yuanxing Liu, Yiheng Sun, Zerui Chen, Ming Liu, Bing Qin Mar 2025

Simulation-Free Hierarchical Latent Policy Planning For Proactive Dialogues, Tao He, Lizi Liao, Yixin Cao, Yuanxing Liu, Yiheng Sun, Zerui Chen, Ming Liu, Bing Qin

Research Collection School Of Computing and Information Systems

Recent advancements in proactive dialogues have garnered significant attention, particularly for more complex objectives (e.g. emotion support and persuasion). Unlike traditional task-oriented dialogues, proactive dialogues demand advanced policy planning and adaptability, requiring rich scenarios and comprehensive policy repositories to develop such systems. However, existing approaches tend to rely on Large Language Models (LLMs) for user simulation and online learning, leading to biases that diverge from realistic scenarios and result in suboptimal efficiency. Moreover, these methods depend on manually defined, context-independent, coarse-grained policies, which not only incur high expert costs but also raise concerns regarding their completeness. In our work, we …

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Unlocking The Potential Of Black-Box Pre-Trained Gnns For Graph Few-Shot Learning, Qiannan Zhang, Shichao Pei, Yuan Fang, Xiangliang Zhang Mar 2025

Unlocking The Potential Of Black-Box Pre-Trained Gnns For Graph Few-Shot Learning, Qiannan Zhang, Shichao Pei, Yuan Fang, Xiangliang Zhang

Research Collection School Of Computing and Information Systems

Few-shot learning has emerged as an important problem on graphs to combat label scarcity, which can be approached by current trends in pre-trained graph neural networks (GNNs) and meta-learning. Recent efforts integrate both paradigms in a white-box setting, leaving the more realistic black-box setting largely underexplored, where the parameters and gradients in the pre-trained GNNs are inaccessible. In this paper, we study the critical problem: Leveraging black-box pre-trained GNNs for graph few-shot learning. Despite its appeal, two key issues hinder the unlocking of its potential: the inherent task gap between pre-training and downstream stages, which can introduce irrelevant knowledge and …

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Enhancing Item‑Level Bundle Representation For Bundle Recommendation, Xiaoyu Du, Kun Qian, Yunshan Ma, Xinguang Xiang Mar 2025

Enhancing Item‑Level Bundle Representation For Bundle Recommendation, Xiaoyu Du, Kun Qian, Yunshan Ma, Xinguang Xiang

Research Collection School Of Computing and Information Systems

Bundle recommendation approaches offer users a set of related items on a particular topic. The current state-of-the-art (SOTA) method utilizes contrastive learning to learn representations at both the bundle and item levels. However, due to the inherent difference between the bundle-level and item-level preferences, the item-level representations may not receive sufficient information from the bundle affiliations to make accurate predictions. In this article, we propose a novel approach, Enhanced Bundle Recommendation (EBRec), which incorporates two enhanced modules to explore inherent item-level bundle representations. First, we propose to incorporate the bundle-user-item (B-U-I) high-order correlations to explore more collaborative information, thus to …

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Understanding The Oss Communities Of Deep Learning Frameworks: A Comparative Case Study Of Pytorch And Tensorflow, Yunqi Chen, Zhiyuan Wan, Yifei Zhuang, Ning Liu, David Lo, Xiaohu Yang Mar 2025

Understanding The Oss Communities Of Deep Learning Frameworks: A Comparative Case Study Of Pytorch And Tensorflow, Yunqi Chen, Zhiyuan Wan, Yifei Zhuang, Ning Liu, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Over the past two decades, deep learning has received tremendous success in developing software systems across various domains. Deep learning frameworks have been proposed to facilitate the development of such software systems, among which, PyTorch and TensorFlow stand out as notable examples. Considerable attention focuses on exploring software engineering practices and addressing diverse technical aspects in developing and deploying deep learning frameworks and software systems. Despite these efforts, little is known about the open source software communities involved in the development of deep learning frameworks. In this article, we perform a comparative investigation into the open source software communities of …

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Beyond A Chapter: Libraries As Key Characters In Smu’S Sustainability Saga, Shameem Nilofar Maideen Feb 2025

Beyond A Chapter: Libraries As Key Characters In Smu’S Sustainability Saga, Shameem Nilofar Maideen

Research Collection Library

No abstract provided.

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Impact Tracing: Identifying The Culprit Of Misinformation In Encrypted Messaging Systems, Zhongming Wang, Tao Xiang, Xiaoguo Li, Biwen Chen, Guomin Yang, Chuan Ma, Robert H. Deng Feb 2025

Impact Tracing: Identifying The Culprit Of Misinformation In Encrypted Messaging Systems, Zhongming Wang, Tao Xiang, Xiaoguo Li, Biwen Chen, Guomin Yang, Chuan Ma, Robert H. Deng

Research Collection School Of Computing and Information Systems

Encrypted messaging systems obstruct content moderation, although they provide end-to-end security. As a result, misinformation proliferates in these systems, thereby exacerbating online hate and harassment. The paradigm of “Reporting-then-Tracing” shows great potential in mitigating the spread of misinformation. For instance, message traceback (CCS’19) traces all the dissemination paths of a message, while source tracing (CCS’21) traces its originator. However, message traceback lacks privacy preservation for non-influential users (e.g., users who only receive the message once), while source tracing maintains privacy but only provides limited traceability. In this paper, we initiate the study of impact tracing. Intuitively, impact tracing traces influential …

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Bridging Expert Knowledge With Deep Learning Techniques For Just-In-Time Defect Prediction, Xin Zhou, Donggyun Han, David Lo Feb 2025

Bridging Expert Knowledge With Deep Learning Techniques For Just-In-Time Defect Prediction, Xin Zhou, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Just-In-Time (JIT) defect prediction aims to automatically predict whether a commit is defective or not, and has been widely studied in recent years. In general, most studies can be classified into two categories: 1) simple models using traditional machine learning classifiers with hand-crafted features, and 2) complex models using deep learning techniques to automatically extract features from commit contents. Hand-crafted features used by simple models are based on expert knowledge but may not fully represent the semantic meaning of the commits. On the other hand, deep learning-based features used by complex models represent the semantic meaning of commits but may …

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Proactive Conversational Ai: A Comprehensive Survey Of Advancements And Opportunities, Yang Deng, Lizi Liao, Wenqiang Lei, Grace Hui Yang, Wai Lam, Tat-Seng Chua Feb 2025

Proactive Conversational Ai: A Comprehensive Survey Of Advancements And Opportunities, Yang Deng, Lizi Liao, Wenqiang Lei, Grace Hui Yang, Wai Lam, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Dialogue systems are designed to offer human users social support or functional services through natural language interactions. Traditional conversation research has put significant emphasis on a system's response-ability, including its capacity to understand dialogue context and generate appropriate responses. However, the key element of proactive behavior-a crucial aspect of intelligent conversations-is often overlooked in these studies. Proactivity empowers conversational agents to lead conversations towards achieving pre-defined targets or fulfilling specific goals on the system side. Proactive dialogue systems are equipped with advanced techniques to handle complex tasks, requiring strategic and motivational interactions, thus representing a significant step towards artificial general …

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Loco: Low-Bit Communication Adaptor For Large-Scale Model Training, Xingyu Xie, Zhijie Lin, Kim-Chuan Toh, Pan Zhou Feb 2025

Loco: Low-Bit Communication Adaptor For Large-Scale Model Training, Xingyu Xie, Zhijie Lin, Kim-Chuan Toh, Pan Zhou

Research Collection School Of Computing and Information Systems

To efficiently train large-scale models, low-bit gradient communication compresses full-precision gradients on local GPU nodes into low-precision ones for higher gradient synchronization efficiency among GPU nodes. However, it often degrades training quality due to compression information loss. To address this, we propose the Low-bit Communication Adaptor (LoCo), which compensates gradients on local GPU nodes before compression, ensuring efficient synchronization without compromising training quality. Specifically, LoCo designs a moving average of historical compensation errors to stably estimate concurrent compression error and then adopts it to compensate for the concurrent gradient compression, yielding a less lossless compression. This mechanism allows it to …

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Siniel: Distributed Privacy-Preserving Zksnark, Yunbo Yang, Yuejia Cheng, Kailun Wang, Xiaoguo Li, Jianfei Sun, Jiachen Shen, Xiaolei Dong, Zhenfu Cao, Guomin Yang, Robert H. Deng Feb 2025

Siniel: Distributed Privacy-Preserving Zksnark, Yunbo Yang, Yuejia Cheng, Kailun Wang, Xiaoguo Li, Jianfei Sun, Jiachen Shen, Xiaolei Dong, Zhenfu Cao, Guomin Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive, in which a prover convinces a verifier that a given statement is true without leaking any additional information. However, existing zkSNARKs suffer from high computation overhead in the proof generation. This limits the applications of zkSNARKs, such as private payments, private smart contracts, and anonymous credentials. Private delegation has become a prominent way to accelerate proof generation. In this work, we propose Siniel, an efficient private delegation framework for zkSNARKs constructed from polynomial interactive oracle proof (PIOP) and polynomial commitment scheme (PCS). Our protocol allows a computationally limited …

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Human‑Ai And Human‑Robot Collaboration In The Age Of Generative Ai, Agentic Ai, And Artificial General Intelligence: Opportunities And Challenges, Keng Siau Feb 2025

Human‑Ai And Human‑Robot Collaboration In The Age Of Generative Ai, Agentic Ai, And Artificial General Intelligence: Opportunities And Challenges, Keng Siau

Research Collection School Of Computing and Information Systems

The advancement of Artificial Intelligence (AI) has been exponential, especially in the past few years. Most, if not all, of the AI systems we encounter and are exposed to at this point are Artificial Narrow Intelligence (ANI). ANI specializes in one area and solves problems in one area. Generative AI (GenAI) and Agentic AI (i.e., independent AI agent), at the current stage of development, are regarded as ANI. The race is currently on to develop Artificial General Intelligence (AGI). AGI refers to AI systems as smart as humans across a wide range of cognitive tasks. Recently, OpenAI’s o3 system received …

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Is Carbon Risk Priced In The Cross Section Of Corporate Bond Returns?, Tinghua Duan, Frank Weikai Li, Quan Wen Feb 2025

Is Carbon Risk Priced In The Cross Section Of Corporate Bond Returns?, Tinghua Duan, Frank Weikai Li, Quan Wen

Research Collection Lee Kong Chian School Of Business

This article examines the pricing of a firm’s carbon risk in the corporate bond market. Contrary to the “carbon risk premium” hypothesis, bonds of more carbon-intensive firms earn significantly lower returns. This effect cannot be explained by a comprehensive list of bond characteristics and exposure to known risk factors. Investigating sources of the low carbon alpha, we find the underperformance of bonds issued by carbon-intensive firms cannot be fully explained by divestment from institutional investors. Instead, our evidence is most consistent with investor underreaction to the predictability of carbon intensity for firm cash-flow news, creditworthiness, and environmental incidents.

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