Entire DC Network | Open Access Articles | Digital Commons Network™

When Performance Is Not Enough: Managing Ngos In An Age Of Digital Mistrust, Christianna Sirindah Parr

Asian Management Insights

As NGOs lack formal authority, their influence depends on legitimacy, which can be contested at scale online. Leaders must treat social media as a governance decision, with clear scope, speaking authority and response protocols.

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Finding Missing Input Validation In Tees Via Llm-Assisted Symbolic Execution, Chengyan Ma, Jieke Shi, Ruidong Han, Ye Liu, Yuqing Niu, David Lo

Research Collection School Of Computing and Information Systems

Trusted Execution Environments (TEEs) provide hardware-enforced isolation that protects sensitive code and data from untrusted software. Despite their strong security guarantees, analyzing TEE applications remains challenging due to the high cost and complexity of configuring complete TEE build and runtime environments, as well as the limited observability imposed by hardware isolation. This paper presents SymTEE, a novel large language model (LLM)-assisted symbolic execution framework for detecting missing input validation issues in TEE applications without requiring real TEE setups. SymTEE begins by leveraging Abstract Syntax Tree (AST) analysis to extract TEE code slices that may lack sufficient input validation, and then …

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Context Engineering For Ai Agents In Open-Source Software, Seyedmoein Mohsenimofidi, Matthias Galster, Christoph Treude, Sebastian Baltes

Research Collection School Of Computing and Information Systems

GenAI-based coding assistants have disrupted software development. The next generation of these tools is agent-based, operating with more autonomy and potentially without human oversight. Like human developers, AI agents require contextual information to develop solutions that are in line with the standards, policies, and workflows of the software projects they operate in. Vendors of popular agentic tools (e.g., Claude Code) recommend maintaining version-controlled Markdown files that describe aspects such as the project structure, code style, or building and testing. The content of these files is then automatically added to each prompt. Recently, AGENTS.md has emerged as a potential standard that …

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Woulda, Shoulda, Coulda? The Impact Of Predictive, Prescriptive, And Prospective Expectations On Stakeholder Reactions, Yuri Mishina, Maxine Yu, David Gomulya

Research Collection Lee Kong Chian School Of Business

When and why might stakeholders react to firm activities in ways that might be different than, or even contradictory to, what we might expect based on the extant research? We draw on expectancy violation theory (EVT) and bring in the notion of heuristics and future-oriented expectations to examine this question, using a sample of investor reactions to earnings surprises from 2013 to 2019. We find that, in addition to comparing earnings to consensus earnings estimates, investors appear to compare the earnings surprises to the firm’s past performance and to its peers. Importantly, their expectations regarding future interactions with the firm …

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Advancing The Understanding Of Palliative Care In Singapore: Knowledge, Attitudes, Receptiveness, And The Moderating Role Of Media Information-Seeking Preferences, Su Lin Yeo, Angel Lee, Raymond Han Lip Ng, May O. Lwin, Yumin Lin

Research Collection Lee Kong Chian School Of Business

Introduction: Despite the growing demand for palliative care, this specialized medical care remains severely underutilized, with almost 50% of countries worldwide lacking access. Given that misperceptions and stigma continue to hinder progress in healthcare policy, this study aims to examine the relationships between palliative care knowledge, attitudes, and receptiveness to better understand how public health communication can enhance awareness and understanding of the topic in Singapore. Applying the adapted knowledge-attitude-practice (KAP) model, it further extends theory by examining the moderating role of media information-seeking between knowledge and receptiveness.Methods: Mixed-mode surveys involving 1,226 participants (926 online and 300 in-person respondents), representing …

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Hypersiniel: Guaranteed Output Delivery Comes (Almost) Free In Private Delegation Of Zksnarks, Yunbo Yang, Yuejia Cheng, Junkai Liang, Kailun Wang, Xuanming Liu, Xiaoguo Li, Jianfei Sun, Jiachen Shen, Xiaolei Dong, Zhenfu Cao, Meng Hao, Guomin Yang, Deng, Robert H., Kui Ren

Research Collection School Of Computing and Information Systems

Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness.Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to …

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Distributional Vision-Language Alignment By Cauchy-Schwarz Divergence, Wenzhe Yin, Zehao Xiao, Pan Zhou, Shujian Yu, Jiayi Shen, Jan-Jakob Sonke, Stratis Gavves

Research Collection School Of Computing and Information Systems

Vision-language alignment is crucial for various downstream tasks such as cross-modal generation and retrieval. Previous multimodal approaches like CLIP utilize InfoNCE to maximize mutual information, primarily aligning pairwise samples across modalities while overlooking distributional differences. In addition, InfoNCE has inherent conflict in terms of alignment and uniformity in multimodality, leading to suboptimal alignment with modality gaps. To overcome the limitations, we propose CS-Aligner, a novel framework that performs distributional vision-language alignment by integrating Cauchy-Schwarz (CS) divergence with mutual information. CS-Aligner captures both the global distribution information of each modality and the pairwise semantic relationships. We find that the CS divergence …

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Price Discovery On Decentralized Exchanges, Agostino Capponi, Ruizhe Jia, Shihao Yu

Research Collection Lee Kong Chian School Of Business

Decentralized exchanges (DEXs) allow traders to express their willingness to pay for quick execution through a public priority fee bidding mechanism. We provide evidence that high-fee DEX trades are more informative and contribute more to price discovery. Using address-level blockchain transaction data, we show that informed traders persistently bid higher fees to secure early execution, revealing a strong willingness to pay for execution priority. Further, analysis of Ethereum mempool data demonstrates that informed traders employ a “jump bidding” strategy, placing high initial bids to deter potential competitors.

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A Market Of Care Skills: Profiting From Professional Education And Practice, Yasmin Y. Ortiga

Research Collection School of Social Sciences

Who profits from the commodification of care and how does such accumulation occur? Focusing on the case of the nursing care market, this paper shows how professional schools and local hospitals within source nations like the Philippines profit from nurses seeking the credentials to qualify for jobs overseas. While existing studies show how nursing skills are exploited and undervalued, this paper argues that it is the process of skilling itself that allows for the accumulation of profit, regardless of whether nurses leave their origin countries to work abroad. This study reveals how global care economies are tightly linked to local …

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Rethinking Artifact Evaluation For Software Engineering In The Age Of Generative Ai, Christoph Treude, Christopher M. Poskitt, Rashina Hoda

Research Collection School Of Computing and Information Systems

Peer review in software engineering research operates under tight time constraints, while generative AI has substantially reduced the human effort required to produce polished research narratives. Reviewer attention is often spent on aspects of submissions such as writing quality or literature positioning that have become relatively less effort-intensive to address, rather than on evaluating the scientific substance of a paper. At the same time, assessing whether methods are implemented correctly, analyses are sound, and claims are supported by evidence remains effort-intensive and dependent on human expertise. In software engineering research, this substance is frequently embodied in artifacts, including code, data, …

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Trustgen: A Platform Of Dynamic Benchmarking On The Trustworthiness Of Generative Foundation Models, Yue Huang, Et. Al

Research Collection School Of Computing and Information Systems

Generative foundation models (GenFMs), such as large language models and text-to-image systems, have demonstrated remarkable capabilities in various downstream applications. As they are increasingly deployed in high-stakes applications, assessing their trustworthiness has become both a critical necessity and a substantial challenge. Existing evaluation efforts are fragmented, rapidly outdated, and often lack extensibility across modalities. This raises a fundamental question: how can we systematically, reliably, and continuously assess the trustworthiness of rapidly advancing GenFMs across diverse modalities and use cases? To address these gaps, we introduce TrustGen, a dynamic and modular benchmarking system designed to systematically evaluate the trustworthiness of GenFMs …

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Fixed Effects Estimation Of Spatial Panel Model With Missing Responses: An Application To Us State Tax Competition, Xiaoyu Meng, Zhenlin Yang

Research Collection School Of Economics

We consider estimation and inferences for general spatial panel data models with randomly missing observations on responses. It allows for unobserved spatiotemporal heterogeneity, time-varying endogenous and contextual spatial interactions, time-varying cross-sectional error dependence, and serial correlation. A general M-estimation method is proposed for model estimation and a novel corrected plug-in method is proposed for model inference. Both take into account the estimation of fixed effects. Asymptotic properties of the proposed methods are studied, and finite sample properties are investigated. An empirical application is given using U.S. state tax competition data. The proposed methods apply to matrix exponential spatial specification and …

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Genuinely Unbalanced Spatial Panel Data Models: Fixed Effects M-Estimation And Inference, Xiaoyu Meng, Zhenlin Yang

Research Collection School Of Economics

We consider spatial panel data models with genuine unbalancedness arising from the non-presence of some spatial units in certain time periods. General M-estimation methods are proposed for model estimation, which take into account the estimation of the incidental fixed effects parameters and allow for spatiotemporal heteroskedasticity and high-order time-varying spatial effects. Corrected plug-in methods are proposed for standard error estimation. The proposed estimation and inference methods are rigorously studied for their asymptotic properties and finite sample performance. An application to China’s provincial FDI inflows shows that properly accounting for genuine unbalancedness uncovers significant positive spatial spillovers that are masked when …

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Semat: Semantic Enhanced Natural Image Interactive Matting, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Qianru Sun, Yang Tang, Bo Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Recent approaches attempt to adapt powerful interactive segmentation models, such as SAM, to interactive matting and fine-tune the models based on synthetic matting datasets. However, models trained on synthetic data fail to generalize to complex and occlusion scenes. We address this challenge by proposing a new matting dataset based on the COCO dataset, namely COCO-Matting. It selects real-world complex images from COCO and converts semantic segmentation masks to matting labels. The built COCO-Matting comprises an extensive collection of 36,980 human instance-level alpha mattes in complex natural scenarios. Furthermore, existing SAM-based matting methods extract intermediate features and masks from a frozen …

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Bridging Bug Localization And Issue Fixing: A Hierarchical Localization Framework Leveraging Large Language Models, Jianming Chang, Xin Zhou, Lulu Wang, David Lo, Bixin Li

Research Collection School Of Computing and Information Systems

Automated issue fixing is a critical task in software debugging and has recently garnered significant attention from academia and industry. However, existing fixing techniques predominantly focus on the repair phase, often overlooking the importance of improving the preceding bug localization phase. As a foundational step in issue fixing, bug localization plays a pivotal role in determining the overall effectiveness of the entire process. To enhance the precision of issue fixing by accurately identifying bug locations in large-scale projects, this paper presents BugCerberus, the first hierarchical bug localization framework powered by three customized large language models. First, BugCerberus analyzes intermediate representations …

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The Citizen And The Smart City: A Global Comparison Of Institutional Logics, Tim Bunnell, Zachary Spicer, Byron Miller, Teresa Abbruzzese, Paolo Cardullo, Sangwon Chae, I-Chun Catherine Chang, Greig Charnock, Ming-Kuang Chung, Kwon Heo, Sue-Ching Jou, Andrew Karvonen, Olga Kordas, Kong, Lily, Ramon Ribera-Fumaz, Haeran Shin, Orlando Woods

Research Collection College of Integrative Studies

Crucial urbanists’ placement of smart city initiatives in Western Europe or North America mostly on the “low” rungs of participatory ladders or scaffolds does not mean that smart urban development necessarily connotes democracy-eroding neoliberalization everywhere. This article offers a more globally variegated and dynamic understanding of the relationship between citizens and smart cities. We apply an institutional logics framework to the citizen-smart city nexus in seven cities spanning three world regions, considering the interplay of citizen-centric logics with techno-innovation-oriented and bureaucratic/managerial logics. Appreciating contextually specific institutional orders helps to explain why similar initiatives and intentions yield different outcomes across time …

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Weather Rescue At Sea: Recovering Historical Weather Observations From 19th Century British Naval Ships, Praveen Teleti, Ed Hawkins, Clive Wilkinson

Research Collection College of Integrative Studies

Ship logbooks represent a critical source of historical meteorological data, providing valuable observations of barometric pressure, air temperature, sea surface temperature, wind force and direction, and other variables. Substantial quantities of these records are unavailable to climate science as they have not yet been transcribed. We present ‘Weather Rescue at Sea’, a citizen-science project which transcribed millions of weather observations contained in 19th Century UK Royal Navy ship logbooks. We describe the logbook structure and weather observation-taking instructions and discuss significant challenges with the translation of handwritten text into accurate data due to errors arising from ambiguous handwriting, historical terminology, …

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Unraveling The Intractable Trilemma In Urban Weather And Climate Modeling, Peiyuan Li, Ashish Sharma, Rao Kotamarthi, Alberto Martilli, Subimal Ghosh, Cristina Negri, Scott Collis, Lee Chapman, Fei Chen, Luis M. A. Bettencourt, Winston T. L. Chow

Research Collection College of Integrative Studies

Urban weather and climate modeling is constrained by the highly heterogeneous and dynamic nature of cities. It exhibits a persistent trilemma between spatial granularity, spatiotemporal coverage, and physical interpretability. We articulate this challenge and propose a hybrid framework that integrates physics -based models, urban observations, and machine learning. Framing urban modeling as an integration problem across methods and scales, we provide a structured guide for next -generation, decision -relevant urban climate capabilities.

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Online Safety (Relief And Accountability) Bill: Further Experiments In Balancing Rights And Responsibilities, Siyuan Chen, Zavier Yu Wu, Jie Ting Lee

Research Collection Yong Pung How School Of Law

Singapore’s latest legislation to address online harms, the Online Safety (Relief and Accountability) Bill, was passed in Parliament in November 2025. The legislation, which takes a complainant-centric approach, provides the Commissioner of Online Safety a broad range of powers so that timely redress can be given for reports of online harms such as image abuse, sexual harassment, doxxing, and stalking. Various torts that impose duties on administrators and platforms have also been created. Legislation of this nature invariably raises issues about whether an appropriate balance between freedom and responsibility has been struck. This Legislative Comment analyses some of the more …

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Negotiating At A Distance: The Impact Of Communication Media And Negotiator Traits, Dorcas Quek Anderson, Tra My Ngo

Research Collection Yong Pung How School Of Law

Purpose – Prior research has yet to provide a coherent theoretical framework explaining how communication media hinder or advance negotiation success, and many dated studies are unlikely to be relevant. This study aims to examine the impact of four communication media on negotiation outcomes. It also examines the potential moderating effects of the following negotiator characteristics: conflict management style, personality traits and indirect communication style.Design/methodology/approach – A total of 400 participants formed 200 dyads to negotiate a mixed- motive relational conflict through face-to-face (FTF) interaction, videoconferencing, audio call or synchronous text messaging. Linear mixed regression was used to assess the …

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Portrait Shadow Removal Via Self-Exemplar Illumination Equalization, Qian Huang, Cheng Xu, Guiqing Li, Ziheng Wu, Shengxin Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

We introduce the Self-Exemplar Illumination Equalization Network, designed specifically for effective portrait shadow removal. The core idea of our method is that partially shadowed portraits can find ideal exemplars within their non-shadowed facial regions. Rather than directly fusing two distinct classes of facial features, our approach utilizes non-shadowed regions as an illumination indicator to equalize the shadowed regions, generating deshadowed results without boundary-merging artifacts. Our network comprises cascaded Self-Exemplar Illumination Equalization Blocks (SExmBlock), each containing two modules: a self-exemplar feature matching module and a feature-level illumination rectification module. The former identifies and applies internal illumination exemplars to shadowed areas, producing …

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Trace: Securing Smart Contract Repository Against Access Control Vulnerability, Chong Chen, Lingfeng Bao, David Lo, Yanlin Wang, Zhenyu Shan, Ting Chen, Guangqiang Yin, Jianxing Yu, Zibin Zheng, Jiachi Chen

Research Collection School Of Computing and Information Systems

Smart contract vulnerabilities have led to billions of dollars in economic losses. Among these, improper Access Control, which allows unauthorized users to execute restricted functions, is particularly prevalent and has caused significant financial damage. Smart contract repositories contain source code, documentation, configuration files, and other artifacts necessary for building and deploying smart contracts. GitHub hosts numerous open-source repositories of this kind, which serve as intermediate artifacts in development and require compilation and packaging to produce deployable contracts. Third-party developers often reference, reuse, or fork code from these repositories during custom development. However, if the referenced code contains vulnerabilities, it can …

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Stacked From One: Multi-Scale Self-Injection For Context Window Extension, Wei Han, Pan Zhou, Shuicheng Yan

Research Collection School Of Computing and Information Systems

The limited context window of contemporary large language models (LLMs) remains a primary bottleneck for their broader application across diverse domains. Although continual pre-training on long-context data offers a straightforward solution, it incurs prohibitive data acquisition and computational costs. To address this challenge, we propose SHAREDLLM, a novel framework based on multi-grained context compression and query-aware information acquisition. SHAREDLLM comprises two stacked short-context LLMs: a lower model serving as a compressor and an upper model acting as a decoder. The lower model compresses long inputs into compact, multi-grained representations, which are then forwarded to the upper model for context-aware processing. …

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Dreamcs: Geometry-Aware Text-To-3d Generation With Unpaired 3d Reward Supervision, Xiandong Zou, Ruihao Xia, Hongsong Wang, Pan Zhou

Research Collection School Of Computing and Information Systems

While text-to-3D generation has attracted growing interest, existing methods often struggle to produce 3D assets that align well with human preferences. Current preference alignment techniques for 3D content typically rely on hardly-collected preference-paired multi-view 2D images to train 2D reward models, when then guide 3D generation — leading to geometric artifacts, such as the Janus face problem and geometric incompleteness, due to their inherent 2D bias. To address these limitations, we construct 3D-MeshPref, the first large-scale unpaired 3D preference dataset, featuring diverse 3D meshes annotated by a large language model and refined by human evaluators. We then develop RewardCS, the …

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Where Did It Go Wrong? Attributing Undesirable Llm Behaviors Via Representation Gradient Tracing, Zhe Li, Wei Zhao, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their deployment is frequently undermined by undesirable behaviors such as generating harmful content, factual inaccuracies, and societal biases. Diagnosing the root causes of these failures poses a critical challenge for AI safety. Existing attribution methods, particularly those based on parameter gradients, often fall short due to prohibitive noisy signals and computational complexity. In this work, we introduce a novel and efficient framework that diagnoses a range of undesirable LLM behaviors by analyzing representation and its gradients, which operates directly in the model's activation space to provide a semantically meaningful signal linking …

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Propaganda Ai: An Analysis Of Semantic Divergence In Large Language Models, Nay Myat Min, Long H. Pham, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) can exhibit concept-conditioned semantic divergence: common high-level cues (e.g., ideologies, public figures) elicit unusually uniform, stance-like responses that evade token-trigger audits. This behavior falls in a blind spot of current safety evaluations, yet carries major societal stakes, as such concept cues can steer content exposure at scale. We formalize this phenomenon and present RAVEN (Response Anomaly Vigilance), a black-box audit that flags cases where a model is simultaneously highly certain and atypical among peers by coupling semantic entropy over paraphrastic samples with cross-model disagreement. In a controlled LoRA fine-tuning study, we implant a concept-conditioned stance using …

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Managing Reproducibility Debt In Scientific Software: A Practical Framework, Zara Hassan, Christoph Treude, Graham Williams, Michael Norrish, Alex Potanin

Research Collection School Of Computing and Information Systems

Scientific software includes end-user applications, modelling tools, research software for publications, and production systems for real users. It plays a key role across various scientific disciplines by enabling large-scale computation, simulation, and data analysis. Unlike commercial software, scientific software is often developed in dynamic research environments with limited engineering practices, documentation, or testing. This makes it fragile and difficult to reproduce results, even when code and data are available, conditions in which Reproducibility Debt (RpD) accumulates. This paper presents the Reproducibility Debt Management Framework (RpD-MF), which is grounded in evidence from a systematic literature review, practitioner interviews, and a global …

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Llmqua: Practical Backdoor Injection On Large Language Model Quantization, Xiangxiang Chen, Peixin Zhang, Jun Sun, Jin Song Dong, Wenhai Wang, Jingyi Wang

Research Collection School Of Computing and Information Systems

Quantization is widely used to enable local deployment of large language models (LLMs) on resource-constrained devices. Recent work (e.g., QuRA) shows quantization can be exploited via rounding manipulation to implant backdoors. However, such an attack has been evaluated only on small models and does not directly apply to LLMs due to three key constraints: (1) limited poisoning data from small, task-agnostic calibration sets; (2) layer-wise quantization restricting adversarial access to global representations; and (3) lack of gradient access in quantization pipelines, blocking gradient-based attacks.We propose LLMQuA, a practical quantization-phase backdoor attack tailored to the LLM setting. LLMQuA (i) injects backdoors …

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Prompting Frameworks For Large Language Models: A Survey, Xiaoxia Liu, Jingyi Wang, Jun Sun, Xiaohan Yuan, Guoliang Dong, Peng Di, Wenhai Wang, Dongxia Wang

Research Collection School Of Computing and Information Systems

Since the launch of ChatGPT, a powerful AI Chatbot developed by OpenAI, large language models (LLMs) have made significant advancements in both academia and industry, bringing about a fundamental engineering paradigm shift in many areas. While LLMs are powerful, it is also crucial to best use their power where “prompt” plays a core role. However, the booming LLMs themselves, including excellent APIs like ChatGPT, have several inherent limitations: (1) temporal lag of training data, and (2) the lack of physical capabilities to perform external actions. Recently, we have observed the trend of utilizing prompt-based tools to better utilize the power …

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Teamwise: Exploring Virtually Embodied Ai Facilitation For Video-Based Team Onboarding, Venkata Akhila Rani Obilisetty, Mikkeline Elleby, Anthony Tang, April Yi Wang

Research Collection School Of Computing and Information Systems

AI-mediated facilitation has emerged as a scalable approach to supporting onboarding and coordination in newly formed remote teams, yet existing systems are predominantly text-based. To explore how video-based, virtually embodied AI facilitators shape team experiences, we present TeamWise, which joins video-based onboarding meetings as an on-screen avatar. TeamWise guides teams through a structured facilitation flow of low-stakes activities to foster rapport, mutual awareness, and shared identity. While the overall sequence of activities and facilitation goals is predefined, the facilitator’s turn-by-turn utterances are generated dynamically by an LLM in response to participant input. We conducted a formative study of TeamWise to …

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Full-Text Articles in Entire DC Network

When Performance Is Not Enough: Managing Ngos In An Age Of Digital Mistrust, Christianna Sirindah Parr

Asian Management Insights

Finding Missing Input Validation In Tees Via Llm-Assisted Symbolic Execution, Chengyan Ma, Jieke Shi, Ruidong Han, Ye Liu, Yuqing Niu, David Lo

Research Collection School Of Computing and Information Systems

Context Engineering For Ai Agents In Open-Source Software, Seyedmoein Mohsenimofidi, Matthias Galster, Christoph Treude, Sebastian Baltes

Research Collection School Of Computing and Information Systems

Woulda, Shoulda, Coulda? The Impact Of Predictive, Prescriptive, And Prospective Expectations On Stakeholder Reactions, Yuri Mishina, Maxine Yu, David Gomulya

Research Collection Lee Kong Chian School Of Business

Advancing The Understanding Of Palliative Care In Singapore: Knowledge, Attitudes, Receptiveness, And The Moderating Role Of Media Information-Seeking Preferences, Su Lin Yeo, Angel Lee, Raymond Han Lip Ng, May O. Lwin, Yumin Lin

Research Collection Lee Kong Chian School Of Business

Hypersiniel: Guaranteed Output Delivery Comes (Almost) Free In Private Delegation Of Zksnarks, Yunbo Yang, Yuejia Cheng, Junkai Liang, Kailun Wang, Xuanming Liu, Xiaoguo Li, Jianfei Sun, Jiachen Shen, Xiaolei Dong, Zhenfu Cao, Meng Hao, Guomin Yang, Deng, Robert H., Kui Ren

Research Collection School Of Computing and Information Systems

Distributional Vision-Language Alignment By Cauchy-Schwarz Divergence, Wenzhe Yin, Zehao Xiao, Pan Zhou, Shujian Yu, Jiayi Shen, Jan-Jakob Sonke, Stratis Gavves

Research Collection School Of Computing and Information Systems

Price Discovery On Decentralized Exchanges, Agostino Capponi, Ruizhe Jia, Shihao Yu

Research Collection Lee Kong Chian School Of Business

A Market Of Care Skills: Profiting From Professional Education And Practice, Yasmin Y. Ortiga

Research Collection School of Social Sciences

Rethinking Artifact Evaluation For Software Engineering In The Age Of Generative Ai, Christoph Treude, Christopher M. Poskitt, Rashina Hoda

Research Collection School Of Computing and Information Systems

Trustgen: A Platform Of Dynamic Benchmarking On The Trustworthiness Of Generative Foundation Models, Yue Huang, Et. Al

Research Collection School Of Computing and Information Systems

Fixed Effects Estimation Of Spatial Panel Model With Missing Responses: An Application To Us State Tax Competition, Xiaoyu Meng, Zhenlin Yang

Research Collection School Of Economics

Genuinely Unbalanced Spatial Panel Data Models: Fixed Effects M-Estimation And Inference, Xiaoyu Meng, Zhenlin Yang

Research Collection School Of Economics

Semat: Semantic Enhanced Natural Image Interactive Matting, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Qianru Sun, Yang Tang, Bo Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Bridging Bug Localization And Issue Fixing: A Hierarchical Localization Framework Leveraging Large Language Models, Jianming Chang, Xin Zhou, Lulu Wang, David Lo, Bixin Li

Research Collection School Of Computing and Information Systems

Research Collection College of Integrative Studies

Weather Rescue At Sea: Recovering Historical Weather Observations From 19th Century British Naval Ships, Praveen Teleti, Ed Hawkins, Clive Wilkinson

Research Collection College of Integrative Studies

Unraveling The Intractable Trilemma In Urban Weather And Climate Modeling, Peiyuan Li, Ashish Sharma, Rao Kotamarthi, Alberto Martilli, Subimal Ghosh, Cristina Negri, Scott Collis, Lee Chapman, Fei Chen, Luis M. A. Bettencourt, Winston T. L. Chow

Research Collection College of Integrative Studies

Online Safety (Relief And Accountability) Bill: Further Experiments In Balancing Rights And Responsibilities, Siyuan Chen, Zavier Yu Wu, Jie Ting Lee

Research Collection Yong Pung How School Of Law

Negotiating At A Distance: The Impact Of Communication Media And Negotiator Traits, Dorcas Quek Anderson, Tra My Ngo

Research Collection Yong Pung How School Of Law

Portrait Shadow Removal Via Self-Exemplar Illumination Equalization, Qian Huang, Cheng Xu, Guiqing Li, Ziheng Wu, Shengxin Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

Trace: Securing Smart Contract Repository Against Access Control Vulnerability, Chong Chen, Lingfeng Bao, David Lo, Yanlin Wang, Zhenyu Shan, Ting Chen, Guangqiang Yin, Jianxing Yu, Zibin Zheng, Jiachi Chen

Research Collection School Of Computing and Information Systems

Stacked From One: Multi-Scale Self-Injection For Context Window Extension, Wei Han, Pan Zhou, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Dreamcs: Geometry-Aware Text-To-3d Generation With Unpaired 3d Reward Supervision, Xiandong Zou, Ruihao Xia, Hongsong Wang, Pan Zhou

Research Collection School Of Computing and Information Systems

Where Did It Go Wrong? Attributing Undesirable Llm Behaviors Via Representation Gradient Tracing, Zhe Li, Wei Zhao, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Propaganda Ai: An Analysis Of Semantic Divergence In Large Language Models, Nay Myat Min, Long H. Pham, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Managing Reproducibility Debt In Scientific Software: A Practical Framework, Zara Hassan, Christoph Treude, Graham Williams, Michael Norrish, Alex Potanin

Research Collection School Of Computing and Information Systems

Llmqua: Practical Backdoor Injection On Large Language Model Quantization, Xiangxiang Chen, Peixin Zhang, Jun Sun, Jin Song Dong, Wenhai Wang, Jingyi Wang

Research Collection School Of Computing and Information Systems

Prompting Frameworks For Large Language Models: A Survey, Xiaoxia Liu, Jingyi Wang, Jun Sun, Xiaohan Yuan, Guoliang Dong, Peng Di, Wenhai Wang, Dongxia Wang

Research Collection School Of Computing and Information Systems

Teamwise: Exploring Virtually Embodied Ai Facilitation For Video-Based Team Onboarding, Venkata Akhila Rani Obilisetty, Mikkeline Elleby, Anthony Tang, April Yi Wang

Research Collection School Of Computing and Information Systems