Digital Commons Network^™

Supporting Fair Practices In Scholarly Publishing With The Editorial Reference Handbook, Susanna-Assunta Sansone, Allyson Lister, Rebecca Taylor-Grant, Matthew Cannon

FORCE 2026

Co-produced by academics and publishers (incl. CUP, Cell Press, EMBO Press, Taylor & Francis, GigaScience Press, OUP, PLOS, Springer Nature), the Editorial Reference Handbook (https://publishers.fairassist.org) assists scholarly publishers in supporting the sharing of digital research objects and in operationalising FAIR research practices by addressing gaps in editorial workflows, policy implementation and stakeholder alignment. The Handbook comprises three interrelated components—a checklist, detailed guidance documentation, and a flowchart—intended primarily for in-house editorial staff while also providing value to reviewers, authors, and service providers.

Beside this practical collaboratively developed product, the Handbook is also a socio-technical pilot to improve the culture …

Managing Ai Bot Access To Open Scholarly Infrastructures, Petr Knoth

FORCE 2026

The rapid rise of generative AI has created unprecedented demand for large, high-quality research corpora. Open access repositories and other open scholarly infrastructures have therefore become primary sources for AI bots, because they host research that is not universally reliable, but remains far more evidence-based than most web content. This is both an opportunity and a strain: repositories are now more valuable than ever, but machine traffic brings sustainability, capacity and policy challenges. Repositories may be able to scale, but who should fund that scaling, and under what conditions?

The core dilemma is how to curb abusive high-load bot activity …

[Birds-Of-A-Feather] Understanding And Handling Software Plagiarism In The Age Of Generative Ai, Daniel S. Katz, Scott C. Edmunds

FORCE 2026

Computing and software have supported research since their inception, and continue to play a significant role in knowledge production. However, the means of communicating research methods and results were developed long before computing existed, and the research community lacks best practices for documenting computational research elements transparently, reproducibly, and reusably.
Publishers are now more accepting of the inclusion of software (typically, source code) associated with submitted manuscripts, and many want to support processes to vouch for the integrity of software just as they do for other content, such as ensuring that ethical and legal concerns such as authorship, plagiarism, copyrights …

Research Integrity Without Borders: How Getftr Extends Trust Across The Scholarly Ecosystem, Joris Van Rossum

FORCE 2026

As research discovery increasingly happens beyond publisher platforms (through aggregators, institutional repositories, AI tools, and citation networks) maintaining trust in the integrity of the scholarly record becomes ever more complex. Visibility is not enough; researchers need confidence that what they find, read, and cite is current, authoritative, and trustworthy.

GetFTR (Get Full Text Research) addresses this challenge by embedding integrity and transparency signals, including retractions, errata, updates, and license information, directly into the researcher’s discovery experience. Through its cross-publisher API and integrations with major discovery tools, GetFTR ensures that researchers are always connected to the version of record and alerted …

Mapping Themes, Aligning Strategies: Institutional Research Analytics With Bertopic And Genai, Danping Dong, Pin Pin Yeo

FORCE 2026

Libraries and research offices are increasingly tasked with making sense of unstructured institutional data, such as research publications and strategic documents. These insights help inform decision-making and leadership planning. This session shares how SMU Libraries leveraged BERTopic, an open-source topic modeling tool, together with generative AI tools such as ChatGPT and Claude, to extract meaningful insights from faculty publications and support institutional sensemaking.

In one use case, we applied BERTopic to university-authored publications to surface thematic groupings related to research about Asia. Generative AI tools were then used to summarize each cluster into human-readable narratives, enabling research landscape mapping, identification …

Pre-Conference Workshop 4: Global Perspectives On Open Research Information, Dominika Tkaczyk, Bianca Kramer

FORCE 2026

Research information (or metadata) relates to the conduct and communication of research. his includes bibliographic metadata, information about research software, research data, funding, research contributors, and more. It is located in systems such as bibliographic databases, software archives, data repositories, and current research information systems. Many research information infrastructures are closed, and yet they play an important role in research assessment and resource allocation in many countries. The Barcelona Declaration on Open research Information seeks to drive fundamental changes in this landscape. How do these concerns play out in other regions—are they shared globally? In this session, with a series …

Ppg-Sport: A Dataset For Reliable Heart Rate Monitoring From Wrist Ppg Under Dynamic Sports Conditions, Changshuo Hu, Hung Manh Pham, Yiming Zhang, Guanru Yan, Xiao Ma, Yuezhong Wu, Thivya Kandappu, Archan Misra, Dong Ma

Research Collection School Of Computing and Information Systems

Photoplethysmography (PPG) has become a cornerstone of physiological sensing in wearable devices, enabling non-invasive monitoring of heart rate and related biomarkers. However, its reliability deteriorates sharply under dynamic, high-intensity, or non-periodic motions such as those in sports, where existing datasets fail to capture realistic wrist dynamics. To address this gap, we introduce PPG-Sport, the first large-scale dataset designed for heart rate monitoring from wrist-worn PPG under real sports conditions. The PPG-Sport dataset includes synchronized PPG, inertial measurement unit (IMU), and electrocardiography (ECG) recordings from both wrists of 30 participants across six representative activities: stationary, walking, running, badminton, table tennis, and …

“From Remembering To Shaping”: Narrating Shared Experiences By Co-Designing Cultural Heritage Artifacts In Collaborative Vr, Yushang Yang, Fanxu Meng, Fiona Fui-Hoon Nah, L. C. Ray

Research Collection School Of Computing and Information Systems

The ways people remember and recall places reveal an invisible aspect of cultural heritage (CH), reflecting how individuals and communities relate to these places. Heritage is communal, emerging through collaboratively constructed narratives rather than individual records. To probe how people may share collective memories, we designed an immersive two-person workflow for collaboratively co-designing 3D artifacts and environments in virtual heritage locations, using Generative AI (GenAI) to instantiate these intangible memories. Observations of the co-creation process revealed that participants merged prompts and model placements when negotiating different perspectives. They used spatial operations to compose scenes, and also to express personal and …

“Megadeal” Subsidies, Local Spillovers And Corporate Innovation, Yoojin Lee, Shaphan Ng, Aruhn Venkat

Research Collection School Of Accountancy

We examine whether the largest place-based, firm-specific corporate subsidies (“Megadeals”) awarded by state and local governments affect local firms’ innovation. First, we document that 1) subsidy firms innovate in the subsidized county and 2) subsidy firms bring inventors from other counties into the subsidized county, consistent with subsidy firms generating new knowledge locally. In our main test, we use a stacked cohort design with stringent fixed effects to document that local firms increase patenting following a Megadeal. Cross-sectionally, effects are increasing 1) in subsidy firm innovativeness, 2) in the technological closeness of subsidy firms and local firms, 3) when subsidy …

Daily Problematic Smartphone Use Predicts Decreases In Self-Control Capacity: Evidence From Random-Intercept Cross-Lagged Panel Model, Adalia Yin Hui Goh, Andree Hartanto

Research Collection School of Social Sciences

Amidst the widespread increase in global smartphone screen time and the phenomenon of ‘The Great Exhaustion’, where multiple surveys indicate pervasive tiredness and feelings of being drained, there is growing concern about the link between problematic smartphone use and the capacity for self-control. While numerous studies have investigated the relationship between problematic smartphone use and self-control capacity, they are mostly cross-sectional. Thus, it is unclear whether the lack of capacity for self-control is an antecedent or consequence of problematic smartphone use. Addressing the research gap, we conducted a 7-day diary study to investigate the bidirectional relationship between problematic smartphone use …

On-The-Fly Generation-Quality Enhancement Of Deep Code Models Via Model Collaboration, Weifeng Sun, Naiqi Huang, Meng Yan, Zhongxin Liu, Hongyan Li, Yan Lei, David Lo

Research Collection School Of Computing and Information Systems

The growing prominence of deep code models in automating software engineering tasks is undeniable. However, their deployment encounters significant challenges in on-the-fly performance enhancement, which refers to dynamically improving the performance of deep code models during real-time execution. Conventional techniques, such as retraining or fine-tuning, are effective in controlled pre-deployment scenarios but fall short when adapting to on-the-fly adjustments post-deployment. CodeDenoise, a notable on-the-fly performance enhancement technology, leverages uncertainty-based methods to identify misclassified inputs and applies an input modification strategy to rectify classification errors. While effective for classification tasks, this approach is inapplicable to generative tasks due to two key …

Sam3-Litetext: An Anatomical Study Of The Sam3 Text Encoder For Efficient Vision-Language Segmentation, Chengxi Zeng, Yuxuan Jiang, Ge Gao, Shuai Wang, Duolikun Danier, Bin Zhu, Stevan Rudinac, David Bull, Fan Zhang

Research Collection School Of Computing and Information Systems

Vision-language segmentation models such as SAM3 enable flexible, prompt-driven visual grounding, but inherit large, general-purpose text encoders originally designed for open-ended language understanding. In practice, segmentation prompts are short, structured, and semantically constrained, leading to substantial over-provisioning in text encoder capacity and persistent computational and memory overhead. In this paper, we perform a large-scale anatomical analysis of text prompting in vision–language segmentation, covering 404,796 real prompts across multiple benchmarks. Our analysis reveals severe redundancy: most context windows are underutilized, vocabulary usage is highly sparse, and text embeddings lie on a low-dimensional manifold despite high-dimensional representations. Motivated by these findings, we …

Co-Designing With Autistic Livestreamers: Care, Constraints, And Trade-Offs In Livestreaming, Terrance Mok, Anthony Tang, Lora Oehlberg

Research Collection School Of Computing and Information Systems

Autistic livestreamers use platforms like Twitch for social connection, self-expression, and community, but these spaces also impose ongoing social and emotional demands. Prior work has documented these experiences, but less is known about what autistic creators themselves envision for the tools and platforms they use. We address this gap through a Research through Design (RtD) co-design study with three autistic Twitch streamers, using speculative artefacts as discussion prompts to explore how participants reasoned about potential livestreaming technologies. Across three co-design activities, we identify three overarching tensions shaping autistic streaming practice: Expression versus Misinterpretation and Harm; Public Participation versus Control and …

“Grandpa, Can You Speak Nicer?”: Envisioned Chatbot Roles And Design Tensions In Intergenerational Communication Conflicts, Tianyi Zhang, Emran Bin Elias Poh, Yueyue Hou, Yi-Chieh Lee, Renwen Zhang, Jiannan Li, Anthony Tang

Research Collection School Of Computing and Information Systems

Intergenerational conversations often break down when differences in tone, language, or expectations lead participants to feel dismissed or misunderstood. In this work, we explore how people envision AI-driven chatbot interventions for addressing communication problems in text-based intergenerational family chat. We conducted a scenario-based design interview with 10 pairs of family members from different generations, in which participants designed chatbot interventions that varied in intervention target and timing. Our findings show that participants expect chatbots to perform multiple themes of intervention, including mediating understanding, providing emotional support, offering evaluative commentary, and guiding interaction through behavioral suggestions. These expectations varied systematically across …

The Stars Align: Modeling User Rating Calibration With Sparse Semantic Review Features, Rodrigo Alves, Antoine Ledent

Research Collection School Of Computing and Information Systems

User ratings are often treated as comparable across users, although identical scores may reflect different experiences. We study whether ratings can be viewed as user-specific discretizations of a shared semantic continuum derived from review text. Our method maps reviews into sparse semantic features with a sparse autoencoder and learns user-specific filters for each rating level. On Amazon Electronics, the learned embeddings align along a shared low-dimensional rating axis. Users differ mainly in how they anchor and partition this continuum, while preserving its overall ordinal structure. These findings support a semantic view of calibration beyond scalar bias correction.

Hide-And-Sweep: Detecting Concealed Cameras Via Led Illumination Sweeps, Jonghyuk Yun, Jaeyoung Moon, Yunseo Park, Sean Rui Xiang Tan, Byunghyun Kim, Rajesh Krishna Balan, Jun Han

Research Collection School Of Computing and Information Systems

Hidden cameras have increasingly infiltrated hotel and Airbnb rooms, posing serious privacy risks. Detecting such cameras is challenging because they are visually inconspicuous and often embedded inside everyday objects. Even worse, existing handheld detectors are manual and also rely on single-angle illumination and hence suffer from high false-positive rates. We present SweepLED (pronounced "sweepled")1, a practical hidden camera detection system that operates on a commodity smartphone augmented with an unobtrusive LED-embedded case. SweepLED performs LED sweeping - a controlled sequence of multi-angle illumination - while the user simply holds the phone still by hand, enabling the camera to capture how …

Rc-Nf: Robot-Conditioned Normalizing Flow For Real-Time Anomaly Detection In Robotic Manipulation, Shijie Zhou, Bin Zhu, Jiarui Yang, Xiangyu Zhao, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Recent advances in Vision-Language-Action (VLA) models have enabled robots to execute increasingly complex tasks. However, VLA models trained through imitation learning struggle to operate reliably in dynamic environments and often fail under Out-of-Distribution (OOD) conditions. To address this issue, we propose Robot-Conditioned Normalizing Flow(RC-NF), a real-time monitoring model for robotic anomaly detection and intervention that ensures the robot's state and the object's motion trajectory align with the task. RC-NF decouples the processing of task-aware robot and object states within the normalizing flow. It requires only positive samples for unsupervised training and calculates accurate robotic anomaly scores during inference through the …

Towards Auto-Evaluation For Large Language Models, Jiahao Ying

Dissertations and Theses Collection (Open Access)

The rapid advancement of large language models (LLMs) has created an urgent need for evaluation methodologies that are timely, scalable, reliable, and informative. Conventional evaluation benchmarks, although essential for measuring model capabilities and guiding model development, are often constructed and maintained through labor-intensive human annotation. As LLMs continue to improve through increases in model scale, training data, and computational resources, static benchmarks may quickly lose discriminative power. Moreover, the growing use of large and diverse training corpora increases the risk of benchmark leakage, which can inflate evaluation results and obscure the true capabilities of models. These challenges call for a …

Queuing Uncertainty Of Limit Orders, Bart Yueshen Zhou

Research Collection Lee Kong Chian School Of Business

Limit orders submitted around the same time are subject to random latencies and will be queued accordingly. In equilibrium, end-of-queue limit orders always lose money—the liquidity supply appears excessive. The model generates empirical predictions regarding such “overshooting” liquidity: (i) new limit orders appear fleeting—clustered submissions are followed by immediate cancellations, (ii) the resulting cancel-to-add count ratio reflects adverse selection, and (iii) the cancel-to-add size ratio measures high-frequency market-making activity. Welfare can be hurt by the overshooting liquidity if it induces excessive speculation. Overall, the model contributes to a more comprehensive understanding and better utilization of order book data.

Public Acceptance Of Meat-Reduction Policies Across Cultures: Comparing Singapore And Switzerland, Bianca Wassmann, Shu Tian Ng, Mark Chong, Angela K. Y. Leung, Michael Siegrist

Research Collection Lee Kong Chian School Of Business

Purpose – As meat-reduction policies are discussed across the globe, many are met with public resistance. Cultural values may help explain this pushback, yet their role in shaping support for food policy remains poorly understood. This study is the first to apply cultural cognition theory to food policy by examining how cultural worldviews shape the acceptance of meat-reduction interventions in Singapore and Switzerland—two economically developed countries with contrasting cultural profiles. Design/methodology/approach – In an online survey, participants (Singapore: n = 357; Switzerland: n = 495) rated their acceptance of 11 meat-reduction interventions (e.g. taxes, subsidies, labelling). We then analysed to …

History To Future: Evolving Agent With Experience And Thought For Zero-Shot Vision-And-Language Navigation, Guangzhao Dai, Shuo Wang, Zihan Wang, Guo-Sen Xie, Yang Yang, Jinshan Pan, Qianru Sun, Xiangbo Shu

Research Collection School Of Computing and Information Systems

Vision-and-Language Navigation in Continuous Environment (VLN-CE) requires an agent to follow language instructions to navigate the target destination. With the advancement of large language models (LLMs), recent efforts have explored adapting them for zero-shot VLN-CE, offering a promising solution in addressing the drawbacks of poor generalization in the training-based paradigm. However, existing LLM-based works primarily perform naive reasoning for decision-making and lack feedback, e.g., reviewing historical errors and predicting future potentials. Consequently, it may suffer from continuous failure for those initial error tasks. In this paper, we rethink LLM-based zero-shot VLN-CE and propose a new paradigm, named EvoNav, to improve …

Task Complexity Matters: An Empirical Study Of Reasoning In Llms For Sentiment Analysis, Donghao Huang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) with reasoning capabilities have fueled a compelling narrative that reasoning universally improves performance across language tasks. We test this claim through a comprehensive evaluation of 504 configurations across seven model families—including adaptive, conditional, and reinforcement learning-based reasoning architectures—on sentiment analysis datasets of varying granularity (binary, five-class, and 27-class emotion). Our findings reveal that reasoning effectiveness is strongly task-dependent, challenging prevailing assumptions: (1) Reasoning shows task-complexity dependence—binary classification degrades up to -19.9 F1% points (pp), while 27-class emotion recognition gains up to  +16.0 pp; (2) Distilled reasoning variants underperform base models by 3–18 pp on simpler tasks, …

Cfalr: Collaborative Filtering-Augmented Large Language Model For Personalized Fashion Outfit Recommendation, Yujuan Ding, Junrong Liao, Yunshan Ma, Yi Bin, Wenqi Fan, Tat-Seng Chua, Qing Li

Research Collection School Of Computing and Information Systems

Personalized outfit recommendation poses a significant challenge in e-commerce and social media platforms, requiring systems that balance user preferences with aesthetic compatibility. Collaborative filtering (CF) provides a traditional solution for this, but it struggles with data-sparse scenarios and complex user-item-outfit relationships. Meanwhile, existing template-based approaches are constrained by rigid pre-designed structures. To bridge these research gaps, we introduce CFALR (Collaborative Filtering-Augmented Large Language Model for Recommendation), a novel framework that synergizes collaborative filtering with large language models for personalized outfit recommendation. Specifically, CFALR describes user-outfit interactions in natural language and leverages LLMs to capture fashion semantics while employing CF-enhanced embeddings …

A Pruning-Based Question-Answering For Interactive Video Search: A Simple Baseline, Yu Tong Cheng, Phuong Anh Nguyen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

There are various factors affecting the performance of video search. An imprecise query will enlarge search space and reduce the discriminative power of ranking functions. This problem is further exacerbated by the presence of numerous visually or semantically similar videos in large datasets. Consequently, users need to painstakingly browse through many highly similar candidates to locate the search target, leading to increased cognitive load and inefficient searching. Ideally, engaging users through interactive questioning to resolve uncertainties in the search process is an effective strategy for progressively narrowing down the search space. However, despite rapid advances in deep learning, generating informative …

Interfold: Learning Interpretable Diffusion Manifolds Beyond Binary Samples, Alexander Vincent Lewi, Rainer Tan, Shengfeng He

Research Collection School Of Computing and Information Systems

We propose InterFold, a framework for learning and applying interpretable semantic manifolds in latent diffusion models, without requiring binary or paired supervision. Existing methods for semantic editing either rely on limited paired data or uncover only coarse, unsupervised directions that fail to capture user-specific, fine-grained attributes. InterFold addresses these limitations by learning a target attribute manifold in the H-space of diffusion models using only a set of positive, unlabeled examples. To edit a new image, InterFold projects its H-space representation toward this learned manifold through test-time optimization, enabling precise, identity-preserving modifications of complex, non-binary concepts. To make these edits effective …

A Survey Of Earable Technology: Trends, Tools, And The Road Ahead, Changshuo Hu, Qiang Yang, Yang Liu, Tobias Röddiger, Kayla-Jade Butkow, Mathias Ciliberto, Adam Luke Pullin, Jake Stuchbury-Wass, Mahbub Hassan, Cecilia Mascolo, Dong Ma

Research Collection School Of Computing and Information Systems

Earable devices, wearables positioned in or around the ear, are undergoing a rapid transformation from audio-centric accessories into multifunctional systems for interaction, contextual awareness, and health monitoring. This evolution is driven by commercial trends emphasizing sensor integration and by a surge of academic interest exploring novel sensing capabilities. Building on the foundation established by earlier surveys, this work presents a timely and comprehensive review of earable research published since 2022. We attempt to answer three core questions: (1) how has earable research evolved in recent years, (2) what enabling resources are now available, and (3) what opportunities remain for future …

Adaptive Outlier Detection Over Data Stream, Rui Zhu, Mingyuan Jiang, Xiaochun Yang, Baihua Zheng, Bin Wang, Tao Qiu

Research Collection School Of Computing and Information Systems

Continuous distance-based outlier detection in streaming data poses significant challenges and has a wide range of practical applications. Traditional threshold-based methods perform well under stable streaming conditions, where fixed parameters remain effective. However, they often struggle with dynamic data distributions and high stream speeds, leading to suboptimal performance, limited control over the number of returned outliers, and failure to meet real-time detection requirements. To address these issues, this paper introduces a novel Recall and Proportion-Aware Outlier Detection (RPA-OD) query. In RPA-OD, ρ defines a distance relaxation that enables real-time outlier detection. Specifically, objects with fewer than k neighbors within the …

A Novel Hierarchical Multi-Agent System For Payments Using Llms, Donghao Huang, Joon Kiat Chua, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Large language model (LLM) agents, such as OpenAI’s Operator and Claude’s Computer Use, can automate workflows but unable to handle payment tasks. Existing agentic solutions have gained significant attention; however, even the latest approaches face challenges in implementing end-to-end agentic payment workflows. To address this gap, this research proposes the Hierarchical Multi-Agent System for Payments (HMASP), which provides an end-to-end agentic method for completing payment workflows. The proposed HMASP leverages either open-weight or proprietary LLMs and employs a modular architecture consisting of the Conversational Payment Agent (CPA - first agent level), Supervisor agents (second agent level), Routing agents (third agent …

“Alexa, Do Not Say That In Front Of My Boss!” A Cross-Cultural Comparison Of User And Ai Preferences For Privacy-Aware Smart Speaker Interactions Across Contexts, Lynne Warin, Emily Aurelia, Anthony Tang, Emily Aurelia, Delphine Reinhardt

Research Collection School Of Computing and Information Systems

Due to their limited ability to reason about the social context in which they are used, smart speakers pose significant privacy risks by responding in ways that may violate people's implicit social boundaries. We conducted a cross-cultural vignette study (N = 944) in Germany and Singapore to investigate how situational factors—specifically social context (bystander relationships and closeness), physical context (location), and interaction context (topic and deceptive intent)—regulate user preferences for smart speaker responses. Our results demonstrate that these factors are superior predictors of response preferences than dispositional user traits (i.e., intrinsic personal traits). We identify two distinct social dynamics: a …

Calibrations And Compliance: The Role Of Motivations In Policy Instrument Design, Panchali Guha, Ishani Mukherjee

Research Collection School of Social Sciences

Most research on the role of policy calibrations in fostering policy target compliance has focused on the calibration of incentives and deterrents; less attention has been paid to examining the deployment and calibration of a wider range of policy instruments with the intention of eliciting a greater degree of compliance from policy targets with heterogeneous motivations. This article addresses this gap in the literature by empirically testing multiple hypotheses on the relationship between the calibration of different kinds of policy instruments and policy compliance for policy targets characterized by different motivations. Using data from a vignette experiment set in the …