Digital Commons Network^™

Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel. Chen, Nan Hu, Peng. Liang, Morgan. Swink

Research Collection School Of Computing and Information Systems

Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …

Towards Llm-Based Fact Verification On News Claims With A Hierarchical Step-By-Step Prompting Method, Xuan Zhang, Wei Gao

Research Collection School Of Computing and Information Systems

While large pre-trained language models (LLMs) have shown their impressive capabilities in various NLP tasks, they are still underexplored in the misinformation domain. In this paper, we examine LLMs with in-context learning (ICL) for news claim verification, and find that only with 4-shot demonstration examples, the performance of several prompting methods can be comparable with previous supervised models. To further boost performance, we introduce a Hierarchical Step-by-Step (HiSS) prompting method which directs LLMs to separate a claim into several subclaims and then verify each of them via multiple questionsanswering steps progressively. Experiment results on two public misinformation datasets show that …

Complex Knowledge Base Question Answering: A Survey, Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Zhao Wayne Xin, Ji Rong Wen

Research Collection School Of Computing and Information Systems

Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Early studies mainly focused on answering simple questions over KBs and achieved great success. However, their performances on complex questions are still far from satisfactory. Therefore, in recent years, researchers propose a large number of novel methods, which looked into the challenges of answering complex questions. In this survey, we review recent advances in KBQA with the focus on solving complex questions, which usually contain multiple subjects, express compound relations, or involve numerical operations. In detail, we begin with introducing the complex KBQA task and …

The Relationship Between Constitutional Equality And Substantive Review, Wei Yao, Kenny Chng

Research Collection Yong Pung How School Of Law

General equality rights in written constitutions – rights stating the ideal of equality without specifying categories of impermissible differentiation – have often been effected through the idea of equality as rationality. Equality as rationality demands that differentiations between like entities have to be rationally justifiable. Such equality rights are applicable to legislation and executive action. This presents a prima facie overlap with substantive review in common law administrative law, since substantive review is also concerned about the rational justifiability of executive action. This raises three questions: (1) Are both sets of legal principles indeed similar? (2) Have courts managed to …

Why Your Organisation Needs A Language Management Strategy, John Fiset, Devasheesh P. Bhave

Research Collection Lee Kong Chian School Of Business

This article discusses the challenges and benefits of managing language dynamics at work, where both standard and hybrid forms of language coexist. It reviews the literature on language-related misunderstanding and its effects on employee outcomes through three pathways: relational, emotional, and informational. It also suggests some interventions to enhance communication effectiveness and foster an inclusive culture in linguistically diverse workplaces.

Boots On The Ground: Foreign Direct Investment By Born Digital Firms, Maximilian Stalkkamp, Liang Chen, Sali Li

Research Collection Lee Kong Chian School Of Business

Research Summary: Recent global strategy research on born digital firms (i.e., firms with digital products distributed through digital channels) has paid only limited attention to the role of foreign direct investment (FDI) in the internationalization of such firms. We argue that exploiting digital technologies requires a range of complementary, non-digital resources. Born digitals typically deploy FDI when large cultural and geographic distances limit the fungibility and scalability of such complementary resources, leading to a positive relationship between distance (cultural and geographic) and FDI. The positive distance effect is moderated by business model type. Using a sample of US-based born digital …

Revisiting Disentanglement And Fusion On Modality And Context In Conversational Multimodal Emotion Recognition, Bobo Li, Hao Fei, Lizi Liao, Yu Zhao, Chong Teng, Tat-Seng Chua, Donghong Ji, Fei Li

Research Collection School Of Computing and Information Systems

It has been a hot research topic to enable machines to understand human emotions in multimodal contexts under dialogue scenarios, which is tasked with multimodal emotion analysis in conversation (MM-ERC). MM-ERC has received consistent attention in recent years, where a diverse range of methods has been proposed for securing better task performance. Most existing works treat MM-ERC as a standard multimodal classification problem and perform multimodal feature disentanglement and fusion for maximizing feature utility. Yet after revisiting the characteristic of MM-ERC, we argue that both the feature multimodality and conversational contextualization should be properly modeled simultaneously during the feature disentanglement …

Pro-Cap: Leveraging A Frozen Vision-Language Model For Hateful Meme Detection, Rui Cao, Ming Shan Hee, Adriel Kuek, Wen Haw Chong, Roy Ka-Wei Lee, Jing Jiang

Research Collection School Of Computing and Information Systems

Hateful meme detection is a challenging multimodal task that requires comprehension of both vision and language, as well as cross-modal interactions. Recent studies have tried to fine-tune pre-trained vision-language models (PVLMs) for this task. However, with increasing model sizes, it becomes important to leverage powerful PVLMs more efficiently, rather than simply fine-tuning them. Recently, researchers have attempted to convert meme images into textual captions and prompt language models for predictions. This approach has shown good performance but suffers from non-informative image captions. Considering the two factors mentioned above, we propose a probing-based captioning approach to leverage PVLMs in a zero-shot …

Cgt-Gan: Clip-Guided Text Gan For Image Captioning, Jiarui Yu, Haoran Li, Yanbin Hao, Bin Zhu, Tong Xu, Xiangnan He

Research Collection School Of Computing and Information Systems

The large-scale visual-language pre-trained model, Contrastive Language-Image Pre-training (CLIP), has significantly improved image captioning for scenarios without human-annotated image-caption pairs. Recent advanced CLIP-based image captioning without human annotations follows a text-only training paradigm, i.e., reconstructing text from shared embedding space. Nevertheless, these approaches are limited by the training/inference gap or huge storage requirements for text embeddings. Given that it is trivial to obtain images in the real world, we propose CLIP-guided text GAN (CgT-GAN), which incorporates images into the training process to enable the model to "see" real visual modality. Particularly, we use adversarial training to teach CgT-GAN to mimic …

Multimodal Fashion Knowledge Extraction As Captioning, Yifei Yuan, Wenxuan Zhang, Yang Deng, Wai Lam

Research Collection School Of Computing and Information Systems

Social media plays a significant role in boosting the fashion industry, where a massive amount of fashion-related posts are generated every day. In order to obtain the rich fashion information from the posts, we study the task of social media fashion knowledge extraction. Fashion knowledge, which typically consists of the occasion, person attributes, and fashion item information, can be effectively represented as a set of tuples. Most previous studies on fashion knowledge extraction are based on the fashion product images without considering the rich text information in social media posts. Existing work on fashion knowledge extraction in social media is …

Constructing Holistic Spatio-Temporal Scene Graph For Video Semantic Role Labeling, Yu Zhao, Hao Fei, Yixin Cao, Bobo Li, Meishan Zhang, Jianguo Wei, Min Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

As one of the core video semantic understanding tasks, Video Semantic Role Labeling (VidSRL) aims to detect the salient events from given videos, by recognizing the predict-argument event structures and the interrelationships between events. While recent endeavors have put forth methods for VidSRL, they can be mostly subject to two key drawbacks, including the lack of fine-grained spatial scene perception and the insufficiently modeling of video temporality. Towards this end, this work explores a novel holistic spatio-temporal scene graph (namely HostSG) representation based on the existing dynamic scene graph structures, which well model both the fine-grained spatial semantics and temporal …

Followupqg: Towards Information-Seeking Follow-Up Question Generation, Yan Meng, Liangming Pan, Yixin Cao, Min-Yen Kan

Research Collection School Of Computing and Information Systems

Humans ask follow-up questions driven by curiosity, which reflects a creative human cognitive process. We introduce the task of realworld information-seeking follow-up question generation (FQG), which aims to generate follow-up questions seeking a more in-depth understanding of an initial question and answer. We construct FOLLOWUPQG, a dataset1 of over 3K real-world (initial question, answer, follow-up question) tuples collected from a Reddit forum providing layman-friendly explanations for open-ended questions. In contrast to existing datasets, questions in FOLLOWUPQG use more diverse pragmatic strategies to seek information, and they also show higher-order cognitive skills (such as applying and relating). We evaluate current question …

Matk: The Meme Analytical Tool Kit, Ming Shan Hee, Aditi Kumaresan, Nguyen Khoi Hoang, Nirmalendu Prakash, Rui Cao, Roy Ka-Wei Lee

Research Collection School Of Computing and Information Systems

The rise of social media platforms has brought about a new digital culture called memes. Memes, which combine visuals and text, can strongly influence public opinions on social and cultural issues. As a result, people have become interested in categorizing memes, leading to the development of various datasets and multimodal models that show promising results in this field. However, there is currently a lack of a single library that allows for the reproduction, evaluation, and comparison of these models using fair benchmarks and settings. To fill this gap, we introduce the Meme Analytical Tool Kit (MATK), an open-source toolkit specifically …

Voucher Abuse Detection With Prompt-Based Fine-Tuning On Graph Neural Networks, Zhihao Wen, Yuan Fang, Yihan Liu, Yang Guo, Shuji Hao

Research Collection School Of Computing and Information Systems

Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-training using label-free data, and further fine-tune on a downstream task with limited labels. Nevertheless, the "pre-train, fine-tune" paradigm is often plagued by the objective gap between pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based fine-tuning framework on GNNs for voucher abuse detection. We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task …

Wait, Wasn't That Code Here Before? Detecting Outdated Software Documentation, Wen Siang Tan, Markus Wagner, Christoph Treude

Research Collection School Of Computing and Information Systems

Encountering outdated documentation is not a rare occurrence for developers and users in the software engineering community. To ensure that software documentation is up-to-date, developers often have to manually check whether the documentation needs to be updated whenever changes are made to the source code. In our previous work, we proposed an approach to automatically detect outdated code element references in software repositories and found that more than a quarter of the 1000 most popular projects on GitHub contained at least one outdated reference. In this paper, we present a GitHub Actions tool that builds on our previous work’s approach …

Hallucination Detection: Robustly Discerning Reliable Answers In Large Language Models, Yuyuan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, Yanghua Xiao

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major drawback of LLMs is the issue of hallucination, where they generate unfaithful or inconsistent content that deviates from the input source, leading to severe consequences. In this paper, we propose a robust discriminator named RelD to effectively detect hallucination in LLMs' generated answers. RelD is trained on the constructed RelQA, a bilingual question-answering dialogue dataset along with answers generated by LLMs and a comprehensive set of metrics. Our experimental results demonstrate that the proposed RelD successfully detects …

When Planetary Cosmopolitanism Meets The Buddhist Ethic: Recycling, Karma And Popular Ecology In Singapore, Siew Ying Shee, Orlando Woods, Lily Kong

Research Collection College of Integrative Studies

By thinking with and through Buddhist cosmology, this paper explores the emergence of an ethical sensibility—what we call planetary cosmopolitanism—that is based on not just a spatially expanded ethic of care to ecological worlds, but also a temporally extended sense of justice to the future Earth. This transtemporal sense of ethical becoming reflects how the possibility of future ‘rebirth’ and accountability for past actions can motivate new ecological consciousness in the present. We forge these ideas through an empirical focus on popular Buddhist ecological practices in Singapore, where green recovery visions have primarily been driven by a secular and technocratic …

Evlog: Identifying Anomalous Logs Over Software Evolution, Yintong Huo, Cheryl Lee, Yuxin Su, Shiwen Shan, Jinyang Liu, Michael Lyu

Research Collection School Of Computing and Information Systems

Software logs record system activities, aiding maintainers in identifying the underlying causes for failures and enabling prompt mitigation actions. However, maintainers need to inspect a large volume of daily logs to identify the anomalous logs that reveal failure details for further diagnosis. Thus, how to automatically distinguish these anomalous logs from normal logs becomes a critical problem. Existing approaches alleviate the burden on software maintainers, but they are built upon an improper yet critical assumption: logging statements in the software remain unchanged. While software keeps evolving, our empirical study finds that evolving software brings three challenges: log parsing errors, evolving …

Unpacking Constitutional Literacy, De Maartje Visser, Brian Christopher Jones

Research Collection Yong Pung How School Of Law

The contemporary crisis in relation to constitutional literacy relates not to the lack of knowledge that citizens possess about fundamental constitutional texts, but to the considerable lack of development in relation to what constitutional literacy itself entails. This article accordingly unpacks the notion of constitutional literacy: its importance, its characteristics, its variable nature, and its potential downsides. Using a comparative lens, the article invites reflection on the role that we expect citizens to play in our democracies, and especially the associated knowledge and skills required for successful state performance. We suggest that constitutional literacy is exceptionally multifaceted and fluid in …

Tackling Misperceptions About Immigrants With Fact-Checking Interventions: A Randomized Survey Experiment, Syngjoo Choi, Chung-Yoon Choi, Kim

Research Collection School Of Economics

We conduct a randomized online survey experiment to study the impact of fact-checking offers and financial incentives on misperceptions about immigrants. We find that natives overestimate the number of immigrants and the social and economic costs of immigration. Offering a free check of the factual information about immigrants reduces these misperceptions; it becomes more effective when combined with financial incentives. However, more than half of the participants never took up offers to check factual information. Using a model of information search with limited attention, we identify the presence of non-negligible costs of information search and processing, which limits the effectiveness …

Flacgec: A Chinese Grammatical Error Correction Dataset With Fine-Grained Linguistic Annotation, Hanyue Du, Yike Zhao, Qingyuan Tian, Jiani Wang, Lei Wang, Yunshi Lan, Xuesong Lu

Research Collection School Of Computing and Information Systems

Chinese Grammatical Error Correction (CGEC) has been attracting growing attention from researchers recently. In spite of the fact that multiple CGEC datasets have been developed to support the research, these datasets lack the ability to provide a deep linguistic topology of grammar errors, which is critical for interpreting and diagnosing CGEC approaches. To address this limitation, we introduce FlaCGEC, which is a new CGEC dataset featured with fine-grained linguistic annotation. Specifically, we collect raw corpus from the linguistic schema defined by Chinese language experts, conduct edits on sentences via rules, and refine generated samples manually, which results in 10k sentences …

Ai In Universities And Libraries, Aaron Tay

Research Collection Library

Everyone is familiar with ChatGPT. But can ChatGPT alone be used for information retrieval? In this Keynote speech, Aaron argues that ChatGPT or similar large language models alone is not sufficient for information retrieval. As databases such as Scopus, Dimensions announce new beta search tools that leverage generative AI, he provides a overview of how these tools that blend search with generative AI (large language models) work (using a technique known as Retrieval Augmented Generation or RAG) and explains the pros and cons of this new class of search tools.

He ends with a prediction of what the next 3 …

Grasp Solution Approach For The E-Waste Collection Problem, Aldy Gunawan, Dang Viet Anh Nguyen, Pham Kien Minh Nguyen, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

The digital economy has brought significant advancements in electronic devices, increasing convenience and comfort in people’s lives. However, this progress has also led to a shorter life cycle for these devices due to rapid advancements in hardware and software technology. As a result, e-waste collection and recycling have become vital for protecting the environment and people’s health. From the operations research perspective, the e-waste collection problem can be modeled as the Heterogeneous Vehicle Routing Problem with Multiple Time Windows (HVRP-MTW). This study proposes a metaheuristic based on the Greedy Randomized Adaptive Search Procedure complemented by Path Relinking (GRASP-PR) to solve …

The Power Of Identity Cues In Text-Based Customer Service: Evidence From Twitter, Yang Gao, Huaxia Rui, Shujing Sun

Research Collection School Of Computing and Information Systems

Text-based customer service is emerging as an important channel through which companies can assist customers. However, the use of few identity cues may cause customers to feel limited social presence and even suspect the human identity of agents, especially in the current age of advanced algorithms. Does such a lack of social presence affect service interactions? We studied this timely question by evaluating the impact of customers’ perceived social presence on service outcomes and customers’ attitudes toward agents. Our identification strategy hinged on Southwest Airlines’ sudden requirement to include a first name in response to service requests on Twitter, which …

Does Abstract Thinking Facilitate Information Processing? Evidence From Financial Analysts, Frank Weikai Li, Rong Wang, Yang Yu, Gloria Yang Yu

Research Collection Lee Kong Chian School Of Business

We study whether abstract thinking – an essential cognitive trait established by psychological and neuroscientific studies – facilitates analysts’ information processing. Exploiting analysts’ questions during earnings calls, we construct an Abstract Thinking Index (ATI) that measures their tendency to involve abstract words, logical reasoning, broader topics, and future outlooks. We find that abstract thinking improves analysts’ forecast accuracy and recommendation informativeness. Consistent with abstract thinking featuring identifying central characteristics and comprehending intangible things, ATI has stronger effects for firms with fundamentals co-moving more with peers and less tangible information. Additional analyses suggest that ATI captures analysts’ cognitive traits rather than …

Are We Ready To Embrace Generative Ai For Software Q&A?, Bowen Xu, Thanh-Dat Nguyen, Thanh Le Cong, Thong Hoang, Jiakun Liu, Kisub Kim, Chen Gong, Changan Niu, Chenyu Wang, Xuan-Bach Dinh Le, David Lo

Research Collection School Of Computing and Information Systems

Stack Overflow, the world's largest software Q&A (SQA) website, is facing a significant traffic drop due to the emergence of generative AI techniques. ChatGPT is banned by Stack Overflow after only 6 days from its release. The main reason provided by the official Stack Overflow is that the answers generated by ChatGPT are of low quality. To verify this, we conduct a comparative evaluation of human-written and ChatGPT-generated answers. Our methodology employs both automatic comparison and a manual study. Our results suggest that human-written and ChatGPT-generated answers are semantically similar, however, human-written answers outperform ChatGPT-generated ones consistently across multiple aspects, …

Real: A Representative Error-Driven Approach For Active Learning, Cheng Chen, Yong Wang, Lizi Liao, Yueguo Chen, Xiaoyong Du

Research Collection School Of Computing and Information Systems

Given a limited labeling budget, active learning (al) aims to sample the most informative instances from an unlabeled pool to acquire labels for subsequent model training. To achieve this, al typically measures the informativeness of unlabeled instances based on uncertainty and diversity. However, it does not consider erroneous instances with their neighborhood error density, which have great potential to improve the model performance. To address this limitation, we propose Real, a novel approach to select data instances with Representative Errors for Active Learning. It identifies minority predictions as pseudo errors within a cluster and allocates an adaptive sampling budget for …

Chinese Visions Of Self And Other: The International Politics Of Noses, William A. Callahan

Research Collection School of Social Sciences

How does China view the world? While much has been written about how China's international relations are best seen as intercultural relations that do not exclude the Other, this article examines how self/Other relations are visualized in terms of the human face in China, in particular through iconic images that construct the Chinese self with and against foreigners with large noses. It employs interpretivist methods to consider the interplay of social relations and international relations in order to see how ‘external appearance’ and ‘external relations’ inform each other in Chinese theory and practice. It mobilizes the classical Chinese concept ‘deep-set …

An Activity System-Based Perspective Of Generative Ai: Challenges And Research Directions, Fiona Fui-Hoon Nah, Jingyuan Cai, Ruilin Zheng, Natalie Pang

Research Collection School Of Computing and Information Systems

With its remarkable ability to generate content, generative artificial intelligence (GAI) has been recognized as a milestone in the development of artificial general intelligence. To understand the challenges, potential impact, and implications associated with GAI, we adopt a socio-technical perspective to analyze them. First, we identify the key characteristics of GAI, which include content generation, generalization ability, and reinforcement learning based on human feedback. Next, we address technological, ethical, societal, economic, regulatory, and governance challenges. Finally, we deploy activity theory to explore research directions in GAI. Research questions that warrant further investigation include how GAI may impact the future of …

Rethinking Acting In Concert: Activist Esg Stewardship Is Shareholder Democracy, Dan W. Puchniak, Umakanth Varottil

Research Collection Yong Pung How School Of Law

In May 2021, Engine No. 1, an investment fund, was lauded by the responsible investment community for successfully placing three dissident independent directors on ExxonMobil’s board. It achieved this by being a catalyst for institutional investors to become backers of environmental shareholder activism. The unprecedented success of Engine No. 1’s campaign has spurred calls for a new, more sustained, activist engagement model by institutional investors, now known as “activist stewardship”.However, there is a significant legal hurdle that has been almost entirely overlooked by those calling for this new approach for institutional investors to become activist stewards: acting in concert rules. …