Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Physical Sciences and Mathematics (9364)
- Computer Sciences (9056)
- Databases and Information Systems (3567)
- Software Engineering (2215)
- Artificial Intelligence and Robotics (1920)
-
- Social and Behavioral Sciences (1630)
- Business (1306)
- Information Security (1112)
- Numerical Analysis and Scientific Computing (1060)
- Graphics and Human Computer Interfaces (952)
- Engineering (935)
- Theory and Algorithms (513)
- Computer Engineering (469)
- Economics (426)
- Operations Research, Systems Engineering and Industrial Engineering (425)
- Programming Languages and Compilers (413)
- Communication (348)
- OS and Networks (346)
- International and Area Studies (336)
- Asian Studies (327)
- Public Affairs, Public Policy and Public Administration (310)
- Education (294)
- Social Media (271)
- Finance and Financial Management (248)
- Environmental Sciences (240)
- Medicine and Health Sciences (228)
- Transportation (220)
- Econometrics (214)
- Technology and Innovation (201)
- Management Information Systems (187)
- Keyword
-
- Machine learning (148)
- Deep learning (130)
- Artificial intelligence (127)
- Singapore (125)
- Social media (82)
-
- Reinforcement learning (74)
- Data mining (70)
- Privacy (67)
- Security (62)
- Sustainability (61)
- Cloud computing (60)
- Deep Learning (58)
- Optimization (56)
- Empirical study (55)
- Software engineering (55)
- Blockchain (52)
- Online learning (52)
- Visualization (52)
- Natural language processing (51)
- Neural networks (50)
- Training (50)
- Anomaly detection (49)
- Large Language Models (49)
- Twitter (49)
- Task analysis (48)
- Collaboration (47)
- Machine Learning (47)
- Feature extraction (45)
- Algorithms (44)
- Semantics (44)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (8567)
- Research Collection Lee Kong Chian School Of Business (428)
- Research Collection School Of Economics (340)
- Dissertations and Theses Collection (Open Access) (272)
- Research Collection School of Social Sciences (193)
-
- Research Collection College of Integrative Studies (138)
- Research Collection Yong Pung How School Of Law (110)
- Research Collection School Of Accountancy (65)
- Asian Management Insights (63)
- Perspectives@SMU (49)
- Dissertations and Theses Collection (20)
- Research Collection Library (19)
- SMU Press Releases and News (18)
- Knowledge@SMU (17)
- FORCE 2026 (14)
- Sim Kee Boon Institute for Financial Economics (14)
- Research@SMU: Connecting the Dots (13)
- Social Space (12)
- MITB Thought Leadership Series (11)
- Research Collection School of Computing and Information Systems (11)
- Oral History Collection (9)
- Report to Stakeholders (9)
- PhD Student’s Publications Collection (8)
- LARC Research Publications (7)
- Research Collection School of Economics (7)
- Student Publications (7)
- Centre for Computational Law (2022-2025) (6)
- CCX Research (5)
- SMU Research Data (4)
- 2024 AI for Research Week (3)
- Publication Type
- File Type
Articles 1711 - 1740 of 10474
Full-Text Articles in Entire DC Network
Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink
Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, 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 …
Non-Monotonic Generation Of Knowledge Paths For Context Understanding, Pei-Chi Lo, Ee-Peng Lim
Non-Monotonic Generation Of Knowledge Paths For Context Understanding, Pei-Chi Lo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Knowledge graphs can be used to enhance text search and access by augmenting textual content with relevant background knowledge. While many large knowledge graphs are available, using them to make semantic connections between entities mentioned in the textual content remains to be a difficult task. In this work, we therefore introduce contextual path generation (CPG) which refers to the task of generating knowledge paths, contextual path, to explain the semantic connections between entities mentioned in textual documents with given knowledge graph. To perform CPG task well, one has to address its three challenges, namely path relevance, incomplete knowledge graph, and …
Ddos Family: A Novel Perspective For Massive Types Of Ddos Attacks, Ziming Zhao, Zhaoxuan Li, Zhihao Zhou, Jiongchi Yu, Zhuoxue Song, Xiaofei Xie, Fan Zhang, Rui Zhang
Ddos Family: A Novel Perspective For Massive Types Of Ddos Attacks, Ziming Zhao, Zhaoxuan Li, Zhihao Zhou, Jiongchi Yu, Zhuoxue Song, Xiaofei Xie, Fan Zhang, Rui Zhang
Research Collection School Of Computing and Information Systems
Distributed Denial of Service (DDoS) defense is a profound research problem. In recent years, adversaries tend to complicate their attack strategies by crafting vast DDoS variants. On the one hand, this trend exacerbates both extremes of classification granularity (i.e., binary and attack level) in existing machine learning methods. On the other hand, massive attack categories make the filter rule table bulky, as well as cause problems of slow reaction presented in the recent state-of-the-art DDoS mitigation system. Therefore, we propose the concept of a DDoS family to reconcile/cope with these issues. The specific technical roadmap includes traffic pattern characterization, attack …
Fixing Your Own Smells: Adding A Mistake-Based Familiarization Step When Teaching Code Refactoring, Ivan Wei Han Tan, Christopher M. Poskitt
Fixing Your Own Smells: Adding A Mistake-Based Familiarization Step When Teaching Code Refactoring, Ivan Wei Han Tan, Christopher M. Poskitt
Research Collection School Of Computing and Information Systems
Programming problems can be solved in a multitude of functionally correct ways, but the quality of these solutions (e.g. readability, maintainability) can vary immensely. When code quality is poor, symptoms emerge in the form of 'code smells', which are specific negative characteristics (e.g. duplicate code) that can be resolved by applying refactoring patterns. Many undergraduate computing curricula train students on this software engineering practice, often doing so via exercises on unfamiliar instructor-provided code. Our observation, however, is that this makes it harder for novices to internalise refactoring as part of their own development practices. In this paper, we propose a …
Hypergraphs With Attention On Reviews For Explainable Recommendation, Theis E. Jendal, Trung Hoang Le, Hady Wirawan Lauw, Matteo Lissandrini, Peter Dolog, Katja Hose
Hypergraphs With Attention On Reviews For Explainable Recommendation, Theis E. Jendal, Trung Hoang Le, Hady Wirawan Lauw, Matteo Lissandrini, Peter Dolog, Katja Hose
Research Collection School Of Computing and Information Systems
Given a recommender system based on reviews, the challenges are how to effectively represent the review data and how to explain the produced recommendations. We propose a novel review-specific Hypergraph (HG) model, and further introduce a model-agnostic explainability module. The HG model captures high-order connections between users, items, aspects, and opinions while maintaining information about the review. The explainability module can use the HG model to explain a prediction generated by any model. We propose a path-restricted review-selection method biased by the user preference for item reviews and propose a novel explanation method based on a review graph. Experiments on …
Revisiting The Markov Property For Machine Translation, Cunxiao Du, Hao Zhou, Zhaopeng Tu, Jing Jiang
Revisiting The Markov Property For Machine Translation, Cunxiao Du, Hao Zhou, Zhaopeng Tu, Jing Jiang
Research Collection School Of Computing and Information Systems
In this paper, we re-examine the Markov property in the context of neural machine translation. We design a Markov Autoregressive Transformer (MAT) and undertake a comprehensive assessment of its performance across four WMT benchmarks. Our findings indicate that MAT with an order larger than 4 can generate translations with quality on par with that of conventional autoregressive transformers. In addition, counter-intuitively, we also find that the advantages of utilizing a higher-order MAT do not specifically contribute to the translation of longer sentences.
Meta-Interpretive Learning With Reuse, Rong Wang, Jun Sun, Cong Tian, Zhenhua Duan
Meta-Interpretive Learning With Reuse, Rong Wang, Jun Sun, Cong Tian, Zhenhua Duan
Research Collection School Of Computing and Information Systems
Inductive Logic Programming (ILP) is a research field at the intersection between machine learning and logic programming, focusing on developing a formal framework for inductively learning relational descriptions in the form of logic programs from examples and background knowledge. As an emerging method of ILP, Meta-Interpretive Learning (MIL) leverages the specialization of a set of higher-order metarules to learn logic programs. In MIL, the input includes a set of examples, background knowledge, and a set of metarules, while the output is a logic program. MIL executes a depth-first traversal search, where its program search space expands polynomially with the number …
Ur2m: Uncertainty And Resource-Aware Event Detection On Microcontrollers, Hong Jia, Young D. Kwon, Dong Ma, Nhat Pham, Lorena Qendro, Tam Vu, Cecilia Mascolo
Ur2m: Uncertainty And Resource-Aware Event Detection On Microcontrollers, Hong Jia, Young D. Kwon, Dong Ma, Nhat Pham, Lorena Qendro, Tam Vu, Cecilia Mascolo
Research Collection School Of Computing and Information Systems
Traditional machine learning techniques are prone to generating inaccurate predictions when confronted with shifts in the distribution of data between the training and testing phases. This vulnerability can lead to severe consequences, especially in applications such as mobile healthcare. Uncertainty estimation has the potential to mitigate this issue by assessing the reliability of a model's output. However, existing uncertainty estimation techniques often require substantial computational resources and memory, making them impractical for implementation on microcontrollers (MCUs). This limitation hinders the feasibility of many important on-device wearable event detection (WED) applications, such as heart attack detection. In this paper, we present …
Application Of Collaborative Learning Paradigms Within Software Engineering Education: A Systematic Mapping Study, Rita Garcia, Christoph Treude, Andrew Valentine
Application Of Collaborative Learning Paradigms Within Software Engineering Education: A Systematic Mapping Study, Rita Garcia, Christoph Treude, Andrew Valentine
Research Collection School Of Computing and Information Systems
Collaboration is used in Software Engineering (SE) to develop software. Industry seeks SE graduates with collaboration skills to contribute to productive software development. SE educators can use Collaborative Learning (CL) to help students develop collaboration skills. This paper uses a Systematic Mapping Study (SMS) to examine the application of the CL educational theory in SE Education. The SMS identified 14 papers published between 2011 and 2022. We used qualitative analysis to classify the papers into four CL paradigms: Conditions, Effect, Interactions, and Computer-Supported Collaborative Learning (CSCL). We found a high interest in CSCL, with a shift in student interaction research …
Representation Learning For Stack Overflow Posts: How Far Are We?, Junda He, Xin Zhou, Bowen Xu, Ting Zhang, Kisub Kim, Zhou Yang, Thung Ferdian, Ivana Clairine Irsan, David Lo
Representation Learning For Stack Overflow Posts: How Far Are We?, Junda He, Xin Zhou, Bowen Xu, Ting Zhang, Kisub Kim, Zhou Yang, Thung Ferdian, Ivana Clairine Irsan, David Lo
Research Collection School Of Computing and Information Systems
The tremendous success of Stack Overflow has accumulated an extensive corpus of software engineering knowledge, thus motivating researchers to propose various solutions for analyzing its content. The performance of such solutions hinges significantly on the selection of representation models for Stack Overflow posts. As the volume of literature on Stack Overflow continues to burgeon, it highlights the need for a powerful Stack Overflow post representation model and drives researchers’ interest in developing specialized representation models that can adeptly capture the intricacies of Stack Overflow posts. The state-of-the-art (SOTA) Stack Overflow post representation models are Post2Vec and BERTOverflow, which are built …
Generative Ai In Finance: Risks And Potential Solutions, Nydia Remolina Leon
Generative Ai In Finance: Risks And Potential Solutions, Nydia Remolina Leon
Research Collection Yong Pung How School Of Law
Generative Artificial Intelligence captured the attention of academics, policymakers, the private sector, and some regulators in 2022 after the launch of ChatGPT and its widespread adoption worldwide. Then, during the World Economic Forum session in 2023, Microsoft Chairman and Chief Executive Officer Satya Nadella said that the ‘golden age’ of AI is underway and generative AI is set to play a big role in it. Accordingly, Microsoft has invested billions of dollars into OpenAI, the company behind the launch of ChatGPT. Additionally, Google and Meta have also created their own generative AI models. Given the multiplicity of potential use cases …
Navigating Through Chaos, Hoong Chuin Lau
Navigating Through Chaos, Hoong Chuin Lau
Asian Management Insights
How AI and optimisation models can strengthen supply chain resilience.
Maximizing The Ai Revolution In Southeast Asia, Shoeb Kagda
Maximizing The Ai Revolution In Southeast Asia, Shoeb Kagda
Asian Management Insights
For that, the region must narrow the digital divide.
Forests Are Chill: The Interplay Between Thermal Comfort And Mental Wellbeing, Loïc Gillerot, Kevin Rozario, Pieter De Frenne, Rachel Oh, Quentin Ponette, Aletta Bonn, Winston T. L. Chow, Douglas Godbold, Matthias Steinparzaer, Daniela Haluza, Dries Landuyt, Bart Muys, Kris Verheyen
Forests Are Chill: The Interplay Between Thermal Comfort And Mental Wellbeing, Loïc Gillerot, Kevin Rozario, Pieter De Frenne, Rachel Oh, Quentin Ponette, Aletta Bonn, Winston T. L. Chow, Douglas Godbold, Matthias Steinparzaer, Daniela Haluza, Dries Landuyt, Bart Muys, Kris Verheyen
Research Collection College of Integrative Studies
As global warming and urbanisation intensify unabated, a growing share of the human population is exposed to dangerous heat levels. Trees and forests can effectively mitigate such heat alongside numerous health co-benefits like improved mental wellbeing. Yet, which forest types are objectively and subjectively coolest to humans, and how thermal and mental wellbeing interact, remain understudied. We surveyed 223 participants in peri-urban forests with varying biodiversity levels in Austria, Belgium and Germany. Using microclimate sensors, questionnaires and saliva cortisol measures, we monitored intra-individual changes in thermal and mental states from non-forest baseline to forest conditions. Forests reduced daytime modified Physiologically …
Catnet: Cross-Modal Fusion For Audio-Visual Speech Recognition, Xingmei Wang, Jianchen Mi, Boquan Li, Yixu Zhao, Jiaxiang Meng
Catnet: Cross-Modal Fusion For Audio-Visual Speech Recognition, Xingmei Wang, Jianchen Mi, Boquan Li, Yixu Zhao, Jiaxiang Meng
Research Collection School Of Computing and Information Systems
Automatic speech recognition (ASR) is a typical pattern recognition technology that converts human speeches into texts. With the aid of advanced deep learning models, the performance of speech recognition is significantly improved. Especially, the emerging Audio–Visual Speech Recognition (AVSR) methods achieve satisfactory performance by combining audio-modal and visual-modal information. However, various complex environments, especially noises, limit the effectiveness of existing methods. In response to the noisy problem, in this paper, we propose a novel cross-modal audio–visual speech recognition model, named CATNet. First, we devise a cross-modal bidirectional fusion model to analyze the close relationship between audio and visual modalities. Second, …
Hgprompt: Bridging Homogeneous And Heterogeneous Graphs For Few-Shot Prompt Learning, Xingtong Yu, Yuan Fang, Zemin Liu, Xinming Zhang
Hgprompt: Bridging Homogeneous And Heterogeneous Graphs For Few-Shot Prompt Learning, Xingtong Yu, Yuan Fang, Zemin Liu, Xinming Zhang
Research Collection School Of Computing and Information Systems
Graph neural networks (GNNs) and heterogeneous graph neural networks (HGNNs) are prominent techniques for homogeneous and heterogeneous graph representation learning, yet their performance in an end-to-end supervised framework greatly depends on the availability of task-specific supervision. To reduce the labeling cost, pre-training on selfsupervised pretext tasks has become a popular paradigm, but there is often a gap between the pre-trained model and downstream tasks, stemming from the divergence in their objectives. To bridge the gap, prompt learning has risen as a promising direction especially in few-shot settings, without the need to fully fine-tune the pre-trained model. While there has been …
Public Acceptance Of Using Artificial Intelligence-Assisted Weight Management Apps In High-Income Southeast Asian Adults With Overweight And Obesity: A Cross-Sectional Study, Han Shi Jocelyn Chew, Palakorn Achananuparp, Palakorn Achananuparp, Nicholas W. S. Chew, Yip Han Chin, Yujia Gao, Bok Yan Jimmy So, Asim Shabbir, Ee-Peng Lim, Kee Yuan Ngiam
Public Acceptance Of Using Artificial Intelligence-Assisted Weight Management Apps In High-Income Southeast Asian Adults With Overweight And Obesity: A Cross-Sectional Study, Han Shi Jocelyn Chew, Palakorn Achananuparp, Palakorn Achananuparp, Nicholas W. S. Chew, Yip Han Chin, Yujia Gao, Bok Yan Jimmy So, Asim Shabbir, Ee-Peng Lim, Kee Yuan Ngiam
Research Collection School Of Computing and Information Systems
Introduction: With in increase in interest to incorporate artificial intelligence (AI) into weight management programs, we aimed to examine user perceptions of AI-based mobile apps for weight management in adults with overweight and obesity. Methods: 280 participants were recruited between May and November 2022. Participants completed a questionnaire on sociodemographic profiles, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Self-Regulation of Eating Behavior Questionnaire. Structural equation modeling was performed using R. Model fit was tested using maximum-likelihood generalized unweighted least squares. Associations between influencing factors were analyzed using correlation and linear regression. Results: 271 participant responses were …
M3sa: Multimodal Sentiment Analysis Based On Multi-Scale Feature Extraction And Multi-Task Learning, Changkai Lin, Hongju Cheng, Qiang Rao, Yang Yang
M3sa: Multimodal Sentiment Analysis Based On Multi-Scale Feature Extraction And Multi-Task Learning, Changkai Lin, Hongju Cheng, Qiang Rao, Yang Yang
Research Collection School Of Computing and Information Systems
Sentiment analysis plays an indispensable part in human-computer interaction. Multimodal sentiment analysis can overcome the shortcomings of unimodal sentiment analysis by fusing multimodal data. However, how to extracte improved feature representations and how to execute effective modality fusion are two crucial problems in multimodal sentiment analysis. Traditional work uses simple sub-models for feature extraction, and they ignore features of different scales and fuse different modalities of data equally, making it easier to incorporate extraneous information and affect analysis accuracy. In this paper, we propose a Multimodal Sentiment Analysis model based on Multi-scale feature extraction and Multi-task learning (M 3 SA). …
Detecting Outdated Code Element References In Software Repository Documentation, Wen Siang Tan, Markus Wagner, Christoph Treude
Detecting Outdated Code Element References In Software Repository Documentation, Wen Siang Tan, Markus Wagner, Christoph Treude
Research Collection School Of Computing and Information Systems
Outdated documentation is a pervasive problem in software development, preventing effective use of software, and misleading users and developers alike. We posit that one possible reason why documentation becomes out of sync so easily is that developers are unaware of when their source code modifications render the documentation obsolete. Ensuring that the documentation is always in sync with the source code takes considerable effort, especially for large codebases. To address this situation, we propose an approach that can automatically detect code element references that survive in the documentation after all source code instances have been deleted. In this work, we …
Mutation Analysis For Evaluating Code Translation, Giovani Guizzo, Jie M. Zhang, Federica Sarro, Christoph Treude, Mark Harman
Mutation Analysis For Evaluating Code Translation, Giovani Guizzo, Jie M. Zhang, Federica Sarro, Christoph Treude, Mark Harman
Research Collection School Of Computing and Information Systems
Source-to-source code translation automatically translates a program from one programming language to another. The existing research on code translation evaluates the effectiveness of their approaches by using either syntactic similarities (e.g., BLEU score), or test execution results. The former does not consider semantics, the latter considers semantics but falls short on the problem of insufficient data and tests. In this paper, we propose MBTA (Mutation-based Code Translation Analysis), a novel application of mutation analysis for code translation assessment. We also introduce MTS (Mutation-based Translation Score), a measure to compute the level of trustworthiness of a translator. If a mutant of …
Handling Long And Richly Constrained Tasks Through Constrained Hierarchical Reinforcement Learning, Yuxiao Lu, Arunesh Sinha, Pradeep Varakantham
Handling Long And Richly Constrained Tasks Through Constrained Hierarchical Reinforcement Learning, Yuxiao Lu, Arunesh Sinha, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Safety in goal directed Reinforcement Learning (RL) settings has typically been handled through constraints over trajectories and have demonstrated good performance in primarily short horizon tasks. In this paper, we are specifically interested in the problem of solving temporally extended decision making problems such as robots cleaning different areas in a house while avoiding slippery and unsafe areas (e.g., stairs) and retaining enough charge to move to a charging dock; in the presence of complex safety constraints. Our key contribution is a (safety) Constrained Search with Hierarchical Reinforcement Learning (CoSHRL) mechanism that combines an upper level constrained search agent (which …
Frameworks For Measuring Population Health: A Scoping Review, Sze Ling Chan, Clement Zhong Hao Ho, Nang Ei Ei Khaing, Ezra Ho, Candelyn Pong, Jia Sheng Guan, Calida Chua, Zongbin Li, Trudi Lim Wenqi, Sean Shao Wei Lam, Lian Leng Low, Choon How How
Frameworks For Measuring Population Health: A Scoping Review, Sze Ling Chan, Clement Zhong Hao Ho, Nang Ei Ei Khaing, Ezra Ho, Candelyn Pong, Jia Sheng Guan, Calida Chua, Zongbin Li, Trudi Lim Wenqi, Sean Shao Wei Lam, Lian Leng Low, Choon How How
Research Collection School Of Computing and Information Systems
Introduction Many regions in the world are using the population health approach and require a means to measure the health of their population of interest. Population health frameworks provide a theoretical grounding for conceptualization of population health and therefore a logical basis for selection of indicators. The aim of this scoping review was to provide an overview and summary of the characteristics of existing population health frameworks that have been used to conceptualize the measurement of population health. Methods We used the Population, Concept and Context (PCC) framework to define eligibility criteria of frameworks. We were interested in frameworks applicable …
Better Pay Attention Whilst Fuzzing, Shunkai Zhu, Jingyi Wang, Jun Sun, Jie Yang, Xingwei Lin, Tianyi Wang, Liyi Zhang, Peng Cheng
Better Pay Attention Whilst Fuzzing, Shunkai Zhu, Jingyi Wang, Jun Sun, Jie Yang, Xingwei Lin, Tianyi Wang, Liyi Zhang, Peng Cheng
Research Collection School Of Computing and Information Systems
Fuzzing is one of the prevailing methods for vulnerability detection. However, even state-of-the-art fuzzing methods become ineffective after some period of time, i.e., the coverage hardly improves as existing methods are ineffective to focus the attention of fuzzing on covering the hard-to-trigger program paths. In other words, they cannot generate inputs that can break the bottleneck due to the fundamental difficulty in capturing the complex relations between the test inputs and program coverage. In particular, existing fuzzers suffer from the following main limitations: 1) lacking an overall analysis of the program to identify the most “rewarding” seeds, and 2) lacking …
From Canteen Food To Daily Meals: Generalizing Food Recognition To More Practical Scenarios, Guoshan Liu, Yang Jiao, Jingjing Chen, Bin Zhu, Yu-Gang Jiang
From Canteen Food To Daily Meals: Generalizing Food Recognition To More Practical Scenarios, Guoshan Liu, Yang Jiao, Jingjing Chen, Bin Zhu, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
The precise recognition of food categories plays a pivotal role for intelligent health management, attracting significant research attention in recent years. Prominent benchmarks, such as Food-101 and VIREO Food-172, provide abundant food image resources that catalyze the prosperity of research in this field. Nevertheless, these datasets are well-curated from canteen scenarios and thus deviate from food appearances in daily life. This discrepancy poses great challenges in effectively transferring classifiers trained on these canteen datasets to broader daily-life scenarios encountered by humans. Toward this end, we present two new benchmarks, namely DailyFood-172 and DailyFood-16, specifically designed to curate food images from …
Transition-Informed Reinforcement Learning For Large-Scale Stackelberg Mean-Field Games., Pengdeng Li, Runsheng Yu, Xinrun Wang, Bo An
Transition-Informed Reinforcement Learning For Large-Scale Stackelberg Mean-Field Games., Pengdeng Li, Runsheng Yu, Xinrun Wang, Bo An
Research Collection School Of Computing and Information Systems
Many real-world scenarios including fleet management and Ad auctions can be modeled as Stackelberg mean-field games (SMFGs) where a leader aims to incentivize a large number of homogeneous self-interested followers to maximize her utility. Existing works focus on cases with a small number of heterogeneous followers, e.g., 5-10, and suffer from scalability issue when the number of followers increases. There are three major challenges in solving large-scale SMFGs: i) classical methods based on solving differential equations fail as they require exact dynamics parameters, ii) learning by interacting with environment is data-inefficient, and iii) complex interaction between the leader and followers …
Earnhft: Efficient Hierarchical Reinforcement Learning For High Frequency Trading, Molei Qin, Shuo Sun, Wentao Zhang, Haochong Xia, Xinrun Wang, Bo An
Earnhft: Efficient Hierarchical Reinforcement Learning For High Frequency Trading, Molei Qin, Shuo Sun, Wentao Zhang, Haochong Xia, Xinrun Wang, Bo An
Research Collection School Of Computing and Information Systems
High-frequency trading (HFT) is using computer algorithms to make trading decisions in short time scales (e.g., second-level), which is widely used in the Cryptocurrency (Crypto) market, (e.g., Bitcoin). Reinforcement learning (RL) in financial research has shown stellar performance on many quantitative trading tasks. However, most methods focus on low-frequency trading, e.g., day-level, which cannot be directly applied to HFT because of two challenges. First, RL for HFT involves dealing with extremely long trajectories (e.g., 2.4 million steps per month), which is hard to optimize and evaluate. Second, the dramatic price fluctuations and market trend changes of Crypto make existing algorithms …
Market-Gan: Adding Control To Financial Market Data Generation With Semantic Context, Haochong Xia, Shuo Sun, Xinrun Wang, Bo An
Market-Gan: Adding Control To Financial Market Data Generation With Semantic Context, Haochong Xia, Shuo Sun, Xinrun Wang, Bo An
Research Collection School Of Computing and Information Systems
Financial simulators play an important role in enhancing forecasting accuracy, managing risks, and fostering strategic financial decision-making. Despite the development of financial market simulation methodologies, existing frameworks often struggle with adapting to specialized simulation context. We pinpoint the challenges as i) current financial datasets do not contain context labels; ii) current techniques are not designed to generate financial data with context as control, which demands greater precision compared to other modalities; iii) the inherent difficulties in generating context-aligned, high-fidelity data given the non-stationary, noisy nature of financial data. To address these challenges, our contributions are: i) we proposed the Contextual …
Out-Of-Distribution Detection In Long-Tailed Recognition With Calibrated Outlier Class Learning, Wenjun Miao, Guansong Pang, Xiao Bai, Tianqi Li, Jin Zheng
Out-Of-Distribution Detection In Long-Tailed Recognition With Calibrated Outlier Class Learning, Wenjun Miao, Guansong Pang, Xiao Bai, Tianqi Li, Jin Zheng
Research Collection School Of Computing and Information Systems
Existing out-of-distribution (OOD) methods have shown great success on balanced datasets but become ineffective in long-tailed recognition (LTR) scenarios where 1) OOD samples are often wrongly classified into head classes and/or 2) tail-class samples are treated as OOD samples. To address these issues, current studies fit a prior distribution of auxiliary/pseudo OOD data to the long-tailed in-distribution (ID) data. However, it is difficult to obtain such an accurate prior distribution given the unknowingness of real OOD samples and heavy class imbalance in LTR. A straightforward solution to avoid the requirement of this prior is to learn an outlier class to …
Unsupervised Training Sequence Design: Efficient And Generalizable Agent Training, Wenjun Li, Pradeep Varakantham
Unsupervised Training Sequence Design: Efficient And Generalizable Agent Training, Wenjun Li, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
To train generalizable Reinforcement Learning (RL) agents, researchers recently proposed the Unsupervised Environment Design (UED) framework, in which a teacher agent creates a very large number of training environments and a student agent trains on the experiences in these environments to be robust against unseen testing scenarios. For example, to train a student to master the “stepping over stumps” task, the teacher will create numerous training environments with varying stump heights and shapes. In this paper, we argue that UED neglects training efficiency and its need for very large number of environments (henceforth referred to as infinite horizon training) makes …
Imitate The Good And Avoid The Bad: An Incremental Approach To Safe Reinforcement Learning, Minh Huy Hoang, Tien Mai, Pradeep Varakantham
Imitate The Good And Avoid The Bad: An Incremental Approach To Safe Reinforcement Learning, Minh Huy Hoang, Tien Mai, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
A popular framework for enforcing safe actions in Rein- forcement Learning (RL) is Constrained RL, where trajectory based constraints on expected cost (or other cost measures) are employed to enforce safety and more importantly these constraints are enforced while maximizing expected reward. Most recent approaches for solving Constrained RL convert the trajectory based cost constraint into a surrogate problem that can be solved using minor modifications to RL methods. A key drawback with such approaches is an over or under- estimation of the cost constraint at each state. Therefore, we provide an approach that does not modify the trajectory based …