Open Access. Powered by Scholars. Published by Universities.®

Physical Sciences and Mathematics Commons™

Open Access. Powered by Scholars. Published by Universities.®

Discipline
Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 40111 - 40140 of 294685

Full-Text Articles in Physical Sciences and Mathematics

Generative Pre-Trained Transformers (Gpt) And Space Health: A Potential Frontier In Astronaut Health During Exploration Missions, Ethan Waisberg, Joshua Ong, Mouayad Masalkhi, Nasif Zaman, Sharif Amit Kamran, Andrew G Lee Aug 2023

Generative Pre-Trained Transformers (Gpt) And Space Health: A Potential Frontier In Astronaut Health During Exploration Missions, Ethan Waisberg, Joshua Ong, Mouayad Masalkhi, Nasif Zaman, Sharif Amit Kamran, Andrew G Lee

Faculty, Staff and Student Publications

In anticipation of space exploration where astronauts are traveling away from Earth, and for longer durations with an increasing communication lag, artificial intelligence (AI) frameworks such as large language learning models (LLMs) that can be trained on Earth can provide real-time answers. This emerging technology may be helpful for acute medical emergencies, particularly in austere and distant space environments. In this manuscript, we provide an overview of generative pre-trained transformer (GPT) technology, a rapidly emerging AI technology, and implications, considerations, and limitations of such technology for space health.

Go to article

Atomically Dispersed Cu-N3 On Hollow Spherical Carbon Nitride For Acetaminophen Degradation: Generation Of 1o2 From H2o2, Shuoyuan Tian, Yu Yin, Mengxuan Liu, Lei Shi, Shu Zhang, Abdul Hannan Asif, Xuan Li, Mengqiong Liu, Xiaoguang Duan, Shaobin Wang, Hongqi Sun Aug 2023

Atomically Dispersed Cu-N3 On Hollow Spherical Carbon Nitride For Acetaminophen Degradation: Generation Of 1o2 From H2o2, Shuoyuan Tian, Yu Yin, Mengxuan Liu, Lei Shi, Shu Zhang, Abdul Hannan Asif, Xuan Li, Mengqiong Liu, Xiaoguang Duan, Shaobin Wang, Hongqi Sun

Research outputs 2022 to 2026

Discharge of recalcitrant pharmaceuticals into aquatic systems has caused severe impacts on public health and ecosystem. Advanced oxidation processes (AOPs) are effective for eliminating these refractory pollutants, for which single-atom catalysts (SACs) become the state-of-the-art materials owing to the maximized exposure of active metal sites. In this work, hollow spherical graphitic carbon nitride (hsCN) was fabricated to incorporate copper species to develop Fenton-like catalysts for acetaminophen (ACT) removal. Through pyrolysis of supramolecular assemblies derived from melamine-Cu complex and cyanuric acid, single atom Cu-N3 sites were anchored on hsCN by N-coordination to obtain SACu-hsCN. In virtue …

Go to article

Survey On Sentiment Analysis: Evolution Of Research Methods And Topics, Jingfeng Cui, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria Aug 2023

Survey On Sentiment Analysis: Evolution Of Research Methods And Topics, Jingfeng Cui, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates …

Go to article

Decoding The Underlying Meaning Of Multimodal Hateful Memes, Ming Shan Hee, Wen Haw Chong, Roy Ka-Wei Lee Aug 2023

Decoding The Underlying Meaning Of Multimodal Hateful Memes, Ming Shan Hee, Wen Haw Chong, Roy Ka-Wei Lee

Research Collection School Of Computing and Information Systems

Recent studies have proposed models that yielded promising performance for the hateful meme classification task. Nevertheless, these proposed models do not generate interpretable explanations that uncover the underlying meaning and support the classification output. A major reason for the lack of explainable hateful meme methods is the absence of a hateful meme dataset that contains ground truth explanations for benchmarking or training. Intuitively, having such explanations can educate and assist content moderators in interpreting and removing flagged hateful memes. This paper address this research gap by introducing Hateful meme with Reasons Dataset (HatReD), which is a new multimodal hateful meme …

Go to article

Transferable Curricula Through Difficulty Conditioned Generators, Sidney Tio, Pradeep Varakantham Aug 2023

Transferable Curricula Through Difficulty Conditioned Generators, Sidney Tio, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Advancements in reinforcement learning (RL) have demonstrated superhuman performance in complex tasks such as Starcraft, Go, Chess etc. However, knowledge transfer from Artificial "Experts" to humans remain a significant challenge. A promising avenue for such transfer would be the use of curricula. Recent methods in curricula generation focuses on training RL agents efficiently, yet such methods rely on surrogate measures to track student progress, and are not suited for training robots in the real world (or more ambitiously humans). In this paper, we introduce a method named Parameterized Environment Response Model (PERM) that shows promising results in training RL agents …

Go to article

Learning To Send Reinforcements: Coordinating Multi-Agent Dynamic Police Patrol Dispatching And Rescheduling Via Reinforcement Learning, Waldy Joe, Hoong Chuin Lau Aug 2023

Learning To Send Reinforcements: Coordinating Multi-Agent Dynamic Police Patrol Dispatching And Rescheduling Via Reinforcement Learning, Waldy Joe, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of coordinating multiple agents in a dynamic police patrol scheduling via a Reinforcement Learning (RL) approach. Our approach utilizes Multi-Agent Value Function Approximation (MAVFA) with a rescheduling heuristic to learn dispatching and rescheduling policies jointly. Often, police operations are divided into multiple sectors for more effective and efficient operations. In a dynamic setting, incidents occur throughout the day across different sectors, disrupting initially-planned patrol schedules. To maximize policing effectiveness, police agents from different sectors cooperate by sending reinforcements to support one another in their incident response and even routine patrol. This poses an interesting research challenge …

Go to article

Camper: An Effective Framework For Privacy-Aware Deep Entity Resolution, Yuxiang Guo, Lu Chen, Zhengjie Zhou, Baihua Zheng, Ziquan Fang, Zhikun Zhang, Yuren Mao, Yunjun Gao Aug 2023

Camper: An Effective Framework For Privacy-Aware Deep Entity Resolution, Yuxiang Guo, Lu Chen, Zhengjie Zhou, Baihua Zheng, Ziquan Fang, Zhikun Zhang, Yuren Mao, Yunjun Gao

Research Collection School Of Computing and Information Systems

Entity Resolution (ER) is a fundamental problem in data preparation. Standard deep ER methods have achieved state-of-the-art efectiveness, assuming that relations from diferent organizations are centrally stored. However, due to privacy concerns, it can be difcult to centralize data in practice, rendering standard deep ER solutions inapplicable. Despite eforts to develop rule-based privacy-preserving ER methods, they often neglect subtle matching mechanisms and have poor efectiveness as a result. To bridge efectiveness and privacy, in this paper, we propose CampER, an efective framework for privacy-aware deep entity resolution. Specifcally, we frst design a training pair self-generation strategy to overcome the absence …

Go to article

Multi-View Graph Contrastive Learning For Solving Vehicle Routing Problems, Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang Aug 2023

Multi-View Graph Contrastive Learning For Solving Vehicle Routing Problems, Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang

Research Collection School Of Computing and Information Systems

Recently, neural heuristics based on deep learning have reported encouraging results for solving vehicle routing problems (VRPs), especially on independent and identically distributed (i.i.d.) instances, e.g. uniform. However, in the presence of a distribution shift for the testing instances, their performance becomes considerably inferior. In this paper, we propose a multi-view graph contrastive learning (MVGCL) approach to enhance the generalization across different distributions, which exploits a graph pattern learner in a self-supervised fashion to facilitate a neural heuristic equipped with an active search scheme. Specifically, our MVGCL first leverages graph contrastive learning to extract transferable patterns from VRP graphs to …

Go to article

Decompiling X86 Deep Neural Network Executables, Zhibo Liu, Yuanyuan Yuan, Shuai Wang, Xiaofei Xie, Lei Ma Aug 2023

Decompiling X86 Deep Neural Network Executables, Zhibo Liu, Yuanyuan Yuan, Shuai Wang, Xiaofei Xie, Lei Ma

Research Collection School Of Computing and Information Systems

Due to their widespread use on heterogeneous hardware devices, deep learning (DL) models are compiled into executables by DL compilers to fully leverage low-level hardware primitives. This approach allows DL computations to be undertaken at low cost across a variety of computing platforms, including CPUs, GPUs, and various hardware accelerators. We present BTD (Bin to DNN), a decompiler for deep neural network (DNN) executables. BTD takes DNN executables and outputs full model specifications, including types of DNN operators, network topology, dimensions, and parameters that are (nearly) identical to those of the input models. BTD delivers a practical framework to process …

Go to article

Fintech Data Infrastructure For Esg Disclosure Compliance, Randall E. Duran, Peter Tierney Aug 2023

Fintech Data Infrastructure For Esg Disclosure Compliance, Randall E. Duran, Peter Tierney

Research Collection School Of Computing and Information Systems

Regulations related to the disclosure of environmental, governance, and social (ESG) factors are evolving rapidly and are a major concern for financial compliance worldwide. Information technology has the potential to reduce the effort and cost of ESG disclosure compliance. However, comprehensive and accurate ESG data are necessary for disclosures. Currently, the availability and quality of underlying data for ESG disclosures vary widely and are often deficient. The process involved with obtaining ESG data is also often inefficient and prone to error. This paper compares the models used and the evolution of Fintech data infrastructure developed to support financial services with …

Go to article

Document-Level Relation Extraction Via Separate Relation Representation And Logical Reasoning, Heyan Huang, Changsen Yuan, Qian Liu, Yixin Cao Aug 2023

Document-Level Relation Extraction Via Separate Relation Representation And Logical Reasoning, Heyan Huang, Changsen Yuan, Qian Liu, Yixin Cao

Research Collection School Of Computing and Information Systems

Document-level relation extraction (RE) extends the identification of entity/mentions’ relation from the single sentence to the long document. It is more realistic and poses new challenges to relation representation and reasoning skills. In this article, we propose a novel model, SRLR, using Separate Relation Representation and Logical Reasoning considering the indirect relation representation and complex reasoning of evidence sentence problems. Specifically, we first expand the judgment of relational facts from the entity-level to the mention-level, highlighting fine-grained information to capture the relation representation for the entity pair. Second, we propose a logical reasoning module to identify evidence sentences and conduct …

Go to article

Hyperbolic Graph Topic Modeling Network With Continuously Updated Topic Tree, Ce Zhang, Rex Ying, Hady Wirawan Lauw Aug 2023

Hyperbolic Graph Topic Modeling Network With Continuously Updated Topic Tree, Ce Zhang, Rex Ying, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Connectivity across documents often exhibits a hierarchical network structure. Hyperbolic Graph Neural Networks (HGNNs) have shown promise in preserving network hierarchy. However, they do not model the notion of topics, thus document representations lack semantic interpretability. On the other hand, a corpus of documents usually has high variability in degrees of topic specificity. For example, some documents contain general content (e.g., sports), while others focus on specific themes (e.g., basketball and swimming). Topic models indeed model latent topics for semantic interpretability, but most assume a flat topic structure and ignore such semantic hierarchy. Given these two challenges, we propose a …

Go to article

Semantically Constitutive Entities In Knowledge Graphs, Chong Cher Chia, Maksim Tkachenko, Hady Wirawan Lauw Aug 2023

Semantically Constitutive Entities In Knowledge Graphs, Chong Cher Chia, Maksim Tkachenko, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Knowledge graphs are repositories of facts about a world. In this work, we seek to distill the set of entities or nodes in a knowledge graph into a specified number of constitutive nodes, whose embeddings would be retained. Intuitively, the remaining accessory nodes could have their original embeddings “forgotten”, and yet reconstitutable from those of the retained constitutive nodes. The constitutive nodes thus represent the semantically constitutive entities, which retain the core semantics of the knowledge graph. We propose a formulation as well as algorithmic solutions to minimize the reconstitution errors. The derived constitutive nodes are validated empirically both in …

Go to article

An Adaptive Large Neighborhood Search For Heterogeneous Vehicle Routing Problem With Time Windows, Minh Pham Kien Nguyen, Aldy Gunawan, Vincent F. Yu, Mustafa Misir Aug 2023

An Adaptive Large Neighborhood Search For Heterogeneous Vehicle Routing Problem With Time Windows, Minh Pham Kien Nguyen, Aldy Gunawan, Vincent F. Yu, Mustafa Misir

Research Collection School Of Computing and Information Systems

The heterogeneous vehicle routing problem with time windows (HVRPTW) employs various vehicles with different capacities to serve upcoming pickup and delivery orders. We introduce a HVRPTW variant for reflecting the practical needs of crowd-shipping by considering the mass-rapid-transit stations, as the additional terminal points. A mixed integer linear programming model is formulated. An Adaptive Large Neighborhood Search based meta-heuristic is also developed by utilizing a basic probabilistic selection strategy, i.e. roulette wheel, and Simulated Annealing. The proposed approach is empirically evaluated on a new set of benchmark instances. The computational results revealed that ALNS shows its clear advantage on the …

Go to article

Grasp Based Metaheuristic To Solve The Mixed Fleet E-Waste Collection Route Planning Problem, Aldy Gunawan, Dang V.A. Nguyen, Pham K.M. Nguyen, Pieter. Vansteenwegen Aug 2023

Grasp Based Metaheuristic To Solve The Mixed Fleet E-Waste Collection Route Planning Problem, Aldy Gunawan, Dang V.A. Nguyen, Pham K.M. 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 …

Go to article

The 4th International Workshop On Talent And Management Computing (Tmc'2023): Editorial, Hengshu Zhu, Hui Xiong, Yong Ge, Ee-Peng Lim Aug 2023

The 4th International Workshop On Talent And Management Computing (Tmc'2023): Editorial, Hengshu Zhu, Hui Xiong, Yong Ge, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with the talent and management related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for business leaders to understand the rules of talent and management, which in turn deliver intelligence for effective decision making and management for their organizations. In the past few years, talent and management computing have increasingly attracted attentions from KDD communities, and a number of research/applied data science efforts have been devoted. To …

Go to article

Extraction, Purification, And Characterization Of Potential Bioactive Compounds Produced By Janthinobacterium Lividum Tajx1901, Andy Elorm Agbakpo Aug 2023

Extraction, Purification, And Characterization Of Potential Bioactive Compounds Produced By Janthinobacterium Lividum Tajx1901, Andy Elorm Agbakpo

Electronic Theses and Dissertations

Underexplored environments such as soil samples continue to be an untapped source of bacterial strains with great potential to produce secondary metabolites for medicinal applications. As a result, these microorganisms represent a broad and yet unknown reservoir of new strains capable of producing these novel compounds. The current research primarily seeks to perform the isolation, purification, and characterization of secondary metabolites from a soil bacterium (Janthinobacterium lividum TAJX1901). The isolated soil bacterium was successfully cultured on rich media agar plates, followed by extraction using methanol and chloroform. The purification methods utilized include flash column chromatography, preparative thin-layer chromatography, and …

Go to article

The Analysis Of Extended Producer Responsibility (Epr) For E-Waste Management Policy Drivers And Challenges In Singapore, Aldy Gunawan, Tasaporn Visawameteekul, Aidan Marc Wong, Linh Chi Tran Aug 2023

The Analysis Of Extended Producer Responsibility (Epr) For E-Waste Management Policy Drivers And Challenges In Singapore, Aldy Gunawan, Tasaporn Visawameteekul, Aidan Marc Wong, Linh Chi Tran

Research Collection School Of Computing and Information Systems

This paper examines Singapore’s Extended Producer Responsibility (EPR) scheme for managing e-waste, focusing on policy drivers, challenges, and young consumers’ attitudes through an online survey. Using the Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB), it models the relationships between awareness, norms, and perceived convenience. Findings emphasize the need for tailored policies, formal recycling promotion, and analysis of disposal factors, cost preferences, and incentives. Text analysis also highlights concerns from respondents’ recommendations.

Go to article

Deep Weakly-Supervised Anomaly Detection, Guansong Pang, Chunhua Shen, Huidong Jin, Anton Van Den Hengel Aug 2023

Deep Weakly-Supervised Anomaly Detection, Guansong Pang, Chunhua Shen, Huidong Jin, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved performance over unsupervised methods. However, these methods often focus on fitting abnormalities illustrated by the given anomaly examples only (i.e., seen anomalies), and consequently they fail to generalize to those that are not, i.e., new types/classes of anomaly unseen during training. To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by predicting the relation of any two …

Go to article

Sparsity Brings Vulnerabilities: Exploring New Metrics In Backdoor Attacks, Jianwen Tian, Kefan Qiu, Debin Gao, Zhi Wang, Xiaohui Kuang, Gang Zhao Aug 2023

Sparsity Brings Vulnerabilities: Exploring New Metrics In Backdoor Attacks, Jianwen Tian, Kefan Qiu, Debin Gao, Zhi Wang, Xiaohui Kuang, Gang Zhao

Research Collection School Of Computing and Information Systems

Nowadays, using AI-based detectors to keep pace with the fast iterating of malware has attracted a great attention. However, most AI-based malware detectors use features with vast sparse subspaces to characterize applications, which brings significant vulnerabilities to the model. To exploit this sparsityrelated vulnerability, we propose a clean-label backdoor attack consisting of a dissimilarity metric-based candidate selection and a variation ratio-based trigger construction. The proposed backdoor is verified on different datasets, including a Windows PE dataset, an Android dataset with numerical and boolean feature values, and a PDF dataset. The experimental results show that the attack can slash the accuracy …

Go to article

Single-View View Synthesis With Self-Rectified Pseudo-Stereo, Yang Zhou, Hanjie Wu, Wenxi Liu, Zheng Xiong, Jing Qin, Shengfeng He Aug 2023

Single-View View Synthesis With Self-Rectified Pseudo-Stereo, Yang Zhou, Hanjie Wu, Wenxi Liu, Zheng Xiong, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Synthesizing novel views from a single view image is a highly ill-posed problem. We discover an effective solution to reduce the learning ambiguity by expanding the single-view view synthesis problem to a multi-view setting. Specifically, we leverage the reliable and explicit stereo prior to generate a pseudo-stereo viewpoint, which serves as an auxiliary input to construct the 3D space. In this way, the challenging novel view synthesis process is decoupled into two simpler problems of stereo synthesis and 3D reconstruction. In order to synthesize a structurally correct and detail-preserved stereo image, we propose a self-rectified stereo synthesis to amend erroneous …

Go to article

Balancing Utility And Fairness In Submodular Maximization, Yanhao Wang, Yuchen Li, Francesco Bonchi, Ying Wang Aug 2023

Balancing Utility And Fairness In Submodular Maximization, Yanhao Wang, Yuchen Li, Francesco Bonchi, Ying Wang

Research Collection School Of Computing and Information Systems

Submodular function maximization is a fundamental combinatorial optimization problem with plenty of applications – including data summarization, influence maximization, and recommendation. In many of these problems, the goal is to find a solution that maximizes the average utility over all users, for each of whom the utility is defined by a monotone submodular function. However, when the population of users is composed of several demographic groups, another critical problem is whether the utility is fairly distributed across different groups. Although the utility and fairness objectives are both desirable, they might contradict each other, and, to the best of our knowledge, …

Go to article

Ai For Connectivism Learning: Undergraduate Students’ Experiences Of Chatgpt In Advanced Programming Courses, Swapna Gottipati, Kyong Jin Shim, Venky Shankararaman Aug 2023

Ai For Connectivism Learning: Undergraduate Students’ Experiences Of Chatgpt In Advanced Programming Courses, Swapna Gottipati, Kyong Jin Shim, Venky Shankararaman

Research Collection School Of Computing and Information Systems

Advanced programming skills are required for computing courses on merging topics, and students often struggle to develop these skills to solve complex problems. To address this challenge, faculty members provide additional lectures, practice sessions, and educational technology tools. This paper discusses the challenges faced by computer science students in developing advanced programming skills and explores the use of AI chatbots, specifically ChatGPT, as a support tool for learning. We study the engagement and effectiveness of ChatGPT in helping students learn advanced programming skills using two engagement learning frameworks (CIE and MELT) for evaluation. The study involves designing a computing lab …

Go to article

Multi-Granularity Detector For Vulnerability Fixes, Truong Giang Nguyen, Cong, Thanh Le, Hong Jin Kang, Ratnadira Widyasari, Chengran Yang, Zhipeng Zhao, Bowen Xu, Jiayuan Zhou, Xin Xia, Ahmed E. Hassan, David Lo, David Lo Aug 2023

Multi-Granularity Detector For Vulnerability Fixes, Truong Giang Nguyen, Cong, Thanh Le, Hong Jin Kang, Ratnadira Widyasari, Chengran Yang, Zhipeng Zhao, Bowen Xu, Jiayuan Zhou, Xin Xia, Ahmed E. Hassan, David Lo, David Lo

Research Collection School Of Computing and Information Systems

With the increasing reliance on Open Source Software, users are exposed to third-party library vulnerabilities. Software Composition Analysis (SCA) tools have been created to alert users of such vulnerabilities. SCA requires the identification of vulnerability-fixing commits. Prior works have proposed methods that can automatically identify such vulnerability-fixing commits. However, identifying such commits is highly challenging, as only a very small minority of commits are vulnerability fixing. Moreover, code changes can be noisy and difficult to analyze. We observe that noise can occur at different levels of detail, making it challenging to detect vulnerability fixes accurately. To address these challenges and …

Go to article

Lean Manufacturing Approach To Increase Packaging Efficiency, Lina Gozali, Irsandy Kurniawan, Aldo Salim, Iveline Anne Marie, Benny Tjahjono, Yun Chia Liang, Aldy Gunawan, Nnovia Hardjo Sie, Yuliani Suseno Aug 2023

Lean Manufacturing Approach To Increase Packaging Efficiency, Lina Gozali, Irsandy Kurniawan, Aldo Salim, Iveline Anne Marie, Benny Tjahjono, Yun Chia Liang, Aldy Gunawan, Nnovia Hardjo Sie, Yuliani Suseno

Research Collection School Of Computing and Information Systems

The company upon which this paper is based engages in flexible packaging production, especially pharmaceutical products with guaranteed quality, trusted by consumers. Its production process includes printing, laminating, and assembling processes. Production activities are done manually and automatically using machines, so various types of waste are often found in these processes, making the level of plant efficiency nonoptimal. This study aims to identify wastes occurring in the production process, especially the production of pollycelonium with three colour variants as the highest demand product, by applying lean manufacturing concepts. The Current Value Stream Mapping (CVSM) used to map the production process …

Go to article

Knowledge Representation For Conceptual, Motivational, And Affective Processes In Natural Language Communication, Seng Beng Ho, Zhaoxia Wang, Boon-Kiat Quek, Erik Cambria Aug 2023

Knowledge Representation For Conceptual, Motivational, And Affective Processes In Natural Language Communication, Seng Beng Ho, Zhaoxia Wang, Boon-Kiat Quek, Erik Cambria

Research Collection School Of Computing and Information Systems

Natural language communication is an intricate and complex process. The speaker usually begins with an intention and motivation of what is to be communicated, and what outcomes are expected from the communication, while taking into consideration the listener’s mental model to concoct an appropriate sentence. Likewise, the listener has to interpret the speaker’s message, and respond accordingly, also with the speaker’s mental model in mind. Doing this successfully entails the appropriate representation of the conceptual, motivational, and affective processes that underlie language generation and understanding. Whereas big-data approaches in language processing (such as chatbots and machine translation) have performed well, …

Go to article

Mastering Stock Markets With Efficient Mixture Of Diversified Trading Experts, Shuo Sun, Xinrun Wang, Wanqi Xue, Xiaoxuan Lou, Bo An Aug 2023

Mastering Stock Markets With Efficient Mixture Of Diversified Trading Experts, Shuo Sun, Xinrun Wang, Wanqi Xue, Xiaoxuan Lou, Bo An

Research Collection School Of Computing and Information Systems

Quantitative stock investment is a fundamental financial task that highly relies on accurate prediction of market status and profitable investment decision making. Despite recent advances in deep learning (DL) have shown stellar performance on capturing trading opportunities in the stochastic stock market, the performance of existing DL methods is unstable with sensitivity to network initialization and hyperparameter selection. One major limitation of existing works is that investment decisions are made based on one individual neural network predictor with high uncertainty, which is inconsistent with the workflow in real-world trading firms. To tackle this limitation, we propose AlphaMix, a novel three-stage …

Go to article

A Survey On Proactive Dialogue Systems: Problems, Methods, And Prospects, Yang Deng, Wenqiang Lei, Wai Lam, Tat-Seng Chua Aug 2023

A Survey On Proactive Dialogue Systems: Problems, Methods, And Prospects, Yang Deng, Wenqiang Lei, Wai Lam, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Proactive dialogue systems, related to a wide range of real-world conversational applications, equip the conversational agent with the capability of leading the conversation direction towards achieving pre-defined targets or fulfilling certain goals from the system side. It is empowered by advanced techniques to progress to more complicated tasks that require strategical and motivational interactions. In this survey, we provide a comprehensive overview of the prominent problems and advanced designs for conversational agent's proactivity in different types of dialogues. Furthermore, we discuss challenges that meet the real-world application needs but require a greater research focus in the future. We hope that …

Go to article

Corporate Trade War Uncertainty And Patent Bubble, Xu Yang, Nan Hu, Peng Liang Aug 2023

Corporate Trade War Uncertainty And Patent Bubble, Xu Yang, Nan Hu, Peng Liang

Research Collection School Of Computing and Information Systems

This paper draws upon resource dependence theory and investigates how trade policy uncertainty affects firm strategic innovation management in China. Adopting a machine learning approach called Word2Vec from computational linguistics, we construct and validate a measure of firm-level managers’ perceived trade war uncertainty (TWU). We find that TWU has a positive effect on the number of total patent applications, but this positive effect is totally driven by low-quality patents instead of high-quality patents. Moreover, we document that firms have stronger incentives for such strategic innovation behavior when the underlying firms are more financially constrained, and/or when the management is more …

Go to article

Generative Artificial Intelligence And Metaverse: Future Of Work, Future Of Society, And Future Of Humanity, Yuxin Liu, Keng Siau Aug 2023

Generative Artificial Intelligence And Metaverse: Future Of Work, Future Of Society, And Future Of Humanity, Yuxin Liu, Keng Siau

Research Collection School Of Computing and Information Systems

The rapid development of Generative Artificial Intelligence (GenAI) and the emergence of the Metaverse are dynamically reshaping our lives and societies. GenAI can enhance the development of Metaverse and empower the applications in Metaverse. Metaverse is also an excellent environment for GenAI to demonstrate its power and usefulness. This interwoven relationship fuels the potential of integrating GenAI and Metaverse. The paper discusses the integration potential of GenAI and Metaverse from four aspects. We further investigate how GenAI, Metaverse, and the integration of GenAI and Metaverse can reshape our future across the realms of work, society, and humanity. This paper offers …

Go to article

First Prev Next Last