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Integrating Forward And Reverse Logistics In Vehicle Routing Problem With Cross-Docking, Vincent F. Yu, Pham T. Anh, Aldy Gunawan Aug 2022

Integrating Forward And Reverse Logistics In Vehicle Routing Problem With Cross-Docking, Vincent F. Yu, Pham T. Anh, Aldy Gunawan

Research Collection School Of Computing and Information Systems

A closed-loop supply chain is one of the vital parts for maintaining the success of enterprises, where forward and reverse logistics are integrated to eliminate wastes (e.g., transportation costs). However, previous studies related to the Vehicle Routing Problem have almost overlooked this integration. This research therefore introduces a variant of the Vehicle Routing Problem with cross-docking (VRPCD) by simultaneously considering three additional factors: (1) various types of vehicles in terms of their capacities and unit travel costs; (2) multiple cross-docks; and (3) the integration of forward and reverse logistics. In particular, the flows of the network consist of distributing goods …

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Legion: Massively Composing Rankers For Improved Bug Localization At Adobe, Darryl Jarman, Jeffrey Berry, Riley Smith, Ferdian Thung, David Lo Aug 2022

Legion: Massively Composing Rankers For Improved Bug Localization At Adobe, Darryl Jarman, Jeffrey Berry, Riley Smith, Ferdian Thung, David Lo

Research Collection School Of Computing and Information Systems

Studies have estimated that, in industrial settings, developers spend between 30 and 90 percent of their time fixing bugs. As such, tools that assist in identifying the location of bugs provide value by reducing debugging costs. One such tool is BugLocator. This study initially aimed to determine if developers working on the Adobe Analytics product could use BugLocator. The initial results show that BugLocator achieves a similar accuracy on five of seven Adobe Analytics repositories and on open-source projects. However, these results do not meet the minimum applicability requirement deemed necessary by Adobe Analytics developers prior to possible adoption. Thus, …

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Efficient Resource Allocation With Fairness Constraints In Restless Multi-Armed Bandits, Dexun Li, Pradeep Varakantham Aug 2022

Efficient Resource Allocation With Fairness Constraints In Restless Multi-Armed Bandits, Dexun Li, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Restless Multi-Armed Bandits (RMAB) is an apt model to represent decision-making problems in public health interventions (e.g., tuberculosis, maternal, and child care), anti-poaching planning, sensor monitoring, personalized recommendations and many more. Existing research in RMAB has contributed mechanisms and theoretical results to a wide variety of settings, where the focus is on maximizing expected value. In this paper, we are interested in ensuring that RMAB decision making is also fair to different arms while maximizing expected value. In the context of public health settings, this would ensure that different people and/or communities are fairly represented while making public health intervention …

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Joint Chance-Constrained Staffing Optimization In Multi-Skill Call Centers, Tien Thanh Dam, Thuy Anh Ta, Tien Mai Aug 2022

Joint Chance-Constrained Staffing Optimization In Multi-Skill Call Centers, Tien Thanh Dam, Thuy Anh Ta, Tien Mai

Research Collection School Of Computing and Information Systems

This paper concerns the staffing optimization problem in multi-skill call centers. The objective is to find a minimal cost staffing solution while meeting a target level for the quality of service (QoS) to customers. We consider a staffing problem in which joint chance constraints are imposed on the QoS of the day. Our joint chance-constrained formulation is more rational capturing the correlation between different call types, as compared to separate chance-constrained versions considered in previous studies. We show that, in general, the probability functions in the joint-chance constraints display S-shaped curves, and the optimal solutions should belong to the concave …

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Fed-Ltd: Towards Cross-Platform Ride Hailing Via Federated Learning To Dispatch, Yansheng Wang, Yongxin Tong, Zimu Zhou, Ziyao Ren, Yi Xu, Guobin Wu, Weifeng Lv Aug 2022

Fed-Ltd: Towards Cross-Platform Ride Hailing Via Federated Learning To Dispatch, Yansheng Wang, Yongxin Tong, Zimu Zhou, Ziyao Ren, Yi Xu, Guobin Wu, Weifeng Lv

Research Collection School Of Computing and Information Systems

Learning based order dispatching has witnessed tremendous success in ride hailing. However, the success halts within individual ride hailing platforms because sharing raw order dispatching data across platforms may leak user privacy and business secrets. Such data isolation not only impairs user experience but also decreases the potential revenues of the platforms. In this paper, we advocate federated order dispatching for cross-platform ride hailing, where multiple platforms collaboratively make dispatching decisions without sharing their local data. Realizing this concept calls for new federated learning strategies that tackle the unique challenges on effectiveness, privacy and efficiency in the context of order …

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Finding Meta Winning Ticket To Train Your Maml, Dawei Gao, Yuexiang Xie, Zimu Zhou, Zhen Wang, Yaliang Li, Bolin. Ding Aug 2022

Finding Meta Winning Ticket To Train Your Maml, Dawei Gao, Yuexiang Xie, Zimu Zhou, Zhen Wang, Yaliang Li, Bolin. Ding

Research Collection School Of Computing and Information Systems

The lottery ticket hypothesis (LTH) states that a randomly initialized dense network contains sub-networks that can be trained in isolation to the performance of the dense network. In this paper, to achieve rapid learning with less computational cost, we explore LTH in the context of meta learning. First, we experimentally show that there are sparse sub-networks, known as meta winning tickets, which can be meta-trained to few-shot classification accuracy to the original backbone. The application of LTH in meta learning enables the adaptation of meta-trained networks on various IoT devices with fewer computation. However, the status quo to identify winning …

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Finding Meta Winning Ticket To Train Your Maml, Dawei Gao, Yuexiang Xie, Zimu Zhou, Zhen Wang, Yaliang Li, Bolin. Ding Aug 2022

Finding Meta Winning Ticket To Train Your Maml, Dawei Gao, Yuexiang Xie, Zimu Zhou, Zhen Wang, Yaliang Li, Bolin. Ding

Research Collection School Of Computing and Information Systems

The lottery ticket hypothesis (LTH) states that a randomly initialized dense network contains sub-networks that can be trained in isolation to the performance of the dense network. In this paper, to achieve rapid learning with less computational cost, we explore LTH in the context of meta learning. First, we experimentally show that there are sparse sub-networks, known as meta winning tickets, which can be meta-trained to few-shot classification accuracy to the original backbone. The application of LTH in meta learning enables the adaptation of meta-trained networks on various IoT devices with fewer computation. However, the status quo to identify winning …

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P-Meta: Towards On-Device Deep Model Adaptation, Zhongnan Qu, Zimu Zhou, Yongxin Tong, Lothar Thiele Aug 2022

P-Meta: Towards On-Device Deep Model Adaptation, Zhongnan Qu, Zimu Zhou, Yongxin Tong, Lothar Thiele

Research Collection School Of Computing and Information Systems

Data collected by IoT devices are often private and have a large diversity across users. Therefore, learning requires pre-training a model with available representative data samples, deploying the pre-trained model on IoT devices, and adapting the deployed model on the device with local data. Such an on-device adaption for deep learning empowered applications demands data and memory efficiency. However, existing gradient-based meta learning schemes fail to support memory-efficient adaptation. To this end, we propose p-Meta, a new meta learning method that enforces structure-wise partial parameter updates while ensuring fast generalization to unseen tasks. Evaluations on few-shot image classification and reinforcement …

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Mobile Health With Head-Worn Devices: Challenges And Opportunities, Andrea Ferlini, Dong Ma, Lorena Qendro, Cecilia Mascolo Aug 2022

Mobile Health With Head-Worn Devices: Challenges And Opportunities, Andrea Ferlini, Dong Ma, Lorena Qendro, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

Monitoring human behavior and health status using mobile devices, a.k.a. Mobile Health, has gained increasing attention from both academia and industry in recent years. It allows imperceptible health tracking from the users and remote health management from the healthcare service providers. Headworn devices, such as earbuds, glasses, and BCIs (Brain Computer Interfaces), exhibit great potential for mobile health due to their advantageous wearing position, the human head, which is motion-resilient and full of human bio-signals. Although initial attempts have been conducted for different healthcare applications with head-worn devices, this fast-growing area is still under-explored and retains great promises. With this …

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Self-Adaptive Systems: A Systematic Literature Review Across Categories And Domains, Terence Wong, Markus Wagner, Christoph Treude Aug 2022

Self-Adaptive Systems: A Systematic Literature Review Across Categories And Domains, Terence Wong, Markus Wagner, Christoph Treude

Research Collection School Of Computing and Information Systems

Context: Championed by IBM’s vision of autonomic computing paper in 2003, the autonomic computing research field has seen increased research activity over the last 20 years. Several conferences (SEAMS, SASO, ICAC) and workshops (SISSY) have been established and have contributed to the autonomic computing knowledge base in search of a new kind of system — a self-adaptive system (SAS). These systems are characterized by being context-aware and can act on that awareness. The actions carried out could be on the system or on the context (or environment). The underlying goal of a SAS is the sustained achievement of its goals …

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Destress: Computation-Optimal And Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization, Boyue Li, Zhize Li, Yuejie Chi Aug 2022

Destress: Computation-Optimal And Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization, Boyue Li, Zhize Li, Yuejie Chi

Research Collection School Of Computing and Information Systems

Emerging applications in multiagent environments such as internet-of-things, networked sensing, autonomous systems, and federated learning, call for decentralized algorithms for finite-sum optimizations that are resource efficient in terms of both computation and communication. In this paper, we consider the prototypical setting where the agents work collaboratively to minimize the sum of local loss functions by only communicating with their neighbors over a predetermined network topology. We develop a new algorithm, called DEcentralized STochastic REcurSive gradient methodS (DESTRESS) for nonconvex finite-sum optimization, which matches the optimal incremental first-order oracle complexity of centralized algorithms for finding first-order stationary points, while maintaining communication …

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Investigating Toxicity Changes Of Cross-Community Redditors From 2 Billion Posts And Comments, Hind Almerekhi, Haewoon Kwak, Bernard J. Jansen Aug 2022

Investigating Toxicity Changes Of Cross-Community Redditors From 2 Billion Posts And Comments, Hind Almerekhi, Haewoon Kwak, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

This research investigates changes in online behavior of users who publish in multiple communities on Reddit by measuring their toxicity at two levels. With the aid of crowdsourcing, we built a labeled dataset of 10,083 Reddit comments, then used the dataset to train and fine-tune a Bidirectional Encoder Representations from Transformers (BERT) neural network model. The model predicted the toxicity levels of 87,376,912 posts from 577,835 users and 2,205,581,786 comments from 890,913 users on Reddit over 16 years, from 2005 to 2020. This study utilized the toxicity levels of user content to identify toxicity changes by the user within the …

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El Niño And The Human-Environment Nexus: Drought And Vulnerability In Singapore 1877-1911, Fiona Williamson Aug 2022

El Niño And The Human-Environment Nexus: Drought And Vulnerability In Singapore 1877-1911, Fiona Williamson

Research Collection College of Integrative Studies

This chapter brings a climatic perspective to the study of Singaporean history by exploring the impacts of the strong El Niño inspired droughts of 1877, 1902 and 1911. The narrative focuses on unpacking the nexus of nature-inspired versus human-induced vulnerability to drought within the contexts of colonial urbanisation and looks at the short-to medium-term impacts of the events on society. It also explores how such events inspired new questions about the climate and regional teleconnections, as a wealth of evidence became available due to the increasingly connected nature of scientific institutions, scientific literature, and communications systems across the Indian Ocean …

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Integrated Assessment Of Urban Overheating Impacts On Human Life, N. Nazarian, E. S. Krayenhoff, B. Bechtel, D. M. Hondula, R. Paolini, J. Vanos, T. Cheung, Winston T. L. Chow, Et Al Aug 2022

Integrated Assessment Of Urban Overheating Impacts On Human Life, N. Nazarian, E. S. Krayenhoff, B. Bechtel, D. M. Hondula, R. Paolini, J. Vanos, T. Cheung, Winston T. L. Chow, Et Al

Research Collection College of Integrative Studies

Urban overheating, driven by global climate change and urban development, is a major contemporary challenge that substantially impacts urban livability and sustainability. Overheating represents a multifaceted threat to the well-being, performance, and health of individuals as well as the energy efficiency and economy of cities, and it is influenced by complex interactions between building, city, and global scale climates. In recent decades, extensive discipline-specific research has characterized urban heat and assessed its implications on human life, including ongoing efforts to bridge neighboring disciplines. The research horizon now encompasses complex problems involving a wide range of disciplines, and therefore comprehensive and …

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Artificial Intelligence In Financial Technology, Keng Siau, Fiona Fui-Hoon Nah, Brenda L. Eschenbrenner, Langtao Chen Aug 2022

Artificial Intelligence In Financial Technology, Keng Siau, Fiona Fui-Hoon Nah, Brenda L. Eschenbrenner, Langtao Chen

Research Collection School Of Computing and Information Systems

AI applications in health care, communications, and arts have brought about rapid and dramatic advances in these fields. Nevertheless, the rapidly expanding potential of AI in the economy and society has raised a set of challenging issues. The fields of AI and financial technology are not spared. How can artificial intelligence (AI) be utilized in financial technology (fintech)? What will be the impact? What actionable objectives are needed to realize value from AI? This research uses a systematic qualitative research methodology, Value-Focused Thinking, to identify the actionable objectives for deriving value from AI in the fintech industry. The results of …

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Systems And Methods For Contrastive Learning With Self-Labeling Refinement, Pan Zhou, Caiming Xiong, Steven Hoi Aug 2022

Systems And Methods For Contrastive Learning With Self-Labeling Refinement, Pan Zhou, Caiming Xiong, Steven Hoi

Research Collection School Of Computing and Information Systems

Embodiments described herein provide a contrastive learning mechanism with self - labeling refinement , which iteratively employs the network and data themselves to generate more accurate and informative soft labels for contrastive learning . Specifically , the contrastive learning framework includes a self - labeling refinery module to explicitly generate accurate labels , and a momentum mix - up module to increase similarity between a query and its positive , which in turn implicitly improves label accuracy.

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Crosscbr: Cross‑View Contrastive Learning For Bundle Recommendation, Yunshan Ma, Yingzhi He, An Zhang, Xiang Wang, Tat-Seng Chua Aug 2022

Crosscbr: Cross‑View Contrastive Learning For Bundle Recommendation, Yunshan Ma, Yingzhi He, An Zhang, Xiang Wang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Bundle recommendation aims to recommend a bundle of related items to users, which can satisfy the users' various needs with one-stop convenience. Recent methods usually take advantage of both user-bundle and user-item interactions information to obtain informative representations for users and bundles, corresponding to bundle view and item view, respectively. However, they either use a unified view without differentiation or loosely combine the predictions of two separate views, while the crucial cooperative association between the two views' representations is overlooked.In this work, we propose to model the cooperative association between the two different views through cross-view contrastive learning. By encouraging …

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Efficient Resource Allocation With Fairness Constraints In Restless Multi-Armed Bandits, Dexun Li, Pradeep Varakantham Aug 2022

Efficient Resource Allocation With Fairness Constraints In Restless Multi-Armed Bandits, Dexun Li, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Restless Multi-Armed Bandits (RMAB) is an apt model to represent decision-making problems in public health interventions (e.g., tuberculosis, maternal, and child care), anti-poaching planning, sensor monitoring, personalized recommendations and many more. Existing research in RMAB has contributed mechanisms and theoretical results to a wide variety of settings, where the focus is on maximizing expected value. In this paper, we are interested in ensuring that RMAB decision making is also fair to different arms while maximizing expected value. In the context of public health settings, this would ensure that different people and/or communities are fairly represented while making public health intervention …

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Structured Encryption For Knowledge Graphs, Yujie Xue, Lanxiang Chen, Yu Mi, Lingfang Zeng, Fatemeh Rezaeibagha, Robert H. Deng Aug 2022

Structured Encryption For Knowledge Graphs, Yujie Xue, Lanxiang Chen, Yu Mi, Lingfang Zeng, Fatemeh Rezaeibagha, Robert H. Deng

Research Collection School Of Computing and Information Systems

We investigate the problem of structured encryption (STE) for knowledge graphs (KGs) where the knowledge of data can be efficiently and privately queried. Presently, the application of natural language processing (NLP) for knowledge-based search is gradually emerging. Compared with the traditional search based only on keywords of documents-symmetric searchable encryption (SSE), the knowledge-based search system transforms the latent knowledge contained in documents into a semantic network as a knowledge base, which greatly improves the accuracy and relevance of search results. In order to develop a knowledge-based search, the contents of documents are analyzed and extracted using KG techniques (e.g. multi-relational …

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Ransomsoc: A More Effective Security Operations Center To Detect And Respond To Ransomware Attacks, Anthony Cheuk Tung Lai, Ping Fan Ke, Kelvin Chan, Siu Ming Yiu, Dongsun Kim, Wai Kin Wong, Shuai Wang, Joseph Muppala, Alan Ho Aug 2022

Ransomsoc: A More Effective Security Operations Center To Detect And Respond To Ransomware Attacks, Anthony Cheuk Tung Lai, Ping Fan Ke, Kelvin Chan, Siu Ming Yiu, Dongsun Kim, Wai Kin Wong, Shuai Wang, Joseph Muppala, Alan Ho

Research Collection School Of Computing and Information Systems

Ransomware remains a major threat for organizations. Despite a lot of research done, existing solutions still have at least two shortcomings. (I) Slow detection time: by the time we realize that the system is under ransomware attack, almost all files have been encrypted. (II) Without a ransomwareaware backup scheme: Most existing systems, in particular those in SMEs (small and medium enterprises), do not have a proper backup system. Even they have it, either it is not a remote-site backup (i.e., files in the backup system may also be encrypted) or it is not designed for ransomware attacks. In this paper, …

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Self-Checking Deep Neural Networks For Anomalies And Adversaries In Deployment, Yan Xiao, Ivan Beschastnikh, Yun Lin, Rajdeep Singh Hundal, Xiaofei Xie, David S. Rosenblum, Jin Song Dong Aug 2022

Self-Checking Deep Neural Networks For Anomalies And Adversaries In Deployment, Yan Xiao, Ivan Beschastnikh, Yun Lin, Rajdeep Singh Hundal, Xiaofei Xie, David S. Rosenblum, Jin Song Dong

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) have been widely adopted, yet DNN models are surprisingly unreliable, which raises significant concerns about their use in critical domains. In this work, we propose that runtime DNN mistakes can be quickly detected and properly dealt with in deployment, especially in settings like self-driving vehicles. Just as software engineering (SE) community has developed effective mechanisms and techniques to monitor and check programmed components, our previous work, SelfChecker, is designed to monitor and correct DNN predictions given unintended abnormal test data. SelfChecker triggers an alarm if the decisions given by the internal layer features of the model …

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Trajectory Optimization For Safe Navigation In Maritime Traffic Using Historical Data, Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, T. K. Satish Kumar Aug 2022

Trajectory Optimization For Safe Navigation In Maritime Traffic Using Historical Data, Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, T. K. Satish Kumar

Research Collection School Of Computing and Information Systems

Increasing maritime trade often results in congestion in busy ports, thereby necessitating planning methods to avoid close quarter risky situations among vessels. Rapid digitization and automation of port operations and vessel navigation provide unique opportunities for significantly improving navigation safety. Our key contributions are as follows. First, given a set of future candidate trajectories for vessels in a traffic hotspot zone, we develop a multiagent trajectory optimization method to choose trajectories that result in the best overall close quarter risk reduction. Our novel MILP-based optimization method is more than an order-of-magnitude faster than a standard MILP for this problem, and …

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Neural-Progressive Hedging: Enforcing Constraints In Reinforcement Learning With Stochastic Programming, Supriyo Ghosh, Laura Wynter, Shiau Hong Lim, Duc Thien Nguyen Aug 2022

Neural-Progressive Hedging: Enforcing Constraints In Reinforcement Learning With Stochastic Programming, Supriyo Ghosh, Laura Wynter, Shiau Hong Lim, Duc Thien Nguyen

Research Collection School Of Computing and Information Systems

We propose a framework, called neural-progressive hedging (NP), that leverages stochastic programming during the online phase of executing a reinforcement learning (RL) policy. The goal is to ensure feasibility with respect to constraints and risk-based objectives such as conditional value-at-risk (CVaR) during the execution of the policy, using probabilistic models of the state transitions to guide policy adjustments. The framework is particularly amenable to the class of sequential resource allocation problems since feasibility with respect to typical resource constraints cannot be enforced in a scalable manner. The NP framework provides an alternative that adds modest overhead during the online phase. …

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Extract Human Mobility Patterns Powered By City Semantic Diagram, Zhangqing Shan, Weiwei Shan, Baihua Zheng Aug 2022

Extract Human Mobility Patterns Powered By City Semantic Diagram, Zhangqing Shan, Weiwei Shan, Baihua Zheng

Research Collection School Of Computing and Information Systems

With widespread deployment of GPS devices, massive spatiotemporal trajectories became more accessible. This booming trend paved the solid data ground for researchers to discover the regularities or patterns of human mobility. However, there are still three challenges in semantic pattern extraction including semantic absence, semantic bias and semantic complexity. In this paper, we invent and apply a novel data structure namely City Semantic Diagram to overcome above three challenges. First, our approach resolves semantic absence by exactly identifying semantic behaviours from raw trajectories. Second, the delicate design of semantic purification helps us to detect semantic complexity from human mobility. Third, …

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Resumable Zero-Knowledge For Circuits From Symmetric Key Primitives, Handong Zhang, Puwen Wei, Haiyang Xue, Yi Deng, Jinsong Li, Wei Wang, Guoxiao Liu Aug 2022

Resumable Zero-Knowledge For Circuits From Symmetric Key Primitives, Handong Zhang, Puwen Wei, Haiyang Xue, Yi Deng, Jinsong Li, Wei Wang, Guoxiao Liu

Research Collection School Of Computing and Information Systems

Consider the scenario that the prover and the verifier perform the zero-knowledge (ZK) proof protocol for the same statement multiple times sequentially, where each proof is modeled as a session. We focus on the problem of how to resume a ZK proof efficiently in such scenario. We introduce a new primitive called resumable honest verifier zero-knowledge proof of knowledge (resumable HVZKPoK) and propose a general construction of the resumable HVZKPoK for circuits based on the “MPC-in-the-head" paradigm, where the complexity of the resumed session is less than that of the original ZK proofs. To ensure the knowledge soundness for the …

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A Comprehensive Examination Of The Cross-Validity Of Pareto-Optimal Versus Fixed-Weight Selection Systems In The Biobjective Selection Context., Wilfried De Corte, Filip Lievens, Paul R. Sackett Aug 2022

A Comprehensive Examination Of The Cross-Validity Of Pareto-Optimal Versus Fixed-Weight Selection Systems In The Biobjective Selection Context., Wilfried De Corte, Filip Lievens, Paul R. Sackett

Research Collection Lee Kong Chian School Of Business

The article presents evidence for the cross-validity potential of fixed-weight (FW) versus Pareto-Optimal (PO) selection systems in biobjective selection situations where both the goals of diversity and quality are valued and the importance of the goals is undecided a priori. The article extends previous research by also studying the cross-validity potential of selection systems in the practically most important sample-to-sample cross-validity scenario. We address three research questions: (a) Do different PO systems show comparable levels of relative (i.e., proportional) achievement upon cross-validation? (b) Do PO systems achieve higher levels of relative achievement upon cross-validation than FW selection systems?, and (c) …

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A Climate Resilience Research Renewal Agenda: Learning Lessons From The Covid-19 Pandemic For Urban Climate Resilience, Mark Pelling, Winston T. L. Chow, Eric Chu, Richard Dawson, David Dodman, Arabella Fraser, Bronwyn Hayward, Luna Khirfan, Timon Mcphearson, Anjal Prakash, Gina Ziervogel Aug 2022

A Climate Resilience Research Renewal Agenda: Learning Lessons From The Covid-19 Pandemic For Urban Climate Resilience, Mark Pelling, Winston T. L. Chow, Eric Chu, Richard Dawson, David Dodman, Arabella Fraser, Bronwyn Hayward, Luna Khirfan, Timon Mcphearson, Anjal Prakash, Gina Ziervogel

Research Collection School of Social Sciences

Learning lessons from the COVID-19 pandemic opens an opportunity for enhanced research and action on inclusive urban resilience to climate change. Lessons and their implications are used to describe a climate resilience research renewal agenda. Three key lessons are identified. The first lesson is generic, that climate change risk coexists and interacts with other risks through overlapping social processes, conditions and decision-making contexts. Two further lessons are urban specific: that networks of connectivity bring risk as well as resilience and that overcrowding is a key indicator of the multiple determinants of vulnerability to both COVID-19 and climate change impacts. From …

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Efficient Bilateral Trade Via Two-Stage Mechanisms: Comparison Between One-Sided And Two-Sided Asymmetric Information Environments, Takashi Kunimoto, Cuiling Zhang Aug 2022

Efficient Bilateral Trade Via Two-Stage Mechanisms: Comparison Between One-Sided And Two-Sided Asymmetric Information Environments, Takashi Kunimoto, Cuiling Zhang

Research Collection School Of Economics

This paper first considers a bilateral-trade model with one-sided asymmetric information in which one agent (seller) initially owns an indivisible object and is fully informed of its value, while the other agent (buyer) intends to obtain the object whose value is unknown to himself. As no mechanisms can generally result in efficient, voluntary bilateral trades (Jehiel and Pauzner, 2006), we aim to overturn this impossibility result by employing two-stage mechanisms (Mezzetti, 2004) in which first, the outcome (e.g., allocation of the goods) is determined, then the agents observe their own outcome-decision payoffs, and finally, transfers are made. We show that …

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Learning To Ask Critical Questions For Assisting Product Search, Zixuan Li, Lizi Liao, Tat-Seng Chua Jul 2022

Learning To Ask Critical Questions For Assisting Product Search, Zixuan Li, Lizi Liao, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Product search plays an essential role in eCommerce. It was treated as a special type of information retrieval problem. Most existing works make use of historical data to improve the search performance, which do not take the opportunity to ask for user’s current interest directly. Some session-aware methods take the user’s clicks within the session as implicit feedback, but it is still just a guess on user’s preference. To address this problem, recent conversational or question-based search models interact with users directly for understanding the user’s interest explicitly. However, most users do not have a clear picture on what to …

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Enhancing A Qubo Solver Via Data Driven Multi-Start And Its Application To Vehicle Routing Problem, Whei Yeap Suen, Matthieu Parizy, Hoong Chuin Lau Jul 2022

Enhancing A Qubo Solver Via Data Driven Multi-Start And Its Application To Vehicle Routing Problem, Whei Yeap Suen, Matthieu Parizy, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Quadratic unconstrained binary optimization (QUBO) models have garnered growing interests as a strong alternative modelling framework for solving combinatorial optimization problems. A wide variety of optimization problems that are usually studied using conventional Operations Research approaches can be formulated as QUBO problems. However, QUBO solvers do not guarantee optimality when solving optimization problems. Instead, obtaining high quality solutions using QUBO solvers entails tuning of multiple parameters. Here in our work, we conjecture that the initial states adjustment method used in QUBO solvers can be improved, where careful tuning will yield overall better results. We propose a data-driven multi-start algorithm that …

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