Physical Sciences and Mathematics Commons^™

Bismuth And Lanthanides In Mixed Metal Oxides To Metal– Organic Frameworks: Synthesis, Cherectarization, And Photocatalytic Performance, Reem H. Alzard

Dissertations

Known as rare–earth elements (REE), lanthanides (Ln) found in f–blocks of the periodic table, have gained much interest because of their unique characteristics including magnetism, photoluminescence and catalysis. Having a diverse range of coordination geometries, lanthanides can result in different complexes and materials. Herein, three classes of materials containing selected lanthanides such as lanthanide bismuth mixed metal oxides (Ln–Bi oxides), 1D lanthanide coordination polymers (Ln–CPs) and lanthanide metal–organic frameworks (Ln–MOFs) were investigated. Simple route sol– gel method was used to prepare Ln–Bi oxides while Ln–CPs and MOFs were prepared by solvothermal condition. The materials were characterized using multiple analytical, spectroscopic …

Digital Transformation In Local Governments: A Case Study Of Abu Dhabi Municipality Transport Department, Alia Sahmi Al Ahbabi

Theses

The new generation is rapidly adapting to the digital era, where government and private services are being transformed into electronic services, commonly known as Eservices. Cities are leveraging digitalization to streamline their business processes and business services. This digitalization has improved service delivery time and quality for individuals. With digitalization, business processes align with technology, enhancing performance and customer satisfaction. However, there are challenges associated with digitalization, particularly people working in various municipality departments who find it challenging to adapt to digitization. Employees may take time to adjust to the new techniques and technologies, which may hamper the actions of …

Invalidator: Automated Patch Correctness Assessment Via Semantic And Syntactic Reasoning, Tranh Le-Cong, Duc Minh Luong, Xuan Bach D. Le, David Lo, Nhat-Hoa Tran, Bui Quang-Huy, Quyet-Thang Huynh

Research Collection School Of Computing and Information Systems

Automated program repair (APR) has been gaining ground recently. However, a significant challenge that still remains is test overfitting, in which APR-generated patches plausibly pass the validation test suite but fail to generalize. A common practice to assess the correctness of APR-generated patches is to judge whether they are equivalent to ground truth, i.e., developer-written patches, by either generating additional test cases or employing human manual inspections. The former often requires the generation of at least one test that shows behavioral differences between the APR-patched and developer-patched programs. Searching for this test, however, can be difficult as the search space …

Lessons Learned From The Hospital To Home Community Care Program In Singapore And The Supporting Ai Multiple Readmissions Prediction Model, John Abisheganaden, Kheng Hock Lee, Lian Leng Low, Eugene Shum, Han Leong Goh, Christine Gia Lee Ang, Adny An Ta Wee, Steven M. Miller

Research Collection School Of Computing and Information Systems

In a prior practice and policy article published in Healthcare Science, we introduced the deployed application of an artificial intelligence (AI) model to predict longer-term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home (H2H) program that has been operating since 2017. In this follow on practice and policy article, we further elaborate on Singapore's H2H program and care model, and its supporting AI model for multiple readmission prediction, in the following ways: (1) by providing updates on the AI and supporting information systems, (2) by reporting on customer …

Tracing The Twenty-Year Evolution Of Developing Ai For Eye Screening In Singapore: A Master Chronology Of Sidrp, Selena+ And Eyris, Steven M. Miller

Research Collection School Of Computing and Information Systems

This working paper is entirely comprised of a timeline table that begins in 2002 and runs through mid-2023. Across these two decades, this timeline traces the evolutionary development of the following:

• The early Singapore R&D efforts to apply software-based image analysis algorithms and methods to analyse eye retina images for diabetic retinopathy and other eye diseases. This was based on a collaboration between the Singapore Eye Research Institute (SERI) and its parent organization, the Singapore National Eye Centre (SNEC), with faculty from the School of Computing at National University of Singapore.
• The establishment and operation of the Singapore Integrated Diabetic …

Mosaic: Spatially-Multiplexed Edge Ai Optimization Over Multiple Concurrent Video Sensing Streams, Ila Gokarn, Hemanth Sabbella, Yigong Hu, Tarek Abdelzaher, Archan Misra

Research Collection School Of Computing and Information Systems

Sustaining high fidelity and high throughput of perception tasks over vision sensor streams on edge devices remains a formidable challenge, especially given the continuing increase in image sizes (e.g., generated by 4K cameras) and complexity of DNN models. One promising approach involves criticality-aware processing, where the computation is directed selectively to "critical" portions of individual image frames. We introduce MOSAIC, a novel system for such criticality-aware concurrent processing of multiple vision sensing streams that provides a multiplicative increase in the achievable throughput with negligible loss in perception fidelity. MOSAIC determines critical regions from images received from multiple vision …

Catching The Fast Payments Trend: Optimal Designs And Leadership Strategies Of Retail Payment And Settlement Systems, Zhiling Guo, Dan Ma

Research Collection School Of Computing and Information Systems

Recent financial technologies have enabled fast payments and are reshaping retail payment and settlement systems globally. We developed an analytical model to study the optimal design of a new retail payment system in terms of settlement speed and system capability under both bank and fintech firm heterogeneous participation incentives. We found that three types of payment systems emerge as equilibrium outcomes: batch retail (BR), expedited retail (ER), and real-time retail (RR) payment systems. Although the base value of the payment service positively affects both settlement speed and system capability, the expected liquidity cost negatively impacts settlement speed, and total transaction …

Ldptrace: Locally Differentially Private Trajectory Synthesis, Yuntao Du, Yujia Hu, Zhikun Zhang, Ziquan Fang, Lu Chen, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Trajectory data has the potential to greatly benefit a wide-range of real-world applications, such as tracking the spread of the disease through people's movement patterns and providing personalized location-based services based on travel preference. However, privacy concerns and data protection regulations have limited the extent to which this data is shared and utilized. To overcome this challenge, local differential privacy provides a solution by allowing people to share a perturbed version of their data, ensuring privacy as only the data owners have access to the original information. Despite its potential, existing point-based perturbation mechanisms are not suitable for real-world scenarios …

How Does Credit Risk Affect Cost Management Strategies? Evidence On The Initiation Of Credit Default Swap And Sticky Cost Behavior, Jing Dai, Nan Hu, Rong Huang, Yan Yan

Research Collection School Of Computing and Information Systems

In this paper, we examine the effect of credit defaults swaps (CDS) initiation on reference firms' cost management strategies. CDS contracts provide insurance protection for creditors, inducing a shift in bargaining power from borrowers to creditors and an excessive incidence of bankruptcy. Anticipating more intransigent creditors in debt renegotiations and higher bankruptcy risk, CDS firms are incentivized to mitigate risk through decreasing cost stickiness after CDS initiation, as cost stickiness lowers liquidity and triggers early covenant violations. We find that, on average, CDS initiation is associated with a decline in reference firms' cost stickiness. This association is more pronounced for …

Glocal Energy-Based Learning For Few-Shot Open-Set Recognition, Haoyu Wang, Guansong Pang, Peng Wang, Lei Zhang, Wei Wei, Yanning Zhang

Research Collection School Of Computing and Information Systems

Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few examples while being able to reject the sample from unknown classes. In this work, we approach the FSOR task by proposing a novel energy-based hybrid model. The model is composed of two branches, where a classification branch learns a metric to classify a sample to one of closedset classes and the energy branch explicitly estimates the open-set probability. To achieve holistic detection of openset samples, our model leverages both class-wise and pixelwise …

Class-Incremental Exemplar Compression For Class-Incremental Learning, Zilin Luo, Yaoyao Liu, Bernt Schiele, Qianru Sun

Research Collection School Of Computing and Information Systems

Exemplar-based class-incremental learning (CIL) finetunes the model with all samples of new classes but few-shot exemplars of old classes in each incremental phase, where the "few-shot" abides by the limited memory budget. In this paper, we break this "few-shot" limit based on a simple yet surprisingly effective idea: compressing exemplars by downsampling non-discriminative pixels and saving "many-shot" compressed exemplars in the memory. Without needing any manual annotation, we achieve this compression by generating 0-1 masks on discriminative pixels from class activation maps (CAM). We propose an adaptive mask generation model called class-incremental masking (CIM) to explicitly resolve two difficulties of …

Extracting Class Activation Maps From Non-Discriminative Features As Well, Zhaozheng Chen, Qianru Sun

Research Collection School Of Computing and Information Systems

Extracting class activation maps (CAM) from a classification model often results in poor coverage on foreground objects, i.e., only the discriminative region (e.g., the “head” of “sheep”) is recognized and the rest (e.g., the “leg” of “sheep”) mistakenly as background. The crux behind is that the weight of the classifier (used to compute CAM) captures only the discriminative features of objects. We tackle this by introducing a new computation method for CAM that explicitly captures non-discriminative features as well, thereby expanding CAM to cover whole objects. Specifically, we omit the last pooling layer of the classification model, and perform clustering …

Freestyle Layout-To-Image Synthesis, Han Xue, Zhiwu Huang, Qianru Sun, Li Song, Wenjun Zhang

Research Collection School Of Computing and Information Systems

Typical layout-to-image synthesis (LIS) models generate images for a close set of semantic classes, e.g., 182 common objects in COCO-Stuff. In this work, we explore the freestyle capability of the model, i.e., how far can it generate unseen semantics (e.g., classes, attributes, and styles) onto a given layout, and call the task Freestyle LIS (FLIS). Thanks to the development of large-scale pre-trained language-image models, a number of discriminative models (e.g., image classification and object detection) trained on limited base classes are empowered with the ability of unseen class prediction. Inspired by this, we opt to leverage large-scale pre-trained text-to-image diffusion …

3d Dental Biometrics: Transformer-Based Dental Arch Extraction And Matching, Zhiyuan Zhang, Zhong Xin

Research Collection School Of Computing and Information Systems

The dental arch is a significant anatomical feature that is crucial in assessing tooth arrangement and configuration and has a potential for human identification in biometrics and digital forensic dentistry. In a previous study, we proposed an auto pose-invariant arch feature extraction Radial Ray Algorithm (RRA) and a matching framework [1] based solely on 3D dental geometry. To enhance the identification accuracy and speed of our previous work, we propose in this study a transformer architecture that can extract dental keypoints by encoding both local and global features. The dental arch is then constructed through robust interpolation of the dental …

Preference-Aware Delivery Planning For Last-Mile Logistics, Qian Shao, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Optimizing delivery routes for last-mile logistics service is challenging and has attracted the attention of many researchers. These problems are usually modeled and solved as variants of vehicle routing problems (VRPs) with challenging real-world constraints (e.g., time windows, precedence). However, despite many decades of solid research on solving these VRP instances, we still see significant gaps between optimized routes and the routes that are actually preferred by the practitioners. Most of these gaps are due to the difference between what's being optimized, and what the practitioners actually care about, which is hard to be defined exactly in many instances. In …

A Mixed-Integer Linear Programming Reduction Of Disjoint Bilinear Programs Via Symbolic Variable Elimination, Jihwan Jeong, Scott Sanner, Akshat Kumar

Research Collection School Of Computing and Information Systems

A disjointly constrained bilinear program (DBLP) has various practical and industrial applications, e.g., in game theory, facility location, supply chain management, and multi-agent planning problems. Although earlier work has noted the equivalence of DBLP and mixed-integer linear programming (MILP) from an abstract theoretical perspective, a practical and exact closed-form reduction of a DBLP to a MILP has remained elusive. Such explicit reduction would allow us to leverage modern MILP solvers and techniques along with their solution optimality and anytime approximation guarantees. To this end, we provide the first constructive closed-form MILP reduction of a DBLP by extending the technique of …

Scanet: Self-Paced Semi-Curricular Attention Network For Non-Homogeneous Image Dehazing, Yu Guo, Yuan Gao, Ryan Wen Liu, Yuxu Lu, Jingxiang Qu, Shengfeng He, Ren Wenqi

Research Collection School Of Computing and Information Systems

The presence of non-homogeneous haze can cause scene blurring, color distortion, low contrast, and other degradations that obscure texture details. Existing homogeneous dehazing methods struggle to handle the non-uniform distribution of haze in a robust manner. The crucial challenge of non-homogeneous dehazing is to effectively extract the non-uniform distribution features and reconstruct the details of hazy areas with high quality. In this paper, we propose a novel self-paced semi-curricular attention network, called SCANet, for non-homogeneous image dehazing that focuses on enhancing haze-occluded regions. Our approach consists of an attention generator network and a scene re-construction network. We use the luminance …

How To Resuscitate A Sick Vm In The Cloud, Xuhua Ding

Research Collection School Of Computing and Information Systems

A guest virtual machine in a cloud platform may fall “sick” when its kernel encounters a fatal low-level bug or is subverted by an adversary. The VM owner is hence likely to lose her control over it due to a kernel hang or being denied of remote accesses. While the VM can be rebooted with the assistance from the cloud server, the owner not only faces service disruption but also is left with no opportunity to make an in-depth diagnosis and forensics on the spot, not to mention a live rectification. Currently, the cloud service provider has neither incentive nor …

Semantic Scene Completion With Cleaner Self, Fengyun Wang, Dong Zhang, Hanwang Zhang, Jinhui Tang, Qianru Sun

Research Collection School Of Computing and Information Systems

Semantic Scene Completion (SSC) transforms an image of single-view depth and/or RGB 2D pixels into 3D voxels, each of whose semantic labels are predicted. SSC is a well-known ill-posed problem as the prediction model has to “imagine” what is behind the visible surface, which is usually represented by Truncated Signed Distance Function (TSDF). Due to the sensory imperfection of the depth camera, most existing methods based on the noisy TSDF estimated from depth values suffer from 1) incomplete volumetric predictions and 2) confused semantic labels. To this end, we use the ground-truth 3D voxels to generate a perfect visible surface, …

Livoauth: Liveness Detection In Voiceprint Authentication With Random Challenges And Detection Modes, Rui Zhang, Zheng Yan, Xueru Wang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Voiceprint authentication provides great convenience to users in many application scenarios. However, it easily suffers from spoofing attacks including speech synthesis, speech conversion, and speech replay. Liveness detection is an effective way to resist these attacks. But existing methods suffer from many disadvantages, such as extra deployment costs due to precise data collection, environmental disturbance, high computational overhead, and operational complexity. A uniform platform that can offer voiceprint authentication as a service (VAaS) over the cloud is also lacked. Hence, it is imperative to design an economic and effective method for liveness detection in voiceprint authentication. In this article, we …

The Bemi Stardust: A Structured Ensemble Of Binarized Neural Networks, Ambrogio Maria Bernardelli, Stefano Gualandi, Hoong Chuin Lau, Simone Milanesi

Research Collection School Of Computing and Information Systems

Binarized Neural Networks (BNNs) are receiving increasing attention due to their lightweight architecture and ability to run on low-power devices, given the fact that they can be implemented using Boolean operations. The state-of-the-art for training classification BNNs restricted to few-shot learning is based on a Mixed Integer Programming (MIP) approach. This paper proposes the BeMi ensemble, a structured architecture of classification-designed BNNs based on training a single BNN for each possible pair of classes and applying a majority voting scheme to predict the final output. The training of a single BNN discriminating between two classes is achieved by a MIP …

Imitating Opponent To Win: Adversarial Policy Imitation Learning In Two-Player Competitive Games, The Viet Bui, Tien Mai, Thanh H. Nguyen

Research Collection School Of Computing and Information Systems

Recent research on vulnerabilities of deep reinforcement learning (RL) has shown that adversarial policies adopted by an adversary agent can influence a target RL agent (victim agent) to perform poorly in a multi-agent environment. In existing studies, adversarial policies are directly trained based on experiences of interacting with the victim agent. There is a key shortcoming of this approach --- knowledge derived from historical interactions may not be properly generalized to unexplored policy regions of the victim agent, making the trained adversarial policy significantly less effective. In this work, we design a new effective adversarial policy learning algorithm that overcomes …

Dynamic Police Patrol Scheduling With Multi-Agent Reinforcement Learning, Songhan Wong, Waldy Joe, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Effective police patrol scheduling is essential in projecting police presence and ensuring readiness in responding to unexpected events in urban environments. However, scheduling patrols can be a challenging task as it requires balancing between two conflicting objectives namely projecting presence (proactive patrol) and incident response (reactive patrol). This task is made even more challenging with the fact that patrol schedules do not remain static as occurrences of dynamic incidents can disrupt the existing schedules. In this paper, we propose a solution to this problem using Multi-Agent Reinforcement Learning (MARL) to address the Dynamic Bi-objective Police Patrol Dispatching and Rescheduling Problem …

Curricular Contrastive Regularization For Physics-Aware Single Image Dehazing, Yu Zheng, Jiahui Zhan, Shengfeng He, Yong Du

Research Collection School Of Computing and Information Systems

Considering the ill-posed nature, contrastive regularization has been developed for single image dehazing, introducing the information from negative images as a lower bound. However, the contrastive samples are non-consensual, as the negatives are usually represented distantly from the clear (i.e., positive) image, leaving the solution space still under-constricted. Moreover, the interpretability of deep dehazing models is underexplored towards the physics of the hazing process. In this paper, we propose a novel curricular contrastive regularization targeted at a consensual contrastive space as opposed to a non-consensual one. Our negatives, which provide better lower-bound constraints, can be assembled from 1) the hazy …

Where Is My Spot? Few-Shot Image Generation Via Latent Subspace Optimization, Chenxi Zheng, Bangzhen Liu, Huaidong Zhang, Xuemiao Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

Image generation relies on massive training data that can hardly produce diverse images of an unseen category according to a few examples. In this paper, we address this dilemma by projecting sparse few-shot samples into a continuous latent space that can potentially generate infinite unseen samples. The rationale behind is that we aim to locate a centroid latent position in a conditional StyleGAN, where the corresponding output image on that centroid can maximize the similarity with the given samples. Although the given samples are unseen for the conditional StyleGAN, we assume the neighboring latent subspace around the centroid belongs to …

Towards A Smaller Student: Capacity Dynamic Distillation For Efficient Image Retrieval, Yi Xie, Huaidong Zhang, Xuemiao Xu, Jianqing Zhu, Shengfeng He

Research Collection School Of Computing and Information Systems

Previous Knowledge Distillation based efficient image retrieval methods employ a lightweight network as the student model for fast inference. However, the lightweight student model lacks adequate representation capacity for effective knowledge imitation during the most critical early training period, causing final performance degeneration. To tackle this issue, we propose a Capacity Dynamic Distillation framework, which constructs a student model with editable representation capacity. Specifically, the employed student model is initially a heavy model to fruitfully learn distilled knowledge in the early training epochs, and the student model is gradually compressed during the training. To dynamically adjust the model capacity, our …

Knowledge Compilation For Constrained Combinatorial Action Spaces In Reinforcement Learning, Jiajing Ling, Moritz Lukas Schuler, Akshat Kumar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Action-constrained reinforcement learning (ACRL), where any action taken in a state must satisfy given constraints, has several practical applications such as resource allocation in supply-demand matching, and path planning among others. A key challenge is to enforce constraints when the action space is discrete and combinatorial. To address this, first, we assume an action is represented using propositional variables, and action constraints are represented using Boolean functions. Second, we compactly encode the set of all valid actions that satisfy action constraints using a probabilistic sentential decision diagram (PSDD), a recently proposed knowledge compilation framework. Parameters of the PSDD compactly encode …

Avoiding Starvation Of Arms In Restless Multi-Armed Bandit, Dexun Li, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Restless multi-armed bandits (RMAB) is a popular framework for optimizing performance with limited resources under uncertainty. It is an extremely useful model for monitoring beneficiaries (arms) and executing timely interventions using health workers (limited resources) to ensure optimal benefit in public health settings. For instance, RMAB has been used to track patients' health and monitor their adherence in tuberculosis settings, ensure pregnant mothers listen to automated calls about good pregnancy practices, etc. Due to the limited resources, typically certain individuals, communities, or regions are starved of interventions, which can potentially have a significant negative impact on the individual/community in the …

Enhancing Third-Party Software Reliability Through Bug Bounty Programs, Tianlu Zhou, Dan Ma, Nan Feng

Research Collection School Of Computing and Information Systems

Bug Bounty Programs (BBPs) reward external hackers for identifying and reporting software vulnerabilities. As the number of security issues caused by third-party applications has been significantly increased recently, many digital platforms are considering launching BBPs to help enhance the reliability of third-party software. BBPs bring benefits to the platform and vendors, meanwhile impose additional costs on them as well. As a result, the overall impact of using BBP is unclear. In this paper, we present an analytical model to examine the strategic decisions of launching and participating in a BBP for the platform and the third-party vendor, respectively. We find …

Venus: A Geometrical Representation For Quantum State Visualization, Shaolun Ruan, Ribo Yuan, Qiang Guan, Yanna Lin, Ying Mao, Weiwen Jiang, Zhepeng Wang, Wei Xu, Yong Wang

Research Collection School Of Computing and Information Systems

Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum …