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Full-Text Articles in Physical Sciences and Mathematics
Designing Large-Scale Intelligent Collaborative Platform For Freight Forwarders, Pang Jin Tan, Shih-Fen Cheng, Richard Chen
Designing Large-Scale Intelligent Collaborative Platform For Freight Forwarders, Pang Jin Tan, Shih-Fen Cheng, Richard Chen
Research Collection School Of Computing and Information Systems
In this paper, we propose to design a large-scale intelligent collaborative platform for freight forwarders. This platform is based on a mathematical programming formulation and an efficient solution approach. Forwarders are middlemen who procure container capacities from carriers and sell them to shippers to serve their transport requests. However, due to demand uncertainty, they often either over-procure or under-procure capacities. We address this with our proposed platform where forwarders can collaborate and share capacities, allowing one's transport requests to be potentially shipped on another forwarder's container. The result is lower total costs for all participating forwarders. The collaboration can be …
Clustering-Nn Based Cfo Estimation Using Random Access Preambles For 5g Non-Terrestrial Networks, Li Zhen, Luyao Cheng, Zheng Chu, Keping Yu, Pei Xiao, Mohsen Guizani
Clustering-Nn Based Cfo Estimation Using Random Access Preambles For 5g Non-Terrestrial Networks, Li Zhen, Luyao Cheng, Zheng Chu, Keping Yu, Pei Xiao, Mohsen Guizani
Machine Learning Faculty Publications
Non-terrestrial networks (NTNs) are expected to play a pivotal role in the future wireless ecosystem. Due to its high-dynamic characteristics, the accurate estimation and compensation of carrier frequency offset (CFO) are crucial for supporting 5G new radio (NR) enabled satellite direct access. With emphasis on ensuring reliable uplink synchronization, we propose a clustering-neural network based CFO estimation scheme by virtue of NR random access preambles. By leveraging the sparsity and regularity of input samples, the proposed scheme can achieve fast and precise prediction of CFOs, while establishing robustness against time uncertainty and channel variation within a satellite beam. Simulation results …
Optimizing Uncertainty Quantification Of Vision Transformers In Deep Learning On Novel Ai Architectures, Erik Pautsch, John Li, Silvio Rizzi, George K. Thiruvathukal, Maria Pantoja
Optimizing Uncertainty Quantification Of Vision Transformers In Deep Learning On Novel Ai Architectures, Erik Pautsch, John Li, Silvio Rizzi, George K. Thiruvathukal, Maria Pantoja
Computer Science: Faculty Publications and Other Works
Deep Learning (DL) methods have shown substantial efficacy in computer vision (CV) and natural language processing (NLP). Despite their proficiency, the inconsistency in input data distributions can compromise prediction reliability. This study mitigates this issue by introducing uncertainty evaluations in DL models, thereby enhancing dependability through a distribution of predictions. Our focus lies on the Vision Transformer (ViT), a DL model that harmonizes both local and global behavior. We conduct extensive experiments on the ImageNet-1K dataset, a vast resource with over a million images across 1,000 categories. ViTs, while competitive, are vulnerable to adversarial attacks, making uncertainty estimation crucial for …
Optimized Uncertainty Estimation For Vision Transformers: Enhancing Adversarial Robustness And Performance Using Selective Classification, Erik Pautsch, John Li, Silvio Rizzi, George K. Thiruvathukal, Maria Pantoja
Optimized Uncertainty Estimation For Vision Transformers: Enhancing Adversarial Robustness And Performance Using Selective Classification, Erik Pautsch, John Li, Silvio Rizzi, George K. Thiruvathukal, Maria Pantoja
Computer Science: Faculty Publications and Other Works
Deep Learning models often exhibit undue confidence when encountering out-of-distribution (OOD) inputs, misclassifying with high confidence. The ideal outcome, in these cases, would be an "I do not know" verdict. We enhance the trustworthiness of our models through selective classification, allowing the model to abstain from making predictions when facing uncertainty. Rather than a singular prediction, the model offers a prediction distribution, enabling users to gauge the model’s trustworthiness and determine the need for human intervention. We assess uncertainty in two baseline models: a Convolutional Neural Network (CNN) and a Vision Transformer (ViT). By leveraging these uncertainty values, we minimize …
Uncertainty-Adjusted Inductive Matrix Completion With Graph Neural Networks, Petr Kasalicky, Antoine Ledent, Rodrigo Alves
Uncertainty-Adjusted Inductive Matrix Completion With Graph Neural Networks, Petr Kasalicky, Antoine Ledent, Rodrigo Alves
Research Collection School Of Computing and Information Systems
We propose a robust recommender systems model which performs matrix completion and a ratings-wise uncertainty estimation jointly. Whilst the prediction module is purely based on an implicit low-rank assumption imposed via nuclear norm regularization, our loss function is augmented by an uncertainty estimation module which learns an anomaly score for each individual rating via a Graph Neural Network: data points deemed more anomalous by the GNN are downregulated in the loss function used to train the low-rank module. The whole model is trained in an end-to-end fashion, allowing the anomaly detection module to tap on the supervised information available in …
Research On Period Emergency Supply Distribution Optimization Under Uncertainty, Li Zhang, Mingling He, Qiushuang Yin, Ning Li, Le'an Yu
Research On Period Emergency Supply Distribution Optimization Under Uncertainty, Li Zhang, Mingling He, Qiushuang Yin, Ning Li, Le'an Yu
Journal of System Simulation
Abstract: Aiming at the uncertainty and multi-periodicity of emergency supply distribution, a novel period vehicle routing problem(PVRP) multi-objective optimization model is built and a three-step optimization method is proposed. A triangular fuzzy number is used to eliminate the uncertainty. An AHP approach is used to transform the multi-objective function into the single objective function. An improved ACO algorithm is proposed to solve the single objective optimization problem. By classical data set, the time effectiveness of proposed method on emergency supply distribution problem is verified. The computational advantage in convergence speed is proved by the comparative analysis of the proposed …
Multiclass Confidence And Localization Calibration For Object Detection, Bimsara Pathiraja, Malitha Gunawardhana, Muhammad Haris Khan
Multiclass Confidence And Localization Calibration For Object Detection, Bimsara Pathiraja, Malitha Gunawardhana, Muhammad Haris Khan
Computer Vision Faculty Publications
Albeit achieving high predictive accuracy across many challenging computer vision problems, recent studies suggest that deep neural networks (DNNs) tend to make over-confident predictions, rendering them poorly calibrated. Most of the existing attempts for improving DNN calibration are limited to classification tasks and restricted to calibrating in-domain predictions. Surprisingly, very little to no attempts have been made in studying the calibration of object detection methods, which occupy a pivotal space in vision-based security-sensitive, and safety-critical applications. In this paper, we propose a new train-time technique for calibrating modern object detection methods. It is capable of jointly calibrating multiclass confidence and …
Avoiding Starvation Of Arms In Restless Multi-Armed Bandit, Dexun Li, Pradeep Varakantham
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 …
Performance Analysis Of Empirical Open-Circuit Voltage Modeling In Lithium-Ion Batteries, Part-3: Experimental Results, Prarthana Pillai, James Nguyen, Balakumar Balasingam
Performance Analysis Of Empirical Open-Circuit Voltage Modeling In Lithium-Ion Batteries, Part-3: Experimental Results, Prarthana Pillai, James Nguyen, Balakumar Balasingam
Computer Science Publications
This paper is the third part of a series of papers about empirical approaches to open circuit voltage (OCV) modeling of lithium-ion batteries. The first part of the series proposed models to quantify various sources of uncertainties in the OCV models; the second part of the series presented systematic data collection approaches to compute the uncertainties in the OCV to state of charge (SOC) models. This paper uses data collected from 28 OCV characterization experiments, performed according to the data collection plan presented in the second part, to compute and analyze three OCV uncertainty metrics: cell-to-cell variations, C-Rate error, and …
A Comparative Assessment Of Human Factors In Cybersecurity: Implications For Cyber Governance, Muhammad Umair Shah, Farkhund Iqbal, Umair Rehman, Patrick C.K. Hung
A Comparative Assessment Of Human Factors In Cybersecurity: Implications For Cyber Governance, Muhammad Umair Shah, Farkhund Iqbal, Umair Rehman, Patrick C.K. Hung
All Works
This paper provides an extensive overview of cybersecurity awareness in the young, educated, and technology-savvy population of the United Arab Emirates (UAE), compared to the United States of America (USA) for advancing the scholarship and practice of global cyber governance. We conducted comparative empirical studies to identify differences in specific human factors that affect cybersecurity behaviour in the UAE and the USA. In addition, we employed several control variables to observe reliable results. We used Hofstede’s theoretical framework on culture to advance our investigation. The results show that the targeted population in the UAE exhibits contrasting interpretations of cybersecurity awareness …