Identifying And Sharing Per-And Polyfluoroalkyl Substances Hot-Spot Areas And Exposures In Drinking Water,
2023
University of Kentucky
Identifying And Sharing Per-And Polyfluoroalkyl Substances Hot-Spot Areas And Exposures In Drinking Water, Sweta Ojha, P. Travis Thompson, Christian D. Powell, Hunter N. B. Moseley, Kelly G. Pennell
Markey Cancer Center Faculty Publications
Exposure to per- and polyfluoroalkyl substances (PFAS) in drinking water is widely recognized as a public health concern. Decision-makers who are responsible for managing PFAS drinking water risks lack the tools to acquire the information they need. In response to this need, we provide a detailed description of a Kentucky dataset that allows decision-makers to visualize potential hot-spot areas and evaluate drinking water systems that may be susceptible to PFAS contamination. The dataset includes information extracted from publicly available sources to create five different maps in ArcGIS Online and highlights potential sources of PFAS contamination in the environment in relation …
Improving The Efficiency Of Exponential Ratio-Type Estimator For Population Median: A Calibration Weight Adjustment Approach,
2023
Department of Statistics, Akwa Ibom State University, Nigeria.
Improving The Efficiency Of Exponential Ratio-Type Estimator For Population Median: A Calibration Weight Adjustment Approach, Mathew J. Iseh, Kufre J. Bassey
CBN Journal of Applied Statistics (JAS)
This paper modifies the Bahl and Tuteja exponential ratio-type estimator for population median under simple random and stratified sampling schemes using calibration weight adjustment technique with supplementary information to vary the stratum weights. The bias and mean square error of the modified estimator were obtained up to the second-order approximation, which satisfies the necessary conditions for efficiency. The findings show that the new estimator surpasses existing estimators in efficiency gain. This suggests the appropriateness of calibration weight modification in boosting the efficiency of a population parameter estimator under stratified random sampling especially where the population parameter of the auxiliary variable …
Lessons Learned From The Hospital To Home Community Care Program In Singapore And The Supporting Ai Multiple Readmissions Prediction Model,
2023
Tan Tock Seng Hospital
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 …
Catching The Fast Payments Trend: Optimal Designs And Leadership Strategies Of Retail Payment And Settlement Systems,
2023
Singapore Management University
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 …
Class-Incremental Exemplar Compression For Class-Incremental Learning,
2023
Singapore Management University
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 …
Freestyle Layout-To-Image Synthesis,
2023
Singapore Management University
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 …
Strategic Planning For Flexible Agent Availability In Large Taxi Fleets,
2023
Singapore Management University
Strategic Planning For Flexible Agent Availability In Large Taxi Fleets, Rajiv Ranjan Kumar, Pradeep Varakantham, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
In large scale multi-agent systems like taxi fleets, individual agents (taxi drivers) are self interested (maximizing their own profits) and this can introduce inefficiencies in the system. One such inefficiency is with regards to the "required" availability of taxis at different time periods during the day. Since a taxi driver can work for limited number of hours in a day (e.g., 8-10 hours in a city like Singapore), there is a need to optimize the specific hours, so as to maximize individual as well as social welfare. Technically, this corresponds to solving a large scale multi-stage selfish routing game with …
Preference-Aware Delivery Planning For Last-Mile Logistics,
2023
Singapore Management University
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 …
Towards Explaining Sequences Of Actions In Multi-Agent Deep Reinforcement Learning Models,
2023
Singapore Management University
Towards Explaining Sequences Of Actions In Multi-Agent Deep Reinforcement Learning Models, Phyo Wai Khaing, Minghong Geng, Budhitama Subagdja, Shubham Pateria, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Although Multi-agent Deep Reinforcement Learning (MADRL) has shown promising results in solving complex real-world problems, the applicability and reliability of MADRL models are often limited by a lack of understanding of their inner workings for explaining the decisions made. To address this issue, this paper proposes a novel method for explaining MADRL by generalizing the sequences of action events performed by agents into high-level abstract strategies using a spatio-temporal neural network model. Specifically, an interval-based memory retrieval procedure is developed to generalize the encoded sequences of action events over time into short sequential patterns. In addition, two abstraction algorithms are …
Semantic Scene Completion With Cleaner Self,
2023
Singapore Management University
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, …
Unbiased Multiple Instance Learning For Weakly Supervised Video Anomaly Detection,
2023
Singapore Management University
Unbiased Multiple Instance Learning For Weakly Supervised Video Anomaly Detection, Hui Lyu, Zhongqi Yue, Qianru Sun, Bin Luo, Zhen Cui, Hanwang Zhang
Research Collection School Of Computing and Information Systems
Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the binary anomaly label is only given on the video level, but the output requires snippet-level predictions. So, Multiple Instance Learning (MIL) is prevailing in WSVAD. However, MIL is notoriously known to suffer from many false alarms because the snippet-level detector is easily biased towards the abnormal snippets with simple context, confused by the normality with the same bias, and missing the anomaly with a different pattern. To this end, we propose a new MIL framework: Unbiased MIL (UMIL), to learn unbiased anomaly features that improve WSVAD. At each MIL training …
Groundnlq @ Ego4d Natural Language Queries Challenge 2023,
2023
Singapore Management University
Groundnlq @ Ego4d Natural Language Queries Challenge 2023, Zhijian Hou, Lei Ji, Difei Gao, Wanjun Zhong, Kun Yan, Chong-Wah Ngo, Wing-Kwong Chan, Chong-Wah Ngo, Nan Duan, Mike Zheng Shou
Research Collection School Of Computing and Information Systems
In this report, we present our champion solution for Ego4D Natural Language Queries (NLQ) Challenge in CVPR 2023. Essentially, to accurately ground in a video, an effective egocentric feature extractor and a powerful grounding model are required. Motivated by this, we leverage a two-stage pre-training strategy to train egocentric feature extractors and the grounding model on video narrations, and further fine-tune the model on annotated data. In addition, we introduce a novel grounding model GroundNLQ, which employs a multi-modal multiscale grounding module for effective video and text fusion and various temporal intervals, especially for long videos. On the blind test …
Enhancing Third-Party Software Reliability Through Bug Bounty Programs,
2023
Singapore Management University
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 …
Digital Transformation In Local Governments: A Case Study Of Abu Dhabi Municipality Transport Department,
2023
United Arab Emirates University
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 …
An Efficient Strategy For Deploying Deception Technology,
2023
United Arab Emirates University
An Efficient Strategy For Deploying Deception Technology, Noora Abdulla Alhosani
Theses
Implementations of deception technology is crucial in discovering attacks by creating a controlled and monitored environment for detecting malicious activity. This technology involves the deployment of decoys, traps, and honeypots that mimic natural systems and network assets to attract and identify attackers. The use of deception technology provides an early warning system for detecting cyber-attacks, allowing organizations to respond quickly and mitigate damage. This article proposed a framework that focuses on maximizing the efficiency of deception technology in detecting sophisticated attacks. The framework employs multi-layered deception techniques at various levels of the network, system, and application to provide comprehensive coverage …
Efficient Convoy Routing And Bridge Load Optimization User Interface,
2023
University of Nebraska at Omaha
Efficient Convoy Routing And Bridge Load Optimization User Interface, Brandon Lacy, Will Heller, Yonas Kassa, Brian Ricks, Robin Gandhi
Information Systems and Quantitative Analysis Faculty Proceedings & Presentations
No abstract provided.
How To Select Simple-Yet-Accurate Model Of Bridge Maintenance?,
2023
University of Nebraska at Omaha
How To Select Simple-Yet-Accurate Model Of Bridge Maintenance?, Akshay Kale, Yonas Kassa, Brian Ricks, Robin Gandhi
Information Systems and Quantitative Analysis Faculty Proceedings & Presentations
No abstract provided.
Developing Architecture For A Routing System Using Bridge Data And Adversary Avoidance,
2023
University of Nebraska at Omaha
Developing Architecture For A Routing System Using Bridge Data And Adversary Avoidance, Will Heller, Brian Ricks, Yonas Kassa, Brandon Lacy, Rahul Kamar Nethakani
Information Systems and Quantitative Analysis Faculty Proceedings & Presentations
No abstract provided.
Building Explainable Machine Learning Lifecycle: Model Training, Selection, And Deployment With Explainability,
2023
University of Nebraska at Omaha
Building Explainable Machine Learning Lifecycle: Model Training, Selection, And Deployment With Explainability, Vidit Singh, Yonas Kassa, Brian Ricks, Robin Gandhi
Information Systems and Quantitative Analysis Faculty Proceedings & Presentations
No abstract provided.
Progress In A New Visualization Strategy For Ml Models,
2023
University of Nebraska at Omaha
Progress In A New Visualization Strategy For Ml Models, Alex Wissing, Brian Ricks, Robin Gandhi, Yonas Kassa, Akshay Kale
Information Systems and Quantitative Analysis Faculty Proceedings & Presentations
No abstract provided.
