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 …
How Does Credit Risk Affect Cost Management Strategies? Evidence On The Initiation Of Credit Default Swap And Sticky Cost Behavior,
2023
Singapore Management University
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,
2023
Singapore Management University
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,
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 …
Improving Quantal Cognitive Hierarchy Model Through Iterative Population Learning,
2023
Singapore Management University
Improving Quantal Cognitive Hierarchy Model Through Iterative Population Learning, Yuhong Xu, Shih-Fen Cheng, Xinyu Chen
Research Collection School Of Computing and Information Systems
In this paper, we propose to enhance the state-of-the-art quantal cognitive hierarchy (QCH) model with iterative population learning (IPL) to estimate the empirical distribution of agents’ reasoning levels and fit human agents’ behavioral data. We apply our approach to a real-world dataset from the Swedish lowest unique positive integer (LUPI) game and show that our proposed approach outperforms the theoretical Poisson Nash equilibrium predictions and the QCH approach by 49.8% and 46.6% in Wasserstein distance respectively. Our approach also allows us to explicitly measure an agent’s reasoning level distribution, which is not previously possible.
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 …
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 …
Knowledge Compilation For Constrained Combinatorial Action Spaces In Reinforcement Learning,
2023
Singapore Management University
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 …
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 …
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.
Fair Signposting Profile,
2023
Old Dominion University
Fair Signposting Profile, Herbert Van De Sompel, Martin Klein, Shawn Jones, Michael L. Nelson, Simeon Warner, Anusuriya Devaraju, Robert Huber, Wilko Steinhoff, Vyacheslav Tykhonov, Luc Boruta, Enno Meijers, Stian Soiland-Reyes, Mark Wilkonson
Computer Science Faculty Publications
[First paragraph] This page details concrete recipes that platforms that host research outputs (e.g. data repositories, institutional repositories, publisher platforms, etc.) can follow to implement Signposting, a lightweight yet powerful approach to increase the FAIRness of scholarly objects.
Feature Selection From Clinical Surveys Using Semantic Textual Similarity,
2023
Washington University in St. Louis
Feature Selection From Clinical Surveys Using Semantic Textual Similarity, Benjamin Warner
McKelvey School of Engineering Graduate Student Theses & Dissertations
Survey data collected from human subjects can contain a high number of features while having a comparatively low quantity of examples. Machine learning models that attempt to predict outcomes from survey data under these conditions can overfit and result in poor generalizability. One remedy to this issue is feature selection, which attempts to select an optimal subset of features to learn upon. A relatively unexplored source of information in the feature selection process is the usage of textual names of features, which may be semantically indicative of which features are relevant to a target outcome. The relationships between feature names …
An Analysis And Examination Of Consensus Attacks In Blockchain Networks,
2023
James Madison University
An Analysis And Examination Of Consensus Attacks In Blockchain Networks, Thomas R. Clark
Senior Honors Projects, 2020-current
This paper examines consensus attacks as they relate to blockchain networks. Consensus attacks are a significant threat to the security and integrity of blockchain networks, and understanding these attacks is crucial for developers and stakeholders. The primary contribution of the paper is to present blockchain and consensus attacks in a clear and accessible manner, with the aim of making these complex concepts easily understandable for a general audience. Using literature review, the paper identifies various methods to prevent consensus attacks, including multi-chain networks, proof-of-work consensus algorithms, and network auditing and monitoring. An analysis revealed that these methods for preventing consensus …
Understanding Data Mining And Its Relation To Information Systems,
2023
CUNY New York City College of Technology
Understanding Data Mining And Its Relation To Information Systems, Malak Alammari
Publications and Research
This research project aims to enrich an Open Educational Resource (OER) textbook on Introduction to Information Systems/Technology with a focus on data mining and its relation to hardware and software components of information systems. The study will address the following research questions: (1) What is data mining? and (2) How does data relate to the hardware and software components of information systems? To answer these questions, the researcher will conduct research to ascertain the current state of data mining and its relevance in the field of information systems/technology. The results of the research will be incorporated into an existing OER …
