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 …
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 …
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 …
Non-Binary Evaluation Of Next-Basket Food Recommendation,
2023
Singapore Management University
Non-Binary Evaluation Of Next-Basket Food Recommendation, Yue Liu, Palakorn Achananuparp, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Next-basket recommendation (NBR) is a recommendation task that predicts a basket or a set of items a user is likely to adopt next based on his/her history of basket adoption sequences. It enables a wide range of novel applications and services from predicting next basket of items for grocery shopping to recommending food items a user is likely to consume together in the next meal. Even though much progress has been made in the algorithmic NBR research over the years, little research has been done to broaden knowledge about the evaluation of NBR methods, which is largely based on the …
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 …
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 …
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.
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 …
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.
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.
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.
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.
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.
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 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 …
Cinema Trends And Viewer Preferences: An Analysis Of Movie Trends, Factors Leading To Box-Office Success, And Viewer Ratings,
2023
La Salle University
Cinema Trends And Viewer Preferences: An Analysis Of Movie Trends, Factors Leading To Box-Office Success, And Viewer Ratings, Brandon Crossland
Analytics Capstones
This research paper investigates the critical factors that impact the success and profitability of feature films in the entertainment industry. The study is divided into two primary parts. The first part aims to identify trends in cinema and predict box office earnings using advanced data analytics techniques. The second part examines user reviews to determine the key factors that influence film viewership. The objective is to provide valuable insights to cinema enthusiasts, film executives, and streaming platforms, helping them make informed decisions on film production and recommendations. The methods utilized include descriptive data visualizations in Excel and Python and predictive …
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 …
Blockchain Security: Double-Spending Attack And Prevention,
2023
Stephen F. Austin State University
Blockchain Security: Double-Spending Attack And Prevention, William Henry Scott Iii
Electronic Theses and Dissertations
This thesis shows that distributed consensus systems based on proof of work are vulnerable to hashrate-based double-spending attacks due to abuse of majority rule. Through building a private fork of Litecoin and executing a double-spending attack this thesis examines the mechanics and principles behind the attack. This thesis also conducts a survey of preventative measures used to deter double-spending attacks, concluding that a decentralized peer-to-peer network using proof of work is best protected by the addition of an observer system whether internal or external.
