Computer Sciences Commons^™

Reference Dependence In Queue Design And Pricing Strategies, Jian Liu, Yongpin Zhou, Jian Chen, Peng Li

Electrical and Computer Engineering Faculty Research & Creative Works

This research investigates the effect of reference dependence on waiting times in service systems which formerly used a first-in-first-out (FIFO) service but have introduced a priority line with a fee. Our model combines reference-dependent gain-loss utility with standard customer utility, and we posit that customers are pleased with shorter-than-expected waiting times, whereas longer-than-expected times lead to dissatisfaction and an increased likelihood of balking. The study explores two scenarios: a captive customer system (CCS) and a noncaptive customer system (NCCS), with a focus on optimal pricing and segmentation strategies for revenue and social welfare maximization. The results reveal that, in a …

Multi-Dimensional Visualization Strategies Using Web Scraping Tools: A Word Formation Synthesis Case Study For Russian Verbs Of Sound, John Simmons

Graduate Student Research & Creative Works

No abstract provided.

Tackling Selfish Clients In Federated Learning, Andrea Augello, Ashish Gupta, Giuseppe Lo Re, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL) is a distributed machine learning paradigm facilitating participants to collaboratively train a model without revealing their local data. However, when FL is deployed into the wild, some intelligent clients can deliberately deviate from the standard training process to make the global model inclined toward their local model, thereby prioritizing their local data distribution. We refer to this novel category of misbehaving clients as selfish. in this paper, we propose a Robust aggregation strategy for the FL server to mitigate the effect of Selfishness (in short RFL-Self). RFL-Self incorporates an innovative method to recover (or estimate) the true …

Prevention Of Attacks Via Requested Displayable Content, Mingkui Wei, Yao Liu, Zhuo Lu, Junjie Xiong

Computer Science Faculty Research & Creative Works

A method and system disable executable script in requested displayable content. Responsive to requesting displayable content, a non-executable code sequence and a mis-matched font file that maps a plurality of characters of the requested displayable content to the non-executable code sequence is received. The non-executable code sequence is displayed as a text string in accordance with the received mis-matched font file.

Prompt And Accurate Grb Source Localization Aboard The Advanced Particle Astrophysics Telescope (Apt) And Its Antarctic Demonstrator (Adapt), Ye Htet, Marion Sudvarg, Jeremy Buhler, Roger Chamberlain, Wenlei Chen, James Buckley, Roger D. Chamberlain, Corrado Altomare, Matthew Andrew, Blake Bal, Richard G. Bose, Dana Braun, Eric Burns, Michael L. Cherry, Leonardo Di Venere, Jeffrey Dumonthier, Manel Errando, Stefan Funk

Computer Science Faculty Research & Creative Works

We characterize the performance of our computational pipeline for real-time gamma-ray burst (GRB) detection and localization aboard the Advanced Particle-astrophysics Telescope (APT) – a space-based observatory for MeV to TeV gamma-ray astronomy – and its smaller, balloon-borne prototype, the Antarctic Demonstrator for APT (ADAPT), whose scientific focus will be the detection of MeV transients. These instruments observe scintillation light from multiple Compton scattering and photoabsorption of gamma-ray photons across a series of CsI detector layers. We infer the incident angle of each photon's first scattering to localize its source direction to a Compton ring about the vector defined by its …

Front-End Computational Modeling And Design For The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope, Marion Sudvarg, Ye Htet, Roger Chamberlain, Jeremy Buhler, Blake Bal, Blake Bal, Corrado Altomare, Corrado Altomare, Davide Serini, Davide Serini, Mario Nicola Mazziotta, Mario Nicola Mazziotta, Leonardo Di Venere, Leonardo Di Venere, Wenlei Chen, Wenlei Chen, James H. Buckley, Roger D. Chamberlain

Computer Science Faculty Research & Creative Works

The Advanced Particle-astrophysics Telescope (APT) is a planned space-based observatory designed to localize MeV to TeV transients such as gamma-ray bursts in real time using onboard computational hardware. The Antarctic Demonstrator for APT (ADAPT) is a prototype high-altitude balloon mission scheduled to fly during the 2025–26 season. Gamma-ray-induced scintillations in CsI tiles will be captured by perpendicular arrays of optical fibers running across both tile surfaces, as well as SiPM-based edge detectors to improve light collection and calorimetry. Signal samples are captured by analog waveform digitizer ASICs then sent to the front end of the computational pipeline, which is designed …

Optical Lens Attack On Deep Learning Based Monocular Depth Estimation, Ce Zhou, Qiben Yan, Daniel Kent, Guangjing Wang, Ziqi Zhang, Haydar Radha

Computer Science Faculty Research & Creative Works

Monocular Depth Estimation (MDE) plays a crucial role in vision-based Autonomous Driving (AD) systems. It utilizes a singlecamera image to determine the depth of objects, facilitating driving decisions such as braking a few meters in front of a detected obstacle or changing lanes to avoid collision. In this paper, we investigate the security risks associated with monocular vision-based depth estimation algorithms utilized by AD systems. By exploiting the vulnerabilities of MDE and the principles of optical lenses, we introduce 𝐿𝑒𝑛𝑠𝐴𝑡𝑡𝑎𝑐𝑘, a physical attack that involves strategically placing optical lenses on the camera of an autonomous vehicle to manipulate the perceived …

Extending Segment Tree For Polygon Clipping And Parallelizing Using Openmp And Openacc Compiler Directives, M. K. Buddhi Ashan, Satish Puri, Sushil K. Prasad

Computer Science Faculty Research & Creative Works

A segment tree is a versatile tree-based data structure over intervals or line segments efficiently supporting several computational operations such as stabbing query, segment arrangement, and planar point location, both theoretically and practically. Polygon clipping is a basic operation in domains such as Computer Graphics, Computer-aided Design, and Geographic Information Science (GIS). Given two polygons with n vertices, polygon clipping algorithms find the geometric intersection or union in O(n2) time using Foster's all-to-all edge intersection testing and O((n + k) logn) time using Vatti's sweep line-based method, where k is the number of intersections. No known segment tree implementation, including …

A Human-Centered Power Conservation Framework Based On Reverse Auction Theory And Machine Learning, Enrico Casella, Simone Silvestri, Denise A. Baker, Sajal K. Das

Computer Science Faculty Research & Creative Works

Extreme outside temperatures resulting from heat waves, winter storms, and similar weather-related events trigger the Heating Ventilation and Air Conditioning (HVAC) systems, resulting in challenging, and potentially catastrophic, peak loads. As a consequence, such extreme outside temperatures put a strain on power grids and may thus lead to blackouts. To avoid the financial and personal repercussions of peak loads, demand response and power conservation represent promising solutions. Despite numerous efforts, it has been shown that the current state-of-the-art fails to consider (1) the complexity of human behavior when interacting with power conservation systems and (2) realistic home-level power dynamics. As …

Doing Personal Laps: Llm-Augmented Dialogue Construction For Personalized Multi-Session Conversational Search, Hideaki Joko, Shubham Chatterjee, Andrew Ramsay, Arjen P. De Vries, Jeff Dalton, Faegheh Hasibi

Computer Science Faculty Research & Creative Works

The future of conversational agents will provide users with personalized information responses. However, a significant challenge in developing models is the lack of large-scale dialogue datasets that span multiple sessions and reflect real-world user preferences. Previous approaches rely on experts in a wizard-of-oz setup that is difficult to scale, particularly for personalized tasks. Our method, LAPS, addresses this by using large language models (LLMs) to guide a single human worker in generating personalized dialogues. This method has proven to speed up the creation process and improve quality. LAPS can collect large-scale, human-written, multi-session, and multi-domain conversations, including extracting user preferences. …

Trec Ikat 2023: A Test Collection For Evaluating Conversational And Interactive Knowledge Assistants, Mohammad Aliannejadi, Zahra Abbasiantaeb, Shubham Chatterjee, Jeffrey Dalton, Leif Azzopardi

Computer Science Faculty Research & Creative Works

Conversational information seeking has evolved rapidly in the last few years with the development of Large Language Models (LLMs), providing the basis for interpreting and responding in a naturalistic manner to user requests. The extended TREC Interactive Knowledge Assistance Track (iKAT) collection aims to enable researchers to test and evaluate their Conversational Search Agent (CSA). The collection contains a set of 36 personalized dialogues over 20 different topics each coupled with a Personal Text Knowledge Base (PTKB) that defines the bespoke user personas. A total of 344 turns with approximately 26,000 passages are provided as assessments on relevance, as well …

Effective Data Sharing In An Edge-Cloud Model: Security Challenges And Solutions, Arijit Karati, Sajal K. Das

Computer Science Faculty Research & Creative Works

The proposed protocol offers privacy-preserving authentication across several cloud platforms, flexible key management for consumer data protection, and effective user revocation. Performance evaluation demonstrates that the proposed framework supports low latency, safe unified remote access, and data privacy in the contemporary edge-enabled environment.

Reinforcement Learning Based Proactive Entanglement Swapping For Quantum Networks, Tasdiqul Islam, Md Arifuzzaman, Engin Arslan

Computer Science Faculty Research & Creative Works

Entanglement generation and swapping is a difficult process due to probabilistic nature of quantum mechanics. To overcome this issue, existing quantum routing algorithms try to create entanglement on multiple paths between source and destination. Although it is possible to save entangled qubits on unused links using quantum memories, the quantum routing algorithms discard them and try creating new entanglement in each time slot. In this work, we leverage the longevity of entanglement and introduce two enhancements to improve the performance of existing routing algorithms: (i) The generation and caching of entanglements across multiple time slots, and (ii) the proactively executing …

Predicting Iot Distributed Ledger Fraud Transactions With A Lightweight Gan Network, Charles Rawlins, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Decision-making and consensus in traditional blockchain protocols is formulated as a repeated Bernoulli trial that solves a computationally intense lottery puzzle, called Proof-of-Work (PoW) in Bitcoin. This approach has shown robustness through practice but does not scale with increasing network size and generation of new transactions. Resource constrained Internet of Things (IoT) networks are incompatible with full computation of schemes like Bitcoin's PoW. Our effort proposes a first step towards an alternative consensus using machine learning-based decision-making with prediction of fraud transactions to alleviate need for intense computation. To improve base approval probabilities for fraud detection in an ideal security …

Maximizing Network Throughput In Heterogeneous Uav Networks, Shuyue Li, Jing Li, Chaocan Xiang, Wenzheng Xu, Jian Peng, Ziming Wang, Weifa Liang, Xinwei Yao, Xiaohua Jia, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper we study the deployment of an Unmanned Aerial Vehicle (UAV) network that consists of multiple UAVs to provide emergent communication service for people who are trapped in a disaster area, where each UAV is equipped with a base station that has limited computing capacity and power supply, and thus can only serve a limited number of people. Unlike most existing studies that focused on homogeneous UAVs, we consider the deployment of heterogeneous UAVs where different UAVs have different computing capacities. We study a problem of deploying K heterogeneous UAVs in the air to form a temporarily connected …

Prescribed-Time Nash Equilibrium Seeking For Pursuit-Evasion Game, Lei Xue, Jianfeng Ye, Yongbao Wu, Jian Liu, D. C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Dear Editor, this letter is concerned with prescribed-time Nash equilibrium (PTNE) seeking problem in a pursuit-evasion game (PEG) involving agents with second-order dynamics. In order to achieve the prior given and user-defined convergence time for the PEG, a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global information. Then, it is theoretically proved that the prescribed-time convergence of the designed algorithm for achieving Nash equilibrium of PEG. Eventually, the effectiveness of the PTNE method was validated by numerical simulation results.

Prevention Of Web Scraping And Copy And Paste Of Content By Font Obfuscation, Mingkui Wei, Yao Liu, Zhuo Lu, Junjie Xiong

Computer Science Faculty Research & Creative Works

A method and system provide and utilize obfuscated fonts for displayable content. Responsive to a request for displayable content having text, a text portion of the requested displayable content to be obfuscated is determined. For that text portion, obfuscated fonts are provided, by retrieving obfuscated fonts or by generating obfuscated fonts from a set of obfuscated glyphs created from a plurality of glyphs representative of a plurality of characters of the text portion of the displayable content. The obfuscated fonts can be created by assigning obfuscated glyphs of the set into the obfuscated fonts in accordance with one or more …

Scalable Pythagorean Mean-Based Incident Detection In Smart Transportation Systems, Md Jaminur Islam, Jose Paolo Talusan, Shameek Bhattacharjee, Francis Tiausas, Abhishek Dubey, Keiichi Yasumoto, Sajal K. Das

Computer Science Faculty Research & Creative Works

Modern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. to materialize this, roadside units and ambient transportation sensors are being deployed to collect speed data that enables the monitoring of traffic conditions on each road segment. in this article, we first propose a scalable data-driven anomaly-Based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. …

Hls Taking Flight: Toward Using High-Level Synthesis Techniques In A Space-Borne Instrument, Marion Sudvarg, Chenfeng Zhao, Ye Htet, Meagan Konst, Thomas Lang, Nick Song, Roger D. Chamberlain, Jeremy Buhler, James H. Buckley

Computer Science Faculty Research & Creative Works

FPGAs are widely deployed on high-energy astrophysics telescopes to preprocess and reduce sensor data read out by front-end electronics. Across instruments, these computational pipelines have similar semantics, sharing common stages such as pedestal subtraction, signal integration, zero-suppression, island detection, and centroiding. However, diverse telescope designs require unique implementations of these algorithms, and the logic is often rewritten from scratch for a new instrument. As an alternative, High-Level Synthesis (HLS) tools enable these algorithms to be implemented in a high-level language, which eases modifications and enables fast prototyping and deployment. Nonetheless, writing performant HLS code requires augmentation of the code with …

Russian Verbs Of Sound’S Web-Scraping Results From The A.A. Zalizniak Grammatical Dictionary And The Russian National Corpus. Multi-Dimensional Scaling Techniques And Visualization Strategies, John Simmons, Irina V. Ivliyeva

Graduate Student Research & Creative Works

This project aims to enhance web extraction techniques pertaining to a specific lexical-semantic group of Russian verbs of sounds, which undergo semantic modifications at the word-formation level (affixation). Additionally, it seeks to organize search results in a manner conducive to linguistic research using Multi-Dimensional Scaling (MDS) techniques and novel visualization strategies.

The primary objective in this phase of the research was to gather, consolidate, analyze, and present a comprehensive index of all forms of verbs of sound sourced from the A.A. Zalizniak Grammatical Dictionary of the Russian language, hyperlink each verbal form in this index with the Russian National Corpus …

Rainbowcake: Mitigating Cold-Starts In Serverless With Layer-Wise Container Caching And Sharing, Hanfei Yu, Rohan Basu Roy, Christian Fontenot, Devesh Tiwari, Jian Li, Hong Zhang, Hao Wang, Seung Jong Park

Computer Science Faculty Research & Creative Works

Serverless Computing Has Grown Rapidly as a New Cloud Computing Paradigm that Promises Ease-Of-Management, Cost-Efficiency, and Auto-Scaling by Shipping Functions Via Self-Contained Virtualized Containers. Unfortunately, Serverless Computing Suffers from Severe Cold-Start Problems - -Starting Containers Incurs Non-Trivial Latency. Full Container Caching is Widely Applied to Mitigate Cold-Starts Yet Has Recently Been Outperformed by Two Lines of Research: Partial Container Caching and Container Sharing. However, Either Partial Container Caching or Container Sharing Techniques Exhibit their Drawbacks. Partial Container Caching Effectively Deals with Burstiness While Leaving Cold-Start Mitigation Halfway; Container Sharing Reduces Cold-Starts by Enabling Containers to Serve Multiple Functions While Suffering …

Drone-Based Bug Detection In Orchards With Nets: A Novel Orienteering Approach, Francesco Betti Sorbelli, Federico Coró, Sajal K. Das, Lorenzo Palazzetti, Cristina M. Pinotti

Computer Science Faculty Research & Creative Works

The Use of Drones for Collecting Information and Detecting Bugs in Orchards Covered by Nets is a Challenging Problem. the Nets Help in Reducing Pest Damage, But They Also Constrain the Drone's Flight Path, Making It Longer and More Complex. to Address This Issue, We Model the Orchard as an Aisle-Graph, a Regular Data Structure that Represents Consecutive Aisles Where Trees Are Arranged in Straight Lines. the Drone Flies Close to the Trees and Takes Pictures at Specific Positions for Monitoring the Presence of Bugs, But its Energy is Limited, So It Can Only Visit a Subset of Positions. to …

A Reputation System For Provably-Robust Decision Making In Iot Blockchain Networks, Charles C. Rawlins, Sarangapani Jagannathan, Venkata Sriram Siddhardh Nadendla

Electrical and Computer Engineering Faculty Research & Creative Works

Blockchain systems have been successful in discerning truthful information from interagent interaction amidst possible attackers or conflicts, which is crucial for the completion of nontrivial tasks in distributed networking. However, the state-of-the-art blockchain protocols are limited to resource-rich applications where reliably connected nodes within the network are equipped with significant computing power to run lottery-based proof-of-work (pow) consensus. The purpose of this work is to address these challenges for implementation in a severely resource-constrained distributed network with internet of things (iot) devices. The contribution of this work is a novel lightweight alternative, called weight-based reputation (wbr) scheme, to classify new …

Application Of T Gates For Anti-Concentration In Clifford Circuits, Matthew Dominicis, Mason Toombs, Gabriel Riddle, Parineeta Puja Saha, Himanth Bobba

Undergraduate Research Conference at Missouri S&T

Our study examines the integration of non-Clifford T gates into randomly generated Clifford circuits to enhance their universal unitary capacity. We investigate the impact of T gates on circuit output randomness, focusing on generating random Clifford circuits and analyzing the effects of T gates. Through simulations and analysis, we assess the effectiveness of this modification in achieving outputs consistent with Anti concentration properties while minimizing the required number of Clifford gates. Our findings provide valuable insights into quantum circuits, with implications for quantum computing applications.

A Fisher Information-Based Approach To Improve Labeling Efficiency Of Neural Network Models In Image Classification, Joshua Caruso

Undergraduate Research Conference at Missouri S&T

Active learning is a framework for training machine learning models where the goal is to reduce the number of labels used during training. Neural network models used for image classification require a large training dataset to achieve good accuracy. This project will use active learning to reduce the number of labels needed for training neural network models. We propose to use the Fisher information of the neural network parameters to actively select which images are labelled and included in the training data. A key challenge is the large number of parameters in commonly used neural network models, which significantly increases …

Ta Theoretical Framework For Comparing Rlhf Method, Matthew Dominicis

Undergraduate Research Conference at Missouri S&T

Reinforcement Learning from Human Feedback (RLHF) can be used as a means to align Al agents and Large Language Models (LLM) to better represent human expectations. There is a myriad of RLHF methods that exist, however it is difficult to benchmark and compare such methods in terms of alignment, training cost, data collection cost, and other metrics. This project aims to create a robust classification for different RLHF methods from a theoretical point of view. Additionally, this project will attempt to propose bounds for the degree of influence on LLMs that stems from human feedback. Open source LLMs will be …

Enhancing Galaxy Surveys With Machine Learning, Steven Karst

Undergraduate Research Conference at Missouri S&T

Applications of machine learning (ML) or artificial intelligence (Al) to problems in astrophysics and cosmology have recently entered a golden era. In response, we have updated two of our recent ML/Al efforts that contribute to galaxy surveys whose main scientific target is to reveal the nature of the Comsic Acceleration or Dark Energy. We first revised our effort to infer cosmological information beyond the survey geometry using Graph Neural Networks (GNNs) to take advantage of supercomputing resources on campus. We then updated our methods for galaxy target selection in the Subaru Prime Focus Spectrograph (PFS) survey with modern reinforcement learning …

Enhancing Galaxy Surveys With Machine Learning, Steven Karst

Undergraduate Research Conference at Missouri S&T

Applications of machine learning (ML) or artificial intelligence (AI) to problems in astrophysics and cosmology have recently entered a golden era. In response, we have updated two of our recent ML/ AI efforts that contribute to galaxy surveys whose main scientific target is to reveal the nature of the Cosmic Acceleration or Dark Energy. We first revised our effort to infer cosmological information beyond the survey geometry using Graph Neural Networks (GNN) to take advantage of supercomputing resources on campus. We then updated our reinforcement learning methods for galaxy target selection in the Subaru Prime Focus Spectrograph (PFS) survey with …

Effects Of Reproduction On Senescence In Local Species, Nathan Smith

Undergraduate Research Conference at Missouri S&T

Understanding the impact of reproduction on the aging process within species populations remains an ongoing challenge in ecological and evolutionary research. In this study, we aim to clarify the relationship between reproduction and senescence using agent-based modeling using wild species data sets. Our objectives include investigating how variations in reproductive rates influence the lifespan and aging trajectories of individuals within populations, as well as identifying potential mechanisms underlying these effects. We will employ agent-based modeling to simulate populations and explore the dynamics of reproduction and senescence using publicly available datasets of local wild species. By manipulating parameters related to reproductive …

Simulating Inter-Species Competition In C. Elegans, Kevin Lai

Undergraduate Research Conference at Missouri S&T

In biological research, understanding the life cycles of Caenorhabditis elegans (C. elegans) is pivotal for insights into developmental biology, genetics, and population dynamics. Our project builds on Worm-Pop, a Python-based multi-agent simulation of Caenorhabditis elegans (C. elegans) , to enhance its capabilities in simulating survival strategies, reproductive success, and genetic drift. The current model simulates a uniform population without inter-agent interactions. I plan to introduce multiple species of worms into the simulation to study competitive dynamics and determine which variants are most successful under various conditions. Pheromones significantly influence C. elegans behavior, affecting mating, foraging, and social interactions. To address …