Digital Commons Network^™

Unsafe Events Detection In Smart Water Meter Infrastructure Via Noise-Resilient Learning, Ayanfeoluwa Oluyomi, Sahar Abedzadeh, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

Residential smart water meters (SWMs) collect real-time water consumption data, enabling automated billing and peak period forecasting. The presence of unsafe events is typically detected via deviations from the benign profile of water usage. However, profiling the benign behavior is non-trivial for large-scale SWM networks because once deployed, the collected data already contain those events, biasing the benign profile. To address this challenge, we propose a real-time data-driven unsafe event detection framework for city-scale SWM networks that automatically learns the profile of benign behavior of water usage. Specifically, we first propose an optimal clustering of SWMs based on the recognition …

Log Sequence Anomaly Detection Based On Template And Parameter Parsing Via Bert, Xiaolin Chai, Hang Zhang, Jue Zhang, Yan Sun, Sajal K. Das

Computer Science Faculty Research & Creative Works

Logs record various operations and events during system running in text format, which is an essential basis for detecting and identifying potential security threats or system failures and is widely used in system management to ensure security and reliability. Existing log sequence anomaly detection is limited by log parsing and does not consider all key features of logs, which may cause false or missed detection. In this paper, we propose a fast and accurate log parsing method and feed the entire log content into the deep learning network for analysis. To avoid semantic loss during parsing, we replace some variables …

Mobilytics: Mobility Analytics Framework For Transferring Semantic Knowledge, Shreya Ghosh, Soumya K. Ghosh, Sajal K. Das, Prasenjit Mitra

Computer Science Faculty Research & Creative Works

The proliferation of sensor-equipped smartphones has led to the generation of vast amounts of GPS data, such as timestamped location points, enabling a range of location-based services. However, deciphering the spatio-temporal dynamics of mobility to understand the underlying motivations behind travel patterns presents a significant challenge. his paper focuses on how individuals' GPS traces (latitude, longitude, timestamp) interpret the connection and correlations among different entities such as people, locations or point-of-interests (POIs), and semantic contexts (trip-purpose). We introduce a mobility analytics framework, named Mobilytics designed to identify trip purposes from individual GPS traces by leveraging a “mobility knowledge graph” (MKG) …

Traffic Prediction-Based Vnf Auto-Scaling And Deployment Mechanism For Flexible And Elastic Service Provision, Bo Yi, Jiacheng Wang, Qiang He, Xingwei Wang, Min Huang, Sajal K. Das, Keqin Li

Computer Science Faculty Research & Creative Works

Network Function Virtualization (NFV) provides a flexible way to provision new services by decoupling network functions from hardware and implementing them as Virtual Network Functions (VNFs). However, the rapid development of technologies greatly promotes the explosion of diverse services, which directly results in the exponential increase of heterogeneous traffic. In addition, such a tremendous amount of heterogeneous traffic will generate bursts in a more dynamic and unexpected manner, so it becomes extremely hard to satisfy the customer demands. Aiming at addressing these challenges, this work proposes a positive and elastic VNF deployment mechanism for service provisioning, which introduces three novelties: …

L3geocast: Enabling P4-Based Customizable Network-Layer Geocast At The Network Edge, Xindi Hou, Shuai Gao, Ningchun Liu, Fangtao Yao, Hongke Zhang, Sajal K. Das

Computer Science Faculty Research & Creative Works

Geocast is a one-to-many communication paradigm that enables the transmission of data packets to a designated area rather than an IP address. The most common geocast solutions rely on the application-layer Geolocation-to-IP database. But these IP-based approaches cannot cope with the challenges of flexibility and mobility in a granularity-customizable geocast scenario. While some non-IP network-layer (L3) attempts have resulted in low addressing accuracy and poor routing scalability. Besides, the clean-slate design is incompatible with the existing network. To address these issues, this article proposes an innovative network-layer geographic addressing scheme that leverages P4-based Software Defined Networks (SDN) to enable flexible …

Federated Graph Anomaly Detection Via Contrastive Self-Supervised Learning, Xiangjie Kong, Wenyi Zhang, Hui Wang, Mingliang Hou, Xin Chen, Xiaoran Yan, Sajal K. Das

Computer Science Faculty Research & Creative Works

Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain sensitive situations. Although federated graph learning offers a promising solution by enabling collaborative model training in distributed systems while preserving data privacy, a practical challenge arises as each client typically possesses a limited amount of graph data. Consequently, naively applying federated graph learning directly to …

Real-Time Analysis Of Encrypted Dns Traffic For Threat Detection, Marta Moure-Garrido, Sajal K. Das, Celeste Campo, Carlos Garcia-Rubio

Computer Science Faculty Research & Creative Works

Domain Name System (DNS) tunneling is a well-known cyber-attack that allows data exfiltration - the attackers exploit this tunnel to extract sensitive information from the system. Advanced Persistent Threat (APT) attackers encapsulate malicious traffic in a DNS connection to elude security mechanisms such as Intrusion Detection System (IDS). Although different techniques have been implemented to detect these targeted attacks, their rise induces a threat to Cyber-Physical Systems (CPS). The DNS over HTTPS (DoH) tunnel detection is a challenge because the encrypted data prevents an analysis of DNS traffic content. In this paper, we present a novel detection system that identifies …

Secure Location-Based Authenticated Key Establishment Scheme For Maritime Communication, Saurabh Agrawal, Anusha Vangala, Ashok Kumar Das, Neeraj Kumar, Sachin Shetty, Sajal K. Das

Computer Science Faculty Research & Creative Works

Maritime communication helps vessels and ports plan their movements, exchange environmental information, and communicate among themselves. The vessels' movement and changing location are critical to keep them secure from data interception and data tampering by unauthorized parties during transmission. To secure maritime communication, we propose a novel lightweight authentication scheme sensitive to the current ship location. We assess the effectiveness of the proposed protocol in defending against a range of security threats while keeping communication and computation costs low and meeting the desired security and functional requirements of anonymity and untraceability. The detailed security analysis using the widely accepted Scyther …

Approximation Algorithm For Connected Submodular Function Maximization Problems, Wenzheng Xu, He Xue, Jing Li, Weifa Liang, Zichuan Xu, Pan Zhou, Xiaohua Jia, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget K, the problem is to find a subset S with K nodes from a graph G so that a given submodular function f (S) on S is maximized while the induced subgraph G[S] by the nodes in S is connected, where the submodular function f can be used to model many practical application problems, such as the number of users within different service areas …

Approximation Algorithm And Applications For Connected Submodular Function Maximization Problems, Ziming Wang, Jing Li, He Xue, Wenzheng Xu, Weifa Liang, Zichuan Xu, Jian Peng, Pan Zhou, Xiaohua Jia, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget $K$ , the problem is to find a subset $S$ with $K$ nodes from a graph $G$ , so that a given submodular function $f(S)$ on $S$ is maximized and the induced subgraph $G[S]$ by the nodes in $S$ is connected, where the submodular function $f$ can be used to model many practical application problems, such as the number of users within different service …

Risk-Reward Allocation Among Integrated Project Delivery Method Stakeholders: A Gamified Cooperative Data Simulation Approach, Radwa Eissa, Mohamad Abdul Nabi, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Integrated project delivery (IPD) method is associated with numerous benefits, including enhanced project performance, collaboration, and information sharing among project stakeholders. However, the lack of adequate incentive and reward mechanisms is still considered as the main reason for slowing down the adoption of IPD. As such, the goal of this paper is to identify a fair and efficient risk pool distribution among IPD project stakeholders. The adopted methodology included (1) assigning the risks associated with each stakeholder, (2) computing valuations for all possible subset coalitions among IPD project stakeholders that reflect their risk control capabilities as well as their coordination …

Achieving Project Objectives And Improving Functions: The Benefits Of Ai And Construction Technologies, Fareed Salih, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The Construction Industry Is Increasingly Incorporating Artificial Intelligence (AI) And Construction Technologies Into Projects. However, It Lags Behind Other Industries In Terms Of Digital Transformation. One Of The Reasons For This Disparity Is The Lack Of Evidence-Based Information On AI And Technologies In Construction Projects. Hence, This Research Aims To Comprehensively Understand The Benefits Of AI Techniques And Construction Technologies And Their Role In Achieving Objectives And Improving Functions. To This End, The Authors Followed A Three-Step Research Methodology. Firstly, An Extensive Literature Review Was Conducted To Identify 4 AI Techniques, 13 Construction Technologies, And 28 Benefits Relevant To Their …

A Hypergraph Approach To Deep Learning Based Routing In Software-Defined Vehicular Networks, Ankur Nahar, Nishit Bhardwaj, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

Software-Defined Vehicular Networks (SDVNs) revolutionize modern transportation by enabling dynamic and adaptable communication infrastructures. However, accurately capturing the dynamic communication patterns in vehicular networks, characterized by intricate spatio-temporal dynamics, remains a challenge with traditional graph-Based models. Hypergraphs, due to their ability to represent multi-way relationships, provide a more nuanced representation of these dynamics. Building on this hypergraph foundation, we introduce a novel hypergraph-Based routing algorithm. We jointly train a model that incorporates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) using a Deep Deterministic Policy Gradient (DDPG) approach. This model carefully extracts spatial and temporal traffic matrices, capturing elements …

Persistent Monitoring Of Insect-Pests On Sticky Traps Through Hierarchical Transfer Learning And Slicing-Aided Hyper Inference, Fateme Fotouhi, Kevin Menke, Aaron Prestholt, Ashish Gupta, Matthew E. Carroll, Hsin Jung Yang, Edwin J. Skidmore, Matthew O'Neal, Nirav Merchant, Sajal K. Das, Peter Kyveryga, Baskar Ganapathysubramanian, Asheesh K. Singh, Arti Singh, Soumik Sarkar

Computer Science Faculty Research & Creative Works

Introduction: Effective monitoring of insect-pests is vital for safeguarding agricultural yields and ensuring food security. Recent advances in computer vision and machine learning have opened up significant possibilities of automated persistent monitoring of insect-pests through reliable detection and counting of insects in setups such as yellow sticky traps. However, this task is fraught with complexities, encompassing challenges such as, laborious dataset annotation, recognizing small insect-pests in low-resolution or distant images, and the intricate variations across insect-pests life stages and species classes. Methods: to tackle these obstacles, this work investigates combining two solutions, Hierarchical Transfer Learning (HTL) and Slicing-Aided Hyper Inference …

Lessons Learned From Laboratory Study And Field Application Of Re-Crosslinkable Preformed Particle Gels Rppg For Conformance Control In Mature Oilfields With Conduits/Fractures/Fracture-Like Channels, Baojun Bai, Thomas P. Schuman, David Smith, Tao Song

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This Paper Surveys the Role of Re-Crosslink able Preformed Particle Gels (RPPG) in Addressing Conformance Challenges within Mature Oilfields. Despite Widespread Preformed Particle Gel (PPG) Application in 15,000+ Wells, their Limitations in Sealing Fractures and Conduits Prevalent in Mature Reservoirs Have Driven the Development of RPPG Formulations. Synthesized in Various Sizes from Micrometer to Millimeter Levels, These Environmentally Friendly RPPGs Are Tailored for Diverse Reservoir Conditions. Findings Showcase the Successful Laboratory-Scale Creation and Upscaling of RPPG Products, Offering Adaptability to Temperatures from 20 to 175°C, Customizable Sizes, Swelling Ratios (5 to 40 Times), and Re-Crosslinking Times Spanning Minutes to Days. …

Low Finesse Extrinsic Fabry-Perot Interferometer (Efpi) Demodulation For High Temperature Gap Measurements, Abhishek Prakash Hungund

Masters Theses

"In continuous casting steel industries, mold flux is added to provide thermal and chemical insulation for molten steel. The mold flux absorbs detrimental inclusions from the steel and promotes uniform heat distribution to prevent sticking. To promote flux infiltration, mold oscillation is used, but this creates oscillation marks that reduce local shell growth and increase temperatures. Wide (2-3 mm) and deep (0.5-0.9 mm) oscillation marks with areas of 1.1-2.5 mm² are observed, affecting the steel quality, which results in a loss of 6% per billet to the industry. To address this challenge, we propose an extrinsic Fabry-Perot interferometer (EFPI) sensor …

Radiofrequency Interference Detection Using Lstmand Statistical Analysis Discriminator, Luke Smith

Masters Theses

"Wireless devices are becoming increasingly pervasive across all aspects of society. Examples of such devices include radios, routers, mobile phones, tablets, and more. As the number of radio frequency (RF) devices continues to rise, so does the amount of interference and noise increase. This is why an efficient approach to interference detection is explored. Most research within this area has been done strictly within the frequency domain as viewing a signal within this domain provides many insights into what makes the signal. This has, however, led to the time domain being underutilized for this area of research.

To explore the …

The Exponential Function In Discrete Fractional Calculus Under The Delta Operator, Brayton James Link

Masters Theses

"Previously the exponential problem in discrete fractional calculus under the nabla operator was solved with the discrete Mittag--Leffler function. We now show the solution to the exponential problem in discrete fractional calculus under the delta operator, providing multiple derivations of the solution with recursion and Laplace transforms. We also share some computational and numerical results of experiments with different orders of difference to display the nature of the solution" -- Abstract, p. iii

Discrete Event Simulation (Des) In Swapping/Charging Of Battery Electric Vehicles In Underground Mining, Albert Einstein Amponsem

Masters Theses

"With climate change concerns escalating, international agreements such as the Kyoto Protocol, the US Presidential Policy, and the Paris Agreement aim to reduce greenhouse gas (GHG) emissions, targeting significant reductions by 2050. The mining sector, a notable contributor to GHG emissions primarily through diesel-powered material haulage, emits approximately 68 million tons of CO₂ annually. Transitioning to Battery Electric Trucks (BETs) presents a viable mitigation strategy by replacing diesel trucks with electric alternatives, thus eliminating CO₂ emissions.

However, the effectiveness of BETs hinges on optimized battery swapping and charging procedures. This study employs Discrete Event Simulation (DES), a computational …

Comparative Study Of Crypto Volatility And Price Forecasting Using A Mixture Of Time Series And Machine Learning Models, Abhishek Kafle

Masters Theses

"Forecasting financial product volatility and price is crucial for informed decision-making in investment and risk management. The models considered include GARCH, LSTM, GRU, BiLSTM, and hybrid models that incorporate various combinations of these models. We present a comparative analysis of forecasting volatility and price using the aforementioned models.

We also introduce a user-friendly dashboard for model training and evaluation, enabling users to upload datasets and customize model parameters. The dashboard allows users to select the type of model, specify the dataset range for training, determine the number of epochs, adjust the number of layers for deep …

Ontogenetically Driven Changes In The Endocranial Anatomy Of Maiasaura Peeblesorum And Its Implications For Sensory Functions, Emma Puetz

Masters Theses

"Hadrosaurids exhibited extreme morphological diversity and behavioral characteristics that can be compared with other, closely related members of Dinosauria – particularly through the study of neuroanatomy. X-ray CT generated endocrania of the Late Cretaceous saurolophine hadrosaur Maiasaura peeblesorum, known as the “good mother” dinosaur, offers a unique opportunity to compare complex ecological behaviors between non-avian dinosaurs and their modern archosaur relatives. The 3D reconstruction software ORS Dragonfly provided linear and volumetric measurements taken from the digital cranial endocast and endosseous labyrinths of three ontogenetically varied M. peeblesorum specimens from the Two Medicine Formation in Montana that were used to …

Learn From The Past: Using Peer Data To Improve Course Recommendations In Personalized Education, Colton Walker

Masters Theses

"This work describes a recommendation approach designed to enhance student success by identifying semester schedules and graduation paths. The primary objective is to provide personalized graduation path recommendations rooted in individual student performance and draw insights from the academic journeys of similar students who successfully graduated. The original research contribution of this work lies in the development of a graduation path recommender system that leverages a combination of Markov Decision Process, Q-Learning, and collaborative filtering techniques to pinpoint graduation paths with a higher likelihood of leading students to success based on their academic progress thus far. The effectiveness of the …

Evaluating Electromagnetic Interference On Gnss Receivers, Giorgi Tsintsadze

Masters Theses

"Due to high power and efficiency requirements of modern electrical drive system, inverters are utilizing fast-edge pulses as a drive signal. The high frequency pulses contain higher order harmonics can radiate electromagnetic interference above accepted levels. Modelling and understanding the mechanism that causes the radiation is studied, modelled and presented as gray-box model which contains circuit level modelling of coupling between electrical machine phase windings and low power sensor harness. The thesis consists of two main papers. In the first paper, radiated emissions from an electrical drive system is charac- terized thru gray-box modelling where coupling between electrical machine phase …

Iterative Power Flow Control Architecture For Dc-Ac-Ac Triple Active Bridge Converters: Design, Simulation, And Hardware Testing, Jonathan Henri Saelens

Masters Theses

Driven by widespread electrification and escalating energy demands, efficient and cost-effective converter systems are necessary to maintain and improve our nation's electrical grid. Facilitating this necessity are power electronic converters in applications such as smart grids, renewable energy integration, and electric vehicles. Guided by the demand for improved converter topologies and conversion efficiency, much research has been conducted on varying designs, development, and control implementations. One such topology gaining significant research attention is the triple active bridge (TAB) converter. The TAB is a three-port power converter enabling both bidirectional power flow on each port and galvanic isolation for safety and …

A Gat-Bilstma Model For Weather-Aware Prediction Of Traffic Speed, Bikis Muhammed

Masters Theses

This thesis presented a method for incorporating the effect of weather conditions in the prediction of the average speed of vehicular traffic for each segment of a road network. The proposed approach utilized two different deep learning methods: graph attention networks and bidirectional long short-term memory with attention layers. The accuracy of predictions is increased by considering the real-world driving distance between road segments, in contrast to the Haversine distance used in several existing prediction methods. Categorization of input data as weekend or weekday further increased the prediction accuracy. The proposed approach was validated using two data sets published by …

Time Series Anomaly Detection Using Generative Adversarial Networks, Shyam Sundar Saravanan

Masters Theses

"Anomaly detection is widely used in network intrusion detection, autonomous driving, medical diagnosis, credit card frauds, etc. However, several key challenges remain open, such as lack of ground truth labels, presence of complex temporal patterns, and generalizing over different datasets. In this work, we propose TSI-GAN, an unsupervised anomaly detection model for time-series that can learn complex temporal patterns automatically and generalize well, i.e., no need for choosing dataset-specific parameters, making statistical assumptions about underlying data, or changing model architectures. To achieve these goals, we convert each input time-series into a sequence of 2D images using two encoding techniques with …

Simulation Of Lc Circuits In Cylindrical Photoemission Driven Cavities Using Coupled Monte Carlo And Particle-In-Cell Codes, Ravi Sanjay Shastri

Masters Theses

"This work presents a computational experiment simulating irradiation of gold in a cylindrical cavity. The full 3-D simulation with a B-Dot diagnostic is modeled, and a subset of the results are compared to modeling the system as a coupled anode/cathode (AK) gap and transmission line. The cavity is filled with N2 and Ne gasses at pressures ranging from vacuum to 1000 mTorr. Space-charge limited emission physics, electric discharge physics, LC circuit physics, and Monte-Carlo methods are explored.

The computation process happens in two steps. First, the electron emission material is irradiated with an input x-ray photon spectrum to produce a …

Dig Limits Optimization Using Binary Integer Linear Programming Method In Open Pit Mines, Hussam Naif Altalhi

Masters Theses

"Dig limits optimization is the process for classifying different materials (e.g., ore, stockpile material, and waste) into appropriately sized contiguous zones for open pit mining. The efficient determination of dig-limits is crucial for profitable and sustainable resource extraction in mining. Previous research has focused on defining dig-limits manually or using optimization approaches, but these methods are limited to only handling two material destinations (ore and waste). Thus, there is a need for operations research methods that consider the selectivity of mining equipment and can optimize dig-limits for metal mining operations with more than two material destinations. Consequently, the objective of …

A Binary Integer Linear Programming Model For Optimizing Underground Stope Layout, Theophilus Mensah

Masters Theses

"Underground mine planning engineers face significant challenges when determining what geometry provides the most profitable and safe stope for extraction. Several techniques and optimization algorithms have been developed in recent years, but most fail to find optimal solutions because they are heuristic or LP-based without efficient geometric constraints. This thesis work proposes a two-dimensional binary linear programming (BILP) model for determining the optimal combination of blocks in a stope that maximizes the economic value of the layout of stopes for a sublevel deposit. The work draws from Queyranne and Wolsey’s (2017 & 2018) formulations of tight constraints for bounded up/down …

The Deep Bsde Method, Daniel Kovach

Masters Theses

"The curse of dimensionality is the non-linear growth in computing time as the dimension of a problem increases. Using the Deep Backwards Stochastic Differential Equation (Deep BSDE) method developed in [HJE18], I approximate the solution at an initial time to a one-dimensional diffusion equation. Although we only approximate a one-dimensional equation, this method extends well to higher dimensions because it overcomes the curse of dimensionality by evaluating the given partial differential equation along "random characteristics''. In addition to the implementation, I also present most of the mathematical theory needed to understand this method"-- Abstract, p. iii