Engineering Commons^™

The Digital Loophole: Evaluating The Effectiveness Of Child Age Verification Methods On Social Media, Fatmaelzahraa Eltaher, Rahul Gajula, Luis Miralles-Pechuán, Christina Thorpe, Susan Mckeever

Conference papers

Social media platforms are an integral part of daily life for nearly five billion people worldwide. However, the growing presence of underage users on these platforms raises significant concerns regarding children's exposure to harmful content and its impact on their mental health. This paper examines the effectiveness of age verification measures implemented on leading platforms Facebook, YouTube, Instagram, TikTok, Snapchat, and X. We evaluate the age verification processes required for account creation by simulating the registration steps for minors on these platforms. We also compare these methods to best practices in online age assurance in finance, betting and public transportation …

Delivery Of Medical Supplies To Remote Locations Via Unmanned Aerial Vehicles: Approaches, Challenges, And Solutions, Meshari Aljohani, Ravi Mukkamala, Stephanie Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming more important in improving healthcare logistics, in particular due to their cost effectiveness, minimized risk, and versatile operational capabilities. This study explores the deployment of autonomous UAVs to deliver medical supplies to remote areas. Advances in ledger technology, smart contracts, and machine learning have transformed tasks previously managed by human teams or manually controlled UAVs into fully autonomous missions. We present a comprehensive analysis of the challenges and initial solutions vital for the effective use of autonomous UAVs in the delivery of medical supplies. In addition, we propose a machine-learning model to optimize UAV …

Vaim-Cff: A Variational Autoencoder Inverse Mapper Solution To Compton Form Factor Extraction From Deeply Virtual Compton Scattering, Manal Almaeen, Tareq Alghamdi, Brandon Kriesten, Douglas Adams, Yaohang Li, Huey-Wen Lin, Simonetta Liuti

Computer Science Faculty Publications

We develop a new methodology for extracting Compton form factors (CFFs) from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted …

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Computer Science Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …

Uncertainty-Aware Deep Learning Framework For Forecasting Coastal Water Level In Virginia Beach, Md Mahmudul Hasan, Malachi Schram, Sridhar Katragadda, Diana Mcspadden, Alisa N. Udomvisawakul, Heather Richter, Frank Liu

Computer Science Faculty Publications

Coastal areas like Virginia Beach, USA, are increasingly vulnerable to flooding. To mitigate the impact of flooding, it is crucial for the City of Virginia Beach to have reliable 72-hour-ahead (3 days) forecasts of water levels at key gauge locations. To support this effort, several sensors have been installed throughout the city to monitor water levels and other environmental parameters such as wind speed, precipitation, and atmospheric pressure. Leveraging sensor data from one of these locations, we developed an uncertainty-aware deep learning model to forecast water levels. We employed deep quantile regression (DQR) to quantify variability in the predictions and …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

Normalizing Images In Various Weather And Lighting Conditions Using Colorpix2pix Generative Adversarial Network, Sanjida Tasnim, Ashif Mahmud Mostafa, Azmain Morshed, Namreen Shaiyaz, Shakib Mahmud Dipto, Saad Aloteibi, Mohammad Ali Moni, Md. Golam Rabiul Alam, Md. Ashraful Alam

Computer Science Faculty Publications

Autonomous vehicles (AVs) are widely regarded as the future of transportation due to their tremendous benefits and user comfort. However, the AVs have been struggling with very crucial challenges, such as achieving reliable accuracy in object detection as well as faster computation required for quick decision-making. In recent years, perception systems in driverless cars have been significantly enhanced, mainly due to advances in deep-learning-based object detection systems. However, these perception systems are still heavily affected by environmental variables, such as changes in illumination, refractive interference, and adverse weather conditions, which may compromise their reliability and safety. This research proposes an …

Insights In Adaptation: Examining Self-Reflection Strategies Of Job Seekers With Visual Impairments In India, Akshay Kolgar Nayak, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Significant changes in the digital employment landscape, driven by rapid technological advancements and the COVID-19 pandemic, have introduced new opportunities for blind and visually impaired (BVI) individuals in developing countries like India. However, a significant portion of the BVI population in India remains unemployed despite extensive accessibility advancements and job search interventions. Therefore, we conducted semi-structured interviews with 20 BVI persons who were either pursuing or recently sought employment in the digital industry. Our findings reveal that despite gaining digital literacy and extensive training, BVI individuals struggle to meet industry requirements for fulfilling job openings. While they engage in self-reflection …

S²Il: Structurally Stable Incremental Learning, S. Balasubramanian, P. Yedu Krishna, Talasu Sai Sriram, M. Sai Subramaniam, Manepalli Pranav Phanindra Sai, Ravi Mukkamala

Computer Science Faculty Publications

Feature Distillation (FD) strategies are proven to be effective in mitigating Catastrophic Forgetting (CF) seen in Class Incremental Learning (CIL). However, current FD approaches enforce strict alignment of feature magnitudes and directions across incremental steps, limiting the model’s ability to adapt to new knowledge. In this paper, we propose Structurally Stable Incremental Learning (S²IL), a FD method for CIL that mitigates forgetting by focusing on preserving the overall spatial patterns of features which promote flexible (plasticity) yet stable representations that preserve old knowledge (stability). We also demonstrate that our proposed method S²IL achieves strong incremental accuracy and outperforms other FD …

An Optimized Generalized Multi-Color Point Implicit Solver For Intel Gpus Using Oneapi Esimd, Joseph Wassell, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Timothée Ewart

Computer Science Faculty Publications

This paper presents an efficient implementation of a linear-solver kernel relevant to FUN3D, a suite of computational fluid dynamics software developed at NASA’s Langley Research Center. The linear solver is optimized for a range of block sizes commonly used in FUN3D. The implementation targets Aurora, the Argonne Leadership Computing Facility’s (ALCF) exascale machine featuring Intel Data Center Max 1550 GPUs. The linear solver’s performance is memory bandwidth-bound due to its low arithmetic intensity. The primary performance challenges stem from variable matrix row lengths and indirect memory access patterns inherent in unstructured-grid applications. Variable block sizes introduce additional complexity through differing …

Energy-Based Deep Incomplete Multi-View Clustering, Ziyu Wang, Yiming Du, Rui Ning, Lusi Li

Computer Science Faculty Publications

Incomplete multi-view clustering (IMVC) deals with real-world scenarios where certain views are partially missing, posing significant challenges to effective clustering. Most existing IMVC approaches face a trade-off: imputation-free methods suffer from information bias and imbalance, while full-imputation methods risk introducing and propagating noise. To overcome these limitations, we propose Energy-Based Deep Incomplete Multi-View Clustering (Energy-DIMC), a novel selective-imputation framework that leverages energy-based models (EBMs) to guide reliable imputations and robust clustering. EBMs assess data compatibility by assigning lower energy to more coherent structures, effectively modeling complex inter-view and inter-sample dependencies. Inspired by EBMs, Energy-DIMC integrates four key components: 1) a …

Survey: A Study On Image Encryption Using Dna In Bioinformatics, Rana M. Zaki, Zaed S. Mahdi, Matheel E. Abdulmunim

Journal of Soft Computing and Computer Applications

One area of study between computer science and biology is bioinformatics, which deals with methods for collecting, processing, storing, and evaluating biological data. Sequences of RiboNucleic Acid (RNA), DeoxyriboNucleic Acid (DNA), and proteins make up biological data, which has a wide range of uses in domains such as feature extraction, data segmentation, data security, and more. In cryptography, DNA sequences are used as data carriers, enhancing the unique properties of biomolecules. This approach involves using DNA sequences to enhance the security of confidential data that must be transmitted over networks or stored securely. Several DNA-based security techniques have been developed, …

New Feature Selection Using Principal Component Analysis, Zaid Mundher Radeef, Soukaena Hassan Hashem, Ekhlas Khalaf Gbashi

Journal of Soft Computing and Computer Applications

Dimensionality reduction techniques streamline machine learning by reducing data complexity, improving model accuracy, and cutting computational costs. They remove noise and irrelevant features, making models faster and more efficient. These techniques also enhance data visualization and interpretation by condensing data into manageable, insightful dimensions. Ultimately, dimensionality reduction leads to simpler, more interpretable models without sacrificing critical information, making it a cornerstone of efficient data analysis and machine learning applications. Theoretically, feature extraction tends to create new features that encapsulate more information by combining multiple existing features, resulting in more concentrated and informative features. In contrast, feature selection involves choosing a …

Development Of A Hybrid Methodology Of Deep Learning And Machine Learning For Lung Nodule Detection In Medical Computed Tomography Images, Zaed S. Mahdi, Rana M. Zaki, Alaa Kadhim Farhan, Negar Majma

Journal of Soft Computing and Computer Applications

Deep learning and machine learning play an important role in the medical field, helping doctors make accurate, fast and effective diagnosis. Despite the progress achieved in the use of modern technologies in detecting cancerous nodes, current studies still suffer from some challenges and limitations that must be addressed to obtain high efficiency in identifying cancerous nodes. These challenges include using image pre-processing, combining deep learning and machine learning techniques, and constantly adapting to clinical changes, in order to address this. A hybrid methodology has been proposed for detecting cancerous nodules in the lung in medical Computed Tomography (CT) images. It …

Enhancing Image Classification Using A Convolutional Neural Network Model, Zena M. Saadi, Ahmed T. Sadiq, Omar Z. Akif, Marwa M. Eid

Journal of Soft Computing and Computer Applications

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as …

Improved Rapidly-Exploring Random Tree Using Firefly Algorithm For Robot Path Planning, Dena Kadhim Muhsen, Firas Abdulrazzaq Raheem, Yuhanis Yusof, Ahmed T. Sadiq, Faiz Al Alawy

Journal of Soft Computing and Computer Applications

In robotics, efficient path planning makes robots work independently and move through changing environments over time. This study combines the Rapidly-exploring Random Tree (RRT) architecture with the Firefly Algorithm (FA) to make robot’s path-planning better. The proposed ERRT-FA, which stands for "Enhanced RRT with Firefly Algorithm", generates better routes using Firefly social habits. Plan routes using Firefly social habits can effectively aid in exploring configuration space. The role of the FA is to enhance the RRT algorithm by providing an optimized exploration of the search space, ultimately leading to optimizing the path found by the RRT algorithm and better paths …

Foreword From Editor - 16th Edition: Toward An Inclusive Community Engagement, Yandi Andri Yatmo

ASEAN Journal of Community Engagement

This edition of AJCE defines and elaborates on the idea of inclusive community engagement as a means to involve the community in a meaningful process. ‘Inclusive’ refers to the principles of encompassing everyone, all individuals and groups alike, regardless of their identity, background, characteristics, needs, and perspectives, thereby ensuring that all voices are represented (Hodkinson, 2011). This practice extends beyond individuals with disabilities and embodies broader ideas of equality. Inclusive engagement plays a crucial part in fostering a constructive dialog that incorporates diverse perspectives within a community. Such engagements prioritize community participation in the decision-making process that affects their well-being …

Exploring Student Satisfaction In Learning With Podcast Applications: A Qualitative Study Based On Open-Ended Questions, Indah Permatasari, Peny Meliaty Hutabarat, Erni Adelina

Jurnal Vokasi Indonesia

This study aims to explore student satisfaction with the use of podcasts as a learning medium in the Non-News Radio Production course. A qualitative approach was used, with three open-ended questions posed to students: (1) What different experiences did you have when listening to the course material via podcast?(2) Did listening to the course material through podcasts help you focus on understanding the material? And why? And (3) provide your opinion on the Adapto podcast material shared during the Non-News Radio Production course in the 4th semester. The data obtained was thematically analyzed to identify the main emerging themes. The …

First-Principles Study Of Ferroelectric Properties And Co2 Reduction Reaction Capabilities In Two-Dimensional Monolayers And Heterostructures, Mo Li

Dissertations

Two-dimensional (2D) materials hold significant potential for CO₂ reduction reactions (CO₂RR) due to their high surface-to-volume ratio. However, achieving high selectivity for desired products and overcoming limitations posed by scaling relationships remain challenging. Recent studies suggest that ferroelectric (FE) materials with switchable out-of-plane polarization (OOP) can effectively tune the adsorption behavior, thermodynamics, and kinetics of CO₂RR, offering promising solutions to these challenges. Using density functional theory (DFT) and the Berry phase approach, this work expands the family of 2D ferroelectrics by theoretically identifying Y₂CO₂, Y₂CS₂, and Sc …

Determination Of Electrochemical Parameters For Predicting Reaction Mechanism And Algorithmic Approaches To Pain Assessment, Huize Xue

Dissertations

This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine …

2024 Scholarly Productivity Report, Missouri University Of Science And Technology

Civil, Architectural and Environmental Engineering Scholarly Productivity Reports

No abstract provided.

Gas-Generating Reactive Materials: Design, Evaluation And Effect Of Morphology On Their Ignition And Combustion, Purvam Mehulkumar Gandhi

Dissertations

This work investigates optimizing gas-generating reactive materials, focusing on particle morphology's effect on ignition and combustion behavior. Metal-based gas-generating energetic materials (EMs) are promising alternatives to replace traditional CHNO compounds. Design of advanced metal-based EMs requires consistent ways of evaluating how their characteristics affect their ignition and combustion. Many relevant evaluation approaches exist; however, the results may be difficult to compare directly across different studies. Commonly, experimentalists ignite metal-based EMs in enclosed chambers and report pressures, P. However, direct comparison of these pressures is hindered by variations in the chamber volume, V, and the EM mass, m. To standardize the …

Decomposition Of Diisopropyl Methylphosphonate (Dimp) Exposed To Elevated Temperatures And Combustion Products Of Reactive Materials, Elif Irem Senyurt

Dissertations

The safe and efficient destruction of chemical weapon agents (CWAs) stockpiles remains a critical global challenge. Understanding the thermophysical properties, decomposition mechanisms, and interactions of CWAs with their environment is crucial for developing effective strategies to mitigate their threats. Diisopropyl methylphosphonate (DIMP) and Dimethyl methylphosphonate are commonly used nerve agent surrogates. This dissertation focuses on DIMP and DMMP, investigates their properties and behavior under conditions relevant to prompt defeat scenarios.

The thermophysical properties of DIMP and DMMP were experimentally characterized, including vapor-liquid surface tension (3-60 °C) and viscosities of neat and aqueous solutions. Results revealed significant non-ideal behavior in aqueous …

Optimizing Bioethanol Production From Solanum Torvum (Devil's Thorn): An Evaluation Of Conversion Efficiency And Invasive Plant Management Potential, Jaya Ashwin D S, Navaneeth K, Abhay Mahesh Baadkar, Supriya S Sundar, Bhoomika B J

Manipal Journal of Science and Technology

Solanum torvum, commonly known as the Devil’s thorn, is an invasive species present in India that causes negative ecological consequences. However, given its abundance and high starch content in the plant, it could be utilized as a potential feedstock for sustainable biofuel production. We aim to explore the feasibility of bioethanol production from S. torvum and its potential as a means of managing this invasive species. The study will take on a comprehensive approach, including techniques for optimizing starch extraction and improving its accessibility. Fermentation with suitable strains of microorganisms, and analysis of bioethanol yield. In addition, the study will …

Genetic Algorithm-Based Design Solution Of An Area Lighting Scheme - A Case Study, Prabhat Mishra, Arnab Ganguly, Amartya Roy, Mihir Kumar Manna, Abhik Hazra

Manipal Journal of Science and Technology

In this paper, a Genetic Algorithm (GA)-based approach is taken for the lighting design of a specified area. The design of an area lighting scheme primarily depends upon the application of that area and accordingly target values of lighting design parameters are to be decided from relevant BIS (Bureau of Indian Standard) lighting codes. There are several design variables, viz., light distribution, aiming of the luminaire, pole spacing, luminaire mounting height, grid dimension over the field, etc. The task of a lighting designer is to achieve the target design parameters through a suitable combination of set design variables and design …

Field Oriented Control (Foc) Of Permanent Magnet Synchronous Motor (Pmsm) Applied In Electric Vehicle (Ev), Unnikrishnan P C, Delgin Saji, Ashwini M, Georgee Cleetus, Joshua Andrews Chandy

Manipal Journal of Science and Technology

Synchronous motors are the most commonly used steady-state three-phase AC motors in electrical systems. The synchronous speed of these motors remains constant, being equal to the supply frequency and its rotational period corresponding to the integral number of AC cycles. So, these motors are mainly used to improve the power factor in power systems.

The paper focuses on field-oriented control of a permanent magnet synchronous motor to effectively control its speed and torque. AC motors only have stator currents, so separate control mechanisms such as vector controls are required to control the motor’s operation. Field-oriented control is the most commonly …

Contingency Analysis On Transmission Line Of Ieee 9 Bus System, Padmashree K S

Manipal Journal of Science and Technology

Ensuring power system security poses a significant challenge for engineers in the field. Conducting security assessments is crucial as it provides insight into the system's condition in the event of a contingency. The widely employed contingency analysis technique serves to anticipate the impact of outages, such as equipment failures or transmission line disruptions, enabling pre-emptive measures to maintain system reliability. However, analyzing each contingency offline is arduous due to the extensive number of system components, with only select contingencies posing severe threats to the system. Computing performance indices for every scenario is a step in the contingency selection process, which …

An Energy-Efficient Clustering Technique Of Heterogeneous And Homogeneous Wireless Sensor Networks, Ahmad Alkhayyat, Rohit Sharma

NJF Intelligent Engineering Journal

The technology of wireless sensor networks (WSN) has recently gained widespread recognition as an emerging one. A WSN consists of a set of sensors powered by batteries. Inaccessible locations usually make it difficult to replace or recharge sensors' batteries. These networks are plagued by energy consumption, which is the main problem. The clustering algorithms are remarkably effective in dealing with such problems in this regard. As a result, this technique appears to help reduce node energy consumption, which ultimately increases the network's lifespan. Heterogeneous and homogeneous clustering algorithms exist. In homogeneous clustering algorithms, all nodes have the same technical characteristics, …

Optimized Thyroid Disease Classification Using Nature-Inspired Algorithms: Gwo & Woa, Sayan Mondal

NJF Intelligent Engineering Journal

Recently, there has been an upsurge in the number of cases of thyroid disease. Thyroid function is essential for metabolism, making the early diagnosis of thyroid dysfunction an urgent matter. The issue of class imbalance has not been thoroughly examined, even though there are multiple publications on the topic of thyroid disease detection. Furthermore, the binary-class problem has been the primary emphasis of previous research. This study intends to address these concerns by using the suggested strategy, which takes into account ten distinct thyroid illnesses. In order to choose the best features from the Thyroid dataset, this research proposes two …